[Federal Register Volume 83, Number 165 (Friday, August 24, 2018)]
[Proposed Rules]
[Pages 42986-43500]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2018-16820]

[[Page 42985]]

Vol. 83


No. 165

August 24, 2018

Part II

Book 2 of 2 Books

Pages 42985-43500

Department of Transportation


National Highway Traffic Safety Administration


49 CFR Parts 523, 531, 533, et al.

Environmental Protection Agency


40 CFR Parts 85 and 86

The Safer Affordable Fuel-Efficient (SAFE) Vehicles Rule for Model 
Years 2021-2026 Passenger Cars and Light Trucks; Proposed Rule

Federal Register / Vol. 83 , No. 165 / Friday, August 24, 2018 / 
Proposed Rules

[[Page 42986]]



National Highway Traffic Safety Administration

49 CFR Parts 523, 531, 533, 536, and 537


40 CFR Parts 85 and 86

[NHTSA-2018-0067; EPA-HQ-OAR-2018-0283; FRL-9981-74-OAR]
RIN 2127-AL76; RIN 2060-AU09

The Safer Affordable Fuel-Efficient (SAFE) Vehicles Rule for 
Model Years 2021-2026 Passenger Cars and Light Trucks

AGENCY: Environmental Protection Agency and National Highway Traffic 
Safety Administration.

ACTION: Notice of proposed rulemaking.


SUMMARY: The National Highway Traffic Safety Administration (NHTSA) and 
the Environmental Protection Agency (EPA) are proposing the ``Safer 
Affordable Fuel-Efficient (SAFE) Vehicles Rule for Model Years 2021-
2026 Passenger Cars and Light Trucks'' (SAFE Vehicles Rule). The SAFE 
Vehicles Rule, if finalized, would amend certain existing Corporate 
Average Fuel Economy (CAFE) and tailpipe carbon dioxide emissions 
standards for passenger cars and light trucks and establish new 
standards, all covering model years 2021 through 2026. More 
specifically, NHTSA is proposing new CAFE standards for model years 
2022 through 2026 and amending its 2021 model year CAFE standards 
because they are no longer maximum feasible standards, and EPA is 
proposing to amend its carbon dioxide emissions standards for model 
years 2021 through 2025 because they are no longer appropriate and 
reasonable in addition to establishing new standards for model year 
2026. The preferred alternative is to retain the model year 2020 
standards (specifically, the footprint target curves for passenger cars 
and light trucks) for both programs through model year 2026, but 
comment is sought on a range of alternatives discussed throughout this 
document. Compared to maintaining the post-2020 standards set forth in 
2012, current estimates indicate that the proposed SAFE Vehicles Rule 
would save over 500 billion dollars in societal costs and reduce 
highway fatalities by 12,700 lives (over the lifetimes of vehicles 
through MY 2029). U.S. fuel consumption would increase by about half a 
million barrels per day (2-3 percent of total daily consumption, 
according to the Energy Information Administration) and would impact 
the global climate by 3/1000th of one degree Celsius by 2100, also when 
compared to the standards set forth in 2012.

DATES: Comments: Comments are requested on or before October 23, 2018. 
Under the Paperwork Reduction Act, comments on the information 
collection provisions must be received by the Office of Management and 
Budget (OMB) on or before October 23, 2018. See the SUPPLEMENTARY 
INFORMATION section on ``Public Participation,'' below, for more 
information about written comments.
    Public Hearings: NHTSA and EPA will jointly hold three public 
hearings in Washington, DC; the Detroit, MI area; and in the Los 
Angeles, CA area. The agencies will announce the specific dates and 
addresses for each hearing location in a supplemental Federal Register 
notice. The agencies will accept oral and written comments to the 
rulemaking documents, and NHTSA will also accept comments to the Draft 
Environmental Impact Statement (DEIS) at these hearings. The hearings 
will start at 10 a.m. local 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 hearings.

ADDRESSES: You may send comments, identified by Docket No. EPA-HQ-OAR-
2018-0283 and/or NHTSA-2018-0067, by any of the following methods:
     Federal eRulemaking Portal: http://www.regulations.gov. 
Follow the instructions for sending comments.
     Fax: EPA: (202) 566-9744; NHTSA: (202) 493-2251.
    [cir] EPA: Environmental Protection Agency, EPA Docket Center (EPA/
DC), Air and Radiation Docket, Mail Code 28221T, 1200 Pennsylvania 
Avenue NW, Washington, DC 20460, Attention Docket ID No. EPA-HQ-OAR-
2018-0283. In addition, please mail a copy of your comments on the 
information collection provisions for the EPA proposal to the Office of 
Information and Regulatory Affairs, Office of Management and Budget 
(OMB), Attn: Desk Officer for EPA, 725 17th St. NW, Washington, DC 
    [cir] NHTSA: 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:
    [cir] EPA: Docket Center (EPA/DC), EPA West, Room B102, 1301 
Constitution Avenue NW, Washington, DC, Attention Docket ID No. EPA-HQ-
OAR-2018-0283. Such deliveries are only accepted during the Docket's 
normal hours of operation, and special arrangements should be made for 
deliveries of boxed information.
    [cir] NHTSA: 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.
    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 to read background documents or 
comments received, go to http://www.regulations.gov, and/or:
     For EPA: EPA Docket Center (EPA/DC), EPA West, Room 3334, 
1301 Constitution Avenue NW, Washington, DC 20460. The Public Reading 
Room is open from 8:30 a.m. to 4:30 p.m., Monday through Friday, 
excluding legal holidays. The telephone number for the Public Reading 
Room is (202) 566-1744.
     For NHTSA: 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: EPA: Christopher Lieske, Office of 
Transportation and Air Quality, Assessment and Standards Division, 
Environmental Protection Agency, 2000 Traverwood Drive, Ann Arbor, MI 
48105; telephone number: (734) 214-4584; fax number: (734) 214-4816; 
email address: [email protected], or contact the Assessment 
and Standards Division, email address: [email protected]. NHTSA: 
James Tamm, Office of Rulemaking, Fuel Economy Division, National 
Highway Traffic Safety Administration, 1200 New Jersey Avenue SE, 
Washington, DC 20590; telephone number: (202) 493-0515.

[[Page 42987]]


I. Overview of Joint NHTSA/EPA Proposal
II. Technical Foundation for NPRM Analysis
III. Proposed CAFE and CO2 Standards for MYs 2021-2026
IV. Alternative CAFE and GHG Standards Considered for MYs 2021/22-
V. Proposed Standards, the Agencies' Statutory Obligations, and Why 
the Agencies Propose To Choose Them Over the Alternatives
VI. Preemption of State and Local Laws
VII. Impacts of the Proposed CAFE and CO2 Standards
VIII. Impacts of Alternative CAFE and CO2 Standards 
Considered for MYs 2021/22-2026
IX. Vehicle Classification
X. Compliance and Enforcement
XI. Public Participation
XII. Regulatory Notices and Analyses

I. Overview of Joint NHTSA/EPA Proposal

A. Executive Summary

    In this notice, the National Highway Traffic Safety Administration 
(NHTSA) and the Environmental Protection Agency (EPA) (collectively, 
``the agencies'') are proposing the ``Safer Affordable Fuel-Efficient 
(SAFE) Vehicles Rule for Model Years 2021-2026 Passenger Cars and Light 
Trucks'' (SAFE Vehicles Rule). The proposed SAFE Vehicles Rule would 
set Corporate Average Fuel Economy (CAFE) and carbon dioxide 
(CO2) emissions standards, respectively, for passenger cars 
and light trucks manufactured for sale in the United States in model 
years (MYs) 2021 through 2026.\1\ CAFE and CO2 standards 
have the power to transform the vehicle fleet and affect Americans' 
lives in significant, if not always immediately obvious, ways. The 
proposed SAFE Vehicles Rule seeks to ensure that government action on 
these standards is appropriate, reasonable, consistent with law, 
consistent with current and foreseeable future economic realities, and 
supported by a transparent assessment of current facts and data.

    \1\ NHTSA sets CAFE standards under the Energy Policy and 
Conservation Act of 1975 (EPCA), as amended by the Energy 
Independence and Security Act of 2007 (EISA). EPA sets 
CO2 standards under the Clean Air Act (CAA).

    The agencies must act to propose and finalize these standards and 
do not have discretion to decline to regulate. Congress requires NHTSA 
to set CAFE standards for each model year.\2\ Congress also requires 
EPA to set emissions standards for light-duty vehicles if EPA has made 
an ``endangerment finding'' that the pollutant in question--in this 
case, CO2--``cause[s] or contribute[s] to air pollution 
which may reasonably be anticipated to endanger public health or 
welfare.'' \3\ NHTSA and EPA are proposing these standards concurrently 
because tailpipe CO2 emissions standards are directly and 
inherently related to fuel economy standards,\4\ and if finalized, 
these rules would apply concurrently to the same fleet of vehicles. By 
working together to develop these proposals, the agencies reduce 
regulatory burden on industry and improve administrative efficiency.

    \2\ 49 U.S.C. 32902.
    \3\ 42 U.S.C. 7521, see also 74 FR 66495 (Dec. 15, 2009) 
(``Endangerment and Cause or Contribute Findings for Greenhouse 
Gases under Section 202(a) of the Clean Air Act'').
    \4\ See, e.g., 75 FR 25324, at 25327 (May 7, 2010) (``The 
National Program is both needed and possible because the 
relationship between improving fuel economy and reducing tailpipe 
CO2 emissions is a very direct and close one. The amount 
of those CO2 emissions is essentially constant per gallon 
combusted of a given type of fuel. Thus, the more fuel efficient a 
vehicle is, the less fuel it burns to travel a given distance. The 
less fuel it burns, the less CO2 it emits in traveling 
that distance. [citation omitted] While there are emission control 
technologies that reduce the pollutants (e.g., carbon monoxide) 
produced by imperfect combustion of fuel by capturing or converting 
them to other compounds, there is no such technology for 
CO2. Further, while some of those pollutants can also be 
reduced by achieving a more complete combustion of fuel, doing so 
only increases the tailpipe emissions of CO2. Thus, there 
is a single pool of technologies for addressing these twin problems, 
i.e., those that reduce fuel consumption and thereby reduce 
CO2 emissions as well.'')

    Consistent with both agencies' statutes, this proposal is entirely 
de novo, based on an entirely new analysis reflecting the best and most 
up-to-date information available to the agencies at the time of this 
rulemaking. The agencies worked together in 2012 to develop CAFE and 
CO2 standards for MYs 2017 and beyond; in that rulemaking 
action, EPA set CO2 standards for MYs 2017-2025, while NHTSA 
set final CAFE standards for MYs 2017-2021 and also put forth 
``augural'' CAFE standards for MYs 2022-2025, consistent with EPA's 
CO2 standards for those model years. EPA's CO2 
standards for MYs 2022-2025 were subject to a ``mid-term evaluation,'' 
by which EPA bound itself through regulation to re-evaluate the 
CO2 standards for those model years and to undertake to 
develop new CO2 standards through a regulatory process if it 
concluded that the previously finalized standards were no longer 
appropriate. EPA regulations on the mid-term evaluation process 
required EPA to issue a Final Determination no later than April 1, 2018 
on whether the GHG standards for MY 2022-2025 light-duty vehicles 
remain appropriate under section 202(a) of the Clean Air Act.\5\ The 
regulations also required the issuance of a draft Technical Assessment 
Report (TAR) by November 15, 2017, an opportunity for public comment on 
the draft TAR, and, before making a Final Determination, an opportunity 
for public comment on whether the GHG standards for MY 2022-2025 remain 
appropriate. In July 2016, the draft TAR was issued for public comment 
jointly by the EPA, NHTSA, and the California Air Resources Board 
(CARB).\6\ Following the draft TAR, EPA published a Proposed 
Determination for public comment on December 6, 2016 and provided less 
than 30 days for public comments over major holidays.\7\ EPA published 
the January 2017 Determination on EPA's website and regulations.gov 
finding that the MY 2022-2025 standards remained appropriate.\8\

    \5\ 40 CFR 86.1818-12(h)(1); see also 77 FR 62624 (Oct. 15, 
    \6\ 81 FR 49217 (Jul. 27, 2016).
    \7\ 81 FR 87927 (Dec. 6, 2016).
    \8\ Docket item EPA-HQ-OAR-2015-0827-6270 (EPA-420-R-17-001). 
This conclusion generated a significant amount of public concern. 
See, e.g., Letter from Auto Alliance to Scott Pruitt, Administrator, 
Environmental Protection Agency (Feb. 21, 2017); Letter from Global 
Automakers to Scott Pruitt, Administrator, Environmental Protection 
Agency (Feb. 21, 2017).

    On March 15, 2017, President Trump announced a restoration of the 
original mid-term review timeline. The President made clear in his 
remarks, ``[i]f the standards threatened auto jobs, then commonsense 
changes'' would be made in order to protect the economic viability of 
the U.S. automotive industry.'' \9\ In response to the President's 
direction, EPA announced in a March 22, 2017, Federal Register notice, 
its intention to reconsider the Final Determination of the mid-term 
evaluation of GHGs emissions standards for MY 2022-2025 light-duty 
vehicles.\10\ The Administrator stated that EPA would coordinate its 
reconsideration with the rulemaking process to be undertaken by NHTSA 
regarding CAFE standards for cars and light trucks for the same model 

    \9\ See https://www.whitehouse.gov/briefings-statements/remarks-president-trump-american-center-mobility-detroit-mi/.
    \10\ 82 FR 14671 (Mar. 22, 2017).

    On August 21, 2017, EPA published a notice in the Federal Register 
announcing the opening of a 45-day public comment period and inviting 
stakeholders to submit any additional comments, data, and information 
they believed were relevant to the Administrator's reconsideration of 

[[Page 42988]]

January 2017 Determination.\11\ EPA held a public hearing in Washington 
DC on September 6, 2017.\12\ EPA received more than 290,000 comments in 
response to the August 21, 2017 notice.\13\

    \11\ 82 FR 39551 (Aug. 21, 2017).
    \12\ 82 FR 39976 (Aug. 23, 2017).
    \13\ The public comments, public hearing transcript, and other 
information relevant to the Mid-term Evaluation are available in 
docket EPA-HQ-OAR-2015-0827.

    EPA has since concluded, based on more recent information, that 
those standards are no longer appropriate.\14\ NHTSA's ``augural'' CAFE 
standards for MYs 2022-2025 were not final in 2012 because Congress 
prohibits NHTSA from finalizing new CAFE standards for more than five 
model years in a single rulemaking.\15\ NHTSA was therefore obligated 
from the beginning to undertake a new rulemaking to set CAFE standards 
for MYs 2022-2025.

    \14\ 83 FR 16077 (Apr. 2, 2018).
    \15\ 49 U.S.C. 32902.

    The proposed SAFE Vehicles Rule begins the rulemaking process for 
both agencies to establish new standards for MYs 2022-2025 passenger 
cars and light trucks. Standards are concurrently being proposed for MY 
2026 in order to provide regulatory stability for as many years as is 
legally permissible for both agencies together.
    Separately, the proposed SAFE Vehicles Rule includes revised 
standards for MY 2021 passenger cars and light trucks. The information 
now available and the current analysis suggest that the CAFE standards 
previously set for MY 2021 are no longer maximum feasible, and the 
CO2 standards previously set for MY 2021 are no longer 
appropriate. Agencies always have authority under the Administrative 
Procedure Act to revisit previous decisions in light of new facts, as 
long as they provide notice and an opportunity for comment, and it is 
plainly the best practice to do so when changed circumstances so 

    \16\ See FCC v. Fox Television, 556 U.S. 502 (2009).

    Thus, the proposed SAFE Vehicles Rule would maintain the CAFE and 
CO2 standards applicable in MY 2020 for MYs 2021-2026, while 
taking comment on a wide range of alternatives, including different 
stringencies and retaining existing CO2 standards and the 
augural CAFE standards.\17\ Table I-4 below presents those 
alternatives. We note further that prior to MY 2021, CO2 
targets include adjustments reflecting the use of automotive 
refrigerants with reduced global warming potential (GWP) and/or the use 
of technologies that reduce the refrigerant leaks, and optionally 
offsets for nitrous oxide and methane emissions. In the interests of 
harmonizing with the CAFE program, EPA is proposing to exclude air 
conditioning refrigerants and leakage, and nitrous oxide and methane 
emissions for compliance with CO2 standards after model year 
2020 but seeks comment on whether to retain these element, and reinsert 
A/C leakage offsets, and remain disharmonized with the CAFE program. 
EPA also seeks comment on whether to change existing methane and 
nitrous oxide standards that were finalized in the 2012 rule. 
Specifically, EPA seeks information from the public on whether those 
existing standards are appropriate, or whether they should be revised 
to be less stringent or more stringent based on any updated data.

    \17\ Note: This does not mean that the miles per gallon and 
grams per mile levels that were estimated for the MY 2020 fleet in 
2012 would be the ``standards'' going forward into MYs 2021-2026. 
Both NHTSA and EPA set CAFE and CO2 standards, 
respectively, as mathematical functions based on vehicle footprint. 
These mathematical functions that are the actual standards are 
defined as ``curves'' that are separate for passenger cars and light 
trucks, under which each vehicle manufacturer's compliance 
obligation varies depending on the footprints of the cars and trucks 
that it ultimately produces for sale in a given model year. It is 
the MY 2020 CAFE and CO2 curves which we propose would 
continue to apply to the passenger car and light truck fleets for 
MYs 2021-2026. The mpg and g/mi values which those curves would 
eventually require of the fleets in those model years would be known 
for certain only at the ends of each of those model years. While it 
is convenient to discuss CAFE and CO2 standards as a set 
``mpg,'' ``g/mi,'' or ``mpg-e'' number, attempting to define those 
values today will end up being inaccurate.

    While actual requirements will ultimately vary for automakers 
depending upon their individual fleet mix of vehicles, many 
stakeholders will likely be interested in the current estimate of what 
the MY 2020 CAFE and CO2 curves would translate to, in terms 
of miles per gallon (mpg) and grams per mile (g/mi), in MYs 2021-2026. 
These estimates are shown in the following tables.

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[[Page 42990]]

    In the tables above, estimated required CO2 increases 
between MY 2020 and MY 2021 because, again, EPA is proposing to exclude 
CO2-equivalent emission improvements associated with air 
conditioning refrigerants and leakage (and, optionally, offsets for 
nitrous oxide and methane emissions) after model year 2020.
    As explained above, the agencies are taking comment on a wide range 
of alternatives and have specifically modeled eight alternatives 
(including the proposed alternative) and the current requirements 
(i.e., baseline/no-action). The modeled alternatives are provided 

    \18\ Carbon dioxide equivalent of air conditioning refrigerant 
leakage, nitrous oxide and methane emissions are included for 
compliance with the EPA standards for all MYs under the baseline/no 
action alternative. Carbon dioxide equivalent is calculated using 
the Global Warming Potential (GWP) of each of the emissions.
    \19\ Beginning in MY 2021, the proposal provides that the GWP 
equivalents of air conditioning refrigerant leakage, nitrous oxide 
and methane emissions would no longer be able to be included with 
the tailpipe CO2 for compliance with tailpipe 
CO2 standards.

Summary of Rationale
    Since finalizing the agencies' previous joint rulemaking in 2012 
titled ``Final Rule for Model Year 2017 and Later Light-Duty Vehicle 
Greenhouse Gas Emission and Corporate Average Fuel Economy Standards,'' 
and even since EPA's 2016 and early 2017 ``mid-term evaluation'' 
process, the agencies have gathered new information, and have performed 
new analysis. That new information and analysis has led the

[[Page 42991]]

agencies to the tentative conclusion that holding standards constant at 
MY 2020 levels through MY 2026 is maximum feasible, for CAFE purposes, 
and appropriate, for CO2 purposes.
    Technologies have played out differently in the fleet from what the 
agencies assumed in 2012.
    The technology to improve fuel economy and reduce CO2 
emissions has not changed dramatically since prior analyses were 
conducted: A wide variety of technologies are still available to 
accomplish the goals of the programs, and a wide variety of 
technologies would likely be used by industry to accomplish these 
goals. There remains no single technology that the majority of vehicles 
made by the majority of manufacturers can implement at low cost without 
affecting other vehicle attributes that consumers value more than fuel 
economy and CO2 emissions. Even when used in combination, 
technologies that can improve fuel economy and reduce CO2 
emissions still need to (1) actually work together and (2) be 
acceptable to consumers and avoid sacrificing other vehicle attributes 
while also avoiding undue increases in vehicle cost. Optimism about the 
costs and effectiveness of many individual technologies, as compared to 
recent prior rounds of rulemaking, is somewhat tempered; a clearer 
understanding of what technologies are already on vehicles in the fleet 
and how they are being used, again as compared to recent prior rounds 
of rulemaking, means that technologies that previously appeared to 
offer significant ``bang for the buck'' may no longer do so. 
Additionally, in light of the reality that vehicle manufacturers may 
choose the relatively cost-effective technology option of vehicle 
lightweighting for a wide array of vehicles and not just the largest 
and heaviest, it is now recognized that as the stringency of standards 
increases, so does the likelihood that higher stringency will increase 
on-road fatalities. As it turns out, there is no such thing as a free 

    \20\ Mankiw, N. Gregory, Principles of Macroeconomics, Sixth 
Edition, 2012, at 4.

    Technology that can improve both fuel economy and/or performance 
may not be dedicated solely to fuel economy.
    As fleet-wide fuel efficiency has improved over time, additional 
improvements have become both more complicated and more costly. There 
are two primary reasons for this phenomenon. First, as discussed, there 
is a known pool of technologies for improving fuel economy and reducing 
CO2 emissions. Many of these technologies, when actually 
implemented on vehicles, can be used to improve other vehicle 
attributes such as ``zero to 60'' performance, towing, and hauling, 
etc., either instead of or in addition to improving fuel economy and 
reducing CO2 emissions. As one example, a V6 engine can be 
turbocharged and downsized so that it consumes only as much fuel as an 
inline 4-cylinder engine, or it can be turbocharged and downsized so 
that it consumes less fuel than it would originally have consumed (but 
more than the inline 4-cylinder would) while also providing more low-
end torque. As another example, a vehicle can be lightweighted so that 
it consumes less fuel than it would originally have consumed, or so 
that it consumes the same amount of fuel it would originally have 
consumed but can carry more content, like additional safety or 
infotainment equipment. Manufacturers employing ``fuel-saving/
emissions-reducing'' technologies in the real world make decisions 
regarding how to employ that technology such that fewer than 100% of 
the possible fuel-saving/emissions-reducing benefits result. They do 
this because this is what consumers want, and more so than exclusively 
fuel economy improvements.
    This makes actual fuel economy gains more expensive.
    Thus, even though the technologies may be largely the same, 
previous assumptions about how much fuel can be saved or how much 
emissions can be reduced by employing various technologies may not have 
played out as prior analyses suggested, meaning that previous 
assumptions about how much it would cost to save that much fuel or 
reduce that much in emissions fall correspondingly short. For example, 
the agencies assumed in the 2010 final rule that dual clutch 
transmissions would be widely used to improve fuel economy due to 
expectations of strong effectiveness and very low cost: In practice, 
dual clutch transmissions had significant customer acceptance issues, 
and few manufacturers employ them in the U.S. market today.\21\ The 
agencies included some ``technologies'' in the 2012 final rule analysis 
that were defined ambiguously and/or in ways that precluded observation 
in the known (MYs 2008 and 2010) fleets, likely leading to double 
counting in cases where the known vehicles already reflected the 
assumed efficiency improvement. For example, the agencies assumed that 
transmission ``shift optimizers'' would be available and fairly widely 
used in MYs 2017-2025, but involving software controls, a 
``technology'' not defined in a way that would be observed in the fleet 
(unlike, for example, a dual clutch transmission).

    \21\ In fact, one manufacturer saw enough customer pushback that 
it launched a buyback program. See, e.g., Steve Lehto, ``What you 
need to know about the settlement for Ford Powershift owners,'' Road 
and Track, Oct. 19, 2017. Available at https://www.roadandtrack.com/car-culture/a10316276/what-you-need-to-know-about-the-proposed-settlement-for-ford-powershift-owners/ (last accessed Jul. 2, 2018).

    To be clear, this is no one's ``fault''--the CAFE and 
CO2 standards do not require manufacturers to use particular 
technologies in particular ways, and both agencies' past analyses 
generally sought to illustrate technology paths to compliance that were 
assumed to be as cost-effective as possible. If manufacturers choose 
different paths for reasons not accounted for in regulatory analysis, 
or choose to use technologies differently from what the agencies 
previously assumed, it does not necessarily mean that the analyses were 
unreasonable when performed. It does mean, however, that the fleet 
ought to be reflected as it stands today, with the technology it has 
and as that technology has been used, and consider what technology 
remains on the table at this point, whether and when it can 
realistically be available for widespread use in production, and how 
much it would cost to implement.
    Incremental additional fuel economy benefits are subject to 
diminishing returns.
    As fleet-wide fuel efficiency improves and CO2 emissions 
are reduced, the incremental benefit of continuing to improve/reduce 
inevitably decreases. This is because, as the base level of fuel 
economy improves, fewer gallons are saved from subsequent incremental 
improvements. Put simply, a one mpg increase for vehicles with low fuel 
economy will result in far greater savings than an identical 1 mpg 
increase for vehicles with higher fuel economy, and the cost for 
achieving a one-mpg increase for low fuel economy vehicles is far less 
than for higher fuel economy vehicles. This means that improving fuel 
economy is subject to diminishing returns. Annual fuel consumption can 
be calculated as follows:

[[Page 42992]]


    For purposes of illustration, assume a vehicle owner who drives a 
light vehicle 15,000 miles per year (a typical assumption for 
analytical purposes).\22\ If that owner trades in a vehicle with fuel 
economy of 15 mpg for one with fuel economy of 20 mpg, the owner's 
annual fuel consumption would drop from 1,000 gallons to 750 gallons--
saving 250 gallons annually. If, however, that owner were to trade in a 
vehicle with fuel economy of 30 mpg for one with fuel economy of 40 
mpg, the owner's annual gasoline consumption would drop from 500 
gallons/year to 375 gallons/year--only 125 gallons even though the mpg 
improvement is twice as large. Going from 40 to 50 mpg would save only 
75 gallons/year. Yet, each additional fuel economy improvement becomes 
much more expensive as the low-hanging fruit of low-cost technological 
improvement options are picked.\23\ Automakers, who must nonetheless 
continue adding technology to improve fuel economy and reduce 
CO2 emissions, will either sacrifice other performance 
attributes or raise the price of vehicles--neither of which is 
attractive to most consumers.

    \22\ A different vehicle-miles-traveled (VMT) assumption would 
change the absolute numbers in the example, but would not change the 
mathematical principles. Today's analysis uses mileage accumulation 
schedules that average about 15,000 miles annually over the first 
six years of vehicle operation.
    \23\ The examples in the text above are presented in mpg because 
that is a metric which should be readily understandable to most 
readers, but the example would hold true for grams of CO2 
per mile as well. If a vehicle emits 300 g/mi CO2, a 20 
percent improvement is 60 g/mi, so that the vehicle would emit 240 
g/mi. At 180 g/mi, a 20% improvement is 36 g/mi, so the vehicle 
would get 144 g/mi. In order to continue achieving similarly large 
(on an absolute basis) emissions reductions, mathematics require the 
percentage reduction to continue increasing.

    If fuel prices are high, the value of those gallons may be enough 
to offset the cost of further fuel economy improvements, but (1) the 
most recent reference case projections in the Energy Information 
Administration's (EIA's) Annual Energy Outlook (AEO 2017 and AEO 2018) 
do not indicate particularly high fuel prices in the foreseeable 
future, given underlying assumptions,\24\ and (2) as the baseline level 
of fuel economy continues to increase, the marginal cost of the next 
gallon saved similarly increases with the cost of the technologies 
required to meet the savings. The following figure illustrates the fact 
that fuel savings and corresponding avoided costs diminish with 
increasing fuel economy, showing the same basic pattern as a 2014 
illustration developed by EIA.\25\

    \24\ The U.S. Energy Information Administration (EIA) is the 
statistical and analytical agency within the U.S. Department of 
Energy (DOE). EIA is the nation's premiere source of energy 
information, and every fuel economy rulemaking since 2002 (and every 
joint CAFE and CO2 rulemaking since 2009) has applied 
fuel price projections from EIA's Annual Energy Outlook (AEO). AEO 
projections, documentation, and underlying data and estimates are 
available at https://www.eia.gov/outlooks/aeo/.
    \25\ Today in Energy: Fuel economy improvements show diminishing 
returns in fuel savings, U.S. Energy Information Administration 
(Jul. 11, 2014), https://www.eia.gov/todayinenergy/detail.php?id=17071.

[[Page 42993]]

    This effect is mathematical in nature and long-established, but 
when combined with relatively low fuel prices potentially through 2050, 
and the likelihood that a large majority of American consumers could 
consequently continue to place a higher value on vehicle attributes 
other than fuel economy, it makes manufacturers' ability to sell light 
vehicles with ever-higher fuel economy and ever-lower carbon dioxide 
emissions increasingly difficult. Put more simply, if gas is cheap and 
each additional improvement saves less gas anyway, most consumers would 
rather spend their money on attributes other than fuel economy when 
they are considering a new vehicle purchase, whether that is more 
safety technology, a better infotainment package, a more powerful 
powertrain, or other features (or, indeed, they may prefer to spend the 
savings on something other than automobiles). Manufacturers trying to 
sell consumers more fuel economy in such circumstances may convince 
consumers who place weight on efficiency and reduced carbon emissions, 
but consumers decide for themselves what attributes are worth to them. 
And while some contend that consumers do not sufficiently consider or 
value future fuel savings when making vehicle purchasing decisions,\26\ 
information regarding the benefits of higher fuel economy has never 
been made more readily available than today, with a host of online 
tools and mandatory prominent disclosures on new vehicles on the 
Monroney label showing fuel savings compared to average vehicles. This 
is not a question of ``if you build it, they will come.'' Despite the 
widespread availability of fuel economy information, and despite 
manufacturers building and marketing vehicles with higher fuel economy 
and increasing their offerings of hybrid and electric vehicles, in the 
past several years as gas prices have remained low, consumer 
preferences have shifted markedly away from higher-fuel-economy smaller 
and midsize passenger vehicles toward crossovers and truck-based 
utility vehicles.\27\ Some consumers plainly value fuel economy and low 
CO2 emissions above other attributes, and thanks in part to 
CAFE and CO2 standards, they have a plentiful selection of 
high-fuel economy and low CO2-emitting vehicles to choose 
from, but those consumers represent a relatively small percentage of 

    \26\ In docket numbers EPA-HQ-OAR-2015-0827 and NHTSA-2016-0068, 
see comments submitted by, e.g., Consumer Federation of America 
(NHTSA-2016-0068-0054, at p. 57, et seq.) and the Environmental 
Defense Fund (EPA-HQ-OAR-2015-0827-4086, at p. 18, et seq.).
    \27\ Carey, N. Lured by rising SUV sales, automakers flood 
market with models, Reuters (Mar. 29, 2018), available at https://www.reuters.com/article/us-autoshow-new-york-suvs/lured-by-rising-suv-sales-automakers-flood-market-with-models-idUSKBN1H50KI (last 
accessed Jun. 13, 2018). Many commentators have recently argued that 
manufacturers are deliberately increasing vehicle footprint size in 
order to get ``easier'' CAFE and CO2 standards. This 
misunderstands, somewhat, how the footprint-based standards work. 
While it is correct that larger-footprint vehicles have less 
stringent ``targets,'' the difficulty of compliance rests in how far 
above or below those vehicles are as compared to their targets, and 
more specifically, whether the manufacturer is selling so many 
vehicles that are far short of their targets that they cannot 
average out to compliant levels through other vehicles sold that 
beat their targets. For example, under the CAFE program, a 
manufacturer building a fleet of larger-footprint vehicles may have 
an objectively lower mpg-value compliance obligation than a 
manufacturer building a more mixed fleet, but it may still be more 
challenging for the first manufacturer to reach its compliance 
obligation if it is selling only very-low-mpg variants at any given 
footprint. There is only so much that increasing footprint makes it 
``easier'' for a manufacturer to reach compliance.

    Changed petroleum market has supported a shift in consumer 
    In 2012, the agencies projected fuel prices would rise 
significantly, and the United States would continue to rely heavily 
upon imports of oil, subjecting the country to heightened risk of price 
shocks.\28\ Things have changed significantly since 2012, with fuel 
prices significantly lower than anticipated, and projected to remain 
low through 2050. Furthermore, the global petroleum market has shifted 
dramatically with the United States taking advantage of its own oil 
supplies through technological advances that allow for cost-effective 
extraction of shale oil. The U.S. is now the world's largest oil 
producer and expected to become a net petroleum exporter in the next 

    \28\ The 2012 final rule analysis relied on the Energy 
Information Administration's Annual Energy Outlook 2012 Early 
Release, which assumed significantly higher fuel prices than the AEO 
2017 (or AEO 2018) currently available. See 77 FR 62624, 62715 (Oct. 
15, 2012) for the 2012 final rule's description of the fuel price 
estimates used.
    \29\ Annual Energy Outlook 2018, U.S. Energy Information 
Administration, at 53 (Feb. 6, 2018), https://www.eia.gov/outlooks/aeo/pdf/AEO2018.pdf.

    At least partially in response to lower fuel prices, consumers have 
moved more heavily into crossovers, sport utility vehicles and pickup 
trucks, than anticipated at the time of the last rulemaking. Because 
standards are based on footprint and specified separately for passenger 
cars and light trucks, these shifts do not necessarily pose compliance 
challenges by themselves, but they tend to reduce the overall average 
fuel economy rates and increase the overall average CO2 
emission rates of the new vehicle fleet. Consumers are also 
demonstrating a preference for more powerful engines and vehicles with 
higher seating positions and ride height (and accompanying mass 
increase relative to footprint) \30\--all of which present challenges 
for achieving increased fuel economy levels and lower CO2 
emission rates.

    \30\ See id.

    The Consequence of Unreasonable Fuel Economy and CO2 Standards: 
Increased vehicle prices keep consumers in older, dirtier, and less 
safe vehicles.
    Consumers tend to avoid purchasing things that they neither want or 
need. The analysis in today's proposal moves closer to being able to 
represent this fact through an improved model for vehicle scrappage 
rates. While neither this nor a sales response model, also included in 
today's analysis, nor the combination of the two, are consumer choice 
models, today's analysis illustrates market-wide impacts on the sale of 
new vehicles and the retention of used vehicles. Higher vehicle prices, 
which result from more-stringent fuel economy standards, have an effect 
on consumer purchasing decisions. As prices increase, the market-wide 
incentive to extract additional travel from used vehicles increases. 
The average age of the in-service fleet has been increasing, and when 
fleet turnover slows, not only does it take longer for fleet-wide fuel 
economy and CO2 emissions to improve, but also safety 
improvements, criteria pollutant emissions improvements, many other 
vehicle attributes that also provide societal benefits take longer to 
be reflected in the overall U.S. fleet as well because of reduced 
turnover. Raising vehicle prices too far, too fast, such as through 
very stringent fuel economy and CO2 emissions standards 
(especially considering that, on a fleet-wide basis, new vehicle sales 
and turnover do not appear strongly responsive to fuel economy), has 
effects beyond simply a slowdown in sales. Improvements over time have 
better longer-term effects simply by not alienating consumers, as 
compared to great leaps forward that drive people out of the new car 
market or into vehicles that do not meet their needs. The industry has 
achieved tremendous gains in fuel economy over the past decade, and 
these increases will continue at least through 2020.
    Along with these gains, there have also been tremendous increases 
in vehicle prices, as new vehicles become increasingly unaffordable--
with the average new vehicle transaction price

[[Page 42994]]

recently exceeding $36,000--up by more than $3,000 since 2014 
alone.\31\ In fact, a recent independent study indicated that the 
average new car price is unaffordable to median-income families in 
every metropolitan region in the United States except one: Washington, 
DC.\32\ That analysis used the historically accepted approach that 
consumers should make a down-payment of at least 20% of a vehicle's 
purchase price, finance for no longer than four years, and make 
payments of 10% or less of the consumer's annual income to car payments 
and insurance. But the market looks nothing like that these days, with 
average financing terms of 68 months, and an increasing proportion 
exceeding 72 or even 84 months.\33\ Longer financing terms may allow a 
consumer to keep their monthly payment affordable but can have serious 
potential financial consequences. Longer-term financing leads 
(generally) to higher interest rates, larger finance charges and total 
consumer costs, and a longer period of time with negative equity. In 
2012, the agencies expected prices to increase under the standards 
announced at that time. The agencies estimated that, compared to a 
continuation of the model year 2016 standards, the standards issued 
through model year 2025 would eventually increase average prices by 
about $1,500-$1,800.\34\ \35\ \36\ Circumstances have changed, the 
analytical methods and inputs have been updated (including updates to 
address issues still present in analyses published in 2016, 2017, and 
early 2018), and today, the analysis suggests that, compared to the 
proposed standards today, the previously-issued standards would 
increase average vehicle prices by about $2,100. While today's estimate 
is similar in magnitude to the 2012 estimate, it is relative to a 
baseline that includes increases in stringency between MY 2016 and MY 
2020. Compared to leaving vehicle technology at MY 2016 levels, today's 
analysis shows the previously-issued standards through model year 2025 
could eventually increase average vehicle prices by approximately 
$2,700. A pause in continued increases in fuel economy standards, and 
cost increases attributable thereto, is appropriate.

    \31\ See, e.g., Average New-Car Prices Rise Nearly 4 Percent for 
January 2018 On Shifting Sales Mix, According To Kelley Blue Book, 
Kelley Blue Book, https://mediaroom.kbb.com/2018-02-01-Average-New-Car-Prices-Rise-Nearly-4-Percent-For-January-2018-On-Shifting-Sales-Mix-According-To-Kelley-Blue-Book (last accessed Jun. 15, 2018).
    \32\ Bell, C. What's an `affordable' car where you live? The 
answer may surprise you, Bankrate.com (Jun. 28, 2017), available at 
https://www.bankrate.com/auto/new-car-affordability-survey/ (last 
accessed Jun. 15, 2018).
    \33\ Average Auto Loan Interest Rates: 2018 Facts and Figures, 
ValuePenguin, available at https://www.valuepenguin.com/auto-loans/average-auto-loan-interest-rates (last accessed Jun. 15, 2018).
    \34\ 77 FR 62624, 62666 (Oct. 15, 2012).
    \35\ The $1,500 figure reported in 2012 by NHTSA reflected 
application of carried-forward credits in model year 2025, rather 
than an achieved CAFE level that could be sustainably compliant 
beyond 2025 (with standards remaining at 2025 levels). As for the 
2016 draft TAR, NHTSA has since updated its modeling approach to 
extend far enough into the future that any unsustainable credit 
deficits are eliminated. Like analyses published by EPA in 2016, 
2017, and early 2018, the $1,800 figure reported in 2012 by EPA did 
not reflect either simulation of manufacturers' multiyear plans to 
progress from the initial MY 2008 fleet to the MY 2025 fleet or any 
accounting for manufacturers' potential application of banked 
credits. Today's analysis of both CAFE and CO2 standards 
accounts explicitly for multiyear planning and credit banking.
    \36\ While EPA did not refer to the reported $1,800 as an 
estimate of the increase in average prices, because EPA did not 
assume that manufacturers would reduce profit margins, the $1,800 
estimate is appropriately interpreted as an estimate of the average 
increase in vehicle prices.


[[Page 42995]]


Preferred Alternative
    For all of these reasons, the agencies are proposing to maintain 
the MY 2020 fuel economy and CO2 emissions standards for MYs 
2021-2026. Our goal is to establish standards that promote both energy 
conservation and safety, in light of what is technologically feasible 
and economically practicable, as directed by Congress.

    \37\ Data on new vehicle prices are from U.S. Bureau of Economic 
Analysis, National Income and Product Accounts, Supplemental Table 
7.2.5S, Auto and Truck Unit Sales, Production, Inventories, 
Expenditures, and Price (https://www.bea.gov/iTable/iTable.cfm?reqid=19&step=2#reqid=19&step=3&isuri=1&1921=underlying&1903=2055, last accessed Jul. 20, 2018). Median Household Income data 
are from U.S. Census Bureau, Table A-1, Households by Total Money 
Income, Race, and Hispanic Origin of Householder: 1967 to 2016 
(https://www.census.gov/data/tables/2017/demo/income-poverty/p60-259.html, last accessed Jul. 20, 2018).

Energy Conservation
    EPCA requires that NHTSA, when determining the maximum feasible 
levels of CAFE standards, consider the need of the Nation to conserve 
energy. However, EPCA also requires that NHTSA consider other factors, 
such as technological feasibility and economic practicability. The 
analysis suggests that, compared to the standards issued previously for 
MYs 2021-2025, today's proposed rule will eventually (by the early 
2030s) increase U.S. petroleum consumption by about 0.5 million barrels 
per day--about two to three percent of projected total U.S. 
consumption. While significant, this additional petroleum consumption 
is, from an economic perspective, dwarfed by the cost savings also 
projected to result from today's proposal, as indicated by the 
consideration of net benefits appearing below.
Safety Benefits From Preferred Alternative
    Today's proposed rule is anticipated to prevent more than 12,700 
on-road fatalities \38\ and significantly more injuries as compared to 
the standards set forth in the 2012 final rule over the lifetimes of 
vehicles as more new, safer vehicles are purchased than the current 
(and augural) standards. A large portion of these safety benefits will 
come from improved fleet turnover as more consumers will be able to 
afford newer and safer vehicles.

    \38\ Over the lifetime of vehicles through MY 2029.

    Recent NHTSA analysis shows that the proportion of passengers 
killed in a vehicle 18 or more model years old is nearly double that of 
a vehicle three model years old or newer.\39\ As the average car on the 
road is approaching 12 years old, apparently the oldest in our 
history,\40\ major safety benefits will occur by reducing fleet age. 
Other safety benefits will occur from other areas such as avoiding the 
increased driving

[[Page 42996]]

that would otherwise result from higher fuel efficiency (known as the 
rebound effect) and avoiding the mass reductions in passenger cars that 
might otherwise be required to meet the standards established in 
2012.\41\ Together these and other factors lead to estimated annual 
fatalities under the proposed standards that are significantly reduced 
\42\ relative to those that would occur under current (and augural) 

    \39\ Passenger Vehicle Occupant Injury Severity by Vehicle Age 
and Model Year in Fatal Crashes, Traffic Safety Facts Research Note, 
DOT HS 812 528. Washington, DC: National Highway Traffic Safety 
Administration. April 2018.
    \40\ See, e.g., IHS Markit, Vehicles Getting Older: Average Age 
of Light Cars and Trucks in U.S. Rises Again in 2016 to 11.5 years, 
IHS Markit Says, IHS Markit (Nov. 22, 2016), http://news.ihsmarkit.com/press-release/automotive/vehicles-getting-older-average-age-light-cars-and-trucks-us-rises-again-201 (``. . . 
consumers are continuing the trend of holding onto their vehicles 
longer than ever. As of the end of 2015, the average length of 
ownership measured a record 79.3 months, more than 1.5 months longer 
than reported in the previous year. For used vehicles, it is nearly 
66 months. Both are significantly longer lengths of ownership since 
the same measure a decade ago.'').
    \41\ The agencies are specifically requesting comment on the 
appropriateness and level of the effects of the rebound effect. The 
agencies also seek comment on changes as compared to the 2012 
modeling relating to mass reduction assumptions. During that 
rulemaking, the analysis limited the amount of mass reduction 
assumed for certain vehicles, which impacted the results regarding 
potential for adverse safety effects, even while acknowledging that 
manufacturers would not necessarily choose to avoid mass reductions 
in the ways that the agencies assumed. See, 77 FR 623624, 62763 
(Oct. 15, 2012). By choosing where and how to limit assumed mass 
reduction, the 2012 rule's safety analysis reduced the projected 
apparent risk to safety associated with aggressive fuel economy and 
CO2 targets. That specific assumption has been removed 
for today's analysis.
    \42\ The reduction in annual fatalities varies each calendar 
year, averaging 894 fewer fatalities annually for the CAFE program 
and 1,150 fewer fatalities for the CO2 program over 
calendar years 2036-2045.

The Preferred Alternative Would Have Negligible Environmental Impacts 
on Air Quality
    Improving fleet turnover will result in consumers getting into 
newer and cleaner vehicles, accelerating the rate at which older, more-
polluting vehicles are removed from the roadways. Also, reducing fuel 
economy (relative to levels that would occur under previously-issued 
standards) would increase the marginal cost of driving newer vehicles, 
reducing mileage accumulated by those vehicles, and reducing 
corresponding emissions. On the other hand, increasing fuel consumption 
would increase emissions resulting from petroleum refining and related 
``upstream'' processes. Our analysis shows that none of the regulatory 
alternatives considered in this proposal would noticeably impact net 
emissions of smog-forming or other ``criteria'' or toxic air 
pollutants, as illustrated by the following graph. That said, the 
resultant tailpipe emissions reductions should be especially beneficial 
to highly trafficked corridors.

Climate Change Impacts From Preferred Alternative
    The estimated effects of this proposal in terms of fuel savings and 
CO2 emissions, again perhaps somewhat counter-intuitively, 
is relatively small as compared to the 2012 final rule.\43\ NHTSA's 
Environmental Impact Statement performed for this rulemaking shows that 
the preferred alternative would result in 3/1,000ths of a degree 
Celsius increase in global average temperatures by 2100, relative to 
the standards finalized in 2012. On a net CO2 basis, the 
results are similarly minimal. The following graph compares the 
estimated atmospheric CO2 concentration (789.76 ppm) in 2100 
under the proposed standards to the estimated level (789.11 ppm) under 
the standards set forth in 2012--or an 8/100ths of a percentage 

    \43\ Counter-intuitiveness is relative, however. The estimated 
effects of the 2012 final rule on climate were similarly small in 
magnitude, as shown in the Final EIS accompanying that rule and 
available on NHTSA's website.


[[Page 42997]]


Net Benefits From Preferred Alternative
    Maintaining the MY 2020 curves for MYs 2021-2026 will save American 
consumers, the auto industry, and the public a considerable amount of 
money as compared to if EPA retained the previously-set CO2 
standards and NHTSA finalized the augural standards. This was 
identified as the preferred alternative, in part, because it maximizes 
net benefits compared to the other alternatives analyzed, recognizing 
the statutory considerations for both agencies. Comment is sought on 
whether this is an appropriate basis for selection.

[[Page 42998]]


    These estimates, reported as changes relative to impacts under the 
standards issued in 2012, account for impacts on vehicles produced 
during model years 2016-2029, as well as (through changes in 
utilization) vehicles produced in earlier model years, throughout those 
vehicles' useful lives. Reported values are in 2016 dollars, and 
reflect three-percent and seven-percent discount rates. Under CAFE 
standards, costs are estimated to decrease by $502 billion overall at a 
three-percent discount rate ($335 billion at a seven-percent discount 
rate); benefits are estimated to decrease by $326 billion at a three-
percent discount rate ($204 billion at a seven-percent discount rate). 
Thus, net benefits are estimated to increase by $176 billion at a 
three-percent discount rate and $132 billion at a seven-percent 
discount rate. The estimated impacts under CO2 standards are 
similar, with net benefits estimated to increase by $201 billion at a 
three-percent discount rate and $141 billion at a seven-percent 
discount rate.
Compliance Flexibilities
    This proposal also seeks comment on a variety of changes to NHTSA's 
and EPA's compliance programs for CAFE and CO2 as well as 
related programs. Compliance flexibilities can generally be grouped 
into two categories. The first category are those compliance 
flexibilities that reduce unnecessary compliance costs and provide for 
a more efficient program. The second category of compliance 
flexibilities are those that distort the market--such as by 
incentivizing the implementation of one type of technology by providing 
credit for compliance in excess of real-world fuel savings.
    Both programs provide for the generation of credits based upon 
fleet-wide over-compliance, provide for adjustments to the test 
measured value of each individual vehicle based upon the implementation 
of certain fuel saving technologies, and provide additional incentives 
for the implementation of certain preferred technologies (regardless of 
actual fuel savings). Auto manufacturers and others have petitioned for 
a host of additional adjustment- and incentive-type flexibilities, 
where there is not always consumer interest in the technologies to be 
incentivized nor is there necessarily clear fuel-saving and emissions-
reducing benefit to be derived from that incentivization. The agencies 
seek comment on all of those requests as part of this proposal.
    Over-compliance credits, which can be built up in part through use 
of the above-described per-vehicle adjustments and incentives, can be 
saved and either applied retroactively to accounts for previous non-
compliance, or carried forward to mitigate future non-compliance. Such 
credits can also be traded to other automakers for cash or for other 
credits for different fleets. But such trading is not pursued openly. 
Under the CAFE program, the public is not made aware of inter-automaker 
trades, nor are shareholders. And even the agencies are not informed of 
the price of credits. With the exception of statutorily-mandated 
credits, the agencies seek comment on all aspects of the current 
system. The agencies are particularly interested in comments on 
flexibilities that may distort the market.

[[Page 42999]]

The agencies seek comment as to whether some adjustments and non-
statutory incentives and other provisions should be eliminated and 
stringency levels adjusted accordingly. In general, well-functioning 
banking and trading provisions increase market efficiency and reduce 
the overall costs of compliance with regulatory objectives. The 
agencies request comment on whether the current system as implemented 
might need improvements to achieve greater efficiencies. We seek 
comment on specific programmatic changes that could improve compliance 
with current standards in the most efficient way, ranging from 
requiring public disclosure of some or all aspects of credit trades, to 
potentially eliminating credit trading in the CAFE program. We request 
commenters to provide any data, evidence, or existing literature to 
help agency decision-making.
One National Standard
    Setting appropriate and maximum feasible fuel economy and tailpipe 
CO2 emissions standards requires regulatory efficiency. This 
proposal addresses a fundamental and unnecessary complication in the 
currently-existing regulatory framework, which is the regulation of GHG 
emissions from passenger cars and light trucks by the State of 
California through its GHG standards and Zero Emission Vehicle (ZEV) 
mandate and subsequent adoption of these standards by other States. 
Both EPCA and the CAA preempt State regulation of motor vehicle 
emissions (in EPCA's case, standards that are related to fuel economy 
standards). The CAA gives EPA the authority to waive preemption for 
California under certain circumstances. EPCA does not provide for a 
waiver of preemption under any circumstances. In short, the agencies 
propose to maintain one national standard--a standard that is set 
exclusively by the Federal government.
Proposed Withdrawal of California's Clean Air Act Preemption Waiver
    EPA granted a waiver of preemption to California in 2013 for its 
``Advanced Clean Car'' regulations, composed of its GHG standards, its 
``Low Emission Vehicle (LEV)'' program and the ZEV program,\44\ and, as 
allowed under the CAA, a number of other States adopted California's 
standards.\45\ The CAA states that EPA shall not grant a waiver of 
preemption if EPA finds that California's determination that its 
standards are, in the aggregate, at least as protective of public 
health and welfare as applicable Federal standards, is arbitrary and 
capricious; that California does not need its own standards to meet 
compelling or extraordinary conditions; or that such California 
standards and accompanying enforcement procedures are not consistent 
with Section 202(a) of the CAA. In this proposal, EPA is proposing to 
withdraw the waiver granted to California in 2013 for the GHG and ZEV 
requirements of its Advanced Clean Cars program, in light of all of 
these factors.

    \44\ 78 FR 2112 (Jan. 9, 2013).
    \45\ CAA Section 177, 42 U.S.C. 7507.

    Attempting to solve climate change, even in part, through the 
Section 209 waiver provision is fundamentally different from that 
section's original purpose of addressing smog-related air quality 
problems. When California was merely trying to solve its air quality 
issues, there was a relatively-straightforward technology solution to 
the problems, implementation of which did not affect how consumers 
lived and drove. Section 209 allowed California to pursue additional 
reductions to address its notorious smog problems by requiring more 
stringent standards, and allowed California and other States that 
failed to comply with Federal air quality standards to make progress 
toward compliance. Trying to reduce carbon emissions from motor 
vehicles in any significant way involves changes to the entire vehicle, 
not simply the addition of a single or a handful of control 
technologies. The greater the emissions reductions are sought, the 
greater the likelihood that the characteristics and capabilities of the 
vehicle currently sought by most American consumers will have to change 
significantly. Yet, even decades later, California continues to be in 
widespread non-attainment with Federal air quality standards.\46\ In 
the past decade, California has disproportionately focused on GHG 
emissions. Parts of California have a real and significant local air 
pollution problem, but CO2 is not part of that local 

    \46\ See California Nonattainment/Maintenance Status for Each 
County by Year for All Criteria Pollutants, current as of May 31, 
2018, at https://www3.epa.gov/airquality/greenbook/anayo_ca.html 
(last accessed June 15, 2018).

California's Tailpipe CO2 Emissions Standards and ZEV 
Mandate Conflict With EPCA
    Moreover, California regulation of tailpipe CO2 
emissions, both through its GHG standards and ZEV program, conflicts 
directly and indirectly with EPCA and the CAFE program. EPCA expressly 
preempts State standards related to fuel economy. Tailpipe 
CO2 standards, whether in the form of fleet-wide 
CO2 limits or in the form of requirements that manufacturers 
selling vehicles in California sell a certain number of low- and no-
tailpipe-CO2 emissions vehicles as part of their overall 
sales, are unquestionably related to fuel economy standards. Standards 
that control tailpipe CO2 emissions are de facto fuel 
economy standards because CO2 is a direct and inevitable 
byproduct of the combustion of carbon-based fuels to make energy, and 
the vast majority of the energy that powers passenger cars and light 
trucks comes from carbon-based fuels.
    Improving fuel economy means getting the vehicle to go farther on a 
gallon of gas; a vehicle that goes farther on a gallon of gas produces 
less CO2 per unit of distance; therefore, improving fuel 
economy necessarily reduces tailpipe CO2 emissions, and 
reducing CO2 emissions necessarily improves fuel economy. 
EPCA therefore necessarily preempts California's Advanced Clean Cars 
program to the extent that it regulates or prohibits tailpipe 
CO2 emissions. Section VI of this proposal, below, discusses 
the CAA waiver and EPCA preemption in more detail.
    Eliminating California's regulation of fuel economy pursuant to 
Congressional direction will provide benefits to the American public. 
The automotive industry will, appropriately, deal with fuel economy 
standards on a national basis--eliminating duplicative regulatory 
requirements. Further, elimination of California's ZEV program will 
allow automakers to develop such vehicles in response to consumer 
demand instead of regulatory mandate. This regulatory mandate has 
required automakers to spend tens of billions of dollars to develop 
products that a significant majority of consumers have not adopted, and 
consequently to sell such products at a loss. All of this is paid for 
through cross subsidization by increasing prices of other vehicles not 
just in California and other States that have adopted California's ZEV 
mandate, but throughout the country.
Request for Comment
    The agencies look forward to all comments on this proposal, and 
wish to emphasize that obtaining public input is extremely important to 
us in selecting from among the alternatives in a final rule. While the 
agencies and the Administration met with a variety of stakeholders 
prior to issuance of this proposal, those meetings have not resulted in 
a predetermined final rule outcome. The Administrative Procedure Act 
requires that agencies provide the

[[Page 43000]]

public with adequate notice of a proposed rule followed by a meaningful 
opportunity to comment on the rule's content. The agencies are 
committed to following that directive.

II. Technical Foundation for NPRM Analysis

A. Basics of CAFE and CO2 Standards Analysis

    The agencies' analysis of CAFE and CO2 standards 
involves two basic elements: first, estimating ways each manufacturer 
could potentially respond to a given set of standards in a manner that 
considers potential consumer response; and second, estimating various 
impacts of those responses. Estimating manufacturers' potential 
responses involves simulating manufacturers' decision-making processes 
regarding the year-by-year application of fuel-saving technologies to 
specific vehicles. 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 scrapped, and estimating the monetary value of these effects. 
Estimating impacts also involves consideration of the response of 
consumers--e.g., whether consumers will purchase the vehicles and in 
what quantities. Both of these basic analytical elements involve the 
application of many analytical inputs.
    The agencies' analysis uses the CAFE model to estimate 
manufacturers' potential responses to new CAFE and CO2 
standards and to estimate various impacts of those responses. The model 
makes use of many inputs, values of which are developed outside of the 
model and not by the model. For example, the model applies fuel prices; 
it does not estimate fuel prices. 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 effects of manufacturers working to meet those 
standards, which become the basis for comparing between different 
potential stringencies.
    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, and the 2016 rulemaking 
regarding heavy-duty pickup and van fuel consumption and GHG emissions 
also used the CAFE model for analysis.\47\

    \47\ While this rulemaking employed the CAFE model for analysis, 
EPA and DOT used different versions of the CAFE model for 
establishing their respective standards, and EPA also used the EPA 
MOVES model. See 81 FR 73478, 73743 (Oct. 25, 2016).

    DOT recently arranged for a formal peer review of the model. In 
general, reviewers' comments strongly supported the model's conceptual 
basis and implementation, and commenters provided several specific 
recommendations. DOT staff agreed with many of these recommendations 
and have worked to implement them wherever practicable. Implementing 
some of them would require considerable further research, development, 
and testing, and will be considered going forward. For a handful of 
other recommendations, DOT staff disagreed, often finding the 
recommendations involved considerations (e.g., other policies, such as 
those involving fuel taxation) beyond the model itself or were based on 
concerns with inputs rather than how the model itself functioned. A 
report available in the docket for this rulemaking presents peer 
reviewers' detailed comments and recommendations, and provides DOT's 
detailed responses.\48\

    \48\ Docket No. NHTSA-2018-0067.

    The agencies also use four DOE and DOE-sponsored models to develop 
inputs to the CAFE model, including three developed and maintained by 
DOE's Argonne National Laboratory. The agencies use the DOE Energy 
Information Administration's (EIA's) National Energy Modeling System 
(NEMS) to estimate fuel prices,\49\ and used Argonne's Greenhouse 
gases, Regulated Emissions, and Energy use in Transportation (GREET) 
model to estimate emissions rates from fuel production and distribution 
processes.\50\ DOT also sponsored DOE/Argonne to use their Autonomie 
full-vehicle simulation system to estimate the fuel economy impacts for 
roughly a million combinations of technologies and vehicle types.\51\ 

    \49\ See https://www.eia.gov/outlooks/aeo/info_nems_archive.php. 
Today's notice uses fuel prices estimated using the Annual Energy 
Outlook (AEO) 2017 version of NEMS (see https://www.eia.gov/outlooks/archive/aeo17/ and https://www.eia.gov/outlooks/aeo/data/browser/#/?id=3-AEO2017&cases=ref2017&sourcekey=0).
    \50\ Information regarding GREET is available at https://greet.es.anl.gov/index.php. Availability of NEMS is discussed at 
https://www.eia.gov/outlooks/aeo/info_nems_archive.php. Today's 
notice uses fuel prices estimated using the AEO 2017 version of 
    \51\ 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 http://www.cse.anl.gov/batpac/.
    \52\ Additionally, 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.

    EPA developed two models after 2009, referred to as the ``ALPHA'' 
and ``OMEGA'' models, which provide some of the same capabilities as 
the Autonomie and CAFE models. EPA applied the OMEGA model to conduct 
analysis of GHG standards promulgated in 2010 and 2012, and the ALPHA 
and OMEGA models to conduct analysis discussed in the above-mentioned 
2016 Draft TAR and Proposed and Final Determinations regarding 
standards beyond 2021. In an August 2017 notice, the agencies requested 
comments on, among other things, whether EPA should use alternative 
methodologies and modeling, including DOE/Argonne's Autonomie full-
vehicle simulation tool and DOT's CAFE model.\53\

    \53\ 82 FR 39533 (Aug. 21, 2017).

    Having reviewed comments on the subject and having considered the 
matter fully, the agencies have determined it is reasonable and 
appropriate to use DOE/Argonne's model for full-vehicle simulation, and 
to use DOT's CAFE model for analysis of regulatory alternatives. EPA 
interprets Section 202(a) of the CAA as giving the agency broad 
discretion in how it develops and sets GHG standards for light-duty 
vehicles. Nothing in Section 202(a) mandates that EPA use any specific 
model or set of models for analysis of potential CO2 
standards for light-duty vehicles. EPA weighs many factors when 
determining appropriate levels for CO2 standards, including 
the cost of compliance (see Section 202(a)(2)), lead time necessary for 
compliance (also Section 202(a)(2)), safety (see NRDC v. EPA, 655 F.2d 
318, 336 n. 31 (D.C. Cir. 1981) and other impacts on consumers,\54\ and 
energy impacts associated with use of the technology.\55\ Using the 
CAFE model

[[Page 43001]]

allows consideration of the following factors: the CAFE model 
explicitly evaluates the cost of compliance for each manufacturer, each 
fleet, and each model year; it accounts for lead time necessary for 
compliance by directly incorporating estimated manufacturer production 
cycles for every vehicle in the fleet, ensuring that the analysis does 
not assume vehicles can be redesigned to incorporate more technology 
without regard to lead time considerations; it provides information on 
safety effects associated with different levels of standards and 
information about many other impacts on consumers, and it calculates 
energy impacts (i.e., fuel saved or consumed) as a primary function, 
besides being capable of providing information about many other factors 
within EPA's broad CAA discretion to consider.

    \54\ Since its earliest Title II regulations, EPA has considered 
the safety of pollution control technologies. See 45 FR 14496, 14503 
    \55\ See George E. Warren Corp. v. EPA, 159 F.3d 616, 623-624 
(D.C. Cir. 1998) (ordinarily permissible for EPA to consider factors 
not specifically enumerated in the Act).

    Because the CAFE model simulates a wide range of actual constraints 
and practices related to automotive engineering, planning, and 
production, such as common vehicle platforms, sharing of engines among 
different vehicle models, and timing of major vehicle redesigns, the 
analysis produced by the CAFE model provides a transparent and 
realistic basis to show pathways manufacturers could follow over time 
in applying new technologies, which helps better assess impacts of 
potential future standards. Furthermore, because the CAFE model also 
accounts fully for regulatory compliance provisions (now including 
CO2 compliance provisions), such as adjustments for reduced 
refrigerant leakage, production ``multipliers'' for some specific types 
of vehicles (e.g., PHEVs), and carried-forward (i.e., banked) credits, 
the CAFE model provides a transparent and realistic basis to estimate 
how such technologies might be applied over time in response to CAFE or 
CO2 standards.
    There are sound reasons for the agencies to use the CAFE model 
going forward in this rulemaking. First, the CAFE and CO2 
fact analyses are inextricably linked. Furthermore, the analysis 
produced by the CAFE model and DOE/Argonne's Autonomie addresses 
several analytical needs. The CAFE model provides an explicit year-by-
year simulation of manufacturers' application of technology to their 
products in response to a year-by-year progression of CAFE standards 
and accounts for sharing of technologies and the implications for 
timing, scope, and limits on the potential to optimize powertrains for 
fuel economy. In the real world, standards actually are specified on a 
year-by-year basis, not simply some single year well into the future, 
and manufacturers' year-by-year plans involve some vehicles ``carrying 
forward'' technology from prior model years and some other vehicles 
possibly applying ``extra'' technology in anticipation of standards in 
ensuing model years, and manufacturers' planning also involves applying 
credits carried forward between model years. Furthermore, manufacturers 
cannot optimize the powertrain for fuel economy on every vehicle model 
configuration--for example, a given engine shared among multiple 
vehicle models cannot practicably be split into different versions for 
each configuration of each model, each with a slightly different 
displacement. The CAFE model is designed to account for these real-
world factors.
    Considering the technological heterogeneity of manufacturers' 
current product offerings, and the wide range of ways in which the many 
fuel economy-improving/CO2 emissions-reducing technologies 
included in the analysis can be combined, the CAFE model has been 
designed to use inputs that provide an estimate of the fuel economy 
achieved for many tens of thousands of different potential combinations 
of fuel-saving technologies. Across the range of technology classes 
encompassed by the analysis fleet, today's analysis involves more than 
a million such estimates. While the CAFE model requires no specific 
approach to developing these inputs, the National Academy of Sciences 
(NAS) has recommended, and stakeholders have commented, that full-
vehicle simulation provides the best balance between realism and 
practicality. DOE/Argonne has spent several years developing, applying, 
and expanding means to use distributed computing to exercise its 
Autonomie full-vehicle simulation tool over the scale necessary for 
realistic analysis of CAFE or average CO2 standards. This 
scalability and related flexibility (in terms of expanding the set of 
technologies to be simulated) makes Autonomie well-suited for 
developing inputs to the CAFE model.
    Additionally, DOE/Argonne's Autonomie also has a long history of 
development and widespread application by a much wider range of users 
in government, academia, and industry. Many of these users apply 
Autonomie to inform funding and design decisions. These real-world 
exercises have contributed significantly to aspects of Autonomie 
important to producing realistic estimates of fuel economy levels and 
CO2 emission rates, such as estimation and consideration of 
performance, utility, and driveability metrics (e.g., towing 
capability, shift business, frequency of engine on/off transitions). 
This steadily increasing realism has, in turn, steadily increased 
confidence in the appropriateness of using Autonomie to make 
significant investment decisions. Notably, DOE uses Autonomie for 
analysis supporting budget priorities and plans for programs managed by 
its Vehicle Technologies Office (VTO). Considering the advantages of 
DOE/Argonne's Autonomie model, it is reasonable and appropriate to use 
Autonomie to estimate fuel economy levels and CO2 emission 
rates for different combinations of technologies as applied to 
different types of vehicles.
    Commenters have also suggested that the CAFE model's graphical user 
interface (GUI) facilitates others' ability to use the model quickly--
and without specialized knowledge or training--and to comment 
accordingly.\56\ For today's proposal, DOT has significantly expanded 
and refined this GUI, providing the ability to observe the model's 
real-time progress much more closely as it simulates year-by-year 
compliance with either CAFE or CO2 standards.\57\ Although 
the model's ability to produce realistic results is independent of the 
model's GUI, it is anticipated the CAFE model's GUI will facilitate 
stakeholders' meaningful review and comment during the comment period.

    \56\ From Docket Number EPA-HQ-OAR-2015-0827, see Comment by 
Global Automakers, Docket ID EPA-HQ-OAR-2015-0827-9728, at 34.
    \57\ The updated GUI provides a range of graphs updated in real 
time as the model operates. These graphs can be used to monitor fuel 
economy or CO2 ratings of vehicles in manufacturers' 
fleets and to monitor year-by-year CAFE (or average CO2 
ratings), costs, avoided fuel outlays, and avoided CO2-
related damages for specific manufacturers and/or specific fleets 
(e.g., domestic passenger car, light truck). Because these graphs 
update as the model progresses, they should greatly increase users' 
understanding of the model's approach to considerations such as 
multiyear planning, payment of civil penalties, and credit use.

    Beyond these general considerations, several specific related 
technical comments and considerations underlie the agencies' decision 
in this area, as discussed, where applicable, in the remainder of this 
    Other commenters expressed a number of concerns with whether DOT's 
CAFE model could be used for CAA analysis. Many of these concerns 
focused on inputs used by the CAFE model for prior rulemaking 
analyses.\58\ \59\ \60\ Because inputs are

[[Page 43002]]

exogenous to any model, they do not determine whether it would be 
reasonable and appropriate for EPA to use DOT's model for analysis. 
Other concerns focused on characteristics of the CAFE model that were 
developed to better align the model with EPCA and EISA; the model has 
been revised to accommodate both EPCA/EISA and CAA analysis, as 
explained further below. Some commenters also argued that use of any 
models other than ALPHA and OMEGA for CAA analysis would constitute an 
arbitrary and capricious delegation of EPA's decision-making authority 
to DOT, if DOT models are used for analysis instead. These comments 
were made prior to the development of the CAA analysis function in the 
CAFE model, and, moreover, appear to conflate the analytical tool used 
to inform decision-making with the action of making the decision. As 
explained elsewhere in this document and as made repeatedly clear over 
the past several rulemakings, the CAFE model neither sets standards nor 
dictates where and how to set standards; it simply informs as to the 
effects of setting different levels of standards. In this rulemaking, 
EPA will be making its own decisions regarding what CO2 
standards would be appropriate under the CAA. The CAA does not require 
EPA to create a specific model or use a specific model of its own 
creation in setting GHG standards. The fact EPA's decision may be 
informed by non-EPA-created models does not, in any way, constitute a 
delegation of its statutory power to set standards or decision-making 
authority.\61\ Arguing to the contrary would suggest, for example, that 
EPA's decision would be invalid because it relied on EIA's Annual 
Energy Outlook for fuel prices rather than developing its own model for 
estimating future trends in fuel prices. Yet, all Federal agencies that 
have occasion to use forecasts of future fuel prices regularly (and 
appropriately) defer to EIA's expertise in this area and rely on EIA's 
NEMS-based analysis in the AEO, even when those same agencies are using 
EIA's forecasts to inform their own decision-making.

    \58\ For example, EDF's recent comments (EDF at 12, Docket ID. 
EPA-HQ-OAR-2015-0827-9203) stated ``the data that NHTSA needs to 
input into its model is sensitive confidential business information 
that is not transparent and cannot be independently verified . . .'' 
and claimed ``the OMEGA model's focus on direct technological inputs 
and costs--as opposed to industry self-reported data--ensures the 
model more accurately characterizes the true feasibility and cost 
effectiveness of deploying greenhouse gas reducing technologies.'' 
Neither statement is correct, as nothing about either the CAFE or 
OMEGA model either obviates or necessitates the use of CBI to 
develop inputs.
    \59\ In recent comments (CARB at 28, Docket ID. EPA-HQ-OAR-2015-
0827-9197), CARB stated ``another promising technology entering the 
market was not even included in the NHTSA compliance modeling'' and 
that EPA assumes a five-year redesign cycle, whereas NHTSA assumes a 
six to seven-year cycle.'' Though presented as criticisms of the 
models, these comments--at least with respect to the CAFE model--
actually concern model inputs. NHTSA did not agree with CARB about 
the commercialization potential of the engine technology in question 
(``Atkinson 2'') and applied model inputs accordingly. Also, rather 
than applying a one-size-fits-all assumption regarding redesign 
cadence, NHTSA developed estimates specific to each vehicle model 
and applied these as model inputs.
    \60\ NRDC's recent comments (NRDC at 37, Docket ID. EPA-HQ-OAR-
2015-0827-9826) state EPA should not use the CAFE model because it 
``allows manufacturers to pay civil penalties in lieu of meeting the 
standards, an alternative compliance pathway currently allowed under 
EISA and EPCA.'' While the CAFE model can simulate civil penalty 
payment, NRDC's comment appears to overlook the fact that this 
result depends on model inputs; the inputs can easily be specified 
such that the CAFE model will set aside civil penalty payment as an 
alternative to compliance.
    \61\ ``[A] federal agency may turn to an outside entity for 
advice and policy recommendations, provided the agency makes the 
final decisions itself.'' U.S. Telecom. Ass'n v. FCC, 359 F.3d 554, 
565-66 (D.C. Cir. 2004). To the extent commenters meant to suggest 
outside parties have a reliance interest in EPA using ALPHA and 
OMEGA to set standards, EPA does not agree a reliance interest is 
properly placed on an analytical methodology (as opposed to on the 
standards themselves). Even if it were, all parties that closely 
examined ALPHA and OMEGA-based analyses in the past either also 
simultaneously closely examined CAFE and Autonomie-based analyses in 
the past, or were fully capable of doing so, and thus, should face 
no additional difficulty now they have only one set of models and 
inputs/outputs to examine.

    Moreover, DOT's CAFE model with inputs from DOE/Argonne's Autonomie 
model has produced analysis supporting rulemaking under the CAA. In 
2015, EPA proposed new GHG standards for MY 2021-2027 heavy-duty 
pickups and vans, finalizing standards in 2016. Supporting the NPRM and 
final rule, EPA relied on analysis implemented by DOT using DOT's CAFE 
model, and DOT used inputs developed by DOE/Argonne using DOE/Argonne's 
Autonomie model.
    The following sections provide a brief technical overview of the 
CAFE model, including changes NHTSA made to the model since 2012, 
before discussing inputs to the model and then diving more deeply into 
how the model works. For more information on the latter topic, see the 
CAFE model documentation July 2018 draft, available in the docket for 
this rulemaking and on NHTSA's website.
1. Brief Technical Overview of the Model
    The CAFE model is designed to simulate compliance with a given set 
of CAFE or CO2 standards for each manufacturer selling 
vehicles in the United States. The model begins with a representation 
of the current (for today's analysis, model year 2016) vehicle model 
offerings for each manufacturer that includes the specific engines and 
transmissions on each model variant, observed sales volumes, and all 
fuel economy improvement technology that is already present on those 
vehicles. From there it adds technology, in response to the standards 
being considered, in a way that minimizes the cost of compliance and 
reflects many real-world constraints faced by automobile manufacturers. 
After simulating compliance, the model calculates impacts of the 
simulated standard: technology costs, fuel savings (both in gallons and 
dollars), CO2 reductions, social costs and benefits, and 
safety impacts.
    Today's analysis reflects several changes made to the CAFE model 
since 2012, when NHTSA used the model to estimate the effects, costs, 
and benefits of final CAFE standards for light-duty vehicles produced 
during MYs 2017-2021 and augural standards for MYs 2022-2025. Key 
changes relevant to this analysis include the following:
     Expansion of model inputs, procedures, and outputs to 
accommodate technologies not included in prior analyses,
     Updated approach to estimating the combined effect of 
fuel-saving technologies using large scale simulation modeling,
     Modules that dynamically estimate new vehicle sales and 
existing vehicle scrappage in response to changes to new vehicle prices 
that result from manufacturers' compliance actions,
     A safety module that estimates the changes in light-duty 
traffic fatalities resulting from changes to vehicle exposure, vehicle 
retirement rates, and reductions in vehicle mass to improve fuel 
     Disaggregation of each manufacturer's fleet into separate 
``domestic'' passenger car and ``import'' passenger car fleets to 
better represent the statutory requirements of the CAFE program,
     Changes to the algorithm used to apply technologies, 
enabling more explicit accounting of shared vehicle components 
(engines, transmissions, platforms) and ``inheritance'' of major 
technology within or across powertrains and/or platforms over time,
     An industry labor quantity module, which estimates net 
changes in the amount of U.S. automobile labor for dealerships, Tier 1 
and 2 supplier companies, and original equipment manufacturers (OEMs),
     Cost estimation of batteries for electrification 
technologies incorporates an updated version of Argonne National 
Laboratory's BatPAC (battery) model for hybrid electric vehicles 
(HEVs), plug-in

[[Page 43003]]

hybrid electric vehicles (PHEVs), and battery electric vehicles (BEVs), 
consistent with how we estimate effectiveness for those values,
     Expanded accounting for CAFE credits carried over from 
years prior to those included in the analysis (a.k.a. ``banked'' 
credits) and application to future CAFE deficits to better evaluate 
anticipated manufacturer responses to proposed standards,\62\

    \62\ While EPCA/EISA precludes NHTSA from considering 
manufacturers' potential use of credits in model years for which the 
agency is establishing new standards, NHTSA considers credit use in 
earlier model years. Also, as allowed by NEPA, NHTSA's EISs present 
results of analysis that considers manufacturers' potential use of 
credits in all model years, including those for which the agency is 
establishing new standards.

     The ability to represent a manufacturer's preference for 
fine payment (rather than achieving full compliance exclusively through 
fuel economy improvements) on a year-by-year basis,
     Year-by-year simulation of how manufacturers could comply 
with EPA's CO2 standards, including
    [cir] Calculation of vehicle models' CO2 emission rates 
before and after application of fuel-saving (and, therefore, 
CO2-reducing) technologies,
    [cir] Calculation of manufacturers' fleet average CO2 
emission rates,
    [cir] Calculation of manufacturers' fleet average CO2 
emission rates under attribute-based CO2 standards,
    [cir] Accounting for adjustments to average CO2 emission 
rates reflecting reduction of air conditioner refrigerant leakage,
    [cir] Accounting for the treatment of alternative fuel vehicles for 
CO2 compliance,
    [cir] Accounting for production ``multipliers'' for PHEVs, BEVs, 
compressed natural gas (CNG) vehicles, and fuel cell vehicles (FCVs),
    [cir] Accounting for transfer of CO2 credits between 
regulated fleets,
    [cir] Accounting for carried-forward (a.k.a. ``banked'') 
CO2 credits, including credits from model years earlier than 
modeled explicitly.
2. Sensitivity Cases and Why We Examine Them
    Today's notice presents estimated impacts of the proposed CAFE and 
CO2 standards defining the proposals, relative to a baseline 
``no action'' regulatory alternative under which the standards 
announced in 2012 remain in place through MY 2025 and continue 
unchanged thereafter. Relative to this same baseline, today's notice 
also presents analysis estimating impacts under a range of other 
regulatory alternatives the agencies are considering. All but one 
involve different standards, and three involve a gradual 
discontinuation of CAFE and GHG adjustments reflecting the application 
of technologies that improve air conditioner efficiency or, in other 
ways, improve fuel economy under conditions not represented by long-
standing fuel economy test procedures. Like the baseline no action 
alternative, all of these alternatives are more stringent than the 
preferred alternative. Section III and Section IV describe the 
preferred and other regulatory alternatives, respectively.
    These alternatives were examined because they will be considered as 
options for the final rule. The agencies seek comment on these 
alternatives, seek any relevant data and information, and will review 
responses. That review could lead to the selection of one of the other 
regulatory alternatives for the final rule or some combination of the 
other regulatory alternatives (e.g., combining passenger cars standards 
from one alternative with light truck standards from a different 
    Because outputs depend on inputs (e.g., the results of the analysis 
in terms of quantities and kinds of technologies required to meet 
different levels of standards, and the societal and private benefits 
associated with manufacturers meeting different levels of standards 
depend on input data, estimates, and assumptions), the analysis also 
explores the sensitivity of results to many of these inputs. For 
example, the net benefits of any regulatory alternative will depend 
strongly on fuel prices well beyond 2025. Fuel prices a decade and more 
from now are not knowable with certainty. The sensitivity analysis 
involves repeating the ``central'' or ``reference case'' analysis under 
alternative inputs (e.g., higher fuel prices in one case, lower fuel 
prices in another case), and exploring changes in analytical results, 
which is discussed further in the agencies' Preliminary Regulatory 
Impact Analysis (PRIA) accompanying today's notice.

B. Developing the Analysis Fleet for Assessing Costs, Benefits, and 
Effects of Alternative CAFE Standards

    The following sections describe what the analysis fleet is and why 
it is used, how it was developed for this NPRM, and the analysis-fleet-
related topics on which comment is sought.
1. Purpose of Developing and Using an Analysis Fleet
    The starting point for the evaluation of the potential feasibility 
of different stringency levels for future CAFE and CO2 
standards is the analysis fleet, which is a snapshot of the recent 
vehicle market. The analysis fleet provides a snapshot to project what 
vehicles will exist in future model years covered by the standards and 
what technologies they will have, and then evaluate what additional 
technologies can feasibly be applied to those vehicles in a cost-
effective way to raise their fuel economy and lower their 
CO2 emission levels.\63\

    \63\ 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 in the CAFE model.

    Part of reflecting what vehicles will exist in future model years 
is knowing which vehicles are produced by which manufacturers, how many 
of each are sold, and whether they are passenger cars or light trucks. 
This is important because it improves our understanding of the overall 
impacts of different levels of CAFE and CO2 standards; 
overall impacts result from industry's response to standards, and 
industry's response is made up of individual manufacturer responses to 
the standards in light of the overall market and their individual 
assessment of consumer acceptance. Having an accurate picture of 
manufacturers' existing fleets (and the vehicle models in them) that 
will be subject to future standards helps us better understand 
individual manufacturer responses to those future standards in addition 
to potential changes in those standards.
    Another part of reflecting what vehicles will exist in future model 
years is knowing what technologies are already on those vehicles. 
Accounting for technologies already being on vehicles helps avoid 
``double-counting'' the value of those technologies, by assuming they 
are still available to be applied to improve fuel economy and reduce 
CO2 emissions. It also promotes more realistic 
determinations of what additional technologies can feasibly be applied 
to those vehicles: if a manufacturer has already started down a 
technological path to fuel economy or performance improvements, we do 
not assume it will completely abandon that path because that would be 
unrealistic and would not accurately represent manufacturer responses 
to standards. Each vehicle model (and configurations of each model) in 
the analysis fleet, therefore, has a comprehensive list of its 
technologies, which is important because different configurations may 
have different technologies applied to

[[Page 43004]]

them.\64\ Additionally, the analysis accounts for platforms within 
manufacturers' fleets, recognizing platforms will share technologies, 
and the vehicles that make up that platform should receive (or not 
receive) additional technological improvements together. The specific 
engineering characteristics of each model/configuration are available 
in the aforementioned input file.\65\ For the regulatory alternatives 
considered in today's proposal, estimates of rates at which various 
technologies might be expected to penetrate manufacturers' fleets (and 
the overall market) are summarized below in Sections VI and VII, and in 
Chapter 6 of the accompanying PRIA and in detailed model output files 
available at NHTSA's website. A solid characterization of a recent 
model year as an analytical starting point helps to realistically 
estimate ways manufacturers could potentially respond to different 
levels of standards, and the modeling strives to realistically simulate 
how manufacturers could progress from that starting point. 
Nevertheless, manufacturers can respond in many ways beyond those 
represented in the analysis (e.g., applying other technologies, 
shifting production volumes, changing vehicle footprint), such that it 
is impossible to predict with any certainty exactly how each 
manufacturer will respond. Therefore, recent trends in manufacturer 
performance and technology application, although of interest in terms 
of understanding manufacturers' current compliance positions, are not 
in themselves innately indicative of future potential.

    \64\ Considering each vehicle model/configuration also improves 
the ability to consider the differential impacts of different levels 
of potential standards on different manufacturers, since all vehicle 
model/configurations ``start'' at different places, in terms of the 
technologies they already have and how those technologies are used.
    \65\ Available with the model and other input files supporting 
today's announcement at https://www.nhtsa.gov/corporate-average-fuel-economy/compliance-and-effects-modeling-system.

    Yet, another part of reflecting what vehicles will exist in future 
model years is having reasonable real-world assumptions about when 
certain technologies can be applied to vehicles. Each vehicle model/
configuration in the analysis fleet also has information about its 
redesign schedule, i.e., the last year it was redesigned and when the 
agencies expect it to be redesigned again. Redesign schedules are a key 
part of manufacturers' business plans, as each new product can cost 
more than $1.0B and involve a significant portion of a manufacturer's 
scarce research, development, and manufacturing and equipment budgets 
and resources.\66\ Manufacturers have repeatedly told the agencies that 
sustainable business plans require careful management of resources and 
capital spending, and that the length of time each product remains in 
production is crucial to recouping the upfront product development and 
plant/equipment costs, as well as the capital needed to fund the 
development and manufacturing equipment needed for future products. 
Because the production volume of any given vehicle model varies within 
a manufacturer's product line and also varies among different 
manufacturers, redesign schedules typically vary for each model and 
manufacturer. Some (relatively few) technological improvements are 
small enough they can be applied in any model year; others are major 
enough they can only be cost-effectively applied at a vehicle redesign, 
when many other things about the vehicle are already changing. Ensuring 
the CAFE model makes technological improvements to vehicles only when 
it is feasible to do so also helps the analysis better represent 
manufacturer responses to different levels of standards.

    \66\ Shea, T. Why Does It Cost So Much For Automakers To Develop 
New Models?, Autoblog (Jul. 27, 2010), https://www.autoblog.com/2010/07/27/why-does-it-cost-so-much-for-automakers-to-develop-new-models/.

    A final important aspect of reflecting what vehicles will exist in 
future model years and potential manufacturer responses to standards is 
estimating how future sales might change in response to different 
potential standards. If potential future standards appear likely to 
have major effects in terms of shifting production from cars to trucks 
(or vice versa), or in terms of shifting sales between manufacturers or 
groups of manufacturers, that is important for the agencies to 
consider. For previous analyses, the CAFE model used a static forecast 
contained in the analysis fleet input file, which specified changes in 
production volumes over time for each vehicle model/configuration. This 
approach yielded results that, in terms of production volumes, did not 
change between scenarios or with changes in important model inputs. For 
example, very stringent standards with very high technology costs would 
result in the same estimated production volumes as less stringent 
standards with very low technology costs.
    New for today's proposal, the CAFE model begins with the first-year 
production volumes (i.e., MY 2016 for today's analysis) and adjusts 
ensuing sales mix year by year (between cars and trucks, and between 
manufacturers) endogenously as part of the analysis, rather than using 
external forecasts of future car/truck split and future manufacturer 
sales volumes. This leads the model to produce different estimates of 
future production volumes under different standards and in response to 
different inputs, reflecting the expectation that regulatory standards 
and other external factors will, in fact, impact the market.
    The input file for the CAFE model characterizing the analysis fleet 
\67\ includes a large amount of data about vehicle models/
configurations, their technological characteristics, the manufacturers 
and fleets to which they belong, and initial prices and production 
volumes which provide the starting points for projection (by the sales 
model) to ensuing model years. The following sections discuss aspects 
of how the analysis fleet was built for this proposal and seek comment 
on those topics.

    \67\ Available with the model and other input files supporting 
today's announcement at https://www.nhtsa.gov/corporate-average-fuel-economy/compliance-and-effects-modeling-system.

2. Source Data for Building the Analysis Fleet
    The source data for the vehicle models/configurations in the 
analysis fleet and their technologies is a central input for the 
analysis. The sections below discuss pros and cons of different 
potential sources and what was used for this proposal.
(a) Use of Confidential Business Information Versus Publicly-Releasable 
    Since 2001, CAFE analysis has used either confidential, forward-
estimating product plans from manufacturers, or publicly available data 
on vehicles already sold, as a starting point for determining what 
technologies can be applied to what vehicles in response to potential 
different levels of standards. These two sources present a tradeoff. 
Confidential product plans comprehensively represent what vehicles a 
manufacturer expects to produce in coming years, accounting for plans 
to introduce new vehicles and fuel-saving technologies and, for 
example, plans to discontinue other vehicles and even brands. This 
information can be very thorough and can improve the accuracy of the 
analysis, but for competitive reasons, most of this information must be 
redacted prior to publication with rulemaking documentation. This makes 
it difficult for public commenters to reproduce the analysis for 
themselves as

[[Page 43005]]

they develop their comments. Some non-industry commenters have also 
expressed concern manufacturers would have an incentive in the 
submitted plans to (deliberately or not) underestimate their future 
fuel economy capabilities and overstate their expectations about, for 
example, the levels of performance of future vehicle models in order to 
affect the analysis. Since 2010, EPA and NHTSA have based analysis 
fleets almost exclusively on information from commercial and public 
sources, starting with CAFE compliance data and adding information from 
other sources.
    An analysis fleet based primarily on public sources can be released 
to the public, solving the issue of commenters being unable to 
reproduce the overall analysis when they want to. However, industry 
commenters have argued such an analysis fleet cannot accurately reflect 
manufacturers actual plans to apply fuel-saving technologies (e.g., 
manufacturers may apply turbocharging to improve not just fuel economy, 
but also to improve vehicle performance) or manufacturers' plans to 
change product offerings by introducing some vehicles and brands and 
discontinuing other vehicles and brands, precisely because that 
information is typically confidential business information (CBI). A 
fully-publicly-releasable analysis fleet holds vehicle characteristics 
unchanged over time and arguably lacks some level of accuracy when 
projected into the future. For example, over time, manufacturers 
introduce new products and even entire brands. On the other hand, plans 
announced in press releases do not always ultimately bear out, nor do 
commercially-available third-party forecasts. Assumptions could be made 
about these issues to improve the accuracy of a publicly-releasable 
analysis fleet, but concerns include that this information would either 
be largely incorrect, or information would be released that 
manufacturers would consider CBI. We seek comment on how to address 
this issue going forward, recognizing the competing interests involved 
and also recognizing typical timeframes for CAFE and CO2 
standards rulemakings.
(b) Use of MY 2016 CAFE Compliance Data Versus Other Starting Points
    Based on the assumption that a publicly-available analysis fleet 
continues to be desirable, for this NPRM, an analysis fleet was 
constructed starting with CAFE compliance information from 
manufacturers.\68\ Information from MY 2016 was chosen as the 
foundation for today's analysis fleet because, at the time the 
rulemaking analysis was initiated, the 2016 fleet represented the most 
up-to-date information available in terms of individual vehicle models 
and configurations, production technology levels, and production 
volumes. If MY 2017 data had been used while this analysis was being 
developed, the agencies would have needed to use product planning 
information that could not be made available to the public until a 
later date.

