[Federal Register Volume 86, Number 169 (Friday, September 3, 2021)]
[Proposed Rules]
[Pages 49602-49883]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2021-17496]



[[Page 49601]]

Vol. 86

Friday,

No. 169

September 3, 2021

Part II





Department of Transportation





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 National Highway Traffic Safety Administration





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49 CFR Parts 531, 533 et al.





Corporate Average Fuel Economy Standards for Model Years 2024-2026 
Passenger Cars and Light Trucks; Proposed Rule

Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / 
Proposed Rules

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DEPARTMENT OF TRANSPORTATION

National Highway Traffic Safety Administration

49 CFR Parts 531, 533, 536, and 537

[NHTSA-2021-0053]
RIN 2127-AM34


Corporate Average Fuel Economy Standards for Model Years 2024-
2026 Passenger Cars and Light Trucks

AGENCY: National Highway Traffic Safety Administration (NHTSA), 
Department of Transportation (DOT).

ACTION: Notice of proposed rulemaking.

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SUMMARY: NHTSA, on behalf of the Department of Transportation, is 
proposing revised fuel economy standards for passenger cars and light 
trucks for model years 2024-2026. On January 20, 2021, President Biden 
signed an Executive order (E.O.) entitled, ``Protecting Public Health 
and the Environment and Restoring Science To Tackle the Climate 
Crisis.'' In it, the President directed that ``The Safer Affordable 
Fuel-Efficient (SAFE) Vehicles Rule for Model Years 2021-2026 Passenger 
Cars and Light Trucks'' (hereafter, ``the 2020 final rule'') be 
immediately reviewed for consistency with our Nation's abiding 
commitment to empower our workers and communities; promote and protect 
our public health and the environment; and conserve our national 
treasures and monuments, places that secure our national memory. 
President Biden further directed that the 2020 final rule be reviewed 
at once and that (in this case) the Secretary of Transportation 
consider ``suspending, revising, or rescinding'' it, via a new 
proposal, by July 2021. Because of the President's direction in the 
E.O., NHTSA reexamined the 2020 final rule under its authority to set 
corporate average fuel economy (CAFE) standards. In doing so, NHTSA 
tentatively concluded that the fuel economy standards set in 2020 
should be revised so that they increase at a rate of 8 percent year 
over year for each model year from 2024 through 2026, for both 
passenger cars and light trucks. This responds to the agency's 
statutory mandate to improve energy conservation. This proposal also 
makes certain minor changes to fuel economy reporting requirements.

DATES: Comments: Comments are requested on or before October 26, 2021. 
In compliance with the Paperwork Reduction Act, NHTSA is also seeking 
comment on a revision to an existing information collection. For 
additional information, see the Paperwork Reduction Act Section under 
Section IX, below. All comments relating to the information collection 
requirements should be submitted to NHTSA and to the Office of 
Management and Budget (OMB) at the address listed in the ADDRESSES 
section on or before October 26, 2021. See the SUPPLEMENTARY 
INFORMATION section on ``Public Participation,'' below, for more 
information about written comments.
    Public Hearings: NHTSA will hold one virtual public hearing during 
the public comment period. The agency will announce the specific date 
and web address for the hearing in a supplemental Federal Register 
notification. The agency will accept oral and written comments on the 
rulemaking documents and will also accept comments on the Supplemental 
Environmental Impact Statement (SEIS) at this hearing. The hearing will 
start at 9 a.m. Eastern standard time and continue until everyone has 
had a chance to speak. See the SUPPLEMENTARY INFORMATION section on 
``Public Participation,'' below, for more information about the public 
hearing.

ADDRESSES: You may send comments, identified by Docket No. NHTSA-2021-
0053, by any of the following methods:
     Federal eRulemaking Portal: http://www.regulations.gov. 
Follow the instructions for submitting comments.
     Fax: (202) 493-2251.
     Mail: Docket Management Facility, M-30, U.S. Department of 
Transportation, West Building, Ground Floor, Rm. W12-140, 1200 New 
Jersey Avenue SE, Washington, DC 20590.
     Hand Delivery: Docket Management Facility, M-30, U.S. 
Department of Transportation, West Building, Ground Floor, Rm. W12-140, 
1200 New Jersey Avenue SE, Washington, DC 20590, between 9 a.m. and 4 
p.m. Eastern Time, Monday through Friday, except Federal holidays.
    Comments on the proposed information collection requirements should 
be submitted to: Office of Management and Budget at www.reginfo.gov/public/do/PRAMain. To find this particular information collection, 
select ``Currently under Review--Open for Public Comment'' or use the 
search function. NHTSA requests that comments sent to the OMB also be 
sent to the NHTSA rulemaking docket identified in the heading of this 
document.
    Instructions: All submissions received must include the agency name 
and docket number or Regulatory Information Number (RIN) for this 
rulemaking. All comments received will be posted without change to 
http://www.regulations.gov, including any personal information 
provided. For detailed instructions on sending comments and additional 
information on the rulemaking process, see the ``Public Participation'' 
heading of the SUPPLEMENTARY INFORMATION section of this document.
    Docket: For access to the dockets or to read background documents 
or comments received, please visit http://www.regulations.gov, and/or 
Docket Management Facility, M-30, U.S. Department of Transportation, 
West Building, Ground Floor, Rm. W12-140, 1200 New Jersey Avenue SE, 
Washington, DC 20590. The Docket Management Facility is open between 9 
a.m. and 4 p.m. Eastern Time, Monday through Friday, except Federal 
holidays.

FOR FURTHER INFORMATION CONTACT: Rebecca Schade, NHTSA Office of Chief 
Counsel, National Highway Traffic Safety Administration, 1200 New 
Jersey Avenue SE, Washington, DC 20590; email: [email protected].

SUPPLEMENTARY INFORMATION:

Does this action apply to me?

    This action affects companies that manufacture or sell new 
passenger automobiles (passenger cars) and non-passenger automobiles 
(light trucks) as defined under NHTSA's CAFE regulations.\1\ Regulated 
categories and entities include:
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    \1\ ``Passenger car'' and ``light truck'' are defined in 49 CFR 
part 523.

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------------------------------------------------------------------------
                                  NAICS  Codes   Examples of potentially
            Category                   \A\          regulated entities
------------------------------------------------------------------------
Industry.......................          335111  Motor Vehicle
                                                  Manufacturers.
                                         336112
Industry.......................          811111  Commercial Importers of
                                                  Vehicles and Vehicle
                                                  Components.
                                         811112
                                         811198
                                         423110
Industry.......................          335312  Alternative Fuel
                                                  Vehicle Converters.
                                         336312
                                         336399
                                         811198
------------------------------------------------------------------------
\A\ North American Industry Classification System (NAICS).

    This list is not intended to be exhaustive, but rather provides a 
guide regarding entities likely to be regulated by this action. To 
determine whether particular activities may be regulated by this 
action, you should carefully examine the regulations. You may direct 
questions regarding the applicability of this action to the person 
listed in FOR FURTHER INFORMATION CONTACT.

I. Executive Summary

    NHTSA, on behalf of the Department of Transportation, is proposing 
to amend standards regulating corporate average fuel economy (CAFE) for 
passenger cars and light trucks for model years (MYs) 2024-2026. This 
proposal responds to NHTSA's statutory obligation to set maximum 
feasible CAFE standards to improve energy conservation, and to 
President Biden's directive in Executive Order 13990 of January 20, 
2021 that ``The Safer Affordable Fuel-Efficient (SAFE) Vehicles Rule 
for Model Years 2021-2026 Passenger Cars and Light Trucks'', 2020 final 
rule or 2020 CAFE rule (85 FR 24174 (April 30, 2020)), be immediately 
reviewed for consistency with our Nation's abiding commitment to 
promote and protect our public health and the environment, among other 
things. NHTSA undertook that review immediately, and this proposal is 
the result of that process.
    The proposed amended CAFE standards would increase in stringency 
from MY 2023 levels by 8 percent per year, for both passenger cars and 
light trucks over MYs 2024-2026. NHTSA tentatively concludes that this 
level is maximum feasible for these model years, as discussed in more 
detail in Section VI, and seeks comment on that conclusion. The 
proposal considers a range of regulatory alternatives, consistent with 
NHTSA's obligations under the National Environmental Policy Act (NEPA) 
and Executive Order 12866. While E.O. 13990 directed the review of CAFE 
standards for MYs 2021-2026, statutory lead time requirements mean that 
the soonest model year that can currently be amended in the CAFE 
program is MY 2024. The proposed standards would remain vehicle 
footprint-based, like the CAFE standards in effect since MY 2011. 
Recognizing that many readers think about CAFE standards in terms of 
the miles per gallon (mpg) values that the standards are projected to 
eventually require, NHTSA currently projects that the proposed 
standards would require, on an average industry fleet-wide basis, 
roughly 48 mpg in MY 2026. NHTSA notes both that real-world fuel 
economy is generally 20-30 percent lower than the estimated required 
CAFE level stated above, and also that the actual CAFE standards are 
the footprint target curves for passenger cars and light trucks, 
meaning that ultimate fleet-wide levels will vary depending on the mix 
of vehicles that industry produces for sale in those model years. Table 
I-1 shows the incremental differences in stringency levels for 
passenger cars and light trucks, by regulatory alternative, in the 
model years subject to regulation.
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    This proposal is significantly different from the conclusion that 
NHTSA reached in the 2020 final rule, but this is because important 
facts have changed, and because NHTSA has reconsidered how to balance 
the relevant statutory considerations in light of those facts. NHTSA 
tentatively concludes that significantly more stringent standards are 
maximum feasible. Contrary to the 2020 final rule, NHTSA recognizes 
that the need of the United States to conserve energy must include 
serious consideration of the energy security risks of continuing to 
consume oil, which more stringent fuel economy standards can reduce. 
Reducing our Nation's climate impacts can also benefit our national 
security. Additionally, at least part of the automobile industry 
appears increasingly convinced that improving fuel economy and reducing 
greenhouse gas (GHG) emissions is a growth market for them, and that 
the market rewards investment in advanced technology. Nearly all auto 
manufacturers have announced forthcoming new higher fuel-economy and 
electric vehicle models, and five major manufacturers voluntarily bound 
themselves to stricter GHG requirements than set forth by NHTSA and the 
Environmental Protection Agency (EPA) in 2020 through contractual 
agreements with the State of California, which will result in their 
achieving fuel economy levels well above the standards set forth in the 
2020 final rule. These companies are sophisticated, for-profit 
enterprises. If they are taking these steps, NHTSA can be more 
confident than the agency was in 2020 that the market is getting ready 
to make the leap to significantly higher fuel economy. The California 
Framework and the clear planning by industry to migrate toward more 
advanced fuel economy technologies are evidence of the practicability 
of more stringent standards. Moreover, more stringent CAFE standards 
will help to encourage industry to continue improving the fuel economy 
of all vehicles, rather than simply producing a few electric vehicles, 
such that all Americans can benefit from higher fuel economy and save 
money on fuel. NHTSA cannot consider the fuel economy of dedicated 
alternative fuel vehicles like battery electric vehicles when 
determining maximum feasible standards, but the fact that industry 
increasingly appears to believe that there is a market for these 
vehicles is broader evidence of market (and consumer) interest in fuel 
economy, which is relevant to NHTSA's determination of whether more 
stringent standards would be economically practicable. For all of these 
reasons, NHTSA tentatively concludes that standards that increase at 8 
percent per year are maximum feasible.
    This proposal is also different from the 2020 final rule in that it 
is issued by NHTSA alone, and EPA has issued a separate proposal. The 
primary reason for this is the difference in statutory authority--EPA 
does not have the same lead time requirements as NHTSA and is thus able 
to amend MY 2023 in addition to MYs 2024-2026. An important consequence 
of this is that EPA's proposed rate of stringency increase, after 
taking a big leap in MY 2023, looks slower than NHTSA's over the same 
time period. NHTSA emphasizes, however, that the proposed standards are 
what NHTSA believes best fulfills our statutory directive of energy 
conservation, and in the context of the EPA standards, the analysis we 
have done is tackling the core question of whether compliance with both 
standards should be achievable with the same vehicle fleet, after 
manufacturers fully understand the requirements from both proposals. 
The differences in what the two agencies' standards require become 
smaller each year, until alignment is achieved. While NHTSA recognizes 
that the last several CAFE standard rulemakings have been issued 
jointly with EPA, and that issuing separate proposals represents a 
change in approach, the agencies worked together to avoid 
inconsistencies and to create proposals that would continue to allow 
manufacturers to build a single fleet of vehicles to meet both 
agencies' proposed standards. Additionally, and importantly, NHTSA has 
also considered and accounted for California's Zero Emission Vehicle 
(ZEV) program (and its adoption by a number of other states) in 
developing the baseline for this proposal, and has accounted for the 
aforementioned ``Framework Agreements'' between California and BMW, 
Ford, Honda, Volkswagen of America (VWA), and Volvo, which are 
national-level GHG standards to which these companies committed for 
several model years.
    A number of other improvements and updates have been made to the 
analysis since the 2020 final rule. Table I-2 summarizes these, and 
they are discussed in much more detail below and in the documents 
accompanying this preamble.
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    NHTSA estimates that this proposal could reduce average 
undiscounted fuel outlays over the lifetimes of MY 2029 vehicles by 
about $1,280, while increasing the average cost of those vehicles by 
about $960 over the baseline described above. With the social cost of 
carbon (SCC) discounted at 2.5 percent and other benefits and costs 
discounted at 3 percent, for the three affected model years NHTSA finds 
$65.8 billion in benefits attributable to the proposed standards and 
$37.4 billion in proposed costs so that present net benefits could be 
$28.4 billion.\2\ Applied to the entire fleet for MYs 1981-2029, NHTSA 
estimates $120 billion in costs and $121

[[Page 49606]]

billion in benefits attributable to the proposed standards, such that 
the present value of aggregate net benefits to society could be $1 
billion. Like any analysis of this magnitude attempting to forecast 
future effects of current policies, significant uncertainty exists 
about many key inputs. Changes in the price of fuel or in the social 
cost of carbon could dramatically change benefits, for example, and 
readers should expect that the eventual final rule will reflect any 
updates made to those (and many other) values that occur between now 
and then. It is also worth stressing that NHTSA's statutory authority 
requires that its standards be maximum feasible, taking into account 
four statutory factors. While NHTSA's estimates of costs and benefits 
are important considerations, it is the maximum feasible analysis that 
controls the setting of CAFE standards.
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    \2\ As discussed in Section III.G.2.b), NHTSA has discounted the 
SCC at 2.5% when other benefits and costs are discounted at 3% but 
seeks comment on this approach.
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    Like many other types of regulations, CAFE standards apply only to 
new vehicles. The costs attributable to new CAFE standards are thus 
``front-loaded,'' because they result primarily from the application of 
fuel-saving technology to new vehicles. On the other hand, the impact 
of new CAFE standards on fuel consumption and greenhouse gases--and the 
associated benefits to society--occur over an extended time, as drivers 
buy, use, and eventually scrap these new vehicles. By accounting for 
many model years and extending well into the future (2050), our 
analysis accounts for these differing patterns in impacts, benefits, 
and costs. Our analysis also accounts for the potential that, by 
changing new vehicle prices and fuel economy levels, CAFE standards 
could indirectly impact the operation of vehicles produced before or 
after the model years (2024-2026) for which we are proposing new CAFE 
standards. This means that some of the proposal's impacts and 
corresponding benefits and costs are actually attributable to indirect 
impacts on vehicles produced before and after model years 2024-2026.
    The bulk of our analysis considers a ``model year'' (MY) 
perspective that considers the lifetime impacts attributable to all 
vehicles produced prior to model year 2030, accounting for the 
operation of these vehicles over their entire useful lives (with some 
model year 2029 vehicles estimated to be in service as late as 2068). 
This approach emphasizes the role of model years 2024-2026, while 
accounting for the potential that it may take manufacturers a few 
additional years to produce fleets fully responsive to the proposed MY 
2026 standards, and for the potential that the proposal could induce 
some changes in the operation of vehicles produced prior to MY 2024.
    Our analysis also considers a ``calendar year'' (CY) perspective 
that includes the annual impacts attributable to all vehicles estimated 
to be in service in each calendar year for which our analysis includes 
a representation of the entire registered light-duty fleet. For this 
NPRM, this calendar year perspective covers each of calendar years 
2021-2050, with differential impacts accruing as early as model year 
2023. Compared to the ``model year'' perspective, this calendar year 
perspective emphasizes model years of vehicles produced in the longer 
term, beyond those model years for which standards are currently being 
proposed. Table I-3 summarizes estimates of selected physical impacts 
viewed from each of these two perspectives, as well as corresponding 
estimates of the present values of cumulative benefits, costs, and net 
benefits.
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[GRAPHIC] [TIFF OMITTED] TP03SE21.002

    Finally, for purposes of comparing the benefits and costs of new 
CAFE standards to the benefits and costs of other Federal regulations, 
policies, and programs, we have computed ``annualized'' benefits and 
costs. These are the annual averages of the cumulative benefits and 
costs over the covered model or calendar years, after expressing these 
in present value terms.
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    As discussed in detail below, the monetized estimated costs and 
benefits of this proposal are relevant and important to the agency's 
tentative conclusion, but they are not the whole of the conclusion.

[[Page 49609]]

    Additionally, although NHTSA is prohibited from considering the 
availability of certain flexibilities in making our determination about 
the levels of CAFE standards that would be maximum feasible, 
manufacturers have a variety of flexibilities available to them to 
reduce their compliance burden. Table I-10 through Table I-13 below 
summarizes available compliance flexibilities. NHTSA seeks comment on 
whether to retain non-statutory flexibilities for the final rule.
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BILLING CODE 4910-59-C
    NHTSA recognizes that the lead time for this proposal is shorter 
than past rulemakings have provided, and that the economy and the 
country are in the process of recovering from a global pandemic and the 
resulting economic distress. At the same time, NHTSA also recognizes 
that at least parts of the industry are nonetheless stepping up their 
product offerings and releasing more and more high fuel-economy vehicle 
models, and many companies did not deviate significantly from product 
plans established in response to the standards set forth in the 2012 
final rule (77 FR 62624, Oct. 15, 2012) and confirmed by EPA in its 
January 2017 Final Determination. With these considerations in mind, 
NHTSA is proposing to amend the CAFE standards for MYs 2024-2026. 
NHTSA, like any other Federal agency, is afforded an opportunity to 
reconsider prior views and, when warranted, to adopt new positions. 
Indeed, as a matter of good governance, agencies should revisit their 
positions when appropriate, especially to ensure that their actions and 
regulations reflect legally sound interpretations of the agency's 
authority and remain consistent with the agency's views and practices. 
As a matter of law, ``an Agency is entitled to change its 
interpretation of a statute.'' \3\ Nonetheless, ``[w]hen an Agency 
adopts a materially changed interpretation of a statute, it must in 
addition provide a `reasoned analysis' supporting its decision to 
revise its interpretation.'' \4\ The analysis presented in this 
preamble and in the accompanying Technical Support Document (TSD), 
Preliminary Regulatory Impact Analysis (PRIA), Supplemental 
Environmental Impact Statement (SEIS), CAFE Model documentation, and 
extensive rulemaking docket fully supports the proposed decision and 
revised balancing of the statutory factors for MYs 2024-2026 standards. 
NHTSA seeks comment on the entirety of the rulemaking record.
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    \3\ Phoenix Hydro Corp. v. FERC, 775 F.2d 1187, 1191 (D.C. Cir. 
1985).
    \4\ Alabama Educ. Ass'n v. Chao, 455 F.3d 386, 392 (D.C. Cir. 
2006) (quoting Motor Vehicle Mfrs. Ass'n of U.S., Inc. v. State Farm 
Mut. Auto. Ins. Co., 463 U.S. 29, 57 (1983)); see also Encino 
Motorcars, LLC v. Navarro, 136 S.Ct. 2117, 2125 (2016) (``Agencies 
are free to change their existing policies as long as they provide a 
reasoned explanation for the change.'') (citations omitted).
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II. Introduction

    In this notice of proposed rulemaking (NPRM), NHTSA is proposing to 
revise CAFE standards for model years (MYs) 2024-2026. On January 20, 
2021, the President signed Executive Order (E.O.) 13990, ``Protecting 
Public Health and the Environment and Restoring Science To Tackle the 
Climate Crisis.'' \5\ In it, the President directed that ``The Safer 
Affordable Fuel-Efficient (SAFE) Vehicles Rule for Model Years 2021-
2026 Passenger Cars and Light Trucks'' (hereafter, ``the 2020 final 
rule''), 85 FR 24174 (April 30, 2020), must be immediately reviewed for 
consistency with our Nation's abiding commitment to empower our workers 
and communities; promote and protect our public health and the 
environment; and conserve our national treasures and monuments, places 
that secure our national memory. E.O. 13990 states expressly that the 
Administration prioritizes listening to the science, improving public 
health and protecting the environment, reducing greenhouse gas 
emissions, and improving environmental justice while creating well-
paying union jobs. The E.O. thus directs that the 2020 final rule be 
reviewed at once and that (in this case) the Secretary of 
Transportation consider ``suspending, revising, or rescinding'' it, via 
an NPRM, by July 2021.\6\
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    \5\ 86 FR 7037 (Jan. 25, 2021).
    \6\ Id., Sec. 2(a)(ii).
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    Section 32902(g)(1) of Title 49, United States Code allows the 
Secretary (by delegation to NHTSA) to prescribe regulations amending an 
average fuel economy standard prescribed under 49 U.S.C. 32902(a), like 
those prescribed in the 2020 final rule, if the amended standard meets 
the requirements of 32902(a). The Secretary's authority to set fuel 
economy standards is delegated to NHTSA at 49 CFR 1.95(a); therefore, 
in this NPRM, NHTSA proposes revised fuel economy standards for MYs 
2024-2026. Section 32902(g)(2) states that when the amendment makes an 
average fuel economy standard more stringent, it must be prescribed at 
least 18 months before the beginning of the model year to which the 
amendment applies. NHTSA generally calculates the 18-month lead time 
requirement as April of the calendar year prior to the start of the 
model year. Thus, 18 months before MY 2023 would be April 2021, because 
MY 2023 begins in September 2022. Because of this lead time 
requirement, NHTSA is not proposing to amend the CAFE standards for MYs 
2021-2023, even though the 2020 final rule also covered those model 
years. For purposes of the CAFE program, the 2020 final rule's 
standards for MYs 2021-2023 will remain in effect.
    For the MYs for which there is statutory lead time to amend the 
standards, however, NHTSA is proposing amendments to the currently 
applicable fuel economy standards. Although only one year has passed 
since the 2020 final rule, the agency believes it is reasonable and 
appropriate to revisit the CAFE standards for MYs 2024-2026. In 
particular, the agency has further considered the serious adverse 
effects on energy conservation that the standards finalized in 2020 
would cause

[[Page 49611]]

as compared to the proposed standards. The need of the U.S. to conserve 
energy is greater than understood in the 2020 final rule. In addition, 
standards that are more stringent than those that were finalized in 
2020 appear economically practicable. Nearly all auto manufacturers 
have announced forthcoming new advanced technology vehicle models with 
higher fuel economy, making strong public commitments that mirror those 
of the Administration. Five major manufacturers voluntarily bound 
themselves to stricter national-level GHG requirements as part of the 
California Framework agreement. Meanwhile, certain facts on the ground 
remain similar to what was before NHTSA in the prior analysis--gas 
prices still remain relatively low in the U.S., for example, and while 
light-duty vehicle sales fell sharply in MY 2020, the vehicles that did 
sell tended to be, on average, larger, heavier, and more powerful, all 
factors that increase fuel consumption. However, the renewed focus on 
addressing energy conservation and the industry's apparent ability to 
meet more stringent standards show that a rebalancing of the EPCA 
factors, and the proposal of more stringent standards, is appropriate 
for model years 2024-2026.
    The following sections introduce the proposal in more detail.

A. What is NHTSA proposing?

    NHTSA is proposing to set CAFE standards for passenger cars and 
light trucks manufactured for sale in the United States in MYs 2024-
2026. Passenger cars are generally sedans, station wagons, and two-
wheel drive crossovers and sport utility vehicles (CUVs and SUVs), 
while light trucks are generally four-wheel drive vehicles, larger/
heavier two-wheel drive sport utility vehicles, pickups, minivans, and 
passenger/cargo vans.\7\ The proposed standards would increase at 8 
percent per year for both cars and trucks, and are represented by 
regulatory Alternative 2 in the agency's analysis. The proposed 
standards would be defined by a mathematical equation that represents a 
constrained linear function relating vehicle footprint to fuel economy 
targets for both cars and trucks; vehicle footprint is roughly measured 
as the rectangle that is made by the four points where the vehicle's 
tires touch the ground. Generally, passenger cars will have more 
stringent targets than light trucks regardless of footprint, and 
smaller vehicles will have more stringent targets than larger vehicles. 
No individual vehicle or vehicle model need meet its target exactly, 
but a manufacturer's compliance is determined by how its average fleet 
fuel economy compares to the average fuel economy of the targets of the 
vehicles it manufactures.
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    \7\ ``Passenger car'' and ``light truck'' are defined at 49 CFR 
part 523.
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    The proposed target curves \8\ for passenger cars and light trucks 
are as follows; curves for MYs 2020-2023 are included in Figure II-1 
and Figure II-2 for context.
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    \8\ NHTSA underscores that the equations and coefficients 
defining the curves are what the agency is proposing, and not the 
mpg numbers that the agency currently estimates could result from 
manufacturers complying with the curves. Because the estimated mpg 
numbers are an effect of the proposed curves, they are presented in 
the following section.
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BILLING CODE 4910-59-C
    NHTSA is also proposing to amend the minimum domestic passenger car 
CAFE standards for MYs 2024-2026. The provision at 49 U.S.C. 
32902(b)(4) requires NHTSA to project the minimum standard when it 
promulgates passenger car standards for a model year, so it is 
appropriate to revisit the minimum standards at this time. NHTSA is 
proposing to retain the 1.9 percent offset used in the 2020 final rule, 
such that the minimum domestic passenger car standard would be as shown 
in Table II-1.
[GRAPHIC] [TIFF OMITTED] TP03SE21.015

    The next section describes some of the effects that NHTSA estimates 
would follow from this proposal, including how the curves shown above 
translate to estimated average mile per gallon requirements for the 
industry.

B. What does NHTSA estimate the effects of proposing this would be?

    As for past CAFE rulemakings, NHTSA has used the CAFE Model to 
estimate the effects of proposed CAFE standards, and of other 
regulatory alternatives under consideration. Some inputs to the CAFE 
Model are derived from other models, such as Argonne National 
Laboratory's ``Autonomie'' vehicle simulation tool and Argonne's 
Greenhouse gases, Regulated Emissions, and Energy use in Transportation 
(GREET) fuel-cycle emissions analysis model, the U.S. Energy 
Information Administration's (EIA's) National Energy Modeling System 
(NEMS), and EPA's Motor Vehicle Emission Simulator (MOVES) vehicle 
emissions model. Especially given the scope of the

[[Page 49614]]

NHTSA's analysis (through model years 2050, with driving of model year 
2029 vehicles accounted for through calendar year 2068), these inputs 
involve a multitude of uncertainties. For example, a set of inputs with 
significant uncertainty could include future population and economic 
growth, future gasoline and electricity prices, future petroleum market 
characteristics (e.g., imports and exports), future battery costs, 
manufacturers' future responses to standards and fuel prices, buyers' 
future responses to changes in vehicle prices and fuel economy levels, 
and future emission rates for ``upstream'' processes (e.g., refining, 
finished fuel transportation, electricity generation). Considering that 
all of this is uncertain from a 2021 vantage point, NHTSA underscores 
that all results of this analysis are, in turn, uncertain, and simply 
represent the agency's best estimates based on the information 
currently before us.
    NHTSA estimates that this proposal would increase the eventual \9\ 
average of manufacturers' CAFE requirements to about 48 mpg by 2026 
rather than, under the No-Action Alternative (i.e., the baseline 
standards issued in 2020), about 40 mpg. For passenger cars, the 
average in 2026 is estimated to reach about 58 mpg, and for light 
trucks, about 42. This compares with 47 mpg and 34 mpg for cars and 
trucks, respectively, under the No-Action Alternative.
---------------------------------------------------------------------------

    \9\ Here, ``eventual'' means by MY 2029, after most of the fleet 
will have been redesigned under the MY 2026 standards. NHTSA allows 
the CAFE Model to continue working out compliance solutions for the 
regulated model years for three model years after the last regulated 
model year, in recognition of the fact that manufacturers do not 
comply perfectly with CAFE standards in each model year.
[GRAPHIC] [TIFF OMITTED] TP03SE21.016

    Because manufacturers do not comply exactly with each standard in 
each model year, but rather focus their compliance efforts when and 
where it is most cost-effective to do so, ``estimated achieved'' fuel 
economy levels differ somewhat from ``estimated required'' levels for 
each fleet, for each year. NHTSA estimates that the industry-wide 
average fuel economy achieved in MY 2029 could increase from about 44 
mpg under the No-Action Alternative to about 49 mpg under the proposal.
[GRAPHIC] [TIFF OMITTED] TP03SE21.017

    As discussed above, NHTSA's analysis--unlike its previous CAFE 
analyses--estimates manufacturers' potential responses to the combined 
effect of CAFE standards and separate CO2 standards 
(including agreements some manufacturers have reached with California), 
ZEV mandates, and fuel prices. Together, the aforementioned regulatory 
programs are more binding than any single program considered in 
isolation, and this analysis, like past analyses, shows some estimated 
overcompliance with the proposed CAFE standards, albeit by much less 
than what was shown in the NPRM that preceded the 2020 final rule, and 
any overcompliance is highly manufacturer-dependent.
    Expressed as equivalent required and achieved average 
CO2 levels (using 8887 grams of CO2 per gallon of 
gasoline vehicle certification fuel), the above CAFE levels appear as 
shown in Table II-4 and Table II-5.
[GRAPHIC] [TIFF OMITTED] TP03SE21.018


[[Page 49615]]


[GRAPHIC] [TIFF OMITTED] TP03SE21.019

    Average requirements and achieved CAFE levels would ultimately 
depend on manufacturers' and consumers' responses to standards, 
technology developments, economic conditions, fuel prices, and other 
factors.
    NHTSA estimates that over the lives of vehicles produced prior to 
MY 2030, the proposal would save about 50 billion gallons of gasoline 
and increase electricity consumption (as the percentage of electric 
vehicles increases over time) by about 275 terawatts (TWh), compared to 
levels of gasoline and electricity consumption NHTSA projects would 
occur under the baseline standards (i.e., the No-Action Alternative).
[GRAPHIC] [TIFF OMITTED] TP03SE21.020

    NHTSA's analysis also estimates total annual consumption of fuel by 
the entire on-road fleet from calendar year 2020 through calendar year 
2050. On this basis, gasoline and electricity consumption by the U.S. 
light-duty vehicle fleet evolves as shown in Figure II-3 and Figure II-
4, each of which shows projections for the No-Action Alternative 
(Alternative 0, i.e., the baseline), Alternative 1, Alternative 2 (the 
proposal), and Alternative 3.
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[[Page 49616]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.021


[[Page 49617]]


[GRAPHIC] [TIFF OMITTED] TP03SE21.022

    Accounting for emissions from both vehicles and upstream energy 
sector processes (e.g., petroleum refining and electricity generation), 
NHTSA estimates that the proposal would reduce greenhouse gas emissions 
by about 465 million metric tons of carbon dioxide (CO2), 
about 500 thousand metric tons of methane (CH4), and about 
12 thousand tons of nitrous oxide (N2O).
[GRAPHIC] [TIFF OMITTED] TP03SE21.023

    As for fuel consumption, NHTSA's analysis also estimates annual 
emissions attributable to the entire on-road fleet from calendar year 
2020 through calendar year 2050. Also accounting for both vehicles and 
upstream processes, NHTSA estimates that CO2 emissions could 
evolve over time as shown in Figure II-5, which accounts for both 
emissions from both vehicles and upstream processes.

[[Page 49618]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.024

    Estimated emissions of methane and nitrous oxides follow similar 
trends. As discussed in the TSD, PRIA, and this NPRM, NHTSA has 
performed two types of supporting analysis. This NPRM and PRIA focus on 
the ``standard setting'' analysis, which sets aside the potential that 
manufacturers could respond to standards by using compliance credits or 
introducing new alternative fuel vehicle (including BEVs) models during 
the ``decision years'' (for this NPRM, 2024, 2025, and 2026). The 
accompanying SEIS focuses on an ``unconstrained'' analysis, which does 
not set aside these potential manufacturer actions. The SEIS presents 
much more information regarding projected GHG emissions, as well as 
model-based estimates of corresponding impacts on several measures of 
global climate change.
    Also accounting for vehicular and upstream emissions, NHTSA has 
estimated annual emissions of most criteria pollutants (i.e., 
pollutants for which EPA has issued National Ambient Air Quality 
Standards). NHTSA estimates that under each regulatory alternative, 
annual emissions of carbon monoxide (CO), volatile organic compounds 
(VOC), nitrogen oxide (NOX), and fine particulate matter 
(PM2.5) attributable to the light-duty on-road fleet will 
decline dramatically between 2020 and 2050, and that emissions in any 
given year could be very nearly the same under each regulatory 
alternative. For example, Figure II-6 shows NHTSA's estimate of future 
NOX emissions under each alternative.

[[Page 49619]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.025

BILLING CODE 4910-59-C
    On the other hand, as discussed in the PRIA and SEIS, NHTSA 
projects that annual SO2 emissions attributable to the 
light-duty on-road fleet could increase modestly under the action 
alternatives, because, as discussed above, NHTSA projects that each of 
the action alternatives could lead to greater use of electricity (for 
PHEVs and BEVs). The adoption of actions--such as actions prompted by 
President Biden's Executive order directing agencies to develop a 
Federal Clean Electricity and Vehicle Procurement Strategy--to reduce 
electricity generation emission rates beyond projections underlying 
NHTSA's analysis (discussed in the TSD) could dramatically reduce 
SO2 emissions under all regulatory alternatives considered 
here.\10\
---------------------------------------------------------------------------

    \10\ https://www.whitehouse.gov/briefing-room/presidential-actions/2021/01/27/executive-order-on-tackling-the-climate-crisis-at-home-and-abroad/, accessed June 17, 2021.
---------------------------------------------------------------------------

    For the ``standard setting'' analysis, the PRIA accompanying this 
NPRM provides additional detail regarding projected criteria pollutant 
emissions and health effects, as well as the inclusion of these impacts 
in this benefit-cost analysis. For the ``unconstrained'' or ``EIS'' 
type of analysis, the SEIS accompanying this NPRM presents much more 
information regarding projected criteria pollutant emissions, as well 
as model-based estimates of corresponding impacts on several measures 
of urban air quality and public health. As mentioned above, these 
estimates of criteria pollutant emissions are based on a complex 
analysis involving interacting simulation techniques and a myriad of 
input estimates and assumptions. Especially extending well past 2040, 
the analysis involves a multitude of uncertainties. Therefore, actual 
criteria pollutant emissions could ultimately be different from NHTSA's 
current estimates.
    To illustrate the effectiveness of the technology added in response 
to this proposal, Table II-8 presents NHTSA's estimates for increased 
vehicle cost and lifetime fuel expenditures if we assumed the 
behavioral response to the lower cost of driving were zero.\11\ These 
numbers are presented in lieu of NHTSA's primary estimate of lifetime 
fuel savings, which would give an incomplete picture of technological 
effectiveness because the analysis accounts for consumers' behavioral 
response to the lower cost-per-mile of driving a more fuel-efficient 
vehicle.
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    \11\ While this comparison illustrates the effectiveness of the 
technology added in response to this proposal, it does not represent 
a full consumer welfare analysis, which would account for drivers' 
likely response to the lower cost-per-mile of driving, as well as a 
variety of other benefits and costs they will experience. The 
agency's complete analysis of the proposal's likely impacts on 
passenger car and light truck buyers appears in the PRIA, Appendix 
I, Table A-23-1.

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[[Page 49620]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.026

    With the SCC discounted at 2.5% and other benefits and costs 
discounted at 3%, NHTSA estimates that costs and benefits could be 
approximately $120 billion and $121 billion, respectively, such that 
the present value of aggregate net benefits to society could be 
somewhat less than $1 billion. With the social cost of carbon (SCC) 
discounted at 3% and other benefits and costs discounted at 7%, NHTSA 
estimates approximately $90 billion in costs and $76 billion in 
benefits could be attributable to vehicles produced prior to MY 2030 
over the course of their lives, such that the present value of 
aggregate net costs to society could be approximately $15 billion.\13\
---------------------------------------------------------------------------

    \12\ Assumes no rebound effect.
    \13\ NHTSA interprets the 2021 IWG draft guidance as indicating 
that a 2.5% discount rate for the SCC is consistent with discounting 
near-term benefits and costs of the proposal at the OMB-recommended 
consumption discount rate of 3%. For the OMB-recommended discount 
rate of 7%, NHTSA concluded that a 3% discount rate for the SCC was 
reasonable given that the IWG draft guidance suggested that the 
appropriate discount rate for the SCC was likely lower than 3%. 
NHTSA refers readers specifically to pp. 16-17 of that guidance, 
available at https://www.whitehouse.gov/wp-content/uploads/2021/02/TechnicalSupportDocument_SocialCostofCarbonMethaneNitrousOxide.pdf?source=email.
[GRAPHIC] [TIFF OMITTED] TP03SE21.027

    Model results can be viewed many different ways, and NHTSA's 
rulemaking considers both ``model year'' and ``calendar year'' 
perspectives. The ``model year'' perspective, above, considers vehicles 
projected to be produced in some range of model years, and accounts for 
impacts, benefits, and costs attributable to these vehicles from the 
present (from the model year's perspective, 2020) until they are 
projected to be scrapped. The bulk of NHTSA's analysis considers 
vehicles produced prior to model year 2030, accounting for the 
estimated indirect impacts new standards could have on the remaining 
operation of vehicles already in service. This perspective emphasizes 
impacts on those model years nearest to those (2024-2026) for which 
NHTSA is proposing new standards. NHTSA's analysis also presents some 
results focused only on model years 2024-2026, setting aside the 
estimated indirect impacts on earlier model years, and the impacts 
estimated to occur during model years 2027-2029, as some manufacturers 
and products ``catch up'' to the standards.
    Another way to present the benefits and costs of the proposal is 
the ``calendar year'' perspective shown in Table II-10, which is 
similar to how EPA presents benefits and costs in its proposal for GHG 
standards for MYs 2023-2026. The calendar year perspective considers 
all vehicles projected to be in service in each of some range of future 
calendar years. NHTSA's presentation of results from this perspective 
considers calendar years 2020-2050, because the model's representation 
of the full on-road fleet extends through 2050. Unlike the model year 
perspective, this perspective includes vehicles projected produced 
during model years 2030-2050. This perspective emphasizes longer-term 
impacts that could accrue if standards were to continue without change. 
Table II-10 shows costs and benefits for MYs 2023-2026 while Table II-9 
shows costs and benefits through MY 2029.

[[Page 49621]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.028

    Though based on the exact same model results, these two 
perspectives provide considerably different views of estimated costs 
and benefits. Because technology costs account for a large share of 
overall estimated costs, and are also projected to decline over time 
(as manufacturers gain more experience with new technologies), costs 
tend to be ``front loaded''--occurring early in a vehicle's life and 
tending to be higher in earlier model years than in later model years. 
Conversely, because social benefits of standards occur as vehicles are 
driven, and because both fuel prices and the social cost of 
CO2 emissions are projected to increase in the future, 
benefits tend to be ``back loaded.'' As a result, estimates of future 
fuel savings, CO2 reductions, and net social benefits are 
higher under the calendar year perspective than under the model year 
perspective. On the other hand, with longer-term impacts playing a 
greater role, the calendar year perspective is more subject to 
uncertainties regarding, for example, future technology costs and fuel 
prices.
    Even though NHTSA and EPA estimate benefits, costs, and net 
benefits using similar methodologies and achieve similar results, 
different approaches to accounting may give the false appearance of 
significant divergences. Table II-10 above presents NHTSA's results 
using comparable accounting to EPA's preamble Table 5. EPA also 
presents cost and benefit information in its RIA over calendar years 
2021 through 2050. The numbers most comparable to those presented in 
EPA's RIA are those NHTSA developed to complete its Supplemental 
Environmental Impact Statement (SEIS) using an identical accounting 
approach. This is because the statutory limitations constraining 
NHTSA's standard setting analysis, such as those in 49 U.S.C. 32902(h) 
prohibiting consideration of full vehicle electrification during the 
rulemaking timeframe, or consideration of the trading or transferring 
of overcompliance credits, do not similarly apply to its EIS 
analysis.\14\ NHTSA's EIS analysis estimates $312 billion in costs, 
$443 billion in benefits, and $132 billion in net benefits using a 3% 
discount rate over calendar years 2021 through 2050.\15\ NHTSA 
describes its cost and benefit accounting approach in Section V of this 
preamble.
---------------------------------------------------------------------------

    \14\ As the EIS analysis contains information that NHTSA is 
statutorily prevented from considering, the agency does not rely on 
this analysis in regulatory decision-making.
    \15\ See PRIA Chapter 6.5 for more information regarding NHTSA's 
estimates of annual benefits and costs using NHTSA's standard 
setting analysis. See Tables B-7-25 through B-7-30 in Appendix II of 
the PRIA for a more detailed breakdown of NHTSA's EIS analysis.
---------------------------------------------------------------------------

C. Why does NHTSA tentatively believe the proposal would be maximum 
feasible, and how and why is this tentative conclusion different from 
the 2020 final rule?

    NHTSA's tentative conclusion, after consideration of the factors 
described below and information in the administrative record for this 
action, is that 8 percent increases in stringency for MYs 2024-2026 
(Alternative 2 of this analysis) are maximum feasible. The Department 
of Transportation is deeply committed to working aggressively to 
improve energy conservation and reduce security risks associated with 
energy use, and higher standards appear increasingly likely to be 
economically practicable given almost-daily announcements by major 
automakers about forthcoming new high-fuel-economy vehicle models, as 
described in more detail below. Despite only one year having passed 
since the 2020 final rule, enough has changed in the U.S. and the world 
that revisiting the CAFE standards for MYs 2024-2026, and raising their 
stringency considerably, is both appropriate and reasonable.
    The 2020 final rule set CAFE standards that increased at 1.5 
percent per year for cars and trucks for MYs 2021-2026, in large part 
because it prioritized industry concerns and reducing vehicle purchase 
costs to consumers and manufacturers. This proposed rule acknowledges 
the priority of energy conservation, consistent with NHTSA's statutory 
authority. Moreover, NHTSA is also legally required to consider the 
environmental implications of this action under NEPA, and while the 
2020 final rule did undertake a NEPA analysis, it did not prioritize 
the environmental considerations aspects of the statutory need of the 
U.S. to conserve energy.
    NHTSA recognizes that the amount of lead time available before MY 
2024 is less than what was provided in the 2012 rule. As will be 
discussed further in Section VI, NHTSA believes that the evidence 
suggests that the proposed standards are still economically 
practicable.
    We note further that while this proposal is different from the 2020 
final rule (and also from the 2012 final rule), NHTSA, like any other 
Federal agency, is afforded an opportunity to reconsider prior views 
and, when warranted, to adopt new positions. Indeed, as a matter of 
good governance, agencies should revisit their positions when 
appropriate, especially to ensure that their actions and regulations 
reflect legally sound interpretations of the agency's authority and 
remain consistent with the agency's views and practices. As a matter of 
law, ``an Agency is entitled to change its interpretation of a 
statute.'' \16\ Nonetheless, ``[w]hen an Agency adopts a materially 
changed interpretation of a statute, it must in addition provide a 
`reasoned analysis' supporting its decision to revise its 
interpretation.'' \17\

[[Page 49622]]

This preamble and the accompanying TSD and PRIA all provide extensive 
detail on the agency's updated analysis, and Section VI contains the 
agency's explanation of how the agency has considered that analysis and 
other relevant information in tentatively determining that the proposed 
CAFE standards are maximum feasible for MYs 2024-2026 passenger cars 
and light trucks.
---------------------------------------------------------------------------

    \16\ Phoenix Hydro Corp. v. FERC, 775 F.2d 1187, 1191 (D.C. Cir. 
1985).
    \17\ Alabama Educ. Ass'n v. Chao, 455 F.3d 386, 392 (D.C. Cir. 
2006) (quoting Motor Vehicle Mfrs. Ass'n of U.S., Inc. v. State Farm 
Mut. Auto. Ins. Co., 463 U.S. 29, 57 (1983)); see also Encino 
Motorcars, LLC v. Navarro, 136 S.Ct. 2117, 2125 (2016) (``Agencies 
are free to change their existing policies as long as they provide a 
reasoned explanation for the change.'') (citations omitted).
---------------------------------------------------------------------------

D. How is this proposal consistent with EPA's proposal and with 
California's programs?

    The NHTSA and EPA proposals remain coordinated despite being issued 
as separate regulatory actions. Because NHTSA and EPA are regulating 
the exact same vehicles and manufacturer will use the same technologies 
to meet both sets of standards, NHTSA and EPA coordinated during the 
development of each agency's independent proposal to revise the 
standards set forth in the 2020 final rule. The NHTSA-proposed CAFE and 
EPA-proposed CO2 standards for MY 2026 represent roughly 
equivalent levels of stringency and may serve as a coordinated starting 
point for subsequent standards. While the proposed CAFE and 
CO2 standards for MYs 2024-2025 are different, this is 
largely due to the difference in the ``start year'' for the revised 
regulations--EPA is proposing to revise standards for MY 2023, while 
EPCA's lead time requirements, which do not apply to EPA, prevent NHTSA 
from proposing revised standards until MY 2024. In order to set 
standards for MY 2023, EPA intends to issue its final rule by December 
31, 2021, whereas NHTSA has until April 2022 to finalize standards for 
MY 2024. The difference in timing makes separate rulemaking actions 
reasonable and prudent. The specific differences in what the two 
agencies' standards require become smaller each year, until alignment 
is achieved. The agencies still have coordinated closely to minimize 
inconsistency between the programs and will continue to do so through 
the final rule stage.
    While NHTSA's and EPA's programs differ in certain other respects, 
like programmatic flexibilities, those differences are not new in this 
proposal. Some parts of the programs are harmonized, and others differ, 
often as a result of statute. Since NHTSA and EPA began regulating 
together under President Obama, differences in programmatic 
flexibilities have meant that manufacturers have had (and will have) to 
plan their compliance strategies considering both the CAFE standards 
and the GHG standards and assure that they are in compliance with both, 
while still building a single fleet of vehicles to accomplish that 
goal. NHTSA is proposing CAFE standards that increase at 8 percent per 
year over MYs 2024-2026 because that is what NHTSA has tentatively 
concluded is maximum feasible in those model years, under the EPCA 
factors, and is confident that industry would still be able to build a 
single fleet of vehicles to meet both the NHTSA and EPA standards. Auto 
manufacturers are extremely sophisticated companies, well-able to 
manage complex compliance strategies that account for multiple 
regulatory programs concurrently. If different agencies' standards are 
more binding for some companies in certain years, this does not mean 
that manufacturers must build multiple fleets of vehicles, simply that 
they will have to be more strategic about how they build their fleet.
    NHTSA has also considered and accounted for California's ZEV 
mandate (and its adoption by a number of other states) in developing 
the baseline for this proposal, and has also accounted for the 
Framework Agreements between California, BMW, Ford, Honda, VWA, and 
Volvo. NHTSA believes that it is reasonable to include ZEV in the 
baseline for this proposal regardless of whether California receives a 
waiver of preemption under the Clean Air Act (CAA) because, according 
to California, industry overcompliance with the ZEV mandate has been 
extensive, which indicates that whether or not a waiver exists, many 
companies intend to produce ZEVs in volumes comparable to what a ZEV 
mandate would require. Because no decision has yet been made on a CAA 
waiver for California, and because modeling a sub-national fleet is not 
currently an analytical option for NHTSA, NHTSA has not expressly 
accounted for California GHG standards in the analysis for this 
proposal, although we seek comment on whether and how to account for 
them in the final rule. Chapter 6 of the accompanying PRIA shows the 
estimated effects of all of these programs simultaneously.

III. Technical Foundation for NPRM Analysis

A. Why does NHTSA conduct this analysis?

    NHTSA is proposing to establish revised CAFE standards for 
passenger cars and light trucks produced for model years (MYs) 2024-
2026. NHTSA's review of the existing standards is consistent with 
Executive Order 13990, Protecting Public Health and the Environment and 
Restoring Science to Tackle the Climate Crisis, signed on January 20, 
2021, directing the review of the 2020 final rule that established CAFE 
standards for MYs 2021-2026 and the consideration of whether to 
suspend, revise, or rescind that action by July 2021.\18\ NHTSA 
establishes CAFE standards under the Energy Policy and Conservation 
Act, as amended, and this proposal is undertaken pursuant to that 
authority. This proposal would require CAFE stringency for both 
passenger cars and light trucks to increase at a rate of 8 percent per 
year annually from MY 2024 through MY 2026. NHTSA estimates that over 
the useful lives of vehicles produced prior to MY 2030, the proposal 
would save about 50 billion gallons of gasoline and increase 
electricity consumption by about 275 TWh. Accounting for emissions from 
both vehicles and upstream energy sector processes (e.g., petroleum 
refining and electricity generation), NHTSA estimates that the proposal 
would reduce greenhouse gas emissions by about 465 million metric tons 
of carbon dioxide (CO2), about 500 thousand tons metric tons 
of methane (CH4), and about 12 thousand tons of nitrous 
oxide (N2O).
---------------------------------------------------------------------------

    \18\ 86 FR 7037 (Jan. 25, 2021).
---------------------------------------------------------------------------

    When NHTSA promulgates new regulations, it generally presents an 
analysis that estimates the impacts of such regulations, and the 
impacts of other regulatory alternatives. These analyses derive from 
statutes such as the Administrative Procedure Act (APA) and National 
Environmental Policy Act (NEPA), from Executive orders (such as 
Executive Order 12866 and 13653), and from other administrative 
guidance (e.g., Office of Management Budget Circular A-4). For CAFE, 
the Energy Policy and Conservation Act (EPCA), as amended by the Energy 
Independence and Security Act (EISA), contains a variety of provisions 
that require NHTSA to consider certain compliance elements in certain 
ways and avoid considering other things, in determining maximum 
feasible CAFE standards. Collectively, capturing all of these 
requirements and guidance elements analytically means that, at least 
for CAFE, NHTSA presents an analysis that spans a meaningful range of 
regulatory alternatives, that quantifies a range of technological, 
economic, and environmental impacts, and that does so in a manner that 
accounts for EPCA's express requirements for the CAFE program

[[Page 49623]]

(e.g., passenger cars and light trucks are regulated separately, and 
the standard for each fleet must be set at the maximum feasible level 
in each model year).
    NHTSA's decision regarding the proposed standards is thus supported 
by extensive analysis of potential impacts of the regulatory 
alternatives under consideration. Along with this preamble, a Technical 
Support Document (TSD), a Preliminary Regulatory Impact Analysis 
(PRIA), and a Supplemental Environmental Impact Statement (SEIS), 
together provide an extensive and detailed enumeration of related 
methods, estimates, assumptions, and results. NHTSA's analysis has been 
constructed specifically to reflect various aspects of governing law 
applicable to CAFE standards and has been expanded and improved in 
response to comments received to the prior rulemaking and based on 
additional work conducted over the last year. Further improvements may 
be made based on comments received to this proposal, the 2021 NAS 
Report,\19\ and other additional work generally previewed in these 
rulemaking documents. The analysis for this proposal aided NHTSA in 
implementing its statutory obligations, including the weighing of 
various considerations, by reasonably informing decision-makers about 
the estimated effects of choosing different regulatory alternatives.
---------------------------------------------------------------------------

    \19\ National Academies of Sciences, Engineering, and Medicine 
(NASEM), 2021. Assessment of Technologies for Improving Fuel Economy 
of Light-Duty Vehicles--2025-2035, Washington, DC: The National 
Academies Press (hereafter, ``2021 NAS Report''). Available at 
https://www.nationalacademies.org/our-work/assessment-of-technologies-for-improving-fuel-economy-of-light-duty-vehicles-phase-3 and for hard-copy review at DOT headquarters.
---------------------------------------------------------------------------

    NHTSA's analysis makes use of a range of data (i.e., observations 
of things that have occurred), estimates (i.e., things that may occur 
in the future), and models (i.e., methods for making estimates). Two 
examples of data include (1) records of actual odometer readings used 
to estimate annual mileage accumulation at different vehicle ages and 
(2) CAFE compliance data used as the foundation for the ``analysis 
fleet'' containing, among other things, production volumes and fuel 
economy levels of specific configurations of specific vehicle models 
produced for sale in the U.S. Two examples of estimates include (1) 
forecasts of future GDP growth used, with other estimates, to forecast 
future vehicle sales volumes and (2) the ``retail price equivalent'' 
(RPE) factor used to estimate the ultimate cost to consumers of a given 
fuel-saving technology, given accompanying estimates of the 
technology's ``direct cost,'' as adjusted to account for estimated 
``cost learning effects'' (i.e., the tendency that it will cost a 
manufacturer less to apply a technology as the manufacturer gains more 
experience doing so).
    NHTSA uses the CAFE Compliance and Effects Modeling System (usually 
shortened to the ``CAFE Model'') to estimate manufacturers' potential 
responses to new CAFE and CO2 standards and to estimate 
various impacts of those responses. DOT's Volpe National Transportation 
Systems Center (often simply referred to as the ``Volpe Center'') 
develops, maintains, and applies the model for NHTSA. NHTSA has used 
the CAFE Model to perform analyses supporting every CAFE rulemaking 
since 2001. The 2016 rulemaking regarding heavy-duty pickup and van 
fuel consumption and CO2 emissions also used the CAFE Model 
for analysis (81 FR 73478, October 25, 2016).
    The basic design of the CAFE Model is as follows: the system first 
estimates how vehicle manufacturers might respond to a given regulatory 
scenario, and from that potential compliance solution, the system 
estimates what impact that response will have on fuel consumption, 
emissions, and economic externalities. In a highly-summarized form, 
Figure III-1 shows the basic categories of CAFE Model procedures and 
the sequential flow between different stages of the modeling. The 
diagram does not present specific model inputs or outputs, as well as 
many specific procedures and model interactions. The model 
documentation accompanying this preamble presents these details, and 
Chapter 1 of the TSD contains a more detailed version of this flow 
diagram for readers who are interested.
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[[Page 49624]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.029

BILLING CODE 4910-59-C
    More specifically, the model may be characterized as an integrated 
system of models. For example, one model estimates manufacturers' 
responses, another estimates resultant changes in total vehicle sales, 
and still another estimates resultant changes in fleet turnover (i.e., 
scrappage). Additionally, and importantly, the model does not determine 
the form or stringency of the standards. Instead, the model applies 
inputs specifying the form and stringency of standards to be analyzed 
and produces outputs showing the impacts of manufacturers working to 
meet those standards, which become the basis for comparing between 
different potential stringencies. A regulatory scenario, meanwhile, 
involves specification of the form, or shape, of the standards (e.g., 
flat standards, or linear or logistic attribute-based standards), scope 
of passenger car and truck regulatory classes, and stringency of the 
CAFE standards for each model year to be analyzed. For example, a 
regulatory scenario may define CAFE standards that increase in 
stringency by 8 percent per year for 3 consecutive years.
    Manufacturer compliance simulation and the ensuing effects 
estimation, collectively referred to as compliance modeling, encompass 
numerous subsidiary elements. Compliance simulation begins with a 
detailed user-provided \20\ initial forecast of the vehicle models 
offered for sale during the simulation period. The compliance 
simulation then attempts to bring each manufacturer into compliance 
with the standards \21\ defined by the regulatory scenario contained 
within an input file developed by the user.
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    \20\ Because the CAFE Model is publicly available, anyone can 
develop their own initial forecast (or other inputs) for the model 
to use. The DOT-developed market data file that contains the 
forecast used for this proposal is available on NHTSA's website.
    \21\ With appropriate inputs, the model can also be used to 
estimate impacts of manufacturers' potential responses to new 
CO2 standards and to California's ZEV program.
---------------------------------------------------------------------------

    Estimating impacts involves calculating resultant changes in new 
vehicle costs, estimating a variety of costs (e.g., for fuel) and 
effects (e.g., CO2 emissions from fuel combustion) occurring 
as vehicles are driven over their lifetimes before eventually being

[[Page 49625]]

scrapped, and estimating the monetary value of these effects. 
Estimating impacts also involves consideration of consumer responses--
e.g., the impact of vehicle fuel economy, operating costs, and vehicle 
price on consumer demand for passenger cars and light trucks. Both 
basic analytical elements involve the application of many analytical 
inputs. Many of these inputs are developed outside of the model and not 
by the model. For example, the model applies fuel prices; it does not 
estimate fuel prices.
    NHTSA also uses EPA's MOVES model to estimate ``tailpipe'' (a.k.a. 
``vehicle'' or ``downstream'') emission factors for criteria 
pollutants,\22\ and uses four Department of Energy (DOE) and DOE-
sponsored models to develop inputs to the CAFE Model, including three 
developed and maintained by DOE's Argonne National Laboratory. The 
agency uses the DOE Energy Information Administration's (EIA's) 
National Energy Modeling System (NEMS) to estimate fuel prices,\23\ and 
uses Argonne's Greenhouse gases, Regulated Emissions, and Energy use in 
Transportation (GREET) model to estimate emissions rates from fuel 
production and distribution processes.\24\ DOT also sponsored DOE/
Argonne to use Argonne's Autonomie full-vehicle modeling and simulation 
system to estimate the fuel economy impacts for roughly a million 
combinations of technologies and vehicle types.25 26 The TSD 
and PRIA describe details of the agency's use of these models. In 
addition, as discussed in the SEIS accompanying this NPRM, DOT relied 
on a range of climate models to estimate impacts on climate, air 
quality, and public health. The SEIS discusses and describes the use of 
these models.
---------------------------------------------------------------------------

    \22\ See https://www.epa.gov/moves. This proposal uses version 
MOVES3, available at https://www.epa.gov/moves/latest-version-motor-vehicle-emission-simulator-moves.
    \23\ See https://www.eia.gov/outlooks/aeo/info_nems_archive.php. 
This proposal uses fuel prices estimated using the Annual Energy 
Outlook (AEO) 2021 version of NEMS (see https://www.eia.gov/outlooks/aeo/pdf/02%20AEO2021%20Petroleum.pdf).
    \24\ Information regarding GREET is available at https://greet.es.anl.gov/index.php. This NPRM uses the 2020 version of 
GREET.
    \25\ As part of the Argonne simulation effort, individual 
technology combinations simulated in Autonomie were paired with 
Argonne's BatPaC model to estimate the battery cost associated with 
each technology combination based on characteristics of the 
simulated vehicle and its level of electrification. Information 
regarding Argonne's BatPaC model is available at https://www.anl.gov/cse/batpac-model-software.
    \26\ In addition, the impact of engine technologies on fuel 
consumption, torque, and other metrics was characterized using GT-
POWER simulation modeling in combination with other engine modeling 
that was conducted by IAV Automotive Engineering, Inc. (IAV). The 
engine characterization ``maps'' resulting from this analysis were 
used as inputs for the Autonomie full-vehicle simulation modeling. 
Information regarding GT-POWER is available at https://www.gtisoft.com/gt-suite-applications/propulsion-systems/gt-power-engine-simulation-software.
---------------------------------------------------------------------------

    To prepare for analysis supporting this proposal, DOT has refined 
and expanded the CAFE Model through ongoing development. Examples of 
such changes, some informed by past external comments, made since early 
2020 include:
     Inclusion of 400- and 500-mile BEVs;
     Inclusion of high compression ratio (HCR) engines with 
cylinder deactivation;
     Accounting for manufacturers' responses to both CAFE and 
CO2 standards jointly (rather than only separately)
     Accounting for the ZEV mandates applicable in California 
and the ``Section 177'' states;
     Accounting for some vehicle manufacturers' (BMW, Ford, 
Honda, VW, and Volvo) voluntary agreement with the State of California 
to continued annual national-level reductions of vehicle greenhouse gas 
emissions through MY 2026, with greater rates of electrification than 
would have been required under the 2020 Federal final rule; \27\
---------------------------------------------------------------------------

    \27\ For more information on the Framework Agreements for Clean 
Cars, including the specific agreements signed by individual 
manufacturers, see https://ww2.arb.ca.gov/news/framework-agreements-clean-cars.
---------------------------------------------------------------------------

    [cir] Inclusion of CAFE civil penalties in the ``effective cost'' 
metric used when simulating manufacturers' potential application of 
fuel-saving technologies;
    [cir] Refined procedures to estimate health effects and 
corresponding monetized damages attributable to criteria pollutant 
emissions;
    [cir] New procedures to estimate the impacts and corresponding 
monetized damages of highway vehicle crashes that do not result in 
fatalities;
    [cir] Procedures to ensure that modeled technology application and 
production volumes are the same across all regulatory alternatives in 
the earliest model years; and
    [cir] Procedures to more precisely focus application of EPCA's 
``standard setting constraints'' (i.e., regarding the consideration of 
compliance credits and additional dedicated alternative fueled 
vehicles) to only those model years for which NHTSA is proposing or 
finalizing new standards.
    These changes reflect DOT's long-standing commitment to ongoing 
refinement of its approach to estimating the potential impacts of new 
CAFE standards.
    NHTSA underscores that this analysis exercises the CAFE Model in a 
manner that explicitly accounts for the fact that in producing a single 
fleet of vehicles for sale in the United States, manufacturers face the 
combination of CAFE standards, EPA CO2 standards, and ZEV 
mandates, and for five manufacturers, the voluntary agreement with 
California to more stringent CO2 reduction requirements 
(also applicable to these manufacturers' total production for the U.S. 
market) through model year 2026. These regulations and contracts have 
important structural and other differences that affect the strategy a 
manufacturer could use to comply with each of the above.
    As explained, the analysis is designed to reflect a number of 
statutory and regulatory requirements applicable to CAFE and tailpipe 
CO2 standard-setting. EPCA contains a number of requirements 
governing the scope and nature of CAFE standard setting. Among these, 
some have been in place since EPCA was first signed into law in 1975, 
and some were added in 2007, when Congress passed EISA and amended 
EPCA. EPCA/EISA requirements regarding the technical characteristics of 
CAFE standards and the analysis thereof include, but are not limited 
to, the following, and the analysis reflects these requirements as 
summarized:
    Corporate Average Standards: The provision at 49 U.S.C. 32902 
requires standards that apply to the average fuel economy levels 
achieved by each corporation's fleets of vehicles produced for sale in 
the U.S.\28\ The CAFE Model calculates the CAFE and CO2 
levels of each manufacturer's fleets based on estimated production 
volumes and characteristics, including fuel economy levels, of distinct 
vehicle models that could be produced for sale in the U.S.
---------------------------------------------------------------------------

    \28\ This differs from safety standards and traditional 
emissions standards, which apply separately to each vehicle. For 
example, every vehicle produced for sale in the U.S. must, on its 
own, meet all applicable Federal motor vehicle safety standards 
(FMVSS), but no vehicle produced for sale must, on its own, meet 
Federal fuel economy standards. Rather, each manufacturer is 
required to produce a mix of vehicles that, taken together, achieve 
an average fuel economy level no less than the applicable minimum 
level.
---------------------------------------------------------------------------

    Separate Standards for Passenger Cars and Light Trucks: The 
provision at 49 U.S.C. 32902 requires the Secretary of Transportation 
to set CAFE standards separately for passenger cars and light trucks. 
The CAFE Model accounts separately for passenger cars and light trucks 
when it analyzes CAFE or CO2 standards, including 
differentiated standards and compliance.

[[Page 49626]]

    Attribute-Based Standards: The provision at 49 U.S.C. 32902 
requires the Secretary of Transportation to define CAFE standards as 
mathematical functions expressed in terms of one or more vehicle 
attributes related to fuel economy. This means that for a given 
manufacturer's fleet of vehicles produced for sale in the U.S. in a 
given regulatory class and model year, the applicable minimum CAFE 
requirement (i.e., the numerical value of the requirement) is computed 
based on the applicable mathematical function, and the mix and 
attributes of vehicles in the manufacturer's fleet. The CAFE Model 
accounts for such functions and vehicle attributes explicitly.
    Separately Defined Standards for Each Model Year: The provision at 
49 U.S.C. 32902 requires the Secretary to set CAFE standards 
(separately for passenger cars and light trucks \29\) at the maximum 
feasible levels in each model year. The CAFE Model represents each 
model year explicitly, and accounts for the production relationships 
between model years.\30\
---------------------------------------------------------------------------

    \29\ 49 U.S.C. chapter 329 uses the term ``non-passenger 
automobiles,'' while NHTSA uses the term ``light trucks'' in its 
CAFE regulations. The terms' meanings are identical.
    \30\ For example, a new engine first applied to given vehicle 
model/configuration in model year 2020 will most likely be ``carried 
forward'' to model year 2021 of that same vehicle model/
configuration, in order to reflect the fact that manufacturers do 
not apply brand-new engines to a given vehicle model every single 
year. The CAFE Model is designed to account for these real-world 
factors.
---------------------------------------------------------------------------

    Separate Compliance for Domestic and Imported Passenger Car Fleets: 
The provision at 49 U.S.C. 32904 requires the EPA Administrator to 
determine CAFE compliance separately for each manufacturers' fleets of 
domestic passenger cars and imported passenger cars, which 
manufacturers must consider as they decide how to improve the fuel 
economy of their passenger car fleets. The CAFE Model accounts 
explicitly for this requirement when simulating manufacturers' 
potential responses to CAFE standards, and combines any given 
manufacturer's domestic and imported cars into a single fleet when 
simulating that manufacturer's potential response to CO2 
standards (because EPA does not have separate standards for domestic 
and imported passenger cars).
    Minimum CAFE Standards for Domestic Passenger Car Fleets: The 
provision at 49 U.S.C. 32902 requires that domestic passenger car 
fleets meet a minimum standard, which is calculated as 92 percent of 
the industry-wide average level required under the applicable 
attribute-based CAFE standard, as projected by the Secretary at the 
time the standard is promulgated. The CAFE Model accounts explicitly 
for this requirement for CAFE standards and sets this requirement aside 
for CO2 standards.
    Civil Penalties for Noncompliance: The provision at 49 U.S.C. 32912 
(and implementing regulations) prescribes a rate (in dollars per tenth 
of a mpg) at which the Secretary is to levy civil penalties if a 
manufacturer fails to comply with a CAFE standard for a given fleet in 
a given model year, after considering available credits. Some 
manufacturers have historically demonstrated a willingness to pay civil 
penalties rather than achieving full numerical compliance across all 
fleets. The CAFE Model calculates civil penalties for CAFE shortfalls 
and provides means to estimate that a manufacturer might stop adding 
fuel-saving technologies once continuing to do so would be effectively 
more ``expensive'' (after accounting for fuel prices and buyers' 
willingness to pay for fuel economy) than paying civil penalties. The 
CAFE Model does not allow civil penalty payment as an option for 
CO2 standards.
    Dual-Fueled and Dedicated Alternative Fuel Vehicles: For purposes 
of calculating CAFE levels used to determine compliance, 49 U.S.C. 
32905 and 32906 specify methods for calculating the fuel economy levels 
of vehicles operating on alternative fuels to gasoline or diesel 
through MY 2020. After MY 2020, methods for calculating alternative 
fuel vehicle (AFV) fuel economy are governed by regulation. The CAFE 
Model is able to account for these requirements explicitly for each 
vehicle model. However, 49 U.S.C. 32902 prohibits consideration of the 
fuel economy of dedicated alternative fuel vehicle (AFV) models when 
NHTSA determines what levels of CAFE standards are maximum feasible. 
The CAFE Model therefore has an option to be run in a manner that 
excludes the additional application of dedicated AFV technologies in 
model years for which maximum feasible standards are under 
consideration. As allowed under NEPA for analysis appearing in EISs 
informing decisions regarding CAFE standards, the CAFE Model can also 
be run without this analytical constraint. The CAFE Model does account 
for dual- and alternative fuel vehicles when simulating manufacturers' 
potential responses to CO2 standards. For natural gas 
vehicles, both dedicated and dual-fueled, EPA has a multiplier of 2.0 
for model years 2022-2026.\31\
---------------------------------------------------------------------------

    \31\ While EPA is proposing changes to this and other 
flexibility provisions in its separate NPRM, for purposes of this 
NPRM, the CAFE Model only reflects the current EPA regulatory 
flexibilities.
---------------------------------------------------------------------------

    ZEV Mandates: The CAFE Model can simulate manufacturers' compliance 
with ZEV mandates applicable in California and ``Section 177'' \32\ 
states. The approach involves identifying specific vehicle model/
configurations that could be replaced with PHEVs or BEVs, and 
immediately making these changes in each model year, before beginning 
to consider the potential that other technologies could be applied 
toward compliance with CAFE or CO2 standards.
---------------------------------------------------------------------------

    \32\ The term ``Section 177'' states refers to states which have 
elected to adopt California's standards in lieu of Federal 
requirements, as allowed under Section 177 of the CAA.
---------------------------------------------------------------------------

    Creation and Use of Compliance Credits: The provision at 49 U.S.C. 
32903 provides that manufacturers may earn CAFE ``credits'' by 
achieving a CAFE level beyond that required of a given fleet in a given 
model year, and specifies how these credits may be used to offset the 
amount by which a different fleet falls short of its corresponding 
requirement. These provisions allow credits to be ``carried forward'' 
and ``carried back'' between model years, transferred between regulated 
classes (domestic passenger cars, imported passenger cars, and light 
trucks), and traded between manufacturers. However, credit use is also 
subject to specific statutory limits. For example, CAFE compliance 
credits can be carried forward a maximum of five model years and 
carried back a maximum of three model years. Also, EPCA/EISA caps the 
amount of credit that can be transferred between passenger car and 
light truck fleets and prohibits manufacturers from applying traded or 
transferred credits to offset a failure to achieve the applicable 
minimum standard for domestic passenger cars. The CAFE Model explicitly 
simulates manufacturers' potential use of credits carried forward from 
prior model years or transferred from other fleets.\33\ The provision 
at 49

[[Page 49627]]

U.S.C. 32902 prohibits consideration of manufacturers' potential 
application of CAFE compliance credits when setting maximum feasible 
CAFE standards. The CAFE Model can be operated in a manner that 
excludes the application of CAFE credits for a given model year under 
consideration for standard setting. For modeling CO2 
standards, the CAFE Model does not limit transfers. Insofar as the CAFE 
Model can be exercised in a manner that simulates trading of 
CO2 compliance credits, such simulations treat trading as 
unlimited.\34\
---------------------------------------------------------------------------

    \33\ The CAFE Model does not explicitly simulate the potential 
that manufacturers would carry CAFE or CO2 credits back 
(i.e., borrow) from future model years, or acquire and use CAFE 
compliance credits from other manufacturers. At the same time, 
because EPA has currently elected not to limit credit trading, the 
CAFE Model can be exercised in a manner that simulates unlimited 
(a.k.a. ``perfect'') CO2 compliance credit trading 
throughout the industry (or, potentially, within discrete trading 
``blocs''). NHTSA believes there is significant uncertainty in how 
manufacturers may choose to employ these particular flexibilities in 
the future: For example, while it is reasonably foreseeable that a 
manufacturer who over-complies in one year may ``coast'' through 
several subsequent years relying on those credits rather than 
continuing to make technology improvements, it is harder to assume 
with confidence that manufacturers will rely on future technology 
investments to offset prior-year shortfalls, or whether/how 
manufacturers will trade credits with market competitors rather than 
making their own technology investments. Historically, carry-back 
and trading have been much less utilized than carry-forward, for a 
variety of reasons including higher risk and preference not to `pay 
competitors to make fuel economy improvements we should be making' 
(to paraphrase one manufacturer), although NHTSA recognizes that 
carry-back and trading are used more frequently when standards 
increase in stringency more rapidly. Given the uncertainty just 
discussed, and given also the fact that the agency has yet to 
resolve some of the analytical challenges associated with simulating 
use of these flexibilities, the agency considers borrowing and 
trading to involve sufficient risk that it is prudent to support 
this proposal with analysis that sets aside the potential that 
manufacturers could come to depend widely on borrowing and trading. 
While compliance costs in real life may be somewhat different from 
what is modeled today as a result of this analytical decision, that 
is broadly true no matter what, and the agency does not believe that 
the difference would be so great that it would change the policy 
outcome. Furthermore, a manufacturer employing a trading strategy 
would presumably do so because it represents a lower-cost compliance 
option. Thus, the estimates derived from this modeling approach are 
likely to be conservative in this respect, with real-world 
compliance costs possibly being lower.
    \34\ To avoid making judgments about possible future trading 
activity, the model simulates trading by combining all manufacturers 
into a single entity, so that the most cost-effective choices are 
made for the fleet as a whole.
---------------------------------------------------------------------------

    Statutory Basis for Stringency: The provision at 49 U.S.C. 32902 
requires the Secretary to set CAFE standards at the maximum feasible 
levels, considering technological feasibility, economic practicability, 
the need of the United States to conserve energy, and the impact of 
other motor vehicle standards of the Government. EPCA/EISA authorizes 
the Secretary to interpret these factors, and as the Department's 
interpretation has evolved, NHTSA has continued to expand and refine 
its qualitative and quantitative analysis to account for these 
statutory factors. For example, one of the ways that economic 
practicability considerations are incorporated into the analysis is 
through the technology effectiveness determinations: The Autonomie 
simulations reflect the agency's judgment that it would not be 
economically practicable for a manufacturer to ``split'' an engine 
shared among many vehicle model/configurations into myriad versions 
each optimized to a single vehicle model/configuration.
    National Environmental Policy Act: In addition, NEPA requires the 
Secretary to issue an EIS that documents the estimated impacts of 
regulatory alternatives under consideration. The SEIS accompanying this 
NPRM documents changes in emission inventories as estimated using the 
CAFE Model, but also documents corresponding estimates--based on the 
application of other models documented in the SEIS, of impacts on the 
global climate, on tropospheric air quality, and on human health.
    Other Aspects of Compliance: Beyond these statutory requirements 
applicable to DOT and/or EPA are a number of specific technical 
characteristics of CAFE and/or CO2 regulations that are also 
relevant to the construction of this analysis. For example, EPA has 
defined procedures for calculating average CO2 levels, and 
has revised procedures for calculating CAFE levels, to reflect 
manufacturers' application of ``off-cycle'' technologies that increase 
fuel economy (and reduce CO2 emissions). Although too little 
information is available to account for these provisions explicitly in 
the same way that the agency has accounted for other technologies, the 
CAFE Model does include and makes use of inputs reflecting the agency's 
expectations regarding the extent to which manufacturers may earn such 
credits, along with estimates of corresponding costs. Similarly, the 
CAFE Model includes and makes use of inputs regarding credits EPA has 
elected to allow manufacturers to earn toward CO2 levels 
(not CAFE) based on the use of air conditioner refrigerants with lower 
global warming potential (GWP), or on the application of technologies 
to reduce refrigerant leakage. In addition, the CAFE Model accounts for 
EPA ``multipliers'' for certain alternative fueled vehicles, based on 
current regulatory provisions or on alternative approaches. Although 
these are examples of regulatory provisions that arise from the 
exercise of discretion rather than specific statutory mandate, they can 
materially impact outcomes.
    Besides the updates to the model described above, any analysis of 
regulatory actions that will be implemented several years in the 
future, and whose benefits and costs accrue over decades, requires a 
large number of assumptions. Over such time horizons, many, if not 
most, of the relevant assumptions in such an analysis are inevitably 
uncertain. Each successive CAFE analysis seeks to update assumptions to 
reflect better the current state of the world and the best current 
estimates of future conditions.
    A number of assumptions have been updated since the 2020 final rule 
for this proposal. While NHTSA would have made these updates as a 
matter of course, we note that that the COVID-19 pandemic has been 
profoundly disruptive, including in ways directly material to major 
analytical inputs such as fuel prices, gross domestic product (GDP), 
vehicle production and sales, and highway travel. As discussed below, 
NHTSA has updated its ``analysis fleet'' from a model year 2017 
reference to a model year 2020 reference, updated estimates of 
manufacturers' compliance credit ``holdings,'' updated fuel price 
projections to reflect the U.S. Energy Information Administration's 
(EIA's) 2021 Annual Energy Outlook (AEO), updated projections of GDP 
and related macroeconomic measures, and updated projections of future 
highway travel. In addition, through Executive Order 13990, President 
Biden has required the formation of an Interagency Working Group (IWG) 
on the Social Cost of Greenhouse Gases and charged this body with 
updating estimates of the social costs of carbon, nitrous oxide, and 
methane. As discussed in the TSD, NHTSA has applied the IWG's interim 
guidance, which contains cost estimates (per ton of emissions) 
considerably greater than those applied in the analysis supporting the 
2020 SAFE rule. These and other updated analytical inputs are discussed 
in detail in the TSD. NHTSA seeks comment on the above discussion.

B. What is NHTSA analyzing?

    As in the CAFE and CO2 rulemakings in 2010, 2012, and 
2020, NHTSA is proposing to set attribute-based CAFE standards defined 
by a mathematical function of vehicle footprint, which has observable 
correlation with fuel economy. EPCA, as amended by EISA, expressly 
requires that CAFE standards for passenger cars and light trucks be 
based on one or more vehicle attributes related to fuel economy and be 
expressed in the form of a mathematical function.\35\ Thus, the 
proposed standards (and regulatory alternatives) take the form of fuel 
economy targets expressed as functions of vehicle footprint (the 
product of vehicle wheelbase and average track width) that are separate 
for passenger cars and light trucks. Chapter 1.2.3 of the TSD discusses 
in detail NHTSA's continued

[[Page 49628]]

reliance on footprint as the relevant attribute in this proposal.
---------------------------------------------------------------------------

    \35\ 49 U.S.C. 32902(a)(3)(A).
---------------------------------------------------------------------------

    Under the footprint-based standards, the function defines a fuel 
economy performance target for each unique footprint combination within 
a car or truck model type. Using the functions, each manufacturer thus 
will have a CAFE average standard for each year that is almost 
certainly unique to each of its fleets,\36\ based upon the footprints 
and production volumes of the vehicle models produced by that 
manufacturer. A manufacturer will have separate footprint-based 
standards for cars and for trucks, consistent with 49 U.S.C. 32902(b)'s 
direction that NHTSA must set separate standards for cars and for 
trucks. The functions are mostly sloped, so that generally, larger 
vehicles (i.e., vehicles with larger footprints) will be subject to 
lower mpg targets than smaller vehicles. This is because, generally 
speaking, smaller vehicles are more capable of achieving higher levels 
of fuel economy, mostly because they tend not to have to work as hard 
(and therefore require as much energy) to perform their driving task. 
Although a manufacturer's fleet average standards could be estimated 
throughout the model year based on the projected production volume of 
its vehicle fleet (and are estimated as part of EPA's certification 
process), the standards with which the manufacturer must comply are 
determined by its final model year production figures. A manufacturer's 
calculation of its fleet average standards, as well as its fleets' 
average performance at the end of the model year, will thus be based on 
the production-weighted average target and performance of each model in 
its fleet.\37\
---------------------------------------------------------------------------

    \36\ EPCA/EISA requires NHTSA and EPA to separate passenger cars 
into domestic and import passenger car fleets for CAFE compliance 
purposes (49 U.S.C. 32904(b)), whereas EPA combines all passenger 
cars into one fleet.
    \37\ As discussed in prior rulemakings, a manufacturer may have 
some vehicle models that exceed their target and some that are below 
their target. Compliance with a fleet average standard is determined 
by comparing the fleet average standard (based on the production-
weighted average of the target levels for each model) with fleet 
average performance (based on the production-weighted average of the 
performance of each model).
---------------------------------------------------------------------------

    For passenger cars, consistent with prior rulemakings, NHTSA is 
proposing to define fuel economy targets as shown in Equation III-1.
[GRAPHIC] [TIFF OMITTED] TP03SE21.030

Where:

TARGETFE is the fuel economy target (in mpg) applicable to a 
specific vehicle model type with a unique footprint combination,
a is a minimum fuel economy target (in mpg),
b is a maximum fuel economy target (in mpg),
c is the slope (in gallons per mile per square foot, or gpm, per 
square foot) of a line relating fuel consumption (the inverse of 
fuel economy) to footprint, and
d is an intercept (in gpm) of the same line.
Here, MIN and MAX are functions that take the minimum and maximum 
values, respectively, of the set of included values. For example, 
MIN[40, 35] = 35 and MAX(40, 25) = 40, such that MIN[MAX(40, 25), 
35] = 35.

    For the preferred alternative, this equation is represented 
graphically as the curves in Figure III-2.
BILLING CODE 4910-59-P

[[Page 49629]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.031

    For light trucks, also consistent with prior rulemakings, NHTSA is 
proposing to define fuel economy targets as shown in Equation III-2.
[GRAPHIC] [TIFF OMITTED] TP03SE21.032

Where:

TARGETFE is the fuel economy target (in mpg) applicable to a 
specific vehicle model type with a unique footprint combination,
a, b, c, and d are as for passenger cars, but taking values specific 
to light trucks,
e is a second minimum fuel economy target (in mpg),
f is a second maximum fuel economy target (in mpg),
g is the slope (in gpm per square foot) of a second line relating 
fuel consumption (the inverse of fuel economy) to footprint, and
h is an intercept (in gpm) of the same second line.

    For the preferred alternative, this equation is represented 
graphically as the curves in Figure III-3.

[[Page 49630]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.033

BILLING CODE 4910-59-C
    Although the general model of the target function equation is the 
same for each vehicle category (passenger cars and light trucks) and 
each model year, the parameters of the function equation differ for 
cars and trucks. The actual parameters for both the preferred 
alternative and the other regulatory alternatives are presented in 
Section IV.B of this preamble.
    As has been the case since NHTSA began establishing attribute-based 
standards, no vehicle need meet the specific applicable fuel economy 
target, because compliance with CAFE standards is determined based on 
corporate average fuel economy. In this respect, CAFE standards are 
unlike, for example, Federal Motor Vehicle Safety Standards (FMVSS) and 
certain vehicle criteria pollutant emissions standards where each car 
must meet the requirements. CAFE standards apply to the average fuel 
economy levels achieved by manufacturers' entire fleets of vehicles 
produced for sale in the U.S. Safety standards apply on a vehicle-by-
vehicle basis, such that every single vehicle produced for sale in the 
U.S. must, on its own, comply with minimum FMVSS. When first mandating 
CAFE standards in the 1970s, Congress specified a more flexible 
averaging-based approach that allows some vehicles to ``under comply'' 
(i.e., fall short of the overall flat standard, or fall short of their 
target under attribute-based standards) as long as a manufacturer's 
overall fleet is in compliance.
    The required CAFE level applicable to a given fleet in a given 
model year is determined by calculating the production-weighted 
harmonic average of fuel economy targets applicable to specific vehicle 
model configurations in the fleet, as shown in Equation III-3.

[[Page 49631]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.034

Where:

CAFErequired is the CAFE level the fleet is required to achieve,
i refers to specific vehicle model/configurations in the fleet,
PRODUCTIONi is the number of model configuration i produced for sale 
in the U.S., and
TARGETFE,I is the fuel economy target (as defined above) for model 
configuration i.

    Chapter 1 of the TSD describes the use of attribute-based 
standards, generally, and explains the specific decision, in past rules 
and for the current rule, to continue to use vehicle footprint as the 
attribute over which to vary stringency. That chapter also discusses 
the policy in selecting the specific mathematical function; the 
methodologies used to develop the current attribute-based standards; 
and methodologies previously used to reconsider the mathematical 
function for CAFE standards. NHTSA refers readers to the TSD for a full 
discussion of these topics.
    While Chapter 1 of the TSD explains why the proposed standards for 
MYs 2024-2026 continue to be footprint-based, the question has arisen 
periodically of whether NHTSA should instead consider multi-attribute 
standards, such as those that also depend on weight, torque, power, 
towing capability, and/or off-road capability. To date, every time 
NHTSA has considered options for which attribute(s) to select, the 
agency has concluded that a properly-designed footprint-based approach 
provides the best means of achieving the basic policy goals (i.e., by 
increasing the likelihood of improved fuel economy across the entire 
fleet of vehicles; by reducing disparities between manufacturers' 
compliance burdens; and by reducing incentives for manufacturers to 
respond to standards in ways that could compromise overall highway 
safety) involved in applying an attribute-based standard. At the same 
time, footprint-based standards need also to be structured in a way 
that furthers the energy and environmental policy goals of EPCA without 
creating inappropriate incentives to increase vehicle size in ways that 
could increase fuel consumption or compromise safety. That said, as 
NHTSA moves forward with the CAFE program, and continues to refine our 
understanding of the light-duty vehicle market and trends in vehicle 
and highway safety, NHTSA will also continue to revisit whether other 
approaches (or other ways of applying the same basic approaches) could 
foreseeably provide better means of achieving policy goals.
    For example, in the 2021 NAS Report, the committee recommended that 
if Congress does not act to remove the prohibition at 49 U.S.C. 
32902(h) on considering the fuel economy of dedicated alternative fuel 
vehicles (like BEVs) in determining maximum feasible CAFE standards, 
then NHTSA should account for the fuel economy benefits of ZEVs by 
``setting the standard as a function of a second attribute in addition 
to footprint--for example, the expected market share of ZEVs in the 
total U.S. fleet of new light-duty vehicles--such that the standards 
increase as the share of ZEVs in the total U.S. fleet increases.'' \38\ 
DOE seconded this suggestion in its comments during interagency review 
of this proposal. Chapter 1 of the TSD contains an examination of this 
suggestion, and NHTSA seeks comment on whether and how NHTSA might 
consider adding electrification as an attribute on which to base CAFE 
standards.
---------------------------------------------------------------------------

    \38\ National Academies of Sciences, Engineering, and Medicine, 
2021. Assessment of Technologies for Improving Fuel Economy of 
Light-Duty Vehicles--2025-2035, Washington, DC: The National 
Academies Press (hereafter, ``2021 NAS Report''), at Summary 
Recommendation 5. Available at https://www.nationalacademies.org/our-work/assessment-of-technologies-for-improving-fuel-economy-of-light-duty-vehicles-phase-3 and for hard-copy review at DOT 
headquarters.
---------------------------------------------------------------------------

    Changes in the market that have occurred since NHTSA last examined 
the appropriateness of the footprint curves have been, for the most 
part, consistent with the trends that the agency identified in 2018. 
For the most part, the fleet has continued to grow somewhat in vehicle 
size, as vehicle manufacturers have continued over the past several 
years to reduce their offerings of smaller footprint vehicles and 
increase their sales of larger footprint vehicles and continue to sell 
many small to mid-size crossovers and SUVs, some of which are 
classified as passenger cars and some of which are light trucks. 
Although this trend has had the effect of reducing the achieved fuel 
economy of the fleet (and thus increasing its carbon dioxide emissions) 
as compared to if vehicles had instead remained the same size or gotten 
smaller, NHTSA does not believe that there have been sufficiently major 
changes in the relationship between footprint and fuel economy over the 
last three years to warrant a detailed re-examination of that 
relationship as part of this proposal. Moreover, changes to the 
footprint curves can significantly affect manufacturers' ability to 
comply. Given the available lead time between now and the beginning of 
MY 2024, NHTSA believes it is unlikely any potential benefit of 
changing the shape of the footprint curves (when we are already 
proposing to change standard stringency) would outweigh the costs of 
doing so.
    NHTSA seeks comment on the choice of footprint as the attribute on 
which the proposed standards are based, and particularly seeks comment 
on the 2021 NAS report recommendation described above. If commenters 
wish to provide comments on possible changes to the attribute(s) on 
which fuel economy standards should be based, including approaches for 
considering vehicle electrification in ways that would further a zero 
emissions fleet as discussed in Chapter 1 of the TSD, NHTSA would 
appreciate commenters including a discussion of the timeframe in which 
those changes should be made--for example, whether and how much lead 
time would be preferable for making such changes, particularly 
recognizing the available lead time for MY 2024. NHTSA also seeks 
comment on whether, to the extent that vehicle upsizing trends and fuel 
economy curves are causally related instead of correlated, it is the 
curve shape versus the choice of footprint that creates this 
relationship (or, alternatively, whether the relationship if any 
derives from vehicle classification). Again, if commenters wish to 
provide comments on possible changes to the curve shapes, NHTSA would 
appreciate commenters including a discussion of the timeframe in which 
those changes should be made.
    NHTSA seeks comment on the discussion above and in the TSD.

[[Page 49632]]

C. What inputs does the compliance analysis require?

    The CAFE Model applies various technologies to different vehicle 
models in each manufacturer's product line to simulate how each 
manufacturer might make progress toward compliance with the specified 
standard. Subject to a variety of user-controlled constraints, the 
model applies technologies based on their relative cost-effectiveness, 
as determined by several input assumptions regarding the cost and 
effectiveness of each technology, the cost of compliance (determined by 
the change in CAFE or CO2 credits, CAFE-related civil 
penalties, or value of CO2 credits, depending on the 
compliance program being evaluated), and the value of avoided fuel 
expenses. For a given manufacturer, the compliance simulation algorithm 
applies technologies either until the manufacturer runs out of cost-
effective technologies,\39\ until the manufacturer exhausts all 
available technologies, or, if the manufacturer is assumed to be 
willing to pay civil penalties or acquire credits from another 
manufacturer, until paying civil penalties or purchasing credits 
becomes more cost-effective than increasing vehicle fuel economy. At 
this stage, the system assigns an incurred technology cost and updated 
fuel economy to each vehicle model, as well as any civil penalties 
incurred/credits purchased by each manufacturer. This compliance 
simulation process is repeated for each model year included in the 
study period (through model year 2050 in this analysis).
---------------------------------------------------------------------------

    \39\ Generally, the model considers a technology cost-effective 
if it pays for itself in fuel savings within 30 months. Depending on 
the settings applied, the model can continue to apply technologies 
that are not cost-effective rather than choosing other compliance 
options; if it does so, it will apply those additional technologies 
in order of cost-effectiveness (i.e., most cost-effective first).
---------------------------------------------------------------------------

    At the conclusion of the compliance simulation for a given 
regulatory scenario the system transitions between compliance 
simulation and effects calculations. This is the point where the system 
produces a full representation of the registered light-duty vehicle 
population in the United States. The CAFE Model then uses this fleet to 
generate estimates of the following (for each model year and calendar 
year included in the analysis): Lifetime travel, fuel consumption, 
carbon dioxide and criteria pollutant emissions, the magnitude of 
various economic externalities related to vehicular travel (e.g., 
congestion and noise), and energy consumption (e.g., the economic costs 
of short-term increases in petroleum prices, or social damages 
associated with GHG emissions). The system then uses these estimates to 
measure the benefits and costs associated with each regulatory 
alternative (relative to the no-action alternative).
    To perform this analysis, the CAFE Model uses millions of data 
points contained in several input files that have been populated by 
engineers, economists, and safety and environmental program analysts at 
both NHTSA and the DOT's Volpe National Transportations Systems Center 
(Volpe). In addition, some of the input data comes from modeling and 
simulation analysis performed by experts at Argonne National Laboratory 
using their Autonomie full vehicle simulation model and BatPaC battery 
cost model. Other inputs are derived from other models, such as the 
U.S. Energy Information Administration's (EIA's) National Energy 
Modeling System (NEMS), Argonne's ``GREET'' fuel-cycle emissions 
analysis model, and U.S. EPA's ``MOVES'' vehicle emissions analysis 
model. As NHTSA and Volpe are both organizations within DOT, we use DOT 
throughout these sections to refer to the collaborative work performed 
for this analysis.
    This section and Section III.D describe the inputs that the 
compliance simulation requires, including an in-depth discussion of the 
technologies used in the analysis, how they are defined in the CAFE 
Model, how they are characterized on vehicles that already exist in the 
market, how they can be applied to realistically simulate 
manufacturer's decisions, their effectiveness, and their cost. The 
inputs and analyses for the effects calculations, including economic, 
safety, and environmental effects, are discussed later in Sections 
III.C through III.H. NHTSA seeks comment on the following discussion.
1. Overview of Inputs to the Analysis
    As discussed above, the current analysis involves estimating four 
major swaths of effects. First, the analysis estimates how the 
application of various combinations of technologies could impact 
vehicles' costs and fuel economy levels (and CO2 emission 
rates). Second, the analysis estimates how vehicle manufacturers might 
respond to standards by adding fuel-saving technologies to new 
vehicles. Third, the analysis estimates how changes in new vehicles 
might impact vehicle sales and operation. Finally, the analysis 
estimates how the combination of these changes might impact national-
scale energy consumption, emissions, highway safety, and public health.
    There are several CAFE Model input files important to the 
discussion these first two steps, and these input files are discussed 
in detail later in this section and in Section III.D. The Market Data 
file contains the detailed description of the vehicle models and model 
configurations each manufacturer produces for sale in the U.S. The file 
also contains a range of other inputs that, though not specific to 
individual vehicle models, may be specific to individual manufacturers. 
The Technologies file identifies about six dozen technologies to be 
included in the analysis, indicates when and how widely each technology 
can be applied to specific types of vehicles, provides most of the 
inputs involved in estimating what costs will be incurred, and provides 
some of the inputs involved in estimating impacts on vehicle fuel 
consumption and weight.
    The CAFE Model also makes use of databases of estimates of fuel 
consumption impacts and, as applicable, battery costs for different 
combinations of fuel saving technologies.\40\ These databases are 
termed the FE1 and FE2 Adjustments databases (the main database and the 
database specific to plug-in hybrid electric vehicles, applicable to 
those vehicles' operation on electricity) and the Battery Costs 
database. DOT developed these databases using a large set of full 
vehicle and accompanying battery cost model simulations developed by 
Argonne National Laboratory. The Argonne simulation outputs, battery 
costs, and other reference materials are also discussed in the 
following sections.\41\
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    \40\ To be used as files provided separately from the model and 
loaded every time the model is executed, these databases are 
prohibitively large, spanning more than a million records and more 
than half a gigabyte. To conserve memory and speed model operation, 
DOT has integrated the databases into the CAFE Model executable 
file. When the model is run, however, the databases are extracted 
and placed in an accessible location on the user's disk drive.
    \41\ The Argonne workbooks included in the docket for this 
proposal include ten databases that contain the outputs of the 
Autonomie full vehicle simulations, two summary workbooks of 
assumptions used for the full vehicle simulations, a data 
dictionary, and the lookup tables for battery costs generated using 
the BatPaC battery cost model.
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    The following discussion in this section and in Section III.D 
expands on the inputs used in the compliance analysis. Further detail 
is included in Chapters 2 and 3 of the TSD accompanying this proposal, 
and all input values relevant to the compliance analysis can be seen in 
the Market Data, Technologies, fuel consumption and battery cost 
database files, and Argonne

[[Page 49633]]

summary files included in the docket for this proposal. As previously 
mentioned, other model input files underlie the effects analysis, and 
these are discussed in detail in Sections III.C through III.H. NHTSA 
seeks comment on the above discussion.
2. The Market Data File
    The Market Data file contains the detailed description of the 
vehicle models and model configurations each manufacturer produces for 
sale in the U.S. This snapshot of the recent light duty vehicle market, 
termed the analysis fleet, or baseline fleet, is the starting point for 
the evaluation of different stringency levels for future fuel economy 
standards. The analysis fleet provides a reference from which to 
project how manufacturers could apply additional technologies to 
vehicles to cost-effectively improve vehicle fuel economy, in response 
to regulatory action and market conditions.\42\ For this analysis, the 
MY 2020 light duty fleet was selected as the baseline for further 
evaluation of the effects of different fuel economy standards. The 
Market Data file also contains a range of other inputs that, though not 
specific to individual vehicle models, may be specific to individual 
manufacturers.
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    \42\ The CAFE Model does not generate compliance paths a 
manufacturer should, must, or will deploy. It is intended as a tool 
to demonstrate a compliance pathway a manufacturer could choose. It 
is almost certain all manufacturers will make compliance choices 
differing from those projected by the CAFE Model.
---------------------------------------------------------------------------

    The Market Data file is an Excel spreadsheet that contains five 
worksheets. Three worksheets, the Vehicles worksheet, Engines 
worksheet, and Transmissions worksheet, characterize the baseline fleet 
for this analysis. The three worksheets contain a characterization of 
every vehicle sold in MY 2020 and their relevant technology content, 
including the engines and transmissions that a manufacturer uses in its 
vehicle platforms and how those technologies are shared across 
platforms. In addition, the Vehicles worksheet includes baseline 
economic and safety inputs linked to each vehicle that allow the CAFE 
Model to estimate economic and safety impacts resulting from any 
simulated compliance pathway. The remaining two worksheets, the 
Manufacturers worksheet and Credits and Adjustments worksheet, include 
baseline compliance positions for each manufacturer, including each 
manufacturer's starting CAFE credit banks and whether the manufacturer 
is willing to pay civil penalties for noncompliance with CAFE 
standards, among other inputs.
    New inputs have been added for this analysis in the Vehicles 
worksheet and Manufacturers worksheet. The new inputs indicate which 
vehicles a manufacturer may reasonably be expected to convert to a zero 
emissions vehicle (ZEV) at first redesign opportunity, to comply with 
several States' ZEV program provisions. The new inputs also indicate if 
a manufacturer has entered into an agreement with California to achieve 
more stringent CO2 emissions reductions targets than those 
promulgated in the 2020 final rule.
    The following sections discuss how we built the Market Data file, 
including characterizing vehicles sold in MY 2020 and their technology 
content, and baseline safety, economic, and manufacturer compliance 
positions. A detailed discussion of the Market Data file development 
process is in TSD Chapter 2.2. NHTSA seeks comment on the below 
discussion and the agency's approach to developing the Market Data file 
for this proposal.
(a) Characterizing Vehicles and Their Technology Content
    The Market Data file integrates information from many sources, 
including manufacturer compliance submissions, publicly available 
information, and confidential business information. At times, DOT must 
populate inputs using analyst judgment, either because information is 
still incomplete or confidential, or because the information does not 
yet exist.\43\ For this analysis DOT uses mid-model year 2020 
compliance data as the basis of the analysis fleet. The compliance data 
is supplemented for each vehicle nameplate with manufacturer 
specification sheets, usually from the manufacturer media website, or 
from online marketing brochures.\44\ For additional information about 
how specification sheets inform MY 2020 vehicle technology assignments, 
see the technology specific assignments sections in Section III.D.
---------------------------------------------------------------------------

    \43\ Forward looking refresh/redesign cycles are one example of 
when analyst judgement is necessary.
    \44\ The catalogue of reference specification sheets (broken 
down by manufacturer, by nameplate) used to populate information in 
the market data file is available in the docket.
---------------------------------------------------------------------------

    DOT uses the mid-model year 2020 compliance data to create a row on 
the Vehicles worksheet in the Market Data file for each vehicle (or 
vehicle variant \45\) that lists a certification fuel economy, sales 
volume, regulatory class, and footprint. DOT identifies which 
combination of modeled technologies reasonably represents the fuel 
saving technologies already on each vehicle, and assigns those 
technologies to each vehicle, either on the Vehicles worksheet, the 
Engines worksheet, or the Transmissions worksheet. The fuel saving 
technologies considered in this analysis are listed in Table III-1.
---------------------------------------------------------------------------

    \45\ The market data file often includes a few rows for vehicles 
that may have identical certification fuel economies, regulatory 
classes, and footprints (with compliance sales volumes divided out 
among rows), because other pieces of information used in the CAFE 
Model may be dissimilar. For instance, in the reference materials 
used to create the Market Data file, for a nameplate curb weight may 
vary by trim level (with premium trim levels often weighing more on 
account of additional equipment on the vehicle), or a manufacturer 
may provide consumers the option to purchase a larger fuel tank size 
for their vehicle. These pieces of information may not impact the 
observed compliance position directly, but curb weight (in relation 
to other vehicle attributes) is important to assess mass reduction 
technology already used on the vehicle, and fuel tank size is 
directly relevant to saving time at the gas pump, which the CAFE 
Model uses when calculating the value of avoided time spent 
refueling.
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    For additional information on the characterization of these 
technologies (including the cost, prevalence in the 2020 fleet, 
effectiveness estimates, and considerations for their adoption) see the 
appropriate technology section in Section III.D or TSD Chapter 3.
    DOT also assigns each vehicle a technology class. The CAFE Model 
uses the technology class (and engine class, discussed below) in the 
Market Data file to reference the most relevant technology costs for 
each vehicle, and fuel saving technology combinations. We assign each 
vehicle in the fleet a technology class using a two-step algorithm that 
takes into account key characteristics of vehicles in the fleet 
compared to the baseline characteristics of each technology class.\46\ 
As discussed further in Section III.C.4.b), there are ten technology 
classes used in the CAFE analysis that span five vehicle types and two 
performance variants. The

[[Page 49637]]

technology class algorithm and assignment process is discussed in more 
detail in TSD Chapter 2.4.2.
---------------------------------------------------------------------------

    \46\ Baseline 0 to 60 mph accelerations times are assumed for 
each technology class as part of the Autonomie full vehicle 
simulations. DOT calculates class baseline curb weights and 
footprints by averaging the curb weights and footprints of vehicles 
within each technology class as assigned in previous analyses.
---------------------------------------------------------------------------

    We also assign each vehicle an engine technology class so that the 
CAFE Model can reference the powertrain costs in the Technologies file 
that most reasonably align with the observed vehicle. DOT assigns 
engine technology classes for all vehicles, including electric 
vehicles. If an electric powertrain replaces and internal combustion 
engine, the electric motor specifications may be different (and hence 
costs may be different) depending on the capabilities of the internal 
combustion engine it is replacing, and the costs in the technologies 
file (on the engine tab) account for the power output and capability of 
the gasoline or electric drivetrain.
    Parts sharing helps manufacturers achieve economies of scale, 
deploy capital efficiently, and make the most of shared research and 
development expenses, while still presenting a wide array of consumer 
choices to the market. The CAFE Model simulates part sharing by 
implementing shared engines, shared transmissions, and shared mass 
reduction platforms. Vehicles sharing a part (as recognized in the CAFE 
Model), will adopt fuel saving technologies affecting that part 
together. To account for parts sharing across products, vehicle model/
configurations that share engines are assigned the same engine 
code,\47\ vehicle model/configurations that share transmissions have 
the same transmission code, and vehicles that adopt mass reduction 
technologies together share the same platform. For more information 
about engine codes, transmission codes, and mass reduction platforms 
see TSD Chapter 3.
---------------------------------------------------------------------------

    \47\ Engines (or transmissions) may not be exactly identical, as 
specifications or vehicle integration features may be different. 
However, the architectures are similar enough that it is likely the 
powertrain systems share research and development (R&D), tooling, 
and production resources in a meaningful way.
---------------------------------------------------------------------------

    Manufacturers often introduce fuel saving technologies at a major 
redesign of their product or adopt technologies at minor refreshes in 
between major product redesigns. To support the CAFE Model accounting 
for new fuel saving technology introduction as it relates to product 
lifecycle, the Market Data file includes a projection of redesign and 
refresh years for each vehicle. DOT projects future redesign years and 
refresh years based on the historical cadence of that vehicle's product 
lifecycle. For new nameplates, DOT considers the manufacturer's 
treatment of product lifecycles for past products in similar market 
segments. When considering year-by-year analysis of standards, the 
sizing of redesign and refresh intervals will affect projected 
compliance pathways and how quickly manufacturers can respond to 
standards. TSD Chapter 2.2.1.7 includes additional information about 
the product design cycles assumed for this proposal based on historical 
manufacturer product design cycles.
    The Market Data file also includes information about air 
conditioning (A/C) and off-cycle technologies, but the information is 
not currently broken out at a row level, vehicle by vehicle.\48\ 
Instead, historical data (and forecast projections, which are used for 
analysis regardless of regulatory scenario) are listed by manufacturer, 
by fleet on the Credits and Adjustments worksheet of the Market Data 
file. Section III.D.8 shows model inputs specifying estimated 
adjustments (all in grams/mile) for improvements to air conditioner 
efficiency and other off-cycle energy consumption, and for reduced 
leakage of air conditioner refrigerants with high global warming 
potential (GWP). DOT estimated future values based on an expectation 
that manufacturers already relying heavily on these adjustments would 
continue do so, and that other manufacturers would, over time, also 
approach the limits on adjustments allowed for such improvements.
---------------------------------------------------------------------------

    \48\ Regulatory provisions regarding off-cycle technologies are 
new, and manufacturers have only recently begun including related 
detailed information in compliance reporting data. For this 
analysis, though, such information was not sufficiently complete to 
support a detailed representation of the application of off-cycle 
technology to specific vehicle model/configurations in the MY 2020 
fleet.
---------------------------------------------------------------------------

(b) Characterizing Baseline Safety, Economic, and Compliance Positions
    In addition to characterizing vehicles and their technology 
content, the Market Data file contains a range of other inputs that, 
though not specific to individual vehicle models, may be specific to 
individual manufacturers, or that characterize baseline safety or 
economic information.
    First, the CAFE Model considers the potential safety effect of mass 
reduction technologies and crash compatibility of different vehicle 
types. Mass reduction technologies lower the vehicle's curb weight, 
which may improve crash compatibility and safety, or not, depending on 
the type of vehicle. DOT assigns each vehicle in the Market Data file a 
safety class that best aligns with the mass-size-safety analysis. This 
analysis is discussed in more detail in Section III.H of this proposal 
and TSD Chapter 7.
    The CAFE Model also includes procedures to consider the direct 
labor impacts of manufacturer's response to CAFE regulations, 
considering the assembly location of vehicles, engines, and 
transmissions, the percent U.S. content (that reflects percent U.S. and 
Canada content),\49\ and the dealership employment associated with new 
vehicle sales. The Market Data file therefore includes baseline labor 
information, by vehicle. Sales volumes also influence total estimated 
direct labor projections in the analysis.
---------------------------------------------------------------------------

    \49\ Percent U.S. content was informed by the 2020 Part 583 
American Automobile Labeling Act Reports, appearing on NHTSA's 
website.
---------------------------------------------------------------------------

    We hold the percent U.S. content constant for each vehicle row for 
the duration of the analysis. In practice, this may not be the case. 
Changes to trade policy and tariff policy may affect percent U.S. 
content in the future. Also, some technologies may be more or less 
likely to be produced in the U.S., and if that is the case, their 
adoption could affect future U.S. content. NHTSA does not have data at 
this time to support varying the percent U.S. content.
    We also hold the labor hours projected in the Market Data file per 
unit transacted at dealerships, per unit produced for final assembly, 
per unit produced for engine assembly, and per unit produced for 
transmission assembly constant for the duration of the analysis, and 
project that the origin of these activities to remain unchanged. In 
practice, it is reasonable to expect that plants could move locations, 
or engine and transmission technologies are replaced by another fuel 
saving technology (like electric motors and fixed gear boxes) that 
could require a meaningfully different amount of assembly labor hours. 
NHTSA does not have data at this time to support varying labor hours 
projected in the Market Data file, but we will continue to explore 
methods to estimate the direct labor impacts of manufacturer's 
responses to CAFE standards in future analyses.
    As observed from Table III-2, manufacturers employ U.S. labor with 
varying intensity. In many cases, vehicles certifying in the light 
truck (LT) regulatory class have a larger percent U.S. content than 
vehicles certifying in the passenger car (PC) regulatory class.

[[Page 49638]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.038

    Next, manufacturers may over-comply with CAFE standards and bank 
so-called over compliance credits. As discussed further in Section 
III.C.7, manufacturers may use these credits later, sell them to other 
manufacturers, or let them expire. The CAFE Model does not explicitly 
trade credits between and among manufacturers, but staff have adjusted 
starting credit banks in the Market Data file to reflect trades that 
are likely to happen when the simulation begins (in MY 2020). 
Considering information manufacturers have reported regarding 
compliance credits, and considering recent manufacturers' compliance 
positions, DOT estimates manufacturers' potential use of compliance 
credits in earlier MYs. This aligns to an extent that represents how 
manufacturers could deplete their credit banks rather than producing 
high volume vehicles with fuel saving technologies in earlier MYs. This 
also avoids the unrealistic application of technologies for 
manufacturers in early analysis years that typically rely on credits. 
For a complete discussion about how this data is collected and assigned 
in the Market Data file, see TSD Chapter 2.2.2.3.
---------------------------------------------------------------------------

    \50\ Tesla does not have internal combustion engines, or multi-
speed transmissions, even though they are identified as producing 
engine and transmission systems in the United States in the Market 
Data file.
---------------------------------------------------------------------------

    The Market Data file also includes assumptions about a vehicle 
manufacturer's preferences towards civil penalty payments. EPCA 
requires that if a manufacturer does not achieve compliance with a CAFE 
standard in a given model year and cannot apply credits sufficient to 
cover the compliance shortfall, the manufacturer must pay civil 
penalties (i.e., fines) to the Federal Government. If inputs indicate 
that a manufacturer treats civil penalty payment as an economic choice 
(i.e., one to be taken if doing so would be economically preferable to 
applying further technology toward compliance), the CAFE Model, when 
evaluating the manufacturer's response to CAFE standards in a given 
model year, will apply fuel-saving technology only up to the point 
beyond which doing so would be more expensive (after subtracting the 
value of avoided fuel outlays) than paying civil penalties.
    For this analysis, DOT exercises the CAFE Model with inputs 
treating all manufacturers as treating civil penalty

[[Page 49639]]

payment as an economic choice through model year 2023. While DOT 
expects that only manufacturers with some history of paying civil 
penalties would actually treat civil penalty payment as an acceptable 
option, the CAFE Model does not currently simulate compliance credit 
trading between manufacturers, and DOT expects that this treatment of 
civil penalty payment will serve as a reasonable proxy for compliance 
credit purchases some manufacturers might actually make through model 
year 2023. These input assumptions for model years through 2023 reduce 
the potential that the model will overestimate technology application 
in the model years leading up to those for which the agency is 
proposing new standards. As in past CAFE rulemaking analyses (except 
that supporting the 2020 final rule), DOT has treated manufacturers 
with some history of civil penalty payment (i.e., BMW, Daimler, FCA, 
Jaguar-Land Rover, Volvo, and Volkswagen) as continuing to treat civil 
penalty payment as an acceptable option beyond model year 2023, but has 
treated all other manufacturers as unwilling to do so beyond model year 
2023.
    Next, the CAFE Model uses an ``effective cost'' metric to evaluate 
options to apply specific technologies to specific engines, 
transmissions, and vehicle model configurations. Expressed on a $/
gallon basis, the analysis computes this metric by subtracting the 
estimated values of avoided fuel outlays and civil penalties from the 
corresponding technology costs, and then dividing the result by the 
quantity of avoided fuel consumption. The analysis computes the value 
of fuel outlays over a ``payback period'' representing the 
manufacturer's expectation that the market will be willing to pay for 
some portion of fuel savings achieved through higher fuel economy. Once 
the model has applied enough technology to a manufacturer's fleet to 
achieve compliance with CAFE standards (and CO2 standards 
and ZEV mandates) in a given model year, the model will apply any 
further fuel economy improvements estimated to produce a negative 
effective cost (i.e., any technology applications for which avoided 
fuel outlays during the payback period are larger than the 
corresponding technology costs). As discussed above in Section III.A 
and below in Section III.C, DOT anticipates that manufacturers are 
likely to act as if the market is willing to pay for avoided fuel 
outlays expected during the first 30 months of vehicle operation.
    We seek comment on whether this expectation is appropriate, or 
whether some other amount of time should be used. If commenters believe 
a different amount of time should be used for the payback assumption, 
it would be most helpful if commenters could define the amount of time, 
provide an explanation of why that amount of time is preferable, 
provide any data or information on which the amount of time is based, 
and provide any discussion of how changing this assumption would 
interact with other elements in the analysis.
    In addition, the Market Data file includes two new sets of inputs 
for this analysis. In 2020, five vehicle manufacturers reached a 
voluntary commitment with the state of California to improve the fuel 
economy of their future nationwide fleets above levels required by the 
2020 final rule. For this analysis, compliance with this agreement is 
in the baseline case for designated manufacturers. The Market Data file 
contains inputs indicating whether each manufacturer has committed to 
exceed Federal requirements per this agreement.
    Finally, when considering other standards that may affect fuel 
economy compliance pathways, DOT includes projected zero emissions 
vehicles (ZEV) that would be required for manufacturers to meet 
standards in California and Section 177 States, per the waiver granted 
under the Clean Air Act. To support the inclusion of the ZEV program in 
the analysis, DOT identifies specific vehicle model/configurations that 
could adopt BEV technology in response to the ZEV program, independent 
of CAFE standards, at the first redesign opportunity. These ZEVs are 
identified in the Market Data file as future BEV200s, BEV300s, or 
BEV400s. Not all announced BEV nameplates appear in the MY 2020 Market 
Data file; in these cases, in consultation with CARB, DOT used the 
volume from a comparable vehicle in the manufacturer's Market Data file 
portfolio as a proxy. The Market Data file also includes information 
about the portion of each manufacturer's sales that occur in California 
and Section 177 states, which is helpful for determining how many ZEV 
credits each manufacturer will need to generate in the future to comply 
with the ZEV program with their own portfolio in the rulemaking 
timeframe. These new procedures are described in detail below and in 
TSD Chapter 2.3.
3. Simulating the Zero Emissions Vehicle Program
    California's Zero Emissions Vehicle (ZEV) program is one part of a 
program of coordinated standards that the California Air Resources 
Board (CARB) has enacted to control emissions of criteria pollutants 
and greenhouse gas emissions from vehicles. The program began in 1990, 
within the low-emission vehicle (LEV) regulation,\51\ and has since 
expanded to include eleven other states.\52\ These states may be 
referred to as Section 177 states, in reference to Section 177 of the 
Clean Air Act's grant of authority to allow these states to adopt 
California's air quality standards,\53\ but it is important to note 
that not all Section 177 states have adopted the ZEV program 
component.\54\ In the following discussion of the incorporation of the 
ZEV program into the CAFE Model, any reference to the Section 177 
states refers to those states that have adopted California's ZEV 
program requirements.
---------------------------------------------------------------------------

    \51\ California Air Resource Board (CARB), Zero-Emission Vehicle 
Program. California Air Resources Board. Accessed April 12, 2021. 
https://ww2.arb.ca.gov/our-work/programs/zero-emission-vehicle-program/about.
    \52\ At the time of writing, the Section 177 states that have 
adopted the ZEV program are Colorado, Connecticut, Maine, Maryland, 
Massachusetts, New Jersey, New York, Oregon, Rhode Island, Vermont, 
and Washington. See Vermont Department of Environmental 
Conservation, Zero Emission Vehicles. Accessed April 12, 2021. 
https://dec.vermont.gov/air-quality/mobile-sources/
zev#:~:text=To%20date%2C%2012%20states%20have,ZEVs%20over%20the%20nex
t%20decade.
    \53\ Section 177 of the Clean Air Act allows other states to 
adopt California's air quality standards.
    \54\ At the time of writing, Delaware and Pennsylvania are the 
two states that have adopted the LEV standards, but not the ZEV 
portion.
---------------------------------------------------------------------------

    To account for the ZEV program, and particularly as other states 
have recently adopted California's ZEV standards, DOT includes the main 
provisions of the ZEV program in the CAFE Model's analysis of 
compliance pathways. As explained below, incorporating the ZEV program 
into the model includes converting vehicles that have been identified 
as potential ZEV candidates into battery-electric vehicles (BEVs) at 
the first redesign opportunity, so that a manufacturer's fleet meets 
calculated ZEV credit requirements. Since ZEV program compliance 
pathways happen independently from the adoption of fuel saving 
technology in response to increasing CAFE standards, the ZEV program is 
considered in the baseline of the analysis, and in all other regulatory 
alternatives.
    Through its ZEV program, California requires that all manufacturers 
that sell cars within the state meet ZEV credit standards. The current 
credit requirements are calculated based on manufacturers' California 
sales volumes. Manufacturers primarily earn ZEV credits through the 
production of BEVs, fuel cell vehicles (FCVs), and

[[Page 49640]]

transitional zero-emissions vehicles (TZEVs), which are vehicles with 
partial electrification, namely plug-in hybrids (PHEVs). Total credits 
are calculated by multiplying the credit value each ZEV receives by the 
vehicle's volume.
    The ZEV and PHEV/TZEV credit value per vehicle is calculated based 
on the vehicle's range; ZEVs may earn up to 4 credits each and PHEVs 
with a US06 all-electric range capability of 10 mi or higher receive an 
additional 0.2 credits on top of the credits received based on all-
electric range.\55\ The maximum PHEV credit amount available per 
vehicle is 1.10.\56\ Note however that CARB only allows intermediate-
volume manufacturers to meet their ZEV credit requirements through PHEV 
production.\57\
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    \55\ US06 is one of the drive cycles used to test fuel economy 
and all-electric range, specifically for the simulation of 
aggressive driving. See Dynamometer Drive Schedules [verbar] Vehicle 
and Fuel Emissions Testing [verbar] U.S. EPA for more information, 
as well as Section III.C.4 and Section III.D.3.d).
    \56\ 13 CCR 1962.2(c)(3).
    \57\ 13 CCR 1962.2(c)(3).
---------------------------------------------------------------------------

    DOT's method for simulating the ZEV program involves several steps; 
first, DOT calculates an approximate ZEV credit target for each 
manufacturer based on the manufacturer's national sales volumes, share 
of sales in Section 177 states, and the CARB credit requirements. Next, 
DOT identifies a general pathway to compliance that involves accounting 
for manufacturers' potential use of ZEV overcompliance credits or other 
credit mechanisms, and the likelihood that manufacturers would choose 
to comply with the requirements with BEVs rather than PHEVs or other 
types of compliant vehicles, in addition to other factors. For this 
analysis, as discussed further below, DOT consulted with CARB to 
determine reasonable assumptions for this compliance pathway. Finally, 
DOT identifies vehicles in the MY 2020 analysis fleet that 
manufacturers could reasonably adapt to comply with the ZEV standards 
at the first opportunity for vehicle redesign, based on publicly 
announced product plans and other information. Each of these steps is 
discussed in turn, below, and a more detailed description of DOT's 
simulation of the ZEV program is included in TSD Chapter 2.3.
    The CAFE Model is designed to present outcomes at a national scale, 
so the ZEV analysis considers the Section 177 states as a group as 
opposed to estimating each state's ZEV credit requirements 
individually. To capture the appropriate volumes subject to the ZEV 
requirement, DOT calculates each manufacturer's total market share in 
Section 177 states. DOT also calculates the overall market share of 
ZEVs in Section 177 states, in order to estimate as closely as possible 
the number of predicted ZEVs we expect all manufacturers to sell in 
those states. These shares are then used to scale down national-level 
information in the CAFE Model to ensure that we represent only Section 
177 states in the final calculation of ZEV credits that we project each 
manufacturer to earn in future years.
    DOT uses model year 2019 National Vehicle Population Profile (NVPP) 
from IHS Markit--Polk to calculate these percentages.\58\ These data 
include vehicle characteristics such as powertrain, fuel type, 
manufacturer, nameplate, and trim level, as well as the state in which 
each vehicle is sold, which allows staff to identify the different 
types of ZEVs manufacturers sell in the Section 177 state group. DOT 
may make use of future Polk data in updating the analysis for the final 
rule and may include other states that join the ZEV program after the 
publication of this proposal, if necessary.
---------------------------------------------------------------------------

    \58\ National Vehicle Population Profile (NVPP) 2020, IHS 
Markit--Polk. At the time of the analysis, model year 2019 data from 
the NVPP contained the most current estimate of market shares by 
manufacturer, and best represented the registered vehicle population 
on January 1, 2020.
---------------------------------------------------------------------------

    We calculate sales volumes for the ZEV credit requirement based on 
each manufacturer's future assumed market share in Section 177 states. 
DOT decided to carry each manufacturer's ZEV market shares forward to 
future years, after examination of past market share data from model 
year 2016, from the 2017 version of the NVPP.\59\ Comparison of these 
data to the 2020 version showed that manufacturers' market shares 
remain fairly constant in terms of geographic distribution. Therefore, 
we determined that it was reasonable to carry forward the recently 
calculated market shares to future years.
---------------------------------------------------------------------------

    \59\ National Vehicle Population Profile (NVPP) 2017, IHS 
Markit--Polk.
---------------------------------------------------------------------------

    We calculate total credits required for ZEV compliance by 
multiplying the percentages from CARB's ZEV requirement schedule by the 
Section 177 state volumes. CARB's credit percentage requirement 
schedule for the years covered in this analysis begins at 9.5% in 2020 
and ramps up in increments to 22% by 2025.\60\ Note that the 
requirements do not currently change after 2025.\61\
---------------------------------------------------------------------------

    \60\ See 13 CCR 1962.2(b). The percentage credit requirements 
are as follows: 9.5% in 2020, 12% in 2021, 14.5% in 2022, 17% in 
2023, 19.5% in 2024, and 22% in 2025 and onward.
    \61\ 13 CCR 1962.2(b).
---------------------------------------------------------------------------

    We generate national sales volume predictions for future years 
using the Compliance Report, a CAFE Model output file that includes 
simulated sales by manufacturer, fleet, and model year. We use a 
Compliance Report that corresponds to the baseline scenario of 1.5% per 
year increases in standards for both passenger car and light truck 
fleets. The resulting national sales volume predictions by manufacturer 
are then multiplied by each manufacturer's total market share in the 
Section 177 states to capture the appropriate volumes in the ZEV 
credits calculation. Required credits by manufacturer, per year, are 
determined by multiplying the Section 177 state volumes by CARB's ZEV 
credit percentage requirement. These required credits are subsequently 
added to the CAFE Model inputs as targets for manufacturer compliance 
with ZEV standards in the CAFE baseline.
    The estimated ZEV credit requirements serve as a target for 
simulating ZEV compliance in the baseline. To achieve this, DOT 
determines a modeling philosophy for ZEV pathways, reviews various 
sources for information regarding upcoming ZEV programs, and inserts 
those programs into the analysis fleet inputs. As manufacturers can 
meet ZEV standards in a variety of different ways, using various 
technology combinations, the analysis must include certain simplifying 
assumptions in choosing ZEV pathways. We made these assumptions in 
conjunction with guidance from CARB staff. The following sections 
discuss the approach used to simulate a pathway to ZEV program 
compliance in this analysis.
    First, DOT targeted 2025 compliance, as opposed to assuming 
manufacturers would perfectly comply with their credit requirements in 
each year prior to 2025. This simplifying assumption was made upon 
review of past history of ZEV credit transfers, existing ZEV credit 
banks, and redesign schedules. DOT focused on integrating ZEV 
technology throughout that timeline with the target of meeting 2025 
obligations; thus, some manufacturers are estimated to over-comply or 
under-comply, depending on their individual situations, in the years 
2021-2024.
    Second, DOT determined that the most reasonable way to model ZEV 
compliance would be to allow under-compliance in certain cases and 
assume that some manufacturers would not meet their ZEV obligation on 
their own in 2025. Instead, these manufacturers were assumed to prefer 
to purchase credits from another manufacturer with a credit surplus. 
Reviews of past ZEV credit transfers between manufacturers informed the 
decision to make this

[[Page 49641]]

simplifying assumption.\62\ CARB advised that for these manufacturers, 
the CAFE Model should still project that each manufacturer meet 
approximately 80% of their ZEV requirements with technology included in 
their own portfolio. Manufacturers that were observed to have generated 
many ZEV credits in the past or had announced major upcoming BEV 
initiatives were projected to meet 100% of their ZEV requirements on 
their own, without purchasing ZEV credits from other manufacturers.\63\
---------------------------------------------------------------------------

    \62\ See https://ww2.arb.ca.gov/our/work/programs/advanced-clean-cars-program/zev-program-zero-emission-vehicle-credit-balances 
for past credit balances and transfer information.
    \63\ The following manufacturers were assumed to meet 100% ZEV 
compliance: Ford, General Motors, Hyundai, Kia, Jaguar Land Rover, 
and Volkswagen Automotive. Tesla was also assumed to meet 100% of 
its required standards, but the analyst team did not need to add 
additional ZEV substitutes to the baseline for this manufacturer.
---------------------------------------------------------------------------

    Third, DOT agreed that manufacturers would meet their ZEV credit 
requirements in 2025 though the production of BEVs. As discussed above, 
manufacturers may choose to build PHEVs or FCVs to earn some portion of 
their required ZEV credits. However, DOT projected that manufacturers 
would rely on BEVs to meet their credit requirements, based on reviews 
of press releases and industry news, as well as discussion with CARB. 
Since nearly all manufacturers have announced some plans to produce 
BEVs at a scale meaningful to future ZEV requirements, DOT agreed that 
this was a reasonable assumption.\64\ Furthermore, as CARB only allows 
intermediate-volume manufacturers to meet their ZEV credit requirements 
through the production of PHEVs, and the volume status of these few 
manufacturers could change over the years, assuming BEV production for 
ZEV compliance is the most straightforward path.
---------------------------------------------------------------------------

    \64\ See TSD Chapter 2.3 for a list of potential BEV programs 
recently announced by manufacturers.
---------------------------------------------------------------------------

    Fourth, to account for the new BEV programs announced by some 
manufacturers, DOT identified vehicles in the 2020 fleet that closely 
matched the upcoming BEVs, by regulatory class, market segment, and 
redesign schedule. DOT made an effort to distribute ZEV candidate 
vehicles by CAFE regulatory class (light truck, passenger car), by 
manufacturer, in a manner consistent with the 2020 manufacturer fleet 
mix. Since passenger car and light truck mixes by manufacturer could 
change in response to the CAFE policy alternative under consideration, 
this effort was deemed necessary in order to avoid redistributing the 
fleet mix in an unrealistic manner. However, there were some exceptions 
to this assumption, as some manufacturers are already closer to meeting 
their ZEV obligation through 2025 with BEVs currently produced, and 
some manufacturers underperform their compliance targets more so in one 
fleet than another. In these cases, DOT deviated from keeping the LT/PC 
mix of BEVs evenly distributed across the manufacturer's portfolio.\65\
---------------------------------------------------------------------------

    \65\ The GM light truck and passenger car distribution is one 
such example.
---------------------------------------------------------------------------

    DOT then identified future ZEV programs that could plausibly 
contribute towards the ZEV requirements for each manufacturer by 2025. 
To obtain this information, DOT examined various sources, including 
trade press releases, industry announcements, and investor reports. In 
many cases, these BEV programs are in addition to programs already in 
production.\66\ Some manufacturers have not yet released details of 
future electric vehicle programs at the time of writing, but have 
indicated goals of reaching certain percentages of electric vehicles in 
their portfolios by a specified year. In these cases, DOT reviewed the 
manufacturer's current fleet characteristics as well as the 
aspirational information in press releases and other news in order to 
make reasonable assumptions about the vehicle segment and range of 
those future BEVs. DOT may reassign some manufacturer's ZEV programs in 
the analysis fleet for the final rule based on stakeholder comments or 
other public information releases that occur in time for the final rule 
analysis.
---------------------------------------------------------------------------

    \66\ Examples of BEV programs already in production include the 
Nissan Leaf and the Chevrolet Bolt.
---------------------------------------------------------------------------

    Overall, analysts assumed that manufacturers would lean towards 
producing BEV300s rather than BEV200s, based on the information 
reviewed and an initial conversation with CARB.\67\ Phase-in caps were 
also considered, especially for BEV200, with the understanding that the 
CAFE Model will always pick BEV200 before BEV300 or BEV400, until the 
quantity of BEV200s is exhausted. See Section III.D.3.c) for details 
regarding BEV phase-in caps.
---------------------------------------------------------------------------

    \67\ BEV300s are 300-mile range battery-electric vehicles. See 
Section III.D.3.b) for further information regarding electrification 
fleet assignments.
---------------------------------------------------------------------------

    BEVs, especially BEVs with smaller battery packs and less range, 
are less likely to meet all the performance needs of traditional pickup 
truck owners today. However, new markets for BEVs may emerge, 
potentially in the form of electric delivery trucks and some light-duty 
electric truck applications in state and local government. The extent 
to which BEVs will be used in these and other new markets is difficult 
to project. DOT did identify certain trucks as upcoming BEVs for ZEV 
compliance, and these BEVs were expected to have higher ranges, due to 
the specific performance needs associated with these vehicles. Outside 
of the ZEV inputs described here, the CAFE Model does not handle the 
application of BEV technology with any special considerations as to 
whether the vehicle is a pickup truck or not. Comments from 
manufacturers are solicited on this issue.
    Finally, in order to simulate manufacturers' compliance with their 
particular ZEV credits target, 142 rows in the analysis fleet were 
identified as substitutes for future ZEV programs. As discussed above, 
the analysis fleet summarizes the roughly 13.6 million light-duty 
vehicles produced and sold in the United States in the 2020 model year 
with more than 3,500 rows, each reflecting information for one vehicle 
type observed. Each row includes the vehicle's nameplate and trim 
level, the sales volume, engine, transmission, drive configuration, 
regulatory class, projected redesign schedule, and fuel saving 
technologies, among other attributes.
    As the goal of the ZEV analysis is to simulate compliance with the 
ZEV program in the baseline, and the analysis fleet only contains 
vehicles produced during model year 2020, DOT identified existing 
models in the analysis fleet that shared certain characteristics with 
upcoming BEVs. DOT also focused on identifying substitute vehicles with 
redesign years similar to the future BEV's introduction year. The sales 
volumes of those existing models, as predicted for 2025, were then used 
to simulate production of the upcoming BEVs. DOT identified a 
combination of rows that would meet the ZEV target, could contribute 
productively towards CAFE program obligations (by manufacturer and by 
fleet), and would introduce BEVs in each manufacturer's portfolio in a 
way that reasonably aligned with projections and announcements. DOT 
tagged each of these rows with information in the Market Data file, 
instructing the CAFE Model to apply the specified BEV technology to the 
row at the first redesign year, regardless of the scenario or type of 
CAFE or GHG simulation.
    The CAFE Model does not optimize compliance with the ZEV mandate; 
it relies upon the inputs described in this section in order to 
estimate each

[[Page 49642]]

manufacturer's resulting ZEV credits. The resulting amount of ZEV 
credits earned by manufacturer for each model year can be found in the 
CAFE Model's Compliance file.
    Not all ZEV-qualifying vehicles in the U.S. earn ZEV credits, as 
they are not all sold in states that have adopted ZEV regulations. In 
order to reflect this in the CAFE Model, which only estimates sales 
volumes at the national level, the percentages calculated for each 
manufacturer are used to scale down the national-level volumes. 
Multiplying national-level ZEV sales volumes by these percentages 
ensures that only the ZEVs sold in Section 177 states count towards the 
ZEV credit targets of each manufacturer.\68\ See Section 5.8 of the 
CAFE Model Documentation for a detailed description of how the model 
applied these ZEV technologies and any changes made to the model's 
programming for the incorporation of the ZEV program into the baseline.
---------------------------------------------------------------------------

    \68\ The single exception to this assumption is Mazda, as Mazda 
has not yet produced any ZEV-qualifying vehicles at the time of 
writing. Thus, the percentage of ZEVs sold in Section 177 states 
cannot be calculated from existing data. However, Mazda has 
indicated its intention to produce ZEV-qualifying vehicles in the 
future, so DOT assumed that 100% of future ZEVs would be sold in 
Section 177 states for the purposes of estimating ZEV credits in the 
CAFE Model.
---------------------------------------------------------------------------

    As discussed above, DOT made an effort to distribute the newly 
identified ZEV candidates between CAFE regulatory classes (light truck 
and passenger car) in a manner consistent with the proportions seen in 
the 2020 analysis fleet, by manufacturer. As mentioned previously, 
there were a few exceptions to this assumption in cases where 
manufacturers' regulatory class distribution of current or planned ZEV 
programs clearly differed from their regulatory class distribution as a 
whole.
    In some instances, the regulatory distribution of flagged ZEV 
candidates leaned towards a higher portion of PCs. The reasoning behind 
this differs in each case, but there is an observed pattern in the 2020 
analysis fleet of fewer BEVs being light trucks, especially pickups. 
The 2020 analysis fleet contains no BEV pickups in the light truck 
segment. The slow emergence of electric pickups could be linked to the 
specific performance needs associated with pickup trucks. However, the 
market for BEVs may emerge in unexpected ways that are difficult to 
project. Examples of this include anticipated electric delivery trucks 
and light-duty electric trucks used by state and local governments. Due 
to these considerations, DOT tagged some trucks as BEVs for ZEV, and 
expected that these would generally be of higher ranges.
    TSD Chapter 2.3 includes more information about the process we use 
to simulate ZEV program compliance in this analysis.
4. Technology Effectiveness Values
    The next input we use to simulate manufacturers' decision-making 
processes for the year-by-year application of technologies to specific 
vehicles are estimates of how effective each technology would be at 
reducing fuel consumption. For this analysis, we use full-vehicle 
modeling and simulation to estimate the fuel economy improvements 
manufacturers could make to a fleet of vehicles, considering the 
vehicles' technical specifications and how combinations of technologies 
interact. Full-vehicle modeling and simulation uses physics-based 
models to predict how combinations of technologies perform as a full 
system under defined conditions. We use full vehicle simulations 
performed in Autonomie, a physics-based full-vehicle modeling and 
simulation software developed and maintained by the U.S. Department of 
Energy's Argonne National Laboratory.\69\
---------------------------------------------------------------------------

    \69\ Islam, E. S., A. Moawad, N. Kim, R. Vijayagopal, and A. 
Rousseau. A Detailed Vehicle Simulation Process to Support CAFE 
Standards for the MY 2024-2026 Analysis. ANL/ESD-21/9 [hereinafter 
Autonomie model documentation].
---------------------------------------------------------------------------

    A model is a mathematical representation of a system, and 
simulation is the behavior of that mathematical representation over 
time. In this analysis, the model is a mathematical representation of 
an entire vehicle,\70\ including its individual components such as the 
engine and transmission, overall vehicle characteristics such as mass 
and aerodynamic drag, and the environmental conditions, such as ambient 
temperature and barometric pressure. We simulate the model's behavior 
over test cycles, including the 2-cycle laboratory compliance tests (or 
2-cycle tests),\71\ to determine how the individual components 
interact.
---------------------------------------------------------------------------

    \70\ Each full vehicle model in this analysis is composed of 
sub-models, which is why the full vehicle model could also be 
referred to as a full system model, composed of sub-system models.
    \71\ EPA's compliance test cycles are used to measure the fuel 
economy of a vehicle. For readers unfamiliar with this process, it 
is like running a car on a treadmill following a program--or more 
specifically, two programs. The ``programs'' are the ``urban 
cycle,'' or Federal Test Procedure (abbreviated as ``FTP''), and the 
``highway cycle,'' or Highway Fuel Economy Test (abbreviated as 
``HFET'' or ``HWFET''), and they have not changed substantively 
since 1975. Each cycle is a designated speed trace (of vehicle speed 
versus time) that all certified vehicles must follow during testing. 
The FTP is meant roughly to simulate stop and go city driving, and 
the HFET is meant roughly to simulate steady flowing highway driving 
at about 50 mph.
---------------------------------------------------------------------------

    Using full-vehicle modeling and simulation to estimate technology 
efficiency improvements has two primary advantages over using single or 
limited point estimates. An analysis using single or limited point 
estimates may assume that, for example, one fuel economy-improving 
technology with an effectiveness value of 5 percent by itself and 
another technology with an effectiveness value of 10 percent by itself, 
when applied together achieve an additive improvement of 15 percent. 
Single point estimates generally do not provide accurate effectiveness 
values because they do not capture complex relationships among 
technologies. Technology effectiveness often differs significantly 
depending on the vehicle type (e.g., sedan versus pickup truck) and the 
way in which the technology interacts with other technologies on the 
vehicle, as different technologies may provide different incremental 
levels of fuel economy improvement if implemented alone or in 
combination with other technologies. Any oversimplification of these 
complex interactions leads to less accurate and often overestimated 
effectiveness estimates.
    In addition, because manufacturers often implement several fuel-
saving technologies simultaneously when redesigning a vehicle, it is 
difficult to isolate the effect of individual technologies using 
laboratory measurement of production vehicles alone. Modeling and 
simulation offer the opportunity to isolate the effects of individual 
technologies by using a single or small number of baseline vehicle 
configurations and incrementally adding technologies to those baseline 
configurations. This provides a consistent reference point for the 
incremental effectiveness estimates for each technology and for 
combinations of technologies for each vehicle type. Vehicle modeling 
also reduces the potential for overcounting or undercounting technology 
effectiveness.
    An important feature of this analysis is that the incremental 
effectiveness of each technology and combinations of technologies 
should be accurate and relative to a consistent baseline vehicle. For 
this analysis, the baseline absolute fuel economy value for each 
vehicle in the analysis fleet is based on CAFE compliance data for each 
make and model.\72\ The absolute fuel economy values of the full 
vehicle simulations are

[[Page 49643]]

used only to determine incremental effectiveness and are never used 
directly to assign an absolute fuel economy value to any vehicle model 
or configuration. For subsequent technology changes, we apply the 
incremental effectiveness values of one or more technologies to the 
baseline fuel economy value to determine the absolute fuel economy 
achieved for applying the technology change.
---------------------------------------------------------------------------

    \72\ See Section III.C.2 for further discussion of CAFE 
compliance data in the Market Data file.
---------------------------------------------------------------------------

    As an example, if a Ford F-150 2-wheel drive crew cab and short bed 
in the analysis fleet has a fuel economy value of 30 mpg for CAFE 
compliance, 30 mpg will be considered the reference absolute fuel 
economy value. A similar full vehicle model node in the Autonomie 
simulation may begin with an average fuel economy value of 32 mpg, and 
with incremental addition of a specific technology X its fuel economy 
improves to 35 mpg, a 9.3 percent improvement. In this example, the 
incremental fuel economy improvement (9.3 percent) from technology X 
would be applied to the F-150's 30 mpg absolute value.
    We determine the incremental effectiveness of technologies as 
applied to the thousands of unique vehicle and technology combinations 
in the analysis fleet. Although, as mentioned above, full-vehicle 
modeling and simulation reduces the work and time required to assess 
the impact of moving a vehicle from one technology state to another, it 
would be impractical--if not impossible--to build a unique vehicle 
model for every individual vehicle in the analysis fleet. Therefore, as 
discussed in the following sections, the Autonomie analysis relies on 
ten vehicle technology class models that are representative of large 
portions of the analysis fleet vehicles. The vehicle technology classes 
ensure that key vehicle characteristics are reasonably represented in 
the full vehicle models. The next sections discuss the details of the 
technology effectiveness analysis input specifications and assumptions. 
NHTSA seeks comment on the following discussion.
(a) Full Vehicle Modeling and Simulation
    As discussed above, for this analysis we use Argonne's full vehicle 
modeling tool, Autonomie, to build vehicle models with different 
technology combinations and simulate the performance of those models 
over regulatory test cycles. The difference in the simulated 
performance between full vehicle models, with differing technology 
combination, is used to determine effectiveness values. We consider 
over 50 individual technologies as inputs to the Autonomie 
modeling.\73\ These inputs consist of engine technologies, transmission 
technologies, powertrain electrification, lightweighting, aerodynamic 
improvements, and tire rolling resistance improvements. Section III.D 
broadly discusses each of the technology groupings definitions, inputs, 
and assumptions. A deeper discussion of the Autonomie modeled 
subsystems, and how inputs feed the sub models resulting in outputs, is 
contained in the Autonomie model documentation that accompanies this 
analysis. The 50 individual technologies, when considered with the ten 
vehicle technology classes, result in over 1.1 million individual 
vehicle technology combination models. For additional discussion on the 
full vehicle modeling used in this analysis see TSD Chapter 2.
---------------------------------------------------------------------------

    \73\ See Autonomie model documentation; ANL--All 
Assumptions_Summary_NPRM_022021.xlsx; ANL--Data Dictionary_January 
2021.xlsx.
---------------------------------------------------------------------------

    While Argonne built full-vehicle models and ran simulations for 
many combinations of technologies, it did not simulate literally every 
single vehicle model/configuration in the analysis fleet. Not only 
would it be impractical to assemble the requisite detailed information 
specific to each vehicle/model configuration, much of which would 
likely only be provided on a confidential basis, doing so would 
increase the scale of the simulation effort by orders of magnitude. 
Instead, Argonne simulated ten different vehicle types, corresponding 
to the five ``technology classes'' generally used in CAFE analysis over 
the past several rulemakings, each with two performance levels and 
corresponding vehicle technical specifications (e.g., small car, small 
performance car, pickup truck, performance pickup truck, etc.).
    Technology classes are a means of specifying common technology 
input assumptions for vehicles that share similar characteristics. 
Because each vehicle technology class has unique characteristics, the 
effectiveness of technologies and combinations of technologies is 
different for each technology class. Conducting Autonomie simulations 
uniquely for each technology class provides a specific set of 
simulations and effectiveness data for each technology class. In this 
analysis the technology classes are compact cars, midsize cars, small 
SUVs, large SUVs, and pickup trucks. In addition, for each vehicle 
class there are two levels of performance attributes (for a total of 10 
technology classes). The high performance and low performance vehicles 
classifications allow for better diversity in estimating technology 
effectiveness across the fleet.
    For additional discussion on the development of the vehicle 
technology classes used in this analysis and the attributes used to 
characterize each vehicle technology class, see TSD Chapter 2.4 and the 
Autonomie model documentation.
    Before any simulation is initiated in Autonomie, Argonne must 
``build'' a vehicle by assigning reference technologies and initial 
attributes to the components of the vehicle model representing each 
technology class. The reference technologies are baseline technologies 
that represent the first step on each technology pathway used in the 
analysis. For example, a compact car is built by assigning it a 
baseline engine (DOHC, VVT, port fuel injection (PFI)), a baseline 
transmission (AT5), a baseline level of aerodynamic improvement 
(AERO0), a baseline level of rolling resistance improvement (ROLL0), a 
baseline level of mass reduction technology (MR0), and corresponding 
attributes from the Argonne vehicle assumptions database like 
individual component weights. A baseline vehicle will have a unique 
starting point for the simulation and a unique set of assigned inputs 
and attributes, based on its technology class. Argonne collected over a 
hundred baseline vehicle attributes to build the baseline vehicle for 
each technology class. In addition, to account for the weight of 
different engine sizes, like 4-cylinder versus 8-cylinder or 
turbocharged versus naturally aspirated engines, Argonne developed a 
relationship curve between peak power and engine weight based on the 
A2Mac1 benchmarking data. Argonne uses the developed relationship to 
estimate mass for all engines. For additional discussion on the 
development and optimization of the baseline vehicle models and the 
baseline attributes used in this analysis see TSD Chapter 2.4 and the 
Autonomie model documentation.
    The next step in the process is to run a powertrain sizing 
algorithm that ensures the built vehicle meets or exceeds defined 
performance metrics, including low-speed acceleration (time required to 
accelerate from 0-60 mph), high-speed passing acceleration (time 
required to accelerate from 50-80 mph), gradeability (the ability of 
the vehicle to maintain constant 65 miles per hour speed on a six 
percent upgrade), and towing capacity. Together, these performance 
criteria are widely used by the automotive industry as metrics to 
quantify vehicle performance attributes

[[Page 49644]]

that consumers observe and that are important for vehicle utility and 
customer satisfaction.
    As with conventional vehicle models, electrified vehicle models 
were also built from the ground up. For MY 2020, the U.S. market has an 
expanded number of available hybrid and electric vehicle models. To 
capture improvements for electrified vehicles for this analysis, DOT 
applied a mass regression analysis process that considers electric 
motor weight versus electric motor power (similar to the regression 
analysis for internal combustion engine weights) for vehicle models 
that have adopted electric motors. Benchmarking data for hybrid and 
electric vehicles from the A2Mac1 database were analyzed to develop a 
regression curve of electric motor peak power versus electric motor 
weight.\74\
---------------------------------------------------------------------------

    \74\ See Autonomie model documentation, Chapter 5.2.10 Electric 
Machines System Weight.
---------------------------------------------------------------------------

    We maintain performance neutrality in the full vehicle simulations 
by resizing engines, electric machines, and hybrid electric vehicle 
battery packs at specific incremental technology steps. To address 
product complexity and economies of scale, engine resizing is limited 
to specific incremental technology changes that would typically be 
associated with a major vehicle or engine redesign. This is intended to 
reflect manufacturers' comments to DOT on how they consider engine 
resizing and product complexity, and DOT's observations on industry 
product complexity. A detailed discussion on powertrain sizing can be 
found in TSD Chapter 2.4 and in the Autonomie model documentation.
    After all vehicle class and technology combination models have been 
built, Autonomie simulates the vehicles' performance on test cycles to 
calculate the effectiveness improvement of adding fuel-economy-
improving technologies to the vehicle. Simulating vehicles' performance 
using tests and procedures specified by Federal law and regulations 
minimizes the potential variation in determining technology 
effectiveness.
    For vehicles with conventional powertrains and micro hybrids, 
Autonomie simulates the vehicles per EPA 2-cycle test procedures and 
guidelines.\75\ For mild and full hybrid electric vehicles and FCVs, 
Autonomie simulates the vehicles using the same EPA 2-cycle test 
procedure and guidelines, and the drive cycles are repeated until the 
initial and final state of charge are within a SAE J1711 tolerance. For 
PHEVs, Autonomie simulates vehicles per similar procedures and 
guidelines as prescribed in SAE J1711.\76\ For BEVs Autonomie simulates 
vehicles per similar procedures and guidelines as prescribed in SAE 
J1634.\77\
---------------------------------------------------------------------------

    \75\ 40 CFR part 600.
    \76\ PHEV testing is broken into several phases based on SAE 
J1711: Charge-sustaining on the city cycle and HWFET cycle, and 
charge-depleting on the city and HWFET cycles.
    \77\ SAE J1634. ``Battery Electric Vehicle Energy Consumption 
and Range Test Procedure.'' July 12, 2017.
---------------------------------------------------------------------------

(b) Performance Neutrality
    The purpose of the CAFE analysis is to examine the impact of 
technology application that can improve fuel economy. When the fuel 
economy-improving technology is applied, often the manufacturer must 
choose how the technology will affect the vehicle. The advantages of 
the new technology can either be completely applied to improving fuel 
economy or be used to increase vehicle performance while maintaining 
the existing fuel economy, or some mix of the two effects. 
Historically, vehicle performance has improved over the years as more 
technology is applied to the fleet. The average horsepower is the 
highest that it has ever been; all vehicle types have improved 
horsepower by at least 42 percent compared to the 1978 model year, and 
pickup trucks have improved by 48 percent.\78\ Fuel economy has also 
improved, but the horsepower and acceleration trends show that not 100 
percent of technological improvements have been applied to fuel 
savings. While future trends are uncertain, the past trends suggest 
vehicle performance is unlikely to decrease, as it seems reasonable to 
assume that customers will, at a minimum, demand vehicles that offer 
the same utility as today's fleet.
---------------------------------------------------------------------------

    \78\ ``The 2020 EPA Automotive Trends Report, Greenhouse Gas 
Emissions, Fuel Economy, and Technology since 1975,'' EPA-420-R-21-
003, January 2021 [hereinafter 2020 EPA Automotive Trends Report].
---------------------------------------------------------------------------

    For this rulemaking analysis, DOT analyzed technology pathways 
manufacturers could use for compliance that attempt to maintain vehicle 
attributes, utility, and performance. Using this approach allows DOT to 
assess the costs and benefits of potential standards under a scenario 
where consumers continue to get the similar vehicle attributes and 
features, other than changes in fuel economy. The purpose of 
constraining vehicle attributes is to simplify the analysis and reduce 
variance in other attributes that consumers may value across the 
analyzed regulatory alternatives. This allows for a streamlined 
accounting of costs and benefits by not requiring the values of other 
vehicle attributes that trade off with fuel economy.
    To confirm minimal differences in performance metrics across 
regulatory alternatives, DOT analyzed the sales-weighted average 0-60 
mph acceleration performance of the entire simulated vehicle fleet for 
MYs 2020 and 2029. The analysis compared performance under the baseline 
standards and preferred alternative. This analysis identified that the 
analysis fleet under no action standards in MY 2029 had a 0.77 percent 
worse 0-60 mph acceleration time than under the preferred alternative, 
indicating there is minimal difference in performance between the 
alternatives. This assessment shows that for this analysis, the 
performance difference is minimal across regulatory alternatives and 
across the simulated model years, which allows for fair, direct 
comparison among the alternatives. Further details about this 
assessment can be found in TSD Chapter 2.4.5.
(c) Implementation in the CAFE Model
    The CAFE Model uses two elements of information from the large 
amount of data generated by the Autonomie simulation runs: Battery 
costs, and fuel consumption on the city and highway cycles. DOT 
combines the fuel economy information from the two cycles to produce a 
composite fuel economy for each vehicle, and for each fuel used in dual 
fuel vehicles. The fuel economy information for each simulation run is 
converted into a single value for use in the CAFE Model.
    In addition to the technologies in the Autonomie simulation, the 
CAFE Model also incorporated a handful of technologies not explicitly 
simulated in Autonomie. These technologies' performance either could 
not be captured on the 2-cycle test, or there was no robust data usable 
as an input for full-vehicle modeling and simulation. The specific 
technologies are discussed in the individual technology sections below 
and in TSD Chapter 3. To calculate fuel economy improvements 
attributable to these additional technologies, estimates of fuel 
consumption improvement factors were developed and scale 
multiplicatively when applied together. See TSD Chapter 3 for a 
complete discussion on how these factors were developed. The Autonomie-
simulated results and additional technologies are combined, forming a 
single dataset used by the CAFE Model.
    Each line in the CAFE Model dataset represents a unique combination 
of technologies. DOT organizes the records using a unique technology 
state vector,

[[Page 49645]]

or technology key (tech key), that describes the technology content 
associated with each unique record. The modeled 2-cycle fuel economy 
(miles per gallon) of each combination is converted into fuel 
consumption (gallons per mile) and then normalized relative to a 
baseline tech key. The improvement factors used by the model are a 
given combination's fuel consumption improvement relative to the 
baseline tech key in its technology class.
    The tech key format was developed by recognizing that most of the 
technology pathways are unrelated and are only logically linked to 
designate the direction in which technologies are allowed to progress. 
As a result, it is possible to condense the paths into groups based on 
the specific technology. These groups are used to define the technology 
vector, or tech key. The following technology groups defined the tech 
key: Engine cam configuration (CONFIG), VVT engine technology (VVT), 
VVL engine technology (VVL), SGDI engine technology (SGDI), DEAC engine 
technology (DEAC), non-basic engine technologies (ADVENG), transmission 
technologies (TRANS), electrification and hybridization (ELEC), low 
rolling resistance tires (ROLL), aerodynamic improvements (AERO), mass 
reduction levels (MR), EFR engine technology (EFR), electric accessory 
improvement technologies (ELECACC), LDB technology (LDB), and SAX 
technology (SAX). This summarizes to a tech key with the following 
fields: CONFIG; VVT; VVL; SGDI; DEAC; ADVENG; TRANS; ELEC; ROLL; AERO; 
MR; EFR; ELECACC; LDB; SAX. It should be noted that some of the fields 
may be blank for some tech key combinations. These fields will be left 
visible for the examples below, but blank fields may be omitted from 
tech keys shown elsewhere in the documentation.
    As an example, a technology state vector describing a vehicle with 
a SOHC engine, variable valve timing (only), a 6-speed automatic 
transmission, a belt-integrated starter generator, rolling resistance 
(level 1), aerodynamic improvements (level 2), mass reduction (level 
1), electric power steering, and low drag brakes, would be specified as 
``SOHC; VVT; ; ; ; ; AT6; BISG; ROLL10; AERO20; MR1; ; EPS; LDB ; .'' 
\79\
---------------------------------------------------------------------------

    \79\ In the example tech key, the series of semicolons between 
VVT and AT6 correspond to the engine technologies which are not 
included as part of the combination, while the gap between MR1 and 
EPS corresponds to EFR and the omitted technology after LDB is SAX. 
The extra semicolons for omitted technologies are preserved in this 
example for clarity and emphasis and will not be included in future 
examples.
---------------------------------------------------------------------------

    Once a vehicle is assigned (or mapped) to an appropriate tech key, 
adding a new technology to the vehicle simply represents progress from 
a previous tech key to a new tech key. The previous tech key refers to 
the technologies that are currently in use on a vehicle. The new tech 
key is determined, in the simulation, by adding a new technology to the 
combination represented by the previous state vector while 
simultaneously removing any technologies that are superseded by the 
newly added one.
    For example, start with a vehicle with the tech key: SOHC; VVT; 
AT6; BISG; ROLL10; AERO20; MR1; EPS; LDB. Assume the simulation is 
evaluating PHEV20 as a candidate technology for application on this 
vehicle. The new tech key for this vehicle is computed by removing 
SOHC, VVT, AT6, and BISG technologies from the previous state 
vector,\80\ and adding PHEV20, resulting a tech key that looks like 
this: PHEV20; ROLL10; AERO20; MR1; EPS; LDB.
---------------------------------------------------------------------------

    \80\ For more discussion of how the CAFE Model handles 
technology supersession, see S4.5 of the CAFE Model Documentation.
---------------------------------------------------------------------------

    From here, the simulation obtains a fuel economy improvement factor 
for the new combination of technologies and applies that factor to the 
fuel economy of a vehicle in the analysis fleet. The resulting 
improvement is applied to the original compliance fuel economy value 
for a discrete vehicle in the MY 2020 analysis fleet.
5. Defining Technology Adoption in the Rulemaking Timeframe
    As discussed in Section III.C.2, starting with a fixed analysis 
fleet (for this analysis, the model year 2020 fleet indicated in 
manufacturers' early CAFE compliance data), the CAFE Model estimates 
ways each manufacturer could potentially apply specific fuel-saving 
technologies to specific vehicle model/configurations in response to, 
among other things (such as fuel prices), CAFE standards, 
CO2 standards, commitments some manufacturers have made to 
CARB's ``Framework Agreement'', and ZEV mandates imposed by California 
and several other States. The CAFE Model follows a year-by-year 
approach to simulating manufacturers' potential decisions to apply 
technology, accounting for multiyear planning within the context of 
estimated schedules for future vehicle redesigns and refreshes during 
which significant technology changes may most practicably be 
implemented.
    The modeled technology adoption for each manufacturer under each 
regulatory alternative depends on this representation of multiyear 
planning, and on a range of other factors represented by other model 
characteristics and inputs, such as the logical progression of 
technologies defined by the model's technology pathways; the 
technologies already present in the analysis fleet; inputs directing 
the model to ``skip'' specific technologies for specific vehicle model/
configurations in the analysis fleet (e.g., because secondary axle 
disconnect cannot be applied to 2-wheel-drive vehicles, and because 
manufacturers already heavily invested in engine turbocharging and 
downsizing are unlikely to abandon this approach in favor of using high 
compression ratios); inputs defining the sharing of engines, 
transmissions, and vehicle platforms in the analysis fleet; the model's 
logical approach to preserving this sharing; inputs defining each 
regulatory alternative's specific requirements; inputs defining 
expected future fuel prices, annual mileage accumulation, and valuation 
of avoided fuel consumption; and inputs defining the estimated efficacy 
and future cost (accounting for projected future ``learning'' effects) 
of included technologies; inputs controlling the maximum pace the 
simulation is to ``phase in'' each technology; and inputs further 
defining the availability of each technology to specific technology 
classes.
    Two of these inputs--the ``phase-in cap'' and the ``phase-in start 
year''--apply to the manufacturer's entire estimated production and, 
for each technology, define a share of production in each model year 
that, once exceeded, will stop the model from further applying that 
technology to that manufacturer's fleet in that model year. The 
influence of these inputs varies with regulatory stringency and other 
model inputs. For example, setting the inputs to allow immediate 100% 
penetration of a technology will not guarantee any application of the 
technology if stringency increases are low and the technology is not at 
all cost effective. Also, even if these are set to allow only very slow 
adoption of a technology, other model aspects and inputs may 
nevertheless force more rapid application than these inputs, alone, 
would suggest (e.g., because an engine technology propagates quickly 
due to sharing across multiple vehicles, or because BEV application 
must increase quickly in response to ZEV requirements). For this 
analysis, nearly

[[Page 49646]]

all of these inputs are set at levels that do not limit the simulation 
at all.
    As discussed below, for the most advanced engines (advanced 
cylinder deactivation, variable compression ratio, variable 
turbocharger geometry, and turbocharging with cylinder deactivation), 
DOT has specified phase-in caps and phase-in start years that limit the 
pace at which the analysis shows the technology being adopted in the 
rulemaking timeframe. For example, this analysis applies a 34% phase-in 
cap and MY 2019 phase-in start year for advanced cylinder deactivation 
(ADEAC), meaning that in MY 2021 (using a MY 2020 fleet, the analysis 
begins simulating further technology application in MY 2021), the model 
will stop adding ADEAC to a manufacturer's MY 2021 fleet once ADEAC 
reaches more than 68% penetration, because 34% x (2021-2019) = 34% x 2 
= 68%.
    This analysis also applies phase-in caps and corresponding start 
years to prevent the simulation from showing inconceivable rates of 
applying battery-electric vehicles (BEVs), such as showing that a 
manufacturer producing very few BEVs in MY 2020 could plausibly replace 
every product with a 300- or 400-mile BEV by MY 2025. Also, as 
discussed in Section III.D.4, this analysis applies phase-in caps and 
corresponding start years intended to ensure that the simulation's 
plausible application of the highest included levels of mass reduction 
(20% and 28.2% reductions of vehicle ``glider'' weight) do not, for 
example, outpace plausible supply of raw materials and development of 
entirely new manufacturing facilities.
    These model logical structures and inputs act together to produce 
estimates of ways each manufacturer could potentially shift to new 
fuel-saving technologies over time, reflecting some measure of 
protection against rates of change not reflected in, for example, 
technology cost inputs. This does not mean that every modeled solution 
would necessarily be economically practicable. Using technology 
adoption features like phase-in caps and phase-in start years is one 
mechanism that can be used so that the analysis better represents the 
potential costs and benefits of technology application in the 
rulemaking timeframe.
6. Technology Costs
    DOT estimates present and future costs for fuel-saving technologies 
taking into consideration the type of vehicle, or type of engine if 
technology costs vary by application. These cost estimates are based on 
three main inputs. First, direct manufacturing costs (DMCs), or the 
component and labor costs of producing and assembling the physical 
parts and systems, are estimated assuming high volume production. DMCs 
generally do not include the indirect costs of tools, capital 
equipment, financing costs, engineering, sales, administrative support 
or return on investment. DOT accounts for these indirect costs via a 
scalar markup of direct manufacturing costs (the retail price 
equivalent, or RPE). Finally, costs for technologies may change over 
time as industry streamlines design and manufacturing processes. To 
reflect this, DOT estimates potential cost improvements with learning 
effects (LE). The retail cost of equipment in any future year is 
estimated to be equal to the product of the DMC, RPE, and LE. 
Considering the retail cost of equipment, instead of merely direct 
manufacturing costs, is important to account for the real-world price 
effects of a technology, as well as market realities. Absent a 
Government mandate, motor vehicle manufacturers will not undertake 
expensive development and production efforts to implement technologies 
without realistic prospects of consumers being willing to pay enough 
for such technology to allow for the manufacturers to recover their 
investment.
(a) Direct Manufacturing Costs
    Direct manufacturing costs (DMCs) are the component and assembly 
costs of the physical parts and systems that make up a complete 
vehicle. The analysis used agency-sponsored tear-down studies of 
vehicles and parts to estimate the DMCs of individual technologies, in 
addition to independent tear-down studies, other publications, and 
confidential business information. In the simplest cases, the agency-
sponsored studies produced results that confirmed third-party industry 
estimates and aligned with confidential information provided by 
manufacturers and suppliers. In cases with a large difference between 
the tear-down study results and credible independent sources, DOT 
scrutinized the study assumptions, and sometimes revised or updated the 
analysis accordingly.
    Due to the variety of technologies and their applications, and the 
cost and time required to conduct detailed tear-down analyses, the 
agency did not sponsor teardown studies for every technology. In 
addition, some fuel-saving technologies were considered that are pre-
production or are sold in very small pilot volumes. For those 
technologies, DOT could not conduct a tear-down study to assess costs 
because the product is not yet in the marketplace for evaluation. In 
these cases, DOT relied upon third-party estimates and confidential 
information from suppliers and manufacturers; however, there are some 
common pitfalls with relying on confidential business information to 
estimate costs. The agency and the source may have had incongruent or 
incompatible definitions of ``baseline.'' The source may have provided 
DMCs at a date many years in the future, and assumed very high 
production volumes, important caveats to consider for agency analysis. 
In addition, a source, under no contractual obligation to DOT, may 
provide incomplete and/or misleading information. In other cases, 
intellectual property considerations and strategic business 
partnerships may have contributed to a manufacturer's cost information 
and could be difficult to account for in the CAFE Model as not all 
manufacturers may have access to proprietary technologies at stated 
costs. The agency carefully evaluates new information in light of these 
common pitfalls, especially regarding emerging technologies.
    While costs for fuel-saving technologies reflect the best estimates 
available today, technology cost estimates will likely change in the 
future as technologies are deployed and as production is expanded. For 
emerging technologies, DOT uses the best information available at the 
time of the analysis and will continue to update cost assumptions for 
any future analysis. The discussion of each category of technologies in 
Section III.D (e.g., engines, transmissions, electrification) and 
corresponding TSD Chapter 3 summarizes the specific cost estimates DOT 
applied for this analysis.
(b) Indirect Costs (Retail Price Equivalent)
    As discussed above, direct costs represent the cost associated with 
acquiring raw materials, fabricating parts, and assembling vehicles 
with the various technologies manufacturers are expected to use to meet 
future CAFE standards. They include materials, labor, and variable 
energy costs required to produce and assemble the vehicle. However, 
they do not include overhead costs required to develop and produce the 
vehicle, costs incurred by manufacturers or dealers to sell vehicles, 
or the profit manufacturers and dealers make from their investments. 
All of these items contribute to the price consumers ultimately pay for 
the vehicle. These components of retail prices are illustrated in Table 
III-3 below.

[[Page 49647]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.039

    To estimate the impact of higher vehicle prices on consumers, both 
direct and indirect costs must be considered. To estimate total 
consumer costs, DOT multiplies direct manufacturing costs by an 
indirect cost factor to represent the average price for fuel-saving 
technologies at retail.
    Historically, the method most commonly used to estimate indirect 
costs of producing a motor vehicle has been the retail price equivalent 
(RPE). The RPE markup factor is based on an examination of historical 
financial data contained in 10-K reports filed by manufacturers with 
the Securities and Exchange Commission (SEC). It represents the ratio 
between the retail price of motor vehicles and the direct costs of all 
activities that manufacturers engage in.
    Figure III-4 indicates that for more than three decades, the retail 
price of motor vehicles has been, on average, roughly 50 percent above 
the direct cost expenditures of manufacturers. This ratio has been 
remarkably consistent, averaging roughly 1.5 with minor variations from 
year to year over this period. At no point has the RPE markup exceeded 
1.6 or fallen below 1.4.\81\ During this time frame, the average annual 
increase in real direct costs was 2.5 percent, and the average annual 
increase in real indirect costs was also 2.5 percent. Figure III-4 
illustrates the historical relationship between retail prices and 
direct manufacturing costs.\82\
---------------------------------------------------------------------------

    \81\ Based on data from 1972-1997 and 2007. Data were not 
available for intervening years, but results for 2007 seem to 
indicate no significant change in the historical trend.
    \82\ Rogozhin, A., Gallaher, M., & McManus, W., 2009, Automobile 
Industry Retail Price Equivalent and Indirect Cost Multipliers. 
Report by RTI International to Office of Transportation Air Quality. 
U.S. Environmental Protection Agency, RTI Project Number 
0211577.002.004, February, Research Triangle Park, NC.
    Spinney, B.C., Faigin, B., Bowie, N., & St. Kratzke, 1999, 
Advanced Air Bag Systems Cost, Weight, and Lead Time analysis 
Summary Report, Contract NO. DTNH22-96-0-12003, Task Orders--001, 
003, and 005. Washington, DC, U.S. Department of Transportation.
---------------------------------------------------------------------------

    An RPE of 1.5 does not imply that manufacturers automatically mark 
up each vehicle by exactly 50 percent. Rather, it means that, over 
time, the competitive marketplace has resulted in pricing structures 
that average out to this relationship across the entire industry. 
Prices for any individual model may be marked up at a higher or lower 
rate depending on market demand. The consumer who buys a popular 
vehicle may, in effect, subsidize the installation of a new technology 
in a less marketable vehicle. But, on average, over time and across the 
vehicle fleet, the retail price paid by consumers has risen by about 
$1.50 for each dollar of direct costs incurred by manufacturers.

[[Page 49648]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.040

    It is also important to note that direct costs associated with any 
specific technology will change over time as some combination of 
learning and resource price changes occurs. Resource costs, such as the 
price of steel, can fluctuate over time and can experience real long-
term trends in either direction, depending on supply and demand. 
However, the normal learning process generally reduces direct 
production costs as manufacturers refine production techniques and seek 
out less costly parts and materials for increasing production volumes. 
By contrast, this learning process does not generally influence 
indirect costs. The implied RPE for any given technology would thus be 
expected to grow over time as direct costs decline relative to indirect 
costs. The RPE for any given year is based on direct costs of 
technologies at different stages in their learning cycles, and that may 
have different implied RPEs than they did in previous years. The RPE 
averages 1.5 across the lifetime of technologies of all ages, with a 
lower average in earlier years of a technology's life, and, because of 
learning effects on direct costs, a higher average in later years.
    The RPE has been used in all NHTSA safety and most previous CAFE 
rulemakings to estimate costs. In 2011, the National Academy of 
Sciences recommended RPEs of 1.5 for suppliers and 2.0 for in-house 
production be used to estimate total costs.\83\ The Alliance of 
Automobile Manufacturers also advocates these values as appropriate 
markup factors for estimating costs of technology changes.\84\ In their 
2015 report, the National Academy of Sciences recommend 1.5 as an 
overall RPE markup.\85\ An RPE of 2.0 has also been adopted by a 
coalition of environmental and research groups (Northeast States Center 
for a Clean Air Future (NESCCAF), International Council on Clean 
Transportation (ICCT), Southwest Research Institute, and TIAX-LLC) in a 
report on reducing heavy truck emissions, and 2.0 is recommended by the 
U.S. Department of Energy for estimating the cost of hybrid-electric 
and automotive fuel cell costs (see Vyas et al. (2000) in Table III-4 
below). Table III-4 below also lists other estimates of the RPE. Note 
that all RPE estimates vary between 1.4 and 2.0, with most in the 1.4 
to 1.7 range.
---------------------------------------------------------------------------

    \83\ Effectiveness and Impact of Corporate Average Fuel Economy 
Standards, Washington, DC--The National Academies Press; NRC, 2011.
    \84\ Communication from Chris Nevers (Alliance) to Christopher 
Lieske (EPA) and James Tamm (NHTSA), http://www.regulations.gov 
Docket ID Nos. NHTSA-2018-0067; EPA-HQ-OAR-2018-0283, p.143.
    \85\ National Research Council 2015. Cost, Effectiveness, and 
Deployment of Fuel Economy Technologies for Light Duty Vehicles. 
Washington, DC: The National Academies Press. https://doi.org/10.17226/21744 [hereinafter 2015 NAS report].
---------------------------------------------------------------------------

    Table III-4--Alternate Estimates of the RPE \86\
---------------------------------------------------------------------------

    \86\ Duleep, K.G. 2008 Analysis of Technology Cost and Retail 
Price. Presentation to Committee on Assessment of Technologies for 
Improving Light Duty Vehicle Fuel Economy, January 25, Detroit, MI.; 
Jack Faucett Associates, September 4, 1985. Update of EPA's Motor 
Vehicle Emission Control Equipment Retail Price Equivalent (RPE) 
Calculation Formula. Chevy Chase, MD--Jack Faucett Associates; 
McKinsey & Company, October 2003. Preface to the Auto Sector Cases. 
New Horizons--Multinational Company Investment in Developing 
Economies, San Francisco, CA.; NRC (National Research Council), 
2002. Effectiveness and Impact of Corporate Average Fuel Economy 
Standards, Washington, DC--The National Academies Press; NRC, 2011. 
Assessment of Fuel Economy Technologies for Light Duty Vehicles. 
Washington, DC--The National Academies Press; Cost, Effectiveness, 
and Deployment of Fuel Economy Technologies in Light Duty Vehicles. 
Washington, DC--The National Academies Press, 2015; Sierra Research, 
Inc., November 21, 2007, Study of Industry-Average Mark-Up Factors 
used to Estimate Changes in Retail Price Equivalent (RPE) for 
Automotive Fuel Economy and Emissions Control Systems, Sacramento, 
CA--Sierra Research, Inc.; Vyas, A. Santini, D., & Cuenca, R. 2000. 
Comparison of Indirect Cost Multipliers for Vehicle Manufacturing. 
Center for Transportation Research, Argonne National Laboratory, 
April. Argonne, Ill.

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[[Page 49649]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.041

    The RPE has thus enjoyed widespread use and acceptance by a variety 
of governmental, academic, and industry organizations.
    In past rulemakings, a second type of indirect cost multiplier has 
also been examined. Known as the ``Indirect Cost Multiplier'' (ICM) 
approach, ICMs were first examined alongside the RPE approach in the 
2010 rulemaking regarding standards for MYs 2012-2016 (75 FR 25324, May 
7, 2010). Both methods have been examined in subsequent rulemakings.
    Consistent with the 2020 final rule, we continue to employ the RPE 
approach to account for indirect manufacturing costs. The RPE accounts 
for indirect costs like engineering, sales, and administrative support, 
as well as other overhead costs, business expenses, warranty costs, and 
return on capital considerations. A detailed discussion of indirect 
cost methods and the basis for our use of the RPE to reflect these 
costs is available in the Final Regulatory Impact Analysis (FRIA) for 
the 2020 final rule.\87\
---------------------------------------------------------------------------

    \87\ Final Regulatory Impact Analysis, The Safer Affordable 
Fuel-Efficient (SAFE) Vehicles Rule for Model Year 2021-2026 
Passenger Cars and Light Trucks, USDOT, EPA, March 2020, at 354-76.
---------------------------------------------------------------------------

(c) Stranded Capital Costs
    The idea behind stranded capital is that manufacturers amortize 
research, development, and tooling expenses over many years, especially 
for engines and transmissions. The traditional production life-cycles 
for transmissions and engines have been a decade or longer. If a 
manufacturer launches or updates a product with fuel-saving technology, 
and then later replaces that technology with an unrelated or different 
fuel-saving technology before the equipment and research and 
development investments have been fully paid off, there will be 
unrecouped, or stranded, capital costs. Quantifying stranded capital 
costs accounts for such lost investments.
    As DOT has observed previously, manufacturers may be shifting their 
investment strategies in ways that may alter how stranded capital could 
be considered. For example, some suppliers sell similar transmissions 
to multiple manufacturers. Such arrangements allow manufacturers to 
share in capital expenditures or amortize expenses more quickly. 
Manufacturers share parts on vehicles around the globe, achieving 
greater scale and greatly affecting tooling strategies and costs.
    As a proxy for stranded capital in recent CAFE analyses, the CAFE 
Model has accounted for platform and engine sharing and includes 
redesign and refresh cycles for significant and less significant 
vehicle updates. This analysis continues to rely on the CAFE Model's 
explicit year-by-year accounting for estimated refresh and redesign 
cycles, and shared vehicle platforms and engines, to moderate the 
cadence of technology adoption and thereby limit the implied occurrence 
of stranded capital and the need to account for it explicitly. In 
addition, confining some manufacturers to specific advanced technology 
pathways through technology adoption features acts as a proxy to 
indirectly account for stranded capital. Adoption features specific to 
each technology, if applied on a manufacturer-by-manufacturer basis, 
are discussed in each technology section. The agency will monitor these 
trends to assess the role of stranded capital moving forward.
(d) Cost Learning
    Manufacturers make improvements to production processes over time, 
which often result in lower costs. ``Cost learning'' reflects the 
effect of experience and volume on the cost of production, which 
generally results in better utilization of resources, leading to higher 
and more efficient production. As manufacturers gain experience through 
production, they refine production techniques, raw material and 
component sources, and assembly methods to maximize efficiency and 
reduce production costs. Typically, a representation of this cost 
learning, or learning curves, reflects initial learning rates that are 
relatively high, followed by slower learning as additional improvements 
are made and production efficiency peaks. This eventually produces an 
asymptotic shape to the learning curve, as small percent decreases are 
applied to gradually declining cost levels. These learning curve 
estimates are applied to various technologies that are used to meet 
CAFE standards.
    We estimate cost learning by considering methods established by 
T.P. Wright and later expanded upon by J.R. Crawford.88 89 
Wright, examining aircraft production, found that every doubling of 
cumulative production of airplanes resulted in decreasing labor hours 
at a fixed percentage. This fixed percentage is commonly referred to as 
the progress rate or progress ratio, where a lower rate implies faster 
learning as cumulative

[[Page 49650]]

production increases. J.R. Crawford expanded upon Wright's learning 
curve theory to develop a single unit cost model, that estimates the 
cost of the nth unit produced given the following information is known: 
(1) Cost to produce the first unit; (2) cumulative production of n 
units; and (3) the progress ratio.
---------------------------------------------------------------------------

    \88\ Wright, T.P., Factors Affecting the Cost of Airplanes. 
Journal of Aeronautical Sciences, Vol. 3 (1936), at 124-25. 
Available at http://www.uvm.edu/pdodds/research/papers/others/1936/wright1936a.pdf.
    \89\ Crawford, J.R., Learning Curve, Ship Curve, Ratios, Related 
Data, Burbank, California-Lockheed Aircraft Corporation (1944).
---------------------------------------------------------------------------

    As pictured in Figure III-5, Wright's learning curve shows the 
first unit is produced at a cost of $1,000. Initially cost per unit 
falls rapidly for each successive unit produced. However, as production 
continues, cost falls more gradually at a decreasing rate. For each 
doubling of cumulative production at any level, cost per unit declines 
20 percent, so that 80 percent of cost is retained. The CAFE Model uses 
the basic approach by Wright, where cost reduction is estimated by 
applying a fixed percentage to the projected cumulative production of a 
given fuel economy technology.
[GRAPHIC] [TIFF OMITTED] TP03SE21.042

    The analysis accounts for learning effects with model year-based 
cost learning forecasts for each technology that reduces direct 
manufacturing costs over time. We evaluate the historical use of 
technologies, and reviews industry forecasts to estimate future volumes 
to develop the model year-based technology cost learning curves.
    The following section discusses the development of model year-based 
cost learning forecasts for this analysis, including how the approach 
has evolved from the 2012 rulemaking for MY 2017-2025 vehicles, and how 
the progress ratios were developed for different technologies 
considered in the analysis. Finally, we discuss how these learning 
effects are applied in the CAFE Model.
(1) Time Versus Volume-Based Learning
    For the 2012 joint CAFE and GHG rulemaking, DOT developed learning 
curves as a function of vehicle model year.\90\ Although the concept of 
this methodology is derived from Wright's cumulative production volume-
based learning curve, its application for CAFE technologies was more of 
a function of time. More than a dozen learning curve schedules were 
developed, varying between fast and slow learning, and assigned to each 
technology corresponding to its level of complexity and maturity. The 
schedules were applied to the base year of direct manufacturing cost 
and incorporate a percentage of cost reduction by model year, declining 
at a decreasing rate through the technology's production life. Some 
newer technologies experience 20 percent cost reductions for 
introductory model years, while mature or less complex technologies 
experience 0-3 percent cost reductions over a few years.
---------------------------------------------------------------------------

    \90\ 77 FR 62624 (Oct. 15, 2012).
---------------------------------------------------------------------------

    In their 2015 report to Congress, the National Academy of Sciences 
(NAS) recommended NHTSA should ``continue to conduct and review 
empirical evidence for the cost reductions that occur in the automobile 
industry with volume, especially for large-volume technologies that 
will be relied on to meet the CAFE/GHG standards.'' \91\
---------------------------------------------------------------------------

    \91\ National Research Council 2015. Cost, Effectiveness, and 
Deployment of Fuel Economy Technologies for Light-Duty Vehicles. 
Washington, DC: The National Academies Press. https://doi.org/10.17226/21744.
---------------------------------------------------------------------------

    In response, we incorporated statically projected cumulative volume 
production data of fuel economy-improving technologies, representing an 
improvement over the previously used time-based method. Dynamic 
projections of cumulative production are not feasible with current CAFE 
Model capabilities, so one set of projected cumulative production data 
for most vehicle technologies was developed for the purpose of 
determining cost impact. We obtained historical cumulative production 
data for many technologies produced and/or sold in the U.S. to 
establish a starting point for learning schedules. Groups of similar 
technologies or technologies of similar complexity may share identical 
learning schedules.
    The slope of the learning curve, which determines the rate at which 
cost reductions occur, has been estimated using research from an 
extensive literature review and automotive cost tear-down reports (see 
below). The slope of the learning curve is derived from the progress 
ratio of manufacturing automotive and other mobile source technologies.
(2) Deriving the Progress Ratio Used in This Analysis
    Learning curves vary among different types of manufactured 
products. Progress ratios can range from 70 to 100

[[Page 49651]]

percent, where 100 percent indicates no learning can be achieved.\92\ 
Learning effects tend to be greatest in operations where workers often 
touch the product, while effects are less substantial in operations 
consisting of more automated processes. As automotive manufacturing 
plant processes become increasingly automated, a progress ratio towards 
the higher end would seem more suitable. We incorporated findings from 
automotive cost-teardown studies with EPA's 2015 literature review of 
learning-related studies to estimate a progress ratio used to determine 
learning schedules of fuel economy-improving technologies.
---------------------------------------------------------------------------

    \92\ Martin, J., ``What is a Learning Curve?'' Management and 
Accounting Web, University of South Florida, available at: https://www.maaw.info/LearningCurveSummary.htm.
---------------------------------------------------------------------------

    EPA's literature review examined and summarized 20 studies related 
to learning in manufacturing industries and mobile source 
manufacturing.\93\ The studies focused on many industries, including 
motor vehicles, ships, aviation, semiconductors, and environmental 
energy. Based on several criteria, EPA selected five studies providing 
quantitative analysis from the mobile source sector (progress ratio 
estimates from each study are summarized in Table III-5, below). 
Further, those studies expand on Wright's learning curve function by 
using cumulative output as a predictor variable, and unit cost as the 
response variable. As a result, EPA determined a best estimate of 84 
percent as the progress ratio in mobile source industries. However, of 
those five studies, EPA at the time placed less weight on the Epple et 
al. (1991) study, because of a disruption in learning due to incomplete 
knowledge transfer from the first shift to introduction of a second 
shift at a North American truck plant. While learning may have 
decelerated immediately after adding a second shift, we note that unit 
costs continued to fall as the organization gained experience operating 
with both shifts. We recognize that disruptions are an essential part 
of the learning process and should not, in and of themselves, be 
discredited. For this reason, the analysis uses a re-estimated average 
progress ratio of 85 percent from those five studies (equally 
weighted).
---------------------------------------------------------------------------

    \93\ Cost Reduction through Learning in Manufacturing Industries 
and in the Manufacture of Mobile Sources, United States 
Environmental Protection Agency (2015). Prepared by ICF 
International and available at https://19january2017snapshot.epa.gov/sites/production/files/2016-11/documents/420r16018.pdf.
[GRAPHIC] [TIFF OMITTED] TP03SE21.043

    In addition to EPA's literature review, this progress ratio 
estimate was informed based on findings from automotive cost-teardown 
studies. NHTSA routinely performs evaluations of costs of previously 
issued Federal Motor Vehicle Safety Standards (FMVSS) for new motor 
vehicles and equipment. NHTSA engages contractors to perform detailed 
engineering ``tear-down'' analyses for representative samples of 
vehicles, to estimate how much specific FMVSS add to the weight and 
retail price of a vehicle. As part of the effort, the agency examines 
cost and production volume for automotive safety technologies. In 
particular, we estimated costs from multiple cost tear-down studies for 
technologies with actual production data from the Cost and weight added 
by the Federal Motor Vehicle Safety Standards for MY 1968-2012 
passenger cars and LTVs (2017).\99\
---------------------------------------------------------------------------

    \94\ Argote, L., Epple, D., Rao, R. D., & Murphy, K., The 
acquisition and depreciation of knowledge in a manufacturing 
organization--Turnover and plant productivity, Working paper, 
Graduate School of Industrial Administration, Carnegie Mellon 
University (1997).
    \95\ Benkard, C. L., Learning and Forgetting--The Dynamics of 
Aircraft Production, The American Economic Review, Vol. 90(4), at 
1034-54 (2000).
    \96\ Epple, D., Argote, L., & Devadas, R., Organizational 
Learning Curves--A Method for Investigating Intra-Plant Transfer of 
Knowledge Acquired through Learning by Doing, Organization Science, 
Vol. 2(1), at 58-70 (1991).
    \97\ Epple, D., Argote, L., & Murphy, K., An Empirical 
Investigation of the Microstructure of Knowledge Acquisition and 
Transfer through Learning by Doing, Operations Research, Vol. 44(1), 
at 77-86 (1996).
    \98\ Levitt, S. D., List, J. A., & Syverson, C., Toward an 
Understanding of Learning by Doing--Evidence from an Automobile 
Assembly Plant, Journal of Political Economy, Vol. 121 (4), at 643-
81 (2013).
    \99\ Simons, J. F., Cost and weight added by the Federal Motor 
Vehicle Safety Standards for MY 1968-2012 Passenger Cars and LTVs 
(Report No. DOT HS 812 354). Washington, DC--National Highway 
Traffic Safety Administration (November 2017), at 30-33.
---------------------------------------------------------------------------

    We chose five vehicle safety technologies with sufficient data to 
estimate progress ratios of each, because these technologies are large-
volume technologies and are used by almost all vehicle manufacturers. 
Table III-6 includes these five technologies and yields an average 
progress rate of 92 percent.

[[Page 49652]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.044

    For the final progress ratio used in the CAFE Model, the five 
progress rates from EPA's literature review and five progress rates 
from NHTSA's evaluation of automotive safety technologies results were 
averaged. This resulted in an average progress rate of approximately 89 
percent. We placed equal weight on progress ratios from all 10 sources. 
More specifically, we placed equal weight on the Epple et al. (1991) 
study, because disruptions have more recently been recognized as an 
essential part in the learning process, especially in an effort to 
increase the rate of output.
(3) Obtaining Appropriate Baseline Years for Direct Manufacturing Costs
    DOT obtained direct manufacturing costs for each fuel economy-
improving technology from various sources, as discussed above. To 
establish a consistent basis for direct manufacturing costs in the 
rulemaking analysis, we adjusted each technology cost to MY 2018 
dollars. For each technology, the DMC is associated with a specific 
model year, and sometimes a specific production volume, or cumulative 
production volume. The base model year is established as the MY in 
which direct manufacturing costs were assessed (with learning factor of 
1.00). With the aforementioned data on cumulative production volume for 
each technology and the assumption of a 0.89 progress ratio for all 
automotive technologies, we can solve for an implied cost for the first 
unit produced. For some technologies, we used modestly different 
progress ratios to match detailed cost projections if available from 
another source (for instance, batteries for plug-in hybrids and battery 
electric vehicles).
    This approach produces reasonable estimates for technologies 
already in production, and some additional steps are required to set 
appropriate learning rates for technologies not yet in production. 
Specifically, for technologies not yet in production in MY 2017, the 
cumulative production volume in MY 2017 is zero, because manufacturers 
have not yet produced the technologies. For pre-production cost 
estimates in previous CAFE rulemakings, we often relied on confidential 
business information sources to predict future costs. Many sources for 
pre-production cost estimates include significant learning effects, 
often providing cost estimates assuming high volume production, and 
often for a timeframe late in the first production generation or early 
in the second generation of the technology. Rapid doubling and re-
doubling of a low cumulative volume base with Wright's learning curves 
can provide unrealistic cost estimates. In addition, direct 
manufacturing cost projections can vary depending on the initial 
production volume assumed. Accordingly, we carefully examined direct 
costs with learning, and made adjustments to the starting point for 
those technologies on the learning curve to better align with the 
assumptions used for the initial direct cost estimate.
(4) Cost Learning Applied in the CAFE Model
    For this analysis, we applied learning effects to the incremental 
cost over the null technology state on the applicable technology tree. 
After this step, we calculated year-by-year incremental costs over 
preceding technologies on the tech tree to create the CAFE Model 
inputs.\100\ The shift from incremental cost accounting to absolute 
cost accounting in recent CAFE analyses made cost inputs more 
transparently relatable to detailed model output, and relevant to this 
discussion, made it easier to apply learning curves in the course of 
developing inputs to the CAFE Model.
---------------------------------------------------------------------------

    \100\ These costs are located in the CAFE Model Technologies 
file.
---------------------------------------------------------------------------

    We grouped certain technologies, such as advanced engines, advanced 
transmissions, and non-battery electric components and assigned them to 
the same learning schedule. While these grouped technologies differ in 
operating characteristics and design, we chose to group them based on 
their complexity, technology integration, and economies of scale across 
manufacturers. The low volume of certain advanced technologies, such as 
hybrid and electric technologies, poses a significant issue for 
suppliers and prevents them from producing components needed for 
advanced transmissions and other technologies at more efficient high 
scale production. The technology groupings consider market 
availability, complexity of technology integration, and production 
volume of the technologies that can be implemented by manufacturers and 
suppliers. For example, technologies like ADEAC and VCR are grouped 
together; these technologies were not in production or were only in 
limited introduction in MY 2017 and are planned to be introduced in 
limited production by a few manufacturers. The details of these 
technologies are discussed in Section III.D.
    In addition, we expanded model inputs to extend the explicit 
simulation of technology application through MY 2050. Accordingly, we 
updated the learning curves for each technology group to cover MYs 
through 2050. For MYs 2017-2032, we expect incremental improvements in 
all technologies, particularly in electrification technologies because 
of increased production volumes, labor efficiency, improved 
manufacturing methods, specialization, network building, and other 
factors. While these and other factors contribute to continual cost 
learning, we believe that many fuel economy-improving technologies 
considered in this rule will approach a flat learning level by the 
early 2030s. Specifically, older and less complex internal combustion 
engine technologies and transmissions will reach a flat learning curve 
sooner when compared to electrification technologies, which have more 
opportunity for improvement. For batteries and non-battery 
electrification components, we estimated a steeper learning curve that

[[Page 49653]]

will gradually flatten after MY 2040. For a more detailed discussion of 
the electrification learning curves, see Section III.D.3.
    Each technology in the CAFE Model is assigned a learning schedule 
developed from the methodology explained previously. For example, the 
following chart shows learning rates for several technologies 
applicable to midsize sedans, demonstrating that while we estimate that 
such learning effects have already been almost entirely realized for 
engine turbocharging (a technology that has been in production for many 
years), we estimate that significant opportunities to reduce the cost 
of the greatest levels of mass reduction (e.g., MR5) remain, and even 
greater opportunities remain to reduce the cost of batteries for HEVs, 
PHEVs, BEVs. In fact, for certain advanced technologies, we determined 
that the results predicted by the standard learning curves progress 
ratio was not realistic, based on unusual market price and production 
relationships. For these technologies, we developed specific learning 
estimates that may diverge from the 0.89 progress rate. As shown in 
Figure III-6, these technologies include: turbocharging and downsizing 
level 1 (TURBO1), variable turbo geometry electric (VTGE), aerodynamic 
drag reduction by 15 percent (AERO15), mass reduction level 5 (MR5), 20 
percent improvement in low-rolling resistance tire technology (ROLL20) 
over the baseline, and battery integrated starter/generator (BISG).
[GRAPHIC] [TIFF OMITTED] TP03SE21.045

(e) Cost Accounting
    To facilitate specification of detailed model inputs and review of 
detailed model outputs, the CAFE Model continues to use absolute cost 
inputs relative to a known base component cost, such that the estimated 
cost of each technology is specified relative to a common reference 
point for the relevant technology pathway. For example, the cost of a 
7-speed transmission is specified relative to a 5-speed transmission, 
as is the cost of every other transmission technology. Conversely, in 
some earlier versions of the CAFE Model, incremental cost inputs were 
estimated relative to the technology immediately preceding on the 
relevant technology pathway. For our 7-speed transmission example, the 
incremental cost would be relative to a 6-speed transmission. This 
change in the structure of cost inputs does not, by itself, change 
model results, but it does make the connection between these inputs and 
corresponding outputs more transparent. The CAFE Model Documentation 
accompanying our analysis presents details of the structure for model 
cost inputs.\101\ The individual technology sections in Section III.D 
provide a detailed discussion of cost accounting for each technology.
---------------------------------------------------------------------------

    \101\ CAFE Model Documentation, S4.7.
---------------------------------------------------------------------------

7. Manufacturer's Credit Compliance Positions
    This proposed rule involves a variety of provisions regarding 
``credits'' and other compliance flexibilities. Some regulatory 
provisions allow a manufacturer to earn ``credits'' that will

[[Page 49654]]

be counted toward a vehicle's rated CO2 emissions level, or 
toward a fleet's rated average CO2 or CAFE level, without 
reference to required levels for these average levels of performance. 
Such flexibilities effectively modify emissions and fuel economy test 
procedures or methods for calculating fleets' CAFE and average 
CO2 levels. Other provisions (for CAFE, statutory 
provisions) allow manufacturers to earn credits by achieving CAFE or 
average CO2 levels beyond required levels; these provisions 
may hence more appropriately be termed ``compliance credits.'' We 
described in the 2020 final rule how the CAFE Model simulates these 
compliance credit provisions for both the CAFE program and for EPA's 
CO2 standards.\102\ For this analysis, we modeled the no-
action and action alternatives as a set of CAFE standards in place 
simultaneously with EPA baseline (i.e., 2020 final) CO2 
standards, related CARB agreements with five manufacturers, and ZEV 
mandates in place in California and some other states. The modeling of 
CO2 standards and standard-like contractual obligations 
includes our representation of applicable credit provisions.
---------------------------------------------------------------------------

    \102\ See 85 FR 24174, 24303 (April 30, 2020).
---------------------------------------------------------------------------

    EPCA has long provided that, by exceeding the CAFE standard 
applicable to a given fleet in a given model year, a manufacturer may 
earn corresponding ``credits'' that the same manufacturer may, within 
the same regulatory class, apply toward compliance in a different model 
year. EISA amended these provisions by providing that manufacturers 
may, subject to specific statutory limitations, transfer compliance 
credits between regulatory classes and trade compliance credits with 
other manufacturers. The CAA provides the EPA with broad standard-
setting authority for the CO2 program, with no specific 
directives regarding CO2 standards or CO2 
compliance credits.
    EPCA also specifies that NHTSA may not consider the availability of 
CAFE credits (for transfer, trade, or direct application) toward 
compliance with new standards when establishing the standards 
themselves.\103\ Therefore, this analysis excludes model years 2024-
2026 from those in which carried-forward or transferred credits can be 
applied for the CAFE program.
---------------------------------------------------------------------------

    \103\ 49 U.S.C. 32902(h)(3).
---------------------------------------------------------------------------

    The ``unconstrained'' perspective acknowledges that these 
flexibilities exist as part of the program and, while not considered by 
NHTSA in setting standards, are nevertheless important to consider when 
attempting to estimate the real impact of any alternative. Under the 
``unconstrained'' perspective, credits may be earned, transferred, and 
applied to deficits in the CAFE program throughout the full range of 
model years in the analysis. The Draft Supplemental Environmental 
Impact Statement (SEIS) accompanying this proposed rule, like the 
corresponding SEIS analysis, presents ``unconstrained'' modeling 
results. Also, because the CAA provides no direction regarding 
consideration of any CO2 credit provisions, this analysis 
includes simulation of carried-forward and transferred CO2 
credits in all model years.
    The CAFE Model, therefore, does provide means to simulate 
manufacturers' potential application of some compliance credits, and 
both the analysis of CO2 standards and the NEPA analysis of 
CAFE standards do make use of this aspect of the model. On the other 
hand, 49 U.S.C. 32902(h) prevents NHTSA from, in its standard setting 
analysis, considering the potential that manufacturers could use 
compliance credits in model years for which the agency is establishing 
maximum feasible CAFE standards. Further, as discussed below, we also 
continue to find it appropriate for the analysis largely to refrain 
from simulating two of the mechanisms allowing the use of compliance 
credits.
    The CAFE Model's approach to simulating compliance decisions 
accounts for the potential to earn and use CAFE credits as provided by 
EPCA/EISA. The model similarly accumulates and applies CO2 
credits when simulating compliance with EPA's standards. Like past 
versions, the current CAFE Model can simulate credit carry-forward 
(i.e., banking) between model years and transfers between the passenger 
car and light truck fleets but not credit carry-back (i.e., borrowing) 
from future model years or trading between manufacturers.
    While NHTSA's ``unconstrained'' evaluation can consider the 
potential to carry back compliance credits from later to earlier model 
years, past examples of failed attempts to carry back CAFE credits 
(e.g., a MY 2014 carry back default leading to a civil penalty payment) 
underscore the riskiness of such ``borrowing.'' Recent evidence 
indicates manufacturers are disinclined to take such risks, and we find 
it reasonable and prudent to refrain from attempting to simulate such 
``borrowing'' in rulemaking analysis.
    Like the previous version, the current CAFE Model provides a basis 
to specify (in model inputs) CAFE credits available from model years 
earlier than those being explicitly simulated. For example, with this 
analysis representing model years 2020-2050 explicitly, credits earned 
in the model year 2015 are made available for use through the model 
year 2020 (given the current five-year limit on carry-forward of 
credits). The banked credits are specific to both the model year and 
fleet in which they were earned.
    To increase the realism with which the model transitions between 
the early model years (MYs 2020-2023) and the later years that are the 
subject of this action, we have accounted for the potential that some 
manufacturers might trade credits earned prior to 2020 to other 
manufacturers. However, the analysis refrains from simulating the 
potential that manufacturers might continue to trade credits during and 
beyond the model years covered by this action. In 2018 and 2020, the 
analysis included idealized cases simulating ``perfect'' (i.e., wholly 
unrestricted) trading of CO2 compliance credits by treating 
all vehicles as being produced by a single manufacturer. Even for 
CO2 compliance credit trading, these scenarios were not 
plausible, because it is exceedingly unlikely that some pairs of 
manufacturers would trade compliance credits. NHTSA did not include 
such cases for CAFE compliance credits, because EPCA provisions (such 
as the minimum domestic passenger car standard requirement) make such 
scenarios impossible. At this time, we remain concerned that any 
realistic simulation of such trading would require assumptions 
regarding which specific pairs of manufacturers might trade compliance 
credits, and the evidence to date makes it clear that the credit market 
is far from fully ``open.''
    We also remain concerned that to set standards based on an analysis 
that presumes the use of program flexibilities risks making the 
corresponding actions mandatory. Some flexibilities--credit carry-
forward (banking) and transfers between fleets in particular--involve 
little risk because they are internal to a manufacturer and known in 
advance. As discussed above, credit carry-back involves significant 
risk because it amounts to borrowing against future improvements, 
standards, and production volume and mix. Similarly, credit trading 
also involves significant risk, because the ability of manufacturer A 
to acquire credits from manufacturer B depends not just on manufacturer 
B actually earning the expected amount of credit, but also on 
manufacturer B being willing to trade with manufacturer A, and on 
potential interest by other manufacturers. Manufacturers' compliance 
plans have

[[Page 49655]]

already evidenced cases of compliance credit trades that were planned 
and subsequently aborted, reinforcing our judgment that, like credit 
banking, credit trading involves too much risk to be included in an 
analysis that informs decisions about the stringency of future 
standards.
    As discussed in the CAFE Model Documentation, the model's default 
logic attempts to maximize credit carry-forward--that is, to ``hold 
on'' to credits for as long as possible. If a manufacturer needs to 
cover a shortfall that occurs when insufficient opportunities exist to 
add technology to achieve compliance with a standard, the model will 
apply credits. Otherwise, the manufacturer carries forward credits 
until they are about to expire, at which point it will use them before 
adding technology that is not considered cost-effective. The model 
attempts to use credits that will expire within the next three years as 
a means to smooth out technology applications over time to avoid both 
compliance shortfalls and high levels of over-compliance that can 
result in a surplus of credits. Although it remains impossible 
precisely to predict the manufacturer's actual earning and use of 
compliance credits, and this aspect of the model may benefit from 
future refinement as manufacturers and regulators continue to gain 
experience with these provisions, this approach is generally consistent 
with manufacturers' observed practices.
    NHTSA introduced the CAFE Public Information Center (PIC) to 
provide public access to a range of information regarding the CAFE 
program,\104\ including manufacturers' credit balances. However, there 
is a data lag in the information presented on the CAFE PIC that may not 
capture credit actions across the industry for as much as several 
months. Furthermore, CAFE credits that are traded between manufacturers 
are adjusted to preserve the gallons saved that each credit 
represents.\105\ The adjustment occurs at the time of application 
rather than at the time the credits are traded. This means that a 
manufacturer who has acquired credits through trade, but has not yet 
applied them, may show a credit balance that is either considerably 
higher or lower than the real value of the credits when they are 
applied. For example, a manufacturer that buys 40 million credits from 
Tesla may show a credit balance in excess of 40 million. However, when 
those credits are applied, they may be worth only 1/10 as much--making 
that manufacturer's true credit balance closer to 4 million than 40 
million (e.g., when another manufacturer uses credits acquired from 
Tesla, the manufacturer may only be able to offset a 1 mpg compliance 
shortfall, even though the credits' ``face value'' suggests the 
manufacturer could offset a 10 mpg compliance shortfall).
---------------------------------------------------------------------------

    \104\ CAFE Public Information Center, https://one.nhtsa.gov/cafe_pic/cafe_pic_home.htm (last visited May 11, 2021).
    \105\ CO2 credits for EPA's program are denominated 
in metric tons of CO2 rather than gram/mile compliance 
credits and require no adjustment when traded between manufacturers 
or fleets.
---------------------------------------------------------------------------

    Specific inputs accounting for manufacturers' accumulated 
compliance credits are discussed in TSD Chapter 2.2.2.3.
    In addition to the inclusion of these existing credit banks, the 
CAFE Model also updated its treatment of credits in the rulemaking 
analysis. EPCA requires that NHTSA set CAFE standards at maximum 
feasible levels for each model year without consideration of the 
program's credit mechanisms. However, as recent CAFE rulemakings have 
evaluated the effects of standards over longer time periods, the early 
actions taken by manufacturers required more nuanced representation. 
Accordingly, the CAFE Model now provides means to exclude the simulated 
application of CAFE compliance credits only from specific model years 
for which standards are being set (for this analysis, 2024-2026), while 
allowing CAFE credits to be applied in other model years.
    In addition to more rigorous accounting of CAFE and CO2 
compliance credits, the model also accounts for air conditioning 
efficiency and off-cycle adjustments. NHTSA's program considers those 
adjustments in a manufacturer's compliance calculation starting in MY 
2017, and specific estimates of each manufacturer's reliance on these 
adjustments are discussed above in Section III.C.2.a). Because air 
conditioning efficiency and off-cycle adjustments are not credits in 
NHTSA's program, but rather adjustments to compliance fuel economy, 
they may be included under either a ``standard setting'' or 
``unconstrained'' analysis perspective.
    The manner in which the CAFE Model treats the EPA and CAFE A/C 
efficiency and off-cycle credit programs is similar, but the model also 
accounts for A/C leakage (which is not part of NHTSA's program). When 
determining the compliance status of a manufacturer's fleet (in the 
case of EPA's program, PC and LT are the only fleet distinctions), the 
CAFE Model weighs future compliance actions against the presence of 
existing (and expiring) CO2 credits resulting from over-
compliance with earlier years' standards, A/C efficiency credits, A/C 
leakage credits, and off-cycle credits.
    The model currently accounts for any off-cycle adjustments 
associated with technologies that are included in the set of fuel-
saving technologies explicitly simulated as part of this proposal (for 
example, start-stop systems that reduce fuel consumption during idle or 
active grille shutters that improve aerodynamic drag at highway speeds) 
and accumulates these adjustments up to the cap. As discussed further 
in Section III.D.8, this analysis considers that some manufacturers may 
apply up to 15.0 g/mi of off-cycle credit by MY 2032. We considered the 
potential to model the application of off-cycle technologies 
explicitly. However, doing so would require data regarding which 
vehicle models already possess these improvements as well as the cost 
and expected value of applying them to other models in the future. Such 
data are currently too limited to support explicit modeling of these 
technologies and adjustments.
    When establishing maximum feasible fuel economy standards, NHTSA is 
prohibited from considering the availability of alternatively fueled 
vehicles,\106\ and credit provisions related to AFVs that significantly 
increase their fuel economy for CAFE compliance purposes. Under the 
``standard setting'' perspective, these technologies (pure battery 
electric vehicles and fuel cell vehicles \107\) are not available in 
the compliance simulation to improve fuel economy. Under the 
``unconstrained'' perspective, such as is documented in the SEIS, the 
CAFE Model considers these technologies in the same manner as other 
available technologies and may apply them if they represent cost-
effective compliance pathways. However, under both perspectives, the 
analysis continues to include dedicated AFVs that could be produced in 
response to CAFE standards outside the model years for which standards 
are being set, or for other reasons (e.g., ZEV mandates, as accounted 
for in this analysis).
---------------------------------------------------------------------------

    \106\ 49 U.S.C. 32902(h).
    \107\ Dedicated compressed natural gas (CNG) vehicles should 
also be excluded in this perspective but are not considered as a 
compliance strategy under any perspective in this analysis.
---------------------------------------------------------------------------

    EPCA also provides that CAFE levels may, subject to limitations, be 
adjusted upward to reflect the sale of flexible fuel vehicles (FFVs). 
Because these adjustments ended in model year 2020, this analysis 
assumes no manufacturer

[[Page 49656]]

will earn FFV credits within the modeling horizon.
    Also, the CAA provides no direction regarding consideration of 
alternative fuels, and EPA has provided that manufacturers selling 
PHEVs, BEVs, and FCVs may, when calculating fleet average 
CO2 levels, ``count'' each unit of production as more than a 
single unit. The CAFE Model accounts for these ``multipliers.'' For 
example, under EPA's current regulation, when calculating the average 
CO2 level achieved by its MY 2019 passenger car fleet, a 
manufacturer may treat each 1,000 BEVs as 2,000 BEVs. When calculating 
the average level required of this fleet, the manufacturer must use the 
actual production volume (in this example, 1,000 units). Similarly, the 
manufacturer must use the actual production volume when calculating 
compliance credit balances.
    There were no natural gas vehicles in the baseline fleet, and the 
analysis did not apply natural gas technology due to cost 
effectiveness. The application of a 2.0 multiplier for natural gas 
vehicles for MYs 2024-2026 would have no impact on the analysis because 
given the state of natural gas vehicle refueling infrastructure, the 
cost to equip vehicles with natural gas tanks, the outlook for 
petroleum prices, and the outlook for battery prices, we have little 
basis to project more than an inconsequential response to this 
incentive in the foreseeable future.

D. Technology Pathways, Effectiveness, and Cost

    Vehicle manufacturers meet increasingly more stringent fuel economy 
standards by applying increasing levels of fuel-economy-improving 
technologies to their vehicles. An appropriate characterization of the 
technologies available to manufacturers to meet fuel economy standards 
is, therefore, an important input required to assess the levels of 
standards that manufacturers can achieve. Like previous CAFE standards 
analyses, this proposal considers over 50 fuel-economy-improving 
technologies that manufacturers could apply to their MY 2020 fleet of 
vehicles to meet proposed levels of CAFE standards in MYs 2024-2026. 
The characterization of these technologies, the technology 
effectiveness values, and technology cost assumptions build on work 
performed by DOT, EPA, the National Academy of Sciences, and other 
Federal and state government agencies including the Department of 
Energy's Argonne National Laboratory and the California Air Resources 
Board.
    After spending approximately a decade refining the technology 
pathways, effectiveness, and cost assumptions used in successive CAFE 
Model analyses, DOT has developed guiding principles to ensure that the 
CAFE Model's simulation of manufacturer compliance pathways results in 
impacts that we would reasonably expect to see in the real world. These 
guiding principles are as follows:
    Even though the analysis considers over 50 individual technologies, 
the fuel economy improvement from any individual technology must be 
considered in conjunction with the other fuel-economy-improving 
technologies applied to the vehicle. For example, there is an obvious 
fuel economy benefit that results from converting a vehicle with a 
traditional internal combustion engine to a battery electric vehicle; 
however, the benefit of the electrification technology depends on the 
other road load reducing technologies (i.e., mass reduction, 
aerodynamic, and rolling resistance) on the vehicle.
    Technologies added in combination to a vehicle will not result in a 
simply additive fuel economy improvement from each individual 
technology. As discussed in Section III.C.4, full vehicle modeling and 
simulation provides the required degree of accuracy to project how 
different technologies will interact in the vehicle system. For 
example, as discussed further in Sections III.D.1 and III.D.3, a 
parallel hybrid architecture powertrain improves fuel economy, in part, 
by allowing the internal combustion engine to spend more time operating 
at efficient engine speed and load conditions. This reduces the 
advantage of adding advanced internal combustion engine technologies, 
which also improve fuel economy, by broadening the range of speed and 
load conditions for the engine to operate at high efficiency. This 
redundancy in fuel savings mechanism results in a reduced effectiveness 
improvement when the technologies are added to each other.
    The effectiveness of a technology depends on the type of vehicle 
the technology is being applied to. For example, applying mass 
reduction technology results in varying effectiveness as the absolute 
mass reduced is a function of the starting vehicle mass, which varies 
across technology classes. See Section III.D.4 for more details.
    The cost and effectiveness values for each technology should be 
reasonably representative of what can be achieved across the entire 
industry. Each technology model employed in the analysis is designed to 
be representative of a wide range of specific technology applications 
used in industry. Some vehicle manufacturer's systems may perform 
better and cost less than our modeled systems and some may perform 
worse and cost more. However, employing this approach will ensure that, 
on balance, the analysis captures a reasonable level of costs and 
benefits that would result from any manufacturer applying the 
technology.
    The baseline for cost and effectiveness values must be identified 
before assuming that a cost or effectiveness value could be employed 
for any individual technology. For example, as discussed further in 
Section III.D.1.d) below, this analysis uses a set of engine map models 
that were developed by starting with a small number of baseline engine 
configurations, and then, in a very systematic and controlled process, 
adding specific well-defined technologies to create a new map for each 
unique technology combination.
    The following sections discuss the engine, transmission, 
electrification, mass reduction, aerodynamic, tire rolling resistance, 
and other vehicle technologies considered in this analysis. Each 
section discusses how we define the technology in the CAFE Model,\108\ 
how we assigned the technology to vehicles in the MY 2020 analysis 
fleet used as a starting point for this analysis, any adoption features 
applied to the technology so the analysis better represents 
manufacturers' real-world decisions, the technology effectiveness 
values, and technology cost.
---------------------------------------------------------------------------

    \108\ Note, due to the diversity of definitions industry 
sometimes employs for technology terms, or in describing the 
specific application of technology, the terms defined here may 
differ from how the technology is defined in the industry.
---------------------------------------------------------------------------

    Please note that the following technology effectiveness sections 
provide examples of the range of effectiveness values that a technology 
could achieve when applied to the entire vehicle system, in conjunction 
with the other fuel-economy-improving technologies already on or also 
applied at the same time to the vehicle. To see the incremental 
effectiveness values for any particular vehicle moving from one 
technology key to a more advanced technology key, see the FE_1 and FE_2 
Adjustments files that are integrated in the CAFE Model executable 
file. Similarly, the technology costs provided in each section are 
examples of absolute costs seen in specific model years (MYs 2020, 
2025, and 2030 for most technologies), for specific vehicle classes. To 
see all absolute technology costs used in the analysis across all model 
years, see the Technologies file.