    \68\ CO2 emissions rates are directly related to fuel 
economy levels, and the CAFE model uses the latter to calculate the 

    The analysis fleet was initially developed with 2016 mid-model year 
compliance data because final compliance data was not available at that 
time, and the timing provided manufacturers the opportunity to review 
and comment on the characterization of their vehicles in the fleet. 
With a view toward developing an accurate characterization of the 2016 
fleet to serve as an analytical starting point, corrections and updates 
to mid-year data (e.g., to production estimates) were sought, in 
addition to corroboration or correction of technical information 
obtained from commercial and other sources (to the extent that 
information was not included in compliance data), although future 
product planning information from manufacturers (e.g., future product 
offerings, products to be discontinued) was not requested, as most 
manufacturers view such information as CBI. Manufacturers offered a 
range of corrections to indicate engineering characteristics (e.g., 
footprint, curb weight, transmission type) of specific vehicle model/
configurations, as well as updates to fuel economy and production 
volume estimates in mid-year reporting. After following up on a case-
by-case basis to investigate significant differences, the analysis 
fleet was updated.
    Sales, footprint, and fuel economy values with final compliance 
data were also updated if that data was available. In a few cases, 
final production and fuel economy values may be slightly different for 
specific model year 2016 vehicle models and configurations than are 
indicated in today's analysis; however, other vehicle characteristics 
(e.g., footprint, curb weight, technology content) important to the 
analysis should be accurate. While some commenters have, in the past, 
raised concerns that non-final CAFE compliance data is subject to 
change, the potential for change is likely not significant enough to 
merit using final data from an earlier model year reflecting a more 
outdated fleet. Moreover, even ostensibly final CAFE compliance data 
can sometimes be subject to later revision (e.g., if errors in fuel 
economy tests are discovered), and the purpose of today's analysis is 
not to support enforcement actions but rather to provide a realistic 
assessment of manufacturers' potential responses to future standards.
    Manufacturers integrated a significant amount of new technology in 
the MY 2016 fleet, and this was especially true for newly-designed 
vehicles launched in MY 2016. While subsequent fleets will involve even 
further application of technology, using available data for MY 2016 
provides the most realistic detailed foundation for analysis that can 
be made available publicly in full detail, allowing stakeholders to 
independently reproduce the analysis presented in this proposal. 
Insofar as future product offerings are likely to be more similar to 
vehicles produced in 2016 than to vehicles produced in earlier model 
years, using available data regarding the 2016 model year provides the 
most realistic, publicly releasable foundation for constructing a 
forecast of the future vehicle market for this proposal.
    A number of comments to the Draft TAR, EPA's Proposed 
Determination, and EPA's 2017 Request for Comment \69\ stated that the 
most up-to-date analysis fleet possible should be used, because a more 
up-to-date analysis fleet will better capture how manufacturers apply 
technology and will account better for vehicle model/configuration 
introductions and deletions.\70\ On the other hand, some commenters 
suggested that because manufacturers continue improving vehicle 
performance and utility over time, an older analysis fleet should be 
used to estimate how the fleet could have evolved had manufacturers 
applied all technological potential to

[[Page 43006]]

fuel economy rather than continuing to improve vehicle performance and 
utility.\71\ Because manufacturers change and improve product offerings 
over time, conducting analysis with an older analysis fleet (or with a 
fleet using fuel economy levels and CO2 emissions rates that 
have been adjusted to reflect an assumed return to levels of 
performance and utility typical of some past model year) would miss 
this real-world trend. While such an analysis could demonstrate what 
industry could do if, for example, manufacturers devoted all 
technological improvements toward raising fuel economy and reducing 
CO2 emissions (and if consumers decided to purchase these 
vehicles), we do not believe it would be consistent with a transparent 
examination of what effects different levels of standards would have on 
individual manufacturers and the fleet as a whole.

    \69\ 82 FR 39551 (Aug. 21, 2017).
    \70\ For example, in 2016 comments to dockets EPA-HQ-OAR-2015-
0827 and NHTSA-2016-0068, the Alliance of Automobile Manufacturers 
commented that ``the Alliance supports the use of the most recent 
data available in establishing the baseline fleet, and therefore 
believes that NHTSA's selection [of, at the time, model year 2015] 
was more appropriate for the Draft TAR.'' (Alliance at 82, Docket 
ID. EPA-HQ-OAR-2015-0827-4089) Global Automakers commented that ``a 
one-year difference constitutes a technology change-over for up to 
20% of a manufacturer's fleet. It was also generally understood by 
industry and the agencies that several new, and potentially 
significant, technologies would be implemented in MY 2015. The use 
of an older, outdated baseline can have significant impacts on the 
modeling of subsequent Reference Case and Control Case 
technologies.'' (Global Automakers at A-10, Docket ID. EPA-HQ-OAR-
    \71\ For example, in 2016 comments to dockets EPA-HQ-OAR-2015-
0827 and NHTSA-2016-0068, UCS stated ``in modeling technology 
effectiveness and use, the agencies should use 2010 levels of 
performance as the baseline.'' (UCS at 4, Docket ID. EPA-HQ-OAR-

    Generally, all else being equal, using a newer analysis fleet will 
produce more realistic estimates of impacts of potential new standards 
than using an outdated analysis fleet. However, among relatively 
current options, a balance must be struck between, on one hand, inputs' 
freshness, and on the other, inputs' completeness and accuracy.\72\ 
During assembly of the inputs for today's analysis, final compliance 
data was available for the MY 2015 model year but not in a few cases 
for MY 2016. However, between mid-year compliance information and 
manufacturers' specific updates discussed above, a robust and detailed 
characterization of the MY 2016 fleet was developed. However, while 
information continued to develop regarding the MY 2017 and, to a lesser 
extent MY 2018 and even MY 2019 fleets, this information was--even in 
mid-2017--too incomplete and inconsistent to be assembled with 
confidence into an analysis fleet for modeling supporting deliberations 
regarding today's proposal.

    \72\ Comments provided through a recent peer review of the CAFE 
model recognize the need for this balance. For example, referring to 
NHTSA's 2016 analysis documented in the draft TAR, one of the peer 
reviewers commented as follows: ``The NHTSA decision to use MY 2015 
data is wise. In the TAR they point out that a MY 2016 foundation 
would require the use of confidential data, which is less desirable. 
Clearly they would also have a qualitative vision of the MY 2016 
landscape while employing MY 2015 as a foundation. Although MY 2015 
data may still be subject to minor revision, this is unlikely to 
impact the predictive ability of the model . . . A more complex 
alternative approach might be to employ some 2016 changes in 
technology, and attempt a blend of MY 2015 and MY 2016, while 
relying of estimation gained from only MY 2015 for sales. This 
approach may add some relevancy in terms of technology, but might 
introduce substantial error in terms of sales.''

    In short, the 2016 fleet was, in fact, the most up-to-date fleet 
that could be produced for this NPRM. Moreover, during late 2016 and 
early 2017, nearly all manufacturers provided comments on the 
characterization of their vehicles in the analysis fleet, and many 
provided specific feedback about their vehicles, including aerodynamic 
drag coefficients, tire rolling resistance values, transmission 
efficiencies, and other information used in the analysis. NHTSA worked 
with manufacturers to clarify and correct some information and 
integrated the information into a single input file for use in the CAFE 
model. Accordingly, the current analysis fleet is reasonable to use for 
purposes of the NPRM analysis.
    As always, however, ways to improve the analysis fleet used for 
subsequent modeling to evaluate potential new CAFE and CO2 
standards will undergo continuous consideration. As described above, 
the compliance data is only the starting point for developing the 
analysis fleet; much additional information comes directly from 
manufacturers (such as details about technologies, platforms, engines, 
transmissions, and other vehicle information, that may not be present 
in compliance data), and other information must come from commercial 
and public sources (for example, fleet-wide information like market 
share, because individual manufacturers do not provide this kind of 
information). If newer compliance data (i.e., MY 2017) becomes 
available and can be analyzed during the pendency of this rulemaking, 
and if all of the other necessary steps can be performed, the analysis 
fleet will be updated, as feasible, and made publicly available. The 
agencies seek comment on the option used today and any other options, 
as well as on the tradeoffs between, on one hand, fidelity with 
manufacturers' actual plans and, on the other, the ability to make 
detailed analysis inputs and outputs publicly available.
(c) Observed Technology Content of 2016 Fleet
    As explained above, the analysis fleet is defined not only by the 
vehicle models/configurations it contains but also by the technologies 
on those vehicles. Each vehicle model/configuration in the analysis 
fleet has an associated list of observed technologies and equipment 
that can improve fuel economy and reduce CO2 emissions.\73\ 
With a portfolio of descriptive technologies arranged by manufacturer 
and model, the analysis fleet can be summarized and project how 
vehicles in that fleet may improve over time via the application of 
additional technology.

    \73\ These technologies are generally grouped into the following 
categories: Vehicle technologies include mass reduction, aerodynamic 
drag reduction, low rolling resistance tires, and others. Engine 
technologies include engine attributes describing fuel type, engine 
aspiration, valvetrain configuration, compression ratio, number of 
cylinders, size of displacement, and others. Transmission 
technologies include different transmission arrangements like 
manual, 6-speed automatic, 8-speed automatic, continuously variable 
transmission, and dual-clutch transmissions. Hybrid and electric 
powertrains may complement traditional engine and transmission 
designs or replace them entirely.

    In many cases, vehicle technology is clearly observable from the 
2016 compliance data (e.g., compliance data indicates clearly which 
vehicles have turbochargers and which have continuously variable 
transmissions), but in some cases technology levels are less 
observable. For the latter, like levels of mass reduction, the analysis 
categorized levels of technology already used in a given vehicle. 
Similarly, engineering judgment was used to determine if higher mass 
reduction levels may be used practicably and safely in a given vehicle.
    Either in mid-year compliance data for MY 2016 or, separately and 
at the agencies' invitation (as discussed above), most manufacturers 
identified most of the technology already present in each of their MY 
2016 vehicle model/configurations. This information was not as complete 
for all manufacturers' products as needed for today's analysis, so in 
some cases, information was supplemented with publicly available data, 
typically from manufacturer media sites. In limited cases, 
manufacturers did not supply information, and information from 
commercial and publicly available sources was used.
(d) Mapping Technology Content of 2016 Fleet to Argonne Technology 
Effectiveness Simulation Work
    While each vehicle model/configuration in the analysis fleet has 
its list of observed technologies and equipment, the ways in which 
manufacturers apply technologies and equipment do not always coincide 
perfectly with how the analysis characterizes the various technologies 
that improve fuel economy and reduce CO2 emissions. To 
improve how the observed vehicle fleet ``fits into'' the analysis, each 
vehicle model/configuration is ``mapped'' to the full-

[[Page 43007]]

vehicle simulation modeling \74\ by Argonne National Laboratory that is 
used to estimate the effectiveness of the fuel economy-improving/
CO2 emissions-reducing technologies considered. Argonne 
produces full-vehicle simulation modeling for many combinations of 
technologies, on many types of vehicles, but it did not simulate 
literally every single vehicle model/configuration in the analysis 
fleet because it would be impractical to assemble the requisite 
detailed information--much of which would likely only be provided on a 
confidential basis--specific to each vehicle model/configuration and 
because the scale of the simulation effort would correspondingly 
increase by at least two orders of magnitude. Instead, Argonne 
simulated 10 different vehicle types, corresponding to the ``technology 
classes'' generally used in CAFE analysis over the past several 
rulemakings (e.g., small car, small performance car, pickup truck, 
etc.). Each of those 10 different vehicle types was assigned a set of 
``baseline characteristics,'' to which Argonne added combinations of 
fuel-saving technologies and then ran simulations to determine the fuel 
economy achieved when applying each combination of technologies to that 
vehicle type given its baseline characteristics. These inputs, 
discussed at greater length in Sections II.D and II.G, provide the 
basis for the CAFE model's estimation of fuel economy levels and 
CO2 emission rates.

    \74\ Full-vehicle simulation modeling uses software and physics 
models to compute and estimate energy use of a vehicle during 
explicit driving conditions. Section II.D below contains more 
information on the Argonne work for this analysis.

    In the analysis fleet, inputs assign each specific vehicle model/
configuration to a technology class, and once there, map to the 
simulation within that technology class most closely matching the 
combination of observed technologies and equipment on that vehicle.\75\ 
This mapping to a specific simulation result most closely representing 
a given vehicle model/configuration's initial technology ``state'' 
enables the CAFE model to estimate the same vehicle model/
configuration's fuel economy after application of some other 
combination of technologies, leading to an alternative technology 

    \75\ Mapping vehicle model/configurations in the analysis fleet 
to Argonne simulations was generally straightforward, but 
occasionally the mapping was complicated by factors like a vehicle 
model/configuration being a great match for simulations within more 
than one technology class (in which case, the model/configuration 
was assigned to the technology class that it best matched), or when 
technologies on the model/configuration were difficult to observe 
directly (like friction reduction or parasitic loss characteristics 
of a transmission, in which case the agencies relied on 
manufacturer-reported data or CBI to help map the vehicle to a 


[[Page 43008]]


[[Page 43009]]


[[Page 43010]]


[[Page 43011]]

(e) Shared Vehicle Platforms, Engines, and Transmissions
    Another aspect of characterizing vehicle model/configurations in 
the analysis fleet is based on whether they share a ``platform'' with 
other vehicle model/configurations. A ``platform'' refers to engineered 
underpinnings shared on several differentiated products. Manufacturers 
share and standardize components, systems, tooling, and assembly 
processes within their products (and occasionally with the products of 
another manufacturer) to cost-effectively maintain vibrant 

    \76\ The concept of platform sharing has evolved with time. 
Years ago, manufacturers rebadged vehicles and offered luxury 
options only on premium nameplates (and manufacturers shared some 
vehicle platforms in limited cases). Today, manufacturers share 
parts across highly differentiated vehicles with different body 
styles, sizes, and capabilities that may share the same platform. 
For instance, the Honda Civic and Honda CR-V share many parts and 
are built on the same platform. Engineers design chassis platforms 
with the ability to vary wheelbase, ride height, and even driveline 
configuration. Assembly lines can produce hatchbacks and sedans to 
cost-effectively utilize manufacturing capacity and respond to 
shifts in market demand. Engines made on the same line may power 
small cars or mid-size sport utility vehicles. Additionally, 
although the agencies' analysis, like past CAFE analyses, considers 
vehicles produced for sale in the U.S., the agency notes these 
platforms are not constrained to vehicle models built for sale in 
the United States; many manufacturers have developed, and use, 
global platforms, and the total number of platforms is decreasing 
across the industry. Several automakers (for example, General Motors 
and Ford) either plan to, or already have, reduced their number of 
platforms to less than 10 and account for the overwhelming majority 
of their production volumes on that small number of platforms.

    Vehicle model/configurations derived from the same platform are so 
identified in the analysis fleet. Many manufacturers' use of vehicle 
platforms is well documented in the public record and widely recognized 
among the vehicle engineering community. Engineering knowledge, 
information from trade publications, and feedback from manufacturers 
and suppliers was also used to assign vehicle platforms in the analysis 
    When the CAFE model is deciding where and how to add technology to 
vehicles, if one vehicle on the platform receives new technology, other 
vehicles on the platform also receive the technology as part of their 
next major redesign or refresh.\77\ Similar to vehicle platforms, 
manufacturers create engines that share parts.\78\ One engine family 
may appear on many vehicles on a platform, and changes to that engine 
may or may not carry through to all the vehicles. Some engines are 
shared across a range of different vehicle platforms. Vehicle model/
configurations in the analysis fleet that share engines belonging to 
the same platform are also identified as such.

    \77\ The CAFE model assigns mass reduction technology at a 
platform level, but many other technologies may be assigned and 
shared at a vehicle nameplate or vehicle model level.
    \78\ For instance, manufacturers may use different piston 
strokes on a common engine block or bore out common engine block 
castings with different diameters to create engines with an array of 
displacements. Head assemblies for different displacement engines 
may share many components and manufacturing processes across the 
engine family. Manufacturers may finish crankshafts with the same 
tools, to similar tolerances. Engines on the same architecture may 
share pistons, connecting rods, and the same engine architecture may 
include both six and eight cylinder engines.

    It is important to note that manufacturers define common engines 
differently. Some manufacturers consider engines as ``common'' if the 
engines shared an architecture, components, or manufacturing processes. 
Other manufacturers take a narrower definition, and only assume 
``common'' engines if the parts in the engine assembly are the same. In 
some cases, manufacturers designate each engine in each application as 
a unique powertrain.\79\ Engine families for each manufacturer were 
tabulated and assigned \80\ based on data-driven criteria. If engines 
shared a common cylinder count and configuration, displacement, 
valvetrain, and fuel type, those engines may have been considered 
together. Additionally, if the compression ratio, horsepower, and 
displacement of engines were only slightly different, those engines 
were considered to be the same for the purposes of redesign and 
sharing. Vehicles in the analysis fleet with the same engine family 
will therefore adopt engine technology in a coordinated fashion.\81\ By 
grouping engines together, the CAFE model controls future engine 
families to retain reasonable powertrain complexity.\82\

    \79\ For instance, a manufacturer may have listed two engines 
for a pair that share designs for the engine block, the crank shaft, 
and the head because the accessory drive components, oil pans, and 
engine calibrations differ between the two. In practice, many 
engines share parts, tooling, and assembly resources, and 
manufacturers often coordinate design updates between two similar 
    \80\ Engine family is referred to in the analysis as an ``engine 
    \81\ Specifically, if such vehicles have different design 
schedules (i.e., refresh and redesign schedules), and a subset of 
vehicles using a given engine add engine technologies in the course 
of a redesign or refresh that occurs in an early model year (e.g., 
2018), other vehicles using the same engine ``inherit'' these 
technologies at the soonest ensuing refresh or redesign. This is 
consistent with a view that, over time, most manufacturers are 
likely to find it more practicable to shift production to a new 
version of an engine than to indefinitely continue production of 
both the new engine and a ``legacy'' engine.
    \82\ This does mean, however, that for manufacturers that 
submitted highly atomized engine and transmission portfolios, there 
is a practical cap on powertrain complexity and the ability of the 
manufacturer to optimize the displacement of (a.k.a. ``right size'') 
engines perfectly for each vehicle configuration.

    Like with engines, manufacturers often use transmissions that are 
the same or similar on multiple vehicles.\83\ To reflect this reality, 
shared transmissions were considered for manufacturers as appropriate. 
To define common transmissions, the agencies considered transmission 
type (manual, automatic, dual-clutch, continuously variable), number of 
gears, and vehicle architecture (front-wheel-drive, rear-wheel-drive, 
all-wheel-drive based on a front-wheel-drive platform, or all-wheel-
drive based on a rear-wheel-drive platform). If vehicles shared these 
attributes, these transmissions were grouped for the analysis. Vehicles 
in the analysis fleet with the same transmission configuration \84\ 
will adopt transmission technology together, as described above.\85\

    \83\ Manufacturers may produce transmissions that have nominally 
different machining to castings, or manufacturers may produce 
transmissions that are internally identical, except for final output 
gear ratio. In some cases, manufacturers sub-contract with suppliers 
that deliver whole transmissions. In other cases, manufacturers form 
joint-ventures to develop shared transmissions, and these 
transmission platforms may be offered in many vehicles across 
manufacturers. Manufacturers use supplier and joint-venture 
transmissions to a greater extent than engines.
    \84\ Transmission configurations are referred to in the analysis 
as ``transmission codes.''
    \85\ Similar to the inheritance approach outlined for engines, 
if one vehicle application of a shared transmission family upgraded 
the transmission, other vehicle applications also upgraded the 
transmission at the next refresh or redesign year.

    Having all vehicles that share a platform (or engines that are part 
of a family) adopt fuel economy-improving/CO2 emissions-
reducing technologies together, subject to refresh/redesign 
constraints, reflects the real-world considerations described above but 
also overlooks some decisions manufacturers might make in the real 
world in response to market pull, meaning that even though the analysis 
fleet is incredibly complex, it is also over-simplified in some 
respects compared to the real world. For example, the CAFE model does 
not currently attempt to simulate the potential for a manufacturer to 
shift the application of technologies to improve performance rather 
than fuel economy. Therefore, the model's representation of the 
``inheritance'' of technology can lead to estimates a manufacturer 
might eventually exceed fuel economy

[[Page 43012]]

standards as technology continues to propagate across shared platforms 
and engines. In the past, there were some examples of extended periods 
during which some manufacturers exceeded one or both of the CAFE and/or 
GHG standards, but in plenty of other examples, manufacturers chose to 
introduce (or even reintroduce) technological complexity into their 
vehicle lineups in response to buyer preferences. Going forward, and 
recognizing the recent trend for consolidating platforms, it seems 
likely manufacturers will be more likely to choose efficiency over 
complexity in this regard; therefore, the potential should be lower 
that today's analysis turns out to be over-simplified compared to the 
real world.
    Options will be considered to further refine the representation of 
sharing and inheritance of technology, possibly including model 
revisions to account for tradeoffs between fuel economy and performance 
when applying technology. Please provide comments on the sharing and 
inheritance-related aspects of the analysis fleet and the CAFE model 
along with information that would support refinement of the current 
approach or development and implementation of alternative approaches.
(f) Estimated Product Design Cycles
    Another aspect of characterizing vehicle model/configurations in 
the analysis fleet is based on when they can next be refreshed or 
redesigned. Redesign schedules play an important role in determining 
when new technologies may be applied. Many technologies that improve 
fuel economy and reduce CO2 emissions may be difficult to 
incorporate without a major product redesign. Therefore, each vehicle 
model in the analysis fleet has an associated redesign schedule, and 
the CAFE model uses that schedule to restrict significant advances in 
some technologies (like major mass reduction) to redesign years, while 
allowing manufacturers to include minor advances (such as improved tire 
rolling resistance) during a vehicle ``refresh,'' or a smaller update 
made to a vehicle, which can happen between redesigns. In addition to 
refresh and redesign schedules associated with vehicle model/
configurations, vehicles that share a platform subsequently have 
platform-wide refresh and redesign schedules for mass reduction 
    To develop the refresh/redesign cycles used for the MY 2016 
vehicles in the analysis fleet, information from commercially available 
sources was used to project redesign cycles through MY 2022, as for 
NHTSA's analysis for the Draft TAR published in 2016.\86\ Commercially 
available sources' estimates through MY 2022 are generally supported by 
detailed consideration of public announcements plus related 
intelligence from suppliers and other sources, and recognize that 
uncertainty increases considerably as the forecasting horizon is 
extended. For MYs 2023-2035, in recognition of that uncertainty, 
redesign schedules were extended considering past pacing for each 
product, estimated schedules through MY 2022, and schedules for other 
products in the same technology classes. As mentioned above, 
potentially confidential forward-looking information was not requested 
from manufacturers; nevertheless, all manufacturers had an opportunity 
to review the estimates of product-specific redesign schedules, a few 
manufacturers provided related forecasts and, for the most part, that 
information corroborated the estimates.

    \86\ In some cases, data from commercially available sources was 
found to be incomplete or inconsistent with other available 
information. For instance, commercially available sources identified 
some newly imported vehicles as new platforms, but the international 
platform was midway through the product lifecycle. While new to the 
U.S. market, treating these vehicles as new entrants would have 
resulted in artificially short redesign cycles if carried forward, 
in some cases. Similarly, commercially available sources labeled 
some product refreshes as redesigns, and vice versa. In these 
limited cases, the data was revised to be consistent with other 
available information or typical redesign and refresh schedules, for 
the purpose of the CAFE modeling. In these limited cases, the 
forecast time between redesigns and refreshes was updated to match 
the observed past product timing.

    Some commenters suggested supplanting these estimated redesign 
schedules with estimates applying faster cycles (e.g., four to five 
years), and this approach was considered for the analysis.\87\ Some 
manufacturers tend to operate with faster redesign cycles and may 
continue to do so, and manufacturers tend to redesign some products 
more frequently than others. However, especially considering that 
information presented by manufacturers largely supports estimates 
discussed above, applying a ``one size fits all'' acceleration of 
redesign cycles would likely not improve the analysis; instead, doing 
so would likely reduce consistency with the real world, especially for 
light trucks. Moreover, if some manufacturers accelerate redesigns in 
response to new standards, doing so would likely involve costs (greater 
levels of stranded capital, reduced opportunity to benefit from 
``learning''-related cost reductions) greater than reflected in other 
inputs to the analysis. However, a wider range of technologies can 
practicably be applied during mid-cycle ``freshenings'' than has been 
represented by past analyses, and this part of the analysis has been 
expanded, as discussed below in Section II.D.\88\ Also, in the 
sensitivity analysis supporting today's proposal and presented in 
Chapter 13 of the PRIA, one case involving faster redesign schedules 
and one involving slower redesign schedules has been analyzed.

    \87\ In response to the EPA's August 21, 2017, Request for 
Comments (docket numbers EPA-HQ-OAR-2015-0827 and NHTSA-2016-0068), 
see, e.g., CARB at 28 (Docket ID. EPA-HQ-OAR-2015-0827-9197), EDF at 
12 (Docket ID. EPA-HQ-OAR-2015-0827-9203), and NRDC, et. al. at 29-
33 (Docket ID. EPA-HQ-OAR-2015-0827-9826).
    \88\ NRDC, et al., at 32.

    Manufacturers use diverse strategies with respect to when, and how 
often they update vehicle designs. While most vehicles have been 
redesigned sometime in the last five years, many vehicles have not. In 
particular, vehicles with lower annual sales volumes tend to be 
redesigned less frequently, perhaps giving manufacturers more time to 
amortize the investment needed to bring the product to market. In some 
cases, manufacturers continue to produce and sell vehicles designed 
more than a decade ago.

[[Page 43013]]


    Each manufacturer may use different strategies throughout their 
product portfolio, and a component of each strategy may include the 
timing of refresh and redesign cycles. Table II-3 below summarizes the 
average time between redesigns, by manufacturer, by vehicle technology 
class.\89\ Dashes mean the manufacturer has no volume in that vehicle 
technology class in the MY 2016 analysis fleet. Across the industry, 
manufacturers average 6.5 years between product redesigns.

    \89\ Technology class, or tech class, refers to a group of fuel-
economy simulations of similar vehicles. As explained, each vehicle 
is assigned to a representative simulation to estimate technology 
effectiveness for purposes of the analysis.


[[Page 43014]]


    There are a few notable observations from this table. Pick-up 
trucks have much longer redesign schedules (7.8 years on average) than 
small cars (5.5 years on average). Some manufacturers redesign vehicles 
often (every 5.2 years in the case of Hyundai), while other 
manufacturers redesign vehicles less often (FCA waits on average 8.6 
years between vehicle redesigns). Across the industry, light-duty 
vehicle designs last for about 6.5 years.
    Even if two manufacturers have similar redesign cadence, the model 
years in which the redesigns occur may still be different and dependent 
on where each of the manufacturer's products are in their life cycle.
    Table II-4 summarizes the average age of manufacturers' offering by 
vehicle technology class. A value of ``0.0'' means that every vehicle 
for a manufacturer in that vehicle technology class, represented in the 
MY 2016 analysis fleet was new in MY 2016. Across the industry, 
manufacturers redesigned MY 2016 vehicles an average of 3.2 years 

[[Page 43015]]


    Based on historical observations and refresh/redesign schedule 
forecasts, careful consideration to redesign cycles for each 
manufacturer and each vehicle is important. Simply assuming every 
vehicle is redesigned by 2021 and by 2025 is not appropriate, as this 
would misrepresent both the likely timing of redesigns and the likely 
time between redesigns in most cases.
C. Development of Footprint-Based Curve Shapes
    As in the past four CAFE rulemakings, the most recent two of which 
included related standards for CO2 emissions, NHTSA and EPA 
are proposing to set attribute-based CAFE standards that are defined by 
a mathematical function of vehicle footprint, which has observable 
correlation with fuel economy and vehicle emissions. 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.\90\ 
While the CAA includes no specific requirements regarding GHG 
regulation, EPA has chosen to adopt standards consistent with the EPCA/
EISA requirements in the interest of simplifying compliance for the 
industry since 2010.\91\ Section II.C.1 describes the advantages of 
attribute standards, generally. Section II.C.2 explains the agencies' 
specific decision to use vehicle footprint as the attribute over which 
to vary stringency for past and current rules. Section II.C.3 discusses 
the policy considerations in selecting the specific mathematical 
function. Section II.C.4 discusses the methodologies used to develop 
current attribute-based standards, and the agencies' current proposal 
to continue to do so for MYs 2022-2026. Section II.C.5 discusses the 
methodologies used to reconsider the mathematical function for the 
proposed standards.

    \90\ 49 U.S.C. 32902(a)(3)(A).
    \91\ Such an approach is permissible under section 202(a) of the 
CAA, and EPA has used the attribute-based approach in issuing 
standards under analogous provisions of the CAA.

1. Why attribute-based standards, and what are the benefits?
    Under attribute-based standards, every vehicle model has fuel 
economy and CO2 targets, the levels of which depend on the 
level of that vehicle's determining attribute (for this proposed rule, 
footprint is the determining attribute, as discussed below). The 
manufacturer's fleet average performance is calculated by the harmonic 
production-weighted average of those targets, as defined below:

[[Page 43016]]


    Here, i represents a given model \92\ in a manufacturer's fleet, 
Productioni represents the U.S. production of that model, and Targeti 
represents the target as defined by the attribute-based standards. This 
means no vehicle is required to meet its target; instead, manufacturers 
are free to balance improvements however they deem best within (and, 
given credit transfers, at least partially across) their fleets.

    \92\ If a model has more than one footprint variant, here each 
of those variants is treated as a unique model, i, since each 
footprint variant will have a unique target.

    The idea is to select the shape of the mathematical function 
relating the standard to the fuel economy-related attribute to reflect 
the trade-offs manufacturers face in producing more of that attribute 
over fuel efficiency (due to technological limits of production and 
relative demand of each attribute). If the shape captures these trade-
offs, every manufacturer is more likely to continue adding fuel 
efficient technology across the distribution of the attribute within 
their fleet, instead of potentially changing the attribute--and other 
correlated attributes, including fuel economy--as a part of their 
compliance strategy. Attribute-based standards that achieve this have 
several advantages.
    First, assuming the attribute is a measurement of vehicle size, 
attribute-based standards reduce the incentive for manufacturers to 
respond to CAFE standards by reducing vehicle size in ways harmful to 
safety.\93\ Larger vehicles, in terms of mass and/or crush space, 
generally consume more fuel, but are also generally better able to 
protect occupants in a crash.\94\ Because each vehicle model has its 
own target (determined by a size-related attribute), properly fitted 
attribute-based standards provide little, if any, incentive to build 
smaller vehicles simply to meet a fleet-wide average, because smaller 
vehicles are subject to more stringent compliance targets.

    \93\ The 2002 NAS Report described at length and quantified the 
potential safety problem with average fuel economy standards that 
specify a single numerical requirement for the entire industry. See 
Transportation Research Board and National Research Council. 2002. 
Effectiveness and Impact of Corporate Average Fuel Economy (CAFE) 
Standards, Washington, DC: The National Academies Press (``2002 NAS 
Report'') at 5, finding 12, available at https://www.nap.edu/catalog/10172/effectiveness-and-impact-of-corporate-average-fuel-economy-cafe-standards (last accessed June 15, 2018). Ensuing 
analyses, including by NHTSA, support the fundamental conclusion 
that standards structured to minimize incentives to downsize all but 
the largest vehicles will tend to produce better safety outcomes 
than flat standards.
    \94\ Bento, A., Gillingham, K., & Roth, K. (2017). The Effect of 
Fuel Economy Standards on Vehicle Weight Dispersion and Accident 
Fatalities. NBER Working Paper No. 23340. Available at http://www.nber.org/papers/w23340 (last accessed June 15, 2018).

    Second, attribute-based standards, if properly fitted, better 
respect heterogeneous consumer preferences than do single-valued 
standards. As discussed above, a single-valued standard encourages a 
fleet mix with a larger share of smaller vehicles by creating 
incentives for manufacturers to use downsizing the average vehicle in 
their fleet (possibly through fleet mixing) as a compliance strategy, 
which may result in manufacturers building vehicles for compliance 
reasons that consumers do not want. Under a size-related, attribute-
based standard, reducing the size of the vehicle is a less viable 
compliance strategy because smaller vehicles have more stringent 
regulatory targets. As a result, the fleet mix under such standards is 
more likely to reflect aggregate consumer demand for the size-related 
attribute used to determine vehicle targets.
    Third, attribute-based standards provide a more equitable 
regulatory framework across heterogeneous manufacturers who may each 
produce different shares of vehicles along attributes correlated with 
fuel economy.\95\ A single, industry-wide CAFE standard imposes 
disproportionate cost burden and compliance challenges on manufacturers 
who produce more vehicles with attributes inherently correlated with 
lower fuel economy--i.e. manufacturers who produce, on average, larger 
vehicles. As discussed above, retaining the ability for manufacturers 
to produce vehicles which respect heterogeneous market preferences is 
an important consideration. Since manufacturers may target different 
markets as a part of their business strategy, ensuring that these 
manufacturers do not incur a disproportionate share of the regulatory 
cost burden is an important part of conserving consumer choices within 
the market.

    \95\ 2002 NAS Report at 4-5, finding 10.

2. Why footprint as the attribute?
    It is important that the CAFE and CO2 standards be set 
in a way that does not encourage manufacturers to respond by selling 
vehicles that are less safe. Vehicle size is highly correlated with 
vehicle safety--for this reason, it is important to choose an attribute 
correlated with vehicle size (mass or some dimensional measure). Given 
this consideration, there are several policy and technical reasons why 
footprint is considered to be the most appropriate attribute upon which 
to base the standards, even though other vehicle size attributes 
(notably, curb weight) are more strongly correlated with fuel economy 
and emissions.
    First, mass is strongly correlated with fuel economy; it takes a 
certain amount of energy to move a certain amount of mass. Footprint 
has some positive correlation with frontal surface area, likely a 
negative correlation with aerodynamics, and therefore fuel economy, but 
the relationship is less deterministic. Mass and crush space 
(correlated with footprint) are both important safety considerations. 
As discussed below and in the accompanying PRIA, NHTSA's research of 
historical crash data indicates that holding footprint constant, and 
decreasing the mass of the largest vehicles, will have a net positive 
safety impact to drivers overall, while holding footprint constant and 
decreasing the mass of the smallest vehicles will have a net decrease 
in fleetwide safety. Properly fitted footprint-based standards provide 
little, if any, incentive to build smaller vehicles to meet CAFE and 
CO2 standards, and therefore help minimize the impact of 
standards on overall fleet safety.
    Second, it is important that the attribute not be easily 
manipulated in a manner that does not achieve the goals of EPCA or 
other goals, such as safety. Although weight is more strongly 
correlated with fuel economy than footprint, there is less risk of 
manipulation (changing the attribute(s) to achieve a more favorable 
target) by increasing footprint under footprint-based standards than 
there would be by increasing vehicle mass under weight-based standards. 
It is relatively easy for a manufacturer to add enough weight to a 
vehicle to decrease its applicable fuel economy target a significant 
amount, as compared to increasing vehicle

[[Page 43017]]

footprint, which is a much more complicated change that typically takes 
place only with a vehicle redesign.
    Further, some commenters on the MY 2011 CAFE rulemaking were 
concerned that there would be greater potential for gaming under multi-
attribute standards, such as those that also depend on weight, torque, 
power, towing capability, and/or off-road capability. As discussed in 
NHTSA's MY 2011 CAFE final rule,\96\ it is anticipated that the 
possibility of gaming is lowest with footprint-based standards, as 
opposed to weight-based or multi-attribute-based standards. 
Specifically, standards that incorporate weight, torque, power, towing 
capability, and/or off-road capability in addition to footprint would 
not only be more complex, but by providing degrees of freedom with 
respect to more easily-adjusted attributes, they could make it less 
certain that the future fleet would actually achieve the projected 
average fuel economy and CO2 levels. This is not to say that 
a footprint-based system will eliminate gaming, or that a footprint-
based system will eliminate the possibility that manufacturers will 
change vehicles in ways that compromise occupant protection, but 
footprint-based standards achieve the best balance among affected 
considerations. Please provide comments on whether vehicular footprint 
is the most suitable attribute upon which to base standards.

    \96\ See 74 FR at 14359 (Mar. 30, 2009).

3. What mathematical function should be used to specify footprint-based 
    In requiring NHTSA to ``prescribe by regulation separate average 
fuel economy standards for passenger and non-passenger automobiles 
based on 1 or more vehicle attributes related to fuel economy and 
express each standard in the form of a mathematical function'', EPCA/
EISA provides ample discretion regarding not only the selection of the 
attribute(s), but also regarding the nature of the function. The CAA 
provides no specific direction regarding CO2 regulation, and 
EPA has continued to harmonize this aspect of its CO2 
regulations with NHTSA's CAFE regulations. The relationship between 
fuel economy (and GHG emissions) and footprint, though directionally 
clear (i.e., fuel economy tends to decrease and CO2 emissions tend to 
increase with increasing footprint), is theoretically vague, and 
quantitatively uncertain; in other words, not so precise as to a priori 
yield only a single possible curve.
    The decision of how to specify this mathematical function therefore 
reflects some amount of judgment. The function can be specified with a 
view toward achieving different environmental and petroleum reduction 
goals, encouraging different levels of application of fuel-saving 
technologies, avoiding any adverse effects on overall highway safety, 
reducing disparities of manufacturers' compliance burdens, and 
preserving consumer choice, among other aims. The following are among 
the specific technical concerns and resultant policy tradeoffs the 
agencies have considered in selecting the details of specific past and 
future curve shapes:
     Flatter standards (i.e., curves) increase the risk that 
both the size of vehicles will be reduced, potentially compromising 
highway safety, and reducing any utility consumers would have gained 
from a larger vehicle.
     Steeper footprint-based standards may create incentives to 
upsize vehicles, potentially oversupplying vehicles of certain 
footprints beyond what consumers would naturally demand, and thus 
increasing the possibility that fuel savings and CO2 
reduction benefits will be forfeited artificially.
     Given the same industry-wide average required fuel economy 
or CO2 standard, flatter standards tend to place greater 
compliance burdens on full-line manufacturers.
     Given the same industry-wide average required fuel economy 
or CO2 standard, dramatically steeper standards tend to 
place greater compliance burdens on limited-line manufacturers 
(depending of course, on which vehicles are being produced).
     If cutpoints are adopted, given the same industry-wide 
average required fuel economy, moving small-vehicle cutpoints to the 
left (i.e., up in terms of fuel economy, down in terms of 
CO2 emissions) discourages the introduction of small 
vehicles, and reduces the incentive to downsize small vehicles in ways 
that could compromise overall highway safety.
     If cutpoints are adopted, given the same industry-wide 
average required fuel economy, moving large-vehicle cutpoints to the 
right (i.e., down in terms of fuel economy, up in terms of 
CO2 emissions) better accommodates the design requirements 
of larger vehicles -- especially large pickups -- and extends the size 
range over which downsizing is discouraged.
4. What mathematical functions have been used previously, and why?
    Notwithstanding the aforementioned discretion under EPCA/EISA, data 
should inform consideration of potential mathematical functions, but 
how relevant data is defined and interpreted, and the choice of 
methodology for fitting a curve to that data, can and should include 
some consideration of specific policy goals. This section summarizes 
the methodologies and policy concerns that were considered in 
developing previous target curves (for a complete discussion see the 
2012 FRIA).
    As discussed below, the MY 2011 final curves followed a constrained 
logistic function defined specifically in the final rule.\97\ The MYs 
2012-2021 final standards and the MYs 2022-2025 augural standards are 
defined by constrained linear target functions of footprint, as shown 
below: \98\

    \97\ See 74 FR 14196, 14363-14370 (Mar. 30, 2009) for NHTSA 
discussion of curve fitting in the MY 2011 CAFE final rule.
    \98\ The right cutpoint for the light truck curve was moved 
further to the right for MYs 2017-2021, so that more possible 
footprints would fall on the sloped part of the curve. In order to 
ensure that, for all possible footprints, future standards would be 
at least as high as MY 2016 levels, the final standards for light 
trucks for MYs 2017-2021 is the maximum of the MY 2016 target curves 
and the target curves for the give MY standard. This is defined 
further in the 2012 final rule. See 77 FR 62624, at 62699-700 (Oct. 
15, 2012).

    Here, Target is the fuel economy target applicable to vehicles of a 
given footprint in square feet (Footprint). The upper asymptote, a, and 
the lower asymptote, b, are specified in mpg; the reciprocal of these 
values represent the lower and upper asymptotes, respectively, when the 
curve is instead specified in gallons per mile (gpm). The

[[Page 43018]]

slope, c, and the intercept, d, of the linear portion of the curve are 
specified as gpm per change in square feet, and gpm, respectively.
    The min and max functions will take the minimum and maximum values 
within their associated parentheses. Thus, the max function will first 
find the maximum of the fitted line at a given footprint value and the 
lower asymptote from the perspective of gpm. If the fitted line is 
below the lower asymptote it is replaced with the floor, which is also 
the minimum of the floor and the ceiling by definition, so that the 
target in mpg space will be the reciprocal of the floor in mpg space, 
or simply, a. If, however, the fitted line is not below the lower 
asymptote, the fitted value is returned from the max function and the 
min function takes the minimum value of the upper asymptote (in gpm 
space) and the fitted line. If the fitted value is below the upper 
asymptote, it is between the two asymptotes and the fitted value is 
appropriately returned from the min function, making the overall target 
in mpg the reciprocal of the fitted line in gpm. If the fitted value is 
above the upper asymptote, the upper asymptote is returned is returned 
from the min function, and the overall target in mpg is the reciprocal 
of the upper asymptote in gpm space, or b.
    In this way curves specified as constrained linear functions are 
specified by the following parameters:

a = upper limit (mpg)
b = lower limit (mpg)
c = slope (gpm per sq. ft.)
d = intercept (gpm)

    The slope and intercept are specified as gpm per sq. ft. and gpm 
instead of mpg per sq. ft. and mpg because fuel consumption and 
emissions appear roughly linearly related to gallons per mile (the 
reciprocal of the miles per gallon).
(a) NHTSA in MY 2008 and MY 2011 CAFE (Constrained Logistic)
    For the MY 2011 CAFE rule, NHTSA estimated fuel economy levels by 
footprint from the MY 2008 fleet after normalization for differences in 
technology,\99\ but did not make adjustments to reflect other vehicle 
attributes (e.g., power-to-weight ratios). Starting with the 
technology-adjusted passenger car and light truck fleets, NHTSA used 
minimum absolute deviation (MAD) regression without sales weighting to 
fit a logistic form as a starting point to develop mathematical 
functions defining the standards. NHTSA then identified footprints at 
which to apply minimum and maximum values (rather than letting the 
standards extend without limit) and transposed these functions 
vertically (i.e., on a gallons-per-mile basis, uniformly downward) to 
produce the promulgated standards. In the preceding rule, for MYs 2008-
2011 light truck standards, NHTSA examined a range of potential 
functional forms, and concluded that, compared to other considered 
forms, the constrained logistic form provided the expected and 
appropriate trend (decreasing fuel economy as footprint increases), but 
avoided creating ``kinks'' the agency was concerned would provide 
distortionary incentives for vehicles with neighboring footprints.\100\

    \99\ See 74 FR 14196, 14363-14370 (Mar. 30, 2009) for NHTSA 
discussion of curve fitting in the MY 2011 CAFE final rule.
    \100\ See 71 FR 17556, 17609-17613 (Apr. 6, 2006) for NHTSA 
discussion of ``kinks'' in the MYs 2008-2011 light truck CAFE final 
rule (there described as ``edge effects''). A ``kink,'' as used 
here, is a portion of the curve where a small change in footprint 
results in a disproportionally large change in stringency.

(b) MYs 2012-2016 Standards (Constrained Linear)
    For the MYs 2012-2016 rule, potential methods for specifying 
mathematical functions to define fuel economy and CO2 
standards were reevaluated. These methods were fit to the same MY 2008 
data as the MY 2011 standard. Considering these further specifications, 
the constrained logistic form, if applied to post-MY 2011 standards, 
would likely contain a steep mid-section that would provide undue 
incentive to increase the footprint of midsize passenger cars.\101\ A 
range of methods to fit the curves would have been reasonable, and a 
minimum absolute deviation (MAD) regression without sales weighting on 
a technology-adjusted car and light truck fleet was used to fit a 
linear equation. This equation was used as a starting point to develop 
mathematical functions defining the standards. Footprints were then 
identified at which to apply minimum and maximum values (rather than 
letting the standards extend without limit). Finally, these 
constrained/piecewise linear functions were transposed vertically 
(i.e., on a gpm or CO2 basis, uniformly downward) by 
multiplying the initial curve by a single factor for each MY standard 
to produce the final attribute-based targets for passenger cars and 
light trucks described in the final rule.\102\ These transformations 
are typically presented as percentage improvements over a previous MY 
target curve.

    \101\ 75 FR at 25362.
    \102\ See generally 74 FR at 49491-96; 75 FR at 25357-62.

(c) MYs 2017 and Beyond Standards (Constrained Linear)
    The mathematical functions finalized in 2012 for MYs 2017 and 
beyond changed somewhat from the functions for the MYs 2012-2016 
standards. These changes were made to both address comments from 
stakeholders, and to further consider some of the technical concerns 
and policy goals judged more preeminent under the increased uncertainty 
of the impacts of finalizing and proposing standards for model years 
further into the future.\103\ Recognizing the concerns raised by full-
line OEMs, it was concluded that continuing increases in the stringency 
of the light truck standards would be more feasible if the light truck 
curve for MYs 2017 and beyond was made steeper than the MY 2016 truck 
curve and the right (large footprint) cut-point was extended only 
gradually to larger footprints. To accommodate these considerations, 
the 2012 final rule finalized the slope fit to the MY 2008 fleet using 
a sales-weighted, ordinary least-squares regression, using a fleet that 
had technology applied to make the technology application across the 
fleet more uniform, and after adjusting the data for the effects of 
weight-to-footprint. Information from an updated MY 2010 fleet was also 
considered to support this decision. As the curve was vertically 
shifted (with fuel economy specified as mpg instead of gpm or 
CO2 emissions) upwards, the right cutpoint was progressively 
moved for the light truck curves with successive model years, reaching 
the final endpoint for MY 2021; this is further discussed and shown in 
Chapter 4.3 of the PRIA.

    \103\ The MYs 2012-2016 final standards were signed April 1st, 
2010--putting 6.5 years between its signing and the last affected 
model year, while the MYs 2017-2021 final standards were signed 
August 28th, 2012--giving just more than nine years between signing 
and the last affected final standards.

5. Reconsidering the Mathematical Functions for This Proposal
(a) Why is it important to reconsider the mathematical functions?
    By shifting the developed curves by a single factor, it is assumed 
that the underlying relationship of fuel consumption (in gallons per 
mile) to vehicle footprint does not change significantly from the model 
year data used to fit the curves to the range of model years for which 
the shifted curve shape is applied to develop the standards. However, 
it must be recognized that the relationship

[[Page 43019]]

between vehicle footprint and fuel economy is not necessarily constant 
over time; newly developed technologies, changes in consumer demand, 
and even the curves themselves could, if unduly susceptible to gaming, 
influence the observed relationships between the two vehicle 
characteristics. For example, if certain technologies are more 
effective or more marketable for certain types of vehicles, their 
application may not be uniform over the range of vehicle footprints. 
Further, if market demand has shifted between vehicle types, so that 
certain vehicles make up a larger share of the fleet, any underlying 
technological or market restrictions which inform the average shape of 
the curves could change. That is, changes in the technology or market 
restrictions themselves, or a mere re-weighting of different vehicles 
types, could reshape the fit curves.
    For the above reasons, the curve shapes were reconsidered using the 
newest available data, from MY 2016. With a view toward corroboration 
through different techniques, a range of descriptive statistical 
analyses were conducted that do not require underlying engineering 
models of how fuel economy and footprint might be expected to be 
related, and a separate analysis that uses vehicle simulation results 
as the basis to estimate the relationship from a perspective more 
explicitly informed by engineering theory was conducted as well. 
Despite changes in the new vehicle fleet both in terms of technologies 
applied and in market demand, the underlying statistical relationship 
between footprint and fuel economy has not changed significantly since 
the MY 2008 fleet used for the 2012 final rule; therefore, it is 
proposed to continue to use the curve shapes fit in 2012. The analysis 
and reasoning supporting this decision follows.
(b) What statistical analyses did NHTSA consider?
    In considering how to address the various policy concerns discussed 
above, data from the MY 2016 fleet was considered, and a number of 
descriptive statistical analyses (i.e., involving observed fuel economy 
levels and footprints) using various statistical methods, weighting 
schemes, and adjustments to the data to make the fleets less 
technologically heterogeneous were performed. There were several 
adjustments to the data that were common to all of the statistical 
analyses considered.
    With a view toward isolating the relationship between fuel economy 
and footprint, the few diesels in the fleet were excluded, as well as 
the limited number of vehicles with partial or full electric 
propulsion; when the fleet is normalized so that technology is more 
homogenous, application of these technologies is not allowed. This is 
consistent with the methodology used in the 2012 final rule.
    The above adjustments were applied to all statistical analyses 
considered, regardless of the specifics of each of the methods, 
weights, and technology level of the data, used to view the 
relationship of vehicle footprint and fuel economy. Table II-5, below, 
summarizes the different assumptions considered and the key attributes 
of each. The analysis was performed considering all possible 
combinations of these assumptions, producing a total of eight footprint 

[[Page 43020]]

(1) Current Technology Level Curves
    The ``current technology'' level curves exclude diesels and 
vehicles with electric propulsion, as discussed above, but make no 
other changes to each model year fleet. Comparing the MY 2016 curves to 
ones built under the same methodology from previous model year fleets 
shows whether the observed curve shape has changed significantly over 
time as standards have become more stringent. Importantly, these curves 
will include any market forces which make technology application 
variable over the distribution of footprint. These market forces will 
not be present in the ``maximum technology'' level curves: By making 
technology levels homogenous, this variation is removed. The current 
technology level curves built using both regression types and both 
regression weight methodologies from the MY 2008, MY 2010, and MY 2016 
fleets, shown in more detail in Chapter of the PRIA, support 
the curve slopes finalized in the 2012 final rule. The curves built 
from most methodologies using each fleet generally shift, but remain 
very similar in slope. This suggests that the relationship of footprint 
to fuel economy, including both technology and market limits, has not 
significantly changed.
(2) Maximum Technology Level Curves
    As in prior rulemakings, technology differences between vehicle 
models were considered to be a significant factor producing uncertainty 
regarding the relationship between fuel consumption and footprint. 
Noting that attribute-based standards are intended to encourage the 
application of additional technology to improve fuel efficiency and 
reduce CO2 emissions across the distribution of footprint in 
the fleet, approaches were considered in which technology application 
is simulated for purposes of the curve fitting analysis in order to 
produce fleets that are less varied in technology content. This 
approach helps reduce ``noise'' (i.e., dispersion) in the plot of 
vehicle footprints and fuel consumption levels and identify a more 
technology-neutral relationship between footprint and fuel consumption. 
The results of updated analysis for maximum technology level curves are 
also shown in Chapter of the PRIA. Especially if vehicles 
progress over time toward more similar size-specific efficiency, 
further removing variation in technology application both better 
isolates the relationship between fuel consumption and footprint and 
further supports the curve slopes finalized in the 2012 final rule.
(c) What other methodologies were considered?
    The methods discussed above are descriptive in nature, using 
statistical analysis to relate observed fuel economy levels to observed 
footprints for known vehicles. As such, these methods are clearly based 
on actual data, answering the question ``how does fuel economy appear 
to be related to footprint?'' However, being independent of explicit 
engineering theory, they do not answer the question ``how might one 
expect fuel economy to be related to footprint?'' Therefore, as an 
alternative to the above methods, an alternative methodology was also 
developed and applied that, using full-vehicle simulation, comes closer 
to answer the second question, providing a basis to either corroborate 
answers to the first, or suggest that further investigation could be 
    As discussed in the 2012 final rule, several manufacturers have 
confidentially shared with the agencies what they described as 
``physics-based'' curves, with each OEM showing significantly different 
shapes for the footprint-fuel economy relationships. This variation 
suggests that manufacturers face different curves given the other 
attributes of the vehicles in their fleets (i.e., performance 
characteristics) and/or that their curves reflected different levels of 
technology application. In reconsidering the shapes of the proposed MYs 
2021-2026 standards, a similar estimation of physics-based curves 
leveraging third-party simulation work form Argonne National 
Laboratories (ANL) was developed. Estimating physics-based curves 
better ensures that technology and performance are held constant for 
all footprints; augmenting a largely statistical analysis with an 
analysis that more explicitly incorporates engineering theory helps to 
corroborate that the relationship between fuel economy and footprint is 
in fact being characterized.
    Tractive energy is the amount of energy it will take to move a 
vehicle.\104\ Here, tractive energy effectiveness is defined as the 
share of the energy content of fuel consumed which is converted into 
mechanical energy and used to move a vehicle--for internal combustion 
engine (ICE) vehicles, this will vary with the relative efficiency of 
specific engines. Data from ANL simulations suggest that the limits of 
tractive energy effectiveness are approximately 25% for vehicles with 
internal combustion engines which do not possess ISG, other hybrid, 
plug-in, pure electric, or fuel cell technology.

    \104\ Thomas, J. ``Drive Cycle Powertrain Efficiencies and 
Trends Derived from EPA Vehicle Dynamometer Results,'' SAE Int. J. 
Passeng. Cars--Mech. Syst. 7(4):2014, doi:10.4271/2014-01-2562. 
Available at https://www.sae.org/publications/technical-papers/content/2014-01-2562/ (last accessed June 15, 2018).

    A tractive energy prediction model was also developed to support 
today's proposal. Given a vehicle's mass, frontal area, aerodynamic 
drag coefficient, and rolling resistance as inputs, the model will 
predict the amount of tractive energy required for the vehicle to 
complete the Federal test cycle. This model was used to predict the 
tractive energy required for the average vehicle of a given footprint 
\105\ and ``body technology package'' to complete the cycle. The body 
technology packages considered are defined in Table II-6, below. Using 
the absolute tractive energy predicted and tractive energy 
effectiveness values spanning possible ICE engines, fuel economy values 
were then estimated for different body technology packages and engine 
tractive energy effectiveness values.

    \105\ The mass reduction curves used elsewhere in this analysis 
were used to predict the mass of a vehicle with a given footprint, 
body style box, and mass reduction level. The `Body style Box' is 1 
for hatchbacks and minivans, 2 for pickups, and 3 for sedans, and is 
an important predictor of aerodynamic drag. Mass is an essential 
input in the tractive energy calculation.


[[Page 43021]]


    Chapter 6 of the PRIA shows the resultant CAFE levels estimated for 
the vehicle classes ANL simulated for this analysis, at different 
footprint values and by vehicle ``box.'' Pickups are considered 1-box, 
hatchbacks and minivans are 2-box, and sedans are 3-box. These 
estimates are compared with the MY 2021 standards finalized in 2012. 
The general trend of the simulated data points follows the pattern of 
the previous MY 2021 standards for all technology packages and tractive 
energy effectiveness values presented in the PRIA. The tractive energy 
curves are intended to validate the curve shapes against a physics-
based alternative, and the analysis suggests that the curve shapes 
track the physical relationship between fuel economy and tractive 
energy for different footprint values.
    Physical limitations are not the only forces manufacturers face; 
they must also produce vehicles that consumers will purchase. For this 
reason, in setting future standards, the analysis will continue to 
consider information from statistical analyses that do not homogenize 
technology applications in addition to statistical analyses which do, 
as well as a tractive energy analysis similar to the one presented 
    The relationship between fuel economy and footprint remains 
directionally discernable but quantitatively uncertain. Nevertheless, 
each standard must commit to only one function. Approaching the 
question ``how is fuel economy related to footprint'' from different 
directions and applying different approaches will provide the greatest 
confidence that the single function defining any given standard 
appropriately and reasonably reflects the relationship between fuel 
economy and footprint. Please provide comments on this tentative 
conclusion and the above discussion.
D. Characterization of Current and Anticipated Fuel-Saving Technologies
    The analysis evaluates a wide array of technologies manufacturers 
could use to improve the fuel economy of new vehicles, in both the near 
future and the timeframe of this proposed rulemaking, to meet the fuel 
economy and CO2 standards proposed in this rulemaking. The 
analysis evaluated costs for these technologies, and looked at how 
these costs may change over time. The analysis also considered how 
fuel-saving technologies may be used on many types of vehicles (ranging 
from small cars to trucks) and how the technologies may perform in 
improving fuel economy and CO2 emissions in combination with 
other technologies. With cost and effectiveness estimates for 
technologies, the analysis can forecast how manufacturers may respond 
to potential standards and can estimate the associated costs and 
benefits related to technology and equipment changes. This assists the 
assessment of technological feasibility and is a building block for the 
consideration of economic practicability of potential standards.
    NHTSA, EPA, and CARB issued the Draft Technical Assessment Report 
(Draft TAR) \106\ as the first step in the EPA MTE process. The Draft 
TAR provided an opportunity for the agencies to share with the public 
updated technical analysis relevant to development of future standards. 
For this NPRM, the analysis relies on portions of the analysis 
presented in the Draft TAR, along with new information that has been 
gathered and developed since conducting that analysis, and the 
significant, substantive input that was received during the public 
comment period.

    \106\ Available at https://www.nhtsa.gov/staticfiles/rulemaking/pdf/cafe/Draft-TAR-Final.pdf (last accessed June 15, 2018).

    The Draft TAR considered many technologies previously assessed in 
the 2012 final rule.\107\ In some cases, manufacturers have nearly 
universally adopted a technology in today's new vehicle fleet (for 
example, electric power steering). In other cases, manufacturers 
occasionally use a technology in today's new vehicle fleet (like 
turbocharged engines). For a few technologies considered in the 2012 
rulemaking, manufacturers began implementing the technologies but have 
since largely pivoted to other technologies due to consumer acceptance 
issues (for instance, in some cases drivability and performance feel 
issues associated with dual clutch transmissions without a torque 
converter) or limited commercial success. The analysis utilizes new 
information as manufacturers' use of technologies evolves.

    \107\ 77 FR 62624 (Oct. 15, 2012).

    Some of the emerging technologies described in the Draft TAR were 
not included in this analysis, but this includes some additional 
technologies not previously considered. As industry invents and 
develops new fuel-savings technologies, and as suppliers and 
manufacturers produce and apply the technologies, and as consumers 
react to the new technologies, efforts are continued to learn more 
about the capabilities and limitations of new technologies. While a 
technology is in early development, theoretical constructs, limited 
access to test data, and CBI is relied on to assess the technology. 
After manufacturers commercialize the technology and bring products to 
market, the technology may be studied in more detail, which generally 
leads to the most reliable information about the technology. In 
addition, once in production, the technology is represented in the fuel 
economy and CO2 status of the baseline fleet. The technology 
analysis is kept as current as possible in light of the ongoing 
technology development and implementation in the automotive industry.
    Some technology assumptions have been updated since the MYs 2017-
2025 final rule and, in many cases, since the 2016 Draft TAR. In some 
cases, EPA and NHTSA presented different analytical approaches in the 
Draft TAR; the analysis is now presented using the

[[Page 43022]]

same direct manufacturing costs, retail costs, and learning rates. In 
addition, the effectiveness of fuel-economy technologies is now 
assessed based on the same assumptions, and with the same tools. 
Finally, manufacturers' response to stringency alternatives is forecast 
with the same simulation model.
    Since the 2017 and later final rule, many cost assessments, 
including tear down studies, were funded and completed, and presented 
as part of the Draft TAR analysis. These studies evaluated 
transmissions, engines, hybrid technologies, and mass reduction.\108\ 
As a result, the analysis uses updated cost estimates for many 
technologies, some of which have been updated since the Draft TAR. In 
addition to those studies, the analysis also leveraged research reports 
from other organizations to assess costs.\109\ Today's analysis also 
updates the costs to 2016 dollars, as in many cases technology costs 
were estimated several years ago.

    \108\ FEV prepared several cost analysis studies for EPA on 
subjects ranging from advanced 8-speed transmissions to belt 
alternator starter, or Start/Stop systems. NHTSA also contracted 
with Electricore, EDAG, and Southwest Research on teardown studies 
evaluating mass reduction and transmissions. The 2015 NAS report on 
fuel economy technologies for light-duty vehicles also evaluated the 
agencies' technology costs developed based on these teardown 
studies, and the technology costs used in this proposal were updated 
accordingly. These studies are discussed in detail in Chapter 6 of 
the PRIA accompanying this proposal.
    \109\ For example, the agencies relied on reports from the 
Department of Energy's Office of Energy Efficiency & Renewable 
Energy's Vehicle Technologies Office. More information on that 
office is available at https://www.energy.gov/eere/vehicles/vehicle-technologies-office. Other agency reports that were relied on for 
technology or other information are referenced throughout this 
proposal and accompanying PRIA.

    The analysis uses an updated, peer-reviewed model developed by ANL 
for the Department of Energy to provide a more rigorous estimate for 
battery costs. The new battery model provides an estimate future for 
battery costs for hybrids, plug-in hybrids, and electric vehicles, 
taking into account the different battery design characteristics and 
taking into account the size of the battery for different 

    \110\ For instance, battery electric vehicles with high levels 
of mass reduction may use a smaller battery than a comparable 
vehicle with less mass reduction technology and still deliver the 
same range on a charge.

    In the Draft TAR, two possible methodologies to estimate indirect 
costs from direct manufacturing costs, described as ``indirect cost 
multipliers'' and ``retail price equivalent'' were presented. Both of 
these methodologies attempted to relate the price of parts for fuel-
saving technologies to a retail price. Today's analysis utilizes the 
direct manufacturing costs (DMC) and the retail price equivalent (RPE) 
methodology published in the Draft TAR.
    Two tools to estimate effectiveness of fuel-saving technologies 
were used in the Draft TAR, and for today's analysis, only one tool was 
used (Autonomie).\111\ Previously, EPA developed ``ALPHA'', an in-house 
model that estimated fuel-savings for technologies, which provided a 
foundation for EPA's analysis. EPA's ``ALPHA'' results were used to 
calibrate a much simpler ``Lumped Parameter Model'' that was developed 
by EPA to estimate technology effectiveness for many technologies. The 
Lumped Parameter Model (LPM) approximated simulation modeling results 
instead of directly using the results and lead to less accurate 
estimates of technology effectiveness. Many stakeholders questioned the 
efficacy of the Lumped Parameter Model and ALPHA assumptions and 
outputs in combination,\112\ especially as the tool was used to 
evaluate increasingly heterogeneous combinations of technologies in the 
baseline fleet.\113\ For today's analysis, EPA and NHTSA used an 
updated version of the Autonomie model--an improved version of what 
NHTSA presented in the 2016 Draft TAR--to assess technology 
effectiveness of technologies and combinations of technologies. The 
Department of Energy's ANL developed Autonomie and the underpinning 
model assumptions leveraged research from the DOE's Vehicle 
Technologies Office and feedback from the public. Autonomie is 
commercially available and widely used; third parties such as 
suppliers, automakers, and academic researchers (who publish findings 
in peer reviewed academic journals) commonly use the Autonomie 
simulation software.

    \111\ ANL's Full-Vehicle Simulation Autonomie Model is discussed 
in Chapter 6 of the PRIA and in the ANL Model Documentation 
available at Docket No. NHTSA-2018-0067.
    \112\ At NHTSA-2016-0068-0082, p. 49, FCA provided the following 
comments, ``FCA believes EPA is overestimating the benefits of 
technology. As the LPM is calibrated to those projections, so too is 
the LPM too optimistic.'' FCA also shared the chart, ``LPM vs. 
Actual for 8 Speed Transmissions.''
    \113\ See e.g., Automotive News ``CAFE math gets trickier as 
industry innovates'' (Kulisch), March 26, 2018.

    Similarly for today's analysis, only one tool is used. Previously, 
EPA developed ``OMEGA,'' a tool that looked at costs of technologies 
and effectiveness of technologies (as estimated by EPA's Lumped 
Parameter Model or ALPHA), and applied cost effective technologies to 
manufacturers' fleets in response to potential standards. Many 
stakeholders commented that the OMEGA model oversimplified fleet-wide 
analysis, resulting in significant shortcomings.\114\ For instance, 
OMEGA assumed manufacturers would redesign all vehicles in the fleet by 
2021, and then again by 2025; stakeholders purported that these 
assumptions did not reflect practical constraints in many 
manufacturers' business models.\115\ Additionally, stakeholders 
commented that OMEGA did not adequately take into consideration common 
parts like shared engines, shared transmissions, and engineering 
platforms. The CAFE model does consider refresh and redesign cycles and 
parts sharing. The CAFE model can evaluate responses to any policy 
alternative on a year-by-year basis, as required by EPCA/EISA \116\ and 
as allowed by the CAA, and can also account for manufacturers' year-by-
year plans that involve ``carrying forward'' technology from prior 
model years, and some other vehicles possibly applying ``extra'' 
technology in anticipation of standards in ensuing model years. For 
today's analysis, an updated version of the CAFE model is used--an 
improved version of what NHTSA presented in the 2016 Draft TAR--to 
assess manufacturers' response to policy alternatives. See Section 
II.A.1 above for further discussion of the decision to use the CAFE 
model for the NPRM analysis.

    \114\ The Alliance of Automobile Manufacturers commented that 
``the OMEGA model is over-optimized and unrealistic . . . many of 
these issues either are not present or are accounted for in DOT's 
Volpe model. The Alliance therefore recommends that EPA focus on 
ensuring needs specific to its regulatory analysis are appropriately 
addressed in the Volpe model.'' Alliance at 48 (Docket ID. EPA-HQ-
    \115\ For example, FCA provided the following comments: ``EPA's 
expectation of 10-20% mass reduction rates across 70% of FCA's 
fleet, which includes a 70% truck mix, is simply unreasonable as the 
magnitude of change would require complete product redesigns in less 
than eight years shortening existing production needed to amortize 
the large capital cost involved.'' FCA at 19 (Docket ID. EPA-HQ-OAR-
    \116\ 49 U.S.C. 32902(b)(2)(B).

    Each aforementioned change is discussed briefly in the remainder of 
this section and in much greater detail in Chapter 6 of the PRIA. A 
brief summary of the technologies considered in this proposal is 
discussed below. Please provide comments on all aspects of the analysis 
as discussed here and as detailed in the PRIA.

[[Page 43023]]

1. Data Sources and Processes for Developing Individual Technology 
    Technology assumptions were developed that provide a foundation for 
conducting a fleet-wide compliance analysis. As part of this effort, 
the analysis estimated technology costs, projected technology 
effectiveness values, and identified possible limitations for some 
fuel-saving technologies. There is a preference to use values developed 
from careful review of commercialized technologies; however, in some 
cases for technologies that are new, and are not yet for sale in any 
vehicle, the analysis relied on information from other sources, 
including CBI and third-party research reports and publications. Many 
emerging technologies are still being evaluated for the analysis 
supporting the final rule, including those that are currently emerging.
    For today's analysis, one set of cost assumptions, one set of 
effectiveness values (developed with one tool), and one set of 
assumptions about the limitations of some technologies are presented. 
Many sources of data were evaluated, in addition to many stakeholder 
comments received on the Draft TAR. Throughout the process of 
developing the assumptions for today's analysis, the preferred approach 
was to harmonize on sources and methodologies that were data-driven and 
reproducible in independent verification, produced using tools utilized 
by OEMs, suppliers, and academic institutions, and using tools that 
could support both CAFE and CO2 analysis. A single set of 
assumptions also facilitates and focuses public comment by reducing 
burden on stakeholders who seek to review all of the supporting 
documentation for this proposal.
(a) Technology Costs
    The analysis estimated 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. Cost estimates were 
developed based on three main inputs. First, direct manufacturing costs 
(DMC), or the component costs of the physical parts and systems, were 
considered, with estimated costs assuming high volume production. DMCs 
generally do not include the indirect costs of tools, capital 
equipment, and financing costs, nor do they cover indirect costs like 
engineering, sales, and administrative support. Second, indirect costs 
via a scalar markup of direct manufacturing costs (the retail price 
equivalent, or RPE) was taken into account. Finally, costs for 
technologies may change over time as industry streamlines design and 
manufacturing processes. Potential cost improvements with learning 
effects (LE) were also considered. 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 
government mandate, a manufacturer will not undertake expensive 
development and support costs to implement technologies without 
realistic prospects of consumer willingness to pay enough for such 
technology to allow for the manufacturer to recover its investment.
(1) Direct Manufacturing Costs
    In many instances, the analysis used agency-sponsored tear-down 
studies of vehicles and parts to estimate the direct manufacturing 
costs of individual technologies. In the simplest cases, the 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, study assumptions were 
scrutinized, and sometimes the analysis was revised or updated 
accordingly.\117\ Studies were conducted on vehicles and technologies 
that would cover a breadth of fuel-savings technologies, but because 
tear-down studies can be time-intensive and expensive, the agencies did 
not sponsor teardown studies for every technology. For some 
technologies, independent tear-down studies were also utilized, in 
addition to other publications and confidential business 
information.\118\ Due to the variety of technologies and their 
applications, a detailed tear-down study could not be conducted for 
every technology, including pre-production technologies.

    \117\ For instance, in previous analysis, EPA referenced an old 
study that purported the first 7-10% of mass reduction to be 
``free'' or at a significant ``cost savings'' to for many vehicles 
and many manufacturers.
    \118\ The analysis referenced studies from private businesses 
and business analysts for emerging technologies and for off-the-
shelf technologies that were commercially mature.

    Many fuel-saving technologies were considered that are pre-
production, or sold in very small pilot volumes. For emerging 
technologies that could be applied in the rulemaking timeframe, a tear-
down study cannot be conducted to assess costs because the product is 
not yet in the marketplace for evaluation. In these cases, third-party 
estimates and confidential information from suppliers and manufacturers 
are relied upon; however, there are some common pitfalls with relying 
on confidential business information to estimate costs. The agencies 
and the source may have had incongruent or incompatible definitions of 
``baseline.'' \119\ The source may have provided direct manufacturer 
costs 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 the agencies, 
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 model as not all 
manufacturer's may have access to proprietary technologies at stated 
costs. New information is carefully evaluated in light of these common 
pitfalls, especially regarding emerging technologies. The analysis used 
third-party, forward looking information for advanced cylinder 
deactivation and variable compression ratio engines, and while these 
cost estimates may be cursory (as is the case with many emerging 
technologies prior to commercialization), the agencies took care to use 
early information provided fairly and reasonably. 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, the best information available at the time of the 
analysis was utilized, and cost assumptions will continue to be 

    \119\ ``Baseline'' here refers to a reference part, piece of 
equipment, or engineering system that efficiency improvements and 
costs are relative to.

(2) Indirect Costs
    As explained above, in addition to direct manufacturing costs, the 
analysis estimates and considers indirect manufacturing costs. To 
estimate indirect costs, direct manufacturing costs are multiplied by a 
factor to represent the average price for fuel-saving technologies at 
retail. This factor, referred to as the retail price equivalence (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

[[Page 43024]]

considerations. This approach to the RPE remains unchanged from the RPE 
approach NHTSA presented in the Draft TAR.
    The RPE was chosen for this analysis instead of indirect cost 
multipliers (ICM) because it provides the best estimate of indirect 
costs. For a more detailed discussion of the approach to indirect 
costs, see PRIA Chapter 9.
(3) Stranded Capital Costs
    Past analyses accounted for costs associated with stranded capital 
when fuel economy standards caused a technology to be replaced before 
its costs were fully amortized. 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 attempted to account for such lost 
investments. In the Draft TAR analysis, there were only a few 
technologies for a few manufacturers that were projected to have 
stranded capital costs.
    As more technologies are included in this analysis, and as the CAFE 
model has been expanded to account for platform and engine sharing and 
updated with redesign and refresh cycles, accounting for stranded 
capital has become increasingly complex. Separately, the fact that 
manufacturers may be shifting their investment strategies in ways that 
may affect stranded capital calculations was considered. For instance, 
Ford and General Motors agreed to jointly develop next generation 
transmission technologies,\120\ and some suppliers sell similar 
transmissions to multiple manufacturers. These arrangements allow 
manufacturers to share in capital expenditures, or amortize expenses 
more quickly. Manufacturers increasingly share parts on vehicles around 
the globe, achieving greater scale and greatly affecting tooling 
strategies and costs. Given these trends in the industry and their 
uncertain effect on capital amortization, and given the difficulty of 
handling this uncertainty in the CAFE model, this analysis does not 
account for stranded capital. However, these trends will be monitored 
to assess the role of stranded capital moving forward.

    \120\ See, e.g., Nick Bunkley, Ford to invest $1.4 billion to 
build 10-speed transmissions for 2017 F-150, Automotive News (Apr. 
26, 2016), http://www.autonews.com/article/20160426/OEM01/160429878/

    The analysis continues to rely on projected refresh and redesign 
cycles in the CAFE model to moderate the cadence for technology 
adoption and limit the occurrence of stranded capital and the need to 
account for it. Stranded capital is an important consideration to 
appropriately account for costs if there is too rapid of a turnover for 
certain technologies.
(4) Cost Learning
    Manufacturers make improvements to production processes over time, 
often resulting in lower costs. Today's analysis estimates cost 
learning by considering Wright's learning theory, which states that as 
every time cumulative volume for a product doubles, the cost lowers by 
a scalar factor. The analysis accounts for learning effects with model 
year-based cost learning forecasts for each technology that reduce 
direct manufacturing costs over time. Historical use of technologies 
were evaluated, and industry forecasts were reviewed to estimate future 
volumes for the purpose of developing the model year-based technology 
cost learning curves. The CAFE model does not dynamically update 
learning curves, based on compliance pathways chosen in simulation.
    As discussed above, cost inputs to the CAFE model incorporate 
estimates of volume-based learning. As an alternative approach, Volpe 
Center staff have considered modifications such that the CAFE model 
would calculate degrees of volume-based learning dynamically, 
responding to the model's application of affected technologies. While 
it is intuitive that the degree of cost reduction achieved through 
experience producing a given technology should depend on the actual 
accumulated experience (i.e., volume) producing that technology, staff 
have thus far found such dynamic implementation in the CAFE model 
infeasible. Insufficient data has been available regarding 
manufacturers' historical application of specific technology. Also, 
insofar as underlying direct manufacturing costs already make some 
assumptions about volume and scale, insufficient information is 
currently available to determine how to dynamically adjust these 
underlying costs. It should be noted that if learning responds 
dynamically to volume, and volume responds dynamically to learning, an 
internally consistent model solution would likely require iteration of 
the CAFE model to seek a stable solution within the model's 
representation multiyear planning. Thus far, these challenges suggest 
it would be infeasible to calculate degrees of volume-based learning in 
a manner that responds dynamically to modeled technology application. 
Nevertheless, the agencies invite comment on the issue, and seek data 
and methods that would provide the basis for a practicable approach to 
doing so.
    Today's analysis also updates the way learning effects apply to 
costs. In the Draft TAR analysis, NHTSA applied learning curves only to 
the incremental direct manufacturing costs or costs over the previous 
technology on the tech tree. In practice, two things were observed: (1) 
If the incremental direct manufacturing costs were positive, 
technologies could not become less expensive than their predecessors on 
the tech tree, and (2) absolute costs over baseline technology depended 
on the learning curves of root technologies on the tech tree. Today's 
analysis applies learning effects to the incremental cost over the null 
technology state on the tech tree. After this step, the analysis 
calculates year-by-year incremental costs over preceding technologies 
on the tech tree to create the CAFE model inputs.
    Direct manufacturing costs and learning effects for many 
technologies were reviewed by evaluating historical use of technologies 
and industry forecasts to estimate future volumes. This approach 
produced reasonable estimates for technologies already in production. 
For technologies not yet in production in MY 2016, the cumulative 
volume in MY 2016 is zero, because manufacturers have not yet produced 
the technologies. For pre-production cost estimates, the analysis often 
relies 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. Direct costs with 
learning were carefully examined, and adjustments were made to the 

[[Page 43025]]

point for those technologies on the learning curve to better align with 
the assumptions used for the initial direct cost estimate. See PRIA 
Chapter 9 for more detailed information on cost learning.
(b) Technology Effectiveness
(1) Technology Effectiveness Simulation Modeling
    Full-vehicle simulation modeling was used to estimate the fuel 
economy improvements manufacturers could make to their fleet by adding 
new technologies, taking into account MY 2016 vehicle specifications, 
as well as how combinations of technologies interact. Full-vehicle 
simulation modeling uses computer software and physics-based models to 
predict how combinations of technologies perform together.
    The simulation and modeling requires detailed specifications for 
each technology that describes its efficiency and performance-related 
characteristics. Those specifications generally come from design 
specifications, laboratory measurements, simulation or modeling, and 
may involve additional analysis. For example, the analysis used engine 
maps showing fuel use vs. engine torque vs. engine speed, and 
transmission maps taking into account gear efficiency for a range of 
loads and speeds. With physics-based technology specifications, full-
vehicle simulation modeling can be used to estimate technology 
effectiveness for various combinations and permutations of technologies 
for many vehicle classes. To develop the specifications used for the 
simulation and modeling, laboratory test data was evaluated for 
production and pre-production technologies, technical publications, 
manufacturer and supplier CBI, and simulation modeling of specific 
technologies. Evaluating recently introduced production products to 
inform the technology effectiveness models of emerging technologies is 
preferred because doing so allows for a more reliable analysis of 
incremental improvements over previous technologies; however, some 
technologies were considered that are not yet in production. As 
technologies evolve and new applications emerge, this work will be 
continued and may include additional technologies and/or updated 
modeling for the final rule. The details of new and emerging 
technologies are discussed in PRIA Chapter 6.
    Using full-vehicle simulation modeling has two primary advantages 
over using single or limited point estimates for fuel efficiency 
improvements of technologies. First, technology effectiveness often 
differs significantly depending on the type of vehicle and the other 
technologies that are on the vehicle, and this is shown in full-vehicle 
simulations. Different technologies may provide different fuel economy 
improvements depending on whether they are implemented alone or in 
tandem with other technologies. Single point estimates often 
oversimplify these important, complex relationships and lead to less 
accurate effectiveness estimates. Also, because manufacturers often 
implement a number of fuel-saving technologies simultaneously at 
vehicle redesigns, it is generally difficult to isolate the effect of 
individual technologies using laboratory measurement of production 
vehicles alone. Simulation modeling offers the opportunity to isolate 
the effects of individual technologies by using a single or small 
number of baseline 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 and reduces 
potential double counting or undercounting technology effectiveness. 
Note: It is most important that the incremental effectiveness of each 
technology and combinations be accurate and relative to a consistent 
baseline, because it is the incremental effectiveness that is applied 
to each vehicle model/configuration in the MY 2016 baseline fleet (and 
to each vehicle model/configuration's absolute fuel economy value) to 
determine the absolute fuel economy of the model/configuration with the 
additional technology. The absolute fuel economy values of the 
simulation modeling runs by themselves are used only to determine the 
incremental effectiveness and are never used directly to assign an 
absolute fuel economy value to any vehicle model/configuration for the 
rulemaking analysis. Therefore, commenters on technology effectiveness 
should be specific about the incremental effectiveness of technologies 
relative to other specifically defined technologies. The fuel economy 
of a specific vehicle or simulation modeling run in isolation may be 
less useful.
    Second, full-vehicle simulation modeling requires explicit 
specifications and assumptions for each technology; therefore, these 
assumptions can be presented for public review and comment. For 
instance, transmission gear efficiencies, shift logic, and gear ratios 
are explicitly stated as model inputs and are available for review and 
comment. For today's analysis, every effort was made to make the input 
specifications and modeling assumptions available for review and 
comment. PRIA Chapter 6 and referenced documents provide more detailed 
    Technology development and application will be monitored to acquire 
more information for the final rule. The agencies may update the 
analysis for the final rule based on comments and/or new information 
that becomes available.
    Today's analysis utilizes effectiveness estimates for technologies 
developed using Autonomie software,\121\ a physics-based full-vehicle 
simulation tool developed and maintained by the Department of Energy's 
ANL. Autonomie has a long history of development and widespread 
application by users in industry, academia, research institutions and 
government.\122\ Real-world use has contributed significantly to 
aspects of Autonomie important to producing realistic estimates of fuel 
economy and CO2 emission rates, such as estimation and 
consideration of performance, utility, and driveability metrics (e.g., 
towing capability, shift business, frequency of engine on/off 
transitions). This steadily increasing realism has, in turn, steadily 
increased confidence in the appropriateness of using Autonomie to make 
significant investment decisions. Notably, DOE uses Autonomie for 
analysis supporting budget priorities and plans for programs managed by 
its Vehicle Technologies Office (VTO) and to decide among competing 
vehicle technology R&D projects.

    \121\ More information about Autonomie is available at https://www.anl.gov/technology/project/autonomie-automotive-system-design 
(last accessed June 21, 2018).
    \122\ ANL Model Documentation, ``A Detailed Vehicle Simulation 
Process To Support CAFE Standards'' ANL/ESD-18/6.

    In the 2015 National Academies of Science (NAS) study of fuel 
economy improving technologies, the Committee recommended that the 
agencies use full-vehicle simulation to improve the analysis method of 
estimating technology effectiveness.\123\ The committee acknowledged 
that developing and executing tens or hundreds of thousands of 
constantly changing vehicle packages models in

[[Page 43026]]

real-time is extremely challenging. While initially this approach was 
not considered practical to implement, a process developed by Argonne 
in collaboration with NHTSA and the DOT Volpe Center has succeeded in 
enabling large scale simulation modeling. For more details about the 
Autonomie simulation model and its submodels and inputs, see PRIA 
Chapter 6.2.

    \123\ National Research Council. 2015. Cost, Effectiveness, and 
Deployment of Fuel Economy Technologies for Light-Duty Vehicles. 
Washington, DC: The National Academies Press [hereinafter ``2015 NAS 
Report''] at pg. 263, available at https://www.nap.edu/catalog/21744/cost-effectiveness-and-deployment-of-fuel-economy-technologies-for-light-duty-vehicles (last accessed June 21, 2018).

    Today's analysis modeled more than 50 fuel economy-improving 
technologies, and combinations thereof, on 10 vehicle types (an 
increase from five vehicle types in NHTSA's Draft TAR analysis). While 
10 vehicle types may seem like a small number, a large portion of the 
production volume in the MY 2016 fleet have specifications that are 
very similar, especially in highly competitive segments (for instance, 
many mid-sized sedans, many small SUVs, and many large SUVs coalesce 
around similar specifications, respectively), and baseline simulations 
have been aligned around these modal specifications. The sequential 
addition of these technologies generated more than 100,000 unique 
technology combinations per vehicle class. The analysis included 10 
technology classes, so more than one million full-vehicle simulations 
were run. In addition, simulation modeling was conducted to determine 
the appropriate amount of engine downsizing needed to maintain baseline 
performance across all modeled vehicle performance metrics when 
advanced mass reduction technology or advanced engine technology was 
applied, so these simulations take into account performance neutrality, 
given logical engine down-sizing opportunities associated with specific 
    Some baseline vehicle assumptions used in the simulation modeling 
were updated based on public comment and the assessment of the MY 2016 
production fleet. The analysis included updated assumptions about curb 
weight, component inertia, as well as technology properties like 
baseline rolling resistance, aerodynamic drag coefficients, and frontal 
areas. Many of the assumptions are aligned with published research from 
the Department of Energy's Vehicle Technologies Office and other 
independent sources.\124\ Additional transmission technologies and more 
levels of aerodynamic technologies than NHTSA presented in the Draft 
TAR analysis were also added for today's analysis. Having additional 
technologies allowed the agencies to assign baselines and estimate 
fuel-savings opportunities with more precision.

    \124\ Pannone, G. ``Technical Analysis of Vehicle Load Reduction 
Potential for Advanced Clear Cars,'' April 29, 2015. Available at 
https://www.arb.ca.gov/research/apr/past/13-313.pdf (last accessed 
June 21, 2018).

    The 10 vehicle types (referred to as ``technology classes'' in the 
modeling documentation) are shown in Table II-7. Each vehicle type 
(technology class) represented a large segment of vehicles, such as 
medium cars, small SUVs, and medium performance SUVs.\125\ Baseline 
parameters were defined with ANL for each technology class, including 
baseline curb weight, time required to accelerate from stop to 60 miles 
per hour, time required to accelerate from 50 miles per hour to 80 
miles per hour, ability of the vehicle to maintain constant 65 miles 
per hour speed on a six percent upgrade, and (for some classes) 
assumptions about towing capability.

    \125\ Separate technology classes were created for high 
performance and low performance vehicles to better account for 
performance diversity across the fleet.


[[Page 43027]]


    From these baseline specifications, incremental combinations of 
fuel saving technologies were applied. As the combinations of 
technologies change, so too may predicted performance.
    The analysis attempts to maintain performance by resizing engines 
at a few specific incremental technology steps. Steps from one 
technology to another typically associated with a major vehicle 
redesign, or engine redesign, were identified, and engine resizing was 
restricted only to these steps. The analysis allowed engine resizing 
when mass reduction of 10% or greater was applied to the vehicle glider 
mass,\126\ and when one powertrain architecture was replaced with 
another architecture.\127\ The analysis resized engines to the extent 
that performance was maintained for the least capable performance 
criteria to maintain vehicle utility for that criteria; therefore, 
sometimes other performance attributes may improve. For instance, the 
amount of engine resizing may be determined based on its high speed 
acceleration time if it is the least capable criteria, but that 
resizing may also improve the low speed acceleration time.\128\ The 
analysis did not re-size the engine in response to adding technologies 
that have small effects on vehicle performance. For instance, if a 
vehicle's weight is reduced by a small amount causing the 0-60 mile per 
hour time to improve slightly, the analysis would not resize the 
engine. Manufacturers have repeatedly told the agencies that the high 
costs for redesign and the increased manufacturing complexity that 
would result from resizing engines for such small changes in the 
vehicle preclude doing so. The analysis should not, in fact, include 
engine resizing with the application of every technology or for 
combinations of technologies that drive small performance changes so 
that the analysis better reflects what is feasible for manufacturers to 

    \126\ The vehicle glider is defined here as the vehicle without 
the engine, transmission, and driveline. See PRIA Chapter 6.3 for 
further information.
    \127\ Some engine and accessory technologies may be added to an 
engine without an engine architecture change. For instance, 
manufacturers may adapt, but not replace engine architectures to 
include cylinder deactivation, variable valve lift, belt-integrated 
starter generators, and other basic technologies. However, switching 
from a naturally aspirated engine to a turbo-downsized engine is an 
engine architecture change typically associated with a major 
redesign and radical change in engine displacement.
    \128\ The simulation database, or summary of simulation outputs, 
includes all of the estimated performance metrics for each 
combination of technology as modeled.
    \129\ For instance, a vehicle would not get a modestly bigger 
engine if the vehicle comes with floor mats, nor would the vehicle 
get a modestly smaller engine without floor mats. This example 
demonstrates small levels of mass reduction. If manufacturers 
resized engines for small changes, manufacturers would have 
dramatically more part complexity, potentially losing economies of 

2. CAFE model
    The CAFE model is designed to simulate compliance with a given set 
of CAFE or CO2 standards for each manufacturer that sells 
vehicles in the United States. The model begins with a

[[Page 43028]]

representation of the MY 2016 vehicle model offerings for each 
manufacturer that includes the specific engines and transmissions on 
each model variant, observed sales volumes, and all fuel economy 
improving technology that is already present on those vehicles. From 
there the model adds technology, in response to the standards being 
considered, in a way that minimizes the cost of compliance and reflects 
many real-world constraints faced by automobile manufacturers. The 
model addresses fleet year-by-year compliance, taking into 
consideration vehicle refresh and redesign schedules and shared 
platforms, engines, and transmissions among vehicles.
    As a result of simulating compliance, the CAFE model provides the 
technology pathways that manufacturers could use to comply with 
regulations, including how technologies could be applied to each of 
their vehicle model/configurations in response to a given set of 
standards. The model calculates the impacts of the simulated standard: 
Technology costs, fuel savings (both in gallons and dollars), 
CO2 reductions, social costs and benefits, and safety 
    The current analysis reflects several changes made to the CAFE 
model since 2012, when NHTSA used the model to estimate the effects, 
costs, and benefits of final CAFE standards for light-duty vehicles 
produced during MYs 2017-2021 and augural standards for MYs 2022-2025. 
The changes are discussed in Section II.A.1, above, and PRIA Chapter 6.
3. Assumptions About Individual Technology Cost and Effectiveness 
    Cost and effectiveness values were estimated for each technology 
included in the analysis, with a summary list of all technologies 
provided in Table II-1 (List of Technologies with Data Sources for 
Technology Assignments) of Preamble Chapter II.B, above. In all, more 
than 50 technologies were considered in today's analysis, and the 
analysis evaluated many combinations of these technologies on many 
applications. Potential issues in assessing technology effectiveness 
and cost were identified, including:
     Baseline (MY 2016) vehicle technology level assessed as 
too low, or too high. Compliance information was extensively reviewed 
and supplemented with available literature on many MY 2016 vehicle 
models. Manufacturers could also review the baseline technology 
assignments for their vehicles, and the analysis incorporates feedback 
received from manufacturers.
     Technology costs too low or too high. Tear down cost 
studies, CBI, literature, and the 2015 NAS study information were 
referenced to estimate technology costs. In cases that one technology 
appeared exemplary on cost and effectiveness relative to all other 
technologies, information was acquired from additional sources to 
confirm or reject assumptions. Cost assumptions for emerging 
technologies are continuously being evaluated.
     Technology effectiveness too high or too low in 
combination with other vehicle technologies. Technology effectiveness 
was evaluated using the Autonomie full-vehicle simulation modeling, 
taking into account the impact of other technologies on the vehicle and 
the vehicle type. Inputs and modeling for the analysis took into 
account laboratory test data for production and some pre-production 
technologies, technical publications, manufacturer and supplier CBI, 
and simulation modeling of specific technologies. Evaluating recently 
introduced production products to inform the technology effectiveness 
models of emerging technologies was preferred; however, some 
technologies that are not yet in production were considered, via CBI. 
Simulation modeling used carefully chosen baseline configurations to 
provide a consistent, reasonable reference point for the incremental 
effectiveness estimates.
     Vehicle performance not considered or applied in an 
infeasible manner. Performance criteria, including low speed 
acceleration (0-60 mph time), high speed acceleration (50-80 mph time), 
towing, and gradeability (six percent grade at 65 mph) were also 
considered. In the simulation modeling, resizing was applied to achieve 
the same performance level as the baseline for the least capable 
performance criteria but only with significant design changes. The 
analysis struck a balance by employing a frequency of engine downsizing 
that took product complexity and economies of scale into account.
     Availability of technologies for production application 
too soon or too late. A number of technologies were evaluated that are 
not yet in production. CBI was gathered on the maturity and timing of 
these technologies and the likely cadence at which manufacturers might 
adopt these technologies.
     Product complexity and design cadence constraints too low 
or too high. Product platforms, refresh and redesign cycles, shared 
engines, and shared transmissions were also considered in the analysis. 
Product complexity and the cadence of product launches were matched to 
historical values for each manufacturer.
     Customer acceptance under estimated or over estimated. 
Resale prices for hybrid vehicles, electric vehicles, and internal 
combustion engine vehicles were evaluated to assess consumer 
willingness to pay for those technologies. The analysis accounts for 
the differential in the cost for those technologies and the amount 
consumers have actually paid for those technologies. Separately, new 
dual-clutch transmissions and manual transmissions were applied to 
vehicles already equipped with these transmission architectures.
    Please provide comments on all assumptions for fuel economy and 
CO2 technology costs, effectiveness, availability, and 
applicability to vehicles in the fleet.
    The technology effectiveness modeling results show effectiveness of 
a technology often varies with the type of vehicle and the other 
technologies that are on the vehicle. Figure II-1 and Figure II-2 show 
the range of effectiveness for each technology for the range of vehicle 
types and technology combinations included in this NPRM analysis. The 
data reflect the change in effectiveness for applying each technology 
by itself while all other technologies are held unchanged. The data 
show the improvement in fuel consumption (in gallons per mile) and 
tailpipe CO2 over the combined 2-cycle test procedures. For 
many technologies, effectiveness values ranged widely; only a few 
technologies for which effectiveness may be reasonably represented as a 
fixed offset were identified.
    For engine technologies, the effectiveness improvement range is 
relative to a comparably equipped vehicle with only variable valve 
timing (VVT) on the engine. For automatic transmission technologies, 
the effectiveness improvement range is over a 5-speed automatic 
transmission. For manual transmission technologies, the effectiveness 
improvement range is over a 5-speed manual transmission. For road load 
technologies like aerodynamics, rolling resistance, and mass reduction, 
the effectiveness improvement ranges are relative to the least advanced 
technology state, respectively. For hybrid and electric drive systems 
that wholly replace an engine and transmission, the effectiveness 
improvement ranges are relative to a comparably equipped vehicle with a 
basic engine with VVT only and a 5-speed automatic transmission. For 
hybrid or electrification technologies that complement other advanced 

[[Page 43029]]

and transmission technologies, the effectiveness improvement ranges are 
relative to a comparably equipped vehicle without the hybrid or 
electrification technologies (for instance, parallel strong hybrids and 
belt integrated starter generators retain engine technologies, such as 
a turbo charged engine or an Atkinson cycle engine). Many technologies 
have a wide range of estimated effectiveness values. Figure II-3 below 
shows a hierarchy of technologies discussed.

[[Page 43030]]


[[Page 43031]]


4. Engine Technologies
    There are a number of engine technologies that manufacturers can 
use to improve fuel economy and CO2. 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.
    In this section and for this analysis, the terms ``basic engine 
technologies'' and ``advanced engine technologies'' are used only to 
define how the CAFE model applies a specific engine technology and 
handles incremental costs and effectiveness improvements. ``Basic 
engine technologies'' refer to technologies that, in many cases, can be 
adapted 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. In the CAFE model, basic engine technologies may be 
applied in combination with other basic engine technologies; advanced 
engine technologies (defined by an engine map) stand alone as an 
exclusive engine technology. The words ``basic'' and ``advanced'' are 
not meant to confer any information about the level of sophistication 
of the technology. Also, many advanced engine technology

[[Page 43032]]

definitions include some basic engine technologies, but these basic 
technologies are already accounted for in the costs and effectiveness 
values of the advance engine. The ``basic engine technologies'' need 
not be (and are not) applied in addition to the ``advanced engine 
technologies'' in the CAFE model.
    Engines come in a wide variety of shapes, sizes, and 
configurations, and the incremental engine costs and effectiveness 
values often depend on engine architecture. The agencies modeled single 
overhead cam (SOHC), dual overhead cam (DOHC), and overhead valve (OHV) 
engines separately to account for differences in engine architecture. 
The agencies adjusted costs for some engine technologies based on the 
number of cylinders and number of banks in the engine, and the agencies 
evaluated many production engines to better understand how costs and 
capabilities may vary with engine configuration. Table II-8, Table II-
9, Table II-10 below shows the summary of absolute costs \130\ for 
different technologies.

    \130\ ``Absolute'' being in reference to cost above the lowest 
level of technology considered in simulations. For instance, an 
engine of the same architecture with no VVT, VVL, SGDI, or DEAC.


[[Page 43033]]


[[Page 43034]]


[[Page 43035]]


    Many types of production powertrains were reviewed and tested for 
this analysis, and engine maps were developed for each combination of

[[Page 43036]]

engine technologies. For a given engine configuration, some production 
engines may be less efficient than the engine maps presented in the 
analysis, and some may be more efficient. Developing engine maps that 
reasonably represented most vehicles equipped with the engine 
technology, and that are in production today, was the preferred 
approach for this analysis. Additionally, some advanced engines were 
included in the simulation that are not yet in production. The engine 
maps for these engines were either based on CBI or were theoretical. 
The most recently released production engines are still being reviewed, 
and the analysis may include updated engine maps in the future or add 
entirely new engine maps to the analysis if either action could improve 
the quality of the fleet-wide analysis.
    Stakeholders provided many comments on the engine maps that were 
presented in the Draft TAR. These comments were considered, and today's 
analysis utilizes several engine maps that were updated since the Draft 
TAR. Most notably, for turbocharged and downsized engines, the engine 
maps were adjusted in high torque, low speed operating conditions to 
address engine knock with regular octane fuel to align with the fuel 
octane that manufacturers recommend be used for the majority of 
vehicles. In the Draft TAR, NHTSA assumed high octane fuel to develop 
engine maps. See the discussion below and in PRIA Chapter 6.3 for more 
details. Please provide comment on the appropriateness of assuming the 
use of lower octane fuels.
(a) ``Basic'' Engine Technologies
    The four ``basic'' engine technologies in today's model are 
Variable Valve Timing (VVT), Variable Valve Lift (VVL), Stoichiometric 
Gasoline Direct Injection (SGDI), and basic Cylinder Deactivation 
(DEAC). Over the last decade, manufacturers upgraded many engines with 
these engine technologies. Implementing these technologies involves 
changes to the cylinder head of the engine, but the engine block, 
crankshaft, pistons, and connecting rods require few, if any, changes. 
In today's analysis, manufacturers may apply the four basic engine 
technologies in various combinations, just as manufacturers have done 
(1) Variable Valve Timing (VVT)
    Variable Valve Timing (VVT) 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. This family of technologies 
reduces pumping losses. VVT is nearly universally used in the MY 2016 
(2) Variable Valve Lift (VVL)
    Variable Valve Lift (VVL) dynamically adjusts the travel of the 
valves to optimize airflow over a broad range of engine operating 
conditions. The technology increases effectiveness by reducing pumping 
losses and may improve efficiency by affecting in-cylinder charge (fuel 
and air mixture), motion, and combustion.
(3) Stoichiometric Gasoline Direct Injection (SGDI)
    Stoichiometric Gasoline Direct Injection (SGDI) 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. SGDI appears in 
about half of basic engines produced in MY 2016, and the technology is 
used in many advanced engines as well.
(4) Basic Cylinder Deactivation (DEAC)
    Basic Cylinder Deactivation (DEAC) disables intake and exhaust 
valves and prevents fuel injection into some cylinders during light-
load operation. The engine runs temporarily as though it were a smaller 
engine, which reduces pumping losses and improves efficiency. 
Manufacturers typically disable one-cylinder bank with basic cylinder 
deactivation. In the MY 2016 fleet, manufacturers used DEAC on V6, V8, 
V10, and V12 engines on OHV, SOHC, and DOHC engine configurations. With 
some engine configurations in some operating conditions, DEAC creates 
noise-vibration-and-harshness (NVH) challenges. NVH challenges are 
significant for V6 and I4 DEAC configurations. For I4 engine 
configurations, manufacturers can operate the DEAC function of an 
engine in very few operating conditions, with limited potential to save 
fuel. No manufacturers sold I4 DEAC engines in the MY 2016 fleet. 
Typically, the smaller the engine displacement, the less opportunity 
DEAC provides to improve fuel consumption.
    Manufacturers and suppliers continue to evaluate more improved 
versions of cylinder deactivation, including advanced cylinder 
deactivation and pairing basic cylinder deactivation with turbo charged 
engines. No manufacturers produced such technologies in the MY 2016 
fleet. Advanced cylinder deactivation and turbo technologies were 
modeled and considered separately in today's analysis.
(b) ``Advanced'' Engine Technologies
    The analysis included ``advanced'' engine technologies that can 
deliver high levels of effectiveness but often require a significant 
engine design change or a new engine architecture. In the CAFE model, 
``basic'' engine technologies may be considered in combination and 
applied before advanced engine technologies. ``Advanced'' engine 
technologies generally include one or more basic engine technologies in 
the simulation, without the need to layer on ``basic'' engine 
technologies on top of ``advanced'' engines. Once an advanced engine 
technology is applied, the model does not reconsider the basic engine 
technologies. The characterization of each advanced engine technology 
takes into account the prerequisite technologies.
    Many of the newest advanced engine technologies improve 
effectiveness over their predecessors, but the engines may also include 
sophisticated materials or manufacturing processes that contribute to 
efficiency improvements. For instance, one recently introduced turbo 
charged engine uses sodium filled valve stems.\131\ Another recently 
introduced high compression ratio engine uses a sophisticated laser 
cladding process to manufacture valve seats and improve airflow.\132\ 
To fully consider these advancements (and their potential benefits), 
the incremental costs of these technologies, as well as the 
effectiveness improvements, must be accounted for.

    \131\ See Honda, ``2018 Honda Accord Press Kit--Powertrain,'' 
Oct. 2, 2017. Available at http://news.honda.com/newsandviews/article.aspx?g=honda-automobiles&id=9932-en. (last accessed June 21, 
    \132\ Hakariya et al., ``The New Toyota Inline 4-Cylinder 2.5L 
Gasoline Engine,'' SAE Technical Paper 2017-01-1021 (Mar. 28, 2017), 
available at https://www.sae.org/publications/technical-papers/content/2017-01-1021/.

(1) Turbocharged Engines
    Turbo engines recover energy from hot exhaust gas and compress 
intake air, thereby increasing available airflow and increasing 
specific power level. Due to specific power improvements on turbo 
engines, engine displacement can be downsized. The downsizing reduces 
pumping losses and improves fuel economy at lower loads. For the NPRM 
analysis, a level of downsizing is assumed to be applied that achieves 
performance similar to the baseline naturally-aspirated engine. This 
assumes manufacturers would apply the benefits toward improved fuel 

[[Page 43037]]

and not trade off fuel economy improvements to increase overall vehicle 
performance. In practice, manufacturers have often also improved some 
vehicle performance attributes at the expense of not maximizing 
potential fuel economy improvements.
    Manufacturers may develop engines to operate on varying levels of 
boost,\133\ with higher levels of boost achieving higher engine 
specific power and enabling greater levels of engine downsizing and 
corresponding reductions in pumping losses for improved efficiency. 
However, engines operating at higher boost levels are generally more 
susceptible to engine knock,\134\ especially at higher torques and low 
engine speeds. Additionally, engines with higher boost levels typically 
require larger induction and exhaust system components, dissipate 
greater amounts of heat, and with greater levels of engine downsizing 
have increased challenges with turbo lag.\135\ For these reasons, three 
levels of turbo downsizing technologies are separately modeled in this 

    \133\ Boost refers to the degree to which the turbocharger 
compresses the intake air for the engine, which may affect the 
specific power of the engine.
    \134\ Knock refers to rapid uncontrolled combustion in the 
cylinder part way through the combustion process, which can create 
an audible sound and can damage the engine.
    \135\ Turbo lag refers to the delay time between power demanded 
and power delivered; it is typically associated with rapid 
accelerations from a stopped vehicle at idle.

    The analysis also modeled turbocharged engines with parallel hybrid 
technology. In simulations with high stringencies, many manufacturers 
produced turbo-hybrid electric vehicles. In the MY 2016 fleet, of the 
vehicles that use parallel hybrid technology, many use turbocharged 
    Since the Draft TAR, the turbo family engine maps were updated to 
reflect operation on 87 AKI regular octane fuel.\136\ In the Draft TAR, 
turbo engine maps were developed assuming premium fuel. For this 
rulemaking analyses, pathways to improving fuel economy and 
CO2 are analyzed, while also maintaining vehicle 
performance, capability, and other attributes. This includes assuming 
there is no change in the fuel octane required to operate the vehicle. 
Using 87 AKI regular octane fuel is consistent with the fuel octane 
that manufacturers specify for the majority of vehicles, and enables 
the modeling to account for important design and calibration issues 
associated with regular octane fuel. Using the updated criteria assures 
the NPRM analysis reflects real-world constraints faced by 
manufacturers to assure engine durability, and acceptable drivability, 
noise and harshness, and addresses the over-estimation of potential 
fuel economy improvements related to the fuel octane assumptions, which 
did not fully account for these constraints, in the Draft TAR. Compared 
with the NHTSA analysis in the Draft TAR, these engine maps adjust the 
fuel use at high torque and low speed operation and at high speed 
operation to fully account for knock limitations with regular octane 

    \136\ Specifically, 87 Anti-Knock Index (AKI) Tier 3 
certification fuel. 87 AKI is also known as 87 (R+M)/2 or 87 
(Research Octane + Motor Octane)/2.

    The analysis assumes engine downsizing with the addition of turbo 
technology. For instance, in the simulations, manufacturers may have 
replaced a naturally-aspirated V8 engine with a turbo V6 engine, and 
manufacturers may have replaced a naturally-aspirated V6 engine with a 
turbo I4 engine. When manufacturers reduced the number of banks or 
cylinders of an engine, some cost savings is projected due to fewer 
cylinders and fewer valves. Such cost savings is projected to help 
offset the additional costs of turbo charger specific hardware, making 
turbo downsizing a very attractive technology progression for some 

    \137\ In particular, the step from a naturally-aspirated V6 to a 
turbo I4 was particularly cost effective in agency simulations.

(a) TURBO1
    Level 1 Turbo Charging (TURBO1) adds a turbo charger to a DOHC 
engine with SGDI, VVT, and continuously VVL. The engine operates at up 
to 18 bar brake mean effective pressure (BMEP).
    Manufacturers used Turbo1 technology in a little less than a 
quarter of the MY 2016 fleet with particularly high concentrations in 
premium vehicles.
(b) TURBO2
    Level 2 Turbo Charging (TURBO2) operates at up to 24 bar BMEP. The 
step from Turbo1 to Turbo2 is accompanied with additional displacement 
downsizing for reduced pumping losses. Very few manufacturers have 
Turbo2 technology in the MY 2016 fleet.
(c) CEGR1
    Turbo Charging with Cooled Exhaust Gas Recirculation (CEGR1) 
improves the knock resistance of Turbo2 engines by mixing cooled inert 
exhaust gases into the engine's air intake. That allows greater boost 
levels, more optimal spark timing for improved fuel economy, and 
performance and greater engine downsizing for lower pumping losses. 
CEGR1 technology is used in only a few vehicles in the MY 2016 fleet, 
and many of these vehicles include high-performance utility either for 
towing or acceleration.
(a) Turbocharged Engine Technologies Not Considered
    Previous analyses considered turbo charged engines with even higher 
BMEP than today's Turbo2 and CEGR1 technologies, but today's analysis 
does not present 27 bar BMEP turbo engines. Turbo engines with very 
high BMEP have demonstrated limited potential to improve fuel economy 
due to practical limitations on engine downsizing and tradeoffs with 
launch performance and drivability. Based on the analysis, and based on 
CBI, CEGR2 turbo engine technology was not included in this NPRM 
(2) High Compression Ratio Engines (Atkinson Cycle Engines)
    Atkinson cycle gasoline engines use changes in valve timing (e.g., 
late-intake-valve-closing or LIVC) to reduce the effective compression 
ratio while maintaining the expansion ratio. This approach allows a 
reduction in top-dead-center (TDC) clearance ratio (e.g., increase in 
``mechanical'' or ``physical'' compression ratio) to increase the 
effective expansion ratio without increasing the effective compression 
ratio to a point that knock-limited operation is encountered. 
Increasing the expansion ratio in this manner improves thermal 
efficiency but also lowers peak BMEP, particularly at lower engine 
    Often knock concerns for these engines limit applications in high 
load, low RPM conditions. Some manufacturers have mitigated knock 
concerns by lowering back pressure with long, intricate exhaust 
systems, but these systems must balance knock performance with 
emissions tradeoffs, and the increased size of the exhaust manifold can 
pose packaging concerns, particularly on V-engine configurations.\138\

    \138\ Some HCR1 4-cylinder (I-4) engines use an intricate 4-2-1 
exhaust manifold to lower backpressure and to improve engine 
efficiency. Manufacturers sometimes fitted such an exhaust system 
into a front-wheel-drive vehicle with an I-4 engine by using a high 
underbody tunnel or rearward dashpanel (trading off some interior 
space), but packaging such systems on rear-wheel-drive vehicles may 
pose challenges, especially if the engine has two banks and would 
therefore require room for two such exhaust manifolds.

    Only a few manufacturers produced internal combustion engine 
vehicles with Atkinson cycle engines in MY

[[Page 43038]]

2016; however, these engines are commonly paired with hybrid electric 
vehicle technologies due to the synergy of peak efficiency of Atkinson 
cycle engines and immediate torque from electric motors in strong 
hybrids. Atkinson cycle engines are very common on power split hybrids 
and are sometimes observed as part of a parallel hybrid system or plug-
in hybrid system.
    Atkinson cycle engines played a prominent role in EPA's January 
2017 final determination, which has since been withdrawn. Today's 
analysis recognizes that the technology is not suitable for many 
vehicles due to performance, emissions and packaging issues, and/or the 
extensive capital and resources that would be required for 
manufacturers to shift from other powertrain technology pathways (such 
as turbocharging and downsizing) to standalone Atkinson cycle engine 
(a) HCR1
    A number of Asian manufacturers have launched Atkinson cycle 
engines in smaller vehicles that do not use hybrid technologies. These 
production engines have been benchmarked to characterize HCR1 
technology for today's analysis.
    Today's analysis restricted the application of stand-alone Atkinson 
cycle engines in the CAFE model in some cases. The engines benchmarked 
for today's analysis were not suitable for MY 2016 baseline vehicle 
models that have 8-cylinder engines and in many cases 6-cylinder 
(b) HCR2
    EPA conceptualized a ``future'' Atkinson cycle engine and published 
the theoretical engine map in an SAE paper.139 140 For this 
engine, EPA staff began with a best-in-class 2.0L Atkinson cycle engine 
and then increased the efficiency of the engine map further, through 
the theoretical application of additional technologies in combination, 
like cylinder deactivation, engine friction reduction, and cooled 
exhaust gas recirculation. This engine remains entirely speculative, as 
no production engine as outlined in the EPA SAE paper has ever been 
commercially produced or even produced as a prototype in a lab setting. 
Furthermore, the engine map has not been validated with hardware and 
bench data, even on a prototype level (as no such engine exists to test 
to validate the engine map).

    \139\ 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. Available at https://www.sae.org/publications/technical-papers/content/2016-01-1007/.
    \140\ Lee, S., Schenk, C., and McDonald, J., ``Air Flow 
Optimization and Calibration in High-Compression-Ratio Naturally 
Aspirated SI Engines with Cooled-EGR,'' SAE Technical Paper 2016-01-
0565, 2016. Available at https://www.sae.org/publications/technical-papers/content/2016-01-0565/.

    Previously, EPA relied heavily on the HCR2 (or sometimes referred 
to as ATK2 in previous EPA analysis) engine as a cost effective pathway 
to compliance for stringent alternatives, but many engine experts 
questioned its technical feasibility and near term commercial 
practicability. Stakeholders asked for the engine to be removed from 
compliance simulations until the performance could be validated with 
engine hardware.\141\ \142\ While for the Draft TAR, the agencies ran 
full-vehicle simulations with the theoretical engine map and made these 
available in the CAFE model, HCR2 technology as described in EPA's SAE 
paper was not included in today's analysis because there has been no 
observable physical demonstration of the speculative technology, and 
many questions remain about its practicability as specified, especially 
in high load, low engine speed operating conditions. Simulations with 
EPA's HCR2 engine map produce results that approach (and sometimes 
exceed) diesel powertrain efficiency.\143\ Given the prominence of this 
unproven technology in previous rule-makings, the CAFE model may be 
configured to consider the application of HCR2 technology for reference 

    \141\ At NHTSA-2016-0068-0082, FCA recommended, ``Remove ATK2 
from OMEGA model until the performance is validated.'', p. viii. And 
FCA stated, ``ATK2--High Compression engines coupled with Cylinder 
Deactivation and Cooled EGR are unlikely to deliver modeled results, 
meet customer needs, or be ready for commercial application.'', p. 
    \142\ At Docket ID No EPA-HQ-OAR-2015-0827-6156, The Alliance of 
Automobile Manufacturers commented, ``[There] is no current example 
of combined Atkinson, plus cooled EGR, plus cylinder deactivation 
technology in the present fleet to verify EPA's modeled benefits and 
. . . EPA could not provide physical test results replicating its 
modeled benefits of these combined technologies,'' p. 40.
    \143\ Thomas, J. ``Drive Cycle Powertrain Efficiencies and 
Trends Derived from EPA Vehicle Dynamometer Results,'' SAE Int. J. 
Passeng. Cars--Mech. Syst. 7(4):2014. Available at https://www.sae.org/publications/technical-papers/content/2014-01-2562/.

    As new engines emerge that achieve high thermal efficiency, 
questions may be raised as to whether the HCR2 engine is a simulation 
proxy for the new engine technology. It is important to conduct a 
thorough evaluation of the actual new production engines to measure the 
brake specific fuel consumption and to characterize the improvements 
attributable to friction and thermal efficiency before drawing 
conclusions. Using vehicle level data may misrepresent or conflate 
complex interactions between a high thermal efficiency engine, engine 
friction reduction, accessory load improvements, transmission 
technologies, mass reduction, aerodynamics, rolling resistance, and 
other vehicle technologies. For instance, some of the newest high 
compression ratio engines show improved thermal efficiency, in large 
part due to improved accessory loads or reduced parasitic losses from 
accessory systems.\144\ The CAFE model allows for incremental 
improvement over existing HCR1 technologies with the addition of 
improved accessory devices (IACC), a technology that is available to be 
applied on many baseline MY 2016 vehicles with HCR1 engines and may be 
applied as part of a pathway of compliance to further improve the 
effectiveness of existing HCR1 engines.

    \144\ For instance, the MY 2018 2.5L Camry engine that uses HCR 
technology also reduces parasitic losses with a variable capacity 
oil pump.

(c) Emerging Gasoline Engine Technologies
    Manufacturers and suppliers continue to invest in many emerging 
engine technologies, and some of these technologies are on the cusp of 
commercialization. Often, manufacturers submit information about new 
engine technologies that they may soon bring into production. When this 
happens, a collaborative effort is undertaken with suppliers and 
manufacturers to learn as much as possible and sometimes begin 
simulation modeling efforts. Bench data, or performance data for 
preproduction vehicles and engines, is usually closely held 
confidential business information. To properly characterize the 
technologies, it is often necessary to wait until the engine 
technologies are in production to study them.
(1) Advanced Cylinder Deactivation (ADEAC)
    Advanced cylinder deactivation systems (or rolling or dynamic 
cylinder deactivation systems) allows a further degree of cylinder 
deactivation than DEAC. The technology 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, so long as the calibration avoids certain 

[[Page 43039]]

    ADEAC systems may be integrated into the valvetrains with moderate 
modifications on OHV engines. However, while the ADEAC operating 
concept remains the same on DOHC engines, the valvetrain hardware 
configuration is very different, and application on DOHC engines is 
projected to be more costly per cylinder due to the valvetrain 
    Some preproduction 8-cylinder OHV prototype vehicles were briefly 
evaluated for this analysis, but no production versions of the 
technology have been studied.
    Today's analysis relied on CBI to estimate costs and effectiveness 
values of ADEAC. Since no engine map was available at the time of the 
NPRM analysis, ADEAC was estimated to improve a basic engine with VVL, 
VVT, SGDI, and DEAC by three percent (for 4 cylinder engines) six 
percent (for engines with more than 4 cylinders).
    ADEAC systems will continue to be studied as production begins.
(2) Variable Compression Ratio Engines (VCR)
    Engines using variable compression ratio (VCR) technology appear to 
be at a production-intent stage of development but also appear to be 
targeted primarily towards limited production, high performance and 
very high BMEP (27-30 bar) applications. Variable compression ratio 
engines work by changing the length of the piston stroke of the engine 
to operate at a more optimal compression ratio and improve thermal 
efficiency over the full range of engine operating conditions.
    A number of manufacturers and suppliers provided information about 
VCR technologies, and several design concepts were reviewed that could 
achieve a similar functional outcome. In addition to design concept 
differences, intellectual property ownership complicates the ability of 
the agencies to define a VCR hardware system that could be widely 
adopted across the industry.
    For today's analysis, VCR engines have a spot on the technology 
simulation tree, but VCR is not actively used in the NPRM simulation. 
Reasonable representations of costs and technology characterizations 
remain open questions for VCR engine technology and the analysis.
    NHTSA is sponsoring work to develop engine maps for additional 
combinations of technologies. Some of these technologies being 
researched presently, including VCR, may be used in the analysis 
supporting the final rule. Please provide comment on variable 
compression ratio engine technology. Should VCR technology be employed 
in the timeframe of this proposed rulemaking? Why or why not? Do 
commenters believe VCR technology will see widespread adoption in the 
US vehicle fleet? Why or why not? What vehicle segments may it best be 
suited for, and which segments would it not be best suited for? Why or 
why not? What cost and effectiveness values should be used if VCR is 
modeled for analysis? Please provide supporting data. Additionally, 
please provide any comments on the sponsored work related to VCR, 
described further in PRIA Chapter 6.3.
(3) Compression Ignition Gasoline Engines (SpCCI, HCCI)
    For many years, engine developers, researchers, manufacturers have 
explored ways to achieve the inherent efficiency of a diesel engine 
while maintaining the operating characteristics of a gasoline engine. A 
potential pathway for striking this balance is utilizing compression 
ignition for gasoline fueled engines, more commonly referred to as 
Homogeneous Charge Compression Ignition (HCCI).
    Ongoing, periodic discussions with manufacturers on future fuel 
saving technologies and powertrain plans have, generally, included HCCI 
as a long-term strategy. The technology appears to always be a strong 
consideration as, in theory, it provides the ``best of both worlds,'' 
meaning a way to provide diesel engine efficiency with gasoline engine 
performance and emissions levels.
    Developments in both the research and the potential production 
implementation of HCCI for the US market is continually assessed. In 
2017, a significant, potentially production breakthrough was announced 
by Mazda regarding a gasoline-fueled engine employing Spark Controlled 
Compression Ignition (SpCCI), where HCCI is employed for a portion of 
its normal operation and spark ignition is used at other times.\145\ 
Soon after, Mazda publicly stated they plan to introduce this engine as 
part of the Skyactiv family of engines in 2019.\146\

    \145\ Mazda Next-Generation Technology--Press Information, Mazda 
USA (Oct. 24, 2017), https://insidemazda.mazdausa.com/press-release/mazda-next-generation-technology-press-information/ (last visited 
Apr. 13, 2018).
    \146\ Mazda introduces updated 2019 CX-3 at 2018 New York 
International Auto Show, Mazda USA (Mar. 28, 2018), https://insidemazda.mazdausa.com/press-release/mazda-introduces-2019-cx-3-2018-new-york-auto-show/ (last visited Apr. 13, 2018).

    However, HCCI was not included in the simulation and vehicle fleet 
modeling for past rulemakings, and is not included in this NPRM 
analysis, primarily because effectiveness, cost, and mass market 
implementation readiness data are not available.
    Please comment on the potential use of HCCI technology in the 
timeframe covered by this rule. More specifically, should HCCI be 
included in the final rulemaking analysis for this proposed rulemaking? 
Why or why not? Please provide supporting data, including effectiveness 
values, costs in relation varying engine types and applications, and 
production timing that supports the timeframe of this rulemaking.
(d) Diesel Engines
    Diesel engines have several characteristics that give superior fuel 
efficiency, including reduced pumping losses due to lack of (or greatly 
reduced) throttling, high pressure direct injection of fuel, a 
combustion cycle that operates at a higher compression ratio, and a 
very lean air/fuel mixture relative to an equivalent-performance 
gasoline engine. This technology requires additional enablers, such as 
a NOX adsorption catalyst system or a urea/ammonia selective 
catalytic reduction system for control of NOX emissions 
during lean (excess air) operation.
(e) Alternative Fuel Engines
(1) Compressed Natural Gas (CNG)
    Compressed Natural Gas (CNG) engines use compressed natural gas as 
a fuel source. The fuel storage and supply systems for these engines 
differ tremendously from gasoline, diesel, and flex fuel vehicles.
(2) Flex Fuel Engines
    Flex fuel engines can run on regular gasoline and fuel blended with 
ethanol. These vehicles may require additional equipment in the fuel 
system to effectively supply different blends of fuel to the engine.
(f) Lubrication and Friction Reduction
    Low-friction lubricants including low viscosity and advanced low 
friction lubricant oils are now available (and widely used). If 
manufacturers choose to make use of these lubricants, they may need to 
make engine changes and conduct durability testing to accommodate the 
lubricants. The level of low friction lubricants exceeded 85% 
penetration in the MY 2016 fleet.
    Reduction of engine friction can be achieved through low-tension 
piston rings, roller cam followers, improved material coatings, more 
optimal thermal management, piston surface treatments, and other 
improvements in the design of

[[Page 43040]]

engine components and subsystems that improve efficient engine 
    Manufacturers have already widely adopted both lubrication and 
friction reduction technologies. This analysis includes advanced engine 
maps that already assume application of low-friction lubricants and 
engine friction reduction technologies. Therefore, additional friction 
reduction is not considered in today's analysis.
    The use and commercial development of improved lubricants and 
friction reduction components will continue to be monitored, including 
conical boring and oblong cylinders, and future analyses may be updated 
if new information becomes available.
5. Fuel Octane
(a) What is fuel octane level?
    Gasoline octane levels are an integral part of potential engine 
performance. According the United States Energy Information 
Administration (EIA), octane ratings are measures of fuel stability. 
These ratings are based on the pressure at which a fuel will 
spontaneously combust (auto-ignite) in a testing engine.\147\ 
Spontaneous combustion is an undesired condition that will lead to 
serious engine damage and costly repairs for consumers if not properly 
managed. The higher an octane number, the more stable the fuel, 
mitigating the potential for spontaneous combustion, also commonly 
known as ``knock.'' Modern engine control systems are sophisticated and 
allow manufacturers to detect when ``knock'' occurs during engine 
operation. These control systems are designed to adjust operating 
parameters to reduce or eliminate ``knock'' once detected.

    \147\ U.S. Energy Information Administration, What is Octane?, 
https://www.eia.gov/energyexplained/index.cfm?page=gasoline_home#tab2 (last visited Mar. 19, 2018).

    In the United States, consumers are typically able to select from 
three distinct grades of fuel, each of which provides a different 
octane rating. The octane levels can vary from region to region, but on 
the majority, the octane levels offered are regular (the lowest octane 
fuel-generally 87 Anti-Knock Index (AKI) also expressed as (the average 
of Research Octane + Motor Octane), midgrade (the middle range octane 
fuel-generally 89-90 AKI), and premium (the highest octane fuel-
generally 91-94 AKI).\148\ At higher elevations, the lowest octane 
rating available can drop to 85 AKI.\149\

    \148\ Id.
    \149\ See e.g., U.S. Department of Energy and U.S. Environmental 
Protection Agency, What is 85 octane, and is it safe to use in my 
vehicle?, https://www.fueleconomy.gov/feg/octane.shtml#85 (last 
visited Mar. 19, 2018). 85 octane fuel is available in high-
elevation regions where the barometric pressure is lower causing 
naturally-aspirated engines to operate with less air and, therefore, 
at lower torque and power. This creates less benefit and need for 
higher octane fuels as compared to at lower elevations where engine 
airflow, torque, and power levels are higher.

    Currently, throughout the United States, pump fuel is a blend of 
90% gasoline and 10% ethanol. It is standard practice for refiners to 
manufacture gasoline and ship it, usually via pipelines, to bulk fuel 
terminals across the country. In many cases, refiners supply lower 
octane fuels than the minimum 87-octane required by law to these 
terminals. The terminals then perform blending operations to bring the 
fuel octane level up to the minimum required by law, and higher. In 
some cases, typically to lowest fuel grade, the ``base fuel'' is 
blended with ethanol, which has a typical octane rating of 
approximately 113. For example, in 2013, the State of Nebraska Ethanol 
Board defined requirements for refiners to 84-octane gas for blending 
to achieve 87-octane prior to final dispensing to consumers.\150\

    \150\ Nebraska Ethanol Board, Oil Refiners Change Nebraska Fuel 
Components, Nebraska.gov, http://ethanol.nebraska.gov/wordpress/oil-refiners-change-nebraska-fuel-components/ (last visited Mar. 19, 

(b) Fuel Octane Level and Engine Performance
    A typical, overarching goal of optimal spark-ignited engine design 
and operation is to maximize the greatest amount of energy from the 
fuel available, without manifesting detrimental impacts to the engine 
over its expected operating conditions. Design factors, such as 
compression ratio, intake and exhaust value control specifications, 
combustion chamber and piston characteristics, among others, are all 
impacted by octane (stability) of the fuel consumers are anticipated to 

    \151\ Additionally, PRIA Chapter 6 contains a brief discussion 
of fuel properties, octane levels used for engine simulation and in 
real-world testing, and how octane levels can impact performance 
under these test conditions.

    Vehicle manufacturers typically develop their engines and engine 
control system calibrations based on the fuel available to consumers. 
In many cases, manufacturers may recommend a fuel grade for best 
performance and to prevent potential damage. In some cases, 
manufacturers may require a specific fuel grade for both best 
performance and/or to prevent potential engine damage.
    Consumers, though, may or may not choose to follow the 
recommendation or requirement for a specific fuel grade. Additionally, 
regional fuel availability could also limit consumer choice, or, in the 
case of higher elevation regions, present an opportunity for consumers 
to use a fuel grade that is below the minimum recommended. As such, 
vehicle manufacturers employ strategies for scenarios where a lower 
than recommended, or required, fuel grade is used, mitigating engine 
damage over the life of a vehicle.
    When knock (also referred to as detonation) is encountered during 
engine operation, at the most basic level, non-turbo charged engines 
can reduce or eliminate knock by adjusting the timing of the spark that 
ignites the fuel, as well as the amounts of fuel injected at each 
intake stroke (``fueling''). In turbo-charged applications, boost 
levels are typically reduced along with spark timing and fueling 
adjustments. Past rulemakings have also discussed other techniques that 
may be employed to allow higher compression ratios, more optimal spark 
timing to be used without knock, such as the addition of cooled exhaust 
gas recirculation (EGR). Regardless of the type of spark-ignition 
engine or technology employed, reducing or preventing knock results in 
the loss of potential power output, creating a ``knock-limited'' 
constraint on performance and efficiency.
    Despite limits imposed by available fuel grades, manufacturers 
continue to make progress in extracting more power and efficiency from 
spark-ignited engines. Production engines are safely operating with 
regular 87 AKI fuel with compression ratios and boost levels once 
viewed as only possible with premium fuel. According to the Department 
of Energy, the average gasoline octane level has remained fundamentally 
flat starting in the early 1980's and decreased slightly starting in 
the early 2000s. During this time, however, the average compression 
ratio for the U.S. fleet has increased from 8.4 to 10.52, a more than 
20% increase, yielding the statement that, ``There is some concern that 
in the future, auto manufacturers will reach the limit of technological 
increases in compression ratios without further increases in the octane 
of the fuel.'' \152\

    \152\ Fact of the Week, Fact #940: August 29, 2016 Diverging 
Trends of Engine Compression Ratio and Gasoline Octane Rating, U.S. 
Department of Energy, https://www.energy.gov/eere/vehicles/fact-940-august-29-2016-diverging-trends-engine-compression-ratio-and-gasoline-octane (last visited Mar. 21, 2018).

    As such, manufacturers are still limited by the available fuel 
grades to consumers and the need to safeguard the durability of their 
products for all of the available fuels; thus, the potential

[[Page 43041]]

improvement in the design of spark-ignition engines continues to be 
overshadowed by the fuel grades available to consumers.
(c) Potential of Higher Octane Fuels
    Automakers and advocacy groups have expressed support for increases 
to fuel octane levels for the U.S. market and are actively 
participating in Department of Energy research programs on the 
potential of higher octane fuel usage.153 154 Some positions 
for potential future octane levels include advocacy for today's premium 
grade becoming the base grade of fuel available, which could enable low 
cost design changes that would improve fuel economy and CO2. 
Challenges associated with this approach include the increased fuel 
cost to consumers who drive vehicles designed for current regular 
octane grade fuel that would not benefit from the use of the higher 
cost higher octane fuel. The net costs for a shift to higher octane 
fuel would persist well into the future. Net benefits for the 
transition would not be achieved until current regular octane fuel is 
not available in the North American market, causing manufacturers to 
redesign all engines to operate the higher octane fuel, and then after 
those vehicles have been in production a sufficient number of model 
years to largely replace the current on-road vehicle fleet. The 
transition to net positive benefits could take many years.

    \153\ Mark Phelan, High octane gas coming--but you'll pay more 
for it, Detroit Free Press (Apr. 25, 2017), https://www.freep.com/story/money/cars/mark-phelan/2017/04/25/new-gasoline-promises-lower-emissions-higher-mpg-and-cost-octane-society-of-automotive-engineers/100716174/.
    \154\ The octane game: Auto industry lobbies for 95 as new 
regular, Automotive News (April 17, 2018), http://www.autonews.com/article/20180417/BLOG06/180419780/the-octane-game-auto-industry-lobbies-for-95-as-new-regular.

    In anticipation of this proposed rulemaking, organizations such as 
the High Octane Low Carbon Alliance (HOLC) and the Fuel Freedom 
Foundation (FFA), have shared their positions on the potential for 
making higher octane fuels available for the U.S. market. Other 
stakeholders also commented to past NHTSA rulemakings and/or the Draft 
TAR regarding the potential for increasing octane levels for the U.S. 
    In the meetings with HOLC and the FFA, the groups advocated for the 
potential benefits high octane fuels could provide via the blending of 
non-petroleum feedstocks to increase octane levels available at the 
pump. The groups' positions on benefits took both a technical approach 
by suggesting an octane level of 100 is desired for the marketplace, as 
well as, the benefits from potential increased national energy security 
by reduced dependencies on foreign petroleum.
(d) Fuel Octane--Request for Comments
    Please comment on the potential benefits, or dis-benefits, of 
considering the impacts of increased fuel octane levels available to 
consumers for purposes of the model. More specifically, please comment 
on how increasing fuel octane levels would play a role in product 
offerings and engine technologies. Are there potential improvements to 
fuel economy and CO2 reductions from higher octane fuels? 
Why or why not? What is an ideal octane level for mass-market 
consumption balanced against cost and potential benefits? What are the 
negatives associated with increasing the available octane levels and, 
potentially, eliminating today's lower octane fuel blends? Please 
provide supporting data for your position(s).
6. Transmission Technologies
    Transmissions transmit torque from the engine to the wheels. 
Transmissions may improve fuel efficiency primarily through two 
mechanisms: (1) Transmissions with more gears allow the engine to 
operate more regularly at the most efficient speed-load points, and (2) 
transmissions may have improvements in friction (gears, bearings, 
seals, and so on), or improvements in shift efficiency that help the 
transmission transfer torque more efficiently, lowering parasitic 
losses. These mechanisms are very different, so full-vehicle simulation 
is helpful to understand how a transmission may work with complementary 
equipment to improve fuel economy.
    Today's analysis significantly increased the number of 
transmissions modeled in full-vehicle simulations, attempting to more 
closely align the Department of Energy full-vehicle simulations with 
existing vehicles. Previously, EPA included just five transmissions 
\155\ by vehicle class in their analysis, and often EPA represented 
upgrades among manual, automatic, continuously variable, and dual 
clutch transmissions with the same effectiveness \156\ and cost values 
\157\ within a vehicle class. Today's analysis simulated nearly 20 
transmissions, with explicit assumptions about gear ratios, gear 
efficiencies, gear spans, shift logic, and transmission 
architecture.158 159 This analysis improves transparency by 
making clear the assumptions underlying the transmissions in the full-
vehicle simulations and by increasing the number of transmissions 
simulated since the Draft TAR. Methods will be continuously evaluated 
to improve transmission models in full-vehicle simulations. For the box 
plots of effectiveness values, as shown in the PRIA Chapter 6, all 
automatic transmissions are relative to a 5-speed automatic, and all 
manual transmissions are relative to a 5-speed manual. Table II-11 
below shows the absolute costs of transmission used for this analysis 
including learning and retail price equivalent.

    \155\ Null, TRX11, TRX12, TRX21, TRX22.
    \156\ Draft TAR, p. 5-297 through 5-298 summarizes effectiveness 
values previously assumed for stepping between transmission 
technologies (Null, TRX11, TRX12, TRX21, TRX22).
    \157\ Draft TAR, p. 5-299. ``For future vehicles, it was assumed 
that the costs for transitioning from one technology level (TRX11-
TRX22) to another level is the same for each transmission type (AT, 
AMT, DCT, and CVT).''
    \158\ See PRIA Chapter 6.3.
    \159\ Ehsan, I.S., Moawad, A., Kim, N., & Rousseau, A. ``A 
Detailed Vehicle Simulation Process To Support CAFE Standards.'' 
ANL/ESD-18/6. Energy Systems Division, Argonne National Laboratory. 


[[Page 43042]]


(a) Automatic Transmissions
    Five-, six-, seven-, eight-, nine- and ten-speed automatic 
transmissions are optimized by changing the gear ratios to enable the 
engine to operate in a more efficient operating range over a broader 
range of vehicle operating conditions. While a six speed transmission 
application was most prevalent for the MYs 2012-2016 final rule, eight 
and higher speed transmissions were more prevalent in the MY 2016 
    ``L2'' and ``L3'' transmissions designate improved gear efficiency 
and reduced parasitic losses. Few transmissions in the MY 2016 fleet 
have achieved ``L2'' efficiency, and the highest level of transmission 
efficiencies modeled are assumed to be available in MY 2022.
(1) Continuously Variable Transmissions
    Continuously variable transmission (CVT) commonly uses V-shaped 
pulleys connected by a metal belt rather than gears to provide ratios 
for operation. Unlike manual and automatic transmissions with fixed 
transmission ratios, continuously variable transmissions can provide 
fully variable and an infinite number of transmission

[[Page 43043]]

ratios that enable the engine to operate in a more efficient operating 
range over a broader range of vehicle operating conditions. In this 
NPRM, two levels of CVTs are considered for future vehicles. The second 
level CVT would have a wider transmission ratio, increased torque 
capacity, improvements in oil pump efficiency, lubrication 
improvements, and friction reduction. While CVTs work well with light 
loads, the technology as modeled is not suitable for larger vehicles 
such as trucks and large SUVs.
(2) Dual Clutch Transmissions
    Dual clutch or automated shift manual transmissions (DCT) are 
similar to manual transmissions except for the vehicle controls 
shifting and launch functions. A dual-clutch automated shift manual 
transmission uses separate clutches for even-numbered and odd-numbered 
gears, so the next expected gear is pre-selected, which allows for 
faster and smoother shifting. The 2012-2016 final rule limited DCT 
applications to a maximum of 6-speeds. Both 6-speed and 8-speed DCT 
transmissions are considered in today's proposal.
    Dual clutch transmissions are very effective transmission 
technologies, and previous rule-making projected rapid, and wide 
adoption into the fleet. However, early DCT product launches in the 
U.S. market experienced shift harshness and poor launch performance, 
resulting in customer satisfaction issues--some so extreme as to prompt 
vehicle buyback campaigns.\160\ Most manufacturers are not using DCTs 
in the U.S. market due to the customer satisfaction issues. 
Manufacturers used DCTs in about three percent of the MY 2016 fleet. 
Today's analysis limits the application of improved DCTs to vehicles 
that already use DCTs. Many of these vehicles are imported performance 

    \160\ Ford Powershift Transmission Settlement, http://fordtransmissionsettlement.com/ (last visited June 21, 2018).

(b) Manual Transmissions
    Manual 6- and 7-speed transmissions offer an additional gear ratio, 
sometimes with a higher overdrive gear ratio, over a 5-speed manual 
transmission. Similar to automatic transmissions, more gears often 
means the engine may operate in the efficient zone more frequently.
7. Vehicle Technologies
    As discussed earlier in Section II.D.1.b)(1), several technologies 
were considered for this analysis, and Table II-12, Table II-13, and 
Table II-14 below shows the full list of vehicle technologies analyzed 
and the associated absolute cost.\161\

    \161\ Mass reduction costs are in $/lb.


[[Page 43044]]


[[Page 43045]]


[[Page 43046]]


(a) Reduced Rolling Resistance
    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 and 
reducing CO2 emissions. New for this proposal, and also 
marking an advance over low rolling resistance tires considered during 
the heavy duty greenhouse gas rulemaking,\162\ is a second level of 
lower rolling resistance tires that reduce frictional losses even 
further. The first level of low rolling resistance tires will have 10% 
rolling resistance reduction while the second level would have 20% 
rolling resistance reduction. In this NPRM, baseline vehicle reference 
rolling resistance values were determined based on the MY 2016 vehicles 
rather than the MY 2008 vehicles used in the 2012 final rule. Rolling 
resistance values were assigned based on CBI shared by manufacturers.

    \162\ See 76 FR 57106, at 57207, 57229 (Sep. 15, 2011).

    In some cases, low rolling resistance tires can affect traction, 
which may be untenable for some high performance vehicles. For cars and 
SUVs with more than 405 horsepower, the analysis restricted the 
application of the highest levels of rolling resistance. For cars and 
SUVs with more than 500 horsepower, the analysis restricted the 
application of any additional rolling resistance technology.
(b) Reduced Aerodynamic Drag Coefficient
    Aerodynamic drag reduction can be achieved via two approaches, 
either by reducing the drag coefficients or reducing vehicle frontal 
area. To reduce the drag coefficient, skirts, air dams, underbody 
covers, and more aerodynamic side view mirrors can be applied. In the 
MY 2017-2025 final rule and the 2016 Draft TAR, the analysis included 
two levels of aerodynamic technologies. The second level included 
active grille shutters, rear visors, and larger under body panels. This 
NPRM expanded the aerodynamic drag improvements from two levels to four 
to provide more discrete levels. The NPRM levels are 5%, 10%, 15%, and 

[[Page 43047]]

improvement relative to baseline reference vehicles. The agencies 
relied on the wind tunnel testing performed by National Research 
Council (NRC), Canada, Transport Canada (TC), and Environment and 
Climate Change, Canada (ECCC) to quantify the aerodynamic drag impacts 
of various OEM aerodynamic technologies and to explore the improvement 
potential of these technologies by expanding the capability and/or 
improving the design of MY 2016 state-of-the-art aerodynamic 
treatments. The agencies estimated the level of aerodynamic drag in 
each vehicle model in the MY 2016 baseline fleet and gathered CBI on 
aerodynamic drag coefficients, so each vehicle has an appropriate 
initial value for further improvements.
    Notably, today's analysis assumes aerodynamic drag reduction can 
only come from reduction in the aerodynamic drag coefficient and not 
from reduction of frontal area.\163\ For some bodystyles, the agencies 
have no evidence that manufacturers may be able to achieve 15% or 20% 
aerodynamic drag coefficient reduction relative to baseline for some 
bodystyles (for instance, with pickup trucks) due to form drag 
limitions. Previously, EPA analysis assumed some vehicles from all 
bodystyles could (and would) reduce aerodynamic forces by 20%, which in 
some cases led to future pickup trucks having aerodynamic drag 
coefficients better than some of today's typical cars, if frontal area 
were held constant. While ANL created full-vehicle simulations for 
trucks with 20% drag reduction, those simulations were not used in the 
CAFE modeling. That level of drag reduction is likely not 
technologically feasible with today's technology, and the analysis 
accordingly restricted the application of advanced levels of 
aerodynamics in some instances, such as in this case, due to bodystyle 
form drag limitations. Separate from form drag limitations, some high 
performance vehicles already use advanced aerodynamics technologies to 
generate down force, and sometimes these applications must trade-off 
between aerodynamic drag coefficient reduction and down force. Today's 
analysis does not apply 15% or 20% aerodynamic drag coefficient 
reduction to cars and SUVs with more than 405 horsepower.

    \163\ EPA previously assumed that manufacturers could reduce 
frontal area as well as aerodynamic drag coefficient to achieve 20% 
aerodynamic force reduction relative to ``Null'' or initial 
aerodynamic technology level; however, reducing frontal area would 
likely degrade other utility features like interior volume, or 

(c) Mass Reduction
    Mass Reduction can be achieved in many ways, such as material 
substitution, design optimization, part consolidation, improving 
manufacturing process, etc. The analysis utilizes mass reduction levels 
of 5, 10, 15, and 20% relative to a reference glider vehicle for each 
vehicle subsegment. For HEV, PHEV, and BEV vehicles, net mass reduction 
was considered, including the mass reduction applied to the glider and 
the added mass of electrification components. An extensive discussion 
of mass reduction technologies as well as the cost of mass reduction is 
located in Chapter 6.3 of the PRIA. The analysis included an estimated 
level of mass reduction technology in each vehicle model in the MY 2016 
baseline fleet so that each vehicle model has an appropriate initial 
value for further improvements.
(d) Low Drag Brakes (LDB)
    Low-drag brakes 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 rotors.
(e) Secondary Axle Disconnect (SAX)
    Front or secondary axle disconnect for all-wheel drive systems 
provides a torque distribution disconnect between front and rear axles 
when torque is not required for the non-driving axle. This results in 
the reduction of associated parasitic energy losses.
8. Electrification Technologies
    For this NPRM, the analysis of electrification technologies relies 
primarily on research published by the Department of Energy, ANL.\164\ 
ANL's assumptions regarding all hybrid systems, including belt-
integrated starter generators, strong parallel and series hybrids, 
plug-in hybrids,\165\ and battery electric vehicles, and most projected 
technology costs were adopted for this analysis. In addition, the most 
recent ANL BatPaC model is used to estimate battery costs. Table II-15 
and Table II-16 below show the absolute costs of all electrification 
technologies estimated for this NPRM analysis relative to a basic 
internal combustion engine vehicle with a 5-speed automatic 

    \164\ Moawad et al., Assessment of vehicle sizing, energy 
consumption, and cost through large-scale simulation of advanced 
engine technologies, Argonne National Laboratory (March 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.
    \165\ Notably all power split hybrids, and all plug-in hybrid 
vehicles were assumed to be paired with a high compression ratio 
internal combustion engine for this analysis.
    \166\ Note: These costs do not include value loss for HEVs, 
PHEVs, and BEVs. Powertrain hardware between cars and small SUV's is 
often similar, especially if technology is used vehicles on the same 
platform; however, battery pack sizes may vary meaningfully to 
deliver similar range in different applications.


[[Page 43048]]



[[Page 43049]]

(a) Hybrid Technologies
(1) 12-Volt Stop-Start
    12-volt Stop-Start, sometimes referred to as idle-stop or 12-volt 
micro hybrid, is the most basic hybrid system that facilitates idle-
stop capability. These systems typically incorporate an enhanced 
performance battery and other features such as electric transmission 
pump and cooling pump to maintain vehicle systems during idle-stop.
(2) Higher Voltage Stop-Start/Belt Integrated Starter Generator
    Higher Voltage Stop-Start/Belt Integrated Starter Generator (BISG), 
sometimes referred to as a mild hybrid system, provides idle-stop 
capability and uses a higher voltage battery with increased energy 
capacity over typical automotive batteries. The higher system voltage 
allows the use of a smaller, more powerful electric motor. This system 
replaces a standard alternator with an enhanced power, higher voltage, 
higher efficiency starter-alternator, that is belt driven and that can 
recover braking energy while the vehicle slows down (regenerative 
braking). Today's analysis assumes 48V systems on cars and small SUVs 
and high voltage systems for large SUVs and trucks. Future analysis may 
reference the application and operation of 48V systems on large SUVs 
and trucks, if applicable.
(3) Integrated Motor Assist (IMA)/Crank Integrated Starter Generator
    Integrated Motor Assist (IMA)/Crank integrated starter generator 
(CISG) provides idle-stop capability and uses a high voltage battery 
with increased energy capacity over typical automotive batteries. The 
higher system voltage allows the use of a smaller, more powerful 
electric motor and reduces the weight of the wiring harness. This 
system replaces a standard alternator with an enhanced power, higher 
voltage, higher efficiency starter alternator that is crankshaft-
mounted and can recover braking energy while the vehicle slows down 
(regenerative braking).
(4) P2 Hybrid
    P2 Hybrid (SHEVP2) is a newly emerging hybrid technology that uses 
a transmission-integrated electric motor placed between the engine and 
a gearbox or CVT, much like the IMA system described above except with 
a wet or dry separation clutch that is used to decouple the motor/
transmission from the engine. In addition, a P2 hybrid would typically 
be equipped with a larger electric machine. Disengaging the clutch 
allows all-electric operation and more efficient brake-energy recovery. 
Engaging the clutch allows efficient coupling of the engine and 
electric motor and, when combined with a DCT transmission, reduces 
gear-train losses relative to power-split or 2-mode hybrid systems. 
Battery costs are now considered separately from other HEV hardware.
    P2 Hybrid systems typically rely on the internal combustion engine 
to deliver high, sustained power levels. While many vehicles may use 
HCR1 engines as part of a hybrid powertrain, HCR1 engines may not be 
suitable for all vehicles, especially high performance vehicles, or 
vehicles designed to carry or tow large loads. Many manufacturers may 
prefer turbo engines (with high specific power output) for P2 Hybrid 
(5) Power-Split Hybrid
    Power-split Hybrid (SHEVPS) is a hybrid electric drive system that 
replaces the traditional transmission with a single planetary gearset 
and a motor/generator. This motor/generator uses the engine to either 
charge the battery or supply additional power to the drive motor. A 
second, more powerful motor/generator is permanently 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 to either charge the battery or supply power to the wheels. 
The power-split hybrid technology is included in this analysis as an 
enabling technology supporting this proposal, (the agencies evaluate 
the P2 hybrid technology discussed above where power-split hybrids 
might otherwise have been appropriate). As stated above, battery costs 
are now considered separately from other HEV hardware. Power-split 
hybrid technology as modeled in this analysis is not suitable for large 
vehicles that must handle high loads.
    The ANL Autonomie simulations assumed all power-split hybrids use a 
high compression ratio engine. Therefore, all vehicles equipped with 
SHEVPS technology in the CAFE model inputs and simulations are assumed 
to have high compression ratio engines.
(6) Plug-in Hybrid Electric
    Plug-in hybrid electric vehicles (PHEV) 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 hybrid electric vehicles. They also 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 is typical of other hybrid electric 
vehicles. These vehicles are sometimes referred to as Range Extended 
Electric Vehicles (REEV). In this NPRM analysis, PHEVs with two all-
electric ranges--both a 30 mile and a 50 mile all-electric range--have 
been included as potential technologies. Again, battery costs are now 
considered separately from other PHEV hardware.
    The ANL Autonomie simulations assumed all PHEVs use a high 
compression ratio engine. Therefore, all vehicles equipped with PHEV 
technology in the CAFE model inputs and simulations are assumed to have 
high compression ratio engines. In practice, many PHEVs recently 
introduced in the marketplace use turbo-charged engines in the PHEV 
system, and this is particularly true for PHEVs produced by European 
manufacturers and for other PHEV performance vehicle applications.
    Please provide comment on the modeling of PHEV systems. Should 
turbo PHEVs be considered instead, or in addition to high compression 
ratio PHEVs? Why or why not? What vehicle segments may turbo PHEVs best 
be suited for, and which segments would it not be best suited for? What 
vehicle segments may high compression ratio PHEVs best be suited for, 
and which segments would it not be best suited for? Similarly, the 
analysis currently considers PHEVs with 30-mile and 50-mile all-
electric range, and should other ranges be considered? For instance, a 
20-mile all-electic range may decrease the battery pack size, and hence 
the battery pack cost relative to a 30-mile all-electric range system, 
while still providing electric-vehicle functionality in many 
applications. Do commenters believe PHEV technology will see widespread 
adoption in the US vehicle fleet? Why or why not? Please provide 
supporting data.
(b) Full Electrification and Fuel Cell Vehicles
(1) Battery Electric
    Electric vehicles (EV) are equipped with all-electric drive and 
with systems powered by energy-optimized batteries charged primarily 
from grid electricity. EVs with range of 200 miles have been included 
as a potential technology in this NPRM. Battery costs are now 
considered separately from other EV hardware.

[[Page 43050]]

(2) Fuel Cell Electric
    Fuel cell electric vehicles (FCEVs) utilize a full electric drive 
platform but consume electricity generated by an onboard fuel cell and 
hydrogen fuel. Fuel cells are electrochemical devices that directly 
convert reactants (hydrogen and oxygen via air) into electricity, with 
the potential of achieving more than twice the efficiency of 
conventional internal combustion engines. High pressure gaseous 
hydrogen storage tanks are used by most automakers for FCEVs. The high 
pressure tanks are similar to those used for compressed gas storage in 
more than 10 million CNG vehicles worldwide, except that they are 
designed to operate at a higher pressure (350 bar or 700 bar vs. 250 
bar for CNG). FCEVs are currently produced in limited numbers and are 
available in limited geographic areas.
(c) Electric Vehicle Infrastructure
    BEVs and PHEVs may be charged at home or elsewhere. Home chargers 
may access electricity from a regular wall outlet, or they may require 
special equipment to be installed at the home. Commercial chargers may 
sometimes be found at businesses or other travel locations. These 
chargers often may supply power to the vehicle at a faster rate of 
charge but often require significant capital investment to install.
    Time to charge, and availability and convenience of charging are 
significant factors for plug-in vehicle operators. For many consumers, 
accessible charging stations present inconveniences that may deter the 
adoption of battery electric and plug-in hybrid vehicles.
    More detail about charging and charging infrastructure, including a 
discussion of potential electric vehicle impacts on the electric grid, 
is available in the PRIA, Chapter 6. For today's analysis, costs for 
installing chargers and charge convenience is not taken into account in 
the CAFE model. Also, today's analysis assumes HEVs, PHEVs, and BEVs 
have the same survival rates and mileage accumulation schedules as 
vehicles with conventional powertrains, and that HEVs, PHEVs, and BEVs 
never receive replacement batteries before being scrapped. The agencies 
invite comment on these assumptions and on data and practicable methods 
to implement any alternatives.
9. Accessory Technologies
    Two accessory technologies, electric power steering (EPS) and 
improved accessories (IACC) (accessory technologies categorized for the 
2012 rule) were considered in this analysis, and are described 
below.\167\ Table II-17 and Table II-18 below shows the estimated 
absolute costs including learning effects and retail price equivalent 
utilized in today's analysis.

    \167\ For further discussion of accessory technologies, see 
Chapter 6 of the PRIA accompanying this NPRM.


(a) Electric Power Steering (EPS)
    Electric power steering (EPS)/Electrohydraulic power steering 
(EHPS) is an electrically-assisted steering system that has advantages 
over traditional hydraulic power steering because it replaces a 
continuously operated hydraulic pump, thereby reducing parasitic losses 
from the accessory drive. Manufacturers have informed the agencies that 
full EPS systems are being developed for all types of light-duty 
vehicles, including large trucks. However, this analysis applies the 
EHPS technology to large trucks and the EPS technology to all other 
light-duty vehicles.

[[Page 43051]]

(b) Improved Accessories (IACC)
    Improved accessories (IACC) may include high efficiency 
alternators, electrically driven (i.e., on-demand) water pumps, 
variable geometry oil pumps, cooling fans, a mild regeneration 
strategy, and high efficiency alternators. It excludes other electrical 
accessories such as electric oil pumps and electrically driven air 
conditioner compressors. In the MY 2017-2025 final rule, two levels of 
IACC were offered as a technology path (a low improvement level and a 
high improvement level). Since much of the market has incorporated some 
of these technologies in the MY 2016 fleet, the analysis assumes all 
vehicles have incorporated what was previously the low level, so only 
the high level remains as an option for some vehicles.
10. Other Technologies Considered but Not Included in This Aanalysis
    Manufacturers, suppliers, and researchers continue to create a 
diverse set of fuel economy technologies. Many high potential 
technologies that are still in the early stages of the development and 
commercialization process are still being evaluated for any final 
analysis. Due to uncertainties in the cost and capabilities of emerging 
technologies, some new and pre-production technologies are not yet a 
part of the CAFE model simulation. Evaluating and benchmarking 
promising fuel economy technologies continues to be a priority as 
commercial development matures.
(a) Engine Technologies
     Variable compression ratio (VCR)--varies the compression 
ratio and swept volume by changing the piston stroke on all cylinders. 
Manufacturers accomplish this by changing the effective length of the 
piston connecting rod, with some prototypes having a range of 8:1 to 
14:1 compression ratio. In turbocharged form, early publications 
suggest VCR may be possible to deliver up to 35% improved efficiency 
over the existing equivalent-output naturally-aspirated engine.\168\

    \168\ See e.g., VC--Turbo--The world's first production-ready 
variable compression ratio engine, Nissan Motor Corporation (Dec. 
13, 2017), https://newsroom.nissan-global.com/releases/release-917079cb4af478a2d26bf8e5ac00ae49-vc-turbo-the-worlds-first-production-ready-variable-compression-ratio-engine.

     Opposed-piston engine--sometimes known as opposed-piston 
opposed-cylinder (OPOC), operates with two pistons per cylinder working 
in opposite reciprocal motion and running on a two-stroke combustion 
cycle. It has no cylinder head or valvetrain but requires a 
turbocharger and supercharger for engine breathing. The efficiency may 
be significantly higher than MY 2016 turbocharged gasoline engines with 
competitive costs. This engine architecture could run on many fuels, 
including gasoline and diesel. Packaging constraints, emissions 
compliance, and performance across a wide range of operating conditions 
remain as open considerations. No production vehicles have been 
publicly announced, and multiple manufacturers continue to evaluate the 
technology.169 170

    \169\ Murphy, T. Achates: Opposed-Piston Engine makers tooling 
up, Wards Auto (Jan. 23, 2017), http://wardsauto.com/engines/achates-opposed-piston-engine-makers-tooling.
    \170\ Our Formula, Achates Power, http://achatespower.com/our-formula/opposed-piston/ (last visited June 21, 2018).

     Dual-fuel--engine concepts such as reactivity controlled 
compression ignition (RCCI) combine multiple fuels (e.g. gasoline and 
diesel) in cylinder to improve brake thermal efficiency while reducing 
NOX and particulate emissions. This technology is still in 
the research phase.\171\

    \171\ Robert Wagner, Enabling the Next Generation of High 
Efficiency Engines, Oak Ridge National Laboratory, U.S. Department 
of Energy (2012), available at https://www.energy.gov/sites/prod/files/2014/03/f8/deer12_wagner_0.pdf.

     Smart accessory technologies--can improve fuel efficiency 
through smarter controls of existing systems given imminent or expected 
controls inputs in real world driving conditions. For instance, a 
vehicle could adjust the use of accessory systems to conserve energy 
and fuel as a vehicle approaches a red light. Vehicle connectivity and 
sensors can further refine the operation for more benefit and smoother 

    \172\ EfficientDynamics--The intelligent route to lower 
emissions, BMW Group (2007), https://www.bmwgroup.com/content/dam/bmw-group-websites/bmwgroup_com/responsibility/downloads/en/2007/Alex_ED__englische_Version.pdf.

     High Compression Miller Cycle Engine with Variable 
Geometry Turbocharger or Electric Supercharger--Atkinson cycle gasoline 
engines with sophisticated forced induction system that requires 
advanced controls. The benefits of these technologies provide better 
control of EGR rates and boost which is achieved with electronically 
controlled turbocharger or supercharger. The electric version of this 
technology which incorporates 48V is called E-boost.173 174

    \173\ Volkswagen at the 37th Vienna Motor Symposium, Volkswagen 
(Apr. 28, 2016), https://www.volkswagen-media-services.com/en/detailpage/-/detail/Volkswagen-at-the-37th-Vienna-Motor-Symposium/view/3451577/5f5a4dcc90111ee56bcca439f2dcc518?p_p_auth=M2yfP3Ze.
    \174\ These engines may be considered in the analysis supporting 
the final rule, but these engine maps were not available in time for 
the NPRM analysis. Please see Chapter 6.3 of the PRIA accompanying 
this proposal for more information.

(b) Electrified Vehicle Powertrain
     Advanced battery chemistries--many emerging battery 
technologies promise to eventually improve the cost, safety, charging 
time, and durability in comparison to the MY 2016 automotive lithium-
ion batteries. For instance, many view solid state batteries as a 
promising medium-term automotive technology. Solid state batteries 
replace the battery's liquid electrolyte with a solid electrolyte to 
potentially improve safety, power and energy density, durability, and 
cost. Some variations use ceramic, polymer, or sulfide-based solid 
electrolytes. Multiple automakers and suppliers are exploring the 
technology and possible commercialization that may occur in the early 
2020s.175 176 177

    \175\ Schmitt, B. Ultrafast-Charging Solid-State EV Batteries 
Around The Corner, Toyota Confirms, Forbes (Jul. 25, 2017), https://www.forbes.com/sites/bertelschmitt/2017/07/25/ultrafast-charging-solid-state-ev-batteries-around-the-corner-toyota-confirms/#5736630244bb.
    \176\ Moving toward clean mobility, Robert Bosch GmbH, https://www.bosch.com/explore-and-experience/moving-toward-clean-mobility/ 
(last visited June 21, 2018).
    \177\ Reuters Staff, Honda considers developing all solid-state 
EV batteries, Reuters (Dec. 21, 2017), https://www.reuters.com/article/us-honda-nissan/honda-considers-developing-all-solid-state-ev-batteries-idUSKBN1EF0FM.

     Supercapacitors/Ultracapacitors--An electrical energy 
storage device with higher power density but lower energy density than 
batteries. Advanced capacitors may reduce battery degradation 
associated with charge and discharge cycles, with some tradeoffs to 
cost and engineering complexity. Supercapacitors/Ultracapacitors are 
currently not used in parallel or as a standalone traction motor energy 
storage device.\178\

    \178\ Burke, A. & Zhao,H. Applications of Supercapacitors in 
Electric and Hybrid Vehicles, Institute of Transportation Studies 
University of California, Davis (Apr. 2015), available at https://steps.ucdavis.edu/wp-content/uploads/2017/05/2015-UCD-ITS-RR-15-09-1.pdf.

    [cir] Lower-cost magnets for Brushless Direct Current (BLDC) 
motors--BLDC motor technology, common in hybrid and battery electric 
vehicles, uses rare earth magnets. By substituting and eliminating rare 
earths from the magnets, motor cost can be significantly reduced. This 
technology is announced, but not yet in production. The capability and 
material configuration of these systems remains a closely guarded trade 

    \179\ Buckland, K. & Sano, N. Toyota Readies Cheaper Electric 
Motor by Halving Rare Earth Use, Bloomberg (Feb, 20, 2018), https://www.bloomberg.com/news/articles/2018-02-20/toyota-readies-cheaper-electric-motor-by-halving-rare-earth-use.


[[Page 43052]]

    [cir] Integrated multi-phase integrated electric vehicle 
drivetrains. Research has been conducted on 6-phase and 9-phase 
integrated systems to potentially reduce cost and improve power 
density. Manufacturers may improve system power density through 
integration of the motor, inverter, control, and gearing. These systems 
are in the research phase.180 181

    \180\ Burkhardt, Y., Spagnolo, A., Lucas, P., Zavesky, M., & 
Brockerhoff, P. ``Design and analysis of a highly integrated 9-phase 
drivetrain for EV applications '' 20 November 2014. DOI. 10.1109/
ICELMACH.2014.6960219. IEEE xplore.
    \181\ Patel, V., Wang, J., Nugraha, D., Vuletic, R., & Tousen, 
J. ``Enhanced Availability of Drivetrain Through Novel Multi-Phase 
Permanent Magnet Machine Drive'' 2016. IEEE Transactions on 
Industrial Electronics Pages. 469-480.

(c) Transmission Technologies
     Beltless CVT--Most MY 2016, commercially available CVTs 
rely on belt technology. A new architecture of CVT replaces belts or 
pulleys with a continuously variable variator, which is a special type 
of planetary set with balls and rings instead of gears. The technology 
promises to improve efficiency, handle higher torques, and change modes 
more quickly. This technology may be commercially available as early as 

    \182\ Murphy, T. Planets Aligning for Dana's VariGlide Beltless 
CVT, Wards Auto (Aug. 22, 2017), http://wardsauto.com/technology/planets-aligning-dana-s-variglide-beltless-cvt.

     Multi-speed electric motor transmission--MY 2016 battery 
electric vehicle transmissions are single-speed. Multiple gears can 
allow for more torque multiplication at lower speeds or a downsized 
electric machine, increased efficiency, and higher top speed. Two-speed 
transmission designs are available but not currently in 

    \183\ Faid, S. A Highly Efficient Two Speed Transmission for 
Electric Vehicles (May 2015), available at http://www.evs28.org/event_file/event_file/1/pfile/EVS28_Saphir_Faid.pdf.

(d) Energy-Harvesting Technology
     Vehicle waste heat recovery systems--Internal combustion 
engines convert the majority of the fuel's energy to heat. 
Thermoelectric generators and heat pipes can convert this heat to 
electricity.\184\ Thermoelectric generators, often made of 
semiconductors, have been tested by automakers but have traditionally 
not been implemented due to low efficiency and high cost.\185\ These 
systems are not yet in production.

    \184\ Orr et al., A review of car waste heat recovery systems 
utilising thermoelectric generators and heat pipes, 101 Applied 
Thermal Engineering 490-495 (May 25, 2016).
    \185\ Patel, P. Powering Your Car with Waste Heat, MIT 
Technology Review (May 25, 2011), https://www.technologyreview.com/s/424092/powering-your-car-with-waste-heat/.

     Suspension energy recovery--Multiple electromechanical and 
electrohydraulic suspension technologies exist that can convert motion 
from uneven roads into electricity.186 187 These 
technologies are limited to luxury vehicles with limited production 
volumes. This technology is not produced in 2016 but planned for 
production as early as 2018.\188\

    \186\ Baeuml, B. et al., The Chassis of the Future, Schaeffler, 
https://www.schaeffler.com/remotemedien/media/_shared_media/08_media_library/01_publications/schaeffler_2/symposia_1/downloads_11/Schaeffler_Kolloquium_2014_27_en.pdf (last visited June 
21, 2018).
    \187\ Advanced Suspension, Tenneco, http://www.tenneco.com/overview/rc_advanced_suspension/ (last visited June 21, 2018).
    \188\ Audi A8 Active Chassis, Audi, https://www.audi.com/en/innovation/design/more_personal_comfort_a8_active_chassis.html (last 
visited June 21, 2018).

11. Air Conditioning Efficiency and Off-Cycle Technologies
(a) Air Conditioning Efficiency Technologies
    Air conditioning (A/C) is a virtually standard automotive 
accessory, with more than 95% of new cars and light trucks sold in the 
United States equipped with mobile air conditioning (MAC) systems. Most 
of the additional air conditioning related load on an engine is due to 
the compressor, which pumps the refrigerant around the system loop. The 
less the compressor operates or the more efficiently it operates, the 
less load the compressor places on the engine, and the better fuel 
consumption will be. This high penetration means A/C systems can 
significantly impact energy consumed by the light duty vehicle fleet.
    Vehicle manufacturers can generate credits for improved A/C systems 
under EPA's GHG 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.\189\ Table II-19 provides a 
``menu'' of qualifying A/C technologies, with the magnitude of each 
improvement value or credit estimated based on the expected reduction 
in CO2 emissions from the technology.\190\ NHTSA converts 
the improvement in grams per mile to a FCIV for each vehicle for 
purposes of measuring CAFE compliance. As part of a manufacturer's 
compliance data, manufacturers will provide information about which 
off-cycle technologies are present on which vehicles (see Section X for 
further discussion of reporting off-cycle technology information).

    \189\ 77 FR 62624, 62720 (Oct. 15, 2012).
    \190\ 40 CFR 86.1868-12 (2016).

    The 2012 final rule for MYs 2017 and later outlined two test 
procedures to determine credit or FCIV eligibility for A/C efficiency 
menu credits, the idle test, and the AC17 test. The idle test, 
performed while the vehicle is at idle, determined the additional 
CO2 generated at idle when the A/C system is operated.\191\ 
The AC17 test is a four-part performance test that combines the 
existing SC03 driving cycle, the fuel economy highway test cycle, and a 
pre-conditioning cycle, and solar soak period.\192\ Manufacturers could 
use the idle test or AC17 test to determine improvement values for MYs 
2014-2016, while for MYs 2017 and later, the AC17 test is the exclusive 
test that manufacturers can use to demonstrate eligibility for menu A/C 
improvement values.

    \191\ 75 FR 25324, 25431 (May 7, 2010). The A/C CO2 
Idle Test is run with and without the A/C system cooling the 
interior cabin while the vehicle's engine is operating at idle and 
with the system under complete control of the engine and climate 
control system.
    \192\ 77 FR 62624, 62723 (Oct. 15, 2012).

    In MYs 2020 and later, manufacturers will use the AC17 test to 
demonstrate eligibility for A/C credits and to partially quantify the 
amount of the credit earned. AC17 test results equal to or greater than 
the menu value will allow manufacturers to claim the full menu value 
for the credit. A test result less than the menu value will limit the 
amount of credit to that demonstrated on the AC17 test. In addition, 
for MYs 2017 and beyond, A/C fuel consumption improvement values will 
be available for CAFE calculations, whereas efficiency credits were 
previously only available for GHG compliance. The agencies proposed 
these changes in the 2012 final rule for MYs 2017 and later largely as 
a result of new data collected, as well as the extensive technical 
comments submitted on the proposal.\193\

    \193\ Id.

    The pre-defined technology menu and associated car and light truck 
credit value is shown in Table II-19 below. The regulations include a 
definition of each technology that must be met to be eligible for the 
menu credit.\194\ Manufacturers are not required to submit any other 
emissions data or information beyond meeting the definition and useful 
life requirements \195\ to use the pre-defined

[[Page 43053]]

credit value. Manufacturers' use of menu-based credits for A/C 
efficiency is subject to a regulatory cap: 5.7 g/mi for cars and trucks 
through MY 2016 and separate caps of 5.0 g/mi for cars and 7.2g/mi for 
trucks for later MYs.\196\

    \194\ Id. at 62725.
    \195\ Lifetime vehicle miles travelled (VMT) for MY 2017-2025 
are 195,264 miles and 225,865 miles for passenger cars and light 
trucks, respectively. The manufacturer must also demonstrate that 
the off-cycle technology is effective for the full useful life of 
the vehicle. Unless the manufacturer demonstrates that the 
technology is not subject to in-use deterioration, the manufacturer 
must account for the deterioration in their analysis.
    \196\ 40 CFR 86.1868-12(b)(2) (2016).

    In the 2012 final rule for MYs 2017 and later, the agencies 
estimated that manufacturers would employ significant advanced A/C 
technologies throughout their fleets to improve fuel economy, and this 
was reflected in the stringency of the standards.\197\ Many 
manufacturers have since incorporated A/C technology throughout their 
fleets, and the utilization of advanced A/C technologies has become a 
significant contributor to industry compliance plans. As summarized in 
the EPA Manufacturer Performance Report for the 2016 model year,\198\ 
15 auto manufacturers included A/C efficiency credits as part of their 
compliance demonstration in the 2016 MY. These amounted to more than 12 
million Mg of fuel consumption improvement values of the total net fuel 
consumption improvement values reported. This is equivalent to 
approximately four grams per mile across the 2016 fleet. Accordingly, a 
significant amount of new information about A/C technology and the 
efficacy of test procedures has become available since the 2012 final 

    \197\ See e.g., 77 FR 62623, 62803-62806 (Oct. 15, 2012).
    \198\ See Greenhouse Gas Emission Standards for Light-Duty 
Vehicles: Manufacturer Performance Report for the 2016 Model Year 
(EPA Report 420-R18-002), U.S. EPA (Jan. 2018), available at https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100TGIA.pdf.

    The sections below provide a brief history of the AC17 test 
procedure for evaluating A/C efficiency improving technology and 
discuss stakeholder comments on the AC17 test procedure approach and 
discuss A/C efficiency technology valuation through the off-cycle 

[[Page 43054]]


(1) Evaluation of the AC17 Test Procedure Since the Draft TAR
    In developing the AC17 test procedure, the agencies sought to 
develop a test procedure that could more reliably generate an 
appropriate fuel consumption improvement value based on an ``A'' to 
``B'' comparison, that is, a comparison of substantially similar 
vehicles in which one has the technology and the other does not.\199\ 
The agencies believe that the AC17 test procedure is more capable of 
detecting the effect of more efficient A/C components and controls 
strategies during a transient drive cycle rather than during just idle 
(as measured in the old idle test procedure). As described above and in 
the 2012 final rule,\200\ the AC17 test is a four-part performance test 
that combines the existing SC03 driving

[[Page 43055]]

cycle, the fuel economy highway cycle, as well as a pre-conditioning 
cycle, and a solar soak period.

    \199\ For an explanation of how the agencies, in collaboration 
with stakeholders, developed the AC17 test procedure, see the 2017 
and later final rule at 77 FR 62624, 62723 (Oct. 15, 2012).
    \200\ See 77 FR 62624, 62723 (Oct. 15, 2012); Joint Technical 
Support Document: Final Rulemaking for 2017-2025 Light-Duty Vehicle 
Greenhouse Gas Emission and Corporate Average Fuel Economy 
Standards, U.S. EPA, National Highway Traffic Safety Administration 
at 5-40 (August 2012) .

    The agencies received several comments on the Draft TAR evaluation 
of the AC17 test procedure. FCA commented generally that A/C efficiency 
technologies ``are not showing their full effect on this AC17 test as 
most technologies provide benefit at different temperatures and 
humidity conditions in comparison to a standard test conditions. All of 
these technologies are effective at different levels at different 
conditions. So there is not one size fits all in this very complex 
testing approach. Selecting one test that captures benefits of all of 
these conditions has not been possible.'' \201\

    \201\ See Comment by FCA US LLC, Docket ID NHTSA 2016-0068-0082, 
at 123-124.

    The agencies acknowledge that any single test procedure is unlikely 
to equally capture the real-world effect of every potential technology 
in every potential use case. Both the agencies and stakeholders 
understood this difficulty when developing the AC17 test procedure. 
While no test is perfect, the AC17 test procedure represents an 
industry best effort at identifying a test that would greatly improve 
upon the idle test by capturing a greater range of operating 
conditions. General industry evaluation of the AC17 test procedure is 
in agreement that the test achieves this objective.
    FCA also noted that ``[i]t is a major problem to find a baseline 
vehicle that is identical to the new vehicle but without the new A/C 
technology. This alone makes the test unworkable.'' \202\ The agencies 
disagree this makes the test unworkable. The regulation describes the 
baseline vehicle as a ``similar'' vehicle, selected with good 
engineering judgment (such that the test comparison is not unduly 
affected by other differences). Also, OEMs expressed confidence in 
using A-to-B testing to qualify for fuel consumption improvement values 
for software-based A/C efficiency technologies. While hardware 
technologies may pose a greater challenge in locating a sufficiently 
similar ``A'' baseline vehicle, the engineering analysis provision 
under 40 CFR 86.1868-12(g)(2) provides an alternative to locating and 
performing an AC17 test on such a vehicle. Further, as the USCAR 
program in general and the GM Denso SAS compressor application 
specifically have shown, the test is able to resolve small differences 
in CO2 effectiveness (1.3 grams in the latter case) when 
carefully conducted.

    \202\ Id. at 124.

    Commenters on the Draft TAR also expressed a desire for 
improvements in the process by which manufacturers without an ``A'' 
vehicle (for the A-to-B comparison) could apply under the engineering 
analysis provision, such as development of standardized engineering 
analysis and bench testing procedures that could support such 
applications. For example, Toyota requested that ``EPA consider an 
optional method for validation via an engineering analysis, as is 
currently being developed by industry.'' \203\ Similarly, the Alliance 
commented that, ``[t]he future success of the MAC credit program in 
generating emissions reductions will depend to a large extent on the 
manner in which it is administered by EPA, especially with respect to 
making the AC17 A-to-B provisions function smoothly, without becoming a 
prohibitive obstacle to fully achieving the MAC indirect credits.'' 

    \203\ See Comment by Toyota (revised), Docket ID NHTSA-2016-
0068-0088, at 23.
    \204\ See Comment by Alliance of Automobile Manufacturers, 
Docket ID EPA-HQ-OAR-2015-0827-4089 and NHTSA-2016-0068-0072, at 

    As described in the Draft TAR, in 2016, USCAR members initiated a 
Cooperative Research Program (CRP) through the Society of Automotive 
Engineers (SAE) to develop bench testing standards for the four 
hardware technologies in the fuel consumption improvement value menu 
(blower motor control, internal heat exchanger, improved evaporators 
and condensers, and oil separator). The intent of the program is to 
streamline the process of conducting bench testing and engineering 
analysis in support of an application for A/C credits under 40 CFR part 
86.1868-12(g)(2), by creating uniform standards for bench testing and 
for establishing the expected GHG effect of the technology in a vehicle 
    An update to the list of SAE standards under development originally 
presented in the Draft TAR is listed in Table II-20. Since completion 
of the Draft TAR, work has continued on these standards, which appear 
to be nearing completion. The agencies seek comment with the latest 
completion of these SAE standards.

(2) A/C Efficiency Technology Valuation Through the Off-Cycle Program
    The A/C technology menu, discussed at length above, includes 
several A/C efficiency-improving technologies that were well defined 
and had been quantified for effectiveness at the time of the 2012 final 
rule for MYs 2017 and beyond. Manufacturers claimed the vast majority 
of A/C efficiency credits to date by utilizing technologies on the 
menu; however, the agencies recognize that manufacturers will develop 
additional technologies that are not currently listed on the menu. 
These additional A/C efficiency-improving technologies are eligible for 
fuel consumption improvement values on a case-by-case basis under the 
off-cycle program. Approval under the off-cycle program also requires 
``A-to-B'' comparison testing under the AC17 test, that is, testing 
substantially similar vehicles in which one has the technology and the 
other does not.
    To date, the agencies have received one type off-cycle application 
for an A/C efficiency technology. In December 2014, General Motors 
submitted an off-cycle application for the Denso SAS A/

[[Page 43056]]

C compressor with variable crankcase suction valve technology, 
requesting an off-cycle GHG credit of 1.1 grams CO2 per 
mile. In December 2017, BMW of North America, Ford Motor Company, 
Hyundai Motor Company, and Toyota petitioned and received approval to 
receive the off-cycle improvement value for the same A/C efficiency 
technology.205 206 EPA, in consultation with NHTSA, 
evaluated the applications and found methodologies described therein 
were sound and appropriate.\207\ Accordingly, the agencies approved the 
fuel economy improvement value applications.

    \205\ EPA Decision Document: Off-Cycle Credits for BMW Group, 
Ford Motor Company, and Hyundai Motor Company, U.S. EPA (Dec. 2017), 
available at https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100TF06.pdf.
    \206\ Alternative Method for Calculating Off-cycle Credits under 
the Light-Duty Vehicle Greenhouse Gas Emissions Program: 
Applications from General Motors and Toyota Motor North America, 83 
FR 8262 (Feb. 26, 2018).
    \207\ Id.

    The agencies received additional stakeholder comments on the off-
cycle approval process as an alternate route to receiving A/C 
technology credit values. The Alliance requested that EPA ``simplify 
and standardize the procedures for claiming off-cycle credits for the 
new MAC technologies that have been developed since the creation of the 
MAC indirect credit menu.'' \208\ Other commenters noted the importance 
of continuing to incentivize further innovation in A/C efficiency 
technologies as new technologies emerge that are not listed on the menu 
or when manufacturers begin to reach regulatory caps. The commenters 
suggested that EPA should consider adding new A/C efficiency 
technologies to the menu and/or update the fuel consumption improvement 
values for technology already listed on the menu, particularly in cases 
where manufacturers can show through an off-cycle application that the 
technology actually deserves more credit than that listed on the menu. 
For example, Toyota commented that ``the incentive values for A/C 
efficiency should be updated along with including new technologies 
being deployed.'' \209\

    \208\ Comment by Alliance of Automobile Manufacturers, Docket ID 
EPA-HQ-OAR-2015-0827-4089 and NHTSA-2016-0068-0072, at 152.
    \209\ Comment by Toyota (revised), Docket ID NHTSA-2016-0068-
0088, at 23.

    The agencies note that some of these comments are directed towards 
the off-cycle technology approval process generally, which is described 
in more detail in Section X of this preamble. Regarding the A/C 
technology menu specifically, the agencies do anticipate that new A/C 
technologies not currently on the menu will emerge over the time frame 
of the MY 2021-2026 standards. This proposal requests comment on adding 
one additional A/C technology to the menu--the A/C compressor with 
variable crankcase suction valve technology, discussed below (and also 
one off-cycle technology, discussed below). The agencies also request 
comment on whether to change any fuel economy improvement values 
currently assigned to technologies on the menu.
    Next, as mentioned above, the menu-based improvement values for A/C 
efficiency established in the 2012 final rule for MYs 2017 and by end 
are subject to a regulatory cap. The rule set a cap of 5.7 g/mi for 
cars and trucks through MY 2016 and separate caps of 5.0 g/mi for cars 
and 7.2g/mi for trucks for later MYs.\210\ Several commenters asked EPA 
to reconsider the applicability of the cap to non-menu A/C efficiency 
technologies claimed through the off-cycle process and questioned the 
applicability of this cap on several different grounds. These comments 
appear to be in response to a Draft TAR passage that stated: 
``Applications for A/C efficiency credits made under the off-cycle 
credit program rather than the A/C credit program will continue to be 
subject to the A/C efficiency credit cap'' (Draft TAR, p. 5-210). The 
agencies considered these comments and present clarification below. As 
additional context, the 2012 TSD states:

    \210\ 40 C.F.R Sec.  86.1868-12(b)(2) (2016).

    ``. . . air conditioner efficiency is an off-cycle technology. 
It is thus appropriate [. . .] to employ the standard off-cycle 
credit approval process [to pursue a larger credit than the menu 
value]. Utilization of bench tests in combination with dynamometer 
tests and simulations [. . .] would be an appropriate alternate 
method of demonstrating and quantifying technology credits (up to 
the maximum level of credits allowed for A/C efficiency) [emphasis 
added]. A manufacturer can choose this method even for technologies 
that are not currently included in the menu.'' \211\

    \211\ Joint Technical Support Document: Final Rulemaking for 
2017-2025 Light-Duty Vehicle Greenhouse Gas Emission and Corporate 
Average Fuel Economy Standards, U.S. EPA, National Highway Traffic 
Safety Administration at 5-58 (August 2012).

    This suggests the concept of placing a limit on total A/C fuel 
consumption improvement values, even when some are granted under the 
off-cycle program, is not entirely new and that EPA considered the menu 
cap as being appropriate at the time.
    A/C regulatory caps specified under 40 CFR 86.1868-12(b)(2) apply 
to A/C efficiency menu-based improvement values and are not part of the 
off-cycle regulation (40 CFR 86.1869-12). However, it should be noted 
that off-cycle applications submitted via the public process pathway 
are decided individually on merits through a process involving public 
notice and opportunity for comment. In deciding whether to approve or 
deny a request, the agencies may take into account any factors deemed 
relevant, including such issues as the realization of claimed fuel 
consumption improvement value in real-world use. Such considerations 
could include synergies or interactions among applied technologies, 
which could potentially be addressed by application of some form of cap 
or other applicable limit, if warranted. Therefore, applying for A/C 
efficiency fuel consumption improvement values through the off-cycle 
provisions in 40 CFR 86.1869-12 should not be seen as a route to 
unlimited A/C fuel consumption improvement values. The agencies discuss 
air conditioning efficiency improvement values further in Section X of 
this NPRM.
(b) Off-Cycle Technologies
    ``Off-cycle'' emission reductions and fuel consumption improvements 
can be achieved by employing off-cycle technologies resulting in real-
world benefits but where that benefit is not adequately captured on the 
test procedures used to demonstrate compliance with fuel economy 
emission standards. EPA initially included off-cycle technology credits 
in the MY 2012-2016 rule and revised the program in the MY 2017-2025 
rule.\212\ NHTSA adopted equivalent off-cycle fuel consumption 
improvement values for MYs 2017 and later in the MY 2017-2025 

    \212\ 77 FR 62624, 62832 (Oct. 15, 2012).
    \213\ Id. at 62839.