[[Page 49657]]

NHTSA seeks comment on the following discussion.
1. Engine Paths
    For this analysis, the extensive variety of light duty vehicle 
internal combustion (IC) engine technologies are classified into 
discrete engine technology paths. These paths are used to model the 
most representative characteristics, costs, and performance of the 
fuel-economy improving technologies most likely available during the 
rulemaking time frame, MYs 2024-2026. Due to uncertainties in the cost 
and capabilities of emerging technologies, some new and pre- production 
technologies are not part of this analysis. We did not include 
technologies unlikely to be feasible in the rulemaking timeframe, 
technologies unlikely to be compatible with U.S. fuels, or technologies 
for which there was not appropriate data available to allow the 
simulation of effectiveness across all vehicle technology classes in 
this analysis.
    The following sections discuss IC engine technologies considered in 
this analysis, general technology categories used by the CAFE Model, 
and how the engine technologies are assigned in the MY 2020 analysis 
fleet. The following sections also discuss adoption features applicable 
to engine technologies, engine technologies' effectiveness when 
combined in a full vehicle model, and the engine technologies' costs.
(a) Engine Modeling in the CAFE Model
    DOT models IC engine technologies that manufacturers can use to 
improve fuel economy. Some engine technologies can be incorporated into 
existing engines with minor or moderate changes to the engines, but 
many engine technologies require an entirely new engine architecture.
    We divide engine technologies into two categories, ``basic engine 
technologies'' and ``advanced engine technologies.'' ``Basic engine 
technologies'' refer to technologies adaptable to an existing engine 
with minor or moderate changes to the engine. ``Advanced engine 
technologies'' refer to technologies that generally require significant 
changes or an entirely new engine architecture. The words ``basic'' and 
``advanced'' are not meant to confer any information about the level of 
sophistication of the technology. Many advanced engine technology 
definitions also include some basic engine technologies, and these 
basic technologies are accounted for in the costs and effectiveness 
values of the advance engine. Figure III-7 shows how the basic and 
other engines are laid out on pathways evaluated in the compliance 
simulation. Each engine technology is briefly described, below. It is 
important to note the ``Basic Engine Path'' shows that every engine 
starts with VVT and can add one, some, or all the technologies in the 
dotted box, as discussed in Section III.D.1.a)(1).
[GRAPHIC] [TIFF OMITTED] TP03SE21.046

(1) Basic Engines
    In the CAFE Model, basic engine technologies may be applied 
individually or in combination with other basic engine technologies. 
The basic engine technologies include variable valve timing (VVT), 
variable valve lift (VVL), stoichiometric gasoline direct injection 
(SGDI), and cylinder deactivation. Cylinder deactivation includes a 
basic level (DEAC) and an advanced level (ADEAC). DOT applies the basic 
engine technologies across two engine architectures: dual over-head 
camshaft (DOHC) engine architecture and single over-head camshaft 
(SOHC) engine architecture.
    VVT: Variable valve timing is a family of valve-train designs that 
dynamically adjusts the timing of the intake valves, exhaust valves, or 
both, in relation to piston position. VVT can reduce pumping losses, 
provide increased engine torque and horsepower over a broad engine 
operating range, and allow unique operating modes, such as Atkinson 
cycle operation, to further enhance efficiency.\109\ VVT is nearly 
universally used in the MY 2020 fleet. VVT enables more control of in-
cylinder

[[Page 49658]]

air flow for exhaust scavenging and combustion relative to fixed valve 
timing engines. Engine parameters such as volumetric efficiency, 
effective compression ratio, and internal exhaust gas recirculation 
(iEGR) can all be enabled and accurately controlled by a VVT system.
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    \109\ 2015 NAS report, at 31.
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    VVL: Variable valve lift dynamically adjusts the distance a valve 
travels from the valve seat. The dynamic adjustment can optimize 
airflow over a broad range of engine operating conditions. The 
technology can increase effectiveness by reducing pumping losses and by 
affecting the fuel and air mixture motion and combustion in-
cylinder.\110\ VVL is less common in the MY 2020 fleet than VVT, but 
still prevalent. Some manufacturers have implemented a limited, 
discrete approach to VVL. The discrete approach allows only limited 
(e.g., two) valve lift profiles versus allowing a continuous range of 
lift profiles.
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    \110\ 2015 NAS report, at 32.
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    SGDI: Stoichiometric gasoline direct injection sprays fuel at high 
pressure directly into the combustion chamber, which provides cooling 
of the in-cylinder charge via in-cylinder fuel vaporization to improve 
spark knock tolerance and enable an increase in compression ratio and/
or more optimal spark timing for improved efficiency.\111\ SGDI is 
common in the MY 2020 fleet, and the technology is used in many 
advanced engines as well.
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    \111\ 2015 NAS report, at 34.
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    DEAC: Basic cylinder deactivation disables intake and exhaust 
valves and turns off fuel injection for the deactivated cylinders 
during light load operation. DEAC is characterized by a small number of 
discrete operating configurations.\112\ The engine runs temporarily as 
though it were a smaller engine, reducing pumping losses and improving 
efficiency. DEAC is present in the MY 2020 baseline fleet.
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    \112\ 2015 NAS report, at 33.
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    ADEAC: Advanced cylinder deactivation systems, also known as 
rolling or dynamic cylinder deactivation systems, allow a further 
degree of cylinder deactivation than the base DEAC. ADEAC allows the 
engine to vary the percentage of cylinders deactivated and the sequence 
in which cylinders are deactivated, essentially providing 
``displacement on demand'' for low load operations. A small number of 
vehicles have ADEAC in the MY 2020 baseline fleet.
    Section III.D.1.d) contains additional information about each basic 
engine technology used in this analysis, including information about 
the engine map models used in the full vehicle technology effectiveness 
modeling.
(2) Advanced Engines
    DOT defines advanced engine technologies in the analysis as 
technologies that require significant changes in engine structure, or 
an entirely new engine architecture.\113\ The advanced engine 
technologies represent the application of alternate combustion cycles 
or changes in the application of forced induction to the engine. Each 
advanced engine technology has a discrete pathway for progression to 
improved versions of the technology, as seen above in Figure III-7. The 
advanced engine technology pathways include a turbocharged pathway, a 
high compression ratio (Atkinson) engine pathway, a variable turbo 
geometry (Miller Cycle) engine pathway, a variable compression ratio 
pathway, and a diesel engine pathway. Although the CAFE Model includes 
a compressed natural gas (CNG) pathway, that technology is a baseline-
only technology and was not included in the analysis; currently, there 
are no dedicated CNG vehicles in the MY 2020 analysis fleet.
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    \113\ Examples of this include but are not limited to changes in 
cylinder count, block geometry or combustion cycle changes.
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    TURBO: Forced induction engines, or turbocharged downsized engines, 
are characterized by technology that can create greater-than-
atmospheric pressure in the engine intake manifold when higher output 
is needed. The raised pressure results in an increased amount of 
airflow into the cylinder supporting combustion, increasing the 
specific power of the engine. Increased specific power means the engine 
can generate more power per unit of cylinder volume. The higher power 
per cylinder volume allows the overall engine volume to be reduced, 
while maintaining performance. The overall engine volume decrease 
results in an increase in fuel efficiency by reducing parasitic loads 
associated with larger engine volumes.\114\
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    \114\ 2015 NAS report, at 34.
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    Cooled exhaust gas recirculation is also part of the advanced 
forced induction technology path. The basic recycling of exhaust gases 
using VVT is called internal EGR (iEGR) and is included as part of the 
performance improvements provided by the VVT basic engine technology. 
Cooled EGR (cEGR) is a second method for diluting the incoming air that 
takes exhaust gases, passes them through a heat exchanger to reduce 
their temperature, and then mixes them with incoming air in the intake 
manifold.\115\ As discussed in Section III.D.1.d), many advanced engine 
maps include EGR.
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    \115\ 2015 NAS report, at 35.
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    Five levels of turbocharged engine downsizing technologies are 
considered in this analysis: A `basic' level of turbocharged downsized 
technology (TURBO1), an advanced turbocharged downsized technology 
(TURBO2), an advanced turbocharged downsized technology with cooled 
exhaust gas recirculation applied (cEGR), a turbocharged downsized 
technology with basic cylinder deactivation applied (TURBOD), and a 
turbocharged downsized technology with advanced cylinder deactivation 
applied (TURBOAD).
    HCR: Atkinson engines, or high compression ratio engines, represent 
a class of engines that achieve a higher level of fuel efficiency by 
implementing an alternate combustion cycle.\116\ Historically, the Otto 
combustion cycle has been used by most gasoline-based spark ignition 
engines. Increased research into improving fuel economy has resulted in 
the development of alternate combustion cycles that allow for greater 
levels of thermal efficiency. One such alternative combustion cycle is 
the Atkinson cycle. Atkinson cycle operation is achieved by allowing 
the expansion stroke of the engine to overextend allowing the 
combustion products to achieve the lowest possible pressure before the 
exhaust stroke.117 118 119
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    \116\ See the 2015 NAS report, Appendix D, for a short 
discussion on thermodynamic engine cycles.
    \117\ Otto cycle is a four-stroke cycle that has four piston 
movements over two engine revolutions for each cycle. First stroke: 
Intake or induction; seconds stroke: Compression; third stroke: 
Expansion or power stroke; and finally, fourth stroke: Exhaust.
    \118\ Compression ratio is the ratio of the maximum to minimum 
volume in the cylinder of an internal combustion engine.
    \119\ Expansion ratio is the ratio of maximum to minimum volume 
in the cylinder of an IC engine when the valves are closed (i.e., 
the piston is traveling from top to bottom to produce work).
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    Descriptions of Atkinson cycle engines and Atkinson mode or 
Atkinson-enabled engine technologies have been used interchangeably in 
association with high compression ratio (HCR) engines, for past 
rulemaking analyses. Both technologies achieve a higher thermal 
efficiency than traditional Otto cycle-only engines, however, the two 
engine types operate differently. For purposes of this analysis, 
Atkinson technologies can be categorized into two groups to reduce 
confusion: (1) Atkinson-enabled engines and (2) Atkinson engines.
    Atkinson-enabled engines, or high compression ratio engines (HCR),

[[Page 49659]]

dynamically swing between operating closer to an Otto cycle or an 
Atkinson cycle based on engine loads. During high loads the engine will 
use the lower-efficiency, power-dense Otto cycle mode, while at low 
loads the engine will use the higher-efficiency, lower power-dense 
Atkinson cycle mode. The hybrid combustion cycle operation is used to 
address the low power density issues that can limit the Atkinson-only 
engine and allow for a wider application of the technology.
    The level of efficiency improvement experienced by a vehicle 
employing Atkinson cycle operation is directly related to how much of 
the engine's operation time is spent in Atkinson mode. Vehicles that 
can experience operation at a high load for long portions of their 
operating cycle will see little to no benefit from this technology. 
This limitation to performance results in manufacturers typically 
limiting the application of this technology to vehicles with a use 
profile that can take advantage of the technology's behavior.
    Three HCR or Atkinson-enabled engines are available in the 
analysis: (1) The baseline Atkinson-enabled engine (HCR0), (2) the 
enhanced Atkinson enabled engine (HCR1), and finally, (3) the enhanced 
Atkinson enabled engine with cylinder deactivation (HCR1D).
    In contrast, Atkinson engines in this analysis are defined as 
engines that operate full-time in the Atkinson cycle. The most common 
method of achieving Atkinson operation is the use of late intake valve 
closing. This method allows backflow from the combustion chamber into 
the intake manifold, reducing the dynamic compression ratio, and 
providing a higher expansion ratio. The higher expansion ratio improves 
thermal efficiency but reduces power density. The low power density 
generally relegates these engines to hybrid vehicle (SHEVPS) 
applications only in this analysis. Coupling the engines to electric 
motors and significantly reducing road loads can compensate for the 
lower power density and maintain desired performance levels for the 
vehicle.\120\ The Toyota Prius is an example of a vehicle that uses an 
Atkinson engine. The 2017 Toyota Prius achieved a peak thermal 
efficiency of 40 percent.\121\
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    \120\ Toyota. ``Under the Hood of the All-new Toyota Prius.'' 
Oct. 13, 2015. Available at https://global.toyota/en/detail/9827044. 
Last accessed Nov. 22, 2019.
    \121\ Matsuo, S., Ikeda, E., Ito, Y., and Nishiura, H., ``The 
New Toyota Inline 4 Cylinder 1.8L ESTEC 2ZR-FXE Gasoline Engine for 
Hybrid Car,'' SAE Technical Paper 2016-01-0684, 2016, https://doi.org/10.4271/2016-01-0684.
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    NHTSA seeks comment on whether and how to consider ``HCR2'' in the 
analysis for the final rule.
    VTG: The Miller cycle is another type of overexpansion combustion 
cycle, similar to the Atkinson cycle. The Miller cycle, however, 
operates in combination with a forced induction system that helps 
address the impacts of reduced power density during high load operating 
conditions. Miller cycle-enabled engines use a similar technology 
approach as seen in Atkinson-enabled engines to effectively create an 
expanded expansion stroke of the combustion cycle.
    In the analysis, the baseline Miller cycle-enabled engine includes 
the application of a variable turbo geometry technology (VTG). The 
advanced Miller cycle enabled system includes the application of a 48V-
based electronic boost system (VTGE). VTG technology allows the system 
to vary boost level based on engine operational needs. The use of a 
variable geometry turbocharger also supports the use of cooled exhaust 
gas recirculation.\122\ An electronic boost system has an electric 
motor added to assist a turbocharger at low engine speeds. The motor 
assist mitigates turbocharger lag and low boost pressure at low engine 
speeds. The electronic assist system can provide extra boost needed to 
overcome the torque deficits at low engine speeds.\123\
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    \122\ 2015 NAS report, at 116.
    \123\ 2015 NAS report, at 62.
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    VCR: Variable compression ratio (VCR) engines work by changing the 
length of the piston stroke of the engine to optimize the compression 
ratio and improve thermal efficiency over the full range of engine 
operating conditions. Engines using VCR technology are currently in 
production, but appear to be targeted primarily towards limited 
production, high performance applications. Nissan is the only 
manufacturer to use this technology in the MY 2020 baseline fleet. Few 
manufacturers and suppliers provided information about VCR 
technologies, and DOT reviewed several design concepts that could 
achieve a similar functional outcome. In addition to design concept 
differences, intellectual property ownership complicates the ability to 
define a VCR hardware system that could be widely adopted across the 
industry. Because of these issues, adoption of the VCR engine 
technology is limited to Nissan only.
    ADSL: Diesel engines have several characteristics that result in 
superior fuel efficiency over traditional gasoline engines. These 
advantages include reduced pumping losses due to lack of (or greatly 
reduced) throttling, high pressure direct injection of fuel, a more 
efficient combustion cycle,\124\ and a very lean air/fuel mixture 
relative to an equivalent-performance gasoline engine.\125\ However, 
diesel technologies require additional enablers, such as a NOx 
adsorption catalyst system or a urea/ammonia selective catalytic 
reduction system, for control of NOx emissions.
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    \124\ Diesel cycle is also a four-stroke cycle like the Otto 
Cycle, except in the intake stroke no fuel is injected and fuel is 
injected late in the compression stroke at higher pressure and 
temperature.
    \125\ See the 2015 NAS report, Appendix D, for a short 
discussion on thermodynamic engine cycles.
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    DOT considered three levels of diesel engine technology: the 
baseline diesel engine technology (ADSL) is based on a standard 2.2L 
turbocharged diesel engine; the more advanced diesel engine (DSLI) 
starts with the ADSL system and incorporates a combination of low 
pressure and high pressure EGR, reduced parasitic loss, friction 
reduction, a highly-integrated exhaust catalyst with low temp light off 
temperatures, and closed loop combustion control; and finally the most 
advanced diesel system (DSLIAD) is the DSLI system with advanced 
cylinder deactivation technology added.
    EFR: Engine friction reduction technology is a general engine 
improvement meant to represent future technologies that reduce the 
internal friction of an engine. EFR technology is not available for 
application until MY 2023. The future technologies do not significantly 
change the function or operation of the engine but reduce the energy 
loss due to the rotational or rubbing friction experienced in the 
bearings or cylinder during normal operation. These technologies can 
include improved surface coatings, lower-tension piston rings, roller 
cam followers, optimal thermal management and piston surface 
treatments, improved bearing design, reduced inertial loads, improved 
materials, or improved geometry.
(b) Engine Analysis Fleet Assignments
    As a first step in assigning baseline levels of engine technologies 
in the analysis fleet, DOT used data for each manufacturer to determine 
which platforms shared engines. Within each manufacturer's fleet, DOT 
assigned unique identification designations (engine codes) based on 
configuration, technologies applied, displacement, compression ratio, 
and power output. DOT used power output to distinguish between engines 
that might have the same displacement and configuration

[[Page 49660]]

but significantly different horsepower ratings.
    The CAFE Model identifies leaders and followers for a 
manufacturer's vehicles that use the same engine, indicated by sharing 
the same engine code. The model automatically determines which engines 
are leaders by using the highest sales volume row of the highest sales 
volume nameplate that is assigned an engine code. This leader-follower 
relationship allows the CAFE Model simulation to maintain engine 
sharing as more technology is applied to engines.
    DOT accurately represents each engine using engine technologies and 
engine technology classes. The first step is to assign engine 
technologies to each engine code. Technology assignment is based on the 
identified characteristics of the engine being modeled, and based on 
technologies assigned, the engine will be aligned with an engine map 
model that most closely corresponds.
    The engine technology classes are a second identifier used to 
accurately account for engine costs. The engine technology class is 
formatted as number of cylinders followed by the letter C, number of 
banks followed by the letter B, and an engine head configuration 
designator, which is _SOHC for single overhead cam, _ohv for overhead 
valve, or blank for dual overhead cam. As an example, one variant of 
the GMC Acadia has a naturally aspirated DOHC inline 4-cylinder engine, 
so DOT assigned the vehicle to the `4C1B' engine technology class and 
assigned the technology VVT and SGDI. Table III-7 shows examples of 
observed engines with their corresponding assigned engine technologies 
as well as engine technology classes.
[GRAPHIC] [TIFF OMITTED] TP03SE21.047

    The cost tables for a given engine class include downsizing (to an 
engine architecture with fewer cylinders) when turbocharging technology 
is applied, and therefore, the turbocharged engines observed in the 
2020 fleet (that have already been downsized) often map to an engine 
class with more cylinders. For instance, an observed TURBO1 V6 engine 
would map to an 8C2B (V8) engine class, because the turbo costs on the 
8C2B engine class worksheet assume a V6 (6C2B) engine architecture. 
Diesel engines map to engine technology classes that match the observed 
cylinder count since naturally aspirated diesel engines are not found 
in new light duty vehicles in the U.S. market. Similarly, as indicated 
above, the TURBO1 I3 in the Ford Escape maps to the 4C1B_L (I4) engine 
class, because the turbo costs on the 4C1B_L engine class worksheet 
assume a I3 (3C1B) engine architecture. Some instances can be more 
complex, including low horsepower variants for 4-cylinder engines, and 
are shown in Table III-8.
    For this analysis, we have allowed additional downsizing beyond 
what has been previously modeled. We allow enhanced downsizing because 
manufacturers have downsized low output naturally aspirated engines to 
turbo engines with smaller architectures than traditionally 
observed.126 127 128 To capture this new level of turbo 
downsizing we created a new category of low output naturally aspirated 
engines, which is only applied to 4-cylinder engines in the MY 2020 
fleet. These engines use the costing tabs in the Technologies file with 
the `L' designation and are assumed to downsize to turbocharged 3-
cylinder engines for costing purposes. We seek comment regarding the 
expected further application of this technology to larger cylinder 
count engines, such as 8-cylinder engines that may be turbo

[[Page 49661]]

downsized to 4-cylinder engines. We would also like comment on how to 
define the characteristic of an engine that may be targeted for 
enhanced downsizing.
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    \126\ Richard Truett, ``GM Brining 3-Cylinder back to North 
America.'' Automotive News, December 01, 2019. https://www.autonews.com/cars-concepts/gm-bringing-3-cylinder-back-na.
    \127\ Stoklosa, Alexander, ``2021 Mini Cooper Hardtop.'' Car and 
Driver, December 2, 2014. https://www.caranddriver.com/reviews/a15109143/2014-mini-cooper-hardtop-manual-test-review/.
    \128\ Leanse, Alex ``2020 For Escape Options: Hybrid vs. 3-
Cylinder EcoBoost vs. 4-Cylinder EcoBoost.'' MotorTrend, Sept 24, 
2019. https://www.motortrend.com/news/2020-ford-escape-engine-options-pros-and-cons-comparison/.
[GRAPHIC] [TIFF OMITTED] TP03SE21.048

    TSD Chapter 3.1.2 includes more details about baseline engine 
technology assignment logic, and details about the levels of engine 
technology penetration in the MY 2020 fleet.
(c) Engine Adoption Features
    Engine adoption features are defined through a combination of (1) 
refresh and redesign cycles, (2) technology path logic, (3) phase-in 
capacity limits, and (4) SKIP logic. Figure III-7 above shows the 
technology paths available for engines in the CAFE Model. Engine 
technology development and application typically results in an engine 
design moving from the basic engine tree to one of the advanced engine 
trees. Once an engine design moves to the advanced engine tree it is 
not allowed to move to alternate advanced engine trees. Specific path 
logic, phase-in caps, and SKIP logic applied to each engine technology 
are discussed by engine technology, in turn.
    Refresh and redesign cycles dictate when engine technology can be 
applied. Technologies applicable only during a platform redesign can be 
applied during a platform refresh if another vehicle platform that 
shares engine codes (uses the same engine) has already applied the 
technology during a redesign. For example, models of the GMC Acadia and 
the Cadillac XT4 use the same engine (assigned engine code 112011 in 
the Market Data file); if the XT4 adds a new engine technology during a 
redesign, then the Acadia may also add the same engine technology 
during the next refresh or redesign. This allows the model to maintain 
engine sharing relationships while also maintaining refresh and 
redesign schedules.\129\ For engine technologies, DOHC, OHV, VVT, and 
CNG engine technologies are baseline only, while all other engine 
technologies can only be applied at a vehicle redesign.
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    \129\ See Section III.C.2.a) for more discussion on platform 
refresh and redesign cycles.
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    Basic engine technologies in the CAFE Model are represented by four 
technologies: VVT, VVL, SGDI, and DEAC. DOT assumes that 100% of basic 
engine platforms use VVT as a baseline, based on wide proliferation of 
the technology in the U.S. fleet. The remaining three technologies, 
VVL, SGDI, and DEAC, can all be applied individually or in any 
combination of the three. An engine can jump from the basic engines 
path to any other engine path except the Alternative Fuel Engine Path.
    Turbo downsizing allows manufacturers to maintain vehicle 
performance characteristics while reducing engine displacement and 
cylinder count. Any basic engine can adopt one of the turbo engine 
technologies (TURBO1, TURBO2 and CEGR1). Vehicles that have 
turbocharged engines in the baseline fleet will stay on the turbo 
engine path to prevent unrealistic engine technology change in the 
short timeframe considered in the rulemaking analysis. Turbo technology 
is a mutually exclusive technology in that it cannot be adopted for 
HCR, diesel, ADEAC, or CNG engines.
    Non-HEV Atkinson mode engines are a collection of engines in the 
HCR engine pathway (HCR0, HCR1, HCR1D and HCR2). Atkinson engines excel 
in lower power applications for lower load conditions, such as driving 
around a city or steady state highway driving without large payloads, 
thus their adoption is more limited than some other technologies. DOT 
expanded the availability of HCR technology compared to the 2020 final 
rule because of new observed applications in the market.\130\ However, 
there are three categories of adoption features specific to the HCR 
engine pathway: \131\
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    \130\ For example, the Hyundai Palisade and Kia Telluride have a 
291 hp V6 HCR1 engine. The specification sheets for these vehicles 
are located in the docket for this action.
    \131\ See Section III.D.1.d)(1) Engine Maps, for a discussion of 
why HCR2 and P2HCR2 were not used in the central analysis. ``SKIP'' 
logic was used to remove this engine technology from application, 
however as discussed below, we maintain HCR2 and P2HCR2 in the model 
architecture for sensitivity analysis and for future engine map 
model updates.
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     DOT does not allow vehicles with 405 or more horsepower to 
adopt HCR engines due to their prescribed duty cycle being more 
demanding and likely not supported by the lower power density found in 
HCR-based engines.\132\
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    \132\ Heywood, John B. Internal Combustion Engine Fundamentals. 
McGraw-Hill Education, 2018. Chapter 5.
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     Pickup trucks and vehicles that share engines with pickup 
trucks are

[[Page 49662]]

also excluded from receiving HCR engines; the duty cycle for these 
heavy vehicles, particularly when hauling cargo or towing, are likely 
unable to take full advantage of Atkinson cycle use, and would 
ultimately spend the majority of operation as an Otto cycle engine, 
negating the benefits of HCR technology.\133\
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    \133\ This is based on CBI conversation with manufacturers that 
currently employ HCR-based technology but saw no benefit when the 
technology was applied to truck platforms in their fleet.
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     HCR engine application is also restricted for some 
manufacturers that are heavily performance-focused and have 
demonstrated a significant commitment to power dense technologies such 
as turbocharged downsizing.\134\
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    \134\ There are three manufacturers that met the criteria (near 
100% turbo downsized fleet, and future hybrid systems are based on 
turbo-downsized engines) described and were excluded: BMW, Daimler, 
and Jaguar Land Rover.
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NHTSA seeks comment on the appropriateness of these restrictions for 
the final rule.
    Advanced cylinder deactivation technology (ADEAC), or dynamic 
cylinder deactivation (e.g., Dynamic Skip Fire), can be applied to any 
engine with basic technology. This technology represents a naturally 
aspirated engine with ADEAC. Additional technology can be applied to 
these engines by moving to the Advanced Turbo Engine Path.
    Miller cycle (VTG and VTGE) engines can be applied to any basic and 
turbocharged engine. VTGE technology is enabled by the use of a 48V 
system that presents an improvement from traditional turbocharged 
engines, and accordingly VTGE includes the application of a mild hybrid 
(BISG) system.
    VCR engines can be applied to basic and turbocharged engines, but 
the technology is limited to Nissan and Mitsubishi.\135\ VCR technology 
requires a complete redesign of the engine, and in the analysis fleet, 
only two of Nissan's models had incorporated this technology. The 
agency does not believe any other manufacturers will invest to develop 
and market this technology in their fleet in the rulemaking time frame.
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    \135\ Nissan and Mitsubishi are strategic partners and members 
of the Renault-Nissan-Mitsubishi Alliance.
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    Advanced turbo engines are becoming more prevalent as the 
technologies mature. TURBOD combines TURBO1 and DEAC technologies and 
represents the first advanced turbo. TURBOAD combines TURBO1 and ADEAC 
technologies and is the second and last level of advanced turbos. 
Engines from either the Turbo Engine Path or the ADEAC Engine Path can 
adopt these technologies.
    Any basic engine technologies (VVT, VVL, SGDI, and DEAC) can adopt 
ADSL and DSLI engine technologies. Any basic engine and diesel engine 
can adopt DSLIAD technology in this analysis; however, DOT applied a 
phase in cap and year for this technology at 34 percent and MY 2023, 
respectively. In DOT's engineering judgement, this is a rather complex 
and costly technology to adopt and it would take significant investment 
for a manufacturer to develop. For more than a decade, diesel engine 
technologies have been used in less than one percent of the total 
light-duty fleet production and have been found mostly on medium and 
heavy-duty vehicles.
    Finally, DOT allows the CAFE Model to apply EFR to any engine 
technology except for DSLI and DSLIAD. DSLI and DSLIAD inherently have 
incorporated engine friction technologies from ADSL. In addition, 
friction reduction technologies that apply to gasoline engines cannot 
necessarily be applied to diesel engines due to the higher temperature 
and pressure operation in diesel engines.
(d) Engine Effectiveness Modeling
    Effectiveness values used for engine technologies were simulated in 
two ways. The value was either calculated based on the difference in 
full vehicle simulation results created using the Autonomie modeling 
tool, or effectiveness values were determined using an alternate 
calculation method, including analogous improvement or fuel economy 
improvement factors.
(1) Engine Maps
    Most effectiveness values used as inputs for the CAFE Model were 
determined by comparing results of full vehicle simulations using the 
Autonomie simulation tool. For a full discussion about how Autonomie 
was used, see Section III.C.4 and TSD Chapter 2.4, in addition to the 
Autonomie model documentation. Engine map models were the primary 
inputs used to simulate the effects of different engine technologies in 
the Autonomie full vehicle simulations.
    Engine maps provide a three-dimensional representation of engine 
performance characteristics at each engine speed and load point across 
the operating range of the engine. Engine maps have the appearance of 
topographical maps, typically with engine speed on the horizontal axis 
and engine torque, power, or brake mean effective pressure (BMEP) \136\ 
on the vertical axis. A third engine characteristic, such as brake-
specific fuel consumption (BSFC),\137\ is displayed using contours 
overlaid across the speed and load map. The contours provide the values 
for the third characteristic in the regions of operation covered on the 
map. Other characteristics typically overlaid on an engine map include 
engine emissions, engine efficiency, and engine power. The engine maps 
developed to model the behavior of the engines used in this analysis 
are referred to as engine map models.
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    \136\ Brake mean effective pressure is an engineering measure, 
independent of engine displacement, that indicates the actual work 
an engine performs.
    \137\ Brake-specific fuel consumption is the rate of fuel 
consumption divided by the power being produced.
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    The engine map models used in this analysis are representative of 
technologies that are currently in production or are expected to be 
available in the rulemaking timeframe, MYs 2024-2026. The engine map 
models were developed to be representative of the performance 
achievable across industry for a given technology and are not intended 
to represent the performance of a single manufacturer's specific 
engine. The broadly representative performance level was targeted 
because the same combination of technologies produced by different 
manufacturers will have differences in performance, due to 
manufacturer-specific designs for engine hardware, control software, 
and emissions calibration.
    Accordingly, DOT expects that the engine maps developed for this 
analysis will differ from engine maps for manufacturers' specific 
engines. However, DOT intends and expects that the incremental changes 
in performance modeled for this analysis, due to changes in 
technologies or technology combinations, will be similar to the 
incremental changes in performance observed in manufacturers' engines 
for the same changes in technologies or technology combinations.
    The analysis never applies absolute BSFC levels from the engine 
maps to any vehicle model or configuration for the rulemaking analysis. 
The absolute fuel economy values from the full vehicle Autonomie 
simulations are used only to determine incremental effectiveness for 
switching from one technology to another technology. The incremental 
effectiveness is applied to the absolute fuel economy of vehicles in 
the analysis fleet, which are based on CAFE compliance data. For 
subsequent

[[Page 49663]]

technology changes, incremental effectiveness is applied to the 
absolute fuel economy level of the previous technology configuration. 
Therefore, for a technically sound analysis, it is most important that 
the differences in BSFC among the engine maps be accurate, and not the 
absolute values of the individual engine maps. However, achieving this 
can be challenging.
    For this analysis, DOT used a small number of baseline engine 
configurations with well-defined BSFC maps, and then, in a very 
systematic and controlled process, added specific well-defined 
technologies to create a BSFC map for each unique technology 
combination. This could theoretically be done through engine or vehicle 
testing, but testing would need to be conducted on a single engine, and 
each configuration would require physical parts and associated engine 
calibrations to assess the impact of each technology configuration, 
which is impractical for the rulemaking analysis because of the 
extensive design, prototype part fabrication, development, and 
laboratory resources that are required to evaluate each unique 
configuration. Modeling is an approach used by industry to assess an 
array of technologies with more limited testing. Modeling offers the 
opportunity to isolate the effects of individual technologies by using 
a single or small number of baseline engine configurations and 
incrementally adding technologies to those baseline configurations. 
This provides a consistent reference point for the BSFC maps for each 
technology and for combinations of technologies that enables the 
differences in effectiveness among technologies to be carefully 
identified and quantified.
    The Autonomie model documentation provides a detailed discussion on 
how the engine map models were used as inputs to the full vehicle 
simulations performed using the Autonomie tool. The Autonomie model 
documentation contains the engine map model topographic figures, and 
additional engine map model data can be found in the Autonomie input 
files.\138\
---------------------------------------------------------------------------

    \138\ See additional Autonomie supporting materials in docket 
number NHTSA-2021-0053 for this proposal.
---------------------------------------------------------------------------

    Most of the engine map models used in this analysis were developed 
by IAV GmbH (IAV) Engineering. IAV is one of the world's leading 
automotive industry engineering service partners with an over 35-year 
history of performing research and development for powertrain 
components, electronics, and vehicle design.\139\ The primary outputs 
of IAV's work for this analysis are engine maps that model the 
operating characteristics of engines equipped with specific 
technologies.
---------------------------------------------------------------------------

    \139\ IAV Automotive Engineering, https://www.iav.com/en/.
---------------------------------------------------------------------------

    The generated engine maps were validated against IAV's global 
database of benchmarked data, engine test data, single cylinder test 
data, prior modeling studies, technical studies, and information 
presented at conferences.\140\ The effectiveness values from the 
simulation results were also validated against detailed engine maps 
produced from the Argonne engine benchmarking programs, as well as 
published information from industry and academia, ensuring reasonable 
representation of simulated engine technologies.\141\ The engine map 
models used in this analysis and their specifications are shown in 
Table III-9.
---------------------------------------------------------------------------

    \140\ Friedrich, I., Pucher, H., and Offer, T., ``Automatic 
Model Calibration for Engine-Process Simulation with Heat-Release 
Prediction,'' SAE Technical Paper 2006-01-0655, 2006, https://doi.org/10.4271/2006-01-0655. Rezaei, R., Eckert, P., Seebode, J., 
and Behnk, K., ``Zero-Dimensional Modeling of Combustion and Heat 
Release Rate in DI Diesel Engines,'' SAE Int. J. Engines 5(3):874-
885, 2012, https://doi.org/10.4271/2012-01-1065. Multistage 
Supercharging for Downsizing with Reduced Compression Ratio (2015). 
MTZ Rene Berndt, Rene Pohlke, Christopher Severin and Matthias 
Diezemann IAV GmbH. Symbiosis of Energy Recovery and Downsizing 
(2014). September 2014 MTZ Publication Heiko Neukirchner, Torsten 
Semper, Daniel Luederitz and Oliver Dingel IAV GmbH.
    \141\ Bottcher,. L, Grigoriadis, P. ``ANL--BSFC map prediction 
Engines 22-26.'' IAV (April 30, 2019). 20190430_ANL_Eng 22-26 
Updated_Docket.pdf.
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BILLING CODE 4910-59-P

[[Page 49664]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.049

BILLING CODE 4910-59-C
    Two engine map models shown in Table III-9, Eng24 and Eng25, were 
not developed as part of the IAV modeling effort and only Eng24 is used 
in this

[[Page 49665]]

analysis. The Eng24 and Eng25 engine maps are equivalent to the ATK and 
ATK2 models developed for the 2016 Draft Technical Assessment Report 
(TAR), EPA Proposed Determination, and Final Determination.\142\ The 
ATK1 engine model is based directly on the 2.0L 2014 Mazda SkyActiv-G 
(ATK) engine. The ATK2 represents an Atkinson engine concept based on 
the Mazda engine, adding cEGR, cylinder deactivation, and an increased 
compression ratio (14:1). In this analysis, Eng24 and Eng25 correspond 
to the HCR1 and HCR2 technologies.
---------------------------------------------------------------------------

    \142\ Ellies, B., Schenk, C., and Dekraker, P., ``Benchmarking 
and Hardware-in-the-Loop Operation of a 2014 MAZDA SkyActiv 2.0L 
13:1 Compression Ratio Engine,'' SAE Technical Paper 2016-01-1007, 
2016, doi:10.4271/2016-01-1007.
---------------------------------------------------------------------------

    The HCR2 engine map model application in this analysis follows the 
approach of the 2020 final rule.\143\ The agency believes the use of 
HCR0, HCR1, and the new addition of HCR1D reasonably represents the 
application of Atkinson Cycle engine technologies within the current 
light-duty fleet and the anticipated applications of Atkinson Cycle 
technology in the MY 2024-2026 timeframe.
---------------------------------------------------------------------------

    \143\ 85 FR 24425-27 (April 30, 2020).
---------------------------------------------------------------------------

    We are currently developing an updated family of HCR engine map 
models that will include cEGR, cylinder deactivation and a combination 
thereof. The new engine map models will closely align with the baseline 
assumptions used in the other IAV-based HCR engine map models used for 
the agency's analysis. The updated engine map models will likely not be 
available for the final rule associated with this proposal because of 
engine map model testing and validation requirements but will be 
available for future CAFE analyses. We believe the timing for including 
the new engine map models is reasonable, because a manufacturer that 
could apply this technology in response to CAFE standards is likely not 
do so before MY 2026, as the application of this technology will 
require an engine redesign. We also believe this is reasonable given 
manufacturer's statements that there are diminishing returns to 
additional conventional engine technology improvements considering 
vehicle electrification commitments.
    NHTSA seeks comment on whether and how to change our engine maps 
for HCR2 in the analysis for the final rule.
(2) Analogous Engine Effectiveness Improvements and Fuel Economy 
Improvement Factors
    For some technologies, the effectiveness for applying an 
incremental engine technology was determined by using the effectiveness 
values for applying the same engine technology to a reasonably similar 
base engine. An example of this can be seen in the determination of the 
application of SGDI to the baseline SOHC engine. Currently there is no 
engine map model for the SOHC+VVT+SGDI engine configuration. To create 
the effectiveness data required as an input to the CAFE Model, first, a 
pairwise comparison between technology configurations that included the 
DOHC+VVT engine (Eng1) and the DOHC+VVT+SGDI (Eng18) engine was 
conducted. Then, the results of that comparison were used to generate a 
data set of emulated performance values for adding the SGDI technology 
to the SOHC+VVT engine (Eng5b) systems.
    The pairwise comparison is performed by finding the difference in 
fuel consumption performance between every technology configuration 
using the analogous base technology (e.g., Eng1) and every technology 
configuration that only changes to the analogous technology (e.g., 
Eng18). The individual changes in performance between all the 
technology configurations are then added to the same technology 
configurations that use the new base technology (e.g., Eng5b) to create 
a new set of performance values for the new technology (e.g., 
SOHC+VVT+SGDI). Table III-10 shows the engine technologies where 
analogous effectiveness values were used.
[GRAPHIC] [TIFF OMITTED] TP03SE21.050

    DOT also developed a static fuel efficiency improvement factor to 
simulate applying an engine technology for some technologies where 
there was either no appropriate analogous technology or there were not 
enough data to create a full engine map model. The improvement factors 
were generally developed based on literature review or confidential 
business information (CBI) provided by stakeholders. Table III-11 
provides a summary of the technology

[[Page 49666]]

effectiveness values simulated using improvement factors, and the value 
and rules for how the improvement factors were applied. Advanced 
cylinder deactivation (ADEAC, TURBOAD, DSLIAD), advanced diesel engines 
(DSLIA) and engine friction reduction (EFR) are the three technologies 
modeled using improvement factors.
    The application of the advanced cylinder deactivation is 
responsible for three of the five technologies using an improvement 
factor in this analysis. The initial review of the advanced cylinder 
deactivation technology was based on a technical publication that used 
a MY 2010 SOHC VVT basic engine.\144\ Additional information about the 
technology effectiveness came from a benchmarking analysis of pre-
production 8-cylinder OHV prototype systems.\145\ However, at the time 
of the analysis no studies of production versions of the technology 
were available, and the only available technology effectiveness came 
from existing studies, not operational information. Thus, only 
estimates of effect could be developed and not a full model of 
operation. No engine map model could be developed, and no other 
technology pairs were analogous.
---------------------------------------------------------------------------

    \144\ Wilcutts, M., Switkes, J., Shost, M., and Tripathi, A., 
``Design and Benefits of Dynamic Skip Fire Strategies for Cylinder 
Deactivated Engines,'' SAE Int. J. Engines 6(1):278-288, 2013, 
available at https://doi.org/10.4271/2013-01-0359. Eisazadeh-Far, K. 
and Younkins, M., ``Fuel Economy Gains through Dynamic-Skip-Fire in 
Spark Ignition Engines,'' SAE Technical Paper 2016-01-0672, 2016, 
available at https://doi.org/10.4271/2016-01-0672.
    \145\ EPA, 2018. ``Benchmarking and Characterization of a Full 
Continuous Cylinder Deactivation System.'' Presented at the SAE 
World Congress, April 10-12, 2018. Retrieved from https://www.regulations.gov/document?D=EPA-HQ-OAR-2018-0283-0029.
---------------------------------------------------------------------------

    To model the effects of advanced cylinder deactivation, an 
improvement factor was determined based on the information referenced 
above and applied across the engine technologies. The effectiveness 
values for naturally aspirated engines were predicted by using full 
vehicle simulations of a basic engine with DEAC, SGDI, VVL, and VVT, 
and adding 3 percent or 6 percent improvement based on engine cylinder 
count: 3 percent for engines with 4 cylinders or less and 6 percent for 
all other engines. Effectiveness values for turbocharged engines were 
predicted using full vehicle simulations of the TURBOD engine and 
adding 1.5 percent or 3 percent improvement based on engine cylinder 
count: 1.5 percent for engines with 4 cylinders or less and 3 percent 
for all other engines. For diesel engines, effectiveness values were 
predicted by using the DSLI effectiveness values and adding 4.5 percent 
or 7.5 percent improvement based on vehicle technology class: 4.5 
percent improvement was applied to small and medium non-performance 
cars, small performance cars, and small non-performance SUVs. 7.5 
percent improvement was applied to all other vehicle technology 
classes.
    The analysis modeled advanced engine technology application to the 
baseline diesel engine by applying an improvement factor to the ADSL 
engine technology combinations. A 12.8 percent improvement factor was 
applied to the ADSL technology combinations to create the DSLI 
technology combinations. The improvement in performance was based on 
the application of a combination of low pressure and high pressure EGR, 
reduced parasitic loss, advanced friction reduction, incorporation of 
highly-integrated exhaust catalyst with low temp light off 
temperatures, and closed loop combustion 
control.146 147 148 149
---------------------------------------------------------------------------

    \146\ 2015 NAS report, at 104.
    \147\ Hatano, J., Fukushima, H., Sasaki, Y., Nishimori, K., 
Tabuchi, T., Ishihara, Y. ``The New 1.6L 2-Stage Turbo Diesel Engine 
for HONDA CR-V.'' 24th Aachen Colloquium--Automobile and Engine 
Technology 2015.
    \148\ Steinparzer, F., Nefischer, P., Hiemesch, D., Kaufmann, 
M., Steinmayr, T. ``The New Six-Cylinder Diesel Engines from the BMW 
In-Line Engine Module.'' 24th Aachen Colloquium--Automobile and 
Engine Technology 2015.
    \149\ Eder, T., Weller, R., Spengel, C., B[ouml]hm, J., Herwig, 
H., Sass, H. Tiessen, J., Knauel, P. ``Launch of the New Engine 
Family at Mercedes-Benz.'' 24th Aachen Colloquium--Automobile and 
Engine Technology 2015.
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    As discussed above, the application of the EFR technology does not 
simulate the application of a specific technology, but the application 
of an array of potential improvements to an engine. All reciprocating 
and rotating components in the engine are potential candidates for 
friction reduction, and minute improvements in several components can 
add up to a measurable fuel economy 
improvement.150 151 152 153 Because of the incremental 
nature of this analysis, a range of 1-2 percent improvement was 
identified initially, and narrowed further to a specific 1.39% 
improvement. The final value is likely representative of a typical 
value industry may be able to achieve in future years.
---------------------------------------------------------------------------

    \150\ ``Polyalkylene Glycol (PAG) Based Lubricant for Light- & 
Medium-Duty Axles,'' 2017 DOE Annual Merit Review. Ford Motor 
Company, Gangopadhyay, A., Ved, C., Jost, N. https://energy.gov/sites/prod/files/2017/06/f34/ft023_gangopadhyay_2017_o.pdf.
    \151\ ``Power-Cylinder Friction Reduction through Coatings, 
Surface Finish, and Design,'' 2017 DOE Annual Merit Review. Ford 
Motor Company. Gangopadhay, A. Erdemir, A. https://energy.gov/sites/prod/files/2017/06/f34/ft050_gangopadhyay_2017_o.pdf.
    \152\ ``Nissan licenses energy-efficient engine technology to 
HELLER,'' https://newsroom.nissan-global.com/releases/170914-01-e?lang=en-US&rss&la=1&downloadUrl=%2Freleases%2F170914-01-e%2Fdownload. Last accessed April 2018.
    \153\ ``Infiniti's Brilliantly Downsized V-6 Turbo Shines,'' 
http://wardsauto.com/engines/infiniti-s-brilliantly-downsized-v-6-turbo-shines. Last Accessed April 2018.

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[[Page 49667]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.051

(3) Engine Effectiveness Values
    The effectiveness values for the engine technologies, for all ten 
vehicle technology classes, are shown in Figure III-8. Each of the 
effectiveness values shown is representative of the improvements seen 
for upgrading only the listed engine technology for a given combination 
of other technologies. In other words, the range of effectiveness 
values seen for each specific technology (e.g., TURBO1) represents the 
addition of the TURBO1 technology to every technology combination that 
could select the addition of TURBO1. See Table III-12 for several 
specific examples. It must be emphasized, the change in fuel 
consumption values between entire technology keys is used,\154\ and not 
the individual technology effectiveness values. Using the change 
between whole technology keys captures the complementary or non-
complementary interactions among technologies.
---------------------------------------------------------------------------

    \154\ Technology key is the unique collection of technologies 
that constitutes a specific vehicle, see Section III.C.4.c).
[GRAPHIC] [TIFF OMITTED] TP03SE21.052

    Some of the advanced engine technologies have values that indicate 
seemingly low effectiveness. Investigation of these values shows the 
low effectiveness was a result of applying the advanced engines to 
existing SHEVP2 architectures. This effect is expected and illustrates 
the importance of using the full vehicle modeling to capture 
interactions between technologies and capture instances of both 
complimentary technologies and non-complimentary technologies. In this 
instance, the SHEVP2 powertrain improves fuel economy, in part, by 
allowing the engine to spend more time operating at efficient engine 
speed and load conditions. This reduces the advantage of adding 
advanced engine technologies, which also improve fuel economy, by 
broadening the range of speed and load conditions for the engine to 
operate at high efficiency. This redundancy in fuel savings mechanism 
results in a lower effectiveness when the technologies are added to 
each other.
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    \155\ The full data set we used to generate this example can be 
found in the FE_1 Improvements file.

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[[Page 49668]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.053

(e) Engine Costs
    The CAFE Model considers both cost and effectiveness in selecting 
any technology changes. We have allocated considerable resources to 
sponsoring research to determine direct manufacturing costs (DMCs) for 
fuel saving technologies. As discussed in detail in TSD Chapter 3.1.5, 
the engine costs used in this analysis build on estimates from the 2015 
NAS report, agency-funded teardown studies, and work performed by non-
government organizations.\157\
---------------------------------------------------------------------------

    \156\ The box shows the inner quartile range (IQR) of the 
effectiveness values and whiskers extend out 1.5 x IQR. The dots 
outside this range show effectiveness values outside those 
thresholds. The data used to create this figure can be found in the 
FE_1 Improvements file.
    \157\ FEV prepared several cost analysis studies for EPA on 
subjects ranging from advanced 8-speed transmissions to belt 
alternator starters or start/stop systems. NHTSA contracted 
Electricore, EDAG, and Southwest Research for teardown studies 
evaluating mass reduction and transmissions. The 2015 NAS report 
also evaluated technology costs developed based on these teardown 
studies.
---------------------------------------------------------------------------

    Absolute costs of the engine technology are used in this analysis 
instead of relative costs, which were used prior to the 2020 final 
rule. The absolute costs are used to ensure the full cost of the IC 
engine is removed when electrification technologies are applied 
specifically for the transition to BEVs. This analysis models the cost 
of adoption of BEV technology by first removing the costs associated 
with IC powertrain systems, then applying the BEV systems costs. 
Relative costs can still be determined through comparison of the 
absolute costs for the initial technology combination and the new 
technology combination.
    As discussed in detail in TSD Chapter 3.1.5, engine costs are 
assigned based on the number of cylinders in the engine and whether the 
engine is naturally aspirated or turbocharged and downsized. Table III-
13 below shows an example of absolute costs for engine technologies in 
2018$. The example costs are shown for a straight 4-cylinder DOHC 
engine and V-6-cylinder DOHC engine. The table shows costs declining 
across successive years due to the learning rate applied to each engine 
technology. For a full list of all absolute engine costs used in the 
analysis across all model years, see the Technologies file.

[[Page 49669]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.054

2. Transmission Paths
    For this analysis, DOT classified all light duty vehicle 
transmission technologies into discrete transmission technology paths. 
These paths are used to model the most representative characteristics, 
costs, and performance of the fuel-economy improving transmissions most 
likely available during the rulemaking time frame, MYs 2024-2026.
    The following sections discuss how transmission technologies 
considered in this analysis are defined, the general technology 
categories used by the CAFE Model, and the transmission technologies' 
relative effectiveness and costs. The following sections also provide 
an overview of how the transmission technologies were assigned to the 
MY 2020 fleet, as well as the adoption features applicable to the 
transmission technologies.
(a) Transmission Modeling in the CAFE Model
    DOT modeled two major categories of transmissions for this 
analysis: Automatic and manual. Automatic transmissions are 
characterized by automatically selecting and shifting between 
transmission gears for the driver during vehicle operation. Automatic 
transmissions are further subdivided into four subcategories: 
Traditional automatic transmissions (AT), dual clutch transmissions 
(DCT), continuously variable transmissions (CVT), and direct drive 
transmissions (DD).
    ATs and CVTs also employ different levels of high efficiency 
gearbox (HEG) technology. HEG improvements for transmissions represent 
incremental advancement in technology that improve efficiency, such as 
reduced friction seals, bearings and clutches, super finishing of 
gearbox parts, and improved lubrication. These advancements are all 
aimed at reducing frictional and other parasitic loads in transmissions 
to improve efficiency. DOT considered three levels of HEG improvements 
in this analysis, based on 2015 recommendations by the National Academy 
of Sciences and CBI data.\158\ HEG efficiency improvements are applied 
to ATs and CVTs, as those transmissions inherently have higher friction 
and parasitic loads related to hydraulic control systems and greater 
component complexity, compared to MTs and DCTs. HEG technology 
improvements are noted in the transmission technology pathways by 
increasing ``levels'' of a transmission technology; for example, the 
baseline 8-speed automatic transmission is termed ``AT8'', while an AT8 
with level 2 HEG technology is ``AT8L2'' and an AT8 with level 3 HEG 
technology is ``AT8L3.''
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    \158\ 2015 NAS report, at 191.
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    AT: Conventional planetary gear automatic transmissions are the 
most

[[Page 49670]]

popular transmission.\159\ ATs typically contain three or four 
planetary gear sets that provide the various gear ratios. Gear ratios 
are selected by activating solenoids which engage or release multiple 
clutches and brakes as needed. ATs are packaged with torque converters, 
which provide a fluid coupling between the engine and the driveline and 
provide a significant increase in launch torque. When transmitting 
torque through this fluid coupling, energy is lost due to the churning 
fluid. These losses can be eliminated by engaging the torque convertor 
clutch to directly connect the engine and transmission (``lockup''). 
For the Draft TAR and 2020 final rule, EPA and DOT surveyed automatic 
transmissions in the market to assess trends in gear count and 
purported fuel economy improvements.\160\ Based on that survey, and 
also EPA's more recent 2019 and 2020 Automotive Trends Reports,\161\ 
DOT concluded that modeling ATs with a range of 5 to 10 gears, with 
three levels of HEG technology for this analysis was reasonable.
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    \159\ 2020 EPA Automotive Trends Report, at 57-61.
    \160\ Draft TAR at 5-50, 5-51; Final Regulatory Impact Analysis 
accompanying the 2020 final rule, at 549.
    \161\ The 2019 EPA Automotive Trends Report, EPA-420-R-20-006, 
at 59 (March 2020), https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100YVFS.pdf [hereinafter 2019 EPA Automotive 
Trends Report]; 2020 EPA Automotive Trends Report, at 57.
---------------------------------------------------------------------------

    CVT: Conventional continuously variable transmissions consist of 
two cone-shaped pulleys, connected with a belt or chain. Moving the 
pulley halves allows the belt to ride inward or outward radially on 
each pulley, effectively changing the speed ratio between the pulleys. 
This ratio change is smooth and continuous, unlike the step changes of 
other transmission varieties.\162\ DOT modeled two types of CVT systems 
in the analysis, the baseline CVT and a CVT with HEG technology 
applied.
---------------------------------------------------------------------------

    \162\ 2015 NAS report, at 171.
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    DCT: Dual clutch transmissions, like automatic transmissions, 
automate shift and launch functions. DCTs use separate clutches for 
even-numbered and odd-numbered gears, allowing the next gear needed to 
be pre-selected, resulting in faster shifting. The use of multiple 
clutches in place of a torque converter results in lower parasitic 
losses than ATs.\163\ Because of a history of limited 
appeal,164 165 DOT constrains application of additional DCT 
technology to vehicles already using DCT technology, and only models 
two types of DCTs in the analysis.
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    \163\ 2015 NAS report, at 170.
    \164\ 2020 EPA Automotive Trends Report, at 57.
    \165\ National Academies of Sciences, Engineering, and Medicine 
2021. Assessment of Technologies for Improving Light-Duty Vehicle 
Fuel Economy 2025-2035. Washington, DC: The National Academies 
Press. https://doi.org/10.17226/26092, at 4-56 [hereinafter 2021 NAS 
report].
---------------------------------------------------------------------------

    MT: Manual transmissions are transmissions that require direct 
control by the driver to operate the clutch and shift between gears. In 
a manual transmission, gear pairs along an output shaft and parallel 
layshaft are always engaged. Gears are selected via a shift lever, 
operated by the driver. The lever operates synchronizers, which speed 
match the output shaft and the selected gear before engaging the gear 
with the shaft. During shifting operations (and during idle), a clutch 
between the engine and transmission is disengaged to decouple engine 
output from the transmission. Automakers today offer a minimal 
selection of new vehicles with manual transmissions.\166\ As a result 
of reduced market presence, DOT only included three variants of manual 
transmissions in the analysis.
---------------------------------------------------------------------------

    \166\ 2020 EPA Automotive Trends Report, at 61.
---------------------------------------------------------------------------

    The transmission model paths used in this analysis are shown in 
Figure III-9. Baseline-only technologies (MT5, AT5, AT7L2, AT9L2, and 
CVT) are grayed and can only be assigned as initial vehicle 
transmission configurations. Further details about transmission path 
modeling can be found in TSD Chapter 3.2.
[GRAPHIC] [TIFF OMITTED] TP03SE21.055


[[Page 49671]]


(b) Transmission Analysis Fleet Assignments
    The wide variety of transmissions on the market are classified into 
discrete transmission technology paths for this analysis. These paths 
are used to model the most representative characteristics, costs, and 
performance of the fuel economy-improving technologies most likely 
available during the rulemaking time frame.
    For the 2020 analysis fleet, DOT gathered data on transmissions 
from manufacturer mid-model year CAFE compliance submissions and 
publicly available manufacturer specification sheets. These data were 
used to assign transmissions in the analysis fleet and determine which 
platforms shared transmissions.
    Transmission type, number of gears, and high-efficiency gearbox 
(HEG) level are all specified for the baseline fleet assignment. The 
number of gears in the assignments for automatic and manual 
transmissions usually match the number of gears listed by the data 
sources, with some exceptions. Four-speed transmissions were not 
modeled in Autonomie for this analysis due to their rarity and low 
likelihood of being used in the future, so DOT assigned 2020 vehicles 
with an AT4 or MT4 to an AT5 or MT5 baseline, respectively. Some dual-
clutch transmissions were also an exception; dual-clutch transmissions 
with seven gears were assigned to DCT6.
    For automatic and continuously variable transmissions, the 
identification of the most appropriate transmission path model required 
additional steps; this is because high-efficiency gearboxes are 
considered in the analysis but identifying HEG level from specification 
sheets alone was not always straightforward. DOT conducted a review of 
the age of the transmission design, relative performance versus 
previous designs, and technologies incorporated and used the 
information obtained to assign an HEG level. No automatic transmissions 
in the MY 2020 analysis fleet were determined to be at HEG Level 3. In 
addition, no six-speed automatic transmissions were assigned HEG Level 
2. However, DOT found all 7-speed, all 9-speed, all 10-speed, and some 
8-speed automatic transmissions to be advanced transmissions operating 
at HEG Level 2 equivalence. Eight-speed automatic transmissions 
developed after MY 2017 are assigned HEG Level 2. All other 
transmissions are assigned to their respective transmission's baseline 
level. The baseline (HEG level 1) technologies available include AT6, 
AT8, and CVT.
    DOT assigned any vehicle in the analysis fleet with a hybrid or 
electric powertrain a direct drive (DD) transmission. This designation 
is for informational purposes; if specified, the transmission will not 
be replaced or updated by the model.
    In addition to technology type, gear count, and HEG level, 
transmissions are characterized in the analysis fleet by drive type and 
vehicle architecture. Drive types considered in the analysis include 
front-, rear-, all-, and four-wheel drive. The definition of drive 
types in the analysis does not always align with manufacturers' drive 
type designations; see the end of this subsection for further 
discussion. These characteristics, supplemented by information such as 
gear ratios and production locations, showed that manufacturers use 
transmissions that are the same or similar on multiple vehicle models. 
Manufacturers have told the agency they do this to control component 
complexity and associated costs for development, manufacturing, 
assembly, and service. If multiple vehicle models share technology 
type, gear count, drive configuration, internal gear rations, and 
production location, the transmissions are treated as a single group 
for the analysis. Vehicles in the analysis fleet with the same 
transmission configuration adopt additional fuel-saving transmission 
technology together, as described in Section III.C.2.a).
    Shared transmissions are designated and tracked in the CAFE Model 
input files using transmission codes. Transmission codes are six-digit 
numbers that are assigned to each transmission and encode information 
about them. This information includes the manufacturer, drive 
configuration, transmission type, and number of gears. TSD Chapter 
3.2.2 includes more information on the transmission codes designated in 
the MY 2020 analysis fleet.
    Different transmission codes are assigned to variants of a 
transmission that may have appeared to be similar based on the 
characteristics considered in the analysis but are not mechanically 
identical. DOT analysts distinguish among transmission variants by 
comparing their internal gear ratios and production locations. For 
example, several Ford nameplates carry a rear-wheel drive, 10-speed 
automatic transmission. These nameplates comprise a wide variety of 
body styles and use cases, and so DOT assigned different transmission 
codes to these different nameplates. Because they have different 
transmission codes, they are not treated as ``shared'' for the purposes 
of the analysis and have the opportunity to adopt transmission 
technologies independently.
    Note that when determining the drive type of a transmission, the 
assignment of all-wheel drive versus four-wheel drive is determined by 
vehicle architecture. This assignment does not necessarily match the 
drive type used by the manufacturer in specification sheets and 
marketing materials. Vehicles with a powertrain capable of providing 
power to all wheels and a transverse engine (front-wheel drive 
architecture) are assigned all-wheel drive. Vehicles with power to all 
four wheels and a longitudinal engine (rear-wheel drive architecture) 
are assigned four-wheel drive.
(c) Transmission Adoption Features
    Transmission technology pathways are designed to prevent ``branch 
hopping''--changes in transmission type that would correspond to 
significant changes in transmission architecture--for vehicles that are 
relatively advanced on a given pathway. For example, any automatic 
transmission with more than five gears cannot move to a dual-clutch 
transmission. For a more detailed discussion of path logic applied in 
the analysis, including technology supersession logic and technology 
mutual exclusivity logic, please see CAFE Model Documentation S4.5 
Technology Constraints (Supersession and Mutual Exclusivity). 
Additionally, the CAFE Model prevents ``branch hopping'' to prevent 
stranded capital associated with moving from one transmission 
architecture to another. Stranded capital is discussed in Section 
III.C.6.
    Some technologies that are modeled in the analysis are not yet in 
production, and therefore are not assigned in the baseline fleet. 
Nonetheless, these technologies, which are projected to be available in 
the analysis timeframe, are available for future adoption. For 
instance, an AT10L3 is not observed in the baseline fleet, but it is 
plausible that manufacturers that employ AT10L2 technology may improve 
the efficiency of those AT10L2s in the rulemaking timeframe.
    The following sections discuss specific adoption features applied 
to each type of transmission technology.
    When electrification technologies are adopted, the transmissions 
associated with those technologies will supersede the existing 
transmission on a vehicle. The transmission technology is superseded if 
P2 hybrids, plug-in hybrids, or battery electric vehicle technologies 
are applied. For more information, see Section III.D.3.c).

[[Page 49672]]

    The automatic transmission path precludes adoption of other 
transmission types once a platform progresses past an AT6. This 
restriction is used to avoid the significant level of stranded capital 
loss that could result from adopting a completely different 
transmission type shortly after adopting an advanced transmission, 
which would occur if a different transmission type were adopted after 
AT6 in the rulemaking timeframe.
    Vehicles that did not start out with AT7L2 or AT9L2 transmissions 
cannot adopt those technologies in the model. The agency observed that 
MY 2017 vehicles with those technologies were primarily luxury 
performance vehicles and concluded that other vehicles would likely not 
adopt those technologies. DOT concluded that this was also a reasonable 
assumption for the MY 2020 analysis fleet because vehicles that have 
moved to more advanced automatic transmissions have overwhelmingly 
moved to 8-speed and 10-speed transmissions.\167\
---------------------------------------------------------------------------

    \167\ 2020 EPA Automotive Trends Report, at 64, figure 4.18.
---------------------------------------------------------------------------

    CVT adoption is limited by technology path logic. CVTs cannot be 
adopted by vehicles that do not originate with a CVT or by vehicles 
with multispeed transmissions beyond AT6 in the baseline fleet. 
Vehicles with multispeed transmissions greater than AT6 demonstrate 
increased ability to operate the engine at a highly efficient speed and 
load. Once on the CVT path, the platform is only allowed to apply 
improved CVT technologies. The analysis restricts the application of 
CVT technology on larger vehicles because of the higher torque (load) 
demands of those vehicles and CVT torque limitations based on 
durability constraints. Additionally, this restriction is used to avoid 
the significant level of stranded capital.
    The analysis allows vehicles in the baseline fleet that have DCTs 
to apply an improved DCT and allows vehicles with an AT5 to consider 
DCTs. Drivability and durability issues with some DCTs have resulted in 
a low relative adoption rate over the last decade; this is also broadly 
consistent with manufacturers' technology choices.\168\
---------------------------------------------------------------------------

    \168\ Ibid.
---------------------------------------------------------------------------

    Manual transmissions can only move to more advanced manual 
transmissions for this analysis, because other transmission types do 
not provide a similar driver experience (utility). Manual transmissions 
cannot adopt AT, CVT, or DCT technologies under any circumstance. Other 
transmissions cannot move to MT because manual transmissions lack 
automatic shifting associated with the other transmission types 
(utility) and in recognition of the low customer demand for manual 
transmissions.\169\
---------------------------------------------------------------------------

    \169\ Ibid.
---------------------------------------------------------------------------

(d) Transmission Effectiveness Modeling
    For this analysis, DOT used the Autonomie full vehicle simulation 
tool to model the interaction between transmissions and the full 
vehicle system to improve fuel economy, and how changes to the 
transmission subsystem influence the performance of the full vehicle 
system. The full vehicle simulation approach clearly defines the 
contribution of individual transmission technologies and separates 
those contributions from other technologies in the full vehicle system. 
The modeling approach follows the recommendations of the National 
Academy of Sciences in its 2015 light duty vehicle fuel economy 
technology report to use full vehicle modeling supported by application 
of collected improvements at the sub-model level.\170\ See TSD Chapter 
3.2.4 for more details on transmission modeling inputs and results.
---------------------------------------------------------------------------

    \170\ 2015 NAS report, at 292.
---------------------------------------------------------------------------

    The only technology effectiveness results that were not directly 
calculated using the Autonomie simulation results were for the AT6L2. 
DOT determined that the model for this specific technology was 
inconsistent with the other transmission models and overpredicted 
effectiveness results. Evaluation of the AT6L2 transmission model 
revealed an overestimated efficiency map was developed for the AT6L2 
model. The high level of efficiency assigned to the transmission 
surpassed benchmarked advanced transmissions.\171\ To address the 
issue, DOT replaced the effectiveness values of the AT6L2 model. DOT 
replaced the effectiveness for the AT6L2 technology with analogous 
effectiveness values from the AT7L2 transmission model. For additional 
discussion on how analogous effectiveness values are determined please 
see Section III.D.1.d)(2).
---------------------------------------------------------------------------

    \171\ Autonomie model documentation, Chapter 5.3.4. Transmission 
Performance Data.
---------------------------------------------------------------------------

    The effectiveness values for the transmission technologies, for all 
ten vehicle technology classes, are shown in Figure III-10. Each of the 
effectiveness values shown is representative of the improvements seen 
for upgrading only the listed transmission technology for a given 
combination of other technologies. In other words, the range of 
effectiveness values seen for each specific technology, e.g., AT10L3, 
represents the addition of the AT10L3 technology to every technology 
combination that could select the addition of AT10L3. It must be 
emphasized that the graph shows the change in fuel consumption values 
between entire technology keys,\172\ and not the individual technology 
effectiveness values. Using the change between whole technology keys 
captures the complementary or non-complementary interactions among 
technologies. In the graph, the box shows the inner quartile range 
(IQR) of the effectiveness values and whiskers extend out 1.5 x IQR. 
The dots outside of the whiskers show values for effectiveness that are 
outside these bounds.
---------------------------------------------------------------------------

    \172\ Technology key is the unique collection of technologies 
that constitutes a specific vehicle, see Section III.C.4.c).

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[[Page 49673]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.056

    Note that the effectiveness for the MT5, AT5 and DD technologies 
are not shown. The DD transmission does not have a standalone 
effectiveness because it is only implemented as part of electrified 
powertrains. The MT5 and AT5 also have no effectiveness values because 
both technologies are baseline technologies against which all other 
technologies are compared.
---------------------------------------------------------------------------

    \173\ The data used to create this figure can be found the FE_1 
Improvements file.
---------------------------------------------------------------------------

(e) Transmission Costs
    This analysis uses transmission costs drawn from several sources, 
including the 2015 NAS report and NAS-cited studies. TSD Chapter 3.2.5 
provides a detailed description of the cost sources used for each 
transmission technology. Table III-14 shows an example of absolute 
costs for transmission technologies in 2018$ across select model years, 
which demonstrates how cost learning is applied to the transmission 
technologies over time. Note, because transmission hardware is often 
shared across vehicle classes, transmission costs are the same for all 
vehicle classes. For a full list of all absolute transmission costs 
used in the analysis across all model years, see the Technologies file.

[[Page 49674]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.057

3. Electrification Paths
    The electric paths include a large set of technologies that share 
the common element of using electrical power for certain vehicle 
functions that were traditionally powered mechanically by engine power. 
Electrification technologies thus can range from electrification of 
specific accessories (for example, electric power steering to reduce 
engine loads by eliminating parasitic losses) to electrification of the 
entire powertrain (as in the case of a battery electric vehicle).
    The following subsections discuss how each electrification 
technology is defined in the CAFE Model and the electrification 
pathways down which a vehicle can travel in the compliance simulation. 
The subsections also discuss how the agency assigned electrified 
vehicle technologies to vehicles in the MY 2020 analysis fleet, any 
limitations on electrification technology adoption, and the specific 
effectiveness and cost assumptions used in the Autonomie and CAFE Model 
analysis.
(a) Electrification Modeling in the CAFE Model
    The CAFE Model defines the technology pathway for each type of 
electrification grouping in a logical progression. Whenever the CAFE 
Model converts a vehicle model to one of the available electrified 
systems, both effectiveness and costs are updated according to the 
specific components' modeling algorithms. Additionally, all 
technologies on the different electrification paths are mutually 
exclusive and are evaluated in parallel. For example, the model may 
evaluate PHEV20 technology prior to having to apply 12-volt stop-start 
(SS12V) or strong hybrid technology. The specific set of algorithms and 
rules are discussed further in the sections below, and more detailed 
discussions are included in the CAFE Model Documentation. The 
specifications for each electrification technology used in the analysis 
is discussed below.
    The technologies that are included on the three vehicle-level paths 
pertaining to the electrification and electric improvements defined 
within the modeling system are illustrated in Figure III-11. As shown 
in the Electrification path, the baseline-only CONV technology is 
grayed out. This technology is used to denote whether a vehicle comes 
in with a conventional powertrain (i.e., a vehicle that does not 
include any level of hybridization) and to allow the model to properly 
map to the Autonomie vehicle simulation database results. If multiple 
branches converge on a single technology, the subset of technologies 
that will be disabled from further adoption is extended only up the 
point of convergence.
BILLING CODE 4910-59-P

[[Page 49675]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.058

BILLING CODE 4910-59-C
    SS12V: 12-volt stop-start (SS12V), sometimes referred to as start-
stop, idle-stop, or a 12-volt micro hybrid system, is the most basic 
hybrid system that facilitates idle-stop capability. In this system, 
the integrated starter generator is coupled to the internal combustion 
(IC) engine. When the vehicle comes to an idle-stop the IC engine 
completely shuts off, and, with the help of the 12-volt battery, the 
engine cranks and starts again in response to throttle to move the 
vehicle, application or release of the brake pedal to move the vehicle. 
The 12-volt battery used for the start-stop system is an improved unit 
compared to a traditional 12-volt battery, and is capable of higher 
power, increased life cycle, and capable of minimizing voltage drop on 
restart. This technology is beneficial to reduce fuel consumption and 
emissions when the vehicle frequently stops, such as in city driving 
conditions or in stop and go traffic. 12VSS can be applied to all 
vehicle technology classes.
    BISG: The belt integrated starter generator, sometimes referred to 
as a mild hybrid system or P0 hybrid, provides idle-stop capability and 
uses a higher voltage battery with increased energy capacity over 
conventional automotive batteries. These higher voltages allow the use 
of a smaller, more powerful and efficient electric motor/generator 
which replaces the standard alternator. In BISG systems, the motor/
generator is coupled to the engine via belt (similar to a standard 
alternator). In addition, these motor/generators can assist vehicle 
braking and recover braking energy while the vehicle slows down 
(regenerative braking) and in turn can propel the vehicle at the 
beginning of launch, allowing the engine to be restarted later. Some 
limited electric assist is also provided during acceleration to improve 
engine efficiency. Like the micro hybrids, BISG can be applied to all 
vehicles in the analysis except for Engine 26a (VCR). We assume all 
mild hybrids are 48-volt systems with engine belt-driven motor/
generators.
    SHEVP2/SHEVPS: A strong hybrid vehicle is a vehicle that combines 
two or more propulsion systems, where one uses gasoline (or diesel), 
and the other captures energy from the vehicle during deceleration or 
braking, or from the engine and stores that energy for later used by 
the vehicle. This analysis evaluated the following strong hybrid 
systems: Hybrids with ``P2'' parallel

[[Page 49676]]

drivetrain architectures (SHEVP2),\174\ and hybrids with power-split 
architectures (SHEVPS). Both types provide start-stop or idle-stop 
functionality, regenerative braking capability, and vehicle launch 
assist. A SHEVPS has a higher potential for fuel economy improvement 
than a SHEVP2, although its cost is also higher and engine power 
density is lower.\175\
---------------------------------------------------------------------------

    \174\ Depending on the location of electric machine (motor with 
or without inverter), the parallel hybrid technologies are 
classified as P0-motor located at the primary side of the engine, 
P1-motor located at the flywheel side of the engine, P2-motor 
located between engine and transmission, P3-motor located at the 
transmission output, and P4-motor located on the axle.
    \175\ Kapadia, J., Kok, D., Jennings, M., Kuang, M. et al., 
``Powersplit or Parallel--Selecting the Right Hybrid Architecture,'' 
SAE Int. J. Alt. Power. 6(1):2017, doi:10.4271/2017-01-1154.
---------------------------------------------------------------------------

    P2 parallel hybrids (SHEVP2) are a type of hybrid vehicle that use 
a transmission-integrated electric motor placed between the engine and 
a gearbox or CVT, with a clutch that allows decoupling of the motor/
transmission from the engine. Although similar to the configuration of 
the crank mounted integrated starter generator (CISG) system discussed 
previously, a P2 hybrid is typically equipped with a larger electric 
motor and battery in comparison to the CISG. Disengaging the clutch 
allows all-electric operation and more efficient brake-energy recovery. 
Engaging the clutch allows coupling of the engine and electric motor 
and, when combined with a transmission, reduces gear-train losses 
relative to power-split or 2-mode hybrid systems. P2 hybrid systems 
typically rely on the internal combustion engine to deliver high, 
sustained power levels. Electric-only mode is used when power demands 
are low or moderate.
    An important feature of the SHEVP2 system is that it can be applied 
in conjunction with most engine technologies. Accordingly, once a 
vehicle is converted to a SHEVP2 powertrain in the compliance 
simulation, the CAFE Model allows the vehicle to adopt the conventional 
engine technology that is most cost effective, regardless of relative 
location of the existing engine on the engine technology path. For 
example, a vehicle in the MY 2020 analysis fleet that starts with a 
TURBO2 engine could adopt a TURBO1 engine with the SHEVP2 system, if 
that TURBO1 engine allows the vehicle to meet fuel economy standards 
more cost effectively.
    The power-split hybrid (SHEVPS) is a hybrid electric drive system 
that replaces the traditional transmission with a single planetary gear 
set (the power-split device) and a motor/generator. This motor/
generator uses the engine either to charge the battery or to supply 
additional power to the drive motor. A second, more powerful motor/
generator is connected to the vehicle's final drive and always turns 
with the wheels. The planetary gear splits engine power between the 
first motor/generator and the drive motor either to charge the battery 
or to supply power to the wheels. During vehicle launch, or when the 
battery state of charge (SOC) is high, the engine is turned off and the 
electric motor propels the vehicle.\176\ During normal driving, the 
engine output is used both to propel the vehicle and to generate 
electricity. The electricity generated can be stored in the battery 
and/or used to drive the electric motor. During heavy acceleration, 
both the engine and electric motor (by consuming battery energy) work 
together to propel the vehicle. When braking, the electric motor acts 
as a generator to convert the kinetic energy of the vehicle into 
electricity to charge the battery.
---------------------------------------------------------------------------

    \176\ Autonomie model documentation, Chapter 4.13.2.
---------------------------------------------------------------------------

    Table III-15 below shows the configuration of conventional engines 
and transmissions used with strong hybrids for this analysis. The 
SHEVPS powertrain configuration was paired with a planetary 
transmission (eCVT) and Atkinson engine (Eng26). This configuration was 
designed to maximize efficiency at the cost of reduced towing 
capability and real-world acceleration performance.\177\ In contrast, 
the SHEVP2 powertrains were paired with an advanced 8-speed automatic 
transmissions (AT8L2) and could be paired with most conventional 
engines.\178\
---------------------------------------------------------------------------

    \177\ Kapadia, J., D, Kok, M. Jennings, M. Kuang, B. Masterson, 
R. Isaacs, A. Dona. 2017. Powersplit or Parallel--Selecting the 
Right Hybrid Architecture. SAE International Journal of Alternative 
Powertrains 6 (1): 68-76. https://doi.org/10.4271/2017-01-1154.
    \178\ We did not model SHEVP2s with VTGe (Eng23c) and VCR 
(Eng26a).
[GRAPHIC] [TIFF OMITTED] TP03SE21.059

    PHEV: Plug-in hybrid electric vehicles are hybrid electric vehicles 
with the means to charge their battery packs from an outside source of 
electricity (usually the electric grid). These vehicles have larger 
battery packs with more energy storage and a greater capability to be 
discharged than other non-plug-in hybrid electric vehicles. PHEVs also 
generally use a control system that allows the battery pack to be 
substantially depleted under electric-only or blended mechanical/
electric operation and batteries that can be cycled in charge-
sustaining operation at a lower state of charge than non-plug-in hybrid 
electric vehicles. These vehicles generally have a greater all-electric 
range than typical strong HEVs. Depending on how these vehicles are 
operated, they can use electricity exclusively, operate like a 
conventional hybrid, or operate in some combination of these two modes.
---------------------------------------------------------------------------

    \179\ Engine 01, 02, 03, 04, 5b, 6a, 7a, 8a, 12, 12-DEAC, 13, 
14, 17, 18, 19, 20, 21, 22b, 23b, 24, 24-Deac. See Section III.D.1 
for these engine specifications.

---------------------------------------------------------------------------

[[Page 49677]]

    There are four PHEV architectures included in this analysis that 
reflect combinations of two levels of all-electric range (AER) and two 
engine types. DOT selected 20 miles AER and 50 miles AER to reasonably 
span the various AER in the market, and their effectiveness and cost. 
DOT selected an Atkinson engine and a turbocharged downsized engine to 
span the variety of engines in the market.
    PHEV20/PHEV20H and PHEV50/PHEV50H are essentially a SHEVPS with a 
larger battery and the ability to drive with the engine turned off. In 
the CAFE Model, the designation for ``H'' in PHEVxH could represent 
another type of engine configuration, but for this analysis DOT used 
the same effectiveness values as PHEV20 and PHEV50 to represent PHEV20H 
and PHEV50H, respectively. The PHEV20/PHEV20H represents a ``blended-
type'' plug-in hybrid, which can operate in all-electric (engine off) 
mode only at light loads and low speeds, and must blend electric motor 
and engine power together to propel the vehicle at medium or high loads 
and speeds. The PHEV50/PHEV50H represents an extended range electric 
vehicle (EREV), which can travel in all-electric mode even at higher 
speeds and loads. Further discussion of engine sizing, batteries, and 
motors for these PHEVs is discussed in Section III.D.3.d).
    PHEV20T and PHEV50T are 20 mile and 50 mile AER vehicles based on 
the SHEVP2 engine architecture. The PHEV versions of these 
architectures include larger batteries and motors to meet performance 
in charge sustaining mode at higher speeds and loads as well as similar 
performance and range in all electric mode in city driving, at higher 
speeds and loads. For this analysis, the CAFE Model considers these 
PHEVs to have an advanced 8-speed automatic transmission (AT8L2) and 
TURBO1 (Eng12) in the powertrain configuration. Further discussion of 
engine sizing, batteries, and motors for these PHEVs is discussed in 
Section III.D.3.d).
    Table III-16 shows the different PHEV configurations used in this 
analysis.
[GRAPHIC] [TIFF OMITTED] TP03SE21.060

    BEV: Battery electric vehicles are equipped with all-electric drive 
systems powered by energy-optimized batteries charged primarily by 
electricity from the grid. BEVs do not have a combustion engine or 
traditional transmission. Instead, BEVs rely on all electric 
powertrains, with an advanced transmission packaged with the 
powertrain. The range of battery electric vehicles vary by vehicle and 
battery pack size.
    DOT simulated BEVs with ranges of 200, 300, 400, and 500 miles in 
the CAFE Model. BEV range is measured pursuant to EPA test procedures 
and guidance.\180\ The CAFE Model assumes that BEVs transmissions are 
unique to each vehicle (i.e., the transmissions are not shared by any 
other vehicle) and that no further improvements are available.
---------------------------------------------------------------------------

    \180\ BEV electric ranges are determined per EPA guidance 
Document. ``EPA Test Procedure for Electric Vehicles and Plug-in 
Hybrids.'' https://fueleconomy.gov/feg/pdfs/EPA%20test%20procedure%20for%20EVs-PHEVs-11-14-2017.pdf. November 
14, 2017. Last Accessed May 3, 2021.
---------------------------------------------------------------------------

    A key note about the BEVs offered in this analysis is that the CAFE 
Model does not account for vehicle range when considering additional 
BEV technology adoption. That is, the CAFE Model does not have an 
incentive to build BEV300, 400, and 500s, because the BEV200 is just as 
efficient as those vehicles and counts the same toward compliance, but 
at a significantly lower cost because of the smaller battery. While 
manufacturers have been building 200-mile range BEVs, those vehicles 
have generally been passenger cars. Manufacturers have told DOT that 
greater range is important for meeting the needs of broader range of 
consumers and to increase consumer demand. More recently, there has 
been a trend towards manufacturers building higher range BEVs in the 
market, and manufacturers building CUV/SUV and pickup truck BEVs. To 
simulate the potential relationship of BEV range to consumer demand, 
DOT has included several adoption features for BEVs. These are 
discussed further in Section III.D.3.c).
    Fuel cell electric vehicle (FCEV): Fuel cell electric vehicles are 
equipped with an all-electric drivetrain, but unlike BEVs, FCEVs do not 
solely rely on batteries; rather, electricity to run the FCEV electric 
motor is mainly generated by an onboard fuel cell system. FCEV 
architectures are similar to series hybrids,\181\ but with the engine 
and generator replaced by a fuel cell. Commercially available FCEVs 
consume hydrogen to generate electricity for the fuel cell system, with 
most automakers using high pressure gaseous hydrogen storage tanks. 
FCEVs are currently produced in limited numbers and are available in 
limited geographic areas where hydrogen refueling stations are 
accessible. For reference, in MY 2020, only four FCV models were 
offered for

[[Page 49678]]

sale, and since 2014 only 9,975 FCVs have been sold.182 183
---------------------------------------------------------------------------

    \181\ Series hybrid architecture is a strong hybrid that has the 
engine, electric motor and transmission in series. The engine in a 
series hybrid drives a generator that charges the battery.
    \182\ Argonne National Laboratory, ``Light Duty Electric Drive 
Vehicles Monthly Sales Update.'' Energy Systems Division, https://www.anl.gov/es/light-duty-electric-drive-vehicles-monthly-sales-updates. Last Accessed May 4, 2021.
    \183\ See the MY 2020 Market Data file. The four vehicles are 
the Honda Clarity, Hyundai Nexo and Nexo Blue, and Toyota Mirai.
---------------------------------------------------------------------------

    For this analysis, the CAFE Model simulates a FCEV with a range of 
320 miles. Any type of powertrain could adopt a FCEV powertrain; 
however, to account for limited market penetration and unlikely 
increased adoption in the rulemaking timeframe, technology phase in 
caps were used to control how many FCEVs a manufacturer could build. 
The details of this concept are further discussed in Section 
III.D.3.c).
(b) Electrification Analysis Fleet Assignments
    DOT identified electrification technologies present in the baseline 
fleet and used these as the starting point for the regulatory analysis. 
These assignments were based on manufacturer-submitted CAFE compliance 
information, publicly available technical specifications, marketing 
brochures, articles from reputable media outlets, and data from Wards 
Intelligence.\184\
---------------------------------------------------------------------------

    \184\ ``U.S. Car and Light Truck Specifications and Prices, '20 
Model Year.'' Wards Intelligence, 3 Aug. 2020, 
wardsintelligence.informa.com/WI964244/US-Car-and-Light-Truck-Specifications-and-Prices-20-Model-Year.
---------------------------------------------------------------------------

    Table III-17 gives the baseline fleet penetration rates of 
electrification technologies eligible to be assigned in the baseline 
fleet. Over half the fleet had some level of electrification, with the 
vast majority of these being micro hybrids. BEVs represented less than 
2% of MY 2020 baseline fleet; BEV300 was the most common BEV 
technology, while no BEV500s were observed.
[GRAPHIC] [TIFF OMITTED] TP03SE21.061

    Micro and mild hybrids refer to the presence of SS12V and BISG, 
respectively. The data sources discussed above were used to identify 
the presence of these technologies on vehicles in the fleet. Vehicles 
were assigned one of these technologies only if its presence could be 
confirmed with manufacturer brochures or technical specifications.
    Strong hybrid technologies included SHEVPS and SHEVP2. Note that 
P2HCR0, P2HCR1, P2HCR1D, and P2HCR2 are not assigned in the fleet and 
are only available to be applied by the model. When possible, 
manufacturer specifications were used to identify the strong hybrid 
architecture type. In the absence of more sophisticated information, 
hybrid architecture was determined by number of motors. Hybrids with 
one electric motor were assigned P2, and those with two were assigned 
power-split (PS). DOT seeks comment on additional ways the agency could 
perform initial hybrid assignments based on publicly available 
information.
    Plug-in hybrid technologies PHEV20/20T and PHEV50/50T are assigned 
in the baseline fleet. PHEV20H and PHEV50H are not assigned in the 
fleet and are only available to be applied by the model. Vehicles with 
an electric-only range of 40 miles or less were assigned PHEV20; those 
with a range above 40 miles were assigned PHEV50. They were 
respectively assigned PHEV20T/50T if the engine was turbocharged (i.e., 
if it would qualify for one of technologies on the turbo engine 
technology pathway). DOT also had to calculate baseline fuel economy 
values for PHEV technologies as part of the PHEV analysis fleet 
assignments; that process is described in detail in TSD Chapter 3.3.2.
    Fuel cell and battery electric vehicle technologies included 
BEV200/300/400/500 and FCV. Vehicles with all-electric powertrains that 
used hydrogen fuel were assigned FCV. The BEV technologies were 
assigned to vehicles based on range thresholds that best account for 
vehicles' existing range capabilities while allowing room for the model 
to potentially apply more advanced electrification technologies.

[[Page 49679]]

    For more detail about the electrification analysis fleet assignment 
process, see TSD Chapter 3.3.2.
(c) Electrification Adoption Features
    Multiple types of adoption features applied to the electrification 
technologies. The hybrid/electric technology path logic dictated how 
vehicles could adopt different levels of electrification technology. 
Broadly speaking, more advanced levels of hybridization or 
electrification superseded all prior levels, with certain technologies 
within each level being mutually exclusive. The analysis modeled (from 
least to most electrified) micro hybrids, mild hybrids, strong hybrids, 
plug-in hybrids, and fully electric vehicles.
    As discussed further below, SKIP logic--restrictions on the 
adoption of certain technologies--applied to plug-in (PHEV) and strong 
hybrid vehicles (SHEV). Some technologies on these pathways were 
``skipped'' if a vehicle was high performance, required high towing 
capabilities as a pickup truck, or belonged to certain manufacturers 
who have demonstrated that their future product plans will more than 
likely not include the technology. The specific criteria for SKIP logic 
for each applicable electrification technology will be expanded on 
later in this section.
    This section also discusses the supersession of engines and 
transmissions on vehicles that adopt SHEV or PHEV powertrains. To 
manage the complexity of the analysis, these types of hybrid 
powertrains were modeled with several specific engines and 
transmissions, rather than in multiple configurations. Therefore, the 
cost and effectiveness values SHEV and PHEV technologies take into 
account these specific engines and transmissions.
    Finally, phase-in caps limited the adoption rates of battery 
electric (BEV) and fuel cell vehicles (FCV). These phase-in caps were 
set by DOT, taking into account current market share, scalability, and 
reasonable consumer adoption rates of each technology. TSD Chapter 
3.3.3 discusses the electrification phase-in caps and the reasoning 
behind them in detail.
    The only adoption feature applicable to micro and mild hybrid 
technologies was path logic. The pathway consists of a linear 
progression starting with a conventional powertrain with no 
electrification at all, which is superseded by SS12V, which in turn is 
superseded by BISG. Vehicles could only adopt micro and mild hybrid 
technology if the vehicle did not already have a more advanced level of 
electrification.
    The adoption features applied to strong hybrid technologies 
included path logic, powertrain substitution, and vehicle class 
restrictions. Per the defined technology pathways, SHEVPS, SHEVP2, and 
the P2HCR technologies were considered mutually exclusive. In other 
words, when the model applies one of these technologies, the others are 
immediately disabled from future application. However, all vehicles on 
the strong hybrid pathways could still advance to one or more of the 
plug-in hybrid technologies.
    When the model applied any strong hybrid technology to a vehicle, 
the transmission technology on the vehicle was superseded. Regardless 
of the transmission originally present, P2 hybrids adopt an 8-speed 
automatic transmission (AT8L2), and PS hybrids adopt a continuously 
variable transmission (eCVT).
    When the model applies the SHEVP2 technology, the model can 
consider various engine options to pair with the SHEVP2 architecture 
according to existing engine path constraints, taking into account 
relative cost effectiveness. For SHEVPS technology, the existing engine 
was replaced with Eng26, a full Atkinson cycle engine.
    SKIP logic was also used to constrain adoption for SHEVPS, P2HCR0, 
P2HCR1, and P2HCR1D. No SKIP logic applied to SHEVP2; P2HCR2 was 
restricted from all vehicles in the 2020 fleet, as discussed further in 
Section III.D.1.d)(1). These technologies were ``skipped'' for vehicles 
with engines \185\ that met one of the following conditions:
---------------------------------------------------------------------------

    \185\ This refers to the engine assigned to the vehicle in the 
2020 baseline fleet.
---------------------------------------------------------------------------

     The engine belonged to an excluded manufacturer; \186\
---------------------------------------------------------------------------

    \186\ Excluded manufacturers included BMW, Daimler, and Jaguar 
Land Rover.
---------------------------------------------------------------------------

     The engine belonged to a pickup truck (i.e., the engine 
was on a vehicle assigned the ``pickup'' body style);
     The engine's peak horsepower was more than 405 HP; or if
     The engine was on a non-pickup vehicle but was shared with 
a pickup.
    The reasons for these conditions are similar to those for the SKIP 
logic applied to HCR engine technologies, discussed in more detail 
above. In the real world, pickups and performance vehicles with certain 
powertrain configurations cannot adopt the technologies listed above 
and maintain vehicle performance without redesigning the entire 
powertrain. SKIP logic was put in place to prevent the model from 
pursuing compliance pathways that are ultimately unrealistic.
    PHEV technologies superseded the micro, mild, and strong hybrids, 
and could only be replaced by full electric technologies. Plug-in 
hybrid technology paths were also mutually exclusive, with the PHEV20 
technologies able to progress to the PHEV50 technologies.
    The engine and transmission technologies on a vehicle were 
superseded when PHEV technologies were applied to a vehicle. For all 
plug-in technologies, the model applied an AT8L2 transmission. For 
PHEV20/50 and PHEV20H/50H, the vehicle received a full Atkinson cycle 
engine, Eng26. For PHEV20T/50T, the vehicle received a TURBO1 engine, 
Eng12.
    SKIP logic applied to PHEV20/20H and PHEV50/50H under the same four 
conditions listed for the strong hybrid technologies in the previous 
section, for the same reasons previously discussed.
    For the analysis, the adoption of BEVs and FCEVs was limited by 
both path logic and phase in caps. BEV200/300/400/500 and FCEV were 
applied as end-of-path technologies that superseded previous levels of 
electrification.
    The main adoption feature applicable to BEVs and FCEVs is phase-in 
caps, which are defined in the CAFE Model input files as percentages 
that represent the maximum rate of increase in penetration rate for a 
given technology. They are accompanied by a phase-in start year, which 
determines the first year the phase-in cap applies. Together, the 
phase-in cap and start year determine the maximum penetration rate for 
a given technology in a given year; the maximum penetration rate equals 
the phase-in cap times the number of years elapsed since the phase-in 
start year. Note that phase-in caps do not inherently dictate how much 
a technology is applied by the model. Rather, they represent how much 
of the fleet could have a given technology by a given year. Because 
BEV200 costs less and has higher effectiveness values than other 
advanced electrification technologies,\187\ the model will have 
vehicles adopt it first, until it is restricted by the phase-in cap.
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    \187\ This is because BEV200 uses fewer batteries and weighs 
less than BEVs with greater ranges.
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    Table III-18 shows the phase-in caps, phase-in year, and maximum 
penetration rate through 2050 for BEV and FCEV technologies. For 
comparison, the actual penetration rate of each technology in the 2020 
baseline fleet is also listed in the fourth column from the left.

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    The BEV200 phase-in cap was informed by manufacturers' tendency to 
move away from low-range vehicle offerings, in part because of consumer 
hesitancy to adopt this technology. The advertised range on most 
electric vehicles does not reflect extreme cold and hot real-world 
driving conditions, affecting the utility of already low-range 
vehicles.\188\ Many manufacturers have told DOT that the portion of 
consumers willing to accept a vehicle with less than 300 miles of 
electric range is extremely small, and many manufacturers do not plan 
to offer vehicles with less than 300 miles of electric range. For 
example, in February 2021, Tesla, the U.S.' highest-selling BEV 
manufacturer, discontinued the Standard Range Model Y because its range 
did not meet the company's ``standard of excellence.'' \189\ Tesla does 
sell long-range versions of many of its vehicles.
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    \188\ AAA. ``AAA Electric Vehicle Range Testing.'' February 
2019. https://www.aaa.com/AAA/common/AAR/files/AAA-Electric-Vehicle-Range-Testing-Report.pdf.
    \189\ Baldwin, Roberto. ``Tesla Model Y Standard Range 
Discontinued; CEO Musk Tweets Explanation.'' Car and Driver, 30 Apr. 
2021, www.caranddriver.com/news/a35602581/elon-musk-model-y-discontinued-explanation/. Accessed May 20, 2020.
---------------------------------------------------------------------------

    Furthermore, the average BEV range has steadily increased over the 
past decade,\190\ perhaps in part as batteries become more cost 
effective. EPA observed in its 2020 Automotive Trends Report that ``the 
average range of new EVs has climbed substantially. In model year 2019 
the average new EV is projected to have a 252-mile range, or about 
three and a half times the range of an average EV in 2011. This 
difference is largely attributable to higher production of new EVs with 
much longer ranges.'' \191\ The maximum growth rate for BEV200 in the 
model was set accordingly low to less than 0.1% per year. While this 
rate is significantly lower than that of the other BEV technologies, 
the BEV200 phase-in cap allows the penetration rate of low-range BEVs 
to grow by a multiple of what is currently observed in the market.
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    \190\ 2020 EPA Automotive Trends Report, at 53, figure 4.14.
    \191\ 2020 EPA Automotive Trends Report, at 53.
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    For BEV300, 400, and 500, phase-in caps are largely a reflection of 
the challenges facing the scalability of BEV manufacturing, and 
implementing BEV technology on many vehicle configurations, including 
larger vehicles. In the short term, the penetration of BEVs is largely 
limited by battery availability.\192\ For example, Tesla has struggled 
to scale production of new cells for its vehicles, and it remains a 
bottleneck in the company's production capability.\193\ The Director of 
Energy and Environmental Research at Toyota acknowledged in March 2021 
that BEV adoption faces many challenges beyond battery availability, 
including ``the cost of batteries, the need for national 
infrastructure, long recharging times, limited driving range and the 
need for consumer behavioral change.'' \194\ Incorporating battery 
packs that provide greater amounts of electric range into vehicles also 
poses its own engineering challenges. Heavy batteries and large packs 
may be difficult to integrate for many vehicle configurations. Pickup 
trucks and large SUVs in particular require higher levels of energy as 
the number of passengers and/or payload increases, for towing and other 
high-torque applications. DOT selected the BEV400 and 500 phase-in caps 
to reflect these concerns.
---------------------------------------------------------------------------

    \192\ See, e.g., Cohen, Ariel. ``Manufacturers Are Struggling To 
Supply Electric Vehicles With Batteries.'' Forbes, Forbes Magazine, 
25 March 2020, www.forbes.com/sites/arielcohen/2020/03/25/manufacturers-are-struggling-to-supply-electric-vehicles-with-batteries. Accessed May 20, 2021.
    \193\ Hyatt, Kyle. ``Tesla Will Build an Electric Van 
Eventually, Elon Musk Says.'' Roadshow, CNET, 28 Jan. 2021, 
www.cnet.com/roadshow/news/tesla-electric-van-elon-musk/. Accessed 
May 20, 2021.
    \194\ https://www.energy.senate.gov/services/files/E2EA0E4F-BAD9-452D-99CC-35BC204DE6F0.
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    The phase-in cap for FCEVs was assigned based on existing market 
share as well as historical trends in FCEV production. FCEV production 
share in the past five years has been extremely low, and DOT set the 
phase-in cap accordingly.\195\ As with BEV200, however, the phase-in 
cap still allows for the market share of FCVs to grow several times 
over.
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    \195\ 2020 EPA Automotive Trends Report, at 52, figure 4.13.
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(d) Electrification Effectiveness Modeling
    For this analysis, DOT considers a range of electrification 
technologies which, when modeled, result in varying levels of 
effectiveness at reducing fuel consumption. As discussed above, the 
modeled electrification technologies include micro hybrids, mild 
hybrids, two different strong hybrids, two different plug-in hybrids 
with two separate all electric ranges, full electric vehicles and 
FCEVs. Each electrification technology consists of many complex sub-
systems with unique component

[[Page 49681]]

characteristics and operational modes. As discussed further below, the 
systems that contribute to the effectiveness of an electrified 
powertrain in the analysis include the vehicle's battery, electric 
motors, power electronics, and accessory loads. Procedures for modeling 
each of these sub-systems are broadly discussed below, in Section 
III.C.4, and the Autonomie model documentation.
    Argonne used data from their Advanced Mobility Technology 
Laboratory (AMTL) to develop Autonomie's electrified powertrain models. 
The modeled powertrains are not intended to represent any specific 
manufacturer's architecture but are intended to act as surrogates 
predicting representative levels of effectiveness for each 
electrification technology.
    Autonomie determines the effectiveness of each electrified 
powertrain type by modeling the basic components, or building blocks, 
for each powertrain, and then combining the components modularly to 
determine the overall efficiency of the entire powertrain. The basic 
building blocks that comprise an electrified powertrain in the analysis 
include the battery, electric motors, power electronics, and accessory 
loads. Autonomie identifies components for each electrified powertrain 
type, and then interlinks those components to create a powertrain 
architecture. Autonomie then models each electrified powertrain 
architecture and provides an effectiveness value for each architecture. 
For example, Autonomie determines a BEV's overall efficiency by 
considering the efficiencies of the battery, the electric traction 
drive system (the electric machine and power electronics) and 
mechanical power transmission devices. Or, for a SHEVP2, Autonomie 
combines a very similar set of components to model the electric portion 
of the hybrid powertrain, and then also includes the combustion engine 
and related power for transmission components. See TSD Chapter 3.3.4 
for a complete discussion of electrification component modeling.
    As discussed earlier in Section III.C.4, Autonomie applies 
different powertrain sizing algorithms depending on the type of vehicle 
considered because different types of vehicles not only contain 
different powertrain components to be optimized, but they must also 
operate in different driving modes. While the conventional powertrain 
sizing algorithm must consider only the power of the engine, the more 
complex algorithm for electrified powertrains must simultaneously 
consider multiple factors, which could include the engine power, 
electric machine power, battery power, and battery capacity. Also, 
while the resizing algorithm for all vehicles must satisfy the same 
performance criteria, the algorithm for some electric powertrains must 
also allow those electrified vehicles to operate in certain driving 
cycles, like the US06 cycle, without assistance of the combustion 
engine, and ensure the electric motor/generator and battery can handle 
the vehicle's regenerative braking power, all-electric mode operation, 
and intended range of travel.
    To establish the effectiveness of the technology packages, 
Autonomie simulates the vehicles' performance on compliance test 
cycles, as discussed in Section III.C.4.196 197 198 The 
range of effectiveness for the electrification technologies in this 
analysis is a result of the interactions between the components listed 
above and how the modeled vehicle operates on its respective test 
cycle. This range of values will result in some modeled effectiveness 
values being close to real-world measured values, and some modeled 
values that will depart from measured values, depending on the level of 
similarity between the modeled hardware configuration and the real-
world hardware and software configurations. This modeling approach 
comports with the National Academy of Science 2015 recommendation to 
use full vehicle modeling supported by application of lumped 
improvements at the sub-model level.\199\ The approach allows the 
isolation of technology effects in the analysis supporting an accurate 
assessment.
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    \196\ See U.S. EPA, ``How Vehicles are Tested.'' https://www.fueleconomy.gov/feg/how_tested.shtml. Last accessed May 6, 2021.
    \197\ See Autonomie model documentation, Chapter 6: Test 
Procedures and Energy Consumption Calculations.
    \198\ EPA Guidance Letter. ``EPA Test Procedures for Electric 
Vehicles and Plug-in Hybrids.'' Nov. 14, 2017. https://www.fueleconomy.gov/feg/pdfs/EPA%20test%20procedure%20for%20EVs-PHEVs-11-14-2017.pdf. Last accessed May 6, 2021.
    \199\ 2015 NAS report, at 292.
---------------------------------------------------------------------------

    The range of effectiveness values for the electrification 
technologies, for all ten vehicle technology classes, is shown in 
Figure III-12. In the graph, the box shows the inner quartile range 
(IQR) of the effectiveness values and whiskers extend out 1.5 x IQR. 
The dots outside of the whiskers show values outside these bounds.
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BILLING CODE 4910-59-C
(e) Electrification Costs
    The total cost to electrify a vehicle in this analysis is based on 
the battery the vehicle requires, the non-battery electrification 
component costs the vehicle requires, and the traditional powertrain 
components that must be added or removed from the vehicle to build the 
electrified powertrain.
---------------------------------------------------------------------------

    \200\ The data used to create this figure can be found in the 
FE_1 Adjustments file.
---------------------------------------------------------------------------

    We worked collaboratively with the experts at Argonne National 
Laboratory to generate battery costs using BatPaC, which is a model 
designed to calculate the cost of a vehicle battery for a specified 
battery power, energy, and type. Argonne used BatPaC v4.0 (October 2020 
release) to create lookup tables for battery cost and mass that the 
Autonomie simulations referenced when a vehicle received an electrified 
powertrain. The BatPaC battery cost estimates are generated for a base 
year, in this case for MY 2020. Accordingly, our BatPaC inputs 
characterized the state of the market in MY 2020 and employed a widely 
utilized cell chemistry (NMC622),\201\ average estimated battery pack 
production volume per plant (25,000), and a plant efficiency or plant 
cell yield value of 95%.
---------------------------------------------------------------------------

    \201\ Autonomie model documentation, Chapter 5.9. Argonne 
surveyed A2Mac1 and TBS teardown reports for electrified vehicle 
batteries and of the five fully electrified vehicles surveyed, four 
of those vehicles used NMC622 and one used NMC532. See also Georg 
Bieker, A Global Comparison of the Life-Cycle Greenhouse Gas 
Emissions of Combustion Engine and Electric Passenger Cars, 
International Council on Clean Transportation (July 2021), https://theicct.org/sites/default/files/publications/Global-LCA-passenger-cars-jul2021_0.pdf (``For cars registered in 2021, the GHG emission 
factors of the battery production are based on the most common 
battery chemistry, NMC622-graphite batteries. . . .''); 2021 NAS 
report, at 5-92 (``. . . NMC622 is the most common cathode chemistry 
in 2019. . . .'').
---------------------------------------------------------------------------

    For two specific electrified vehicle applications, BEV400 and 
BEV500, we did not use BatPaC to generate battery pack costs. Rather, 
we scaled the BatPaC-generated BEV300 costs to match the range of 
BEV400 and BEV500 vehicles to compute a direct manufacturing cost for 
those vehicles' batteries. We initially examined using BatPaC to model 
the cost and weight of BEV400 and BEV500 packs, however, initial values 
from the model could not be validated and were based on assumptions for 
smaller sized battery packs. The initial results provided cost and 
weight estimates for BEV400 battery packs out of alignment with current 
examples of BEV400s in the market, and there are currently no examples 
of BEV500 battery packs in the market against which to validate the 
pack results.
    Finally, to reflect how we expect batteries could fall in cost over 
the timeframe considered in the analysis, we applied a learning rate to 
the direct manufacturing cost. Broadly, the learning rate applied in 
this analysis reflects middle-of-the-road year-over-year improvements 
until MY 2032, and then the learning rates incrementally become 
shallower as battery technology is expected to mature in MY 2033 and 
beyond. Applying learning curves to the battery pack DMC in subsequent 
analysis years lowers the cost such that the cost of a battery pack in 
any future model year could be representative of the cost to 
manufacture a battery pack, regardless of potentially diverse 
parameters such as cell chemistry, cell format, or production volume.