    Manufacturers can demonstrate the value of off-cycle technologies 
in three ways: First, they may select fuel economy improvement values 
and CO2 credit values from a pre-defined ``menu'' for off-
cycle technologies that meet certain regulatory specifications. As part 
of a manufacturer's compliance data, manufacturers will provide 
information about which off-cycle technologies are present on which 
    The pre-defined list of technologies and associated off-cycle 
light-duty vehicle fuel economy improvement values and GHG credits is 
shown in Table II-21 and Table II-22 below.\214\ A

[[Page 43057]]

definition of each technology equipment must meet to be eligible for 
the menu credit is included at 40 CFR 86.1869-12(b)(4). Manufacturers 
are not required to submit any other emissions data or information 
beyond meeting the definition and useful life requirements to use the 
pre-defined credit value. Credits based on the pre-defined list are 
subject to an annual manufacturer fleet-wide cap of 10 g/mile.

    \214\ For a description of each technology and the derivation of 
the pre-defined credit levels, see Chapter 5 of the Joint Technical 
Support Document: Final Rulemaking for 2017-2025 Light-Duty Vehicle 
Greenhouse Gas Emission and Corporate Average Fuel Economy 
Standards, U.S. EPA, National Highway Traffic Safety Administration 
(August 2012). 

    Manufacturers can also perform their own 5-cycle testing and submit 
test results to the agencies with a request explaining the off-cycle 
technology. The additional three test cycles have different operating 
conditions including high speeds, rapid accelerations, high temperature 
with A/C operation and cold temperature, enabling improvements to be 
measured for technologies that do not impact operation on the 2-cycle 
tests. Credits determined according to this methodology do not undergo 
public review.
    The third pathway allows manufacturers to seek EPA approval to use 
an alternative methodology for determining the value of an off-cycle 
technology. This option is only available if the benefit of the 
technology cannot be adequately demonstrated using the 5-cycle 
methodology. Manufacturers may also use this option to demonstrate 
reductions that exceed

[[Page 43058]]

those available via use of the predetermined menu list. The 
manufacturer must also demonstrate that the off-cycle technology is 
effective for the full useful life of the vehicle. Unless the 
manufacturer demonstrates that the technology is not subject to in-use 
deterioration, the manufacturer must account for the deterioration in 
their analysis.
    Manufacturers must develop a methodology for demonstrating the 
benefit of the off-cycle technology, and EPA makes the methodology 
available for public comment prior to an EPA determination, in 
consultation with NHTSA, on whether to allow the use of the methodology 
to measure improvements. The data needed for this demonstration may be 
    Several manufacturers have requested and been granted use of 
alternative test methodologies for measuring improvements. In 2013, 
Mercedes requested off-cycle credits for the following off-cycle 
technologies in use or planned for implementation in the 2012-2016 
model years: Stop-start systems, high-efficiency lighting, infrared 
glass glazing, and active seat ventilation. EPA approved methodologies 
for Mercedes to determine these off-cycle credits in September 
2014.\215\ Subsequently, FCA, Ford, and GM requested off-cycle credits 
using this same methodology. FCA and Ford submitted applications for 
off-cycle credits from high efficiency exterior lighting, solar 
reflective glass/glazing, solar reflective paint, and active seat 
ventilation. Ford's application also demonstrated off-cycle benefits 
from active aerodynamic improvements (grille shutters), active 
transmission warm-up, active engine warm-up technologies, and engine 
idle stop-start. GM's application described real-world benefits of an 
air conditioning compressor with variable crankcase suction valve 
technology. EPA approved the credits for FCA, Ford, and GM in September 
2015.\216\ Note, however, that although EPA granted the use of 
alternative methodologies to determine credit values, manufacturers 
have yet to report credits to EPA based on those alternative 

    \215\ EPA Decision Document: Mercedes-Benz Off-cycle Credits for 
MYs 2012-2016, U.S. EPA (Sept. 2014), available at https://nepis.epa.gov/Exe/ZyPDF.cgi/P100KB8U.PDF?Dockey=P100KB8U.PDF.
    \216\ EPA Decision Document: Off-cycle Credits for Fiat Chrysler 
Automobiles, Ford Motor Company, and General Motors Corporation, 
U.S. EPA (Sept. 2015), available at https://nepis.epa.gov/Exe/ZyPDF.cgi/P100N19E.PDF?Dockey=P100N19E.PDF.

    As discussed below, all three methods have been used by 
manufacturers to generate off-cycle improvement values and credits.
(1) Use of Off-Cycle Technologies to Date
    Manufacturers used a wide array of off-cycle technologies in MY 
2016 to generate off-cycle GHG credits using the pre-defined menu. 
Table II-23 below shows the percent of each manufacturer's production 
volume using each menu technology reported to EPA for MY 2016 by 
manufacturer. Table II-24 shows the g/mile benefit each manufacturer 
reported across its fleet from each off-cycle technology. Like Table 
II-23, Table II-24 provides the mix of technologies used in MY 2016 by 
manufacturer and the extent to which each technology benefits each 
manufacturer's fleet. Fuel consumption improvement values for off-cycle 
technologies were not available in the CAFE program until MY 2017; 
therefore, only GHG off-cycle credits have been generated by 
manufacturers thus far.

[[Page 43059]]


[[Page 43060]]


    In 2016, manufacturers generated the vast majority of credits using 
the pre-defined menu.\217\ Although MY 2014 was the first year that 
manufacturers could generate credits using pre-defined menu values, 
manufacturers have acted quickly to generate substantial off-cycle 
improvements. FCA and Jaguar Land Rover generated the most off-cycle 
credits on a fleet-wide basis, reporting credits equivalent to 
approximately 6 g/mile and 5 g/mile, respectively. Several other 
manufacturers report fleet-wide credits in the range of approximately 1 
to 4 g/mile. In MY 2016, the fleet total across manufacturers equaled 
approximately 2.5 g/mile. The agencies

[[Page 43061]]

expect that as manufacturers continue expanding their use of off-cycle 
technologies, the fleet-wide effects will continue to grow with some 
manufacturers potentially approaching the 10 g/mile fleet-wide cap.

    \217\ Thus far, the agencies have only granted one manufacturer 
(GM) off-cycle credits for technology based on 5-cycle testing. 
These credits are for an off-cycle technology used on certain GM 
gasoline-electric hybrid vehicles, an auxiliary electric pump, which 
keeps engine coolant circulating in cold weather while the vehicle 
is stopped and the engine is off, thus allowing the engine stop-
start system to be active more frequently in cold weather.

E. Development of Economic Assumptions and Information Used as Inputs 
to the Analysis

1. Purpose of Developing Economic Assumptions for Use in Modeling 
(a) Overall Framework of Costs and Benefits
    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 that 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 the first objective to the extent that it clarifies the 
benefits and costs of the proposed action's impacts on car and light 
truck producers, illustrates how these are transmitted to buyers of new 
vehicles, shows the action's collateral economic effects on owners of 
used cars and light trucks, and identifies how these impacts create 
costs and benefits for the remainder of the U.S. economy. It will 
achieve the second objective by showing clearly 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 
    Table II-25 through Table II-28 present the economic benefits and 
costs of the proposed action to reduce CAFE and CO2 
emissions standards for model years 2021-26 at three percent and seven 
percent discount rates in a format that is intended to meet these 
objectives. Note: They 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. As it indicates, the proposed action first 
reduces costs to manufacturers for adding technology necessary to 
enable new cars and light trucks to comply with fuel economy and 
emission regulations (line 1). It may also reduce fine payments by 
manufacturers who would have failed to comply with the more demanding 
baseline standards. Manufacturers are assumed to transfer these cost 
savings on to buyers by charging lower prices (line 5); although this 
reduces their revenues (line 3), on balance, the reduction in 
compliance costs and lower sales revenue leaves them financially 
unaffected (line 4).

[[Page 43062]]


[[Page 43063]]


[[Page 43064]]


[[Page 43065]]


[[Page 43066]]


    As the tables show, most impacts of the proposed action will fall 
on the businesses and individuals who design, manufacture, and sell (at 
retail and wholesale) cars and light trucks, the consumers who 
purchase, drive, and subsequently sell or trade-in new models (and 
ultimately bear the cost of fuel economy technology), and owners of 
used cars and light trucks produced during model years prior to those 
covered by this action. Compared to the baseline standards, if the 
preferred alternative is finalized, buyers of new cars and light trucks 
will benefit from

[[Page 43067]]

their lower purchase prices and financing costs (line 5). They will 
also avoid the increased risks of being injured in crashes that would 
have resulted from manufacturers' efforts to reduce the weight of new 
models to comply with the baseline standards, which represents another 
benefit from reducing stringency vis-[agrave]-vis the baseline (line 
    At the same time, new cars and light trucks will offer lower fuel 
economy with more lenient standards in place, and this imposes various 
costs on their buyers and users. Drivers will experience higher costs 
as a consequence of new vehicles' increased fuel consumption (line 7), 
and from the added inconvenience of more frequent refueling stops 
required by their reduced driving range (line 8). They will also forego 
some mobility benefits as they use newly-purchased cars and light 
trucks less in response to their higher fueling costs, although this 
loss will be almost fully offset by the fuel and other costs they save 
by driving less (line 9). On balance, consumers of new cars and light 
trucks produced during the model years subject to this proposed action 
will experience significant economic benefits (line 10).
    By lowering prices for new cars and light trucks, this proposed 
action will cause some owners of used vehicles to retire them from 
service earlier than they would otherwise have done, and replace them 
with new models. In effect, it will transfer some driving that would 
have been done in used cars and light trucks under the baseline 
scenario to newer and safer models, thus reducing costs for injuries 
(both fatal and less severe) and property damages sustained in motor 
vehicle crashes. This improvement in safety results 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 older to 
newer models reduces injuries and damages sustained by drivers and 
passengers because they are traveling in inherently safer vehicles and 
not because it changes the risk profiles of drivers themselves. This 
reduction in injury risks and other damage costs produces benefits to 
owners and drivers of older cars and light trucks. This also results in 
benefits in terms of improved fuel economy and significant reductions 
of emissions from newer vehicles (line 11).
    Table II-27 through Table II-28 also show that the changes in fuel 
consumption and vehicle use resulting from this proposed action will in 
turn generate both benefits and costs to the remainder of the U.S. 
economy. 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 
the U.S. economy rather than by the firms and individuals who 
indirectly cause them. Increased refining and consumption of petroleum-
based fuel will increase emissions of carbon dioxide and other 
greenhouse gases that theoretically contribute to climate change, and 
some of the resulting (albeit uncertain) increase in economic damages 
from future changes in the global climate will be borne throughout the 
U.S. economy (line 13). Similarly, added fuel production and use will 
increase emissions of more localized air pollutants (or their chemical 
precursors), and the resulting increase in the U.S. population's 
exposure to harmful levels of these pollutants will lead to somewhat 
higher costs from its adverse effects on health (line 14). On the other 
hand, it is expected that the proposed standards, by reducing new 
vehicle prices relative to the baseline, will accelerate fleet turnover 
to cleaner, safer, more efficient vehicles (as compared to used 
vehicles that might otherwise continue to be driven or purchased).
    As discussed in PRIA Section 9.8, increased consumption and imports 
of crude petroleum for refining higher volumes of gasoline and diesel 
will also impose some external costs throughout the U.S. economy, in 
the form of potential losses in production and costs for businesses and 
households to adjust rapidly to sudden changes in energy prices (line 
15 of the table), although these costs should be tempered by increasing 
U.S. oil production.\218\ Reductions in driving by buyers of new cars 
and light trucks in response to their higher operating costs will also 
reduce the external costs associated with their contributions to 
traffic delays and noise levels in urban areas, and these additional 
benefits will be experienced throughout much of the U.S. economy (line 
17). Finally, some of the higher fuel costs to buyers of new cars and 
light trucks will consist of increased fuel taxes; this increase in 
revenue will enable Federal and State government agencies to provide 
higher levels of road capacity or maintenance, producing benefits for 
all road and transit users (line 18).

    \218\ Note: This output was based upon the EIA Annual Energy 
Outlook from 2017. The 2018 Annual Energy Outlook projects the U.S. 
will be a net exporter by around 2029, with net exports peaking at 
around 0.5 mbd circa 2040. See Annual Energy Outlook 2018, U.S. 
Energy Information Administration, at 53 (Feb, 6, 2018), https://www.eia.gov/outlooks/aeo/pdf/AEO2018.pdf. Furthermore, pursuant to 
Executive Order 13783 (Promoting Energy Independence and Economy 
Growth), agencies are expected to review and revise or rescind 
policies that unduly burden the development of domestic energy 
resources beyond what is necessary to protect the public interest or 
otherwise comply with the law. Therefore, it is reasonable to 
anticipate further increases in domestic production of petroleum. 
The agencies may update the analysis and table to account for this 
revised information.

    On balance, Table II-27 through Table II-28 show that the U.S. 
economy as a whole will experience large net economic benefits from the 
proposed action (line 22). While the proposal to establish less 
stringent CAFE and GHG emission standards will produce net external 
economic costs, as the increase in environmental and energy security 
externalities outweighs external benefits from reduced driving and 
higher fuel tax revenue (line 19), the table also shows that combined 
benefits to vehicle manufacturers, buyers, and users of cars and light 
trucks, and the general public (line 20), including the value of the 
lives saved and injuries avoided, will greatly outweigh the combined 
economic costs they experience as a consequence of this proposed action 
(line 21).
    The finding that this action to reduce the stringency of 
previously-established CAFE and GHG standards will create significant 
net economic benefits--when it was initially claimed that establishing 
those standards would also generate large economic benefits to vehicle 
buyers and others throughout the economy--is notable. This contrast 
with the earlier finding is explained by the availability of updated 
information on the costs and effectiveness of technologies that will 
remain available to improve fuel economy in model years 2021 and 
beyond, the fleet-wide consequences for vehicle use, fuel consumption, 
and safety from requiring higher fuel economy (that is, considering 
these consequences for used cars and light trucks as well as new ones), 
and new estimates of some external costs of fuel in petroleum use.
2. Macroeconomic Assumptions That Affect the Benefit Cost Analysis
    Unlike previous CAFE and GHG rulemaking analyses, the economic 
context in which the alternatives are simulated is more explicit. While 
both this analysis and previous analyses contained fuel price 
projections from the Annual Energy Outlook, which has embedded 
assumptions about future macroeconomic conditions, this analysis 
requires explicit assumptions about future GDP growth, labor force 
participation, and interest rates in order to evaluate the 

[[Page 43068]]


    The analysis simulates compliance through MY 2032 explicitly and 
must consider the full useful lives of those vehicles, approximately 40 
years, in order to estimate their lifetime mileage accumulation and 
fuel consumption. This means that any macroeconomic forecast 
influencing those factors must cover a similar span of years. Due to 
the long time horizon, a source that regularly produces such lengthy 
forecasts of these factors was selected:

[[Page 43069]]

the 2017 OASDI Trustees Report from the U.S. Social Security 
Administration. While Table-II-29 only displays assumptions through CY 
2050, the remaining years merely continue the trends present in the 
    The analysis once again uses fuel price projections from the 2017 
Annual Energy Outlook.\219\ The projections by fuel calendar year and 
fuel type are presented in Table-II-30, in real 2016 dollars. Fuel 
prices in this analysis affect not only the value of each gallon of 
fuel consumed but relative valuation of fuel-saving technologies 
demanded by the market as a result of their associated fuel savings.

    \219\ The central analysis supporting today's proposal uses 
reference case estimates of fuel prices reported in the Energy 
Information Administration's (EIA's) Annual Energy Outlook 2017 (AEO 
2017). Today's proposal also examines the sensitivity of this 
analysis to changes in key inputs, including fuel prices, and 
includes cases that apply fuel prices from the AEO 2017 low oil 
price and high oil price cases. The reference case prices are 
considerably lower than AEO 2011-based reference cases prices 
applied in the 2012 rulemaking, and this is one of several important 
changes in circumstances supporting revision of previously-issued 
    After significant portions of today's analysis had already been 
completed, EIA released AEO 2018, which reports reference case fuel 
prices about 10% higher than reported in AEO 2017, though still well 
below the above-mentioned prices from AEO 2011. The sensitivity 
analysis therefore includes a case that applies fuel prices from the 
AEO 2018 reference case. The AEO 2018 low oil price case reports 
fuel prices somewhat higher than the AEO 2017 low oil price case, 
and the AEO 2018 high oil price case reports fuel prices very 
similar to the AEO 2017 high oil price case. Adding the AEO 2018 low 
and high oil price cases to the sensitivity analysis would thus have 
provided little, if any, additional insight into the sensitivity of 
the analysis to fuel prices. As shown in the summary of the 
sensitivity analysis, results obtained applying AEO 2018-based fuel 
prices are similar to those obtained applying AEO 2017-based fuel 
prices. For example, net benefits between the two are about five 
percent different, especially considering that decisions regarding 
future standards are not single-factor decisions, but rather reflect 
a balancing of factors, applying AEO 2018-based fuel prices would 
not materially change the extent to which today's analysis supports 
the selection of the preferred alternative.
    Like other inputs to the analysis, fuel prices will be updated 
for the analysis supporting the final rule after consideration of 
related new information and public comment.


[[Page 43070]]


3. New Vehicle Sales and Employment Assumptions
    In all previous CAFE and GHG rulemaking analyses, static fleet 
forecasts that were based on a combination of manufacturer compliance 
data, public data sources, and proprietary forecasts were used. When 
simulating compliance with regulatory alternatives, the analysis 
projected identical sales across the alternatives, for each 
manufacturer down to the make/model level where the exact same number 
of each model variant was simulated to be sold in a given model year 
under both the least stringent alternative (typically the

[[Page 43071]]

baseline) and the most stringent alternative considered. 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, it seems intuitive that 
any sufficiently large span of regulatory alternatives would contain 
alternatives for which that static forecast was unrepresentative. A 
number of commenters have encouraged consideration of the potential 
impact of CAFE/GHG standards on new vehicle prices and sales, and the 
changes to compliance strategies that those shifts could 
necessitate.\220\ In particular, the continued growth of the utility 
vehicle segment creates compliance challenges within some 
manufacturers' fleets as sales volumes shift from one region of the 
footprint curve to another.

    \220\ See e.g., Comment by Alliance of Automobile Manufacturers, 
Docket ID EPA-HQ-OAR-2015-0827-4089 and NHTSA-2016-0068-0072.

    Any model of sales response must satisfy two requirements: It must 
be appropriate for use in the CAFE model, and it must be 
econometrically reasonable. The first of these requirements implies 
that any variable used in the estimation of the econometric model, must 
also be available as a forecast throughout the duration of the years 
covered by the simulations (this analysis explicitly simulates 
compliance through MY 2032). Some values the model calculates 
endogenously, making them available in future years for sales 
estimation, but others must be known in advance of the simulation. As 
the CAFE model simulates compliance, it accumulates technology costs 
across the industry and over time. By starting with the last known 
transaction price and adding the accumulated technology cost to that 
value, the model is able to represent the average selling price in each 
future model year assuming that manufacturers are able to pass all of 
their compliance costs on to buyers of new vehicles. Other variables 
used in the estimation must enter the model as inputs prior to the 
start of the compliance simulation.
(a) How do car and light truck buyers value improved fuel economy?
    How potential buyers value improvements in the fuel economy of new 
cars and light trucks is an important issue in assessing the benefits 
and costs of government regulation. If buyers fully value the savings 
in fuel costs that result from higher fuel economy, manufacturers will 
presumably supply any improvements that buyers demand, and vehicle 
prices will fully reflect future fuel cost savings consumers would 
realize from owning--and potentially re-selling--more fuel-efficient 
models. In this case, more stringent fuel economy standards will impose 
net costs on vehicle owners and can only result in social benefits by 
correcting externalities, since consumers would already fully 
incorporate private savings into their purchase decisions. If instead 
consumers systematically undervalue the cost savings generated by 
improvements in fuel economy when choosing among competing models, more 
stringent fuel economy standards will also lead manufacturers to adopt 
improvements in fuel economy that buyers might not choose despite the 
cost savings they offer.
    The potential for car buyers 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 
individuals will purchase more energy-efficient products only if the 
savings in future energy costs they offer promise to offset their 
higher initial costs. However, the additional cost of a more energy-
efficient product includes more than just the cost of the technology 
necessary to improve its efficiency; it also includes the opportunity 
cost of any other desirable features that consumers give up when they 
choose the more efficient alternative. In the context of vehicles, 
whether the expected fuel savings outweigh the opportunity cost of 
purchasing a model offering higher fuel economy will depend on how much 
its buyer expects to drive, his or her expectations about future fuel 
prices, the discount rate he or she uses to value future expenses, the 
expected effect on resale value, and whether more efficient models 
offer equivalent attributes such as performance, carrying capacity, 
reliability, quality, or other characteristics.
    Published literature has offered little consensus about consumers' 
willingness-to-pay for greater fuel economy, and whether it implies 
over-, under- or full-valuation of the expected fuel savings from 
purchasing a model with higher fuel economy. Most studies have relied 
on car buyers' purchasing behavior to estimate their willingness-to-pay 
for future fuel savings; a typical approach has been to use ``discrete 
choice'' models that relate individual buyers' choices among competing 
vehicles to their purchase prices, fuel economy, and other attributes 
(such as performance, carrying capacity, and reliability), and to infer 
buyers' valuation of higher fuel economy from the relative importance 
of purchase prices and fuel economy.\221\ Empirical estimates using 
this approach span a wide range, extending from substantial 
undervaluation of fuel savings to significant overvaluation, thus 
making it difficult to draw solid conclusions about the influence of 
fuel economy on vehicle buyers' choices (see Helfand & Wolverton, 2011; 
Green (2010) for detailed reviews of these cross-sectional studies). 
Because a vehicle's price is often correlated with its other attributes 
(both measured and unobserved), analysts have often used instrumental 
variables or other approaches to address endogeneity and other 
resulting concerns (e.g., Barry, et al. 1995).

    \221\ In a typical vehicle choice model, the ratio of estimated 
coefficients on fuel economy--or more commonly, fuel cost per mile 
driven--and purchase price is used to infer the dollar value buyers 
attach to slightly higher fuel economy.

    Despite these efforts, more recent research has criticized these 
cross-sectional studies; some have questioned the effectiveness of the 
instruments they use (Allcott & Greenstone, 2012), while others have 
observed that coefficients estimated using non-linear statistical 
methods can be sensitive to the optimization algorithm and starting 
values (Knittel & Metaxoglou, 2014). Collinearity (i.e., high 
correlations) among vehicle attributes--most notably among fuel 
economy, performance or power, and vehicle size--and between vehicles' 
measured and unobserved features also raises questions about the 
reliability and interpretation of coefficients that may conflate the 
value of fuel economy with other attributes (Sallee, et al., 2016; 
Busse, et al., 2013; Allcott & Wozny, 2014; Allcott & Greenstone, 2012; 
Helfand & Wolverton, 2011).
    In an effort to overcome shortcomings of past analyses, three 
recently published studies rely on panel data from sales of individual 
vehicle models to improve their reliability in identifying the 
association between vehicles' prices and their fuel economy (Sallee, et 
al. 2016; Allcott & Wozny, 2014; Busse, et al., 2013). Although they 
differ in certain details, each of these

[[Page 43072]]

analyses relates changes over time in individual models' selling prices 
to fluctuations in fuel prices, differences in their fuel economy, and 
increases in their age and accumulated use, which affects their 
expected remaining life, and thus their market value. Because a 
vehicle's future fuel costs are a function of both its fuel economy and 
expected gasoline prices, changes in fuel prices have different effects 
on the market values of vehicles with different fuel economy; comparing 
these effects over time and among vehicle models reveals the fraction 
of changes in fuel costs that is reflected in changes in their selling 
prices (Allcott & Wozny, 2014). Using very large samples of sales 
enables these studies to define vehicle models at an extremely 
disaggregated level, which enables their authors to isolate differences 
in their fuel economy from the many other attributes, including those 
that are difficult to observe or measure, that affect their sale 

    \222\ These studies rely on individual vehicle transaction data 
from dealer sales and wholesale auctions, which includes actual sale 
prices and allows their authors to define vehicle models at a highly 
disaggregated level. For instance, Allcott & Wozny (2014) 
differentiate vehicles by manufacturer, model or nameplate, trim 
level, body type, fuel economy, engine displacement, number of 
cylinders, and ``generation'' (a group of successive model years 
during which a model's design remains largely unchanged). All three 
studies include transactions only through mid-2008 to limit the 
effect of the recession on vehicle prices. To ensure that the 
vehicle choice set consists of true substitutes, Allcott & Wozny 
(2014) define the choice set as all gasoline-fueled light-duty cars, 
trucks, SUVs, and minivans that are less than 25 years old (i.e., 
they exclude vehicles where the substitution elasticity is expected 
to be small). Sallee et al. (2016) exclude diesels, hybrids, and 
used vehicles with less than 10,000 or more than 100,000 miles.

    These studies point to a somewhat narrower range of estimates than 
suggested by previous cross-sectional studies; more importantly, they 
consistently suggest that buyers value a large proportion--and perhaps 
even all--of the future savings that models with higher fuel economy 
offer.\223\ Because they rely on estimates of fuel costs over vehicles' 
expected remaining lifetimes, these studies' estimates of how buyers 
value fuel economy are sensitive to the strategies they use to isolate 
differences among individual models' fuel economy, as well as to their 
assumptions about buyers' discount rates and gasoline price 
expectations, among others. Since Anderson et al. (2013) find evidence 
that consumers expect future gasoline prices to resemble current 
prices, we use this assumption to compare the findings of the three 
studies and examine how their findings vary with the discount rates 
buyers apply to future fuel savings.\224\

    \223\ Killian & Sims (2006) and Sawhill (2008) rely on similar 
longitudinal approaches to examine consumer valuation of fuel 
economy except that they use average values or list prices instead 
of actual transaction prices. Since these studies remain 
unpublished, their empirical results are subject to change, and they 
are excluded from this discussion.
    \224\ Each of the studies makes slightly different assumptions 
about appropriate discount rates. Sallee et al. (2016) use five 
percent in their base specification, while Allcott & Wozny (2014) 
rely on six percent. As some authors note, a five to six percent 
discount rate is consistent with current interest rates on car 
loans, but they also acknowledge that borrowing rates could be 
higher in some cases, which could be justify higher discount rates. 
Rather than assuming a specific discount rate, Busse et al. (2013) 
directly estimate implicit discount rates at which future fuel costs 
would be fully internalized; they find discount rates of six to 21% 
for used cars and one to 13% for new cars at assumed demand 
elasticities ranging from -2 to -3. Their estimates can be 
translated into the percent of fuel costs internalized by consumers, 
assuming a particular discount rate. To make these results more 
directly comparable to the other two studies, we assume a range of 
discount rates and uses the authors' spreadsheet tool to translate 
their results into the percent of fuel costs internalized into the 
purchase price at each rate. Because Busse et al. (2013) estimate 
the effects of future fuel costs on vehicle prices separately by 
fuel economy quartile, these results depend on which quartiles of 
the fuel economy distribution are compared; our summary shows 
results using the full range of quartile comparisons.

    As Table 1 indicates, Allcott & Wozny (2014) find that consumers 
incorporate 55% of future fuel costs into vehicle purchase decisions at 
a six percent discount rate, when their expectations for future 
gasoline prices are assumed to reflect prevailing prices at the time of 
their purchases. With the same expectation about future fuel prices, 
the authors report that consumers would fully value fuel costs only if 
they apply discount rates of 24% or higher. However, these authors' 
estimates are closer to full valuation when using gasoline price 
forecasts that mirror oil futures markets because the petroleum market 
expected prices to fall during this period (this outlook reduces the 
discounted value of a vehicle's expected remaining lifetime fuel 
costs). With this expectation, Allcott & Wozny (2014) find that buyers 
value 76% of future cost savings (discounted at six percent) from 
choosing a model that offers higher fuel economy, and that a discount 
rate of 15% would imply that they fully value future cost savings. 
Sallee et al. (2016) begin with the perspective that buyers fully 
internalize future fuel costs into vehicles' purchase prices and cannot 
reliably reject that hypothesis; their base specification suggests that 
changes in vehicle prices incorporate slightly more than 100% of 
changes in future fuel costs. For discount rates of five to six 
percent, the Busse et al. (2013) results imply that vehicle prices 
reflect 60 to 100% of future fuel costs. As Table II-31 suggests, 
higher private discount rates move all of the estimates closer to full 
valuation or to over-valuation, while lower discount rates imply less 
complete valuation in all three studies.

[[Page 43073]]


    The studies also explore the sensitivity of the results to other 
parameters that could influence their results. Busse et al. (2013) and 
Allcott & Wozny (2014) find that relying on data that suggest lower 
annual vehicle use or survival probabilities, which imply that vehicles 
will not last as long, moves their estimates closer to full valuation, 
an unsurprising result because both reduce the changes in expected 
future fuel costs caused by fuel price fluctuations. Allcott & Wozny's 
(2014) base results rely on an instrumental variables estimator that 
groups miles-per-gallon (MPG) into two quantiles to mitigate potential 
attenuation bias due to measurement error in fuel economy, but they 
find that greater disaggregation of the MPG groups implies greater 
undervaluation (for example, it reduces the 55% estimated reported in 
Table 1 to 49%). Busse et al. (2013) allow gasoline prices to vary 
across local markets in their main specification; using national 
average gasoline prices, an approach more directly comparable to the 
other studies, results in estimates that are closer to or above full 
valuation. Sallee et al. (2016) find modest undervaluation by vehicle 
fleet operators or manufacturers making large-scale purchases, compared 
to retail dealer sales (i.e., 70 to 86%).
    Since they rely predominantly on changes in vehicles' prices 
between repeat sales, most of the valuation estimates reported in these 
studies apply most directly to buyers of used vehicles. Only Busse et 
al. (2013) examine new vehicle sales; they find that consumers value 
between 75 to 133% of future fuel costs for new vehicles, a higher 
range than they estimate for used vehicles. Allcott & Wozny (2014) 
examine how their estimates vary by vehicle age and find that 
fluctuations in purchase prices of younger vehicles imply that buyers 
whose fuel price expectations mirror the petroleum futures market value 
a higher fraction of future fuel costs: 93% for one- to three-year-old 
vehicles, compared to their estimate of 76% for all used vehicles 
assuming the same price expectation.\225\

    \225\ Allcott & Wozny (2014) and Sallee, et al. (2016) also find 
that future fuel costs for older vehicles are substantially 
undervalued (26-30%). The pattern of Allcott and Wozny's results for 
different vehicle ages is similar when they use retail transaction 
prices (adjusted for customer cash rebates and trade-in values) 
instead of wholesale auction prices, although the degree of 
valuation falls substantially in all age cohorts with the smaller, 
retail price based sample.

    Accounting for differences in their data and estimation procedures, 
the three studies described here suggest that car buyers who use 
discount rates of five to six percent value at least half--and perhaps 
all--of the savings in future fuel costs they expect from choosing 
models that offer higher fuel economy. Perhaps more important in 
assessing the case for regulating fuel economy, one study suggests that 
buyers of new cars and light trucks value three-quarters or more of the 
savings in future fuel costs they anticipate from purchasing higher-mpg 
models, although this result is based on more limited information.
    In contrast, previous regulatory analyses of fuel economy standards 
implicitly assumed that buyers undervalue even more of the benefits 
they would experience from purchasing models with higher fuel economy 
so that without increases in fuel economy standards little improvement 
would occur, and the entire value of fuel savings from raising CAFE 
standards represented private benefits to car and light truck buyers 
themselves. For instance, in the EPA analysis of the 2017-2025 model 
year greenhouse gas emission standards, fuel savings alone added up to 
$475 billion (at three percent discount rate) over the lifetime of the 
vehicles, far outweighing the compliance costs: $150 billion). The 
assertion that buyers were unwilling to take voluntary advantage of 
this opportunity implies that collectively, they must have valued less 
than a third ($150 billion/$475 billion = 32%) of the fuel savings that 
would have resulted from those standards.\226\ The evidence

[[Page 43074]]

reviewed here makes that perspective extremely difficult to justify and 
would call into question any analysis that claims to show large private 
net benefits for vehicle buyers.

    \226\ In fact, those earlier analyses assumed that new car and 
light truck buyers attach relatively little value to higher fuel 
economy, since their baseline scenarios assumed that fuel economy 
levels would not increase in the absence of progressively tighter 

    What analysts assume about consumers' vehicle purchasing behavior, 
particularly about potential buyers' perspectives on the value of 
increased fuel economy, clearly matters a great deal in the context of 
benefit-cost analysis for fuel economy regulation. In light of recent 
evidence on this question, a more nuanced approach than assuming that 
buyers drastically undervalue benefits from higher fuel economy, and 
that as a consequence, these benefits are unlikely to be realized 
without stringent fuel economy standards, seems warranted. One possible 
approach would be to use a baseline scenario where fuel economy levels 
of new cars and light trucks reflected full (or nearly so) valuation of 
fuel savings by potential buyers in order to reveal whether setting 
fuel economy standards above market-determined levels could produce net 
social benefits. Another might be to assume that, unlike in the 
agencies' previous analyses, where buyers were assumed to greatly 
undervalue higher fuel economy under the baseline but to value it fully 
under the proposed standards, buyers value improved fuel economy 
identically under both the baseline scenario and with stricter CAFE 
standards in place. The agencies ask for comment on these and any 
alternative approaches they should consider for valuing fuel savings, 
new peer-reviewed evidence on vehicle buyers' behavior that casts light 
on how they value improved fuel economy, the appropriate private 
discount rate to apply to future fuel savings, and thus the degree to 
which private fuel savings should be considered as private benefits of 
increasing fuel economy standards.
(b) Sales Data and Relevant Macroeconomic Factors
    Developing a procedure to predict the effects of changes in prices 
and attributes of new vehicles is complicated by the fact that their 
sales are highly pro-cyclical--that is, they are very sensitive to 
changes in macroeconomic conditions--and also statistically ``noisy,'' 
because they reflect the transient effects of other factors such as 
consumers' confidence in the future, which can be difficult to observe 
and measure accurately. At the same time, their average sales price 
tends to move in parallel with changes in economic growth; that is, 
average new vehicle prices tend to be higher when the total number of 
new vehicles sold is increasing and lower when the total number of new 
sales decreases (typically during periods of low economic growth or 
recessions). Finally, counts of the total number of new cars and light 
trucks that are sold do not capture shifts in demand among vehicle size 
classes or body styles (``market segments''); nor do they measure 
changes in the durability, safety, fuel economy, carrying capacity, 
comfort, or other aspects of vehicles' quality.
    The historical series of new light-duty vehicle sales exhibits 
cyclic behavior over time that is most responsive to larger cycles in 
the macro economy--but has not increased over time in the same way the 
population, for example, has. While U.S. population has grown over 35 
percent since 1980, the registered vehicle population has grown at an 
even faster pace--nearly doubling between 1980 and 2015.\227\ But 
annual vehicle sales did not grow at a similar pace -even accounting 
for the cyclical nature of the industry. Total new light-duty sales 
prior to the 2008 recession climbed as high as 16 million, though 
similarly high sales years occurred in the 1980's and 1990's as well. 
In fact, when considering a 10-year moving average to smooth out the 
effect of cycles, most 10-year averages between 1992 and 2015 are 
within a few percent of the 10-year average in 1992. And although 
average transaction prices for new vehicles have been rising steadily 
since the recession ended, prices are not yet at historical highs when 
adjusted for inflation. The period of highest inflation-adjusted 
transaction prices occurred from 1996-2006, when the average 
transaction price for a new light-duty vehicle was consistently higher 
than the price in 2015.

    \227\ There are two measurements of the size of the registered 
vehicle population that are considered to be authoritative. One is 
produced by the Federal Highway Adminstration, and the other by R.L. 
Polk (now part of IHS). The Polk measurement shows fleet growth 
between 1980 and 2015 of about 85%, while the FHWA measurement shows 
a slower growth rate over that period; only about 60%. Both are 
still considerably larger than the growth in new vehicle sales over 
the same period.

    In an attempt to overcome these analytical challenges, various 
approaches were experimented with to predict the response of new 
vehicle sales to the changes in prices, fuel economy, and other 
features. These included treating new vehicle demand as a product of 
changes in total demand for vehicle ownership and demand necessary to 
replace used vehicles that are retired, analyzing total expenditures to 
purchase new cars and light trucks in conjunction with the total number 
sold, and other approaches. However, none of these methods offered a 
significant improvement over estimating the total number of vehicles 
sold directly from its historical relationship to directly measurable 
factors such as their average sales price, macroeconomic variables such 
as GDP or Personal Disposable Income, U.S. labor force participation, 
and regularly published surveys of consumer sentiment or confidence.
    Quarterly, rather than annual data on total sales of new cars and 
light trucks, their average selling price, and macroeconomic variables 
was used to develop an econometric model of sales, in order to increase 
the number of observations and more accurately capture the causal 
effects of individual explanatory variables. Applying conventional data 
diagnostics for time-series economic data revealed that most variables 
were non-stationary (i.e., they reflected strong underlying time 
trends) and displayed unit roots, and statistical tests revealed co-
integration between the total vehicle sales--the model's dependent 
variable--and most candidate explanatory variables.
(c) Current Estimation of Sales Impacts
    To address the complications of the time series data, the analysis 
estimated an autoregressive distributed-lag (ARDL) model that employs a 
combination of lagged values of its dependent variable--in this case, 
last year's and the prior year's vehicle sales--and the change in 
average vehicle price, quarterly changes in the U.S. GDP growth rate, 
as well as current and lagged values of quarterly estimates of U.S. 
labor force participation. The number of lagged values of each 
explanatory variable to include was determined empirically (using the 
Bayesian information criterion), by examining the effects of including 
different combinations of their lagged values on how well the model 
``explained'' historical variation in car and light truck sales.
    The results of this approach were encouraging: The model's 
predictions fit the historical data on sales well, each of its 
explanatory variables displayed the expected effect on sales, and 
analysis of its unexplained residual terms revealed little evidence of 
autocorrelation or other indications of statistical problems. The model 
coefficients suggest that positive GDP growth rates and increases in 
labor force participation are both indicators of increases in new 
vehicle sales, while positive changes in average new vehicle price 
reduce new sales. However, the magnitude of the

[[Page 43075]]

coefficient on change in average price is not as determinative of total 
sales as the other variables.
    Based on the model, a $1,000 increase in the average new vehicle 
price causes approximately 170,000 lost units in the first year, 
followed by a reduction of another 600,000 units over the next ten 
years as the initial sales decrease propagates over time through the 
lagged variables and their coefficients. The price elasticity of new 
car and light truck sales implied by alternative estimates of the 
model's coefficients ranged from -0.2 to -0.3--meaning that changes in 
their prices have moderate effects on total sales--which contrasts with 
estimates of higher sensitivity to prices implied by some models.\228\ 
The analysis was unable to incorporate any measure of new car and light 
truck fuel economy in the model that added to its ability to explain 
historical variation in sales, even after experimenting with 
alternative measures of such as the unweighted and sales-weighted 
averages fuel economy of models sold in each quarter, the level of fuel 
economy they were required to achieve, and the change in their fuel 
economy from previous periods.

    \228\ Effects on the used car market are accounted for 

    Despite the evidence in the literature, summarized above, that 
consumers value most, if not all, of the fuel economy improvements when 
purchasing new vehicles, the model described here operates at too high 
a level of aggregation to capture these preferences. By modeling the 
total number of new vehicles sold in a given year, it is necessary to 
quantify important measures, like sales price or fuel economy, by 
averages. Our model operates at a high level of aggregation, where the 
average fuel economy represents an average across many vehicle types, 
usage profiles, and fuel economy levels. In this context, the average 
fuel economy was not a meaningful value with respect to its influence 
on the total number of new vehicles sold. A number of recent studies 
have indeed shown that consumers value fuel savings (almost) fully. 
Those studies are frequently based on large datasets that are able to 
control for all other vehicle attributes through a variety of 
econometric techniques. They represent micro-level decisions, where a 
buyer is (at least theoretically) choosing between a more or less 
efficient version of a pickup truck (for example) that is otherwise 
identical. In an aggregate sense, the average is not comparable to the 
decision an individual consumer faces.
    Estimating the sales response at the level of total new vehicle 
sales likely fails to address valid concerns about changes to the 
quality or attributes of new vehicles sold--both over time and in 
response to price increases resulting from CAFE standards. However, 
attempts to address such concerns would require significant additional 
data, new statistical approaches, and structural changes to the CAFE 
model over several years. It is also the case that using absolute 
changes in the average price may be more limited than another 
characterization of price that relies on distributions of household 
income over time or percentage change in the new vehicle price. The 
former would require forecasting a deeply uncertain quantity many years 
into the future, and the latter only become relevant once the 
simulation moves beyond the magnitude of observed price changes in the 
historical series. Future versions of this model may use a different 
characterization of cost that accounts for some of these factors if 
their inclusion improves the model estimation and corresponding 
forecast projections are available.
    The changes in selling prices, fuel economy, and other features of 
cars and light trucks produced during future model years that result 
from manufacturers' responses to lower CAFE and GHG emission standards 
are likely to affect both sales of individual models and the total 
number of new vehicles sold. Because the values of changes in fuel 
economy and other features to potential buyers are not completely 
understood; however, the magnitude, and possibly even the direction, of 
their effect on sales of new vehicles is difficult to anticipate. On 
balance, it is reasonable to assume that the changes in prices, fuel 
economy, and other attributes expected to result from their proposed 
action to amend and establish fuel economy and GHG emission standards 
are likely to increase total sales of new cars and light trucks during 
future model years. Please provide comment on the relationship between 
price increases, fuel economy, and new vehicle sales, as well as 
methods to appropriately account for these relationships.
(d) Projecting New Vehicle Sales and Comparisons to Other Forecasts
    The purpose of the sales response model is to allow the CAFE model 
to simulate new vehicle sales in a given future model year, accounting 
for the impact of a regulatory alternative's stringency on new vehicle 
prices (in a macro-economic context that is identical across 
alternatives). In order to accomplish this, it is important that the 
model of sales response be dynamically stable, meaning that it responds 
to shocks not by ``exploding,'' increasing or decreasing in a way that 
is unbounded, but rather returns to a stable path, allowing the shock 
to dissipate. The CAFE model uses the sales model described above to 
dynamically project future sales; after the first year of the 
simulation, lagged values of new vehicle sales are those that were 
produced by the model itself rather than observed. The sales response 
model constructed here uses two lagged dependent variables and simple 
econometric conditions determine if the model is dynamically stable. 
The coefficients of the one-year lag and the two-year lag, 
[beta]1 and [beta]2, respectively must satisfy 
three conditions. Their sum must be less than one, [beta]2 - 
[beta]1 <1, and the absolute value of [beta]2 
must be less than one. The coefficients of this model satisfy all three 
    Using the Augural CAFE standards as the baseline, it is possible to 
produce a series of future total sales as shown in Table-II-32. For 
comparison, the table includes the calculated total light-duty sales of 
a proprietary forecast purchased to support the 2016 Draft TAR 
analysis, the total new light-duty sales in EIA's 2017 Annual Energy 
Outlook, and a (short) forecast published in the Center for Automotive 
Research's Q4 2017 Automotive Outlook. All of the forecasts in Table-
II-32 assume the Augural Standards are in place through MY 2025, though 
assumptions about the costs required to comply with them likely differ. 
As the table shows, despite differences among them, the dynamically 
produced sales projection from the CAFE model is not qualitatively 
different from the others.

[[Page 43076]]


    While this forecast projects a relatively high, but flat, level of 
new vehicle sales into the future, it is worth noting that it continues 
another trend observed in the historical data. The time series of 
annual new vehicle sales is volatile from year to year, but multi-year 
averages are less so being sufficient to wash out the variation 
associated with them peaks and valleys of the series. Despite the fact 
that the moving average annual new vehicle sales has been growing over 
the last four decades, it has not kept pace with U.S. population 
growth. Data from the Federal Reserve Bank of St. Louis shows that the 
per-capita sales of new vehicles peaked in 1986 and has declined more 
than 25% from this peak to today's level.\231\ While the sales 
projection in Table-II-32 would represent a historically high average 
of new vehicle sales over the analysis period, it would not be 
sufficient to reverse the trend of declining per-capita sales of new 
vehicles during the analysis period, though it would continue the trend 
at a slower rate.

    \229\ Out of necessity, the analysis in today's rule conflates 
production year (or ``model year'') and calendar year. The volumes 
cited in the CAFE model forecast represent forecasted production 
volumes for those model years, while the other represent calendar 
year sales (rather than production)--during which two, or possibly 
three, different model year vehicles are sold. In the long run, the 
difference is not important. In the early years, there are likely to 
be discrepancies.
    \230\ U.S. Total Sales by Make, Automotive News, http://www.autonews.com/section/datalist18 (last visited June 22, 2018).
    \231\ Mislinski, J. Light Vehicle Sales Per Capita: Our Latest 
Look at the Long-Term Trend, Advisor Perspectives (June 1, 2018), 

    In addition to the statistical model that estimates the response of 
total new vehicle sales to changes in the average new vehicle price, 
the CAFE model incorporates a dynamic fleet share model that modifies 
the light truck (and, symmetrically, passenger car) share of the new 
vehicle market. A version of this model first appeared in the 2012 
final rule, when this fleet share component was introduced to ensure 
greater internal consistency within inputs in the uncertainty analysis. 
For today's analysis, this dynamic fleet share is enabled throughout 
the analysis of alternatives.
    The dynamic fleet share model is a series of difference equations 
that determine the relative share of light trucks and passenger cars 
based on the average fuel economy of each, the fuel price, and average 
vehicle attributes like horsepower and vehicle mass (the latter of 
which explicitly evolves as a result of the compliance simulation). 
While this model was taken from EIA's National Energy Modeling System 
(NEMS), it is applied at a different level. Rather than apply the 
shares based on the regulatory class distinction, the CAFE model 
applies the shares to body-style. This is done to account for the 
large-scale shift in recent years to crossover utility vehicles that 
have model variants in both the passenger car and light truck 
regulatory fleets. The agencies have always modified their static 
forecasts of new vehicle sales to reflect the PC/LT split present in 
the Annual Energy Outlook; this integration continues that approach in 
a way that ensures greater internal consistency when simulating 
multiple regulatory alternatives (and conducting sensitivity analysis 
on any of the factors that influence fleet share).
(e) Vehicle Choice Models as an Alternative Method To Estimate New 
Vehicle Sales
    Another potential option to estimate future new vehicle sales would 
be to use a full consumer choice model. The agencies simulate 
compliance with CAFE and CO2 standards for each manufacturer 
using a disaggregated representation of its regulated vehicle fleets. 
This means that each manufacturer may have hundreds of vehicle model 
variants (e.g., the Honda Civic with the 6-cylinder engine, and the 
Honda Civic with the 4-cylinder engine would each be treated as 
different, in some ways, during the compliance simulation).\232\ While 
the analysis accounts for a wide variety of attributes across these 
vehicles, only a few of them change during the compliance simulation. 
However, all of those attributes are relevant in the context of 
consumer choice models.

    \232\ For more detail about the compliance simulation and 
manufacturer fleet representation, see Section II.G.

    Aside from the computational intensity of simulating new vehicle 
sales at the level of individual models--for all manufacturers, under 
each regulatory alternative, over the next decade or more--it would be 
necessary to include additional relationships

[[Page 43077]]

about how consumers trade off among vehicle attributes, which types of 
consumers prefer which types of attributes (and how much), and how 
manufacturers might strategically price these modified vehicles. This 
requires a strategic pricing model, which each manufacturer has and 
would likely be unwilling to share. Some of this strategic pricing 
behavior occurs on small time-scale through the use of dealer 
incentives, rebates on specific models, and creative financing offers. 
When simulating compliance at the annual scale, it is effectively 
impossible to account for these types of strategic decisions.
    It is also true consumers have heterogeneous preferences that 
change over time and determine willingness-to-pay for a variety of 
vehicle attributes. These preferences change in response to marketing, 
distribution, pricing, and product strategies that manufacturers may 
change over time. With enough data, a consumer choice model could 
stratify new vehicle buyers into types and attempt to measure the 
strength of each type's preference for fuel economy, acceleration, 
safety rating, perceived quality and reliability, interior volume, or 
comfort. However, other factors also influence customers' purchase 
decision, and some of these can be challenging to model. Consumer 
proximity to dealerships, quality of service and customer experience at 
dealerships, availability and terms of financing, and basic product 
awareness may significantly factor into sales success.
    Manufacturers' marketing choices may significantly and 
unpredictably affect sales. Ad campaigns may increase awareness in the 
market, and campaigns may reposition consumers' perception of the 
brands and products. For example, in 2011 the Volkswagen Passat 
featured an ad with a child in a Darth Vader costume (and showcased 
remote start technology on the Passat). In MY 2012, Kia established the 
Kia Soul with party rocking, hip-hop hamster commercials showcasing 
push-button ignition, a roomy interior, and design features in the 
brake lights. Both commercials raised awareness and highlighted basic 
product features. Each commercial also impressed demographic groups 
with pop culture references, product placement, and co-branding. While 
the marketing budget of individual manufacturers may help a consumer 
choice model estimate market share for a given brand, estimating the 
impact of a given campaign on new sales is more challenging as 
consumers make purchasing decisions based upon their own needs and 
    Modelers must understand how consumers and commercial buyers select 
vehicles in order to effectively develop and implement a consumer 
choice model in a compliance simulation. Consumers purchase vehicles 
for a variety of reasons such as family need, need for more space, new 
technology, changes to income and affordability of a new vehicle, 
improved fuel economy, operating costs of current vehicles, and others. 
Once committed to buying a vehicle, consumers use different processes 
to narrow down their shopping list. Consumer choice decision attributes 
include factors both related and not related to the vehicle design. The 
vehicle's utility for those attributes is researched across many 
different information sources as listed in the table below.

    An objective, attribute-based consumer choice model could lead to 
projected swings in manufacturer market shares and individual model 
volumes. The current approach simulates compliance for each 
manufacturer assuming that it produces the same set of vehicles that it 
produced in the initial year of the simulation (MY 2016 in today's 
analysis). If a consumer choice model were to drive projected sales of 
a given vehicle model below some threshold, as consumers have done in 
the real market, the simulation currently has no way to generate a new 
vehicle model to take its place. As demand changes across specific 
market segments and models, manufacturers adapt by supplying new 
vehicle nameplates and models (e.g., the proliferation of crossover 
utility vehicles in recent years). Absent that flexibility in the 
compliance simulation, even the more accurate consumer choice model may 
produce unrealistic projections of future sales volumes at the model, 
segment, or manufacturer level.
    Comment is sought on the development and use of potential consumer 
choice model in compliance simulations. Comment is also sought on the 
appropriate breadth, depth, and complexity of considerations in a 
consumer choice model.
(f) Industry Employment Baseline (Including Multiplier Effect) and Data 
    In the first two joint CAFE/CO2 rulemakings, the 
agencies considered an analysis of industry employment impacts in some 
form in setting both CAFE and emissions standards; NHTSA conducted an 
industry employment analysis in part to determine whether the standards 
the agency set were economically practicable, that is, whether the 
standards were ``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.'' \233\ EPA similarly conducted an industry employment analysis 
under the broad authority granted to the agency under the Clean Air 
Act.\234\ Both agencies recognized the uncertainties inherent in 
estimating industry employment impacts; in fact, both agencies 
dedicated a substantial amount of discussion to uncertainty in industry 
employment analyses in the 2012 final rule for MYs 2017 and 
beyond.\235\ Notwithstanding these uncertainties, CAFE and 
CO2 standards do impact industry labor hours, and providing 
the best analysis practicable better informs stakeholders

[[Page 43078]]

and the public about the standards' impact than would omitting any 
estimates of potential labor impacts.

    \233\ 67 FR 77015, 77021 (Dec. 16, 2002).
    \234\ See George E. Warren Corp. v. EPA, 159 F.3d 616, 623-624 
(D.C. Cir. 1998) (ordinarily permissible for EPA to consider factors 
not specifically enumerated in the Act).
    \235\ See 77 FR 62624, 62952, 63102 (Oct. 15, 2012).

    Today many of the effects that were previously qualitatively 
identified, but not considered, are quantified. For instance, in the 
PRIA for the 2017-2025 rule EPA identified ``demand effects,'' ``cost 
effects,'' and ``factor shift effects'' as important considerations for 
industry labor, but the analysis did not attempt to quantify either the 
demand effect or the factor shift effect.\236\ Today's industry labor 
analysis quantifies direct labor changes that were qualitatively 
discussed previously.

    \236\ Regulatory Impact Analysis: Final Rulemaking for 2017-2025 
Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate 
Average Fuel Economy Standards, U.S. EPA at 8-24 to 8-32 (Aug. 

    Previous analyses and new methodologies to consider direct labor 
effects on the automotive sector in the United States were improved 
upon and developed. Potential changes that were evaluated include (1) 
dealership labor related to new light duty vehicle unit sales; (2) 
changes in assembly labor for vehicles, for engines and for 
transmissions related to new vehicle unit sales; and (3) changes in 
industry labor related to additional fuel savings technologies, 
accounting for new vehicle unit sales. All automotive labor effects 
were estimated and reported at a national level,\237\ in job-years, 
assuming 2,000 hours of labor per job-year.

    \237\ The agencies recognize a few local production facilities 
may contribute meaningfully to local economies, but the analysis 
reported only on national effects.

    The analysis estimated labor effects from the forecasted CAFE model 
technology costs and from review of automotive labor for the MY 2016 
fleet. For each vehicle in the CAFE model analysis, the locations for 
vehicle assembly, engine assembly, and transmission assembly and 
estimated labor in MY 2016 were recorded. The percent U.S. content for 
each vehicle was also recorded. Not all parts are made in the United 
States, so the analysis also took into account the percent U.S. content 
for each vehicle as manufacturers add fuel-savings technologies. As 
manufacturers added fuel-economy technologies in the CAFE model 
simulations, the analysis assumed percent U.S. content would remain 
constant in the future, and that the U.S. labor added would be 
proportional to U.S. content. From this foundation, the analysis 
forecasted automotive labor effects as the CAFE model added fuel 
economy technology and adjusted future sales for each vehicle.
    The analysis also accounts for sales projections in response to the 
different regulatory alternatives; the labor analysis considers changes 
in new vehicle prices and new vehicle sales (for further discussion of 
the sales model, see Section 2.E). As vehicle prices rise, the analysis 
expected consumers to purchase fewer vehicles than they would have at 
lower prices. As manufacturers sell fewer vehicles, the manufacturers 
may need less labor to produce the vehicles and less labor to sell the 
vehicles. However, as manufacturers add equipment to each new vehicle, 
the manufacturers will require human resources to develop, sell, and 
produce additional fuel-saving technologies. The analysis also accounts 
for the potential that new standards could shift the relative shares of 
passenger cars and light trucks in the overall fleet (see Section 2.E); 
insofar as different vehicles involved different amounts of labor, this 
shifting impacts the quantity of estimated labor. The CAFE model 
automotive labor analysis takes into account reduction in vehicle 
sales, shifts in the mix of passenger cars and light trucks, and 
addition of fuel-savings technologies.
    For today's analysis, it was assumed that some observations about 
the production of MY 2016 vehicles would carry forward, unchanged into 
the future. For instance, assembly plants would remain the same as MY 
2016 for all products now, and in the future. The analysis assumed 
percent U.S. content would remain constant, even as manufacturers 
updated vehicles and introduced new fuel-saving technologies. It was 
assumed that assembly labor hours per unit would remain at estimated MY 
2016 levels for vehicles, engines, and transmissions, and the factor 
between direct assembly labor and parts production jobs would remain 
the same. When considering shifts from one technology to another, the 
analysis assumed revenue per employee at suppliers and original 
equipment manufacturers would remain in line with MY 2016 levels, even 
as manufacturers added fuel-saving technologies and realized cost 
reductions from learning.
    The analysis focused on automotive labor because adjacent 
employment factors and consumer spending factors for other goods and 
services are uncertain and difficult to predict. The analysis did not 
consider how direct labor changes may affect the macro economy and 
possibly change employment in adjacent industries. For instance, the 
analysis did not consider possible labor changes in vehicle maintenance 
and repair, nor did it consider changes in labor at retail gas 
stations. The analysis did not consider possible labor changes due to 
raw material production, such as production of aluminum, steel, copper 
and lithium, nor did the agencies consider possible labor impacts due 
to changes in production of oil and gas, ethanol, and electricity. The 
analysis did not analyze effects of how consumers could spend money 
saved due to improved fuel economy, nor did the analysis assess the 
effects of how consumers would pay for more expensive fuel savings 
technologies at the time of purchase; either could affect consumption 
of other goods and services, and hence affect labor in other 
industries. The effects of increased usage of car-sharing, ride-
sharing, and automated vehicles were not analyzed. The analysis did not 
estimate how changes in labor from any industry could affect gross 
domestic product and possibly affect other industries as a result.
    Finally, no assumptions were made about full-employment or not 
full-employment and the availability of human resources to fill 
positions. When the economy is at full employment, a fuel economy 
regulation is unlikely to have much impact on net overall U.S. 
employment; instead, labor would primarily be shifted from one sector 
to another. These shifts in employment impose an opportunity cost on 
society, approximated by the wages of the employees, as regulation 
diverts workers from other activities in the economy. In this 
situation, any effects on net employment are likely to be transitory as 
workers change jobs (e.g., some workers may need to be retrained or 
require time to search for new jobs, while shortages in some sectors or 
regions could bid up wages to attract workers). On the other hand, if a 
regulation comes into effect during a period of high unemployment, a 
change in labor demand due to regulation may affect net overall U.S. 
employment because the labor market is not in equilibrium. Schmalansee 
and Stavins point out that net positive employment effects are possible 
in the near term when the economy is at less than full employment due 
to the potential hiring of idle labor resources by the regulated sector 
to meet new requirements (e.g., to install new equipment) and new 
economic activity in sectors related to the regulated sector longer 
run, the net effect on employment is more difficult to predict and will 
depend on the way in which the related industries respond to the 
regulatory requirements. For that reason, this analysis does not 
include multiplier effects but instead focuses on

[[Page 43079]]

labor impacts in the most directly affected industries. Those sectors 
are likely to face the most concentrated labor impacts.
    Comment is sought on these assumptions and approaches in the labor 
4. Estimating Labor for Fuel Economy Technologies, Vehicle Components, 
Final Assembly, and Retailers
    The following sections discuss the approaches to estimating factors 
related to dealership labor, final assembly labor and parts production, 
and fuel economy technology labor.
(a) Dealership Labor
    The analysis evaluated dealership labor related to new light-duty 
vehicle sales, and estimated the labor hours per new vehicle sold at 
dealerships, including labor from sales, finance, insurance, and 
management. The effect of new car sales on the maintenance, repair, and 
parts department labor is expected to be limited, as this need is based 
on the vehicle miles traveled of the total fleet. To estimate the labor 
hours at dealerships per new vehicle sold, the National Automobile 
Dealers Association 2016 Annual Report, which provides franchise dealer 
employment by department and function, was referenced.\238\ The 
analysis estimated that slightly less than 20% of dealership employees' 
work relates to new car sales (versus approximately 80% in service, 
parts, and used car sales), and that on average dealership employees 
working on new vehicle sales labor for 27.8 hours per new vehicle sold.

    \238\ NADA Data 2016: Annaul Financial Profile of America's 
Franchised New-Car Dealerships, National Automobile Dealers 
Association, https://www.nada.org/2016NADAdata/ (last visited June 
22, 2018).

(b) Final Assembly Labor and Parts Production
    How the quantity of assembly labor and parts production labor for 
MY 2016 vehicles would increase or decrease in the future as new 
vehicle unit sales increased or decreased was estimated.
    Specific assembly locations for final vehicle assembly, engine 
assembly, and transmission assembly for each MY 2016 vehicle were 
identified. In some cases, manufacturers assembled products in more 
than one location, and the analysis identified such products and 
considered parallel production in the labor analysis.
    The analysis estimated industry average direct assembly labor per 
vehicle (30 hours), per engine (four hours), and per transmission (five 
hours) based on a sample of U.S. assembly plant employment and 
production statistics and other publicly available information. The 
analysis recognizes that some plants may use less labor than the 
analysis estimates to produce the vehicle, the engine, or the 
transmission, and other plants may have used more labor. The analysis 
used the assembly locations and industry averages for labor per unit to 
estimate U.S. assembly labor hours for each vehicle. U.S. assembly 
labor hours per vehicle ranged from as high as 39 hours if the 
manufacturer assembled the vehicle, engine, and transmission at U.S. 
plants, to as low as zero hours if the manufacturer imported the 
vehicle, engine, and transmission.
    The analysis also considered labor for part production in addition 
to labor for final assembly. Motor vehicle and equipment manufacturing 
labor statistics from the U.S. Census Bureau, the Bureau of Labor 
Statistics,\239\ and other publicly available sources were surveyed. 
Based on these sources, the analysis noted that the historical average 
ratio of vehicle assembly manufacturing employment to employment for 
total motor vehicle and equipment manufacturing for new vehicles 
remained roughly constant over the period from 2001 through 2013, at a 
ratio of 5.26. Observations from 2001-2013 spanned many years, many 
combinations of technologies and technology trends, and many economic 
conditions, yet the ratio remained about the same. Accordingly, the 
analysis scaled up estimated U.S. assembly labor hours by a factor of 
5.26 to consider U.S. parts production labor in addition to assembly 
labor for each vehicle.

    \239\ NAICS Code 3361, 3363.

    The industry estimates for vehicle assembly labor and parts 
production labor for each vehicle scaled up or down as unit sales 
scaled up or down over time in the CAFE model.
(c) Fuel Economy Technology Labor
    As manufacturers spend additional dollars on fuel-saving 
technologies, parts suppliers and manufacturers require human resources 
to bring those technologies to market. Manufacturers may add, shift, or 
replace employees in ways that are difficult for the agencies to 
predict in response to adding fuel-savings technologies; however, it is 
expected that the revenue per labor hour at original equipment 
manufacturers (OEMs) and suppliers will remain about the same as in MY 
2016 even as industry includes additional fuel-saving technology.
    To estimate the average revenue per labor hour at OEMs and 
suppliers, the analysis looked at financial reports from publicly 
traded automotive businesses.\240\ Based on recent figures, it was 
estimated that OEMs would add one labor year per $633,066 revenue \241\ 
and that suppliers would add one labor year per $247,648 in 
revenue.\242\ These global estimates are applied to all revenues, and 
U.S. content is applied as a later adjustment. In today's analysis, it 
was assumed these ratios would remain constant for all technologies 
rather than that the increased labor costs would be shifted toward 
foreign countries. Comment is sought on the realism of this assumption.

    \240\ The analysis considered suppliers that won the Automotive 
News ``PACE Award'' from 2013-2017, covering more than 40 suppliers, 
more than 30 of which are publicly traded companies. Automotive News 
gives ``PACE Awards'' to innovative manufacturers, with most recent 
winners earning awards for new fuel-savings technologies.
    \241\ The analysis assumed incremental OEM revenue as the retail 
price equivalent for technologies, adjusting for changes in sales 
    \242\ The analysis assumed incremental supplier revenue as the 
technology cost for technologies before retail price equivalent 
mark-up, adjusting for changes in sales volume.

(d) Labor Calculations
    The analysis estimated the total labor as the sum of three 
components: Dealership hours, final assembly and parts production, and 
labor for fuel-economy technologies (at OEM's and suppliers). The CAFE 
model calculated additional labor hours for each vehicle, based on 
current vehicle manufacturing locations and simulation outputs for 
additional technologies, and sales changes. The analysis applied some 
constants to all vehicles,\243\ but other constants were vehicle 
specific,\244\ or year specific for a vehicle.\245\

    \243\ The analysis applied the same assumptions to all 
manufacturers for annual labor hours per employee, dealership hours 
per unit sold, OEM revenue per employee, supplier revenue per 
employee, and factor for the jobs multiplier.
    \244\ The analysis made vehicle specific assumptions about 
percent U.S. content and U.S. assembly employment hours.
    \245\ The analysis estimated technology cost for each vehicle, 
for each year based on the technology content applied in the CAFE 
model, year-by-year.

    While a multiplier effect of all U.S. automotive related jobs on 
non-auto related U.S. jobs was not considered for today's analysis, the 
analysis did program a ``global multiplier'' that can be used to scale 
up or scale down the total labor hours. This multiplier exists in the 
parameters file, and for today's analysis the analysis set the value at 
5. Additional Costs and Benefits Incurred by New Vehicle Buyers
    Some costs of purchasing and owning a new or used vehicle scale 
with the

[[Page 43080]]

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. Below the 
assumptions made about how each of these additional costs of vehicle 
purchase and ownership scale with the MSRP and how the analysis arrived 
at these assumptions are discussed.
(a) Sales Taxes
    The analysis took auto sales taxes by state \246\ and weighted them 
by population by state to determine a national weighted-average sales 
tax of 5.46%. The analysis sought to weight sales taxes by new vehicle 
sales by state; however, such data were unavailable. It is recognized 
that for this purpose, new vehicle sales by state is a superior 
weighting mechanism to Census population; in effort to approximate new 
vehicle sales by state, a study of the change in new vehicle 
registrations (using R.L. Polk data) by state across recent years was 
conducted, resulting in a corresponding set of weights. Use of the 
weights derived from the study of vehicle registration data resulted in 
a national weighted-average sales tax rate almost identical to that 
resulting from the use of Census population estimates as weights, just 
slightly above 5.5%. The analysis opted to utilize Census population 
rather than the registration-based proxy of new vehicle sales as the 
basis for computing this weighted average, as the end results were 
negligibly different and the analytical approach involving new vehicle 
registrations had not been as thoroughly reviewed. Note: Sales taxes 
and registration fees are transfer payments between consumers and the 
Federal government and are therefore not considered a cost in the 
societal perspective. However, these costs are considered as additional 
costs in the private consumer perspective.

    \246\ See Car Tax by State, FactoryWarrantyList.com, http://www.factorywarrantylist.com/car-tax-by-state.html (last visited June 
22, 2018). Note: County, city, and other municipality-specific taxes 
were excluded from weighted averages, as the variation in locality 
taxes within states, lack of accessible documentation of locality 
rates, and lack of availability of weights to apply to locality 
taxes complicate the ability to reliably analyze the subject at this 
level of detail. Localities with relatively high automobile sales 
taxes may have relatively fewer auto dealerships, as consumers would 
endeavor to purchase vehicles in areas with lower locality taxes, 
therefore reducing the effect of the exclusion of municipality-
specific taxes from this analysis.

(b) Financing Costs
    The analysis assumes 85% of automobiles are financed based on 
Experian's quarter 4, 2016 ``State of the Automotive Finance Market,'' 
which notes that 85.2% of 2016 new vehicles were financed, as were 
85.9% of 2015 new vehicle purchases.\247\ The analysis used data from 
Wards Automotive and JD Power on the average transaction price of new 
vehicle purchases, average financed new auto beginning principal, and 
the average incentive as a percent of MSRP to compute the ratio of the 
average financed new auto principal to the average new vehicle MSRP for 
calendar years 2011-2016. Table-II-34 shows that the average financed 
auto principal is between 82 and 84% of the average new vehicle MSRP. 
Using the assumption that 85% of new vehicle purchases involve some 
financing, the average share of the MSRP financed for all vehicles 
purchased, including non-financed transactions, rather than only those 
that are financed, was computed. Table-II-34 shows that this share 
ranges between 70 and 72%. From this, the analysis assumed that on an 
aggregate level, including all new vehicle purchases, 70% of the value 
of all vehicles' MSRP is financed. It is likely that the share financed 
is correlated with the MSRP of the new vehicle purchased, but for 
simplification purposes, it is assumed that 70% of all vehicle costs 
are financed, regardless of the MSRP of the vehicle. In measurements of 
the impacts on the average consumer, this assumption will not affect 
the outcome of our calculation, though this assumption will matter for 
any discussions about how many, or which, consumers bear the brunt of 
the additional cost of owning more expensive new vehicles. For sake of 
simplicity, the model also assumes that increasing the cost of new 
vehicles will not change the share of new vehicle MSRP that is 
financed; the relatively constant share from 2011-2016 when the average 
MSRP of a vehicle increased 10% supports this assumption. It is 
recognized that this is not indicative of average individual consumer 
transactions but provides a useful tool to analyze the aggregate 

    \247\ Zabritski, M. State of the Automotive Finance Market: A 
look at loans and leases in Q4 2016, Experian, https://www.experian.com/assets/automotive/quarterly-webinars/2016-Q4-SAFM-revised.pdf (last visited June 22, 2018).

    From Wards Auto data, the average 48- and 60-month new auto 
interest rates were 4.25% in 2016, and the average finance term length 
for new autos was 68 months. It is recognized that longer financing 
terms generally include higher interest rates. The share financed, 
interest rate, and finance term length are added as inputs in the

[[Page 43081]]

parameters file so that they are easier to update in the future. Using 
these inputs the model computes the stream of financing payments paid 
for the average financed purchases as the following:

    Note: The above assumes the interest is distributed evenly over the 
period, when in reality more of the interest is paid during the 
beginning of the term. However, the incremental amount calculated as 
attributable to the standard will represent the difference in the 
annual payments at the time that they are paid, assuming that a 
consumer does not repay early. This will represent the expected change 
in the stream of financing payments at the time of financing.
    The above stream does not equate to the average amount paid to 
finance the purchase of a new vehicle. In order to compute this amount, 
the share of financed transactions at each interest rate and term 
combination would have to be known. Without having projections of the 
full distribution of the auto finance market into the future, the above 
methodology reasonably accounts for the increased amount of financing 
costs due to the purchase of a more expensive vehicle, on an average 
basis taking into account non-financed transactions. Financing payments 
are also assumed to be an intertemporal transfer of wealth for a 
consumer; for this reason, it is not included in the societal cost and 
benefit analysis. However, because it is an additional cost paid by the 
consumer, it is calculated as a part of the private consumer welfare 
    It is recognized that increased finance terms, combined with rising 
interest rates, lead to a longer period of time before a consumer will 
have positive equity in the vehicle to trade in toward the purchase of 
a newer vehicle. This has impacts in terms of consumers either trading 
vehicles with negative equity (thereby increasing the amount financed 
and potentially subjecting the consumer to higher interest rates and/or 
rendering the consumer unable to obtaining financing) or delaying the 
replacement of the vehicle until they achieve suitably positive equity 
to allow for a trade. Comment is sought on the effect these 
developments will have on the new vehicle market, both in general, and 
in light of increased stringency of fuel economy and GHG emission 
standards. Comment is also sought on whether and how the model should 
account for consumer decisions to purchase a used vehicle instead of a 
new vehicle based upon increased new vehicle prices in response to 
increased CAFE standard stringency.
(c) Insurance Costs
    More expensive vehicles will require more expensive collision and 
comprehensive (e.g., fire and theft) car insurance. Actuarially fair 
insurance premiums for these components of value-based insurance will 
be the amount an insurance company will pay out in the case of an 
incident type weighted by the risk of that type of incident occurring. 
It is expected that the same driver in the same vehicle type will have 
the same risk of occurrence for the entirety of a vehicle's life so 
that the share of the value of a vehicle paid out should be constant 
over the life of a vehicle. However, the value of vehicles will decline 
at some depreciation rate so that the absolute amount paid in value-
related insurance will decline as the vehicle depreciates. This is 
represented in the model as the following stream of expected collision 
and comprehensive insurance payments:

    To utilize the above framework, estimates of the share of MSRP paid 
on collision and comprehensive insurance and of annual vehicle 
depreciations are needed to implement the above equation. Wards has 
data on the average annual amount paid by model year for new light 
trucks and passenger cars on collision, comprehensive and damage and 
liability insurance for model years 1992-2003; for model years 2004-
2016, they only offer the total amount paid for insurance premiums. The 
share of total insurance premiums paid for collision and comprehensive 
coverage was computed for 1979-2003. For cars the share ranges from 49 
to 55%, with the share tending to be largest towards the end of the 
series. For trucks the share ranges from 43 to 61%, again, with the 
share increasing towards the end of the series. It is assumed that for 
model years 2004-2016, 60% of insurance premiums for trucks, and 55% 
for cars, is paid for collision and comprehensive. Using these shares 
the absolute amount paid for collision and comprehensive coverage for 
cars and trucks is computed. Then each regulatory class in the fleet is 
weighted by share to estimate the overall average amount paid for 
collision and comprehensive insurance by model year as shown in Table-
II-35. The average share of the initial MSRP paid in collision and 
comprehensive insurance by model year is then computed. The average 
share paid for model years 2010-2016 is 1.83% of the initial MSRP. This 
is used as the share of the value of a new vehicle paid for collision 
and comprehensive in the future.

[[Page 43082]]


    2017 data from Fitch Black Book was used as a source for vehicle 
depreciation rates; two- to six-year-old vehicles in 2016 had an 
average annual depreciation rate of 17.3%.\248\ It is assumed that 
future depreciation rates will be like recent depreciation, and the 
analysis used the same assumed depreciation. Table-II-36 shows the 
cumulative share of the initial MSRP of a vehicle assumed to be paid in 
collision and comprehensive insurance in five-year age increments under 
this depreciation assumption, conditional on a vehicle surviving to 
that age--that is, the expected insurance payments at the time of 
purchase will be weighted by the probability of surviving to that age. 
If a vehicle lives to 10 years, 9.9% of the initial MSRP is expected to 
be paid in collision and comprehensive payments; by 20 years 11.9% of 
the initial MSRP; finally, if a vehicle lives to age 40, 12.4% of the 
initial MSRP. As can be seen, the majority of collision and 
comprehensive payments are paid by the time the vehicle is 10 years 

    \248\ Fitch Ratings Vehicle Depreciation Report February 2017, 
Black Book, http://www.blackbook.com/wp-content/uploads/2017/02/Final-February-Fitch-Report.pdf (last visited June 22, 2018).

    The increase in insurance premiums resulting from an increase in 
the average value of a vehicle is a result of an increase in the 
expected amount insurance companies will have to pay out in the case of 
damage occurring to the driver's vehicle. In this way, it is a cost to 
the private consumer, attributable to the CAFE standard that caused the 
price increase.
(d) Consumer Acceptance of Specific Technologies
    In previous rulemaking analyses, NHTSA imposed an economic cost of 
lost welfare to buyers of advanced electric vehicles. NHTSA chose to 
model a 75-mile EV for early adopters, who we assume would not be 
concerned with the lower range, and a 150-mile EV for the broader 
market. The initial five percent of EV sales were assumed to go to 
early adopters, with the remainder being 150-mile EVs. The broader 
market was assumed to have some lower utility for the 150-mile EV, due 
to the lower driving range between refueling events relative to a 
conventional vehicle. Thus, an additional social cost of about $3,500 
per vehicle was assigned to the EV150 to capture the lost utility to 
consumers.\249\ Additionally, NHTSA imposed a ``relative value loss'' 
of 1.94% of the vehicle's MSRP to reflect the economic value of the 
difference between the useful life of a conventional ICE and the 150-
mile EV when it reaches a 55% battery capacity (as a result of battery 
deteroriation).\250\ In subsequent analyses (the 2016 Draft TAR 
analysis and today's analysis), NHTSA removed the low-range EVs from 
its technology set due to both weak consumer demand for low-range EVs 
in the marketplace and subsequent technology advances that make 200-
mile EVs a more practical option for new EVs produced in future model 
years. The exclusion of low-range EVs in the technology set reduced the 
need to account for consumer welfare losses

[[Page 43083]]

attributable to reduced driving range. While the sensitivity analysis 
explores some potential for continuing consumer value loss, even in the 
improved electrified powertrain vehicles, the central analysis assumes 
that no value loss exists for electrified powertrains. However, ongoing 
low sales volumes and a growing body of literature suggest that 
consumer welfare losses may still exist if manufacturers are forced to 
produce electric vehicles in place of vehicles with internal combustion 
engines (forcing sacrifices to cargo capacity or driving range) in 
order to comply with standards. This topic will receive ongoing 
investigation and revision before the publication of the final rule. 
Please provide comments and any relevant data that would help to inform 
the estimation of implementation of any value loss related to 
sacrificed attributes in electric vehicles.

    \249\ Based on Michael K. Hidrue, George R. Parsons, Willett 
Kempton, Meryl P. Gardner, Willingness to pay for electric vehicles 
and their attributes, Resource and Energy Economics,Volume 33, Issue 
3, 2011, Pages 686-705.
    \250\ The vehicle was assumed to be retired once the capacity 
reached 55 percent of its initial capacity, and the residual 
lifetime miles from that point forward were valued, discounted, and 
expressed as a fraction of initial MSRP.

    One reason it was necessary to account for welfare losses from 
reduced driving range in this way is that, in previous rulemakings, the 
agencies implicitly assumed that every vehicle in the forecast would be 
produced and purchased and that manufacturers would pass on the entire 
incremental cost of fuel-saving technologies to new car (and truck) 
buyers. However, many stakeholders commented that consumers are not 
willing to pay the full incremental costs for hybrids, plug-in hybrids, 
and battery electric vehicles.\251\ For this analysis, consumer 
willingness to pay for HEVs, PHEVs, BEVs relative to comparable ICE 
vehicles was investigated. The analysis compared the estimated price 
premium the electrified vehicles command in the used car market and 
estimated the willingness to pay premium for new vehicles with 
electrification technologies at age zero relative to their internal 
combustion engine counterparts. For the analysis, the willingness to 
pay was compared with the expected incremental cost to produce 
electrification technologies. Manufacturers also contributed 
confidential business information about the costs, revenues, and 
profitability of their electrified vehicle lines. The CBI provided a 
valuable check on the empirical work described below. As a result of 
this examination, we no longer assume manufacturers can pass on the 
entire incremental cost of hybrid, plug-in hybrid, and battery electric 
vehicles to buyers of those vehicles. The difference between the 
buyer's willingness-to-pay for those technologies, and the cost to 
produce them, must be recovered from buyers of other vehicles in a 
manufacturer's product portfolio or sacrificed from its profits, or 
sacrificed from dealership profits, or supplemented with State or 
Federal incentives (or, some combination of the four).

    \251\ See e.g., Comment by Alliance of Automobile Manufacturers, 
Docket ID EPA-HQ-OAR-2015-0827-4089 and NHTSA-2016-0068-0072.

    Using data from the used vehicle market, statistical models were 
fit to estimate consumer willingness to pay for new vehicles with 
varying levels of electrification relative to comparable internal 
combustion engine vehicles was evaluated in four steps. The analysis 
(1) gathered used car fair market value for select vehicles; (2) 
developed regression models to estimate the portion of vehicle 
depreciation rate attributable to the vehicle nameplate and the portion 
attributable to the vehicle's technology content at each age (using 
fixed effects for nameplates and specific electrification 
technologies); (3) estimated the value of vehicles at age zero (i.e., 
when the vehicles were new); and (4) compared new vehicle values for 
comparable vehicles across different electrification levels (i.e., 
internal combustion, HEV, PHEV, and BEV) to estimate willingness-to-pay 
for the electric technology relative to an ICE.
    The dataset used for estimation consisted of vehicle attribute data 
from Edmunds and transaction data from Kelley Blue Book published 
online in June and July of 2017 for select vehicles of interest.\252\ 
\253\ The dataset was constructed to contain pairs of vehicles that 
were nearly the same, except for type of powertrain (internal 
combustion versus some amount of electrification). For instance, the 
dataset contained used vehicle prices for the Honda Accord and Honda 
Accord Hybrid, Toyota Camry and Toyota Camry Hybrid, Ford Fusion and 
Ford Fusion Hybrid, Kia Soul and Kia Soul EV, and so on for several 
model years. In some cases, the manufacturer produced no identically 
equivalent internal combustion engine vehicle, so a similar internal 
combustion vehicle produced by the same manufacturer was used as the 
point of comparison. For example, the Nissan Leaf was paired with the 
Nissan Versa, as well as the Toyota Prius and Toyota Corolla. Only 
vehicles available for private sale, and in good vehicle condition were 
included in the analysis.\254\ The dataset contains fewer observations 
for PHEVs and BEVs because manufacturers have produced fewer examples 
of vehicles with these technologies, compared to HEV and ICE vehicles. 
In all of these cases, trim level and options packages were matched 
between ICE and electric powertrains to minimize the degree of non-
powertrain difference between vehicle pairs. The resale price data 
spanned many model years, but most observations in the dataset 
represent MY 2013 through MY 2016.

    \252\ See Edmunds, https://www.edmunds.com/ (last visited June 
22, 2018). Edmunds publishes automotive data, reviews, and advice.
    \253\ See Kelley Blue Book, https://www.kbb.com/ (last visited 
June 22, 2018). Kelley Blue Book, part of Cox Automotive's 
Autotrader brand, provides automotive research, reviews, and advice, 
including estimated market values of new and used vehicles.
    \254\ It is possible ``good'' vehicles for all ages may have 
inadvertently introduced a small bias in the sample, as a ``good'' 
conditioning rating on a vehicle just a year or two old may not be 
in average condition relative to other vehicles of the vintage, but 
a ``good'' rating for a much older car may reflect an impeccably 
maintained vehicle.

    The regression models used to estimate the transaction price (or 
``Value'') as a function of age, control for the type of powertrain 
(ICE, HEV, PHEV, and BEV) and nameplate to account for their impact on 
the value of the vehicle as it ages.\255\ The regression takes the 
following form, with ICE, HEV, PHEV, and BEV binary variables (0, or 
1), and age defined as 2017 minus the model year was used:

    \255\ In the case of electrified vehicles with no internal 
combustion engine equivalent, the analysis grouped like vehicle 
pairs together under the same nameplate fixed effects (or 
FENameplate). Tesla vehicles have no internal combustion 
engine equivalent, and the used vehicle market for Tesla has not 
cleared in the same way because of a variety of unique business 
factors (previously, Tesla guaranteed resale value prices for their 
products, which was a factory incentive program that only recently 
ended, no longer applying to vehicles sold after July 1, 2016). 
These two factors impaired the quality of used Tesla data for the 
purposes of the analysis, so the agencies excluded Tesla vehicles 
from today's analysis on customer willingness-to-pay for electrified 

1n(Value = ,[beta]1(ICE * Age) + [beta]2(HEV * 
Age) + [beta]3(PHEV * Age) + [beta]4(BEV * Age) + 
[beta]5(HEV) + [beta]6(PHEV) + 
[beta]7(BEV) + FENameplate

    For each observation in the dataset, the ``Value'' at age zero is 
determined by setting the age variable to zero and solving.

[[Page 43084]]

    The estimated willingness-to-pay for electrified powertrain 
packages over an internal combustion engine in an otherwise similar 
vehicle is computed as the difference between their estimated initial 
values, using the functions above. These pair-wise differences are 
averaged to estimate a price premium for new vehicles with HEV, PHEV, 
and BEV technologies. This analysis suggests that consumers are willing 
to pay more for new electrified vehicles than their new internal engine 
combustion counterparts, but only a little more, and not necessarily 
enough to cover the relatively large projected incremental cost to 
produce these vehicles. Specifically, the analysis estimated consumers 
are willing to pay between $2,000 and $3,000 more for the electrified 
powertrains considered here than their internal combustion engine 

    Table-II-37 illustrates the variation in willingness-to-pay by 
electrification level (although the statistical model did not 
distinguish between PHEV30 and PHEV50 due to the small number of 
available operations for plug-in hybrids). As the table demonstrates, 
the difference between the median and mean predicted price premium for 
PHEVs is significant. The limited number of PHEV observations were not 
uniformly distributed among the nameplates present, and some of the 
luxury vehicles in the set retained value in a way that skewed the 
average. The CBI acquired from manufacturers was more consistent with 
the mean than median value (except for the PHEVs).
    Additionally, the Kelley Blue Book data suggest that the used 
electrified vehicles were often worth less than their used internal 
combustion engine counterpart vehicles after a few years of use.\256\ 
As Table-II-38 illustrates, the value of the price premium shrinks as 
the vehicles age and depreciate. Using the statistical model, we 
estimate that strong hybrids hold less than $100 of the initial price 
premium by age eight (on average). While the battery electric vehicles 
appear to be worth less than their ICE counterparts by age eight, there 
is limited data about this emerging segment of the new vehicle market. 
These independently-produced results using publicly available data were 
in line with manufacturers' reported confidential business information.

    \256\ The analysis did not identify an underlying reason for 
this observation, but the agencies posit for discussion purposes 
there could be some interaction between maintenance costs and 
batteries or maintenance costs and low volume vehicles. 
Alternatively, new electrified vehicles may be superior to previous 
generation vehicles, and new electrified vehicles may be offered at 
lower prices still because of a variety of market conditions.

    The ``technology cost burden'' numbers used in today's analysis 
represent the amount of a given technology's incremental cost that 
manufacturers are unable to pass along to the buyer of a given vehicle 
at the time of purchase. The burden is defined as the difference 
between estimated willingness-to-pay, itself a combination of the 
estimated values and confidential business information received from 
manufacturers any tax credits that can be passed through in the price, 
and the cost of the technology. In general, the incremental 
willingness-to-pay falls well short of the costs currently projected 
for HEVs, PHEVs, and BEVs; for example, BEV technology can add roughly 
$18,000 in equipment costs to the vehicle after standard retail price 
equivalent markups (with a large portion of those costs being 
batteries), but the estimated willingness-to-pay is only about $3,000. 
While tax credits offset some, if not most of that difference for PHEVs 
and BEVs, there is some residual amount that buyers of new electrified 
vehicles are currently unwilling to cover, and that must either come 
from forgone profits or be passed

[[Page 43085]]

along to buyers of other vehicles in a manufacturer's portfolio.
    Manufacturers may be able to recover some or all of these costs by 
charging higher prices for their other models, in which case it will 
represent a welfare loss to buyers of other vehicles (even if not to 
buyers of HEVs, PHEVs, or BEVs themselves). To the extent that they are 
unable to do so and must absorb part or all of these costs, their 
profits will decline, and in effect this cost will be borne by their 
investors. In practice, the analysis estimates benefits and costs to 
car and light truck manufacturers and buyers under the assumption that 
each manufacturer recovers all technology costs and civil penalties it 
incurs from buyers via higher average prices for the models it produces 
and sells, although sufficient information to support specific 
assumptions about price increases for individual models is not present. 
In effect, this means that any part of a manufacturer's costs to 
convert specific models to electric drive technologies that it cannot 
recover by charging higher prices to their buyers will be borne 
collectively by buyers of the other models they produce. Each of those 
buyers is in effect assumed to pay a slight premium (or ``markup'') 
over the manufacturer's cost to produce the models they purchase 
(including the cost of any technology used to improve its fuel 
economy), this premium on average is modeled to recover the full cost 
of technology applied to all vehicles to improve the fuel economy of 
the fleet. So, even though electrified vehicles are modeled as if their 
buyers are unwilling to pay the full cost of the technology associated 
with their fuel economy improvement, the price borne by the average new 
vehicle buyer represents the average incremental technology cost for 
all applied technology, the sum of all technology costs divided by the 
number of units sold, across all classes, for each manufacturer.
    The willingness-to-pay analysis described above relies on used 
vehicle data that is widely available to the public. Market tracking 
services update used vehicle price estimates regularly as fuel prices 
and other market conditions change, making the data easy to update in 
the future as market conditions change. The used vehicle data also 
account for consumer willingness-to-pay absent State and Federal 
rebates at the time of sale, which are reflected in both the initial 
purchase price of the vehicle and its later value in the used vehicle 
market. As such, the analysis would continue to be relevant even if 
incentive programs for vehicle electrification change or phase out in 
the future. By considering a variety of nameplates and body styles 
produced by several manufacturers, this analysis produces average 
willingness-to-pay estimates that can be applied to the whole industry. 
By evaluating matched pairs of vehicles from the same manufacturer, the 
analysis accounts for many additional factors that may be tied to the 
brand, rather than the technology, and influence the fair market price 
of vehicles. In particular, the data inherently include customer 
valuations for fuel-savings and vehicle maintenance schedules, as well 
as other factors like noise-vibration-and-harshness, interior 
space,\257\ and fueling convenience in the context of the vehicles 

    \257\ Often HEVs and PHEVs place batteries in functional storage 
space, such as the trunk or floor storage bins, thereby forcing 
consumers to trade-off fuel-savings with other functional vehicle 

    There are some limitations to this approach. There are currently 
few observations of PHEV and BEV technologies in the data, and most of 
the observations for BEVs are sedans and small cars, the values for 
which are extrapolated to other market segments. Additionally, the used 
vehicle data supporting these estimates inherently includes both older 
and newer generations of technology, so the historical regression may 
be slow to react to rapid changes in the new vehicle marketplace. As 
new vehicle nameplates emerge, and existing nameplates improve their 
implementation of electrification technologies, this model will require 
re-estimation to determine how these new entrants impact the estimated 
industry average willingness-to-pay.
    Additionally, the willingness-to-pay analysis does not consider 
electric vehicles with no direct ICE counterpart. For example, today's 
evaluation does not consider Tesla because the Tesla brand has no ICE 
equivalent, and because the free-market prices for used Tesla vehicles 
have been difficult (if not impossible) to obtain, primarily due to 
factory guaranteed resale values (which is a program that still affects 
the used market for many Tesla vehicles). Still, Tesla vehicles have a 
large share of the BEV market by both unit sales and dollar sales, it 
may be possible to include Tesla data in a future update to this 
analysis. Similarly, the analysis did not include ICE vehicles with no 
similar HEV, PHEV, or BEV nameplate or counterpart, so the analysis 
presented here looks at a small portion of all transactions and is more 
likely to include fuel efficient models where market demand for hybrid 
(or higher) versions may exist. One possible alternative is to rely on 
new vehicle transaction prices to estimate consumer willingness-to-pay 
for new vehicles with certain attributes. However, new vehicle 
transaction data is highly proprietary and difficult to obtain in a 
form that may be disclosed to the public.
    While estimating willingness-to-pay for electrification 
technologies from depreciation and MSRP data is appealing, many 
manufacturers handle MSRP and pricing strategies differently, with some 
preferring to deviate only a little from sticker price and others 
preferring to offer high discounts. There is evidence of large 
differences between MSRP and effective market prices to consumers for 
many vehicles, especially BEVs.
    Please provide comments on methods and data used to evaluate 
consumer willingness-to-pay for electrification technologies.