[[Page 49683]]

    TSD Chapter 3.3.5.1 includes more detail about the process we used 
to develop battery costs for this analysis. In addition, all BatPaC-
generated direct manufacturing costs for all technology keys can be 
found in the CAFE Model's Battery Costs file, and the Argonne BatPaC 
Assumptions file includes the assumptions used to generate the costs, 
and pack costs, pack mass, cell capacity, $/kW at the pack level, and 
W/kg at the pack level for all vehicle classes.
    Table III-19 and Table III-20 show an example of our battery pack 
direct manufacturing costs per kilowatt hour for BEV300s for all 
vehicle classes for the base year, MY 2020. The tables shown here 
demonstrate how the cost per kWh varies with the size of the battery 
pack. While the overall cost of a battery pack will go up for larger 
kWh battery packs, the cost per kWh goes down. The amortization of 
costs for components required in all battery packs across a larger 
number of cells results in this reduced cost per kWh.
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BILLING CODE 4910-59-C
    A range of parameters can ultimately influence battery pack 
manufacturing costs, including other vehicle improvements (e.g., mass 
reduction technology, aerodynamic improvements, or tire rolling 
resistance improvements all affect the size and energy of a battery 
required to propel a vehicle where all else is equal), and the 
availability of materials required to manufacture the 
battery.202 203 Or, if manufacturers adopt more 
electrification technology than projected in this analysis, increases 
in battery pack production volume will likely lower actual battery pack 
costs.
---------------------------------------------------------------------------

    \202\ The cost of raw material also has a meaningful influence 
on the future cost of the battery pack. As the production volume 
goes up, the demand for battery critical raw materials also goes up, 
which has an offsetting impact on the efficiency gains achieved 
through economies of scale, improved plant efficiency, and advanced 
battery cell chemistries. We do not consider future battery raw 
material price fluctuations for this analysis, however that may be 
an area for further exploration in future analyses.
    \203\ See, e.g., Jacky Wong, EV Batteries: The Next Victim of 
High Commodity Prices?, The Wall Street Journal (July 22, 2021), 
https://www.wsj.com/articles/ev-batteries-the-next-victim-of-high-commodity-prices-11626950276.
---------------------------------------------------------------------------

    Like the 2020 final rule, we compared our battery pack costs in 
future years to battery pack costs from other sources that may or may 
not account for some of these additional parameters, including varying 
potential future battery chemistry and learning rates. As discussed in 
TSD Chapter 3.3.5.1.4, our battery pack costs in 2025 and 2030 fell 
fairly well in the middle of other sources' cost projections, with 
Bloomberg New Energy Finance (BNEF) projections presenting the highest 
year-over-year cost reductions,\204\ and MIT's Insights into Future 
Mobility report providing an upper bound of potential future 
costs.\205\ ICCT presented a similar comparison of costs from several 
sources in its 2019 working paper, Update on Electric Vehicle Costs in 
the United States through 2030, and predicted battery pack costs in 
2025 and 2030 would drop to approximately $104/kWh and $72/kWh, 
respectively,\206\ which put their projections slightly higher than 
BNEF's 2019 projections. BNEF's more recent 2020 Electric Vehicle 
Outlook projected average pack cost to fall below $100/kWh by 
2024,\207\ while the 2021 NAS report projected that pack costs are 
projected to reach $90-115 kWh by 2025.\208\
---------------------------------------------------------------------------

    \204\ See Logan Goldie-Scot, A Behind the Scenes Take on 
Lithium-ion Battery Prices, Bloomberg New Energy Finance (March 5, 
2019), https://about.bnef.com/blog/behind-scenes-take-lithium-ion-battery-prices/.
    \205\ MIT Energy Initiative. 2019. Insights into Future 
Mobility. Cambridge, MA: MIT Energy Initiative. Available at http://energy.mit.edu/insightsintofuturemobility.
    \206\ Nic Lutsey and Michael Nicholas, Update on electric 
vehicle costs in the United States through 2030, ICCT (April 2, 
2019), available at https://theicct.org/publications/update-US-2030-electric-vehicle-cost.
    \207\ Bloomberg New Energy Finance (BNEF), ``Electric Vehicle 
Outlook 2020,'' https://about.bnef.com/electric-vehicle-outlook/, 
last accessed July 29, 2021.
    \208\ 2021 NAS report, at 5-121. The 2021 NAS report assumed a 7 
percent cost reduction per year from 2018 through 2030.
---------------------------------------------------------------------------

    That our projected costs seem to fall between several projections 
gives us some confidence that the costs in this NPRM could reasonably 
represent future battery pack costs across the industry during the 
rulemaking time frame. That said, we recognize that battery technology 
is currently under intensive development, and that characteristics such 
as cost and capability are rapidly changing. These advances are 
reflected in recent aggressive projections, like those from ICCT, BNEF, 
and the 2021 NAS report. As a result, we would like to seek comments, 
supported by data elements as outlined below, on these characteristics.
    We seek comment on the input assumptions used to generate battery 
pack costs in BatPaC and the BatPaC-generated direct manufacturing 
costs for the base year (MY 2020). If commenters believe that different 
input assumptions should be used for battery chemistry,\209\

[[Page 49685]]

plant manufacturing volume, or plant efficiency in MY 2020, they should 
provide data or other information validating such assumptions. In 
addition, commenters should explain how these assumptions reasonably 
represent applications across the industry in MY 2020. This is 
important to align with our guiding principles to ensure that the CAFE 
Model's simulation of manufacturer compliance pathways results in 
impacts that we would reasonably expect to see in the real world. As 
discussed above, each technology model employed in the analysis is 
designed to be representative of a wide range of specific technology 
applications used in industry. Some vehicle manufacturer's systems may 
perform better and cost less than our modeled systems and some may 
perform worse and cost more. However, employing this approach will 
ensure that, on balance, the analysis captures a reasonable level of 
costs and benefits that would result from any manufacturer applying the 
technology. In this case, vehicle and battery manufacturers use 
different chemistries, cell types, and production processes to 
manufacture electric vehicle battery packs. Any proposed alternative 
costs for base year direct manufacturing costs should be able to 
represent the range of costs across the industry in MY 2020 based on 
different manufacturers using different approaches.
---------------------------------------------------------------------------

    \209\ Note that stakeholders had commented to the 2020 final 
rule that batteries using NMC811 chemistry had either recently come 
into the market or was imminently coming into the market, and 
therefore DOT should have selected NMC811 as the appropriate 
chemistry for modeling battery pack costs. Similar to the other 
technologies considered in this analysis, DOT endeavors to use 
technology that is a reasonable representation of what the industry 
could achieve in the model year or years under consideration, in 
this case the base DMC year of 2020, as discussed above. At the time 
of this current analysis, the referenced A2Mac1 teardown reports and 
other reports provided the best available information about the 
range of battery chemistry actually employed in the industry. At the 
time of writing, DOT still has not found examples of NMC811 in 
commercial application across the industry in a way that DOT 
believes selecting NMC811 would have represented industry average 
performance in MY 2020. As discussed in TSD Chapter 3.3.5.1.4, DOT 
did analyze the potential future cost of NMC811 in the composite 
learning curve generated to ensure the battery learning curve 
projections are reasonable.
---------------------------------------------------------------------------

    We also seek comment on the scaling used to generate direct 
manufacturing costs for BEV400 and BEV500 technologies. If commenters 
have additional data or information on the relationship between cost 
and weight for heavier battery packs used for these higher-range BEV 
applications, particularly in light truck vehicle segments, that would 
be helpful as well.
    In addition, we seek comment on the learning rates applied to the 
battery pack costs and on the battery pack costs in future years. 
Recognizing that any battery pack cost projections for future years 
from our analysis or external analyses will involve assumptions that 
may or may not come to pass, it would be most helpful if commenters 
thoroughly explained the basis for any recommended learning rates, 
including references to publicly available data or models (and if such 
models are peer reviewed) where appropriate. Similarly, it would be 
helpful for commenters to note where external analyses may or may not 
take into account certain parameters in their battery pack cost 
projections, and whether we should attempt to incorporate those 
parameters in our analysis. For example, as discussed above, our 
analysis does not consider raw material price fluctuations; however, 
the price of battery pack raw materials will put a lower bound on NMC-
based battery prices.\210\
---------------------------------------------------------------------------

    \210\ See, e.g., MIT Energy Initiative. 2019. Insights into 
Future Mobility. Cambridge, MA: MIT Energy Initiative. Available at 
http://energy.mit.edu/insightsintofuturemobility, at 78-9.
---------------------------------------------------------------------------

    It would also be helpful if commenters explained how learning rates 
or future cost projections could represent the state of battery 
technology across the industry. Like other technologies considered in 
this analysis, some battery and vehicle manufacturers have more 
experience manufacturing electric vehicle battery packs, and some have 
less, meaning that different manufacturers will be at different places 
along the learning curve in future years. Note also that comments 
should specify whether their referenced costs, either for MY 2020 or 
for future years, are for the battery cell or the battery pack.
    Ensuring our learning rates encompass these diverse parameters will 
ensure that the analysis best predicts the costs and benefits 
associated with future standards. We will incorporate any new 
information received to the extent possible for the final rule and 
future analyses.
    Recognizing again that battery technology is a rapidly evolving 
field and there are a range of external analyses that project battery 
pack costs declining at different rates across the next decade, as 
discussed above and further in the TSD, we performed four sensitivity 
studies around battery pack costs that are described in PRIA Chapter 
7.2.2.5. The sensitivity studies examined the impacts of increasing and 
decreasing the direct cost of batteries and battery learning costs by 
20 percent from central analysis levels, based on our survey of 
external analyses' battery pack cost projections that fell generally 
within +/-20% of our central analysis costs. We found that changing the 
battery direct manufacturing costs in MY 2020 without changing the 
learning rate did not produce meaningfully different outcomes for 
electric vehicle technology penetration in later years, although it 
resulted in the lowest technology costs. Keeping the same direct 
manufacturing costs and using a steeper battery learning rate produced 
slightly higher technology costs, compared to the sensitivity results 
that changed battery pack direct manufacturing cost and kept learning 
rate the same.
    We seek comment on these conclusions, their implications for any 
potential updates to battery pack costs for the final rule, and any 
other external analyses that the agency should consider when validating 
future battery pack cost projections.
    Next, each vehicle powertrain type also receives different non-
battery electrification components. When researching costs for 
different non-battery electrification components, DOT found that 
different reports vary in components considered and cost breakdown. 
This is not surprising, as vehicle manufacturers use different non-
battery electrification components in different vehicle's systems, or 
even in the same vehicle type, depending the application.\211\ DOT 
developed costs for the major non-battery electrification components on 
a dollar per kilowatt hour basis using the costs presented in two 
reports. DOT used a $/kW cost metric for non-battery components to 
align with the normalized costs for a system's peak power rating as 
presented in U.S. DRIVE's Electrical and Electronics Technical Team 
(EETT) Roadmap report.\212\ This approach captures components in some 
manufacturer's systems, but not all systems; however, DOT believes this 
is a reasonable metric and approach to use for this analysis given the 
differences in non-battery electrification component systems. This 
approach allows us to scale the cost of non-battery electrification 
components based on the requirements of the system. We also relied on a 
teardown study of a MY 2016 Chevrolet Bolt for non-battery component 
costs that were not explicitly estimated in the EETT Roadmap 
report.\213\
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    \211\ For example, the MY 2020 Nissan Leaf does not have an 
active cooling system whereas Chevy Bolt uses an active cooling 
system.
    \212\ U.S. DRIVE, Electrical and Electronics Technical Team 
Roadmap (Oct. 2017), available at https://www.energy.gov/sites/prod/files/2017/11/f39/EETT%20Roadmap%2010-27-17.pdf.
    \213\ Hummel et al., UBS Evidence Lab Electric Car Teardown--
Disruption Ahead?, UBS (May 18, 2017), https://neo.ubs.com/shared/d1wkuDlEbYPjF/.

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[[Page 49686]]

    To develop the learning curves for non-battery electrification 
components, DOT used cost information from Argonne's 2016 Assessment of 
Vehicle Sizing, Energy Consumption, and Cost through Large-Scale 
Simulation of Advanced Vehicle Technologies report.\214\ The report 
provided estimated cost projections from the 2010 lab year to the 2045 
lab year for individual vehicle components.215 216 DOT 
considered the component costs used in electrified vehicles, and 
determined the learning curve by evaluating the year over year cost 
change for those components. Argonne recently published a 2020 version 
of the same report that included high and low cost estimates for many 
of the same components, that also included a learning rate.\217\ DOT's 
learning estimates generated using the 2016 report fall fairly well in 
the middle of these two ranges, and therefore staff decided that 
continuing to apply the learning curve estimates based on the 2016 
report was reasonable. There are many sources that DOT staff could have 
picked to develop learning curves for non-battery electrification 
component costs, however given the uncertainty surrounding 
extrapolating costs out to MY 2050, DOT believes these learning curves 
provide a reasonable estimate.
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    \214\ Moawad, Ayman, Kim, Namdoo, Shidore, Neeraj, and Rousseau, 
Aymeric. Assessment of Vehicle Sizing, Energy Consumption and Cost 
Through Large Scale Simulation of Advanced Vehicle Technologies 
(ANL/ESD-15/28). United States (2016). Available at https://www.autonomie.net/pdfs/Report%20ANL%20ESD-1528%20-%20Assessment%20of%20Vehicle%20Sizing,%20Energy%20Consumption%20and%20Cost%20through%20Large%20Scale%20Simulation%20of%20Advanced%20Vehicle%20Technologies%20-%201603.pdf.
    \215\ ANL/ESD-15/28 at 116.
    \216\ DOE's lab year equates to five years after a model year, 
e.g., DOE's 2010 lab year equates to MY 2015.
    \217\ Islam, E., Kim, N., Moawad, A., Rousseau, A. ``Energy 
Consumption and Cost Reduction of Future Light-Duty Vehicles through 
Advanced Vehicle Technologies: A Modeling Simulation Study Through 
2050'', Report to the U.S. Department of Energy, Contract ANL/ESD-
19/10, June 2020 https://www.autonomie.net/pdfs/ANL%20-%20Islam%20-%202020%20-%20Energy%20Consumption%20and%20Cost%20Reduction%20of%20Future%20Light-Duty%20Vehicles%20through%20Advanced%20Vehicle%20Technologies%20A%20Modeling%20Simulation%20Study%20Through%202050.pdf.
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    Table III-21 shows an example of how the non-battery 
electrification component costs are computed for the Medium Car and 
Medium SUV non-performance vehicle classes.
BILLING CODE 4910-59-P
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[[Page 49687]]


    TSD Chapter 3.3.5.2 contains more information about the non-battery 
electrification components relevant to each specific electrification 
technology and the sources used to develop these costs. We seek comment 
on these costs, the appropriateness of the sources used to develop 
these costs, and the $/kW metric used to size specific non-battery 
electrification components. In addition, we seek comment on the 
learning rate applied to non-battery electrification components.
    Finally, the cost of electrifying a vehicle depends on the other 
powertrain components that must be added or removed from a vehicle with 
the addition of the electrification technology. Table III-22 below 
provides a breakdown of each electrification component included for 
each electrification technology type, as well as where to find the 
costs in each CAFE Model input file.
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    As shown in Table III-22, DOT used the cost of the CVTL2 as a proxy 
for the cost of an eCVT used in PS hybrid vehicles. In its recent 2021 
report, the NAS estimated the cost of eCVTs to be lower than DOT's cost 
estimate for CVTL2.\218\ DOT is investigating the cost assumptions used 
for the PS hybrid transmission and may update those costs for the final 
rule depending on information submitted by stakeholders or other 
research. DOT seeks comment on the appropriateness of the cost estimate 
for eCVTs in the 2021 NAS report, or any other data that could be made 
public on the costs of eCVTs.
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    \218\ A detailed cost comparison between our costs and the 2021 
NAS report costs is discussed in TSD Chapter 3.3.5.3.3.
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    The following example in Table III-23 shows how the costs are 
computed for a vehicle that progresses from a lower level to a higher 
level of electrified powertrain. The table shows the components that 
are removed and the components that are added as a GMC Acadia 
progresses from a MY 2024 vehicle with only SS12V electrification 
technology to a BEV300 in MY 2025. The total cost in MY 2025 is a net 
cost addition to the vehicle. The same methodology could be used for 
any other technology advancement in the electric technology tree 
path.\219\
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    \219\ Please note that in this calculation the CAFE Model 
accounts for the air conditioning and off-cycle technologies (g/
mile) applied to each vehicle model. The cost for the AC/OC 
adjustments are located in the CAFE Model Scenarios file. The air 
conditioning and off-cycle cost values are discussed further in TSD 
Chapter 3.8.

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[[Page 49688]]

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BILLING CODE 4910-59-C
    TSD Chapter 3.3.5.3 includes more details about how the costs 
associated with the internal combustion engine, transmission, electric 
machine(s), non-battery electrification components, and battery pack 
for each electrified technology type are combined to create a full 
electrification system cost.
4. Mass Reduction
    Mass reduction is a relatively cost-effective means of improving 
fuel economy, and vehicle manufacturers are expected to apply various 
mass reduction technologies to meet fuel economy standards. Reducing 
vehicle mass can be accomplished through several different techniques, 
such as modifying and optimizing vehicle component and system designs, 
part consolidation, and adopting lighter weight materials (advanced 
high strength steel, aluminum, magnesium, and plastics including carbon 
fiber reinforced plastics).
    The cost for mass reduction depends on the type and amount of 
materials used, the manufacturing and assembly processes required, and 
the degree to which changes to plants and new manufacturing and 
assembly equipment is needed. In addition, manufacturers may develop 
expertise and invest in certain mass reduction strategies that may 
affect the approaches for mass reduction they consider and the 
associated costs. Manufacturers may also consider vehicle attributes 
like noise-vibration-harshness (NVH), ride quality, handling, crash 
safety and various acceleration metrics when considering how to 
implement any mass reduction strategy. These are considered to be 
aspects of performance, and for this analysis any identified pathways 
to compliance are intended to maintain performance neutrality. 
Therefore, mass reduction via elimination of, for example, luxury items 
such as climate control, or interior vanity mirrors, leather padding, 
etc., is not considered in the mass reduction pathways for this 
analysis.
    The automotive industry uses different metrics to measure vehicle 
weight. Some commonly used measurements are vehicle curb weight,\220\ 
gross vehicle weight (GVW),\221\ gross vehicle weight rating 
(GVWR),\222\ gross combined weight (GCVW),\223\ and equivalent test 
weight (ETW),\224\ among others. The vehicle curb weight is the most 
commonly used measurement when comparing vehicles. A vehicle's curb 
weight is the weight of the vehicle including fluids, but without a 
driver, passengers, and cargo. A vehicle's glider weight, which is 
vehicle curb weight minus the powertrain weight, is used to track the 
potential opportunities for weight reduction not including the 
powertrain. A glider's subsystems may consist of the vehicle body, 
chassis, interior, steering,

[[Page 49689]]

electrical accessory, brake, and wheels systems. The percentage of 
weight assigned to the glider will remain constant for any given rule 
but may change overall. For example, as electric powertrains including 
motors, batteries, inverters, etc. become a greater percent of the 
fleet, glider weight percentage will change compared to earlier fleets 
with higher dominance of internal combustion engine (ICE) powertrains.
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    \220\ This is the weight of the vehicle with all fluids and 
components but without the drivers, passengers, and cargo.
    \221\ This weight includes all cargo, extra added equipment, and 
passengers aboard.
    \222\ This is the maximum total weight of the vehicle, 
passengers, and cargo to avoid damaging the vehicle or compromising 
safety.
    \223\ This weight includes the vehicle and a trailer attached to 
the vehicle, if used.
    \224\ For the EPA two-cycle regulatory test on a dynamometer, an 
additional weight of 300 lbs is added to the vehicle curb weight. 
This additional 300 lbs represents the weight of the driver, 
passenger, and luggage. Depending on the final test weight of the 
vehicle (vehicle curb weight plus 300 lbs), a test weight category 
is identified using the table published by EPA according to 40 CFR 
1066.805. This test weight category is called ``Equivalent Test 
Weight'' (ETW).
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    For this analysis, DOT considered six levels of mass reduction 
technology that include increasing amounts of advanced materials and 
mass reduction techniques applied to the glider. The mass change 
associated with powertrain changes is accounted for separately. The 
following sections discuss the assumptions for the six mass reduction 
technology levels, the process used to assign initial analysis fleet 
mass reduction assignments, the effectiveness for applying mass 
reduction technology, and mass reduction costs.
(a) Mass Reduction in the CAFE Model
    The CAFE Model considers six levels of mass reduction technologies 
that manufacturers could use to comply with CAFE standards. The 
magnitude of mass reduction in percent for each of these levels is 
shown in Table III-24 for mass reductions for light trucks, passenger 
cars and for gliders.
[GRAPHIC] [TIFF OMITTED] TP03SE21.069

    For this analysis, DOT considers mass reduction opportunities from 
the glider subsystems of a vehicle first, and then consider associated 
opportunities to downsize the powertrain, which are accounted for 
separately.\225\ As explained below, in the Autonomie simulations, the 
glider system includes both primary and secondary systems from which a 
percentage of mass is reduced for different glider weight reduction 
levels; specifically, the glider includes the body, chassis, interior, 
electrical accessories, steering, brakes and wheels. In this analysis, 
DOT assumed the glider share is 71% of vehicle curb weight. The 
Autonomie model sizes the powertrain based on the glider weight and the 
mass of some of the powertrain components in an iterative process. The 
mass of the powertrain depends on the powertrain size. Therefore, the 
weight of the glider impacts the weight of the powertrain.\226\
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    \225\ When the mass of the vehicle is reduced by an appropriate 
amount, the engine may be downsized to maintain performance. See 
Section III.C.4 for more details.
    \226\ Since powertrains are sized based on the glider weight for 
the analysis, glider weight reduction beyond a threshold amount 
during a redesign will lead to re-sizing of the powertrain. For the 
analysis, the glider was used as a base for the application of any 
type of powertrain. A conventional powertrain consists of an engine, 
transmission, exhaust system, fuel tank, radiator and associated 
components. A hybrid powertrain also includes a battery pack, 
electric motor(s), generator, high voltage wiring harness, high 
voltage connectors, inverter, battery management system(s), battery 
pack thermal system, and electric motor thermal system.
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    DOT uses glider weight to apply non-powertrain mass reduction 
technology in the CAFE Model and use Autonomie simulations to determine 
the size of the powertrain and corresponding powertrain weight for the 
respective glider weight. The combination of glider weight (after mass 
reduction) and re-sized powertrain weight equal the vehicle curb 
weight.
    While there are a range of specific mass reduction technologies 
that may be applied to vehicles to achieve each of the six mass 
reduction levels, there are some general trends that are helpful to 
illustrate some of the more widely used approaches. Typically, MR0 
reflects vehicles with widespread use of mild steel structures and body 
panels, and very little or no use of high strength steel or aluminum. 
MR0 reflects materials applied to average vehicles in the MY 2008 
timeframe. MR1-MR3 can be achieved with a steel body structure. In 
going from MR1 to MR3, expect that mild steel to be replaced by high 
strength and then advanced high strength steels. In going from MR3 to 
MR4 aluminum is required. This will start at using aluminum closure 
panels and then to get to MR4 the vehicle's primary structure will need 
to be mostly made from aluminum. In the vast majority of cases, carbon 
fiber technology is necessary to reach MR5, perhaps with a mix of some 
aluminum. MR6 can really only be attained in anything resembling a 
passenger car by make nearly every structural component from carbon 
fiber. This mean the body structure and closure panels like hoods and 
door skins are wholly made from carbon fiber. There may be some use of 
aluminum in the suspension. TSD Chapter 3.4 includes more discussion of 
the challenges involved with adopting large amounts of carbon fiber in 
the vehicle fleet in the coming years.
    As discussed further below, the cost studies used to generate the 
cost curves assume mass can be reduced in levels that require different 
materials and different components to be utilized, in a specific order. 
DOT's mass reduction levels are loosely based on what materials and 
components that would be required to be used for each percent of mass 
reduction, based on the conclusions of those studies.
(b) Mass Reduction Analysis Fleet Assignments
    To assign baseline mass reduction levels (MR0 through MR6) for 
vehicles in the MY 2020 analysis fleet, DOT used previously developed 
regression models to estimate curb weight for each vehicle based on 
observable vehicle attributes.

[[Page 49690]]

DOT used these models to establish a baseline (MR0) curb weight for 
each vehicle, and then determined the existing mass reduction 
technology level by finding the difference between the vehicles actual 
curb weight to the estimated regression-based value, and comparing the 
difference to the values in Table III-24. DOT originally developed the 
mass reduction regression models using MY 2015 fleet data; for this 
analysis, DOT used MY 2016 and 2017 analysis fleet data to update the 
models.
    DOT believes the regression methodology is a technically sound 
approach for estimating mass reduction levels in the analysis fleet. 
For a detailed discussion about the regression development and use 
please see TSD Chapter 3.4.2.
    Manufacturers generally apply mass reduction technology at a 
vehicle platform level (i.e., using the same components across multiple 
vehicle models that share a common platform) to leverage economies of 
scale and to manage component and manufacturing complexity, so 
conducting the regression analysis at the platform level leads to more 
accurate estimates for the real-world vehicle platform mass reduction 
levels. The platform approach also addresses the impact of potential 
weight variations that might exist for specific vehicle models, as all 
the individual vehicle models are aggregated into the platform group, 
and are effectively averaged using sales weighting, which minimizes the 
impact of any outlier vehicle configurations.
(c) Mass Reduction Adoption Features
    Given the degree of commonality among the vehicle models built on a 
single platform, manufacturers do not have complete freedom to apply 
unique technologies to each vehicle that shares the platform. While 
some technologies (e.g., low rolling resistance tires) are very nearly 
``bolt-on'' technologies, others involve substantial changes to the 
structure and design of the vehicle, and therefore affect all vehicle 
models that share a platform. In most cases, mass reduction 
technologies are applied to platform level components and therefore the 
same design and components are used on all vehicle models that share 
the platform.
    Each vehicle in the analysis fleet is associated with a specific 
platform. Similar to the application of engine and transmission 
technologies, the CAFE Model defines a platform ``leader'' as the 
vehicle variant of a given platform that has the highest level of 
observed mass reduction present in the analysis fleet. If there is a 
tie, the CAFE Model begins mass reduction technology on the vehicle 
with the highest sales volume in model year 2020. If there remains a 
tie, the model begins by choosing the vehicle with the highest 
manufacturer suggested retail price (MSRP) in MY 2020. As the model 
applies technologies, it effectively levels up all variants on a 
platform to the highest level of mass reduction technology on the 
platform. For example, if the platform leader model is already at MR3 
in MY 2020, and a ``follower'' platform model starts at MR0 in MY 2020, 
the follower platform model will get MR3 at its next redesign, assuming 
no further mass reduction technology is applied to the leader model 
before the follower models next redesign.
    In addition to the platform-sharing logic employed in the model, 
DOT applied phase-in caps for MR5 and MR6 (15 percent and 20 percent 
reduction of a vehicle's curb weight, respectively), based on the 
current state of mass reduction technology. As discussed above, for 
nearly every type of vehicle, with the exception of the smallest sports 
cars, a manufacturer's strategy to achieve mass reduction consistent 
with MR5 and MR6 will require extensive use of carbon fiber 
technologies in the vehicles' primary structures. For example, one way 
of using carbon fiber technology to achieve MR6 is to develop a carbon 
fiber monocoque structure. A monocoque structure is one where the outer 
most skins support the primary loads of the vehicle. For example, they 
do not have separate non-load bearing aero surfaces. All of the 
vehicle's primary loads are supported by the monocoque. In the most 
structurally efficient automotive versions, the monocoque is made from 
multiple well-consolidated plies of carbon fiber infused with resin. 
Such structures can require low hundreds of pounds of carbon fiber for 
most passenger vehicles. Add to this another roughly equivalent mass of 
petroleum-derived resins and even at aspirational prices for dry carbon 
fiber of $10-20 per pound it is easy to see how direct materials alone 
can easily climb into the five-figure dollar range per vehicle.
    High CAFE stringency levels will push the CAFE Model to select 
compliance pathways that include these higher levels of mass reduction 
for vehicles produced in the mid and high hundreds of thousands of 
vehicles per year. DOT assumes, based on material costs and 
availability, that achieving MR6 levels of mass reduction will cost 
tens of thousands of dollars per car. Therefore, application of such 
technology to high volume vehicles is unrealistic today and will, with 
certainty, remain so for the next several years.
    The CAFE Model applies technologies to vehicles that provide a 
cost-effective pathway to compliance. In some cases, the direct 
manufacturing cost, indirect costs, and applied learning factor do not 
capture all the considerations that make a technology more or less 
costly for manufacturers to apply in the real world. For example, there 
are direct labor, R&D overhead, manufacturing overhead, and amortized 
tooling costs that will likely be higher for carbon fiber production 
than current automotive steel production, due to fiber handling 
complexities. In addition, R&D overhead will also increase because of 
the knowledge base for composite materials in automotive applications 
is simply not as deep as it is for steel and aluminum. Indeed, the 
intrinsic anisotropic mechanical properties of composite materials 
compared to the isotropic properties of metals complicates the design 
process. Added testing of these novel anisotropic structures and their 
associated costs will be necessary for decades. Adding up all these 
contributing costs, the price tag for a passenger car or truck 
monocoque would likely be multiple tens of thousands of dollars per 
vehicle. This would be significantly more expensive than transitioning 
to hybrid or fully electric powertrains and potentially less effective 
at achieving CAFE compliance.
    In addition, the CAFE Model does not currently enable direct 
accounting for the stranded capital associated with a transition away 
from stamped sheet metal construction to molded composite materials 
construction. For decades, or in some cases half-centuries, car 
manufacturers have invested billions of dollars in capital for 
equipment that supports the industry's sheet metal forming paradigm. A 
paradigm change to tooling and equipment developed to support molding 
carbon fiber panels and monocoque chassis structures would leave that 
capital stranded in equipment that would be rendered obsolete. Doing 
this is possible, but the financial ramifications are not currently 
reflected in the CAFE Model for MR5 and MR6 compliance pathways.
    Financial matters aside, carbon fiber technology and how it is best 
used to produce lightweight primary automotive structures is far from 
mature. In fact, no car company knows for sure the best way to use 
carbon fiber to make a passenger car's primary structure. Using this 
technology in passenger cars is far more complex than using it in 
racing cars where passenger egress, longevity, corrosion protection, 
crash protection,

[[Page 49691]]

etc. are lower on the list of priorities for the design team. BMW may 
be the manufacturer most able accurately opine on the viability of 
carbon fiber technology for primary structure on high-volume passenger 
cars, and even it decided to use a mixed materials solution for their 
next generation of EVs (the iX and i4) after the i3, thus eschewing a 
wholly carbon fiber monocoque structure.
    Another factor limiting the application of carbon fiber technology 
to mass volume passenger vehicles is indeed the availability of dry 
carbon fibers. There is high global demand from a variety of industries 
for a limited supply of carbon fibers. Aerospace, military/defense, and 
industrial applications demand most of the carbon fiber currently 
produced. Today, only roughly 10% of the global dry fiber supply goes 
to the automotive industry, which translates to the global supply base 
only being able to support approximately 70k cars.\227\
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    \227\ J. Sloan, ``Carbon Fiber Suppliers Gear up for Next 
Generation Growth,'' compositesworld.com, February 11, 2020.
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    To account for these cost and production considerations, including 
the limited global supply of dry carbon fiber, DOT applied phase-in 
caps that limited the number of vehicles that can achieve MR5 and M6 
levels of mass reduction in the CAFE Model. DOT applied a phase-in cap 
for MR5 level technology so that 75 percent of the vehicle fleet 
starting in 2020 could employ the technology, and the technology could 
be applied to 100 percent of the fleet by MY 2022. DOT also applied a 
phase-in cap for MR6 technology so that five percent of the vehicle 
fleet starting in MY 2020 could employ the technology, and the 
technology could be applied to 10 percent of the fleet by MY 2025.
    To develop these phase-in caps, DOT chose a 40,000 unit thresholds 
for both MR5 and MR6 technology (80,000 units total), because it 
roughly reflects the number of BMW i3 cars produced per year 
worldwide.\228\ As discussed above, the BMW i3 is the only high-volume 
vehicle currently produced with a primary structure mostly made from 
carbon fiber (except the skateboard, which is aluminum). Because mass 
reduction is applied at the platform level (meaning that every car of a 
given platform would receive the technology, not just special low 
volume versions of that platform), only platforms representing 40,000 
vehicles or less are eligible to apply MR5 and MR6 toward CAFE 
compliance. Platforms representing high volume sales, like a Chevrolet 
Traverse, for example, where hundreds of thousands are sold per year, 
are therefore blocked from access to MR5 and MR6 technology. There are 
no phase in caps for mass reduction levels MR1, MR2, MR3, or MR4.
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    \228\ However, even this number is optimistic because only a 
small fraction of i3 cars are sold in the U.S. market, and combining 
MR5 and MR6 allocations equates to 80k vehicles, not 40k. 
Regardless, if the auto industry ever seriously committed to using 
carbon fiber in mainstream high-volume vehicles, competition with 
the other industries would rapidly result in a dramatic increase in 
price for dry fiber. This would further stymie the deployment of 
this technology in the automotive industry.
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    In addition to determining that the caps were reasonable based on 
current global carbon fiber production, DOT determined that the MR5 
phase-in cap is consistent with the DOT lightweighting study that found 
that a 15 percent curb weight reduction for the fleet is possible 
within the rulemaking timeframe.\229\
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    \229\ Singh, Harry. (2012, August). Mass Reduction for Light-
Duty Vehicles for Model Years 2017-2025. (Report No. DOT HS 811 
666). Program Reference: DOT Contract DTNH22-11-C-00193. Contract 
Prime: Electricore, Inc, at 356, Figure 397.
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    These phase-in caps appropriately function as a proxy for the cost 
and complexity currently required (and that likely will continue to be 
required until manufacturing processes evolve) to produce carbon fiber 
components. Again, MR6 technology in this analysis reflects the use of 
a significant share of carbon fiber content, as seen through the BMW i3 
and Alfa Romeo 4c as discussed above.
    Given the uncertainty and fluid nature of knowledge around higher 
levels of mass reduction technology, DOT welcomes comments on how to 
most cost effectively use carbon fiber technology in high-volume 
passenger cars. Financial implementation estimates for this technology 
are equally as welcome.
(d) Mass Reduction Effectiveness Modeling
    As discussed in Section III.C.4, Argonne developed a database of 
vehicle attributes and characteristics for each vehicle technology 
class that included over 100 different attributes. Some examples from 
these 100 attributes include frontal area, drag coefficient, fuel tank 
weight, transmission housing weight, transmission clutch weight, hybrid 
vehicle components, and weights for components that comprise engines 
and electric machines, tire rolling resistance, transmission gear 
ratios, and final drive ratio. Argonne used these attributes to 
``build'' each vehicle that it used for the effectiveness modeling and 
simulation. Important for precisely estimating the effectiveness of 
different levels of mass reduction is an accurate list of initial 
component weights that make up each vehicle subsystem, from which 
Autonomie considered potential mass reduction opportunities.
    As stated above, glider weight, or the vehicle curb weight minus 
the powertrain weight, is used to determine the potential opportunities 
for weight reduction irrespective of the type of powertrain.\230\ This 
is because weight reduction can vary depending on the type of 
powertrain. For example, an 8-speed transmission may weigh more than a 
6-speed transmission, and a basic engine without variable valve timing 
may weigh more than an advanced engine with variable valve timing. 
Autonomie simulations account for the weight of the powertrain system 
inherently as part of the analysis, and the powertrain mass accounting 
is separate from the application and accounting for mass reduction 
technology levels that are applied to the glider in the simulations. 
Similarly, Autonomie also accounts for battery and motor mass used in 
hybrid and electric vehicles separately. This secondary mass reduction 
is discussed further below.
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    \230\ Depending on the powertrain combination, the total curb 
weight of the vehicle includes glider, engine, transmission and/or 
battery pack and motor(s).
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    Accordingly, in the Autonomie simulations, mass reduction 
technology is simulated as a percentage of mass removed from the 
specific subsystems that make up the glider, as defined for that set of 
simulations (including the non-powertrain secondary mass systems such 
as the brake system). For the purposes of determining a reasonable 
percentage for the glider, DOT in consultation with Argonne examined 
glider weight data available in the A2Mac1 database,\231\ in addition 
to the NHTSA MY 2014 Chevrolet Silverado lightweighting study 
(discussed further below). Based on these studies, DOT assumed that the 
glider weight comprised 71 percent of the vehicle curb weight. TSD 
Chapter 3.4.4 includes a detailed breakdown of the components that DOT 
considered to arrive at the conclusion that a glider, on average, 
represents 71% of a vehicle's curb weight.
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    \231\ A2Mac1: Automotive Benchmarking, https://a2mac1.com.
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    Any mass reduction due to powertrain improvements is accounted for 
separately from glider mass reduction. Autonomie considers several 
components for powertrain mass reduction, including engine downsizing,

[[Page 49692]]

and transmission, fuel tank, exhaust systems, and cooling system 
lightweighting.
    The 2015 NAS report suggested an engine downsizing opportunity 
exists when the glider mass is lightweighted by at least 10%. The 2015 
NAS report also suggested that 10% lightweighting of the glider mass 
alone would boost fuel economy by 3% and any engine downsizing 
following the 10% glider mass reduction would provide an additional 3% 
increase in fuel economy.\232\ The 2011 Honda Accord and 2014 Chevrolet 
Silverado lightweighting studies applied engine downsizing (for some 
vehicle types but not all) when the glider weight was reduced by 10 
percent. Accordingly, this analysis limited engine resizing to several 
specific incremental technology steps as in the 2018 CAFE NPRM (83 FR 
42986, Aug. 24, 2018) and 2020 final rule; important for this 
discussion, engines in the analysis were only resized when mass 
reduction of 10% or greater was applied to the glider mass, or when one 
powertrain architecture was replaced with another architecture.
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    \232\ National Research Council. 2015. Cost, Effectiveness, and 
Deployment of Fuel Economy Technologies for Light-Duty Vehicles. 
Washington, DC--The National Academies Press. https://doi.org/10.17226/21744.
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    Specifically, we allow engine resizing upon adoption of 7.1%, 
10.7%, 14.2%, and 20% curb weight reduction, but not at 3.6% and 
5.3%.\233\ Resizing is also allowed upon changes in powertrain type or 
the inheritance of a powertrain from another vehicle in the same 
platform. The increments of these higher levels of mass reduction, or 
complete powertrain changes, more appropriately match the typical 
engine displacement increments that are available in a manufacturer's 
engine portfolio.
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    \233\ These curb weight reductions equate to the following 
levels of mass reduction as defined in the analysis: MR3, MR4, MR5 
and MR6, but not MR1 and MR2; additional discussion of engine 
resizing for mass reduction can be found in Section III.C.4 and TSD 
Chapter 2.4.
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    Argonne performed a regression analysis of engine peak power versus 
weight for a previous analysis based on attribute data taken from the 
A2Mac1 benchmarking database, to account for the difference in weight 
for different engine types. For example, to account for weight of 
different engine sizes like 4-cylinder versus 8-cylinder, Argonne 
developed a relationship curve between peak power and engine weight 
based on the A2Mac1 benchmarking data. We use this relationship to 
estimate mass for all engine types regardless of technology type (e.g., 
variable valve lift and direct injection). DOT applied weight 
associated with changes in engine technology by using this linear 
relationship between engine power and engine weight from the A2Mac1 
benchmarking database. When a vehicle in the analysis fleet with an 8-
cylinder engine adopted a more fuel-efficient 6-cylinder engine, the 
total vehicle weight would reflect the updated engine weight with two 
less cylinders based on the peak power versus engine weight 
relationship.
    When Autonomie selects a powertrain combination for a lightweighted 
glider, the engine and transmission are selected such that there is no 
degradation in the performance of the vehicle relative to the baseline 
vehicle. The resulting curb weight is a combination of the 
lightweighted glider with the resized and potentially new engine and 
transmission. This methodology also helps in accurately accounting for 
the cost of the glider and cost of the engine and transmission in the 
CAFE Model.
    Secondary mass reduction is possible from some of the components in 
the glider after mass reduction has been incorporated in primary 
subsystems (body, chassis, and interior). Similarly, engine downsizing 
and powertrain secondary mass reduction is possible after certain level 
of mass reduction is incorporated in the glider. For the analysis, the 
agencies include both primary mass reduction, and when there is 
sufficient primary mass reduction, additional secondary mass reduction. 
The Autonomie simulations account for the aggregate of both primary and 
secondary glider mass reduction, and separately for powertrain mass.
    Note that secondary mass reduction is integrated into the mass 
reduction cost curves. Specifically, the NHTSA studies, upon which the 
cost curves depend, first generated costs for lightweighting the 
vehicle body, chassis, interior, and other primary components, and then 
calculated costs for lightweighting secondary components. Accordingly, 
the cost curves reflect that, for example, secondary mass reduction for 
the brake system is only applied after there has been sufficient 
primary mass reduction to allow the smaller brake system to provide 
safe braking performance and to maintain mechanical functionality.
    DOT enhanced the accuracy of estimated engine weights by creating 
two curves to represent separately naturally aspirated engine designs 
and turbocharged engine designs.\234\ This achieves two benefits. 
First, small naturally aspirated 4-cylinder engines that adopted 
turbocharging technology reflected the increased weight of associated 
components like ducting, clamps, the turbocharger itself, a charged air 
cooler, wiring, fasteners, and a modified exhaust manifold. Second, 
larger cylinder count engines like naturally aspirated 8-cylinder and 
6-cylinder engines that adopted turbocharging and downsized 
technologies would have lower weight due to having fewer engine 
cylinders. For this analysis, a naturally aspirated 8-cylinder engine 
that adopts turbocharging technology and is downsized to a 6-cylinder 
turbocharged engine appropriately reflects the added weight of the 
turbocharging components, and the lower weight of fewer cylinders.
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    \234\ See Autonomie model documentation, Chapter 5.2.9. Engine 
Weight Determination.
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    The range of effectiveness values for the mass reduction 
technologies, for all ten vehicle technology classes are shown in 
Figure III-13. In the graph, the box shows the inner quartile range 
(IQR) of the effectiveness values and whiskers extend out 1.5 x IQR. 
The dots outside of the whiskers show a few values outside these 
ranges. As discussed earlier, Autonomie simulates all possible 
combinations of technologies for fuel consumption improvements. For a 
few technology combinations mass reduction has minimal impact on 
effectiveness on the regulatory 2-cycle test. For example, if an engine 
is operating in an efficient region of the fuel map on the 2-cycle test 
further reduction of mass may have smaller improvement on the 
regulatory cycles. Figure III-13 shows the range improvements based on 
the full range of other technology combinations considered in the 
analysis.

[[Page 49693]]

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(e) Mass Reduction Costs
    The CAFE Model analysis handles mass reduction technology costs 
differently than all other technology costs. Mass reduction costs are 
calculated as an average cost per pound over the baseline (MR0) for a 
vehicle's glider weight. While the definitions of glider may vary, DOT 
referenced the same dollar per pound of curb weight to develop costs 
for different glider definitions. In translating these values, DOT took 
care to track units ($/kg vs. $/lb) and the reference for percentage 
improvements (glider vs. curb weight).
    DOT calculated the cost of mass reduction on a glider weight basis 
so that the weight of each powertrain configuration could be directly 
and separately accounted for. This approach provides the true cost of 
mass reduction without conflating the mass change and costs associated 
with downsizing a powertrain or adding additional advanced powertrain 
technologies. Hence, the mass reduction costs in this proposal reflect 
the cost of mass reduction in the glider and do not include the mass 
reduction associated with engine downsizing. The mass reduction and 
costs associated with engine downsizing are accounted for separately.
    A second reason for using glider share instead of curb weight is 
that it affects the absolute amount of curb weight reduction applied, 
and therefore cost per pound for the mass reduction changes with the 
change in the glider share. The cost for removing 20 percent of the 
glider weight when the glider represents 75 percent of a vehicle's curb 
weight is not the same as the cost for removing 20 percent of the 
glider weight when the glider represents 50 percent of the vehicle's 
curb weight. For example, the glider share of 79 percent of a 3,000-
pound curb weight vehicle is 2,370 lbs, while the glider share of 50 
percent of a 3,000-pound curb weight vehicle is 1,500 lbs, and the 
glider share of 71 percent of a 3,000-pound curb weight vehicle is 
2,130 lbs. The mass change associated with 20 percent mass reduction is 
474 lbs for 79 percent glider share (=[3,000 lbs x 79% x 20%]), 300 lbs 
for 50 percent glider share (=[3,000 lbs x 50% x 20%]), and 426 lbs for 
71 percent glider share (=[3,000 lbs x 71% x 20%]). The mass reduction 
cost studies that DOT relied on to develop mass reduction costs for 
this analysis show that the cost for mass reduction varies with the 
amount of mass reduction. Therefore, for a fixed glider mass reduction 
percentage, different glider share assumptions will have different 
costs.
    DOT considered several sources to develop the mass reduction 
technology cost curves. Several mass reduction studies have used either 
a mid-size passenger car or a full-size pickup truck as an exemplar 
vehicle to demonstrate the technical and cost feasibility of mass 
reduction. While the findings of these studies may not apply directly 
to different vehicle classes, the cost estimates derived for the mass 
reduction technologies identified in these studies can be useful for 
formulating general estimates of costs. As discussed further below, the 
mass reduction cost curves developed for this analysis are based on two 
lightweighting studies, and DOT also updated the curves based on more

[[Page 49694]]

recent studies to better account for the cost of carbon fiber needed 
for the highest levels of mass reduction technology. The two studies 
used for MR1 through MR4 costs included the teardown of a MY 2011 Honda 
Accord and a MY 2014 Chevrolet Silverado pickup truck, and the carbon 
fiber costs required for MR5 and MR6 were updated based on the 2021 NAS 
report.\235\
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    \235\ This analysis applied the cost estimates per pound derived 
from passenger cars to all passenger car segments, and the cost 
estimates per pound derived from full-size pickup trucks to all 
light-duty truck and SUV segments. The cost estimates per pound for 
carbon fiber (MR5 and MR6) were the same for all segments.
---------------------------------------------------------------------------

    Both teardown studies are structured to derive the estimated cost 
for each of the mass reduction technology levels. DOT relied on the 
results of those studies because they considered an extensive range of 
material types, material gauge, and component redesign while taking 
into account real world constraints such as manufacturing and assembly 
methods and complexity, platform-sharing, and maintaining vehicle 
utility, functionality and attributes, including safety, performance, 
payload capacity, towing capacity, handling, NVH, and other 
characteristics. In addition, DOT determined that the baseline vehicles 
and mass reduction technologies assessed in the studies are still 
reasonably representative of the technologies that may be applied to 
vehicles in the MY 2020 analysis fleet to achieve up to MR4 level mass 
reduction in the rulemaking timeframe. DOT adjusted the cost estimates 
derived from the two studies to reflect the assumption that a vehicle's 
glider weight consisted of 71% of the vehicle's curb weight, and mass 
reduction as it pertains to achieving MR0-MR6 levels would only come 
from the glider.
    As discussed above, achieving the highest levels of mass reduction 
often necessitates extensive use of advanced materials like higher 
grades of aluminum, magnesium, or carbon fiber. For the 2020 final 
rule, DOT provided a survey of information available regarding carbon 
fiber costs compared to the costs DOT presented in the final rule based 
on the Honda Accord and Chevrolet Silverado teardown studies. In the 
Honda Accord study, the estimated cost of carbon fiber was $5.37/kg, 
and the cost of carbon fiber used in the Chevy Silverado study was 
$15.50/kg. The $15.50 estimate closely matched the cost estimates from 
a BMW i3 teardown analysis,\236\ the cost figures provided by Oak Ridge 
National Laboratory for a study from the IACMI Composites 
Institute,\237\ and from a Ducker Worldwide presentation at the CAR 
Management Briefing Seminar.\238\
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    \236\ Singh, Harry, FSV Body Structure Comparison with 2014 BMW 
i3, Munro and Associates for World Auto Steel (June 3, 2015).
    \237\ IACMI Baseline Cost and Energy Metrics (March 2017), 
available at https://iacmi.org/wp-content/uploads/2017/12/IACMI-Baseline-Cost-and-Energy-Metrics-March-2017.pdf.
    \238\ Ducker Worldwide, The Road Ahead--Automotive Materials 
(2016), https://societyofautomotiveanalysts.wildapricot.org/resources/Pictures/SAA%20Sumit%20slides%20for%20Abey%20Abraham%20of%20Ducker.pdf.
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    For this analysis, DOT relied on the cost estimates for carbon 
fiber construction that the National Academies detailed in the 2021 
Assessment of Technologies for Improving Fuel Economy of Light-Duty 
Vehicles--Phase 3 recently completed by the National Academies.\239\ 
The study indicates that the sum of direct materials costs plus 
manufacturing costs for carbon fiber composite automotive components is 
$25.97 per pound in high volume production. In order to use this cost 
in the CAFE Model it must be put in terms of dollars per pound saved. 
Using an average vehicle curb weight of 4000 lbs, a 71% glider share 
and the percent mass savings associated with MR5 and MR6, it is 
possible to calculate the number of pounds to be removed to attain MR5 
and MR6. Also taken from the NAS study is the assertion that carbon 
fiber substitution for steel in an automotive component results in a 
50% mass reduction. Combining all this together, carbon fiber 
technology offers weight savings at $24.60 per pound saved. This dollar 
per pound savings figure must also be converted to a retail price 
equivalent (RPE) to account for various commercial costs associated 
with all automotive components. This is accomplished by multiplying 
$24.60 by the factor 1.5. This brings the cost per pound saved for 
using carbon fiber to $36.90 per pound saved.\240\ The analysis uses 
this cost for achieving MR5 and MR6.
---------------------------------------------------------------------------

    \239\ 2021 NAS report, at 7-242-3.
    \240\ See MR5 and MR6 CFRP Cost Increase Calculator.xlsx in the 
docket for this action.
---------------------------------------------------------------------------

    Table III-25 and Table III-26 show the cost values (in dollars per 
pound) used in the CAFE Model with MR1-4 costs based on the cost curves 
developed from the MY 2011 Honda Accord and MY 2014 Chevrolet Silverado 
studies, and the updated MR5 and MR6 values that account for the 
updated carbon fiber costs from the 2021 NAS report. Both tables assume 
a 71% glider share.
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[GRAPHIC] [TIFF OMITTED] TP03SE21.072


[[Page 49695]]


    There is a dramatic increase in cost going from MR4 to MR5 and MR6 
for all classes of vehicles. However, while the increase in cost going 
from MR4 to MR5 and MR6 is dramatic, the MY 2011 Honda Accord study, 
the MY 2014 Chevrolet Silverado study, and the 2021 NAS report all 
included a steep increase to achieve the highest levels of mass 
reduction technology. As noted above, DOT seeks comment on any 
additional information about the costs of achieving the highest levels 
of mass reduction technology, including from publicly available sources 
or data that could be made publicly available.
    Table III-27 provides an example of mass reduction costs in 2018$ 
over select model years for the medium car and pickup truck technology 
classes as a dollar per pound value. The table shows how the $/lb value 
for each mass reduction level decreases over time because of cost 
learning. For a full list of the $/lb mass reduction costs used in the 
analysis across all model years, see the Technologies file.
[GRAPHIC] [TIFF OMITTED] TP03SE21.073

5. Aerodynamics
    The energy required to overcome aerodynamic drag accounts for a 
significant portion of the energy consumed by a vehicle and can become 
the dominant factor for a vehicle's energy consumption at high speeds. 
Reducing aerodynamic drag can, therefore, be an effective way to reduce 
fuel consumption and emissions.
    Aerodynamic drag is proportional to the frontal area (A) of the 
vehicle and coefficient of drag (Cd), such that aerodynamic 
performance is often expressed as the product of the two values, 
CdA, which is also known as the drag area of a vehicle. The 
coefficient of drag (Cd) is a dimensionless value that 
essentially represents the aerodynamic efficiency of the vehicle shape. 
The frontal area (A) is the cross-sectional area of the vehicle as 
viewed from the front. It acts with the coefficient of drag as a sort 
of scaling factor, representing the relative size of the vehicle shape 
that the coefficient of drag describes. The force imposed by 
aerodynamic drag increases with the square of vehicle velocity, 
accounting for the largest contribution to road loads at higher speeds.
    Aerodynamic drag reduction can be achieved via two approaches, 
either by reducing the drag coefficient or reducing vehicle frontal 
area, with two different categories of technologies, passive and active 
aerodynamic technologies. Passive aerodynamics refers to aerodynamic 
attributes that are inherent to the shape and size of the vehicle, 
including any components of a fixed nature. Active aerodynamics refers 
to technologies that variably deploy in response to driving conditions. 
These include technologies such as active grille shutters, active air 
dams, and active ride height adjustment. It is important to note that 
manufacturers may employ both passive and active aerodynamic 
technologies to achieve aerodynamic drag values.
    The greatest opportunity for improving aerodynamic performance is 
during a vehicle redesign cycle when significant changes to the shape 
and size of the vehicle can be made. Incremental improvements may also 
be achieved during mid-cycle vehicle refresh using restyled exterior 
components and add-on devices. Some examples of potential technologies 
applied during mid-cycle refresh are restyled front and rear fascia, 
modified front air dams and rear valances, addition of rear deck lips 
and underbody panels, and low-drag exterior mirrors. While 
manufacturers may nudge the frontal area of the vehicle during 
redesigns, large changes in frontal area are typically not possible 
without impacting the utility and interior space of the vehicle. 
Similarly, manufacturers may improve Cd by changing the 
frontal shape of the vehicle or lowering the height of the vehicle, 
among other approaches, but the form drag of certain body styles and 
airflow needs for engine cooling often limit how much Cd may 
be improved.
    The following sections discuss the four levels of aerodynamic 
improvements considered in the CAFE Model, how the agency assigned 
baseline aerodynamic technology levels to vehicles in the MY 2020 
fleet, the effectiveness improvements for the addition of aerodynamic 
technologies to vehicles, and the costs for adding that aerodynamic 
technology.
(a) Aerodynamic Technologies in the CAFE Model
    DOT bins aerodynamic improvements into four levels--5%, 10%, 15% 
and 20% aerodynamic drag improvement values over a baseline computed 
for each vehicle body style--which correspond to AERO5, AERO10, AERO15, 
and AERO20, respectively.
    The aerodynamic improvements technology pathway consists of a 
linear progression, with each level superseding all previous levels, as 
seen in Figure III-14.

[[Page 49696]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.074

    While the four levels of aerodynamic improvements are technology-
agnostic, DOT built a pathway to compliance for each level based on 
aerodynamic data from a National Research Council (NRC) of Canada-
sponsored wind tunnel testing program. The program included an 
extensive review of production vehicles utilizing these technologies, 
and industry comments.241 242 Again, these technology 
combinations are intended to show a potential way for a manufacturer to 
achieve each aerodynamic improvement level; however, in the real world, 
manufacturers may implement different combinations of aerodynamic 
technologies to achieve a percentage improvement over their baseline 
vehicles.
---------------------------------------------------------------------------

    \241\ Larose, G., Belluz, L., Whittal, I., Belzile, M. et al., 
``Evaluation of the Aerodynamics of Drag Reduction Technologies for 
Light-duty Vehicles--a Comprehensive Wind Tunnel Study,'' SAE Int. 
J. Passeng. Cars--Mech. Syst. 9(2):772-784, 2016, https://doi.org/10.4271/2016-01-1613.
    \242\ Larose, Guy & Belluz, Leanna & Whittal, Ian & Belzile, 
Marc & Klomp, Ryan & Schmitt, Andreas. (2016). Evaluation of the 
Aerodynamics of Drag Reduction Technologies for Light-duty 
Vehicles--a Comprehensive Wind Tunnel Study. SAE International 
Journal of Passenger Cars--Mechanical Systems. 9. 10.4271/2016-01-
1613.
---------------------------------------------------------------------------

    Table III-28 and Table III-29 show the aerodynamic technologies 
that could be used to achieve 5%, 10%, 15% and 20% improvements in 
passenger cars, SUVs, and pickup trucks. As discussed further in 
Section III.D.5.c, AERO20 cannot be applied to pickup trucks in the 
model, which is why there is no pathway to AERO20 shown in Table III-
29. While some aerodynamic improvement technologies can be applied 
across vehicle classes, like active grille shutters (used in the 2015 
Chevrolet Colorado),\243\ DOT determined that there are limitations 
that make it infeasible for vehicles with some body styles to achieve a 
20% reduction in the coefficient of drag from their baseline. This 
technology path is an example of how a manufacturer could reach each 
AERO level, but they would not necessarily be required to use the 
technologies.
---------------------------------------------------------------------------

    \243\ Chevrolet Product Information, available at https://media.chevrolet.com/content/media/us/en/chevrolet/vehicles/colorado/2015/_jcr_content/iconrow/textfile/file.res/15-PG-Chevrolet-Colorado-082218.pdf.

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[[Page 49697]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.075

[GRAPHIC] [TIFF OMITTED] TP03SE21.076

BILLING CODE 4910-59-C
    As discussed further in Section III.D.8, this analysis assumes 
manufacturers apply off-cycle technology at rates defined in the Market 
Data file. While the AERO levels in the analysis are technology-
agnostic, achieving AERO20 improvements does assume the use of active 
grille shutters, which is an off-cycle technology.
(b) Aerodynamics Analysis Fleet Assignments
    DOT uses a relative performance approach to assign an initial level 
of aerodynamic drag reduction technology to each vehicle. Each AERO 
level represents a percent reduction in a vehicle's aerodynamic drag 
coefficient (Cd) from a baseline value for its body style. 
For a vehicle to achieve AERO5, the Cd must be at least 5% 
below the baseline for the body style; for AERO10, 10% below the 
baseline, and so on. Baseline aerodynamic assignment is therefore a 
three step process: Each vehicle in the fleet is assigned a body style, 
the average drag coefficient is calculated for each body style, and the 
drag coefficient for each vehicle model is compared to the average for 
the body style.
    Every vehicle in the fleet is assigned a body style; available body 
styles included convertible, coupe, sedan, hatchback, wagon, SUV, 
pickup, minivan, and van. These assignments do not necessarily match 
the body styles used by manufacturers for marketing purposes. Instead, 
they are assigned based on analyst judgement, taking into account how a 
vehicle's AERO and vehicle technology class assignments are affected. 
Different body styles offer different utility and have varying levels

[[Page 49698]]

of baseline form drag. In addition, frontal area is a major factor in 
aerodynamic forces, and the frontal area varies by vehicle. This 
analysis considers both frontal area and body style as utility factors 
affecting aerodynamic forces; therefore, the analysis assumes all 
reduction in aerodynamic drag forces come from improvement in the drag 
coefficient.
    Average drag coefficients for each body style were computed using 
the MY 2015 drag coefficients published by manufacturers, which were 
used as the baseline values in the analysis. DOT harmonizes the 
Autonomie simulation baselines with the analysis fleet assignment 
baselines to the fullest extent possible.\244\
---------------------------------------------------------------------------

    \244\ See TSD Chapter 2.4.1 for a table of vehicle attributes 
used to build the Autonomie baseline vehicle models. That table 
includes a drag coefficient for each vehicle class.
---------------------------------------------------------------------------

    The drag coefficients used for each vehicle in the MY 2020 analysis 
fleet are sourced from manufacturer specification sheets, when 
possible. However, drag coefficients for the MY 2020 vehicles were not 
consistently reported publicly. If no drag coefficient was reported, 
analyst judgment is sometimes used to assign an AERO level. If no level 
was manually assigned, the drag coefficient obtained from manufacturers 
to build the MY 2016 fleet,\245\ was used, if available. The MY 2016 
drag coefficient values may not accurately reflect the current 
technology content of newer vehicles but are, in many cases, the most 
recent data available.
---------------------------------------------------------------------------

    \245\ See 83 FR 42986 (Aug. 24, 2018). The MY 2016 fleet was 
built to support the 2018 NPRM.
---------------------------------------------------------------------------

(c) Aerodynamics Adoption Features
    As already discussed, DOT engineers use a relative performance 
approach to assign current aerodynamic technology (AERO) level to a 
vehicle. For some body styles with different utility, such as pickup 
trucks, SUVs and minivans, frontal area can vary, and this can affect 
the overall aerodynamic drag forces. In order to maintain vehicle 
utility and functionality related to passenger space and cargo space, 
we assume all technologies that improve aerodynamic drag forces do so 
by reducing Cd while maintaining frontal area.
    Technology pathway logic for levels of aerodynamic improvement 
consists of a linear progression, with each level superseding all 
previous ones. Technology paths for AERO are illustrated in Figure III-
14.
    The highest levels of AERO are not considered for certain body 
styles. In these cases, this means that AERO20, and sometimes AERO15, 
can neither be assigned in the baseline fleet nor adopted by the model. 
For these body styles, there are no commercial examples of drag 
coefficients that demonstrate the required AERO15 or AERO20 improvement 
over baseline levels. DOT also deemed the most advanced levels of 
aerodynamic drag simulated as not technically practicable given the 
form drag of the body style and costed technology, especially given the 
need to maintain vehicle functionality and utility, such as interior 
volume, cargo area, and ground clearance. In short, DOT `skipped' 
AERO15 for minivan body styles, and `skipped' AERO20 for convertible, 
minivan, pickup, and wagon body styles.
    DOT also does not allow application of AERO15 and AERO20 technology 
to vehicles with more than 780 horsepower. There are two main types of 
vehicles that informed this threshold: performance internal combustion 
engine (ICE) vehicles and high-power battery electric vehicles (BEVs). 
In the case of the former, the agency recognizes that manufacturers 
tune aerodynamic features on these vehicles to provide desirable 
downforce at high speeds and to provide sufficient cooling for the 
powertrain, rather than reducing drag, resulting in middling drag 
coefficients despite advanced aerodynamic features. Therefore, 
manufacturers may have limited ability to improve aerodynamic drag 
coefficients for high performance vehicles with internal combustion 
engines without reducing horsepower. The baseline fleet includes 1,655 
units of sales volume with limited application of aerodynamic 
technologies because of ICE vehicle performance.\246\
---------------------------------------------------------------------------

    \246\ Market Data file.
---------------------------------------------------------------------------

    In the case of high-power battery electric vehicles, the 780-
horsepower threshold is set above the highest peak system horsepower 
present on a BEV in the 2020 fleet. BEVs have different aerodynamic 
behavior and considerations than ICE vehicles, allowing for features 
such as flat underbodies that significantly reduce drag.\247\ BEVs are 
therefore more likely to achieve higher AERO levels, so the horsepower 
threshold is set high enough that it does not restrict AERO15 and 
AERO20 application. Note that the CAFE Model does not force high levels 
of AERO adoption; rather, higher AERO levels are usually adopted 
organically by BEVs because significant drag reduction allows for 
smaller batteries and, by extension, cost savings. BEVs represent 
252,023 units of sales volume in the baseline fleet.\248\
---------------------------------------------------------------------------

    \247\ 2020 EPA Automotive Trends Report, at 227.
    \248\ Market Data file.
---------------------------------------------------------------------------

(d) Aerodynamics Effectiveness Modeling
    To determine aerodynamic effectiveness, the CAFE Model and 
Autonomie used individually assigned road load technologies for each 
vehicle to appropriately assign initial road load levels and 
appropriately capture benefits of subsequent individual road load 
improving technologies.
    The current analysis included four levels of aerodynamic 
improvements, AERO5, AERO10, AERO15, and AERO20, representing 5, 10, 
15, and 20 percent reduction in drag coefficient (Cd), 
respectively. DOT assumed that aerodynamic drag reduction could only 
come from reduction in Cd and not from reduction of frontal 
area, to maintain vehicle functionality and utility, such as passenger 
space, ingress/egress ergonomics, and cargo space.
    The effectiveness values for the aerodynamic improvement levels 
relative to AERO0, for all ten vehicle technology classes, are shown in 
Figure III-15. Each of the effectiveness values shown is representative 
of the improvements seen for upgrading only the listed aerodynamic 
technology level for a given combination of other technologies. In 
other words, the range of effectiveness values seen for each specific 
technology (e.g., AERO 15) represents the addition of AERO15 technology 
(relative to AERO0 level) for every technology combination that could 
select the addition of AERO15. It must be emphasized that the change in 
fuel consumption values between entire technology keys is used,\249\ 
and not the individual technology effectiveness values. Using the 
change between whole technology keys captures the complementary or non-
complementary interactions among technologies. The box shows the inner 
quartile range (IQR) of the effectiveness values and whiskers extend 
out 1.5 x IQR. The dots outside the whiskers show effectiveness values 
outside those thresholds.
---------------------------------------------------------------------------

    \249\ Technology key is the unique collection of technologies 
that constitutes a specific vehicle, see TSD Chapter 2.4.7 for more 
detail.

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[[Page 49699]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.077

(e) Aerodynamics Costs
    This analysis uses the AERO technology costs established in the 
2020 final rule that are based on confidential business information 
submitted by the automotive industry in advance of the 2018 NPRM,\251\ 
and on DOT's assessment of manufacturing costs for specific aerodynamic 
technologies.\252\ DOT received no additional comments from 
stakeholders regarding the costs established in the 2018 NPRM, and 
continued to use the established costs for the 2020 final rule and this 
analysis.
---------------------------------------------------------------------------

    \250\ The data used to create this figure can be found in the 
FE_1 Improvements file.
    \251\ See the PRIA accompanying the 2018 NPRM, Chapter 
6.3.10.1.2.1.2 for a discussion of these cost estimates.
    \252\ See the FRIA accompanying the 2020 final rule, Chapter 
VI.C.5.e.
---------------------------------------------------------------------------

    Table III-30 shows examples of costs for AERO technologies as 
applied to the medium car and pickup truck vehicle classes in select 
model years. The cost to achieve AERO5 is relatively low, as most of 
the improvements can be made through body styling changes. The cost to 
achieve AERO10 is higher than AERO5, due to the addition of several 
passive aerodynamic technologies, and the cost to achieve AERO15 and 
AERO20 is higher than AERO10 due to use of both passive and active 
aerodynamic technologies. For a full list of all absolute aerodynamic 
technology costs used in the analysis across all model years see the 
Technologies file.

[[Page 49700]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.078

6. Tire Rolling Resistance
    Tire rolling resistance is a road load force that arises primarily 
from the energy dissipated by elastic deformation of the tires as they 
roll. Tire design characteristics (for example, materials, 
construction, and tread design) have a strong influence on the amount 
and type of deformation and the energy it dissipates. Designers can 
select these characteristics to minimize rolling resistance. However, 
these characteristics may also influence other performance attributes, 
such as durability, wet and dry traction, handling, and ride comfort.
    Lower-rolling-resistance tires have characteristics that reduce 
frictional losses associated with the energy dissipated mainly in the 
deformation of the tires under load, thereby improving fuel economy. 
Low rolling resistance tires are increasingly specified by OEMs in new 
vehicles and are also increasingly available from aftermarket tire 
vendors. They commonly include attributes such as higher inflation 
pressure, material changes, tire construction optimized for lower 
hysteresis, geometry changes (e.g., reduced aspect ratios), and reduced 
sidewall and tread deflection. These changes are commonly accompanied 
by additional changes to vehicle suspension tuning and/or suspension 
design to mitigate any potential impact on other performance attributes 
of the vehicle.
    DOT continues to assess the potential impact of tire rolling 
resistance changes on vehicle safety. DOT has been following the 
industry developments and trends in application of rolling resistance 
technologies to light duty vehicles. As stated in the National 
Academies Press (NAP) special report on Tires and Passenger Vehicle 
Fuel Economy,\253\ national crash data does not provide data about tire 
structural failures specifically related to tire rolling resistance, 
because the rolling resistance of a tire at a crash scene cannot be 
determined. However, other metrics like brake performance compliance 
test data are helpful to show trends like that stopping distance has 
not changed in the last ten years,\254\ during which time many 
manufacturers have installed low rolling resistance tires in their 
fleet--meaning that manufacturers were successful in improving rolling 
resistance while maintaining stopping distances through tire design, 
tire materials, and/or braking system improvements. In addition, NHTSA 
has addressed other tire-related issues through rulemaking,\255\ and 
continues to research tire problems such as blowouts, flat tires, tire 
or wheel deficiency, tire or wheel failure, and tire degradation.\256\ 
However, there are currently no data connecting low rolling resistance 
tires to accident or fatality rates.
---------------------------------------------------------------------------

    \253\ Tires and Passenger Vehicle Fuel Economy: Informing 
Consumers, Improving Performance--Special Report 286 (2006), 
available at https://www.nap.edu/read/11620/chapter/6.
    \254\ See, e.g., NHTSA Office of Vehicle Safety Compliance, 
Compliance Database, https://one.nhtsa.gov/cars/problems/comply/index.cfm.
    \255\ 49 CFR 571.138, Tire pressure monitoring systems.
    \256\ Tire-Related Factors in the Pre-Crash Phase, DOT HS 811 
617 (April 2012), available at https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/811617.

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[[Page 49701]]

    NHTSA conducted tire rolling resistance tests and wet grip index 
tests on original equipment tires installed on new vehicles. The tests 
showed that there is no degradation in wet grip index values (no 
degradation in traction) for tires with improved rolling resistance 
technology. With better tire design, tire compound formulations and 
improved tread design, tire manufacturers have tools to balance 
stopping distance and reduced rolling resistance. Tire manufacturers 
can use ``higher performance materials in the tread compound, more 
silica as reinforcing fillers and advanced tread design features'' to 
mitigate issues related to stopping distance.\257\
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    \257\ Jesse Snyder, A big fuel saver: Easy-rolling tires (but 
watch braking) (July 21, 2008), https://www.autonews.com/article/20080721/OEM01/307219960/a-big-fuel-saver-easy-rolling-tires-but-watch-braking. Last visited December 3, 2019.
---------------------------------------------------------------------------

    The following sections discuss levels of tire rolling resistance 
technology considered in the CAFE Model, how the technology was 
assigned in the analysis fleet, adoption features specified to maintain 
performance, effectiveness, and cost.
(a) Tire Rolling Resistance in the CAFE Model
    DOT continues to consider two levels of improvement for low rolling 
resistance tires in the analysis: The first level of low rolling 
resistance tires considered reduced rolling resistance 10 percent from 
an industry-average baseline rolling resistance coefficient (RRC) 
value, while the second level reduced rolling resistance 20 percent 
from the baseline.\258\
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    \258\ To achieve ROLL10, the tire rolling resistance must be at 
least 10 percent better than baseline (.0081 or better). To achieve 
ROLL20, the tire rolling resistance must be at least 20 percent 
better than baseline (.0072 or better).
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    DOT selected the industry-average RRC baseline of 0.009 based on a 
CONTROLTEC study prepared for the California Air Resources Board,\259\ 
in addition to confidential business information submitted by 
manufacturers prior to the 2018 NPRM analysis. The average RRC from the 
CONTROLTEC study, which surveyed 1,358 vehicle models, was 0.009.\260\ 
CONTROLTEC also compared the findings of their survey with values 
provided by Rubber Manufacturers Association (renamed as USTMA-U.S. 
Tire Manufacturers Association) for original equipment tires. The 
average RRC from the data provided by RMA was 0.0092,\261\ compared to 
average of 0.009 from CONTROLTEC.
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    \259\ Technical Analysis of Vehicle Load Reduction by CONTROLTEC 
for California Air Resources Board (April 29, 2015).
    \260\ The RRC values used in this study were a combination of 
manufacturer information, estimates from coast down tests for some 
vehicles, and application of tire RRC values across other vehicles 
on the same platform.
    \261\ Technical Analysis of Vehicle Load Reduction by CONTROLTEC 
for California Air Resources Board (April 29, 2015) at page 40.
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    In past agency actions, commenters have argued that based on 
available data on current vehicle models and the likely possibility 
that there would be additional tire improvements over the next decade, 
DOT should consider ROLL30 technology, or a 30 percent reduction of 
tire rolling resistance over the baseline.\262\
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    \262\ NHTSA-2018-0067-11985.
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    As stated in the Joint TSD for the MY 2017-2025 final rule (77 FR 
62624, Oct. 15, 2012) and 2020 final rule, tire technologies that 
enable rolling resistance improvements of 10 and 20 percent have been 
in existence for many years.\263\ Achieving improvements of up to 20 
percent involves optimizing and integrating multiple technologies, with 
a primary contributor being the adoption of a silica tread technology. 
Tire suppliers have indicated that additional innovations are necessary 
to achieve the next level of low rolling resistance technology on a 
commercial basis, such as improvements in material to retain tire 
pressure, tread design to manage both stopping distance and wet 
traction, and development of carbon black material for low rolling 
resistance without the use of silica to reduce cost and weight.\264\
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    \263\ EPA-420-R-12-901, at page 3-210.
    \264\ 2011 NAS report, at 103.
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    The agency believes that the tire industry is in the process of 
moving automotive manufacturers towards higher levels of rolling 
resistance technology in the vehicle fleet. Importantly, as shown 
below, the MY 2020 fleet does include a higher percentage of vehicles 
with ROLL20 technology than the MY 2017 fleet. However, DOT believes 
that at this time, the emerging tire technologies that would achieve 30 
percent improvement in rolling resistance, like changing tire profile, 
stiffening tire walls, or adopting improved tires along with active 
chassis control,\265\ among other technologies, will not be available 
for widespread commercial adoption in the fleet during the rulemaking 
timeframe. As a result, the agency continues to not to incorporate 30 
percent reduction in rolling resistance technology. DOT will consider 
adding an advanced level of tire rolling resistance technology to 
future analyses, and invites comment on any updated information on 
manufacturers' capabilities to add tires with higher levels of rolling 
resistance to their vehicles, and consumers' willingness to accept 
these tires on their vehicles.
---------------------------------------------------------------------------

    \265\ Mohammad Mehdi Davari, Rolling resistance and energy loss 
in tyres (May 20, 2015), available at https://www.sveafordon.com/media/42060/SVEA-Presentation_Davari_public.pdf. Last visited 
December 30, 2019.
---------------------------------------------------------------------------

(b) Tire Rolling Resistance Analysis Fleet Assignments
    Tire rolling resistance is not a part of tire manufacturers' 
publicly released specifications and thus it is difficult to assign 
this technology to the analysis fleet. Manufacturers also often offer 
multiple wheel and tire packages for the same nameplates, further 
increasing the complexity of this assignment. DOT employed an approach 
consistent with previous rulemaking in assigning this technology. DOT 
relied on previously submitted rolling resistance values that were 
supplied by manufacturers in the process of building older fleets and 
bolstered it with agency-sponsored tire rolling testing by 
Smithers.\266\
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    \266\ See memo to Docket No. NHTSA-2021-0053, Evaluation of 
Rolling Resistance and Wet Grip Performance of OEM Stock Tires 
Obtained from NCAP Crash Tested Vehicles Phase One and Two. NHTSA 
used tire rolling resistance coefficient values from this project to 
assign baseline tire rolling resistance technology in the MY 2020 
analysis fleet and is therefore providing the draft project 
appendices for public review and comment.
---------------------------------------------------------------------------

    DOT carried over rolling resistance assignments for nameplates 
where manufacturers had submitted data on the vehicles' rolling 
resistance values, even if the vehicle was redesigned. If Smithers data 
was available, DOT replaced any older or missing values with that 
updated data. Those vehicles for which no information was available 
from either previous manufacturer submission or Smithers data were 
assigned to ROLL0. All vehicles under the same nameplate were assigned 
the same rolling resistance technology level even if manufacturers do 
outfit different trim levels with different wheels and tires.
    The MY 2020 analysis fleet includes the following breakdown of 
rolling resistance technology: 44% at ROLL0, 20% at ROLL10, and 36% at 
ROLL20, which shows that the majority of the fleet has now adopted some 
form of improved rolling resistance technology. The majority of the 
change from the MY 2017 analysis fleet has been in implementing ROLL20 
technology. There is likely more proliferation of rolling resistance 
technology, but we would need further information from manufacturers in 
order to account for it. DOT invites comment from manufacturers on 
whether these rolling

[[Page 49702]]

resistance values are still applicable, or any updated rolling 
resistance values that could be incorporated in a publicly available 
analysis fleet. If manufacturers submit updated information on baseline 
rolling resistance assignments DOT may update those assignments for the 
final rule.
(c) Tire Rolling Resistance Adoption Features
    Rolling resistance technology can be adopted with either a vehicle 
refresh or redesign. In some cases, low rolling resistance tires can 
affect traction, which may adversely impact acceleration, braking, and 
handling characteristics for some high-performance vehicles. Similar to 
past rulemakings, the agency recognizes that to maintain performance, 
braking, and handling functionality, some high-performance vehicles 
would not adopt low rolling resistance tire technology. For cars and 
SUVs with more than 405 horsepower (hp), the agency restricted the 
application of ROLL20. For cars and SUVs with more than 500 hp, the 
agency restricted the application of any additional rolling resistance 
technology (ROLL10 or ROLL20). The agency developed these cutoffs based 
on a review of confidential business information and the distribution 
of rolling resistance values in the fleet.
(d) Tire Rolling Resistance Effectiveness Modeling
    As discussed above, the baseline rolling resistance value from 
which rolling resistance improvements are measured is 0.009, based on a 
thorough review of confidential business information submitted by 
industry, and a review of other literature. To achieve ROLL10, the tire 
rolling resistance must be at least 10 percent better than baseline 
(.0081 or better). To achieve ROLL20, the tire rolling resistance must 
be at least 20 percent better than baseline (.0072 or better).
    DOT determined effectiveness values for rolling resistance 
technology adoption using Autonomie modeling. Figure III-16 below shows 
the range of effectiveness values used for adding tire rolling 
resistance technology to a vehicle in this analysis. The graph shows 
the change in fuel consumption values between entire technology 
keys,\267\ and not the individual technology effectiveness values. 
Using the change between whole technology keys captures the 
complementary or non-complementary interactions among technologies. In 
the graph, the box shows the interquartile range (IQR) of the 
effectiveness values and whiskers extend out 1.5 x IQR. The dots 
outside of the whiskers show values for effectiveness that are outside 
these bounds.
---------------------------------------------------------------------------

    \267\ Technology key is the unique collection of technologies 
that constitutes a specific vehicle, see TSD Chapter 2.4.7 for more 
information.
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    The data points with the highest effectiveness values are almost 
all exclusively BEV and FCV technology combinations for medium sized 
nonperformance cars. The effectiveness for these vehicles, when the low 
rolling resistance technology is applied, is amplified by a 
complementary effect, where the lower rolling resistance reduces road 
load and allows a smaller battery pack to be used (and still meet range 
requirements). The smaller battery pack reduces the overall weight of 
the vehicle, further reducing road load, and improving fuel efficiency. 
This complimentary effect is experience by all the vehicle technology 
classes, but the strongest effect is on the midsized vehicle non-
performance classes and is only captured in the analysis through the 
use of full vehicle simulations, demonstrating the full interactions of 
the technologies.

[[Page 49703]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.079

(e) Tire Rolling Resistance Costs
    DOT continues to use the same DMC values for ROLL technology that 
were used for the 2020 final rule which are based on NHTSA's MY 2011 
CAFE final rule (74 FR 14196, March 30, 2009) and the 2006 NAS/NRC 
report.\268\ Table III-31 shows the different levels of tire rolling 
resistance technology cost for all vehicle classes across select model 
years, which shows how the learning rate for ROLL technologies impacts 
the cost. For all ROLL absolute technology costs used in the analysis 
across all model years see the Technologies file.
---------------------------------------------------------------------------

    \268\ ``Tires and Passenger Vehicle Fuel Economy,'' 
Transportation Research Board Special Report 286, National Research 
Council of the National Academies, 2006, Docket No. EPA-HQ-OAR-2009-
0472-0146.
[GRAPHIC] [TIFF OMITTED] TP03SE21.080

7. Other Vehicle Technologies
    Four other vehicle technologies were included in the analysis--
electric power steering (EPS), improved accessory devices (IACC), low 
drag brakes (LDB), and secondary axle disconnect (SAX). The 
effectiveness of these technologies was applied directly in the CAFE 
Model with unique effectiveness values for each technology and for each 
technology class, rather than using Autonomie effectiveness estimates. 
This methodology was used in these four cases because the effectiveness 
of these technologies varies little with combinations of other 
technologies. Also, applying these technologies directly in the CAFE 
Model significantly reduces the number of Autonomie simulations that 
are needed.
(a) Electric Power Steering
    Electric power steering reduces fuel consumption by reducing load 
on the engine. Specifically, it reduces or eliminates the parasitic 
losses associated with engine-driven power

[[Page 49704]]

steering pumps, which pump hydraulic fluid continuously through the 
steering actuation system even when no steering input is present. By 
selectively powering the electric assist only when steering input is 
applied, the power consumption of the system is reduced in comparison 
to the traditional ``always-on'' hydraulic steering system. Power 
steering may be electrified on light duty vehicles with standard 12V 
electrical systems and is also an enabler for vehicle electrification 
because it provides power steering when the engine is off (or when no 
combustion engine is present).
    Power steering systems can be electrified in two ways. 
Manufacturers may choose to eliminate the hydraulic portion of the 
steering system and provide electric-only power steering (EPS) driven 
by an independent electric motor, or they may choose to move the 
hydraulic pump from a belt-driven configuration to a stand-alone 
electrically driven hydraulic pump. The latter system is commonly 
referred to as electro-hydraulic power steering (EHPS). As discussed in 
the rulemakings, manufacturers have informed DOT that full EPS systems 
are being developed for all types of light-duty vehicles, including 
large trucks.
    DOT described in past rulemakings that, like low drag brakes, EPS 
can be difficult to observe and assign to the analysis fleet, however, 
it is found more frequently in publicly available information than low 
drag brakes. Based on comments received during the 2020 rulemaking, the 
agency increased EPS application rate to nearly 90 percent for the 2020 
final rule. The agency is maintaining this level of EPS fleet 
penetration for this analysis, recognizing that some specialized, 
unique vehicle types or configurations still implement hydraulically 
actuated power steering systems for the baseline fleet model year.
    The effectiveness of both EPS and EHPS is derived from the 
decoupling of the pump from the crankshaft and is considered to be 
practically the same for both. Thus, a single effectiveness value is 
used for both EPS and EHPS. As indicated in the following table, the 
effectiveness of EPS and EHPS varies based on the vehicle technology 
class it is being applied to. This variance is a direct result of 
vehicle size and the amount of energy required to turn the vehicle's 
two front wheels about their vertical axis. More simply put, more 
energy is required for vehicles that weigh more and, typically, have 
larger tire contact patches.
[GRAPHIC] [TIFF OMITTED] TP03SE21.081

(b) Improved Accessories
    Engine accessories typically include the alternator, coolant pump, 
cooling fan, and oil pump, and are traditionally mechanically driven 
via belts, gears, or directly by other rotating engine components such 
as camshafts or the crankshaft. These can be replaced with improved 
accessories (IACC), which may include high efficiency alternators, 
electrically driven (i.e., on-demand) coolant pumps, electric cooling 
fans, variable geometry oil pumps, and a mild regeneration strategy. 
Replacing lower-efficiency and/or mechanically-driven components with 
these improved accessories results in a reduction in fuel consumption, 
as the improved accessories can conserve energy by being turned on/off 
``on demand'' in some cases, driven at partial load as needed, or by 
operating more efficiently.
    For example, electric coolant pumps and electric powertrain cooling 
fans provide better control of engine cooling. Flow from an electric 
coolant pump can be varied, and the cooling fan can be shut off during 
engine warm-up or cold ambient temperature conditions, reducing warm-up 
time, fuel enrichment requirements, and, ultimately reducing parasitic 
losses.
    IACC technology is difficult to observe and therefore there is 
uncertainty in assigning it to the analysis fleet. As in the past, DOT 
relies on industry-provided information and comments to assess the 
level of IACC technology applied in the fleet. DOT believes there 
continues to be opportunity for further implementation of IACC. The MY 
2020 analysis fleet has an IACC fleet penetration of approximately 
eight percent compared to the six percent value in the MY 2017 analysis 
fleet used for the 2020 final rule analysis.
    The agency believes improved accessories may be incorporated in 
coordination with powertrain related changes occurring at either a 
vehicle refresh or vehicle redesign. This coordination with powertrain 
changes enables related design and tooling changes to be implemented 
and systems development, functionality and durability testing to be 
conducted in a single product change program to efficiently manage 
resources and costs.
    This analysis carries forward work on the effectiveness of IACC 
systems conducted in the Draft TAR and EPA Proposed Determination that 
is originally founded in the 2002 NAS Report \269\ and confidential 
manufacturer data. This work involved gathering information by 
monitoring

[[Page 49705]]

press reports, holding meetings with suppliers and OEMs, and attending 
industry technical conferences. The resulting effectiveness estimates 
we use are shown below. As indicated in the following table, the 
effectiveness of IACC is simulated with differing values based on the 
vehicle technology class it is being applied to. This variance, like 
EPS, is a direct result of vehicle size and the amount of energy 
required perform the work necessary for the vehicle to operate as 
expected. This variance is related to the amount energy generated by 
the alternator, the size of the coolant pump to the cool the necessary 
systems, the size of the cooling fan required, among other 
characteristics and it directed related to a vehicle size and mass.
---------------------------------------------------------------------------

    \269\ National Research Council 2002. Effectiveness and Impact 
of Corporate Average Fuel Economy (CAFE) Standards. Washington, DC: 
The National Academies Press. https://doi.org/10.17226/10172.
[GRAPHIC] [TIFF OMITTED] TP03SE21.082

(c) Low Drag Brakes
    Since 2009, for the MY 2011 CAFE final rule, DOT has defined low 
drag brakes (LDB) as brakes that reduce the sliding friction of disc 
brake pads on rotors when the brakes are not engaged because the brake 
pads are pulled away from the rotating disc either by mechanical or 
electric methods.\270\ DOT estimated the effectiveness of LDB 
technology to be a range from 0.5-1.0 percent, based on CBI data. DOT 
applied a learning curve to the estimated cost for LDB, but noted that 
the technology was considered high volume, mature, and stable. DOT 
explained that confidential manufacturer comments in response to the 
NPRM for MY 2011 (73 FR 24352, May 2, 2008) indicated that most 
passenger cars have already adopted LDB technology, but ladder frame 
trucks have not.
---------------------------------------------------------------------------

    \270\ Final Regulatory Impact Analysis, Corporate Average Fuel 
Economy for MY 2011 Passenger Cars and Light Trucks (March 2009), at 
V-135.
---------------------------------------------------------------------------

    DOT and EPA continued to use the same definition for LDB in the MY 
2012-2016 rule (75 FR 25324, May 7, 2010), with an estimated 
effectiveness of up to 1 percent based on CBI data.\271\ DOT only 
allowed LDB technology to be applied to large car, minivan, medium and 
large truck, and SUV classes because the agency determined the 
technology was already largely utilized in most other subclasses. The 
2011 NAS committee also utilized NHTSA and EPA's definition for LDB and 
added that most new vehicles have low-drag brakes.\272\ The committee 
confirmed that the impact over conventional brakes may be about a 1 
percent reduction of fuel consumption.
---------------------------------------------------------------------------

    \271\ Final Regulatory Impact Analysis, Corporate Average Fuel 
Economy for MY 2012-MY 2016 Passenger Cars and Light Trucks (March 
2010), at 249.
    \272\ 2011 NAS report, at 104.
---------------------------------------------------------------------------

    For the MY 2017-2025 rule, however, DOT and EPA updated the 
effectiveness estimate for LDB to 0.8 percent based on a 2011 Ricardo 
study and updated lumped-parameter model.\273\ The agencies considered 
LDB technology to be off the learning curve (i.e., the DMC does not 
change year-over-year). The 2015 NAS report continued to use the 
agencies' definition for LDB and commented that the 0.8 percent 
effectiveness estimate is a reasonable estimate.\274\ The 2015 NAS 
committee did not opine on the application of LDB technology in the 
fleet. The agencies used the same definition, cost, and effectiveness 
estimates for LDB in the Draft TAR, but also noted the existence of 
zero drag brake systems which use electrical actuators that allow brake 
pads to move farther away from the rotor.\275\ However, the agencies 
did not include zero drag brake technology in either compliance 
simulation. EPA continued with this approach in its first 2017 Final 
Determination that the standards through 2025 were appropriate.\276\
---------------------------------------------------------------------------

    \273\ Joint Technical Support Document: Final Rulemaking for 
2017-2025 Light-Duty Vehicle Greenhouse Gas Emission Standards and 
Corporate Average Fuel Economy Standards (August 2012), at 3-211.
    \274\ 2015 NAS report, at 231.
    \275\ Draft TAR, at 5-207.
    \276\ EPA Proposed Determination TSD, at 2-422.
---------------------------------------------------------------------------

    In the 2020 final rule, the agencies applied LDB sparingly in the 
MY 2017 analysis fleet using the same cost and effectiveness estimates 
from the 2011 Ricardo study, with approximately less than 15% of 
vehicles being assigned the technology. In addition, DOT noted the 
existence of zero drag brakes in production for some BEVs, similar to 
the summary in the Draft TAR, but did not opine on the existence of 
zero drag brakes in the fleet. Some stakeholders commented to the 2020 
final rule that other vehicle technologies, including LDB, were 
actually overapplied in the analysis fleet.
    For this action, DOT considered the conflicting statements that LDB 
were both universally applied in new vehicles and that the new vehicle 
fleet still had space to improve LDB technology. DOT determined that 
LDB technology as previously defined going back to the MY 2011 rule (74 
FR 14196, March 30, 2009) was universally

[[Page 49706]]

applied in the MY 2020 fleet. However, DOT determined that zero drag 
brakes, the next level of brake technology, was sparingly applied in 
the MY 2020 analysis fleet. Currently, DOT does not believe that zero 
drag brake systems will be available for wide scale application in the 
rulemaking timeframe and did not include it as a technology for this 
analysis. DOT will consider how to define a new level of low drag brake 
technology that either encompasses the definition of zero drag brakes 
or similar technology in future rulemakings. We invite comment on the 
issue, and any available data regarding use of such systems on current 
and forthcoming production vehicles, any available data regarding 
system costs and efficacy in reducing drag (i.e., force at different 
speeds) and vehicle fuel economy levels (i.e., through coastdown 
testing).
(d) Secondary Axle Disconnect
    All-wheel drive (AWD) and four-wheel drive (4WD) vehicles provide 
improved traction by delivering torque to the front and rear axles, 
rather than just one axle. When a second axle is rotating, it tends to 
consume more energy because of additional losses related to lubricant 
churning, seal friction, bearing friction, and gear train 
inefficiencies.\277\ Some of these losses may be reduced by providing a 
secondary axle disconnect function that disconnects one of the axles 
when driving conditions do not call for torque to be delivered to both.
---------------------------------------------------------------------------

    \277\ Pilot Systems, ``AWD Component Analysis'', Project Report, 
performed for Transport Canada, Contract T8080-
    150132, May 31, 2016.
---------------------------------------------------------------------------

    The terms AWD and 4WD are often used interchangeably, although they 
have also developed a colloquial distinction, and are two separate 
systems. The term AWD has come to be associated with light-duty 
passenger vehicles providing variable operation of one or both axles on 
ordinary roads. The term 4WD is often associated with larger truck-
based vehicle platforms providing a locked driveline configuration and/
or a low range gearing meant primarily for off-road use.
    Many 4WD vehicles provide for a single-axle (or two-wheel) drive 
mode that may be manually selected by the user. In this mode, a primary 
axle (usually the rear axle) will be powered, while the other axle 
(known as the secondary axle) is not. However, even though the 
secondary axle and associated driveline components are not receiving 
engine power, they are still connected to the non-driven wheels and 
will rotate when the vehicle is in motion. This unnecessary rotation 
consumes energy,\278\ and leads to increased fuel consumption that 
could be avoided if the secondary axle components were completely 
disconnected and not rotating.
---------------------------------------------------------------------------

    \278\ Any time a drivetrain component spins it consumes some 
energy, primarily to overcome frictional forces.
---------------------------------------------------------------------------

    Light-duty AWD systems are often designed to divide variably torque 
between the front and rear axles in normal driving to optimize traction 
and handling in response to driving conditions. However, even when the 
secondary axle is not necessary for enhanced traction or handling, in 
traditional AWD systems it typically remains engaged with the driveline 
and continues to generate losses that could be avoided if the axle was 
instead disconnected. The SAX technology observed in the marketplace 
disengages one axle (typically the rear axle) for two-wheel drive (2WD) 
operation but detects changes in driving conditions and automatically 
engages AWD mode when it is necessary. The operation in 2WD can result 
in reduced fuel consumption. For example, Chrysler has estimated the 
secondary axle disconnect feature in the Jeep Cherokee reduces friction 
and drag attributable to the secondary axle by 80% when in disconnect 
mode.\279\
---------------------------------------------------------------------------

    \279\ Brooke, L. ``Systems Engineering a new 4x4 benchmark'', 
SAE Automotive Engineering, June 2, 2014.
---------------------------------------------------------------------------

    Observing SAX technology on actual vehicles is very difficult. 
Manufacturers do not typically identify the technology on technical 
specifications or other widely available information. The agency 
employed an approach consistent with previous rulemaking in assigning 
this technology. Specifically, the agency assigned SAX technology based 
on a combination of publicly available information and previously 
submitted confidential information. In the analysis fleet, 38% of the 
vehicles that had AWD or 4WD are determined to have SAX technology. All 
vehicles in the analysis fleet with front-wheel drive (FWD) or rear-
wheel drive (RWD) have SAX skipped since SAX technology is a way to 
emulate FWD or RWD in AWD and 4WD vehicles, respectively. The agency 
does not allow for the application of SAX technology to FWD or RWD 
vehicles because they do not have a secondary driven axle to 
disconnect.
    SAX technology can be adopted by any vehicle in the analysis fleet, 
including those with a HEV or BEV powertrain,\280\ which was identified 
as having AWD or 4WD. It does not supersede any technology or result in 
any other technology being excluded for future implementation for that 
vehicle. SAX technology can be applied during any refresh or redesign. 
DOT seeks comment on whether it is appropriate for SAX technology to be 
allowed to be applied to BEVs, or if the technology only provides 
benefits to ICE vehicles.
---------------------------------------------------------------------------

    \280\ The inefficiencies addressed on ICEs by SAX technology may 
not be similar enough, or even present, in HEVs or BEVs.
---------------------------------------------------------------------------

    This analysis carries forward work on the effectiveness of SAX 
systems conducted in the Draft TAR and EPA Proposed Determination.\281\ 
This work involved gathering information by monitoring press reports, 
holding meetings with suppliers and OEMs, and attending industry 
technical conferences. DOT does not simulate SAX effectiveness in the 
Autonomie modeling because, similar to LDB, IACC, and EFR, the fuel 
economy benefits from the technology are not fully captured on the two-
cycle test. The secondary axle disconnect effectiveness values, for the 
most part, have been accepted as plausible based on the rulemaking 
record and absence of contrary comments. As such, the agency has 
prioritized its extensive Autonomie vehicle simulation work toward 
other technologies that are emerging or considered more critical for 
total system effectiveness. The resulting effectiveness estimates we 
use are shown below. The agency welcomes comment on these effectiveness 
values and will consider any material data providing revised, or 
confirmatory, values for those being used in the analysis.
---------------------------------------------------------------------------

    \281\ Draft TAR, at 5-412; Proposed Determination TSD, at 2-422.

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[[Page 49707]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.083

(e) Other Vehicle Technology Costs
    The cost estimates for EPS, IACC, SAX, and LDB \282\ rely on 
previous work published as part of past rulemakings with learning 
applied to those cost values which is founded in the 2002 NAS 
report.\283\ The cost values are the same values that were used for the 
Draft TAR and 2020 final rule, updated to 2018 dollars. Table III-35 
shows examples of costs for these technologies across select model 
years. Note that these costs are the same for all vehicle technology 
classes. For all absolute EPS, IACC, LDB, and SAX technology costs 
across all model years, see the Technologies file.
---------------------------------------------------------------------------

    \282\ Note that because LDB technology is applied universally as 
a baseline technology in the MY 2020 fleet, there is functionally 
zero costs for this technology associated with this proposed 
rulemaking.
    \283\ National Research Council 2002. Effectiveness and Impact 
of Corporate Average Fuel Economy (CAFE) Standards. Washington, DC: 
The National Academies Press. https://doi.org/10.17226/10172.
[GRAPHIC] [TIFF OMITTED] TP03SE21.084

8. Simulating Air Conditioning Efficiency and Off-Cycle Technologies
    Off-cycle and air conditioning (A/C) efficiency technologies can 
provide fuel economy benefits in real-world vehicle operation, but 
those benefits cannot be fully captured by the traditional 2-cycle test 
procedures used to measure fuel economy.\284\ Off-cycle technologies 
include technologies like high efficiency alternators and high 
efficiency exterior lighting.\285\ A/C efficiency technologies are 
technologies that reduce the operation of or the loads on the 
compressor, which pressurizes A/C refrigerant. The less the compressor 
operates or the more efficiently it operates, the less load the 
compressor places on the engine, resulting in better fuel efficiency.
---------------------------------------------------------------------------

    \284\ See 49 U.S.C 32904(c) (``The Administrator shall measure 
fuel economy for each model and calculate average fuel economy for a 
manufacturer under testing and calculation procedures prescribed by 
the Administrator. . . . the Administrator shall use the same 
procedures for passenger automobiles the Administrator used for 
model year 1975 (weighted 55 percent urban cycle and 45 percent 
highway cycle), or procedures that give comparable results.'').
    \285\ 40 CFR 86.1869-12(b)--Credit available for certain off-
cycle technologies.
---------------------------------------------------------------------------

    Vehicle manufacturers have the option to generate credits for off-
cycle technologies and improved A/C systems under the EPA's 
CO2 program and receive a fuel consumption improvement value 
(FCIV) equal to the value of the benefit not captured on the 2-cycle 
test under NHTSA's CAFE program. The FCIV is not a ``credit'' in the 
NHTSA CAFE program,\286\ but the FCIVs increase the reported fuel 
economy of a manufacturer's fleet, which is used to determine 
compliance. EPA applies FCIVs during determination of a fleet's final 
average fuel economy reported to NHTSA.\287\

[[Page 49708]]

FCIVs are only calculated and applied at a fleet level for a 
manufacturer and are based on the volume of the manufacturer's fleet 
that contain qualifying technologies.\288\
---------------------------------------------------------------------------

    \286\ Unlike, for example, the statutory overcompliance credits 
prescribed in 49 U.S.C. 32903.
    \287\ 49 U.S.C. 32904(c)-(e). EPCA granted EPA authority to 
establish fuel economy testing and calculation procedures. See 
Section VII for more information.
    \288\ 40 CFR 600.510-12(c).
---------------------------------------------------------------------------

    There are three pathways that can be used to determine the value of 
A/C efficiency and off-cycle adjustments. First, manufacturers can use 
a predetermined list or ``menu'' of g/mi values that EPA established 
for specific off-cycle technologies.\289\ Second, manufacturers can use 
5-cycle testing to demonstrate off-cycle CO2 benefit; \290\ 
the additional tests allow emissions benefits to be demonstrated over 
some elements of real-world driving not captured by the 2-cycle 
compliance tests, including high speeds, rapid accelerations, hot 
temperatures, and cold temperatures. Third, manufacturers can seek EPA 
approval, through a notice and comment process, to use an alternative 
methodology other than the menu or 5-cycle methodology for determining 
the off-cycle technology improvement values.\291\ For further 
discussion of the A/C and off-cycle compliance and application process, 
see Section VII.
---------------------------------------------------------------------------

    \289\ See 40 CFR 86.1869-12(b). The TSD for the 2012 final rule 
for MYs 2017 and beyond provides technology examples and guidance 
with respect to the potential pathways to achieve the desired 
physical impact of a specific off-cycle technology from the menu and 
provides the foundation for the analysis justifying the credits 
provided by the menu. The expectation is that manufacturers will use 
the information in the TSD to design and implement off-cycle 
technologies that meet or exceed those expectations in order to 
achieve the real-world benefits of off-cycle technologies from the 
menu.
    \290\ See 40 CFR 86.1869-12(c). EPA proposed a correction for 
the 5-cycle pathway in a separate technical amendments rulemaking. 
See 83 FR 49344 (Oct. 1, 2019). EPA is not approving credits based 
on the 5-cycle pathway pending the finalization of the technical 
amendments rule.
    \291\ See 40 CFR 86.1869-12(d).
---------------------------------------------------------------------------

    DOT and EPA have been collecting data on the application of these 
technologies since implementing the A/C and off-cycle 
programs.292 293 Most manufacturers are applying A/C 
efficiency and off-cycle technologies; in MY 2019, 17 manufacturers 
employed A/C efficiency technologies and 20 manufacturers employed off-
cycle technologies, though the level of deployment varies by 
manufacturer.\294\
---------------------------------------------------------------------------

    \292\ See 77 FR at 62832, 62839 (Oct. 15, 2012). EPA introduced 
A/C and off-cycle technology credits for the CO2 program 
in the MY 2012-2016 rule and revised the program in the MY 2017-2025 
rule and NHTSA adopted equivalent provisions for MYs 2017 and later 
in the MY 2017-2025 rule.
    \293\ Vehicle and Engine Certification. Compliance Information 
for Light-Duty Gas (GHG) Standards. Compliance Information for 
Light-Duty Greenhouse Gas (GHG) Standards [verbar] Certification and 
Compliance for Vehicles and Engines [verbar] U.S. EPA. Last Accessed 
May 24, 2021.
    \294\ See 2020 EPA Automotive Trends Report, at 91.
---------------------------------------------------------------------------

    Manufacturers have only recently begun including detailed 
information on off-cycle and A/C efficiency technologies equipped on 
vehicles in compliance reporting data. For this analysis, though, such 
information was not sufficiently complete to support a detailed 
representation of the application of off-cycle technology to specific 
vehicle model/configurations in the MY 2020 fleet. To account for the 
A/C and off-cycle technologies equipped on vehicles and the potential 
that manufacturers will apply additional A/C and off-cycle technologies 
in the rulemaking timeframe, DOT specified model inputs for A/C 
efficiency and off-cycle fuel consumption improvement values in grams/
mile for each manufacturer's fleet in each model year. DOT estimated 
future values based on an expectation that manufacturers already 
relying heavily on these adjustments would continue do so, and that 
other manufacturers would, over time, also approach the limits on 
adjustments allowed for such improvements.
    The next sections discuss how the CAFE Model simulates the 
effectiveness and cost for A/C efficiency and off-cycle technology 
adjustments.
(a) A/C and Off-Cycle Effectiveness Modeling in the CAFE Model
    In this analysis, the CAFE Model applies A/C and off-cycle 
flexibilities to manufacturer's CAFE regulatory fleet performance in a 
similar way to the regulation.\295\ In the analysis and after the first 
MY, A/C efficiency and off-cycle FCIVs apply to each manufacturer's 
regulatory fleet after the CAFE Model applies conventional technologies 
for a given standard. That is, conventional technologies are applied to 
each manufacturers' vehicles in each MY to assess the 2-cycle sales 
weighted harmonic average CAFE rating. Then, the CAFE Model assesses 
the CAFE rating to use for a manufacturer's compliance value after 
applying the A/C efficiency and off-cycle FCIVs designated in the 
Market Data file. This assessment of adoption of conventional 
technology and the A/C efficiency and off-cycle technology occurs on a 
year-by-year basis in the CAFE Model. The CAFE Model attempts to apply 
technologies and flexibilities in a way that both minimizes cost and 
allows the manufacturer to meet their standards without over or under 
complying.
---------------------------------------------------------------------------

    \295\ 49 CFR 531.6 and 49 CFR 533.6 Measurement and Calculation 
procedures.
---------------------------------------------------------------------------

    To determine how manufacturers might adopt A/C efficiency and off-
cycle technologies in the rulemaking timeframe, DOT began with data 
from EPA's 2020 Trends Report and CBI compliance material from 
manufacturers.296 297 DOT used manufacturer's MY 2020 A/C 
efficiency and off-cycle FCIVs as a starting point, and then 
extrapolated values in each MY until MY 2026, for light trucks to the 
proposed regulatory cap, for each manufacturer's fleets by regulatory 
class.
---------------------------------------------------------------------------

    \296\ Vehicle and Engine Certification. Compliance Information 
for Light-Duty Gas (GHG) Standards. Compliance Information for 
Light-Duty Greenhouse Gas (GHG) Standards [verbar] Certification and 
Compliance for Vehicles and Engines [verbar] U.S. EPA. Last Accessed 
May 24, 2021.
    \297\ 49 U.S.C. 32907.
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    To determine the rate at which to extrapolate the addition of A/C 
and off-cycle technology adoption for each manufacturer, DOT reviewed 
historical A/C and off-cycle technology applications, each 
manufacturer's fleet composition (i.e., breakdown between passenger 
cars (PCs) and light trucks (LTs)), availability of A/C and off-cycle 
technologies that manufacturers could still use, and CBI compliance 
data. Different manufacturers showed different levels of historical A/C 
efficiency and off-cycle technology adoption; therefore, different 
manufacturers hit the proposed regulatory caps for A/C efficiency 
technology for both their PC and LT fleets, and different manufacturers 
hit caps for off-cycle technologies in the LT regulatory class. DOT 
declined to extrapolate off-cycle technology adoption for PCs to the 
proposed regulatory cap for a few reasons. First, past EPA Trends 
Reports showed that many manufacturers did not adopt off-cycle 
technology to their passenger car fleets. Next, manufacturers limited 
PC offerings in MY 2020 as compared to historical trends. Last, CBI 
compliance data available to DOT indicated a lower adoption of menu 
item off-cycle technologies to PCs compared to LTs. DOT accordingly 
limited the application of off-cycle FCIVs to 10 g/mi for PCs but 
allowed LTs to apply 15 g/mi of off-cycle FCIVs. The inputs for A/C 
efficiency technologies were set to 5 g/mi and 7.2 g/mi for PCs and 
LTs, respectively. DOT allowed A/C efficiency technologies to reach the 
regulatory caps by MY 2024, which is the first year of standards 
assessed in this analysis.
    DOT decided to apply the FCIVs in this way because the A/C and off-
cycle

[[Page 49709]]

technologies are generally more cost-effective than other technologies. 
The details of this assessment (and the calculation) are further 
discussed in the CAFE Model Documentation.\298\ The A/C efficiency and 
off-cycle adjustment schedules used in this analysis are shown in TSD 
Chapter 3.8 and in the Market Data file's Credits and Adjustments 
worksheet.
---------------------------------------------------------------------------

    \298\ CAFE Model Documentation, S5.
---------------------------------------------------------------------------

(b) A/C and Off-Cycle Costs
    For this analysis, A/C and off-cycle technologies are applied 
independently of the decision trees using the extrapolated values shown 
above, so it is necessary to account for the costs of those 
technologies independently. Table III-36 shows the costs used for A/C 
and off-cycle FCIVs in this analysis. The costs are shown in dollars 
per gram of CO2 per mile ($ per g/mile). The A/C efficiency 
and off-cycle technology costs are the same costs used in the EPA 
Proposed Determination and described in the EPA Proposed Determination 
TSD.\299\
---------------------------------------------------------------------------

    \299\ EPA PD TSD. EPA-420-R-16-021. November 2016. At 2-423-2-
245. https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100Q3L4.pdf. Last 
accessed May 24, 2021.
---------------------------------------------------------------------------

    To develop the off-cycle technology costs, DOT selected the 2nd 
generic 3 gram/mile package estimated to cost $170 (in 2015$) to apply 
in this analysis in $ per gram/mile. DOT updated the costs used in the 
Proposed Determination TSD from 2015$ to 2018$, adjusted the costs for 
RPE, and applied a relatively flat learning rate. We seek comment on 
whether these costs are still appropriate, or whether a different $ per 
gram/mile cost should be used. If commenters believe a different $ per 
gram/mile cost should be used, we request commenters provide any data 
or information on which any alternative costs are based. This should 
include a description of how the alternative costs are representative 
of costs across the industry, and whether the $ per gram/mile estimate 
is based on a package of specific off-cycle technologies.
    Similar to off-cycle technology costs, DOT used the cost estimates 
from EPA Proposed Determination TSD for A/C efficiency technologies 
that relied on the 2012 rulemaking TSD.\300\ DOT updated these costs to 
2018$ and adjusted for RPE for this analysis, and applied the same 
mature learning rate that DOT applied for off-cycle technologies.
---------------------------------------------------------------------------

    \300\ Joint NHTSA and EPA 2012 TSD, see Section 5.1.
    [GRAPHIC] [TIFF OMITTED] TP03SE21.085
    
E. Consumer Responses to Manufacturer Compliance Strategies

    The previous subsections in Section III have so far discussed how 
manufacturers might respond to changes to the standards. While the 
technology analysis is informative of the different compliance 
strategies available to manufactures, the tangible costs and benefits 
that accrue because of CAFE standards are dependent on how consumers 
respond to the decisions made by manufacturers. Many, if not most, of 
the benefits and costs resulting from changes to CAFE standards are 
private benefits that accrue to the buyers of new cars and trucks, 
produced in the model years under consideration. These benefits and 
costs largely flow from the changes to vehicle ownership and operating 
costs that result from improved fuel economy, and the cost of the 
technology required to achieve those improvements. The remaining 
external benefits are also derived from how consumers use--or do not 
use--vehicles. The next few subsections walk through how the analysis 
models consumer responses to changing vehicles and prices. NHTSA 
requests comment on the following discussion.
1. Macroeconomic and Consumer Behavior Assumptions
    This proposal includes a comprehensive economic analysis of the 
impacts of altering the CAFE standards. Most of the effects measured 
are influenced by macroeconomic conditions that are exogenous to the 
agency's influence. For example, fuel prices are mainly determined by 
global demand, and yet they determine how much fuel efficiency 
technology manufacturers will apply to U.S.-bound vehicles, how much 
consumers are willing to pay for a new vehicle, the amount of travel in 
which all users engage, and the value of each gallon saved from higher 
CAFE standards. Constructing these forecasts requires robust 
projections of macroeconomic variables that span the timeframe of the 
analysis, including real U.S. Gross Domestic Product (GDP), consumer 
confidence, U.S. population, and real disposable personal income.
    In order to ensure internal consistency within the analysis, 
relevant economic assumptions are derived from the same source. The 
analysis presented in this analysis employs forecasts developed by DOT 
using the U.S. Energy Information Administration's (EIA's) National 
Energy Model System (NEMS). EIA is an agency within the U.S. Department 
of Energy (DOE) which collects, analyzes, and disseminates independent 
and impartial energy information to promote sound policymaking, 
efficient markets, and public understanding of energy and its 
interaction with the economy and the environment. EIA uses NEMS to 
produce its Annual Energy Outlook (AEO), which presents forecasts of 
future fuel prices, among many other energy-related variables. The 
analysis employs forecasts of fuel prices, real U.S. GDP, real 
disposable personal income, U.S. population, and fuel prices from the 
AEO 2021 Reference Case. The agency also uses a forecast of consumer 
confidence to project sales from the IHS Markit Global Insight long-
term macroeconomic model. The IHS Markit Global Insight model is also 
used by EIA for the AOE.
    While these macroeconomic assumptions are some of the most critical 
inputs to the analysis, they are also subject to the most uncertainty--
particularly over the full lifetimes of the vehicles affected by this 
proposed rule. The agency uses low and high cases from the AEO as 
bounding cases for sensitivity analyses. The purpose of the sensitivity 
analyses, discussed in greater

[[Page 49710]]

detail in PRIA Chapter 6 and PRIA Chapter 7, is not to posit a more 
credible future state of the world than the central case assumes--we 
assume the central case is the most likely future state of the world--
but rather to measure the degree to which important outcomes can change 
under different assumptions about fuel prices.
    The first year simulated in this analysis is 2020, though it is 
based on observational data (rather than forecasts) to the greatest 
extent possible. The elements of the analysis that rely most heavily on 
the macroeconomic inputs--aggregate demand for VMT, new vehicle sales, 
used vehicle retirement rates--all reflect the relatively rapid climb 
back to pre-pandemic growth rates (in all the regulatory alternatives).
    See TSD Chapter 4.1 for a more complete discussion of the 
macroeconomic assumptions made for the analysis.
    Another key assumption that permeates throughout the analysis is 
how much consumers are willing to pay for fuel economy. Increased fuel 
efficiency offers vehicle owners significant savings; in fact, the 
analysis shows that fuel savings exceed the technology cost to comply 
with even the most stringent standards analyzed by this proposal at a 
3% discount rate. It would be reasonable to assume that consumers value 
the full value of fuel savings as they would be better off not having 
to spend more of their disposable income on fuel. If consumers did 
value the full amount of fuel savings, fuel-efficient vehicles would 
functionally be cheaper for consumers to own when considering both 
purchasing and operational costs, and thus making the vehicles offered 
under the stricter alternatives more attractive than similar models 
offered in the baseline. Recent econometric research remains divided 
between studies that conclude has shown that consumers may value most, 
if not all of potential fuel savings, and those that conclude that 
consumers significantly undervalue expected fuel savings (NASEM, 2021, 
p. 11-351).301 302 303
---------------------------------------------------------------------------

    \301\ There is a great deal of work attempting to test the 
question whether consumers are adequately informed about, and 
sufficiently attentive to, potential fuel savings at the time of 
purchase. The existing research is not conclusive and leaves many 
open questions. On the one hand, there is significant support for 
the proposition that consumers are responsive to changes in fuel 
costs. See, e.g., Busse et al.; Sallee, et al. On the other hand, 
there is also support for the proposition that many consumers do 
not, in fact, give full or sufficient attention to potential savings 
from fuel-efficient vehicles, and thus make suboptimal decisions. 
See Duncan et al.; Gillingham et al.
    \302\ Allcott, H. and C. Knittel, 2019. ``Are Consumers Poorly 
Informed about Fuel Economy? Evidence from Two Experiments'', AEJ: 
Economic Policy, 11(1): 1-37.
    \303\ D. Duncan, A. Ku, A. Julian, S. Carley, S. Siddiki, N. 
Zirogiannis and J. Graham, 2019. ``Most Consumers Don't Buy Hybrids: 
Is Rational Choice a Sufficient Explanation?'', J. of Benefit-Cost 
Analysis, 10(1): 1-38.
---------------------------------------------------------------------------

    If buyers fully value the savings in fuel costs that result from 
higher fuel economy, manufacturers would be expected to supply the 
improvements that buyers demand, and vehicle demand would be expected 
to fully consider both future fuel cost savings consumers would realize 
from owning--and potentially re-selling--more fuel-efficient models and 
increased cost of vehicles due to technological and design changes made 
to increase fuel economy. If instead, consumers systematically 
undervalue future fuel savings, the result would be an underinvestment 
in fuel-saving technology. In that case, more stringent fuel economy 
standards would also lead manufacturers to adopt improvements in fuel 
economy that improve consumer welfare (e.g., Allcott et al., 2014; 
Heutel, 2015).
    There is substantial evidence that consumers do not fully value 
lifetime fuel savings. Even though the average fuel economy of new 
vehicles reached an all-time high in MY 2020 of 25.7 MPG,\304\ this is 
still significantly below the fuel economy of the fleet's most 
efficient vehicles that are readily available to consumers.\305\ 
Manufacturers have repeatedly informed the agency that consumers only 
value between 2 to 3 years-worth of fuel savings when making purchasing 
decisions. The potential for car buyers voluntarily to forego 
improvements in fuel economy that offer savings exceeding their initial 
costs is one example of what is often termed the ``energy-efficiency 
gap.'' This appearance of such a gap, between the level of energy 
efficiency that would minimize consumers' overall expenses and what 
they actually purchase, is typically based on engineering calculations 
that compare the initial cost for providing higher energy efficiency to 
the discounted present value of the resulting savings in future energy 
costs. There has long been an active debate about why such a gap might 
arise and whether it actually exists. Economic theory predicts that 
economically rational individuals will purchase more energy-efficient 
products only if the savings in future energy costs they offer promise 
to offset their higher initial costs. On the other hand, behavioral 
economics has documented numerous situations in which the decision-
making of consumers differs in important ways from the predictions of 
economic consumer model (e.g., Dellavigna, 2009).
---------------------------------------------------------------------------

    \304\ See EPA 2020 Automotive Trends Report at 6, available at 
https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P1010U68.pdf.
    \305\ Id. At 9.
---------------------------------------------------------------------------

    A behavioral explanation of such `undervaluation' of the savings 
from purchasing higher-mpg models is myopia or present bias; consumers 
may give undue focus to short-term costs and insufficient attention to 
long-term benefits.\306\ This situation could arise because they are 
unsure of the fuel savings that will be achieved in real-world driving, 
what future fuel prices will be, how long they will own a new vehicle, 
whether they will drive it enough to realize the promised savings. As a 
consequence, they may view choosing to purchase or not purchase a fuel-
efficient technology as a risky bet; behavioral economics has 
demonstrated that faced with the decision to accept or reject a risky 
choice, some consumers weigh potential losses approximately twice as 
heavily as potential gains, significantly undervaluing the choice 
relative to its expected value (e.g., Kahneman and Tversky, 1979; 
Kahneman, 2011). In the context of a choice to pay more for a fuel-
saving technology, loss aversion has been shown to have the potential 
to cause undervaluation of future fuel savings similar to that reported 
by manufacturers (Greene, 2011; Greene et al., 2013).\307\ The 
behavioral model holds that consumers' decisions are affected by the 
context, or framing, of choices. As explained in NASEM (2021), Ch. 
11.3.3, it is possible that consumers respond to changes in fuel 
economy regulations differently than they respond to manufacturers 
voluntarily offering the option to purchase fuel economy technology to 
new car buyers. We explain this differential more thoroughly in TSD 
Chapter 4.2.1.1, but here is the contextual explanation for the 
differential valuation. If a consumer is thinking about buying a new 
car and is looking at two models, one that includes voluntarily added 
fuel economy technology and is more expensive and another that does 
not, she may buy the cheaper, less fuel efficient version even if the 
more expensive model will save

[[Page 49711]]

money in the long run. But if, instead, the consumer is faced with 
whether to buy a new car at all as opposed to keeping an older one, if 
all new cars contain technology to meet fuel economy standards, then 
she may view the decision differently. Will, for example, an extra 
$1,000 for a new car--a $1,000 that the consumer will more than recoup 
in fuel savings--deter her from buying the new car, especially when 
most consumers finance cars over a number of years rather than paying 
the $1,000 cost up front (therefore any increase in monthly payment 
would be partly or entirely offset with lower fuel costs)? In additon, 
the fact that standards generally increase gradually over a period of 
years allows time for consumers and other information sources to verify 
that fuel savings are real and of substantial value.
---------------------------------------------------------------------------

    \306\ Gillingham et al., 2021, which is an AEJ: Economic Policy 
paper, just published on consumer myopia in vehicle purchases; a 
standard reference on present bias generally is O'Donoghue and 
Rabin, AER: Papers and Proceedings, 2015.
    \307\ Application of investment under uncertainty will yield 
similar results as costs may be more certain and up front while the 
fuel savings or benefits of the investment may be perceived as more 
uncertain and farther into future, thereby reducing investments in 
fuel saving technologies.
---------------------------------------------------------------------------

    Another alternative is that consumers view the increase in 
immediate costs associated with fuel economy technology in the context 
of tradeoffs they must make amongst their purchasing decisions. 
American households must choose how to spend their income amongst many 
competing goods and services, including how much to spend on a new 
vehicle. They may also decide to opt for another form of 
transportation. While a consumer may recognize and value the potential 
long-term value of fuel savings, they may also prefer to spend their 
money on other items, either in the form of other vehicle attributes--
such as picking a truck with a larger flatbed or upgrading to a more 
luxurious trim package--or other unrelated goods and services. The same 
technologies that can be used to increase fuel economy can also be used 
to enable increased vehicle power or weight while maintaining fuel 
economy. While increased fuel efficiency will free up disposable income 
throughout the lifetime of the vehicle (and may even exceed the 
additional upfront costs to purchase a more expensive fuel-efficient 
vehicle), the value of owning a different good sooner may provide 
consumers even more benefit.
    As explained more thoroughly in TSD Chapter 4.2.1.1, the analysis 
assumes that potential car and light truck buyers value only the 
undiscounted savings in fuel costs from purchasing a higher-mpg model 
they expect to realize over the first 30 months they own it. Depending 
on the discount rate buyers are assumed to apply, this amounts to 25-
30% of the expected savings in fuel costs over its entire lifetime. 
These savings would offset only a fraction of the expected increase in 
new car and light truck prices that the agency estimates will be 
required for manufacturers to recover their increased costs for making 
required improvements to fuel economy. The agency seeks comment on 
whether 30 months of undiscounted fuel savings is an appropriate 
measure for the analysis of consumer willingness to pay for fuel 
economy. The assumption also has important implications for other 
outcomes of the model, including for VMT, safety, and air pollution 
emissions projections. If NHTSA is incorrect about the undervaluation 
of fuel economy in the context of regulatory standards and its effect 
on car sales, correcting the assumption should result in improved 
safety outcomes and additional declines in conventional air pollutants. 
If commenters believe a different amount of time should be used for the 
payback assumption, it would be most helpful to NHTSA if commenters 
could define the amount of time, provide an explanation of why that 
amount of time is preferable, provide any data or information on which 
the amount of time is based, and provide any discussion of how changing 
this assumption would interact with other elements in the analysis.
2. Fleet Composition
    The composition of the on-road fleet--and how it changes in 
response to CAFE standards--determines many of the costs and benefits 
of the proposal. For example, how much fuel the light-duty consumes is 
dependent on the number of new vehicles sold, older (and less 
efficient) vehicles retired, and how much those vehicles are driven.
    Prior to the 2020 CAFE standards, all previous CAFE rulemaking 
analyses used static fleet forecasts that were based on a combination 
of manufacturer compliance data, public data sources, and proprietary 
forecasts (or product plans submitted by manufacturers). When 
simulating compliance with regulatory alternatives, those analyses 
projected identical sales and retirements across the alternatives, for 
each manufacturer down to the make/model level--where the exact same 
number of each model variant was assumed to be sold in a given model 
year under both the least stringent alternative (typically the 
baseline) and the most stringent alternative considered (intended to 
represent ``maximum technology'' scenarios in some cases). To the 
extent that an alternative matched the assumptions made in the 
production of the proprietary forecast, using a static fleet based upon 
those assumptions may have been warranted.
    However, a fleet forecast is unlikely to be representative of a 
broad set of regulatory alternatives with significant variation in the 
cost of new vehicles. A number of commenters on previous regulatory 
actions and peer reviewers of the CAFE Model encouraged consideration 
of the potential impact of fuel efficiency standards on new vehicle 
prices and sales, the changes to compliance strategies that those 
shifts could necessitate, and the downstream impact on vehicle 
retirement rates. In particular, the continued growth of the utility 
vehicle segment causes changes within some manufacturers' fleets as 
sales volumes shift from one region of the footprint curve to another, 
or as mass is added to increase the ride height of a vehicle on a sedan 
platform to create a crossover utility vehicle, which exists on the 
same place of the footprint curve as the sedan upon which it might be 
based.
    The analysis now dynamically simulates changes in the vehicle 
fleet's size, composition, and usage as manufacturers and consumers 
respond to regulatory alternatives, fuel prices, and macroeconomic 
conditions. The analysis of fleet composition is comprised of two 
forces, how new vehicle sales--the flow of new vehicles into the 
registered population--changes in response to regulatory alternatives, 
and the influence of economic and regulatory factors on vehicle 
retirement (otherwise known as scrappage). Below are brief descriptions 
that of how the agency models sales and scrappage. For a full 
explanation, refer to TSD Chapter 4.2. Particularly given the broad 
uncertainty discussed in TSD Chapter 4.2, NHTSA seeks comment on the 
discussion below and the associated discussions in the TSD, on the 
internal structure of the sales and scrappage modules, and whether and 
how to change the sales and scrappage analyses for the final rule.
(a) Sales
    For the purposes of regulatory evaluation, the relevant sales 
metric is the difference between alternatives rather than the absolute 
number of sales in any of the alternatives. As such, the sales response 
model currently contains three parts: A nominal forecast that provides 
the level of sales in the baseline (based upon macroeconomic inputs, 
exclusively), a price elasticity that creates sales differences 
relative to that baseline in each year, and a fleet share model that 
produces differences in the passenger car and light truck market share 
in each alternative. The nominal forecast does not include price and is 
merely a (continuous) function of several macroeconomic variables that 
are provided to the model as inputs. The price elasticity is also 
specified as an

[[Page 49712]]

input, but this analysis assumes a unit elastic response of -1.0--
meaning that a one percent increase in the average price of a new 
vehicle produces a one percent decrease in total sales. NHTSA seeks 
comment on this assumption. The price change on which the elasticity 
acts is calculated net of some portion of the future fuel savings that 
accrue to new vehicle buyers (2.5 years' worth, in this analysis, as 
discussed in the previous section).
    The current baseline sales module reflects the idea that total new 
vehicle sales are primarily driven by conditions in the economy that 
are exogenous to the automobile industry. Over time, new vehicle sales 
have been cyclical--rising when prevailing economic conditions are 
positive (periods of growth) and falling during periods of economic 
contraction. While the kinds of changes to vehicle offerings that occur 
as a result of manufacturers' compliance actions exert some influence 
on the total volume of new vehicle sales, they are not determinative. 
Instead, they drive the kinds of marginal differences between 
regulatory alternatives that the current sales module is designed to 
simulate--more expensive vehicles, generally, reduce total sales but 
only marginally.
    The first component of the sales response model is the nominal 
forecast, which is a function (with a small set of inputs) that 
determines the size of the new vehicle market in each calendar year in 
the analysis for the baseline. It is of some relevance that this 
statistical model is intended only as a means to project a baseline 
sales series. Past reviewers expressed concerns about the possibility 
of econometrically estimating an industry average price elasticity in a 
way that isolates the causal effect of new vehicle prices on new 
vehicle sales (and properly addresses the issue of endogeneity between 
sales and price). The nominal forecast model does not include prices 
and is not intended for statistical inference around the question of 
price response in the new vehicle market. The economic response to the 
pandemic has created uncertainty, particularly in the near-term, around 
pace at which the market for automobiles will recover--and the scale 
and timing of the recovery's peak--before returning to its long-term 
trend. DOT will continue to monitor macroeconomic data and new vehicle 
sales and update its baseline forecast as appropriate.
    The second component of the sales response model captures how price 
changes affect the number of vehicles sold. The price elasticity is 
applied to the percentage change in average price (in each year). The 
price change does not represent an increase/decrease over the last 
observed year, but rather the percentage change relative to the 
baseline for that year. In the baseline, the average price is defined 
as the observed new vehicle price in 2019 (the last historical year 
before the simulation begins) plus the average regulatory cost 
associated with the baseline alternative.\308\ The central analysis in 
this proposal simulates multiple programs simultaneously (CAFE final 
standards, EPA final greenhouse gas standards, ZEV, and the California 
Framework Agreement), and the regulatory cost includes both technology 
costs and civil penalties paid for non-compliance (with CAFE standards) 
in a model year. Because the elasticity assumes no perceived change in 
the quality of the product, and the vehicles produced under different 
regulatory scenarios have inherently different operating costs, the 
price metric must account for this difference. The price to which the 
unit elasticity is applied in this analysis represents the residual 
price change between scenarios after accounting for 2.5 years' worth of 
fuel savings to the new vehicle buyer.
---------------------------------------------------------------------------

    \308\ The CAFE Model currently operates as if all costs incurred 
by the manufacturer as a consequence of meeting regulatory 
requirements, whether those are the cost of additional technology 
applied to vehicles in order to improve fleetwide fuel economy or 
civil penalties paid when fleets fail to achieve their standard, are 
``passed through'' to buyers of new vehicles in the form of price 
increases.
---------------------------------------------------------------------------

    The third and final component of the sales model is the dynamic 
fleet share module (DFS). Some commenters to previous rules noted that 
the market share of SUVs continues to grow, while conventional 
passenger car body-styles continue to lose market share. For instance, 
in the 2012 final rule, the agencies projected fleet shares based on 
the continuation of the baseline standards (MYs 2012-2016) and a fuel 
price forecast that was much higher than the realized prices since that 
time. As a result, that analysis assumed passenger car body-styles 
comprising about 70 percent of the new vehicle market by 2025, which 
was internally consistent. The reality, however, has been quite 
different. The CAFE Model includes the DFS model in an attempt to 
address these market realities.
    The DFS distributes the total industry sales across two different 
body-types: ``cars'' and ``light trucks.'' While there are specific 
definitions of ``passenger cars'' and ``light trucks'' that determine a 
vehicle's regulatory class, the distinction used in this phase of the 
analysis is more simplistic. All body-styles that are obviously cars--
sedans, coupes, convertibles, hatchbacks, and station wagons--are 
defined as ``cars'' for the purpose of determining fleet share. 
Everything else--SUVs, smaller SUVs (crossovers), vans, and pickup 
trucks--are defined as ``light trucks''--even though they may not be 
treated as such for compliance purposes. The DFS uses two functions 
from the National Energy Modeling System (NEMS) used in the 2017 AEO to 
independently estimate the share of passenger cars and light trucks, 
respectively, given average new market attributes (fuel economy, 
horsepower, and curb weight) for each group and current fuel prices, as 
well as the prior year's market share and prior year's attributes. The 
two independently estimated shares are then normalized to ensure that 
they sum to one.
    These shares are applied to the total industry sales derived in the 
first stage of the sales response. This produces total industry volumes 
of car and light truck body styles. Individual model sales are then 
determined from there based on the following sequence: (1) Individual 
manufacturer shares of each body style (either car or light truck) 
times the total industry sales of that body style, then (2) each 
vehicle within a manufacturer's volume of that body-style is given the 
same percentage of sales as appear in the 2020 fleet. This implicitly 
assumes that consumer preferences for particular styles of vehicles are 
determined in the aggregate (at the industry level), but that 
manufacturers' sales shares of those body styles are consistent with MY 
2020 sales. Within a given body style, a manufacturer's sales shares of 
individual models are also assumed to be constant over time. This 
approach implicitly assumes that manufacturers are currently pricing 
individual vehicle models within market segments in a way that 
maximizes their profit. Without more information about each OEM's true 
cost of production and operation, fixed and variables costs, and both 
desired and achievable profit margins on individual vehicle models, 
there is no basis to assume that strategic shifts within a 
manufacturer's portfolio will occur in response to standards.
    The DFS model show passenger car styles gaining share with higher 
fuel prices and losing them when prices are decline. Similarly, as fuel 
economy increases in light truck models, which offer consumers other 
desirable attributes beyond fuel economy (ride height or interior 
volume, for example) their relative share increases. However, this 
approach does not suggest that consumers dislike fuel economy in 
passenger cars, but merely recognizes

[[Page 49713]]

the fact that fuel economy has diminishing returns in terms of fuel 
savings. As the fuel economy of light trucks increases, the tradeoff 
between passenger car and light truck purchases increasingly involves a 
consideration of other attributes. The coefficients also show a 
relatively stronger preference for power improvements in cars than 
light trucks because that is an attribute where trucks have typically 
outperformed cars, just as cars have outperformed trucks for fuel 
economy.
    For years, some commenters encouraged the agency to consider 
vehicle attributes beyond price and fuel economy when estimating a 
sales response to fuel economy standards, and suggested that a more 
detailed representation of the new vehicle market would allow the 
agency to simulate strategic mix shifting responses from manufacturers 
and diverse attribute preferences among consumers. Doing so would have 
required a discrete choice model (at some level). Discrete models are 
highly sensitive on their inputs and typically fit well on a single 
year of data (a cross-section of vehicles and buyers). This approach 
misses relevant trends that build over time, such as rising GDP or 
shifting consumer sentiment toward emerging technologies and are better 
used for analysis as opposed to prediction. While the agency believes 
that these challenges provide a reasonable basis for not employing a 
discrete choice model in the current CAFE Model, the agency also 
believes these challenges are not insurmountable, and that some 
suitable variant of such models may yet be developed for use in future 
fuel economy rulemakings. The agency has not abandoned the idea and 
plans to continue experimenting with econometric specifications that 
address heterogeneous consumer preferences in the new vehicle market as 
they further refine the analytical tools used for regulatory analysis. 
The agency seeks suggestions on how to incorporate other vehicle 
attributes into the current analysis, or, alternatively, methods to 
implement a discrete choice model that can capture changing 
technologies and consumer trends over an extended time-period.
(b) Scrappage
    New and used vehicles are substitutes. When the price of a good's 
substitute increases/decreases, the demand curve for that good shifts 
upwards/downwards and the equilibrium price and quantity supplied also 
increases/decreases. Thus, increasing the quality-adjusted price of new 
vehicles will result in an increase in equilibrium price and quantity 
of used vehicles. Since, by definition, used vehicles are not being 
``produced'' but rather ``supplied'' from the existing fleet, the 
increase in quantity must come via a reduction in their scrappage 
rates. Practically, when new vehicles become more expensive, demand for 
used vehicles increases (and they become more expensive). Because used 
vehicles are more valuable in such circumstances, they are scrapped at 
a lower rate, and just as rising new vehicle prices push marginal 
prospective buyers into the used vehicle market, rising used vehicle 
prices force marginal prospective buyers of used vehicles to acquire 
older vehicles or vehicles with fewer desired attributes. The effect of 
fuel economy standards on scrappage is partially dependent on how 
consumers value future fuel savings and our assumption that consumers 
value only the first 30 months of fuel savings.
    Many competing factors influence the decision to scrap a vehicle, 
including the cost to maintain and operate it, the household's demand 
for VMT, the cost of alternative means of transportation, and the value 
that can be attained through reselling or scrapping the vehicle for 
parts. A car owner will decide to scrap a vehicle when the value of the 
vehicle is less than the value of the vehicle as scrap metal, plus the 
cost to maintain or repair the vehicle. In other words, the owner gets 
more value from scrapping the vehicle than continuing to drive it, or 
from selling it. Typically, the owner that scraps the vehicle is not 
the first owner.
    While scrappage decisions are made at the household level, the 
agency is unaware of sufficient household data to sufficiently capture 
scrappage at that level. Instead, the agency uses aggregate data 
measures that capture broader market trends. Additionally, the 
aggregate results are consistent with the rest of the CAFE Model as the 
model does not attempt to model how manufacturers will price new 
vehicles; the model instead assumes that all regulatory costs to make a 
particular vehicle compliant are passed onto the purchaser who buys the 
vehicle. It is more likely that manufacturers will defray a portion of 
the increased regulatory cost across its vehicles or to other 
manufacturers' buyers through the sale of credits.
    The most predictive element of vehicle scrappage is `engineering 
scrappage.' This source of scrappage is largely determined by the age 
of a vehicle and the durability of a specific model year vintage, which 
the agency uses proprietary vehicle registration data from IHS/Polk to 
collect vehicle age and durability. Other factors include fuel economy 
and new vehicle prices. For historical data on new vehicle transaction 
prices, the agency uses National Automobile Dealers Association (NADA) 
Data.\309\ The data consists of the average transaction price of all 
light-duty vehicles; since the transaction prices are not broken-down 
by body style, the model may miss unique trends within a particular 
vehicle body style. The transaction prices are the amount consumers 
paid for new vehicles and exclude any trade-in value credited towards 
the purchase. This may be particularly relevant for pickup trucks, 
which have experienced considerable changes in average price as luxury 
and high-end options entered the market over the past decade. Future 
models will further consider incorporating price series that consider 
the price trends for cars, SUVs and vans, and pickups separately. The 
other source of vehicle scrappage is from cyclical effects, which the 
model captures using forecasts of GDP and fuel prices.
---------------------------------------------------------------------------

    \309\ The data can be obtained from NADA. For reference, the 
data for MY 2020 may be found at https://www.nada.org/nadadata/.
---------------------------------------------------------------------------

    Vehicle scrappage follows a roughly logistic function with age--
that is, when a vintage is young, few vehicles in the cohort are 
scrapped, as they age, more and more of the cohort are retired and the 
instantaneous scrappage (the rate at which vehicles are scrapped) 
reaches a peak, and then scrappage declines as vehicles enter their 
later years as fewer and fewer of the cohort remains on the road. The 
analysis uses a logistic function to capture this trend of vehicle 
scrappage with age. The data shows that the durability of successive 
model years generally increases over time, or put another way, 
historically newer vehicles last longer than older vintages. However, 
this trend is not constant across all vehicle ages--the instantaneous 
scrappage rate of vehicles is generally lower for later vintages up to 
a certain age, but increases thereafter so that the final share of 
vehicles remaining converges to a similar share remaining for 
historically observed vintages.\310\ The agency uses fixed effects to 
capture potential changes in durability across model years and to 
ensure that vehicles approaching the end of their life are scrapped in 
the analysis, the agency applies a decay function to vehicles after 
they reach age 30. The macroeconomic conditions variables discussed 
above are included

[[Page 49714]]

in the logistic model to capture cyclical effects. Finally, the change 
in new vehicle prices projected in the model (technology costs minus 30 
months of fuel savings) are included which generates differing 
scrappage rates across the alternatives.
---------------------------------------------------------------------------

    \310\ Examples of why durability may have changed are new 
automakers entering the market or general changes to manufacturing 
practices like switching some models from a car chassis to a truck 
chassis.
---------------------------------------------------------------------------

    In addition to the variables included in the scrappage model, the 
agency considered several other variables that likely either directly 
or indirectly influence scrappage in the real world including, 
maintenance and repair costs, the value of scrapped metal, vehicle 
characteristics, the quantity of new vehicles purchased, higher 
interest rates, and unemployment. These variables were excluded from 
the model either because of a lack of underlying data or modeling 
constraints. Their exclusion from the model is not intended to diminish 
their importance, but rather highlights the practical constraints of 
modeling intricate decisions like scrappage.
3. Changes in Vehicle Miles Traveled (VMT)
    In the CAFE Model, VMT is the product of average usage per vehicle 
in the fleet and fleet composition, which is itself a function of new 
vehicle sales and vehicle retirement decisions, otherwise known as 
scrappage. These three components--average vehicle usage, new vehicle 
sales, and older vehicle scrappage--jointly determine total VMT 
projections for each alternative. VMT directly influences many of the 
various effects of fuel economy standards that decision-makers consider 
in determining what levels of standards to set. For example, the value 
of fuel savings is a function of a vehicle's efficiency, miles driven, 
and fuel price. Similarly, factors like criteria pollutant emissions, 
congestion, and fatalities are direct functions of VMT.
    It is the agency's perspective that the total demand for VMT should 
not vary excessively across alternatives. The basic travel needs for an 
average household are unlikely to be influenced heavily by the 
stringency of the CAFE standards, as the daily need for a vehicle will 
remain the same. That said, it is reasonable to assume that fleets with 
differing age distributions and inherent cost of operation will have 
slightly different annual VMT (even without considering VMT associated 
with rebound miles); however, the difference could conceivably be 
small. Based on the structure of the CAFE Model, the combined effect of 
the sales and scrappage responses would create small percentage 
differences in total VMT across the range of regulatory alternatives if 
steps are not taken to constrain VMT. Because VMT is related to many of 
the costs and benefits of the program, even small magnitude differences 
in VMT across alternatives can have meaningful impacts on the 
incremental net benefit analysis. Furthermore, since decisions about 
alternative stringencies look at the incremental costs and benefits 
across alternatives, it is more important that the analysis capture the 
variation of VMT across alternatives than to accurately predict total 
VMT within a scenario.
    To ensure that travel demand remains consistent across the 
different regulatory scenarios, the CAFE Model begins with a model of 
aggregate VMT developed by the Federal Highway Administration (FHWA) 
that is used to produce their official annual VMT forecasts. These 
estimates provide the aggregate VMT of all model years and body styles 
for any given calendar year and are same across regulatory alternatives 
for each year in the analysis.
    Since vehicles of different ages and body styles carry different 
costs and benefits, to account properly for the average value of 
consumer and societal costs and benefits associated with vehicle usage 
under various CAFE alternatives, it is necessary to partition miles by 
age and body type. The agency created ``mileage accumulation 
schedules'' using IHS-Polk odometer data to construct mileage 
accumulation schedules as an initial estimate of how much a vehicle 
expected to drive at each age throughout its life. The agency uses 
simulated new vehicle sales, annual rates of retirement for used 
vehicles, and the mileage accumulation schedules to distribute VMT 
across the age distribution of registered vehicles in each calendar 
year to preserve the non-rebound VMT constraint.
    The fuel economy rebound effect--a specific example of the well-
documented energy efficiency rebound effect for energy-consuming 
capital goods--refers to the tendency of motor vehicles' use (as 
measured by VMT) to increase when their fuel economy is improved and, 
as a result, the cost per mile (CPM) of driving declines. Establishing 
more stringent CAFE standards than the baseline level will lead to 
comparatively higher fuel economy for new cars and light trucks, thus 
decreasing the amount of fuel consumed and increasing the amount of 
travel in which new car and truck buyers engage. The agency recognizes 
that the value selected for the rebound effect influences overall costs 
and benefits associated with the regulatory alternatives under 
consideration as well as the estimates of lives saved under various 
regulatory alternatives, and that the rebound estimate, along with fuel 
prices, technology costs, and other analytical inputs, is part of the 
body of information that agency decision-makers have considered in 
determining the appropriate levels of the CAFE standards in this 
proposal. We also note that the rebound effect diminishes the economic 
and environmental benefits associated with increased fuel efficiency.
    The agency conducted a review of the literature related to the fuel 
economy rebound effect, which is extensive and covers multiple decades 
and geographic regions. The totality of evidence, without categorically 
excluding studies on grounds that they fail to meet certain criteria, 
and evaluating individual studies based on their particular strengths, 
suggests that a plausible range for the rebound effect is 10-50 
percent. The central tendency of this range appears to be at or 
slightly above its midpoint, which is 30 percent. Considering only 
those studies that the agency believes are derived from extremely 
robust and reliable data, employ identification strategies that are 
likely to prove effective at isolating the rebound effect, and apply 
rigorous estimation methods suggests a range of approximately 10-45 
percent, with most of their estimates falling in the 15-30 percent 
range.
    A case can also be made to support values of the rebound effect 
falling in the 5-15 percent range. There is empirical evidence 
supported by theory, that the rebound effect has been declining over 
time due to factors such as increasing income that affects the value of 
time, increasing fuel economy that makes the fuel cost of driving a 
smaller share of the total costs of vehicle travel, as well as 
diminishing impacts of increased car ownership and rates of license 
holding on vehicle travel. Lower rebound estimates are associated with 
studies that include recently published analyses using U.S. data, and 
to accord the most weight to research that relies on measures of 
vehicle use derived from odometer readings, controls for the potential 
endogeneity of fuel economy, and estimates the response of vehicle use 
to variation in fuel economy itself, rather than to fuel cost per 
distance driven or fuel prices. This approach suggests that the rebound 
effect is likely in the range from 5-15 percent and is more likely to 
lie toward the lower end of that range.
    The agency selected a rebound magnitude of 15% for the analysis 
because it was well-supported by the totality of the evidence and 
aligned well with FHWA's estimated elasticity for

[[Page 49715]]

travel (14.6%). However, recognizing the uncertainty surrounding the 
rebound value, we also examine the sensitivity of estimated impacts to 
values of the rebound ranging from 10 percent to 20 percent. NHTSA 
seeks comment on the above discussion, and whether to consider a 
different value for the rebound effect for the final rule analysis.
    In order to calculate total VMT with rebound, the CAFE Model 
applies the price elasticity of VMT (taken from the FHWA forecasting 
model) to the full change in CPM and the initial VMT schedule, but 
applies the (user defined) rebound parameter to the incremental 
percentage change in CPM between the non-rebound and full CPM 
calculations to the miles applied to each vehicle during the 
reallocation step that ensured adjusted non-rebound VMT matched the 
non-rebound VMT constraint.
    The approach in the model is a combination of top-down (relying on 
the FHWA forecasting model to determine total light-duty VMT in a given 
calendar year), and bottom-up (where the composition and utilization of 
the on-road fleet determines a base level of VMT in a calendar year, 
which is constrained to match the FHWA model). While the agency and the 
model developers agree that a joint household consumer choice model--if 
one could be developed adequately and reliably to capture the myriad 
circumstances under which families and individuals make decisions 
relating to vehicle purchase, use, and disposal--would reflect 
decisions that are made at the household level, it is not obvious, or 
necessarily appropriate, to model the national program at that scale in 
order to produce meaningful results that can be used to inform policy 
decisions.
    The most useful information for policymakers relates to national 
impacts of potential policy choices. No other element of the rulemaking 
analysis occurs at the household level, and the error associated with 
allocating specific vehicles to specific households over the course of 
three decades would easily dwarf any error associated with the 
estimation of these effects in aggregate. We have attempted to 
incorporate estimates of changes to the new and used vehicle markets at 
the highest practical levels of aggregation, and worked to ensure that 
these effects produce fleetwide VMT estimates that are consistent with 
the best, current projections given our economic assumptions. While 
future work will always continue to explore approaches to improve the 
realism of CAFE policy simulation, there are important differences 
between small-scale econometric studies and the kind of flexibility 
that is required to assess the impacts of a broad range of regulatory 
alternatives over multiple decades. To assist with creating even more 
precise estimates of VMT, the agency requests comment on alternative 
approaches to simulate VMT demand.
    See TSD Chapter 4.3 for a complete accounting of how the agency 
models VMT.
4. Changes to Fuel Consumption
    The agency uses the fuel economy and age and body-style VMT 
estimates to determine changes in fuel consumption. The agency divides 
the expected vehicle use by the anticipated MPG to calculate the 
gallons consumed by each simulated vehicle, and when aggregated, the 
total fuel consumed in each alternative.

F. Simulating Environmental Impacts of Regulatory Alternatives

    This proposal includes the adoption of electric vehicles and other 
fuel-saving technologies, which produce additional co-benefits. These 
co-benefits include reduced vehicle tailpipe emissions during operation 
as well as reduced upstream emissions during petroleum extraction, 
transportation, refining, and finally fuel transportation, storage, and 
distribution. This section provides an overview of how we developed 
input parameters for criteria pollutants, greenhouse gases, and air 
toxics. This section also describes how we generated estimates of how 
these emissions could affect human health, in particular criteria 
pollutants known to cause poor air quality and damage human health when 
inhaled.
    The rule implements an emissions inventory methodology for 
estimating impacts. Vehicle emissions inventories are often described 
as three-legged stools, comprised of activity (i.e., miles traveled, 
hours operated, or gallons of fuel burned), population (or number of 
vehicles), and emission factors. An emissions factor is a 
representative rate that attempts to relate the quantity of a pollutant 
released to the atmosphere per unit of activity.\311\
---------------------------------------------------------------------------

    \311\ USEPA, Basics Information of Air Emissions Factors and 
Quantification, https://www.epa.gov/air-emissions-factors-and-quantification/basic-information-air-emissions-factors-and-quantification.
---------------------------------------------------------------------------

    In this rulemaking, upstream emission factors are on a fuel volume 
basis and tailpipe emission factors are on a distance basis. Simply 
stated, the rule's upstream emission inventory is the product of the 
per-gallon emission factor and the corresponding number of gallons of 
gasoline or diesel consumed. Similarly, the tailpipe emission inventory 
is the product of the per-mile emission factor and the appropriate 
miles traveled estimate. The only exceptions are that tailpipe sulfur 
oxides (SOX) and carbon dioxide (CO2) also use a 
per-gallon emission factor in the CAFE Model. The activity levels--both 
miles traveled and fuel consumption--are generated by the CAFE Model, 
while the emission factors have been incorporated from other Federal 
models.
    For this rule, vehicle tailpipe (downstream) and upstream emission 
factors and subsequent inventories were developed independently from 
separate data sources. Upstream emission factors are estimated from a 
lifecycle emissions model developed by the U.S. Department of Energy's 
(DOE) Argonne National Laboratory, the Greenhouse gases, Regulated 
Emissions, and Energy use in Transportation (GREET) Model.\312\ 
Tailpipe emission factors are estimated from the regulatory highway 
emissions inventory model developed by the U.S. Environmental 
Protection Agency's (EPA) National Vehicle and Fuel Emissions 
Laboratory, the Motor Vehicle Emission Simulator (MOVES3). Data from 
GREET and MOVES3 have been utilized to update the CAFE Model for this 
rulemaking.
---------------------------------------------------------------------------

    \312\ U.S. Department of Energy, Argonne National Laboratory, 
Greenhouse gases, Regulated Emissions, and Energy use in 
Transportation (GREET) Model, Last Update: 9 Oct. 2020, https://greet.es.anl.gov/.
---------------------------------------------------------------------------

    The changes in adverse health outcomes due to criteria pollutants 
emitted, such as differences in asthmatic episodes and hospitalizations 
due to respiratory or cardiovascular distress, are generally reported 
in incidence per ton values. Incidence values were developed using 
several EPA studies and recently updated from the 2020 final rule to 
better account for the emissions source sectors used in the CAFE Model 
analysis.
    Chapter 5 of the TSD accompanying this proposal includes the 
detailed discussion of the procedures we used to simulate the 
environmental impact of regulatory alternatives, and the implementation 
of these procedures into the CAFE Model is discussed in detail in the 
CAFE Model Documentation. Further discussion of how the health impacts 
of upstream and tailpipe criteria pollutant emissions have been 
monetized in the analysis can be found in Section III.G.2.b)(2). The 
Supplemental Environmental Impact Statement accompanying this analysis 
also includes a detailed discussion of both criteria pollutant and GHG 
emissions and their impacts. NHTSA

[[Page 49716]]

seeks comment on the following discussion.
1. Activity Levels Used To Calculate Emissions Impacts
    Emission inventories in this rule vary by several key activity 
parameters, especially relating to the vehicle's model year and 
relative age. Most importantly, the CAFE Model accounts for vehicle 
sales, turnover, and scrappage as well as travel demands over its 
lifetime. Like other models, the CAFE Model includes procedures to 
estimate annual rates at which new vehicles are purchased, driven, and 
subsequently scrapped. Together, these procedures result in, for each 
vehicle model in each model year, estimates of the number remaining in 
service in each calendar year, as well as the annual mileage 
accumulation (i.e., VMT) at each age. Inventories by model year are 
derived from the annual mileage accumulation rates and corresponding 
emission factors.
    As discussed in Section III.C.2, for each vehicle model/
configuration in each model year from 2020 to 2050 for upstream 
estimates and 2060 for tailpipe estimates, the CAFE Model estimates and 
records the fuel type (e.g., gasoline, diesel, electricity), fuel 
economy, and number of units sold in the U.S. The model also makes use 
of an aggregated representation of vehicles sold in the U.S. during 
1975-2019. The model estimates the numbers of each cohort of vehicles 
remaining in service in each calendar year, and the amount of driving 
accumulated by each such cohort in each calendar year.
    The CAFE Model estimates annual vehicle-miles of travel (VMT) for 
each individual car and light truck model produced in each model year 
at each age of their lifetimes, which extend for a maximum of 40 years. 
Since a vehicle's age is equal to the current calendar year minus the 
model year in which it was originally produced, the age span of each 
vehicle model's lifetime corresponds to a sequence of 40 calendar years 
beginning in the calendar year corresponding to the model year it was 
produced.\313\ These estimates reflect the gradual decline in the 
fraction of each car and light truck model's original model year 
production volume that is expected to remain in service during each 
year of its lifetime, as well as the well-documented decline in their 
typical use as they age. Using this relationship, the CAFE Model 
calculates fleet-wide VMT for cars and light trucks in service during 
each calendar year spanned in this analysis.
---------------------------------------------------------------------------

    \313\ In practice, many vehicle models bearing a given model 
year designation become available for sale in the preceding calendar 
year, and their sales can extend through the following calendar year 
as well. However, the CAFE Model does not attempt to distinguish 
between model years and calendar years; vehicles bearing a model 
year designation are assumed to be produced and sold in that same 
calendar year.
---------------------------------------------------------------------------

    Based on these estimates, the model also calculates quantities of 
each type of fuel or energy, including gasoline, diesel, and 
electricity, consumed in each calendar year. By combining these with 
estimates of each model's fuel or energy efficiency, the model also 
estimates the quantity and energy content of each type of fuel consumed 
by cars and light trucks at each age, or viewed another way, during 
each calendar year of their lifetimes. As with the accounting of VMT, 
these estimates of annual fuel or energy consumption for each vehicle 
model and model year combination are combined to calculate the total 
volume of each type of fuel or energy consumed during each calendar 
year, as well as its aggregate energy content.
    The procedures the CAFE Model uses to estimate annual VMT for 
individual car and light truck models produced during each model year 
over their lifetimes and to combine these into estimates of annual 
fleet-wide travel during each future calendar year, together with the 
sources of its estimates of their survival rates and average use at 
each age, are described in detail in Section III.E.2. The data and 
procedures it employs to convert these estimates of VMT to fuel and 
energy consumption by individual model, and to aggregate the results to 
calculate total consumption and energy content of each fuel type during 
future calendar years, are also described in detail in that same 
section.
    The model documentation accompanying this NPRM describes these 
procedures in detail.\314\ The quantities of travel and fuel 
consumption estimated for the cross section of model years and calendar 
years constitutes a set of ``activity levels'' based on which the model 
calculates emissions. The model does so by multiplying activity levels 
by emission factors. As indicated in the previous section, the 
resulting estimates of vehicle use (VMT), fuel consumption, and fuel 
energy content are combined with emission factors drawn from various 
sources to estimate emissions of GHGs, criteria air pollutants, and 
airborne toxic compounds that occur throughout the fuel supply and 
distribution process, as well as during vehicle operation, storage, and 
refueling. Emission factors measure the mass of each GHG or criteria 
pollutant emitted per vehicle-mile of travel, gallon of fuel consumed, 
or unit of fuel energy content. The following sections identifies the 
sources of these emission factors and explains in detail how the CAFE 
Model applies them to its estimates of vehicle travel, fuel use, and 
fuel energy consumption to estimate total annual emissions of each GHG, 
criteria pollutant, and airborne toxic.
---------------------------------------------------------------------------

    \314\ CAFE Model documentation is available at https://www.nhtsa.gov/corporate-average-fuel-economy/compliance-and-effects-modeling-system.
---------------------------------------------------------------------------

2. Simulating Upstream Emissions Impacts
    Building on the methodology for simulating upstream emissions 
impacts used in prior CAFE rules, this analysis uses emissions factors 
developed with the U.S. Department of Energy's Greenhouse gases, 
Regulated Emissions, and Energy use in Transportation (GREET) Model, 
specifically GREET 2020.\315\ The analysis includes emissions impacts 
estimates for regulated criteria pollutants,\316\ greenhouse 
gases,\317\ and air toxics.\318\
---------------------------------------------------------------------------

    \315\ U.S. Department of Energy, Argonne National Laboratory, 
Greenhouse gases, Regulated Emissions, and Energy use in 
Transportation (GREET) Model, Last Update: 9 Oct. 2020, https://greet.es.anl.gov/.
    \316\ Carbon monoxide (CO), volatile organic compounds (VOCs), 
nitrogen oxides (NOX), sulfur oxides (SOX), 
and particulate matter with 2.5-micron ([micro]m) diameters or less 
(PM2.5).
    \317\ Carbon dioxide (CO2), methane (CH4), 
and nitrous oxide (N2O).
    \318\ Acetaldehyde, acrolein, benzene, butadiene, formaldehyde, 
diesel particulate matter with 10-micron ([micro]m) diameters or 
less (PM10).
---------------------------------------------------------------------------

    The upstream emissions factors included in the CAFE Model input 
files include parameters for 2020 through 2050 in five-year intervals 
(e.g., 2020, 2025, 2030, and so on). For gasoline and diesel fuels, 
each analysis year includes upstream emissions factors for the four 
following upstream emissions processes: Petroleum extraction, petroleum 
transportation, petroleum refining, and fuel transportation, storage, 
and distribution (TS&D). In contrast, the upstream electricity 
emissions factor is only a single value per analysis year. We briefly 
discuss the components included in each upstream emissions factor here, 
and a more detailed discussion is included in Chapter 5 of the TSD 
accompanying this proposal and the CAFE Model Documentation.
    The first step in the process for calculating upstream emissions 
includes any emissions related to the extraction, recovery, and 
production of petroleum-based feedstocks, namely conventional crude 
oil, oil sands, and shale oils. Then, the petroleum transportation 
process accounts for the transport

[[Page 49717]]

processes of crude feedstocks sent for domestic refining. The petroleum 
refining calculations are based on the aggregation of fuel blendstock 
processes rather than the crude feedstock processes, like the petroleum 
extraction and petroleum transportation calculations. The final 
upstream process after refining is the transportation, storage, and 
distribution (TS&D) of the finished fuel product.
    The upstream gasoline and diesel emissions factors are aggregated 
in the CAFE Model based on the share of fuel savings leading to reduced 
domestic oil fuel refining and the share of reduced domestic refining 
from domestic crude oil. The CAFE Model applies a fuel savings 
adjustment factor to the petroleum refining process and a combined fuel 
savings and reduced domestic refining adjustment to both the petroleum 
extraction and petroleum transportation processes for both gasoline and 
diesel fuels and for each pollutant. These adjustments are consistent 
across fuel types, analysis years, and pollutants, and are unchanged 
from the 2020 final rule. Additional discussion of the methodology for 
estimating the share of fuel savings leading to reduced domestic oil 
refining is located in Chapter 6.2.4.3 of the TSD. NHTSA seeks comment 
on the methodology used and specifically whether all of the change in 
refining would happen domestically, rather than the current division 
between domestic and non-domestic refining.
    Upstream electricity emissions factors are also calculated using 
GREET 2020. GREET 2020 projects a national default electricity 
generation mix for transportation use from the latest Annual Energy 
Outlook (AEO) data available from the previous year. As discussed 
above, the CAFE Model uses a single upstream electricity factor for 
each analysis year.
3. Simulating Tailpipe Emissions Impacts
    Tailpipe emission factors are generated using the latest regulatory 
model for on-road emission inventories from the U.S. Environmental 
Protection Agency, the Motor Vehicle Emission Simulator (MOVES3), 
November 2020 release. MOVES3 is a state-of-the-science, mobile-source 
emissions inventory model for regulatory applications.\319\ New MOVES3 
tailpipe emission factors have been incorporated into the CAFE 
parameters, and these updates supersede tailpipe data previously 
provided by EPA from MOVES2014 for past CAFE analyses. MOVES3 accounts 
for a variety of processes related to emissions impacts from vehicle 
use, including running exhaust, start exhaust, refueling displacement 
vapor loss, brakewear, and tirewear, among others.
---------------------------------------------------------------------------

    \319\ U.S. Environmental Protection Agency, Office of 
Transportation and Air Quality, Motor Vehicle Emission Simulator 
(MOVES), Last Updated: March 2021, https://www.epa.gov/moves/latest-version-motor-vehicle-emission-simulator-moves.
---------------------------------------------------------------------------

    The CAFE Model uses tailpipe emissions factors for all model years 
from 2020 to 2060 for criteria pollutants and air toxics. To maintain 
continuity in the historical inventories, only emission factors for 
model years 2020 and after were updated; all emission factors prior to 
MY 2020 were unchanged from previous CAFE rulemakings. In addition, the 
updated tailpipe data in the current CAFE reference case no longer 
account for any fuel economy improvements or changes in vehicle miles 
traveled from the 2020 final rule. In order to avoid double-counting 
effects from the previous rulemaking in the current rulemaking, the new 
tailpipe baseline backs out 1.5% year-over-year stringency increases in 
fuel economy, and 0.3% VMT increases assumed each year (20% rebound on 
the 1.5% improvements in stringency). Note that the MOVES3 data do not 
cover all the model years and ages required by the CAFE Model, MOVES 
only generates emissions data for vehicles made in the last 30 model 
years for each calendar year being run. This means emissions data for 
some calendar year and vehicle age combinations are missing. To remedy 
this, we take the last vehicle age that has emissions data and forward 
fill those data for the following vehicle ages. Due to incomplete 
available data for years prior to MY 2020, tailpipe emission factors 
for MY 2019 and earlier have not been modified and continue to utilize 
MOVES2014 data.
    For tailpipe CO2 emissions, these factors are defined 
based on the fraction of each fuel type's mass that represents carbon 
(the carbon content) along with the mass density per unit of the 
specific type of fuel. To obtain the emission factors associated with 
each fuel, the carbon content is then multiplied by the mass density of 
a particular fuel as well as by the ratio of the molecular weight of 
carbon dioxide to that of elemental carbon. This ratio, a constant 
value of 44/12, measures the mass of carbon dioxide that is produced by 
complete combustion of mass of carbon contained in each unit of fuel. 
The resulting value defines the emission factor attributed to 
CO2 as the amount of grams of CO2 emitted during 
vehicle operation from each type of fuel. This calculation is repeated 
for gasoline, E85, diesel, and compressed natural gas (CNG) fuel types. 
In the case of CNG, the mass density and the calculated CO2 
emission factor are denoted as grams per standard cubic feet (scf), 
while for the remainder of fuels, these are defined as grams per gallon 
of the given fuel source. Since electricity and hydrogen fuel types do 
not cause CO2 emissions to be emitted during vehicle 
operation, the carbon content, and the CO2 emission factors 
for these two fuel types are assumed to be zero. The mass density, 
carbon content, and CO2 emission factors for each fuel type 
are defined in the Parameters file.
    The CAFE Model calculates CO2 tailpipe emissions 
associated with vehicle operation of the surviving on-road fleet by 
multiplying the number of gallons (or scf for CNG) of a specific fuel 
consumed by the CO2 emissions factor for the associated fuel 
type. More specifically, the amount of gallons or scf of a particular 
fuel are multiplied by the carbon content and the mass density per unit 
of that fuel type, and then applying the ratio of carbon dioxide 
emissions generated per unit of carbon consumed during the combustion 
process.\320\
---------------------------------------------------------------------------

    \320\ Chapter 3, Section 4 of the CAFE Model Documentation 
provides additional description for calculation of CO2 
tailpipe emissions with the model.
---------------------------------------------------------------------------

4. Estimating Health Impacts From Changes in Criteria Pollutant 
Emissions
    The CAFE Model computes select health impacts resulting from three 
criteria pollutants: NOX, SOX,\321\ and 
PM2.5. Out of the six criteria pollutants currently 
regulated, NOX, SOX, and PM2.5 are 
known to be emitted regularly from mobile sources and have the most 
adverse effects to human health. These health impacts include several 
different morbidity measures, as well as low and high mortality 
estimates, and are measured by the number of instances predicted to 
occur per ton of emitted pollutant.\322\ The model reports total health 
impacts by multiplying the estimated tons of each criteria pollutant by 
the corresponding health incidence per ton value. The inputs that 
inform the calculation of the total tons of emissions resulting from 
criteria pollutants are discussed above. This section discusses how the 
health

[[Page 49718]]

incidence per ton values were obtained. See Section III.G.2.b)(2) and 
Chapter 6.2.2 of the TSD accompanying this proposal for information 
regarding the monetized damages arising from these health impacts.
---------------------------------------------------------------------------

    \321\ Any reference to SOX in this section refers to 
the sum of sulfur dioxide (SO2) and sulfate particulate 
matter (pSO4) emissions, following the methodology of the 
EPA papers cited.
    \322\ The complete list of morbidity impacts estimated in the 
CAFE Model is as follows: Acute bronchitis, asthma exacerbation, 
cardiovascular hospital admissions, lower respiratory symptoms, 
minor restricted activity days, non-fatal heart attacks, respiratory 
emergency hospital admissions, respiratory emergency room visits, 
upper respiratory symptoms, and work loss days.
---------------------------------------------------------------------------

    The SEIS that accompanies this proposal also includes a detailed 
discussion of the criteria pollutants and air toxics analyzed and their 
potential health effects. In addition, consistent with past analyses, 
NHTSA will perform full-scale photochemical air quality modeling and 
present those results in the Final SEIS associated with the final rule. 
That analysis will provide additional assessment of the human health 
impacts from changes in PM2.5 and ozone associated with this 
rule. NHTSA will also consider whether such modeling could practicably 
and meaningfully be included in the FRIA, noting that compliance with 
CAFE standards is based on the average performance of manufacturers' 
production for sale throughout the U.S., and that the FRIA will involve 
sensitivity analysis spanning a range of model inputs, many of which 
impact estimates of future emissions from passenger cars and light 
trucks. Chapter 6 of the PRIA includes a discussion of overall changes 
in health impacts associated with criteria pollutant changes across the 
different rulemaking scenarios.
    In previous rulemakings, health impacts were split into two 
categories based on whether they arose from upstream emissions or 
tailpipe emissions. In the current analysis, these health incidence per 
ton values have been updated to reflect the differences in health 
impacts arising from each emission source sector, according to the 
latest publicly available EPA reports. Five different upstream emission 
source sectors (Petroleum Extraction, Petroleum Transportation, 
Refineries, Fuel Transportation, Storage and Distribution, and 
Electricity Generation) are now represented. As the health incidences 
for the different source sectors are all based on the emission of one 
ton of the same pollutants, NOX, SOX, and 
PM2.5, the differences in the incidence per ton values arise 
from differences in the geographic distribution of the pollutants, a 
factor which affects the number of people impacted by the 
pollutants.\323\
---------------------------------------------------------------------------

    \323\ See Environmental Protection Agency (EPA). 2018. 
Estimating the Benefit per Ton of Reducing PM2.5 
Precursors from 17 Sectors. https://www.epa.gov/sites/production/files/2018-02/documents/sourceapportionmentbpttsd_2018.pdf.
---------------------------------------------------------------------------

    The CAFE Model health impacts inputs are based partially on the 
structure of EPA's 2018 technical support document, Estimating the 
Benefit per Ton of Reducing PM2.5 Precursors from 17 Sectors 
(referred to here as the 2018 EPA source apportionment TSD),\324\ which 
reported benefit per ton values for the years 2016, 2020, 2025, and 
2030.\325\ For the years in between the source years used in the input 
structure, the CAFE Model applies values from the closest source year. 
For instance, 2020 values are applied for 2020-2022, and 2025 values 
are applied for 2023-2027. For further details, see the CAFE Model 
documentation, which contains a description of the model's computation 
of health impacts from criteria pollutant emissions.
---------------------------------------------------------------------------

    \324\ Environmental Protection Agency (EPA). 2018. Estimating 
the Benefit per Ton of Reducing PM2.5 Precursors from 17 
Sectors. https://www.epa.gov/sites/production/files/2018-02/documents/sourceapportionmentbpttsd_2018.pdf.
    \325\ As the year 2016 is not included in this analysis, the 
2016 values were not used.
---------------------------------------------------------------------------

    Despite efforts to be as consistent as possible between the 
upstream emissions sectors utilized in the CAFE Model with the 2018 EPA 
source apportionment TSD, the need to use up-to-date sources based on 
newer air quality modeling updates led to the use of multiple papers. 
In addition to the 2018 EPA source apportionment TSD used in the 2020 
final rule, DOT used additional EPA sources and conversations with EPA 
staff to appropriately map health incidence per ton values to the 
appropriate CAFE Model emissions source category.
    We understand that uncertainty exists around the contribution of 
VOCs to PM2.5 formation in the modeled health impacts from 
the petroleum extraction sector; however, based on feedback to the 2020 
final rule we believe that the updated health incidence values specific 
to petroleum extraction sector emissions may provide a more appropriate 
estimate of potential health impacts from that sector's emissions than 
the previous approach of applying refinery sector emissions impacts to 
the petroleum extraction sector. That said, we are aware of work that 
EPA has been doing to address concerns about the BPT estimates, and 
NHTSA will work further with EPA to update and synchronize approaches 
to the BPT estimates.
    The basis for the health impacts from the petroleum extraction 
sector was a 2018 oil and natural gas sector paper written by EPA staff 
(Fann et al.), which estimated health impacts for this sector in the 
year 2025.\326\ This paper defined the oil and gas sector's emissions 
not only as arising from petroleum extraction but also from 
transportation to refineries, while the CAFE/GREET component is 
composed of only petroleum extraction. After consultation with the 
authors of the EPA paper, it was determined that these were the best 
available estimates for the petroleum extraction sector, 
notwithstanding this difference. Specific health incidence per 
pollutant were not reported in the paper, so EPA staff sent BenMAP 
health incidence files for the oil and natural gas sector upon request. 
DOT staff then calculated per ton values based on these files and the 
tons reported in the Fann et al. paper.\327\ The only available health 
impacts corresponded to the year 2025. Rather than trying to 
extrapolate, these 2025 values were used for all the years in the CAFE 
Model structure: 2020, 2025, and 2030.\328\ This simplification implies 
an overestimate of damages in 2020 and an underestimate in 2030.\329\
---------------------------------------------------------------------------

    \326\ Fann, N., Baker, K. R., Chan, E., Eyth, A., Macpherson, 
A., Miller, E., & Snyder, J. (2018). Assessing Human Health 
PM2.5 and Ozone Impacts from U.S. Oil and Natural Gas 
Sector Emissions in 2025. Environmental science & technology, 
52(15), 8095-8103 (hereinafter Fann et al.).
    \327\ Nitrate-related health incidents were divided by the total 
tons of NOX projected to be emitted in 2025, sulfate-
related health incidents were divided by the total tons of projected 
SOX, and EC/OC (elemental carbon and organic carbon) 
related health incidents were divided by the total tons of projected 
EC/OC. Both Fann et al. and the 2018 EPA source apportionment TSD 
define primary PM2.5 as being composed of elemental 
carbon, organic carbon, and small amounts of crustal material. Thus, 
the EC/OC BenMAP file was used for the calculation of the incidents 
per ton attributable to PM2.5.
    \328\ These three years are used in the CAFE Model structure 
because it was originally based on the estimate provided in the 2018 
EPA source apportionment TSD.
    \329\ See EPA. 2018. Estimating the Benefit per Ton of Reducing 
PM2.5 Precursors from 17 Sectors. https://www.epa.gov/sites/production/files/2018-02/documents/sourceapportionmentbpttsd_2018.pdf p.9.
---------------------------------------------------------------------------

    The petroleum transportation sector and fuel TS&D sector did not 
correspond to any one EPA source sector in the 2018 EPA source 
apportionment TSD, so a weighted average of multiple different EPA 
sectors was used to determine the health impact per ton values for 
those sectors. We used a combination of different EPA mobile source 
sectors from two different papers, the 2018 EPA source apportionment 
TSD,\330\ and a 2019 mobile source sectors paper (Wolfe et al.)\331\ to 
generate these values. The health incidence per ton values associated 
with the refineries sector and

[[Page 49719]]

electricity generation sector were drawn solely from the 2018 EPA 
source apportionment TSD.
---------------------------------------------------------------------------

    \330\ Environmental Protection Agency (EPA). 2018. Estimating 
the Benefit per Ton of Reducing PM2.5 Precursors from 17 
Sectors. https://www.epa.gov/sites/production/files/2018-02/documents/sourceapportionmentbpttsd_2018.pdf.
    \331\ Wolfe et al. 2019. Monetized health benefits attributable 
to mobile source emissions reductions across the United States in 
2025. https://pubmed.ncbi.nlm.nih.gov/30296769/.
---------------------------------------------------------------------------

    The CAFE Model follows a similar process for computing health 
impacts resulting from tailpipe emissions as it does for calculating 
health impacts from upstream emissions. Previous rulemakings used the 
2018 EPA source apportionment TSD as the source for the health 
incidence per ton, matching the CAFE Model tailpipe emissions inventory 
to the ``on-road mobile sources sector'' in the TSD. However, a more 
recent EPA paper from 2019 (Wolfe et al.) \332\ computes monetized 
damage costs per ton values at a more disaggregated level, separating 
on-road mobile sources into multiple categories based on vehicle type 
and fuel type. Wolfe et al. did not report incidences per ton, but that 
information was obtained through communications with EPA staff.
---------------------------------------------------------------------------

    \332\ Wolfe et al. 2019. Monetized health benefits attributable 
to mobile source emissions reductions across the United States in 
2025. https://pubmed.ncbi.nlm.nih.gov/30296769/.
---------------------------------------------------------------------------

    The methodology for generating values for each emissions category 
in the CAFE Model is discussed in detail in Chapter 5 of the TSD 
accompanying this proposal. The Parameters file contains all of the 
health impact per ton of emissions values used in this proposal.

G. Simulating Economic Impacts of Regulatory Alternatives

    This section describes the agency's approach for measuring the 
economic costs and benefits that will result from establishing 
alternative CAFE standards for future model years. The benefit and cost 
measures the agency uses are important considerations, because as 
Office of Management and Budget (OMB) Circular A-4 states, benefits and 
costs reported in regulatory analyses must be defined and measured 
consistently with economic theory, and should also reflect how 
alternative regulations are anticipated to change the behavior of 
producers and consumers from a baseline scenario.\333\ For CAFE 
standards, those include vehicle manufacturers, buyers of new cars and 
light trucks, owners of used vehicles, and suppliers of fuel, all of 
whose behavior is likely to respond in complex ways to the level of 
CAFE standards that DOT establishes for future model years.
---------------------------------------------------------------------------

    \333\ White House Office of Management and Budget, Circular A-4: 
Regulatory Analysis, September 17, 2003 (https://obamawhitehouse.archives.gov/omb/circulars_a004_a-4/), Section E.
---------------------------------------------------------------------------

    It is important to report the benefits and costs of this proposed 
action in a format that conveys useful information about how those 
impacts are generated and also distinguishes the impacts of those 
economic consequences for private businesses and households from the 
effects on the remainder of the U.S. economy. A reporting format will 
accomplish this objective to the extent that it clarifies who incurs 
the benefits and costs of the proposed, and shows how the economy-wide 
or ``social'' benefits and costs of the proposed action are composed of 
its direct effects on vehicle producers, buyers, and users, plus the 
indirect or ``external'' benefits and costs it creates for the general 
public.
    Table III-37 and Table III-38 present the incremental economic 
benefits and costs of the proposed action and the alternatives 
(described in detail in Section IV) to increase CAFE standards for 
model years 2024-26 at three percent and seven percent discount rates 
in a format that is intended to meet these objectives. The tables 
include costs which are transfers between different economic actors--
these will appear as both a cost and a benefit in equal amounts (to 
separate affected parties). Societal cost and benefit values shown 
elsewhere in this document do not show costs which are transfers for 
the sake of simplicity but report the same net societal costs and 
benefits. The proposed action and the alternatives would increase costs 
to manufacturers for adding technology necessary to enable new cars and 
light trucks to comply with fuel economy and emission regulations. It 
may also increase fine payments by manufacturers who would have 
achieved compliance with the less demanding baseline standards. 
Manufacturers are assumed to transfer these costs on to buyers by 
charging higher prices; although this reduces their revenues, on 
balance, the increase in compliance costs and higher sales revenue 
leaves them financially unaffected. Since the analysis assumes that 
manufacturers are left in the same economic position regardless of the 
standards, they are excluded from the tables.
BILLING CODE 4910-59-P

[[Page 49720]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.086

     
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    \334\ A portion of Reduced Fuel Costs represent the benefit to 
consumers of not having to pay taxes on avoided gasoline 
consumption. This amount offsets the Loss in Fuel Tax Revenue in 
External Costs. For example, the $47.9 billion in Reduced Fuel Costs 
in alternative 1 represents $11 billion of avoided fuel taxes and 
$36.9 billion in gasoline savings.

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[[Page 49721]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.087

BILLING CODE 4910-59-C
    Compared to the baseline standards, if the preferred alternative is 
finalized, the analysis shows that buyers of new cars and light trucks 
will incur higher purchasing prices and financing costs, which will 
lead to some buyers dropping out of the new vehicle market. Drivers of 
new vehicles will also experience a slight uptick in the risk of being 
injured in a crash because of mass reduction technologies employed to 
meet the increased standards. While this effect is not statistically 
significant, NHTSA provides these results for transparency, and to 
demonstrate that their inclusion does not affect NHTSA's proposed 
policy decision. Because of the increasing price of new vehicles, some 
owners may delay retiring and replacing their older vehicles with newer 
models. In effect, this will transfer some driving that would have been 
done in newer vehicles under the baseline scenario to older models 
within the legacy fleet, thus increasing costs for injuries (both fatal 
and less severe) and property damages sustained in motor vehicle 
crashes. This stems from the fact that cars and light trucks have 
become progressively more protective in crashes over time (and also 
slightly less prone to certain types of crashes, such as rollovers). 
Thus, shifting some travel from newer to older models would increase 
injuries and damages sustained by drivers and passengers because they 
are traveling in less safe vehicles and not because it changes the risk 
profiles of drivers themselves. These costs are largely driven by 
assumptions regarding consumer valuation of fuel efficiency and an 
assumption that more fuel-efficient vehicles are less preferable to 
consumers than their total cost to improve fuel economy. These are 
issues on which we seek comments.
    In exchange for these costs, consumers will benefit from new cars 
and light trucks with better fuel economy. Drivers will experience 
lower costs as a consequence of new vehicles' decreased fuel 
consumption, and from fewer refueling stops required because of their 
increased driving range. They will experience mobility benefits as they 
use newly purchased cars and light trucks more in response to their 
lower operating costs. On balance, consumers of new cars and light 
trucks produced during the model years subject to this proposed action 
will experience significant economic benefits.
    Table III-37 and Table III-38 also show that the changes in fuel 
consumption and vehicle use resulting

[[Page 49722]]

from this proposed action will in turn generate both benefits and costs 
to society writ large. These impacts are ``external,'' in the sense 
that they are by-products of decisions by private firms and individuals 
that alter vehicle use and fuel consumption but are experienced broadly 
throughout society rather than by the firms and individuals who 
indirectly cause them. In terms of costs, additional driving by 
consumers of new vehicles in response to their lower operating costs 
will increase the external costs associated with their contributions to 
traffic delays and noise levels in urban areas, and these additional 
costs will be experienced throughout much of the society. While most of 
the risk of additional driving or delaying purchasing a newer vehicle 
are internalized by those who make those decisions, a portion of the 
costs are borne by other road users. Finally, since owners of new 
vehicles will be consuming less fuel, they will pay less in fuel taxes.
    Society will also benefit from more stringent standards. Increased 
fuel efficiency will reduce the amount of petroleum-based fuel consumed 
and refined domestically, which will decrease the emissions of carbon 
dioxide and other greenhouse gases that contribute to climate change, 
and, as a result, the U.S. (and the rest of world) will avoid some of 
the economic damages from future changes in the global climate. 
Similarly, reduced fuel production and use will decrease emissions of 
more localized air pollutants (or their chemical precursors), and the 
resulting decrease in the U.S. population's exposure to harmful levels 
of these pollutants will lead to lower costs from its adverse effects 
on health. Decreasing consumption and imports of crude petroleum for 
refining lower volumes of gasoline and diesel will also accrue some 
benefits throughout to the U.S., in the form of potential gains of 
energy security as businesses and households that are dependent on fuel 
are subject to less sudden and sharp changes in energy prices.
    On balance, Table III-37 and Table III-38 show that both consumers 
and society as a whole will experience net economic benefits from the 
proposed action. The following subsections will briefly describe the 
economic costs and benefits considered by the agency. For a complete 
discussion of the methodology employed and the results, see TSD Chapter 
6 and PRIA Chapter 6, respectively. The safety implications of the 
proposal--including the monetary impacts--are reserved for Section 
III.H. NHTSA seeks comment on the following discussion.
1. Private Costs and Benefits
(a) Costs to Consumers
(1) Technology Costs
    The proposed action and the alternatives would increase costs to 
manufacturers for adding technology necessary to enable new cars and 
light trucks to comply with fuel economy and emission regulations. 
Manufacturers are assumed to transfer these costs on to buyers by 
charging higher prices. See Section III.C.6 and TSD Chapter 2.5.
(2) Consumer Sales Surplus
    Buyers who would have purchased a new vehicle with the baseline 
standards in effect but decide not to do so in response to the changes 
in new vehicles' prices due to more stringent standards in place will 
experience a decrease in welfare. The collective welfare loss to those 
``potential'' new vehicle buyers is measured by the foregone consumer 
surplus they would have received from their purchase of a new vehicle 
in the baseline.
    Consumer surplus is a fundamental economic concept and represents 
the net value (or net benefit) a good or service provides to consumers. 
It is measured as the difference between what a consumer is willing to 
pay for a good or service and the market price. OMB Circular A-4 
explicitly identifies consumer surplus as a benefit that should be 
accounted for in cost-benefit analysis. For instance, OMB Circular A-4 
states the ``net reduction in total surplus (consumer plus producer) is 
a real cost to society,'' and elsewhere elaborates that consumer 
surplus values be monetized ``when they are significant.'' \335\
---------------------------------------------------------------------------

    \335\ OMB Circular A-4, at 37-38.
---------------------------------------------------------------------------

    Accounting for the portion of fuel savings that the average new 
vehicle buyer demands, and holding all else equal, higher average 
prices should depress new vehicle sales and by extension reduce 
consumer surplus. The inclusion of consumer surplus is not only 
consistent with OMB guidance, but with other parts of the regulatory 
analysis. For instance, we calculate the increase in consumer surplus 
associated with increased driving that results from the decrease in the 
cost per mile of operation under more stringent regulatory 
alternatives, as discussed in Section III.G.1.b)(3). The surpluses 
associated with sales and additional mobility are inextricably linked 
as they capture the direct costs and benefits accrued by purchasers of 
new vehicles. The sales surplus captures the welfare loss to consumers 
when they forego a new vehicle purchase in the presence of higher 
prices and the additional mobility measures the benefit increased 
mobility under lower operating expenses.
    The agency estimates the loss of sales surplus based on the change 
in quantity of vehicles projected to be sold after adjusting for 
quality improvements attributable to fuel economy. For additional 
information about consumer sales surplus, see TSD Chapter 6.1.5.
(3) Ancillary Costs of Higher Vehicle Prices
    Some costs of purchasing and owning a new or used vehicle scale 
with the value of the vehicle. Where fuel economy standards increase 
the transaction price of vehicles, they will affect both the absolute 
amount paid in sales tax and the average amount of financing required 
to purchase the vehicle. Further, where they increase the MSRP, they 
increase the appraised value upon which both value-related registration 
fees and a portion of insurance premiums are based. The analysis 
assumes that the transaction price is a set share of the MSRP, which 
allows calculation of these factors as shares of MSRP. For a detailed 
explanation of how the agency estimates these costs, see TSD Chapter 
6.1.1.
    These costs are included in the consumer per-vehicle cost-benefit 
analysis but are not included in the societal cost-benefit analysis 
because they are assumed to be transfers from consumers to governments, 
financial institutions, and insurance companies.
(b) Benefits to Consumers
(1) Fuel Savings
    The primary benefit to consumers of increasing CAFE standards are 
the additional fuel savings that accrue to new vehicle owners. Fuel 
savings are calculated by multiplying avoided fuel consumption by fuel 
prices. Each vehicle of a given body style is assumed to be driven the 
same as all the others of a comparable age and body style in each 
calendar year. The ratio of that cohort's VMT to its fuel efficiency 
produces an estimate of fuel consumption. The difference between fuel 
consumption in the baseline, and in each alternative, represents the 
gallons (or energy) saved. Under this assumption, our estimates of fuel 
consumption from increasing the fuel economy of each individual model 
depend only on how much its fuel economy is increased, and do not 
reflect whether its actual use differs from other

[[Page 49723]]

models of the same body type. Neither do our estimates of fuel 
consumption account for variation in how much vehicles of the same body 
type and age are driven each year, which appears to be significant (see 
TSD Chapter 4.3.1.2). Consumers save money on fuel expenditures at the 
average retail fuel price (fuel price assumptions are discussed in 
detail in TSD Chapter 4.1.2), which includes all taxes and represents 
an average across octane blends. For gasoline and diesel, the included 
taxes reflect both the Federal tax and a calculated average state fuel 
tax. Expenditures on alternative fuels (E85 and electricity, primarily) 
are also included in the calculation of fuel expenditures, on which 
fuel savings are based. And while the included taxes net out of the 
social benefit cost analysis (as they are a transfer), consumers value 
each gallon saved at retail fuel prices including any additional fees 
such as taxes.
    See TSD Chapter 6.1.3 for additional details. In the TSD, the 
agency considers the possibility that several of the assumptions made 
about vehicle use could lead to misstating the benefits of fuel 
savings. The agency notes that these assumptions are necessary to model 
fuel savings and likely have minimal impact to the accuracy of this 
analysis.
    Technologies that can be used to improve fuel economy can also be 
used to increase other vehicle attributes, especially acceleration 
performance, weight, and energy-using accessories. While this is most 
obvious for technologies that improve the efficiency of engines and 
transmissions, it is also true of technologies that reduce mass, 
aerodynamic drag, rolling resistance or any road or accessory load. The 
exact nature of the potential to trade-off attributes for fuel economy 
varies with the technology, but at a minimum, increasing vehicle 
efficiency or reducing loads allows a more powerful engine to be used 
while achieving the same level of fuel economy. How consumers value 
increased fuel economy and how fuel economy regulations affect 
manufacturers' decisions about how to use efficiency improving 
technologies can have important effects on the estimated costs, 
benefits, and indirect impacts of fuel economy standards.
    NHTSA's preliminary regulatory impact analysis assumes that 
consumers will purchase, and manufacturers will supply, fuel economy 
technologies in the absence of fuel economy standards if the technology 
``pays for itself'' in fuel savings over the first 30 months vehicle 
use. This assumption is based on statements manufacturers have made to 
us and to NASEM CAFE committees and has been deployed in NHTSA's prior 
analyses of fuel economy standards. However, classical economic 
concepts suggest that deploying this assumption may be problematic when 
the baseline standards are binding--meaning that they constrain 
consumers' behavior to vehicles that are more fuel efficient than they 
would have chosen in the absence of fuel economy standards. To 
demonstrate this, we introduce a standard economic model of consumer 
optimization subject to a budgetary constraint.\336\
---------------------------------------------------------------------------

    \336\ Note that the following section examines whether consumers 
are rational in their fuel economy consumption patterns. This 
analysis could represent a scenario where consumers are rational, or 
one in which the underweight future fuel savings in their car 
purchasing decisions.
[GRAPHIC] [TIFF OMITTED] TP03SE21.088

    Figure III-17 models consumer behavior when constrained by a 
budget. Line B1 represents the consumer's original budget constraint. 
Curve I1 is called an indifference curve, which shows each combination 
of horsepower, which we use here to represent a variety of attributes 
that could be traded-off for increased fuel economy, and fuel savings 
between which a consumer is indifferent. The curvature of the 
indifference curve reflects the principle of diminishing marginal 
utility--the idea that consumers value consumption of the first unit of 
any product greater than subsequent units. Curve I1 represents the 
highest utility achievable when subject to budget constraint B1, as the 
consumer may select the combination of performance and fuel economy 
represented by point (HP1, FS1)--which is the point of tangency between 
I1 and B1. When new technology becomes available that

[[Page 49724]]

makes either fuel economy or performance (or both) more affordable, the 
consumer's budget constraint shifts from B1 to B2, and the consumer can 
now achieve the point of tangency between I2 and B2 (HP2, FS2). In this 
case, both fuel economy and performance are modeled as normal goods--
meaning that as they become more affordable, consumers will elect to 
consume more of each.
[GRAPHIC] [TIFF OMITTED] TP03SE21.089

    A different analysis is required when fuel economy standards also 
bind on consumer decisions. Here, minimum fuel economy standards 
eliminate some combinations of performance and fuel economy, creating a 
corner solution in the budget constraint. Figure III-18 shows this 
effect, as the consumer will elect the point of tangency with budget 
constraint B1 at the corner solution at (HP1 and FS1), which is also 
the minimum fuel economy standard. When new technology is introduced 
(or becomes cheaper) which makes fuel economy and performance more 
affordable, the consumer's budget constraint shifts from B1 to B2 
again, but the existing fuel economy standard is still binding, so a 
corner solution remains at FS1. The consumer will choose the corner 
combination of fuel economy and performance again, where I2 is tangent 
with B2, at point (FS1, HP2). Note that the consumer has elected to 
improve performance from HP1 to HP2 but has not elected to improve fuel 
economy.
    This model implies that fuel economy standards prevent consumers 
from achieving their optimal bundle of fuel economy and performance 
given their current preferences, creating an opportunity cost to 
consumers in the form of lost performance. The constrained optimization 
model can be slightly tweaked to show this loss to consumers. In this 
example, the y-axis uses the composite good M reflecting all other 
goods and services, including performance. This makes the 
interpretation of the y axis simpler, as it can be more easily 
translated into dollars.

[[Page 49725]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.090

    Figure III-19 shows the effect of new binding fuel economy 
standards on consumer behavior. The consumer begins at point (M1, FS1) 
on indifference curve I1. If more stringent fuel economy standards were 
in place, the consumer would shift to the lower indifference curve I2--
reflecting a lower level of utility--and would consume at point (M2, 
FS2). One concept from the economics literature for valuing the change 
in welfare from a change in prices or quality (or in this case fuel 
economy standards) is to look at the compensating variation between the 
original and final equilibrium. The compensating variation is the 
amount of money that a consumer would need to return to their original 
indifference curve.\337\ It is found by finding the point of tangency 
with the new indifference curve at the new marginal rate of 
substitution between the two products and finding the equivalent point 
on the old indifference curve. Figure III-19 shows this as the distance 
between points A and B on the Y-axis.\338\
---------------------------------------------------------------------------

    \337\ There is a very similar concept for valuing this 
opportunity cost known as the equivalent variation. NHTSA presents 
the compensating variation here for simplicity but acknowledges that 
the equivalent variation is an equally valid approach.
    \338\ Boardman, Greenberg, Vining, Weimer (2011). Cost-Benefit 
Analysis; Concepts and Practice. Pgs. 69-73.
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    The above logic appears to explain the trends in fuel economy and 
vehicle performance (measured by horsepower/pound) between 1986 and 
2004, when gasoline prices fluctuated between $2.00 and $2.50 per 
gallon and new light duty vehicle fuel economy standards remained 
nearly constant Figure III-20. Over the same period numerous advanced 
technologies with the potential to increase fuel economy were adopted. 
However, the fuel economy of new light duty vehicles did not increase. 
In fact, increases in the market share of light trucks caused fuel 
economy to decline somewhat.

[[Page 49726]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.091

    On the other hand, from 1986-2004 the acceleration performance of 
light-duty vehicles increased by 45% (Figure III-21). Advances in 
engine technology are reflected in the steadily increasing ratio of 
power output to engine size, measured by displacement. Without 
increased fuel economy standards, all the potential of advanced 
technology appears to have gone into increasing performance and other 
attributes (for example average weight also increased by 27% from 1986-
2004) and none to increasing fuel economy. Fuel economy remained nearly 
constant at the levels required by the car and light truck standards, 
consistent with the idea the standards were a binding constraint on the 
fuel economy of new vehicles. The pattern for periods of price shocks 
and increasing standards is different, however, as can be seen in 
Figure III-20. In the early period up to 1986, there is almost no 
change in performance and vehicle weight decreased. However, in the 
more recent period post-2004, performance continued to increase 
although apparently at a slower rate than during the 1986-2004 period 
and vehicle weight changed very little. The large and rapid price 
increases appear to have been an important factor. Even before 
manufacturers can respond to prices and regulations by adding fuel 
economy technologies to new vehicles, demand can respond by shifting 
towards smaller, lighter and less powerful makes and models. The period 
of voluntary increase in fuel economy is consistent with the 
constrained optimization problem presented above if fuel economy 
standards no longer constrained consumer behavior after the change in 
fuel prices.

[[Page 49727]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.092

    If this constrained optimization model is a reliable predictor of 
consumer behavior for some substantive portion of the new vehicle 
market, it would have important implications for how NHTSA models 
baseline consumer choices. In this case, it would mean that as 
technology that could improve fuel economy is added absent standards, 
it would be primarily geared towards enhancing performance rather than 
fuel economy. Depending on how consumers value future fuel savings, it 
might be appropriate for NHTSA to change its methods of analysis to 
reflect consumer preferences for performance, and to develop methods 
for valuing the opportunity cost to consumers for constraining them to 
more fuel efficient options. NHTSA seeks comment on the analysis 
presented in this section and its implications for the assumptions that 
consumers will add technologies that payback within thirty months. It 
also seeks comment on possible approaches to valuing the opportunity 
cost to consumers.
Potential Implications of Behavioral Theories for Fuel Economy 
Standards
    In this proposed rule, the cost-effectiveness of technology-based 
fuel economy improvements is used to estimate fuel economy improvements 
by manufacturers in the No-Policy case and to estimate components of 
the benefits and costs of alternative increases in fuel economy 
standards. In the interest of insuring that our theory and methods 
reflect the best current understanding of how consumers perceive the 
value of technology-based fuel economy improvements, we are seeking 
comment on our current, and possible alternative representations of how 
consumers value fuel economy when purchasing a new vehicle and while 
owning and operating it, and how manufacturers decide to implement fuel 
economy technologies.\339\ We are particularly interested in comments 
on our assumption that in our Alternative 0 (no change in existing 
standards) manufacturers will implement technologies to improve fuel 
economy even if existing standards do not require them to do so, 
provided that the first 30 months of fuel savings will be greater than 
or equal to the cost of the technology. We are also interested in 
comments concerning our use of the difference between the price 
consumers pay for increased fuel economy and the value of fuel savings 
over the first 30 month for estimating the impacts of the standards on 
new and used vehicle markets. Finally, we are interested in comments on 
when attributes that can be traded-off for increased fuel economy 
should be considered opportunity costs of increasing fuel economy.
---------------------------------------------------------------------------

    \339\ We are making a distinction between consumers choices when 
presented with technology-based fuel economy improvements versus 
consumers' choices among various makes and models of vehicles. The 
latter topic is also of interest and is discussed in (see TSD, Ch. 
4.2.1).
---------------------------------------------------------------------------

    How manufacturers choose to implement technologies that can 
increase fuel economy depends on consumers' willingness to pay (WTP) 
for fuel economy and the other attributes the technologies can improve. 
Consumers' WTP for increasing levels of an attribute defines the 
consumers' demand function for that attribute. Here, we consider how 
consumers' WTP for increased fuel economy (WTPFE) and for 
performance (WTPHP), where FE stands for fuel 
economy and HP stands for ``Horse Power''/performance, and 
the cost of technology (C) affect manufacturers' decisions about how to 
implement the technologies with and without fuel economy standards. For 
the purpose of this discussion, it is convenient to think of fuel 
economy in terms of its inverse, the rate of fuel consumption per mile. 
While miles per gallon (mpg) delivers decreasing fuel savings per mpg, 
decreasing fuel consumption delivers constant fuel savings per gallon 
per mile (gpm) reduced. Thinking in terms of gpm is appropriate because 
fuel economy standards are in fact defined in terms of the inverse of 
fuel economy, i.e., gpm.
    In the CAFE Model we typically assume that for a technology that 
can improve fuel economy, consumers are willing to pay an amount equal 
to the first thirty months of fuel savings (WTP30FE). This 
is an important assumption for several reasons. The market will tend to 
equilibrate the ratio of consumers' WTP for fuel economy

[[Page 49728]]

divided by its cost to the ratio of consumers' WTP for other attributes 
divided by their cost. The value of the first thirty months of fuel 
savings is typically about one-fourth of the value of savings over the 
expected life of a vehicle, discounted at annual rates between 3% and 
7%. Arguably, this represents an important undervaluing of technology-
based fuel economy improvement relative to its true economic value. Our 
use of the 30-month payback assumption is based on statements 
manufacturers have made to us and to NASEM CAFE committees. It is also 
based on the fact that repeated assessments of the potential for 
technology to improve fuel economy have consistently found a 
substantial potential to cost-effectively increase fuel economy. But it 
is also partly based on the fact that the substantial literature that 
has endeavored to infer consumers' WTP for fuel economy is 
approximately evenly divided between studies that support severe 
undervaluation and those that support valuation at approximately full 
lifetime discounted present value (e.g., Greene et al., 2018; Helfand 
and Wolverton, 2011; Greene, 2010; for a more complete discussion see 
TSD, Ch. 6.1.6). The most recent studies based on detailed data and 
advanced methods of statistical inference have not resolved the issue 
(NASEM, 2021, Ch. 11.3).
    If consumers value technology-based fuel economy improvements at 
only a small fraction of their lifetime present value and the market 
equates WTP30FE/C to WTPHP/C, the market will 
tend to oversupply performance relative to fuel economy (Allcott et 
al., 2014; Heutel, 2015). The WTP30FE assumption also has 
important consequences when fuel economy standards are in effect. 
Alternative 0 in this proposed rule assumes not only that the SAFE 
standards are in effect but that the manufacturers who agreed to the 
California Framework will be bound by that agreement. If those existing 
regulations are binding, it is likely that WTPHP > 
WTP30FE. (For simplicity we assume that over the range of 
fuel economy and performance achievable by the technology, both WTP 
values are constant.)\340\ This outcome would be expected in a market 
where consumers undervalue fuel savings in their normal car buying 
decisions and standards require levels of fuel economy beyond what they 
are willing to pay.\341\ This is illustrated in Figure III-22. The 
initial consumer demand function for vehicles (D0) is 
shifted upward by WTP30FE to represent the consumer demand 
function for the increased fuel economy the technology could produce 
(D30FE) and by WTPHP to represent the demand 
function (DHP) for the potential increase in performance. 
Because the technology has a cost (C), the manufacturers' supply 
function (S0) shifts upward to S1 = S0 
+ C.\342\ If the cost of the technology exceeds consumers' WTP for 
either the fuel economy or the performance it can deliver, the 
technology will not be adopted in the absence of regulations requiring 
it. In Figure III-22 we show the case where C < WTP30FE < 
WTPHP. In this case, using the technology to increase 
performance provides the greatest increase in sales and revenues: 
QHP > Q30FE > Q0. Since both WTP 
values are assumed to be approximately constant over the range of 
improvement the technology can provide, there is no possible 
combination of fuel economy and performance improvement that would 
produce a larger increase in sales than using the technology entirely 
to increase performance.\343\ Importantly, as long as C < 
WTPHP, the actual cost of the technology does not affect the 
manufacturer's decision to use 100% of its potential to increase 
performance and 0% to increase fuel economy. The technology's payback 
period for the increase in fuel economy is irrelevant. If we reverse 
the relative WTP values (i.e., WTP30FE > WTPHP), 
then the manufacturer will choose to use 100% of the technology's 
potential to increase fuel economy and 0% to increase performance, 
assuming constant WTP values.\344\ This conclusion may contradict our 
current method, which assumes that even with increasing fuel economy 
standards in Alternative 0, manufacturers will adopt fuel economy 
technologies with WTP30FE < C and use them to increase fuel 
economy rather than performance.
---------------------------------------------------------------------------

    \340\ Although there are diminishing returns to increased miles 
per gallon, in terms of fuel savings in gallons or dollars, there 
are not diminishing returns to reductions in fuel consumption per 
mile, except due to decreasing marginal utility of income. 
WTPHP likely decreases with increasing performance, but 
if the changes are not too large, the assumption of constant WTP is 
reasonable.
    \341\ If there are no binding regulatory constraints and fuel 
economy and other vehicle attributes are normal goods, consumers 
will elect more of each in the event technological progress makes it 
possible to afford them. This simplifying assumption is consistent 
with a scenario where consumers' baseline vehicle choices are 
constrained by regulatory standards. See above for more discussion.
    \342\ The supply function for new cars is assumed to be 
perfectly elastic for the sake of simplicity of exposition. Note 
that if the cost of the technology exceeds consumers' WTP for both 
fuel economy and performance, the technology will not be adopted in 
the absence of regulations requiring it.
    \343\ In fact, all that is required is that over the range of 
increases achievable by the technology, WTPHP > 
WTPFE.
    \344\ However, as noted above, the market will tend to equate 
WTPHP/C to WTPFE/C, so if there is sufficient 
variation in WTPHP over the range of values achievable by 
the technology, some of each will be provided.

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[[Page 49729]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.093

    Because the expected present value of fuel savings is several times 
the 30-month value, it is quite possible that the WTP for performance 
lies between the lifetime present value of fuel savings and the 30-
month value: WTPPVFE > WTPHP > 
WTP30FE. This possibility is illustrated in Figure III-23, 
in which there are three demand functions in addition to the initial 
demand function, D0. In Figure III-23, if the consumer were 
willing to pay for the full present value of fuel savings, the 
technology would be applied 100% to increasing fuel economy, provided C 
< WTPPVFE. But if standards were binding and the consumer 
were willing to pay for only 30 months of fuel savings, the technology 
would be applied 100% to increasing performance, provided C < 
WTPHP. Suppose that the cost of the technology is not C, but 
a much smaller value, say c < C and c < WTP30FE. Assuming 
consumers value increased fuel economy at WTP30FE, it 
remains the case that all the technology's potential will be applied to 
increasing performance because that gives the greatest increase in 
sales. The implication is that when there is a binding fuel economy 
standard, as long as WTPHP > WTP30FE, no 
technologies would be used to increase fuel economy in the absence of a 
regulatory requirement to do so. If consumers' WTP for fuel economy is 
WTP30FE and regulatory standards are binding, 
WTPHP > WTPFE seems likely.
    If WTP30FE < WTPHP (recalling that HP can 
represent attributes in addition to fuel economy), the above analysis 
of producer behavior contradicts the current operation of the CAFE 
Model, which assumes that manufacturers will apply technologies whose 
costs are less than WTP30FE to improving fuel economy in the 
absence of regulations requiring them to do so. For the final rule, 
NHTSA is considering changing the assumption that in the absence of 
standards that require it, manufactures will adopt technologies to 
improve fuel economy that have a payback period of 30 months or less, 
in favor of the above analysis. We are interested in receiving comments 
that specifically address the validity of the current and proposed 
approach.
    As discussed in TSD Chapter 4.2.1.1, there is no consensus in the 
literature about how consumers value fuel economy improvements when 
making vehicle purchases. In this and past analyses, we have assumed 
that consumers value only the first 30 months of fuel savings when 
making vehicle purchase decisions. This value is a small fraction, 
approximately one fourth of the expected present value of future fuel 
savings over the typical life of a light-duty vehicle, assuming 
discount rates in the range of 3% to 7% per year. On the other hand, 
when estimating the societal value of fuel economy improvements, we use 
the full present value of discounted fuel savings over the expected 
life of the vehicle because it represents a real resource savings. 
However, the possibility that consumers' perceptions of utility at the 
time of purchase (decision utility) may differ from the utility 
consumers experience while consuming a good and that experienced 
utility may be the preferrable metric for policy evaluation has been 
raised in the economic literature (Kahneman and Sugden, 2005). In our 
methods, we use WTP30FE to represent consumers' decision 
utility. Gallons saved over the life of a vehicle, valued at the 
current price of gasoline, and discounted to present value appears to 
be an appropriate measure of experienced utility. The large difference 
between our measure of decision utility and lifetime present value fuel 
savings as a measure of experienced utility has potentially important 
implications for how we estimate the impacts of fuel economy standards 
on new vehicle sales and the used vehicle market. It seems plausible 
that as consumers experience the fuel savings benefits of increased 
fuel economy, their valuation of the fuel economy increases required by 
regulation may adjust over time towards the full lifetime discounted 
present value. In addition, behavioral economic theory accepts that 
consumers' willingness to pay for fuel economy may change depending on 
the context of consumers' car purchase decisions. The implications of 
such possibilities are analyzed below. We are interested in

[[Page 49730]]

how they might affect our current methods for estimate the impacts of 
standards on new vehicle sales and the used vehicle market, and whether 
any changes to our current methods are appropriate.
    The existence of fuel economy standards changes manufacturers' 
decision making. First, if a standard is set at a level that requires 
only part of the technological potential to increase fuel economy, if C 
< WTPHP, and WTPHP > WTP30FE, the 
remainder of the technology's potential will be used to provide some 
increase in performance. This appears to have occurred post 2004 when 
the rate of improvement in performance slowed while fuel economy 
improved. Assuming that consumers value fuel economy improvement at 
time of purchase at WTP30FE, there would be a consumers' 
surplus cost of foregone performance equal to the cross-hatched 
trapezoid in Figure III-23. The foregone performance cost will be less 
than what it would have been if none of the technology's potential to 
increase fuel economy were used to increase performance. Even if the 
cost of the technology is less than WTP30FE, the technology 
will be applied to improve fuel economy only up to the required level 
and the remainder of its potential will be used to increase 
performance. If the cost of applying enough of the technology to 
achieve the fuel economy standard is greater than WTPHP, 
there would be no cost of foregone performance since the cost of 
applying the technology to increasing fuel economy exceeds its 
opportunity cost when applied to increase performance.\345\ In that 
case, the technology cost represents the full cost of the fuel economy 
improvement, since that cost exceeds consumers' WTP for the performance 
it could produce. On the other hand, if under regulatory standards 
consumers valued fuel economy at WTPPVFE, there would also 
be no opportunity cost of performance because WTPPVFE > 
WTPHP.
---------------------------------------------------------------------------

    \345\ This is because using the technology to increase 
performance would not be the second-best use of the cost of 
increasing fuel economy. The second-best use would instead be to 
invest the cost at a market rate of return.
[GRAPHIC] [TIFF OMITTED] TP03SE21.094

    Because the CAFE Model estimates the effects of standards on new 
vehicle sales and scrappage based on the difference between the cost of 
technology and the perceived value of fuel savings at the time a new 
vehicle is purchased, whether consumers perceive the value differently 
in regulated and unregulated markets is an important question. 
Traditional utility theory of consumer decision making does not allow 
that consumers' preference rankings depend on the context of the 
choices they make. However, in addition to the theory of utility 
maximizing rational economic behavior, modern economics includes the 
insights and findings of behavioral economics, which has established 
many examples of human decision making that differ in important ways 
from the rational economic model. In particular, the behavioral model 
allows the possibility that consumers' preferences and decision-making 
processes often do change depending on the context or framing of 
choices. The possibility that behavioral theories of decision making 
may be useful for understanding how consumers value fuel economy and 
for evaluating the costs and benefits of fuel economy standards was 
noted in the most recent NASEM (2021) report. An explanation of the 
different contexts helps to illustrate this point. If a consumer is 
thinking about buying a new car and is looking at two models, one that 
includes fuel economy technology and is more expensive and another that 
does not, she may buy the cheaper, less fuel efficient version even if 
the more expensive model will save

[[Page 49731]]

money in the long run. But if, instead, the consumer is faced with 
whether to buy a new car at all as opposed to keeping an older one, if 
all new cars contain technology to meet fuel economy standards then she 
may view the decision differently. Will, for example, an extra $1,000 
for a new car--a $1,000 that the consumer will more than recoup in fuel 
savings--deter her from buying the new car, especially when most 
consumers finance cars over a number of years rather than paying the 
$1,000 cost up front and will therefore partly or entirely offset any 
increase in monthly payment with lower fuel costs? In addition, the 
fact that standards generally increase gradually over a period of years 
allows time for consumers and other information sources to verify that 
fuel savings are real and of substantial value.
    The CAFE Model's representation of consumers' vehicle choices under 
regulation reflects the ``Gruenspecht Effect'', the theory that 
regulation will inevitably cause new vehicles to be less desirable than 
they would have been in the absence of regulation, which will 
inevitably lead to reduced new vehicle sales, higher prices for used 
vehicles and slower turnover of the vehicle stock. However, if 
consumers severely undervalue fuel savings at the time of vehicle 
purchase, not only is that itself a market failure (a large discrepancy 
between decision and experienced utility) but it raises important 
questions about what causes such undervaluation and whether consumers' 
perceptions may change as the benefits of increased fuel economy are 
realized or whether the different framing of new vehicle choices in a 
regulated market might partially or entirely mitigate that 
undervaluation. The 2021 NASEM report asserts that if the behavioral 
model is correct, consumers might value fuel savings at or near their 
full lifetime discounted present value, potentially reversing the 
Gruenspecht Effect.
    ``On the other hand, the Gruenspecht effect is not predicted by the 
behavioral model, under which it is not only possible but likely that 
if the fuel savings from increased fuel economy exceed its cost, 
consumers will find the more fuel-efficient vehicles required by 
regulation to be preferable to those that would otherwise have been 
produced.'' ``It is possible that sales would increase rather than 
decrease and likewise manufacturers' profits. In that case, increased 
new vehicle sales would reduce used vehicle prices, benefiting buyers 
of used vehicles and accelerating the turnover of the vehicle stock.'' 
\346\
---------------------------------------------------------------------------

    \346\ NASEM, 2021, p. 11-357.
---------------------------------------------------------------------------

    NHTSA is interested in comments that can help contribute to 
resolving or improving our understanding of this issue and its 
implications for how the costs and benefits of fuel economy standards 
should be estimated.
(2) Refueling Benefit
    Increasing CAFE standards, all else being equal, affect the amount 
of time drivers spend refueling their vehicles in several ways. First, 
they increase the fuel economy of ICE vehicles produced in the future, 
which increases vehicle range and decreases the number of refueling 
events for those vehicles. Conversely, to the extent that more 
stringent standards increase the purchase price of new vehicles, they 
may reduce sales of new vehicles and scrappage of existing ones, 
causing more VMT to be driven by older and less efficient vehicles 
which require more refueling events for the same amount of VMT driven. 
Finally, sufficiently stringent standards may also change the number of 
electric vehicles that are produced, and shift refueling to occur at a 
charging station, rather than at the pump--changing per-vehicle 
lifetime expected refueling costs.
    The agency estimates these savings by calculating the amount of 
refueling time avoided--including the time it takes to find, refuel, 
and pay--and multiplying it by DOT's value of time of travel savings 
estimate. For a full description of the methodology, refer to TSD 
Chapter 6.1.4.
(3) Additional Mobility
    Any increase in travel demand provides benefits that reflect the 
value to drivers and other vehicle occupants of the added--or more 
desirable--social and economic opportunities that become accessible 
with additional travel. Under the alternatives in this analysis, the 
fuel cost per mile of driving would decrease as a consequence of the 
higher fuel economy levels they require, thus increasing the number of 
miles that buyers of new cars and light trucks would drive as a 
consequence of the well-documented fuel economy rebound effect.
    The fact that drivers and their passengers elect to make more 
frequent or longer trips to gain access to these opportunities when the 
cost of driving declines demonstrates that the benefits they gain by 
doing so exceed the costs they incur. At a minimum, the benefits must 
equal the cost of the fuel consumed to travel the additional miles (or 
they would not have occurred). The cost of that energy is subsumed in 
the simulated fuel expenditures, so it is necessary to account for the 
benefits associated with those miles traveled here. But the benefits 
must also offset the economic value of their (and their passengers') 
travel time, other vehicle operating costs, and the economic cost of 
safety risks due to the increase in exposure that occurs with 
additional travel. The amount by which the benefits of this additional 
travel exceeds its economic costs measures the net benefits drivers and 
their passengers experience, usually referred to as increased consumer 
surplus.
    TSD Chapter 6.1.5 explains the agency's methodology for calculating 
additional mobility.
2. External Costs and Benefits
(a) Costs
(1) Congestion and Noise
    Increased vehicle use associated with the rebound effect also 
contributes to increased traffic congestion and highway noise. Although 
drivers obviously experience these impacts, they do not fully value 
their impacts on other system users, just as they do not fully value 
the emissions impacts of their own driving. Congestion and noise costs 
are ``external'' to the vehicle owners whose decisions about how much, 
where, and when to drive more--or less--in response to changes in fuel 
economy result in these costs. Therefore, unlike changes in the costs 
incurred by drivers for fuel consumption or safety risks they willingly 
assume, changes in congestion and noise costs are not offset by 
corresponding changes in the travel benefits drivers experience.
    Congestion costs are limited to road users; however, since road 
users include a significant fraction of the U.S. population, changes in 
congestion costs are treated as part of the rule's economic impact on 
the broader society instead of as a cost or benefit to private parties. 
Costs resulting from road and highway noise are even more widely 
dispersed, because they are borne partly by surrounding residents, 
pedestrians, and other non-road users, and for this reason are also 
considered as a cost to the society as a whole.
    To estimate the economic costs associated with changes in 
congestion and noise caused by differences in miles driven, the agency 
updated the underlying components of the cost estimates of per-mile 
congestion and noise costs from increased automobile and light truck 
use provided in FHWA's 1997 Highway Cost Allocation Study. The agencies 
previously relied on this study in the 2010, 2011, and 2012 final 
rules, and updating the individual underlying components for congestion

[[Page 49732]]

costs in this analysis improves currency and internal consistency with 
the rest of the analysis. See TSD Chapter 6.2 for details on how the 
agency calculated estimate the economic costs associated with changes 
in congestion and noise caused by differences in miles driven. NHTSA 
specifically seeks comment on the congestion costs employed in this 
analysis, and whether and how to change them for the analysis for the 
final rule.
(2) Fuel Tax Revenue
    As mentioned in III.G.1.b)(1), a portion of the fuel savings 
experienced by consumers includes avoided fuel taxes. While fuel taxes 
are treated as a transfer within the analysis and do not affect net 
benefits, the agency provides an estimate here to show the potential 
impact to state and local governments.
(b) Benefits
(1) Reduced Climate Damages
    Extracting and transporting crude petroleum, refining it to produce 
transportation fuels, and distributing fuel generate additional 
emissions of GHGs and criteria air pollutants beyond those from cars' 
and light trucks' use of fuel. By reducing the volume of petroleum-
based fuel produced and consumed, adopting higher CAFE standards will 
thus mitigate global climate-related economic damages caused by 
accumulation of GHGs in the atmosphere, as well as the more immediate 
and localized health damages caused by exposure to criteria pollutants. 
Because they fall broadly on the U.S.--and global, in the case of 
climate damages--population, reducing them represents an external 
benefit from requiring higher fuel economy.
    NHTSA estimates the global social benefits of CO2, 
CH4, and N2O emission reductions expected from 
this proposed rule using the social cost of greenhouse gases (SC-GHG) 
estimates presented in the Technical Support Document: Social Cost of 
Carbon, Methane, and Nitrous Oxide Interim Estimates under Executive 
Order 13990 (``February 2021 TSD''). These SC-GHG estimates are interim 
values developed under Executive Order (E.O.) 13990 for use in benefit-
cost analyses until updated estimates of the impacts of climate change 
can be developed based on the best available science and economics. 
NHTSA uses the SC-GHG interim values to estimate the benefits of 
decreased fuel consumption stemming from the proposal.
    The SC-GHG estimates used in our analysis were developed over many 
years, using transparent process, peer-reviewed methodologies, the best 
science available at the time of that process, and with input from the 
public. Specifically, in 2009, an interagency working group (IWG) that 
included the DOT and other executive branch agencies and offices was 
established to ensure that agencies were using the best available 
science and to promote consistency in the social cost of carbon dioxide 
(SC-CO2) values used across agencies. The IWG published SC-
CO2 estimates in 2010. These estimates were updated in 2013 
based on new versions of each IAM. In August 2016 the IWG published 
estimates of the social cost of methane (SC-CH4) and nitrous 
oxide (SC-N2O) using methodologies that are consistent with 
the methodology underlying the SC-CO2 estimates. Executive 
Order 13990 (issued on January 20, 2021) re-established the IWG and 
directed it to publish interim SC-GHG values for CO2, 
CH4, and N2O within thirty days. Furthermore, the 
E.O. tasked the IWG with devising long-term recommendations to update 
the methodologies used in calculating these SC-GHG values, based on 
``the best available economics and science,'' and incorporating 
principles of ``climate risk, environmental justice, and 
intergenerational equity''.\347\ The E.O. also instructed the IWG to 
take into account the recommendations from the NAS committee convened 
on this topic, published in 2017.\348\ The February 2021 TSD provides a 
complete discussion of the IWG's initial review conducted under E.O. 
13990.
---------------------------------------------------------------------------

    \347\ Executive Order on Protecting Public Health and the 
Environment and Restoring Science to Tackle the Climate Crisis. 
(2021). Available at https://www.whitehouse.gov/briefing-room/presidential-actions/2021/01/20/executive-order-protecting-public-health-and-environment-and-restoring-science-to-tackle-climate-crisis/.
    \348\ National Academies of Science (NAS). (2017). Valuing 
Climate Damage: Updating Estimation of the Social Cost of Carbon 
Dioxide. Available at https://www.nap.edu/catalog/24651/valuing-climate-damages-updating-estimation-of-the-social-cost-of.
---------------------------------------------------------------------------

    NHTSA is using the IWG's interim values, published in February 2021 
in a technical support document, for the CAFE analysis in this 
NPRM.\349\ This approach is the same as that taken in DOT regulatory 
analyses over 2009 through 2016. If the IWG issues new estimates before 
the final rule, the agency will consider revising the estimates within 
the CAFE Model time permitting. We request comment on this approach to 
estimating social benefits of reducing GHG emissions in this rulemaking 
in light of the ongoing interagency process.
---------------------------------------------------------------------------

    \349\ Interagency Working Group on Social Cost of Greenhouse 
Gases, United States Government. (2021). Technical Support Document: 
Social Cost of Carbon, Methane, and Nitrous Oxide Interim Estimates 
under Executive Order 13990, available at https://www.whitehouse.gov/wp-content/uploads/2021/02/TechnicalSupportDocument_SocialCostofCarbonMethaneNitrousOxide.pdf?source=email.
---------------------------------------------------------------------------

    NHTSA notes that the primary analysis for this proposal estimates 
benefits from reducing emissions of CO2 and other GHGs that 
incorporate a 2.5% discount rate for distant future climate damages, 
while discounting costs and non-climate related benefits using a 3% 
rate. NHTSA also presents cost and benefits estimates in the primary 
analysis that reflect a 3% discount rate for reductions in climate-
related damages while discounting costs and non-climate related 
benefits at 7%. NHTSA believes this approach represents an appropriate 
treatment of the intergenerational issues presented by emissions that 
result in climate-related damages over a very-long time horizon, and is 
within scope of the IWG's Technical Support Document: Social Cost of 
Carbon, Methane, and Nitrous Oxide that recommends discounting future 
climate damages at rates of 2.5%, 3%, and 5%.\350\
---------------------------------------------------------------------------

    \350\ Interagency Working Group on Social Cost of Greenhouse 
Gases, United States Government, Technical Support Document: Social 
Cost of Carbon, Methane, and Nitrous Oxide, Interim Estimates under 
Executive Order 13990, February 2021.
---------------------------------------------------------------------------

    In addition, NHTSA emphasize the importance and value of 
considering the benefits calculated using all four SC-GHG estimates for 
each of three greenhouse gases. NHTSA includes the social costs of 
CO2, CH4, and N2O calculated using the 
four different estimates recommended in the February 2021 TSD (model 
average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th 
percentile at 3 percent discount rate) in the PRIA.
    The February 2021 TSD does not specify how agencies should combine 
its estimates of benefits from reducing GHG emissions that reflect 
these alternative discount rates with the discount rates for nearer-
term benefits and costs prescribed in OMB Circular A-4. Instead, it 
provides agencies with broad flexibility in implementing the February 
2021 TSD. However, the February 2021 TSD does identify 2.5% as the 
``average certainty-equivalent rate using the mean-reverting and random 
walk approaches from Newell and Pizer (2003) starting at a discount 
rate of 3 percent.'' \351\ As such, NHTSA believes using a 2.5% 
discount rate for climate-related damages is consistent with the IWG 
guidance.
---------------------------------------------------------------------------

    \351\ Ibid.
---------------------------------------------------------------------------

    This section provides further discussion of the discount rates that 
NHTSA uses in its regulatory analysis

[[Page 49733]]

and presents results of a sensitivity analysis using a 3% discount rate 
for reductions in climate-related damages. NHTSA welcomes public 
comment on its selection of 2.5% for climate-related damages and will 
consider other discount rates for the final rule.
    For a full discussion of the agency's quantification of GHGs, see 
TSD Chapter 6.2.1 and the PRIA.
(a) Discount Rates Accounting for Intergenerational Impacts
    A standard function of regulatory analysis is to evaluate tradeoffs 
between impacts that occur at different points in time. Many, if not 
most, Federal regulations involve costly upfront investments that 
generate future benefits in the form of reductions in health, safety, 
or environmental damages. To evaluate these tradeoffs, the analysis 
must account for the social rate of time preference--the broadly 
observed social preference for benefits that occur sooner versus those 
that occur further in the future.\352\ This is accomplished by 
discounting impacts that occur further in the future more than impacts 
that occur sooner.
---------------------------------------------------------------------------

    \352\ This preference is observed in many market transactions, 
including by savers that expect a return on their investments in 
stocks, bonds, and other equities; firms that expect positive rates 
of return on major capital investments; and banks that demand 
positive interest rates in lending markets.
---------------------------------------------------------------------------

    OMB Circular A-4 affirmed the appropriateness of accounting for the 
social rate of time preference in regulatory analyses and prescribed 
discount rates of 3% and 7% for doing so. The 3% discount rate was 
chosen to represent the ``consumption rate of interest'' approach, 
which discounts future costs and benefits to their present values using 
the rate at which consumers appear to make tradeoffs between current 
consumption and equal consumption opportunities deferred to the future. 
OMB Circular A-4 reports a real rate of return on 10-year Treasury 
notes of 3.1% between 1973 and its 2003 publication date and interprets 
this as approximating the rate at which society is indifferent between 
consumption today and in the future.
    The 7% rate reflects the opportunity cost of capital approach to 
discounting, where the discount rate approximates the foregone return 
on private investment if the regulation were to divert resources from 
capital formation. OMB Circular A-4 cites pre-tax rates of return on 
capital as part of its selection of the 7% rate.\353\ The IWG rejected 
the use of the opportunity cost of capital approach to discounting 
reductions in climate-related damages because ``consumption rate of 
interest is the correct discounting concept to use when future damages 
from elevated temperatures are estimated in consumption-equivalent 
units as is done in the IAMs used to estimate the SC-GHG (National 
Academies 2017).'' \354\
---------------------------------------------------------------------------

    \353\ OMB Circular A-4.
    \354\ Interagency Working Group on Social Cost of Greenhouse 
Gases, United States Government, Technical Support Document: Social 
Cost of Carbon, Methane, and Nitrous Oxide, Interim Estimates under 
Executive Order 13990, February 2021.
---------------------------------------------------------------------------

    As the IWG states, ``GHG emissions are stock pollutants, where 
damages are associated with what has accumulated in the atmosphere over 
time, and they are long lived such that subsequent damages resulting 
from emissions today occur over many decades or centuries depending on 
the specific greenhouse gas under consideration.''\355\ OMB Circular A-
4 states that impacts occurring over such intergenerational time 
horizons require special treatment:
---------------------------------------------------------------------------

    \355\ Ibid.