(e) Refueling Surplus

    Direct estimates of the value of extended vehicle range are not 
available in the literature, so the reduction in the required annual 
number of refueling cycles due to improved fuel economy was calculated 
and the economic value of the resulting benefits assessed. Chief among 
these benefits is the time that owners save by spending less time both 
in search of fueling stations and in the act of pumping and paying for 
    The economic value of refueling time savings was calculated by 
applying DOT-recommended valuations for travel time savings to 
estimates of how much time is saved.\258\ The value of travel time 
depends on average hourly valuations of personal and business time, 
which are functions of total hourly compensation costs to employers. 
The total hourly compensation cost to employers, inclusive of benefits, 
in 2010$ is $29.68.\259\ Table-II-39 below demonstrates the approach to 
estimating the value of travel time ($/hour) for both urban and rural 
(intercity) driving. This approach relies on the use of DOT-recommended 
weights that assign a lesser valuation to personal travel time than to 
business travel time, as well as

[[Page 43086]]

weights that adjust for the distribution between personal and business 

    \258\ See https://www.transportation.gov/sites/dot.gov/files/docs/ValueofTravelTimeMemorandum.pdf (last accessed July 3, 2018).
    \259\ Total hourly employer compensation costs for 2010 (average 
of quarterly observations across all occupations for all civilians). 
See https://www.bls.gov/ncs/ect/tables.htm (last accessed July 3, 

    The estimates of the hourly value of urban and rural travel time 
($15.67 and $21.93, respectively) shown in Table-II-39 above must be 
adjusted to account for the nationwide ratio of urban to rural driving. 
By applying this adjustment (as shown in Table-II-40 below), an overall 
estimate of the hourly value of travel time--independent of urban or 
rural status--may be produced.

    \260\ Time spent on personal travel during rural (intercity) 
travel is valued at a greater rate than that of urban travel. There 
are several reasons behind the divergence in these values: (1) Time 
is scarcer on a long trip; (2) a long trip involves complementary 
expenditures on travel, lodging, food, and entertainment because 
time at the destination is worth such high costs.

    Note: The calculations above assume only one adult occupant per 
vehicle. To fully estimate the average value of vehicle travel time, 
the presence of additional adult passengers during refueling trips 
must be accounted for. The analysis applies such an adjustment as 
shown in Table-II-40; this adjustment is performed separately for 
passenger cars and for light trucks, yielding occupancy-adjusted 
valuations of vehicle travel time during refueling trips for each 

    Note: Children (persons under age 16) are excluded from average 
vehicle occupancy counts, as it is assumed that the opportunity cost 
of children's time is zero.

[[Page 43087]]


    The analysis estimated the amount of refueling time saved using 
(preliminary) survey data gathered as part of our 2010-2011 National 
Automotive Sampling System's Tire Pressure Monitoring System (TPMS) 
study.\263\ The study was conducted at fueling stations nationwide, and 
researchers made observations regarding a variety of characteristics of 
thousands of individual fueling station visits from August 2010 through 
April 2011.\264\ Among these characteristics of fueling station visits 
is the total amount of time spent pumping and paying for fuel. From a 
separate sample (also part of the TPMS study), researchers conducted 
interviews at the pump to gauge the distances that drivers travel in 
transit to and from fueling stations, how long that transit takes, and 
how many gallons of fuel are being purchased.

    \261\ See Travel Monitoring, Traffic Volume Trends, U.S. 
Department of Transportation Federal Highway Administration, https://www.fhwa.dot.gov/policyinformation/travel_monitoring/tvt.cfm (last 
visited June 22, 2018). Weights used for urban versus rural travel 
are computed using cumulative 2011 estimates of urban versus rural 
miles driven provided by the Federal Highway Administration.
    \262\ Source: National Automotive Sampling System 2010-2011 Tire 
Pressure Monitoring System (TPMS) study. See next page for further 
background on the TPMS study. TPMS data are preliminary at this 
time, and rates are subject to change pending availability of 
finalized TPMS data. Average occupancy rates shown here are specific 
to refueling trips and do not include children under 16 years of 
    \263\ TPMS data are preliminary and not yet published. Estimates 
derived from TPMS data are therefore preliminary and subject to 
change. Observational and interview data are from distinct 
subsamples, each consisting of approximately 7,000 vehicles. For 
more information on the National Automotive Sampling System and to 
access TPMS data when they are made available, see http://www.nhtsa.gov/NASS.
    \264\ The data collection period for the TPMS study ranged from 
October 10, 2010, through April 15, 2011.

    This analysis of refueling benefits considers only those refueling 
trips which interview respondents indicated the primary reason was due 
to a low reading on the gas gauge.\265\ This restriction was imposed so 
as to exclude drivers who refuel on a fixed (e.g., weekly) schedule and 
may be unlikely to alter refueling patterns as a result of increased 
driving range. The relevant TPMS survey data on average refueling trip 
characteristics are presented below in Table-II-41.

    \265\ Approximately 60% of respondents indicated ``gas tank 
low'' as the primary reason for the refueling trip in question.

    As an illustration of how the value of extended refueling range was 
estimated, assume a small light truck model has an average fuel tank 
size of approximately 20 gallons and a baseline actual on-road fuel 
economy of 24 mpg (its assumed level in the absence of a higher CAFE 
standard for the given model year). TPMS survey data indicate that 
drivers who indicated the primary reason for their refueling trips was 
a low reading on the gas gauge typically refuel when their tanks are 
35% full (i.e. as shown in Table-II-41, with 7.0 gallons in reserve, 
and the consumer purchases 13 gallons). By this measure, a typical 
driver would have an effective driving range of 312 miles (= 13.0 
gallons x 24

[[Page 43088]]

mpg) before he or she is likely to refuel. Increasing this model's 
actual on-road fuel economy from 24 to 25 mpg would therefore extend 
its effective driving range to 325 miles (= 13.0 gallons x 25 mpg). 
Assuming that the truck is driven 12,000 miles/year,\266\ this one mpg 
improvement in actual on-road fuel economy reduces the expected number 
of refueling trips per year from 38.5 (= 12,000 miles per year/312 
miles per refueling) to 36.9 (= 12,000 miles per year/325 miles per 
refueling), or by 1.6 refuelings per year. If a typical fueling cycle 
for a light truck requires a total of 6.83 minutes, then the annual 
value of time saved due to that one mpg improvement would amount to 
$3.97 (= (6.83/60) x $21.81 x 1.6).

    \266\ 2009 National Household Travel Survey (NHTS), U.S 
Department of Transportation Federal Highway Administration at 48 
(June 2011), available at http://nhts.ornl.gov/2009/pub/stt.pdf. 
12,000 miles/year is an approximation of a light duty vehicle's 
annual mileage during its initial decade of use (the period in which 
the bulk of benefits are realized). The CAFE model estimates VMT by 
model year and vehicle age, taking into account the rebound effect, 
secular growth rates in VMT, and fleet survivability; these 
complexities are omitted in the above example for simplicity.

    In the central analysis, this calculation was repeated for each 
future calendar year that light-duty vehicles of each model year 
affected by the standards considered in this rule would remain in 
service. The resulting cumulative lifetime valuations of time savings 
account for both the reduction over time in the number of vehicles of a 
given model year that remain in service and the reduction in the number 
of miles (VMT) driven by those that stay in service. The analysis also 
adjusts the value of time savings that will occur in future years both 
to account for expected annual growth in real wages \267\ and to apply 
a discount rate to determine the net present value of time saved.\268\ 
A further adjustment is made to account for evidence from the 
interview-based portion of the TPMS study which suggests that 40% of 
refueling trips are for reasons other than a low reading on the gas 
gauge. It is therefore assumed that only 60% of the theoretical 
refueling time savings will be realized, as it was assumed that owners 
who refuel on a fixed schedule will continue to do. Based on peer 
reviewer comments to NHTSA's initial implementation of refueling time 
savings (subsequent to the CAFE NPRM issued in 2011), the analysis of 
refueling time savings was updated for the final rule to reflect peer 
reviewer suggestions.\269\ Beyond updating time values to current 
dollars, that analysis has been used, unchanged, in today's analysis as 

    \267\ See The Economics Daily, The compensation-productivity 
gap, U.S. Department of Labor Bureau of Labor Statistics (Feb. 24, 
2011), http://www.bls.gov/opub/ted/2011/ted_20110224.htm. A 1.1% 
annual rate of growth in real wages is used to adjust the value of 
travel time per vehicle ($/hour) for future years for which a given 
model is expected to remain in service. This rate is supported by a 
BLS analysis of growth in real wages from 2000-2009.
    \268\ Note: Here, as elsewhere in the analysis, discounting is 
applied on an annual basis from CY 2017.
    \269\ Peer review materials, peer reviewer backgrounds, 
comments, and NHTSA responses for this prior assessment are 
available at Docket NHTSA-2012-0001.

    Because a reduction in the expected number of annual refueling 
trips leads to a decrease in miles driven to and from fueling stations, 
the value of consumers' fuel savings associated with this decrease can 
also be calculated. As shown in Table-II-41, the typical incremental 
round-trip mileage per refueling cycle is 1.08 miles for light trucks 
and 0.97 miles for passenger cars. Going back to the earlier example of 
a light truck model, a decrease of 1.6 in the number of refuelings per 
year leads to a reduction of 1.73 miles driven per year (= 1.6 
refuelings x 1.08 miles driven per refueling). Again, if this model's 
actual on-road fuel economy was 24 mpg, the reduction in miles driven 
yields an annual savings of approximately 0.07 gallons of fuel (= 1.73 
miles/24 mpg), which at $3.25/gallon \270\ results in a savings of 
$0.23 per year to the owner.

    Note: This example is illustrative only of the approach used to 
quantify this benefit. In practice, the societal value of this 
benefit excludes fuel taxes (as they are transfer payments) from the 
calculation and is modeled using fuel price forecasts specific to 
each year the given fleet will remain in service.

    \270\ Estimate of $3.25/gallon is the forecasted cost per gallon 
(including taxes, as individual consumers consider reduced tax 
expenditures to be savings) for motor gasoline in 2025. Source of 
price projections: U.S. Energy Information Administration, Annual 
Energy Outlook Early 2018.

    The annual savings to each consumer shown in the above example may 
seem like a small amount, but the reader should recognize that the 
valuation of the cumulative lifetime benefit of this savings to owners 
is determined separately for passenger car and light truck fleets and 
then aggregated to show the net benefit across all light-duty vehicles, 
which is much more significant at the macro level. Calculations of 
benefits realized in future years are adjusted for expected real growth 
in the price of gasoline, for the decline in the number of vehicles of 
a given model year that remain in service as they age, for the decrease 
in the number of miles (VMT) driven by those that stay in service, and 
for the percentage of refueling trips that occur for reasons other than 
a low reading on the gas gauge; a discount rate is also applied in the 
valuation of future benefits. Using this direct estimation approach to 
quantify the value of this benefit by model year was considered; 
however, it was concluded that the value of this benefit is implicitly 
captured in the separate measure of overall valuation of fuel savings. 
Therefore, direct estimates of this benefit are not added to net 
benefits calculations. It is noted that there are other benefits 
resulting from the reduction in miles driven to and from fueling 
stations, such as a reduction in greenhouse gas emissions--
CO2 in particular--which, as per the case of fuel savings 
discussed in the preceding paragraph, are implicitly accounted for 
    Special mention must be made with regard to the value of refueling 
time savings benefits to owners of electric and plug-in electric (both 
referred to here as EV) vehicles. EV owners who routinely drive daily 
distances that do not require recharging on-the-go may eliminate the 
need for trips to fueling or charging stations. It is likely that early 
adopters of EVs will factor this benefit into their purchasing 
decisions and maintain driving patterns that require once-daily at-home 
recharging (a process which generally takes five to eleven hours for a 
full charge) \271\ for those EV owners who have purchased and installed 
a Level Two charging station to a high-voltage outlet at their home or 
parking place. However, EV owners who regularly or periodically need to 
drive distances further than the fully-charged EV range may need to 
recharge at fixed locations. A distributed network of charging stations 
(e.g., in parking lots, at parking meters) may allow some EV owners to 
recharge their vehicles while at work or while shopping, yet the 
lengthy charging cycles of current charging technology may pose a cost 
to owners due to the value of time spent waiting for EVs to charge and 
potential EV shoppers who do not have access to charging at home (e.g., 
because they live in an apartment without a vehicle charging station, 

[[Page 43089]]

have street parking, or have garages with insufficient voltage). 
Moreover, EV owners who primarily recharge their vehicles at home will 
still experience some level of inconvenience due to their vehicle being 
either unavailable for unplanned use or to its range being limited 
during this time should they interrupt the charging process. Therefore, 
at present EVs hold potential in offering significant time savings but 
only to owners with driving patterns optimally suited for EV 
characteristics. If fast-charging technologies emerge and a widespread 
network of fast-charging stations is established, it is expected that a 
larger segment of EV vehicle owners will fully realize the potential 
refueling time savings benefits that EVs offer. This is an area of 
significant uncertainty.

    \271\ See generally All-New Nissan Leaf Range & Charging, Nissan 
USA, https://www.nissanusa.com/vehicles/electric-cars/leaf/range-charging.html (last visited June 22, 2018); Home Charging 
Calculator, Tesla, https://www.tesla.com/support/home-charging-calculator (last visited June 22, 2018); 2018 Chevrolet Bolt EV, GM, 
https://media.gm.com/content/media/us/en/chevrolet/vehicles/bolt-ev/2018/_jcr_content/iconrow/textfile/file.res/2018-Chevrolet-Bolt-EV-Product-Guide.pdf (last visited June 22, 2018).

6. Vehicle Use and Survival
    To properly account for the average value of consumer and societal 
costs and benefits associated with vehicle usage under various CAFE and 
GHG alternatives, it is necessary to estimate the portion of these 
costs and benefits that will occur at each age (or calendar year) for 
each model year cohort. Doing so requires some estimate of how many 
miles the average vehicle of a given type \272\ is expected to drive at 
each age and what share of the initial model year cohort is expected to 
remain at each age. The first estimates are referred to as the vehicle 
miles travelled (VMT) schedules and the second as the survival rate 
schedules. In this section the data sources and general methodologies 
used to develop these two essential inputs are briefly discussed. More 
complete discussions of the development of both the VMT schedules and 
the survival rate schedules are present in the PRIA Chapter 8.

    \272\ Type here refers to the following body styles: Pickups, 
vans/SUVs, and other cars.

(a) Updates to Vehicle Miles Traveled Schedules Since 2012 FR
    The MY 2017-2021 FRM built estimates of average lifetime mileage 
accumulation by body style and age using the 2009 National Household 
Travel Survey (NHTS), which surveys odometer readings of the vehicles 
present from the approximately 113,000 households sampled. 
Approximately 210,000 vehicles were in the sample of self-reported 
odometer readings collected between April 2008 and April 2009. This 
represents a sample size of less than one percent of the more than 250 
million light-duty vehicles registered in 2008 and 2009. The NHTS 
sample is now 10 years old and taken during the Great Recession. The 
2017 NHTS was not available at the time of this rulemaking. Because of 
the age of the last available NHTS and the unusual economic conditions 
under which it was collected, NHTSA built the new schedule using a 
similar method from a proprietary dataset collected in the fall of 
2015. This new data source has the advantages of both being newer, a 
larger sample, and collected by a third party.
(1) Data Sources and Estimation (Polk Odometer Data)
    To develop new mileage accumulation schedules for vehicles 
regulated under the CAFE program (classes 1-3), NHTSA purchased a data 
set of vehicle odometer readings from IHS/Polk (Polk). Polk collects 
odometer readings from registered vehicles when they encounter 
maintenance facilities, state inspection programs, or interactions with 
dealerships and OEMs--these readings are more likely to be precise than 
the self-reported odometer readings collected in the NHTS. The average 
odometer readings in the data set NHTSA purchased are based on more 
than 74 million unique odometer readings across 16 model years (2000-
2015) and vehicle classes present in the data purchase (all registered 
vehicles less than 14,000 lbs. GVW). This sample represents 
approximately 28% of the light-duty vehicles registered in 2015, and 
thus has the benefit of not only being a newer, but also, a larger, 
    Comparably to the NHTS, the Polk data provide a measure of the 
cumulative lifetime vehicle miles traveled (VMT) for vehicles, at the 
time of measurement, aggregated by the following parameters: Make, 
model, model year, fuel type, drive type, door count, and ownership 
type (commercial or personal). Within each of these subcategories they 
provide the average odometer reading, the number of odometer readings 
in the sample from which Polk calculated the averages, and the total 
number of that subcategory of vehicles in operation.
    In estimating the VMT models, each data point was weighted (make/
model classification) by the share of each make/model in the total 
population of the corresponding vehicle body style. This weighting 
ensures that the predicted odometer readings, by body style and model 
year, represent each vehicle classification among observed vehicles 
(i.e., the vehicles for which Polk has odometer readings), based on 
each vehicles' representation in the registered vehicle population of 
its body style. Implicit in this weighting scheme is the assumption 
that the samples used to calculate each average odometer reading by 
make, model, and model year are representative of the total population 
of vehicles of that type. Several indicators suggest that this is a 
reasonable assumption.
    First, the majority of vehicle make/models is well-represented in 
the sample. For more than 85% of make/model combinations, the average 
odometer readings are collected for 20% or more of the total 
population. Most make/model observations have sufficient sample sizes, 
relative to their representation in the vehicle population, to produce 
meaningful average odometer totals at that level. Second, we considered 
whether the representativeness of the odometer sample varies by vehicle 
age because VMT schedules in the CAFE model are specific to each age. 
It is possible that, for some of those models, an insufficient number 
of odometer readings is recorded to create an average that is likely to 
be representative of all of those models in operation for a given year. 
For all model years other than 2015, approximately 95% or more of 
vehicles types are represented by at least five percent of their 
population. For this reason, observations from all model years, other 
than 2015, were included in the estimation of the new VMT schedules.
    Because model years are sold in in the Fall of the previous 
calendar year, throughout the same calendar year, and even into the 
following calendar year--not all registered vehicles of a make/model/
model year will have been registered for at least a year (or more) 
until age three. The result is that some MY 2014 vehicles may have been 
driven for longer than one year, and some less, at the time the 
odometer was observed. In order to consider this in the definition of 
age, an age of a vehicle is assigned to be the difference between the 
average reading date of a make/model and the average first registration 
date of that make/model. The result is that the continuous age variable 
reflects the amount of time that a car has been registered at the time 
of odometer reading and presumably the time span that the car has 
accumulated the miles.
    After creating the ``age'' variable, the analysis fits the make/
model lifetime VMT data points to a weighted quartic polynomial 
regression of the age of the vehicle (stratified by vehicle body 
styles). The predicted values of the quartic regressions are used to 
calculate the marginal annual VMT by age for each body style by 
calculating differences in estimated lifetime mileage accumulation by 
age. However, the Polk data acquired by NHTSA only contains

[[Page 43090]]

observations for vehicles newer than 16 years of age. In order to 
estimate the schedule for vehicles older than the age 15 vehicles in 
the Polk data, information about that portion of the schedule from the 
VMT schedules used in both the 2017-2021 Final Light Duty Rule and 
2019-2025 Medium-Duty NPRM was combined. The light-duty schedules were 
derived from the survey data contained in the 2009 National Household 
Travel Survey (NHTS).
    From the old schedules, the annual VMT is expected to be decreasing 
for all ages. Towards the end of the sample, the predictions for annual 
VMT increase. In order to force the expected monotonicity, a triangular 
smoothing algorithm is performed until the schedule is monotonic. This 
performs a weighted average which weights the observations close to the 
observation more than those farther from it. The result is a monotonic 
function, that predicts similar lifetime VMT for the sample span as the 
original function. Because the analysis does not have data beyond 15 
years of age, it is not able to correctly capture that part of the 
annual VMT curve using only the new dataset. For this reason, trends in 
the old data to extrapolate the new schedule for ages beyond the sample 
range are used.
    To use the VMT information from the newer data source for ages 
outside of the sample, final in-sample age (15 years) are used as a 
seed and then applied to the proportional trend from the old schedules 
to extrapolate the new schedules out to age 40. To do this, the annual 
percentage difference in VMT of the old schedule for ages 15-40 is 
calculated. The same annual percentage difference in VMT is applied to 
the new schedule to extend beyond the final in-sample value. This 
assumes that the overall proportional trend in the outer years is 
correctly modeled in the old VMT schedule and imposes this same trend 
for the outer years of the new schedule. The extrapolated schedules are 
the final input for the VMT schedules in the CAFE model. PRIA Chapter 8 
contains a lengthier discussion of both the data source and the 
methodology used to create the new schedules.
(2) Using New Schedules in the CAFE Model/Analysis
    While the Polk registration data set contains odometer readings for 
individual vehicles, the CAFE model tabulates ``mileage accumulation'' 
schedules, which relate average annual miles driven to vehicle age, 
based on vehicles' body style. For the purposes of VMT accounting, the 
CAFE model classifies vehicles in the analysis fleet as being one of 
the following: Passenger car, SUV, pickup truck, passenger van, or 
medium-duty pickup/van.\273\ In order to use the Polk data to develop 
VMT schedules for each of these vehicle classes in the CAFE model, a 
mapping between the classification of each model in the Polk data and 
the classes in the CAFE model was first constructed. This mapping 
enabled separate tabulations of average annual miles driven at each age 
for each of the vehicle classes included in the CAFE model.

    \273\ Though not included in today's analysis, corresponding 
schedules for heavy-duty pickups and vans were developed using the 
same methodology.

    The only revision made to the mappings used to construct the new 
VMT schedules was to merge the SUV and passenger van body styles into a 
single class. These body styles were merged because there were very few 
examples of vans--only 38 models were in use during 2014, where every 
other body style had at least three times as many models. Further, as 
shown in the PRIA Chapter 8, there was not a significant difference 
between the 2009 NHTS van and SUV mileage schedules, nor was there a 
significant difference between the schedules built with the two body 
styles merged or kept separate using the 2015 Polk data. Merging these 
body styles does not change the workings of the CAFE model in any way, 
and the merged schedule is simply entered as an input for both vans and 
    Although there is a single VMT by age schedule used as an input for 
each body style, the assumptions about the rebound effect require that 
this schedule be scaled for future analysis years to reflect changes in 
the cost of travel from the time the Polk sample was originally 
collected. These changes result from both changes in fuel prices 
between the time the sample was collected and any future analysis year 
and differences in fuel economy between the vehicles included in the 
sample used to build the mileage schedules and the future-year vehicles 
analyzed within the CAFE Model simulation.
    As discussed in Section 0, recent literature supports a 20% 
``rebound effect'' for light-duty vehicle use, which represents an 
elasticity of annual use with respect to fuel cost per mile of -0.2. 
Because fuel cost per mile is calculated as fuel price per gallon 
divided by fuel economy (in miles per gallon), this same elasticity 
applies to changes in fuel cost per mile that result from variation in 
fuel prices or differences in fuel economy. It suggests that a five 
percent reduction in the cost per mile of travel for vehicles of a 
certain body style will result in a one percent increase in the average 
number of miles they are driven annually.
    The average cost per mile (CPM) of a vehicle of a given age and 
vehicle style in CY 2016 (the first analysis year of the simulation) 
was used as the reference point to calculate the rebound effect within 
the CAFE model. However, this does not perfectly align with the time of 
the collection of the Polk dataset. The Polk data were collected in 
2015 (so that 2014 fuel prices were the last to influence sampled 
vehicles' odometer readings), and represents the average odometer 
reading at a single point in time for age (model year) included in the 
cross-section. We use the difference in the average odometer reading 
for each vintage during 2014 to calculate the number of miles vehicles 
are driven at each age (see PRIA Chapter 8 for specific details on the 
analysis). For example, we interpret the difference in the average 
odometer reading between the five- and six-year-old vehicles of a given 
body style as the average number of miles they are driven during the 
year when they were five years old. However, vehicles produced during 
different model years do not have the same average fuel economy, so it 
is important to consider the average fuel economy of each vintage (or 
model year) used to measure mileage accumulation at a given age when 
scaling VMT for the rebound calculation.
    The first step in doing so is to adjust for any change in average 
annual use that would have been caused by differences in fuel prices 
between CYs 2014 and 2016. This is done by scaling the original 
schedules of annual VMT by age tabulated from the Polk sample using the 
following equation:

[[Page 43091]]


    Here, the average fuel economy for vehicles of a given body style 
and age refers to a different MY in 2016 than it did in 2014; for 
example, a MY 2014 vehicle had reached age two vehicle during CY 2016, 
whereas a 2012 model year vehicle was age two during CY 2014.
    To estimate the average annual use of vehicles of a specified body 
type and age during future calendar years under a specific regulatory 
alternative, the CAFE model adjusts the resulting estimates of vehicle 
use by age for that body type during CY 2016 to reflect (1) the 
projected change in fuel prices from 2016 to each future calendar year; 
and (2) the difference between the average fuel economy for vehicles of 
that body type and age during a future calendar year and the average 
fuel economy for vehicles of that same body type and age during 2016. 
These two factors combine to determine the average fuel cost per mile 
for vehicles of that body type and age during each future calendar year 
and the average fuel cost per mile for vehicles of that same body type 
and age during 2016.
    The elasticity of annual vehicle use with respect to fuel cost per 
mile is applied to the difference between these two values because 
vehicle use is assumed to respond identically to differences in fuel 
cost per mile that result from changes in fuel prices or from 
differences in fuel economy. The model then repeats this calculation 
for each calendar year during the lifetimes of vehicles of other body 
types, and subsequently repeats this entire set of calculations for 
each regulatory alternative under consideration. The resulting 
differences in average annual use of vehicles of each body type at each 
age interact with the number estimated to remain in use at that age to 
determine total annual VMT by vehicles of each body type.
    This adjustment is defined by the equation below:
    This equation uses the observed cost per mile of a vehicle of each 
age and style in CY 2016 as the reference point for all future calendar 
years. That is, the reference fuel price is fixed at 2016 levels, and 
the reference fuel economy of vehicles of each age is fixed to the 
average fuel economy of the vintage that had reached that age in 2016. 
For example, the reference CPM for a one-year-old SUV is always the CPM 
of the average MY 2015 SUV in CY 2016, and the CPM for a two-year-old 
SUV is always the CPM of the average MYv2014 SUV in CY 2016.
    This referencing ensures that the model's estimates of annual 
mileage accumulation for future calendar years reflect differences in 
the CPM of vehicles of each given type and age relative to CPM 
resulting from the average fuel economy of vehicles of that type and 
age and observed fuel prices during the year when the mileage 
accumulation schedules were originally measured. This is consistent 
with a definition of the rebound effect as the elasticity of annual 
vehicle use with respect to changes in the fuel cost per mile of 
travel, regardless of the source of changes in fuel cost per mile. 
Alternative forms of referencing are possible, but none can guarantee 
that projected future vehicle use will respond to both projected 
changes in fuel prices and differences in individual models' fuel 
economy among regulatory alternatives.
    The mileage estimates described above are a crucial input in the 
CAFE model's calculation of fuel consumption and savings, energy 
security benefits, consumer surplus from cheaper travel, recovered 
refueling time, tailpipe emissions, and changes in crashes, fatalities, 
noise and congestion.
(3) Comparison to other VMT projections (2012 FR, AEO average lifetime 
miles, totals?)
    Across all body styles and ages, the previous VMT schedules 
estimate higher average annual VMT than the updated schedules. Table-
II--42 compares the lifetime VMT under the 2009 NHTS and the 2015 Polk 
dataset. The 40-year lifetime VMT gives the

[[Page 43092]]

expected lifetime VMT of a vehicle conditional on surviving to age 40. 
The new schedules predict between 24 and 31% fewer miles for a 40-year 
old vehicle depending on the body style. The new schedules predict that 
the average 40-year old vehicle will drive between approximately 260k 
and 280k miles depending on the body style versus between approximately 
350k and 380k for the previous schedules.
    The static survival-weighted lifetime VMT represents the expected 
number of miles the average vehicle of each body style will drive, 
weighting by the likelihood it survives to each age using the previous 
static scrappage schedules. The dynamic survival-weighted lifetime VMT 
represents the expected number of miles driven by each body style, 
weighting by the dynamic survival schedules under baseline 
assumptions.\274\ There is a similar proportional reduction in expected 
lifetime VMT under both survival assumptions, with the dynamic 
scrappage model predicting lifetime mileage accumulation within 10,000 
miles of the previous static model under both VMT schedules. The 
expected lifetime mileage accumulation reduces between 13 and 15% under 
the current VMT schedules when compared to the previous schedules--a 
smaller proportional reduction than the unweighted lifetime 
assumptions. Using the updated schedules, the expected lifetime mileage 
accumulation is between approximately 150k and 170k miles depending on 
the body style, rather than the approximately 180k to 210k miles under 
the previous schedules. For more detail on when the mileage and 
survival rates occur, chapter 8 of the PRIA gives the full VMT 
schedules by age. The section below gives further estimates of how 
lifetime VMT estimates vary under different assumptions within the 
dynamic scrappage model.

    We have several reasons for preferring the new VMT schedules over 
the prior iterations. Before discussing these reasons, it is important 
to note that NHTSA uses the same general methodology in developing both 
schedules. We consider data on average odometer readings by age and 
body style collected once during a given window of time; we then 
estimate a weighted polynomial function between vehicle age and 
lifetime accumulation for a given vehicle style. As with the previous 
schedules, we use the inter-annual differences as the estimate of 
annual miles traveled for a given age.

    \274\ In estimating the dynamic survival rate to weight the 
annual VMT schedules, we make the following input assumptions: The 
reference vehicle is MY 2016, GDP growth rates and fuel prices are 
our central estimates, and the future average new vehicle fuel 
economies by body style and overall average new vehicle prices are 
those simulated by the CAFE model when CAFE standards are omitted 
(by setting standards at 1 mpg), such that only technologies that 
pay back within 30 months are applied.

    The primary advantage of the current schedules is the data source. 
The previous schedules are based on data that is outdated and self-
reported, while the observations from Polk are between five and seven 
years newer than those in the NHTS and represent valid odometer 
readings (rather than self-reported information). Further, the 2009 
NHTS represents approximately one percent of the sample of vehicles 
registered in 2008/2009, while the 2015 Polk dataset represents 
approximately 30% of all registered light-duty vehicles; it is a much 
larger dataset, and less likely to oversample certain vehicles. 
Additionally, while the NHTS may be a representative sample of 
households, it is less likely to be a representative sample of 
vehicles. However, by properly accounting for vehicle population 
weights in the new averages and models, we corrected for this issue in 
the derivation of the new schedules.
    Importantly, this methodology treats the cross-section of ages in a 
single calendar year as a panel of the same model year vehicle, when in 
reality each age represents a single model year, and not a true panel. 
We have some concern that where the most heavily driven vehicles drop 
out of the sample that the lifetime odometer readings will be lower 
than they would be if the scrapped vehicles had been left in the 
dataset without additional mileage accumulation. This would bias our 
estimates of inter-annual mileage accumulation downward and may result 
in an undervaluation of costs and benefits associated with additional 
travel for vehicles of older ages. For the next VMT schedule iteration, 
NHTSA intends to use panel data to test the magnitude of any attrition 
effect that may exist. While this caveat is important, all previous 
iterations were also built from a single calendar year cross-section 
and contain the same inherent bias.
(b) How does CAFE affect vehicle retirement rates?
    Lightly used vehicles are a close substitute for new vehicles; 
thus, there is relationship between the two markets. As the price for 
new vehicles increases, there is an upward shift in the demand for used 
vehicles. As a result of the upward shift in the demand curve, the 
equilibrium price and quantity of used vehicles both increase; the 
value of used vehicles increases as a result. The decision to scrap or 
maintain a used vehicle is closely linked with the value of the 
vehicle; when the value is lesser than the cost to maintain the 
vehicle, it will be scrapped. In general, as a result of new vehicle 
price increases, the scrappage rate, or the proportion of vehicles 
remaining on the road unregistered in a given year, of used vehicles 
will decline. Because older vehicles are on average less efficient and 
less safe, this will have important implications for the evaluations of 

[[Page 43093]]

and benefits of fuel economy standards, which increase the cost of new 
vehicles and reduce the average cost per mile of fuel costs.
    Fuel economy standards result in the application of more fuel 
saving technologies for at least some models, which result in a higher 
cost for manufacturers to produce otherwise identical vehicles. This 
increase in production cost amounts to an upward shift in the supply 
curve for new vehicles. This increases the equilibrium price and 
reduces the quantity of vehicles demanded. While the cost of new 
vehicles increases under increased fuel economy standards, the fuel 
cost per mile of travel declines. Consumers will place some value on 
the fuel savings associated with the additional technology, to the 
extent that they value reduced operating expenses against the increased 
price of a new vehicle, increased financing costs (and impediments to 
obtaining financing), and increased insurance costs.
    There is a trade-off between fuel economy and other attributes that 
consumers value such as: Vehicle performance, interior volume, etc. 
Where the additional value of fuel savings associated with a technology 
is greater than any loss of value from trade-offs with other 
attributes, the demand for new vehicles will also shift upwards. Where 
the additional evaluation of fuel savings is lesser than any loss of 
value from changes to other attributes, the demand will shift 
downwards. Thus, the direction of the demand shift is unknown. However, 
if we assume that manufacturers pass all costs associated with a model 
off to the consumer of that vehicle, then the per vehicle profit 
remains constant. If we also assume that manufacturers are good 
predictors of the valuation and elasticity of certain vehicle 
attributes, then we can assume that even if there is some positive 
demand shift, it is not enough to increase demand above the original 
equilibrium levels, or manufacturers would apply those technologies 
even in the absence of regulation.
    As noted above, the increase in the price of new vehicles will 
result in increased demand for used vehicles as substitutes, extending 
the expected age and lifetime vehicle miles travelled of less 
efficient, and generally, less safe vehicles. The additional usage of 
older vehicles will result in fewer gallons saved and more total on-
road fatalities under more stringent CAFE alternatives. For more on the 
topic of safety, the relative safety of specific model year vehicles is 
discussed in Section 0 of the preamble and PRIA Chapter 11. Both the 
erosion of fuel savings and the increase in incremental fatalities will 
decrease the societal net benefits of increasing new vehicle fuel 
economy standards.
    Our previous estimates of vehicle scrappage did not include a 
dynamic response to new vehicle price, but recent literature has 
continued to illustrate that this an omission which could rival the 
rebound effect in magnitude (Jacobsen & van Bentham, 2015). For this 
reason, we worked to develop an econometric survival model which 
captures the effect of increasing the price of new vehicles on the 
survival rate of used vehicles discussed in the following sections and 
in more detail in the PRIA Chapter 8. We discuss the literature on 
vehicle scrappage rate and discuss in the succeeding section. A brief 
explanation of why we develop our own models and the data sources and 
econometric estimations we use to do so, follows. We conclude the 
discussion of the updates to vehicle survival estimates with a summary 
of the results, a description of how we use them in the CAFE model, and 
finally, how the updated schedules compare with the previous static 
scrappage schedules.
(1) What does the literature say about the relationship?
(a) How Fuel Economy Standards Impact Vehicle Scrappage
    The effects of differentiated regulation \275\ in the context of 
fuel economy (particularly, emission standards only affecting new 
vehicles) was discussed in detail in Gruenspecht (1981) and (1982), and 
has since been coined the ``Gruenspecht effect.'' Gruenspecht 
recognized that because fuel economy standards affect only new 
vehicles, any increase in price (net of the portion of reduced fuel 
savings valued by consumers) will increase the expected life of used 
vehicles and reduce the number of new vehicles entering the fleet. In 
this way, increased fuel economy standards slow the turnover of the 
fleet and the entrance of any regulated attributes tied only to new 
vehicles. Although Gruenspecht acknowledges that a structural model 
which allows new vehicle prices to affect used vehicle scrappage only 
through their effect on used vehicle prices would be preferable, the 
data available on used vehicle prices was (and still is) limited. 
Instead he tested his hypothesis in his 1981 dissertation using new 
vehicle price and other determinants of used car prices as a reduced 
form to approximate used car scrappage in response to increasing fuel 
economy standards.

    \275\ Differentiated regulations are regulations affecting 
segments of the market differently; here, it references the fact 
that emission and fuel economy standards have largely only applied 
to new and not used vehicles.

    Greenspan & Cohen (1996) offer additional foundations from which to 
think about vehicle stock and scrappage. Their work identifies two 
types of scrappage: Engineering scrappage and cyclical scrappage. 
Engineering scrappage represents the physical wear on vehicles, which 
results in their being scrapped. Cyclical scrappage represents the 
effects of macroeconomic conditions on the relative value of new and 
used vehicles; under economic growth the demand for new vehicles 
increases and the value of used vehicles declines, resulting in 
increased scrappage. In addition to allowing new vehicle prices to 
affect cyclical vehicle scrappage [agrave] la the Gruenspecht effect, 
Greenspan and Cohen also note that engineering scrappage seems to 
increase where EPA emission standards also increase; as more costs goes 
towards compliance technologies, it becomes more expensive to maintain 
and repair more complicated parts, and scrappage increases. In this 
way, Greenspan and Cohen identify two ways that fuel economy standards 
could affect vehicle scrappage: (1) Through increasing new vehicle 
prices, thereby increasing used vehicle prices, and finally, reducing 
on-road vehicle scrappage, and (2) by shifting resources towards fuel-
saving technologies--potentially reducing the durability of new 
vehicles by making them more complex.
(b) Aggregate vs. Atomic Data Source in the Literature
    One important distinction between the literatures on vehicles 
scrappage is between those that use atomic vehicle data, data following 
specific individual vehicles, and those that use some level of 
aggregated data, data that counts the total number of vehicles of a 
given type. The decision to scrap a vehicle is an atomic one--that is, 
made on an individual vehicle basis. The decision relates to the cost 
of maintaining a vehicle, and the value of the vehicle both on the used 
car market, and as scrap metal. Generally, a used car owner will decide 
to scrap a vehicle where 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.
    Recent work is able to model scrappage as an atomic decision due to 
the availability of a large database of used vehicle transactions. 
Following works by other authors including:

[[Page 43094]]

Busse, Knittel, & Zettelmeyer (2013); Sallee, West, & Fan (2010); 
Alcott & Wozny (2013); and Li, Timmins, & von Haefen (2009)--Jacobsen & 
van Benthem (2015) considers the impact of changes in gasoline prices 
on used vehicle values and scrappage rates. In turn, they consider the 
impact of an increase in used vehicle values on the scrappage rate of 
those vehicles. They find that increases in gasoline price result in a 
reduction in the scrappage rate of the most fuel efficient vehicles and 
an increase in the scrappage rate of the least fuel efficient vehicles. 
This has important implications for the validity of the average fuel 
economy values linked to model years and assumed to be constant over 
the life of that model year fleet within this study. Future iterations 
of this study could further investigate the relationship between fuel 
economy, vehicle usage, and scrappage, as noted in other places in this 
    While the decision to scrap a vehicle is made atomically, the data 
available to NHTSA on scrappage rates and variables that influence 
these scrappage rates are aggregate measures. This influences the best 
available methods to measure the impacts of new vehicle prices on 
existing vehicle scrappage. The result is that this study models 
aggregate trends in vehicle scrappage and not the atomic decisions that 
make up these trends. Many other works within the literature use the 
same data source and general scrappage construct, such as: Walker 
(1968); Park (1977), Greene & Chen (1981); Gruenspecht (1981); 
Gruenspecht (1982); Feeney & Cardebring (1988); Greenspan & Cohen 
(1996); Jacobsen & van Bentham (2015); and Bento, Roth, & Zhuo (2016) 
all use the same aggregate vehicle registration data as the source to 
compute vehicle scrappage.
    Walker (1968) and Bento, Roth, & Zhuo (2016) use aggregate data to 
directly compute the elasticity of scrappage from measures of used 
vehicle prices. Walker (1968) uses the ratio of used vehicle Consumer 
Price Index (CPI) to repair and maintenance CPI. Bento, Roth, & Zhuo 
(2016) use used vehicle prices directly. While the direct measurement 
of the elasticity of scrappage is preferable in a theoretical sense, 
the CAFE model does not predict future values of used vehicles, only 
future prices of new vehicles. For this reason, any model compatible 
with the current CAFE model must estimate a reduced form similar to 
Park (1977); Gruenspecht (1981); Greenspan & Cohen (1996), who use some 
form of new vehicle prices or the ratio of new vehicle prices to 
maintenance and repair prices to impute some measure of the effect of 
new vehicle prices on vehicle scrappage.
(c) Historical Trends in Vehicle Durability
    Waker (1968); Park (1977); Feeney & Cardebring (1988); Hamilton & 
Macauley (1999); and Bento, Ruth, & Zhuo (2016) all note that vehicles 
change in durability over time. Walker (1968) simply notes a 
significant distinction in expected vehicle lifetimes pre- and post-
World War I. Park (1977) discusses a `durability factor' set by the 
producer for each year so that different vintages and makes will have 
varying expected lifecycles. Feeney & Cardebring (1988) show that 
durability of vehicles appears to have generally increased over time 
both in the U.S. and Swedish fleets using registration data from each 
country. They also note that the changes in median lifetime between the 
Swedish and U.S. fleet track well, with a 1.5 year lag in the U.S. 
fleet. This lag is likely due to variation in how the data is 
collected--the Swedish vehicle registry requires a title to unregister 
a vehicle, and therefore gets immediate responses, where the U.S. 
vehicle registry requires re-registration, which creates a lag in 
    Hamilton & Macauley (1999) argue for a clear distinction between 
embodied versus disembodied impacts on vehicle longevity. They define 
embodied impacts as inherent durability similar to Park's producer 
supplied `durability factor' and Greenspan's `engineering scrappage' 
and disembodied effects those which are environmental, not unlike 
Greenspan and Cohen's `cyclical scrappage.' They use calendar year and 
vintage dummy variables to isolate the effects--concluding that the 
environmental factors are greater than any pre-defined `durability 
factor.' Some of their results could be due to some inflexibility of 
assuming model year coefficients are constant over the life of a 
vehicle, and there may be some correlation between the observed life of 
the later model years of their sample and the `stagflation' \276\ of 
the 1970's. Bento, Ruth, & Zhuo (2016) find that the average vehicle 
lifetime has increased 27% from 1969 to 2014 by sub-setting their data 
into three model year cohorts. To implement these findings in the 
scrappage model incorporated into the CAFE model, this study takes 
pains to estimate the effect of durability changes in such a way that 
the historical durability trend can be projected into the future; for 
this reason, a continuous `durability' factor as a function of model 
year vintage is included.

    \276\ Continued high inflation combined with high unemployment 
and slow economic growth.

(d) Models of the Gruenspecht Effect Used in Other Policy Analyses
    This is not the first estimation of the `Gruenspecht Effect' for 
policy considerations. In their Technical Support Document (TSD) for 
the 2004 proposal to reduce greenhouse gas emissions from motor 
vehicles, California Air Resources Board (CARB) outlines how they 
utilized the CARBITS vehicle transaction choice model in an attempt to 
capture the effect of increasing new vehicle prices on vehicle 
replacement rates. They consider data from the National Personal 
Transportation Survey (NPTS) as a source of revealed preferences and a 
University of California (UC) study as a source of stated preferences 
for the purchase and sale of household fleets under different prices 
and attributes (including fuel economy) of new vehicles.
    The transaction choice model represents the addition and deletion 
of a vehicle from a household fleet within a short period of time as a 
``replacement'' of a vehicle, rather than as two separate actions. 
Their final data set consists of 790 vehicle replacements, 292 
additions, and 213 deletions; they do not include the deletions, but 
assume any vehicle over 19 years old that is sold is scrapped. This 
allows them to capture a slowing of vehicle replacement under higher 
new vehicle prices, but because their model does not include deletions, 
does not explicitly model vehicle scrappage, but assumes all vehicles 
aged 20 and older are scrapped rather than resold. They calibrate the 
model so that the overall fleet size is benchmarked to Emissions 
FACtors (EMFAC) fleet predictions for the starting year; the simulation 
then produces estimates that match the EMFAC predictions without 
further calibration.
    The CARB study captures the effect on new vehicle prices on the 
fleet replacement rates and offers some precedence for including some 
estimate of the Gruenspecht Effect. One important thing to note is that 
because vehicles that exited the fleet without replacement were 
excluded, the effect of new vehicle prices on scrappage rates where the 
scrapped vehicle is not replaced is not captured. Because new and used 
vehicles are substitutes, it is expected that used vehicle prices will 
increase with new vehicle prices. Because higher used vehicle prices 
will lower the number of vehicles whose cost of maintenance is higher 
than their value, it is expected that not only will

[[Page 43095]]

replacements of used vehicles slow, but also, that some vehicles that 
would have been scrapped without replacement under lower new vehicle 
prices will now remain on the road because their value will have 
increased. Aggregate measures of the Gruenspecht effect will include 
changes to scrappage rates both from slower replacement rates, and 
slower non-replacement scrappage rates.
(2) Description of Data Sources
    NHTSA purchases proprietary data on the registered vehicle 
population from IHS/Polk for safety analyses. IHS/Polk has annual 
snapshots of registered vehicle counts beginning in calendar year (CY) 
1975 and continuing until calendar year 2015. The data includes the 
following regulatory classes as defined by NHTSA: Passenger cars, light 
trucks (classes 1 and 2a), and medium and heavy-duty trucks (classes 2b 
and 3). Polk separates these vehicles into another classification 
scheme: Cars and trucks. Under their schema, pickups, vans, and SUVs 
are treated as trucks, and all other body styles are included as cars. 
In order to build scrappage models to support the model year (MY) 2021-
2026 light duty vehicle (LDV) standards, it was important to separate 
these vehicle types in a way compatible with the existing CAFE model.
    There were two compatible choices to aggregate scrappage rates: (1) 
By regulatory class or (2) by body style. Because for NHTSA's purposes 
vans/SUVs are sometimes classified as passenger cars and sometimes as 
light trucks, and there was no quick way to reclassify some SUVs as 
passenger cars within the Polk dataset, NHTSA chose to aggregate 
survival schedules by body style. This approach is also preferable 
because NHTSA uses body style specific lifetime VMT schedules. Vehicles 
experience increased wear with use; many maintenance and repair events 
are closely tied to the number of miles on a vehicle. The current 
version of the CAFE model considers separate lifetime VMT schedules for 
cars, vans/SUVs, pickups and classes 2b and 3 vehicles. These vehicles 
are assumed to serve different purposes and, as a result, are modelled 
to have different average lifetime VMT patterns. These different uses 
likely also result in different lifetime scrappage patterns.
    Once stratified into body style level buckets, the data can be 
aggregated into population counts by vintage and age. These counts 
represent the population of vehicles of a given body style and vintage 
in a given calendar year. The difference between the counts of a given 
vintage and vehicle type from one calendar year to the next is assumed 
to represent the number of vehicles of that vintage and type scrapped 
in a given year. There were a couple other important data 
considerations for the calculations of the historical scrappage rates 
not discussed here but discussed in detail in the PRIA Chapter 8.\277\

    \277\ The first is any discontinuity caused by a change in how 
Polk collected their data beginning in calendar year 2010, and the 
second is the use of the adjustment described in Greenspan & Cohen 

    For historical data on vehicle transaction prices, the models use 
data from the National Automobile Dealers Association (NADA), which 
records the average transaction price of all light-duty vehicles. These 
transaction prices represent the prices consumers paid for new vehicles 
but do not include any value of vehicles that may have been traded in 
to dealers. Importantly, these transaction prices were not available by 
vehicle body styles; thus, the models will miss any unique trends that 
may have occurred for a particular vehicle body style. This may be 
particularly relevant for pickup trucks, which observed considerable 
average price increases as luxury and high option pickups entered the 
market. Future models will further consider incorporating price series 
that consider the price trends for cars, SUVs and vans, and pickups 

    \278\ Note: Using historical data aggregated by body styles to 
capture differences in price trends by body style does not require 
the assertion technology costs are or are not borne by the body 
style to which they are applied. If the body-style level average 
price change is used, then the assumption is manufacturers do not 
cross-subsidize across body styles, whereas if the average price 
change is used then the assumption is they would proportion costs 
equally for each vehicle. These are implementation questions to be 
worked out once NHTSA has a historical data source separating price 
series by body styles, but these do not matter in the current model 
which only considers the average price of all light-duty vehicles.

    The models use the NADA price series rather than the Bureau of 
Labor Statistics (BLS) New Vehicle Consumer Price Index (CPI), used by 
Park (1977) and Greenspan & Cohen (1997), because the BLS New Vehicle 
CPI makes quality adjustments to the new vehicle prices. BLS assumes 
that additions of safety and fuel economy equipment are a quality 
adjustment to a vehicle model, which changes the good and should not be 
represented as an increase in its price. While this is good for some 
purposes, it presumes consumers fully value technologies that improve 
fuel economy. Because it is the purpose to this study to measure 
whether this is true, it is important that vehicle prices adjusted to 
fully value fuel economy improving technologies, which would obscure 
the ability to measure the preference for more fuel efficient and 
expensive new vehicles, are not used. As further justification for 
using the NADA price series over the BLS New Vehicle CPI, Park (1977) 
cites a discontinuity found in the amount of quality adjustments made 
to the series so that more adjustments are made over time. This could 
further limit the ability for the BLS New Vehicle CPI to predict 
changes in vehicle scrappage.
    Vehicle scrappage rates are also influenced by fuel economy and 
fuel prices. Historical data on the fuel economy by vehicle style from 
model years 1979-2016 was obtained from the 2016 EPA Motor Trends 
Report.\279\ The van/SUV fuel economy values represent a sales-weighted 
harmonic average of the individual body styles. Fuel prices were 
obtained from Department of Energy (DOE) historical values, and future 
fuel prices within the CAFE model use the Annual Energy Outlook (AEO) 
future oil price projections.\280\ From these values the average cost 
per 100 miles of travel for the cohort of new vehicles in a given 
calendar year and the average cost per 100 miles of travel for each 
used model year cohort in that same calendar year are computed.\281\ It 
is expected that as the new vehicle fleet becomes more efficient 
(holding all other attributes constant) that it will be more desirable, 
and the demand for used vehicles should decrease (increasing their 
scrappage). As a given model year cohort becomes more expensive to 
operate due to increases in fuel prices, it is expected the scrappage 
of that model year will increase. It is perhaps worth noting that more 
efficient model year vintages will be less susceptible to changes in 
fuel prices, as

[[Page 43096]]

absolute changes in their cost per mile will be smaller. The functional 
forms of the cost per mile measures are further discussed in the model 
specification subsection 3 below.

    \279\ Light-Duty Automotive Technology, Carbon Dioxide 
Emissions, and Fuel Economy Trends: 1975 Through 2016, U.S. EPA 
(Nov. 2016), available at https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100PKK8.pdf.
    \280\ Note: The central analysis uses the AEO reference fuel 
price case, but sensitivity analysis also considers the possibility 
of AEO's low and high fuel price cases.
    \281\ Work by Jacobsen and van Bentham suggests that these 
initial average fuel economy values may not represent the average 
fuel economy of a model year cohort as it ages--mainly, they find 
that the most fuel efficient vehicles scrap earlier than the least 
fuel efficient models in a given cohort. This may be an important 
consideration in future endeavors that work to link fuel economy, 
vehicle miles travelled (VMT), and scrappage. Studies on ``the 
rebound effect'' suggest that lowering the fuel cost per driven mile 
increases the demand for VMT. With more miles, a vehicle will be 
worth less as its perceived remaining useful life will be shorter; 
this will result in the vehicle being more likely to be scrapped. A 
rebound effect is included in the CAFE model, but because reliable 
data on how average VMT by age has varied over calendar year and 
model year vintage is not available, expected lifetime VMT is not 
included within the current dynamic scrappage model.

    Aggregate measures that cyclically affect the value of used 
vehicles include macroeconomic factors like the real interest rate, the 
GDP growth rate, unemployment rates, cost of maintenance and repairs, 
and the value of a vehicle as scrap metal or as parts. Here only the 
GDP growth rate is discussed, as this is the only measure included in 
the final model. Extended reasoning as to why other variables are not 
included in the final model in the PRIA Chapter 8 is offered, but the 
discussion was omitted here for brevity in describing only the final 
model. Generally economic growth will result in a higher demand for new 
vehicles--cars in aggregate are normal goods--and a reduction in the 
value of used vehicles. The result should be an increase in the 
scrappage rate of existing vehicles so that we expect the GDP growth 
rate to be an important predictor of vehicle scrappage rates.
    NHTSA sourced the GDP growth rate from the 2017 OASDI Trustees 
Report.\282\ The Trustees Report offers credible projections beyond 
2032. Because the purpose of building this scrappage model is to 
project vehicle survival rates under different fuel economy 
alternatives and the current fuel economy projections go as far forward 
as calendar year 2032, using a data set that encompasses projections at 
least through 2032 is an essential characteristic of any source used 
for this analysis.

    \282\ The 2017 Annual Report of the Board of Trustees of the 
Federal Old-Age and Survivors Insurance and Federal Disability 
Insurance Trust Funds, Social Security Administration (2017), 
available at https://www.ssa.gov/oact/tr/2017/tr2017.pdf.

(3) Summary of Model Estimation
    The most predictive element of vehicle scrappage is what Greenspan 
and Cohen deem `engineering scrappage.' This source of scrappage is 
largely determined by the age of a vehicle and the durability of a 
specific model year vintage. Vehicle scrappage typically follows a 
roughly logistic function with age--that is, instantaneous scrappage 
increases to some peak, and then declines, with age as noted in Walker 
(1968); Park (1977); Greene & Chen (1981); Gruenspecht (1981); Feeney & 
Cardebring (1988); Greenspan & Cohen (1996); Hamilton & Macauley 
(1999); and Bento, Roth, & Zhuo (2016). Thus, this analysis also uses a 
logistic function to capture this trend of vehicle scrappage with age 
but allows non-linear terms to capture any skew to the logistic 
relationship. Specific details about the final and considered forms of 
engineering scrappage by body styles is presented in the PRIA Chapter 
    The final and considered independent variables intended to capture 
cyclical elements of vehicle scrappage and the considered forms of each 
are discussed in PRIA Chapter 8; here only inclusion of the GDP growth 
rate is discussed. The GDP growth rate is not a single-period effect; 
both the current and previous GDP growth rates will affect vehicle 
scrappage rates. A single year increase will affect scrappage 
differently than a multi-period trend. For this reason, an optimal 
number of lagged terms are included: The within-period GDP growth rate, 
the previous period GDP growth rate, and the growth rate from two prior 
years for the car model, while for vans/SUVs, and pickups, the current 
and previous period GDP growth rate are sufficient.
    Similarly, the considered model allows that one-period changes in 
new vehicle prices will affect the used vehicle market differently than 
a consistent trend in new vehicle prices. The optimal number of lags is 
three so that the price trend from the current year and the three prior 
years influences the demand for and scrappage of used vehicles. Note: 
The average lease length is three years \283\ so that the price of an 
average vehicle coming off lease is estimated to affect the scrappage 
rate of used vehicles--this is a major source of the newest used 
vehicles that enter the used car fleet. Further, because increases in 
new vehicle prices due to increased stringency of CAFE standards is the 
primary mechanism through which CAFE standards influence vehicle 
scrappage and the CAFE Model assumes that usage, efficiency, and safety 
vary with the age of the vehicle, particular attention is paid to the 
form of this effect. It is important to know the likelihood of 
scrappage by the age of the vehicle to correctly account for the 
additional costs of additional fatalities and increased fuel 
consumption from deferred scrappage. Thus, the influence of increasing 
new vehicle prices is allowed to influence the demand for used vehicles 
(and reduce their scrappage) differently for different ages of vehicles 
in the scrappage model. We discuss both how we determined the correct 
form and number of lags for each body style in PRIA Chapter 8.

    \283\ See e.g., Edmunds January 2017 Lease Market Report, 
Edmunds (Jan. 2017), https://dealers.edmunds.com/static/assets/articles/lease-report-jan-2017.pdf.

    The final cyclical factor affecting vehicle scrappage in the 
preferred model is the cost per 100 miles of travel both of new 
vehicles and of the vehicle which is the subject of the decision to 
scrap or not to scrap. The new vehicle cost per 100 miles is defined as 
the ratio of the average fuel price faced by new vehicles in a given 
calendar year and the average new vehicle fuel economy for 100 miles in 
the same calendar year, and varies only with calendar year:

    The cost per 100 miles of the potentially scrapped vehicle is 
described as the ratio of the average fuel price faced by that model 
year vintage in a given calendar year and the average fuel economy for 
100 miles of travel for that model year when it was new, and varies 
both with calendar year and model year:

[[Page 43097]]

    The average per-gallon fuel price faced by a model year vintage in 
a given calendar year is the annual average fuel price of all fuel 
types present in that model year fleet for the given calendar year, 
weighted by the share of each fuel type in that model year fleet. Or 
the following, where FT represents the set of fuel types present in a 
given model year vintage:

    For these variables, the best fit model includes the cost per mile 
of both the new and the used vehicle for the current and prior year. 
This is congruent with research that suggests consumers respond to 
current fuel prices and fuel price changes. The selection process of 
this form for the cost per mile and the implications is discussed in 
PRIA Chapter 8.
    There are a couple other controlling factors considered in our 
final model. The 2009 Car Allowance Rebate System (CARS) is not 
outlined here but is outlined in PRIA Chapter 8. This program aimed to 
accelerate the retirement of less fuel efficient vehicles and replace 
them with more fuel efficient vehicles. Further discussion of how this 
is controlled for is located in PRIA Chapter 8. Finally, evidence of 
autocorrelation was found, and including three lagged values of the 
dependent variable addresses the concern. Treatment of autocorrelation 
is discussed in PRIA Chapter 8.
    One additional issue encountered in the estimations of scrappage 
rates is that the models predict too many vehicles remain on the road 
in the later years. This issue occurs because the data beyond age 15 
are progressively more sparsely populated; vehicles over 15 years were 
not captured in the Polk data until 1994, when each successive 
collection year added an additional age of vehicles until 2005 when all 
ages began to be collected. This means that for vehicles over the age 
of 25 there are only 10 years of data. In order to correct for this 
issue the fact that the final fleet share converges to roughly the same 
share for most model years for a given vehicle type is used. The 
predicted versus historical relationships seem to deviate beginning 
around age 20; thus, for scrappage rates for vehicles beyond age 20 an 
exponential decay function which guarantees that by age 40 the final 
fleet share reaches the convergence level observed in the historical 
data is applied. The application of the decay function and mathematical 
definition is further defended in PRIA Chapter 8.
    A sensitivity case is also developed to isolate the magnitude of 
the Greunspecht effect. The impacts on costs and benefits are presented 
in section VII.H.1 of this document. In order to isolate the effect, 
the price of new vehicles is held constant at CY 2016 levels. The 
specific methodology used to do so is described in detail in PRIA 
Chapter 8, as is the leakage implied by comparing the reference and no 
Gruenspecht effect sensitivity cases. It is important to note here that 
the leakage calculated ranges between 12 and 18% across regulatory 
alternatives. This is in line with Jacobsen & van Bentham (2015) 
estimates which put leakage for their central case between 13 and 16%. 
Their high gasoline price case is more in line this analysis' central 
case--with fuel prices of $3/gallon--and predicts leakage of 21%. This 
further validates the scrappage model effects against examples in the 
    The models used for this analysis are able to capture the 
relationship for vehicle scrappage as it varies with age and how this 
relationship changes with increases to new vehicle price, the cost per 
mile of travel of new and used vehicles, and how the rate varies 
cyclically with the GDP growth rate. It also controls for the CARS 
program and checks the influence of a change in Polk's data collection 
procedures. The goodness of fit measures and the plausibility of the 
predictions of the model are discussed at some length in PRIA Chapter 
8. In the next section, the impacts of updating the static scrappage 
models to the dynamic models on average vehicle age and usage, by body 
styles, and across different regulatory assumptions are discussed.
(c) What is the estimated effect on vehicle retirement and how do 
results compare to previously estimated fleets and VMT?
    The expected lifetime of a car estimated using the static scrappage 
schedule from the 2012 final rule, both in years and miles, is between 
the expected lifetime of the dynamic scrappage model in the absence of 
CAFE standards and under the baseline standards. Estimated by the 
dynamic scrappage model, the average vehicle is expected to live 15.1 
years under the influence of only market demand for new technology, and 
15.6 years under the baseline scenario, a four percent increase. 
However, given the distribution of the mileage accumulation schedule by 
age, this amounts only to a two percent increase in the expected 
lifetime mileage accumulation of an individual vehicle. This range is 
consistent with DOT expectations in terms of direction and magnitude.
    The use of a static retirement schedule, while deemed a reasonable 
approach in the past, is a limited representation of scrappage 
behavior. It fails to account for increasing vehicle durability--
occurring for the last several decades--and the resulting increase in 
average vehicle age in the on-road fleet, which has nearly doubled 
since 1980.\284\ Thus, turning off the dynamic scrappage model 
described above would not impose a perspective on the analysis that is 
neutral with respect to observed scrappage behavior but would instead 
represent a strong assumption that asserts important trends in the 
historical record will abruptly cease or change direction.

    \284\ Based on data from FHWA and IHS/Polk.

    As discussed above, the dynamic scrappage model implemented to 
support this proposal affects total fleet size through several 
mechanisms. Although the model accounts for the influence of changes to 
average new vehicle price and U.S. GDP growth, the most influential 
mechanism, by far, is the observed trend of increasing vehicle 
durability over successive model years. This phenomenon is prominently 
discussed in the academic literature related to vehicle retirement, 
where there is no disagreement about its existence or direction.\285\ 
In fact, when the CAFE model is exercised in a way that keeps average 
new vehicle prices at (approximately) MY 2016 levels, the on-road fleet 
grows from an initial level of 228 million in 2016 to 340 million in 
2050, an increase of 49% over the 35-year period from 2016 to 2050.

    \285\ Waker (1968); Park (1977); Feeney & Cardebring (1988); 
Hamilton & Macauley (1999); and Bento, Ruth, & Zhuo (2016) note that 
vehicles change in durability over time.

    The historical data show the size of the registered vehicle 
population (i.e., the on-road fleet) growing by about 60% in the 35 
years between 1980 and

[[Page 43098]]

2015.\286\ In the 35 years between 2016 and 2050, our simulation shows 
the on-road fleet growing from about 230 million vehicles to about 345 
million vehicles when the market adopts only the amount of fuel 
economy, which it naturally demands. The simulated growth over this 
period is about 50% from today's level, rather than the 60% observed in 
the historical data over the last 35 years. Under the baseline 
regulatory scenario, the growth over the next 35 years is simulated to 
be about 54%--still short of the observed growth over a comparable 
period of time. In fact, the simulated annual growth rate in the size 
of the on-road fleet in this analysis, about 1.3%, is lower than the 
long-term average annual growth rate of about two percent dating back 
to the 1970s.\287\

    \286\ There are two measurements of the size of the registered 
vehicle population that are considered to be authoritative. One is 
produced by the Federal Highway Administration, and the other by 
R.L. Polk (now part of IHS). The Polk measurement shows fleet growth 
between 1980 and 2015 of about 85%, while the FHWA measurement shows 
a slower growth rate over that period, only about 60%.
    \287\ Based on calculations using Polk's National Vehicle 
Population Profile (NVPP).

    Additionally, there are inherent precision limitations in measuring 
something as vast and complex as the registered vehicle population. For 
decades, the two authoritative sources for the size of the on-road 
fleet have been R.L. Polk (now IHS/Polk) and FHWA. For two decades 
these two sources differed by more than 10% each year, only lately 
converging to within a few percent of each other. These discrepancies 
over the correct interpretation of the data by each source have 
consistently represented differences of more than 10 million vehicles.
    The total number of new vehicles projected to enter the fleet is 
slightly higher than the historical trend (though the impact of the 
great recession makes it hard to say by how much). More generally, the 
projections used in the analysis cover long periods of time without 
exhibiting the kinds of fluctuation that are present in the historical 
record. For example, the forecast of GDP growth in our analysis posits 
a world in which the United States sees uninterrupted positive annual 
growth in real GDP for four decades. The longest such period in the 
historical record is 17 years and still included several years of low 
(but positive) growth during that interval.
    Over such a long period of time, in the absence of deep insight 
into the future of the U.S. auto industry, it is sensible to assume 
that the trends observed over the course of decades are likely to 
persist. Analyzing fuel economy standards requires an understanding of 
the mechanisms that influence new vehicle sales, the size of the on-
road fleet, and vehicle miles traveled. It is upon these mechanisms 
that the policy acts: Increasing/decreasing new vehicle prices changes 
the rate at which new vehicles are sold, changing the attributes and 
prices of these vehicles influences the rates at which all used 
vehicles are retired, the overall size of the on-road fleet determines 
the total amount of VMT, which in turn affects total fuel consumption, 
fatalities, and other externalities. The fact that DOT's bottom-up 
approach produces results in line with historical trends is both 
expected and intended.
    This is not to say that all details of this new approach will be 
immediately intuitive for reviewers accustomed to results that do not 
include a dynamic sales model or dynamic scrappage model, much less 
results that combine the two. For example, some reviewers may observe 
that today's analysis shows that, compared to the baseline standards, 
the proposed standards produce a somewhat smaller on-road fleet (i.e., 
fewer vehicles in service) despite somewhat increased sales of new 
vehicles (consistent with reduced new vehicle prices) and decreased 
prices for used vehicles. While it might be natural to assume that 
reduced prices of new vehicles and increased sales should lead to a 
larger on-road fleet, in our modelling, the increased sales are more 
than offset by the somewhat accelerated scrappage that accompanies the 
estimated decrease in new vehicle prices. This outcome represents an 
on-road fleet that is both smaller and a little younger on average 
(relative to the baseline) and ``turns over'' more quickly.
    To further test the validity of the scrappage model, a dynamic 
forecast was constructed for calendar years 2005 through 2015 to see 
how well it predicts the fleet size for this period. The last true 
population the scrappage model ``sees'' is the 2005 registered vehicle 
population. It then takes in known production volumes for the new model 
year vehicles and dynamically estimates instantaneous scrappage rates 
for all registered vehicles at each age for CYs 2006-2015, based only 
on the observed exogenous values that inform the model (GDP growth 
rate, observed new vehicle prices, and cost per mile of operation), 
fleet attributes of the vehicles (body style, age, cost per mile of 
operation), and estimated scrappage rates at earlier ages. Within this 
exercise, the scrappage model relies on its own estimated values as the 
previous scrappage rates at earlier ages, forcing any estimation errors 
to propagate through to future years. This exercise is discussed 
further in PRIA Chapter VII. While the years of the recession represent 
a significant shock to the size of the fleet, briefly reversing many 
years of annual growth, the model recovers quickly and produces results 
within one percent of the actual fleet size, as it did prior to the 
    In order to compare the magnitudes of the sales and scrappage 
effects across different fuel economy standards considered it is 
important to define comparable measures. The sales effect in a single 
calendar year is simply the difference in new vehicle sales across 
alternatives. However, the scrappage effect in a single calendar year 
is not simply the change in fleet size across regulatory alternatives. 
The scrappage model predicts the probability that a vehicle will be 
scrapped in the next year conditional on surviving to that age; the 
absolute probability that a vehicle survives to a given age is 
conditional on the scrappage effect for all previous analysis years. In 
other words, if successive calendar years observe lower average new 
vehicle prices, the effect of increased scrappage on fleet size will 
accumulate with each successive calendar year--because fewer vehicles 
survived to previous ages, the same probability of scrappage would 
result in a smaller fleet size for the following year as well, though 
fewer vehicles will have been scrapped than in the previous year.
    To isolate the number of vehicles not scrapped in a single calendar 
year because of the change in standards, the first step is to calculate 
the number of vehicles scrapped in every calendar year for both the 
proposed standards and the baseline; this is calculated by the inter-
annual change in the size of the used vehicle fleet (vehicles ages 1-
39) for each alternative. The difference in this measure across 
regulatory alternatives represents the change in vehicle scrappage 
because of a change in the standards. The resulting scrappage effect 
for a single calendar year can be compared to the difference across 
regulatory alternatives in new vehicle sales for the same calendar year 
as a comparison of the relative magnitudes of the two effects. In most 
years, under the proposed standards relative to the baseline standards, 
the analysis shows that for each additional new vehicles sold, two to 
four used vehicles are removed from the fleet. Over the time period of 
the analysis these predicted differences in the numbers of vehicles 
accumulate, resulting in a maximum of

[[Page 43099]]

seven million fewer vehicles by CY 2033 for the proposed CAFE standards 
relative to the augural standards, and nine million fewer vehicles by 
CY 2035 for the proposed GHG standards relative to the current GHG 
standards. Tables 11-29 and 11-30 in the PRIA show the difference in 
the fleet size by calendar year for the proposed standards relative to 
the augural standards for the CAFE and GHG programs, respectively.
    To understand why the sales and scrappage effects do not perfectly 
offset each other to produce a constant fleet size across regulatory 
alternatives it is important to remember that the decision to buy a new 
vehicle and the decision to scrap a used vehicle are often not made by 
the same household as a joint decision. The average length of initial 
ownership for new vehicles is approximately 6.5 years (and increasing 
over time). Cumulative scrappage up to age seven is typically less than 
10%of the initial fleet. This suggests that most vehicles belong to 
more than one household over the course of their lifetimes. The 
household that is deciding whether or not to purchase a new vehicle is 
rarely the same household deciding whether or not to scrap a vehicle. 
So a vehicle not scrapped in a given year is seldom the direct 
substitute for a new vehicle purchased by that household. Considering 
this, it is not expected that for every additional vehicle scrapped, 
there is also an additional new one sold, under the proposed standards 
relative to the baseline standards.
    Further, while sales and scrappage decisions are both influenced by 
changes in new vehicle prices, the mechanism through which these 
decisions change are different for the two effects. A decrease in 
average new vehicle prices will directly increase the demand for new 
vehicles along the same demand curve. This decrease in new vehicle 
prices will cause a substitution towards new vehicles and away from 
used vehicles, shifting the entire demand curve for used vehicles 
downwards. This will decrease both the equilibrium prices of used 
vehicles, as shown in Figure 8-16 of the PRIA. Since the decision to 
scrap a vehicle in a given year is closely related to the difference 
between the vehicle's value and the cost to maintain it, if the value 
of a vehicle is lower than the cost to maintain it, the current owner 
will not choose to maintain the vehicle for their own use or for resale 
in the used car market, and the vehicle will be scrapped. That is, a 
current owner will only supply a vehicle to the used car market if the 
price of the vehicle is greater than the cost of supplying it. Lowering 
the equilibrium price of used vehicles will lower the increase the 
number of scrapped vehicles, lowering the supply of used vehicles, and 
decreasing the equilibrium quantity. The change in new vehicle sales is 
related to demand of new vehicles at a given price, but the change in 
used vehicle scrappage is related to the shift in the demand curve for 
used vehicles, and the resulting change in the quantity current owners 
will supply; these effects are likely not exactly offsetting.
    Our models indicate that the ratio of the magnitude of the 
scrappage effect to the sales effect is greater than one so that the 
fleet grows under more stringent scenarios. However, it is important to 
remember that not all vehicles are driven equally; used vehicles are 
estimated to deliver considerably less annual travel than new vehicles. 
Further, used vehicles only have a portion of their original life left 
so that it will take more than one used vehicle to replace the full 
lifetime of a new vehicle, at least in the long-run. The result of the 
lower annual VMT and shorter remaining lifetimes of used vehicles, is 
that although the fleet is 1.5% bigger in CY 2050 for the augural 
baseline than it is for the proposed standards, the total non-rebound 
VMT for CY 2050 is 0.4% larger in the augural baseline than in the 
proposed standards. This small increase in VMT is consistent with a 
larger fleet size; if more used vehicles are supplied, there likely is 
some small resulting increase in VMT.
    Our models face some limitations, and work will continue toward 
developing methods for estimating vehicle sales, scrappage, and mileage 
accumulation. For example, our scrappage model assumes that the average 
VMT for a vehicle of a particular vintage is fixed--that is, aside from 
rebound effects, vehicles of a particular vintage drive the same amount 
annually, regardless of changes to the average expected lifetimes. The 
agencies seek comment on ways to further integrate the survival and 
mileage accumulation schedules. Also, our analysis uses sales and 
scrappage models that do not dynamically interact (though they are 
based on similar sets of underlying factors); while both models are 
informed by new vehicle prices, the model of vehicle sales does not 
respond to the size and age profile of the on-road fleet, and the model 
of vehicle scrappage rates does not respond to the quantity of new 
vehicles sold. As one potential option for development, the potential 
for an integrated model of sales and scrappage, or for a dynamic 
connection between the two models will be considered. Comment is sought 
on both the sales and scrappage models, on potential alternatives, and 
on data and methods that may enable practicable integration of any 
alternative models into the CAFE model.
7. Accounting for the Rebound Effect Caused by Higher Fuel Economy
(a) What is the rebound effect and how is it measured?
    Amending and establishing fuel economy and GHG standards at a 
lesser stringency than the augural standards for future model years 
will lead to comparatively lower fuel economy for new cars and light 
trucks, thus increasing the amount of fuel they consume in traveling 
each mile than they would under the augural standard. The resulting 
increase in their per-mile fuel and total driving costs will lead to a 
reduction in the number of miles they are driven each year over their 
lifetimes, and example of the rebound effect that is usually associated 
with energy efficiency improvements working in reverse. The fuel 
economy rebound effect--a specific example of the energy efficiency 
rebound effect for the case of motor vehicles--refers to the well-
documented tendency of vehicles' use to increase when their fuel 
economy is improved and the cost of driving each mile declines as a 
(b) What does the literature say about the magnitude of this effect?
    Table-II-43 summarizes estimates of the fuel economy rebound effect 
for light-duty vehicles from studies conducted through 2008, when the 
agencies originally surveyed research on this subject.\288\ After 
summarizing all of the estimates reported in published and other 
publicly-available research available at that time, it distinguishes 
among estimates based on the type of data used to develop them. As the 
table reports, estimates of the rebound effect ranged from 6% to as 
high as 75%, and the range spanned by published estimates was nearly as 
wide (7-75%).

    \288\ Complete references to the studies summarized in Table 8-2 
are included in the PRIA, and many of the unpublished studies are 
available in the docket for this rulemaking.


[[Page 43100]]

Most studies reported more than one empirical estimate, and the authors 
of published studies typically identified the single estimate in which 
they were most confident; these preferred estimates spanned only a 
slightly narrower range (9-75%).

    Despite their wide range, these estimates displayed a strong 
central tendency, as Table-II-43 also shows. The average values of all 
estimates, those that were published, and authors' preferred estimates 
from published studies were 22-23%, and the median estimates in each 
category were close to these values, indicating nearly symmetric 
distributions. The estimates in each category also clustered fairly 
tightly around their respective average values, as shown by their 
standard deviations in the table's last column. When classified by the 
type of data they relied on, U.S. aggregate time-series data produced 
slightly smaller values (averaging 18%) than did panel-type data for 
individual states (23%) or household survey data (25%). In each 
category, the median estimate was again quite close to the average 
reported value, and comparing the standard deviations of estimates 
based on each type of data again suggests a fairly tight scatter around 
their respective means.
    Of these studies, a then recently-published analysis by Small & Van 
Dender (2007), which reported that the rebound effect appeared to be 
declining over time in response to increasing income of drivers, was 
singled out. These authors theorized that rising income increased the 
opportunity cost of drivers' time, leading them to be less responsive 
over time to reductions in the fuel cost of driving each mile. Small 
and Van Dender reported that while the rebound effect averaged 22% over 
the entire time period they analyzed (1967-2001), its value had 
declined by half--or to 11%--during the last five years they studied 
(1997-2001). Relying primarily on forecasts of its continued decline 
over time, the analysis reduced the 20% rebound effect that NHTSA used 
to analyze the effects of CAFE standards for light trucks produced 
during model years 2005-07 and 2008-11 to 10% for their analysis of 
CAFE and GHG standards for MY 2012-16 passenger cars and light trucks.
    Table-II-44 summarizes estimates of the rebound effect reported in 
research that has become available since the agencies' original survey, 
which extended through 2008, and the following discussion briefly 
summarizes the approaches used by these more recent studies. Bento et 
al. (2009) combined demographic characteristics of more than 20,000 
U.S. households, the manufacturer and model of each vehicle they owned, 
and their annual usage of each vehicle from the 2001 National Household 
Travel Survey with detailed data on fuel economy and other attributes 
for each vehicle model obtained from commercial publications. The 
authors aggregated vehicle models into 350 categories representing 
combinations of manufacturer, vehicle type, and age, and use the 
resulting data to estimate the parameters of a complex model of 
households' joint choices of the number and types of vehicles to own, 
and their annual use of each vehicle.

[[Page 43101]]


    Bento et al. estimate the effect of vehicles' operating costs per 
mile, including fuel costs, which depend in part on each vehicle's fuel 
economy, as well as maintenance and insurance expenses, on households' 
annual use of each vehicle they own. Combining the authors' estimates 
of the elasticity of vehicle use with respect to per-mile operating 
costs with the reported fraction of total operating costs accounted for 
by fuel (slightly less than one-half) yields estimates of the rebound 
effect. The resulting values vary by household composition, vehicle 
size and type, and vehicle age, ranging from 21 to 38%, with a 
composite estimate of 34% for all households, vehicle models, and ages. 
The smallest values apply to new luxury cars, while the largest 
estimates are for light trucks and households with children, but the 
implied rebound effects differ little by vehicle age.
    Barla et al. (2009) analyzed the responses of car and light truck 
ownership, vehicle travel, and average fuel efficiency to variation in 
fuel prices and aggregate economic activity (measured by gross product) 
using panel-type data for the 10 Canadian provinces over the period 
from 1990 through 2004. The authors estimated a system of equations for 
these three variables using statistical procedures appropriate for 
models where the variables of interest are simultaneously determined 
(that is, where each variable is one of the factors explaining 
variation in the others). This procedure enabled them to control for 
the potential ``reverse influence'' of households' demand for vehicle 
travel on their choices of how many vehicles to own and their fuel 
efficiency levels when estimating the effect of variation in fuel 
efficiency on vehicle use.
    Their analysis found that provincial-level aggregate economic 
activity had moderately strong effects on car and light truck ownership 
and use but that fuel prices had only modest effects on driving and the 
average fuel efficiency of the light-duty vehicle fleet. Each of these 
effects became considerably stronger over the long term than in the 
year when changes in economic activity and fuel prices initially 
occurred, with three to five years typically required for behavioral 
adjustments to stabilize. After controlling for the joint relationship 
among vehicle ownership, driving demand, and the fuel efficiency of 
cars and light trucks, Barla et al. estimated elasticities of average 
vehicle use with respect to fuel efficiency that corresponded to a 
rebound effect of eight percent in the short run, rising to nearly 20% 
within five years. A notable feature of their analysis was that 
variation in average fuel efficiency among the individual Canadian 
provinces and over the time period they studied was adequate to 
identify its effect on vehicle use, without the need to combine it with 
variation in fuel prices in order to identify its effect.
    Wadud et al. (2009) combine data on U.S. households' demographic 
characteristics and expenditures on gasoline over the period 1984-2003 
from the Consumer Expenditure Survey with data on gasoline prices and 
an estimate of the average fuel economy of

[[Page 43102]]

vehicles owned by individual households (constructed from a variety of 
sources). They employ these data to explore variation in the 
sensitivity of individual households' gasoline consumption to 
differences in income, gasoline prices, the number of vehicles owned by 
each household, and their average fuel economy. Using an estimation 
procedure intended to account for correlation among unmeasured 
characteristics of households and among estimation errors for 
successive years, the authors explore variation in the response of fuel 
consumption to fuel economy and other variables among households in 
different income categories and between those residing in urban and 
rural areas.
    Dividing U.S. households into five equally-sized income categories, 
Wadud et al. estimate rebound effects ranging from 1-25%, with the 
smallest estimates (8% and 1%) for the two lowest income categories, 
and significantly larger estimates for the middle (18%) and two highest 
income groups (18 and 25%). In a separate analysis, the authors 
estimate rebound effects of seven percent for households of all income 
levels residing in U.S. urban areas and 21% for rural households.
    West & Pickrell (2011) analyzed data on more than 100,000 
households and 300,000 vehicles from the 2009 Nationwide Household 
Transportation Survey to explore how households owning multiple 
vehicles chose which of them to use and how much to drive each one on 
the day the household was surveyed. Their study focused on how the type 
and fuel economy of each vehicle a household owned, as well as its 
demographic characteristics and location, influenced household members' 
decisions about whether and how much to drive each vehicle. They also 
investigated whether fuel economy and fuel prices exerted similar 
influences on vehicle use, and whether households owning more than one 
vehicle tended to substitute use of one for another--or vary their use 
of all of them similarly--in response to fluctuations in fuel prices 
and differences in their vehicles' fuel economy.
    Their estimates of the fuel economy rebound effect ranged from as 
low as nine percent to as high as 34%, with their lowest estimates 
typically applying to single-vehicle households and their highest 
values to households owning three or more vehicles. They generally 
found that differences in fuel prices faced by households who were 
surveyed on different dates or who lived in different regions of the 
U.S. explained more of the observed variation in daily vehicle use than 
did differences in vehicles' fuel economy. West and Pickrell also found 
that while the rebound effect for households' use of passenger cars 
appeared to be quite large--ranging from 17% to nearly twice that 
value--it was difficult to detect a consistent rebound effect for SUVs.
    Anjovic & Haas (2012) examined variation in vehicle use and fuel 
efficiency among six European nations over an extended period (1970-
2006), using an elaborate model and estimation procedure intended to 
account for the existence of common underlying trends among the 
variables analyzed and thus avoid identifying spurious or misleading 
relationships among them. The six nations included in their analysis 
were Austria, Germany, Denmark, France, Italy, and Sweden; the authors 
also conducted similar analyses for the six nations combined. The 
authors focused on the effects of average income levels, fuel prices, 
and the fuel efficiency of each nation's fleet of cars on the total 
distance they were driven each year and their total fuel energy 
consumption. They also tested whether the responses of energy 
consumption to rising and falling fuel prices appeared to be symmetric 
in the different nations.
    Anjovic and Haas report a long-run aggregate rebound effect of 44% 
for the six nations their study included, with corresponding values for 
individual nations ranging from a low of 19% (for Austria) to as high 
as 56% (Italy). These estimates are based on the estimated response of 
vehicle use to variation in average fuel cost per kilometer driven in 
each of the six nations and for their combined total. Other information 
reported in their study, however, suggests lower rebound effects; their 
estimates of the response of total fuel energy consumption to fuel 
efficiency appear to imply an aggregate rebound effect of 24% for the 
six nations, with values ranging from as low as 0-3% (for Austria and 
Denmark) to as high as 70% (Sweden), although the latter is very 
uncertain. These results suggest that vehicle use in European nations 
may be somewhat less sensitive to variation in driving costs caused by 
changes in fuel efficiency than to changes in driving costs arising 
from variation in fuel prices, but they find no evidence of asymmetric 
responses of total fuel consumption to rising and falling prices. Using 
data on household characteristics and vehicle use from the 2009 
Nationwide Household Transportation Survey (NHTS), Su (2012) analyzes 
the effects of locational and demographic factors on household vehicle 
use and investigates how the magnitude of the rebound effect varies 
with vehicles' annual use. Using variation in the fuel economy and per-
mile cost of and detailed controls for the demographic, economic, and 
locational characteristics of the households that owned them (e.g., 
road and population density) and each vehicle's main driver (as 
identified by survey respondents), the author employs specialized 
regression methods to capture the variation in the rebound effect 
across 10 different categories of vehicle use.
    Su estimated the overall rebound effect for all vehicles in the 
sample averaged 13%, and that its magnitude varied from 11-19% among 
the 10 different categories of annual vehicle use. The smallest rebound 
effects were estimated for vehicles at the two extremes of the 
distribution of annual use--those driven comparatively little, and 
those used most intensively--while the largest estimated effects 
applied to vehicles that were driven slightly more than average. 
Controlling for the possibility that high-mileage drivers respond to 
the increased importance of fuel costs by choosing vehicles that offer 
higher fuel economy narrowed the range of Su's estimated rebound 
effects slightly (to 11-17%), but did not alter the finding that they 
are smallest for lightly- and heavily-driven vehicles and largest for 
those with slightly above average use.
    Linn (2013) also uses the 2009 NHTS to develop a linear regression 
approach to estimate the relationship between the VMT of vehicles 
belonging to each household and a variety of different factors: Fuel 
costs, vehicle characteristics other than fuel economy (e.g., 
horsepower, the overall ``quality'' of the vehicle), and household 
characteristics (e.g., age, income). Linn reports a fuel economy 
rebound effect with respect to VMT of between 20-40%.
    One interesting result of the study is that when the fuel 
efficiency of all vehicles increases, which would be the long-run 
effect of rising fuel efficiency standards, two factors have opposing 
effects on the VMT of a particular vehicle. First, VMT increases when 
that vehicle's fuel efficiency increases. But the increase in the fuel 
efficiency of the household's other vehicles causes the vehicle's own 
VMT to decrease. Because the effect of a vehicle's own fuel efficiency 
is larger than the other vehicles' fuel efficiency, VMT increases if 
the fuel efficiency of all vehicles increases proportionately. Linn 
also finds that VMT responds much more strongly to vehicle fuel economy 
than to gasoline prices, which is at variance with the Hymel et al. and 
Greene results discussed above.

[[Page 43103]]

    Like Su and Linn, Liu et al. (2014) employed the 2009 NHTS to 
develop an elaborate model of an individual household's choices about 
how many vehicles to own, what types and ages of vehicles to purchase, 
and how much combined driving to do using all of them. Their analysis 
used a complex mathematical formulation and statistical methods to 
represent and measure the interdependence among households' choices of 
the number, types, and ages of vehicles to purchase, as well as how 
intensively to use them.
    Liu et al. employed their model to simulate variation in 
households' total vehicle use to changes in their income levels, 
neighborhood characteristics, and the per-mile fuel cost of driving 
averaged over all vehicles each household owns. The complexity of the 
relationships among the number of vehicles owned, their specific types 
and ages, fuel economy levels, and use incorporated in their model 
required them to measure these effects by introducing variation in 
income, neighborhood attributes, and fuel costs, and observing the 
response of households' annual driving. Their results imply a rebound 
effect of approximately 40% in response to significant (25-50%) 
variation in fuel costs, with almost exactly symmetrical responses to 
increases and declines.
    A study of the rebound effect by Frondel et al. (2012) used data 
from travel diaries recorded by more than 2,000 German households from 
1997 through 2009 to estimate alternative measures of the rebound 
effect, and to explore variation in their magnitude among households. 
Each household participating in the survey recorded its automobile 
travel and fuel purchases over a period of one to three years and 
supplied information on its composition and the personal 
characteristics of each of its members. The authors converted 
households' travel and fuel consumption to a monthly basis, and used 
specialized estimation procedures (quantile and random-effects panel 
regression) to analyze monthly variation in their travel and fuel use 
in relation to differences in fuel prices, the fuel efficiency of each 
vehicle a household owned, and the fuel cost per mile of driving each 
    Frondel et al. estimate four separate measures of the rebound 
effect, three of which capture the response of vehicle use to variation 
in fuel efficiency, fuel price, and fuel cost per mile traveled, and a 
fourth capturing the response of fuel consumption to changes in fuel 
price. Their first three estimates range from 42% to 57%, while their 
fourth estimate corresponds to a rebound effect of 90%. Although their 
analysis finds no significant variation of the rebound effect with 
household income, vehicle ownership, or urban versus rural location, it 
concludes that the rebound effect is substantially larger for 
households that drive less (90%) than for those who use their vehicles 
most intensively (56%).
    Gillingham (2014) analyzed variation in the use of approximately 
five million new vehicles sold in California from 2001 to 2003 during 
the first several years after their purchase, focusing particularly on 
how their use responded to geographic and temporal variation in fuel 
prices. His sample consisted primarily of personal or household 
vehicles (87%) but also included some that were purchased by 
businesses, rental car companies, and government agencies. Using 
county-level data, he analyzed the effect of differences in the monthly 
average fuel price paid by their drivers on variation in their monthly 
use and explored how that effect varied with drivers' demographic 
characteristics and household incomes.
    Gillingham's analysis did not include a measure of vehicles' fuel 
economy or fuel cost per mile driven, so he could not measure the 
rebound effect directly, but his estimates of the effect of fuel prices 
on vehicle use correspond to a rebound effect of 22-23% (depending on 
whether he controlled for the potential effect of gasoline demand on 
its retail price). His estimation procedure and results imply that 
vehicle use requires nearly two years to adjust fully to changes in 
fuel prices. He found little variation in the sensitivity of vehicle 
use to fuel prices among car buyers with different demographic 
characteristics, although his results suggested that it increases with 
their income levels.
    Weber & Farsi (2014) analyzed variation in the use of more than 
70,000 individual cars owned by Swiss households who were included in a 
2010 survey of travel behavior. Their analysis focuses on the 
simultaneous relationships among households' choices of the fuel 
efficiency and size (weight) of the vehicles they own, and how much 
they drive each one, although they recognize that fuel efficiency 
cannot be chosen independently of vehicle weight. The authors employ a 
model specification and statistical estimation procedures that account 
for the likelihood that households intending to drive more will 
purchase more fuel-efficient cars but may also choose more spacious and 
comfortable--and thus heavier--models, which affects their fuel 
efficiency indirectly, since heavier vehicles are generally less fuel-
efficient. The survey data they rely on includes both owners' estimates 
of their annual use of each car and the distance it was actually driven 
on a specific day; because they are not closely correlated, the authors 
employ them as alternative measures of vehicle use to estimate the 
rebound effect, but this restricts their sample to the roughly 8,100 
cars for which both measures are available. Weber and Farsi's estimates 
of the rebound effect are extremely large: 75% using estimated annual 
driving and 81% when they measure vehicle use by actual daily driving. 
Excluding vehicle size (weight) and limiting the choices that 
households are assumed to consider simultaneously to just vehicles' 
fuel efficiency and how much to drive approximately reverses these 
estimates, but both are still very large. Using a simpler procedure 
that does not account for the potential effect of driving demand on 
households' choices among vehicle models of different size and fuel 
efficiency produces much smaller values for the rebound effect: 37% 
using annual driving and 19% using daily travel. The authors interpret 
these latter estimates as likely to be too low because actual on-road 
fuel efficiency has not improved as rapidly as suggested by the 
manufacturer-reported measure they employ. This introduces an error in 
their measure that may be related to a vehicle's age, and their more 
complex estimation procedure may reduce its effect on their estimates. 
Nevertheless, even their lower estimates exceed those from many other 
studies of the rebound effect, as Table 8-2 shows.
    Hymel, Small, & Van Dender (2010)--and more recently, Hymel & Small 
(2015)--extended the simultaneous equations analysis of time-series and 
state-level variation in vehicle use originally reported in Small & Van 
Dender (2007) and to test the effect of including more recent data. As 
in the original 2007 study, both subsequent extensions found that the 
fuel economy rebound effect had declined over time in response to 
increasing personal income and urbanization but had risen during 
periods when fuel prices increased. Because they rely on the response 
of vehicle use to fuel cost per mile to estimate the rebound effect, 
however, none of these three studies is able to detect whether its 
apparent decline in response to rising income levels over time truly 
reflects its effect on drivers' responses to changing fuel economy--the 
rebound effect itself--or simply captures the effect of rising income 
on their sensitivity to fuel

[[Page 43104]]

prices.\289\ These updated studies each revised Small and Van Dender's 
original estimate of an 11% rebound effect for 1997-2011 upward when 
they included more recent experience: To 13% for the period 2001-04, 
and subsequently to 18% for 2000-2009.