    Special ethical considerations arise when comparing benefits and 
costs across generations. Although most people demonstrate time 
preference in their own consumption behavior, it may not be 
appropriate for society to demonstrate a similar preference when 
deciding between the well-being of current and future generations. 
Future citizens who are affected by such choices cannot take part in 
making them, and today's society must act with some consideration of 
their interest.\356\
---------------------------------------------------------------------------

    \356\ OMB Circular A-4.

    In addition to the ethical considerations, Circular A-4 also 
identifies uncertainty in long-run interest rates as a potential 
justification for using lower rates to discount intergenerational 
impacts. As Circular A-4 states, ``Private market rates provide a 
reliable reference for determining how society values time within a 
generation, but for extremely long time periods no comparable private 
rates exist.''\357\ The social costs of distant future climate 
damages--and by implication, the value of reducing them by lowering 
emissions of GHGs--are highly sensitive to the discount rate, and the 
present value of reducing climate damages grows at an increasing rate 
as the discount rate used in the analysis declines. This ``non-
linearity'' means that even if uncertainty about the exact value of the 
long-run interest rate is equally distributed between values above and 
below the 3% consumption rate of interest, the probability-weighted (or 
``expected'') present value of a unit reduction in climate damages will 
be higher than the value calculated using a 3% discount rate. The 
effect of such uncertainty about the correct discount rate can thus be 
accounted for by using a lower ``certainty-equivalent'' rate to 
discount distant future damages.
---------------------------------------------------------------------------

    \357\ Ibid.
---------------------------------------------------------------------------

    The IWG identifies ``a plausible range of certainty-equivalent 
constant consumption discount rates: 2.5, 3, and 5 percent per year.'' 
The IWG's justification for its selection of these rates is summarized 
in this excerpt from its 2021 guidance:
    The 3 percent value was included as consistent with estimates 
provided in OMB's Circular A-4 (OMB 2003) guidance for the consumption 
rate of interest. . . .The upper value of 5 percent was included to 
represent the possibility that climate-related damages are positively 
correlated with market returns, which would imply a certainty 
equivalent value higher than the consumption rate of interest. The low 
value, 2.5 percent, was included to incorporate the concern that 
interest rates are highly uncertain over time. It represents the 
average certainty-equivalent rate using the mean-reverting and random 
walk approaches from Newell and Pizer (2003) starting at a discount 
rate of 3 percent. Using this approach, the certainty equivalent is 
about 2.2 percent using the random walk model and 2.8 percent using the 
mean reverting approach. Without giving preference to a particular 
model, the average of the two rates is 2.5 percent. Additionally, a 
rate below the consumption rate of interest would also be justified if 
the return to investments in climate mitigation are negatively 
correlated with the overall market rate of return. Use of this lower 
value was also deemed responsive to certain judgments based on the 
prescriptive or normative approach for selecting a discount rate and to 
related ethical objections that have been raised about rates of 3 
percent or higher.
    Because the certainty-equivalent discount rate will lie 
progressively farther below the best estimate of the current rate as 
the time horizon when future impacts occur is extended, the IWG's 
recent guidance also suggest that it may be appropriate to use a 
discount rate that declines over time to account for interest rate 
uncertainty, as has been recommended by the National Academies and 
EPA's Science Advisory Board.\358\ The IWG mentioned that it will 
consider these recommendations and the relevant academic literature on 
declining rates in developing its final

[[Page 49734]]

guidance on the social cost of greenhouse gases.
---------------------------------------------------------------------------

    \358\ Interagency Working Group on Social Cost of Greenhouse 
Gases, United States Government, Technical Support Document: Social 
Cost of Carbon, Methane, and Nitrous Oxide, Interim Estimates under 
Executive Order 13990, February 2021.
---------------------------------------------------------------------------

    The IWG 2021 interim guidance also presented new evidence on the 
consumption-based discount rate suggesting that a rate lower than 3% 
may be appropriate. For example, the IWG replicated OMB Circular A-4's 
original 2003 methodology for estimating the consumption rate using the 
average return on 10-year Treasury notes over the last 30 years and 
found a discount rate close to 2%. They also presented rates over a 
longer time horizon, finding an average rate of 2.3% from 1962 to the 
present. Finally, they summarized results from surveys of experts on 
the topic and found a ``surprising degree of consensus'' for using a 2% 
consumption rate of interest to discount future climate-related 
impacts.\359\
---------------------------------------------------------------------------

    \359\ Ibid.
---------------------------------------------------------------------------

    NHTSA expects that the Interagency Working Group will continue to 
develop its final guidance on the appropriate discount rates to use for 
reductions in climate damages as NHTSA develops its final rule. If new 
guidance is issued in time for NHTSA's final rule, NHTSA will 
incorporate the IWG's updated guidance in the final regulatory 
analysis.
(b) Discount Rates Used in This Proposal for Climate-Related Benefits
    As indicated above, NHTSA's primary analysis presents cost and 
benefit estimates using a 2.5% discount rate for reductions in climate-
related damages and 3% for non-climate related impacts. NHTSA also 
presents cost and benefits estimates using a 3% discount rate for 
reductions in climate-related damages alongside estimates of non-
climate related impacts discounted at 7%. This latter pairing of a 3% 
rate for discounting benefits from reducing climate-related damages 
with a 7% discount rate for non-climate related impacts is consistent 
with NHTSA's past practice.\360\ However, NHTSA's pairing of 2.5% for 
climate-related damage reductions with 3% for non-climate related 
impacts is novel in this proposal.
---------------------------------------------------------------------------

    \360\ See, e.g., the 2012 and 2020 final CAFE rules.
---------------------------------------------------------------------------

    As discussed above, the IWG's guidance indicates that uncertainty 
in long-run interest rates suggests that a lower ``certainty-
equivalent'' discount rate is appropriate for intergenerational 
impacts, and identifies 2.5%, 3%, and 5% as ``certainty-equivalent'' 
discount rates. NHTSA emphasizes the importance and value of 
considering the benefits calculated using all four SC-GHG estimates for 
each of three greenhouse gases. NHTSA includes the social costs of 
CO2, CH4, and N2O calculated using the 
four different estimates recommended in the February 2021 TSD (model 
average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th 
percentile at 3 percent discount rate) in the PRIA. For presentation 
purposes in this rule, NHTSA shows two primary estimates. NHTSA 
believes that pairing OMB's 3% estimate of the consumption discount 
rate for near-term costs and benefits with the IWG's lower certainty-
equivalent rate of 2.5% is consistent with current interim guidance in 
the February 2021 TSD. NHTSA also believe that its pairing of the 3% 
certainty-equivalent rate for climate-related benefits with OMB's 7% 
discount rate is consistent with guidance from the February 2021 TSD 
for GHGs and OMB Circular A-4 for other costs and benefits.
    In addition, NHTSA presents a sensitivity analysis where both 
distant future and nearer-term GHG impacts are discounted using the 3% 
rate combined with all other costs and benefits discounted at 3%.
[GRAPHIC] [TIFF OMITTED] TP03SE21.095

[GRAPHIC] [TIFF OMITTED] TP03SE21.096


[[Page 49735]]


NHTSA seeks comment on the above discussion.
(2) Reduced Health Damages
    The CAFE Model estimates monetized health effects associated with 
emissions from three criteria pollutants: NOX, 
SOx, and PM2.5. As discussed in Section III.F 
above, although other criteria pollutants are currently regulated, only 
impacts from these three pollutants are calculated since they are known 
to be emitted regularly from mobile sources, have the most adverse 
effects to human health, and there exist several papers from the EPA 
estimating the benefits per ton of reducing these pollutants. Other 
pollutants, especially those that are precursors to ozone, are more 
difficult to model due to the complexity of their formation in the 
atmosphere, and EPA does not calculate benefit-per-ton estimates for 
these. The CAFE Model computes the monetized impacts associated with 
health damages from each pollutant by multiplying monetized health 
impact per ton values by the total tons of these pollutants, which are 
emitted from both upstream and tailpipe sources. Chapter 5 of the TSD 
accompanying this proposal includes a detailed description of the 
emission factors that inform the CAFE Model's calculation of the total 
tons of each pollutant associated with upstream and tailpipe emissions.
    These monetized health impacts per ton values are closely related 
to the health incidence per ton values described above in Section III.F 
and in detail in Chapter 5.4 of the TSD. We use the same EPA sources 
that provided health incidence values to determine which monetized 
health impacts per ton values to use as inputs in the CAFE Model. Like 
the estimates associated with health incidences per ton of criteria 
pollutant emissions, we used multiple EPA papers and conversations with 
EPA staff to appropriately account for monetized damages for each 
pollutant associated with the source sectors included in the CAFE 
Model, based on which papers contained the most up-to-date data.\361\ 
The various emission source sectors included in the EPA papers do not 
always correspond exactly to the emission source categories used in the 
CAFE Model.\362\ In those cases, we mapped multiple EPA sectors to a 
single CAFE source category and computed a weighted average of the 
health impact per ton values.
---------------------------------------------------------------------------

    \361\ Environmental Protection Agency (EPA). 2018. Estimating 
the Benefit per Ton of Reducing PM2.5 Precursors from 17 
Sectors. https://www.epa.gov/sites/production/files/2018-02/documents/sourceapportionmentbpttsd_2018.pdf; Wolfe et al. 2019. 
Monetized health benefits attributable to mobile source emissions 
reductions across the United States in 2025. https://pubmed.ncbi.nlm.nih.gov/30296769/; Fann et al. 2018. Assessing Human 
Health PM2.5 and Ozone Impacts from U.S. Oil and Natural 
Gas Sector Emissions in 2025. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6718951/.
    \362\ The CAFE Model's emission source sectors follow a similar 
structure to the inputs from GREET. See Chapter 5.2 of the TSD 
accompanying this proposal for further information.
---------------------------------------------------------------------------

    The EPA uses the value of a statistical life (VSL) to estimate 
premature mortality impacts, and a combination of willingness to pay 
estimates and costs of treating the health impact for estimating the 
morbidity impacts.\363\ EPA's 2018 technical support document, 
``Estimating the Benefit per Ton of Reducing PM2.5 
Precursors from 17 Sectors,'' \364\ (referred to here as the 2018 EPA 
source apportionment TSD) contains a more detailed account of how 
health incidences are monetized. It is important to note that the EPA 
sources cited frequently refer to these monetized health impacts per 
ton as ``benefits per ton,'' since they describe these estimates in 
terms of emissions avoided. In the CAFE Model input structure, these 
are generally referred to as monetized health impacts or damage costs 
associated with pollutants emitted, not avoided, unless the context 
states otherwise.
---------------------------------------------------------------------------

    \363\ Although EPA and DOT's VSL values differ, DOT staff 
determined that using EPA's VSL was appropriate here, since it was 
already included in these monetized health impact values, which were 
best suited for the purposes of the CAFE Model.
    \364\ See Environmental Protection Agency (EPA). 2018. 
Estimating the Benefit per Ton of Reducing PM2.5 
Precursors from 17 Sectors. https://www.epa.gov/sites/production/files/2018-02/documents/sourceapportionmentbpttsd_2018.pdf.
---------------------------------------------------------------------------

    The CAFE Model health impacts inputs are based partially on the 
structure the 2018 EPA source apportionment TSD, which reported 
benefits per ton values for the years 2020, 2025, and 2030. For the 
years in between the source years used in the input structure, the CAFE 
Model applies values from the closest source year. For instance, the 
model applies 2020 monetized health impact per ton values for calendar 
years 2020-2022 and applies 2025 values for calendar years 2023-2027. 
For some of the monetized health damage values, in order to match the 
structure of other impacts costs, DOT staff developed proxies for 7% 
discounted values for specific source sectors by using the ratio 
between a comparable sector's 3% and 7% discounted values. In addition, 
we used implicit price deflators from the Bureau of Economic Analysis 
(BEA) to convert different monetized estimates to 2018 dollars, in 
order to be consistent with the rest of the CAFE Model inputs.
    This process is described in more detail in Chapter 6.2.2 of the 
TSD accompanying this proposal. In addition, the CAFE Model 
documentation contains more details of the model's computation of 
monetized health impacts. All resulting emissions damage costs for 
criteria pollutants are located in the Criteria Emissions Cost 
worksheet of the Parameters file.
(3) Reduction in Petroleum Market Externality
    By amending existing standards, the proposal would decrease 
domestic consumption of gasoline, producing a correspondingly decrease 
in the Nation's demand for crude petroleum, a commodity that is traded 
actively in a worldwide market. Although the U.S. accounts for a 
sufficient (albeit diminishing) share of global oil consumption that 
the resulting decrease in global petroleum demand will exert some 
downward pressure on worldwide prices.
    U.S. consumption and imports of petroleum products have three 
potential effects on the domestic economy that are often referred to 
collectively as ``energy security externalities,'' and increases in 
their magnitude are sometimes cited as possible social costs of 
increased U.S. demand for petroleum. First, any increase in global 
petroleum prices that results from higher U.S. gasoline demand will 
cause a transfer of revenue to oil producers worldwide from consumers 
of petroleum, because consumers throughout the world are ultimately 
subject to the higher global price that results. Although this transfer 
is simply a shift of resources that produces no change in global 
economic welfare, the financial drain it produces on the U.S. economy 
is sometimes cited as an external cost of increased U.S. petroleum 
consumption because consumers of petroleum products are unlikely to 
consider it.
    As the U.S. approaches self-sufficiency in petroleum production 
(the Nation became a net exporter of petroleum in 2020), this transfer 
is increasingly from U.S. consumers of refined petroleum products to 
U.S. petroleum producers, so it not only leaves welfare unaffected, but 
even ceases to be a financial burden on the U.S. economy. In fact, as 
the U.S. becomes a larger net petroleum exporter, any transfer from 
global consumers to petroleum producers would become a financial 
benefit to the U.S. economy. Nevertheless, uncertainty in the Nation's 
long-term import-export balance makes it difficult to project precisely 
how these effects might

[[Page 49736]]

change in response to increased consumption.
    Higher U.S. petroleum consumption can also increase domestic 
consumers' exposure to oil price shocks and thus increase potential 
costs to all U.S. petroleum users (including those outside the light 
duty vehicle sector, whose consumption would be unaffected by this 
proposed rule) from possible interruptions in the global supply of 
petroleum or rapid increases in global oil prices. Because users of 
petroleum products are unlikely to consider the effect of their 
increased purchases on these risks, their economic value is often cited 
as an external cost of increased U.S. consumption.
    Finally, some analysts argue that domestic demand for imported 
petroleum may also influence U.S. military spending; because the 
increased cost of military activities would not be reflected in the 
price paid at the gas pump, this is often suggested to represent a 
third category of external costs form increased U.S. petroleum 
consumption. For example, NHTSA has received extensive comments to past 
actions from the group Securing America's Energy Future on this topic.
    Each of these three factors would be expected to decrease--albeit 
by a limited magnitude--as a consequence of decrease in U.S. petroleum 
consumption resulting from the proposed standards. TSD Chapter 6.2.4 
provides a comprehensive explanation of the agency's analysis of these 
three impacts.
(4) Changes in Labor
    As vehicle prices rise, we expect consumers to purchase fewer 
vehicles than they would have at lower prices. If manufacturers produce 
fewer vehicles as a consequence of lower demand, manufacturers may need 
less labor to produce their fleet and dealers may need less labor to 
sell the vehicles. Conversely, as manufacturers add equipment to each 
new vehicle, the industry will require labor resources to develop, 
sell, and produce additional fuel-saving technologies.\365\ We also 
account for the possibility that new standards could shift the relative 
shares of passenger cars and light trucks in the overall fleet. Since 
the production of different vehicles involves different amounts of 
labor, this shift impacts the quantity of estimated labor.
---------------------------------------------------------------------------

    \365\ For the purposes of this analysis, DOT assumes a linear 
relationship between labor and production volumes.
---------------------------------------------------------------------------

    The analysis considers the direct labor effects that the CAFE 
standards have across the automotive sector. The facets include (1) 
dealership labor related to new light-duty vehicle unit sales; (2) 
assembly labor for vehicles, engines, and transmissions related to new 
vehicle unit sales; and (3) labor related to mandated additional fuel 
savings technologies, accounting for new vehicle unit sales. The labor 
utilization analysis is intentionally narrow in its focus and does not 
represent an attempt to quantify the overall labor or economic effects 
of this rulemaking because adjacent employment factors and consumer 
spending factors for other goods and services are uncertain and 
difficult to predict. We do not consider how direct labor changes may 
affect the macro economy and potentially change employment in adjacent 
industries. For instance, we do not consider possible labor changes in 
vehicle maintenance and repair, nor changes in labor at retail gas 
stations. We also do not consider possible labor changes due to raw 
material production, such as production of aluminum, steel, copper, and 
lithium, nor does the agency consider possible labor impacts due to 
changes in production of oil and gas, ethanol, and electricity.
    All labor effects are estimated and reported at a national level, 
in person-years, assuming 2,000 hours of labor per person-year.\366\ 
These labor hours are not converted into monetized values because we 
assume that the labor costs are included into a new vehicle's 
purchasing price. The analysis estimates labor effects from the 
forecasted CAFE Model technology costs and from review of automotive 
labor for the MY 2020 fleet. The agency uses information about the 
locations of vehicle assembly, engine assembly, and transmission 
assembly, and the percent of U.S. content of vehicles collected from 
American Automotive Labeling Act (AALA) submissions for each vehicle in 
the reference fleet.\367\ The analysis assumes the portion of parts 
that are made in the U.S. will remain constant for each vehicle as 
manufacturers add fuel-savings technologies. This should not be 
misconstrued as a prediction that the percentage of U.S.-made parts--
and by extension U.S. labor--will remain constant, but rather that the 
agency does not have a clear basis to project where future productions 
may shift. The analysis also uses data from the National Automotive 
Dealers Association (NADA) annual report to derive dealership labor 
estimates.
---------------------------------------------------------------------------

    \366\ The agencies recognize a few local production facilities 
may contribute meaningfully to local economies, but the analysis 
reports only on national effects.
    \367\ 49 CFR part 583.
---------------------------------------------------------------------------

    In sum, the analysis shows that the increased labor from production 
of new technologies used to meet the preferred alternative will 
outweigh any decreases attributable to the change in new vehicle sales. 
For a full description of the process the agency uses to estimate labor 
impacts, see TSD Chapter 6.2.5.
3. Costs and Benefits Not Quantified
    In addition to the costs and benefits described above, Table III-37 
and Table III-38 each include two line-items without values. The first 
is maintenance and repair costs. Many of the technologies manufacturers 
apply to vehicles to meet CAFE standards are sophisticated and costly. 
The technology costs capture only the initial or ``upfront'' costs to 
incorporate this equipment into new vehicles; however, if the equipment 
is costlier to maintain or repair--which is likely either because the 
materials used to produce the equipment are more expensive or the 
equipment is significantly more complex than less fuel efficient 
alternatives and requires more time and labor--then consumers will also 
experience increased costs throughout the lifetime of the vehicle to 
keep it operational. The agency does not calculate the additional cost 
of repair and maintenance currently because it lacks a basis for 
estimating the incremental change attributable to the standards. The 
agency seeks comment on methods for estimating these costs.
    The second item is the potential sacrifice in other vehicle 
attributes. In addition to fuel economy, potential buyers of new cars 
and light trucks value other features such as their seating and cargo-
carrying capacity, ride comfort, safety, and performance. Changing some 
of these other features, however, can affect vehicles' fuel economy, so 
manufacturers will carefully consider tradeoffs among them when 
deciding how to comply with stricter CAFE standards. Currently the 
analysis assumes that these vehicle attributes will not change as a 
result of these rules,\368\ but in practice manufacturers may need to 
make practical design changes to meet the standards. Even if 
manufacturers are able to hold vehicles' other attributes at today's 
levels while meeting higher fuel economy targets, manufacturers may 
have to dedicate additional resources to comply with stricter CAFE 
targets and forego improvements in other vehicle attributes. The 
potential loss of other

[[Page 49737]]

vehicle attributes is an opportunity cost to consumers.
---------------------------------------------------------------------------

    \368\ See TSD Chapter 2.4.5.
---------------------------------------------------------------------------

    The agency has previously attempted to model the potential 
sacrifice in other vehicle attributes in sensitivity analyses. In those 
other rulemakings, the agency acknowledged that it is extremely 
difficult to quantify the potential loss of other vehicle attributes. 
To accurately do so requires extensive projections about which and how 
much of other attributes will be sacrificed and a detailed accounting 
of how much value consumers assigned to those attributes. The agency 
modeled the loss in other vehicle attributes using published empirical 
estimates of tradeoffs between higher fuel economy and improvements to 
other attributes, together with estimates of the values buyers attach 
to those attributes. The agency is unsure whether this is an 
appropriate methodology since there is uncertainty about how much fuel 
economy consumers are willing to pay for and how consumers value other 
vehicle attributes. The agency seeks comment on alternative methods for 
estimating the potential sacrifice in other vehicle attributes.

H. Simulating Safety Effects of Regulatory Alternatives

    The primary objective of CAFE standards is to achieve maximum 
feasible fuel economy, thereby reducing fuel consumption. In setting 
standards to achieve this intended effect, the potential of the 
standards to affect vehicle safety is also considered. As a safety 
agency, the agency has long considered the potential for adverse safety 
consequences when establishing CAFE standards.
    This safety analysis includes the comprehensive measure of safety 
impacts from three factors:
    1. Changes in Vehicle Mass. Similar to previous analyses, the 
agency calculates the safety impact of changes in vehicle mass made to 
reduce fuel consumption and comply with the standards. Statistical 
analysis of historical crash data indicates reducing mass in heavier 
vehicles generally improves safety, while reducing mass in lighter 
vehicles generally reduces safety. The agency's crash simulation 
modeling of vehicle design concepts for reducing mass revealed similar 
effects. These observations align with the role of mass disparity in 
crashes; when vehicles of different masses collide, the smaller vehicle 
will experience a larger change in velocity (and, by extension, force) 
which increases the risk to its occupants.
    2. Impacts of Vehicle Prices on Fleet Turnover. Vehicles have 
become safer over time through a combination of new safety regulations 
and voluntary safety improvements. The agency expects this trend to 
continue as emerging technologies, such as advanced driver assistance 
systems, are incorporated into new vehicles. Safety improvements will 
likely continue regardless of changes to CAFE standards.
    As discussed in Section III.E.2, technologies added to comply with 
fuel economy standards have an impact on vehicle prices, therefore 
slowing the acquisition of newer vehicles and retirement of older ones. 
The delay in fleet turnover caused by the effect of new vehicle prices 
affect safety by slowing the penetration of new safety technologies 
into the fleet.
    The standards also influence the composition of the light-duty 
fleet. As the safety provided by light trucks, SUVs and passenger cars 
responds differently to technology that manufacturers employ to meet 
the standards--particularly mass reduction--fleets with different 
compositions of body styles will have varying numbers of fatalities, so 
changing the share of each type of light-duty vehicle in the projected 
future fleet impacts safety outcomes.
    3. Increased driving because of better fuel economy. The ``rebound 
effect'' predicts consumers will drive more when the cost of driving 
declines. More stringent standards reduce vehicle operating costs, and 
in response, some consumers may choose to drive more. Additional 
driving increases exposure to risks associated with motor vehicle 
travel, and this added exposure translates into higher fatalities and 
injuries.
    The contributions of the three factors described above generate the 
differences in safety outcomes among regulatory alternatives.\369\ The 
agency's analysis makes extensive efforts to allocate the differences 
in safety outcomes between the three factors. Fatalities expected 
during future years under each alternative are projected by deriving a 
fleet-wide fatality rate (fatalities per vehicle mile of travel) that 
incorporates the effects of differences in each of the three factors 
from baseline conditions and multiplying it by that alternative's 
expected VMT. Fatalities are converted into a societal cost by 
multiplying fatalities with the DOT-recommended value of a statistical 
life (VSL) supplemented by economic impacts that are external to VSL 
measurements. Traffic injuries and property damage are also modeled 
directly using the same process and valued using costs that are 
specific to each injury severity level.
---------------------------------------------------------------------------

    \369\ The terms safety performance and safety outcome are 
related but represent different concepts. When we use the term 
safety performance, we are discussing the intrinsic safety of a 
vehicle based on its design and features, while safety outcome is 
used to describe whether a vehicle has been involved in an accident 
and the severity of the accident. While safety performance 
influences safety outcomes, other factors such as environmental and 
behavioral characteristics also play a significant role.
---------------------------------------------------------------------------

    All three factors influence predicted fatalities, but only two of 
them--changes in vehicle mass and in the composition of the light-duty 
fleet in response to changes in vehicle prices--impose increased risks 
on drivers and passengers that are not compensated for by accompanying 
benefits. In contrast, increased driving associated with the rebound 
effect is a consumer choice that reveals the benefit of additional 
travel. Consumers who choose to drive more have apparently concluded 
that the utility of additional driving exceeds the additional costs for 
doing so, including the crash risk that they perceive additional 
driving involves. As discussed in Chapter 7 of the accompanying 
Technical Support Document, the benefits of rebound driving are 
accounted for by offsetting a portion of the added safety costs.
    The agency categorizes safety outcome through three measures of 
light-duty vehicle safety: Fatalities to occupants occurring in 
crashes, serious injuries sustained by occupants, and the number of 
vehicles involved in crashes that cause property damage but no 
injuries. Counts of fatalities to occupants of automobiles and light 
trucks are obtained from the agency's Fatal Accident Reporting System 
(FARS). Estimates of the number of serious injuries to drivers and 
passengers of light-duty vehicles are tabulated from the agency's 
General Estimates System (GES), an annual sampling of motor vehicle 
crashes occurring throughout the U.S. Weights for different types of 
crashes were used to expand the samples of each type to estimates of 
the total number of crashes occurring during each year. Finally, 
estimates of the number of automobiles and light trucks involved in 
property damage-only (PDO) crashes each year were also developed using 
GES. NHTSA seeks comment on the following discussion.
1. Mass Reduction Impacts
    Vehicle mass reduction can be one of the more cost-effective means 
of improving fuel economy, particularly for makes and models not 
already built with much high-strength steel or aluminum closures or 
low-mass components. Manufacturers have stated that they will continue 
to reduce vehicle

[[Page 49738]]

mass to meet more stringent standards, and therefore, this expectation 
is incorporated into the modeling analysis supporting the standards. 
Safety trade-offs associated with mass-reduction have occurred in the 
past, particularly before CAFE standards were attribute-based; past 
safety trade-offs may have occurred because manufacturers chose at the 
time, in response to CAFE standards, to build smaller and lighter 
vehicles. In cases where fuel economy improvements were achieved 
through reductions in vehicle size and mass, the smaller, lighter 
vehicles did not fare as well in crashes as larger, heavier vehicles, 
on average. Although The agency now uses attribute-based standards, in 
part to reduce or eliminate the incentive to downsize vehicles to 
comply with CAFE standards, the agency must be mindful of the 
possibility of related safety trade-offs.
    For this proposed rule, the agency employed the modeling technique 
developed in the 2016 Puckett and Kindelberger report to analyze the 
updated crash and exposure data by examining the cross sections of the 
societal fatality rate per billion vehicle miles of travel (VMT) by 
mass and footprint, while controlling for driver age, gender, and other 
factors, in separate logistic regressions for five vehicle groups and 
nine crash types.\370\ The agency utilized the relationships between 
weight and safety from this analysis, expressed as percentage increases 
in fatalities per 100-pound weight reduction (which is how mass 
reduction is applied in the technology analysis; see Section III.D.4), 
to examine the weight impacts applied in this CAFE analysis. The 
effects of mass reduction on safety were estimated relative to 
(incremental to) the regulatory baseline in the CAFE analysis, across 
all vehicles for MY 2021 and beyond.
---------------------------------------------------------------------------

    \370\ Puckett, S.M. and Kindelberger, J.C. (2016, June). 
Relationships between Fatality Risk, Mass, and Footprint in Model 
Year 2003-2010 Passenger Cars and LTVs--Preliminary Report. (Docket 
No. 2016-0068). Washington, DC: National Highway Traffic Safety 
Administration.
---------------------------------------------------------------------------

    In computing the impact of changes in mass on safety, the agency is 
faced with competing challenges. Research has consistently shown that 
mass reduction affects ``lighter'' and ``heavier'' vehicles differently 
across crash types. The 2016 Puckett and Kindelberger report found mass 
reduction concentrated among the heaviest vehicles is likely to have a 
beneficial effect on overall societal fatalities, while mass reduction 
concentrated among the lightest vehicles is likely to have a 
detrimental effect on fatalities. This represents a relationship 
between the dispersion of mass across vehicles in the fleet and 
societal fatalities: Decreasing dispersion is associated with a 
decrease in fatalities. Mass reduction in heavier vehicles is more 
beneficial to the occupants of lighter vehicles than it is harmful to 
the occupants of the heavier vehicles. Mass reduction in lighter 
vehicles is more harmful to the occupants of lighter vehicles than it 
is beneficial to the occupants of the heavier vehicles.
    To accurately capture the differing effect on lighter and heavier 
vehicles, the agency splits vehicles into lighter and heavier vehicle 
classifications in the analysis. However, this poses a challenge of 
creating statistically meaningful results. There is limited relevant 
crash data to use for the analysis. Each partition of the data reduces 
the number of observations per vehicle classification and crash type, 
and thus reduces the statistical robustness of the results. The 
methodology employed by the agency was designed to balance these 
competing forces as an optimal trade-off to accurately capture the 
impact of mass-reduction across vehicle curb weights and crash types 
while preserving the potential to identify robust estimates.
    Comments on the NPRM (83 FR 42986, August 24, 2018) for the 2020 
CAFE rule included suggestions that the sample of LTVs in the analysis 
should not include the medium- or heavy-duty (i.e., truck-based 
vehicles with GVWR above 8,500 pounds) equivalents of light-duty 
vehicles in the sample (e.g., Ford F-250 versus F-150, RAM 2500 versus 
RAM 1500, Chevrolet Suburban 2500 versus Chevrolet Suburban 1500), or 
Class 2b and 3 vehicles. For the proposal, NHTSA explored revising the 
analysis consistent with such comments. The process involved two key 
analytical steps: (1) Removing all case vehicles from the analysis 
whose GVWR exceeded 8,500 pounds; and (2) re-classifying all crash 
partners with GVWR above 8,500 pounds as heavy vehicles. The direct 
effects of these changes are: (1) The range of curb weights in the LTV 
sample is reduced, lowering the median curb weight from 5,014 pounds to 
4,808 pounds; (2) the sample size of LTVs is reduced (the number of 
case LTVs under this alternative specification is approximately 18 
percent lower than in the central analysis); and (3) the relative 
impact of crashes with LTVs on overall impacts on societal fatality 
rates decreases, while the corresponding impact of crashes with heavy 
vehicles increases.
    The results from the exploratory analysis of this alternative 
approach are provided in Table III-41. The agency seeks comment on this 
alternative approach; public comment will inform the decision whether 
to incorporate the results into the CAFE Model. The primary functional 
change offered by the alternative approach is that the sample of 
vehicles classified as LTVs would be restricted to vehicles that would 
be subject to CAFE regulations. At the statistical level, the concerns 
raised in the agency's response to comment on the 2018 CAFE NPRM 
remain. In particular, including Class 2b and 3 vehicles in the 
analysis to determine the relationship of vehicle mass on safety has 
the added benefit of improving correlation constraints. Notably, curb 
weight increases faster than footprint for large light trucks and Class 
2b and 3 pickup trucks and SUVs, in part because the widths of vehicles 
are constrained more tightly (i.e., due to lane widths) than their curb 
weights. Including data from Class 2b and 3 pick-up truck and SUV fatal 
crashes provides data over a wider range of vehicle weights, which 
improves the ability to estimate the mass-crash fatality relationship. 
That is, by extending the footprint-curb weight-fatality data to 
include Class 2b and 3 trucks that are functionally and structurally 
similar to corresponding \1/2\-ton models that are subject to CAFE 
regulation, the sample size and ranges of curb weights and footprint 
are improved. Sample size is a challenge for estimating relationships 
between curb weight and fatality risk for individual crash types in the 
main analysis; dividing the sample further or removing observations 
makes it increasingly difficult to identify meaningful estimates and 
the relationships that are present in the data, as shown in the 
sensitivity analysis below. For the proposal, the agency has determined 
that the benefit of the additional data points outweighs the concern 
that some of the vehicles used to determine the mass-safety 
coefficients are not regulated by CAFE vehicles.
    The agency also explored three other alternative model 
specifications that are presented in Table III-41. The first 
alternative centers on aligning CUVs and minivans with the rest of the 
sample, by splitting these vehicles into two weight classes. The key 
factor restricting this change historically has been a low sample size 
for these vehicles; the exploratory analysis examined whether the 
current database (which, due to the range of CYs covered, contains a 
smaller share of CUVs and

[[Page 49739]]

minivans than the current fleet) contains a sufficient sample size to 
evaluate two weight classes for CUVs and minivans. A complicating 
factor in this analysis is that minivans tend to have higher curb 
weights than other CUVs, adding statistical burden in identifying 
meaningful effects of mass on societal fatality rates after accounting 
for body type in the weight class with the fewest minivans (i.e., 
lighter CUVs and minivans).
    The second alternative centers on aligning passenger cars with the 
rest of the sample by including cars that are equipped with all-wheel 
drive (AWD). In previous analyses, passenger cars with AWD were 
excluded from the analysis because they represented a sufficiently low 
share of the vehicle fleet that statistical relationships between AWD 
status and societal fatality risk were highly prone to being conflated 
with other factors associated with AWD status (e.g., location, luxury 
vehicle status). However, the share of AWD passenger cars in the fleet 
has grown. Approximately one-quarter of the passenger cars in the 
database have AWD, compared to an approximately five-percent share in 
the MY 2000-2007 database. Furthermore, all other vehicle types in the 
analysis include AWD as an explanatory variable. Thus, the agency finds 
the inclusion of a considerable portion of the real-world fleet (i.e., 
passenger cars with AWD) to be a meaningful consideration.
    The third alternative is a minor procedural question: Whether to 
expand the CYs and MYs used to identify the distribution of fatalities 
across crash types. The timing of the safety databases places the years 
of the analysis used to establish the distribution of fatalities by 
crash type firmly within the central years of the economic downturn of 
the late 2000s and early 2010s. During these years, travel demand was 
below long-term trends, resulting in fewer crashes. In turn, applying 
the same window of CYs and MYs to the identification of the 
distribution of fatalities across crash types results in notably fewer 
crashes to incorporate into the analysis. The agency conducted 
exploratory analysis on the question of whether to add CYs and MYs to 
the range of crashes used to identify the distribution of fatalities 
across crash types; this analysis was conducted in concert with the two 
alternatives discussed directly above. Results incorporating these 
three alternatives are presented in Table III-41.
[GRAPHIC] [TIFF OMITTED] TP03SE21.097

    Under the alternative specification excluding Class 2b and Class 3 
truck-based vehicles as case vehicles, the median curb weight for LTVs 
is 4,808 pounds, or 206 pounds lighter than in the central analysis. 
When splitting CUVs and minivans into two weight classes, the median 
curb weight for the vehicles is 3,955 pounds. Under this alternative 
specification, where Class 2b and Class 3 truck-based crash partners 
are shifted from truck-based LTVs to heavy-duty vehicles, the median 
curb weight for LTV crash partners is 4,216 pounds, or 144 pounds 
lighter than in the central analysis.
    Re-classifying Class 2b and Class 3 truck-based vehicles has a 
strong effect on the point estimate for heavier LTVs. Critically, 
removing the heaviest trucks as case vehicles yields a much smaller 
point estimate (reduction in societal fatality rates of between 0.16% 
and 0.17% per 100-pound mass reduction, versus 0.61% in the central 
analysis). This result is consistent with a relationship where a key 
share of the sensitivity of fatality risk is attributed to the mass of 
the heaviest vehicles in the fleet (i.e., supporting the role of mass 
dispersion in societal fatality rates). Importantly, the point estimate 
for lighter LTVs is not meaningfully different from the corresponding 
estimate in the central analysis (increase in societal fatality rates 
of between 0.26% and 0.29% per 100-pound mass reduction, versus 0.3% in 
the central analysis). Considered in concert, these results indicate 
that the most effective reductions in societal fatality rates via mass 
reduction in truck-based vehicles would arise not from lightweighting 
the heaviest vehicles subject to CAFE

[[Page 49740]]

regulation, but rather from lightweighting similar, medium- and heavy-
duty vehicles.
    Including passenger cars with AWD in the analysis has little effect 
on the point estimate for lighter passenger cars (increase in societal 
fatality rates of approximately 1.1% per 100-pound mass reduction, 
versus 1.2% in the central analysis). However, this revision has a 
strong effect on the point estimate for heavier passenger cars 
(increase in societal fatality rates of between 0.84% and 0.89% per 
100-pound mass reduction, versus 0.42% in the central analysis). This 
result supports a hypothesis that, after taking AWD status into 
account, mass reduction in heavier passenger cars is a more important 
driver of societal fatality rates than previously estimated. Although 
this result could be spurious, estimated confidence bounds (presented 
below) indicate that accounting for AWD status reduces uncertainty in 
the point estimate. The agency seeks comment on the inclusion of 
passenger cars with AWD when estimating the effects of mass reduction 
on societal fatality rates.
    Splitting CUVs and minivans into two vehicle classes yields point 
estimates that are consistent with the point estimate for the 
consolidated CUV-minivan vehicle class (an average decrease in societal 
fatality rates of approximately 0.16% to 0.18% per 100-pound mass 
reduction across the two vehicle classes, versus a decrease of 0.25% in 
the central analysis). However, sample sizes half as large in the two 
vehicle classes relative to the consolidated vehicle class lead to very 
large estimated confidence bounds, as shown below. Due to this 
uncertainty, The agency does not feel that the current databases 
contain a large enough sample of CUVs and minivans to split these 
vehicles into two classes in the analysis; however, this issue will be 
re-examined when the next iteration of the databases is complete.
    Extending the range of CYs and MYs used to establish the 
distribution of fatalities across crash types has a negligible effect 
on the point estimates. Based on the narrow ranges of results in Table 
III-41, The agency finds evidence supporting a flexible approach in the 
choice of CYs and MYs used in this manner. All else being equal, 
extending the range helps to mitigate the potential for individual 
crash types with large estimated effects to drive spurious effects on 
overall estimates through unrepresentatively high estimated shares of 
overall fatalities. As a hedge in this direction, the agency applied 
the estimates from the alternative specification with two additional 
CYs and MYs (i.e., the second column from the right in Table III-41) 
when evaluating 95-percent confidence bounds for the alternative models 
considered here. The agency seeks comment on this approach to 
representing the distribution of fatalities across crash types.
    A more detailed description of the mass-safety analysis can be 
found in Chapter 7 of the accompanying TSD.
2. Sales/Scrappage Impacts
    The sales and scrappage responses to higher vehicle prices 
discussed in Section III.E.2 have important safety consequences and 
influence safety through the same basic mechanism, fleet turnover. In 
the case of the scrappage response, delaying fleet turnover keeps 
drivers in older vehicles which tend to be less safe than newer 
vehicles.\371\ Similarly, the sales response slows the rate at which 
newer vehicles, and their associated safety improvements, enter the on-
road population. The sales response also influences the mix of vehicles 
on the road--with more stringent CAFE standards leading to a higher 
share of light trucks sold in the new vehicle market, assuming all else 
is equal. This occurs because there is diminishing value to marginal 
improvements in fuel economy (there are fewer gallons to be saved), and 
as the difference in consumption between light trucks and passenger 
cars diminishes, the other attributes of the trucks will likely lead to 
increases in their market share--especially under lower gas prices. 
Light trucks have higher rates of fatal crashes when interacting with 
passenger cars and, as earlier discussed, different directional 
responses to mass reduction technology based on the existing mass and 
body style of the vehicle.
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    \371\ See Passenger Vehicle Occupant Injury Severity by Vehicle 
Age and Model Year in Fatal Crashes, Traffic Safety Facts Research 
Note, DOT-HS-812-528, National Highway Traffic Safety 
Administration, April, 2018, and The Relationship Between Passenger 
Vehicle Occupant Injury Outcomes and Vehicle Age or Model Year in 
Police-Reported Crashes, Traffic Safety Facts Research Note, DOT-HS-
812-937, National Highway Traffic Safety Administration, March, 
2020.
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    Any effects on fleet turnover (either from delayed vehicle 
retirement or deferred sales of new vehicles) will affect the 
distribution of both ages and model years present in the on-road fleet. 
Because each of these vintages carries with it inherent rates of fatal 
crashes, and newer vintages are generally safer than older ones, 
changing that distribution will change the total number of on-road 
fatalities under each regulatory alternative. Similarly, the dynamic 
fleet share model captures the changes in the fleet's composition of 
cars and trucks. As cars and trucks have different fatality rates, 
differences in fleet composition across the alternatives will affect 
fatalities.
    At the highest level, the agency calculates the impact of the sales 
and scrappage effects by multiplying the VMT of a vehicle by the 
fatality risk of that vehicle. For this analysis, calculating VMT is 
rather simple: The agency uses the distribution of miles calculated in 
TSD Chapter 4.3. The trickier aspect of the analysis is creating 
fatality rate coefficients. The fatality risk measures the likelihood 
that a vehicle will be involved in a fatal accident per mile driven. 
The agency calculates the fatality risk of a vehicle based on the 
vehicle's model year, age, and style, while controlling for factors 
which are independent of the intrinsic nature of the vehicle, such as 
behavioral characteristics. Using this same approach, the agency 
designed separate models for fatalities, non-fatal injuries, and 
property damaged vehicles.
    The fatality risk projections described above capture the 
historical evolution of safety. Given that modern technologies are 
proliferating faster than ever and offer greater safety benefits than 
traditional safety improvements, the agency augmented the fatality risk 
projections with knowledge about forthcoming safety improvements. The 
agency applied detailed empirical estimates of the market uptake and 
improving effectiveness of crash avoidance technologies to estimate 
their effect on the fleet-wide fatality rate, including explicitly 
incorporating both the direct effect of those technologies on the crash 
involvement rates of new vehicles equipped with them, as well as the 
``spillover'' effect of those technologies on improving the safety of 
occupants of vehicles that are not equipped with these 
technologies.\372\
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    \372\ These technologies included Forward Collision Warning 
(FCW), Crash Imminent Braking (CIB), Dynamic Brake Support (DBS), 
Pedestrian AEB (PAEB), Rear Automatic Braking, Semi-automatic 
Headlamp Beam Switching, Lane Departure Warning (LDW), Lane Keep 
Assist (LKA), and Blind Spot Detection (BSD). While Autonomous 
vehicles offer the possibility of significantly reducing or 
eventually even eliminating the effect of human error in crash 
causation, a contributing factor in roughly 94% of all crashes, 
there is insufficient information and certainty regarding autonomous 
vehicles eventual impact to include them in this analysis.
---------------------------------------------------------------------------

    The agency's approach to measuring these impacts is to derive 
effectiveness rates for these advanced crash-avoidance technologies 
from safety technology literature. The agency then applies these 
effectiveness rates to specific crash target populations for

[[Page 49741]]

which the crash avoidance technology is designed to mitigate and 
adjusted to reflect the current pace of adoption of the technology, 
including the public commitment by manufactures to install these 
technologies. The products of these factors, combined across all 6 
advanced technologies, produce a fatality rate reduction percentage 
that is applied to the fatality rate trend model discussed above, which 
projects both vehicle and non-vehicle safety trends. The combined model 
produces a projection of impacts of changes in vehicle safety 
technology as well as behavioral and infrastructural trends. A much 
more detailed discussion of the methods and inputs used to make these 
projections of safety impacts from advanced technologies is included in 
Chapter 7 of the accompanying TSD.
3. Rebound Effect Impacts
    The additional VMT demanded due to the rebound effect is 
accompanied by more exposure to risk, however, rebound miles are not 
imposed on consumers by regulation. They are a freely chosen activity 
resulting from reduced vehicle operational costs. As such, the agencies 
believe a large portion of the safety risks associated with additional 
driving are offset by the benefits drivers gain from added driving. The 
level of risk internalized by drivers is uncertain. This analysis 
assumes that consumers internalize 90 percent of this risk, which 
mostly offsets the societal impact of any added fatalities from this 
voluntary consumer choice. Additional discussion of internalized risk 
is contained in TSD Chapter 7.4.
4. Value of Safety Impacts
    Fatalities, nonfatal injuries, and property damage crashes are 
valued as a societal cost within the CAFE Model's cost and benefit 
accounting. Their value is based on the comprehensive value of a 
fatality, which includes lost quality of life and is quantified in the 
value of a statistical life (VSL) as well as economic consequences such 
as medical and emergency care, insurance administrative costs, legal 
costs, and other economic impacts not captured in the VSL alone. These 
values were derived from data in Blincoe et al. (2015), adjusted to 
2018 dollars, and updated to reflect the official DOT guidance on the 
value of a statistical life. Nonfatal injury costs, which differ by 
severity, were weighted according to the relative incidence of injuries 
across the Abbreviated Injury Scale (AIS). To determine this incidence, 
the agency applied a KABCO \373\/maximum abbreviated injury scale 
(MAIS) translator to GES KABCO based injury counts from 2010 through 
2015. This produced the MAIS based injury profile. This profile was 
used to weight nonfatal injury unit costs derived from Blincoe et al., 
adjusted to 2018 economics and updated to reflect the official DOT 
guidance on the value of a statistical life. Property-damaged vehicle 
costs were also taken from Blincoe et al. and adjusted to 2018 
economics. VSL does not affect property damage. This gives societal 
values of $10.8 million for each fatality, $132,000 for each nonfatal 
injury, and $7,100 for each property damaged vehicle.
---------------------------------------------------------------------------

    \373\ The ``KABCO'' injury scale also can be used for 
establishing crash costs. This scale was developed by the National 
Safety Council (NSC) and is frequently used by law enforcement for 
classifying injuries: K--Fatal; A--Incapacitating injury; B--Non-
incapacitating injury; C--Possible injury; and O--No injury.
---------------------------------------------------------------------------

5. Impacts of the Proposal on Safety
    Table III-42 through Table III-44 summarize the safety impacts of 
the proposed standards on safety broken down by factor. These impacts 
are summarized over the lifetimes of model year 1981 through 2029 
vehicles for all light passenger vehicles (including passenger cars and 
light trucks). Economic impacts are shown separately under both 3% and 
7% discount rates. Model years 1981 through 2029 were examined because 
they represent the model years that might be affected by shifts in 
fleet composition due to the impact of higher new vehicle prices on 
sales of new vehicles and retention of older vehicles. Earlier years 
will be affected by slower scrappage rates and we expect the impacts of 
these standards will be fully realized in vehicle designs by MY 2029.
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[GRAPHIC] [TIFF OMITTED] TP03SE21.100

BILLING CODE 4910-59-C
    As seen in the tables, all three safety factors--changes in mass, 
fleet turnover, and rebound--increase as the standards become more 
stringent. As expected, rebound fatalities grow at a constant rate as 
vehicles become more fuel efficient and are used more frequently. Mass 
reduction has a relatively minimal impact on safety and diminishes as 
stringency increases. This may point to either the fleet becoming more 
homogeneous and hence less mass disparate in crashes. Alternatively, 
the model may be capturing that there's little room for more mass 
reductions in particular models. The slowing of fleet turnover due to 
higher vehicle prices has the largest impact of the three factors and 
accelerates with higher alternatives. Of course, if the agency's 
assumptions overstate the rebound effect and/or slower fleet turnover, 
fatalities, injuries and property damage would be lower, and vice 
versa.
    PRIA Chapter 5.5 discusses the results of the analysis in more 
detail and PRIA Chapter 5.6--Safety Impacts provides an overview of 
sensitivity analyses performed to isolate the uncertainty parameters of 
each of the three safety impacts.

IV. Regulatory Alternatives Considered in this NPRM

A. Basis for Alternatives Considered

    Agencies typically consider regulatory alternatives in proposals as 
a way of evaluating the comparative effects of different potential ways 
of accomplishing their desired goal. NEPA requires agencies to compare 
the potential environmental impacts of their proposed actions to those 
of a reasonable range of alternatives. Executive Orders 12866 and 
13563, as well as OMB Circular A-4, also encourage agencies to evaluate 
regulatory alternatives in their rulemaking analyses.
    Alternatives analysis begins with a ``no-action'' alternative, 
typically described as what would occur in the absence of any 
regulatory action. This proposal includes a no-action alternative, 
described below, and three ``action alternatives.'' The proposed 
standards may, in places, be referred to as the ``preferred 
alternative,'' which is NEPA parlance, but NHTSA intends ``proposal'' 
and ``preferred alternative'' to be used interchangeably for purposes 
of this rulemaking.
    Regulations regarding implementation of NEPA require agencies to 
``rigorously explore and objectively evaluate all reasonable 
alternatives, and for alternatives which were eliminated from detailed 
study, briefly discuss the reasons for their having been eliminated.'' 
This does not amount to a requirement that agencies evaluate the widest 
conceivable spectrum of alternatives. Rather, the range of alternatives 
must be reasonable and consistent with the purpose and need of the 
action.
    The different regulatory alternatives are defined in terms of 
percent-increases in CAFE stringency from year to year. Readers should 
recognize that those year-over-year changes in stringency are not 
measured in terms of mile per gallon differences (as in, 1 percent more 
stringent than 30 miles per gallon in one year equals 30.3 miles per 
gallon in the following year), but rather in terms of shifts in the 
footprint functions that form the basis for the actual CAFE standards 
(as in, on a gallon per mile basis, the CAFE standards change by a 
given percentage from one model year to the next). Under some 
alternatives, the rate of change is the same from year to year, while 
under others, it differs, and under some alternatives, the rate of 
change is different for cars and for trucks. One action alternative is 
more stringent than the proposal, while one is less stringent than the 
proposal. The alternatives considered in this proposal represent a 
reasonable range of possible final agency actions.

B. Regulatory Alternatives and Proposed CAFE Standards for MYs 2024-
2026

    The regulatory alternatives for this proposal are presented here as 
the percent-increases-per-year that they represent. The sections that 
follow will present the alternatives as the literal coefficients which 
define standards curves increasing at the given percentage rates and 
will also further explain the basis for the alternatives selected.

[[Page 49745]]

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    As for past rulemaking analyses, NHTSA has analyzed each of the 
regulatory alternatives in a manner that estimates manufacturers' 
potential application of technology in response to the corresponding 
CAFE requirements and the estimated market demand for fuel economy, 
considering estimated fuel prices, estimated product development 
cadence, and the estimated availability, applicability, cost, and 
effectiveness of fuel-saving technologies. The analysis sometimes shows 
that specific manufacturers could increase CAFE levels beyond 
requirements in ways estimated to ``pay buyers back'' very quickly 
(i.e., within 30 months) for the corresponding additional costs to 
purchase new vehicles through avoided fuel outlays. Consistent with the 
analysis published with the 2020 final rule, this analysis shows that 
if battery costs decline as projected while fuel prices increase as 
projected, BEVs should become increasingly attractive on this basis, 
such that the modeled application of BEVs (and some other technologies) 
clearly outstrips regulatory requirements after the mid-2030s.
    The analysis accompanying the 2020 final rule presented such 
results for CAFE standards as well as--separately--CO2 
standards. New in this proposal, DOT has modified the CAFE Model to 
account for the combined effect of both CAFE and CO2 
standards, simulating technology application decisions each 
manufacturer could possibly make when faced with both CAFE standards 
and CO2 standards (and also estimated market demand for fuel 
economy). This capacity was exercised for purposes of creating the 
baseline against which alternatives were analyzed, but not for purposes 
of modeling compliance with both agencies' proposals. Also, new for 
this proposal, DOT has further modified the CAFE Model to account for 
the ``Framework'' agreements California has reached with BMW, Ford, 
Honda, Volkswagen, and Volvo, and for the ZEV mandate that California 
and the ``Section 177'' states have adopted. The TSD elaborates on 
these new model capabilities. Generally speaking, the model treats each 
manufacturer as applying the following logic when making technology 
decisions:
    1. What do I need to carry over from last year?
    2. What should I apply more widely in order to continue sharing 
(of, e.g., engines) across different vehicle models?
    3. What new PHEVs or BEVs do I need to build in order to satisfy 
the ZEV mandates?
    4. What further technology, if any, could I apply that would enable 
buyers to recoup additional costs within 30 months after buying new 
vehicles?
    5. What additional technology, if any, should I apply in order to 
respond to CAFE and CO2 standards?
    All of the regulatory alternatives considered here include, for 
passenger cars, the following coefficients defining the combination of 
baseline Federal CO2 standards and the California Framework 
agreement.
[GRAPHIC] [TIFF OMITTED] TP03SE21.102

    Coefficients a, b, c, d, e, and f define the current Federal 
CO2 standards for passenger cars. Analogous to coefficients 
defining CAFE standards, coefficients a and b specify minimum and 
maximum passenger car CO2 targets in each model year. 
Coefficients c and d specify the slope and intercept of the linear 
portion of the CO2 target function,

[[Page 49746]]

and coefficients e and f bound the region within which CO2 
targets are defined by this linear form. Coefficients g, h, i, and j 
define the CO2 targets applicable to BMW, Ford, Honda, 
Volkswagen, and Volvo, pursuant to the agreement these manufacturers 
have reached with California. Beyond 2026, the MY 2026 Federal 
standards apply to all manufacturers, including these five 
manufacturers. The coefficients shown in Table IV-3 define the 
corresponding CO2 standards for light trucks.
[GRAPHIC] [TIFF OMITTED] TP03SE21.103

    All of the regulatory alternatives considered here also include 
NHTSA's estimates of ways each manufacturer could introduce new PHEVs 
and BEVs in response to ZEV mandates. As discussed in greater detail 
below, these estimates force the model to convert specific vehicle 
model/configurations to either a BEV200, BEV300, or BEV400 at the 
earliest estimated redesign. These ``ZEV Candidates'' define an 
incremental response to ZEV mandates (i.e., beyond PHEV and BEV 
production through MY 2020) comprise the following shares of 
manufacturers' MY 2020 production for the U.S. market as shown in Table 
IV-4.
[GRAPHIC] [TIFF OMITTED] TP03SE21.104

    For example, while Tesla obviously need not introduce additional 
BEVs to comply with ZEV mandates, our analysis indicates Nissan could 
need to increase BEV offerings modestly to do so, and Mazda and some 
other manufacturers may need to do considerably more than Nissan to 
introduce new BEV offerings.

[[Page 49747]]

    This representation of CO2 standards and ZEV mandates 
applies equally to all regulatory alternatives, and NHTSA's analysis 
applies the CAFE Model to examine each alternative treating each 
manufacturer as responding jointly to the entire set of requirements. 
This is distinct from model application of BEVs for compliance purposes 
under the compliance simulations of the different action alternatives 
which inform decision-makers regarding potential effects of the 
standards.
    Chapter 1 of the TSD contains extensive discussion of the 
development of the No-Action Alternative, and explains the reasons for 
and effect of apparent ``over-compliance'' with the No-Action 
Alternative, which reduces costs and benefits attributable to the 
proposed CAFE standards and other action alternatives. NHTSA seeks 
comment broadly on that discussion and whether and how to change its 
approach to developing the No-Action Alternative for the final rule. 
NHTSA also specifically seeks comment on whether and how to add to the 
No-Action Alternative for the final rule an estimation of GHG standards 
that California and the Section 177 states might separately enforce if 
California's waiver of CAA preemption was re-established.
1. No-Action Alternative
    The No-Action Alternative (also sometimes referred to as 
``Alternative 0'') applies the CAFE target curves set in 2020 for MYs 
2024-2026, which raised stringency by 1.5 percent per year for both 
passenger cars and light trucks.
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[GRAPHIC] [TIFF OMITTED] TP03SE21.106

    These equations are presented graphically in Figure IV-1 and Figure 
IV-2, where the x-axis represents vehicle footprint and the y-axis 
represents fuel economy, showing that in ``CAFE space,'' targets are 
higher in fuel economy for smaller footprint vehicles and lower for 
larger footprint vehicles.

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    NHTSA must also set a minimum standard for domestically 
manufactured passenger cars, which is often referred to as the 
``MDPCS.'' Any time NHTSA establishes or changes a passenger car 
standard for a model year, the MDPCS must also be evaluated or re-
evaluated and established accordingly, but for purposes of the No-
Action alternative, the MDPCS is as it was established in the 2020 
final rule, as shown in Table IV-7.
[GRAPHIC] [TIFF OMITTED] TP03SE21.109

BILLING CODE 4910-59-C
    As the baseline against which the Action Alternatives are measured, 
the No-Action Alternative also includes several other actions that 
NHTSA believes will occur in the absence of further regulatory action. 
First, NHTSA has included California's ZEV mandate as part of the No-
Action Alternative. NHTSA has already proposed to rescind the 2019 
``SAFE I'' rule,\374\ and EPA has reopened consideration of whether to 
grant California a waiver to consider its ZEV mandate,\375\ although 
California does not currently possess a waiver of preemption under the 
CAA and NHTSA regulations currently purport to preempt the California 
ZEV program. Although neither of these actions has yet been finalized, 
it is reasonably foreseeable that manufacturers selling vehicles in 
California and in the Section 177 states could be required to comply 
with the ZEV mandate during the timeframe of this rulemaking. Second, 
NHTSA has included the agreements made between California and BMW, 
Ford, Honda, VWA, and Volvo, because these agreements by their terms 
are contracts,

[[Page 49750]]

even though they were entered into voluntarily.\376\ NHTSA did so by 
including EPA's baseline (i.e., 2020) GHG standards in its analysis, 
and introducing more stringent GHG target functions during MYs 2022-
2026, but treating only these five manufacturers as subject to these 
more stringent target functions. Because a significant portion of the 
market voluntarily adopted the California framework, presumably because 
the manufacturers who joined believed it could be met, and because that 
adoption is contractually binding once entered into, it is reasonable 
to assume that it will occur as expected during the rulemaking 
timeframe, and thus, reasonable to include in the No-Action 
Alternative. As in past analyses, NHTSA's analysis further assumes 
that, beyond any technology applied in response to CAFE standards, EPA 
GHG standards, California/OEM agreements, and ZEV mandates applicable 
in California and the Section 177 states, manufacturers could also make 
any additional fuel economy improvements estimated to reduce owners' 
estimated average fuel outlays during the first 30 months of vehicle 
operation by more than the estimated increase in new vehicle price.
---------------------------------------------------------------------------

    \374\ 86 FR 25980 (May 12, 2021).
    \375\ 86 FR 22421 (Apr. 28, 2021).
    \376\ See https://ww2.arb.ca.gov/news/framework-agreements-clean-cars.
---------------------------------------------------------------------------

    NHTSA accomplished much of this through expansion of the CAFE Model 
after the prior rulemaking. The previous version of the model had been 
extended to apply to GHG standards as well as CAFE standards but had 
not been published in a form that simulated simultaneous compliance 
with both sets of standards. As discussed at greater length in the 
current CAFE Model documentation, the updated version of the model 
simulates all the following simultaneously:

1. Compliance with CAFE standards
2. Compliance with GHG standards applicable to all manufacturers
3. Compliance with alternative GHG standards applicable to a subset of 
manufacturers
4. Compliance with ZEV mandates
5. Further fuel economy improvements applied if sufficiently cost-
effective for buyers

    Inclusion of these actions in the No-Action Alternative means that 
they are necessarily included in each of the Action Alternatives. That 
is, the impacts of all the alternatives evaluated in this proposal are 
against the backdrop of these State and voluntary actions by 
automakers. This is important to remember, because it means that 
automakers will be taking actions to improve fuel economy even in the 
absence of new CAFE standards, and that costs and benefits attributable 
to those actions are therefore not attributable to possible future CAFE 
standards.
2. Alternative 1
    Alternative 1 would increase CAFE stringency for MY 2024 by 9.14% 
for passenger cars and 11.02% for light trucks and increase stringency 
in MYs 2025 and 2026 by 3.26% per year for both passenger cars and 
light trucks. NHTSA calculates that the stringency of Alternative 1 in 
each of MYs 2024-2026 is equivalent to the average stringency of the 
California framework agreement applied to all manufacturers in those 
model years. NHTSA calculated the stringency values using a 
spreadsheet, shown in TSD Chapter 1, assuming manufacturers would 
achieve a one percent reduction in stringency each model year under the 
California framework through the application of ZEV vehicle 
multipliers. The spreadsheet applies a normalized stringency value of 
100 percent in MY 2021 for both CO2 standards and CAFE 
standards.
    Informed by these calculations, NHTSA defined Alternative 1 by 
applying the CAFE equivalent stringency increases in MYs 2024-2026, 
resulting in the coefficients listed in Table IV-8 and Table IV-9.
BILLING CODE 4910-59-P
[GRAPHIC] [TIFF OMITTED] TP03SE21.110

[GRAPHIC] [TIFF OMITTED] TP03SE21.111

    These equations are represented graphically in Figure IV-4 and 
Figure IV-4.
---------------------------------------------------------------------------

    \377\ For this and other action alternatives, readers may note 
that the cutpoint for large trucks is further to the right than in 
the 2020 final rule. The 2020 final rule (and its preceding NPRM) 
did not contain an adjustment to the right cutpoint that had been 
finalized in 2012. Because comments were not received to the NPRM, 
the lack of adjustment was finalized. Considering the question again 
for this proposal, NHTSA believes that moving the cutpoint to the 
right for large trucks (consistent with the intent and requirements 
in 2012) is reasonable, given the rate of increase in stringency for 
this proposal.

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[[Page 49751]]

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[[Page 49752]]


[GRAPHIC] [TIFF OMITTED] TP03SE21.113

    Under this alternative, the MDPCS is as shown in Table IV-10.
    [GRAPHIC] [TIFF OMITTED] TP03SE21.114
    
    NHTSA considered this alternative as a way to evaluate the effects 
of industry-wide CAFE standards approximately harmonized with the 
California framework agreement applied to signatory OEMs' production 
for the U.S. market.\378\ The fact that five major manufacturers 
voluntarily bound themselves to the framework levels, not just for MYs 
2024-2026 but for MYs 2021-2026, is a relevant data point in terms of 
their technological feasibility and economic practicability for the 
fleet as a whole. NHTSA seeks comment on whether Alternative 1 (as 
defined by the rate of increase and the curve coefficients) 
appropriately captures its stated goal of approximating the fuel 
savings that would occur under an industry-wide application of fuel 
economy standards harmonized with the California framework, or whether 
changes might be appropriate for the final rule. NHTSA asks that 
commenters explain the specific technical basis for any requested 
changes, as well as the basis for determining that the resultant CAFE 
standards could meet EPCA's

[[Page 49753]]

requirement that NHTSA select the maximum feasible standard for each 
fleet in each model year.
---------------------------------------------------------------------------

    \378\ CAFE standards defining this alternative reflect the fact 
that EPCA does not provide a basis for CAFE standards to include 
``multipliers'' applicable to PHEV and/or BEV production volumes, as 
well as the fact that EPCA's treatment of BEV energy consumption is 
different from the ``0 grams/mile'' treatment for purposes of 
determining compliance with GHG emissions standards.
---------------------------------------------------------------------------

3. Alternative 2
    Alternative 2 would increase CAFE stringency at 8 percent per year, 
which NHTSA calculates would result in total lifetime fuel savings from 
vehicles produced during MYs 2021-2029 similar to total lifetime fuel 
savings that would occur if the fuel economy standards harmonized with 
California framework agreement had applied to all manufacturers during 
MYs 2021-2026.
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[GRAPHIC] [TIFF OMITTED] TP03SE21.116

    Under this alternative, the MDPCS is as shown in Table IV-13.
    [GRAPHIC] [TIFF OMITTED] TP03SE21.117
    
    NHTSA considered this alternative as a way to evaluate the effects 
of CAFE standards that sought to achieve the fuel savings that would be 
achieved if fuel economy standards harmonized with the California 
framework agreement had been applied to all vehicle manufacturers from 
its beginning the time the framework was agreed. As for Alternative 1, 
the fact that five major manufacturers voluntarily bound themselves to 
these levels, not just for MYs 2024-2026 but for MYs 2021-2026, is a 
relevant data point in terms of their technological feasibility and 
economic practicability for the fleet as a whole.\379\ NHTSA seeks 
comment on whether Alternative 2 (as defined by the rate of increase 
and the curve coefficients) appropriately captures its stated goal of 
representing the fuel savings achievement that would be achieved if 
fuel economy standards harmonized with the California framework 
agreement were applied to all companies at a national level over MYs 
2021-2026, or whether changes might be appropriate for the final rule. 
NHTSA asks that commenters explain the specific technical basis for any 
requested changes, as well as the basis for determining that the 
resultant CAFE standards could meet EPCA's requirement that NHTSA 
select the maximum feasible standard for each fleet in each model year.
---------------------------------------------------------------------------

    \379\ Section VI discusses economic practicability in more 
detail, including NHTSA's long-standing interpretation that economic 
practicability need not mean that the standards are comfortably 
achievable for every single manufacturer individually, as long as 
they appear economically practicable for the fleet as a whole.
---------------------------------------------------------------------------

    As another possibility, NHTSA could modify Alternative 2 by 
increasing the stringency of CAFE standards by 10 percent between model 
years 2025 and 2026, rather than by 8 percent. Shown graphically, this 
possibility would look as shown in Figure IV-5.

[[Page 49754]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.118

    NHTSA seeks comment on this option as well as on Alternative 2.
4. Alternative 3
    Alternative 3 would increase CAFE stringency at 10 percent per 
year, which NHTSA calculates would result in total lifetime fuel 
savings from vehicles produced during MYs 2021-2029 similar to total 
lifetime fuel savings that would have occurred if NHTSA had promulgated 
final CAFE standards for MYs 2021-2025 at the augural levels announced 
in 2012 and, in addition, if NHTSA had also promulgated MY 2026 
standards that reflected a continuation of that average rate of 
stringency increase (4.48% for passenger cars and 4.54% for light 
trucks).
[GRAPHIC] [TIFF OMITTED] TP03SE21.119

[GRAPHIC] [TIFF OMITTED] TP03SE21.120


[[Page 49755]]


    These equations are represented graphically in Figure IV-6 and 
Figure IV-7.
[GRAPHIC] [TIFF OMITTED] TP03SE21.121


[[Page 49756]]


[GRAPHIC] [TIFF OMITTED] TP03SE21.122

    Under this alternative, the MDPCS is as follows in Table IV-16.
    [GRAPHIC] [TIFF OMITTED] TP03SE21.123
    
BILLING CODE 4910-59-C
    NHTSA considered this alternative as a way to evaluate the effects 
of CAFE standards that would return to a fuel consumption trajectory 
exemplified by the standards announced in 2012. NHTSA seeks comment on 
whether Alternative 3 (as defined by the rate of increase and the curve 
coefficients) appropriately captures this goal, or whether changes 
might be appropriate for the final rule. NHTSA asks that commenters 
explain the specific technical basis for any requested changes, as well 
as the basis for determining that the resultant CAFE standards could 
meet EPCA's requirement that NHTSA select the maximum feasible standard 
for each fleet in each model year. While NHTSA believes that this 
alternative may be beyond maximum feasible based on the information 
currently before us, as discussed in more detail in Section VI, all 
alternatives remain under consideration for the final rule. Moreover, 
because Alternative 3 produces significant social benefits, NHTSA seeks 
comment on whether to adopt a more stringent increase from MY 2025 to 
MY 2026, as described above, that would parallel the year over year 
increase Alternative 3 analyzes.

[[Page 49757]]

V. Effects of the Regulatory Alternatives

A. Effects on Vehicle Manufacturers

    Each of the regulatory alternatives NHTSA has considered would 
increase the stringency of both passenger car and light truck CAFE 
standards in each of model years 2024-2026. To estimate the potential 
impacts of each of these alternatives, NHTSA has, as for all recent 
rulemakings, assumed that standards would continue unchanged after the 
last model year (in this case, 2026) to be covered by newly issued 
standards. It is possible that the size and composition of the fleet 
(i.e., in terms of distribution across the range of vehicle footprints) 
could change over time, affecting the average fuel economy requirements 
under both the passenger car and light truck standards, and for the 
overall fleet. If fleet changes differ from NHTSA's projections, 
average requirements could, therefore, also differ from NHTSA's 
projections. At this time, NHTSA estimates that, under each of the 
regulatory alternatives, average fuel economy requirements could 
increase as summarized in the following three tables.
BILLING CODE 4910-59-P
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[GRAPHIC] [TIFF OMITTED] TP03SE21.125

[GRAPHIC] [TIFF OMITTED] TP03SE21.126

    Manufacturers do not always comply exactly with each CAFE standard 
in each model year. To date, some manufacturers have tended to 
regularly exceed one or both requirements. Many manufacturers make use 
of EPCA's provisions allowing CAFE compliance credits to be applied 
when a fleet's CAFE level falls short of the corresponding requirement 
in a given model year. Some manufacturers have paid civil penalties 
(i.e., fines) required under EPCA when a fleet falls short of a 
standard in a given model year and the manufacturer cannot provide 
compliance credits sufficient to address the compliance shortfall. As 
discussed in the accompanying PRIA and TSD, NHTSA simulates 
manufacturers' responses to each alternative given a wide range of 
input estimates (e.g., technology cost and efficacy, fuel prices), and, 
per EPCA, setting aside the potential that any manufacturer would 
respond to CAFE standards in model years 2024-2026 by applying CAFE 
compliance credits or introducing new models of alternative fuel 
vehicles. Many of these inputs are subject to uncertainty and, in any 
event, as in all CAFE rulemakings, NHTSA's analysis merely illustrates 
one set of ways manufacturers could potentially respond to each 
regulatory alternative. At this time, NHTSA estimates that 
manufacturers' responses to standards defining each alternative could 
lead average fuel economy levels to increase through model year 2029 as 
summarized in the following three tables. Changes are shown to occur in 
MY 2023 even though NHTSA is not explicitly

[[Page 49758]]

proposing to regulate that model year because NHTSA anticipates that 
manufacturers could make changes as early as that model year to affect 
future compliance positions (i.e., multi-year planning).
[GRAPHIC] [TIFF OMITTED] TP03SE21.127

[GRAPHIC] [TIFF OMITTED] TP03SE21.128

[GRAPHIC] [TIFF OMITTED] TP03SE21.129

    While these increases in average fuel economy account for estimated 
changes in the composition of the fleet (i.e., the relative shares of 
passenger cars and light trucks), they result almost wholly from the 
projected application of fuel-saving technology. As mentioned above, 
NHTSA's analysis merely illustrates one set of ways manufacturers could 
potentially respond to each regulatory alternative. Manufacturers' 
actual responses will almost assuredly differ from NHTSA's current 
estimates.
    At this time, NHTSA estimates that manufacturers' application of 
advanced gasoline engines (i.e., gasoline engines with cylinder 
deactivation, turbocharging, high or variable compression ratios) could 
increase through MY 2029 under the no-action alternative and through at 
least MY 2024 under each of the action alternatives. However, NHTSA 
also estimates that in MY 2024, reliance on advanced gasoline engines 
could begin to decline under the more stringent action alternatives, as 
manufacturers shift toward electrification.
[GRAPHIC] [TIFF OMITTED] TP03SE21.130


[[Page 49759]]


[GRAPHIC] [TIFF OMITTED] TP03SE21.131

[GRAPHIC] [TIFF OMITTED] TP03SE21.132

    The aforementioned estimated shift to electrification under the 
more stringent regulatory alternatives is the most pronounced for 
hybrid-electric vehicles (i.e., ``mild'' ISG HEVs and ``strong'' P2 and 
Power-Split HEVs).
[GRAPHIC] [TIFF OMITTED] TP03SE21.133

[GRAPHIC] [TIFF OMITTED] TP03SE21.134


[[Page 49760]]


[GRAPHIC] [TIFF OMITTED] TP03SE21.135

    Under the more stringent action alternatives, NHTSA estimates that 
manufacturers could increase production of plug-in hybrid electric 
vehicles (PHEVs) well over current rates.
[GRAPHIC] [TIFF OMITTED] TP03SE21.136

[GRAPHIC] [TIFF OMITTED] TP03SE21.137

[GRAPHIC] [TIFF OMITTED] TP03SE21.138

    For this NPRM and accompanying PRIA, NHTSA's analysis excludes the 
introduction of new alternative fuel vehicle (AFV) models during MY 
2024-2026 as a response to CAFE standards.\380\ However, NHTSA's 
analysis does consider the potential that manufacturers might respond 
to CAFE standards by introducing new BEV models outside of MYs 2024-
2026, and NHTSA's analysis does account for the potential that ZEV 
mandates could lead manufacturers to introduce new BEV models even 
during MYs 2024-2026. Also accounting for shifts in fleet mix, NHTSA 
projects increased production of BEVs through MY 2029.
---------------------------------------------------------------------------

    \380\ The SEIS does not make this analytical exclusion.

---------------------------------------------------------------------------

[[Page 49761]]

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[GRAPHIC] [TIFF OMITTED] TP03SE21.140

[GRAPHIC] [TIFF OMITTED] TP03SE21.141

    The PRIA provides a wider-ranging summary of NHTSA's estimates of 
manufacturers' potential application of fuel-saving technologies 
(including other types of technologies, such as advanced transmissions, 
aerodynamic improvements, and reduced vehicle mass) in response to each 
regulatory alternative. Appendices I and II of the accompanying PRIA 
provide much more detailed and comprehensive results, and the 
underlying CAFE Model output files provide all information, including 
the specific combination of technologies estimated to be applied to 
every specific vehicle model/configuration in each of model years 2020-
2050.\381\
---------------------------------------------------------------------------

    \381\ See Appendices I and II of the accompanying PRIA and the 
CAFE Model output files.
---------------------------------------------------------------------------

    NHTSA's analysis shows manufacturers' regulatory costs for CAFE 
standards, CO2 standards, and ZEV mandates increasing 
through MY 2029, and (logically) increasing more under the more 
stringent alternatives. Accounting for fuel-saving technologies 
estimated to be added under each regulatory alternative (including air 
conditioning improvements and other off-cycle technologies), and also 
accounting for CAFE fines that NHTSA estimates some manufacturers could 
elect to pay rather than achieving full compliance with CAFE standards 
in some model years, NHTSA estimates that relative to the continued 
application of MY 2020 technologies, manufacturers' cumulative costs 
during MYs 2023-2029 could total $121b under the no-action alternative, 
and $166b, $208b, and $251b under alternatives 1, 2, and 3, 
respectively. The table below shows how these costs are estimated to 
vary among manufacturers, accounting for differences in the quantities 
of vehicles produced for sale in the U.S. Appendices I and II of the 
accompanying PRIA present results separately for each manufacturer's 
passenger car and light truck fleets in each model year under each 
regulatory alternative, and the underlying CAFE Model output files also 
show results specific to manufacturers' domestic and imported car 
fleets.

[[Page 49762]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.142

    As discussed in the TSD, these estimates reflect technology cost 
inputs that, in turn, reflect a ``markup'' factor that includes 
manufacturers' profits. In other words, if costs to manufacturers' are 
reflected in vehicle price increases as in the past, NHTSA estimates 
that the average costs to new vehicle purchasers could increase through 
MY 2029 as summarized in Table V-20 through Table V-22.
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[GRAPHIC] [TIFF OMITTED] TP03SE21.144


[[Page 49763]]


[GRAPHIC] [TIFF OMITTED] TP03SE21.145

    Table V-23 shows how these costs could vary among manufacturers, 
suggesting that disparities could decrease as the stringency of 
standards increases.
[GRAPHIC] [TIFF OMITTED] TP03SE21.146

    NHTSA estimates that although projected fuel savings under the more 
stringent regulatory alternatives could tend to increase new vehicles 
sales, this tendency could be outweighed by the opposing response to 
higher prices, such that new vehicle sales could decline slightly under 
the more stringent alternatives. The magnitude of these fuel savings 
and vehicle price increases depends on manufacturer compliance 
decisions, especially technology application. In the event that 
manufacturers select technologies with lower prices and/or higher fuel 
economy improvements, vehicle sales effects could differ. For example, 
in the case of the ``unconstrained'' SEIS results, manufacturer costs 
across alternatives are lower.

[[Page 49764]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.147

    The TSD discusses NHTSA's approach to estimating new vehicle sales, 
including NHTSA's estimate that new vehicle sales could recover from 
2020's aberrantly low levels.
    While these slight reductions in new vehicles sales tend to 
slightly reduce projected automobile industry labor, NHTSA estimates 
that the cost increases could reflect an underlying increase in 
employment to produce additional fuel-saving technology, such that 
automobile industry labor could about the same under each of the four 
regulatory alternatives.

[[Page 49765]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.148

    The accompanying TSD discusses NHTSA's approach to estimating 
automobile industry employment, and the accompanying RIA (and its 
Appendices I and II) and CAFE Model output files provide more detailed 
results of NHTSA's analysis.

B. Effects on New Car and Truck Buyers

    As discussed above, NHTSA estimates that the average fuel economy 
and purchase cost of new vehicles could increase between 2020 and 2029 
and increase more quickly under each of the action alternatives than 
under the baseline No-Action Alternative. On one hand, buyers could 
realize the benefits of increase fuel economy: Spending less on fuel. 
On the other, buyers could pay more for new vehicles, for some costs 
tied directly to vehicle value (e.g., sales taxes and collision 
insurance). Table V-24 reports sales-weighted MSRP values for the No-
Action Alternative and relative increases in MSRP for the three 
regulatory alternatives.
[GRAPHIC] [TIFF OMITTED] TP03SE21.149


[[Page 49766]]


    Table V-25 through Table V-27 presents projected consumer costs and 
benefits along with net benefits for model year 2029 and 2039 vehicles 
under the proposed alternatives. Results are shown in 2018 dollars, 
without discounting and with benefits and costs discounted at annual 
rates of 3% and 7%. The TSD and PRIA accompanying this NPRM discuss 
underlying methods, inputs, and results in greater detail, and more 
detailed tables and underlying results are contained in the 
accompanying CAFE Data Book and CAFE Model output files. For all of the 
action alternatives, avoided outlays for fuel purchases account for 
most of the projected benefits to consumers, and increases in the cost 
to purchase new vehicles account for most of the projected costs.
[GRAPHIC] [TIFF OMITTED] TP03SE21.150


[[Page 49767]]


[GRAPHIC] [TIFF OMITTED] TP03SE21.151


[[Page 49768]]


[GRAPHIC] [TIFF OMITTED] TP03SE21.152

BILLING CODE 4910-59-C

C. Effects on Society

    Table V-28 and Table V-29 describe the costs and benefits of 
increasing CAFE standards in each alternative, as well as the party to 
which they accrue. Manufacturers are directly regulated under the 
program and incur additional production costs when they apply 
technology to their vehicle offerings in order to improve their fuel 
economy. In this analysis, we assume that those costs are fully passed 
through to new car and truck buyers, in the form of higher prices. 
Other assumptions are possible, but we do not currently have data to 
support attempting to model cross-subsidization. We also assume that 
any civil penalties--paid by manufacturers for failing to comply with 
their CAFE standards--are passed through to new car and truck buyers 
and are included in the sales price. However, those civil penalties are 
paid to the U.S. Treasury, where they currently fund the general 
business of Government. As such, they are a transfer from new vehicle 
buyers to all U.S. citizens, who then benefit from the additional 
Federal revenue. While they are calculated in the analysis, and do 
influence consumer decisions in the marketplace, they do not contribute 
to the calculation of net benefits (and are omitted from the tables 
below).
    While incremental maintenance and repair costs would accrue to 
buyers of new cars and trucks affected by more stringent CAFE 
standards, we do not carry these costs in the analysis. They are 
difficult to estimate for emerging

[[Page 49769]]

technologies but represent real costs (and benefits in the case of 
alternative fuel vehicles that may require less frequent maintenance 
events). They may be included in future analyses as data become 
available to evaluate lifetime maintenance costs. This analysis assumes 
that drivers of new vehicles internalize 90 percent of the risk 
associated with increased exposure to crashes when they engage in 
additional travel (as a consequence of the rebound effect).
    Private benefits are dominated by the value of fuel savings, which 
accrue to new car and truck buyers at retail fuel prices (inclusive of 
Federal and state taxes). In addition to saving money on fuel 
purchases, new vehicle buyers also benefit from the increased mobility 
that results from the lower cost of driving their vehicle (higher fuel 
economy reduces the per-mile cost of travel) and fewer refueling 
events. The additional travel occurs as drivers take advantage of lower 
operating costs to increase mobility, and this generates benefits to 
those drivers--equivalent to the cost of operating their vehicles to 
travel those miles, the consumer surplus, and the offsetting benefit 
that represents 90 percent of the additional safety risk from travel.
    In addition to private benefits and costs, there are purely 
external benefits and costs that can be attributed to increases in CAFE 
standards. These are benefits and costs that accrue to society more 
generally, rather than to the specific individuals who purchase a new 
vehicle that was produced under more stringent CAFE standards. Of the 
external costs, the largest is the loss in fuel tax revenue that occurs 
as a result of falling fuel consumption. While drivers of new vehicles 
(purchased in years where CAFE stringency is increasing) save fuel 
costs at retail prices, the rest of U.S. road users experience a 
welfare loss, in two ways. First, the revenue generated by fuel taxes 
helps to maintain roads and bridges, and improve infrastructure more 
generally, and that loss in fuel tax revenue is a social cost. And 
second, the additional driving that occurs as new vehicle buyers take 
advantage of lower per-mile fuel costs is a benefit to those drivers, 
but the congestion (and road noise) created by the additional travel 
impose a social cost to all road users.
    Among the purely external benefits created when CAFE standards are 
increased, the largest is the reduction in damages resulting from 
greenhouse gas emissions. The estimates in Table V-28 assume a social 
cost of GHG emissions based on a 2.5% discount rate, and those in Table 
V-29 assume a social cost of GHG emissions based on a 3% discount rate. 
The associated benefits related to reduced health damages from 
conventional pollutants and the benefit of improved energy security are 
both significantly smaller than the associated change in GHG damages 
across alternatives. As the tables also illustrate, the overwhelming 
majority of both costs and benefits are private costs and benefits that 
accrue to buyers of new cars and trucks, rather than external welfare 
changes that affect society more generally. This has been consistently 
true in CAFE rulemakings.
    The choice of discount rate also affects the resulting benefits and 
costs. As the tables show, net social benefits are positive for 
Alternative 1 and 2 at a 3% discount rate, but only for Alternative 1 
when applying a 7% discount rate to benefits and costs. Alternative 3 
has negative net benefits under both discount rates. As mentioned 
above, the benefits of the regulatory alternatives, but especially 
Alternative 3, are concentrated in later years where a higher discount 
rate has a greater contracting effect.
BILLING CODE 4910-59-P

[[Page 49770]]

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[[Page 49771]]


[GRAPHIC] [TIFF OMITTED] TP03SE21.154

    The following tables show the costs and benefits associated with 
external effects to society. As seen in Table V-28 and Table V-29, the 
external benefits are composed of reduced climate damages (Table V-30 
and Table V-31), reduced health damages (Table V-32 and Table V-33), 
and reduced petroleum market externalities (Table V-36). The external 
costs to society include congestion and noise costs (Table V-34 and 
Table V-35) and safety costs (Table V-37). We show the costs and 
benefits by model year (1981-2029), in contrast to the tables above, 
which present incremental and net costs and benefits over the lifetimes 
of the entire fleet produced through 2029, beginning with model year 
1981.

[[Page 49772]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.155

    Table V-30 and Table V-31 present the total costs of GHGs in the 
baseline scenario and the incremental costs relative to the baseline in 
the other three alternatives. Negative incremental values indicate a 
decrease in social costs of GHGs, while positive incremental values 
indicate an increase in costs relative to the baseline for the given 
model year. The GHG costs follow a similar pattern in all three 
alternatives, decreasing across all model years, with the largest 
reductions associated with 2025-2028 model years. The magnitude of 
CO2 emissions is much higher than the magnitudes of 
CH4 and N2O emissions, which is why the total 
costs are so much larger for CO2.
[GRAPHIC] [TIFF OMITTED] TP03SE21.156

    The CAFE Model calculates health costs attributed to criteria 
pollutant emissions of NOX, SOX, and 
PM2.5, shown in Table V-32 and Table V-33. These costs are 
directly related to the tons of each pollutant emitted from

[[Page 49773]]

various upstream and downstream sources, including on-road vehicles, 
electricity generation, fuel refining, and fuel transportation and 
distribution. See Chapter 4 of the SEIS and Chapter 5.4 of the TSD for 
further information regarding the calculations used to estimate health 
impacts, and more details about the types of health effects. The 
following section of the preamble, V.D, discusses the changes in tons 
of emissions themselves across rulemaking alternatives, while the 
current section focuses on the changes in social costs associated with 
those emissions.
    Criteria pollutant health costs (presented in Table V-32 and Table 
V-35) increase slightly in earlier model years (1981-2023), but those 
cost increases are offset by the decrease in health costs in later 
model years. In Table V-32 and Table V-33, the costs in alternatives 1-
3 are shown in terms of percent of the baseline. For instance, the 
total decrease in SOX costs in Alternative 2 is equivalent 
to 0.2% of the total baseline SOX costs. The changes across 
alternatives relative to the baseline are relatively minor, although 
some impacts in later model years are more significant (e.g., 7.5% 
decrease in PM2.5 in 2028, Alternative 3). Since the health 
cost value per ton of emissions differs by pollutant, the pollutants 
that incur the highest costs are not necessarily those with the largest 
amount of emissions.
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[[Page 49774]]


[GRAPHIC] [TIFF OMITTED] TP03SE21.158

    NHTSA estimates social costs of congestion and noise across 
regulatory alternatives, throughout the lifetimes of model years 1981-
2029. Congestion and noise are functions of VMT and fleet mix, and the 
differences between alternatives are due mainly to differences in VMT 
(see Section V.D). Overall, congestion and noise costs increase 
relative to the baseline across all alternatives, but viewed from a 
model year perspective, the congestion and noise costs associated with 
later model years are negative relative to the baseline. It is 
important to note that the overall increases in congestion and noise 
costs are relatively small when compared to the total congestion and 
noise costs in the baseline (No-Action Alternative). For further 
details regarding congestion and noise costs, see Chapter 6.2.3 of the 
TSD and Chapter 6.5 of the PRIA.
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[[Page 49775]]


[GRAPHIC] [TIFF OMITTED] TP03SE21.160

    The CAFE Model accounts for benefits of increased energy security 
by computing changes in social costs of petroleum market externalities. 
These social costs represent the risk to the U.S. economy incurred by 
exposure to price shocks in the global petroleum market that are not 
accounted for by oil prices and are a direct function of gallons of 
fuel consumed. Chapter 6.2.4 of the accompanying TSD describes the 
inputs involved in calculating these petroleum market externality 
costs. Petroleum market externality costs decrease relative to the 
baseline under all alternatives, regardless of the discount rate used. 
This pattern occurs due to the decrease in gallons of fuel consumed 
(see Section V.D) as the stringency of alternatives increases. Only the 
earlier model year cohorts (1981-2023) contribute to slight increases 
in petroleum market externality costs, but these are offset by the 
decreases from later model years.
[GRAPHIC] [TIFF OMITTED] TP03SE21.161

    NHTSA estimates various monetized safety impacts across regulatory 
alternatives, including costs of fatalities, non-fatal crash costs, and 
property damage costs. Table V-37 presents these social costs across 
alternatives and discount rates. Safety effects are discussed at length 
in the PRIA accompanying this NPRM (see Chapter 5 of the PRIA).

[[Page 49776]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.162

BILLING CODE 4910-59-C

D. Physical and Environmental Effects

    NHTSA calculates estimates for the various physical and 
environmental effects associated with the proposed standards. These 
include quantities of fuel and electricity consumption, tons of 
greenhouse gas (GHG) emissions and criteria pollutants, and health and 
safety impacts.
    In terms of fuel and electricity usage, NHTSA estimates that the 
proposal would save about 50 billion gallons of gasoline and increase 
electricity consumption by about 275 TWh over the lives of vehicles 
produced prior to MY 2030, relative to the baseline standards (i.e., 
the No-Action Alternative). From a calendar year perspective, NHTSA's 
analysis also estimates total annual consumption of fuel by the entire 
on-road fleet from calendar year 2020 through calendar year 2050. On 
this basis, gasoline and electricity consumption by the U.S. light-duty 
vehicle fleet evolves as shown in the following two graphs, each of 
which shows projections for the No-Action Alternative (Alternative 0, 
i.e., the baseline), Alternative 1, Alternative 2 (the proposal), and 
Alternative 3.
BILLING CODE 4910-59-P
[GRAPHIC] [TIFF OMITTED] TP03SE21.163


[[Page 49777]]


[GRAPHIC] [TIFF OMITTED] TP03SE21.164

    NHTSA estimates the greenhouse gas emissions (GHGs) attributable to 
the light-duty on-road fleet, from both vehicles and upstream energy 
sector processes (e.g., petroleum refining, fuel transportation and 
distribution, electricity generation). Overall, NHTSA estimates that 
the proposed rule would reduce greenhouse gases by about 465 million 
metric tons of carbon dioxide (CO2), about 500 thousand 
metric tons of methane (CH4), and about 12 thousand tons of 
nitrous oxide (N2O). The following three graphs (Figure V-5, 
Figure V-6, and Figure V-7) present NHTSA's estimate of how emissions 
from these three GHGs could evolve over the years. Note that these 
graphs include emissions from both vehicle and upstream processes. All 
three GHG emissions follow similar trends in the years between 2020-
2050.

[[Page 49778]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.165


[[Page 49779]]


[GRAPHIC] [TIFF OMITTED] TP03SE21.166


[[Page 49780]]


[GRAPHIC] [TIFF OMITTED] TP03SE21.167

    The figures presented here are not the only estimates NHTSA has 
calculated regarding projected GHG emissions in future years. As 
discussed in Section II, the accompanying SEIS uses an 
``unconstrained'' analysis as opposed to the ``standard setting'' 
analysis presented in this NPRM and PRIA. For more information 
regarding projected GHG emissions, as well as model-based estimates of 
corresponding impacts on several measures of global climate change, see 
the SEIS.
    NHTSA also estimates criteria pollutant emissions resulting from 
vehicle and upstream processes attributable to the light-duty on-road 
fleet. NHTSA includes estimates for all of the criteria pollutants for 
which EPA has issued National Ambient Air Quality Standards. Under each 
regulatory alternative, NHTSA projects a dramatic decline in annual 
emissions of carbon monoxide (CO), volatile organic compounds (VOC), 
nitrogen oxide (NOX), and fine particulate matter 
(PM2.5) attributable to the light-duty on-road fleet between 
2020 and 2050. As exemplified in Figure V-8, emissions in any given 
year could be very nearly the same under each regulatory alternative.
    On the other hand, as discussed in the PRIA and SEIS accompanying 
this NPRM, NHTSA projects that annual SO2 emissions 
attributable to the light-duty on-road fleet could increase modestly 
under the action alternatives, because, as discussed above, NHTSA 
projects that each of the action alternatives could lead to greater use 
of electricity (for PHEVs and BEVs). The adoption of actions--such as 
actions prompted by President Biden's Executive order directing 
agencies to develop a Federal Clean Electricity and Vehicle Procurement 
Strategy--to reduce electricity generation emission rates beyond 
projections underlying NHTSA's analysis (discussed in the TSD) could 
dramatically reduce SO2 emissions under all regulatory 
alternatives considered here.\382\
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    \382\ E.O. 14008, 86 FR 7619 (Feb. 1, 2021), https://www.whitehouse.gov/briefing-room/presidential-actions/2021/01/27/executive-order-on-tackling-the-climate-crisis-at-home-and-abroad/, 
accessed June 17, 2021.

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[[Page 49781]]

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[[Page 49782]]


[GRAPHIC] [TIFF OMITTED] TP03SE21.169


[[Page 49783]]


[GRAPHIC] [TIFF OMITTED] TP03SE21.170

    Health impacts quantified by the CAFE Model include various 
instances of hospital visits due to respiratory problems, minor 
restricted activity days, non-fatal heart attacks, acute bronchitis, 
premature mortality, and other effects of criteria pollutant emissions 
on health. Figure V-11 shows the differences in select health impacts 
relative to the baseline, across alternatives 1-3. These changes are 
split between calendar year decades, with the largest differences 
between the baseline and alternatives occurring between 2041-2050. The 
magnitude of the differences relates directly to the changes in tons of 
criteria pollutants emitted. See Chapter 5.4 of the TSD for information 
regarding how the CAFE Model calculates these health impacts.

[[Page 49784]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.171

    Lastly, NHTSA also quantifies safety impacts in its analysis. These 
include estimated counts of fatalities, non-fatal injuries, and 
property damage crashes occurring over the lifetimes of the light-duty 
on-road vehicles considered in the analysis. Chapter 5 in the PRIA 
accompanying this NPRM contains an in-depth discussion on the effects 
of the various alternatives on these safety measures, and TSD Chapter 7 
contains information regarding the construction of the safety 
estimates.

E. Sensitivity Analysis

    The analysis conducted to support this proposal consists of data, 
estimates, and assumptions, all applied within an analytical framework, 
the CAFE Model. Just like in all past CAFE rulemakings, NHTSA 
recognizes that many analytical inputs are uncertain, and some inputs 
are very uncertain. Of those uncertain inputs, some are likely to exert 
considerable influence over specific types of estimated impacts, and 
some are likely to do so for the bulk of the analysis. Yet making 
assumptions in the face of that uncertainty is necessary, if we are 
going to try to analyze meaningfully the effects of something that will 
happen in the future--i.e., the regulatory alternatives being 
considered, that represent different possible CAFE standards for MYs 
2024-2026. To get a sense of the effect that these assumptions have on 
the analytical findings, we conducted additional model runs with 
alternative assumptions, which explored a range of potential inputs and 
the sensitivity of estimated impacts to changes in model inputs. 
Sensitivity cases in this analysis span assumptions related to 
technology applicability and cost, economic conditions, consumer 
preferences, externality values, and safety assumptions, among 
others.\383\ A sensitivity analysis can identify two critical pieces of 
information: How big an influence does each parameter exert on the 
analysis, and how sensitive are the model results to that assumption?
---------------------------------------------------------------------------

    \383\ In contrast to an uncertainty analysis, where many 
assumptions are varied simultaneously, the sensitivity analyses 
included here vary a single assumption and provide information about 
the influence of each individual factor, rather than suggesting that 
an alternative assumption would have justified a different preferred 
alternative.
---------------------------------------------------------------------------

    That said, influence is different from likelihood. NHTSA does not 
mean to suggest that any one of the sensitivity cases presented here is 
inherently more likely than the collection of assumptions that 
represent the reference case in the figures and tables that follow. Nor 
is this sensitivity analysis intended to suggest that only one of the 
many assumptions made is likely to prove off-base with the passage of 
time or new observations. It is more likely that, when assumptions are 
eventually contradicted by future observation (e.g., deviations in 
observed and predicted fuel prices are nearly a given), there will be 
collections of assumptions, rather than individual parameters, that 
simultaneously require updating. For this reason, we do not interpret 
the sensitivity analysis as necessarily providing justification for 
alternative regulatory scenarios to be preferred. Rather, the analysis 
simply provides an indication of which assumptions are most critical, 
and the extent to which future deviations from central analysis

[[Page 49785]]

assumptions could affect costs and benefits of this proposal.
    Table V-38 lists and briefly descries the cases that we examined in 
the sensitivity analysis.
[GRAPHIC] [TIFF OMITTED] TP03SE21.172


[[Page 49786]]


    Complete results for the sensitivity cases are summarized in 
Chapter 7 of the accompanying PRIA, and detailed model inputs and 
outputs for curious readers are available on NHTSA's website.\384\ For 
purposes of this preamble, Figure V-12 below illustrates the relative 
change of the sensitivity effect of selected inputs on the costs and 
benefits that we estimate for the proposal.
---------------------------------------------------------------------------

    \384\ https://www.nhtsa.gov/laws-regulations/corporate-average-fuel-economy.
[GRAPHIC] [TIFF OMITTED] TP03SE21.173

    While Figure V-12 does not show precise values, it gives us a sense 
of which inputs are ones for which a different assumption would have a 
much different effect on analytical findings, and which ones would not 
have much effect. Assuming a more-discounted or lower social cost of 
carbon would have a relatively large effect, as would assuming a 
different oil price, or doubling the assumed ``payback period.'' Making 
very high levels of mass reduction unavailable in the modeling appears 
to have a (relatively) very large effect on costs, but this is to some 
extent an artifact of the ``standard setting'' runs used for the 
preamble and PRIA analysis, where electrification is limited due to 
statutory restrictions. On the other hand, assumptions about which 
there has been significant disagreement in the past, like the rebound 
effect or the sales-scrappage response, appear to cause only relatively 
small changes in net benefits. Chapter 7 of the PRIA provides a much 
fuller discussion of these findings, and presents net benefits 
estimated under each of the cases included in the sensitivity analysis, 
including the subset for which impacts are summarized in Figure V-13.

[[Page 49787]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.174

BILLING CODE 4910-59-C
    The results presented in the earlier subsections of Section V and 
discussed in Section VI reflect the agency's best judgments regarding 
many different factors, and the sensitivity analysis discussed here is 
simply to illustrate the obvious, that differences in assumptions can 
lead to differences in analytical outcomes, some of which can be large 
and some of which may be smaller than expected. Policy-making in the 
face of future uncertainty is inherently complex. Section VI explains 
how NHTSA proposes to balance the statutory factors in light of the 
analytical findings, the uncertainty that we know exists, and our 
Nation's policy goals, to determine the CAFE standards that NHTSA 
tentatively concludes are maximum feasible for MYs 2024-2026.