    \289\ DeBorger et al. (2016) analyze the separate effects of 
variation in household income on the sensitivity of their vehicle 
use to fuel prices and the fuel economy of vehicles they own. Their 
results imply the decline in the fuel economy rebound effect with 
income reported in Small & Van Dender (2007) and its subsequent 
extensions appears to result entirely from a reduction in drivers' 
sensitivity to fuel prices as their incomes rise, rather than from 
any effect of rising income on the sensitivity of vehicle use to 
improving fuel economy; i.e., on the fuel economy rebound effect 

    In their 2015 update, Hymel and Small hypothesized that the recent 
increase in the rebound effect could be traced to a combination of 
expanded media coverage of changing fuel prices, increased price 
volatility, and an asymmetric response by drivers to variation in fuel 
costs. The authors estimated that about half of the apparent increase 
in the rebound effect for recent years could be attributed to greater 
volatility in fuel prices and more media coverage of sudden price 
changes. Their results also suggest that households curtail their 
vehicle use within the first year following an increase in fuel prices 
and driving costs, while the increase in driving that occurs in 
response to declining fuel prices--and by implication, to improvements 
in fuel economy--occurs more slowly.
    West et al. (2015) attempted to infer the fuel economy rebound 
effect using data from Texas households who replaced their vehicles 
with more fuel-efficient models under the 2009 ``Cash for Clunkers'' 
program, which offered sizeable financial incentives to do so. Under 
the program, households that retired older vehicles with fuel economy 
levels of 18 miles per gallon (MPG) or less were eligible for cash 
incentives ranging from $3,500-4,000, while those retiring vehicles 
with higher fuel economy were ineligible for such rebates. The authors 
examined the fuel economy, other features, and subsequent use of new 
vehicles households in Texas purchased to replace older models that 
narrowly qualified for the program's financial incentives because their 
fuel economy was only slightly below the 18 MPG threshold. They then 
compared these to the fuel economy, features, and use of new vehicles 
that demographically comparable households bought to replace older 
models, but whose slightly higher fuel economy--19 MPG or above--made 
them barely ineligible for the program.
    The authors reported that the higher fuel economy of new models 
that eligible households purchased in response to the generous 
financial incentives offered under the ``Cash for Clunkers'' did not 
prompt their buyers to use them more than the older, low-MPG vehicles 
they replaced. They attributed this apparent absence of a fuel economy 
rebound effect--which they described as an ``attribute-adjusted'' 
measure of its magnitude--to the fact that eligible households chose to 
buy less expensive, smaller, and lower-performing models to replace 
those they retired. Because these replacements offered lower-quality 
transportation service, their buyers did not drive them more than the 
vehicles they replaced.
    The applicability of this result to the proposal's analysis is 
doubtful because previous regulatory analyses assumed that 
manufacturers could achieve required improvements in fuel economy 
without compromising the performance, carrying and towing capacity, 
comfort, or safety of cars and light trucks from recent model 
years.\290\ While this may be technically true, doing so would come at 
a combined greater cost. If this argument is correct, then amending 
future standards at a reduced stringency from their previously-adopted 
levels would lead to less driving attributable to rebound, and should 
therefore not lead to artificial constraints in new vehicles' other 
features that offset the reduction in their use stemming from lower 
fuel economy.

    \290\ As discussed, this does not mean attributes of future cars 
and light trucks will be anything close to those manufacturers could 
have offered if lower standards had remained in effect. Instead, the 
agencies asserted features other than fuel economy could be 
maintained at the levels offered in recent model years--that 
features will not likely be removed, but may not be improved.

    Most recently, De Borger et al. (2017) analyze the response of 
vehicle use to changes in fuel economy among a sample of nearly 350,000 
Danish households owning the same model vehicle, of which almost one-
third replaced it with a different model sometime during the period 
from 2001 to 2011. By comparing the changes in households' driving from 
the early years of this period to its later years among those who 
replaced their vehicles during the intervening period to the changes in 
driving among households who kept their original vehicles, the authors 
attempted to isolate the effect of changes in fuel economy on vehicle 
use from those of other factors. They measured the rebound effect as 
the change in households' vehicle use in response to differences in the 
fuel economy between vehicles they had owned previously and the new 
models they purchased to replace them, over and above any change in 
vehicle use among households who did not buy new cars (and thus saw no 
change in fuel economy).
    These authors' data enabled them to control for the effects of 
changes over time in household characteristics and vehicle features 
other than fuel economy that were likely to have contributed to 
observed changes in vehicle use. They also employed complex statistical 
methods to account for the fact that some households replacing their 
vehicles may have done so in anticipation of changes in their driving 
demands (rather than the reverse), as well as for the possibility that 
some households who replaced their cars may have done so because their 
driving behavior was more sensitive to fuel prices than other 
households. Their estimates ranged from 8-10%, varying only minimally 
among alternative model specifications and statistical estimation 
procedures or in response to whether their sample was restricted to 
households that replaced their vehicles or also included households 
that kept their original vehicles throughout the period.\291\ Finally, 
De Borger et al. found no evidence that the rebound effect is smaller 
among lower-income households than among their higher-income 

    \291\ This latter result suggests their estimates were not 
biased by any tendency for households whose demographic 
characteristics, economic circumstances, or driving demands changed 
over the period in ways that prompted them to replace their vehicles 
with models offering different fuel economy.

(c) What value have the agencies assumed in this rule?
    On the basis of all of the evidence summarized here, a fuel economy 
rebound effect of 20% has been chosen to analyze the effects of the 
proposed action. This is a departure from the 10% value used in 
regulatory analyses for MYs 2012-2016 and previous analyses for MYs 
2017-2025 CAFE and GHG standards and represents a return to the value 
employed in the analyses for MYs 2005-2011 CAFE standards. There are 
several reasons the estimate of the fuel economy rebound effect for 
this analysis has been increased.
    First, the 10% value is inconsistent with nearly all research on 
the magnitude of the rebound effect, as Table-II-43 and Table-II-44 
indicate. Instead, it is based almost exclusively

[[Page 43105]]

on the finding of the 2007 study by Small and Van Dender that the 
rebound effect had been declining over time in response to drivers' 
rising incomes and on extending that decline through future years using 
an assumption of steady income growth. As indicated above, however, 
subsequent extensions of Small and Van Dender's original research have 
produced larger estimates of the rebound effect for recent years: While 
their original study estimated the rebound effect at 11% for 1997-2001, 
the 2010 update by Hymel, Small, and Van Dender reported a value of 13% 
for 2004, and Hymel and Small's 2015 update estimated the rebound 
effect at 18% for 2003-09. Further, the issues with state-level 
measures of vehicle use, fuel consumption, and fuel economy identified 
previously raise some doubt about the reliability of these studies' 
estimates of the rebound effect.
    At the same time, the continued increases in income that were 
anticipated to produce a continued decline in the rebound effect have 
not materialized. The income measure (real personal income per Capita) 
used in these analyses has grown only approximately one percent 
annually over the past two decades and is projected to grow at 
approximately 1.5% for the next 30 years, in contrast to the two to 
three percent annual growth assumed by the agencies when developing 
earlier forecasts of the future rebound effect. Further, another recent 
study by DeBorger et al. (2016) analyzed the separate effects of 
variation in household income on the sensitivity of their vehicle use 
to fuel prices and the fuel economy of vehicles they own. These 
authors' results indicate that the decline in the fuel economy rebound 
effect with income reported in Small & Van Dender (2007) and subsequent 
research results entirely from a reduction in drivers' sensitivity to 
fuel prices as their incomes rise rather than from any effect of rising 
income on the sensitivity of vehicle use to fuel economy itself. This 
latter measure, which DeBorger et al. find has not changed 
significantly as incomes have risen over time, is the correct measure 
of the fuel economy rebound effect, so their analysis calls into 
question its assumed sensitivity to income.
    Some studies of households' use of individual vehicles also find 
that the fuel economy rebound effect increases with the number of 
vehicles they own. Because vehicle ownership is strongly associated 
with household income, this common finding suggests that the overall 
value of the rebound effect is unlikely to decline with rising incomes 
as the agencies had previously assumed. In addition, buyers of new cars 
and light trucks belong disproportionately to higher-income households 
that already own multiple vehicles, which further suggests that the 
higher values of the rebound effect estimated by many studies for such 
households are more relevant for analyzing use of newly-purchased cars 
and light trucks.
    Finally, research on the rebound effect conducted since the 
agencies' original 2008 review of evidence almost universally reports 
estimates in the 10-40% (and larger) range, as Table-II-43 shows. Thus, 
the 20% rebound effect used in this analysis more accurately represents 
the findings from both the studies considered in 2008 review and the 
more recent analyses.
(1) What are the implications of the rebound effect for VMT?
    The assumed rebound effect not only influences the use of new 
vehicles in today's analysis but also affects the response of the 
initial registered vehicle population to changes in fuel price 
throughout their remaining useful lives. The fuel prices used in this 
analysis are lower than the projections used to inform the 2012 Final 
Rule but generally increase from today's level over time. As they do 
so, the rebound effect acts as a price elasticity of demand for 
travel--as the cost-per-mile of travel increases, owners of all 
vehicles in the registered population respond by driving less. In 
particular, they drive 20% less than the difference between the cost-
per-mile of travel when they were observed in calendar year 2016 and 
the relevant cost-per-mile at any future age. For the new vehicles 
subject to this proposal (and explicitly simulated by the CAFE model), 
fuel economies increase relative to MY 2016 levels, and generally 
improve enough to offset the effect of rising fuel prices--at least 
during the years covered by the proposal. For those vehicles, the 
difference between the initial cost-per-mile of travel and future 
travel costs is negative. As the vehicles become less expensive to 
operate, they are driven more (20% more than the difference between 
initial and present travel costs, precisely). Of course, each of the 
regulatory alternatives considered in the analysis would result in 
lower fuel economy levels for vehicles produced in model year 2020 and 
later than if the baseline standards remained in effect, so total VMT 
is lower under these alternatives than under the baseline.
(2) What is the mobility benefit that accrues to vehicle owners?
    The increase in travel associated with the rebound effect produces 
additional benefits to vehicle owners, which 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. As evidenced by the fact that they elect to make 
more frequent or longer trips when the cost of driving declines, the 
benefits from this added travel exceed drivers' added outlays for the 
fuel it consumes (measured at the improved level of fuel economy 
resulting from stricter CAFE standards). The amount by which the 
benefits from this increased driving travel exceed its increased fuel 
costs measures the net benefits they receive from the additional 
travel, usually are referred to as increased consumer surplus.
    NHTSA's analysis estimates the economic value of the decreased 
consumer surplus provided by reduced driving using the conventional 
approximation, which is one half of the product of the increase in 
vehicle operating costs per vehicle-mile and the resulting decrease in 
the annual number of miles driven. Because it depends on the extent of 
the change in fuel economy, the value of economic impacts from 
decreased vehicle use changes by model year and varies among 
alternative CAFE standards.
(d) Societal Externalities Associated With CAFE Alternatives
(1) Energy Security Externalities
    Higher U.S. fuel consumption will produce a corresponding increase 
in the nation's demand for crude petroleum, which is traded actively in 
a worldwide market. The U.S. accounts for a large enough share of 
global oil consumption that the resulting boost in global demand will 
raise its worldwide price. The increase in global petroleum prices that 
results from higher U.S. demand causes a transfer of revenue to oil 
producers worldwide from not only buyers of new cars and light trucks, 
but also other consumers of petroleum products in the U.S. and 
throughout the world, all of whom pay the higher price that results.
    Although these effects will be tempered by growing U.S. oil 
production, uncertainty in the long-term import-export balance makes it 
difficult to precisely project how these effects might change in 
response to that increased production. Growing U.S. petroleum 
consumption will also increase potential costs to all U.S. petroleum 
users from possible interruptions in the global supply of petroleum or 
rapid increases in global oil prices, not all of which are borne by

[[Page 43106]]

the households or businesses who increase their petroleum consumption 
(that is, they are partly ``external'' to petroleum users). If U.S. 
demand for imported petroleum increases, it is also possible that 
increased military spending to secure larger oil supplies from unstable 
regions of the globe will be necessary.
    These three effects are often referred to collectively as ``energy 
security externalities'' resulting from U.S. petroleum consumption, and 
increases in their magnitude are sometimes cited as potential social 
costs of increased U.S. demand for oil. To the extent that they 
represent real economic costs that would rise incrementally with 
increases in U.S. petroleum consumption of the magnitude likely to 
result from less stringent CAFE and GHG standards, these effects 
represent potential additional costs of this proposed action. Chapter 7 
of the Regulatory Impact Analysis for this proposed action defines each 
of these energy security externalities in detail, assesses whether its 
magnitude is likely to change as a consequence of this action, and 
identifies whether that change represents a real economic cost or 
benefit of this action.
(2) Environmental Externalities
    The change in criteria pollutant emissions that result from changes 
in vehicle usage and fuel consumption is estimated as part of this 
analysis. Criteria air pollutants include carbon monoxide (CO), 
hydrocarbon compounds (usually referred to as ``volatile organic 
compounds,'' or VOC), nitrogen oxides (NOX), fine 
particulate matter (PM2.5), and sulfur oxides 
(SOX). These pollutants are emitted during vehicle storage 
and use, as well as throughout the fuel production and distribution 
system. While increases in domestic fuel refining, storage, and 
distribution that result from higher fuel consumption will increase 
emissions of these pollutants, reduced vehicle use associated with the 
fuel economy rebound effect will decrease their emissions. The net 
effect of less stringent CAFE standards on total emissions of each 
criteria pollutant depends on the relative magnitudes of increases in 
its emissions during fuel refining and distribution, and decreases in 
its emissions resulting from additional vehicle use. Because the 
relationship between emissions in fuel refining and vehicle use is 
different for each criteria pollutant, the net effect of increased fuel 
consumption from the proposed standards on total emissions of each 
pollutant is likely to differ.
    The social damage costs associated with changes in the emissions of 
criteria pollutants and CO2 was calculated, attributing 
benefits and costs to the regulatory alternatives considered based on 
the sign of the change in each pollutant. In previous rulemakings, the 
agencies have considered the social cost of CO2 emissions 
from a global perspective, accumulating social costs for CO2 
emissions based on adverse outcomes attributable to climate change in 
any country. In this analysis, however, the costs of CO2 
emissions and resulting climate damages from both domestic and global 
perspectives were considered. Chapter 9 of the Regulatory Impact 
Analysis provides a detailed discussion of how the agencies estimate 
changes in emissions of criteria air pollutants and CO2 and 
reports the values the agencies use to estimate benefits or costs 
associated with those changes in emissions.
(3) Traffic Externalities (Congestion, Noise)
    Increased vehicle use associated with the rebound effect also 
contributes to increased traffic congestion and highway noise. To 
estimate the economic costs associated with these consequences of added 
driving, the estimates of per-mile congestion and noise costs caused by 
increased use of automobiles and light trucks developed previously by 
the Federal Highway Administration (FHWA) were applied. These values 
are intended to measure the increased costs resulting from added 
congestion and the delays it causes to other drivers and passengers and 
noise levels contributed by automobiles and light trucks. NHTSA 
previously employed these estimates in its analysis accompanying the MY 
2011 final CAFE rule as well as in its analysis of the effects of 
higher CAFE standards for MY 2012-16 and MY 2017-2021. After reviewing 
the procedures used by FHWA to develop them and considering other 
available estimates of these values and recognizing that no commenters 
have addressed these costs directly in their comments on previous 
rules, the values continue to be appropriate for use in this proposal. 
For this analysis, FHWA's estimates of per-mile costs are multiplied by 
the annual increases in automobile and light truck use from the rebound 
effect to yield the estimated increases in total congestion and noise 
externality costs during each year over the lifetimes of the cars and 
light trucks in the on-road fleet. Due to the fact that this proposal 
represents a decrease in stringency, the fuel economy rebound effect 
results in fewer miles driven under the action alternatives relative to 
the baseline, which generates savings in congestion and road noise 
relative to the baseline.

F. Impact of CAFE Standards on Vehicle Safety

    In past CAFE rulemakings, NHTSA has examined the effect of CAFE 
standards on vehicle mass and the subsequent effect mass changes will 
have on vehicle safety. While setting standards based on vehicle 
footprint helps reduce potential safety impacts associated with CAFE 
standards as compared to setting standards based on some other vehicle 
attribute, footprint-based standards cannot entirely eliminate those 
impacts. Although prior analyses noted that there could also be impacts 
because of other factors besides mass changes, those impacts were not 
estimated quantitatively.\292\ In this current analysis, the safety 
analysis has been expanded to include a broader and more comprehensive 
measure of safety impacts, as discussed below. A number of factors can 
influence motor vehicle fatalities directly by influencing vehicle 
design or indirectly by influencing consumer behavior. These factors 

    \292\ NHTSA included a quantification of rebound-associated 
safety impacts in its Draft TAR analysis, but because the scrappage 
model is new for this rulemaking, did not include safety impacts 
associated with the effect of standards on new vehicle prices and 
thus on fleet turnover. The fact that the scrappage model did not 
exist previously does not mean that the effects that it aims to show 
were not important considerations, simply that the agency was unable 
to account for them quantitatively prior to the current analysis.

    (1) Changes, which affect the crashworthiness of vehicles impact 
other vehicles or roadside objects, in vehicle mass made to reduce fuel 
consumption. NHTSA's statistical analysis of historical crash data to 
understand effects of vehicle mass and size on safety indicates 
reducing mass in light trucks generally improves safety, while reducing 
mass in passenger cars generally reduces safety. NHTSA's crash 
simulation modeling of vehicle design concepts for reducing mass 
revealed similar trends.\293\

    \293\ DOT HS 812051a--Methodology for evaluating fleet 
protection of new vehicle designs Application to lightweight vehicle 
designs, DOT HS 812051b Methodology for evaluating fleet protection 
of new vehicle designs_Appendices.

    (2) The delay in the pace of consumer acquisition of newer safer 
vehicles that results from higher vehicle prices associated with 
technologies needed to meet higher CAFE standards. Because of a 
combination of safety regulations and voluntary safety improvements, 
passenger vehicles have become safer over time. Compared to prior 
decades, fatality rates have declined significantly

[[Page 43107]]

because of technological safety improvements as well as behavioral 
shifts such as increased seat belt use. The results of this analysis 
project that vehicle prices will be nearly $1,900 higher under the 
augural CAFE standards compared to the preferred alternative that would 
hold stringency at MY 2020 levels in MYs 2021-2026. This will induce 
some consumers to delay or forgo the purchase of newer safer vehicles 
and slow the transition of the on-road fleet to one with the improved 
safety available in newer vehicles. This same factor can also shift the 
mix of passenger cars and light trucks.
    (3) Increased driving because of better fuel economy. The ``rebound 
effect'' predicts consumers will drive more when the cost of driving 
declines. More stringent CAFE standards reduce vehicle operating costs, 
and in response, some consumers may choose to drive more. Driving more 
increases exposure to risks associated with on-road transportation, and 
this added exposure translates into higher fatalities.
    Although all three factors influence predicted fatality levels that 
may occur, only two of them, the changes in vehicle mass and the 
changes in the acquisition of safer vehicles--are actually imposed on 
consumers by CAFE standards. The safety of vehicles has improved over 
time and is expected to continue improving in the future commensurate 
with the pace of safety technology innovation and implementation and 
motor vehicle safety regulation. Safety improvements will likely 
continue regardless of changes to CAFE standards. However, its pace may 
be modified if manufacturers choose to delay or forgo investments in 
safety technology because of the demand CAFE standards impose on 
research, development, and manufacturing budgets. Increased driving 
associated with rebound is a consumer choice. Improved CAFE will reduce 
driving costs, but nothing in the higher CAFE standards compels 
consumers to drive additional miles. If consumers choose to do so, they 
are making a decision that the utility of more driving exceeds the 
marginal operating costs as well as the added crash risk it entails. 
Thus, while the predicted fatality impacts with all three factors 
embedded into the model are measured, the fatalities associated with 
consumer choice decisions are accounted for separately from those 
resulting from technologies implemented in response to CAFE regulations 
or economic limitations resulting from CAFE regulation. Only those 
safety impacts associated with mass reduction and those resulting from 
higher vehicle prices are directly attributed to CAFE standards.\294\ 
This is reflected monetarily by valuing extra rebound miles at the full 
value of their added driving cost plus the added safety risk consumers 
experience, which completely offsets the societal impact of any added 
fatalities from this voluntary consumer choice.

    \294\ It could be argued fatalities resulting from consumer's 
decision to delay the purchase of newer safer vehicles is also a 
market decision implying consumers fully accept the added safety 
risk associated with this delay and value the time value of money 
saved by the delayed purchase more than this risk. This scenario is 
likely accurate for some purchasers. For others, the added cost may 
represent a threshold price increase effectively preventing them 
from being financially able to purchase a new vehicle. Presently 
there is no way to determine the proportion of lost sales reflected 
by these two scenarios. The added driving from the rebound effect 
results from a positive benefit of CAFE, which reduces the cost of 
driving. By contrast, the effect of retaining older vehicles longer 
results from costs imposed on consumers, which potentially limit 
their purchase options. Thus, fatalities are attributed to retaining 
older vehicles due to CAFE but not those resulting from decisions to 
drive more. Comments are sought on this assumption.

    The safety component of CAFE analysis has evolved over time. In the 
2012 final rule, the analysis accounted for the change in projected 
fatalities attributable to mass reduction of new vehicles. The model 
assumed that manufacturers would choose mass reduction as a compliance 
method across vehicle classes such that the net effect of mass 
reduction on fatalities was zero. However, in the 2016 draft Technical 
Assessment Report, DOT made two consequential changes to the analysis 
of fatalities associated with the CAFE standards. In particular, first, 
the modelling assumed that mass reduction technology was available to 
all vehicles, regardless of net safety impact, and second, it accounted 
for the incremental safety costs associated with additional miles 
traveled due to the rebound effect. The current analysis extends the 
analysis to report incremental fatality impacts associated with 
additional miles traveled due to the rebound effect, and identifies the 
increase in fatalities associated with additional driving separately 
from changes in fatalities attributable other sources.\295\

    \295\ Drivers who travel additional miles are assumed to 
experience benefits that at least offset the costs they incur in 
doing so, including the increased safety risks they face. Thus while 
the number of additional fatalities resulting from increased driving 
is reported, the associated costs are not included among the social 
costs of the proposal.

    The current analysis adds another element: The effect that higher 
new vehicle prices have on new vehicle sales and on used vehicle 
scrappage, which influences total expected fatalities because older 
vehicle vintages are associated with higher rates of involvement in 
fatal crashes than newer vehicles. Finally, a dynamic fleet share model 
also predicts the effects of changes in the standards on the share of 
light trucks and passenger cars in future model year light-duty vehicle 
fleets. Vehicles of different body styles have different rates of 
involvement in fatal crashes, so that changing the share of each in the 
projected future fleet has safety impacts; the implied safety effects 
are captured in the current modelling. The agencies seek comment on 
changes to the safety analysis made in this proposal, they seek 
particular comment on the following changes:

    (1) The sales scrappage models as independent models: Two 
separate models capture the effects of new vehicle prices on new 
vehicle demand and used vehicle retirement rates--the sales model 
and the scrappage model, respectively. We seek public comment on the 
methods used for each of these models, in particular we seek comment 

 The assumptions and variables included in the independent 
 The techniques and data used to estimate the independent 
 The structure and implementation of the independent models
    (2) Integration of the sales and scrappage models: The new sales 
and scrappage models use many of the same predictors, but are not 
directly integrated. We seek public comment on, and data supporting 
whether integrating the two models is appropriate.
    (3) Integration of the scrappage rates and mileage accumulation: 
The current model assumes that annual mileage accumulation and 
scrappage rates are independent of one another. We seek public 
comment on the appropriateness of this assumption, and data that 
would support developing an interaction between scrappage rates and 
mileage accumulation, or testing whether such an interaction is 
important to include.
    (4) Increased risk of older vehicles: The observed increase in 
crash and injury risk associated with older vehicles is likely due 
to a combination of vehicle factors and driver factors. For example, 
older vehicles are less crashworthy because in general they're 
equipped with fewer or less modern safety features, and drivers of 
older cars are on average younger and may be less skilled drivers or 
less risk-averse than drivers of new vehicles. We fit a model which 
includes both an age and vintage affect, but assume that the age 
effect is entirely a result of changes in average driver 
demographics, and not impacted by changes in CAFE or GHG standards. 
We seek comment on this approach for attributing increased older 
vehicle risk. Is the analysis likely to overestimate or 
underestimate the safety benefits under the proposed alternative?
    (5) Changes in the mix of light trucks and passenger cars: The 
dynamic fleet share model predicts changes in the future share of 
light truck and passenger car vehicles. Changes in the mix of 
vehicles may result in

[[Page 43108]]

increased or decreased fatalities. Does the dynamic fleet share 
model reasonably capture consumers' decisions about how they 
substitute between different types and sizes of vehicles depending 
on changes in fuel economy, relative and absolute prices, and other 
vehicle attributes? We seek comment on whether our safety analysis 
provides a reasonable estimate of the effects of changes in fleet 
mix on future fatalities.

1. Impact of Weight Reduction on Safety
    The primary goals of CAFE and CO2 standards are reducing 
fuel consumption and CO2 emissions from the on-road light-
duty vehicle fleet; in addition to these intended effects, the 
potential of the standards to affect vehicle safety is also 
considered.\296\ As a safety agency, NHTSA has long considered the 
potential for adverse safety consequences when establishing CAFE 
standards, and under the CAA, EPA considers factors related to public 
health and human welfare, including safety, in regulating emissions of 
air pollutants from mobile sources.

    \296\ In this rulemaking document, ``vehicle safety'' is defined 
as societal fatality rates per vehicle mile of travel (VMT), 
including fatalities to occupants of all vehicles involved in 
collisions, plus any pedestrians. Injuries and property damage are 
not within the scope of the statistical models discussed in this 
section because of data limitations (e.g., limited information on 
observed or potential relationships between safety standards and 
injury and property damage outcomes, consistency of reported injury 
severity levels). Rather, injuries and property damage are 
represented within the CAFE model through adjustment factors based 
on observed relationships between societal costs of fatalities and 
societal injury and property damage costs.

    Safety trade-offs associated with fuel economy increases have 
occurred in the past, particularly before NHTSA 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. Although the agency now uses 
attribute-based standards, in part to protect against excessive vehicle 
downsizing, the agency must be mindful of the possibility of related 
safety trade-offs in the future. 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.
    Historically, as shown in FARS data analyzed by NHTSA, the safest 
cars generally have been heavy and large, while cars with the highest 
fatal-crash rates have been light and small. The question, then, is 
whether past is necessarily a prologue when it comes to potential 
changes in vehicle size (both footprint and ``overhang'') and mass in 
response to the more stringent future CAFE and GHG standards.
    Manufacturers stated they will reduce vehicle mass as one of the 
cost-effective means of increasing fuel economy and reducing 
CO2 to meet standards, and this approach is incorporated 
this expectation into the modeling analysis supporting the standards. 
Because the analysis discerns a historical relationship between vehicle 
mass, size, and safety, it is reasonable to assume these relationships 
will continue in the future.
(a) Historical Analyses of Vehicle Mass and Safety
    Researchers have been using statistical analysis to examine the 
relationship of vehicle mass and safety in historical crash data for 
many years and continue to refine their techniques. In the MY 2012-2016 
final rule, the agencies stated we would conduct further study and 
research into the interaction of mass, size, and safety to assist 
future rulemakings and start to work collaboratively by developing an 
interagency working group between NHTSA, EPA, DOE, and CARB to evaluate 
all aspects of mass, size, and safety. The team would seek to 
coordinate government-supported studies and independent research to the 
greatest extent possible to ensure the work is complementary to 
previous and ongoing research and to guide further research in this 
    The agencies also identified three specific areas to direct 
research in preparation for future CAFE/CO2 rulemaking 
regarding statistical analysis of historical data. First, NHTSA would 
contract with an independent institution to review statistical methods 
NHTSA and DRI used to analyze historical data related to mass, size, 
and safety, and to provide recommendations on whether existing or other 
methods should be used for future statistical analysis of historical 
data. This study would include a consideration of potential near 
multicollinearity in the historical data and how best to address it in 
a regression analysis. The 2010 NHTSA report (hereinafter 2010 Kahane 
report) was also peer reviewed by two other experts in the safety 
field--Farmer (Insurance Institute for Highway Safety) and Lie (Swedish 
Transport Administration).\297\

    \297\ All three peer reviews are available in Docket No. NHTSA-
2010-0152, Relationships Between Fatality Risk, Mass, and Footprint, 

    Second, NHTSA and EPA, in consultation with DOE, would update the 
MY 1991-1999 database where safety analyses in the NPRM and final rule 
are based with newer vehicle data and create a common database that 
could be made publicly available to address concerns that differences 
in data were leading to different results in statistical analyses by 
different researchers.
    And third, to assess if the design of recent model year vehicles 
incorporating various mass reduction methods affect relationships among 
vehicle mass, size, and safety, the agencies sought to identify 
vehicles using material substitution and smart design and to assess if 
there is sufficient crash data involving those vehicles for statistical 
analysis. If sufficient data exists, statistical analysis would be 
conducted to compare the relationship among mass, size, and safety of 
these smart design vehicles to vehicles of similar size and mass with 
more traditional designs.
    By the time of the MY 2017-2025 final rule, significant progress 
was made on these tasks: The independent review of recent and updated 
statistical analyses of the relationship between vehicle mass, size, 
and crash fatality rates had been completed. NHTSA contracted with the 
University of Michigan Transportation Research Institute (UMTRI) to 
conduct this review, and the UMTRI team led by Green evaluated more 
than 20 papers, including studies done by NHTSA's Kahane, Wenzel of the 
U.S. Department of Energy's Lawrence Berkeley National Laboratory, 
Dynamic Research, Inc., and others. UMTRI's basic findings are 
discussed in Chapter 11 of the PRIA accompanying this NPRM.
    Some commenters in recent CAFE rulemakings, including some vehicle 
manufacturers, suggested designs and materials of more recent model 
year vehicles may have weakened the historical statistical 
relationships between mass, size, and safety. It was agreed that the 
statistical analysis would be improved by using an updated database 
reflecting more recent safety technologies, vehicle designs and 
materials, and reflecting changes in the vehicle fleet. An updated 
database was created and employed for assessing safety effects for that 
final rule. The agencies also believed, as UMTRI found, different 
statistical analyses may have produced different results because they 
used slightly different datasets for their analyses.
    To try to mitigate this issue and to support the current 
rulemaking, NHTSA created a common, updated database for statistical 
analysis consisting of crash data of model years 2000-2007 vehicles in 
calendar years 2002-2008, as

[[Page 43109]]

compared to the database used in prior NHTSA analyses, which was based 
on model years 1991-1999 vehicles in calendar years 1995-2000. The new 
database was the most up-to-date possible, given the processing lead 
time for crash data and the need for enough crash cases to permit 
statistically meaningful analyses. NHTSA made the preliminary version 
of the new database, which was the basis for NHTSA's 2011 preliminary 
report (hereinafter 2011 Kahane report), available to the public in May 
2011, and an updated version in April 2012 (used in NHTSA's 2012 final 
report, hereinafter 2012 Kahane report),\298\ enabling other 
researchers to analyze the same data and hopefully minimize 
discrepancies in results because of inconsistencies across 

    \298\ Those databases are available at ftp://ftp.nhtsa.dot.gov/CAFE/.
    \299\ See 75 FR 25324, 25395-25396 (May 7, 2010) (for a 
discussion of planned statistical analyses).

    Since the publication of the MYs 2017-2025 final rule, NHTSA has 
sponsored, and is sponsoring, new studies and research to inform the 
current CAFE and CO2 rulemaking. In addition, the National 
Academy of Sciences published a new report in this area.\300\ 
Throughout the rulemaking process, NHTSA's goal is to publish as much 
of our research as possible. In establishing standards, all available 
data, studies, and information objectively without regard to whether 
they were sponsored by the agencies, will be considered.

    \300\ Cost, Effectiveness and Deployment of Fuel Economy 
Technologies for Light-Duty Vehicles, National Academy of Sciences 

    Undertaking these tasks has helped come closer to resolving ongoing 
debates in statistical analysis research of historical crash data. It 
is intended that these conclusions will be applied going forward in 
future rulemakings, and it is believed the research will assist the 
public discussion of the issues. Specific historical analyses (in 
addition to NHTSA's own analysis) on vehicle mass and safety used to 
support this rulemaking include:
     The 2011 and 2013 NHTSA Workshops on Vehicle Mass, Size, 
and Safety;
     the University of Michigan Transportation Research 
Institute (UMTRI) independent review of a set of statistical 
relationships between vehicle curb weight, footprint variables (track 
width, wheelbase), and fatality rates from vehicle crashes;
     the 2012 Lawrence Berkeley National Laboratory (LBNL) 
Phase 1 and Phase 2 reports on the sensitivity of NHTSA's baseline 
results and casualty risk per VMT;
     the 2012 DRI reports on, among other things, the effects 
of mass reduction on crash frequency and fatality risk per crash;
     LBNL's subsequent review of DRI's study;
     the 2015 National Academy of Sciences Report; and
     the 2017 NBER working paper analyzing the relationships 
among traffic fatalities, CAFE standards, and distributions of MY 1989-
2005 light-duty vehicle curb weights.
    A detailed discussion of each analysis is discussed in Chapter 11 
of the PRIA accompanying this proposed rule.
(b) Recent NHTSA Analysis Supporting CAFE Rulemaking
    As mentioned previously, NHTSA and EPA's 2012 joint final rule for 
MYs 2017 and beyond set ``footprint-based'' standards, with footprint 
being defined as roughly equal to the wheelbase multiplied by the 
average of the front and rear track widths. Basing standards on vehicle 
footprint ideally helps to discourage vehicle manufacturers from 
downsizing their vehicles; the agencies set higher (more stringent) 
mile per gallon (mpg) targets for smaller-footprint vehicles but would 
not similarly discourage mass reduction that maintains footprint while 
potentially improving fuel economy. Several technologies, such as 
substitution of light, high-strength materials for conventional 
materials during vehicle redesigns, have the potential to reduce weight 
and conserve fuel while maintaining a vehicle's footprint and 
maintaining or possibly improving the vehicle's structural strength and 
    In considering what technologies are available for improving fuel 
economy, including mass reduction, an important corollary issue for 
NHTSA to consider is the potential effect those technologies may have 
on safety. NHTSA has thus far specifically considered the likely effect 
of mass reduction that maintains footprint on fatal crashes. The 
relationship between a vehicle's mass, size, and fatality risk is 
complex, and it varies in different types of crashes. As mentioned 
above, NHTSA, along with others, has been examining this relationship 
for more than a decade.\301\

    \301\ A complete discussion of the historical analysis of 
vehicle mass and safety is located in Chapter 10 of the PRIA 
accompanying this proposed rulemaking.

    The safety chapter of NHTSA's April 2012 final regulatory impact 
analysis (FRIA) of CAFE standards for MY 2017-2021 passenger cars and 
light trucks included a statistical analysis of relationships between 
fatality risk, mass, and footprint in MY 2000-2007 passenger cars and 
LTVs (light trucks and vans), based on calendar year (CY) 2002-2008 
crash and vehicle-registration data; \302\ this analysis was also 
detailed in the 2012 Kahane report.

    \302\ Kahane, C.J. Relationships Between Fatality Risk, Mass, 
and Footprint in Model Year 2000-2007 Passenger Cars and LTVs--Final 
Report, National Highway Traffic Safety Administration (Aug. 2012), 
available at https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/811665.

    The principal findings and conclusions of the 2012 Kahane report 
were mass reduction in the lighter cars, even while holding footprint 
constant, would significantly increase fatality risk, whereas mass 
reduction in the heavier LTVs would reduce societal fatality risk by 
reducing the fatality risk of occupants of lighter vehicles colliding 
with those heavier LTVs. NHTSA concluded, as a result, any reasonable 
combination of mass reductions that held footprint constant in MY 2017-
2021 vehicles--concentrated, at least to some extent, in the heavier 
LTVs and limited in the lighter cars--would likely be approximately 
safety-neutral; it would not significantly increase fatalities and 
might well decrease them.
    NHTSA released a preliminary report (2016 Puckett and Kindelberger 
report) on the relationship between fatality risk, mass, and footprint 
in June 2016 in advance of the Draft TAR. The preliminary report 
covered the same scope as the 2012 Kahane report, offering a detailed 
description of the databases, modeling approach, and analytical results 
on relationships among vehicle size, mass, and fatalities that informed 
the Draft TAR. Results in the Draft TAR and the 2016 Puckett and 
Kindelberger report are consistent with results in the 2012 Kahane 
report; chiefly, societal effects of mass reduction are small, and mass 
reduction concentrated in larger vehicles is likely to have a 
beneficial effect on fatalities, while mass reduction concentrated in 
smaller vehicles is likely to have a detrimental effect on fatalities.
    For the 2016 Puckett and Kindelberger report and Draft TAR, NHTSA, 
working closely with EPA and the DOE, performed an updated statistical 
analysis of relationships between fatality rates, mass and footprint, 
updating the crash and exposure databases to the latest available model 
years. The agencies analyzed updated databases that included MY 2003-
2010 vehicles in CY 2005-2011 crashes. For this proposed

[[Page 43110]]

rule, databases are the most up-to-date possible (MY 2004-2011 vehicles 
in CY 2006-2012), given the processing time for crash data and the need 
for enough crash cases to permit statistically meaningful analyses. As 
in previous analyses, NHTSA has made the new databases available to the 
public on its website, enabling other researchers to analyze the same 
data and hopefully minimizing discrepancies in results that would have 
been because of inconsistencies across databases.
(c) Updated Analysis for This Rulemaking
    The basic analytical method used to analyze the impacts of weight 
reduction on safety in this proposed rule is the same as in NHTSA's 
2012 Kahane report, 2016 Puckett and Kindelberger report, and the Draft 
TAR: The agency analyzed 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 by vehicle class and crash type. ``Societal'' 
fatality rates include fatalities to occupants of all the vehicles 
involved in the collisions, plus any pedestrians.
    The temporal range of the data is now MY 2004-2011 vehicles in CY 
2006-2012, updated from previous databases of MY 2000-2007 vehicles in 
CY 2002-2008 (2012 Kahane Report) and MY 2003-2010 vehicles in CY 2005-
2011 (2016 Puckett and Kindelberger report and Draft TAR). NHTSA 
purchased a file of odometer readings by make, model, and model year 
from Polk that helped inform the agency's improved VMT estimates. As in 
the 2012 Kahane report, 2016 Puckett and Kindelberger report, and the 
Draft TAR, the vehicles are grouped into three classes: Passenger cars 
(including both two-door and four-door cars); CUVs and minivans; and 
truck-based LTVs.
    There are nine types of crashes specified in the analysis. Single-
vehicle crashes include first-event rollovers, collisions with fixed 
objects, and collisions with pedestrians, bicycles and motorcycles. 
Two-vehicle crashes include collisions with: heavy-duty vehicles; car, 
CUV, or minivan < 3,187 pounds (the median curb weight of other, non-
case, cars, CUVs and minivans in fatal crashes in the database); car, 
CUV, or minivan >= 3,187 pounds; truck-based LTV < 4,360 pounds (the 
median curb weight of other truck-based LTVs in fatal crashes in the 
database); and truck-based LTV >= 4,360 pounds. An additional crash 
type includes all other fatal crash types (e.g., collisions involving 
more than two vehicles, animals, or trains). Splitting the ``other'' 
vehicles into a lighter and a heavier group permits more accurate 
analyses of the mass effect in collisions of two light vehicles. 
Grouping partner-vehicle CUVs and minivans with cars rather than LTVs 
is more appropriate because their front-end profile and rigidity more 
closely resembles a car than a typical truck-based LTV.
    The curb weight of passenger cars is formulated, as in the 2012 
Kahane report, 2016 Puckett and Kindelberger report, and Draft TAR, as 
a two-piece linear variable to estimate one effect of mass reduction in 
the lighter cars and another effect in the heavier cars. The boundary 
between ``lighter'' and ``heavier'' cars is 3,201 pounds (which is the 
median mass of MY 2004-2011 cars in fatal crashes in CY 2006-2012, up 
from 3,106 for MY 2000-2007 cars in CY 2002-2008 in the 2012 NHTSA 
safety database, and up from 3,197 for MY 2003-2010 cars in CY 2005-
2011 in the 2016 NHTSA safety database).
    Likewise, for truck-based LTVs, curb weight is a two-piece linear 
variable with the boundary at 5,014 pounds (again, the MY 2004-2011 
median, higher than the median of 4,594 for MY 2000-2007 LTVs in CY 
2002-2008 and the median of 4,947 for MY 2003-2010 LTVs in CY 2005-
2011). Curb weight is formulated as a simple linear variable for CUVs 
and minivans. Historically, CUVs and minivans have accounted for a 
relatively small share of new-vehicle sales over the range of the data, 
resulting in less crash data available than for cars or truck-based 
    For a given vehicle class and weight range (if applicable), 
regression coefficients for mass (while holding footprint constant) in 
the nine types of crashes are averaged, weighted by the number of 
baseline fatalities that would have occurred for the subgroup MY 2008-
2011 vehicles in CY 2008-2012 if these vehicles had all been equipped 
with electronic stability control (ESC). The adjustment for ESC, a 
feature of the analysis added in 2012, takes into account results will 
be used to analyze effects of mass reduction in future vehicles, which 
will all be ESC-equipped, as required by NHTSA's regulations.
    Techniques developed in the 2011 (preliminary) and 2012 (final) 
Kahane reports have been retained to test statistical significance and 
to estimate 95 percent confidence bounds (sampling error) for mass 
effects and to estimate the combined annual effect of removing 100 
pounds of mass from every vehicle (or of removing different amounts of 
mass from the various classes of vehicles), while holding footprint 
    NHTSA considered the near multicollinearity of mass and footprint 
to be a major issue in the 2010 Kahane report \303\ and voiced concern 
about inaccurately estimated regression coefficients.\304\ High 
correlations between mass and footprint and variance inflation factors 
(VIF) have not changed from MY 1991-1999 to MY 2004-2011; large 
vehicles continued to be, on the average, heavier than small vehicles 
to the same extent as in the previous decade.\305\

    \303\ Kahane, C. J. Relationships Between Fatality Risk, Mass, 
and Footprint in Model Year 1991-1999 and Other Passenger Cars and 
LTVs (Mar. 24, 2010), in Final Regulatory Impact Analysis: Corporate 
Average Fuel Economy for MY 2012-MY 2016 Passenger Cars and Light 
Trucks, National Highway Traffic Safety Administration (Mar. 2010) 
at 464-542.
    \304\ Van Auken and Green also discussed the issue in their 
presentations at the NHTSA Workshop on Vehicle Mass-Size-Safety in 
Washington, DC February 25, 2011. More information on the NHTSA 
Workshop on Vehicle Mass-Size-Safety is available at https://one.nhtsa.gov/Laws-&-Regulations/CAFE-%E2%80%93-Fuel-Economy/NHTSA-Workshop-on-Vehicle-Mass%E2%80%93Size%E2%80%93Safety.
    \305\ Greene, W. H. Econometric Analysis 266-68 (Macmillan 
Publishing Company 2d ed. 1993); Paul D. Allison, Logistic 
Regression Using the SAS System 48-51 (SAS Institute Inc. 2001). VIF 
scores are in the 6-9 range for curb weight and footprint in NHTSA's 
new database--i.e., in the somewhat unfavorable 2.5-10 range where 
near multicollinearity begins to become a concern in logistic 
regression analyses.

    Nevertheless, multicollinearity appears to have become less of a 
problem in the 2012 Kahane, 2016 Puckett and Kindelberger/Draft TAR, 
and current NHTSA analyses. Ultimately, only three of the 27 core 
models of fatality risk by vehicle type in the current analysis 
indicate the potential presence of effects of multicollinearity, with 
estimated effects of mass and footprint reduction greater than two 
percent per 100-pound mass reduction and one-square-foot footprint 
reduction, respectively; these three models include passenger cars and 
CUVs in first-event rollovers, and CUVs in collisions with LTVs greater 
than 4,360 pounds. This result is consistent with the 2016 Puckett and 
Kindelberger report, which also found only three cases out of 27 models 
with estimated effects of mass and footprint reduction greater than two 
percent per 100-pound mass reduction and one-square-foot footprint 
    Table II-45 presents the estimated percent increase in U.S. 
societal fatality risk per 10 billion VMT for each 100-

[[Page 43111]]

pound reduction in vehicle mass, while holding footprint constant, for 
each of the five vehicle classes:

    None of the estimated effects have 95-percent confidence bounds 
that exclude zero, and thus are not statistically significant at the 
95-percent confidence level. Two estimated effects are statistically 
significant at the 85-percent level. Societal fatality risk is 
estimated to: (1) Increase by 1.2 percent if mass is reduced by 100 
pounds in the lighter cars; and (2) decrease by 0.61 percent if mass is 
reduced by 100 pounds in the heavier truck-based LTVs. The estimated 
increases in societal fatality risk for mass reduction in the heavier 
cars and the lighter truck-based LTVs, and the estimated decrease in 
societal fatality risk for mass reduction in CUVs and minivans are not 
significant, even at the 85-percent confidence level.
    Confidence bounds estimate only the sampling error internal to the 
data used in the specific analysis that generated the point estimate. 
Point estimates are also sensitive to the modification of components of 
the analysis, as discussed at the end of this section. However, this 
degree of uncertainty is methodological in nature rather than 
    It is useful to compare the new results in Table II-45 to results 
in the 2012 Kahane report (MY 2000-2007 vehicles in CY 2002-2008) and 
the 2016 Puckett and Kindelberger report and Draft TAR (MY 2003-2010 
vehicles in CY 2005-2011), presented in Table II-46 below:

    \306\ Median curb weights in the 2012 Kahane report: 3,106 
pounds for cars, 4,594 pounds for truck-based LTVs. Median curb 
weights in the 2016 Puckett and Kindelberger report: 3,197 pounds 
for cars, 4,947 pounds for truck-based LTVs.

    New results are directionally the same as in 2012; in the 2016 
analysis, the estimate for lighter LTVs was of opposite sign (but small 
magnitude). Consistent with the 2012 Kahane and 2016 Puckett and 
Kindelberger reports, mass reductions in lighter cars are estimated to 
lead to increases in fatalities, and mass reductions in heavier LTVs 
are estimated to lead to decreases in fatalities. However, NHTSA does 
not consider this conclusion to be definitive because of the relatively 
wide confidence bounds of the estimates. The estimated mass effects are 
similar among analyses for both classes of passenger cars; for all 
reports, the estimate for lighter passenger cars is statistically 
significant at the 85-percent confidence level, while the estimate for 
heavier passenger cars is insignificant.
    The estimated mass effect for heavier truck-based LTVs is stronger 
in this analysis and in the 2016 Puckett and Kindelberger report than 
in the 2012 Kahane report; both estimates are statistically significant 
at the 85-percent confidence level, unlike the corresponding 
insignificant estimate in the 2012 Kahane report. The estimated mass 
effect for lighter truck-based LTVs is insignificant and positive in 
this analysis and the 2012 Kahane report, while the corresponding 
estimate in the 2016 Puckett and Kindelberger report was insignificant 
and negative.
    Vehicle mass continued an historical upward trend across the MYs in 
the newest databases. The average (VMT-weighted) masses of passenger 
cars and CUVs both increased by approximately three percent from MYs 
2004 to 2011 (3,184 pounds to 3,289 pounds for passenger cars, and 
3,821 pounds to 3,924 pounds for CUVs). Over the same period, the 
average mass of minivans increased by six percent (from 4,204 pounds to 
4,462 pounds), and the average mass of LTVs increased by 10% (from 
4,819 pounds to 5,311 pounds).

[[Page 43112]]

Historical reasons for mass increases within vehicle classes include: 
Manufacturers discontinuing lighter models; manufacturers re-designing 
models to be heavier and larger; and shifting consumer preferences with 
respect to cabin size and overall vehicle size.
    The principal difference between heavier vehicles, especially 
truck-based LTVs, and lighter vehicles, especially passenger cars, is 
mass reduction has a different effect in collisions with another car or 
LTV. When two vehicles of unequal mass collide, the change in velocity 
(delta V) is greater in the lighter vehicle. Through conservation of 
momentum, the degree to which the delta V in the lighter vehicle is 
greater than in the heavier vehicle is proportional to the ratio of 
mass in the heavier vehicle to mass in the lighter vehicle:

    Because fatality risk is a positive function of delta V, the 
fatality risk in the lighter vehicle in two-vehicle collisions is also 
higher. Removing some mass from the heavy vehicle reduces delta V in 
the lighter vehicle, where fatality risk is higher, resulting in a 
large benefit, offset by a small penalty because delta V increases in 
the heavy vehicle where fatality risk is low--adding up to a net 
societal benefit. Removing some mass from the lighter vehicle results 
in a large penalty offset by a small benefit--adding up to net harm.
    These considerations drive the overall result: Mass reduction is 
associated with an increase in fatality risk in lighter cars, a 
decrease in fatality risk in heavier LTVs, CUVs, and minivans, and has 
smaller effects in the intermediate groups. Mass reduction may also be 
harmful in a crash with a movable object such as a small tree, which 
may break if hit by a high mass vehicle resulting in a lower delta V 
than may occur if hit by a lower mass vehicle which does not break the 
tree and therefore has a higher delta V. However, in some types of 
crashes not involving collisions between cars and LTVs, especially 
first-event rollovers and impacts with fixed objects, mass reduction 
may not be harmful and may be beneficial. To the extent lighter 
vehicles may respond more quickly to braking and steering, or may be 
more stable because their center of gravity is lower, they may more 
successfully avoid crashes or reduce the severity of crashes.
    Farmer, Green, and Lie, who reviewed the 2010 Kahane report, again 
peer-reviewed the 2011 Kahane report.\307\ In preparing his 2012 report 
(along with the 2016 Puckett and Kindelberger report and Draft TAR), 
Kahane also took into account Wenzel's \308\ assessment of the 
preliminary report and its peer reviews, DRI's analyses published early 
in 2012, and public comments such as the International Council on Clean 
Transportation's comments submitted on NHTSA and EPA's 2010 notice of 
joint rulemaking.\309\ These comments prompted supplementary analyses, 
especially sensitivity tests, discussed at the end of this section.

    \307\ Items 0035 (Lie), 0036 (Farmer) and 0037 (Green) in Docket 
No. NHTSA-2010-0152.
    \308\ Wenzel, T. An Analysis of the Relationship Between 
Casualty Risk Per Crash and Vehicle Mass and Footprint for Model 
Year 2000-2007 Light Duty Vehicles, Lawrence Berkeley National 
Laboratory (Dec. 2011), available at http://eta-publications.lbl.gov/sites/default/files/lbnl-5695e.pdf; Tom Wenzel, 
Lawrence Berkeley National Laboratory -Assessment of NHTSA Report 
Relationships Btw Fatality Risk Mass and Footprint in MY 2000-2007 
PC and LTV,'' Docket NHTSA-2010-0131-0315; and a peer review of 
Wenzel's reports--Peer Review of LBNL Statistical Analysis of the 
Effect of Vehicle Mass & Footprint Reduction on Safety (LBNL Phase 1 
and 2 Reports), prepared for U.S. EPA (Feb. 2012), available at 
Docket ID NHTSA-2010-0131-0328.
    \309\ Comment by International Council on Clean Transportation, 
Docket ID NHTSA-2010-0131-0258.

    The regression results are best suited to predict the effect of a 
small change in mass, leaving all other factors, including footprint, 
the same. With each additional change from the current environment 
(e.g., the scale of mass change, presence and prevalence of safety 
features, demographic characteristics), the model may become less 
accurate. It is recognized that the light-duty vehicle fleet in the MY 
2021-2026 timeframe will be different from the MY 20042011 fleet 
analyzed here.
    Nevertheless, one consideration provides some basis for confidence 
in applying regression results to estimate effects of relatively large 
mass reductions or mass reductions over longer periods. This is NHTSA's 
sixth evaluation of effects of mass reduction and/or downsizing,\310\ 

[[Page 43113]]

databases ranging from MYs 1985 to 2011.

    \310\ As outlined throughout this section, NHTSA's six related 
studies include the new analysis supporting this rulemaking, and: 
Kahane, C. J. Vehicle Weight, Fatality Risk and Crash Compatibility 
of Model Year 1991-99 Passenger Cars and Light Trucks, National 
Highway Traffic Safety Administration (Oct. 2003), available at 
Kahane, C. J. Relationships Between Fatality Risk, Mass, and 
Footprint in Model Year 1991-1999 and Other Passenger Cars and LTVs 
(Mar. 24, 2010), in Final Regulatory Impact Analysis: Corporate 
Average Fuel Economy for MY 2012-MY 2016 Passenger Cars and Light 
Trucks, National Highway Traffic Safety Administration (Mar. 2010) 
at 464-542; Kahane, C. J. Relationships Between Fatality Risk, Mass, 
and Footprint in Model Year 2000-2007 Passenger Cars and LTVs--
Preliminary Report, National Highway Traffic Safety Administration 
(Nov. 2011), available at Docket ID NHTSA-2010-0152- 0023); Kahane, 
C. J. Relationships Between Fatality Risk, Mass, and Footprint in 
Model Year 2000-2007 Passenger Cars and LTVs: Final Report, NHTSA 
Technical Report. Washington, DC: NHTSA, Report No. DOT-HS-811-665; 
and Puckett, S. M., & Kindelberger, J. C. Relationships between 
Fatality Risk, Mass, and Footprint in Model Year 2003-2010 Passenger 
Cars and LTVs--Preliminary Report, National Highway Traffic Safety 
Administration (June 2016), available at https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/2016-prelim-relationship-fatalityrisk-mass-footprint-2003-10.pdf.

    Results of the six studies are not identical, but they have been 
consistent to a point. During this time period, many makes and models 
have increased substantially in mass, sometimes as much as 30-40%.\311\ 
If the statistical analysis has, over the past years, been able to 
accommodate mass increases of this magnitude, perhaps it will also 
succeed in modeling effects of mass reductions of approximately 10-20%, 
should they occur in the future.

    \311\ For example, one of the most popular models of small 4-
door sedans increased in curb weight from 1,939 pounds in MY 1985 to 
2,766 pounds in MY 2007, a 43% increase. A high-sales mid-size sedan 
grew from 2,385 to 3,354 pounds (41%); a best-selling pickup truck 
from 3,390 to 4,742 pounds (40%) in the basic model with two-door 
cab and rear-wheel drive; and a popular minivan from 2,940 to 3,862 
pounds (31%).

(d) Calculation of MY 2021-2026 Safety Impact
    Neither CAFE standards nor this analysis mandate mass reduction, or 
mandate mass reduction occur in any specific manner. However, mass 
reduction is one of the technology applications available to 
manufacturers, and thus a degree of mass reduction is allowed within 
the CAFE model to: (1) Determine capabilities of manufacturers; and (2) 
to predict cost and fuel consumption effects of improved CAFE 
    The agency utilized the relationships between weight and safety 
from the new NHTSA analysis, expressed as percentage increases in 
fatalities per 100-pound weight reduction, and examined the weight 
impacts assumed in this CAFE analysis. The effects of mass reduction on 
safety were estimated relative to estimated baseline levels of safety 
across vehicle classes and model years. To identify baseline levels of 
safety, the agency examined effects of identifiable safety trends over 
lifetimes of vehicles produced in each model year. The projected 
effectiveness of existing and forthcoming safety technologies and 
expected on-road fleet penetration of safety technologies were 
incorporated into observed trends in fatality rates to estimate 
baseline fatality rates in future years across vehicle classes and 
model years.
    The agency assumed safety trends will result in a reduction in the 
target population of fatalities from which the vehicle mass impacts are 
derived. Table II-47 through Table II-52 show results of NHTSA's 
vehicle mass-size-safety analysis over the cumulative lifetime of MY 
1977-2029 vehicles, for both the CAFE and GHG programs, based on the MY 
2016 baseline fleet, accounting for the projected safety baselines. The 
reported fatality impacts are undiscounted, but the monetized safety 
impacts are discounted at three-percent and seven-percent discount 
rates. The reported fatality impacts are estimated increases or 
decreases in fatalities over the lifetime of the model year fleet. A 
positive number means that fatalities are projected to increase; a 
negative number (in parentheses) means that fatalities are projected to 
    Results are driven extensively by the degree to which mass is 
reduced in relatively light passenger cars and in relatively heavy 
vehicles because their coefficients in the logistic regression analysis 
have the most significant values. We assume any impact on fatalities 
will occur over the lifetime of the vehicle, and the chance of a 
fatality occurring in any particular year is directly related to the 
weighted vehicle miles traveled in that year.

[[Page 43114]]


[[Page 43115]]


[[Page 43116]]


[[Page 43117]]


[[Page 43118]]


[[Page 43119]]


    For all light-duty vehicles, mass changes are estimated to lead to 
a decrease in fatalities over the cumulative lifetime of MY 1977-2029 
vehicles in all alternatives evaluated. The effects of mass changes on 

[[Page 43120]]

range from a combined decrease (relative to the augural standards, the 
baseline) of 12 fatalities for Alternative #7 to a combined decrease of 
173 fatalities for Alternative #4. The difference in results by 
alternative depends upon how much weight reduction is used in that 
alternative and the types and sizes of vehicles to which the weight 
reduction applies. The decreases in fatalities are driven by impacts 
within passenger cars (decreases of between 17 and 281 fatalities) and 
are offset by impacts within light trucks (increases of between 6 and 
120 fatalities).
    Additionally, social effects of increasing fatalities can be 
monetized using NHTSA's estimated comprehensive cost per life of 
$9,900,000 in 2016 dollars. This consists of a value of a statistical 
life of $9.6 million in 2015 dollars plus external economic costs 
associated with fatalities such as medical care, insurance 
administration costs and legal costs, updated for inflation to 2016 
    Typically, NHTSA would also estimate the effect on injuries and add 
that to social costs of fatalities, but in this case NHTSA does not 
have a model estimating the effect of vehicle mass on injuries. Blincoe 
et al. estimates that fatalities account for 39.5% of total 
comprehensive costs due to injury.\312\ If vehicle mass impacts non-
fatal injuries proportionally to its impact on fatalities, then total 
costs would be approximately 2.53 (\1/0\.395) times the value of 
fatalities alone or around $25.07 million per fatality. NHTSA has 
selected this value as representative of the relationship between 
fatality costs and injury costs because this approach is internally 
consistent among NHTSA studies.

    \312\ Blincoe, L. et al., The Economic and Social Impact of 
Motor Vehicle Crashes, 2010 (Revised), National Highway Traffic 
Safety Administration (May 2015), available at https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812013. The 
estimate of 39.5% (see Table 1-8) is equal to the estimated value of 
MAIS6 (fatal) injuries in vehicle incidents divided by the estimated 
value of MAIS0-MAIS6 (non-fatal and fatal) injuries in vehicle 

    Changes in vehicle mass are estimated to decrease social safety 
costs over the lifetime of the nine model years by between $176 million 
(for Alternative #7) and $2.7 billion (for Alternative #4) relative to 
the augural standards at a three-percent discount rate and by between 
$97 million and $1.6 billion at a seven-percent discount rate. The 
estimated decreases in social safety costs are driven by estimated 
decreases in costs associated with passenger cars, ranging from $264 
million (for Alternative #7) to $4.4 billion (for Alternative #1) 
relative to the Augural standards at a three-percent discount rate and 
by between $146 million and $2.5 billion at a seven-percent discount 
rate. The estimated decreases in costs associated with passenger cars 
are offset by estimated increases in costs associated with light 
trucks, ranging from $88 million (for Alternative #7) to $2.0 billion 
(for Alternative #1) relative to the Augural standards at a three-
percent discount rate and by between $49 million and $1.3 billion at a 
seven-percent discount rate.
    Table II-53 through Table II-55 presents average annual estimated 
safety effects of vehicle mass changes, for CYs 2035-2045:

[[Page 43121]]


[[Page 43122]]


[[Page 43123]]


[[Page 43124]]


[[Page 43125]]


[[Page 43126]]


    For all light-duty vehicles, mass changes are estimated to lead to 
an average annual decrease in fatalities in all alternatives evaluated 
for CYs 2035-2045. The effects of mass changes on fatalities range from 
a combined

[[Page 43127]]

decrease (relative to the Augural standards) of 1 fatality per year for 
Alternative #7 to a combined increase of 22 fatalities per year for 
Alternative #1. The difference in the results by alternative depends 
upon how much weight reduction is used in that alternative and the 
types and sizes of vehicles to which the weight reduction applies. The 
decreases in fatalities are generally driven by impacts within 
passenger cars (decreases of between 1 and 33 fatalities per year 
relative to the Augural standards) and are generally offset by impacts 
within light trucks (increases of between 1 and 12 fatalities per 
    Changes in vehicle mass are estimated to decrease average annual 
social safety costs in CY 2035-2045 by between $2 million (for 
Alternative #7) and $271 million (for Alternative #1) relative to the 
Augural standards at a three-percent discount rate and by between $1 
million and $111 million at a seven-percent discount rate. The 
estimated decreases in social safety costs are generally driven by 
estimated decreases in costs associated with passenger cars, decreasing 
between $13 million (for Alternative #7) and $424 million (for 
Alternative #1) relative to the Augural standards at a three-percent 
discount rate and decreasing between $5 million and $175 million at a 
seven-percent discount rate. The estimated decreases in costs 
associated with passenger cars are generally offset by estimated 
increases in costs associated with light trucks, decreasing between $11 
million (for Alternative #7) and $153 million (for Alternative #1) 
relative to the Augural standards at a three-percent discount rate and 
decreasing between $5 million and $64 million at a seven-percent 
discount rate.
    To help illuminate effects at the model year level, Table II-59 
presents the lifetime fatality impacts associated with vehicle mass 
changes for passenger cars, light trucks, and all light-duty vehicles 
by model year under Alternative #1, relative to the Augural standards 
for the CAFE Program. Table II-59 presents an analogous table for the 
GHG Program.

[[Page 43128]]


    Under Alternative #1, passenger car fatalities associated with mass 
changes are estimated to decrease generally from MY 2017 (decrease of 
three fatalities) through MY 2029 (decrease of 36 fatalities), peaking 
in MY 2025 (37 fatalities). Corresponding estimates of light truck 
fatalities associated with mass changes are generally positive, ranging 
from a decrease of one fatality in MYs 2017 and 2018 to an increase of 
14 fatalities in MYs 2026 through 2029. Altogether, light-duty vehicle 
fatality reductions associated with mass changes under Alternative #1 

[[Page 43129]]

estimated to be concentrated among MY 2023 through MY 2029 vehicles 
(146 out of 165, or 91% of net fatalities mitigated).
    Table II-61 and Table II-62 present estimates of monetized lifetime 
social safety costs associated with mass changes by model year at 
three-percent and seven-percent discount rates, respectively for the 
CAFE Program. Table II-63 and Table II-64 show comparable tables from 
the perspective of the GHG Program.

[[Page 43130]]


[[Page 43131]]


    Lifetime social safety costs are estimated to decrease generally by 
model year, with decreases associated with passenger cars generally 
offset partially by increases associated with light trucks. At a three-
percent discount

[[Page 43132]]

rate, decreases in lifetime social safety costs related to passenger 
cars are estimated to range from $13 million for existing (MY 1977 
through MY 2016) cars, to $230 million for MY 2025 cars. The 
corresponding estimates at a seven-percent discount rate range from $7 
million to $136 million. At a three-percent discount rate, impacts on 
lifetime social safety costs related to light trucks are estimated to 
range from a decrease of $5 million for MY 2017 light trucks to an 
increase of $96 million for MY 2022 light trucks. The corresponding 
estimates at a seven-percent discount rate range from $3 million to $65 
    Consistent with the analysis of fatality impacts by model year in 
Table II-61, decreases in lifetime social safety costs associated with 
mass changes are generally concentrated in MY 2023 through MY 2029 
light-duty vehicles under Alternative #1. At a three-percent discount 
rate, 93% of the reduction in total lifetime costs ($872 million out of 
$937 million) is attributed to MY 2023 through MY 2029 light-duty 
vehicles; at a seven-percent discount rate, 97% of the reduction in 
total lifetime costs ($486 million out of $501 million) is attributed 
to MY 2023 through MY 2029 light-duty vehicles.
(e) Sensitivity Analyses
    Table II-65 shows the principal findings and includes sampling-
error confidence bounds for the five parameters used in the CAFE model. 
The confidence bounds represent the statistical uncertainty that is a 
consequence of having less than a census of data. NHTSA's 2011, 2012, 
and 2016 reports acknowledged another source of uncertainty: The 
baseline statistical model can be varied by choosing different control 
variables or redefining the vehicle classes or crash types, which for 
example, could produce different point estimates.
    Beginning with the 2012 Kahane report, NHTSA has provided results 
of 11 plausible alternative models that serve as sensitivity tests of 
the baseline model. Each alternative model was tested or proposed by: 
Farmer (IIHS) or Green (UMTRI) in their peer reviews; Van Auken (DRI) 
in his public comments; or Wenzel in his parallel research for DOE. The 
2012 Kahane and 2016 Puckett and Kindelberger reports provide further 
discussion of the models and the rationales behind them.
    Alternative models use NHTSA's databases and regression-analysis 
approach but differ from the baseline model in one or more explanatory 
variables, assumptions, or data restrictions. NHTSA applied the 11 
techniques to the latest databases to generate alternative CAFE model 
coefficients. The range of estimates produced by the sensitivity tests 
offers insight to the uncertainty inherent in the formulation of the 
models, subject to the caveat these 11 tests are, of course, not an 
exhaustive list of conceivable alternatives.
    The baseline and alternative results follow, ordered from the 
lowest to the highest estimated increase in societal risk per 100-pound 
reduction for cars weighing less than 3,201 pounds:

[[Page 43133]]

    The sensitivity tests illustrate both the fragility and the 
robustness of baseline estimates. On the one hand, the variation among 
NHTSA's coefficients is quite large relative to the baseline estimate: 
In the preceding example of cars < 3,201 pounds, the estimated 
coefficients range from almost zero to almost double the baseline 
estimate. This result underscores the key relationship that the 
societal effect of mass reduction is small and, as Wenzel has said, it 
``is overwhelmed by other known vehicle, driver, and crash factors.'' 
\313\ In other words, varying how to model some of these other vehicle, 
driver, and crash factors, which is exactly what sensitivity tests do, 
can appreciably change the estimate of the societal effect of mass 

    \313\ Wenzel, T. Assessment of NHTSA's Report ``Relationships 
Between Fatality Risk, Mass, and Footprint in Model Year 2000-2007 
Passenger Cars and LTVs,'' Lawrence Berkeley National Laboratory at 
iv (Nov. 2011), available at Docket ID NHTSA-2010-0152-0026.

    On the other hand, variations are not particularly large in 
absolute terms. The ranges of alternative estimates are generally in 
line with the sampling-error confidence bounds for the baseline 
estimates. Generally, in alternative models as in the baseline models, 
mass reduction tends to be relatively more harmful in the lighter 
vehicles and more beneficial in the heavier vehicles, just as they are 
in the central analysis. In all models, the point estimate of NHTSA's 
coefficient is positive for the lightest vehicle class, cars < 3,201 
pounds. In nine out of 11 models, the point estimate is negative for 
CUVs and minivans, and in eight out of 11 models the point estimate is 
negative for LTVs >= 5,014 pounds.
(f) Fleet Simulation Model
    NHTSA has traditionally used real world crash data as the basis for 
projecting the future safety implications for regulatory changes. 
However, because lightweight vehicle designs are introducing 
fundamental changes to the structure of the vehicle, there is some 
concern that historical safety trends may not apply. To address this 
concern, NHTSA developed an approach to utilize lightweight vehicle 
designs to evaluate safety in a subset of real-world representative 
crashes. The methodology focused on frontal crashes because of the 
availability of existing vehicle and occupant restraint models. 
Representative crashes were simulated between baseline and lightweight 
vehicles against a range of vehicles and roadside objects using two 
different size belted driver occupants (adult male and small female) 
only. No passenger(s) or unbelted driver occupants were considered in 
this fleet simulation. The occupant injury risk from each simulation 
was calculated and summed to obtain combined occupant injury risk. The 
combined occupant injury risk was weighted according to the frequency 
of real world occurrences to develop overall societal risk for baseline 
and light-weighted vehicles. Note: The generic restraint system 
developed and used in the baseline occupant simulations was also used 
in the light-weighted vehicle occupant simulations as the purpose of 
this fleet simulation was to understand changes in societal injury 
risks because of mass reduction for different classes of vehicles in 
frontal crashes. No modifications to the restraint systems were made 
for light-weighted vehicle occupant simulations. Any modifications to 
restraint systems to improve occupant injury risks or societal injury 
risks in the light-weighted vehicle would have conflated results 
without identifying effects of mass reduction only. The following 
sections provide an overview of the fleet simulation study:
    NHTSA contracted with George Washington University to develop a 
fleet simulation model \314\ to study the impact and relationship of 
light-weighted vehicle design with injuries and fatalities. In this 
study, there were eight vehicles as follows:

    \314\ Samaha, R. R. et al., Methodology for Evaluating Fleet 
Protection of New Vehicle Designs: Application to Lightweight 
Vehicle Designs, National Highway Traffic Safety Administration 
(Aug. 2014), available at https://www.nhtsa.gov/crashworthiness/vehicle-aggressivity-and-fleet-compatibility-research (accessed by 
clicking on the .zip file for DOT HS 812 051).

     2001 model year Ford Taurus finite element model baseline 
and two simple design variants included a 25% lighter vehicle while 
maintaining the same vehicle front end stiffness and 25% overall 
stiffer vehicle while maintaining the same overall vehicle mass.\315\

    \315\ Samaha, R. R. et al., Methodology for Evaluating Fleet 
Protection of New Vehicle Designs: Application to Lightweight 
Vehicle Designs, appendices, National Highway Traffic Safety 
Administration (Aug. 2014), available at https://www.nhtsa.gov/crashworthiness/vehicle-aggressivity-and-fleet-compatibility-research (accessed by clicking on the .zip file for DOT HS 812 051 
[appendices are Part 2]).

     2011 model year Honda Accord finite element baseline 
vehicle and its 20% light-weight vehicle designed by Electricore. (This 
mass reduction study was sponsored by NHTSA).\316\

    \316\ Singh, H. et al., Update to future midsize lightweight 
vehicle findings in response to manufacturer review and IIHS small-
overlap testing, National Highway Traffic Safety Administration 
(Feb. 2016), available at https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/812237_lightweightvehiclereport.pdf.

     2009/2010 model year Toyota Venza finite element baseline 
vehicle and two design variants included a 20% light-weight vehicle 
model (2010 Venza) (Low option mass reduction vehicle funded by EPA and 
International Council on Clean Transportation (ICCT)) and a 35% light-
weight vehicle (2009 Venza) (High option mass reduction vehicle funded 
by California Air Resources Board).\317\

    \317\ Light-Duty Vehicle Mass Reduction and Cost Analysis -- 
Midsize Crossover Utility Vehicle, U.S. EPA (Aug. 2012), https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryID=230748.

    Light weight vehicles were designed to have similar vehicle crash 
pulses as baseline vehicles. More than 440 vehicle crash simulations 
were conducted for the range of crash speeds and crash configurations 
to generate crash pulse and intrusion data points. The crash pulse data 
and intrusion data points will be used as inputs in the occupant 
simulation models.
    For vehicle to vehicle impact simulations, four finite element 
models were chosen to represent the fleet. The partner vehicle models 
were selected to represent a range of vehicle types and weights. It was 
assumed vehicle models would reflect the crash response for all 
vehicles of the same type, e.g. mid-size car. Only the safety or injury 
risk for the driver in the target vehicle and in the partner vehicle 
were evaluated in this study.
    As noted, vehicle simulations generated vehicle deformations and 
acceleration responses utilized to drive occupant restraint simulations 
and predict the risk of injury to the head, neck, chest, and lower 
extremities. In all, more than 1,520 occupant restraint simulations 
were conducted to evaluate the risk of injury for mid-size male and 
small female drivers.
    The computed societal injury risk (SIR) for a target vehicle v in 
frontal crashes is an aggregate of individual serious crash injury 
risks weighted by real-world frequency of occurrence (v) of a frontal 
crash incident. A crash incident corresponds to a crash with different 
partners (Npartner) at a given impact speed (Pspeed), for a given 
driver occupant size (Loccsize), in the target or partner vehicle (T/
P), in a given crash configuration (Mconfig), and in a single- or two-
vehicle crash (Kevent). CIR (v) represents the combined injury risk (by 
body region) in a single crash incident. (v) designates the weighting 
factor, i.e., percent of occurrence, derived from National Automotive 
Sampling System Crashworthiness Data System (NASS CDS) for the crash 
incident. A driver age group of 16 to 50

[[Page 43134]]

years old was chosen to provide a population with a similar, i.e., more 
consistent, injury tolerance.
    The fleet simulation was performed using the best available 
engineering models, with base vehicle restraint and airbag settings, to 
estimate societal risks of future lightweight vehicles. The range of 
the predicted risks for the baseline vehicles is from 1.25% to 1.56%, 
with an average of 1.39%, for the NASS frontal crashes that were 
simulated. The change in driver injury risk between the baseline and 
light-weighted vehicles will provide insight into the estimate of 
modification needed in the restraint and airbag systems of lightweight 
vehicles. If the difference extends beyond the expected baseline 
vehicle restraint and airbag capability, then adjustments to the 
structural designs would be needed. Results from the fleet simulation 
study show the trend of increased societal injury risk for light-
weighted vehicle designs, as compared to their baselines, occurs for 
both single vehicle and two-vehicle crashes. Results are listed in 
Table II-66.
    In general, the societal injury risk in the frontal crash 
simulation associated with the small size driver is elevated when 
compared to that of the mid-size driver. However, both occupant sizes 
had reasonable injury risk in the simulated impact configurations 
representative of the regulatory and consumer information testing. 
NHTSA examined three methods for combining injuries with different body 
regions. One observation was the baseline mid-size CUV model was more 
sensitive to leg injuries.

    This study only looked at lightweight designs for a midsize sedan 
and a mid-size CUV and did not examine safety implications for heavier 
vehicles. The study was also limited to only frontal crash 
configurations and considered just mid-size CUVs whereas the 
statistical regression model considered all CUVs and all crash modes.
    The change in the safety risk from the MY 2010 fleet simulation 
study was directionally consistent with results for passenger cars from 
NHTSA 2012 regression analysis study,\318\ which covered data for MY 
2000-MY 2007. The NHTSA 2012 regression analysis study was updated in 
2016 to reflect newer MY 2003 to MY 2010. Comparing the fleet 
simulation societal risk to the 2016 update of the NHTSA 2012 
regression analysis and the updated analysis used in this NPRM, the 
risk assessment from the fleet simulation is similarly directionally 
consistent with the passenger car risk assessment from the regression 
analysis. As noted, fleet simulations were performed only in frontal 
crash mode and did not consider other crash modes including rollover 

    \318\ The 2012 Kahane study considered only fatalities, whereas, 
the fleet simulation study considered severe (AIS 3+) injuries and 
fatalities (DOT HS 811 665).
    \319\ The risk assessment for CUV in the regression model 
combined CUVs and minivans in all crash modes and included belted 
and unbelted occupants.

    This fleet simulation study does not provide information that can 
be used to modify coefficients derived for the NPRM regression analysis 
because of the restricted types of crashes \320\ and vehicle designs. 
As explained earlier, the fleet simulation study assumed restraint 
equipment to be as in the baseline model, in which restraints/airbags 
are not redesigned to be optimal with light-weighting.

    \320\ The fleet simulation considered only frontal crashes.

2. Impact of Vehicle Scrappage and Sales Response on Fatalities
    Previous versions of the CAFE model, and the accompanying 
regulatory analyses relying on it, did not carry a representation of 
the full on-road vehicle population, only those vehicles from model 
years regulated under proposed (or final) standards. The omission of an 
on-road fleet implicitly assumed the population of vehicles registered 
at the time a set of CAFE standards is promulgated is not affected by 
those standards. However, there are several mechanisms by which CAFE 
standards can affect the existing vehicle

[[Page 43135]]

population. The most significant of these is deferred retirement of 
older vehicles. CAFE standards force manufacturers to apply fuel saving 
technologies to offered vehicles and then pass along the cost of those 
technologies (to the extent possible) to buyers of new vehicles. These 
price increases affect the length of loan terms and the desired length 
of ownership for new vehicle buyers and can discourage some buyers on 
the margin from buying a new vehicle in a given year. To the extent new 
vehicle purchases offset pending vehicle retirements, delaying new 
purchases in favor of continuing to use an aging vehicle affects the 
overall safety of the on-road fleet even if the vehicle whose 
retirement was delayed was not directly subject to a binding CAFE 
standard in the model year during its production.
    The sales response in the CAFE model acts to modify new vehicle 
sales in two ways:
    1. Changes in new vehicle prices either increase or decrease total 
sales (passenger cars and light trucks combined) each year in the 
context of forecasted macroeconomic conditions.
    2. Changes in new vehicle attributes and fuel prices influence the 
share of new vehicles sold that are light trucks, and therefore also 
passenger cars.
    These two responses change the total number of new vehicles sold in 
each model year across regulatory alternatives and the relative 
proportion of new vehicles that are passenger cars and light trucks. 
This response has two effects on safety. The first response slows the 
rate at which new vehicles, and their associated safety improvements, 
enter the on-road population. The second response 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. Light trucks have higher rates of fatal crashes when 
interacting with passenger cars and, as earlier sections discussed, 
different directional responses to mass reduction technology based on 
the existing mass and body style of the vehicle.
    The sales response and scrappage response influence safety outcomes 
through the same basic mechanism, fleet turnover. In the case of the 
scrappage response, delaying fleet turnover keeps drivers in older 
vehicles likely to be less safe than newer model year vehicles that 
could replace them. Similarly, delaying the sale of new vehicles can 
force households to keep older vehicles in use longer, reallocate VMT 
within their household fleet, and generally meet travel demand through 
the use of older, less safe vehicles. As an illustration, if we 
simplify by ignoring that the share of new vehicles that are passenger 
cars changes with the stringency of the alternatives, simply changing 
the number of new vehicles between scenarios affects the mileage 
accumulation of the fleet and therefore all fleet level effects. 
Reducing the number of new vehicles sold, relative to a baseline 
forecasted value, reduces the size of the registered vehicle fleet that 
is able to service the underlying demand for travel.
    Consider a simple example where we show sales effects operating on 
a micro-scale for a single household whose choices of whether to 
purchase a new vehicle is affected by vehicle price. A household starts 
with three vehicles, aged three, five, and eight years old. In a 
scenario with no CAFE standards and therefore no related changes in 
vehicle sales prices, the household buys a new car and scraps the 
eight-year old car; the other two cars in the fleet each get a year 
older. In a scenario where CAFE standards become more stringent causing 
vehicle sales prices to increase, this household chooses to delay 
buying a new car and each of their three existing cars gets a year 
older. In both cases, all three vehicles (including the new car in the 
first scenario, and the year-year-old car in the second scenario) have 
to serve the family's travel demand.
    The scrappage effect is visible in the household's vehicle fleet as 
it moves from the first scenario to the second scenario with changes in 
CAFE standards. In the second scenario, the nine-year-old car remains 
in the household's fleet to service demand for travel, when it would 
otherwise have been retired. While the scrappage effect can be 
symmetrical to the sales effect, it need not be. The ``new car'' in the 
scenario without CAFE standards could be a new vehicle from the current 
model year or a used car that is of a newer vintage than the 8-year-old 
vehicle it replaces. The latter instance is an effect of scrappage 
decisions that do not directly affect new vehicle sales. Eventually, 
new vehicles transition to the used car market, but that on average 
take several years, and the shift is slow. At the household level, the 
scrappage decision occurs in a single year, each year, for every 
vehicle in the fleet. To the extent CAFE standards affect new vehicle 
prices and fuel economies, relative to vehicles already owned, 
scrappage could accelerate or decelerate depending upon the direction 
(and magnitude) of the changes.
3. Safety Model
    The analysis supporting the CAFE rule for MYs 2017 and beyond did 
not account for differences in exposure or inherent safety risk as 
vehicles aged throughout their useful lives. However, the relationship 
between vehicle age and fatality risk is an important one. In a 2013 
Research Note,\321\ NHTSA's National Center for Statistics and Analysis 
concluded a driver of a vehicle that is four to seven years old is 10% 
more likely to be killed in a crash than the driver of a vehicle zero 
to three years old, accounting for the other factors related to the 
crash. This trend continued for older vehicles more generally, with a 
driver of a vehicle 18 years or older being 71% more likely to be 
killed in a crash than a driver in a new vehicle. While there are more 
registered vehicles that are zero to three years old than there are 20 
years or older (nearly three times as many) because most of the 
vehicles in earlier vintages are retired sooner, the average age of 
vehicles in the United States is 11.6 years old and has risen 
significantly in the past decade.\322\ This relationship reflects a 
general trend visible in the Fatality Analysis Reporting System (FARS) 
when looking at a series of calendar years: Newer vintages are safer 
than older vintages, over time, at each age. This is likely because of 
advancements in safety technology, like side-impact airbags, electronic 
stability control, and (more recently) sophisticated crash avoidance 
systems starting to work their way into the vehicle population. In 
fact, the 2013 Research Note indicated that the percentage of occupants 
fatally injured in fatal crashes increased with vehicle age: From 27% 
for vehicles three or fewer years old, to 41% for vehicles 12-14 years 
old, to 50% for vehicles 18 or more years old.

    \321\ National Center for Statistics and Analysis, How Vehicle 
Age and Model Year Relate to Driver Injury Severity in Fatal 
Crashes, National Highway Traffic Safety Administration (Aug. 2013), 
available at https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/811825.
    \322\ Based on data acquired from Ward's Automotive.

    With an integrated fleet model now part of the analytical framework 
for CAFE analysis, 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

[[Page 43136]]

the total number of on-road fatalities under each regulatory 
    To estimate the empirical relationship between vehicle age, model 
year vintage, and fatalities, DOT conducted a statistical analysis 
linking data from the FARS database, a time series of Polk registration 
data to represent the on-road vehicle population, and assumed per-
vehicle mileage accumulation rates (the derivation of which is 
discussed in detail in PRIA Chapter 11). These data were used to 
construct per-mile fatality rates that varied by vehicle vintage, 
accounting for the influence of vehicle age. However, unlike the NCSA 
study referenced above, any attempt to account for this relationship in 
the CAFE analysis faces two challenges. The first challenge is the CAFE 
model lacks the internal structure to account for other factors related 
to observed fatal crashes--for example, vehicle speed, seat belt use, 
drug use, or age of involved drivers or passengers. Vehicle 
interactions are simply not modeled at this level; the safety analysis 
in the CAFE model is statistical, using aggregate values to represent 
the totality of fleet interactions over time. The second challenge is 
perhaps the more significant of the two: The CAFE analysis is 
inherently forward-looking. To implement a statistical model analogous 
to the one developed by NCSA, the CAFE model would require forecasts of 
all factors considered in the NCSA model--about vehicle speeds in 
crashes, driver behavior, driver and passenger ages, vehicle vintages, 
and so on. In particular, the model would require distributions (joint 
distributions, in most cases) of these factors over a period of time 
spanning decades. Any such forecasts would be highly uncertain and 
would be likely to assume a continuation of current conditions.
    Instead of trying to replicate the NCSA work at a similar level of 
detail, DOT conducted a simpler statistical analysis to separate the 
safety impact of the two factors the CAFE model explicitly accounts 
for: The distribution of vehicle ages in the fleet and the number of 
miles driven by those vehicles at each age. To accomplish this, DOT 
used data from the FARS database at a lower level of resolution; rather 
than looking at each crash and the specific factors that contributed to 
its occurrence, staff looked at the total number of fatal crashes 
involving light-duty vehicles over time with a focus on the influence 
of vehicle age and vehicle vintage. When considering the number of 
fatalities relative to the number of registered vehicles for a given 
model year (without regard to the passenger car/light-truck 
distinction, which has evolved over time and can create inconsistent 
comparisons), a somewhat noisy pattern develops. Using data from 
calendar year 1996 through 2015, some consistent stories develop. The 
points in Figure II-4 represent the number of fatalities per registered 
vehicle with darker circles associated with increasingly current 
calendar years.

    As shown in Figure II-4, fatalities per registered vehicle have 
generally declined over time across all vehicle ages (the darker points 
representing newer vintages being closer to the x-axis) and, across 
most recent calendar years, fatality rates (per registered vehicle) 
start out at a low point, rise through age 15 or so, then decline 
through age 30 (at which point little of the initial model year cohort 
is still registered). While this pattern is evident in the registration 
data, it is magnified by imposing a mileage accumulation schedule on 
the registered population and examining fatalities per billion miles of 
    The mileage accumulation schedule used in this analysis was 
developed using odometer readings of vehicles aged 0-15 years in 
calendar year 2015.

[[Page 43137]]

The years spanned by the FARS database cover all model years from 
calendar year 1996 through 2015. Given that there is a significant 
number of years between the older vehicles in the 1996 CY data and the 
most recent model years in the odometer data the informed the mileage 
accumulation schedules, staff applied an elasticity of -0.20 to the 
change in the average cost per mile of vehicles over their lives. While 
the older vehicles had lower fuel economies, which would be associated 
with higher per-mile driving costs, they also (mostly) faced lower fuel 
prices. This adjustment increased the mileage accumulation for older 
vehicles, but not by large amounts. Because the CAFE model uses the 
mileage accumulation schedule and applies it to all vehicles in the 
fleet, it is necessary to use the same schedule to estimate per-mile 
fatality rates in the statistical analysis--even if the schedule is 
based on vehicles that look different than the oldest vehicles in the 
FARS dataset.
    When the per-vehicle fatality rates are converted into per-mile 
fatality rates, the pattern observed in the registration comparison 
becomes clearer. As Figure II-5 shows, the trend present in the 
fatality data on a per-registration basis is even clearer on a per-mile 
basis: Newer vintages are safer than older vintages, at each age, over 

    The shape of the curve in Figure II-5 suggests a polynomial 
relationship between fatality rate and vehicle age, so DOT's 
statistical model is based on that structure.
    The final model is a weighted quartic polynomial regression (by 
number of registered vehicles) on vehicle age with fixed effects for 
the model years present in the dataset: \323\

    \323\ Note: The dataset included MY 1975, but that fixed effect 
is excluded from the set. The constant term acts as the fixed effect 
for 1975 and all others are relative to that one.

    The coefficient estimates and model summary are in Table II-67.

[[Page 43138]]


[[Page 43139]]


    This function is now embedded in the CAFE model, so the combination 
of VMT per vehicle and the distribution of ages and model years present 
in the on-road fleet determine the number of fatalities in a given 
calendar year. The model reproduces the observed fatalities of a given 
model year, at each age, reasonably well with more recent model years 
(to which the VMT schedule is a better match) estimated with smaller 
    While the final specification was not the only one considered, the 
fact this model was intended to live inside the CAFE model to 
dynamically estimate fatalities for a dynamically changing on-road 
vehicle population was a constraining factor.