VI. Basis for NHTSA's Tentative Conclusion That the Proposed Standards 
Are Maximum Feasible

    In this section, NHTSA discusses the factors, data, and analysis 
that the agency has considered in the tentative selection of the 
proposed CAFE standards for MYs 2024-2026. The primary purpose of EPCA, 
as amended by EISA, and codified at 49 U.S.C. chapter 329, is energy 
conservation, and fuel economy standards help to conserve energy by 
requiring automakers to make new vehicles travel a certain distance on 
a gallon of fuel.\385\ The goal of the CAFE standards is to conserve 
energy, while taking into account the statutory factors set forth at 49 
U.S.C. 32902(f), as discussed below.
---------------------------------------------------------------------------

    \385\ While individual vehicles need not meet any particular mpg 
level, as discussed elsewhere in this preamble, fuel economy 
standards do require vehicle manufacturers' fleets to meet certain 
compliance obligations based on fuel economy levels target curves 
set forth by NHTSA in regulation.
---------------------------------------------------------------------------

    The provision at 49 U.S.C. 32902(f) states that when setting 
maximum feasible CAFE standards for new passenger cars and light 
trucks, the Secretary of Transportation\386\ ``shall consider 
technological feasibility, economic practicability, the effect of other 
motor vehicle standards of the Government on fuel economy, and the need 
of the United States to conserve energy.'' In previous rulemakings, 
including the 2012 final rule issued during the Obama Administration 
and the recent 2020 final rule, NHTSA considered technological 
feasibility, including the availability of various fuel-economy-
improving technologies to be applied to new vehicles in the timeframe 
of the standards depending on the ultimate stringency levels, and also 
considered economic practicability, including the differences between a 
range of regulatory alternatives in terms of effects on per-vehicle 
costs, the ability of both the industry and individual manufacturers to 
comply with standards at various levels, as well as effects on vehicle 
sales, industry employment, and consumer demand. NHTSA also considered 
how compliance with other motor vehicle standards of the Government 
might affect manufacturers' ability to meet CAFE standards represented 
by a range of regulatory alternatives, and how the need of the U.S. to 
conserve energy could be more or less addressed under a range of 
regulatory alternatives, in terms of considerations like costs to 
consumers, the national balance of payments, environmental implications 
like climate and smog effects, and foreign policy effects such as the 
likelihood that U.S. military and other expenditures could change as a 
result of more or less oil consumed by the U.S. vehicle fleet. These 
elements are discussed in detail throughout this analysis. As will be 
explained in greater detail below, while NHTSA is considering all of 
the same factors in proposing revised CAFE standards for MYs 2024-2026 
that it considered in previous rulemakings, the agency's balancing of 
those factors has shifted, and NHTSA is therefore choosing to set CAFE 
standards at a different level from what both the 2012 final rule and 
the 2020 final rule set forth. Besides the factors specified in 
32902(f), NHTSA has also historically considered the safety effects of 
potential CAFE standards, and additionally considers relevant case law.
---------------------------------------------------------------------------

    \386\ By delegation, the NHTSA Administrator.
---------------------------------------------------------------------------

    NHTSA and EPA have coordinated in setting standards, and many of 
the factors that NHTSA considers to set maximum feasible standards 
complement factors that EPA considers under the Clean Air Act. The 
balancing of competing factors by both EPA and NHTSA are consistent 
with each agency's statutory authority and recognize the statutory 
obligations the Supreme Court pointed to in Massachusetts v. EPA. NHTSA 
also

[[Page 49788]]

considers the Ninth Circuit's decision in Center for Biological 
Diversity v. NHTSA, which remanded NHTSA's 2006 final rule establishing 
standards for MYs 2008-2011 light trucks and underscored that ``the 
overarching purpose of EPCA is energy conservation.''\387\
---------------------------------------------------------------------------

    \387\ 538 F.3d 1172 (9th Cir. 2008).
---------------------------------------------------------------------------

    This proposal contains a range of regulatory alternatives for MYs 
2024-2026, from retaining the 1.5 percent annual increases set in 2020, 
up to a stringency increase of 10 percent annually. The analysis 
supported this range of alternatives based on factors relevant to 
NHTSA's exercise of its 32902(f) authority, such as fuel saved and 
emissions reduced, the technologies available to meet the standards, 
the costs of compliance for automakers and their abilities to comply by 
applying technologies, the impact on consumers with respect to cost, 
fuel savings, and vehicle choice, and effects on safety, among other 
things.
    NHTSA's tentative conclusion, after consideration of the factors 
described below and information in the administrative record for this 
action, is that 8 percent increases in stringency for MYs 2024-2026 
(Alternative 2 of this analysis) are maximum feasible. The Biden 
Administration is deeply committed to working aggressively to improve 
energy conservation, and higher standards appear increasingly likely to 
be economically practicable given almost-daily announcements by major 
automakers about forthcoming new high-fuel-economy vehicle models, as 
described below. Despite only one year having passed since the 2020 
final rule, enough has changed in the U.S. and the world that 
revisiting the CAFE standards for MYs 2024-2026, and raising their 
stringency considerably, is both appropriate and reasonable.
    The following sections discuss in more detail the statutory 
requirements and considerations involved in NHTSA's tentative 
determination of maximum feasible CAFE standards, and NHTSA's 
explanation of its balancing of factors for this tentative 
determination.

A. EPCA, as Amended by EISA

    EPCA, as amended by EISA, contains a number of provisions regarding 
how NHTSA must set CAFE standards. DOT (by delegation, NHTSA) \388\ 
must establish separate CAFE standards for passenger cars and light 
trucks \389\ for each model year,\390\ and each standard must be the 
maximum feasible that the Secretary (again, by delegation, NHTSA) 
believes the manufacturers can achieve in that model year.\391\ In 
determining the maximum feasible levels of CAFE standards, EPCA 
requires that NHTSA consider four statutory factors: Technological 
feasibility, economic practicability, the effect of other motor vehicle 
standards of the Government on fuel economy, and the need of the United 
States to conserve energy.\392\ In addition, NHTSA has the authority to 
consider (and typically does consider) other relevant factors, such as 
the effect of CAFE standards on motor vehicle safety and consumer 
preferences. The ultimate determination of what standards can be 
considered maximum feasible involves a weighing and balancing of 
factors, and the balance may shift depending on the information before 
NHTSA about the expected circumstances in the model years covered by 
the rulemaking. The agency's decision must also be guided by the 
overarching purpose of EPCA, energy conservation, while balancing these 
factors.\393\
---------------------------------------------------------------------------

    \388\ EPCA and EISA direct the Secretary of Transportation to 
develop, implement, and enforce fuel economy standards (see 49 
U.S.C. 32901 et seq.), which authority the Secretary has delegated 
to NHTSA at 49 CFR 1.95(a).
    \389\ 49 U.S.C. 32902(b)(1) (2007).
    \390\ 49 U.S.C. 32902(a) (2007).
    \391\ Id.
    \392\ 49 U.S.C. 32902(f) (2007).
    \393\ Center for Biological Diversity v. NHTSA, 538 F.3d 1172, 
1197 (9th Cir. 2008) (``Whatever method it uses, NHTSA cannot set 
fuel economy standards that are contrary to Congress's purpose in 
enacting the EPCA--energy conservation.'').
---------------------------------------------------------------------------

    Besides the requirement that the standards be maximum feasible for 
the fleet in question and the model year in question, EPCA/EISA also 
contain several other requirements, as follow.
1. Lead Time
    EPCA requires that NHTSA prescribe new CAFE standards at least 18 
months before the beginning of each model year.\394\ For amendments to 
existing standards (as this NPRM proposes), EPCA requires that if the 
amendments make an average fuel economy standard more stringent, at 
least 18 months of lead time must be provided.\395\ Thus, if the first 
year for which NHTSA is proposing to amend standards in this NPRM is MY 
2024, NHTSA interprets this provision as requiring the agency to issue 
a final rule covering MY 2024 standards no later than April 2022.
---------------------------------------------------------------------------

    \394\ 49 U.S.C. 32902(a) (2007).
    \395\ 49 U.S.C. 32902(g)(2) (2007).
---------------------------------------------------------------------------

2. Separate Standards for Cars and Trucks, and Minimum Standards for 
Domestic Passenger Cars
    As mentioned above, EPCA requires NHTSA to set separate standards 
for passenger cars and light trucks for each model year.\396\ NHTSA has 
long interpreted this requirement as preventing the agency from setting 
a single combined CAFE standard for cars and trucks together, based on 
the plain language of the statute. Congress originally required 
separate CAFE standards for cars and trucks to reflect the different 
fuel economy capabilities of those different types of vehicles, and 
over the history of the CAFE program, has never revised this 
requirement. Even as many cars and trucks have come to resemble each 
other more closely over time--many crossover and sport-utility models, 
for example, come in versions today that may be subject to either the 
car standards or the truck standards depending on their 
characteristics--it is still accurate to say that vehicles with truck-
like characteristics such as 4-wheel drive, cargo-carrying capability, 
etc., currently consume more fuel per mile than vehicles without these 
characteristics.
---------------------------------------------------------------------------

    \396\ 49 U.S.C. 32902(b)(1) (2007).
---------------------------------------------------------------------------

    EPCA, as amended by EISA, also requires another separate standard 
to be set for domestically-manufactured \397\ passenger cars. Unlike 
the generally-applicable standards for passenger cars and light trucks 
described above, the compliance obligation of the minimum domestic 
passenger car standard (MDPCS for brevity) is identical for all 
manufacturers. The statute clearly states that any manufacturer's 
domestically manufactured passenger car fleet must meet the greater of 
either 27.5 mpg on average, or 92 percent of the average fuel economy 
projected by the Secretary for the combined domestic and non-domestic 
passenger automobile fleets manufactured for sale in the United States 
by all manufacturers in the model year, which projection shall be 
published in the Federal Register when the standard for that model year 
is promulgated in accordance with 49 U.S.C. 32902(b).\398\
---------------------------------------------------------------------------

    \397\ In the CAFE program, ``domestically-manufactured'' is 
defined by Congress in 49 U.S.C. 32904(b). The definition roughly 
provides that a passenger car is ``domestically manufactured'' as 
long as at least 75 percent of the cost to the manufacturer is 
attributable to value added in the United States, Canada, or Mexico, 
unless the assembly of the vehicle is completed in Canada or Mexico 
and the vehicle is imported into the United States more than 30 days 
after the end of the model year.
    \398\ 49 U.S.C. 32902(b)(4) (2007).
---------------------------------------------------------------------------

    Since that requirement was promulgated, the ``92 percent'' has 
always been greater than 27.5 mpg, and foreseeably will continue to be 
so in the future. While NHTSA published 92 percent MDPCSs for MYs 2024-
2026 at 49 CFR 531.5(d) as part of the 2020 final rule, the statutory 
language is clear that

[[Page 49789]]

the MDPCS must be determined at the time an overall passenger car 
standards is promulgated and published in the Federal Register. Thus, 
any time NHTSA establishes or changes a passenger car standard for a 
model year, the MDPCS must also be evaluated or re-evaluated and 
established accordingly.
    As in the 2020 final rule, NHTSA recognizes industry concerns that 
actual total passenger car fleet standards have differed significantly 
from past projections, perhaps more so when the agency has projected 
significantly into the future. In that final rule, because the 
compliance data showed that the standards projected in 2012 were 
consistently more stringent than the actual standards, by an average of 
1.9 percent. NHTSA stated that this difference indicated that in 
rulemakings conducted in 2009 through 2012, NHTSA's and EPA's 
projections of passenger car vehicle footprints and production volumes, 
in retrospect, underestimated the production of larger passenger cars 
over the MYs 2011 to 2018 period.\399\
---------------------------------------------------------------------------

    \399\ See 85 FR at 25127 (Apr. 30, 2020).
---------------------------------------------------------------------------

    Unlike the passenger car standards and light truck standards which 
are vehicle-attribute-based and automatically adjust with changes in 
consumer demand, the MDPCS are not attribute-based, and therefore do 
not adjust with changes in consumer demand and production. They are 
instead fixed standards that are established at the time of the 
rulemaking. As a result, by assuming a smaller-footprint fleet, on 
average, than what ended up being produced, the MYs 2011-2018 MDPCS 
ended up being more stringent and placing a greater burden on 
manufacturers of domestic passenger cars than was projected and 
expected at the time of the rulemakings that established those 
standards. In the 2020 final rule, therefore, NHTSA agreed with 
industry concerns over the impact of changes in consumer demand (as 
compared to what was assumed in 2012 about future consumer demand for 
greater fuel economy) on manufacturers' ability to comply with the 
MDPCS and in particular, manufacturers that produce larger passenger 
cars domestically. Some of the largest civil penalties for 
noncompliance in the history of the CAFE program have been paid for 
noncompliance with the MDPCS. NHTSA also expressed concern that 
consumer demand may shift even more in the direction of larger 
passenger cars if fuel prices continue to remain low. Sustained low oil 
prices can be expected to have real effects on consumer demand for 
additional fuel economy, and consumers may foreseeably be even more 
interested in 2WD crossovers and passenger-car-fleet SUVs (and less 
interested in smaller passenger cars) than they are at present.
    Therefore, in the 2020 final rule, to help avoid similar outcomes 
in the 2021-2026 timeframe to what had happened with the MDPCS over the 
preceding model years, NHTSA determined that it was reasonable and 
appropriate to consider the recent projection errors as part of 
estimating the total passenger car fleet fuel economy for MYs 2021-
2026. NHTSA therefore projected the total passenger car fleet fuel 
economy using the central analysis value in each model year, and 
applied an offset based on the historical 1.9 percent difference 
identified for MYs 2011-2018.
    For this proposal, recognizing that we are proposing to increase 
stringency considerably over the baseline standards and that civil 
penalties have also recently increased, NHTSA remains concerned that 
the MDPCS may pose a significant challenge to certain manufacturers. To 
that end, NHTSA is proposing to retain the 1.9 percent offset for the 
MDPCS for MYs 2024-2026, which we have appropriately recalculated based 
on the current projections for passenger cars based on the current 
analysis fleet. Table VI-1 shows the calculation values used to 
determine the total passenger car fleet fuel economy value for each 
model year for the preferred alternative.
BILLING CODE 4910-59-P
[GRAPHIC] [TIFF OMITTED] TP03SE21.175

    Using this approach, the MDPCS under each regulatory alternative 
would thus be as shown in Table VI-2.

[[Page 49790]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.176

    NHTSA is also seeking comment on another approach to offsetting the 
MDPCS. Recognizing that the analysis supporting this proposal does not 
attempt to project how vehicle footprints may change in the future, nor 
how that might affect the average fuel economy of passenger cars sold 
in the U.S., NHTSA could instead attempt to make such a projection 
explicitly.
    Examination of the average footprints of passenger cars sold in the 
U.S. from 2008, when EPA began reporting footprint data, to 2020 
indicates a clear and statistically significant trend of gradually 
increasing average footprint (Figure VI-1). The average annual increase 
in passenger car footprint, estimated by ordinary least squares, 
indicates that the passenger car footprints increased by an average of 
0.1206 square feet annually over the 2008-2020 period. The estimated 
average increase is statistically significant at the 0.000001 level, 
with a 95 percent confidence interval of (0.0929, 0.1483).
[GRAPHIC] [TIFF OMITTED] TP03SE21.177

    The alternate method for calculating an offset to the MDPCS would 
be three steps, as follows:
    1. Starting from the average footprint of passenger cars in 2020 as 
reported by EPA, add 0.1206 square feet per year through 2026.
    2. Calculate the estimated fuel economy of passenger cars using the 
average projected footprint numbers calculated in step 1 and the 
footprint functions that are the passenger car standards for the 
corresponding model year, which then become ``the Secretary's projected 
passenger car fuel economy numbers.''
    3. Apply the 92 percent factor to calculate the MDPCS for 2024, 
2025, and 2026.
    The results of this approach are shown in Table VI-3.
    [GRAPHIC] [TIFF OMITTED] TP03SE21.178
    

[[Page 49791]]


    Comparing all of these, Table VI-4 shows (1) the unadjusted 92 
percent MDPCS for MYs 2024-2026, (2) the proposed 1.9 percent-offset 
MDPCS for MYs 2024-2026, and (3) the alternate approach offset MDPCS 
for MYs 2024-2026.
[GRAPHIC] [TIFF OMITTED] TP03SE21.179

BILLING CODE 4910-59-C
    While the CAFE Model analysis underlying this proposal, the PRIA, 
and the Draft SEIS does not reflect an offset to the unadjusted 92 
percent MDPCS, separate analysis that does reflect the change 
demonstrates that doing so does not change estimated impacts of any of 
the regulatory alternatives under consideration, despite the mpg values 
being slightly different as shown in Table VI-4.
    NHTSA seeks comment on the discussion above. To be clear, the 
agency also seeks comment on whether to apply the MDPCS without any 
modifier.
3. Attribute-Based and Defined by a Mathematical Function
    EISA requires NHTSA to set CAFE standards that are ``based on 1 or 
more attributes related to fuel economy and express[ed] . . . in the 
form of a mathematical function.'' \400\ Historically, NHTSA has based 
standards on vehicle footprint, and proposes to continue to do so for 
the reasons described in Section III.B of this preamble and Chapter 1 
of the accompanying TSD. As in previous rulemakings, NHTSA is proposing 
to define the standards in the form of a constrained linear function 
that generally sets higher (more stringent) targets for smaller-
footprint vehicles and lower (less stringent) targets for larger-
footprint vehicles. These footprint curves are discussed in more detail 
in Section III.B and TSD Chapter 1. NHTSA seeks comment in Section 
III.B both on the continued use of footprint as the relevant attribute 
and on the continued use of the constrained linear curve shapes.
---------------------------------------------------------------------------

    \400\ 49 U.S.C. 32902(b)(3)(A) (2007).
---------------------------------------------------------------------------

4. Number of Model Years for Which Standards May Be Set at a Time
    EISA also states that NHTSA shall ``issue regulations under this 
title prescribing average fuel economy standards for at least 1, but 
not more than 5, model years.'' \401\ In this NPRM, NHTSA is proposing 
to set CAFE standards for three model years, MYs 2024-2026. This 
proposal fits squarely within the plain language of the statute.
---------------------------------------------------------------------------

    \401\ 49 U.S.C. 32902(b)(3)(B) (2007).
---------------------------------------------------------------------------

5. Maximum Feasible Standards
    As discussed above, EPCA requires NHTSA to consider four factors in 
determining what levels of CAFE standards would be maximum feasible. 
NHTSA presents in the sections below its understanding of the meanings 
of those four factors.
(a) Technological Feasibility
    ``Technological feasibility'' refers to whether a particular method 
of improving fuel economy is available for deployment in commercial 
application in the model year for which a standard is being 
established. Thus, NHTSA is not limited in determining the level of new 
standards to technology that is already being applied commercially at 
the time of the rulemaking. For this proposal, NHTSA has considered a 
wide range of technologies that improve fuel economy, while considering 
the need to account for which technologies have already been applied to 
which vehicle model/configuration, as well as the need to estimate 
realistically the cost and fuel

[[Page 49792]]

economy impacts of each technology as applied to different vehicle 
models/configurations. NHTSA has not, however, attempted to account for 
every technology that might conceivably be applied to improve fuel 
economy, nor does NHTSA believe it is necessary to do so given that 
many technologies address fuel economy in similar ways.\402\
---------------------------------------------------------------------------

    \402\ For example, NHTSA has not considered high-speed flywheels 
as potential energy storage devices for hybrid vehicles; while such 
flywheels have been demonstrated in the laboratory and even tested 
in concept vehicles, commercially-available hybrid vehicles 
currently known to NHTSA use chemical batteries as energy storage 
devices, and the agency has considered a range of hybrid vehicle 
technologies that do so.
---------------------------------------------------------------------------

    NHTSA notes that the technological feasibility factor allows NHTSA 
to set standards that force the development and application of new 
fuel-efficient technologies, but this factor does not require NHTSA to 
do so.\403\ In the 2012 final rule, NHTSA stated that ``[i]t is 
important to remember that technological feasibility must also be 
balanced with the other of the four statutory factors. Thus, while 
`technological feasibility' can drive standards higher by assuming the 
use of technologies that are not yet commercial, `maximum feasible' is 
also defined in terms of economic practicability, for example, which 
might caution the agency against basing standards (even fairly distant 
standards) entirely on such technologies.'' \404\ NHTSA further stated 
that ``. . . as the `maximum feasible' balancing may vary depending on 
the circumstances at hand for the model year in which the standards are 
set, the extent to which technological feasibility is simply met or 
plays a more dynamic role may also shift.'' \405\ For purposes of this 
proposal covering standards for MYs 2024-2026, NHTSA is certain that 
sufficient technology exists to meet the standards--even for the most 
stringent regulatory alternative. As will be discussed further below, 
for this proposal, the question is more likely rather, given that the 
technology exists, how much of it should be required to be added to new 
cars and trucks in order to conserve more energy, and how to balance 
that objective against the additional cost of adding that technology.
---------------------------------------------------------------------------

    \403\ See 77 FR at 63015 (Oct. 12, 2012).
    \404\ Id.
    \405\ Id.
---------------------------------------------------------------------------

(b) Economic Practicability
    ``Economic practicability'' has consistently referred to whether a 
standard is one ``within the financial capability of the industry, but 
not so stringent as to'' lead to ``adverse economic consequences, such 
as a significant loss of jobs or unreasonable elimination of consumer 
choice.'' \406\ In evaluating economic practicability, NHTSA considers 
the uncertainty surrounding future market conditions and consumer 
demand for fuel economy alongside consumer demand for other vehicle 
attributes. There is not necessarily a bright-line test for whether a 
regulatory alternative is economically practicable, but there are 
several metrics that we discuss below that we find can be useful for 
making this assessment. In determining whether standards may or may not 
be economically practicable, NHTSA considers:
---------------------------------------------------------------------------

    \406\ 67 FR 77015, 77021 (Dec. 16, 2002).
---------------------------------------------------------------------------

    Application rate of technologies--whether it appears that a 
regulatory alternative would impose undue burden on manufacturers in 
either or both the near and long term in terms of how much and which 
technologies might be required. This metric connects to the next two 
metrics, as well.
    Other technology-related considerations--related to the application 
rate of technologies, whether it appears that the burden on several or 
more manufacturers might cause them to respond to the standards in ways 
that compromise, for example, vehicle safety, or other aspects of 
performance that may be important to consumer acceptance of new 
products.
    Cost of meeting the standards--even if the technology exists and it 
appears that manufacturers can apply it consistent with their product 
cadence, if meeting the standards will raise per-vehicle cost more than 
we believe consumers are likely to accept, which could negatively 
impact sales and employment in this sector, the standards may not be 
economically practicable. While consumer acceptance of additional new 
vehicle cost associated with more stringent CAFE standards is 
uncertain, NHTSA still finds this metric useful for evaluating economic 
practicability. Elsewhere in this preamble, we seek comment 
specifically on consumer valuation of fuel economy.
    Sales and employment responses--as discussed above, sales and 
employment responses have historically been key to NHTSA's 
understanding of economic practicability.
    Uncertainty and consumer acceptance \407\ of technologies--
considerations not accounted for expressly in our modeling analysis, 
but important to an assessment of economic practicability given the 
timeframe of this rulemaking. Consumer acceptance can involve 
consideration of anticipated consumer responses not just to increased 
vehicle cost and consumer valuation of fuel economy, but also the way 
manufacturers may change vehicle models and vehicle sales mix in 
response to CAFE standards.
---------------------------------------------------------------------------

    \407\ See, e.g., Center for Auto Safety v. NHTSA (CAS), 793 F.2d 
1322 (D.C. Cir. 1986) (Administrator's consideration of market 
demand as component of economic practicability found to be 
reasonable).
---------------------------------------------------------------------------

    Over time, NHTSA has tried different methods to account for 
economic practicability. Many years ago, prior to the MYs 2005-2007 
rulemaking under the non-attribute-based (fixed value) CAFE standards, 
NHTSA sought to ensure the economic practicability of standards in part 
by setting them at or near the capability of the ``least capable 
manufacturer'' with a significant share of the market, i.e., typically 
the manufacturer whose fleet mix was, on average, the largest and 
heaviest, generally having the highest capacity and capability so as 
not to limit the availability of those types of vehicles to consumers. 
NHTSA rejected the ``least capable manufacturer'' approach several 
rulemakings ago and no longer believes that it is consistent with our 
root interpretation of economic practicability. Economic practicability 
focuses on the capability of the industry and seeks to avoid adverse 
consequences such as (inter alia) a significant loss of jobs or 
unreasonable elimination of consumer choice. If the overarching purpose 
of EPCA is energy conservation, it seems reasonable to expect that 
maximum feasible standards may be harder for some automakers than for 
others, and that they need not be keyed to the capabilities of the 
least capable manufacturer.
    NHTSA has also sought to account for economic practicability by 
applying marginal cost-benefit analysis since the first rulemakings 
establishing attribute-based standards, considering both overall 
societal impacts and overall consumer impacts. Whether the standards 
maximize net benefits has thus been a significant, but not dispositive, 
factor in the past for NHTSA's consideration of economic 
practicability. Executive Order 12866, as amended by Executive Order 
13563, states that agencies should ``select, in choosing among 
alternative regulatory approaches, those approaches that maximize net 
benefits . . .'' In practice, however, agencies, including NHTSA, must 
consider that the modeling of net benefits does not capture all 
considerations relevant to economic practicability. Therefore, as in 
past rulemakings, NHTSA is considering net societal impacts, net 
consumer impacts,

[[Page 49793]]

and other related elements in the consideration of economic 
practicability. That said, it is well within the agency's discretion to 
deviate from the level at which modeled net benefits are maximized if 
the agency concludes that the level would not represent the maximum 
feasible level for future CAFE standards. Economic practicability is 
complex, and like the other factors must be considered in the context 
of the overall balancing and EPCA's overarching purpose of energy 
conservation.
(c) The Effect of Other Motor Vehicle Standards of the Government on 
Fuel Economy
    ``The effect of other motor vehicle standards of the Government on 
fuel economy'' involves analysis of the effects of compliance with 
emission, safety, noise, or damageability standards on fuel economy 
capability and thus on average fuel economy. In many past CAFE 
rulemakings, NHTSA has said that it considers the adverse effects of 
other motor vehicle standards on fuel economy. It said so because, from 
the CAFE program's earliest years \408\ until recently, the effects of 
such compliance on fuel economy capability over the history of the CAFE 
program have been negative ones. For example, safety standards that 
have the effect of increasing vehicle weight thereby lower fuel economy 
capability, thus decreasing the level of average fuel economy that 
NHTSA can determine to be feasible. NHTSA has also accounted for EPA's 
``Tier 3'' standards for criteria pollutants in its estimates of 
technology effectiveness in this proposal, and State emissions 
standards (like California's) that address the tailpipe NOX, 
NMOG, and CO emissions that occur during cold start.\409\
---------------------------------------------------------------------------

    \408\ 43 FR 63184, 63188 (Dec. 15, 1977). See also 42 FR 33534, 
33537 (Jun. 30, 1977).
    \409\ For most ICE vehicles on the road today, the majority of 
tailpipe NOX, NMOG, and CO emissions occur during ``cold 
start,'' before the three-way catalyst has reached the very high 
temperature (e.g., 900-1000 [deg]F) at which point it is able to 
convert (through oxidation and reduction reactions) those emissions 
into less harmful derivatives. By limiting the amount of those 
emissions, tailpipe smog standards require the catalyst to be 
brought to temperature extremely quickly, so modern vehicles employ 
cold start strategies that intentionally release fuel energy into 
the engine exhaust to heat the catalyst to the right temperature as 
quickly as possible. The additional fuel that must be used to heat 
the catalyst is typically referred to as a ``cold-start penalty,'' 
meaning that the vehicle's fuel economy (over a test cycle) is 
reduced because the fuel consumed to heat the catalyst did not go 
toward the goal of moving the vehicle forward. The Autonomie work 
employed to develop technology effectiveness estimates for this 
proposal accounts for cold-start penalties, as discussed in the 
Autonomie model documentation.
---------------------------------------------------------------------------

    In other cases, the effect of other motor vehicle standards of the 
Government may be neutral, or positive. Since the Obama administration, 
NHTSA has considered the GHG standards set by EPA as ``other motor 
vehicle standards of the Government.'' In the 2012 final rule, NHTSA 
stated that ``To the extent the GHG standards result in increases in 
fuel economy, they would do so almost exclusively as a result of 
inducing manufacturers to install the same types of technologies used 
by manufacturers in complying with the CAFE standards.'' \410\ NHTSA 
concluded in 2012 that ``no further action was needed'' because ``the 
agency had already considered EPA's [action] and the harmonization 
benefits of the National Program in developing its own [action].'' 
\411\ In the 2020 final rule, NHTSA reinforced that conclusion by 
explaining that a textual analysis of the statutory language made it 
clear that EPA's CO2 standards applicable to light-duty 
vehicles are literally ``other motor vehicle standards of the 
Government,'' because they are standards set by a Federal agency that 
apply to motor vehicles. NHTSA and EPA are obligated by Congress to 
exercise their own independent judgment in fulfilling their statutory 
missions, even though both agencies' regulations affect both fuel 
economy and CO2 emissions. There are differences between the 
two agencies' programs that make NHTSA's CAFE standards and EPA's GHG 
standards not perfectly one-to-one (even besides the fact that EPA 
regulates other GHGs besides CO2, EPA's CO2 
standards also differ from NHTSA's in a variety of ways, often because 
NHTSA is bound by statute to a certain aspect of CAFE regulation). 
NHTSA endeavors to create standards that meet our statutory obligations 
and still avoid requiring manufacturers to build multiple fleets of 
vehicles for the U.S. market.\412\ As in 2020, NHTSA has continued to 
do all of these things with this proposal.
---------------------------------------------------------------------------

    \410\ 77 FR 62624, 62669 (Oct. 15, 2012).
    \411\ Id.
    \412\ Massachusetts v. EPA, 549 U.S. 497, 532 (2007) (``[T]here 
is no reason to think that the two agencies cannot both administer 
their obligations and yet avoid inconsistency.'').
---------------------------------------------------------------------------

    Similarly, NHTSA has considered and accounted for California's ZEV 
mandate (and its adoption by the other Section 177 states) in 
developing the baseline for this proposal. As discussed above, NHTSA 
has not expressly accounted for California's GHG standards for the 
model years subject to this rulemaking in the baseline analysis for 
this proposal,\413\ but seeks comment on this approach for the final 
rule. NHTSA notes again that no final decision has yet been made on the 
CAA waiver for California.
---------------------------------------------------------------------------

    \413\ As discussed elsewhere, however, NHTSA has sought to 
account in the baseline for the California Framework Agreement with 
BMW, Ford, Honda, VWA, and Volvo.
---------------------------------------------------------------------------

(d) The Need of the U.S. To Conserve Energy
    NHTSA has consistently interpreted ``the need of the United States 
to conserve energy'' to mean ``the consumer cost, national balance of 
payments, environmental, and foreign policy implications of our need 
for large quantities of petroleum, especially imported petroleum.'' 
\414\
---------------------------------------------------------------------------

    \414\ 42 FR 63184, 63188 (Dec. 15, 1977).
---------------------------------------------------------------------------

(1) Consumer Costs and Fuel Prices
    Fuel for vehicles costs money for vehicle owners and operators, so 
all else equal, consumers benefit from vehicles that need less fuel to 
perform the same amount of work. Future fuel prices are a critical 
input into the economic analysis of potential CAFE standards because 
they determine the value of fuel savings both to new vehicle buyers and 
to society; the amount of fuel economy that the new vehicle market is 
likely to demand in the absence of regulatory action; and they inform 
NHTSA about the ``consumer cost . . . of our need for large quantities 
of petroleum.'' For this proposal, NHTSA relied on fuel price 
projections from the U.S. Energy Information Administration's (EIA) 
Annual Energy Outlook (AEO) for 2021. Federal government agencies 
generally use EIA's price projections in their assessment of future 
energy-related policies.
    In previous CAFE rulemakings, discussions of fuel prices have 
always been intended to reflect the price of motor gasoline. However, a 
growing set of vehicle offerings that rely in part, or entirely, on 
electricity suggests that gasoline prices are no longer the only fuel 
prices relevant to evaluations of proposed CAFE standards. In the 
analysis supporting this proposal, NHTSA considers the energy 
consumption and resulting emissions from the entire on-road fleet, 
which already contains a number of plug-in hybrid and fully electric 
vehicles. Higher CAFE standards encourage manufacturers to improve fuel 
economy; concurrently, manufacturers will foreseeably seek to continue 
to maximize profit (or minimize compliance cost), and some reliance on 
electrification is a viable strategy for some manufacturers, even 
though NHTSA does not consider it in determining maximum feasible CAFE

[[Page 49794]]

stringency. Under the more stringent CAFE alternatives in this 
proposal, we see a greater reliance on electrification technologies in 
the analysis in the years following the explicitly-regulated model 
years, even though internal combustion engines continue to be the most 
common powertrain across the industry in the action years of this 
proposal.
    While the current national average electricity price is 
significantly higher than that of gasoline, on an energy equivalent 
basis ($/MMBtu),\415\ electric motors convert energy into propulsion 
much more efficiently than internal combustion engines. This means 
that, even though the energy-equivalent prices of electricity are 
higher, electric vehicles still produce fuel savings for their owners. 
EIA also projects rising real gasoline prices over the next three 
decades, while projecting real electricity prices to remain relatively 
flat. As the reliance on electricity grows in the light-duty fleet, 
NHTSA will continue to monitor the trends in electricity prices and 
their implications for CAFE standards. Even if NHTSA is prohibited from 
considering electrification as a technology during the model years 
covered by the rulemaking, the consumer (and social) cost implications 
of manufacturers otherwise switching to electrification may remain 
relevant to the agency's considerations.
---------------------------------------------------------------------------

    \415\ Source: AEO 2021, Table 3.
---------------------------------------------------------------------------

    For now, gasoline is still the dominant fuel used in light-duty 
transportation. As such, consumers, and the economy more broadly, are 
subject to fluctuations in price that impact the cost of travel and, 
consequently, the demand for mobility. Over the last decade, the U.S. 
has become a stabilizing force in the global oil market and our 
reliance on imported petroleum has decreased steadily. The most recent 
Annual Energy Outlook, AEO 2021, projects the U.S. to be a net exporter 
of petroleum and other liquids through 2050 in the Reference Case. Over 
the last decade, EIA projections of real fuel prices have generally 
flattened in recognition of the changing dynamics of the oil market and 
slower demand growth, both in the U.S. and in developing markets. For 
example, the International Energy Agency projects that global demand 
for gasoline is unlikely to ever return to its 2019 level (before the 
pandemic).\416\ However, vehicles are long-lived assets and the long-
term price uncertainty of petroleum still represents a risk to 
consumers, albeit one that has decreased in the last decade. Continuing 
to reduce the amount of money consumers spend on vehicle fuel thus 
remains an important consideration for the need of the U.S. to conserve 
energy.
---------------------------------------------------------------------------

    \416\ International Energy Agency, Oil 2021, (p. 30), https://iea.blob.core.windows.net/assets/1fa45234-bac5-4d89-a532-768960f99d07/Oil_2021-PDF.pdf.
---------------------------------------------------------------------------

(2) National Balance of Payments
    NHTSA has consistently included consideration of the ``national 
balance of payments'' as part of the need of the U.S. to conserve 
energy because of concerns that importing large amounts of oil created 
a significant wealth transfer to oil-exporting countries and left the 
U.S. economically vulnerable.\417\ As recently as 2009, nearly half the 
U.S. trade deficit was driven by petroleum,\418\ yet this concern has 
been less critical in more recent CAFE actions, in part because other 
factors besides petroleum consumption have been playing a bigger role 
in the U.S. trade deficit.\419\ While transportation demand is expected 
to increase as the economy recovers from the pandemic, it is 
foreseeable that the trend of trade in consumer goods and services 
continuing to dominate the national balance of payments, as compared to 
petroleum, will continue during the rulemaking timeframe.
---------------------------------------------------------------------------

    \417\ For the earliest discussion of this topic, see 42 FR 
63184, 63192 (Dec. 15, 1977) (``A major reason for this need [to 
reduce petroleum consumption] is that the importation of large 
quantities of petroleum creates serious balance of payments and 
foreign policy problems. The United States currently spends 
approximately $45 billion annually for imported petroleum. But for 
this large expenditure, the current large U.S. trade deficit would 
be a surplus.'').
    \418\ See, Today in Energy: Recent improvements in petroleum 
trade balance mitigate U.S. trade deficit, U.S. Energy Information 
Administration (July 21, 2014). Available at https://www.eia.gov/todayinenergy/detail.php?id=17191 and in the docket for this 
rulemaking, NHTSA-2021-0053.
    \419\ Consumer products are the primary drivers of the trade 
deficit. In 2020, the U.S. imported $2.4 trillion in consumer goods, 
versus $116.4 billion of petroleum, which is the lowest amount since 
2002. The 2020 goods deficit of $904.9 billion was the highest on 
record, while the 2020 petroleum surplus of $18.1 billion was the 
first annual surplus on record. See U.S. Census Bureau, ``Annual 
2020 Press Highlights,'' at census.gov/foreign-trade/statistics/highlights/AnnualPressHighlights.pdf, and available in the docket 
for this rulemaking. While 2020 was an unusual year for U.S. 
transportation demand, given the global pandemic, this is consistent 
with existing trends in which consumer products imports 
significantly outweigh oil imports.
---------------------------------------------------------------------------

    That said, the U.S. continues to rely on oil imports, and NHTSA 
continues to recognize that reducing the vulnerability of the U.S. to 
possible oil price shocks remains important. This proposal aims to 
improve fleet-wide fuel efficiency and to help reduce the amount of 
petroleum consumed in the U.S., and therefore aims to improve this part 
of the U.S. balance of payments.
(3) Environmental Implications
    Higher fleet fuel economy reduces U.S. emissions of CO2 
as well as various other pollutants by reducing the amount of oil that 
is produced and refined for the U.S. vehicle fleet, but can also 
potentially increase emissions by reducing the cost of driving, which 
can result in increased vehicle miles traveled (i.e., the rebound 
effect). Thus, the net effect of more stringent CAFE standards on 
emissions of each pollutant depends on the relative magnitudes of its 
reduced emissions in fuel refining and distribution and increases in 
its emissions from vehicle use. Fuel savings from CAFE standards also 
necessarily result in lower emissions of CO2, the main 
greenhouse gas emitted as a result of refining, distribution, and use 
of transportation fuels.
    NHTSA has considered environmental issues, both within the context 
of EPCA and the context of the National Environmental Policy Act 
(NEPA), in making decisions about the setting of standards since the 
earliest days of the CAFE program. As courts of appeal have noted in 
three decisions stretching over the last 20 years,\420\ NHTSA defined 
``the need of the United States to conserve energy'' in the late 1970s 
as including, among other things, environmental implications. In 1988, 
NHTSA included climate change concepts in its CAFE NPRMs and prepared 
its first environmental assessment addressing that subject.\421\ It 
cited concerns about climate change as one of the reasons for limiting 
the extent of its reduction of the CAFE standard for MY 1989 passenger 
cars.\422\
---------------------------------------------------------------------------

    \420\ CAS, 793 F.2d 1322, 1325 n. 12 (D.C. Cir. 1986); Public 
Citizen, 848 F.2d 256, 262-63 n. 27 (D.C. Cir. 1988) (noting that 
``NHTSA itself has interpreted the factors it must consider in 
setting CAFE standards as including environmental effects''); CBD, 
538 F.3d 1172 (9th Cir. 2007).
    \421\ 53 FR 33080, 33096 (Aug. 29, 1988).
    \422\ 53 FR 39275, 39302 (Oct. 6, 1988).
---------------------------------------------------------------------------

    NHTSA also considers environmental justice issues as part of the 
environmental considerations under the need of the U.S. to conserve 
energy, per Executive Order 12898, ``Federal Actions to Address 
Environmental Justice in Minority Populations'' \423\ and DOT Order 
5610.2(c), ``U.S. Department of Transportation Actions to Address 
Environmental Justice in Minority Populations and Low-Income 
Populations.'' \424\ The affected environment for environmental justice 
is nationwide, with a focus on areas that

[[Page 49795]]

could contain minority and low-income communities who would most likely 
be exposed to the environmental and health effects of oil production, 
distribution, and consumption, or the impacts of climate change. This 
includes areas where oil production and refining occur, areas near 
roadways, coastal flood-prone areas, and urban areas that are subject 
to the heat island effect.
---------------------------------------------------------------------------

    \423\ 59 FR 629 (Feb. 16, 1994).
    \424\ Department of Transportation Updated Environmental Justice 
Order 5610.2(c) (May 14, 2021).
---------------------------------------------------------------------------

    Numerous studies have found that some environmental hazards are 
more prevalent in areas where minority and low-income populations 
represent a higher proportion of the population compared with the 
general population. In terms of effects due to criteria pollutants and 
air toxics emissions, the body of scientific literature points to 
disproportionate representation of minority and low-income populations 
in proximity to a range of industrial, manufacturing, and hazardous 
waste facilities that are stationary sources of air pollution, although 
results of individual studies may vary. While the scientific literature 
specific to oil refineries is limited, disproportionate exposure of 
minority and low-income populations to air pollution from oil 
refineries is suggested by other broader studies of racial and 
socioeconomic disparities in proximity to industrial facilities 
generally. Studies have also consistently demonstrated a 
disproportionate prevalence of minority and low-income populations that 
are living near mobile sources of pollutants (such as roadways) and 
therefore are exposed to higher concentrations of criteria air 
pollutants in multiple locations across the United States. Lower-
positioned socioeconomic groups are also differentially exposed to air 
pollution and differentially vulnerable to effects of exposure.
    In terms of exposure to climate change risks, the literature 
suggests that across all climate risks, low-income communities, some 
communities of color, and those facing discrimination are 
disproportionately affected by climate events. Communities overburdened 
by poor environmental quality experience increased climate risk due to 
a combination of sensitivity and exposure. Urban populations 
experiencing inequities and health issues have greater susceptibility 
to climate change, including substantial temperature increases. Some 
communities of color facing cumulative exposure to multiple pollutants 
also live in areas prone to climate risk. Indigenous peoples in the 
United States face increased health disparities that cause increased 
sensitivity to extreme heat and air pollution. Together, this 
information indicates that climate impacts disproportionately affect 
minority and low-income populations because of socioeconomic 
circumstances, histories of discrimination, and inequity. Furthermore, 
high temperatures can exacerbate poor air quality, further compounding 
the risk to overburdened communities. Finally, health-related 
sensitivities in low-income and minority populations increase risk of 
damaging impacts from poor air quality under climate change, 
underscoring the potential benefits of improving air quality to 
communities overburdened by poor environmental quality.
    In the SEIS, Chapters 3, 4, 5, and 8 discuss the connections 
between oil production, distribution, and consumption, and their health 
and environmental impacts.
    All of the action alternatives considered in this proposal reduce 
carbon dioxide emissions and, thus, the effects of climate change, as 
compared to the baseline. Effects on criteria pollutants and air toxics 
emissions are somewhat more complicated, for a variety of reasons, as 
discussed in Section VI.C, although over time and certainly over the 
lifetimes of the vehicles that would be subject to this proposal, these 
emissions are currently forecast to fall significantly.
    As discussed above, while the majority of light-duty vehicles will 
continue to be powered by internal combustion engines in the near- to 
mid-term under all regulatory alternatives, the more stringent 
alternatives do appear in the analysis to lead to greater 
electrification in the mid- to longer-term. While NHTSA is prohibited 
from considering electric vehicles in determining maximum feasible CAFE 
levels, electric vehicles (which appear both in the agency's baseline 
and which may be produced in model years following the period of 
regulation as an indirect effect of more stringent standards, or in 
response to other standards or to market demand) produce few to zero 
tailpipe emissions, and thus contribute meaningfully to the 
decarbonization of the transportation sector, in addition to having 
environmental, health, and economic development benefits, although 
these benefits may not yet be equally distributed across society. They 
also present new environmental (and social) questions, like those 
associated with reduced tailpipe emissions, upstream electricity 
production, minerals extraction for battery components, and ability to 
charge an electric vehicle. The upstream environmental effects of 
extraction and refining for petroleum are well-recognized; minerals 
extraction and refining can also have significant downsides. As one 
example of documentation of these effects, the United Nations 
Conference on Trade and Development issued a report in July 2020 
describing acid mine drainage and uranium-laced dust associated with 
cobalt mines in the DRC, along with child labor concerns; considerable 
groundwater consumption and dust issues that harm miners and indigenous 
communities in the Andes; issues with fine particulate matter causing 
human health effects and soil contamination in regions near graphite 
mines; and so forth.\425\ NHTSA's SEIS discusses these and other 
effects (such as production and end-of-life issues) in more detail, and 
NHTSA will continue to monitor these issues going forward insofar as 
CAFE standards may increase electrification levels even if NHTSA does 
not expressly consider electrification in setting those standards, 
because NHTSA does not control what technologies manufacturers use to 
meet those standards, and because NHTSA is required to consider the 
environmental effects of its standards under NEPA.
---------------------------------------------------------------------------

    \425\ UNCTAD, ``Commodities at a Glance: Special issue on 
strategic battery raw materials,'' No. 13, Geneva, 2020, at 46. 
Available at https://unctad.org/system/files/official-document/ditccom2019d5_en.pdf and in the docket for this rulemaking, NHTSA-
2021-0053.
---------------------------------------------------------------------------

    NHTSA carefully considered the environmental effects of this 
proposal, both quantitative and qualitative, as discussed in the SEIS 
and in Sections VI.C and VI.D.
(4) Foreign Policy Implications
    U.S. consumption and imports of petroleum products impose costs on 
the domestic economy that are not reflected in the market price for 
crude petroleum or in the prices paid by consumers for petroleum 
products such as gasoline. These costs include (1) higher prices for 
petroleum products resulting from the effect of U.S. oil demand on 
world oil prices; (2) the risk of disruptions to the U.S. economy 
caused by sudden increases in the global price of oil and its resulting 
impact of fuel prices faced by U.S. consumers, and (3) expenses for 
maintaining the strategic petroleum reserve (SPR) to provide a response 
option should a disruption in commercial oil supplies threaten the U.S. 
economy, to allow the U.S. to meet part of its International Energy 
Agency obligation to maintain emergency oil stocks, and to provide a 
national defense fuel reserve. Reducing U.S. consumption of crude oil 
or refined petroleum products (by reducing motor

[[Page 49796]]

fuel use) can reduce these external costs.\426\
---------------------------------------------------------------------------

    \426\ A 2006 report by the Council on Foreign Relations 
identified six foreign policy costs that it said arose from U.S. 
consumption of imported oil. These costs include (1) the adverse 
effect that significant disruptions in oil supply will have for 
political and economic conditions in the U.S. and other importing 
countries; (2) the fears that the current international system is 
unable to ensure secure oil supplies when oil is seemingly scarce 
and oil prices are high; (3) political realignment from dependence 
on imported oil that limits U.S. alliances and partnerships; (4) the 
flexibility that oil revenues give oil-exporting countries to adopt 
policies that are contrary to U.S. interests and values; (5) an 
undermining of sound governance by the revenues from oil and gas 
exports in oil-exporting countries; and (6) an increased U.S. 
military presence in the Middle East that results from the strategic 
interest associated with oil consumption. Council on Foreign 
Relations, National Security Consequences of U.S. Oil Dependency, 
Independent Task Force Report No. 58, October 2006. Available at 
https://cdn.cfr.org/sites/default/files/report_pdf/0876093659.pdf 
and in the docket for this rulemaking, NHTSA-2021-0053. Brown and 
Huntington (2015) find that these six costs are either implicitly 
incorporated in the welfare-theoretic analysis, are not 
externalities, or cannot be quantified. Brown, Stephen and Hillard 
Huntington, Evaluating U.S. oil security and import reliance, Energy 
Policy 108, 2015, at 512-523. Available at https://www.sciencedirect.com/science/article/abs/pii/S0301421515000026 and 
for hard copy review at DOT headquarters. To the extent that these 
costs are externalities that cannot be quantified, the measured 
security costs of U.S. reliance on imported oil will be understated.
---------------------------------------------------------------------------

    Stephen Brown, who has published extensively on price shock and 
foreign policy risks associated with U.S. oil consumption, stated in a 
recent paper that:

    Over the past few years, world oil market conditions have 
changed considerably (with the United States importing much less 
oil), new estimates of the probabilities of world oil supply 
disruptions have become available, and new estimates of the response 
of U.S. real GDP to oil supply shocks and the short-run elasticity 
of oil demand have become available. These developments suggest that 
it is time to update the estimates of the security costs of U.S. oil 
consumption. The new estimates of the oil security premiums suggest 
that U.S. oil security may have become less of an issue than it was 
in the past, mostly as a result of new estimates of the short-run 
elasticity of demand and the response of U.S. real GDP to oil price 
shocks.\427\
---------------------------------------------------------------------------

    \427\ Brown, Stephen. ``New Estimates of the security costs of 
U.S. oil consumption,'' Energy Policy, Vol. 113, Feb. 2018, at 172. 
Available at https://www.sciencedirect.com/science/article/abs/pii/S0301421517307413 and for hard copy review at DOT headquarters.

    Brown notes that ``Because we have not observed a modern economy 
with large oil supply disruptions, we have no reliable method to 
quantify the effects of these disruptions,'' and ``The result could 
be an average of old and new results or estimation problems and a 
poor fit.'' \428\ Geopolitical risk can still affect global oil 
prices, of course, because oil is a global market, and thus can 
affect U.S. oil prices, although possibly by less than in the 
past.\429\ The U.S. still maintains a military presence in certain 
parts of the world to help secure global access to petroleum 
supplies. Chapter 6.2.4 of the TSD discusses this topic in more 
detail. Brown concludes that:
---------------------------------------------------------------------------

    \428\ Id. at 181.
    \429\ Also in 2018, Beccue, Huntington, Leiby, and Vincent 
reported on their findings of an expert panel on oil market 
disruption risks and likelihoods, and stated that based on these 
findings, during the period of 2016-2025, ``It is very likely that a 
disruption greater than 2 MMBD will occur (81%). However, it is 
unlikely that disruptions greater than 15 MMBD will occur (1%).'' 
They further state that ``. . . experts in the current study expect 
that both gross shocks and excess capacity will be lower than 
before, resulting in similar net disruptions [to what was estimated 
in 2005]. Although turmoil remains high in these countries with the 
ongoing Iraq war, tensions between Iran and its Arab neighbors, and 
concern over the ability of terrorists to cut oil supply facilities, 
these conditions do not produce larger oil market disruptions.'' 
They conclude that ``In general, this panel of energy security 
experts has concluded that current world events and energy markets 
have increased the likelihood of oil disruptions since 1996 but 
demonstrated a similar risk profile compared to the 2005 period. 
Moreover, their assessments indicate that lower oil price paths make 
net disruptions of any given size more likely.'' Beccue et al., ``An 
updated assessment of oil market disruption risks,'' Energy Policy, 
Vol. 115, Apr. 2018, at 456. Available at https://www.sciencedirect.com/science/article/abs/pii/S0301421517308285 and 
for hard copy review at DOT headquarters.

    Nonetheless, only the highest estimates of the oil security 
premiums suggest that U.S. oil security is nearly an equally 
important issue to the environmental costs of oil use. The mid-
estimates from the model that may best represent how the world oil 
market and the U.S. economy will respond to world oil supply 
disruptions of various sizes . . . find U.S. consumption of imported 
or domestic oil does yield important security costs, but those costs 
are much lower than the estimated environmental costs of oil use. 
Consistent with Brown and Huntington (2013), the substitution of 
domestic oil for imported oil only slightly improves U.S. oil 
security. Oil conservation is more effective than increased domestic 
oil production at improving U.S. oil security.\430\
---------------------------------------------------------------------------

    \430\ Brown, 2018, at 182.

    NHTSA agrees both that oil conservation improves U.S. oil security, 
and that the environmental costs of oil use are intertwined with the 
security costs of oil use in some ways as climate change destabilizes 
traditional geopolitical power structures over time. The effect of 
climate change on natural resources inevitably has security 
implications--population changes and shifts have already been forced in 
some countries, which can create social and security effects at all 
geopolitical levels--local, national, regional, and global. CAFE 
standards over the last few decades have conserved significant 
quantities of oil, and the petroleum intensity of the U.S. fleet has 
decreased significantly. Continuing to improve energy conservation and 
reduce U.S. oil consumption by raising CAFE standards further has the 
potential to continue to help with all of these considerations.
    As standards and market demand move the U.S. light-duty vehicle 
fleet toward electrification, different potential foreign policy 
implications arise. Most vehicle electrification is enabled by lithium-
ion batteries. Lithium-ion battery global value chains have several 
phases: Sourcing (mining/extraction); processing/refining; cell 
manufacturing; battery manufacturing; installation in an EV; and 
recycling.\431\ Because lithium-ion battery materials have a wide 
global diversity of origin, accessing them can pose varying 
geopolitical challenges.\432\ The U.S. International Trade Commission 
(USITC) recently summarized 2018 data from the U.S. Geological Survey 
on the production/sourcing of the four key lithium-ion battery 
materials, as shown in Table VI-5.
---------------------------------------------------------------------------

    \431\ Scott, Sarah, and Robert Ireland, ``Lithium-Ion Battery 
Materials for Electric Vehicles and their Global Value Chains,'' 
Office of Industries Working Paper ID-068, U.S. International Trade 
Commission, June 2020, at 7. Available at https://www.usitc.gov/publications/332/working_papers/gvc_overview_scott_ireland_508_final_061120.pdf and in the docket 
for this rulemaking, NHTSA-2021-0053.
    \432\ Id. at 8.

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[[Page 49797]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.180

    Of these  sources, the USITC notes that while ``lithium has 
generally not faced political instability risks,'' ``Because of the 
[Democratic Republic of Congo's] ongoing political instability, as well 
as poor labor conditions, sourcing cobalt faces significant 
geopolitical challenges.'' \434\ Nickel is also used extensively in 
stainless steel production, and much of what is produced in Indonesia 
and the Philippines is exported to China for stainless steel 
manufacturing.\435\ Obtaining graphite for batteries does not currently 
pose geopolitical obstacles, but the USITC notes that Turkey has great 
potential to become a large graphite producer, which would make 
stability there a larger concern.\436\
---------------------------------------------------------------------------

    \433\ Id., citing U.S. Geological Survey, Mineral Commodity 
Summaries, Feb. 2019.
    \434\ Id. at 8, 9.
    \435\ Id at 9.
    \436\ Id.
---------------------------------------------------------------------------

    For materials processing and refining, China is the largest 
importer of unprocessed lithium, which it then transforms into 
processed or refined lithium,\437\ the leading producer of refined 
cobalt (with Finland a distant second),\438\ one of the leading 
producers of primary nickel products (along with Indonesia, Japan, 
Russia, and Canada) and one of the leading refiners of nickel into 
nickel sulfate, the chemical compound used for cathodes in lithium-ion 
batteries,\439\ and one of the leading processors of graphite intended 
for use in lithium-ion batteries as well.\440\ In all regions, 
increasing attention is being given to vertical integration in the 
lithium-ion battery industry from material extraction, mining and 
refining, battery materials, cell production, battery systems, reuse, 
and recycling. The United States is lagging in upstream capacity; 
although the U.S. has some domestic lithium deposits, it has very 
little capacity in mining and refining any of the key raw materials. As 
mentioned elsewhere, however, there can be benefits and drawbacks in 
terms of environmental consequences associated with increased mining, 
refining, and battery production.
---------------------------------------------------------------------------

    \437\ Id.
    \438\ Id. at 10.
    \439\ Id.
    \440\ Id.
---------------------------------------------------------------------------

    China and the European Union (EU) are also major consumers of 
lithium-ion batteries, along with Japan, Korea, and others. Lithium-ion 
batteries are used not only in light-duty vehicles, but in many 
ubiquitous consumer goods, and are likely to be used eventually in 
other forms of transportation as well. Thus, securing sufficient 
batteries to enable large-scale shifts to electrification in the U.S. 
light-duty vehicle fleet may face new issues as vehicle companies 
compete with other new sectors. NHTSA will continue to monitor these 
issues going forward.
    President Biden has already issued an Executive Order on 
``America's Supply Chains,'' aiming to strengthen the resilience of 
America's supply chains, including those for automotive batteries.\441\ 
Reports are to be developed within one year of issuance of the 
Executive Order, and NHTSA will monitor these findings as they develop.
---------------------------------------------------------------------------

    \441\ Executive Order 14017, ``America's Supply Chains,'' Feb. 
24, 2021. 86 FR 11849 (Mar. 1, 2021).
---------------------------------------------------------------------------

(e) Factors That NHTSA Is Prohibited From Considering
    EPCA also provides that in determining the level at which it should 
set CAFE standards for a particular model year, NHTSA may not consider 
the ability of manufacturers to take advantage of several EPCA 
provisions that facilitate compliance with CAFE standards and thereby 
reduce the costs of compliance.\442\ NHTSA cannot consider compliance 
credits that manufacturers earn by exceeding the CAFE standards and 
then use to achieve compliance in years in which their measured average 
fuel economy falls below the standards. NHTSA also cannot consider the 
use of alternative fuels by dual fueled automobiles, nor the fuel 
economy (i.e., the availability) of dedicated alternative fueled 
automobiles--including battery-electric vehicles--in any model year. 
EPCA encourages the production of alternative fuel vehicles by 
specifying that their fuel economy is to be determined using a special 
calculation procedure that results in those vehicles being assigned a 
higher equivalent fuel economy level than they actually achieve.
---------------------------------------------------------------------------

    \442\ 49 U.S.C. 32902(h).
---------------------------------------------------------------------------

    The effect of the prohibitions against considering these statutory 
flexibilities in setting the CAFE standards is that the flexibilities 
remain voluntarily-employed measures. If NHTSA were instead to assume 
manufacturer use of those flexibilities in setting new standards (as 
NHTSA does in the ``EIS analysis,'' but not the ``standard setting 
analysis''), compliance with higher standards would appear more cost-
effective and, potentially, more feasible, which would thus effectively 
require manufacturers to use those flexibilities if NHTSA determined 
that standards should be more stringent. By keeping NHTSA from 
including them in our stringency determination, the provision ensures 
that those statutory credits

[[Page 49798]]

remain true compliance flexibilities. However, the flip side of the 
effect described above is that preventing NHTSA from assuming use of 
dedicated alternative fuel vehicles for compliance makes it more 
difficult for the CAFE program to facilitate a complete transition of 
the U.S. light-duty fleet to full electrification.
    In contrast, for the non-statutory fuel economy improvement value 
program that NHTSA developed by regulation, NHTSA does not consider 
these fuel economy adjustments subject to the 32902(h) prohibition on 
considering flexibilities. The statute is very clear as to which 
flexibilities are not to be considered. When the agency has introduced 
additional flexibilities such as A/C efficiency and ``off-cycle'' 
technology fuel improvement values, NHTSA has considered those 
technologies as available in the analysis. Thus, this analysis includes 
assumptions about manufacturers' use of those technologies, as detailed 
in Chapter 3.8 of the accompanying TSD.
    NHTSA notes that one of the recommendations in the 2021 NAS Report 
was for Congress to ``amend the statute to delete the [32902(h)] 
prohibition on considering the fuel economy of dedicated alternative 
fueled vehicles in setting CAFE standards.'' \443\ Recognizing that 
changing statutory text is Congress' affair and not NHTSA's, the 
committee further recommended that if Congress does not change the 
statute, NHTSA should consider adding another attribute to the fuel 
economy standard function, like ``the expected market share of ZEVs in 
the total U.S. fleet of new light-duty vehicles--such that the 
standards increase as the share of ZEVs in the total U.S. fleet 
increases.'' \444\ NHTSA discusses this recommendation further in 
Section III.B.
---------------------------------------------------------------------------

    \443\ 2021 NAS Report, Summary Recommendation 5.
    \444\ Id.
---------------------------------------------------------------------------

    While NHTSA does not consider the prohibited items in its standard-
setting analysis or for making its tentative decision about what levels 
of standards would be maximum feasible, NHTSA notes that it is informed 
by the ``EIS'' analysis presented in the PRIA. The EIS analysis does 
not contain these restrictions, and therefore accounts for credit 
availability and usage, and manufacturers' ability to employ 
alternative fueled vehicles, for purpose of conformance with E.O. 12866 
and NEPA regulations. Under the EIS analysis, compliance generally 
appears less costly. For example, this EIS analysis shows 
manufacturers' costs averaging about $1,070 in MY 2029 under the 
proposed standards, as compared to the $1,175 shown by the standard 
setting analysis. Again, however, for purposes of tentatively 
determining maximum feasible CAFE levels, NHTSA considers only the 
standard setting analysis shown in the NPRM, consistent with Congress' 
direction.
(f) Other Considerations in Determining Maximum Feasible CAFE Standards
    NHTSA has historically considered the potential for adverse safety 
effects in setting CAFE standards. This practice has been upheld in 
case law.\445\ In this proposal, NHTSA has considered the safety 
effects discussed in Section V of this preamble and in Chapter 5 of the 
accompanying PRIA. NHTSA discusses its consideration of these effects 
in Section VI.D.
---------------------------------------------------------------------------

    \445\ As courts have recognized, ``NHTSA has always examined the 
safety consequences of the CAFE standards in its overall 
consideration of relevant factors since its earliest rulemaking 
under the CAFE program.'' Competitive Enterprise Institute v. NHTSA, 
901 F.2d 107, 120 n. 11 (D.C. Cir. 1990) (``CEI-I'') (citing 42 FR 
33534, 33551 (Jun. 30, 1977). Courts have consistently upheld 
NHTSA's implementation of EPCA in this manner. See, e.g., 
Competitive Enterprise Institute v. NHTSA, 956 F. 2d 321, 322 (D.C. 
Cir. 1992) (``CEI-II'') (in determining the maximum feasible 
standard, ``NHTSA has always taken passenger safety into account) 
(citing CEI-I, 901 F.2d at 120 n. 11); Competitive Enterprise 
Institute v. NHTSA, 45 F.3d 481, 482-83 (D.C. Cir. 1995) (CEI-III) 
(same); Center for Biological Diversity v. NHTSA, 538 F.3d 1172, 
1203-04 (9th Cir. 2008) (upholding NHTSA's analysis of vehicle 
safety issues associated with weight in connection with the MYs 
2008-2011 light truck CAFE rulemaking).
---------------------------------------------------------------------------

B. Administrative Procedure Act

    The Administrative Procedure Act governs agency rulemaking 
generally and provides the standard of judicial review for agency 
actions. To be upheld under the ``arbitrary and capricious'' standard 
of judicial review under the APA, an agency rule must be rational, 
based on consideration of the relevant factors, and within the scope of 
the authority delegated to the agency by statute. The agency must 
examine the relevant data and articulate a satisfactory explanation for 
its action including a ``rational connection between the facts found 
and the choice made.'' \446\
---------------------------------------------------------------------------

    \446\ Burlington Truck Lines, Inc. v. United States, 371 U.S. 
156, 168 (1962).
---------------------------------------------------------------------------

    Statutory interpretations included in an agency's rule are subject 
to the two-step analysis of Chevron, U.S.A. v. Natural Resources 
Defense Council.\447\ Under step one, where a statute ``has directly 
spoken to the precise question at issue,'' id. at 842, the court and 
the agency ``must give effect to the unambiguously expressed intent of 
Congress.'' \448\ If the statute is silent or ambiguous regarding the 
specific question, the court proceeds to step two and asks ``whether 
the agency's answer is based on a permissible construction of the 
statute.'' \449\ The APA also requires that agencies provide notice and 
comment to the public when proposing regulations,\450\ as NHTSA is 
doing in this proposal.
---------------------------------------------------------------------------

    \447\ 467 U.S. 837 (1984).
    \448\ Id. at 843.
    \449\ Id.
    \450\ 5 U.S.C. 553.
---------------------------------------------------------------------------

    NHTSA recognizes that this proposal, like the 2020 final rule, is 
reconsidering standards previously promulgated. NHTSA, like any other 
Federal agency, is afforded an opportunity to reconsider prior views 
and, when warranted, to adopt new positions. Indeed, as a matter of 
good governance, agencies should revisit their positions when 
appropriate, especially to ensure that their actions and regulations 
reflect legally sound interpretations of the agency's authority and 
remain consistent with the agency's views and practices. As a matter of 
law, ``an Agency is entitled to change its interpretation of a 
statute.'' \451\ Nonetheless, ``[w]hen an Agency adopts a materially 
changed interpretation of a statute, it must in addition provide a 
`reasoned analysis' supporting its decision to revise its 
interpretation.'' \452\
---------------------------------------------------------------------------

    \451\ Phoenix Hydro Corp. v. FERC, 775 F.2d 1187, 1191 (D.C. 
Cir. 1985).
    \452\ Alabama Educ. Ass'n v. Chao, 455 F.3d 386, 392 (D.C. Cir. 
2006) (quoting Motor Vehicle Mfrs. Ass'n of U.S., Inc. v. State Farm 
Mut. Auto. Ins. Co., 463 U.S. 29, 57 (1983)); see also Encino 
Motorcars, LLC v. Navarro, 136 S Ct. 2117, 2125 (2016) (``Agencies 
are free to change their existing policies as long as they provide a 
reasoned explanation for the change.'') (citations omitted).
---------------------------------------------------------------------------

    ``Changing policy does not, on its own, trigger an especially 
`demanding burden of justification.' '' \453\ Providing a reasoned 
explanation ``would ordinarily demand that [the Agency] display 
awareness that it is changing position.'' \454\ Beyond that, however, 
``[w]hen an agency changes its existing position, it `need not always 
provide a more detailed justification than what would suffice for a new 
policy created on a blank slate.' '' \455\ While the agency ``must show 
that there are good reasons for the new policy,'' the agency ``need not 
demonstrate to a court's satisfaction that the reasons for the new 
policy are

[[Page 49799]]

better than the reasons for the old one.'' \456\ ``[I]t suffices that 
the new policy is permissible under the statute, that there are good 
reasons for it, and that the Agency believes it to be better, which the 
conscious change of course adequately indicates.'' \457\ For instance, 
``evolving notions'' about the appropriate balance of varying policy 
considerations constitute sufficiently good reasons for a change in 
position.\458\ Moreover, it is ``well within an Agency's discretion'' 
to change policy course even when no new facts have arisen: Agencies 
are permitted to conduct a ``reevaluation of which policy would be 
better in light of the facts,'' without ``rely[ing] on new facts.'' 
\459\
---------------------------------------------------------------------------

    \453\ See Mingo Logan Coal Co. v. EPA, 829 F.3d 710, 718 (D.C. 
Cir. 2016) (quoting Ark Initiative v. Tidwell, 816 F.3d 119, 127 
(D.C. Cir. 2016)).
    \454\ FCC v. Fox Television Stations, Inc. 556 U.S. 502, 515 
(2009) (emphasis in original) (``An agency may not, for example, 
depart from a prior policy sub silentio or simply disregard rules 
that are still on the books.'').
    \455\ Encino Motorcars, LLC, 136 S Ct. at 2125-26 (quoting Fox 
Television Stations, Inc. 556 U.S. at 515).
    \456\ Fox Television Stations, Inc., 556 U.S. at 515 (emphasis 
in original).
    \457\ Id. (emphasis in original).
    \458\ N. Am.'s Bldg. Trades Unions v. Occupational Safety & 
Health Admin., 878 F.3d 271, 303 (D.C. Cir. 2017) (quoting the 
agency's rule).
    \459\ Nat'l Ass'n of Home Builders v. EPA, 682 F.3d 1032, 1037-
38 (D.C. Cir. 2012).
---------------------------------------------------------------------------

    To be sure, providing ``a more detailed justification'' is 
appropriate in some cases. ``Sometimes [the agency] must [provide a 
more detailed justification than what would suffice for a new policy 
created on a blank slate]--when, for example, its new policy rests upon 
factual findings that contradict those which underlay its prior policy; 
or when its prior policy has engendered serious reliance interests that 
must be taken into account.'' \460\ This preamble, and the accompanying 
TSD and PRIA, all provide extensive detail on the agency's updated 
analysis, and Section VI.D contains the agency's explanation of how the 
agency has considered that analysis and other relevant information in 
tentatively determining that the proposed CAFE standards are maximum 
feasible for MYs 2024-2026 passenger cars and light trucks.
---------------------------------------------------------------------------

    \460\ See Fox Television Stations, Inc., 556 U.S. at 515 (2009).
---------------------------------------------------------------------------

C. National Environmental Policy Act

    As discussed above, EPCA requires NHTSA to determine the level at 
which to set CAFE standards for each model year by considering the four 
factors of technological feasibility, economic practicability, the 
effect of other motor vehicle standards of the Government on fuel 
economy, and the need of the United States to conserve energy. The 
National Environmental Policy Act (NEPA) directs that environmental 
considerations be integrated into that process.\461\ To explore the 
potential environmental consequences of this rulemaking action, NHTSA 
has prepared a Supplemental Environmental Impact Statement (``SEIS'') 
for this proposal.\462\ The purpose of an EIS is to ``provide full and 
fair discussion of significant environmental impacts and [to] inform 
decisionmakers and the public of the reasonable alternatives which 
would avoid or minimize adverse impacts or enhance the quality of the 
human environment.'' \463\
---------------------------------------------------------------------------

    \461\ NEPA is codified at 42 U.S.C. 4321-47. The Council on 
Environmental Quality (CEQ) NEPA implementing regulations are 
codified at 40 CFR parts 1500-08.
    \462\ Because this proposal revises CAFE standards established 
in the 2020 final rule, NHTSA chose to prepare a SEIS to inform that 
amendment of the MYs 2024-2026 standards. See the SEIS for more 
details.
    \463\ 40 CFR 1502.1.
---------------------------------------------------------------------------

    When preparing an EIS, NEPA requires an agency to compare the 
potential environmental impacts of its proposed action and a reasonable 
range of alternatives. In the SEIS, NHTSA analyzed a No Action 
Alternative and three action alternatives. The alternatives represent a 
range of potential actions the agency could take, and they are 
described more fully in Section IV of this preamble, Chapter 1 of the 
TSD, and Chapter 2 of the PRIA. The environmental impacts of these 
alternatives, in turn, represent a range of potential environmental 
impacts that could result from NHTSA's setting maximum feasible fuel 
economy standards for passenger cars and light trucks.
    To derive the direct and indirect impacts of the action 
alternatives, NHTSA compared each action alternative to the No Action 
Alternative, which reflects baseline trends that would be expected in 
the absence of any further regulatory action. More specifically, the No 
Action Alternative in the SEIS assumed that the CAFE standards set in 
the 2020 final rule for MYs 2021-2026 passenger cars and light trucks 
would remain in effect. In addition, the No Action Alternative also 
includes several other actions that NHTSA believes will occur in the 
absence of further regulatory action, as discussed in more detail in 
Section IV above: (1) California's ZEV mandate; (2) the ``Framework 
Agreements'' between California and BMW, Ford, Honda, VWA, and Volvo, 
which NHTSA implemented by including EPA's baseline GHG standards 
(i.e., those set in the 2020 final rule) and introducing more stringent 
GHG target functions for those manufacturers; and (3) the assumption 
that manufacturers will also make any additional fuel economy 
improvements estimated to reduce owners' estimated average fuel outlays 
during the first 30 months of vehicle operation by more than the 
estimated increase in new vehicle price. The No Action Alternative 
provides a baseline against which to compare the environmental impacts 
of other alternatives presented in the SEIS.\464\
---------------------------------------------------------------------------

    \464\ See 40 CFR 1502.2(e), 1502.14(d). CEQ has explained that 
``[T]he regulations require the analysis of the no action 
alternative even if the agency is under a court order or legislative 
command to act. This analysis provides a benchmark, enabling 
decision makers to compare the magnitude of environmental effects of 
the action alternatives [See 40 CFR 1502.14(c).] . . . Inclusion of 
such an analysis in the EIS is necessary to inform Congress, the 
public, and the President as intended by NEPA. [See 40 CFR 
1500.1(a).]'' Forty Most Asked Questions Concerning CEQ's National 
Environmental Policy Act Regulations, 46 FR 18026 (Mar. 23, 1981).
---------------------------------------------------------------------------

    For the SEIS, NHTSA analyzed three action alternatives, 
Alternatives 1 through 3, which ranged from increasing CAFE stringency 
for MY 2024 by 9.14 percent for passenger cars and 11.02 percent for 
light trucks, and increase stringency in MYs 2025 and 2026 by 3.26 
percent per year for both passenger cars and light trucks (Alternative 
1) to increasing CAFE stringency for each year, for each fleet, at 10 
percent per year (Alternative 3). The range of action alternatives, as 
well as the No Action Alternative, encompass a spectrum of possible 
standards NHTSA could determine was maximum feasible based on the 
different ways the agency could weigh EPCA's four statutory factors. 
Throughout the SEIS, estimated impacts were shown for all of these 
action alternatives, as well as for the No Action Alternative. For a 
more detailed discussion of the environmental impacts associated with 
the alternatives, see Chapters 3-6 of the SEIS, as well as Section V of 
this preamble.
    NHTSA's SEIS describes potential environmental impacts to a variety 
of resources, including fuel and energy use, air quality, climate, land 
use and development, hazardous materials and regulated wastes, 
historical and cultural resources, noise, and environmental justice. 
The SEIS also describes how climate change resulting from global 
greenhouse gas emissions (including CO2 emissions 
attributable to the U.S. light-duty transportation sector under the 
alternatives considered) could affect certain key natural and human 
resources. Resource areas are assessed qualitatively and 
quantitatively, as appropriate, in the SEIS, and the findings of that 
analysis are summarized here.\465\
---------------------------------------------------------------------------

    \465\ The impacts described in this section come from NHTSA's 
SEIS, which is being publicly issued simultaneously with this NPRM. 
As described above, the SEIS is based on ``unconstrained'' modeling 
rather than ``standard setting'' modeling. NHTSA conducts modeling 
both ways in order to reflect the various statutory requirements of 
EPCA/EISA and NEPA. The preamble employs the ``standard setting'' 
modeling in order to aid the decision-maker in avoiding 
consideration of the prohibited items in 49 U.S.C. 32902(h) in 
determining maximum feasible standards, but as a result, the impacts 
reported here may differ from those reported elsewhere in this 
preamble. However, NHTSA considers the impacts reported in the SEIS, 
in addition to the other information presented in this preamble, the 
TSD, and the PRIA, as part of its decision-making process.

---------------------------------------------------------------------------

[[Page 49800]]

    As the stringency of the alternatives increases, total U.S. 
passenger car and light truck fuel consumption for the period of 2020 
to 2050 decreases. Total light-duty vehicle fuel consumption from 2020 
to 2050 under the No Action Alternative is projected to be 3,510 
billion gasoline gallon equivalents (GGE). Light-duty vehicle fuel 
consumption from 2020 to 2050 under the action alternatives is 
projected to range from 3,409 billion GGE under Alternative 1 to 3,282 
billion GGE under Alternative 3. Under Alternative 2, light-duty 
vehicle fuel consumption from 2020 to 2050 is projected to be 3,344 
billion GGE. All of the action alternatives would decrease fuel 
consumption compared to the No-Action Alternative, with fuel 
consumption decreases that range from 100 billion GGE under Alternative 
1 to 227 billion GGE under Alternative 3.
    The relationship between stringency and criteria and air toxics 
pollutant emissions is less straightforward, reflecting the complex 
interactions among the tailpipe emissions rates of the various vehicle 
types (passenger cars and light trucks, ICE vehicles and EVs, older and 
newer vehicles, etc.), the technologies assumed to be incorporated by 
manufacturers in response to CAFE standards, upstream emissions rates, 
the relative proportions of gasoline, diesel, and electricity in total 
fuel consumption, and changes in VMT from the rebound effect. In 
general, emissions of criteria and toxic air pollutants increase very 
slightly in the short term, and then decrease dramatically in the 
longer term, across all action alternatives, with some exceptions. In 
addition, the action alternatives would result in decreased incidence 
of PM2.5-related health impacts in most years and 
alternatives due to the emissions decreases. Decreases in adverse 
health outcomes include decreased incidences of premature mortality, 
acute bronchitis, respiratory emergency room visits, and work-loss 
days.
    The air quality analysis in the SEIS identified the following 
impacts on criteria air pollutants.
    For all criteria pollutants in 2025, emissions increase slightly 
under the action alternatives compared to the No-Action Alternative. 
The emission increases generally get larger (although they are still 
small) from Alternative 1 through Alternative 3 (the most stringent 
alternative in terms of required miles per gallon). This temporary 
increase is largely due to new vehicle prices increasing in the short-
term, which slightly slows new-vehicle sales and encourages consumers 
to buy used vehicles instead or retain existing vehicles for longer. As 
the analysis timeframe progresses, the new, higher fuel-economy 
vehicles become used vehicles, and the impacts of the standards change 
direction. In 2025, across all criteria pollutants and action 
alternatives, the smallest increase in emissions is 0.01 percent for 
VOCs under Alternative 2; the largest increase is 0.6 percent and 
occurs for SO2 under Alternative 3. We underscore that these 
are fractions of a single percent.
    In 2035 and 2050, emissions of CO, NOX, 
PM2.5, and VOCs generally decrease under the action 
alternatives compared to the No-Action Alternative, except for CO in 
2035 under Alternative 1 (0.07 percent increase) and NOX in 
2035 under Alternative 3 (0.5 percent increase) (again, these are 
fractions of a single percent), with the more stringent alternatives 
having the largest decreases, except for NOX and 
PM2.5 in 2035 (emissions decrease less or increase with more 
stringent alternatives) and NOX in 2050 (emissions increase 
under Alternative 3 relative to Alternative 2, due primarily to 
slightly higher upstream emissions associated with greater 
electrification rates). SO2 emissions generally increase 
under the action alternatives compared to the No-Action Alternative 
(except in 2035 under Alternative 1), with the more stringent 
alternatives having the largest increases. SO2 increases are 
largely due to higher upstream emissions associated with electricity 
use by greater numbers of electrified vehicles being produced in 
response to the standards. In 2035 and 2050, across all criteria 
pollutants and action alternatives, the smallest decrease in emissions 
is 0.03 percent and occurs for NOX under Alternative 2; the 
largest decrease is 11.9 percent and occurs for VOCs under Alternative 
3. The smallest increase in emissions is 0.07 percent and occurs for CO 
under Alternative 1; the largest increase is 4.8 percent and occurs for 
SO2 under Alternative 3.
    The air quality analysis identified the following impacts on toxic 
air pollutants.
    Under each action alternative in 2025 compared to the No-Action 
Alternative, increases in emissions would occur for all toxic air 
pollutants by as much as 0.5 (half of 1) percent, except for DPM, for 
which emissions would decrease by as much as 0.5 percent. For 2025, the 
largest relative increases in emissions would occur for benzene and 
1,3-butadiene, for which emissions would increase by as much as 0.5 
percent. Percentage increases in emissions of acetaldehyde, acrolein, 
and formaldehyde would be even smaller.
    Under each action alternative in 2035 and 2050 compared to the No-
Action Alternative, decreases in emissions would occur for all toxic 
air pollutants, except for acetaldehyde, acrolein, and 1,3-butadiene in 
2035 under Alternative 1 where emissions would increase by 0.2 (one-
fifth of 1), 0.01, and 0.1 percent, respectively, with the more 
stringent alternatives having the largest decreases, except for benzene 
(emissions increase in 2035 under Alternative 3 relative to Alternative 
2). The largest relative decreases in emissions would occur for 
formaldehyde, for which emissions would decrease by as much as 10.3 
percent. Percentage decreases in emissions of acetaldehyde, acrolein, 
benzene, 1,3-butadiene, and DPM would be less.
    The air quality analysis identified the following health impacts.
    In 2025, Alternative 3 would result in slightly increased adverse 
health impacts (mortality, acute bronchitis, respiratory emergency room 
visits, and other health effects) nationwide compared to the No-Action 
Alternative as a result of increases in emissions of NOX, 
PM2.5, and SO2. Alternative 2 would also result 
in slightly increased adverse health impacts from mortality and non-
fatal heart attacks due to increases in NOX, 
PM2.5, and SO2 emissions, while Alternative 1 
would result in decreased adverse health impacts. The more stringent 
alternatives are associated with the largest increases in adverse 
health impacts, or the smallest decreases in impacts, relative to the 
No-Action Alternative. Again, in the short-term, these slight changes 
in health impacts are projected under the action alternatives as the 
result of increases in the prices of new vehicles slightly delaying 
sales of new vehicles and encouraging more VMT in older vehicles 
instead, but this trend shifts over time as higher fuel-economy new 
vehicles become used vehicles and older vehicles are removed from the 
fleet.
    In 2035 and 2050, all action alternatives would result in decreased

[[Page 49801]]

adverse health impacts nationwide compared to the No-Action Alternative 
as a result of general decreases in emissions of NOX, 
PM2.5, and DPM. The decreases in adverse health impacts get 
larger from Alternative 1 to Alternative 3.
    In terms of climate effects, all action alternatives would decrease 
U.S. passenger car and light truck fuel consumption compared with the 
No-Action Alternative, resulting in reductions in the anticipated 
increases in global CO2 concentrations, temperature, 
precipitation, and sea level, and increases in ocean pH that would 
otherwise occur. The impacts of the action alternatives on global mean 
surface temperature, precipitation, sea level, and ocean pH would be 
small in relation to global emissions trajectories. Although these 
effects are small, they occur on a global scale and are long lasting; 
therefore, in aggregate, they can have large consequences for health 
and welfare and can make an important contribution to reducing the 
risks associated with climate change.
    The alternatives would have the following impacts related to GHG 
emissions.
    Passenger cars and light trucks are projected to emit 89,600 
million metric tons of carbon dioxide (MMTCO2) from 2021 
through 2100 under the No-Action Alternative. Alternative 1 would 
decrease these emissions by 5 percent through 2100. Alternative 3 would 
decrease these emissions by 10 percent through 2100. Emissions would be 
highest under the No-Action Alternative, and emission reductions would 
increase from Alternative 1 to Alternative 3.
    Compared with total projected CO2 emissions of 984 
MMTCO2 from all passenger cars and light trucks under the 
No-Action Alternative in the year 2100, the action alternatives are 
expected to decrease CO2 emissions from passenger cars and 
light trucks in the year 2100 from 6 percent under Alternative 1 to 12 
percent under Alternative 3.
    The emission reductions in 2025 compared with emissions under the 
No-Action Alternative are approximately equivalent to the annual 
emissions from 1,284,000 vehicles under Alternative 1 to 2,248,000 
vehicles under Alternative 3. For scale, a total of 253,949,000 
passenger cars and light trucks are projected to be on the road in 2025 
under the No-Action Alternative.
    CO2 emissions affect the concentration of CO2 
in the atmosphere, which in turn affects global temperature, sea level, 
precipitation, and ocean pH. For the analysis of direct and indirect 
impacts, NHTSA used the Global Change Assessment Model Reference 
Scenario to represent the Reference Case emissions scenario (i.e., 
future global emissions assuming no comprehensive global actions to 
mitigate GHG emissions).
    Estimated CO2 concentrations in the atmosphere for 2100 
would range from 788.33 pollutant per million parts (ppm) under 
Alternative 3 to approximately 789.11 ppm under the No-Action 
Alternative, indicating a maximum atmospheric CO2 decrease 
of approximately 0.77 ppm compared to the No-Action Alternative. 
Atmospheric CO2 concentration under Alternative 1 would 
decrease by 0.37 ppm compared with the No-Action Alternative.
    Global mean surface temperature is projected to increase by 
approximately 3.48 [deg]C (6.27 [deg]F) under the No-Action Alternative 
by 2100. Implementing the most stringent alternative (Alternative 3) 
would decrease this projected temperature rise by 0.003 [deg]C (0.006 
[deg]F), while implementing Alternative 1 would decrease projected 
temperature rise by 0.002 [deg]C (0.003 [deg]F).
    Projected sea-level rise in 2100 ranges from a high of 76.28 
centimeters (30.03 inches under the No-Action Alternative to a low of 
76.22 centimeters (30.01 inches) under Alternative 3. Alternative 3 
would result in a decrease in sea-level rise equal to 0.06 centimeter 
(0.03 inch) by 2100 compared with the level projected under the No-
Action Alternative compared to a decrease under Alternative 1 of 0.03 
centimeter (0.01 inch) compared with the No-Action Alternative.
    Global mean precipitation is anticipated to increase by 5.85 
percent by 2100 under the No-Action Alternative. Under the action 
alternatives, this increase in precipitation would be reduced by 0.00 
to 0.01 percent.
    Ocean pH is anticipated to be 8.2180 under Alternative 3, about 
0.0004 more than the No-Action Alternative. Under Alternative 1, ocean 
pH in 2100 would be 8.2178, or 0.0002 more than the No-Action 
Alternative.
    The action alternatives would reduce the impacts of climate change 
that would otherwise occur under the No-Action Alternative. Although 
the projected reductions in CO2 and climate effects are 
small compared with total projected future climate change, they are 
quantifiable and directionally consistent and would represent an 
important contribution to reducing the risks associated with climate 
change.
    Although NHTSA does quantify the changes in monetized damages that 
can be attributable to each action alternative, many specific impacts 
of climate change on health, society, and the environment cannot be 
estimated quantitatively. Therefore, NHTSA provides a qualitative 
discussion of these impacts by presenting the findings of peer-reviewed 
panel reports including those from the Intergovernmental Panel on 
Climate Change (IPCC), U.S. Global Change Research Program (GCRP), the 
U.S. Climate Change Science Program (CCSP), the National Research 
Council, and the Arctic Council, among others. While the action 
alternatives would decrease growth in GHG emissions and reduce the 
impact of climate change across resources relative to the No-Action 
Alternative, they would not themselves prevent climate change and 
associated impacts. Long-term climate change impacts identified in the 
scientific literature are briefly summarized below, and vary 
regionally, including in scope, intensity, and directionality 
(particularly for precipitation). While it is difficult to attribute 
any particular impact to emissions that could result from this 
proposal, the following impacts are likely to be beneficially affected 
to some degree by reduced emissions from the action alternatives:
     Impacts on freshwater resources could include changes in 
rainfall and streamflow patterns, warming temperatures and reduced 
snowpack, changes in water availability paired with increasing water 
demand for irrigation and other needs, and decreased water quality from 
increased algal blooms. Inland flood risk could increase in response to 
increasing intensity of precipitation events, drought, changes in 
sediment transport, and changes in snowpack and the timing of snowmelt.
     Impacts on terrestrial and freshwater ecosystems could 
include shifts in the range and seasonal migration patterns of species, 
relative timing of species' life-cycle events, potential extinction of 
sensitive species that are unable to adapt to changing conditions, 
increases in the occurrence of forest fires and pest infestations, and 
changes in habitat productivity due to increased atmospheric 
concentrations of CO2.
     Impacts on ocean systems, coastal regions, and low-lying 
areas could include the loss of coastal areas due to inundation, 
submersion, or erosion from sea-level rise and storm surge, with 
increased vulnerability of the built environment and associated 
economies. Changes in key habitats (e.g., increased temperatures, 
decreased oxygen, decreased ocean pH, increased

[[Page 49802]]

salinization) and reductions in key habitats (e.g., coral reefs) may 
affect the distribution, abundance, and productivity of many marine 
species.
     Impacts on food, fiber, and forestry could include 
increasing tree mortality, forest ecosystem vulnerability, productivity 
losses in crops and livestock, and changes in the nutritional quality 
of pastures and grazing lands in response to fire, insect infestations, 
increases in weeds, drought, disease outbreaks, or extreme weather 
events. Increased concentrations of CO2 in the atmosphere 
can also stimulate plant growth to some degree, a phenomenon known as 
the CO2 fertilization effect, but the impact varies by 
species and location. Many marine fish species could migrate to deeper 
or colder water in response to rising ocean temperatures, and global 
potential fish catches could decrease. Impacts on food and agriculture, 
including yields, food processing, storage, and transportation, could 
affect food prices, socioeconomic conditions, and food security 
globally.
     Impacts on rural and urban areas could affect water and 
energy supplies, wastewater and stormwater systems, transportation, 
telecommunications, provision of social services, incomes (especially 
agricultural), air quality, and safety. The impacts could be greater 
for vulnerable populations such as lower-income populations, 
historically underserved populations, some communities of color and 
tribal and Indigenous communities, the elderly, those with existing 
health conditions, and young children.
     Impacts on human health could include increases in 
mortality and morbidity due to excessive heat and other extreme weather 
events, increases in respiratory conditions due to poor air quality and 
aeroallergens, increases in water and food-borne diseases, increases in 
mental health issues, and changes in the seasonal patterns and range of 
vector-borne diseases. The most disadvantaged groups such as children, 
the elderly, the sick, those experiencing discrimination, historically 
underserved populations, some communities of color and tribal and 
Indigenous communities, and low-income populations are especially 
vulnerable and may experience disproportionate health impacts.
     Impacts on human security could include increased threats 
in response to adversely affected livelihoods, compromised cultures, 
increased or restricted migration, increased risk of armed conflicts, 
reduction in adequate essential services such as water and energy, and 
increased geopolitical rivalry.
    In addition to the individual impacts of climate change on various 
sectors, compound events may occur more frequently. Compound events 
consist of two or more extreme weather events occurring simultaneously 
or in sequence when underlying conditions associated with an initial 
event amplify subsequent events and, in turn, lead to more extreme 
impacts. To the extent the action alternatives would result in 
reductions in projected increases in global CO2 
concentrations, this rulemaking would contribute to reducing the risk 
of compound events.
    NHTSA has considered the SEIS carefully in arriving at its 
tentative conclusion that Alternative 2 is maximum feasible, as 
discussed below. We seek comment on the SEIS associated with this NPRM.

D. Evaluating the EPCA Factors and Other Considerations To Arrive at 
the Proposed Standards

    Despite only one year having passed since the 2020 final rule, 
enough has changed in the United States and in the world that 
revisiting the CAFE standards for MYs 2024-2026 is reasonable and 
appropriate. The global coronavirus pandemic, with all of its tragedy, 
also demonstrated what happens to U.S. and global oil consumption (and 
CO2 and other pollutant emissions) when driving demand 
plummets. The Biden Administration committed itself in its earliest 
moments to improving energy conservation and tackling climate change. 
Nearly all auto manufacturers have announced forthcoming new advanced 
technology, high-fuel-economy vehicle models, making strong public 
commitments that mirror those of the Administration. Five major 
manufacturers voluntarily bound themselves to stricter GHG national-
level requirements as part of the California Framework agreement. While 
some facts on the ground remain similar to what was before NHTSA in the 
prior analysis--gas prices remain relatively low in the U.S., for 
example, and while light-duty vehicle sales fell sharply in MY 2020, 
the vehicles that did sell tended to be, on average, larger, heavier, 
and more powerful, all factors which increase fuel consumption--again, 
enough has changed that a rebalancing of the EPCA factors is 
appropriate for model years 2024-2026.
    In the 2020 final rule, NHTSA interpreted the need of the U.S. to 
conserve energy as less important than in previous rulemakings. This 
was in part because of structural changes in global oil markets as a 
result of shale oil drilling in the U.S., but also because in the 
context of environmental effects, NHTSA interpreted the word 
``conserve'' as ``to avoid waste.'' NHTSA concluded then that the 
ultimate difference to the climate (among the regulatory alternatives) 
of thousandths of a degree Celsius in 2100 did not represent a 
``wasteful'' use of energy, given the other considerations involved in 
the balancing of factors.
    One of those factors was consumer demand for vehicles with higher 
fuel economy levels. In the 2020 final rule, NHTSA expressed concern 
that low gasoline prices and apparent consumer preferences for larger, 
heavier, more powerful vehicles would make it exceedingly difficult for 
manufacturers to achieve higher standards without negative consequences 
to sales and jobs, and would cause consumer welfare losses. Since then, 
however, more and more manufacturers are announcing more and more 
vehicle models with advanced engines and varying levels of 
electrification. It is reasonable to conclude that manufacturers (who 
are all for-profit companies) would not be announcing plans to offer 
these types of vehicles if they did not expect to be able to sell 
them,\466\ and thus that manufacturers are more sanguine about consumer 
demand for fuel efficiency and the market for fully electric vehicles 
going forward than they have been previously.
---------------------------------------------------------------------------

    \466\ To the extent that manufacturers are offering these 
vehicles in response to expected regulations, NHTSA still believes 
that they would not do so if they believed the vehicles were 
unsaleable or unmanageably detrimental to profits. Vehicle 
manufacturers are sophisticated corporate entities well able to 
communicate their views to regulatory agencies.
---------------------------------------------------------------------------

    Additionally, NHTSA no longer believes that it is reasonable or 
appropriate to focus only on ``avoiding waste'' in evaluating the need 
of the U.S. to conserve energy. EPCA's overarching purpose is energy 
conservation. The need of the U.S. to conserve energy may be reasonably 
interpreted as continuing to push the balancing toward greater 
stringency.
    The following sections will walk through the four statutory factors 
in more detail and discuss NHTSA's decision-making process more 
thoroughly. To be clear at the outset, however, the fundamental 
balancing of factors for this proposal is different from the 2020 final 
rule because the evidence suggests that manufacturers believe there is 
a market for advanced technology vehicles with higher fuel economy, and 
CAFE standards are likely to be maximum feasible if they are set at 
levels that reflect that evidence.

[[Page 49803]]

    We may begin with the need of the U.S to conserve energy, which as 
stated is being considered more holistically in this proposal as 
compared to in the 2020 final rule. According to the analysis presented 
in Section V and in the accompanying PRIA and SEIS, Alternative 3 would 
save consumers the most in fuel costs, and would achieve the greatest 
reductions in climate change-causing CO2 emissions. 
Alternative 3 would also maximize fuel consumption reductions, better 
protecting consumers from international oil market instability and 
price spikes. As discussed above, for now, gasoline is still the 
dominant fuel used in light-duty transportation. As such, consumers, 
and the economy more broadly, are subject to fluctuations in price that 
impact the cost of travel and, consequently, the demand for mobility. 
Vehicles are long-lived assets and the long-term price uncertainty of 
petroleum still represents a risk to consumers. By increasing the fuel 
economy of vehicles in the marketplace, more stringent CAFE standards 
better insulate consumers against these risks over longer periods of 
time. Fuel economy improvements that reduce demand for oil are a more 
certain hedging strategy against price volatility than increasing U.S. 
energy production. Continuing to reduce the amount of money consumers 
spend on vehicle fuel thus remains an important consideration for the 
need of the U.S. to conserve energy.
    Additionally, the SEIS finds that overall, projected changes in 
both upstream and downstream emissions of criteria and toxic air 
pollutants are mixed, with emissions of some pollutants remaining 
constant or increasing and emissions of some pollutants decreasing. 
These increases are associated with both upstream and downstream 
sources, and therefore, may disproportionately affect minority and low-
income populations that reside in proximity to these sources. However, 
the magnitude of the change in emissions relative to the No-Action 
alternative is minor for all action alternatives, and would not be 
characterized as high or adverse; over time, adverse health impacts are 
projected to decrease nationwide under each of the action alternatives.
    For the other considerations that contribute to the need of the 
U.S. to conserve energy, it follows reasonably that reducing fuel 
consumption more would improve our national balance of payments more, 
and our energy security, as discussed above. It is therefore likely 
that Alternative 3 best meets the need of the U.S. to conserve energy.
    During interagency review, the Department of Energy urged NHTSA to 
propose Alternative 3, on the basis that ``a faster transition to 
battery electric vehicles (BEVs) is feasible,'' because a variety of 
market analysts and the National Academies of Sciences, Engineering, 
and Medicine find that BEVs will reach cost parity with ICE vehicles by 
or before 2025. DOE further commented that new BEV prices would drop 
over time because ``DOE has set aggressive technology targets for 
battery costs and electric drive technologies, . . . And DOE has a 
consistent track record in meeting its technology targets: DOE met or 
exceeded its technology cost and performance goals for battery and 
electric drive technologies every year between 2012 and 2018.'' 
[citation omitted] While NHTSA appreciates this comment from DOE, as 
stated repeatedly throughout this proposal, NHTSA is statutorily 
prohibited from considering the fuel economy of dedicated alternative 
fuel vehicles during the rulemaking time frame when determining what 
levels of standards would be maximum feasible. NHTSA believes that 
Alternative 3 could potentially end up being maximum feasible in the 
final rule depending on a variety of factors, but NHTSA would be 
prohibited from basing such a finding exclusively on the date by which 
DOE estimates that BEVs will achieve cost parity with ICEs.
    We next evaluate how the regulatory alternatives fare in terms of 
economic practicability. NHTSA recognizes that the amount of lead time 
available before MY 2024 is less than what was provided in the 2012 
rule. As will be discussed further below, NHTSA believes that the 
evidence suggests that the proposed standards are still economically 
practicable, and not out of reach for a significant portion of the 
industry. CAFE standards can help support industry by requiring ongoing 
improvements even if demand for more fuel economy flags unexpectedly.
    For the proposed standards, the annual rates of increase in the 
passenger car and light truck standards represent increases over the 
required levels in MY 2023 and are as shown in Table VI-6.
[GRAPHIC] [TIFF OMITTED] TP03SE21.181

    Part of the way that we try to evaluate economic practicability, 
and thus where the tipping point in the balancing of factors might be, 
is through a variety of metrics, examined in more detail below. If the 
amounts of technology or per-vehicle cost increases required to meet 
the standards appear to be beyond what we believe the market could 
bear; or sales and employment appear to be unduly impacted, the agency 
may decide that the standards under consideration may not be 
economically practicable. We underscore again, as throughout this 
preamble, that the modeling analysis does not dictate the ``answer,'' 
it is merely one source of information among others that aids the 
agency's balancing of the standards. We similarly underscore that there 
is no single bright line beyond which standards might be economically 
practicable, and that these metrics are not intended to suggest one; 
they are simply ways to think about the information before us.
    Economic practicability may be evaluated in terms of how much 
technology manufacturers would have to apply to meet a given regulatory

[[Page 49804]]

alternative. Technology application can be considered as ``which 
technologies, and when''--both the technologies that NHTSA's analysis 
suggests would be used, and how that application occurs given 
manufacturers' product redesign cadence. While the need of the U.S. to 
conserve energy may encourage the agency to be more technology-forcing 
in its balancing, and while technological feasibility is not limiting 
in this rulemaking given the state of technology in the industry, 
regulatory alternatives that require extensive application of very 
advanced technologies (that may have known or unknown consumer 
acceptance issues) or that require manufacturers to apply additional 
technology in earlier model years, in which meeting the standards is 
already challenging, may not be economically practicable, and may thus 
be beyond maximum feasible.
    The first issue is timing of technology application. While the MY 
2024 standards provide less lead time for an increase in stringency 
than was provided by the standards set in 2012, NHTSA believes that the 
standards for MYs 2021-2023 should provide a relative ``break'' for 
compliance purposes. NHTSA does not believe that significant additional 
technology application would be required by the CAFE standards in the 
years immediately preceding the rulemaking time frame. That said, NHTSA 
is aware of, and has accounted for, several manufacturers voluntarily 
agreeing with CARB to increase their fuel economy during those model 
years. Manufacturers would have to apply more technology than would be 
required by the MYs 2021-2023 CAFE standards alone to meet those higher 
fuel economy levels. Again, NHTSA interprets these agreements as 
evidence that the participating companies believe that applying that 
additional technology is practicable, because for-profit companies can 
likely be relied upon to make decisions that maximize their profit. 
Companies who did not agree with CARB to meet higher targets may not 
increase their fuel economy levels by as much over MYs 2021-2023, but 
they, too, will get the relative ``break'' in CAFE obligations 
mentioned above, and have additional time to plan for the higher 
stringency increases in subsequent years. Those manufacturers can opt 
to employ more modest technologies to improve fuel economy (beyond 
their standard) to generate credits to carry forward into more 
challenging years, or concentrate limited research and development 
resources on the next generation of higher fuel economy vehicles that 
will be needed to meet the proposed standards in MYs 2024-2026 (and 
beyond), rather investing in more modest improvements in the near-term.
    NHTSA's analysis estimates manufacturers' product ``cadence,'' 
representing them in terms of estimated schedules for redesigning and 
``freshening'' vehicles, and assuming that significant technology 
changes will be implemented during vehicle redesigns--as they 
historically have been. Once applied, a technology will be carried 
forward to future model years until superseded by a more advanced 
technology. NHTSA does not consider model years in isolation in the 
analysis, because that is not consistent with how industry responds to 
standards, and thus would not accurately reflect practicability. If 
manufacturers are already applying technology widely and intensively to 
meet standards in earlier years, requiring them to add yet more 
technology in the model years subject to the rulemaking may be less 
economically practicable; conversely, if the preceding model years 
require less technology, more technology during the rulemaking time 
frame may be more economically practicable. The tables below illustrate 
how the agency has modeled that process of manufacturers applying 
technologies in order to comply with different alternative standards. 
The technologies themselves are described in detail in Chapters 2 and 3 
of the accompanying TSD.
[GRAPHIC] [TIFF OMITTED] TP03SE21.182


[[Page 49805]]


[GRAPHIC] [TIFF OMITTED] TP03SE21.183

    Although NHTSA's analysis is intended to estimate ways 
manufacturers could respond to new standards, not to predict how 
manufacturers will respond to new standards, manufacturers have 
indicated in meetings with the agency and in public announcements 
(including the CARB Framework Agreements) that they do intend to 
increase technology application over the coming years, and specifically 
electrification technology which NHTSA does not model as part of its 
standard-setting analysis, considered for decision-making, due to the 
49 U.S.C. 32902(h) restrictions for MYs 2024-2026.
    As the tables illustrate, both Alternative 2 and Alternative 3 
appear to require rapid deployment of fuel efficiency technology across 
a variety of vehicle systems--body improvements due to weight reduction 
and improved aerodynamic drag, engine advancements, and 
electrification.\467\ The aggressive application that is simulated to 
occur between MY 2020 (which NHTSA observed and is the starting point 
of this analysis) and MY 2023 occurs in all of the alternatives, for 
both cars and light trucks. This reflects both the task presented to 
signatories by the California Framework and existing compliance 
positions (in some fleets) across the industry to improve fuel economy 
in the near-term. In general, technology market shares for Alternative 
3 look similar to those for Alternative 2, with the notable exception 
of plug-in hybrids which differ by only a couple of percent for cars 
and about 5 percent for light trucks. While still relatively small 
differences on their own, the market share of plug-in hybrids is 
currently less than one percent in total. While manufacturers could 
certainly choose to produce fully electric vehicles instead of PHEVs, 
fully electric vehicles are projected to grow by multiples of their 
current market share as well. The market for high levels of 
electrification is likely to continue growing but NHTSA acknowledges 
that consumer demand, especially in the near-term, remains somewhat 
unclear. If policy decisions are made to extend or expand incentives 
for electric vehicle purchases, NHTSA could potentially consider the 
greater reliance on electrification in Alternative 3 to be a smaller 
risk.
---------------------------------------------------------------------------

    \467\ While these technology pathways reflect NHTSA's statutory 
restrictions under EPCA/EISA, it is worth noting that they represent 
only one possible solution. In the simulations that support the 
SEIS, PHEV market share grows by less, and is mostly offset by an 
increase in BEV market share.
---------------------------------------------------------------------------

    NHTSA's analysis seeks to account for manufacturers' capital and 
resource constraints in several ways--through the restriction of 
technology application to refreshes and redesigns, through the phase-in 
caps applied to certain technologies, and through the explicit 
consideration of vehicle components (like powertrains) and technologies 
(like platforms based on advanced materials) that are shared by models 
throughout a manufacturer's portfolio. NHTSA is aware that there is a 
significant difference in the level of capital and resources required 
to implement one or more new technologies on a single vehicle model, 
and the level of capital and resources required to implement those same 
technologies across the entire vehicle fleet. NHTSA realizes that it 
would not be economically practicable to expand some of the most 
advanced technologies to every vehicle in the fleet within the 
rulemaking time frame, although it should be possible to increase the 
application of advanced technologies across the fleet in a progression 
that accounts for those resource constraints. That is what NHTSA's 
analysis tries to do.
    Another consideration for economic practicability is the extent to 
which new standards could increase the average cost to acquire new 
vehicles, because even insofar as the underlying application of 
technology leads to reduced outlays for fuel over the useful lives of 
the affected vehicles, these per-vehicle cost increases provide both a 
measure of the degree of effort faced by manufacturers, and also the 
degree of adjustment, in the form of potential vehicle price increases, 
that will ultimately be required of vehicle

[[Page 49806]]

purchasers. Table VI-9 and Table VI-10 show the agency's estimates of 
average cost increase under the Preferred Alternative for passenger 
cars and light trucks, respectively. Because our analysis includes 
estimates of manufacturers' indirect costs and profits, as well as 
civil penalties that some manufacturers (as allowed under EPCA/EISA) 
might elect to pay in lieu of achieving compliance with CAFE standards, 
we report cost increases as estimated average increases in vehicle 
price (as MSRP). These are average values, and the agency does not 
expect that the prices of every vehicle would increase by the same 
amount; rather, the agency's underlying analysis shows unit costs 
varying widely between different vehicle models. For example, a small 
SUV that replaces an advanced internal combustion engine with a plug-in 
hybrid system may incur additional production costs in excess of 
$10,000, while a comparable SUV that replaces a basic engine with an 
advanced internal combustion engine incurs a cost closer to $2,000. 
While we recognize that manufacturers will distribute regulatory costs 
throughout their fleet to maximize profit, we have not attempted to 
estimate strategic pricing, having insufficient data (which would 
likely be confidential business information (CBI)) on which to base 
such an attempt. To provide an indication of potential price increases 
relative to today's vehicles, we report increases relative to the 
market forecast using technology in the MY 2020 fleet--the most recent 
actual fleet for which we have information sufficient for use in our 
analysis. We provide results starting in MY 2023 in part to illustrate 
the cost impacts in the first model year that we believe manufacturers 
might actually be able to change their products in preparation for 
compliance with standards in MYs 2024-2026.
[GRAPHIC] [TIFF OMITTED] TP03SE21.184


[[Page 49807]]


[GRAPHIC] [TIFF OMITTED] TP03SE21.185

    Relative to current vehicles (again, as represented here by 
technology in the MY 2020 fleet, the most recent for which NHTSA has 
adequate data), NHTSA judges these cost increases to be significant, 
but not impossible for the market to bear. Cost increases will be 
partially offset by fuel savings, which consumers will experience 
eventually, if not concurrent with the upfront increase in purchase 
price. And as discussed previously, nearly every manufacturer has 
already indicated their intent to continue introducing advanced 
technology vehicles between now and MY 2026. Again, NHTSA believes that 
manufacturers introduce new vehicles (and technologies) expecting that 
there is a market for them--if not immediately, then in the near 
future. For-profit companies cannot afford to lose money indefinitely. 
This trend suggests that manufacturers believe that at least some cost 
increases should be manageable for consumers.
    Relative to the Preferred Alternative, however, NHTSA notes 
significant further cost increases for several major manufacturers 
under Alternative 3. Table VI-11 and Table VI-12 show additional 
technology costs estimated to be incurred under Alternative 3 as 
compared to the Preferred Alternative.