(a) Predicting Future Safety Trends

    The base model predicts a net increase in fatalities due primarily 
to slower adoption of safer vehicles and added driving because of less 
costly vehicle operating costs. In earlier calendar years, the 
improvement in safety of the on-road fleet produces a net reduction in 
fatalities, but from the mid-2020s forward, the baseline model predicts 
no further increase in safety, and the added risk from more VMT and 
older vehicles produces a net increase in fatalities. This model thus 
reflects a conservative limitation; it implicitly assumes the trend 
toward increasingly safe vehicles that has been apparent for the past 3 
decades will flatten in mid-2020s. The agency does not assert this is 
the most likely case. In fact, the development of advanced crash 
avoidance technologies in recent years indicates some level of safety 
improvement is almost certain to occur. The difficulty is for most of 
these technologies, their effectiveness against fatalities and the pace 
of their adoption are highly uncertain. Moreover, 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. This conservative 
assumption may cause the NPRM to understate the beneficial effect of 
proposed standards on improving (reducing) the number of fatalities.
    Advanced technologies that are currently deployed or in development 
    Forward Collision Warning (FCW) systems are intended to passively 
assist the driver in avoiding or mitigating the impact of rear-end 
collisions (i.e., a vehicle striking the rear portion of a vehicle 
traveling in the same direction directly in front of it). FCW uses 
forward-looking vehicle detection capability, such as RADAR, LIDAR 
(laser), camera, etc., to detect other vehicles ahead and use the 
information from these sensors to warn the driver and to prevent 
crashes. FCW systems provide an audible, visual, or haptic warning, or 
any combination thereof, to alert the driver of an FCW-equipped vehicle 
of a potential collision with another vehicle or vehicles in the 
anticipated forward pathway of the vehicle.
    Crash Imminent Braking (CIB) systems are intended to actively 
assist the driver by mitigating the impact of rear-end collisions. 
These safety systems have forward-looking vehicle detection capability 
provided by sensing technologies such as RADAR, LIDAR, video camera, 
etc. CIB systems mitigate crash severity by automatically applying the 
vehicle's brakes shortly before the expected impact (i.e., without 
requiring the driver to apply force to the brake pedal).
    Dynamic Brake Support (DBS) is a technology that actively increases 
the amount of braking provided to the driver during a rear-end crash 
avoidance maneuver. If the driver has applied force to the brake pedal, 
DBS uses forward-looking sensor data provided by technologies such as 
RADAR, LIDAR, video cameras, etc. to assess the potential for a rear-
end crash. Should DBS ascertain a crash is likely (i.e., the sensor 
data indicate the driver has not applied enough braking to avoid the 
crash), DBS automatically intervenes. Although the manner in which DBS 
has been implemented differs among vehicle manufacturers, the objective 
of the interventions is largely the same: To supplement the driver's 
commanded brake input by increasing the output of the foundation brake 
system. In some situations, the increased braking provided by DBS may 
allow the driver to avoid a crash. In other cases, DBS interventions 
mitigate crash severity.
    Pedestrian AEB (PAEB) systems provide automatic braking for 
vehicles when pedestrians are in the forward path of travel and the 
driver has taken insufficient action to avoid an imminent crash. Like 
CIB, PAEB safety systems use information from forward-looking sensors 
to automatically apply or supplement the brakes in certain driving

[[Page 43140]]

situations in which the system determines a pedestrian is in imminent 
danger of being hit by the vehicle. Many PAEB systems use the same 
sensors and technologies used by CIB and DBS.
    Rear Automatic Braking feature means installed vehicle equipment 
that has the ability to sense the presence of objects behind a 
reversing vehicle, alert the driver of the presence of the object(s) 
via auditory and visual alerts, and automatically engage the available 
braking system(s) to stop the vehicle.
    Semi-automatic Headlamp Beam Switching device provides either 
automatic or manual control of headlamp beam switching at the option of 
the driver. When the control is automatic, headlamps switch from the 
upper beam to the lower beam when illuminated by headlamps on an 
approaching vehicle and switch back to the upper beam when the road 
ahead is dark. When the control is manual, the driver may obtain either 
beam manually regardless of the conditions ahead of the vehicle.
    Rear Turn Signal Lamp Color Turn signal lamps are the signaling 
element of a turn signal system, which indicates the intention to turn 
or change direction by giving a flashing light on the side toward which 
the turn will be made. FMVSS No. 108 permits a rear turn signal lamp 
color of amber or red.
    Lane Departure Warning (LDW) system is a driver assistance system 
that monitors lane markings on the road and alerts the driver when 
their vehicle is about to drift beyond a delineated edge line of their 
current travel lane.
    Blind Spot Detection (BSD) systems uses digital camera imaging 
technology or radar sensor technology to detect one or more vehicles in 
either of the adjacent lanes that may not be apparent to the driver. 
The system warns the driver of an approaching vehicle's presence to 
help facilitate safe lane changes.
    These technologies are either under development or are currently 
being offered, typically in luxury vehicles, as either optional or 
standard equipment.
    To estimate baseline fatality rates in future years, NHTSA examined 
predicted results from a previous NCSA study \324\ that measured the 
effect of known safety regulations on fatality rates. This study relied 
on statistical evaluations of the effectiveness of motor vehicle safety 
technologies based on real world performance in the on-road vehicle 
fleet to determine the effectiveness of each safety technology. These 
effectiveness rates were applied to existing fatality target 
populations and adjusted for current technology penetration in the on-
road fleet, taking into account the retirement of existing vehicles and 
the pace of future penetration required to meet statutory compliance 
requirements, as well as adjustments for overlapping target 
populations. Based on these factors, as well as assumptions regarding 
future VMT, the study predicted future fatality levels and rates. 
Because the safety impact in the CAFE model independently predicts 
future VMT, we removed the VMT growth rate from the NCSA study and 
developed a prediction of vehicle fatality trends based only on the 
penetration pace of new safety technologies into the on-road fleet. 
These data were then normalized into relative safety factors with CY 
2015 as the baseline (to match the baseline fatality year used in this 
CAFE analysis). These factors were then converted into equivalent 
fatality rates/100 million VMT by anchoring them to the 2015 fatality 
rate/100 million VMT published by NHTSA. Figure II-6 below illustrates 
the modelling output and projected fatality trend from the analysis of 
the NCSA study, prior to adjustment to fatality rates/100 million VMT.

    \324\ Blincoe, L. & Shankar, U. The Impact of Safety Standards 
and Behavioral Trends on Motor Vehicle Fatality Rates, National 
Highway Traffic Safety Administration (Jan. 2007), available at 

[[Page 43141]]

    This model was based on inputs representing the impact of 
technology improvement through CY 2020. Projecting this trend beyond 
2020 can be justified based on the continued transformation of the on-
road fleet to 100% inclusion of the known safety technologies. Based on 
projections in the NCSA study, significant further technology 
penetration can be expected in the on-road fleet for side impact 
improvements (FMVSSS 214), electronic stability control (FMVSS 126), 
upper interior head impact protection (FMVSS 301), tire pressure 
monitoring systems (FMVSS 138), ejection mitigation (FMVSS 226), and 
heavy truck stopping distance improvements (FMVSS 121). These 
technologies were estimated to be installed in only 40-70% of the on-
road fleet as of CY 2020, implying further safety improvement well 
beyond the 2020 calendar year.
    The NCSA study focused on projections to reflect known technology 
adaptation requirements, but it was conducted prior to the 2008 
recession, which disrupted the economy and changed travel patterns 
throughout the country. Thus, while the relative trends it predicts 
seem reasonable, they cannot account for the real-world disruption and 
recovery that occurred in the 2008-2015 timeframe. In addition, the 
NCSA study did not attempt to adjust for safety impacts that may have 
resulted from changes in the vehicle sales mix (vehicle types and sizes 
creating different interactions in crashes), in commuting patterns, or 
in shopping or socializing habits associated with internet access and 
use. To address this, NHTSA also examined the actual change in the 
fatality rate as measured by fatality counts and VMT estimates. Figure 
II-7 below illustrates the actual fatality rates measured from 2000 
through 2016 and the modeled fatality rate trend based on these 
historical data.

    The effect of the recession and subsequent recovery can be seen in 
chaotic shift in the fatality rate trend starting in 2008. The 
generally gradual decline that had been occurring over the previous 
decade was interrupted by a slowdown in the rate of change followed by 
subsequent upward and downward shifts. More recently, the rate has 
begun to increase. These shifts reflect some combination of factors not 
captured in the NCSA analysis mentioned above. The significance of this 
is that although there was a steady increase in the penetration of 
safety technologies into the on-road fleet between 2008 and 2015, other 
unknown factors offset their positive influence and eventually reversed 
the trend in vehicle safety rates. Because of the upward shift over the 
2014-2015 period, this model, which does not reflect technology trend 
savings after 2015, will predict an upward shift of fatality rates 
after 2020.
    Predicting future safety trends has significant uncertainty. 
Although further safety improvements are expected because of advanced 
safety technologies such as automatic braking and eventually, fully 
automated vehicles, the pace of development and extent of consumer 
acceptance of these improvements is uncertain. Thus, two imperfect 
models exist for predicting future safety trends. The NCSA model 
reflects the expected trend from required technologies and indicates 
continued improvement well beyond the 2020 timeframe, which is when the 
historical fatality rate based model breaks down. By contrast, the 
historical fatality rate model reflects shifts in safety not captured 
by the NCSA model, but gives arguably implausible results after 2020. 
It essentially represents a scenario in which economic, market, or 
behavioral factors minimize or offset much of the potential impact of 
future safety technology.
    For the NPRM, the analysis examines a scenario projecting safety 
improvements beyond 2015 using a simple average of the NCSA and 
historical fatality rate models, accepting each as an illustration of 
different and conflicting possible future scenarios. As

[[Page 43142]]

both models eventually curve up because of their quadratic form, each 
models' results are flattened at the point where they begin to trend 
upward. This occurs in 2045 for the NCSA model and in 2021 for the 
historical model. The results are shown in Figure II-8 below. The 
results indicate roughly a 19% reduction in fatality rates between 2015 
and 2050. This is a slower pace than what has historically occurred 
over the past several decades, but the biggest influence on historical 
rates was significant improvement in safety belt use, which was below 
10% in 1960 and had risen to roughly 70% by 2000, and is now more than 
90%. Because belt use is now above 90%, further such improvements are 
unlikely unless they come from new technologies.

    A difficulty with these trend models is they are based on calendar 
year predictions, which are derived from the full on-road vehicle fleet 
rather than the model year fleet, which is the basis for calculations 
in the CAFE model. As such they are useful primarily as indicators that 
vehicle safety has steadily improved over the past several decades, and 
given the advanced safety technologies under current development, we 
would expect some continuation of improvement in MY vehicle safety over 
the near and mid-term future. To account for this, NHTSA approximated a 
model year safety trend continuing through about 2035 (Figure II-9). 
For this trend the agency used actual data from FARS to calculate the 
change in fatality rates through 2007. The recession, which struck our 
economy in 2008, distorted normal behavioral patterns and affected both 
VMT and the mix of drivers and type of driving to an extent we do not 
believe the recession era gives an accurate picture of the safety 
trends inherent in the vehicles themselves. Therefore, beginning in 
2008, NHTSA approximated a trend for safety improvement through about 
MY 2035 to reflect the continued effect of improved safety technologies 
such as advanced automatic braking, which manufacturers have announced 
will be in all new vehicles by MY 2022. The agency recognize this is 
only an estimate, and actual MY trends could be above or below this 
line. NHTSA examined alternate trends in a sensitivity analysis and 
request comments on the best way to address future safety trends.
    NHTSA also notes although we project vehicles will continue to 
become safer going forward to about 2035, we do not have corresponding 
cost information for technologies enabling this improvement. In a 
standard elasticity model, sales impacts are a function of the percent 
change in vehicle price. Hypothetically, increasing the base price for 
added safety technologies would decrease the impact of higher prices 
due to impacts of CAFE standards on vehicle sales. The percentage 
change in baseline price would decrease, which would mean a lower 
elasticity effect, which would mean a lower impact on sales. NHTSA will 
consider possible ways to address this issue before the final rule, and 
we request comments on the need and/or practicability for such an 
adjustment, as well as any data and other relevant information that 
could support such an analysis of these costs, as well as the future 
pace of technological adoption within the vehicle fleet.

[[Page 43143]]


(b) Adjusting for Behavioral Impacts
    The influence of delayed purchases of new vehicles is estimated to 
have the most significant effect on safety imposed by CAFE standards. 
Because of a combination of safety regulations and voluntary safety 
improvements, passenger vehicles have become safer over time. Compared 
to prior decades, fatality rates have declined significantly because of 
technological improvements, as well as behavioral shifts, such as 
increased seat belt use. As these safer vehicles replace older less 
safe vehicles in the fleet, the on-road fleet is replaced with vehicles 
reflecting the improved fatality rates of newer, safer vehicles. 
However, fatality rates associated with different model year vehicles 
are influenced by the vehicle itself and by driver behavior. Over time, 
used vehicles are purchased by drivers in different demographic 
circumstances who also tend to have different behavioral 
characteristics. Drivers of older vehicles, on average, tend to have 
lower belt use rates, are more likely to drive inebriated, and are more 
likely to drive over the speed limit. Additionally, older vehicles are 
more likely to be driven on rural roadways, which typically have higher 
speeds and produce more serious crashes. These relationships are 
illustrated graphically in Chapter 11 of the PRIA accompanying this 
proposed rule.
    The behavior being modelled and ascribed to CAFE involves decisions 
by drivers who are contemplating buying a new vehicle, and the purchase 
of a newer vehicle will not in itself cause those drivers to suddenly 
stop wearing seat belts, speed, drive under the influence, or shift 
driving to different land use areas. The goal of this analysis is to 
measure the effect of different vehicle designs that change by model 
year. The modelling process for estimating safety essentially involves 
substituting fatality rates of older MY vehicles for improved rates 
that would have been experienced with a newer vehicle. Therefore, it is 
important to control for behavioral aspects associated with vehicle age 
so only vehicle design differences are reflected in the estimate of 
safety impacts. To address this, the CAFE safety model was run to 
control for vehicle age. That is, it does not reflect a decision to 
replace an older model year vehicle that is, for example, 10 years old 
with a new vehicle. Rather, it reflects the difference in the average 
fatality rate of each model year across its entire lifespan. This will 
account for most of the difference because of vehicle age, but it may 
still reflect a bias caused by the upward trend in societal seat belt 
use over time. Because of this secular trend, each subsequent model 
year's useful life will occur under increasingly higher average seat 
belt use rates. This could cause some level of behavioral safety 
improvement to be ascribed to the model year instead of the driver 
cohort. However, it is difficult to separate this effect from the belt 
use impacts of changing driver cohorts as vehicles age.
    Glassbrenner (2012) analyzed the effect of improved safety in newer 
vehicles for model years 2001 through 2008. She developed several 
statistical regression models that specifically controlled for most 
behavioral factors to isolate model year vehicle characteristics. 
However, her study did not specifically report the change in MY 
fatality rates--rather, she reported total fatalities that could have 
been saved in a baseline year (2008) had all vehicles in the on-road 
fleet had the same safety features as the MY 2001 through MY 2008 
vehicles. This study potentially provides a basis for comparison with 
results of the CAFE safety estimates. To make this comparison, the CY 
2008 passenger car and light truck fatalities total from FARS were 
modified by subtracting the values found in Figure II-9 of her study. 
This gives a stream of comparable hypothetical CY 2008 fatality totals 
under progressively less safe model year designs. Results indicated 
that had the 2008 on-road fleet been equipped with MY 2008 safety 
equipment and vehicle characteristics, total fatalities would have been 
reduced by 25% compared to vehicles that were actually on the road in 
2008. Similar results were calculated for each model years' vehicle 
characteristics back to 2001.
    For comparison, predicted MY fatality rates were derived from the 
CAFE safety model and applied to the CY 2008 VMT calculated by that 
model. This gives an estimate of CY 2008 fatalities under each model 
years' fatality rate, which, when compared to the predicted CY fatality 
total, gives a trendline

[[Page 43144]]

comparable to the Glassbrenner trendline illustrating the change in MY 
fatality rates. Both models are sensitive to the initial 2008 baseline 
fatality total, and because the predicted CAFE total is somewhat lower 
than the actual total, the agency ran a third trendline to examine the 
influence of this difference. Results are shown in Figure II-10.
    Using the corrected fatality count, but retaining the predicted VMT 
changes the initial 2018 CY fatality rate to 12.62 (instead of 12.15) 
and produces the result shown in Figure II-10. The CAFE model trendline 
shifts up, which narrows the difference in early years but expands it 
in later years. However, VMT and fatalities are linked in the CAFE 
model, so the actual level of the MY safety predicted by the CAFE curve 
has uncertainty. Perhaps the most meaningful result from this 
comparison is the difference in slopes; the CAFE model predicts more 
rapid change through 2006, but in the last few years change decreases. 
This might reflect the trend in societal belt use, which rose steadily 
through 2005 and levelled off. Later model years' fatality rates would 
benefit from this trend while earlier model years would suffer. This 
seems consistent with our using lifetime MY fatality rates to reflect 
MY change rather than first year MY fatality rates (although even first 
year rates would reflect this bias, but not as much).

    To provide another perspective on safety impacts, NHTSA accessed 
data from a comprehensive study of the effects of safety technologies 
on motor vehicle fatalities. Kahane (2015) \325\ examined all safety 
effects of vehicle safety technologies from 1960 through 2012 and found 
these technologies saved more than 600,000 lives during that time span. 
Kahane is currently working under contract for NHTSA to update this 
study through 2016. At NHTSA's request, Kahane accessed his database to 
provide a measure of relative MY vehicle design safety by controlling 
for seat belt use. The result was a MY safety index illustrating the 
progress in vehicle safety by model year which isolates vehicle design 
from the primary behavioral impact--seat belt usage. We normalized 
Kahane's index to MY 1975 and did the same to the ``fixed effects'' we 
are currently using from our safety model to compare the trends in MY 
safety from the two methods. Results are shown in Figure II-11.

    \325\ Kahane, C.J. Lives Saved by Safety Standards and 
Associated Vehicle Safety Technologies, 1960-2012--Passenger Cars 
and LTVs--with Reviews of 26 FMVSS and the Effectiveness of their 
Associated Safety Technologies in Reducing Fatalities, Injuries, and 
Crashes, National Highway Traffic Safety Administration (Jan. 2015), 
available at https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812069.


[[Page 43145]]


    From Figure II-11 both approaches show similar long-term downward 
trends, but this model shows a steeper slope than Kahane's model. The 
two models involve completely different approaches, so some difference 
is to be expected. However, it is also possible this reflects different 
methods used to isolate vehicle design safety from behavioral impacts. 
As discussed previously, NHTSA addressed this issue by removing vehicle 
age impacts from its model, whereas Kahane's model does it by 
controlling for belt use. As noted previously, aside from the age 
impact on belt use associated with the different demographics driving 
older vehicles, there is a secular trend toward more belt use 
reflecting the increase in societal awareness of belt use importance 
over time. This trend is illustrated in Figure II-12 below.\326\ 
NHTSA's current approach removes the age trend in belt use, but it's 
not clear whether it accounts for the full impacts of the secular trend 
as well. If not, some portion of the gap between the two trendlines 
could reflect behavioral impacts rather than vehicle design.

    \326\ Note: The drop occurring in 1994 reflects a shift in the 
basis for determining belt use rates. Effective in 1994, data were 
reported from the National Occupant Protection Survey (NOPUS). Prior 
to this, a conglomeration of state studies provided the basis. It is 
likely the pre-NOPUS surveys produced inflated results, especially 
in the 1991-1993 period.

    These models (NHTSA, Glassbrenner, and Kahane) involve differing 
approaches and assumptions contributing to uncertainty, and given this, 
their differences are not surprising. It is encouraging they show 
similar directional trends, reinforcing the basic concept we are 
measuring. NHTSA recognizes predicting future fatality impacts, as well 
as sales impacts that cause them, is a difficult and imprecise task. 
NHTSA will continue to investigate this issue, and we seek comment on 
these estimates as well as alternate methods for predicting the safety 
effects associated with delayed new vehicle purchases.

[[Page 43146]]


4. Impact of Rebound Effect on Fatalities
    Based on historical data, it is possible to calculate a baseline 
fatality rate for vehicles of any model year vintage. By simply taking 
the total number of vehicles involved in fatal accidents over all ages 
for a model year and dividing by the cumulative VMT over the useful 
life of every vehicle produced in that model year, one arrives at a 
baseline hazard rate denominated in fatalities per billion miles. The 
fatalities associated with vehicles produced in that model year are 
then proportional to the cumulative lifetime VMT, where total 
fatalities equal the product of the baseline hazard rate and VMT. A 
more comprehensive discussion of the rebound effect and the basis for 
calculating its impact on mileage and risk is in Chapter 8 of the PRIA 
accompanying this proposed rule.
5. Adjustment for Non-Fatal Crashes
    Fatalities estimated to be caused by various alternative CAFE 
standards are valued as a societal cost within the CAFE models' cost/
benefit accounting. Their value is based on the comprehensive value of 
a fatality derived from data in Blincoe et al. (2015), adjusted to 2016 
economics and updated to reflect the official DOT guidance on the value 
of a statistical life in 2016. This gives a societal value of $9.9 
million for each fatality. The CAFE safety model estimates effects on 
traffic fatalities but does not address corresponding effects on non-
fatal injuries and property damage that would result from the same 
factors influencing fatalities. To address this, we developed an 
adjustment factor that would account for these crashes.
    Development of this factor is based on the assumption nonfatal 
crashes will be affected by CAFE standards in proportion to their 
nationwide incidence and severity. That is, NHTSA assumes the same 
injury profile, the relative number of cases of each injury severity 
level, that occur nationwide, will be increased or decreased because of 
CAFE. The agency recognizes this may not be the case, but the agency 
does not have data to support individual estimates across injury 
severities. There are reasons why this may not be true. For example, 
because older model year vehicles are generally less safe than newer 
vehicles, fatalities may make up a larger portion of the total injury 
picture than they do for newer vehicles. This would imply lower ratios 
across the non-fatal injury and PDO profile and would imply our 
adjustment may overstate total societal impacts. NHTSA requests 
comments on this assumption and alternative methods to estimate injury 
    The adjustment factor is derived from Tables 1-8 and I-3 in Blincoe 
et al. (2015). Incidence in Table I-3 reflects the Abbreviated Injury 
Scale (AIS), which ranks nonfatal injury severity based on an ascending 
5 level scale with the most severe injuries ranked as level 5. More 
information on the basis for these classifications is available from 
the Association for the Advancement of Automotive Medicine at https://www.aaam.org/abbreviated-injury-scale-ais/.
    Table 1-3 in Blincoe lists injured persons with their highest 
(maximum) injury determining the AIS level (MAIS). This scale is 
represented in terms of MAIS level, or maximum abbreviated injury 
scale. MAIS0 refers to uninjured occupants in injury vehicles, MAIS1 
are generally considered minor injuries, MAIS2 moderate injuries, MAIS3 
serious injuries, MAIS4 severe injuries, and MAIS5 critical injuries. 
PDO refers to property damage only crashes, and counts for PDOs refer 
to vehicles in which no one was injured. From Table II-68, ratios of 
injury incidence/fatality are derived for each injury severity level as 

[[Page 43147]]


    For each fatality that occurs nationwide in traffic crashes, there 
are 561 vehicles involved in PDOs, 139 uninjured occupants in injury 
vehicles, 105 minor injuries, 10 moderate injuries, 3 serious injuries, 
and fractional numbers of the most serious categories which include 
severe and critical nonfatal injuries. For each fatality ascribed to 
CAFE it is assumed there will be nonfatal crashes in these same ratios.
    Property damage costs associated with delayed new vehicle purchases 
must be treated differently because crashes that subsequently occur 
damage older used vehicles instead of newer vehicles. Used vehicles are 
worth less and will cost less to repair, if they are repaired at all. 
The consumer's property damage loss is thus reduced by longer retention 
of these vehicles. To estimate this loss, average new and used vehicle 
prices were compared. New vehicle transaction prices were estimated 
from a study published by Kelley Blue Book.\327\ Based on these data, 
the average new vehicle transaction price in January 2017 was $34,968. 
Used vehicle transaction prices were obtained from Edmonds Used Vehicle 
Market Report published in February of 2017.\328\ Edmonds data indicate 
the average used vehicle transaction price was $19,189 in 2016. There 
is a minor timing discrepancy in these data because the new vehicle 
data represent January 2017, and the used vehicle price is for the 
average over 2016. NHTSA was unable to locate exact matching data at 
this time, but the agency believes the difference will be minor.

    \327\ Press Release, Kelley Blue Book, New-Car Transaction 
Prices Remain High, Up More Than 3 Percent Year-Over-Year in January 
2017, According to Kelley Blue Book (Feb. 1, 2017), https://mediaroom.kbb.com/2017-02-01-New-Car-Transaction-Prices-Remain-High-Up-More-Than-3-Percent-Year-Over-Year-In-January-2017-According-To-Kelley-Blue-Book.
    \328\ Edmunds Used Vehicle Market Report, Edmunds (Feb. 2017), 

    Based on these data, new vehicles are on average worth 82% more 
than used vehicles. To estimate the effect of higher property damage 
costs for newer vehicles on crashes, the per unit property damage costs 
from Table I-9 in Blincoe et al. (2015) were multiplied by this factor. 
Results are illustrated in Table II-69.

    The total property damage cost reduction was then calculated as a 
function of the number of fatalities reduced or increased by CAFE as 

S = total property damage savings from retaining used vehicles 
F = change in fatalities estimated for CAFE due to retaining used 
r = ratio of nonfatal injuries or PDO vehicles to fatalities (F)
p = value of property damage prevented by retaining older vehicle

[[Page 43148]]

n = the 8 injury severity categories

    The number of fatalities ascribed to CAFE because of older vehicle 
retention was multiplied by the unit cost per fatality from Table I-9 
in Blincoe et al. (2015) to determine the societal impact accounted for 
by these fatalities.\329\ From Table I-8 in Blincoe et al. (2015), 
NHTSA subtracted property damage costs from all injury severity levels 
and recalculated the total comprehensive value of societal losses from 
crashes. The agency then divided the portion of these crashes because 
of fatalities by the resulting total to estimate the portion of crashes 
excluding property damage that are accounted for by fatalities. Results 
indicate fatalities accounted for approximately 40% of all societal 
costs exclusive of property damage. NHTSA then divided the total cost 
of the added fatalities by 0.4 to estimate the total cost of all 
crashes prevented exclusive of the savings in property damage. After 
subtracting the total savings in property damage from this value, we 
divided the fatality cost by it to estimate that overall, fatalities 
account for 43% of the total costs that would result from older vehicle 

    \329\ Note: These calculations used the original values in the 
Blincoe et all (2015) tables without adjusting for economics. These 
calculations produce ratios and are thus not sensitive to 
adjustments for inflation.

    For the fatalities that occur because of mass effects or to the 
rebound effect, the calculation was more direct, a simple application 
of the ratio of the portion of costs produced by fatalities. In this 
case, there is no need to adjust for property damage because all 
impacts were derived from the mix of vehicles in the on-road fleet. 
Again, from Table I-8 in Blincoe et al (2015), we derive this ratio 
based on all cost factors including property damage to be .36. These 
calculations are summarized as follows:


SV = Value of societal Impacts of all crashes
F = change in fatalities estimated for CAFE due to retaining used 
v = Comprehensive societal value of preventing 1 fatality
x = Percent of total societal loss from crashes excluding property 
damage accounted for by fatalities
S = total property damage savings from retaining used vehicles 
M = change in fatalities due to changes in vehicle mass to meet CAFE 
c = Percent of total societal loss from all cost factors in all 
crashes accounted for by fatalities

    For purposes of application in the CAFE model, these two factors 
were combined based on the relative contribution to total fatalities of 
different factors. As noted, although a safety impact from the rebound 
effect is calculated, these impacts are considered to be freely chosen 
rather than imposed by CAFE and imply personal benefits at least equal 
to the sum of their added costs and safety consequences. The impacts of 
this nonfatal crash adjustment affect costs and benefits equally. When 
considering safety impacts actually imposed by CAFE standards, only 
those from mass changes and vehicle purchase delays are considered. 
NHTSA has two different factors depending on which metric is 
considered. The agency created these factors by weighting components by 
the relative contribution to changes in fatalities associated with each 
component. This process and results are shown in Table II-70. Note: For 
the NPRM, NHTSA applied the average weighted factor to all fatalities. 
This will tend to slightly overstate costs because of sales and 
scrappage and understate costs associated with mass and rebound. The 
agency will consider ways to adjust this minor discrepancy for the 
final rule.

    Table II-71, Table II-72, Table II-73, and Table II-74 summarize 
the safety effects of CAFE standards across the various alternatives 
under the 3% and 7% discount rates. As noted in Section II.F.5, 
societal impacts are valued using a $9.9 million value per statistical 
life (VSL). Fatalities in these tables are undiscounted; only the 
monetized societal impact is discounted.

[[Page 43149]]


[[Page 43150]]


[[Page 43151]]


[[Page 43152]]


[[Page 43153]]


    Table II-75 through Table II-78 summarize the safety effects of GHG 
standards across the various alternatives under the 3% and 7% discount 
rates. As noted in Section II.F.5, societal impacts are valued using a 
$9.9 million value per statistical life (VSL). Fatalities in these 
tables are undiscounted; only the monetized societal impact is 

[[Page 43154]]


[[Page 43155]]


[[Page 43156]]


[[Page 43157]]


[[Page 43158]]


    While NHTSA notes the value of rebound effect fatalities, as well 
as total fatalities from all causes, the agency does not add rebound 
effects to the other CAFE-related impacts because rebound-related 
fatalities and injuries result from risk that is freely chosen and 
offset by societal valuations that at a minimum exceed the aggregate 
value of safety consequences plus added vehicle operating and 
maintenance costs.\330\ These costs implicitly involve a cost and a 
benefit that are offsetting. The relevant safety impacts attributable 
to CAFE are highlighted in bold in the above tables.

    \330\ It would also include some level of consumer surplus, 
which we have estimated using the standard triangular function. This 
is discussed in Chapter 8.5.1 of the PRIA.

G. How the Model Analyzes Different Potential CAFE and CO2 Standards

1. Specification of No-Action and Other Regulatory Alternatives
(a) Mathematical Functions Defining Passenger Car and Light Trucks 
Standards for Each Model Year During 2016-2032
    In the U.S. market, the stringency of CAFE and CO2 
standards can influence the design of new vehicles offered for sale by 
requiring manufacturers to produce increasingly fuel efficient vehicles 
in order to meet program

[[Page 43159]]

requirements. This is also true in the CAFE model simulation, where the 
standards can be defined with a great deal of flexibility to examine 
the impact of different program specifications on the auto industry. 
Standards are defined for each model year and can represent different 
slopes that relate fuel economy to footprint, different regions of flat 
slopes, and different rates of increase for each of three regulatory 
classes covered by the CAFE program (domestic passenger cars, imported 
passenger cars, and light trucks).
    The CAFE model takes, as inputs, the coefficients of the 
mathematical functions described in Sections III and IV. It uses these 
coefficients and the function to which they belong to define the target 
for each vehicle in the fleet, then computes the standard using the 
harmonic average of the targets for each manufacturer and fleet. The 
model also allows the user to define the extent and duration of various 
compliance flexibilities (e.g., limits on the amount of credit that a 
manufacturer may claim related to air conditioning efficiency 
improvements or off-cycle fuel economy adjustments) as well as limits 
on the number of years that CAFE credits may be carried forward or the 
amount that may be transferred between a manufacturer's fleets.
(b) Off-Cycle and A/C Efficiency Adjustments Anticipated for Each Model 
    Another aspect of credit accounting is partially implemented in the 
CAFE model at this point--those related to the application of off-cycle 
and A/C efficiency adjustments, which manufacturers earn by taking 
actions such as special window glazing or using reflective paints that 
provide fuel economy improvements in real-world operation but do not 
produce measurable improvements in fuel consumption on the 2-cycle 
    NHTSA's inclusion of off-cycle and A/C efficiency adjustments began 
in MY 2017, while EPA has collected several years' worth of submissions 
from manufacturers about off-cycle and A/C efficiency technology 
deployment. Currently, the level of deployment can vary considerably by 
manufacturer with several claiming extensive Fuel Consumption 
Improvement Values (FCIV) for off-cycle and A/C efficiency technologies 
and others almost none. The analysis of alternatives presented here 
does not attempt to project how future off-cycle and A/C efficiency 
technology use will evolve or speculate about the potential 
proliferation of FCIV proposals submitted to the agencies. Rather, this 
analysis uses the off-cycle credits submitted by each manufacturer for 
MY 2017 compliance and carries these forward to future years with a few 
exceptions. Several of the technologies described in Section II.D are 
associated with A/C efficiency and off-cycle FCIVs. In particular, 
stop-start systems, integrated starter generators, and full hybrids are 
assumed to generate off-cycle adjustments when applied to vehicles to 
improve their fuel economy. Similarly, higher levels of aerodynamic 
improvements are assumed to include active grille shutters on the 
vehicle, which also qualify for off-cycle FCIVs.
    The analysis assumes that any off-cycle FCIVs that are associated 
with actions outside of the technologies discussed in Section II.D 
(either chosen from the pre-approved ``pick list,'' or granted in 
response to individual manufacturer petitions) remain at the levels 
claimed by manufacturers in MY 2017. Any additional A/C efficiency and 
off-cycle adjustments that accrue as the result of explicit technology 
application are calculated dynamically in each model year for each 
alternative. The off-cycle FCIVs for each manufacturer and fleet, 
denominated in grams CO2 per mile,\331\ are provided in 
Table II-79.

    \331\ For the purpose of estimating their contribution to CAFE 
compliance, the grams CO2/mile values in Table II-79 are 
converted to gallons/mile and applied to a manufacturer's 2-cycle 
CAFE performance. When calculating compliance with EPA's GHG 
program, there is no conversion necessary (as standards are also 
denominated in grams/mile).


[[Page 43160]]


    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 10 g/mi cap. As a practical 
matter, most of the adjustments for which manufacturers are claiming 
off-cycle FCIV exist outside of the technology tree, so the cap is 
rarely reached during compliance simulation. If those FCIVs become a 
more important compliance mechanism, it may be necessary to model their 
application explicitly. However, doing so will require data on which 
vehicle models already possess these improvements as well as the cost 
and expected value of applying them to other models in the future. 
Comment is sought on both the data requirements and strategic decisions 
associated with manufacturers' use of A/C efficiency and off-cycle 
technologies to improve CAFE and CO2 compliance.
(c) Civil Penalty Rate and OEMs' Anticipated Willingness To Treat Civil 
Penalties as a Program Flexibility
    Throughout the history of the CAFE program, some manufacturers have 
consistently achieved fuel economy levels below their standard. As in 
previous versions of the CAFE model, the current version allows the 
user to specify inputs identifying such manufacturers and to consider 
their compliance decisions as if they are willing to pay civil 
penalties for non-compliance with the CAFE program. The assumed civil 
penalty rate in the current analysis is $5.50 per 1/10 of a mile per 
gallon, per vehicle sold.
    It is worth noting that treating a manufacturer as if they are 
willing to pay civil penalties does not necessarily mean that it is 
expected to pay penalties in reality. It merely implies that the 
manufacturer will only apply fuel economy technology up to a point, and 
then stop, regardless of whether or not its corporate average fuel 
economy is above its standard. In practice, we expect that many of 
these manufacturers will continue to be active in the credit market, 
using trades with other manufacturers to transfer credits into specific 
fleets that are challenged in any given year, rather than paying 
penalties to resolve CAFE deficits. The CAFE model calculates the 
amount of penalties paid by each manufacturer, but it does not simulate 
trades between manufacturers. In practice, some (possibly most) of the 
total estimated penalties may be a transfer from one OEM to another.
    While the Energy Policy and Conservation Act (EPCA), as amended in 
2007 by the Energy Independence and Security Act, prescribes these 
specific civil penalty provisions for CAFE standards, the Clean Air Act 
(CAA) does not contain similar provisions. Rather, the CAA's provisions 
regarding noncompliance constitute a de facto prohibition against 
selling vehicles failing to comply with emissions standards. Therefore, 
inputs regarding civil penalties--including inputs regarding 
manufacturers' potential willingness to treat civil penalty payment as 
an economic choice--apply only to simulation of CAFE standards.
(d) Treatment of Credit Provisions for ``Standard Setting'' and 
``Unconstrained'' Analyses
    NHTSA may not consider the application of CAFE credits toward 
compliance with new standards when establishing the standards 
themselves.\332\ As such, this analysis considers 2020 to be the last 
model year in which carried-forward or transferred credits can be 
applied for the CAFE program. Beginning in model year 2021,

[[Page 43161]]

today's ``standard setting'' analysis is conducted assuming each fleet 
must comply with the CAFE standard separately in every model year.

    \332\ 49 U.S.C. 32902(h) (2007).

    The ``unconstrained'' perspective acknowledges that these 
flexibilities exist as part of the program and, while not considered in 
NHTSA's decision of the preferred alternative, are 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 
Environmental Impact Analysis (DEIS) accompanying today's NPRM presents 
results of ``unconstrained'' modeling. Also, because the CAA provides 
no direction regarding consideration of any CO2 credit 
provisions, today's analysis includes simulation of carried-forward and 
transferred CO2 credits in all model years.
(e) Treatment of AFVs for ``Standard Setting'' and ``Unconstrained'' 
    NHTSA is also prohibited from considering the possibility that a 
manufacturer might produce alternatively fueled vehicles as a 
compliance mechanism,\333\ taking advantage of 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 
\334\) are not available in the compliance simulation to improve fuel 
economy. Under the ``unconstrained'' perspective, such as is documented 
in the DEIS, the CAFE model considers these technologies in the context 
of all 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 
already exist in the MY 2016 fleet (and their projected future volumes) 
in CAFE calculations. Also, because the CAA provides no direction 
regarding consideration of alternative fuels, today's analysis includes 
simulation of the potential that some manufacturers might introduce new 
AFVs in response to CO2 standards. To fully represent the 
compliance benefit from such a response, NHTSA modified the CAFE model 
to include the specific provisions related to AFVs under the 
CO2 standards. In particular, the CAFE model now carries a 
full representation of the production multipliers related to electric 
vehicles, fuel cell vehicles, plug-in hybrids, and CNG vehicles, all of 
which vary by year through MY 2021.

    \333\ Id.
    \334\ 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.

2. Simulation of Manufacturers' [and Buyers'] Potential Responses to 
Each Alternative
    The CAFE model provides a way of estimating how manufacturers could 
attempt to comply with a given CAFE standard by adding technology to 
fleets that the agencies anticipate they will produce in future model 
years. This exercise constitutes a simulation of manufacturers' 
decisions regarding compliance with CAFE or CO2 standards.
    This compliance simulation begins with the following inputs: (a) 
The analysis fleet of vehicles from model year 2016 discussed above in 
Section II.B, (b) fuel economy improving technology estimates discussed 
above in Section II.D, (c) economic inputs discussed above in Section 
II.E, and (d) inputs defining baseline and potential new CAFE 
standards. For each manufacturer, the model applies technologies in 
both a logical sequence and a cost-minimizing strategy in order to 
identify a set of technologies the manufacturer could apply in response 
to new CAFE or CO2 standards. The model applies technologies 
to each of the projected individual vehicles in a manufacturer's fleet, 
considering the combined effect of regulatory and market incentives 
while attempting to account for manufacturers' production constraints. 
Depending on how the model is exercised, it will apply technology until 
one of the following occurs:

    (1) The manufacturer's fleet achieves compliance \335\ with the 
applicable standard and continuing to add technology in the current 
model year would be attractive neither in terms of stand-alone 
(i.e., absent regulatory need) cost-effectiveness nor in terms of 
facilitating compliance in future model years;

    \335\ When determining whether compliance has been achieved in 
the CAFE program, existing CAFE credits that may be carried over 
from prior model years or transferred between fleets are also used 
to determine compliance status. For purposes of determining the 
effect of maximum feasible CAFE standards, NHTSA cannot consider 
these mechanisms for years being considered (though does so for 
model years that are already final) and exercises the CAFE model 
without enabling these options.

    (2) The manufacturer ``exhausts'' available technologies; \336\ 

    \336\ In a given model year, it is possible that production 
constraints cause a manufacturer to ``run out'' of available 
technology before achieving compliance with standards. This can 
occur when: (a) An insufficient volume of vehicles are expected to 
be redesigned, (b) vehicles have moved to the ends of each 
(relevant) technology pathway, after which no additional options 
exist, or (c) engineering aspects of available vehicles make 
available technology inapplicable (e.g., secondary axle disconnect 
cannot be applied to two-wheel drive vehicles).

    (3) For manufacturers assumed to be willing to pay civil 
penalties (in the CAFE program), the manufacturer reaches the point 
at which doing so would be more cost-effective (from the 
manufacturer's perspective) than adding further technology.

    The model accounts explicitly for each model year, applying 
technologies when vehicles are scheduled to be redesigned or freshened 
and carrying forward technologies between model years once they are 
applied (until, if applicable, they are superseded by other 
technologies). The model then uses these simulated manufacturer fleets 
to generate both a representation of the U.S. auto industry and to 
modify a representation of the entire light-duty registered vehicle 
population. From these fleets, the model estimates changes in physical 
quantities (gallons of fuel, pollutant emissions, traffic fatalities, 
etc.) and calculates the relative costs and benefits of regulatory 
alternatives under consideration.
    The CAFE model accounts explicitly for each model year, in turn, 
because manufacturers actually ``carry forward'' most technologies 
between model years, tending to concentrate the application of new 
technology to vehicle redesigns or mid-cycle ``freshenings,'' and 
design cycles vary widely among manufacturers and specific products. 
Comments by manufacturers and model peer reviewers strongly support 
explicit year-by-year simulation. Year-by-year accounting also enables 
accounting for credit banking (i.e., carry-forward), as discussed 
above, and at least four environmental organizations recently submitted 
comments urging the agencies to consider such credits, citing NHTSA's 
2016 results showing impacts of carried-forward credits.\337\ Moreover, 
EPCA/EISA requires that NHTSA make a year-by-year determination of the 
appropriate level of stringency and then set the standard at that 
level, while ensuring ratable increases in average fuel economy through 
MY 2020. The multi-year planning capability, (optional) simulation of 
``market-driven overcompliance,'' and EPCA credit mechanisms (again, 
for purposes of modeling the CAFE program) increase the model's ability 
to simulate manufacturers' real-world behavior, accounting for the fact 

[[Page 43162]]

manufacturers will seek out compliance paths for several model years at 
a time, while accommodating the year-by-year requirement. This same 
multi-year planning structure is used to simulate responses to 
standards defined in grams CO2/mile, and utilizing the set 
of specific credit provisions defined under EPA's program.

    \337\ Comment by Environmental Law & Policy Center, Natural 
Resources Defense Council (NRDC), Public Citizen, and Sierra Club, 
Docket ID EPA-HQ-OAR-2015-0827-9826, at 28-29.

(a) Representation of Manufacturers' Production Constraints
    After the light-duty rulemaking analysis accompanying the 2012 
final rule that finalized NHTSA's standards through MY 2021, NHTSA 
began work on changes to the CAFE model with the intention of better 
reflecting constraints of product planning and cadence for which 
previous analyses did not account.
(b) Product Cadence
    Past comments on the CAFE model have stressed the importance of 
product cadence--i.e., the development and periodic redesign and 
freshening of vehicles--in terms of involving technical, financial, and 
other practical constraints on applying new technologies, and DOT has 
steadily made changes to both the CAFE model and its inputs with a view 
toward accounting for these considerations. For example, early versions 
of the model added explicit ``carrying forward'' of applied 
technologies between model years, subsequent versions applied 
assumptions that most technologies will be applied when vehicles are 
freshened or redesigned, and more recent versions applied assumptions 
that manufacturers would sometimes apply technology earlier than 
``necessary'' in order to facilitate compliance with standards in 
ensuing model years. Thus, for example, if a manufacturer is expected 
to redesign many of its products in model years 2018 and 2023, and the 
standard's stringency increases significantly in model year 2021, the 
CAFE model will estimate the potential that the manufacturer will add 
more technology than necessary for compliance in MY 2018, in order to 
carry those product changes forward through the next redesign and 
contribute to compliance with the MY 2021 standard. This explicit 
simulation of multiyear planning plays an important role in determining 
year-by-year analytical results.
    As in previous iterations of CAFE rulemaking analysis, the 
simulation of compliance actions that manufacturers might take is 
constrained by the pace at which new technologies can be applied in the 
new vehicle market. Operating at the Make/Model level (e.g., Toyota 
Camry) allows the CAFE model to explicitly account for the fact that 
individual vehicle models undergo significant redesigns relatively 
infrequently. Many popular vehicle models are only redesigned every six 
years or so, with some larger/legacy platforms (the old Ford Econoline 
Vans, for example) stretching more than a decade between significant 
redesigns. Engines, which are often shared among many different models 
and platforms for a single manufacturer, can last even longer--eight to 
ten years in most cases.
    While these characterizations of product cadence are important to 
any evaluation of the impacts of CAFE or CO2 standards, they 
are not known with certainty--even by the manufacturers themselves over 
time horizons as long as those covered by this analysis. However, lack 
of certainty about redesign schedules is not license to ignore them. 
Indeed, when manufacturers meet with the agencies to discuss 
manufacturers' plans vis-[agrave]-vis CAFE and CO2 
requirements, manufacturers typically present specific and detailed 
year-by-year information that explicitly accounts for anticipated 
redesigns. Such year-by-year analysis is also essential to 
manufacturers' plans to make use of provisions (for CAFE, statutory and 
specific) allowing credits to be carried forward to future model years, 
carried back from future model years, transferred between regulated 
fleets, and traded with other manufacturers. Manufacturers are never 
certain about future plans, but they spend considerable effort 
developing, continually adjusting, and implementing them.
    For every model that appears in the MY 2016 analysis fleet, the 
model years have been estimated in which future redesigns (and less 
significant ``freshenings,'' which offer manufacturers the opportunity 
to make less significant changes to models) will occur. These appear in 
the market data file for each model variant. Mid-cycle freshenings 
provide additional opportunities to add some technologies in years 
where smaller shares of a manufacturer's portfolio is scheduled to be 
redesigned. In addition, the analysis accounts for multiyear planning--
that is, the potential that manufacturers may apply ``extra'' 
technology in an early model year with many planned redesigns in order 
to carry technology forward to facilitate compliance in a later model 
year with fewer planned redesigns. Further, the analysis accounts for 
the potential that manufacturers could earn CAFE and/or CO2 
credits in some model years and use those credits in later model years, 
thereby providing another compliance option in years with few planned 
redesigns. Finally, it should be noted that today's analysis does not 
account for future new products (or discontinued products)--past trends 
suggest that some years in which an OEM had few redesigns may have been 
years when that OEM introduced significant new products. Such changes 
in product offerings can obviously be important to manufacturers' 
compliance positions but cannot be systematically and transparently 
accounted for with a fleet forecast extrapolated forward 10 or more 
years from a largely-known fleet. While manufacturers' actual plans 
reflect intentions to discontinue some products and introduce others, 
those plans are considered CBI. Further research would be required in 
order to determine whether and, if so, how it would be practicable to 
simulate such decisions, especially without relying on CBI.
    Additionally, each technology considered for application by the 
CAFE model is assigned to either a ``refresh'' or ``redesign'' cadence 
that dictates when it can be applied to a vehicle. Technologies that 
are assigned to ``refresh/redesign'' can be applied at either a refresh 
or redesign, while technologies that are assigned to ``redesign'' can 
only be applied during a significant vehicle redesign. Table II-80 and 
Table II-81 show the technologies available to manufacturers in the 
compliance simulation, the level at which they are applied (described 
in greater detail in the CAFE model documentation), whether they are 
available outside of a vehicle redesign, and a short description of 
each. A brief examination of the tables shows that most technologies 
are only assumed to be available during a vehicle redesign--and nearly 
all engine improvements are assumed to be available only during 
redesign. In a departure from past CAFE analyses, all transmission 
improvements are assumed to be available during refresh as well as 
redesign. While there are past and recent examples of mid-cycle product 
changes, it seems reasonable to expect that manufacturers will tend to 
attempt to keep engineering and other costs down by applying most major 
changes mainly during vehicle redesigns and some mostly modest changes 
during product freshenings. As mentioned below, comment is sought on 
the approach to account for product cadence.
(c) Component Sharing and Inheritance (Engines, Transmissions, and 
    In practice, manufacturers are limited in the number of engines and 
transmissions that they produce.

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Typically, a manufacturer produces a number of engines--perhaps six or 
eight engines for a large manufacturer--and tunes them for slight 
variants in output for a variety of car and truck applications. 
Manufacturers limit complexity in their engine portfolio for much the 
same reason as they limit complexity in vehicle variants: They face 
engineering manpower limitations, and supplier, production, and service 
costs that scale with the number of parts produced.
    In previous analyses that used the CAFE model (with the exception 
of the 2016 Draft TAR), engines and transmissions in individual vehicle 
models were allowed relative freedom in technology application, 
potentially leading to solutions that would, if followed, create many 
more unique engines and transmissions than exist in the analysis fleet 
(or in the market) for a given model year. This multiplicity likely 
failed to sufficiently account for costs associated with such increased 
complexity in the product portfolio and may have represented an 
unrealistic diffusion of products for manufacturers that are 
consolidating global production to increasingly smaller numbers of 
shared engines and platforms.\338\ The lack of a constraint in this 
area allowed the model to apply different levels of technology to the 
engine in each vehicle in which it was present at the time that vehicle 
was redesigned or refreshed, independent of what was done to other 
vehicles using a previously identical engine.

    \338\ 2015 NAS Report, at pg. 258-259.

    One peer reviewer of the CAFE model recently commented, ``The 
integration of inheritance and sharing of engines, transmissions, and 
platforms across a manufacturer's light duty fleet and separately 
across its light duty truck fleet is standard practice within the 
industry.'' In the current version of the CAFE model, engines and 
transmissions that are shared between vehicles must apply the same 
levels of technology, in all technologies, dictated by engine or 
transmission inheritance. This forced adoption is referred to as 
``engine inheritance'' in the model documentation. In practice, the 
model first chooses an ``engine leader'' among vehicles sharing the 
same engine--the vehicle with the highest sales in MY 2016. If there is 
a tie, the vehicle with the highest average MSRP is chosen, 
representing the idea that manufacturers will choose to pilot the 
newest technology on premium vehicles if possible. The model applies 
the same logic with respect to the application of transmission changes. 
After the model modifies the engine on the ``engine leader'' (or 
``transmission leader''), the changes to that engine propagate through 
to the other vehicles that share that engine (or transmission) in 
subsequent years as those vehicles are redesigned. The CAFE model has 
been modified to provide additional flexibility vis-[agrave]-vis 
product cadence. In a recent public comment, NRDC noted:

    EPA and NHTSA currently constrain their model to apply 
significant fuel-efficient technologies mainly during a product-
redesign as opposed to product-refresh (or mid-cycle). This was 
identified as one of the most sensitive assumptions affecting 
overall program costs by NHTSA in the TAR. By constraining the 
model, the agencies have likely under-estimated the ability of auto 
manufacturers to incorporate some technologies during their product 
refreshes. This is particularly true regarding the critical 
powertrain technologies which are undergoing continuous improvement. 
The agency should account for these trends and incorporate greater 
flexibility for automakers--within their models--to incorporate more 
mid-cycle enhancements.\339\

    \339\ Comment by Environmental Law & Policy Center, Natural 
Resources Defense Council (NRDC), Public Citizen, and Sierra Club, 
Docket ID EPA-HQ-OAR-2015-0827-9826, at 32.

    While engine redesigns are only applied to the engine leader when 
it is redesigned in the model, followers may now inherit upgraded 
engines (that they share with the leader) at either refresh or 
redesign. All transmission changes, whether upgrades to the ``leader'' 
or inheritance to ``followers'' can occur at refresh as well as 
redesign. This provides additional opportunities for technology 
diffusion within manufacturers' product portfolios.
    While ``follower'' vehicles are awaiting redesign (or, for 
transmissions, refreshing as applicable), they carry a legacy version 
of the shared engine or transmission. As one peer reviewer recently 
stated, ``Most of the time a manufacturer will convert only a single 
plant within a model year. Thus both the `old' and `new' variant of the 
engine (or transmission) will produced for a finite number of years.'' 
\340\ The CAFE model currently carries no additional cost associated 
with producing both earlier revisions of an engine and the updated 
version simultaneously. Further research would be needed to determine 
whether sufficient data is likely to be available to explicitly specify 
and apply additional costs involved with continuing to produce an 
existing engine or transmission for some vehicles that have not yet 
progressed to a newer version of that engine or transmission. Comment 
is sought on possible data sources and approaches that could be used to 
represent any additional costs associated with phased introduction of 
new engines or transmissions.

    \340\ CAFE Model Peer Review, p. 19.

    There are some logical consequences of this approach, the first of 
which is that forcing engine and transmission changes to propagate 
through to other vehicles in this way effectively dictates the pace at 
which new technology can be applied and limits the total number of 
unique engines that the model simulates. In the past, NHTSA used 
``phase-in caps'' (see discussion below) to limit the amount of 
technology that can be applied to any vehicle in a given year. However, 
by explicitly tying the engine changes to a specific vehicle's product 
cadence, rather than letting the timing of changes vary across all the 
vehicles that share an engine, the model ensures that an engine is only 
changed when its leader is redesigned (at most). Given that most 
vehicle redesign cycles are five to eight years, this approach still 
represents shorter average lives than most engines in the market, which 
tend to be in production for eight to ten years or more. It is also the 
case that vehicles which share an engine in the analysis fleet (MY 
2016, for this analysis) are assumed to share that same engine 
throughout the analysis--unless one or both of them are converted to 
power-split hybrids (or farther) on the electrification path. In the 
market, this is not true--since a manufacturer will choose an engine 
from among the engines it produces to fulfill the efficiency and power 
demands of a vehicle model upon redesign. That engine need not be from 
the same family of engines as the prior version of that vehicle. This 
is a simplifying assumption in the model. While the model already 
accommodates detailed inputs regarding redesign schedules for specific 
vehicles and commercial information sources are available to inform 
these inputs, further research would be needed to determine whether 
design schedules for specific engines and transmissions can practicably 
be simulated.
    The CAFE model has implemented a similar structure to address 
shared vehicle platforms. The term ``platform'' is used loosely in 
industry but generally refers to a common structure shared by a group 
of vehicle variants. The degree of commonality varies with some 
platform variants exhibiting traditional ``badge engineering'' where 
two products are differentiated by little more than insignias, while 
other platforms may be used to produce a broad suite of vehicles that 
bear little outer resemblance to one another.

[[Page 43164]]

    Given the degree of commonality between variants of a single 
platform, manufacturers do not have complete freedom to apply 
technology to a vehicle: 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 necessarily are constant among vehicles that share a 
common platform. NHTSA has, therefore, modified the CAFE model such 
that all mass reduction technologies are forced to be constant among 
variants of a platform.
    Within the analysis fleet, each vehicle 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 in model year 2016. If there remains a tie, the 
model begins by choosing the vehicle with the highest MSRP in MY 2016. 
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. So, if the platform leader is already at 
MR3 in MY 2016, and a ``follower'' starts at MR0 in MY 2016, the 
follower will get MR3 at its next redesign (unless the leader is 
redesigned again before that time, and further increases the MR level 
associated with that platform, then the follower would receive the new 
MR level).
    In the 2015 NPRM proposing new fuel consumption and GHG standards 
for heavy-duty pickups and vans, NHTSA specifically requested comment 
on the general use of shared engines, transmissions, and platforms 
within CAFE rulemakings. While no commenter responded to this specific 
request, comments from some environmental organizations cited examples 
of technology sharing between light- and heavy-duty products. NHTSA has 
continued to refine its implementation of an approach accounting for 
shared engines, transmissions, and platforms, and again seeks comment 
on the approach, recommendations regarding any other approaches, and 
any information that would facilitate implementation of the agency's 
current approach or any alternative approaches.
(d) Phase-In Caps
    The CAFE model retains the ability to use phase-in caps (specified 
in model inputs) as proxies for a variety of practical restrictions on 
technology application, including the improvements described above. 
Unlike vehicle-specific restrictions related to redesign, refreshes or 
platforms/engines, phase-in caps constrain technology application at 
the vehicle manufacturer level for a given model year. Introduced in 
the 2006 version of the CAFE model, they were intended to reflect a 
manufacturer's overall resource capacity available for implementing new 
technologies (such as engineering research and development personnel 
and financial resources), thereby ensuring that resource capacity is 
accounted for in the modeling process.
    Compared to prior analyses of light-duty standards, these model 
changes result in some changes in the broad characteristics of the 
model's application of technology to manufacturers' fleets. Since the 
use of phase-in caps has been de-emphasized and manufacturer technology 
deployment remains tied strongly to estimated product redesign and 
freshening schedules, technology penetration rates may jump more 
quickly as manufacturers apply technology to high-volume products in 
their portfolio. As a result, the model will ignore a phase-in cap to 
apply inherited technology to vehicles on shared engines, 
transmissions, and platforms.
    In previous CAFE rulemakings, redesign/refresh schedules and phase-
in caps were the primary mechanisms to reflect an OEM's limited pool of 
available resources during the rulemaking time frame and the years 
preceding it, especially in years where many models may be scheduled 
for refresh or redesign. The newly-introduced representation of 
platform-, engine-, and transmission-related considerations discussed 
above augment the model's preexisting representation of redesign cycles 
and eliminate the need to rely on phase-in caps. By design, 
restrictions that enforce commonality of mass reduction on variants of 
a platform, and those that enforce engine and transmission inheritance, 
will result in fewer vehicle-technology combinations in a 
manufacturer's future modeled fleet. The integration of shared 
components and product cadence as a mechanism to control the pace of 
technology application also more accurately represents each 
manufacturer's unique position in the market and its existing 
technology footprint, rather than a technology-specific phase-in cap 
that is uniformly applied to all manufacturers in a given year. Comment 
is sought regarding this shift away from relying on phase-in caps and, 
if greater reliance on phase-in caps is recommended, what approach and 
information can be used to define and apply these caps.
(e) Interactions Between Regulatory Classes
    Like earlier versions, the current CAFE model provides the 
capability for integrated analysis spanning different regulatory 
classes, accounting both for standards that apply separately to 
different classes and for interactions between regulatory classes. 
Light vehicle CAFE and CO2 standards are specified 
separately for passenger cars and light trucks. However, there is 
considerable sharing between these two regulatory classes--where a 
single engine, transmission, or platform can appear in both the 
passenger car and light truck regulatory class. For example, some 
sport-utility vehicles are offered in 2WD versions classified as 
passenger cars and 4WD versions classified as light trucks. Integrated 
analysis of manufacturers' passenger car and light truck fleets 
provides the ability to account for such sharing and reduces the 
likelihood of finding solutions that could involve introducing 
impractical levels of complexity in manufacturers' product lines. 
Additionally, integrated fleet analysis provides the ability to 
simulate the potential that manufacturers could earn CAFE and 
CO2 credits by over complying with the standard in one fleet 
and use those credits toward compliance with the standard in another 
fleet (i.e., to simulate credit transfers between regulatory classes).
    While previous versions of the CAFE model have represented 
manufacturers' fleets by drawing a distinction between passenger cars 
and light trucks, the current version of the CAFE model adds a further 
distinction, capturing the difference between passenger cars classified 
as domestic passenger cars and those classified as imports. The CAFE 
program regulates those passenger cars separately, and the current 
version of the CAFE model simulates all three CAFE regulatory classes 
separately: Domestic Passenger Cars (DC), Imported Passenger Cars (IC), 
and Light Trucks (LT). CAFE regulations state that standards, fuel 
economy levels, and compliance are all calculated separately for each 
class. These requirements are specified explicitly by the Energy Policy 
and Conservation Act (EPCA), with the 2007 Energy Independence and 
Security Act (EISA) having added the requirement to enforce minimum 
standards for domestic passenger cars. This update to the accounting 
imposes two additional constraints on

[[Page 43165]]

manufacturers that sell vehicles in the U.S.: (1) The domestic minimum 
floor, and (2) Limited transfers between cars classified as 
``domestic'' versus those classified as ``imported.'' The domestic 
minimum floor creates a threshold that every manufacturer's domestic 
car fleet must exceed without the application of CAFE credits. If a 
manufacturer's calculated standard is below the domestic minimum floor, 
then the domestic floor is the binding constraint (even for 
manufacturers that are assumed to be willing to pay fines for non-
compliance). The second constraint poses challenges for manufacturers 
that sell cars from both the domestic and imported passenger car 
    While previous versions of the CAFE model considered those fleets 
as a single fleet (i.e., passenger cars), the model now forces them to 
comply separately and limits the volume of credits that can be shifted 
between them for compliance. However, the CAA provides no direction 
regarding compliance by domestic and imported vehicles; EPA has not 
adopted provisions similar to the aforementioned EPCA/EISA requirements 
and is not doing so today. Therefore, consistent with current and 
proposed CO2 regulations, the CAFE model determines 
compliance for manufacturers' overall passenger car fleets for EPA's 
    During 2015-2016, a single version of the CAFE model was applied to 
produce analyses supporting both a rulemaking regarding heavy-duty 
pickups and vans (HD PUV) and the 2016 draft TAR regarding CAFE 
standards for passenger cars and light trucks. Both analyses reflected 
integrated analysis of the light-duty and HD PUV fleets, thereby 
accounting for sharing between the fleets. However, for most OEMs, that 
analysis showed considerably less sharing between light-duty and HD PUV 
fleets than initially expected. Today's analysis includes only vehicles 
subject to CAFE and light-duty CO2 standards, and the 
agencies invite comment on whether integrated analysis of the two 
fleets should be pursued further.
3. Technology Application Algorithm
(a) Technology Representation and Pathways
    While some properties of the technologies included in the analysis 
are specified by the user (e.g., cost of the technology), the set of 
included technologies is part of the model itself, which contains the 
information about the relationships between technologies.\341\ In 
particular, the CAFE model contains the information about the sequence 
of technologies, the paths on which they reside, any prerequisites 
associated with a technology's application, and any exclusions that 
naturally follow once it is applied.

    \341\ Unlike the 2012 Final Rule, where each technology had a 
single effectiveness value for the CAFE analysis, technology 
effectiveness in the current version of the CAFE model is based on 
the ANL simulation project and defined for each combination of 
technologies, resulting in more than 100,000 technology 
effectiveness values for each of ten technology classes. This large 
database is extracted locally the first time the model is run and 
can be modified by the user in that location to reflect alternative 
assumptions about technology effectiveness.

    The ``application level'' describes the system of the vehicle to 
which the technology is applied, which in turn determines the extent to 
which that decision affects other vehicles in a manufacturer's fleet. 
For example, if a technology is applied at the ``engine'' level, it 
naturally affects all other vehicles that share that same engine 
(though not until they themselves are redesigned, if it happens to be 
in a future model year). Technologies applied at the ``vehicle'' level 
can be applied to a vehicle model without impacting the other models 
with which it shares components. Platform-level technologies affect all 
of the vehicles on a given platform, which can easily span technology 
classes, regulatory classes, and redesign cycles.
    The ``application schedule'' identifies when manufacturers are 
assumed to be able to apply a given technology--with many available 
only during vehicle redesigns. The application schedule also accounts 
for which technologies the CAFE model tracks but does not apply. These 
enter as part of the analysis fleet (``Baseline Only''), and while they 
are necessary for accounting related to cost and incremental fuel 
economy improvement, they do not represent a choice that manufacturers 
make in the model. As discussed in Section II.B, the analysis fleet 
contains the information about each vehicle model, engine, and 
transmission selected for simulation and defines the initial technology 
state of the fleet relative to the sets of technologies in Table II-80 
and Table II-81.

[[Page 43166]]


[[Page 43167]]


    As Table II-80 and Table II-81 show, all of the engine technologies 
may only be applied (for the first time) during redesign. New 
transmissions can be applied during either refresh or redesign, except 
for manual transmissions, which can only be upgraded during redesign. 
Unlike previous versions of the model, which only allowed significant 
changes to vehicle powertrains at redesign, this version allows 
vehicles to inherit updates to shared components during refresh. For 
example, assume Vehicle A and Vehicle B share Engine 1, and engine 1 is 
redesigned as part of Vehicle A's redesign in MY 2020. Vehicle B is not 
redesigned until 2025 but is refreshed in MY 2022. In the current 
version of the CAFE model, Vehicle B would inherit the updated version 
of Engine 1 when it is freshened in MY 2022. This change allows more 
rapid diffusion of powertrain updates (for example) throughout a 
manufacturer's portfolio and reduces the number of years during which a 
manufacturer would build both new and legacy versions of the same 
engine. Despite increasing the rate of technology diffusion, this 
change still restricts the pace at which new engines (for example) can 
be designed and built (i.e., no faster than the redesign schedule of 
the ``leader'' vehicle to which they are tied). The only technology for 

[[Page 43168]]

this does not hold is mass reduction improvements; these occur at the 
platform level, and each model on that platform must be redesigned (not 
merely refreshed) in order to receive the newest version of the 
platform that contains the most current mass reduction technology.
    The CAFE model defines several ``technology classes'' and 
``technology pathways'' for logically grouping all available 
technologies for application on a vehicle. Technology classes provide 
costs and improvement factors shared by all vehicles with similar body 
styles, curb weights, footprints, and engine types, while technology 
pathways establish a logical progression of technologies on a vehicle 
within a system or sub-system (e.g., engine technologies).
    Technology classes, shown in Table-II-82, are a means for 
specifying common technology input assumptions for vehicles that share 
similar characteristics. Predominantly, these classes signify the 
degree of applicability of each of the available technologies to a 
specific class of vehicles and represent a specific set of Autonomie 
simulations (conducted as part of the Argonne National Lab large-scale 
simulation study) that determine the effectiveness of each technology 
to improve fuel economy. The vehicle technology classes also define, 
for each technology, the additional cost associated with 
application.\342\ Like the TAR analysis, the model uses separate 
technology classes for compact cars, midsize cars, small SUVs, large 
SUVs, and pickup trucks. However, in this analysis, each of those 
distinctions also has a ``performance'' version, that represents 
another class with similar body style but higher levels of performance 
attributes (for a total of 10 technology classes). As the model 
simulates compliance, identifying technologies that can be applied to a 
given manufacturer's product portfolio to improve fleet fuel economy, 
it relies on the vehicle class to provide relevant cost and 
effectiveness information for each vehicle model.

    \342\ Inputs are specified to assign each vehicle in the 
analysis fleet to one of these technology classes, as discussed in 
Section II.B.

    The model defines technology pathways for grouping and establishing 
a logical progression of technologies on a vehicle. Each pathway (or 
path) is evaluated independently and in parallel, with technologies on 
these paths being considered in sequential order. As the model 
traverses each path, the costs and fuel economy improvements are 
accumulated on an incremental basis with relation to the preceding 
technology. The system stops examining a given path once a combination 
of one or more technologies results in a ``best'' technology solution 
for that path. After evaluating all paths, the model selects the most 
cost-effective solution among all pathways. This parallel path approach 
allows the modeling system to progress through technologies in any 
given pathway without being unnecessarily prevented from considering 
technologies in other paths.
    Rather than rely on a specific set of technology combinations or 
packages, the model considers the universe of applicable technologies, 
dynamically identifying the most cost-effective combination of 
technologies for each manufacturer's vehicle fleet based on each 
vehicle's initial technology content and the assumptions about each 
technology's effectiveness, cost, and interaction with all other 
technologies both present and available.
(b) Technology Paths
    The modeling system incorporates 16 technology pathways for 
evaluation as shown in Table-II--83. Similar to individual 
technologies, each path carries an intrinsic application level that 
denotes the scope of applicability of all technologies present within 
that path and whether the pathway is evaluated on one vehicle at a 
time, or on a collection of vehicles that share the same platform, 
engine, or transmission.

[[Page 43169]]


    The technologies that comprise the five Engine-Level paths 
available within the model are presented in Figure-II-13. Note: The 
baseline-level technologies (SOHC, DOHC, OHV, and CNG) appear in gray 
boxes. These technologies are used to inform the modeling system of the 
initial engine's configuration and are not otherwise applicable during 
the analysis. Additionally, the VCR path (intended to house fuel 
economy improvements from variable compression ratio engines) was not 
used in this analysis but is present within the model. Unlike earlier 
versions of the CAFE model, that enforced strictly sequential 
application of technologies like VVL and SGDI, this version of the CAFE 
model allows basic engine technologies to be applied in any order once 
an engine has VVT (the base state of all ANL simulations). Once the 
model progresses past the basic engine path, it considers all of the 
more advanced engine paths (Turbo, HCR, Diesel, and ADEAC) 
simultaneously. They are assumed to be mutually exclusive. Once one 
path is taken, it locks out the others to avoid situations where the 
model could be perceived to force manufacturers to radically change 
engine architecture with each redesign, incurring stranded capital 
costs and lost opportunities for learning.

    For all pathways, the technologies are evaluated and applied to a 
vehicle in sequential order, as shown from top to bottom. In some 
cases, however, if a technology is deemed ineffective, the system will 
bypass it and skip ahead to the next technology. If the modeling system 
applies a technology that resides later in the pathway, it will 
``backfill'' anything that was previously skipped in order to fully 
account for costs and fuel economy improvements of the full

[[Page 43170]]

technology combination.\343\ For any technology that is already present 
on a vehicle (either from the MY 2016 fleet or previously applied by 
the model), the system skips over those technologies as well and 
proceeds to the next. These skipped technologies, however, will not be 
applied again during backfill.

    \343\ More detail about how the Argonne simulation database was 
integrated into the CAFE model can be found in PRIA Chapter 6.

    While costs are still purely incremental, technology effectiveness 
is no longer constructed that way. The non-sequential nature of the 
basic engine technologies have no obvious preceding technology except 
for VVT, the root of our engine path. It was a natural extension to 
carry this approach to the other branches as well. The technology 
effectiveness estimates are now an integrated part of the CAFE model 
and represent a translation of the Argonne simulation database that 
compares the fuel consumption of any combination of technologies 
(across all paths) to the base vehicle (that has only VVT, 5-speed 
automatic transmission, no electrification, and no body-level 

    \344\ This is true for all combinations other than those 
containing manual transmissions. Because the model does not convert 
automatic transmissions to manual transmissions, nor the inverse, 
technology combinations containing manual transmissions use a 
reference point identical to the base vehicle description, but 
containing a 5-speed manual rather than automatic transmission.

    The Basic Engine path begins with SOHC, DOHC, and OHV technologies 
defining the initial configuration of the vehicle's engine. Since these 
technologies are not available during modeling, the system evaluates 
this pathway starting with VVT. Whenever a technology pathway forks 
into two or more branch points, as the engine path does at the end of 
the basic engine path, all of the branches are treated as mutually 
exclusive. The model evaluates all technologies forming the branch 
simultaneously and selects the most cost-effective for the application, 
while disabling the unchosen remaining paths.
    The technologies that make up the four Transmission-Level paths 
defined by the modeling system are shown in Figure-II-14. The baseline-
level technologies (AT5, MT5 and CVT) appear in gray boxes and are only 
used to represent the initial configuration of a vehicle's 
transmission. For simplicity, all manual transmissions with five 
forward gears or fewer have been assigned the MT5 technology in the 
analysis fleet. Similarly, all automatic transmissions with five 
forward gears or fewer have been assigned the AT5 technology. The model 
preserves the initial configuration for as long as possible, and 
prohibits manual transmissions from becoming automatic transmissions at 
any point. Automatic transmissions may become CVT level 2 after 
progressing though the 6-speed automatic. While the structure of the 
model still allows automatic transmissions to consider the move to DCT, 
in practice they are restricted from doing so in the market data file. 
This allows vehicles that enter with a DCT to improve it (if 
opportunities to do so exist) but does not allow automatic 
transmissions to become DCTs, in recognition of low consumer enthusiasm 
for the earlier versions the transmission that have been introduced 
over the last decade. The model does not attempt to simulate 
``reversion'' to less advanced transmission technologies, such as 
replacing a 6-speed AT with a DCT and then replacing that DCT with a 
10-speed AT. The agencies invite comment on whether or not the model 
should be modified to simulate such ``reversion'' and, if so, how this 
possible behavior might be practicably simulated.

[[Page 43171]]


    The root of the Electrification path, shown in Figure-II-15, is a 
conventional powertrain (CONV) with no electrification. The two strong 
hybrid technologies (SHEVP2 and SHEVPS) on the Hybrid/Electric path, 
are defined as stand-alone and mutually exclusive. These technologies 
are not incremental over each other for cost or effectiveness and do 
not follow a traditional progression logic present on other paths. 
While the SHEVP2 represents a hybrid system paired with the existing 
engine on a given vehicle, the SHEVPS removes and replaces that engine, 
making it the larger architectural change of the two. In general, the 
electrification technologies are applied as vehicle-level technologies, 
meaning that the model applies them without affecting components that 
might be shared with other vehicles. In the case of the more advanced 
electrification technologies, where engines and transmissions are 
removed or replaced, the model will choose a new vehicle to be the 
leader on that component (if necessary) and will not force other 
vehicles sharing that engine or transmission to become hybrids (or 
EVs). In addition to the electrification technologies, there are two 
electrical system improvements, electric power steering (EPS) and 
accessory improvements (IACC), which were not part of the ANL 
simulation project and are applied by the model as fixed percentage 
improvements to all technology combinations in a particular technology 
class. Their improvements are superseded by technologies in the other 
electrification paths, BISG or CISG, in the case of EPS, and strong 
hybrids (and above) in the case of IACC, which are assumed to include 
those improvements already.

[[Page 43172]]


    The technology paths related to load reduction of the vehicle are 
shown in Figure-II-16. Of these, only the Mass Reduction (MR) path is 
applied at the platform level, thus affecting all vehicles (across 
classes and body styles) on a given platform. The remaining technology 
paths are all applied at the vehicle level, and technologies within 
each path are considered purely sequential. For mass reduction, 
aerodynamic improvements, and reductions in rolling resistance, the 
base level of each path is the ``zero state,'' in which a vehicle has 
exhibited none of the improvements associated with the technology path. 
In addition to choosing among possible engine, transmission, and 
electrification improvements to improve a vehicle's fuel economy, the 
CAFE model will consider technologies each of the possible load 
improvement paths simultaneously.

    Even though the model evaluates each technology path independently, 
some of the pathways are interconnected to allow for additional logical 
progression and incremental accounting of technologies. For example, 
the cost of

[[Page 43173]]

SHEVPS (power-split strong hybrid/electric) on the Hybrid/Electric path 
is defined as incremental over the complete basic engine path (an 
engine that contains VVT, VVL, SGDI, and DEAC), the AT5 (5-speed 
automatic) technology on the Automatic Transmission path, and the CISG 
(crank mounted integrated starter/generator) technology on the 
Electrification path. For that reason, whenever the model evaluates the 
SHEVPS technology for application on a vehicle, it ensures that, at a 
minimum, all the aforementioned technologies (as well as their 
predecessors) have already been applied on that vehicle. However, if it 
becomes necessary for a vehicle to progress to the power-split hybrid, 
the model will virtually apply the technologies associated with the 
reference point in order to evaluate the attractiveness of 
transitioning to the strong hybrid.
    Of the 17 technology pathways present in the model, all Engine 
paths, the Automatic Transmission path, the Electrification path, and 
both Hybrid/Electric paths are logically linked for incremental 
technology progression. Some of the technology pathways, as defined in 
the model and shown in Figure-II-17, may not be compatible with a 
vehicle given its state at the time of evaluation. For example, a 
vehicle with a 6-speed automatic transmission will not be able to get 
improvements from a Manual Transmission path. For this reason, the 
model implements logic to explicitly disable certain paths whenever a 
constraining technology from another path is applied on a vehicle. On 
occasion, not all of the technologies present within a pathway may 
produce compatibility constraints with another path. In such a case, 
the model will selectively disable a conflicting pathway (or part of 
the pathway) as required by the incompatible technology.

    For any interlinked technology pathways shown in Figure-II-17, the 
model also disables all preceding technology paths whenever a vehicle 
transitions to a succeeding pathway. For example, if the model applies 
SHEVPS technology on a vehicle, the model disables the Turbo, HCR, 
ADEAC, and Diesel Engine paths, as well as the Basic Engine, the 
Automatic Transmission, and the Electrification paths (all of which 
precede the Hybrid/Electric path).\345\ This implicitly forces vehicles 
to always move in the direction of increasing technological 
sophistication each time they are reevaluated by the model.

    \345\ The only notable exception to this rule occurs whenever 
SHEVP2 technology is applied on a vehicle. This technology may be 
present in conjunction with any engine-level technology, and as 
such, the Basic Engine path is not disabled upon application of 
SHEVP2 technology, even though this pathway precedes the Hybrid/
Electric path.

4. Simulating Manufacturer Compliance With Standards
    As a starting point, the model needs enough information to 
represent each manufacturer covered by the program. As discussed above 
in Section II.B, the MY 2016 analysis fleet contains information about 
each manufacturer's:

     Vehicle models offered for sale--their current (i.e., 
MY 2016) production volumes, manufacturer suggested retail prices 
(MSRPs), fuel saving technology content (relative to the set of 
technologies described in Table II-80 and Table II-81), and other 
attributes (curb weight, drive type, assignment to technology class 
and regulatory class),
     Production constraints--product cadence of vehicle 
models (i.e., schedule of model redesigns and ``freshenings''), 
vehicle platform membership, degree of engine and/or transmission 
sharing (for each model variant) with other vehicles in the fleet,

[[Page 43174]]

     Compliance constraints and flexibilities--historical 
preference for full compliance or penalty payment/credit 
application, willingness to apply additional cost-effective fuel 
saving technology in excess of regulatory requirements, projected 
applicable flexible fuel credits, and current credit balance (by 
model year and regulatory class) in first model year of simulation.
    Each manufacturer's regulatory requirement represents the 
production-weighted harmonic mean of their vehicle's targets in each 
regulated fleet. This means that no individual vehicle has a 
``standard,'' merely a target, and each manufacturer is free to 
identify a compliance strategy that makes the most sense given its 
unique combination of vehicle models, consumers, and competitive 
position in the various market segments. As the CAFE model provides 
flexibility when defining a set of regulatory standards, each 
manufacturer's requirement is dynamically defined based on the 
specification of the standards for any simulation and the distribution 
of footprints within each fleet.
    Given this information, the model attempts to apply technology to 
each manufacturer's fleet in a manner than minimizes ``effective 
costs.'' The effective cost captures more than the incremental cost of 
a given technology; it represents the difference between their 
incremental cost and the value of fuel savings to a potential buyer 
over the first 30 months of ownership.\346\ In addition to the 
technology cost and fuel savings, the effective cost also includes the 
change in fines from applying a given technology and any estimated 
welfare losses associated with the technology (e.g., earlier versions 
of the CAFE model simulated low-range electric vehicles that produced a 
welfare loss to buyers who valued standard operating ranges between re-
fueling events). The effective cost metric applied by the model does 
not attempt to reflect all costs of vehicle ownership. Further research 
would be required in order to support simulation that assumes buyers 
behave as if they actually consider all ownership costs, and that 
assumes manufacturers respond accordingly. The agencies will continue 
to consider the metric applied to represent manufactuers' approach to 
making decisions regarding the application of fuel-saving technologies 
and invite comment regarding any practicable changes that might make 
this aspect of the model even more realistic.

    \346\ The length of time over which to value fuel savings in the 
effective cost calculation is a model input that can be modified by 
the user. This analysis uses 30 months' worth of fuel savings in the 
effective cost calculation, using the price of fuel at the time of 
vehicle purchase.

    This construction allows the model to choose technologies that both 
improve a manufacturer's regulatory compliance position and are most 
likely to be attractive to its consumers. This also means that 
different assumptions about future fuel prices will produce different 
rankings of technologies when the model evaluates available 
technologies for application. For example, in a high fuel price regime, 
an expensive but very efficient technology may look attractive to 
manufacturers because the value of the fuel savings is sufficiently 
high to both counteract the higher cost of the technology and, 
implicitly, satisfy consumer demand to balance price increases with 
reductions in operating cost. Similarly, technologies for which there 
exist consumer welfare losses (discussed in Section II.E) will be seen 
as less attractive to manufacturers who may be concerned about their 
ability to recover the full amount of the technology cost during the 
sale of the vehicle. The model continues to add technology until a 
manufacturer either: (a) Reaches compliance with regulatory standards 
(possibly through the accumulation and application of overcompliance 
credits), (b) reaches a point at which it is more cost effective to pay 
penalties than to add more technology (for CAFE), or (c) reaches a 
point beyond compliance where the manufacturer assumes its consumers 
will be unwilling to pay for additional fuel saving/emissions reducing 
    In general, the model adds technology for several reasons but 
checks these sequentially. The model then applies any ``forced'' 
technologies. Currently, only VVT is forced to be applied to vehicles 
at redesign since it is the root of the engine path and the reference 
point for all future engine technology applications.\347\ The model 
next applies any inherited technologies that were applied to a leader 
vehicle and carried forward into future model years where follower 
vehicles (on the shared system) are freshened or redesigned (and thus 
eligible to receive the updated version of the shared component). In 
practice, very few vehicle models enter without VVT, so inheritance is 
typically the first step in the compliance loop. Then the model 
evaluates the manufacturer's compliance status, applying all cost-
effective technologies regardless of compliance status (essentially any 
technology for which the effective cost is negative). Then the model 
applies expiring overcompliance credits (if allowed to under the 
perspective of either the ``unconstrained'' or ``standard setting'' 
analysis, for CAFE purposes). At this point, the model checks the 
manufacturer's compliance status again. If the manufacturer is still 
not compliant (and is unwilling to pay civil penalties, again for 
CAFE), the model will add technologies that are not cost-effective 
until the manufacturer reaches compliance. If the manufacturer exhausts 
opportunities to comply with the standard by improving fuel economy/
reducing emissions (typically due to a limited percentage of its fleet 
being redesigned in that year), the model will apply banked CAFE or 
CO2 credits to offset the remaining deficit. If no credits 
exist to offset the remaining deficit, the model will reach back in 
time to alter technology solutions in earlier model years.

    \347\ As a practical matter, this affects very few vehicles. 
More than 95% of vehicles in the market file either already have VVT 
present or have surpassed the basic engine path through the 
application of hybrids or electric vehicles.

    The CAFE model implements multi-year planning by looking back, 
rather than forward. When a manufacturer is unable to comply through 
cost-effective (i.e., producing effective cost values less than zero) 
technology improvements or credit application in a given year, the 
model will ``reach back'' to earlier years and apply the most cost-
effective technologies that were not applied at that time and then 
carry those technologies forward into the future and re-evaluate the 
manufacturer's compliance position. The model repeats this process 
until compliance in the current year is achieved, dynamically 
rebuilding previous model year fleets and carrying them forward into 
the future, accumulating CAFE or CO2 credits from over-
compliance with the standard wherever appropriate.
    In a given model year, the model determines applicability of each 
technology to each vehicle model, platform, engine, and transmission. 
The compliance simulation algorithm begins the process of applying 
technologies based on the CAFE or CO2 standards specified 
during the current model year. This involves repeatedly evaluating the 
degree of noncompliance, identifying the next ``best'' technology 
(ranked by the effective cost discussed earlier) available on each of 
the parallel technology paths described above and applying the best of 
these. The algorithm combines some of the pathways, evaluating them 
sequentially instead of in parallel, in order to ensure appropriate 
incremental progression of technologies.
    The algorithm first finds the best next applicable technology in 
each of the technology pathways then selects the

[[Page 43175]]

best among these. For CAFE purposes, the model applies the technology 
to the affected vehicles if a manufacturer is either unwilling to pay 
penalties or if applying the technology is more cost-effective than 
paying penalties. Afterwards, the algorithm reevaluates the 
manufacturer's degree of noncompliance and continues application of 
technology. Once a manufacturer reaches compliance (i.e., the 
manufacturer would no longer need to pay penalties), the algorithm 
proceeds to apply any additional technology determined to be cost-
effective (as discussed above). Conversely, if a manufacturer is 
assumed to prefer to pay penalties, the algorithm only applies 
technology up to the point where doing so is less costly than paying 
penalties. The algorithm stops applying additional technology to this 
manufacturer's products once no more cost-effective solutions are 
encountered. This process is repeated for each manufacturer present in 
the input fleet. It is then repeated again for each model year. Once 
all model years have been processed, the compliance simulation 
algorithm concludes. The process for CO2 standard compliance 
simulation is similar, but without the option of penalty payment.
(a) Compliance Example
    The following example will illustrate the features discussed above 
for the CAFE program. While the example describes the actions that 
General Motors takes to modify the Chevrolet Equinox in order to comply 
with the augural standards (the baseline in this analysis), and the 
logical consequences of these actions, a similar example would develop 
if instead simulating compliance with the EPA standards for those 
years. The structure of GM's fleet and the mechanisms at work in the 
CAFE model are identical in both cases, but different features of each 
program (unlimited credit transfers between fleets, for example) would 
likely cause the model to choose different technology solutions.
    At the start of the simulation in MY 2016, GM has 30 unique engines 
shared across over 33 unique nameplates, 260 model variants, and three 
regulatory classes. As discussed earlier, the CAFE model will attempt 
to preserve that level of sharing across GM's fleets to avoid 
introducing additional production complexity for which the agencies do 
not estimate additional costs. An even smaller number of transmissions 
(16) and platforms (12) are shared across the same set of nameplates, 
model variants, and regulatory classes.
    The Chevrolet Equinox is represented in the model inputs as a 
single nameplate, with five model variants distinguished by the 
presence of all-wheel drive and four distinct powertrain configurations 
(two engines paired with two different transmissions). Across all five 
model variants, GM produced above 220,000 units of the Equinox 
nameplate. About 150,000 units of that production volume is regulated 
as Domestic Passenger Car, with the remainder regulated as Light 
Trucks. The easiest way to describe the actions taken by the CAFE model 
is to focus on a single model variant of the Equinox (one row in the 
market data file). The model variant of the Equinox with the highest 
production volume, about 130,000 units in MY 2016, is vehicle code 
110111.\348\ This unique model variant is the basis for the example. 
However, because it is only one of five variants on the Equinox 
nameplate, the modifications made to that model in the simulation will 
affect the rest of the Equinox variants and other vehicles across all 

    \348\ This numeric designation is not important to understand 
the example but will allow an interested reader to identify the 
vehicle in model outputs to either recreate the example or use it as 
a template to create similar examples for other manufacturers and 

    The example Equinox variant is designated as an engine and platform 
leader. As discussed earlier, this implies that modifications to its 
engine (11031, a 2.4L I-4) are tied to the redesign cadence of this 
Equinox, as are modifications to its platform (Theta/TE). The engine is 
shared by the Buick LaCrosse, Regal, and Verano, and by the GMC Terrain 
(as well as appearing in two other variants of the Equinox). So those 
vehicles, if redesigned after this Equinox, will inherit changes to 
engine 11031 when they are redesigned, carrying the legacy version of 
the engine until then. Similarly, this Equinox shares its platform with 
the Cadillac SRX and GMC Terrain, which will inherit changes made to 
this platform when they are redesigned (if later than the Equinox, as 
is the case with the SRX).
    This specific Equinox is a transmission ``follower,'' getting 
updates made to its transmission leader (the Chevrolet Malibu) when it 
is freshened or redesigned. Additionally, two other variants of the 
Equinox nameplate (the more powerful versions, containing a 3.6L V-6 
engine) are not ``leaders'' on any of the primary components. Those 
variants are built on the same platform as the example Equinox variant 
but share their engine with the Buick Enclave and LaCrosse, the 
Cadillac SRX and XTS,\349\ the Chevrolet Colorado, Impala and Traverse 
(which is the designated ``leader''), and the GMC Acadia, Canyon, and 
Terrain. This is an example of how shared and inherited components 
interact with product cadence: when the Equinox nameplate is 
redesigned, the CAFE model has more leverage over some variants than 
others and cannot make changes to the engines of the variants of the 
Equinox with V-6 unless that change is consistent with all of the other 
nameplates just listed. The transmissions on the other variants of the 
Equinox are similarly widely shared and represent the same kind of 
production constraint just described with respect to the engine. When 
accounting for the full set of engines, transmissions, and platforms 
represented across the Equinox nameplate's five variants, components 
are shared across all three regulatory classes.

    \349\ The agencies recognized that GM last produced the Cadillac 
SRX for MY 2016, and note this as one example of the limitations of 
using an analysis fleet defined in terms of even a recent actual 
model year. Section II.B discusses these tradeoffs, and the 
tentative judgment that, as a foundation for analysis presented 
here, it was better to develop the analysis fleet using the best 
information available for MY 2016 than to have used manufacturers' 
CBI to construct an analysis fleet that, though more current, would 
have limited the agencies' ability to make public all analytical 
inputs and outputs.

    This example uses a ``standard setting'' perspective to minimize 
the amount of credit generation and application, in order to focus on 
the mechanics of technology application and component sharing. The 
actions taken by the CAFE model when operating on the example Equinox 
during GM's compliance simulation are shown in Table-II-84. In general, 
the example Equinox begins the compliance simulation with the 
technology observed in its MY 2016 incarnation--a 2.6L I-4 with VVT and 
SGDI, a 6-speed automatic transmission, low rolling resistance tires 
(ROLL20) and a 10% realized improvement in aerodynamic drag (AERO10). 
In MY 2018, the Equinox is redesigned, at which time the engine adds 
VVL and level-1 turbocharging. The transmission on the Malibu is 
upgraded to an 8-speed automatic in 2018, which the Equinox also gets. 
The platform, for which this Equinox is the designated leader, gets 
level-4 mass reduction. The CAFE model also applies a few vehicle-level 
technologies: low-drag brakes, electronic accessory improvements, and 
additional aerodynamic improvements (AERO20). Upon refresh in MY 2021, 
it acquires an upgraded 10-speed transmission (AT10) from the Malibu.