[[Page 49808]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.186

[GRAPHIC] [TIFF OMITTED] TP03SE21.187

    For example, Honda's light truck fleet appears to hit an inflection 
point in cost where much more aggressive technology application is 
required in order to comply with Alternative 3. In general, light truck 
fleets appear to be pressed harder to comply with Alternative 3 than 
passenger car fleets across the industry. For example, Ford's passenger 
car compliance costs are estimated to increase minimally between 
Alternative 2 and Alternative 3, but light truck compliance costs 
increase by over 40 percent (in most years). A number of other 
manufacturers are pushed in both

[[Page 49809]]

fleets (Honda, Toyota, and Kia, for example), and make significant 
additional investments in fuel economy technology to reach compliance 
with the standards in Alternative 3.
    Changes in costs for new vehicles are not the only costs that NHTSA 
considers in balancing the statutory factors--fuel costs for consumers 
are relevant to the need of the U.S. to conserve energy, and NHTSA 
believes that consumers themselves weigh expected fuel savings against 
increases in purchase price for vehicles with higher fuel economy. Fuel 
costs (or savings) continue to be the largest source of benefits for 
CAFE standards, and GHG reduction benefits, which are also part of the 
need of the U.S. to conserve energy, are also increasing. E.O. 12866 
and Circular A-4 also direct agencies to consider maximizing net 
benefits in rulemakings whenever possible and consistent with 
applicable law. Thus, because it can be relevant to balancing the 
statutory factors and because it is directed by E.O. 12866 and OMB 
guidance, NHTSA also considers the net benefits attributable to the 
different regulatory alternatives, as shown in Table VI-13.
[GRAPHIC] [TIFF OMITTED] TP03SE21.188

    While maximizing net benefits is a valid decision criterion for 
choosing among alternatives, it is not the only reasonable decision 
perspective. When NHTSA recognizes that the need of the U.S. to 
conserve fuel weighs importantly in the overall balancing of factors, 
it is reasonable to consider choosing the regulatory alternative that 
produces the largest reduction in fuel consumption, while remaining net 
beneficial. The benefit-cost analysis is not the sole factor that NHTSA 
considers in determining the maximum feasible stringency, though it 
supports NHTSA's tentative conclusion that Alternative 2 is the maximum 
feasible stringency. While Alternative 1 produces higher net benefits, 
it also continues to allow fuel consumption that could have been 
avoided in a cost-beneficial manner. And while Alternative 3 achieves 
greater reductions in fuel consumption than Alternative 2, it shows 
relatively high negative net benefits under both discount rates.
    While NHTSA estimates that new vehicle sales will be slightly lower 
under Alternative 2 than under the No-Action Alternative, as a 
consequence of the higher retail prices that result from additional 
technology application, the difference is only about 1 percent over the 
entire period covered by MYs 2020-2026. NHTSA does not believe that 
this estimated change in new vehicle sales over the period covered by 
the rule is a persuasive reason to choose another regulatory 
alternative. Similarly, the estimated labor impacts within the 
automotive industry provide no evidence that another alternative should 
be preferred. While the change in sales is estimated to decrease 
industry employment over the period, the decrease is even smaller than 
the impact on new vehicle sales (about 0.1 percent). As NHTSA explained 
earlier in defining economic practicability, standards simply should 
avoid a significant loss of jobs, and may still be economically 
practicable even though they appear to show a negative impact (here, a 
very slight impact) on sales and employment.
    As with any analysis of sufficient complexity, there are a number 
of critical assumptions here that introduce uncertainty about 
manufacturer compliance pathways, consumer responses to fuel economy 
improvements and higher vehicle prices, and future valuations of the 
consequences from higher CAFE standards. While NHTSA considers dozens 
of sensitivity cases to measure the influence of specific parametric 
assumptions and model relationships, only a small number of them 
demonstrate meaningful impacts to net benefits under the proposed 
standards.
    Looking at these cases more closely, the majority of both costs and 
benefits that occur under the proposed standards accrue to buyers of 
new cars and trucks, rather than society in general. It then follows 
that the assumptions that exert the greatest influence over private 
costs and benefits also exert the greatest influence over net 
benefits--chief among these is the assumed trajectory of future fuel 
prices, specifically gasoline. NHTSA considers the ``High Oil Price'' 
and ``Low Oil Price'' cases from AEO 2021 as bounding cases, though 
they are asymmetrical (while the low case is only about 25 percent 
lower than the Reference case on average, the high case is almost 50 
percent higher on average). The sensitivity cases suggest that fuel 
prices exert considerable influence on net benefits--where higher and 
lower prices not only determine the dollar value of each gallon saved, 
but also how market demand responds to higher levels of fuel economy in 
vehicle offerings. Under the low case, net benefits become negative and 
exceed $30 billion, but increase to almost (positive) $50 billion in 
the high case (the largest increase among any sensitivity cases run for 
this proposal). This suggests that the net benefits resulting from this 
proposal are

[[Page 49810]]

dependent upon the future price of gasoline being at least as high as 
the AEO 2021 Reference Case projects.
    Another critical uncertainty that affects private benefits is the 
future cost of advanced electrification technologies, specifically 
batteries. These emerging technologies provide both the greatest fuel 
savings to new car buyers and impose the highest technology costs (at 
the moment). While the cost to produce large vehicle batteries has been 
rapidly declining for years, they are still expensive relative to 
advancements in internal combustion engines and transmissions. However, 
the analysis projects continued cost learning over time and shows 
battery electric vehicles reaching price parity with conventional 
vehicles in the 2030s for most market segments--after which market 
adoption of BEVs accelerates--although other estimates show price 
parity occurring sooner and we seek comment on whether and how to use 
those estimates in our analysis for the final rule. Electrification is 
also a viable compliance strategy, as partially or fully electric 
vehicles benefit from generous compliance incentives that improve their 
estimated fuel economy relative to measured energy consumption. As 
such, the assumption about future battery costs has the ability to 
influence compliance costs to manufacturers and prices to consumers, 
the rate of electric vehicle adoption in the market, and thus the 
emissions associated with their operation. NHTSA considered two 
different mechanisms to affect battery costs: Higher/lower direct 
costs, and faster/slower cost learning rates. The two mechanisms that 
reduce cost (whether by faster cost learning or lower direct costs) 
both increase net benefits relative to the central case, though 
lowering initial direct costs by 20 percent had a greater effect than 
increasing the learning rate by 20 percent. Increasing cost (though 
either mechanism) by 20 percent produced a similar effect, but in the 
opposite direction (reducing net benefits). However, none of those 
cases exerted a level of influence that compares to alternative fuel 
price assumptions.
    There is one assumption that affects the analysis without 
influencing the benefits and costs that accrue to new car buyers: The 
social cost of damages attributable to greenhouse gas emissions. While 
there is no feedback in either the analysis or the policy between the 
assumed social cost of GHGs and metric tons of GHGs emitted (or gallons 
of fuel consumed), it directly controls the valuation of each metric 
ton saved over time. The central analysis assumes a SC-GHG cost based 
on the 2.5 percent discount rate for the 3 percent social discount 
rate, and a SC-GHG cost based on the 3 percent discount rate in the 7 
percent social discount rate case. However, this assumption directly 
scales total benefits by increasing (or decreasing) the value of each 
ton saved. Using the highest SCC-GHG, based on the 95th percentile 
estimate, pushes net benefits above $30 billion under Alternative 2. 
NHTSA does not independently develop the SC-GHG assumptions used in 
this proposal but takes them from the interagency working group on the 
social cost of GHGs. If future analyses by that group determine that 
the SC-GHG should be different from what it currently is, NHTSA will 
consider those values and whether to include them in subsequent 
analyses. As the sensitivity cases illustrate, their inclusion could 
exert enough influence on net benefits to suggest that a different 
alternative could represent the maximum feasible stringency--at least 
based on the decision criteria described in this section. As mentioned 
above, NHTSA is seeking comment on the methodology employed by that 
group for determining the SC-GHG.
    Based on all of the above, NHTSA tentatively concludes that while 
all of the action alternatives are technologically feasible, 
Alternative 3 may be too costly to be economically practicable in the 
rulemaking timeframe, even if choosing it could result in greater fuel 
savings. NHTSA interprets the need of the U.S. to conserve energy as 
pushing the balancing toward greater stringency--consumer savings on 
fuel costs are estimated to be higher under Alternative 3 than under 
Alternative 2, but the additional technology cost required to meet 
Alternative 3 (as evidenced by the negative net benefits at both 
discount rates) may yet make Alternative 3 too stringent for these 
model years. Changes in criteria pollutants, health effects, and 
vehicle safety effects are relatively minor under all action 
alternatives, and thus not dispositive. NHTSA has considered the effect 
of other motor vehicle standards of the Government by incorporating the 
fuel economy effects of California's ZEV program into its baseline, and 
calculating the costs and benefits of CAFE standards as above and 
beyond those baseline costs and benefits. The additional costs of the 
proposed standards are, on average, not far from what NHTSA estimated 
in the 2012 final rule for standards in a similar timeframe; the 
additional benefits are lower, but this is due to a variety of factors, 
including significant addition of fuel-economy-improving technology to 
new vehicles between then and now (including the growing market for 
electric vehicles), and lower fuel price projections from EIA. To the 
extent that higher prices for new vehicles as a result of the 
technology required by the standards could translate to decreases in 
new vehicle sales, we note that those effects appear small, as 
discussed above. Moreover, improving the fuel efficiency of new 
vehicles has effects over time, not just at point of first sale, on 
consumer fuel savings. Somewhat-more-expensive-but-more-efficient new 
vehicles eventually become more-efficient used vehicles, which may be 
purchased by consumers who may be put off by higher new vehicle prices. 
The benefits have the potential to continue across the fleet and over 
time, for all consumers regardless of their current purchasing power.
    NHTSA recognizes, again, that lead time for this proposal is less 
than past rulemakings have provided, and that the economy and the 
country are in the process of recovering from a global pandemic. NHTSA 
also recognizes that at least parts of the industry are nonetheless 
making announcement after announcement of new forthcoming advanced 
technology, high-fuel-economy vehicle models, and does not believe that 
they would be doing so if they thought there was no market at all for 
them. Perhaps some of the introductions are driven by industry 
perceptions of future regulation, but the fact remains that the 
introductions are happening. CAFE standards can help to buttress this 
momentum by continuing to require the fleets as a whole to improve 
their fuel economy levels steadily over the coming years, so that a 
handful of advanced technology vehicles do not inadvertently allow 
backsliding in the majority of the fleet that will continue to be 
powered by internal combustion for likely the next 5-10 years. CAFE 
standards that increase steadily may help industry make this transition 
more smoothly.
    And finally, if the purpose of EPCA is energy conservation, and 
NHTSA is interpreting the need to conserve energy to be largely driven 
by fuel savings, energy security, and environmental concerns, then it 
makes sense to interpret EPCA's factors as asking the agency to push 
stringency as far as possible before benefits become negative. The 
energy conservation benefits of Alternative 3 appear, under the current 
analysis, to be highest, as discussed in the SEIS and in Section VI.C 
above, and better protect consumers from international oil market 
instability and price spikes. By

[[Page 49811]]

increasing the fuel economy of vehicles in the marketplace, more 
stringent CAFE standards better insulate consumers against these risks 
over longer periods of time. Fuel economy improvements that reduce 
demand for oil are a more certain hedging strategy against price 
volatility than increasing U.S. energy production. However, with 
negative net benefits for Alternative 3 under both discount rates, it 
may be that for the moment, the costs of achieving those benefits are 
more than the market is willing to bear. NHTSA thus aims to help 
bolster the industry's trajectory toward higher future standards, by 
keeping stringency high in the mid-term, but not so high as to be 
economically impracticable.
    NHTSA therefore proposes that Alternative 2 is maximum feasible for 
MYs 2024-2026. We seek comment on this tentative conclusion.

VII. Compliance and Enforcement

A. Introduction

1. Overview of the NHTSA Compliance Program
    A manufacturer's fleet is divided into three compliance categories 
of automobiles: Passenger vehicles manufactured domestically, passenger 
vehicles not manufactured domestically; and non-passenger 
automobiles.\468\ Each category has its own CAFE fleet mpg standard 
that a manufacturer is required to meet. The CAFE standard is 
determined for each model year by a combination of the production 
volume of vehicles produced for sale, the footprint of those vehicles, 
and the requisite CAFE footprint-based fuel economy target curves.
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    \468\ See 49 U.S. Code 32903.6. Passenger vehicles not 
manufactured domestically are referenced as import passenger cars 
and non-passenger automobiles as light trucks.
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    For each compliance category, manufacturers self-report data at the 
end of each MY in the form of a Final Model Year Report, and once these 
data are verified by EPA, NHTSA determines final compliance. Using 
EPA's final verified data, a manufacturer fleet is determined to be 
compliant if the 2-cycle CAFE performance of their fleet with the 
addition of the Alternative Motor Fuels Act (AMFA) and AC/OC incentives 
are equal to or greater than the CAFE fleet mpg standard. The 
manufacturer fleet is out of compliance if its fleet mpg falls below 
the CAFE mpg standard, in which case the manufacturer may resolve the 
shortfall through civil penalties or the use of flexibilities. 
Resolving a shortfall through flexibilities may include the application 
of CAFE credits through trade, carry-forward, carry-back, or transfer 
from within the manufacturer's fleet accounts or from another 
manufacturer's fleet accounts.
    The following sections provide a brief overview how CAFE standards 
and compliance values are derived, what compliance flexibilities and 
incentives are available to manufacturers, and the revisions to the 
CAFE program NHTSA is proposing in this rulemaking. In summary, NHTSA 
is proposing to: (1) Increase and clarify flexibilities for its off-
cycle program; (2) revive incentives for hybrid and electric full-size 
pickup trucks through MY 2025; (3) modify its standardized templates 
for CAFE reporting and credit transactions; and (4) add a new template 
for manufacturers to report information on the monetary and non-
monetary costs associated with credit trades.
2. How Manufacturers' Target and Achieved Performances Are Calculated
    Compliance begins each model year with manufacturers testing 
vehicles on a dynamometer in a laboratory over pre-defined test cycles 
and controlled conditions.\469\ EPA and manufacturers use two different 
dynamometer test procedures--the Federal Test Procedure (FTP) and the 
Highway Fuel Economy Test (HFET) to determine fuel economy. These 
procedures originated in the early 1970s and were intended to generally 
represent city and highway driving conditions, respectively. These two 
tests are commonly referred to as the ``2-cycle'' test procedures for 
CAFE. A machine is connected to the vehicle's tailpipe while it 
performs the test cycle, which collects and analyzes exhaust gases, 
such as CO2 quantities.\470\ Fuel economy is determined from 
relating a derived emissions factor to the amount of observed 
CO2 using a reference test fuel.\471\ Manufacturers continue 
to test vehicles over the course of the model year and will test enough 
vehicles to cover approximately 90 percent of the subconfigurations 
within each model type. Manufacturers self-report this information to 
EPA as part of their end-of-the-model year reports, which are due 90 
days after the model year is completed. After manufacturers submit 
their reports, EPA confirms and validates those results by testing a 
random sample of vehicles at the National Vehicle and Fuel Emissions 
Laboratory (NVFEL) in Ann Arbor, Michigan.
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    \469\ For readers unfamiliar with this process, the test is 
similar to running a car on a treadmill following a program--or more 
specifically, two programs. 49 U.S.C. 32904(c) states that, in 
testing for fuel economy, EPA must ``use the same procedures for 
passenger automobiles [that EPA] used for model year 1975 (weighted 
55 percent urban cycle and 45 percent highway cycle), or procedures 
that give comparable results.'' Thus, the ``programs'' are the 
``urban cycle,'' or Federal Test Procedure (abbreviated as ``FTP'') 
and the ``highway cycle,'' or Highway Fuel Economy Test (abbreviated 
as ``HFET''), and they have not changed substantively since 1975. 
Each cycle is a designated speed trace (of vehicle speed versus 
time) that vehicles must follow during testing--the FTP is meant 
roughly to simulate stop and go city driving, and the HFET is meant 
roughly to simulate steady flowing highway driving at about 50 mph. 
The 2-cycle dynamometer test results differ somewhat from what 
consumers will experience in the real-world driving environment 
because of the lack of high speeds, rapid accelerations, and hot and 
cold temperatures evaluations with the A/C operation. These added 
conditions are more so reflected in the EPA 5-cycle test results 
listed on each vehicle's fuel economy label and on the 
fueleconomy.gov website.
    \470\ Vehicles without tailpipe emissions, such as battery 
electric vehicles, have their performance measured differently, as 
discussed below.
    \471\ Technically, for the CAFE program, carbon-based tailpipe 
emissions (including CO2, CH4, and CO) are 
measured, and fuel economy is calculated using a carbon balance 
equation. EPA uses carbon-based emissions (CO2, 
CH4, and CO, the same as for CAFE) to calculate the 
tailpipe CO2 equivalent for the tailpipe portion of its 
standards. CO2 is by far the largest carbon-based exhaust 
constituent.
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    A manufacturer's fleet fuel economy performance (hereafter 
referenced as Base CAFE) for a given model year is calculated through 
the following steps:
     Each vehicle model's mile per gallon (mpg) performance in 
the city and highway test cycles are calculated based off the carbon 
emitted during dynamometer testing. The vehicle's mpg performance is 
combined at 55 percent city and 45 percent highway. Measurement 
incentives for alternative fuel vehicles (such as for electricity, 
counting 15 percent of the actual energy used to determine the gasoline 
equivalent mpg) are applied as part of these procedures;
     Performance improvements not fully captured through 2-
cycle dynamometer testing, such as eligible A/C and off-cycle 
technologies are then added to the vehicle's mpg performance. 
Incentives for full-size pickup trucks with mild or strong HEV 
technology or other technologies that perform significantly better than 
the vehicle's target value are also applied.
     The quantity of vehicles produced of each model type 
within a manufacturer's fleet is divided by its respective fuel economy 
performance (mpg) including any flexibility/incentive increases; The 
resulting numbers for each model type are summed;
     The manufacturer's total production volume is then divided 
by the summed value calculated in the previous step; and

[[Page 49812]]

     That number, which is the harmonic average of the fleet's 
fuel economy, is rounded to the nearest tenth of an mpg and represents 
the manufacturer's achieved fuel economy.
    The Base CAFE of each fleet is compared to the manufacturer's 
unique fleet compliance obligation, which is calculated using the same 
approach as the Base CAFE performance, except that the fuel economy 
target value (based on the unique footprint of each vehicle within a 
model type) is used instead of the measured fuel economy performance 
values. The fuel economy target values of the model types within each 
fleet and production volumes are used to derive the manufacturer's 
fleet standard (also known as the obligation) which is the harmonic 
average of these values.
    To further illustrate how Base CAFE and fuel economy targets are 
calculated, assume that a manufacturer produces two models of cars--a 
hatchback and a sedan. Figure VII-1 shows the two vehicle models 
imposed onto a fuel economy target function. From Figure VII-1, we can 
see that the target function extends from about 30 mpg for the largest 
cars to about 41 mpg for the smallest cars.
[GRAPHIC] [TIFF OMITTED] TP03SE21.189

    The manufacturer's required CAFE obligation would be determined by 
calculating the production-weighted harmonic average of the fuel 
economy target values applicable at the hatchback and sedan footprints 
(from the curve, about 41 mpg for the hatchback and about 33 mpg for 
the sedan). The manufacturer's achieved Base CAFE level is determined 
by calculating the production-weighted harmonic average of the 
hatchback and sedan fuel economy levels (in this example the values 
shown in the boxes in Figure VII-1, 48 mpg for the hatchback and 25 mpg 
for the sedan). Depending on the relative mix of hatchbacks and sedans 
produced, the manufacturer's fleet Base CAFE may be equal to the 
standard, perform better than the standard (if the required fleet CAFE 
is less than the achieved fleet Base CAFE) and thereby earn credits, or 
perform worse than the standard (if the required fleet CAFE is greater 
than achieved fleet Base CAFE) and thereby earn a credit shortfall 
which would need to be made up using CAFE credits, otherwise the 
manufacturer would be subject to civil penalties.
    As illustrated by the example, the CAFE program's use of sales-
weighted harmonic averages makes compliance more intricate than 
comparing a model to its target as not every model type needs to 
precisely meet its target for a manufacturer to achieve compliance. 
Consequently, if a manufacturer finds itself producing large numbers of 
vehicles that fall well-short of its targets, a manufacturer can 
attempt to equally balance its compliance by producing vehicles that 
are excessively over-compliant. However, NHTSA understands that several 
factors determine the ability of manufacturers to change their fleet-
mix mid-year. In response, the CAFE program is structured to provide 
relief to manufacturers in offsetting any shortfalls by offering 
several compliance flexibilities. Many manufacturers use these 
flexibilities to avoid civil penalties.
3. The Use for CAFE Compliance Flexibilities and Incentives
    The CAFE program offers several compliance flexibilities which 
expand options for compliance, and incentives which encourage 
manufacturers to build vehicles with certain technologies to achieve 
longer range policy objectives. For example, since MY 2017, 
manufacturers have had the flexibility to earn credits for air 
conditioning

[[Page 49813]]

(A/C) systems with improved efficiency. These fuel economy improvements 
are added to the 2-cycle performance results of the vehicle and 
increases the calculation of a manufacturer's fleet Base CAFE in 
determining compliance relative to standards.\472\
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    \472\ NHTSA characterizes any programmatic benefit manufacturers 
can use to comply with CAFE standards that fully accounts for fuel 
use as a ``flexibility'' (e.g., credit trading) and any benefit that 
counts less than the full fuel use as an ``incentive'' (e.g., 
adjustment of alternative fuel vehicle fuel economy). NHTSA 
flexibilities and incentives are discussed further in Section 
VII.B.3.a).
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    Some CAFE flexibilities and incentives are codified by statute in 
EPCA or EISA, while others have been implemented by the NHTSA through 
regulations, consistent with the statutory scheme. Compliance 
flexibilities and incentives have a great deal of theoretical 
attractiveness: If designed properly, they can help reduce the overall 
regulatory costs, while maintaining or improving programmatic benefits. 
If designed poorly, they may create significant potential for market 
distortion. Consequently, creating or revising compliance flexibilities 
and incentives requires proper governmental and industry collaboration 
for understanding upcoming technological developments and for 
determining whether a technology is economically feasible for 
compliance. When designing these programmatic elements, the agency must 
be mindful to ensure flexibilities and incentives are provided with 
long term benefits to the CAFE program while avoiding unintended 
windfalls for only certain manufacturers or technologies.
    Compliance incentives and flexibilities are structured to encourage 
implementation of technology that will further increase fuel savings. 
Some incentives are designed to encourage the development of 
technologies that may have high initial costs but offer promising fuel 
efficiency benefits in the long-term. Others are designed to bring low 
cost technologies uniformly into the market that improve fuel economy 
in the real-world but may be missed by the 2-cycle test, such as the 
cost-effective off-cycle menu technologies included by EPA for CAFE 
compliance.
    Below is a summary of all the current and proposed changes to the 
flexibilities and incentives for the CAFE and CO2 programs 
in Table VII-1 through Table VII-4. Note that this proposal only covers 
the CAFE program; the EPA program is listed here to demonstrate the 
congruencies between the two programs. NHTSA is proposing to maintain 
the bulk of its current program with a few modifications. One of the 
changes raised in this proposal is to increase the off-cycle 
flexibility technology benefit cap along with new technology 
definitions as shown in the table. NHTSA is also proposing to reinstate 
incentives for full-size hybrid and game changing advanced technology 
pickup trucks for model years 2022 through 2026. NHTSA believes that 
these incentives will increase the production of environmentally 
beneficial technologies and help achieve economies of scale to reduce 
costs that will enable more stringent CAFE standards in the future. 
These proposals are explained in further detail in Section VII.B.
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BILLING CODE 4910-59-C
4. Light Duty CAFE Compliance Data for MYs 2011-2020
    NHTSA uses compliance data in part to identify industry trends. For 
this proposal, NHTSA examined CAFE compliance data for model years 2011 
through 2020 using final compliance data for MYs 2011 through 
2017,\473\ projections from end-of-the-model year reports submitted by 
manufacturers for MYs 2018 and 2019,\474\ and projections from 
manufacturers' mid model year reports for MY 2020.\475\ Projections 
from the mid-year and end-of-the-model year reports may differ from 
EPA-verified final CAFE values either because of differing test results 
or final sales-volume figures. MY 2011 was selected as the start of the 
data because it represents the first compliance model year for which 
manufacturers were permitted to trade and transfer credits.\476\ The 
data go up to MY 2020, because this was the most recent year compliance 
reports were available.
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    \473\ Final compliance data have been verified by EPA and are 
published on the NHTSA's Public Information Center (PIC) site. MY 
2017 is currently the most-recent model year verified by EPA.
    \474\ MY 2018 data come from information received in 
manufacturers' final reports submitted to EPA according to 40 CFR 
600.512-12.
    \475\ Manufacturers' mid-model year CAFE reports are submitted 
to NHTSA in accordance with 49 CFR part 537. At the time of the 
analysis, end of the model year data had not yet been submitted for 
MY 2020.
    \476\ 49 CFR 535.6(c).
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    Figure VII-2 through Figure VII-5 provide a graphical overview of 
the actual and projected compliance data for MYs 2011 to 2020.\477\
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    \477\ As mentioned previously, the figures include estimated 
values for certain model years based on the most up to date 
information provided to NHTSA from manufacturers.
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    In the figures, an overview is provided for the total fuel economy 
performance of the industry (the combination of all passenger cars and 
light trucks produced for sale during the

[[Page 49817]]

model year) as a single fleet, and for each of the three CAFE 
compliance fleets: Domestic passenger car, import passenger car, and 
light truck fleets. For each of the graphs, a sale-production weighting 
is applied to determine the average total or fleet Base CAFE 
performances.478 479 480 The graphs do not include 
adjustments for full-size pickup trucks because manufactures have yet 
to bring qualifying products into production.
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    \478\ In the figures, the label ``2-Cycle CAFE'' represents the 
maximum increase each year in the average fuel economy set to the 
limitation ``cap'' for manufacturers attributable to dual-fueled 
automobiles as prescribed in 49 U.S.C. 32906. The label ``AC/OC 
contribution'' represents the increase in the average fuel economy 
adjusted for A/C and off-cycle fuel consumption improvement values 
as prescribed by 40 CFR 600.510-12.
    \479\ Consistent with applicable law, NHTSA established 
provisions starting in MY 2017 allowing manufacturers to increase 
compliance performance based on fuel consumption benefits gained by 
technologies not accounted for during normal 2-cycle EPA compliance 
testing (called ``off-cycle technologies'' for technologies such as 
stop-start systems) as well as for A/C systems with improved 
efficiencies and for hybrid or electric full-size pickup trucks.
    \480\ Adjustments for earned credits include those that have 
been adjusted for fuel saving using the manufacturers CAFE values 
for the model years in which they were earned and adjusted to the 
average CAFE values for the fleets they exist within.
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    The figures also show how many credits remain in the market each 
model year. One complicating factor for presenting credits is that the 
mpg-value of a credit is contingent where it was earned and applied. 
Therefore, the actual use of the credits for MYs 2018 and beyond will 
be uncertain until compliance for those model years is completed. Also, 
since credits can be retained for up to 6 MYs after they were earned or 
applied retroactively to the previous 3 model years, it is impossible 
to know the final application of credits for MY 2020 until MY 2023 
compliance data are finalized. Instead of attempting to project how 
credits would be generated and used, the agency opted to value each 
credit based on its actual value when earned, by estimating the value 
when applied assuming it was applied to the overall average fleet and 
across all vehicles. In the figures, two different approaches were used 
to represent the mpg value of credits used to offset shortages (shown 
as CAFE after credit allocation in the figures). The mpg shortages for 
MYs 2011 to 2017 are based upon actual compliance values from EPA and 
the credit allocations or fines manufacturers instructed NHTSA to 
adjust and apply to resolve compliance shortages. For MYs 2018 to 2020, 
NHTSA used a different approach for representing the mpg shortages, 
deriving them from projected estimates adjusted for fuel savings 
calculated from the projected fleet average performances and standards 
for each model year and fleet. To represent the mpg value of 
manufacturers' remaining banked credits in the figures (shown as 
Credits in the Market) the same weighting approach was also applied to 
these credits based upon the fleet averages. For MYs 2011-2017, the 
remaining banked credits include those currently existing in 
manufacturers' credit accounts adjusted for fuel savings and 
subtracting any expired credits for each year. This approach was taken 
to represent these credits for the actual value that would likely exist 
if the credits were applied for compliance purposes. Without adjusting 
the banked credits, it would provide an unrealistic value of the true 
worth of these credits when used for compliance. For MYs 2018-2020, the 
mpg value of the remaining banked credits is shown slightly differently 
where the value represents the difference between the adjusted credits 
carried forward from previous model years (minus expiring credits) and 
the projected earned credits minus any expected credit shortages. Since 
all the credits in these model years were adjusted using the same 
approach it was possible to subtract the credit amounts. However, 
readers are reminded that for MYs 2018-2020 since the final CAFE 
reports have yet to be issued, the credit allocation process has not 
started, and the data shown in the graphs are a projection of potential 
overall compliance. Consequently, the credits included for MYs 2018-
2020 are separated from earlier model years by a dashed line to 
highlight that there is a margin of uncertainty in the estimated 
values. Projecting how and where credits will be used is difficult for 
a number of reasons such as not knowing which flexibilities 
manufacturers will utilize and the fact that credits are not valued the 
same across different fleets. As such, the agency reminds readers that 
the projections may not align with how manufacturers will actually 
approach compliance for these years.
    Table VII-5 provides the numerical CAFE performance values and 
standards for MYs 2011-2020 as shown in the figures.
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BILLING CODE 4910-59-C
    As shown in Figure VII-2, manufacturers' fuel economy performance 
(2-cycle CAFE plus AMFA) for the total fleet was better than the fleet-
wide target through MY 2015. On average, the total fleet exceeded the 
standards by approximately 0.9 mpg for MYs 2011 to 2015. As shown in 
Figure VII-3 through Figure VII-5, domestic and import passenger cars 
exceeded standards on average by 2.1 mpg and 2.3 mpg, respectively. By 
contrast, light truck manufacturers on average fell below the standards 
by 0.3 mpg over the same time period.
    For MYs 2016 through 2020, Figure VII-2 shows that the total fleet 
Base CAFE (including 2-Cycle CAFE plus A/C and OC benefits) falls below 
and appears to remain below the fleet CAFE standards for these model 
years.\481\ The projected compliance shortfall (i.e. the difference 
between CAFE performance values and the standards) remains constant and 
reaches its greatest difference between MYs 2019 and 2020. Compliance 
becomes even more complex when observing individual compliance fleets 
over these years. Only domestic passenger car fleets collectively 
appear to exceed CAFE standards while import passenger car fleets 
appear to have the greatest compliance shortages. In MY 2020, the 
import passenger car fleet appear to reach its highest compliance 
shortfall equal to 3.3 mpg.
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    \481\ Until MY 2023 compliance, the last year where earned 
credits can be retroactively applied to MY 2020, NHTSA will be 
unable to make a determination about the fleet's overall compliance 
over this timespan.
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    The graphs provide an overall representation of the average values 
for each fleet, although they are less helpful for evaluating 
compliance with the minimum domestic passenger car standards given 
statutory prohibitions on manufacturers using traded or transferred 
credits to meet those standards.\482\ Consequently, in MY 2020, 
domestic passenger car manufacturers may improve their performance by 
adding more AC/OC technology, allowing the domestic passenger car fleet 
to once again exceed CAFE standards. However, NHTSA notes that several 
manufacturers have already reported insufficient earned credits and may 
have to make fine payments if they fail to reach the minimum domestic 
passenger car standards.
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    \482\ In accordance with 49 CFR 536.9(c), transferred or traded 
credits may not be used, pursuant to 49 U.S.C. 32903(g)(4) and 
(f)(2), to meet the domestically manufactured passenger automobile 
minimum standard specified in 49 U.S.C. 32902(b)(4) and in 49 CFR 
531.5(d).
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    In summary, MY 2016 is the last compliance model year that 
passenger cars complied with CAFE standards relying solely on Base CAFE 
performance. Prior to this timeframe, passenger car manufacturers 
especially those building domestic fleets could substantially exceed 
CAFE standards. MY 2016 marked the first time in the history of the 
CAFE program where compliance for passenger car manufacturers fell 
below standards thereby increasing shortfalls and forcing the need for 
manufacturers to rely heavily upon credit flexibilities. Despite higher 
shortfalls, domestic passenger car manufacturers have continued to 
generate credits and increase their total credit holdings. The 
projections show that for MYs 2018-2020, domestic passenger car fleets 
will transition from generating to using credits but will maintain 
sizable amounts of banked credits sufficient to sustain compliance 
shortfalls in other regulatory fleets. Figure VII-4 shows residual 
available banked credits even as far as MY 2020. Domestic passenger car 
credits and their off-cycle credits will play an important role in 
sustaining manufacturers in complying with CAFE standards.
    From the projections, it appears that based on the number of 
remaining domestic passenger credits in the market and the rate at 
which they are being used, there will be insufficient credits to cover 
the shortfalls in other compliance fleets in years following MY 2020. 
Figure VII-2 shows that the total remaining combined credits for the 
industry is expected to decline starting in MY 2018. Import passenger 
cars and light truck fleets will play a major role in the decline and 
possible depletion of all available credits to resolve shortfalls after 
MY 2020. Several factors exist that could produce this outcome. First, 
increasing credit shortages are occurring in the import passenger car 
and light truck fleets especially since the reduction and then 
termination of AMFA incentives in MY 2019 (a major contributor for 
light trucks). Next, residual banked credits for the light truck fleet 
are expected to be exhausted starting in MY 2018 and for import

[[Page 49821]]

passenger cars in MY 2020. Finally, the use of AC/OC benefits for 
import passenger cars and lights trucks is not a significant factor for 
these fleets in complying with CAFE standards. Manufacturers will need 
to change their production strategies or introduce substantially more 
fuel saving technologies to sustain compliance in the future.
    Figure VII-6 provides a historical overview of the industry's use 
of CAFE credit flexibilities and fine payments for addressing 
compliance shortfalls.\483\ As mentioned, MY 2017 is the last model 
year for which CAFE compliance determinations are completed, and credit 
application and civil penalty payment determinations finalized. As 
shown in the figure, for MYs 2011-2015, manufacturers generally 
resolved credit shortfalls by carrying forward earned credits from 
previous years. However, since 2011, the rise in manufacturers 
executing credit trades has become increasingly common and, in MY 2017, 
credit trades were the most frequently used flexibility for achieving 
compliance. Credit transfers have also become increasingly more 
prevalent for manufacturers. As a note to readers, credit trades in the 
figures can also involve credit transfers but are aggregated in the 
figure as credit trades to simplify results. In MY 2016, credit 
transfers constituted the highest contributor to credit flexibilities 
but are starting to decline signifying that manufacturers are currently 
exhausting credit transfers within their own fleets. Manufacturers only 
occasionally carry back credits to resolve performance shortfalls. 
NHTSA believes that trading credits between manufacturers and to some 
degree transferring traded credit across fleets will be the most 
commonly used flexibility in complying with future CAFE standards as 
started in MY 2017.
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    \483\ The Figure includes all credits manufacturers have used in 
credit transactions to date. Credits contained in carryback plans 
yet to be executed or in pending enforcement actions are not 
included in the Figure.
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    Credit trading has generally replaced civil penalty payments as a 
compliance mechanism. Only a handful of manufacturers have made civil 
penalty payments since the implementation of the credit trading 
program. As previously shown, NHTSA believes that manufacturers have 
sufficient credits to resolve any import passenger car and light truck 
performance shortfalls expected through MY 2020. As of recent, the only 
fine payments being made or expected in the future are those directly 
resulting from manufacturers failing to comply with the minimum 
domestic passenger car standards.\484\ There were two fine payments 
made in MYs 2016 and 2017 which fit this exact case. By statute, 
manufacturers cannot use traded or transferred credits to address 
performance shortfalls for failing to meet the minimum domestic 
passenger car standards.\485\ Because of this limitation, the fine 
payments made in MY 2016 and 2017 came from one manufacturer that had 
exhausted all of its earned domestic passenger credits and could not 
carryback future credits.\486\ The same condition will occur for other 
manufacturers in the future. NHTSA calculates that six manufacturers 
will meet this same condition and have to make substantial civil 
penalty payments for failing to comply with the minimum domestic 
passenger cars standards in MYs 2018 through 2020.
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    \484\ Six manufacturers have paid CAFE civil penalties since 
credit trading began in 2011. Fiat Chrysler paid the largest civil 
penalty total over the period, followed by Jaguar Land Rover and 
then Volvo. See Summary of CAFE Civil Penalties Collected, CAFE 
Public Information Center, https://one.nhtsa.gov/cafe_pic/CAFE_PIC_Fines_LIVE.html.
    \485\ Congress prescribed minimum domestic passenger car 
standards for domestic passenger car manufacturers and unique 
compliance requirements for these standards in 49 U.S.C. 32902(b)(4) 
and 32903(f)(2).
    \486\ Fiat Chrysler paid $77,268,702.50 in civil penalties for 
MY 2016 and $79,376,643.50 for MY 2017 for failing to comply with 
the minimum domestic passenger car standards for those MYs.
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    In Figure VII-8, additional information is provided on the credit 
flexibilities exercised and fine payments made by manufacturers for MYs 
2011-2017. The figure includes the gasoline gallon equivalent for these 
credit flexibilities or for paying civil penalties. The figure shows 
that manufacturers used carrying forward credits most often to resolve 
shortfalls. Credit trades were the second leading benefit to 
manufacturers in using credit flexibilities and then followed by credit 
transfers. In summary, manufacturers used these flexibilities amounting 
to the equivalent of 2,952,856 gallons of fuel by carrying forward 
credits in 2017 and 583,720 gallons of fuel by trading credits in 2017.

[[Page 49822]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.199

     
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    \487\ For Figure VII-6; in each year some flexibilities were not 
utilized by manufacturers. For example, carry backed credits were 
not utilized in 2011, 2013, 2014, 2015, 2016, or 2017. Transfer 
credits were not used in 2011, 2012 or 2013. No civil penalties were 
paid in 2015.

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[[Page 49823]]

[GRAPHIC] [TIFF OMITTED] TP03SE21.200

    Despite this compliance picture, NHTSA's analysis supporting this 
NPRM shows some amount of overcompliance in the baseline/No-Action 
Alternative for the model years subject to this proposal. This modeled 
overcompliance occurs due to assumptions about a variety of factors, 
including (1) a number of manufacturers voluntarily binding themselves 
to the California Framework Agreements, (2) expected manufacturer 
compliance with California's ZEV program, (3) expected manufacturer 
compliance with the EPA GHG and NHTSA CAFE standards finalized in 2020, 
(4) a small amount of market demand for increased fuel economy (due 
mostly to projected fuel prices), (5) the projected affordability of 
applying certain technologies that are eligible for compliance boosts 
(like off-cycle adjustments), and so on. If these assumptions do not 
come to pass in the real world, the difference between the compliance 
picture over the last several model years and the one shown in the 
analysis for the next several years would accordingly be smaller. 
Overcompliance with the regulatory alternatives is much lower than what 
was shown in the NPRM that preceded the 2020 final rule and is highly 
manufacturer-dependent. NHTSA seeks comment on the amount of 
overcompliance with the regulatory alternatives shown, if any, in light 
of how the agency has described its modeling approach for this 
proposal.
5. Shift in Sales Production From Passenger Cars to Light Trucks
    The apparent stagnant growth in the automotive industry's CAFE 
performance is likely related to a relative decrease in the share of 
passenger cars, where manufacturers made the most gains in fuel economy 
performance combined with an increase in the relative share of light 
trucks purchased beginning with MY 2013. Light trucks experienced sharp 
increases in sales, increasing by a total of 5 percent from MYs 2013 to 
2014. In MY 2014, light trucks comprised approximately 41 percent of 
the total sales production volume of automobiles and has continued to 
grow ever since. In comparison, for model year 2014, domestic passenger 
cars represented 36 percent of the total fleet and import passenger 
cars represented 23 percent. Both domestic and import passenger car 
sales have continued to fall every year since MY 2013. Figure VII-8 
shows the sales production volumes of light trucks and domestic and 
import passenger cars for MYs 2004 to 2020. Historically, light truck 
fleets have fallen below their associated CAFE standards and have had 
larger performance shortages than either import and domestic passenger 
car fleets. For MY 2020, NHTSA expects even greater CAFE performance 
shortages in the light truck and import passenger car fleets than in 
prior model years, based upon manufacturer's mid-model year (MMY) 
reports. MY 2020 light trucks are expected to comprise approximately 53 
percent of the total. As mentioned previously, the combined effect of 
these fuel economy shortages will likely require manufacturers to rely 
on compliance flexibilities or pay civil penalties.
    Out of 25 vehicle types listed in the EPA database, 5 vehicle 
types--namely compact cars, midsize cars, small and standard SUVs with 
4WD, and standard pickup trucks with 4WD have the highest volumes of 
vehicles produced for sale in MYs 2012 to 2017. From 2012 to 2020, 
there was a drastic decrease of 24% and 17% in the production of 
compact cars and midsize cars,

[[Page 49824]]

respectively. On the other side, there was a significant increase in 
the production of 4WD small and standard equaling approximately 41% 
collectively of all sales. Standard pickup trucks with 4WD experienced 
little change in the production volume throughout the years. As shown 
in Figure VII-9, small SUVs, with 4WD and 2WD drivetrains, have 
surpassed the sales production volumes of all other vehicle types over 
these the given model years. The number of small and standard SUVs sold 
in the U.S. for MY 2017 nearly doubled compared to sales in the U.S. 
for MY 2012. During that same period, passenger car sales production as 
a total of vehicle sales production decreased by approximately 11 
percent. The combination of low gas prices and the increased utility 
that SUVs provide, along with aggressive manufacturer marketing, may 
explain the shift in sales production. Nonetheless, if the sales of 
these small SUVs and pickup trucks continue to increase, there may be 
continued stagnation in the CAFE performance of the overall fleet 
unless manufacturers respond with greater adoption of fuel economy 
technology in the SUV and pickup truck portion of their fleets.
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[GRAPHIC] [TIFF OMITTED] TP03SE21.202

6. Electrification
    According to data submitted to EPA and NHTSA for MYs 2012 through 
2017, the population of electrified vehicles in the passenger car fleet 
has steadily increased. The percentage of petroleum-based passenger 
cars in the market has decreased. While the nominal amount of electric 
light trucks has increased, the percentage of electric light trucks has 
decreased due to petroleum-based light trucks growing at

[[Page 49825]]

a faster rate. All electric passenger cars account for up to 3 percent 
of the total production of light-duty vehicles each year. In 
comparison, all electric light trucks account for about 0.2 percent of 
the total fleet each year. The number of passenger cars using 
alternative fuels has also steadily increased while the population of 
alternative fuel light trucks has become non-existent. However, 
comparing the total fleet, the population of electric and hybrid 
vehicles is steadily increasing each year on average.
[GRAPHIC] [TIFF OMITTED] TP03SE21.203

    Despite the small market share currently for electric and hybrid 
trucks, manufacturers are making a strong effort to grow this market. 
Starting in 2020, several manufacturers introduced several new models 
of hybrid and PEV SUVs and crossovers.
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    \488\ 49 U.S. Code 538 discusses Flexible Fuel Vehicle.
    \489\ Definition of Electricity/Hybrids can be found in 49 U.S. 
Code 523.2.
    \490\ If the fuel type is marked as Hybrid, for this table the 
vehicles are automatically counted as Hybrid no matter what type of 
fuel category they have. Flexible Fuel Vehicle is everything else 
except where the fuel type is gasoline and electric/hybrid.
    \491\ Complete data is only available through MY 2017.
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    NHTSA is considering new CAFE compliance strategies for electric 
pickup trucks in this rulemaking. EPA and NHTSA previously provided 
flexibilities for hybrid and electric pickup trucks adopted under the 
2017-2025 CAFE and GHG final rule issued in 2012. These flexibilities 
would have provided manufacturers with an incentive through MY 2025 to 
build additional electric pickup trucks but in the 2020 final rule, 
NHTSA and EPA decided to terminate these incentives early. Further 
discussion of NHTSA's and EPA's incentive programs for hybrid and 
electric pickup trucks is presented in Section B.3.e)(1). As a part of 
the section, a new proposal is also included for EPA and NHTSA to 
reconsider extending the incentives for pickup trucks back to their 
original effective date ending in MY 2025.
7. Vehicle Classification
    Vehicle classification, for purposes of the light-duty CAFE 
program, refers to whether an automobile qualifies as a passenger 
automobile (car) or a non-passenger automobile (light truck). Passenger 
cars and light trucks are subject to different fuel economy standards 
as required by EPCA/EISA and consistent with their different 
capabilities.
    Vehicles are designated as either passenger automobiles or non-
passenger automobiles. Vehicles ``capable of off-highway operation'' 
are, by statute, non-passenger automobiles.\492\ Determining ``off-
highway operation'' was left to NHTSA, and currently is a two-part 
inquiry: First, does the vehicle either have 4-wheel drive or over 
6,000 pounds gross vehicle weight rating (GVWR), and second, does the 
vehicle have a significant feature designed for

[[Page 49826]]

off-highway operation.\493\ NHTSA's regulation on vehicle 
classification contain requirements for vehicles to be classified as 
light trucks either on the basis of off-highway capability or on the 
basis of having ``truck-like characteristics.'' Over time, NHTSA has 
refined the light truck vehicle classification by revising its 
regulations and issuing legal interpretations. However, based on the 
increase in crossover SUVs and advancements in vehicle design trends, 
NHTSA has become aware of vehicle designs that complicate 
classification determinations for the CAFE program. Throughout the past 
decade, NHTSA has identified these changes in compliance testing, data 
analysis, and has discussed the trend in rulemakings, publications, and 
with stakeholders.
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    \492\ 49 U.S. Code 32902.
    \493\ 49 U.S. Code 523.5(A)(5)(ii)(b).
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    NHTSA believes that an objective procedure for classifying vehicles 
is paramount to the agency's continued oversight of the CAFE program. 
When there is uncertainty as to how vehicles should be classified, 
inconsistency in determining manufacturers' compliance obligations can 
result, which is detrimental to the predictability and fairness of the 
program. In the 2020 final rule, NHTSA attempted to resolve several 
classification issues and committed to continuing research to resolve 
others. NHTSA notified the public of its plans to develop a compliance 
test procedure for verifying manufacturers' submitted classification 
data. An objective standard would help avoid manufacturers having to 
reclassify their vehicles, improve consistency and fairness across the 
industry, and introduce areas within the criteria where uncertainties 
existed and research could be conducted in the near future to resolve.
    In this rulemaking, NHTSA is providing additional classification 
guidance and seeking comments on several unknown aspects needed to 
develop its compliance test procedure. Based upon the comments received 
to this NPRM, NHTSA plans to release its draft test procedure later 
this year. No changes are being made in this rulemaking that will 
change how vehicles are classified.
(a) Clarifications for Classifications Based Upon ``Off-Road 
Capability''
    For a vehicle to qualify as off-highway (off-road) capable, in 
addition to either having 4WD or a GVWR more than 6,000 pounds. The 
vehicle must have four out of five characteristics indicative of off-
highway operation. These characteristics are:

 An approach angle of not less than 28 degrees
 A breakover angle of not less than 14 degrees
 A departure angle of not less than 20 degrees
 A running clearance of not less than 20 centimeters
 Front and rear axle clearances of not less than 18 centimeters 
each
(1) Production Measurements
    NHTSA's regulations require manufacturers to measure vehicle 
characteristics when a vehicle is at its curb weight, on a level 
surface, with the front wheels parallel to the automobile's 
longitudinal centerline, and the tires inflated to the manufacturer's 
recommended cold inflation pressure.\494\ NHTSA clarified in the 2020 
final rule that 49 CFR part 537 requires manufacturers to classify 
vehicles for CAFE based upon their physical production characteristics. 
The agency verifies reported values by measuring production vehicles. 
Manufacturers must also use physical vehicle measurements as the basis 
for values reported to the agency for purposes of vehicle 
classification. It may be possible for certain vehicles within a model 
type to qualify as light trucks while others would not because of their 
production differences. Since issuing the 2020 final rule, NHTSA has 
met with manufacturers to reinforce the use of production measurements 
and clarifying here that manufacturers are only required to report 
classification information for those physical measurements used for 
qualification and can omit other measurements.
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    \494\ 49 U.S. Code 523.5(A)(5).
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    In the previous rulemaking, NHTSA also identified that certain 
vehicle designs incorporate rigid (i.e., inflexible) air dams, valance 
panels, exhaust pipes, and other components, equipped as manufacturers' 
standard or optional equipment (e.g., running boards and towing 
hitches), that likely do not meet the 20-centimeter running clearance 
requirement. Despite these rigid features, some manufacturers are not 
taking these components into consideration when making classification 
decisions. Additionally, other manufacturers provide dimensions for 
their base vehicles without considering optional or various trim level 
components that may reduce the vehicle's ground clearance. Consistent 
with our approach to other measurements, NHTSA believes that ground 
clearance, as well as all the other off-highway criteria for a light 
truck determination, should use the measurements from vehicles with all 
standard and optional equipment installed, at the time vehicles are 
shipped to dealerships. These views were shared by manufacturers in 
response to the previous CAFE rulemaking.
    The agency reiterates that the characteristics listed in 49 CFR 
523.5(b)(2) are characteristics indicative of off-highway capability. A 
fixed feature--such as an air dam that does not flex and return to its 
original state or an exhaust that could detach--inherently interferes 
with the off-highway capability of these vehicles. If manufacturers 
seek to classify vehicles as light trucks under 49 CFR 523.5(b)(2) and 
the vehicles have a production feature that does not meet the four 
remaining characteristics to demonstrate off-highway capability, they 
must be classified as passenger cars. NHTSA also clarifies that 
vehicles that have adjustable ride height, such as air suspension, and 
permit variable on-road or off-road running clearances should be 
classified based upon the mode most commonly used or the off-road mode 
for those with this feature. NHTSA seeks comments on how to define the 
mode most commonly used for any adjustable suspensions. For the test 
procedure, would it be more appropriate to allow manufacturers to 
define the mode setting for vehicles with adjustable suspensions?
(2) Testing for Approach, Breakover, and Departure Angles
    Approach angle, breakover angle, and departure angle are relevant 
to determine off-highway capability. Large approach and departure 
angles ensure the front and rear bumpers and valance panels have 
sufficient clearance for obstacle avoidance while driving off-road. The 
breakover angle ensures sufficient body clearance from rocks and other 
objects located between the front and rear wheels while traversing 
rough terrain. Both the approach and departure angles are derived from 
a line tangent to the front (or rear) tire static loaded radius arc 
extending from the ground near the center of the tire patch to the 
lowest contact point on the front or rear of the vehicle. The term 
``static loaded radius arc'' is based upon the definitions in SAE J1100 
and J1544.\495\ The term is defined as the distance from wheel axis of 
rotation to the supporting surface (ground) at a given load of the 
vehicle and stated inflation pressure of

[[Page 49827]]

the tire (manufacturer's recommended cold inflation pressure).
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    \495\ See SAE J1100 published on May 26, 2012 and SAE J1544 
published on Oct 25, 2011.
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    The static loaded radius arc is easy to measure, but the imaginary 
line tangent to the static loaded radius arc is difficult to ascertain 
in the field. The approach and departure angles are the angles between 
the line tangent to the static loaded radius arc and the level ground 
on which the test vehicle rests. For the compliance test procedure, a 
substitute measurement will be used. A measurement that provides a good 
approximation of the approach and departure angles involve using a line 
tangent to the outside diameter or perimeter of the tire and extends to 
the lowest contact point on the front or rear of the vehicle. This 
approach provides an angle slightly greater than the angle derived from 
the true static loaded radius arc. The approach also has the advantage 
to allow measurements to be made quickly for measuring angles in the 
field to verify data submitted by the manufacturers used to determine 
light truck classification decisions. In order to comply, the vehicle 
measurement must be equal to or greater than the required measurements 
to be considered as compliant and if not, the reported value will 
require an investigation which could lead to the manufacturer's vehicle 
becoming reclassified as a passenger car.
(3) Running Clearance
    NHTSA regulations define ``running clearance'' as ``the distance 
from the surface on which an automobile is standing to the lowest point 
on the automobile, excluding unsprung weight.'' Unsprung weight 
includes the components (e.g., suspension, wheels, axles, and other 
components directly connected to the wheels and axles) that are 
connected and translate with the wheels. Sprung weight, on the other 
hand, includes all components fixed underneath the vehicle that 
translate with the vehicle body (e.g., mufflers and subframes). To 
clarify these requirements, NHTSA previously issued a letter of 
interpretation stating that certain parts of a vehicle--such as tire 
aero deflectors that are made of flexible plastic, bend without 
breaking, and return to their original position--would not count 
against the 20-centimeter running clearance requirement. The agency 
explained that this does not mean a vehicle with less than 20 
centimeters running clearance could be elevated by an upward force that 
bends the deflectors and still be considered compliant with the running 
clearance criterion, as it would be inconsistent with the conditions 
listed in the introductory paragraph of 49 CFR 523.5(b)(2). Further, 
NHTSA explained that without a flexible component installed, the 
vehicle must meet the 20-centimeter running clearance requirement along 
its entire underside. This 20-centimeter clearance is required for all 
sprung weight components. For its compliance test procedure, NHTSA will 
include a list of the all the components under the vehicle considered 
as unsprung components. NHTSA will update the list of unsprung 
components as the need arises.
(4) Front and Rear Axle Clearance
    NHTSA regulations state that front and rear axle clearances of not 
less than 18 centimeters are another criterion that can be used for 
designating a vehicle as off-highway capable.\496\ The agency defines 
``axle clearance'' as the vertical distance from the level surface on 
which an automobile is standing to the lowest point on the axle 
differential of the automobile.
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    \496\ 49 U.S. Code 523.5(b)(2).
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    The agency believes this definition may be outdated because of 
vehicle design changes, including axle system components and 
independent front and rear suspension components. In the past, 
traditional light trucks with and without 4WD systems had solid rear 
axles with center-mounted differential on the axle. For these trucks, 
the rear axle differential was closer to the ground than any other axle 
or suspension system component. This traditional axle design still 
exists today for some trucks with a solid chassis (also known as body-
on-frame configuration). Today, however, many SUVs and CUVs that 
qualify as light trucks are constructed with a unibody frame and have 
unsprung (e.g., control arms, tie rods, ball joints, struts, shocks, 
etc.) and sprung components (e.g., the axle subframes) connected 
together as a part of the axle assembly. These unsprung and sprung 
components are located under the axles, making them lower to the ground 
than the axles and the differential, and were not contemplated when 
NHTSA established the definition and the allowable clearance for axles. 
The definition also did not originally account for 2WD vehicles with 
GVWRs greater than 6,000 pounds that had one axle without a 
differential, such as the model year 2018 Ford Expedition. Vehicles 
with axle components that are low enough to interfere with the 
vehicle's ability to perform off-road would seem inconsistent with the 
regulation's intent of ensuring off-highway capability.
    In light of these issues, for the compliance test procedure, NHTSA 
will ask manufacturers to identify those axle components that are 
sprung or unsprung and provide sufficient justification as a part of 
the testing setup request forms sent to manufacturers before testing. 
In addition, for vehicles without a differential, NHTSA will request 
the location each manufacturer used to establish its axle clearance 
qualification. NHTSA will validate the location specified by the 
manufacturer but will challenge any location on the vehicle's axle 
found to be located at a lower elevation to the ground than the 
designed location of its axle clearance measurement.
(5) 49 CFR 571.3 MPV Definition
    The definition for multipurpose passenger vehicle (MPV) is defined 
as a ``a motor vehicle with motive power, except a low-speed vehicle or 
trailer, designed to carry 10 persons or less which is constructed 
either on a truck chassis or with special features for occasional off-
road operation.'' \497\ The regulation is silent, however, in defining 
special features for occasional off-road operation are qualified. In a 
letter of interpretation dated May 31, 1979, the agency responded to a 
question from Subaru requesting the agency's opinion whether a four-
wheel drive hatchback sedan could be classified as an MPV. NHTSA 
responded stating that the agency interprets the definition as 
requiring that the vehicle contain more than a single feature designed 
for off-road use and that four-wheel drive would be useful in snow on 
public streets, roads and highways, so this feature cannot be 
determinative of the vehicle's classification if there are no features 
for off-road use. The interpretation also stated that Subaru needed to 
provide additional information (including, but not limited to, pictures 
or drawings of the vehicle) concerning other special features of the 
vehicle that would make it suitable for off-road operation. Finally, 
the interpretation referenced 49 CFR 523.5(b)(2) for a description of 
some of the characteristics that would be considered ``special 
features'' for off-road operation although that section relates 
primarily related to fuel economy. Considering that the definition for 
MPVs does not list the ``special features,'' NHTSA is seeking comment 
on whether manufacturers use ``special features'' other than those in 
49 CFR 523.5(b)(2) to qualify vehicles as MPVs. Should NHTSA link the 
definition of MPV in 49 CFR 571.3 (as it relates to special features 
for occasional off-road operation) to 49 CFR

[[Page 49828]]

523.5(b)(2)? What drawbacks exist in linking both provisions? Using the 
longstanding off-road features for fuel economy provides could clarify 
the means for certifying that a vehicle meets the definition for MPV in 
571.3 when manufacturers may otherwise be uncertain as to how to 
classify a vehicle.
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    \497\ 49 CFR 571.3.
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B. Complying With the NHTSA CAFE Program

1. Annual Compliance Process
    Manufacturers' production decisions drive the mixture of 
automobiles on the road. Manufacturers largely produce a mixture of 
vehicles both to influence and meet consumer demand and address 
compliance with CAFE standards though the application of fuel economy 
improving technologies to those vehicles, and by using compliance 
flexibilities and incentives that are available in the CAFE program. As 
discussed earlier in this NPRM, each vehicle manufacturer is subject to 
separate CAFE standards for passenger cars and light trucks, and for 
the passenger car standards, a manufacturer's domestically-manufactured 
and imported passenger car fleets are required to comply 
separately.\498\ Additionally, domestically-manufactured passenger cars 
are subject to a statutory minimum standard. Some CAFE program 
flexibilities are described by statute. Other flexibilities are 
established by NHTSA through regulation in accordance with the EPCA and 
EISA, such as fuel economy improvements for air conditioning 
efficiency, off-cycle, and pickup truck advanced technologies that are 
not expressly specified by CAFE statute, but are implemented consistent 
with EPCA's provisions regarding the calculation of fuel economy 
authorized for EPA.
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    \498\ 49 U.S.C. 32904(b).
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    Compliance with the CAFE program begins each year with 
manufacturers submitting required reports to NHTSA in advance and 
during the model year that contain information, specifications, data, 
and projections about their fleets.\499\ Manufacturers report early 
product projections to NHTSA describing their efforts to comply with 
CAFE standards per EPCA's reporting requirements.\500\ Manufacturers' 
early projections are required to identify any of the flexibilities and 
incentives manufacturers plan to use for air-conditioning (A/C) 
efficiency, off-cycle and, through MY 2021, which this action proposes 
to extend through MY 2026, full-size pickup truck advanced 
technologies. EPA consults with NHTSA when reviewing and considering 
manufacturers' requests for fuel consumption improvement values for A/C 
and off-cycle technologies that improve fuel economy. NHTSA evaluates 
and monitors the performance of the industry using compliance data. 
NHTSA also audits manufacturers' projected data for conformance and 
verifies vehicle conformance through measurements (e.g., vehicle 
footprints) to ensure manufacturers are complying. After the model year 
ends, manufacturers submit final reports to EPA, that include final 
information on all the flexibilities and incentives allowed or approved 
for the given model year.\501\ EPA then verifies manufacturers' 
reported information and values and calculates the final fuel economy 
level of each fleet produced by each manufacturer, and transmits that 
information to NHTSA.\502\
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    \499\ 49 U.S.C. 32907(a); 49 CFR 537.7.
    \500\ 49 U.S.C. 32907(a).
    \501\ For example, alternative fueled vehicles get special 
calculations under EPCA (49 U.S.C. 32905-06), and fuel economy 
levels can also be adjusted to reflect air conditioning efficiency 
and ``off-cycle'' improvements.
    \502\ 49 U.S.C. 32904(c)-(e). EPCA granted EPA authority to 
establish fuel economy testing and calculation procedures; EPA uses 
a two-year early certification process to qualify manufacturers to 
start selling vehicles, coordinates manufacturer testing throughout 
the model year, and validates manufacturer-submitted final test 
results after the close of the model year.
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    In previous years, the normal processes for CAFE compliance between 
NHTSA and EPA have been effective at administering the CAFE program for 
decades. EPA sends NHTSA its final CAFE results usually between 
November to December after the given model year. In recent years, this 
process has been disrupted by manufacturers submitting requests for A/C 
and off-cycle benefits during the model year and at times well after 
the end of the model year. As EPA cannot finalize CAFE results until 
all A/C and off-cycle credits for a model year are accounted for, the 
belated submissions have significantly delayed NHTSA receiving final 
CAFE results for many manufacturers. Late submissions place significant 
burdens on the agencies and complicate administering the CAFE program, 
including delaying the exchange and use of credits. In the following 
sections, NHTSA discusses the adverse impacts on the CAFE program 
resulting from late and retro-active A/C and off-cycle requests and 
proposes regulatory modifications to mitigate late submissions and help 
expedite processes for future off-cycle requests.
    After receiving EPA's final reports, NHTSA completes the remainder 
of its compliance processes for manufacturers usually one to three 
months after receiving EPA's final reports. The process starts with 
NHTSA using EPA's final verified information to determine the CAFE 
standard for each of the manufacturer's fleets, and each fleet's 
compliance level. Those results are then used to determine credits, 
credit shortfalls and credit balances, and NHTSA sends letters to 
manufacturers stating the outcome of that assessment. Credit shortfall 
letters specify the obligated credit deficiency a manufacturer must 
resolve to comply with the applicable CAFE standard for the given model 
year. Credit balance letters specify the official balance of credits 
NHTSA has allotted to the manufacturer in each of its credit accounts 
and a ledger of the credit transactions the manufacturer has executed. 
Upon receipt of NHTSA's compliance letters, manufacturers are required 
to submit plans explaining how they plan to resolve any shortfalls. 
NHTSA periodically releases data and reports to the public through its 
CAFE Public Information Center (PIC) based on information in the EPA 
final reports for the given compliance model year and based on the 
projections manufacturers provide to NHTSA for the next two model 
years.\503\
---------------------------------------------------------------------------

    \503\ The NHTSA Public Information Center (PIC) is located at 
https://one.nhtsa.gov/cafe_pic/CAFE_PIC_Home.htm.
---------------------------------------------------------------------------

    Some flexibilities are defined, and sometimes limited by statute--
for example, while Congress allowed manufacturers to transfer credits 
earned for over-compliance from their car fleet to their truck fleet 
and vice versa, Congress also limited the amount by which manufacturers 
could increase their CAFE levels using those transfers.\504\ Consistent 
with the limits Congress placed on certain statutory flexibilities and 
incentives, NHTSA crafted and implemented credit transfer and trading 
regulations authorized by EISA ensure that total fuel savings are 
preserved when manufacturers exercise statutory compliance 
flexibilities required by statute.
---------------------------------------------------------------------------

    \504\ See 49 U.S.C. 32903(g).
---------------------------------------------------------------------------

    NHTSA and EPA have previously developed other compliance 
flexibilities and incentives for the CAFE program consistent with the 
statutory provisions regarding EPA's calculation of manufacturers' fuel 
economy levels. As discussed previously, NHTSA finalized in the 2012 
final rule an approach for manufacturers' ``credits'' under EPA's 
program to be applied as fuel economy

[[Page 49829]]

``adjustments'' or ``improvement values'' under NHTSA's program for: 
(1) Technologies that cannot be measured or cannot be fully measured on 
the 2-cycle test procedure, i.e., ``off-cycle'' technologies; and (2) 
A/C efficiency improvements that also improve fuel economy but cannot 
be measured on the 2-cycle test procedure. Additionally, both agencies' 
programs give manufacturers compliance incentives through MY 2021, and 
proposed to be extended to MY 2026 in this NPRM, for utilizing 
specified technologies on full-size pickup trucks, such as 
hybridization, or full-size pickup trucks that overperform their fuel 
economy stringency target values by greater than a specified amount.
    The following sections outline how NHTSA determines whether 
manufacturers are in compliance with CAFE standards for each model 
year, and how manufacturers may use compliance flexibilities, or 
alternatively address noncompliance through civil penalties. Moreover, 
it explains how manufacturers submit data and information to the 
agency. This includes a detailed discussion of NHTSA's standardized 
CAFE reporting template adopted as a part of the 2020 final rule, and 
the standardized template for reporting credit transactions. In the 
2020 final rule, NHTSA also adopted requirements for manufacturers to 
provide information on terms of credit trades. In this rulemaking, 
NHTSA is proposing to make changes to its reporting and credit 
templates and to issue a new template to clarify the required reporting 
information for credit trades. These new requirements were intended to 
streamline reporting and data collection from manufacturers, in 
addition to helping the agency use the best available data to inform 
CAFE program decision makers.
2. How does NHTSA determine compliance?
(a) Manufacturers Submit Data to NHTSA and EPA and the Agencies 
Validate Results
    EPCA, as amended by EISA, in 49 U.S.C. 32907, requires 
manufacturers to submit reports to the Secretary of Transportation 
explaining how they will comply with the CAFE standards for the model 
year for which the report is made; the actions a manufacturer has taken 
or intends to take to comply with the standard; and other information 
the Secretary requires by regulation.\505\ A manufacturer must submit a 
report containing this information during the 30-day period before the 
beginning of each model year, and during the 30-day period beginning 
the 180th day of the model year.\506\ When a manufacturer determines it 
is unlikely to comply with a CAFE standard, the manufacturer must 
report additional actions it intends to take to comply and include a 
statement about whether those actions are sufficient to ensure 
compliance.\507\
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    \505\ 49 U.S.C. 32907(a).
    \506\ Id.
    \507\ Id.
---------------------------------------------------------------------------

    To implement these reporting requirements, NHTSA issued 49 CFR part 
537, ``Automotive Fuel Economy Reports,'' which specifies three types 
of CAFE reports that manufacturers must submit.\508\ A manufacturer 
must first submit a pre-model year (PMY) report containing the 
manufacturer's projected compliance information for that upcoming model 
year. By regulation, the PMY report must be submitted in December of 
the calendar year prior to the corresponding model year.\509\ 
Manufacturers must then submit a mid-model year (MMY) report containing 
updated information from manufacturers based upon actual and projected 
information known midway through the model year. By regulation, the MMY 
report must be submitted by the end of July for the applicable model 
year.\510\ Finally, manufacturers must submit a supplementary report to 
supplement or correct previously submitted information, as specified in 
NHTSA's regulation.\511\
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    \508\ See 47 FR 34986, Aug. 12, 1982.
    \509\ 49 CFR 537.5(b).
    \510\ Id.
    \511\ 49 CFR 537.8.
---------------------------------------------------------------------------

    If a manufacturer wishes to request confidential treatment for a 
CAFE report, it must submit both a confidential and redacted version of 
the report to NHTSA. CAFE reports submitted to NHTSA contain estimated 
sales production information, which may be protected as confidential 
until the termination of the production period for that model 
year.\512\ NHTSA protects each manufacturer's competitive sales 
production strategies for 12 months, but does not permanently exclude 
sales production information from public disclosure. Sales production 
volumes are part of the information NHTSA routinely makes publicly 
available through the CAFE PIC.
---------------------------------------------------------------------------

    \512\ 49 CFR part 512, appx. B(2).
---------------------------------------------------------------------------

    The manufacturer reports provide information on light-duty 
automobiles such as projected and actual fuel economy standards, fuel 
economy performance, and production volumes, as well as information on 
vehicle design features (e.g., engine displacement and transmission 
class) and other vehicle attribute characteristics (e.g., track width, 
wheelbase, and other off-road features for light trucks). Beginning 
with MY 2017, to obtain credit for fuel economy improvement values 
attributable to additional technologies, manufacturers must also 
provide information regarding A/C systems with improved efficiency, 
off-cycle technologies (e.g., stop-start systems, high-efficiency 
lighting, active engine warm-up), and full-size pickup trucks with 
hybrid technologies or with fuel economy performance that is better 
than footprint-based targets by specified amounts. This includes 
identifying the makes and model types equipped with each technology, 
the compliance category those vehicles belong to, and the associated 
fuel economy improvement value for each technology.\513\ In some cases, 
NHTSA may require manufacturers to provide supplementary information to 
justify or explain the benefits of these technologies and their impact 
on fuel consumption or to evaluate the safety implication of the 
technologies. These details are necessary to facilitate NHTSA's 
technical analyses and to ensure the agency can perform enforcement 
audits as appropriate.
---------------------------------------------------------------------------

    \513\ NHTSA collects model type information based upon the EPA 
definition for ``model type'' in 40 CFR 600.002.
---------------------------------------------------------------------------

    NHTSA uses manufacturer-submitted PMY, MMY, and supplementary 
reports to assist in auditing manufacturer compliance data and 
identifying potential compliance issues as early as possible. 
Additionally, as part of its footprint validation program, NHTSA 
conducts vehicle testing throughout the model year to confirm the 
accuracy of the track width and wheelbase measurements submitted in the 
reports.\514\ These tests help the agency better understand how 
manufacturers may adjust vehicle characteristics to change a vehicle's 
footprint measurement, and ultimately its fuel economy target. NHTSA 
also includes a summary of manufacturers' PMY and MMY data in an annual 
fuel economy performance report made publicly available on its PIC.
---------------------------------------------------------------------------

    \514\ U.S. Department of Transportation, NHTSA, Laboratory Test 
Procedure for 49 CFR part 537, Automobile Fuel Economy Attribute 
Measurements (Mar. 30, 2009), available at http://www.nhtsa.gov/DOT/NHTSA/Vehicle%20Safety/Test%20Procedures/Associated%20Files/TP-537-01.pdf.
---------------------------------------------------------------------------

    As mentioned, NHTSA uses EPA-verified final-model year (FMY) data 
to evaluate manufacturers' compliance with CAFE program requirements 
and draw conclusions about the performance of the industry. After

[[Page 49830]]

manufacturers submit their FMY data, EPA verifies the information, 
accounting for NHTSA and EPA testing, and subsequently forwards the 
final verified data to NHTSA.
(b) New CAFE Reporting Templates Adopted in the 2020 Final Rule
    NHTSA adopted changes to its CAFE reporting requirements in the 
2020 final rule with the intent of streamlining data collection and 
reporting for manufacturers while helping the agency obtain the best 
available data to inform CAFE program decision-makers. The agency 
adopted two new standardized reporting templates for manufacturers. 
NHTSA's goal was to adopt standardized templates to assist 
manufacturers in providing the agency with all the necessary data to 
ensure they comply with CAFE regulations.
    The first template was designed for manufacturers to simplify 
reporting CAFE credit transactions starting in model year 2021. The 
template's purpose was to reduce the burden on credit account holders, 
encourage compliance, and facilitate quicker NHTSA credit transaction 
approval. Before the template, manufacturers would inconsistently 
submit information required by 49 CFR 536.8, creating difficulties in 
processing credit transactions. Using the template simplifies CAFE 
compliance aspects of the credit trading process and helps to ensure 
that trading parties follow the requirements for a credit transaction 
in 49 CFR 536.8(a).\515\
---------------------------------------------------------------------------

    \515\ Submitting a properly completed template and accompanying 
transaction letter will satisfy the trading requirements in 49 CFR 
part 536.
---------------------------------------------------------------------------

    The second template was designed to standardize reporting for CAFE 
PMY and MMY information, as specified in 49 CFR 537.7(b) and (c), as 
well as supplementary information required by 49 CFR 537.8. The 
template organizes the required data in a manner consistent with NHTSA 
and EPA regulations and simplifies the reporting process by 
incorporating standardized responses consistent with those provided to 
EPA. The template collects the relevant data, calculates intermediate 
and final values in accordance with EPA and NHTSA methodologies, and 
aggregates all the final values required by NHTSA regulations in a 
single summary worksheet. Thus, NHTSA believes that the standardized 
templates will benefit both the agency and manufacturers by helping to 
avoid reporting errors, such as data omissions and miscalculations, and 
will ultimately simplify and streamline reporting. Manufacturers are 
required to use the standardized template for all PMY, MMY, and 
supplementary CAFE reports starting in MY 2023. The template also 
allowed manufacturers to enter information to generate the required 
confidential versions of CAFE reports specified in 49 CFR part 537 and 
to produce automatically the required non-confidential versions by 
clicking a button within the template.
    The standardized CAFE reporting templates were made available on 
the NHTSA website and through the DOT docket. Since then, manufacturers 
have downloaded the templates and met with NHTSA to share 
recommendations for changes, such as allowing the PMY and MMY reporting 
templates to accommodate different types of alternative fueled vehicles 
and to clarify and correct the methods for calculating CAFE values. The 
proposed changes are discussed in the following sections. NHTSA plans 
to host a series of workshops to implement the templates and to provide 
an open dialogue for manufacturers to identify any further problems and 
seek clarifications. NHTSA plans to announce the workshops through the 
Federal Register later this year.
(1) Changes to the CAFE Reporting Template
    The changes to the CAFE Reporting Template include several general 
improvements made to simply the use and the effectiveness for 
manufacturers. These include, but are not limited to; wording changes, 
corrections to calculations and codes, and auto-populating fields 
previously requiring manual entry.
    More specifically, NHTSA is proposing to modify the CAFE Reporting 
Template by adding filters and sorting functions to help manufacturers 
connect the data definitions to the location of each of the required 
data fields in the template. Additional information from other parts of 
the CAFE Reporting Template would be pulled forward to display on the 
summary tab. For the information that must be included pursuant to 49 
CFR 537.7(b)(2), manufacturers can also compare the values the template 
calculates to their own internally calculated CAFE values. 
Additionally, we are proposing to expand the CAFE Reporting Template to 
include more of the required information regarding vehicle 
classification, and guidance provided to ease manufacturers reporting 
burden by having them report only the data used for each vehicle's 
qualification pathway ignoring other possible light truck 
classification information.
    NHTSA is also proposing that the CAFE Reporting Template be 
modified to combine the footprint attribute information and model type 
sub-configuration data for the purposes of matching. NHTSA uses this 
information to match test data directly to fuel economy footprint 
values for the purposes of modeling fuel economy standards. Features 
were added to auto-populate redundant information from one worksheet to 
another. The data gathered and the formulas coded within the proposed 
worksheets have also been updated for the calculation of fuel economy 
based on 40 CFR 600.510-12. The changes to the data and formulas will 
allow data to more accurately represent the fuel economy of electric 
and other vehicles using alternative fuels. NHTSA considers this 
information critically important to forming a more complete picture of 
the performances of dual fuel and alternative fuel vehicles.
    We are also proposing several corrections so that manufacturers 
will submit CAFE data at each of the different sub-configuration levels 
they test and will combine CO2 and fuel economy data. As 
mentioned, manufacturers test approximately 90-percent of their 
vehicles within each model type. Each sub-configuration variant within 
a model type has a unique CO2 and CAFE value. Manufacturers 
combine other vehicles at the configuration, base level and then 
finally at the model type level for determining CAFE performance. The 
CAFE performance data for the sub-configurations have been added to the 
proposed template. NHTSA determined that this level of data was needed 
to verify manufacturers reported CAFE values.
    Finally, we are proposing corrections to the CAFE Reporting 
Template to collect information on off-cycle technologies. The proposed 
changes match the format of the data with the EPA off-cycle database 
system. For example, manufacturers report to EPA high efficiency 
lighting as combination packages, so NHTSA is proposing to change its 
form to reflect this same level of information.
    Version 2.21 of the template is available on NHTSA's Public 
Information Center (PIC) site.
(2) Credit Transactions Reporting Template
    NHTSA established mandatory use of the CAFE credit template 
starting on January 1, 2021. However, manufacturers identified several 
calculation errors in the version of the credit reporting template 
available on

[[Page 49831]]

the PIC site. Those calculation errors have been corrected and a new 
version of the template is available for download on the NHTSA PIC. 
Starting January 1, 2022, NHTSA will only accept its credit template as 
the sole source for executing CAFE credit transactions. Until that 
time, manufacturers can deviate from the generated language in the 
NHTSA credit trade confirmation by adding qualifications but, at a 
minimum, must include the core information generated by the template.
(3) Monetary and Non-Monetary Credit Trade Information
    Credit trading became permissible in MY 2011.\516\ To date, NHTSA 
has received numerous credit trades from entities, but has only made 
limited information publicly available.\517\ As discussed earlier, 
NHTSA maintains an online CAFE database with manufacturer and fleetwide 
compliance information that includes year-by-year accounting of credit 
balances for each credit holder. While NHTSA maintains this database, 
the agency's regulations currently state that it will not publish 
information on individual transactions, and NHTSA has not previously 
required trading entities to submit information regarding the 
compensation (whether financial, or other items of value) exchanged for 
credits.518 519 Thus, NHTSA's PIC offers sparse information 
to those looking to determine the value of a credit.
---------------------------------------------------------------------------

    \516\ 49 CFR 536.6(c).
    \517\ Manufacturers may generate credits, but non-manufacturers 
may also hold or trade credits. Thus, the word ``entities'' is used 
to refer to those that may be a party to a credit transaction.
    \518\ 49 CFR 536.5(e)(1).
    \519\ NHTSA understands that not all credits are exchanged for 
monetary compensation. The proposal that NHTSA is adopting in this 
proposed rule requires entities to report compensation exchanged for 
credits and is not limited to reporting monetary compensation.
---------------------------------------------------------------------------

    The lack of information regarding credit transactions means 
entities wishing to trade credits have little, if any, information to 
determine the value of the credits they seek to buy or sell. 
Historically we have assumed that the civil penalty for noncompliance 
with CAFE standards largely determines the upper value of a credit, 
because it is logical to assume that manufacturers would not purchase 
credits if it cost less to pay civil penalties instead, but it is 
unknown how other factors affect the value. For example, a credit 
nearing the end of its five-model-year lifespan would theoretically be 
worth less than a credit within its full five-model-year lifespan. In 
the latter case, the credit holder would likely value the credit more, 
as it can be used for compliance purposes for a longer period of time.
    NHTSA adopted requirements in the 2020 final rule requiring 
manufacturers to submit all credit trade contracts, including cost and 
transactional information, to the agency starting January 1, 2021. 
NHTSA also adopted requirements allowing manufacturers to submit the 
information confidentially, in accordance with 49 CFR part 512.\520\ As 
stated in the final rule, NHTSA intended to use this information to 
determine the true cost of compliance for all manufacturers. This 
information would allow NHTSA to better assess the impact of its 
regulations on the industry and provide more insightful information in 
developing future rulemakings. This confidential information would be 
held by secure electronic means in NHTSA's database systems. As for 
public information, NHTSA would include more information on the PIC on 
aggregated credit transactions, such as the combined flexibilities all 
manufacturers used for compliance as shown in Figure VII-6, or 
information comparable to the credit information EPA makes available to 
the public. In the future, NHTSA will consider what information, if 
any, can be meaningfully shared with the public on credit transactional 
details or costs, while accounting for the concerns raised by the 
automotive industry for protecting manufacturers' competitive sources 
of information.
---------------------------------------------------------------------------

    \520\ See also 49 U.S.C. 32910(c).
---------------------------------------------------------------------------

    However, manufacturers continue to argue that disclosing trading 
terms may not be as simple as a spot purchase at a given price. As 
stated in the 2020 final rule, manufacturers contend a number of 
transactions for both CAFE and CO2 credits involve a range 
of complexity due to numerous factors that are reflective of the 
marketplace, such as the volume of credits, compliance category, credit 
expiration date, a seller's compliance strategy, and even the CAFE 
penalty rate in effect at that time. In addition, automakers have a 
range of partnerships and cooperative agreements with their own 
competitors. Credit transactions can be an offshoot of these broader 
relationships, and difficult to price separately and independently.
    Since then, NHTSA has identified a series of non-monetary factors 
that it believes to be important to the costs associated with credit 
trading in the CAFE program.\521\ The agency believes this information 
will allow for a better assessment of the true costs of compliance. 
NHTSA further notes that greater government oversight is needed over 
the CAFE credit market and it needs to understand the full range of 
complexity in transactions, monetary and non-monetary, in addition to 
the range of partnerships and cooperative agreements between credit 
account holders--which may impact the price of credit trades.\522\ 
Therefore, using the identified series of non-monetary factors, NHTSA 
has developed a new CAFE Credit Reporting Template (Form 1621) for 
capturing the monetary and non-monetary terms of credit trading 
contracts. NHTSA proposes that manufacturers start using the new 
template starting September 1, 2022. The draft template can be viewed 
and downloaded from the NHTSA PIC site.
---------------------------------------------------------------------------

    \521\ UCS, Detailed Comments, NHTSA-2018-0067-12039; Jason 
Schwartz, Detailed Comments, NHTSA-2018-0067-12162.
    \522\ Honda, Detailed Comments, NHTSA-2018-0067-11819.
---------------------------------------------------------------------------

3. What compliance flexibilities and incentives are currently available 
under the CAFE program and how do manufacturers use them?
    Generating, trading, transferring, and applying CAFE credits is 
governed by statute.\523\ Program credits are generated when a vehicle 
manufacturer's fleet over-complies with its standard for a given model 
year, meaning its vehicle fleet achieved a higher corporate average 
fuel economy value than the amount required by the CAFE program for 
that fleet in that model year. Conversely, if the fleet average CAFE 
level does not meet the standard, the fleet incurs debits (also 
referred to as a shortfall or deficit). A manufacturer whose fleet 
generates a credit shortfall in a given model year can resolve its 
shortfall using any one or combination of several credits 
flexibilities, including credit carryback, credit carry-forward, credit 
transfers, and credit trades, and if all credit flexibilities have been 
exhausted, then the manufacturer must resolve its shortfall by making 
civil penalty payments.\524\
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    \523\ 49 U.S.C. 32903.
    \524\ Manufacturers may elect to pay civil penalties rather than 
utilizing credit flexibilities at their discretion. For purposes of 
the analysis, we assume that manufacturers will only pay penalties 
when all flexibilities have been exhausted.
---------------------------------------------------------------------------

    NHTSA has also promulgated compliance flexibilities and incentives 
consistent with EPCA's provisions regarding calculation of fuel economy 
levels for individual vehicles and for fleets.\525\ These compliance 
flexibilities and incentives, which were first adopted in the 2012 rule 
for MYs 2017 and later, include A/C efficiency improvement and off-
cycle adjustments,

[[Page 49832]]

and adjustments for advanced technologies in full-size pickup trucks, 
including adjustments for mild and strong hybrid electric full-size 
pickup trucks and performance-based incentives in full-size pickup 
trucks. The fuel consumption improvement benefits of these technologies 
measured by various testing methods can be used by manufacturers to 
increase the CAFE performance of their fleets.
---------------------------------------------------------------------------

    \525\ 49 U.S.C. 32904.
---------------------------------------------------------------------------

(a) Available Credit Flexibilities
    Under NHTSA regulations, credit holders (including, but not limited 
to manufacturers) have credit accounts with NHTSA where they can, hold 
credits, and use them to achieve compliance with CAFE standards, by 
carrying forward, carrying back, or transferring credits across 
compliance categories, subject to several restrictions. Manufacturers 
with excess credits in their accounts can also trade credits to other 
manufacturers, who may use those credits to resolve a shortfall 
currently or in a future model year. A credit may also be cancelled 
before its expiration date if the credit holder so chooses. Traded and 
transferred credits are subject to an ``adjustment factor'' to ensure 
total oil savings are preserved.\526\
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    \526\ See Section VII.B.3.b) for details.
---------------------------------------------------------------------------

    Credit ``carryback'' means that manufacturers are able to use 
recently earned credits to offset a deficit that had accrued in a prior 
model year, while credit ``carry-forward'' means that manufacturers can 
bank credits and use them towards compliance in future model years. 
EPCA, as amended by EISA, allows manufacturers to carryback credits for 
up to three model years, and to carry-forward credits for up to five 
model years.\527\ Credits expire the model year after which the credits 
may no longer be used to achieve compliance with fuel economy 
regulations.\528\ Manufacturers seeking to use carryback credits must 
submit a carryback plan to NHTSA, for NHTSA's review and approval, 
demonstrating their ability to earn sufficient credits in future MYs 
that can be carried back to resolve the current MY's credit shortfall.
---------------------------------------------------------------------------

    \527\ 49 U.S.C. 32903(a).
    \528\ 49 CFR 536.3(b).
---------------------------------------------------------------------------

    Credit ``trading'' refers to the ability of manufacturers or 
persons to sell credits to, or purchase credits from, one another while 
credit ``transfer'' means the ability to transfer credit between a 
manufacturer's compliance fleets to resolve a credit shortfall. EISA 
gave NHTSA discretion to establish by regulation a CAFE credit trading 
program, to allow credits to be traded between vehicle manufacturers, 
now codified at 49 CFR part 536.\529\ EISA prohibits manufacturers from 
using traded credits to meet the minimum domestic passenger car CAFE 
standard.\530\
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    \529\ 49 U.S.C. 32903(f).
    \530\ 49 U.S.C. 32903(f)(2).
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(b) Fuel Savings Adjustment Factor
    Under NHTSA's credit trading regulations, a fuel savings adjustment 
factor is applied when trading occurs between manufacturers and those 
credits are used, or when a manufacturer transfers credits between its 
compliance fleets and those credits are used, but not when a 
manufacturer carries credits forward or backwards within the same 
fleet.\531\
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    \531\ See Section III.C for details about carry forward and back 
credits.
---------------------------------------------------------------------------

    NHTSA is including in this proposal a restoration of certain 
definitions that are part of the adjustment factor equation that had 
been inadvertently deleted in the 2020 final rule. The 2020 final rule 
had intended to add a sentence to the adjustment factor term in 49 CFR 
536.4(c), simply to make clear that the figure should be rounded to 
four decimal places. While the 2020 final rule implemented this change, 
the amendatory instruction for doing so unintentionally deleted several 
other definitions from that paragraph. NHTSA had not intended to modify 
or delete those definitions, so they are simply being added back into 
the paragraph.
(c) VMT Estimates for Fuel Savings Adjustment Factor
    NHTSA uses VMT estimates as part of its fuel savings adjustment 
equation. Including VMT is important as fuel consumption is directly 
related to vehicle use, and in order to ensure trading credits between 
fleets preserves oil savings, VMT must be considered.\532\ For MYs 2017 
and later, NHTSA finalized VMT values of 195,264 miles for passenger 
car credits, and 225,865 miles for light truck credits.\533\
---------------------------------------------------------------------------

    \532\ See 49 CFR 536.4(c).
    \533\ 77 FR 63130 (Oct. 15, 2012).
---------------------------------------------------------------------------

(d) Fuel Economy Calculations for Dual and Alternative Fueled Vehicles
    As discussed at length in prior rulemakings, EPCA, as amended by 
EISA, encouraged manufacturers to build alternative-fueled and dual- 
(or flexible-) fueled vehicles by providing special fuel economy 
calculations for ``dedicated'' (that is, 100 percent) alternative 
fueled vehicles and ``dual-fueled'' (that is, capable of running on 
either the alternative fuel or gasoline/diesel) vehicles.
    Dedicated alternative-fuel automobiles include electric, fuel cell, 
and compressed natural gas vehicles, among others. The statutory 
provisions for dedicated alternative fuel vehicles in 49 U.S.C. 
32905(a) state that the fuel economy of any dedicated automobile 
manufactured after MY 1992 shall be measured ``based on the fuel 
content of the alternative fuel used to operate the automobile. A 
gallon of liquid alternative fuel used to operate a dedicated 
automobile is deemed to contain 0.15 gallon of fuel.'' There are no 
limits or phase-out for this special fuel economy calculation within 
the statute.
    EPCA's statutory incentive for dual-fueled vehicles at 49 U.S.C. 
32906 and the measurement methodology for dual-fueled vehicles at 49 
U.S.C. 32905(b) and (d) expired after MY 2019. In the 2012 final rule, 
NHTSA and EPA concluded that it would be inappropriate and contrary to 
the intent of EPCA/EISA to measure duel-fueled vehicles' fuel economy 
like that of conventional gasoline vehicles with no recognition of 
their alternative fuel capability. The agencies determined that for MY 
2020 and later vehicles, the general statutory provisions authorizing 
EPA to establish testing and calculation procedures provide discretion 
to set the CAFE calculation procedures for those vehicles. The 
methodology for EPA's approach is outlined in the 2012 final rule for 
MYs 2017 and later at 77 FR 63128 (Oct. 15, 2012).
(e) Flexibilities for Air-Conditioning Efficiency, Off-Cycle 
Technologies, and Full-Size Pickup Trucks
(1) Incentives for Advanced Technologies in Full-Size Pickup Trucks
    Under its EPCA authority for CAFE and under its CAA authority for 
GHGs, EPA established fuel consumption improvement values (FCIVs) for 
manufacturers that hybridize a significant quantity of their full-size 
pickup trucks, or that use other technologies that significantly reduce 
fuel consumption of these full-sized pickup trucks. More specifically, 
CAFE FCIVs were made available to manufacturers that produce full-size 
pickup trucks with Mild HEV or Strong HEV technology, provided the 
percentage of production with the technology is greater than specified 
percentages.\534\ In addition, CAFE FCIVs were made available for 
manufacturers that produce full-size pickups with other technologies 
that enable full-size

[[Page 49833]]

pickup trucks to exceed their CAFE targets based on footprints by 
specified amounts (i.e., electric vehicles and other electric 
components).\535\ These performance-based incentives create a 
technology-neutral path (as opposed to the other technology-encouraging 
path) to achieve the CAFE FCIVs, which would encourage the development 
and application of new technological approaches.
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    \534\ 77 FR 62651 (Oct. 15, 2012).
    \535\ Id.
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    Large pickup trucks represent a significant portion of the overall 
light duty vehicle fleet and generally have higher levels of fuel 
consumption and GHG emissions than most other light duty vehicles. 
Improvements in the fuel economy and GHG emissions of these vehicles 
can have significant impact on the overall light-duty fleet fuel use 
and GHG emissions. NHTSA believes that offering incentives could 
encourage the deployment of technologies that can significantly improve 
the efficiency of these vehicles and that also will foster production 
of those technologies at levels that will help achieve economies of 
scale, would promote greater fuel savings overall and make these 
technologies more cost effective and available in the future model 
years to assist in compliance with CAFE standards.
    EPA and NHTSA also established limits on the eligibility for these 
pickup trucks to qualify for incentives. A truck was required to meet 
minimum criteria for bed size and towing or payload capacities and meet 
minimum production thresholds (in terms of a percentage of a 
manufacturer's full-size pickup truck fleet) in order to qualify for 
these incentives. Under the provisions, Mild HEVs are eligible for a 
per-vehicle CO2 credit of 10 g/mi (equivalent to 0.0011 
gallon/mile for a gasoline-fueled truck) during MYs 2017-2021. To be 
eligible a manufacturer would have to show that the Mild HEV technology 
is utilized in a specified portion of its truck fleet beginning with at 
least 20 percent of a company's full-size pickup production in MY 2017 
and ramping up to at least 80 percent in MY 2021. Strong HEV pickup 
trucks are eligible for a 20 g/mi credit (0.0023 gallon/mile) during 
MYs 2017-2021, and in this rulemaking proposed to be extended through 
MY 2026, if the technology is used on at least 10 percent of a 
company's full-size pickups in that model year. EPA and NHTSA also 
adopted specific definitions for Mild and Strong HEV pickup trucks, 
based on energy flow to the high-voltage battery during testing.
    Furthermore, to incentivize other technologies that can provide 
significant reductions in GHG emissions and fuel consumption for full-
size pickup trucks, EPA also adopted, a performance-based fuel 
consumption improvement value for full-size pickup trucks. Eligible 
pickup trucks certified as performing 15 percent better than their 
applicable CO2 target receive a 10 g/mi credit (0.0011 
gallon/mile), and those certified as performing 20 percent better than 
their target receive a 20 g/mi credit (0.0023 gallon/mile). The 10 g/mi 
performance-based credit is available for MYs 2017 to 2021 and, once 
qualifying; a vehicle model will continue to receive the credit through 
MY 2021, provided its CO2 emissions level does not increase. 
To be eligible a manufacturer would have to show that the technology is 
utilized in a specified portion of its truck fleet beginning with at 
least 20 percent of a company's full-size pickup production in MY 2017 
and ramping up to at least 80 percent in MY 2021. The 20 g/mi 
performance-based credit was available for a vehicle model for a 
maximum of 5 years within the 2017 to 2021 model year period, and in 
this rulemaking proposed to be extended through MY 2026, provided its 
CO2 emissions level does not increase. To be eligible, the 
technology must be applied to at least 10 percent of a company's full-
size pickups in for the model year.
    The agencies designed a definition for full-size pickup truck based 
on minimum bed size and hauling capability, as detailed in 40 CFR 
86.1866-12(e). This definition ensured that the larger pickup trucks, 
which provide significant utility with respect to bed access and 
payload and towing capacities, are captured by the definition, while 
smaller pickup trucks with more limited capacities are not covered. A 
full-size pickup truck is defined as meeting requirements (1) and (2) 
below, as well as either requirement (3) or (4) below.
    (1) Bed Width--The vehicle must have an open cargo box with a 
minimum width between the wheelhouses of 48 inches. And--
    (2) Bed Length--The length of the open cargo box must be at least 
60 inches. And--
    (3) Towing Capability--the gross combined weight rating (GCWR) 
minus the gross vehicle weight rating (GVWR) must be at least 5,000 
pounds. Or--
    (4) Payload Capability--the GVWR minus the curb weight (as defined 
in 40 CFR 86.1803) must be at least 1,700 pounds.
    In the 2020 CAFE rule, the agencies ended the incentives for full-
size pickup trucks after the end of model year 2021 believing expanded 
incentives would likely not result in any further emissions benefits or 
fuel economy improvements since an increase in sales volume was 
unanticipated. At the time, no manufacturer had qualified to use the 
full-size pickup truck incentives since they went into effect in MY 
2017. One vehicle manufacturer introduced a mild hybrid pickup truck in 
MY 2019 but was ineligible for the FCIV because it did not meet the 
minimum production threshold. Other manufacturers had announced 
potential collaborations or started designing future hybrid or electric 
models, but none were expected to meet production requirements within 
the time period of eligibility for these incentives.
    Since the 2020 final rule, many manufacturers have publicly 
announced several new model types of full-size electric pickup trucks 
starting in MY 2022. NHTSA notes that historically its goal has always 
been to promote electric vehicles due to their exceptional fuel saving 
benefits. For this reason, even given the discontinuation in MY 2019 of 
AMFA incentives for dual fueled vehicles, NHTSA retained its benefits 
for alternative dedicated fueled vehicles to focus on the growth of 
electric vehicles in the market. Therefore, after the careful 
consideration of this new information and the potential role incentives 
could play in increasing the production of these technologies, and the 
associated beneficial impacts on fuel consumption, the agency is 
proposing to extend the full-size pickup truck incentive through MY 
2025 for strong hybrids and for full-size pickup trucks performing 20-
percent better than their target. Also, understanding the importance of 
electric vehicles in the market, NHTSA is proposing to allow 
manufacturers to combine both the incentives for alternative fueled 
vehicles and full-size pickup trucks FCIVs when complying with the CAFE 
program.
(2) Flexibilities for Air Conditioning Efficiency
    A/C systems are virtually standard automotive accessories, and more 
than 95 percent of new cars and light trucks sold in the U.S. are 
equipped with mobile A/C systems. A/C system usage places a load on an 
engine, which results in additional fuel consumption; the high 
penetration rate of A/C systems throughout the light-duty vehicle fleet 
means that more efficient systems can significantly impact the total 
energy consumed. A/C systems also have non-CO2 emissions 
associated with

[[Page 49834]]

refrigerant leakage.\536\ Manufacturers can improve the efficiency of 
A/C systems though redesigned and refined A/C system components and 
controls.\537\ That said, such improvements are not measurable or 
recognized using 2-cycle test procedures since A/C is turned off during 
2-cycle testing. Any A/C system efficiency improvements that reduce 
load on the engine and improve fuel economy is therefore not measurable 
on those tests.
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    \536\ Notably, manufacturers cannot claim CAFE-related benefits 
for reducing A/C leakage or switching to an A/C refrigerant with a 
lower global warming potential. While these improvements reduce GHG 
emissions consistent with the purpose of the CAA, they generally do 
not impact fuel economy and, thus, are not relevant to the CAFE 
program.
    \537\ The approach for recognizing potential A/C efficiency 
gains is to utilize, in most cases, existing vehicle technology/
componentry, but with improved energy efficiency of the technology 
designs and operation. For example, most of the additional A/C-
related load on an engine is because of the compressor, which pumps 
the refrigerant around the system loop. The less the compressor 
operates, the less load the compressor places on the engine 
resulting in less fuel consumption. Thus, optimizing compressor 
operation with cabin demand using more sophisticated sensors, 
controls, and control strategies is one path to improving the 
efficiency of the A/C system.
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    The CAFE program includes flexibilities to account for the real-
world fuel economy improvements associated with improved A/C systems 
and to include the improvements for compliance.\538\ The total A/C 
efficiency credits is calculated by summing the individual credit 
values for each efficiency improving technology used on a vehicle, as 
specified in the A/C credit menu. The total A/C efficiency credit sum 
for each vehicle is capped at 5.0 grams/mile for cars and 7.2 grams/
mile for trucks. Additionally, the off-cycle credit program contains 
credit earning opportunities for technologies that reduce the thermal 
loads on a vehicle from environmental conditions (solar loads or parked 
interior air temperature).\539\ These technologies are listed on a 
thermal control menu that provides a predefined improvement value for 
each technology. If a vehicle has more than one thermal load 
improvement technology, the improvement values are added together, but 
subject to a cap of 3.0 grams/mile for cars and 4.3 grams/mile for 
trucks. Under its EPCA authority for CAFE, EPA calculates equivalent 
FCIVs and applies them for the calculation of manufacturer's fleet CAFE 
values. Manufacturers seeking credits beyond the regulated caps must 
request the added benefit for A/C technology under the off-cycle 
program discussed in the next section. The agency is not proposing to 
change its A/C efficiency flexibility and will retain its provisions in 
its current form.
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    \538\ See 40 CFR 86.1868-12.
    \539\ See 40 CFR 86.1869-12(b).
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(3) Flexibilities for Off-Cycle Technologies
    ``Off-cycle'' technologies are those that reduce vehicle fuel 
consumption in the real world, but for which the fuel consumption 
reduction benefits cannot be fully measured under the 2-cycle test 
procedures (city, highway or correspondingly FTP, HFET) used to 
determine compliance with the fleet average standards. The cycles are 
effective in measuring improvements in most fuel economy improving 
technologies; however, they are unable to measure or underrepresent 
certain fuel economy improving technologies because of limitations in 
the test cycles. For example, off-cycle technologies that improve 
emissions and fuel economy at idle (such as ``stop start'' systems) and 
those technologies that improve fuel economy to the greatest extent at 
highway speeds (such as active grille shutters which improve 
aerodynamics) receive less than their real-world benefits in the 2-
cycle compliance tests.
    In the CAFE rule for MYs 2017-2025, EPA, in coordination with 
NHTSA, established regulations extending the off-cycle technology 
flexibility to the CAFE program starting with MY 2017. For the CAFE 
program, EPA calculates off-cycle fuel consumption improvement values 
(FCIVs) that are equivalent to the EPA CO2 credit values, 
and applies them in the calculation of manufacturer's CAFE compliance 
values for each fleet instead of treating them as separate credits as 
for the EPA GHG program.
    For determining benefits, EPA created three compliance pathways for 
the off-cycle program. The first approach allows manufacturers to gain 
credits using a predetermined approach or ``menu'' of credit values for 
specific off-cycle technologies which became effective starting in MY 
2014 for EPA.540 541 This pathway allows manufacturers to 
use credit values established by EPA for a wide range of off-cycle 
technologies, with minimal or no data submittal or testing 
requirements.\542\ Specifically, EPA established a menu with a number 
of technologies that have real-world fuel consumption benefits not 
measured, or not fully measured, by the two-cycle test procedures, and 
those benefits were reasonably quantified by the agencies at that time. 
For each of the pre-approved technologies on the menu, EPA established 
a menu value or approach that is available without testing 
verifications. Manufacturers must demonstrate that they are in fact 
using the menu technology, but not required to submit test results to 
EPA to quantify the technology's effects, unless they wish to receive a 
credit larger than the default value. The default values for these off-
cycle credits were largely determined from research, analysis, and 
simulations, rather than from full vehicle testing, which would have 
been both cost and time prohibitive. EPA generally used conservative 
predefined estimates to avoid any potential credit windfall.\543\
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    \540\ See 40 CFR 86.1869-12(b). The first approach requires some 
technologies to derive their pre-determined credit values through 
EPA's established testing. For example, waste heat recovery 
technologies require manufacturers to use 5-cycle testing to 
determine the electrical load reduction of the waste heat recovery 
system.
    \541\ EPA implemented its off-cycle GHG program starting in MY 
2012.
    \542\ The Technical Support Document (TSD) for the 2012 final 
rule for MYs 2017 and beyond provides technology examples and 
guidance with respect to the potential pathways to achieve the 
desired physical impact of a specific off-cycle technology from the 
menu and provides the foundation for the analysis justifying the 
credits provided by the menu. The expectation is that manufacturers 
will use the information in the TSD to design and implement off-
cycle technologies that meet or exceed those expectations in order 
to achieve the real-world benefits of off-cycle technologies from 
the menu.
    \543\ While many of the assumptions made for the analysis were 
conservative, others were ``central.'' For example, in some cases, 
an average vehicle was selected on which the analysis was conducted. 
In that case, a smaller vehicle may presumably deserve fewer credits 
whereas a larger vehicle may deserve more. Where the estimates are 
central, it would be inappropriate for the agencies to grant greater 
credit for larger vehicles, since this value is already balanced by 
smaller vehicles in the fleet. The agencies take these matters into 
consideration when applications are submitted for credits beyond 
those provided on the menu.
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    For off-cycle technologies not on the pre-defined technology list, 
EPA created a second pathway which allows manufacturers to use 5-cycle 
testing to demonstrate off-cycle improvements.\544\ Starting in MY 
2008, EPA developed the ``five-cycle'' test methodology to measure fuel 
economy for the purpose of improving new car window stickers (labels) 
and giving consumers better information about the fuel economy they 
could expect under real-world driving conditions.\545\ As learned 
through development of the ``five-cycle'' methodology and prior 
rulemakings, there are technologies that provide real-world fuel 
consumption improvements,

[[Page 49835]]

but those improvements are not fully reflected on the ``two-cycle'' 
test. EPA established this alternative for a manufacturer to 
demonstrate the benefits of off-cycle technologies using 5-cycle 
testing. The additional emissions test allows emission benefits to be 
demonstrated over some elements of real-world driving not captured by 
the two-cycle CO2 compliance tests including high speeds, 
rapid accelerations, hot temperatures, and cold temperatures. Under 
this pathway, manufacturers submit test data to EPA, and EPA determines 
whether there is sufficient technical basis to approve the off-cycle 
credits. No public comment period is required for manufacturers seeking 
credits using the EPA menu or using 5-cycle testing.
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    \544\ See 40 CFR 86.1869-12(c). EPA proposed a correction for 
the 5-cycle pathway in a separate technical amendments rulemaking. 
See 83 FR 49344 (Oct. 1, 2019). EPA is not approving credits based 
on the 5-cycle pathway pending the finalization of the technical 
amendments rule.
    \545\ https://www.epa.gov/vehicle-and-fuel-emissions-testing/dynamometer-drive-schedules.
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    The third pathway allows manufacturers to seek EPA review, through 
a notice and comment process, to use an alternative methodology other 
than the menu or 5-cycle methodology for determining the off-cycle 
technology CO2 credits.\546\ Manufacturers must provide 
supporting data on a case-by-case basis demonstrating the benefits of 
the off-cycle technology on their vehicle models. Manufacturers may 
also use the third pathway to apply for credits and FCIVs for menu 
technologies where the manufacturer is able to demonstrate credits and 
FCIVs greater than those provided by the menu.
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    \546\ See 40 CFR 86.1869-12(d).
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(a) The Off-Cycle Process
    In meetings with EPA and manufacturers, NHTSA examined the 
processes for bringing off-cycle technologies into market. Two distinct 
processes were identified: (1) The manufacturer's off-cycle pre-
production process, and; (2) the manufacturer's regulatory compliance 
process. During the pre-production process, the off-cycle program for 
most manufacturers begins as early as four to 6 years in advance of the 
given model year. Manufacturers' design teams or suppliers identify 
technologies to develop capable of qualifying for off-cycle credits 
after careful considering of the possible benefits. Manufacturer then 
identify the opportunities for the technologies finding the most 
optimal condition for equipping the technology given the availability 
in the production cycle of either new or multiple platforms 
capitalizing on any commonalities to increase sales volumes and reduce 
costs. After establishing their new or series platform development 
plans, manufacturers have two processes for off-cycle technologies on 
the pre-defined menu list or using 5-cycle testing and for those for 
which benefits are sought using the alternative approval methodology. 
For those on the menu list or 5-cycle testing, technologies whose 
credit amounts are defined by EPA regulation, manufacturers confirm 
that: (1) New candidate technologies meet regulatory definitions; and 
(2) for qualifying technologies, there is real fuel economy (FE) 
benefit based on good engineering judgement and/or testing. For these 
technologies, manufacturers conduct research and testing independently 
without communicating with EPA or NHTSA. For non-menu technologies, 
those not defined by regulation, manufacturers pre-production processes 
include: (1) Determining the credit amounts based on the effectiveness 
of the technologies; (2) developing suitable test procedures; (3) 
identifying any necessary studies to support effectiveness; (4) and 
identifying the necessary equipment or vehicle testing using good 
engineer judgement to confirm the vehicle platform benefits of the 
technology.
    While for the regulatory compliance process, the first step for 
manufacturers begins by providing EPA with early notification in their 
p