[[Page 43176]]

Then in MY 2025 it is redesigned again and upgrades the engine to 
level-2 turbocharging, replaces the 10-speed automatic transmission 
with a 8-speed automatic transmission, adds a P2 strong hybrid, and 
further reduces the mass of the platform (MR5). Using an 
``unconstrained'' perspective would possibly lead to additional actions 
taken after MY 2025, where GM may have been simulated to use credits 
earned in earlier model years to offset small, persistent CAFE deficits 
in one or more fleets. In the ``standard setting'' perspective, that 
forces compliance without the use of CAFE credits, this is not an 

    The technology applications described in Table-II-84 have 
consequences beyond the single variant of the Equinox shown in the 
table. In particular, two other variants of the Equinox (both of which 
are regulated as Light Trucks) get the upgraded engine, which they 
share with the example, in MY 2018. Thus, this application of engine 
technology to a single variant of the Equinox in the Domestic Car 
fleet, ``spills over'' into the Light Truck fleet, generating 
improvements in fuel economy and additional costs. Furthermore, the 
Buick LaCrosse and Regal, and the GMC Terrain also get the same engine, 
which they share with the example, in MY 2018. Those vehicles also span 
the Domestic Car and Light Truck fleets. However, the Buick Verano, 
which is not redesigned until MY 2019, continues with the legacy (i.e., 
MY 2016) version of the shared engine until it is redesigned. When it 
inherits the new engine in MY 2019, it does so without modification; 
the engine it inherits is the same one that was redesigned in MY 2018. 
This means that the Verano will improve its fuel economy in MY 2019 
when the new engine is inherited but only to the extent that the new 
version of the engine is an improvement over the legacy version in the 
context of the Verano's other technology (which it is--the Verano moves 
from 32 MPG to 44 MPG when accounting for the other technologies added 
during the MY 2019 redesign).
    This same story continues with the diffusion of platform 
improvements simulated by the CAFE model in MY 2018. The GMC Terrain is 
simulated to be redesigned in MY 2018, in conjunction with the Equinox. 
The performance variants of the Equinox, with a 3.5L V-6, also upgrade 
their engines in MY 2018 (in conjunction with the estimated Chevrolet 
Traverse redesign). However, when the Equinox is next redesigned in MY 
2025, the engine shared with the Traverse is not upgraded again until 
MY 2026, so the performance versions of the Equinox continue with the 
2018 version of the engine throughout the remainder of the simulation. 
While these inheritances and sharing dynamics are not a perfect 
representation of each manufacturer's specific constraints, nor the 
flexibilities available to shift strategies in real-time as a response 
to changing market or regulatory conditions, they are a reasonable way 
to consider the resource constraints that prohibit fleet-wide 
technology diffusion over shorter windows than have been observed 
historically and for which the agencies have no way to impose 
additional costs.
    Aside from the technology application and its consequences 
throughout the GM product portfolio, discussed above, there are other 
important conclusions to draw from the technology application example. 
The first of these is that product cadence matters, and only by taking 
a year-by-year perspective can this be seen. When the example Equinox

[[Page 43177]]

is redesigned in MY 2018, the CAFE model takes actions that cause the 
redesigned Equinox to significantly exceed its fuel economy target. 
While no single vehicle has a ``standard,'' having high volume vehicles 
significantly below their individual targets can present compliance 
challenges for manufacturers who must compensate by exceeding targets 
on other vehicles. While the example Equinox exceeds its MY 2018 target 
by almost 9 mpg, this version of the Equinox is not eligible to see 
significant technology changes again before MY 2025 (except for the 
transmission upgrade that occurs in MY 2021). Thus, the CAFE model is 
redesigning the Equinox in MY 2018 with respect to future targets and 
standards--this Equinox is nearly 2 mpg below its target in MY 2024 
before being redesigned in MY 2025. This reflects a real challenge that 
manufacturers face in the context of continually increasing CAFE 
standards, and represents a clear example of why considering two model 
year snapshots where all vehicles are assumed to be redesigned is 
unrealistically simplistic. The MY 2018 version of the example Equinox 
persists (with little change) through six model years and the standards 
present in those years. This is one reason why the CAFE model, rather 
than OMEGA, was chosen to examine the impacts of the proposed standards 
in this analysis.
    Another feature of note in Table-II-84 is the cost of applying 
these technologies. The costs are all denominated in dollars and 
represent incremental cost increases relative to the MY 2016 version of 
the Equinox. Aside from the cost increase of over $5,000 in MY 2025 
when the vehicle is converted to a strong hybrid, the incremental 
technology costs display a consistent trend between application 
events--decreasing steadily over time as the cost associated with each 
given combination of technologies ``learns down.'' By MY 2032, even the 
most expensive version of the example Equinox costs nearly $800 less to 
produce than it did in MY 2025.
    The technology application in the example occurs in the context of 
GM's attempt to comply with the augural standards. As some of the 
components on the Equinox nameplate are shared across all three 
regulated fleets, Table-II-85 shows the compliance status of each fleet 
in MYs 2016-2025. In MY 2017, the CAFE model applies expiring credits 
to offset deficits in the DC and LT fleets. In MY 2028, when GM is 
simulated to aggressively apply technology to the example Equinox, the 
DC fleet exceeds its standard while the LT fleet still generates 
deficits. The CAFE model offset that deficit with expiring (and 
possibly transferred) credits. However, by MY 2020 the ``standard 
setting'' perspective removes the option of using CAFE credits to 
offset deficits and GM exceeds the standard in all three fleets, though 
by almost 2 mpg in DC and LT. As the Equinox example showed, many of 
the vehicles redesigned in MY 2020 will still be produced at the MY 
2020 technology level in MY 2025 where GM is simulated to comply 
exactly across all three fleets. Under an ``unconstrained'' 
perspective, the CAFE model would use the CAFE credits earned through 
over-compliance with the standards in MYs 2020-2023 to offset deficits 
created by under-compliance as the standards continued to increase, 
pushing some technology application until later years when the 
standards stabilized and those credits expired. The CAFE model 
simulates compliance through MY 2032 to account for this behavior.

[[Page 43178]]


(b) Representation of OEMs' Potential Responsiveness to Buyers' 
Willingness To Pay for Fuel Economy Improvements
    The CAFE model simulates manufacturer responses to both regulatory 
standards and technology availability. In order to do so, it requires 
assumptions about how the industry views consumer demand for additional 
fuel economy because manufacturer responses to potential standards 
depend not just on what they think they are best off producing to 
satisfy regulatory requirements (considering the consequences of not 
satisfying those requirements), but also on what they think they can 
sell, technology-wise, to consumers. In the 2012 final rule, the 
agencies analyzed alternatives under the assumption that manufacturers 
would not improve the fuel economy of new vehicles at all unless 
compelled to do so by the existence of increasingly stringent CAFE and 
GHG standards.\350\ This ``flat baseline'' assumption led the agencies 
to attribute all of the fuel savings that occurred in the simulation 
after MY 2016 to the proposed standards because none of the fuel 
economy improvements were considered likely to occur in the absence of 
increasing standards. However, this assumption contradicted much of the 
literature on this topic and the industry's recent experience with CAFE 
compliance, and for CAFE standards, the analysis published in 2016 
applied a reference case estimate that manufacturers will treat all 
technologies that pay for themselves within the first three years

[[Page 43179]]

of ownership (through reduced expenditures on fuel) as if the cost of 
that technology were negative.\351\

    \350\ See, e.g., 75 FR 62844, 75 FR 63105.
    \351\ Draft TAR, p. 13-10, available at https://www.nhtsa.gov/staticfiles/rulemaking/pdf/cafe/Draft-TAR-Final.pdf (last accessed 
June 15, 2018).

    The industry has exceeded the required CAFE level for both 
passenger cars and light trucks in the past; notably, by almost 5 mpg 
during the fuel price spikes of the 2000s when CAFE standards for 
passenger cars were still frozen at levels established for the 1990 
model year.\352\ In fact, a number of manufacturers that traditionally 
paid CAFE civil penalties even reached compliance during years with 
sufficiently high fuel prices.\353\ The model attempts to account for 
this observed consumer preference for fuel economy, above and beyond 
that required by the regulatory standards, by allowing fuel price to 
influence the ranking of technologies that the model considers when 
modifying a manufacturer's fleet in order to achieve compliance. In 
particular, the model ranks available technology not by cost, but by 
``effective cost.''

    \352\ NHTSA, Summary of Fuel Economy Performance, 2014, 
available at https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/performance-summary-report-12152014-v2.pdf (last accessed June 27, 
    \353\ Ibid. Additional data available at https://one.nhtsa.gov/cafe_pic/CAFE_PIC_Mfr_LIVE.html (last accessed June 27, 2018).

    When the model chooses which technology to apply next, it 
calculates the effective cost of available technologies and chooses the 
technology with the lowest effective cost. The ``effective cost'' 
itself is a combination of the technology cost, the fuel savings that 
would occur if that technology were applied to a given vehicle, the 
resulting change in CAFE penalties (as appropriate), and the affected 
volumes. User inputs determine how much fuel savings manufacturers 
believe new car buyers will pay for (denominated in the number of years 
before a technology ``pays back'' its cost).
    Because the civil penalty provisions specified for CAFE in EPCA do 
not apply to CO2 standards, the effective cost calculation 
applied when simulating compliance with CO2 standards uses 
an estimate of the potential value of CO2 credits. Including 
a valuation of CO2 credits in the effective cost metric 
provides a potential basis for future explicit modeling of credit 
trading.\354\ Manufacturers, though, have thus far declined to disclose 
the actual terms of CAFE or CO2 credit trades, so this 
calculation currently uses the CAFE civil penalty rate as the basis to 
estimate this value. It seems reasonable to assume that the CAFE civil 
penalty rate likely sets an effective ceiling on the price of any 
traded CAFE credits, and considering that each manufacturer can only 
produce one fleet of vehicles for sale in the U.S., prices of 
CO2 credits might reasonably be expected to be equivalent to 
prices of CAFE credits. However, the current CAFE model does not 
explicitly simulate credit trading; therefore, the change in the value 
of CO2 credits should only capture the change in 
manufacturer's own cost of compliance, so the compliance simulation 
algorithm applies a ceiling at 0 (zero) to each calculated value of the 
CO2 credits.\355\

    \354\ By treating all passenger cars and light trucks as being 
manufactured by a single ``OEM,'' inputs to the CAFE model can be 
structured to simulate perfect trading. However, competitive and 
other factors make perfect trading exceedingly unlikely, and future 
efforts will focus consideration on more plausible imperfect 
    \355\ Having the model continue to add technology in order to 
build a surplus of credits as warranted by the estimated (whether 
specified as a model input or calculated dynamically as a clearing 
price) market value of credits would provide part of the basis for 
having the model build the supply side of an explicitly-simulated 
credit trading market.

    Just as manufacturers' actual approaches to vehicle pricing are 
closely held, manufacturers' actual future approaches to making 
decisions about technology are not perfectly knowable. The CAFE model 
is intended to illustrate ways manufacturers could respond to 
standards, given a set of production constraints, not to predict how 
they will respond. Alternatives to these ``effective cost'' metrics 
have been considered and will continue to be considered. For example, 
instead of using a dollar value, the model could use a ratio, such as 
the net cost (technology cost minus fuel savings) of an application of 
technology divided by corresponding quantity of avoided fuel 
consumption or CO2 emissions. Any alternative metric has the 
potential to shift simulated choices among technology application 
options, and some metrics would be less suited to the CAFE model's 
consideration of multiyear product planning, or less adaptable than 
others to any future simulation of credit trading. Comment is sought 
regarding the definition and application of criteria to select among 
technology options and determine when to stop applying technology 
(consider not only standards, but also factors such as fuel prices, 
civil penalties for CAFE, and the potential value of credits for both 
programs), and this aspect of the model may be further revised. Any 
future revision to the effective cost would be considered in light of 
manufacturers different compliance positions relative to the standards, 
and in light of the likelihood that some OEMs will continue to use 
civil penalties as a means to resolve CAFE deficits (at least for some 
    While described in greater detail in the CAFE model documentation, 
the effective cost reflects an assumption not about consumers' actual 
willingness to pay for additional fuel economy but about what 
manufacturers believe consumers are willing to pay. The reference case 
estimate for today's analysis is that manufacturers will treat all 
technologies that pay for themselves within the first 2\1/2\ years of 
ownership (through reduced expenditures on fuel) as if the cost of that 
technology were negative. Manufacturers have repeatedly indicated to 
the agencies that new vehicle buyers are only willing to pay for fuel 
economy-improving technology if it pays back within the first two to 
three years of vehicle ownership.\356\ NHTSA has therefore incorporated 
this assumption (of willingness to pay for technology that pays back 
within 30 months) into today's analysis. Alternatives to this 30-month 
estimate are considered in the sensitivity analysis included in today's 
notice. In the current version of the model, this assumption holds 
whether or not a manufacturer has already achieved compliance. This 
means that the most cost-effective technologies (those that pay back 
within the first 2\1/2\ years) are applied to new vehicles even in the 
absence of regulatory pressure. However, because the value of fuel 
savings depends upon the price of fuel, the model will add more 
technology even without regulatory pressure when fuel prices are high 
compared to simulations where fuel prices are assumed to be low. This 
assumption is consistent with observed historical compliance behavior 
(and consumer demand for fuel economy in the new vehicle market), as 
discussed above.

    \356\ This is supported by the 2015 NAS study, which found that 
consumers seek to recoup added upfront purchasing costs within two 
or three years. See 2015 NAS Report, at pg. 317.

    One implication of this assumption is that futures with higher, or 
lower, fuel prices produce different sets of attractive technologies 
(and at different times). For example, if fuel prices were above $7/
gallon, many of the technologies in this analysis could pay for 
themselves within the first year or two and would be applied at high 
rates in all of the alternatives. Similarly, at the other extreme 
(significantly reduced fuel prices), almost no additional fuel economy 
would be observed.

[[Page 43180]]

    While these assumptions about desired payback period and consumer 
preferences for fuel economy may not affect the eventual level of 
achieved CAFE and CO2 emissions in the later years of the 
program, they will affect the amount of additional technology cost and 
fuel savings that are attributable to the standard. The approach 
currently only addresses the inherent trade-off between additional 
technology cost and the value of fuel savings, but other costs could be 
relevant as well. Further research would be required to support 
simulations that assume buyers behave as if they consider all ownership 
costs (e.g., additional excise taxes and insurance costs) at the time 
of purchase and that manufacturers respond accordingly. Comment is 
sought on the approach described above, the current values ascribed to 
manufacturers' belief about consumer willingness-to-pay for fuel 
economy, and practicable suggestions for future improvements and 
refinements, considering the model's purpose and structure.
(c) Representation of Some OEMs' Willingness To Treat Civil Penalties 
as a Program Flexibility
    When considering technology applications to improve fleet fuel 
economy, the model will add technology up to the point at which the 
effective cost of the technology (which includes technology cost, 
consumer fuel savings, consumer welfare changes, and the cost of 
penalties for non-compliance with the standard) is less costly than 
paying civil penalties or purchasing credits. Unlike previous versions 
of the model, the current implementation further acknowledges that some 
manufacturers experience transitions between product lines where they 
rely heavily on credits (either carried forward from earlier model 
years or acquired from other manufacturers) or simply pay penalties in 
one or more fleets for some number of years. The model now allows the 
user to specify, when appropriate for the regulatory program being 
simulated, on a year-by-year basis, whether each manufacturer should be 
considered as willing to pay penalties for non-compliance. This 
provides additional flexibility, particularly in the early years of the 
simulation. As discussed above, this assumption is best considered as a 
method to allow a manufacturer to under-comply with its standard in 
some model years--treating the civil penalty rate and payment option as 
a proxy for other actions it may take that are not represented in the 
CAFE model (e.g., purchasing credits from another manufacturer, carry-
back from future model years, or negotiated settlements with NHTSA to 
resolve deficits).
    In the current analysis, NHTSA has relied on past compliance 
behavior and certified transactions in the credit market to designate 
some manufacturers as being willing to pay CAFE penalties in some model 
years. The full set of assumptions regarding manufacturer behavior with 
respect to civil penalties is presented in Table-II-86, which shows all 
manufacturers are assumed to be willing to pay civil penalties prior to 
MY 2020. This is largely a reflection of either existing credit 
balances (which manufacturers will use to offset CAFE deficits until 
the credits reach their expiration dates) or assumed trades between 
manufacturers that are likely to happen in the near-future based on 
previous behavior. The manufacturers in the table whose names appear in 
bold all had at least one regulated fleet (of three) whose CAFE was 
below its standard in MY 2016. Because the analysis began with the MY 
2016 fleet, and no technology can be added to vehicles that are already 
designed and built, all manufacturers can generate civil penalties in 
MY 2016. However, once a manufacturer is designated as unwilling to pay 
penalties, the CAFE model will attempt to add technology to the 
respective fleets to avoid shortfalls.

[[Page 43181]]

    Several of the manufacturers in Table-II-86 that are assumed to be 
willing to pay civil penalties in the early years of the program have 
no history of paying civil penalties. However, several of those 
manufacturers have either bought or sold credits--or transferred 
credits from one fleet to another to offset a shortfall in the 
underperforming fleet. As the CAFE model does not simulate credit 
trades between manufacturers, providing this additional flexibility in 
the modeling avoids the outcome where the CAFE model applies more 
technology than would be needed in the context of the full set of 
compliance flexibilities at the industry level. By statute, NHTSA 
cannot consider credit flexibilities when setting standards, so most 
manufacturers (those without a history of civil penalty payment) are 
assumed to comply with their standard through fuel economy improvements 
for the model years being considered in this analysis. The notable 
exception to this is FCA, who we expect will still satisfy the 
requirements of the program through a combination of credit application 
and civil penalties through MY 2025 before eventually complying 
exclusively through fuel economy improvements in MY 2026.
    As mentioned above, the CAA does not provide civil penalty 
provisions similar to those specified in EPCA/EISA, and the above-
mentioned corresponding inputs apply only to simulation of compliance 
with CAFE standards.
(d) Representation of CAFE and CO2 Credit Provisions
    The 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 be used to simulate credit carry-
forward (a.k.a. banking) between model years and transfers between the 
passenger car and light truck fleets but not credit carry-back (a.k.a. 
borrowing) from future model years or trading between manufacturers. 
Some manufacturers have made occasional use of credit carry-back 
provisions, although the analysis does not assume use of carry-back as 
a compliance strategy because of the risk in relying on future 
improvements to offset earlier compliance deficits. Thus far, NHTSA has 
not attempted to include simulation of credit carry-back or trading in 
the CAFE model. Unlike past versions, the current CAFE model provides a 
basis to specify (in model inputs) CAFE credits available from model 
years earlier than those being simulated explicitly. For example, with 
this analysis representing model years 2016-2032 explicitly, credits 
earned in model year 2012 are made available for use through model year 
2017 (given the current five-year limit on carry-forward of credits). 
The banked credits are specific to both model year and fleet in which 
they were earned. Comment and supporting information are invited 
regarding whether and, if so, how the CAFE model and inputs might 
practicably be modified to account for trading of credits between 
manufacturers and/or carry-back of credits from later to earlier model 
    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 in order to achieve compliance with a standard, the 
model will apply credits. Otherwise it 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 application over time to avoid both compliance 
shortfalls and high levels of over-compliance that can result in a 
surplus of credits. As further discussed in the CAFE model 
documentation, model inputs can be used to adjust this logic to shift 
the use of credits ahead by one or more model years. In general, the 
logic used to generate credits and apply them to compensate for 
compliance shortfalls, both in a given fleet and across regulatory 
fleets, is an area that requires more attention in the next phase of 
model development. While the current model correctly accounts for 
credits earned when a manufacturer exceeds its standard in a given 
year, the strategic decision of whether to earn additional credits to 
bank for future years (in the current fleet or to transfer into another 
regulatory fleet) and when to optimally apply them to deficits is 
challenging to simulate. This will be an area of focus moving forward.
    NHTSA introduced the CAFE Public Information Center \357\ to 
provide public access to a range of information regarding the CAFE 
program, 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. Additionally, CAFE credits that are traded between 
manufacturers are adjusted to preserve the gallons saved that each 
credit represents.\358\ 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 

    \357\ CAFE Public Information Center, http://www.nhtsa.gov/CAFE_PIC/CAFE_PIC_Home.htm (last visited June 22, 2018).
    \358\ GHG 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.

    Having reviewed credit balances (as of October 23, 2017) and 
estimated the potential that some manufacturers could trade credits, 
NHTSA developed inputs that make carried-forward credits available as 
summarized in Table-II-87, Table-II-88, and Table-II-89, after 
subtracting credits assumed to be traded to other manufacturers, adding 
credits assumed to be acquired from other manufacturers through such 
trades, and adjusting any traded credits (up or down) to reflect their 
true value for the fleet and model year into which they were 
traded.\359\ While the CAFE model will transfer expiring credits into 
another fleet (e.g., moving expiring credits from the domestic car 
credit bank into the light truck fleet), some of these credits were 
moved in the initial banks to improve the efficiency of application and 
to better reflect both the projected shortfalls of each manufacturer's 
regulated fleets, and to represent observed behavior. For context, a 
manufacturer that produces one million vehicles in a given fleet, and 
experiences a shortfall of 2 mpg, would need 20 million credits to 
completely offset the shortfall.

    \359\ The adjustments, which are based upon the standard, CAFE 
and year of both the party originally earning the credits and the 
party applying them, were implemented assuming the credits would be 
applied to the model year in which they were set to expire. For 
example, credits traded into a domestic passenger car fleet for MY 
2014 were adjusted assuming they would be applied in the domestic 
passenger car fleet for MY 2019.


[[Page 43182]]



[[Page 43183]]


    In addition to the inclusion of these existing credit banks, the 
CAFE model also updated its treatment of credits in the rulemaking 
analysis. Congress has declared that NHTSA set CAFE standards at 
maximum feasible levels for each model year under consideration without 
consideration of the program's credit mechanisms. However, as CAFE 
rulemakings have evaluated longer time periods in recent years, the 
early actions taken by manufacturers required more nuanced 
representation. Therefore, the CAFE model now allows a ``last year to 
consider credits,'' set at the last year for which new standards are 
not being considered (MY 2019 in this analysis). This allows the model 
to replicate the practical application of existing credits toward CAFE 
compliance in early years but to examine the impact of proposed 
standards based solely on fuel economy improvements in all years for 
which new standards are being considered. Comment is sought regarding 
the model's representation of the CAFE and CO2 credit 
provisions, recommendations regarding any other options, and any 
information that could help to refine the current approach or develop 
and implement an alternative approach.
    The CAFE model has also been modified to include a similar 
representation of existing credit banks in EPA's CO2 
program. While the life of a CO2 credit, denominated in 
metric tons CO2, has a five-year life, matching the lifespan 
of CAFE credits, credits earned in the early years of the EPA program, 
MY 2009-2011, may be used through MY 2021.\360\ The CAFE model was not 
modified to allow exceptions to the life-span of compliance credits 
treating them all as if they may be carried forward for no more than 
five years, so the initial credit banks were modified to anticipate the 
years in which those credits might be needed. The fact that MY 2016 is 
simulated explicitly prohibited the inclusion of these banked credits 
in MY 2016 (which could be carried forward from MY 2016 to MY 2021), 
and thus underestimates the extent to which individual manufacturers, 
and the industry as a whole, may rely on these early credits to comply 
with EPA standards between MY 2016 and MY 2021. The credit banks with 
which the simulations in this analysis were conducted are presented in 
the following tables:

    \360\ In response to comments, EPA placed limits on credits 
earned in MY 2009, causing them to expire prior to this rule. 
However, credits generated in MYs 2010-2011 may be carried forward, 
or traded, and applied to deficits generated through MY 2021.

[[Page 43184]]



    While the CAFE model does not simulate the ability to trade credits 
between manufacturers, it does simulate the strategic accumulation and 
application of compliance credits, as well as the ability to transfer 
credits between fleets to improve the compliance position of a less 
efficient fleet by leveraging credits earned by a

[[Page 43185]]

more efficient fleet. The model prefers to hold on to earned compliance 
credits within a given fleet, carrying them forward into the future to 
offset potential future deficits. This assumption is consistent with 
observed strategic behavior dating back to 2009.
    From 2009 to present, no manufacturer has transferred CAFE credits 
into a fleet to offset a deficit in the same year in which they were 
earned. This has occurred with credits acquired from other 
manufacturers via trade but not with a manufacturer's own credits. 
Therefore, the current representation of credit transfers between 
fleets--where the model prefers to transfer expiring, or soon-to-be-
expiring credits rather than newly earned credits--is both appropriate 
and consistent with observed industry behavior.
    This may not be the case for GHG standards, though it is difficult 
to be certain at this point. The GHG program seeded the industry with a 
large quantity of early compliance credits (earned in MYs 2009-2011 
\361\) prior to the existence formal standards of the EPA program. 
These early credits do not expire until 2021. So, for manufacturers 
looking to offset deficits, it is more sensible to use current-year 
credits that expire in the next five years, rather than draw down the 
bank of credits that can be used until MY 2021. The first model year 
for which earned credits outlive the initial bank is MY 2017, for which 
final compliance actions and deficit resolutions are still pending. 
Regardless, in order to accurately represent some of the observed 
behavior in the GHG credit system, the CAFE model allows (and 
encourages) within-year transfers between regulated fleets for the 
purpose of simulating compliance with the GHG standards.

    \361\ In response to public comment, EPA eliminated the use of 
credits earned in MY 2009 for future model years. However, credits 
earned in MY 2010 and MY 2011 remain.

    In addition to more rigorous accounting of CAFE and CO2 
credits, the model now 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 the 
current model uses the adjustments claimed by each manufacturer in MY 
2016 as the starting point for all future years. Because the air 
conditioning and off-cycle adjustments are not credits in NHTSA's 
program, but rather adjustments to compliance fuel economy (much like 
the Flexible Fuel Vehicle adjustments that are due to phase out in MY 
2019), they may be included under either a ``standard setting'' or 
``unconstrained'' analysis perspective.
    When the CAFE model simulates EPA's program, the treatment of A/C 
efficiency and off-cycle credits 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.
5. Impacts on Each OEM and Overall Industry
(a) Technology Application and Penetration Rates
    The CAFE model tracks and reports technology application and 
penetration rates for each manufacturer, regulatory class, and model 
year, calculated as the volume of vehicles with a given technology 
divided by the total volume. The ``application rate'' accounts only for 
those technologies applied by the model during the compliance 
simulation, while the ``penetration rate'' accounts for the total 
percentage of a technology present in a given fleet, whether applied by 
the CAFE model or already present at the start of the simulation.
    In addition to the aggregate representation of technology 
penetration, the model also tracks each individual vehicle model on 
which it has operated. Each row in the market data file (the 
representation of vehicles offered for sale in MY 2016 in the U.S., 
discussed in detail in Section II.B.a and PRIA Chapter 6) contains a 
record for every model year and every alternative, that identifies with 
which technologies the vehicle started the simulation, which 
technologies were applied, and whether those technologies were applied 
directly or through inheritance (discussed above). Interested parties 
may use these outputs to assess how the compliance simulation modified 
any vehicle that was offered for sale in MY 2016 in response to a given 
regulatory alternative.
(b) Required and Achieved CAFE and Average CO2 Levels
    The model fully represents the required CAFE (and now, 
CO2) levels for every manufacturer and every fleet. The 
standard for each manufacturer is based on the harmonic average of 
footprint targets (by volume) within a fleet, just as the standards 
prescribe. Unlike earlier versions of the CAFE model, the current 
version further disaggregates passenger cars into domestic and imported 
classes (which manufacturers report to NHTSA and EPA as part of their 
CAFE compliance submissions). This allows the CAFE model to more 
accurately estimate the requirement on the two passenger car fleets, 
represent the domestic passenger car floor (which must be exceeded by 
every manufacturer's domestic fleet, without the use of credits, but 
with the possibility of civil penalty payment), and allows it to 
enforce the transfer cap limit that exists between domestic and 
imported passenger cars, all for purposes of the CAFE program.
    In calculating the achieved CAFE level, the model uses the 
prescribed harmonic average of fuel economy ratings within a vehicle 
fleet. Under an ``unconstrained'' analysis, or in a model year for 
which standards are already final, it is possible for a manufacturer's 
CAFE to fall below its required level without generating penalties 
because the model will apply expiring or transferred credits to 
deficits if it is strategically appropriate to do so. Consistent with 
current EPA regulations, the model applies simple (not harmonic) 
production-weighted averaging to calculate average CO2 
(c) Costs
    For each technology that the model adds to a given vehicle, it 
accumulates cost. The technology costs are defined incrementally and 
vary both over time and by technology class, where the same technology 
may cost more to apply to larger vehicles as it involves more raw 
materials or requires different specifications to preserve some 
performance attributes. While learning-by-doing can bring down cost, 
and should reasonably be implemented in the CAFE model as a rate of 
cost reduction that is applied to the cumulative volume of a given 
technology produced by either a single manufacturer or the industry as 
a whole, in practice this notion is implemented as a function of time, 
rather than production volume. Thus, depending upon where a given 
technology starts along its learning curve, it may appear to be cost-
effective in later years where it was not in earlier years. As the 
model carries forward technologies that it has already applied to 
future model years, it similarly adjusts the costs of those 
technologies based on their individual learning rates.

[[Page 43186]]

    The other costs that manufacturers incur as a result of CAFE 
standards are civil penalties resulting from non-compliance with CAFE 
standards. The CAFE model accumulates costs of $5.50 per 1/10-MPG under 
the standard, multiplied by the number of vehicles produced in that 
fleet, in that model year. The model reports as the full ``regulatory 
cost,'' the sum of total technology cost and total fines by the 
manufacturer, fleet, and model year. As mentioned above, the relevant 
EPCA/EISA provisions do not also appear in the CAA, so this option and 
these costs apply only to simulated compliance with CAFE standards.
(d) Sales
    In all previous versions of the CAFE model, the total number of 
vehicles sold in any model year, in fact the number of each individual 
vehicle model sold in each year, has been a static input that did not 
vary in response to price increases induced by CAFE standards, nor 
changes in fuel prices, or any other input to the model. The only way 
to alter sales, was to update the entire forecast in the market input 
file. However, in the 2012 final rule, NHTSA included a dynamic fleet 
share model that was based on a module in the Energy Information 
Administration's NEMS model. This fleet share model did not change the 
size of the new vehicle fleet in any year, but it did change the share 
of new vehicles that were classified as passenger cars (or light 
trucks). That capability was not included in the central analysis but 
was included in the uncertainty analysis, which looked at the baseline 
and preferred alternative in the context of thousands of possible 
future states of the world. As some of those futures contained extreme 
cases of fuel prices, it was important to ensure consistent modeling 
responses within that context. For example, at a gasoline price of $7/
gallon, it would be unrealistic to expect the new vehicle market's 
light truck share to be the same as the future where gasoline cost $2/
gallon. The current model has slightly modified, and fully integrated, 
the dynamic fleet share model. Every regulatory alternative and 
sensitivity case considered in this analysis reflects a dynamically 
responsive fleet mix in the new vehicle market.
    While the dynamic fleet share model adjusts unit sales across body 
styles (cars, SUVs, and trucks), it does not modify the total number of 
new vehicles sold in a given year. The CAFE model now includes a 
separate function to account for changes in the total number of new 
vehicles sold in a given year (regardless of regulatory class or body 
style), in response to certain macroeconomic inputs and changes in the 
average new vehicle price. The price impact is modest relative to the 
influence of the macroeconomic factors in the model. The combination of 
these two models modify the total number of new vehicles, the share of 
passenger cars and light trucks, and, as a consequence, the number of 
each given model sold by a given manufacturer. However, these two 
factors are insufficient to cause large changes to the composition of 
any of a manufacturer's fleets. In order to significantly change the 
mix of models produced within a given fleet, the CAFE model would 
require a way to trade off the production of one vehicle versus another 
both within a manufacturer's fleet and across the industry. While NHTSA 
has experimented with fully-integrated consumer choice models, their 
performance has yet to satisfy the requirements of a rulemaking 
    There are multiple levels of sales impacts that could result from 
increasing the prices of new vehicles across the industry. Any estimate 
of impacts at the manufacturer, or model, level would be subject to an 
assumed pricing strategy that spreads technology cost increases across 
available models in a way that may cross-subsidize specific models or 
segments at the expense of others. However, at the industry level, it 
is reasonable to assume that all incremental technology costs can be 
captured by the average price of a new vehicle. To the extent that this 
factor influences the total number of new vehicles sold in a given 
model year, it can be included in an empirical model of annual sales. 
However, there is limited historical evidence that the average price of 
a new vehicle is a strong determining factor in the total number of 
annual new vehicle sales.
6. National Impacts
(a) Vehicle Stock and Fleet Turnover
    The CAFE model carries a complete representation of the registered 
vehicle population in each calendar year, starting with an aggregated 
version of the most recent available data about the registered 
population for the first year of the simulation. In this analysis, the 
first model year considered is MY 2016, and the registered vehicle 
population enters the model as it appeared at the end of calendar year 
2015. The initial vehicle population is stratified by age (or model 
year cohort) and regulatory class--to which the CAFE model assigns 
average fuel economies based on the reported regulatory class industry 
average compliance value in each model year (and class). Once the 
simulation begins, new vehicles are added to the population from the 
market data file and age throughout their useful lives during the 
simulation, with some fraction of them being retired (or scrapped) 
along the way. For example, in calendar year 2017, the new vehicles 
(age zero) are MY 2017 vehicles (added by the CAFE model simulation and 
represented at the same level of detail used to simulate compliance), 
the age one vehicles are MY 2016 vehicles (added by the CAFE model 
simulation), and the age two vehicles are MY 2015 vehicles (inherited 
from the registered vehicle population and carried through the analysis 
with less granularity). This national registered fleet is used to 
calculate annual fuel consumption, vehicle miles traveled (VMT), 
pollutant emissions, and safety impacts under each regulatory 
    In addition to dynamically modifying the total number of new 
vehicles sold, a dynamic model of vehicle retirement, or scrappage, has 
also been implemented. The model implements the scrappage response by 
defining the instantaneous scrappage rate at any age using two 
functions. For ages less than 20, instantaneous scrappage is defined as 
a function of vehicle age, new vehicle price, cost per mile of driving 
(the ratio of fuel price and fuel economy), and a small number of 
macroeconomic factors. For ages greater than 20, the instantaneous 
scrappage rate is a simple exponential function of age. While the 
scrappage response does not affect manufacturer compliance 
calculations, it impacts the lifetime mileage accumulation (and thus 
fuel savings) of all vehicles. Previous CAFE analyses have focused 
exclusively on new vehicles, tracing the fuel consumption and social 
costs of these vehicles throughout their useful lives; the scrappage 
effect also impacts the registered vehicle fleet that exists when a set 
of standards is implemented.
    As new vehicles enter the registered population their retirement 
rates are governed by the scrappage model, so are the vehicles already 
registered at the start of model year 2016. To the extent that a given 
set of CAFE or CO2 standards accelerates or decelerates the 
retirement of those vehicles, additional fuel consumption and social 
costs may accrue to those vehicles under that standard. The CAFE model 
accounts for those costs and benefits, as well as tracking all of the 
standard benefits and costs associated with the lifetimes of new 
vehicles produced under the rule. For more detail about the derivation 
of the scrappage functions, see Section

[[Page 43187]]

II.E, and PRIA Chapter 8. Comment is sought on the specification and 
inclusion of these factors in the current model.
(b) Highway Travel
    In support of prior CAFE rulemakings, the CAFE model accounted for 
new travel that results from fuel economy improvements that reduce the 
cost of driving. The magnitude of the increase in travel demand is 
determined by the rebound effect. In both previous versions and the 
current version of the CAFE model, the amount of travel demanded by the 
existing fleet of vehicles is also responsive to the rebound effect 
(representing the price elasticity of demand for travel)--increasing 
when fuel prices decrease relative to the fuel price when the VMT on 
which our mileage accumulation schedules were built was observed. Since 
the fuel economy of those vehicles is already fixed, only the fuel 
price influences their travel demand relative to the mileage 
accumulation schedule and so is identical for all regulatory 
    While the average mileage accumulation per vehicle by age is not 
influenced by the rebound effect in a way that differs by regulatory 
alternative, three other factors influence total VMT in the model in a 
way that produces different total mileage accumulation by regulatory 
alternative. The first factor is the total industry sales response: New 
vehicles are both driven more than older vehicles and are more fuel 
efficient (thus producing more rebound miles). To the extent that more 
(or fewer) of these new models enter the vehicle fleet in each model 
year, total VMT will increase (or decrease) as a result. The second 
factor is the dynamic fleet share model. The fleet share influences not 
only the fuel economy distribution of the fleet, as light trucks are 
less efficient than passenger cars on average, but the total miles are 
influenced by fact that light trucks are driven more than passenger 
cars as well. Both of the first two factors can magnify the influence 
of the rebound effect on vehicles that go through the compliance 
simulation (MY 2016-2032) in the manner discussed above and in Section 
II.E. The third factor influencing total annual VMT is the scrappage 
model. By modifying the retirement rates of on-road vehicles under each 
regulatory alternative, the scrappage model either increases or 
decreases the lifetime miles that accrue to vehicles in a given model 
year cohort.
(c) Fuel Consumption and GHG Emissions
    For every vehicle model in the market file, the model estimates the 
VMT per vehicle (using the assumed VMT schedule, the vehicle fuel 
economy, fuel price, and the rebound assumption). Those miles are 
multiplied by the volume for each vehicle. Fuel consumption is the 
product of miles driven and fuel economy, which can be tracked by model 
year cohort in the model. Carbon dioxide emissions from vehicle 
tailpipes are the simple product of gallons consumed and the carbon 
content of each gallon.
    In order to calculate calendar year fuel consumption, the model 
needs to account for the inherited on-road fleet in addition to the 
model year cohorts affected by this proposed rule. Using the VMT of the 
average passenger car and light truck from each cohort, the model 
computes the fuel consumption of each model year class of vehicles for 
its age in a given CY. The sum across all ages (and thus, model year 
cohorts) in a given CY provides estimated CY fuel consumption.
    Rather than rely on the compliance values of fuel economy for 
either historical vehicles or vehicles that go through the full 
compliance simulation, the model applies an ``on-road gap'' to 
represent the expected difference between fuel economy on the 
laboratory test cycle and fuel economy under real-world operation. This 
was a topic of interest in the recent peer review of the CAFE model. 
While the model currently allows the user to specify an on-road gap 
that varies by fuel type (gasoline, E85, diesel, electricity, hydrogen, 
and CNG), it does not vary over time, by vehicle age, or by technology 
combination. It is possible that the ``gap'' between laboratory fuel 
economy and real-world fuel economy has changed over time, that fuel 
economy degrades over time as a vehicle ages, or that specific 
combinations of fuel-saving technologies have a larger discrepancy 
between laboratory and real-world fuel economy than others. Further 
research would be required to determine whether the model should 
include a functional representation of the on-road gap to address these 
various factors, and comment is sought on the data sources and 
implementation strategies available to do so.
    Because the model produces an estimate of the aggregate number of 
gallons sold in each CY, it is possible to calculate both the total 
expenditures on motor fuel and the total contribution to the Highway 
Trust Fund (HTF) that result from that fuel consumption. The Federal 
fuel excise tax is levied on every gallon of gasoline and diesel sold 
in the U.S., with diesel facing a higher per-gallon tax rate. The model 
uses a national perspective, where the state taxes present in the input 
files represent an estimated average fuel tax across all U.S. states. 
Accordingly, while the CAFE model cannot reasonably estimate potential 
losses to state fuel tax revenue from increasingly the fuel economy of 
new vehicles, it can do so for the HTF, and the agencies invite comment 
on the proposed standards' implications for the HTF.
    In addition to the tailpipe emissions of carbon dioxide, each 
gallon of gasoline produced for consumption by the on-road fleet has 
associated ``upstream'' emissions that occur in the extraction, 
transportation, refining, and distribution of the fuel. The model 
accounts for these emissions as well (on a per-gallon basis) and 
reports them accordingly.
(d) Criteria Pollutant Emissions
    The CAFE model uses the entire on-road fleet, calculated VMT 
(discussed above), and emissions factors (which are an input to the 
CAFE model, specified by model year and age) to calculate tailpipe 
emissions associated with a given alternative. Just as it does for 
additional GHG emissions associated with upstream emissions from fuel 
production, the model captures criteria pollutants that occur during 
other parts of the fuel life cycle. While this is typically a function 
of the number of gallons of gasoline consumed (and miles driven, for 
tailpipe criteria pollutant emissions), the CAFE model also estimates 
electricity consumption and the associated upstream emissions (resource 
extraction and generation, based on U.S. grid mix).
(e) Highway Fatalities
    Earlier versions of the CAFE model accounted for the safety impacts 
associated with reducing vehicle mass in order to improve fuel economy. 
In particular, NHTSA's safety analysis estimated the additional 
fatalities that would occur as a result of new vehicles getting 
lighter, then interacting with the on-road vehicle population. In 
general, taking mass out of the heaviest new vehicles improved safety 
outcomes, while taking mass from the lightest new vehicles resulted in 
a greater number of expected highway fatalities. However, the change in 
fatalities did not adequately account for changes in exposure that 
occur as a result of increased demand for travel as vehicles become 
cheaper to operate. The current version of the model resolves that

[[Page 43188]]

limitation and addresses additional sources of fatalities that can 
result from the implementation of CAFE or CO2 standards. 
These are discussed in greater detail in Section 0 and PRIA Chapter 11.
    NHTSA has observed that older vehicles in the population are 
responsible for a disproportionate number of fatalities, both by number 
of registrations and by number of miles driven. Accordingly, any factor 
that causes the population of vehicles to turn over more slowly will 
induce additional fatalities--as those older vehicles continue to be 
driven, rather than being retired and replaced with newer (even if not 
brand new) vehicle models. The scrappage effect, which delays (or 
accelerates) the retirement of registered vehicles, impacts the number 
of fatalities through this mechanism--importantly affecting not just 
new vehicles sold from model years 2016-2032 but existing vehicles that 
are already part of the on-road fleet. Similarly, to the extent that a 
CAFE or CO2 alternative reduces new vehicle sales, it can 
slow the transition from older vehicles to newer vehicles, reducing the 
share of total vehicle miles that are driven by newer, more 
technologically advanced vehicles. Accounting for the change in vehicle 
miles traveled that occurs when vehicles become cheaper to operate has 
led to a number of fatalities that can be attributed to the rebound 
effect, independent of any changes to new vehicle mass, price, or 
    The CAFE model now estimates fatalities by combining the effects 
discussed above. In particular, the model estimates the fatality rate 
per billion miles VMT for each model year vehicle in the population 
(the newest of which are the new vehicles produced that model year). 
This estimate is independent of regulatory class and varies only by 
year (and not vehicle age). The estimated fatality rate is then 
multiplied by the estimated VMT for each vehicle in the population and 
the product of the change in curb weight and the relevant safety 
coefficient, as in the equation below.

    For the vehicles in the historical fleet, meaning all those 
vehicles that are already part of the registered vehicle population in 
CY 2016, only the model year effect that determines the 
``FatalityEstimate'' is relevant. However, each vehicle that is 
simulated explicitly by the CAFE model, and is eligible to receive mass 
reduction technologies, must also consider the change between its curb 
weight and the threshold weights that are used to define safety 
classes. For vehicles above the threshold, reducing vehicle mass can 
have a smaller negative impact on fatalities (or even reduce 
fatalities, in the case of the heaviest light trucks). The 
``ChangePer100Lbs'' depends upon this difference. The sum of all 
estimated fatalities for each model year vehicle in the on-road fleet 
determines the reported fatalities, which can be summarized by either 
model year or calendar year.
(f) Costs and Benefits
    As the CAFE model simulates manufacturer compliance with regulatory 
alternatives, it estimates and tracks a number of consequences that 
generate social costs. The most obvious cost associated with the 
program is the cost of additional fuel economy improving/CO2 
emissions reducing technology that is added to new vehicles as a result 
of the rule. However, the model does not inherently draw a distinction 
between costs and benefits. For example, the model tracks fuel 
consumption and the dollar value of fuel consumed. This is the cost of 
travel under a given alternative (including the baseline). The ``cost'' 
or ``benefit'' associated with the value of fuel consumed is determined 
by the reference point against which each alternative is considered. 
The CAFE model reports absolute values for the amount of money spent on 
fuel in the baseline, then reports the amount spent on fuel in the 
alternatives relative to the baseline. If the baseline standard were 
fixed at the current level, and an alternative achieves 100 mpg by 
2025, the total expenditures on fuel in the alternative would be lower, 
creating a fuel savings ``benefit.'' This analysis uses a baseline that 
is more stringent than each alternative considered, so the incremental 
fuel expenditures are greater for the alternatives than for the 
    Other social costs and benefits emerge as the result of physical 
phenomena, like tailpipe emissions or highway fatalities, which are the 
result of changes in the composition and use of the on-road fleet. The 
social costs associated with those quantities represent an economic 
estimate of the social damages associated with the changes in each 
quantity. The model tracks and reports each of these quantities by: 
Model year and vehicle age (the combination of which can be used to 
produce calendar year totals), regulatory class, fuel type, and social 
discount rate.
    The full list of potential costs and benefits is presented in 
Table-II-92 as well as the population of vehicles that determines the 
size of the factor (either new vehicles or all registered vehicles) and 
the mechanism that determines the size of the effect (whether driven by 
the number of miles driven, the number of gallons consumed, or the 
number of vehicles produced).

[[Page 43189]]


III. Proposed CAFE and CO2 Standards for MYs 2021-2026

A. Form of the Standards

    NHTSA and EPA are proposing that the form of the CAFE and 
CO2 standards for MYs 2021-2026 would follow the form of 
those standards in prior model years. NHTSA has specific statutory 
requirements for the form of CAFE standards: Specifically, EPCA, as 
amended by EISA, requires that CAFE standards be issued separately for 
passenger cars and light trucks, and that each standard be specified as 
a mathematical function expressed in terms of one or more vehicle 
attributes related to fuel economy. Although the CAA does not have 
comparable specific requirements for the form of CO2 
standards for light-duty vehicles, EPA has concluded that it is 
appropriate to set CO2 standards according to vehicle 
footprint, consistent with the EPCA/EISA requirements, which simplifies 
compliance for the industry.\362\

    \362\ Such an approach is permissible under section 202(a) of 
the CAA and EPA has used the attribute-based approach in issuing 
standards under analogous provisions of the CAA.

    For MYs since 2011 for CAFE and since 2012 for CO2, 
standards have taken the form of fuel economy and CO2 
targets expressed as functions of vehicle footprint (the product of 
vehicle wheelbase and average track width). NHTSA and EPA continue to 
believe that footprint is the most appropriate attribute on which to 
base the proposed standards, as discussed in Section II.C. Under the 
footprint-based standards, the function defines a CO2 or 
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 and CO2 average standard 
for each year that is unique to each of its fleets,\363\ depending on 
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. The functions are mostly sloped, so 
that generally, larger vehicles (i.e., vehicles with larger footprints) 
will be subject to lower CAFE mpg targets and higher CO2 
grams/mile targets than smaller vehicles. This is because, generally 
speaking, smaller vehicles are more capable of achieving higher levels 
of fuel economy/lower levels of CO2 emissions, mostly 
because they tend not to have to work as hard 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 to which the manufacturer must 
comply will be 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.\364\

    \363\ EPCA/EISA requires NHTSA to separate passenger cars into 
domestic and import passenger car fleets whereas EPA combines all 
passenger cars into one fleet.
    \364\ As 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 follows:

[[Page 43190]]



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] = 
    For light trucks, also consistent with prior rulemakings, NHTSA is 
proposing to define fuel economy targets as follows:


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.

    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. For MYs 2020-2026, the parameters are unchanged, 
resulting in the same stringency in each of those model years.
    Mathematical functions defining the proposed CO2 targets 
are expressed as functions that are similar, with coefficients a-h 
corresponding to those listed above.\365\ For passenger cars, EPA is 
proposing to define CO2 targets as follows:

    \365\ EPA regulations use a different but mathematically 
equivalent approach to specify targets. Rather than using a function 
with nested minima and maxima functions, EPA regulations specify 
requirements separately for different ranges of vehicle footprint. 
Because these ranges reflect the combined application of the listed 
minima, maxima, and linear functions, it is mathematically 
equivalent and more efficient to present the targets as in this 


TARGETCO2 is the CO2 target (in grams per mile, or g/mi) 
applicable to a specific vehicle model configuration,
a is a minimum CO2 target (in g/mi),
b is a maximum CO2 target (in g/mi),
c is the slope (in g/mi, per square foot) of a line relating 
CO2 emissions to footprint, and
d is an intercept (in g/mi) of the same line.

    For light trucks, CO2 targets are defined as follows:
TARGETCO2 = MIN[MIN[b, MAX[a,c x FOOTPRINT + d]], MIN[f,MAX[e, g x 


TARGETCO2 is the CO2 target (in g/mi) applicable to a 
specific vehicle model configuration,
a, b, c, and d are as for passenger cars, but taking values specific 
to light trucks,
e is a second minimum CO2 target (in g/mi),
f is a second maximum CO2 target (in g/mi),
g is the slope (in g/mi per square foot) of a second line relating 
CO2 emissions to footprint, and
h is an intercept (in g/mi) of the same second line.

    To be clear, as has been the case since the agencies began 
establishing attribute-based standards, no vehicle need meet the 
specific applicable fuel economy or CO2 targets, because 
compliance with either CAFE or CO2 standards is determined 
based on corporate average fuel economy or fleet average CO2 
emission rates. 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 follows:


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 the fuel economy target (as defined above) for model 
configuration i.

    Similarly, the required average CO2 level applicable to 
a given fleet in a given model year is determined by calculating the 

[[Page 43191]]

average (not harmonic) of CO2 targets applicable to specific 
vehicle model configurations in the fleet, as follows:


CO2required is the average CO2 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
TARGETCO2,i is the CO2 target (as defined above) for 
model configuration i.

    Today's action would set standards that only apply to fuel economy 
and CO2. EPA seeks comment on this approach.
    Comment is sought on the proposed standards and on the analysis 
presented here; we seek any relevant data and information and will 
review responses. That review could lead to selection of one of the 
other regulatory alternatives for the final rule.

B. Passenger Car Standards

    For passenger cars, NHTSA and EPA are proposing CAFE and 
CO2 standards, respectively, for MYs 2021-2026 that are 
defined by the following coefficients:

    Section II.C above discusses in detail how the coefficients in 
Table III-1 were developed for this proposal. The coefficients result 
in the footprint-dependent targets shown graphically below for MYs 
2021-2026. The MYs 2017-2020 standards are also shown for comparison.

[[Page 43192]]


[[Page 43193]]


    While we do not know yet with certainty what CAFE and 
CO2 levels will ultimately be required of individual 
manufacturers, because those levels will depend on the mix of vehicles 
that they produce for sale in future model years, based on the market 
forecast of future sales that was used to examine today's proposed 
standards, we currently estimate that the target functions shown above 
would result in the following average required fuel economy and 
CO2 emissions levels for individual manufacturers during MYs 
2021-2026. Prior to MY 2021, average required CO2 levels 
reflect underlying target functions (specified above) that reflect the 
use of automotive refrigerants with reduced global warming potential 
(GWP) and/or the use of technologies that reduce the refrigerant leaks. 
EPA is proposing to exclude air conditioning refrigerants and leakage, 
and nitrous oxide and methane GHGs from average performance 
calculations after model year 2020; CO2 targets and 
resultant fleet average requirements for model years 2021 and beyond do 
not reflect these adjustments.

    \366\ Prior to MY 2021, CO2 targets include 
adjustments reflecting the use of automotive refrigerants with 
reduced global warming potential (GWP) and/or the use of 
technologies that reduce the refrigerant leaks and optionally 
nitrous oxide and methane emissions. EPA is proposing to exclude air 
conditioning refrigerants and leakage, and nitrous oxide and methane 
GHGs from average performance calculations after model year 2020; 
CO2 targets (and resultant fleet average requirements) 
for model years 2021 and beyond do not reflect these adjustments.

    EPA seeks comments on whether to proceed with this proposal to 
discontinue accounting for A/C leakage, methane emissions, and nitrous 
oxide emissions as part of the CO2 emissions standards to 
provide for better harmony with the CAFE program, or whether to 
continue to consider these factors toward compliance and retain that as 
a feature that differs between the programs. A/C leakage credits, which 
are accounted for in the baseline model, have been extensively 
generated by manufacturers, and make up a portion of their compliance 
with EPA's CO2 standards. In the 2016 MY, manufacturers 
averaged six grams per mile equivalent in A/C leakage credits, ranging 
from three grams per mile equivalent for Hyundai and Kia, to 17 grams 
per mile equivalent for Jaguar Land Rover.\367\ As related to methane 
(CH4) and nitrous oxide (N2O) emissions, 
manufacturers averaged 0.1 grams per mile equivalent in deficits for 
the 2016 MY, with deficits ranging from 0.1 grams per mile equivalent 
for GM, Mazda, and Toyota, to 0.6 grams per mile equivalent for 

    \367\ Other manufacturers' A/C leakage credit grams per mile 
equivalent include: BMW, Honda, Mistubishi, Nissan, Toyota, and 
Volkswagen at 5 g/mi; Mercedes at 6 g/mi; Ford, GM, and Volvo at 7 
g/mi; and FCA at 14 g/mi.
    \368\ Other manufacturers' methane and nitrous oxide deficit 
grams per mile equivalent include BMW at 0.2 g/mi, and Ford at 0.3 
g/mi. FCA and Volkswagen numbers are not reported due to an ongoing 
investigation and/or corrective actions.

    EPA notes that since the 2010 rulemaking on this subject, the 
agencies have accounted for the ability to apply A/C leakage credits by 
increasing EPA's CO2 standard stringency by the average 
anticipated amount of credits when compared to the CAFE stringency 
requirements.\369\ For model years 2021-2025, the A/C leakage offset, 

[[Page 43194]]

equivalent stringency increase compared to the CAFE standard, is 13.8 
g/mi equivalent for passenger cars and 17.2 g/mi equivalent for light 
trucks.\370\ For those model years, manufacturers are currently allowed 
to apply A/C leakage credits capped at 18.8 g/mi equivalent for 
passenger cars and 24.4 g/mi equivalent for light trucks.\371\

    \369\ 75 FR 25330, May 7, 2010.
    \370\ 77 FR 62805, Oct. 15, 2012.
    \371\ 77 FR 62649, Oct. 15, 2012.

    For methane and nitrous oxide emissions, as part of the MY 2012-
2016 rulemaking, EPA finalized standards to cap emissions of 
N2O at 0.010 g/mile and CH4 at 0.030 g/mile for 
MY 2012 and later vehicles.\372\ However, EPA also provided an optional 
CO2-equivalent approach to address industry concerns about 
technological feasibility and leadtime for the CH4 and 
N2O standards for MY 2012-2016 vehicles. The CO2 
equivalent standard option allowed manufacturers to fold all 2-cycle 
weighted N2O and CH4 emissions, on a 
CO2-equivalent basis, along with CO2, into their 
CO2 emissions fleet average compliance level.\373\ EPA 
estimated that on a CO2 equivalent basis, folding in all 
N2O and CH4 emissions could add up to 3-4 g/mile 
to a manufacturer's overall CO2 emissions level because the 
equivalent standard must be used for the entire fleet, not just for 
``problem vehicles.'' \374\ To address this added difficulty, EPA 
amended the MY 2012-2016 standards to allow manufacturers to use 
CO2 credits, on a CO2-equivalent basis, to meet 
the light-duty N2O and CH4 standards in those 
model years. EPA subsequently extended that same credit provision to MY 
2017 and later vehicles. EPA seeks comment on whether to change 
existing methane and nitrous oxide standards that were finalized in the 
2012 rule. Specifically, EPA seeks information from the public on 
whether the existing standards are appropriate, or whether they should 
be revised to be less stringent or more stringent based on any updated 

    \372\ 75 FR 25421-24, May 7, 2010.
    \373\ 77 FR 62798, Oct. 15, 2012.
    \374\ In the final rule for MYs 2012-2016, EPA acknowledged that 
advanced diesel or lean-burn gasoline vehicles of the future may 
face greater challenges meeting the CH4 and 
N2O standards than the rest of the fleet. [See 75 FR 
25422, May 7, 2010].

    If the agency moves forward with its proposal to eliminate these 
factors, EPA would consider whether it is appropriate to initiate a new 
rulemaking to regulate these programs independently, which could 
include an effective date that would result in no lapse in regulation 
of A/C leakage or emissions of nitrous oxide and methane. If the agency 
decides to retain the A/C leakage and nitrous oxide and methane 
emissions provisions for CO2 compliance, it would likely re-
insert the current A/C leakage offset and increase the stringency 
levels for CO2 compliance by the offset amounts described 
above (i.e., 13.8 g/mi equivalent for passenger cars and 17.2 g/mi 
equivalent for light trucks), and retain the current caps (i.e., 18.8 
g/mi equivalent for passenger cars and 24.4 g/mi equivalent for light 
trucks). The agency will publish an analysis of this alternative 
approach in a memo to the docket for this rulemaking. The agency seeks 
comment on whether the current offsets and caps would continue to be 
appropriate in such circumstances or whether changes are warranted.

    We emphasize again that the values in these tables are estimates, 
and not necessarily the ultimate levels with which each of these 
manufacturers will have to comply, for the reasons described above.

C. Minimum Domestic Passenger Car Standards

    EPCA has long required manufacturers to meet the passenger car CAFE 
standard with both their domestically-manufactured and imported 
passenger car fleets--that is, domestic and imported passenger car 
fleets must comply separately with the passenger car CAFE standard in 
each model year.\375\ In doing so, they may use whatever flexibilities 
are available to them under the CAFE program, such as using credits 
``carried forward'' from prior model years, transferred from another 
fleet, or acquired from another manufacturer. On top of this 
requirement, EISA expressly requires each manufacturer to meet a 
minimum flat fuel economy standard for domestically manufactured 
passenger cars.\376\ According to the statute, the minimum standard 
shall be the greater of (A) 27.5 miles per gallon; or (B) 92% of the 
average fuel economy projected by DOT for the combined domestic and

[[Page 43195]]

nondomestic 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.\377\ NHTSA discusses this requirement in 
more detail in Section V.A.1 below.

    \375\ 49 U.S.C. 32904(b) (2007).
    \376\ 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).
    \377\ 49 U.S.C. 32902(b)(4).

    The following table lists the proposed minimum domestic passenger 
car standards (which very likely will be updated for the final rule as 
the agency updates its overall analysis and resultant projection), 
highlighted as ``Preferred (Alternative 3)'' and calculates what those 
standards would be under the no action alternative (as issued in 2012, 
and as updated by today's analysis) and under the other alternatives 
described and discussed further in Section IV, below.

D. Light Truck Standards

    For light trucks, NHTSA and EPA are proposing CAFE and 
CO2 standards, respectively, for MYs 2021-2026 that are 
defined by the following coefficients:

[[Page 43196]]

    Section II.C above discusses in detail how the coefficients in 
Table III-4 were developed for this proposal. The coefficients result 
in the footprint-dependent targets shown graphically below for MYs 
2021-2026. The MYs 2017-2020 standards are also shown for comparison.

    \378\ Prior to MY 2021, average achieved CO2 levels 
include adjustments reflecting the use of automotive refrigerants 
with reduced global warming potential (GWP) and/or the use of 
technologies that reduce the refrigerant leaks. Because EPA is today 
proposing to exclude air conditioning refrigerants and leakage, and 
nitrous oxide and methane GHGs from average performance calculations 
after MY 2020, CO2 targets and resultant fleet average 
requirements for MYs 2021 and beyond do not reflect these 


[[Page 43197]]

    While we do not know yet with certainty what CAFE and 
CO2 levels will ultimately be required of individual 
manufacturers, because those levels will depend on the mix of vehicles 
that they produce for sale in future model years, based on the market 
forecast of future sales that were used to examine today's proposed 
standards, we currently estimate that the target functions shown above 
would result in the following average required fuel economy and 
CO2 emissions levels for individual manufacturers during MYs 
2021-2026. Prior to MY 2021, average required CO2 levels 
reflect underlying target functions (specified above) that reflect the 
use of automotive refrigerants with reduced global warming potential 
(GWP) and/or the use of technologies that reduce the refrigerant leaks. 
Because EPA is today proposing to exclude air conditioning refrigerants 
and leakage, and nitrous oxide and methane GHGs from average 
performance calculations after model year 2020, CO2 targets 
and resultant fleet average requirements for model years 2021 and 
beyond do not reflect these adjustments.

    We emphasize again the values in these tables are estimates and not 
necessarily the ultimate levels with which each of these manufacturers 
will have to comply for reasons described above.

IV. Alternative CAFE and GHG Standards Considered for MYs 2021/22-2026

    Agencies typically consider regulatory alternatives in proposals as 
a way of evaluating the comparative effects of different potential ways 
of accomplishing their desired goal.\379\ Alternatives analysis begins 
with a ``no-action'' alternative, typically described as what would 
occur in the absence of any regulatory action. Today's proposal 
includes a no-action alternative, described below, as well as seven 
``action alternatives'' besides the proposal. The proposal may, in 
places, be referred to as the ``preferred alternative,'' which is NEPA 
parlance, but NHTSA and EPA intend ``proposal,'' ``proposed action,'' 
and ``preferred alternative'' to be used interchangeably for purposes 
of this rulemaking.

    \379\ As Section V.A.3 explains, 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 and OMB Circular A-4 also encourage agencies 
to evaluate regulatory alternatives in their rulemaking analyses.

    As discussed above in Chapter II, today's notice also presents the 
results of analysis estimating impacts under a range of other 
regulatory alternatives the agencies are considering. Aside from the 
no-action alternative, NHTSA and EPA defined the different regulatory 
alternatives in terms of percent-increases in CAFE and GHG stringency 
from year to year. Under some alternatives, the rate of increase is the 
same for both passenger cars and light trucks; under others, the rate 
of increase differs. Two alternatives also involve a gradual 
discontinuation of CAFE and average GHG adjustments reflecting the 
application of technologies that improve air conditioner efficiency or, 
in other ways, improve fuel economy under conditions not represented by 
long-standing fuel economy test procedures. For increased harmonization 
with NHTSA CAFE standards, which cannot account for such issues, under 
Alternatives 1-8, EPA would regulate tailpipe CO2 
independently of A/C refrigerant leakage, nitrous oxide and methane 
emissions. Under the no action alternative, EPA would continue to 
regulate A/C refrigerant leakage, nitrous oxide and methane emissions 
under the overall CO2 standard.\380\ Like the baseline no-
action alternative, all of the alternatives are more stringent than the 
preferred alternative.

    \380\ 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 tailpipe CO2 for its 
standards. In addition, under the no action alternative EPA adds 
CO2 equivalent (using Global Warming Potential (GWP) 
adjustment) for AC refrigerant leakage and nitrous oxide and methane 
emissions. The CAFE program does not include A/C refrigerant 
leakage, nitrous oxide and methane emissions because they do not 
impact fuel economy. Under Alternatives 1-8, the standards are 
completely aligned for gasoline because compliance is based on 
tailpipe CO2, CH4 and CO for both programs and 
not emissions unrelated to fuel economy. Diesel and alternative fuel 
vehicles would continue to be treated differently between the CAFE 
and CO2 programs. While harmonization would be 
significantly improved, standards would not be fully aligned because 
of the small fraction of the fleet that uses diesel and alternative 
fuels (e.g., about four percent of the MY 2016 fleet), as well as 
differences involving EPCA/EISA provisions EPA, lacking any specific 
direction under the CAA, has declined to adopt, such as minimum 
standards for domestic passenger cars and limits on credit transfers 
between regulated fleets.

    EPA also seeks comment on retaining the existing credit program for 
regulation of A/C refrigerant leakage, nitrous oxide, and methane 
emissions as part of the CO2 standard.
    The agencies have examined these alternatives because the agencies 
intend to continue considering them as options for the final rule. The 
agencies seek comment on these alternatives and on the analysis 
presented here, seek any relevant data and information, and will review 
responses. That review could lead the agencies to select one of the

[[Page 43198]]

other regulatory alternatives for the final rule.

A. What alternatives did NHTSA and EPA consider?

    The table below shows the different alternatives evaluated in this 

    Also, as mentioned previously in Section III.B., EPA seeks comments 
on whether to proceed with this proposal to discontinue accounting for 
A/C leakage, methane emissions, and nitrous oxide emissions as part of 
the CO2 emissions standards to provide for better harmony 
with the CAFE program or whether to continue to consider these factors 
toward compliance and retain that as a feature that differs between the 
programs. EPA seeks comment on whether to change existing methane and 
nitrous oxide standards that were finalized in the 2012 rule. 
Specifically, EPA seeks information from the public on whether the 
existing standards are appropriate, or whether they should be

[[Page 43199]]

revised to be less stringent or more stringent based on any updated 

    \381\ Carbon dioxide equivalent of air conditioning refrigerant 
leakage, nitrous oxide and methane emissions are included for 
compliance with the EPA standards for all MYs under the baseline/no 
action alternative. Carbon dioxide equivalent is calculated using 
the Global Warming Potential (GWP) of each of the emissions.
    \382\ Beginning in MY 2021, air conditioning refrigerant 
leakage, nitrous oxide, and methane emissions may be regulated 
independently by EPA. The GWP equivalent of each of the emissions 
would no longer be included with the tailpipe CO2 for 
compliance with tailpipe CO2 standards. A lengthier 
discussion of this issue can be found in Section III.B.

    Additionally, the agencies note that this proposal also seeks 
comment on a number of additional compliance flexibilities for the 
programs. See Section X below, and EPA specifically draws attention the 
discussion of ``enhanced flexibilities'' in Section X.C.

B. Definition of Alternatives

1. No-Action Alternative
    The No-Action Alternative applies the augural CAFE and final GHG 
targets announced in 2012 for MYs 2021-2025. For MY 2026, this 
alternative applies the same targets as for MY 2025. Carbon dioxide 
equivalent of air conditioning refrigerant leakage, nitrous oxide, and 
methane emissions are included for compliance with the EPA standards 
for all model years under the baseline/no action alternative.


2. Alternative 1 (Proposed)
    Alternative 1 holds the stringency of targets constant and MY 2020 
levels through MY 2026. Beginning in MY 2021, air conditioning 
refrigerant leakage, nitrous oxide, and methane emissions are no longer 
included with the tailpipe CO2 for compliance with tailpipe 
CO2 standards. Section III, above, defines this alternative 
in greater detail.
3. Alternative 2
    Alternative 2 increases the stringency of targets annually during 
MYs 2021-2026 (on a gallon per mile basis, starting from MY 2020) by 
0.5% for passenger cars and 0.5% for light trucks. Section III 
describes the proposed standards included in the preferred alternative. 
Beginning in MY 2021, air conditioning refrigerant leakage, nitrous 
oxide, and methane emissions are no longer included with the tailpipe 
CO2 for compliance with tailpipe CO2 standards.

[[Page 43200]]


4. Alternative 3
    Alternative 3 phases out A/C and off-cycle adjustments and 
increases the stringency of targets annually during MYs 2021-2026 (on a 
gallon per mile basis, starting from MY 2020) by 0.5% for passenger 
cars and 0.5% for light trucks. The cap on adjustments for AC 
efficiency improvements declines from 6 grams per mile in MY 2021 to 5, 
4, 3, 2, and 0 grams per mile in MYs 2022, 2023, 2024, 2025, and 2026, 
respectively. The cap on adjustments for off-cycle improvements 
declines from 10 grams per mile in MY 2021 to 8, 6, 4, 2, and 0 grams 
per mile in MYs 2022, 2023, 2024, 2025, and 2026, respectively. 
Beginning in MY 2021, air conditioning refrigerant leakage, nitrous 
oxide, and methane emissions are no longer included with the tailpipe 
CO2 for compliance with tailpipe CO2 standards.

[[Page 43201]]


5. Alternative 4
    Alternative 4 increases the stringency of targets annually during 
MYs 2021-2026 (on a gallon per mile basis, starting from MY 2020) by 
1.0% for passenger cars and 2.0% for light trucks. Beginning in MY 
2021, air conditioning refrigerant leakage, nitrous oxide, and methane 
emissions are no longer included with the tailpipe CO2 for 
compliance with tailpipe CO2 standards.

[[Page 43202]]


6. Alternative 5
    Alternative 5 increases the stringency of targets annually during 
MYs 2022-2026 (on a gallon per mile basis, starting from MY 2021) by 
1.0% for passenger cars and 2.0% for light trucks. Beginning in MY 
2021, air conditioning refrigerant leakage, nitrous oxide, and methane 
emissions are no longer included with the tailpipe CO2 for 
compliance with tailpipe CO2 standards, and MY 2021 
CO2 targets are adjusted accordingly.

[[Page 43203]]


7. Alternative 6
    Alternative 6 increases the stringency of targets annually during 
MYs 2021-2026 (on a gallon per mile basis, starting from MY 2020) by 
2.0% for passenger cars and 3.0% for light trucks. Beginning in MY 
2021, air conditioning refrigerant leakage, nitrous oxide, and methane 
emissions are no longer included with the tailpipe CO2 for 
compliance with tailpipe CO2 standards.

[[Page 43204]]


8. Alternative 7
    Alternative 7 phases out A/C and off-cycle adjustments and 
increases the stringency of targets annually during MYs 2021-2026 (on a 
gallon per mile basis, starting from MY 2020) by 1.0% for passenger 
cars and 2.0% for light trucks. The cap on adjustments for AC 
efficiency improvements declines from 6 grams per mile in MY 2021 to 5, 
4, 3, 2, and 0 grams per mile in MYs 2022, 2023, 2024, 2025, and 2026, 
respectively. The cap on adjustments for off-cycle improvements 
declines from 10 grams per mile in MY 2021 to 8, 6, 4, 2, and 0 grams 
per mile in MYs 2022, 2023, 2024, 2025, and 2026, respectively. 
Beginning in MY 2021, air conditioning refrigerant leakage, nitrous 
oxide, and methane emissions are no longer included with the tailpipe 
CO2 for compliance with tailpipe CO2 standards.

[[Page 43205]]


9. Alternative 8
    Alternative 8 increases the stringency of targets annually during 
MYs 2022-2026 (on a gallon per mile basis, starting from MY 2021) by 
2.0% for passenger cars and 3.0% for light trucks. Beginning in MY 
2021, air conditioning refrigerant leakage, nitrous oxide, and methane 
emissions are no longer included with the tailpipe CO2 for 
compliance with tailpipe CO2 standards, and MY 2021 
CO2 targets are adjusted accordingly.

[[Page 43206]]


V. Proposed Standards, the Agencies' Statutory Obligations, and Why the 
Agencies Propose To Choose Them Over the Alternatives

A. NHTSA's Statutory Obligations and Why the Proposed Standards Appear 
to be Maximum Feasible

1. EPCA, as Amended by EISA
    EPCA, as amended by EISA, contains a number of provisions regarding 
how NHTSA must set CAFE standards. NHTSA must establish separate CAFE 
standards for passenger cars and light trucks \383\ for each model 
year,\384\ and each standard must be the maximum feasible that NHTSA 
believes the manufacturers can achieve in that model year.\385\ In 
determining the maximum feasible level achievable by the manufacturers, 
EPCA requires that NHTSA consider the four statutory 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.\386\ In addition, NHTSA has 
the authority to (and traditionally does) consider other relevant 
factors, such as the effect of the CAFE standards on motor vehicle 
safety and consumer preferences.\387\ The ultimate determination of 
what standards can be considered maximum feasible involves a weighing 
and balancing of these 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 
support the overarching purpose of EPCA, energy conservation, while 
balancing these factors.\388\

    \383\ 49 U.S.C. 32902(b)(1) (2007).
    \384\ 49 U.S.C. 32902(a) (2007).
    \385\ Id.
    \386\ 49 U.S.C. 32902(f) (2007).
    \387\ Both of these additional considerations also relate, to 
some extent, to economic practicability, but NHTSA also has the 
authority to consider them independently of that statutory factor.
    \388\ 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' 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 

[[Page 43207]]

several other requirements as explained below.
(a) Lead Time
    EPCA requires that NHTSA prescribe new CAFE standards at least 18 
months before the beginning of each model year.\389\ For light-duty 
vehicles, NHTSA has consistently interpreted the ``beginning of each 
model year'' as September 1 of the CY prior, such that the beginning of 
MY 2019 would be September 1, 2018. Thus, if the first year for which 
NHTSA is proposing to set new standards in this NPRM is MY 2022, NHTSA 
interprets this provision as requiring us to issue a final rule 
covering MY 2022 standards no later than April 1, 2020.

    \389\ 49 U.S.C. 32902(a) (2007).

    For amendments to existing standards, EPCA requires that if the 
amendments make an average fuel economy standard more stringent, at 
least 18 months of lead time must be provided.\390\ EPCA contains no 
lead time requirement unless amendments make an average fuel economy 
standard less stringent. NHTSA therefore interprets EPCA as allowing 
amendments to reduce a standard's stringency up until the beginning of 
the model year in question. In this rulemaking, NHTSA is proposing to 
amend the standards for model year 2021. Since the agency proposes to 
reduce these standards, this action is not subject to a lead time 

    \390\ 49 U.S.C. 32902(g)(2) (2007).

(b) Separate Standards for Cars and Trucks, and Minimum Standards for 
Domestic Passenger Cars
    As discussed above, EPCA requires NHTSA to set separate CAFE 
standards for passenger cars and light trucks for each model year.\391\ 
NHTSA interprets 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 intended 
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., need to use more fuel per mile to perform those jobs than 
vehicles without these characteristics. Thus, regardless of the plain 
language of the statute, NHTSA believes that the different fuel economy 
capabilities of cars and trucks would generally make separate standards 
appropriate for these different types of vehicles.

    \391\ 49 U.S.C. 32902(b)(1) (2007).

    EPCA, as amended by EISA, also requires another separate standard 
to be set for domestically-manufactured \392\ passenger cars. Unlike 
under the standards for passenger cars and light trucks described 
above, the compliance burden of the minimum domestic passenger car 
standard is the same 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

    \392\ In the CAFE program, ``domestically-manufactured'' is 
defined by Congress in 49 U.S.C. Sec.  32904(b). The specifics of 
the definition are too many for a footnote, but roughly, a passenger 
car is ``domestically manufactured'' as long as at least 75% 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.

. . . 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)].\393\

    \393\ 49 U.S.C. Sec.  32902(b)(4) (2007).

    Since that requirement was promulgated, the ``92 percent'' has 
always been greater than 27.5 mpg. NHTSA published the 92-percent 
minimum domestic passenger car standards for model years 2017-2025 at 
49 CFR 531.5(d) as part of the 2012 final rule. For MYs 2022-2025, 
531.5(e) states that these were to be applied if, when actually 
proposing MY 2022 and subsequent standards, the previously identified 
standards for those years are deemed maximum feasible, but if NHTSA 
determines that the previously identified standards are not maximum 
feasible, the 92-percent minimum domestic passenger car standards would 
also change. This is consistent with the statutory language that the 
92-percent standards must be determined at the time an overall 
passenger car standard is promulgated and published in the Federal 
Register. Thus, any time NHTSA establishes or changes a passenger car 
standard for a model year, the minimum domestic passenger car standard 
for that model year will also be evaluated or reevaluated and 
established accordingly. NHTSA explained this in the rulemaking to 
establish standards for MYs 2017 and beyond and received no 

    \394\ 77 FR 62624, 63028 (Oct. 15, 2012).

    The 2016 Alliance/Global petition for rulemaking asked NHTSA to 
retroactively revise the 92-percent minimum domestic passenger car 
standards for MYs 2012-2016 ``to reflect 92 percent of the required 
average passenger car standard taking into account the fleet mix as it 
actually occurred, rather than what was forecast.'' The petitioners 
stated that doing so would be ``fully consistent with the statute.'' 

    \395\ Automobile Alliance and Global Automakers Petition for 
Direct Final Rule with Regard to Various Aspects of the Corporate 
Average Fuel Economy Program and the Greenhouse Gas Program (June 
20, 2016) at 5, 17-18, available at https://www.epa.gov/sites/production/files/2016-09/documents/petition_to_epa_from_auto_alliance_and_global_automakers.pdf 
[hereinafter Alliance/Global Petition].

    NHTSA understands that determining the 92 percent value ahead of 
the model year to which it applies, based on the information then 
available to the agency, results in a different mpg number than if 
NHTSA determined the 92 percent value based on the information 
available at the end of the model year in question. NHTSA further 
understands that determining the 92 percent value ahead of time can 
make the domestic minimum passenger car standard more stringent than it 
could be if it were determined at the end of the model year, if 
manufacturers end up producing more larger-footprint passenger cars 
than NHTSA originally anticipated.
    Accordingly, NHTSA seeks comment on this request by Alliance/
Global. Additionally, recognizing the uncertainty inherent in 
projecting specific mpg values far into the future, it is possible that 
NHTSA could define the mpg values associated with a CAFE standard 
(i.e., the footprint curve) as a range rather than as a single number. 
For example, the sensitivity analysis included in this proposal and in 
the accompanying PRIA could provide a basis for such an mpg range 
``defining'' the passenger car standard in any given model year. If 
NHTSA took that approach, 92 percent of that ``standard'' would also, 
necessarily, be a range. We also seek comment on this or other similar 
(c) Attribute-Based and Defined by Mathematical Function
    EISA requires NHTSA to set CAFE standards that are ``based on 1 or 

[[Page 43208]]

attributes related to fuel economy and express[ed] . . . in the form of 
a mathematical function.'' \396\ NHTSA has thus far based standards on 
vehicle footprint and proposes to continue to do so for all the reasons 
described in previous rulemakings. As in previous rulemakings, NHTSA 
proposes 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 much 
greater detail in Section II.C above. We seek comment both on the 
choice of footprint as the relevant attribute and on the rationale for 
the constrained linear functions chosen to represent the standards.

    \396\ 49 U.S.C. 32902(b)(3)(A).

(d) 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.'' \397\ In the 2012 final rule, NHTSA 
interpreted this provision as preventing the agency from setting final 
standards for all of MYs 2017-2025 in a single rulemaking action, so 
the MYs 2022-2025 standards were termed ``augural,'' meaning ``that 
they represent[ed] the agency's current judgment, based on the 
information available to the agency [then], of what levels of 
stringency would be maximum feasible in those model years.'' \398\ That 
said, NHTSA also repeatedly clarified that the augural standards were 
in no way final standards and that a future de novo rulemaking would be 
necessary in order to both propose and promulgate final standards for 
MYs 2022-2025.

    \397\ 49 U.S.C. 32902(b)(3)(B).
    \398\ 77 FR 62623, 62630 (Oct. 15, 2012).

    Today, NHTSA proposes to establish new standards for MYs 2022-2026 
and to revise the previously-established final standards for MY 2021. 
Legislative history suggests that Congress included the five year 
maximum limitation so NHTSA would issue standards for a period of time 
where it would have reasonably realistic estimates of market 
conditions, technologies, and economic practicability (i.e., not set 
standards too far into the future).\399\ However, the concerns Congress 
sought to address by imposing those limitations are not present for 
nearer model years where NHTSA already has existing standards. 
Revisiting existing standards is contemplated by both 49 U.S.C. 
32902(c) and 32902(g). We therefore believe that it is reasonable to 
interpret section 32902(b)(3)(B) as applying only to the establishing 
of new standards rather than to the combined action of establishing new 
standards and amending existing standards.

    \399\ See 153 Cong. Rec. 2665 (Dec. 28, 2007).

    Moreover, we believe it would be an absurd result not intended by 
Congress if the five year maximum limitation were interpreted to 
prevent NHTSA from revising a previously-established standard that we 
have determined to be beyond maximum feasible, while concurrently 
setting five years of standards not so distant from today. The concerns 
Congress sought to address are much starker when NHTSA is trying to 
determine what standards would be maximum feasible 10 years from now as 
compared to three years from now.
(e) Maximum Feasible
    As discussed above, EPCA requires NHTSA to consider four factors in 
determining what levels of CAFE standards would be maximum feasible, 
and NHTSA presents in the sections below its understanding of what 
those four factors mean. All factors should be considered, in the 
manner appropriate, and then the maximum feasible standards should be 
(1) 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 commercially applied at 
the time of the rulemaking. For this proposal, NHTSA is considering a 
wide range of technologies that improve fuel economy, subject to the 
constraints of EPCA regarding how to treat alternative fueled vehicles, 
and considering the need to account for which technologies have already 
been applied to which vehicle model/configuration, and the need to 
realistically estimate the cost and fuel economy impacts of each 
technology. NHTSA has not attempted to account for every technology 
that might conceivably be applied to improve fuel economy and considers 
it unnecessary to do so given that many technologies address fuel 
economy in similar ways.\400\ Technological feasibility and economic 
practicability are often conflated, as will be covered further in the 
following section. To be clear, whether a fuel-economy-improving 
technology does or will exist (technological feasibility) is a 
different question from what economic consequences could ensue if NHTSA 
effectively requires that technology to become widespread in the fleet 
and the economic consequences of the absence of consumer demand for 
technology that are projected to be required (economic practicability). 
It is therefore possible for standards to be technologically feasible 
but still beyond the level that NHTSA determines to be maximum feasible 
due to consideration of the other relevant factors.

    \400\ 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.

(2) Economic Practicability
    ``Economic practicability'' has traditionally 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.'' \401\ 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. NHTSA has explained in the past that this factor can be 
especially important during rulemakings in which the auto industry is 
facing significantly adverse economic conditions (with corresponding 
risks to jobs). Consumer acceptability is also a major component to 
economic practicability,\402\ which can involve consideration of 
anticipated consumer responses not just to increased vehicle cost, but 
also to the way manufacturers may change vehicle models and vehicle 
sales mix in response to CAFE standards. In attempting to determine the 
economic practicability of attribute-based standards, NHTSA considers a 
wide variety of elements, including the annual rate at which 
manufacturers can increase the percentage of their fleet that employs a 
particular type of fuel-saving technology,\403\ the specific fleet 
mixes of

[[Page 43209]]

different manufacturers, and assumptions about the cost of standards to 
consumers and consumers' valuation of fuel economy, among other things.

    \401\ 67 FR 77015, 77021 (Dec. 16, 2002).
    \402\ 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); Public Citizen v. NHTSA, 848 F.2d 256 (Congress 
established broad guidelines in the fuel economy statute; agency's 
decision to set lower standards was a reasonable accommodation of 
conflicting policies).
    \403\ For example, if standards effectively require 
manufacturers to widely apply technologies that consumers do not 
want, or to widely apply technologies before they are ready to be 
widespread, NHTSA believes that these standards could potentially be 
beyond economically practicable.

    Prior to the MYs 2005-2007 rulemaking under the non-attribute-based 
(fixed value) CAFE standards, NHTSA generally 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 to not limit the availability of those 
types of vehicles to consumers. In the first several rulemakings 
establishing attribute-based standards, NHTSA applied marginal cost-
benefit analysis, considering both overall societal impacts and overall 
consumer impacts. Whether the standards maximize net benefits has thus 
been a touchstone 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 situations in which the modeling of net benefits does not 
capture all of the relevant considerations of feasibility. Therefore, 
as in past rulemakings, NHTSA is considering net societal impacts, net 
consumer impacts, and other related elements in the consideration of 
economic practicability.
    NHTSA's consideration of economic practicability depends on a 
number of elements. Expected availability of capital to make 
investments in new technologies matters; manufacturers' expected 
ability to sell vehicles with certain technologies matters; likely 
consumer choices matter and so forth. NHTSA's analysis of the impacts 
of this proposal incorporates assumptions to capture aspects of 
consumer preferences, vehicle attributes, safety, and other elements 
relevant to an impacts estimate; however, it is difficult to capture 
every such constraint. Therefore, 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 that level would not represent 
the maximum feasible level for future CAFE standards. Economic 
practicability is complex, and like the other factors must also be 
considered in the context of the overall balancing and EPCA's 
overarching purpose of energy conservation. Depending on the conditions 
of the industry and the assumptions used in the agency's analysis of 
alternative standards, NHTSA could well find that standards that 
maximize net benefits, or that are higher or lower, could be at the 
limits of economic practicability, and thus potentially the maximum 
feasible level, depending on how the other factors are balanced.
    While we discuss safety as a separate consideration, NHTSA also 
considers safety as closely related to, and in some circumstances a 
subcomponent of economic practicability. On a broad level, 
manufacturers have finite resources to invest in research and 
development. Investment into the development and implementation of fuel 
saving technology necessarily comes at the expense of investing in 
other areas such as safety technology. On a more direct level, when 
making decisions on how to equip vehicles, manufacturers must balance 
cost considerations to avoid pricing further consumers out of the 
market. As manufacturers add technology to increase fuel efficiency, 
they may decide against installing new safety equipment to reduce cost 
increases. And as the price of vehicles increase beyond the reach of 
more consumers, such consumers continue to drive or purchase older, 
less safe vehicles. In assessing practicability, NHTSA also considers 
the harm to the nation's economy caused by highway fatalities and 
(3) 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 \404\ 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 considered the additional 
weight that it estimates would be added in response to new safety 
standards during the rulemaking timeframe.\405\ NHTSA has also 
accounted for EPA's ``Tier 3'' standards for criteria pollutants in its 
estimates of technology effectiveness.\406\

    \404\ 42 FR 63184, 63188 (Dec. 15, 1977). See also 42 FR 33534, 
33537 (June 30, 1977).
    \405\ PRIA, Chapter 5.
    \406\ PRIA, Chapter 6.

    In the 2012 final rule establishing CAFE standards for MYs 2017-
2021, NHTSA also discussed whether EPA GHG standards and California GHG 
standards should be considered and accounted for as ``other motor 
vehicle standards of the Government.'' NHTSA recognized 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.'' \407\ NHTSA concluded that ``the 
agency had already considered EPA's [action] and the harmonization 
benefits of the National Program in developing its own [action],'' and 
that ``no further action was needed.'' \408\

    \407\ 77 FR 62624, 62669 (Oct. 15, 2012).
    \408\ Id.

    Considering the issue afresh in this proposal, and looking only at 
the words in the statute, obviously EPA's GHG standards applicable to 
light-duty vehicles are literally ``other motor vehicle standards of 
the Government,'' in that they are standards set by a Federal agency 
that apply to motor vehicles. Basic chemistry makes fuel economy and 
tailpipe CO2 emissions two sides of the same coin, as 
discussed at length above, and when two agencies functionally regulate 
both (because by regulating fuel economy, you regulate CO2 
emissions, and vice versa), it would be absurd not to link their 
standards.\409\ The global warming potential of N2O, 
CH4, and HFC emissions are not closely linked with fuel 
economy, but neither do they affect fuel economy capabilities. How, 
then, should NHTSA consider EPA's various GHG standards?

    \409\ In fact, EPA includes tailpipe CH4, CO, and 
CO2 in the measurement of tailpipe CO2 for GHG 
compliance using a carbon balance equation so that the measurement 
of tailpipe CO2 exactly aligns with the measurement of 
fuel economy for the CAFE compliance.

    NHTSA is aware that some stakeholders believe that NHTSA's 
obligation to set maximum feasible CAFE standards can best be executed 
by letting EPA decide what GHG standards

[[Page 43210]]

are appropriate and reasonable under the CAA. NHTSA disagrees. While 
EPA and NHTSA consider some similar factors under the CAA and EPCA/
EISA, respectively, they are not identical. Standards that are 
appropriate under the CAA may not be ``maximum feasible'' under EPCA/
EISA, and vice versa. Moreover, considering EPCA's language in the 
context in which it was written, it seems unreasonable to conclude that 
Congress intended EPA to dictate CAFE stringency. In fact, Congress 
clearly separated NHTSA's and EPA's responsibilities for CAFE under 
EPCA by giving NHTSA authority to set standards and EPA authority to 
measure and calculate fuel economy. If Congress had wanted EPA to set 
CAFE standards, it could have given that authority to EPA in EPCA or at 
any point since Congress amended EPCA.\410\

    \410\ We note, for instance, that EISA was passed after the 
Massachusetts v. EPA decision by the Supreme Court. If Congress had 
wanted to amend EPCA in light of that decision, they would have done 
so at the time. They did not.

    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. Because of this relationship, it is incumbent 
on both agencies to coordinate and look to one another's actions to 
avoid unreasonably burdening industry through inconsistent regulations, 
but both agencies must be able to defend their programs on their own 
merits. As with other recent CAFE and GHG rulemakings, the agencies are 
continuing do all of these things in this proposal.
    With regard to standards issued by the State of California, State 
tailpipe standards (whether for greenhouse gases or for other 
pollutants) do not qualify as ``other motor vehicle standards of the 
Government'' under 49 U.S.C. 32902(f); therefore, NHTSA will not 
consider them as such in proposing maximum feasible average fuel 
economy standards. States may not adopt or enforce tailpipe greenhouse 
gas emissions standards when such standards relate to fuel economy 
standards and are therefore preempted under EPCA, regardless of whether 
EPA granted any waivers under the Clean Air Act (CAA).\411\

    \411\ This topic is discussed further in Section VI below.

    Preempted standards of a State or a political subdivision of a 
State include, for example:
    (1) A fuel economy standard; and
    (2) A law or regulation that has the direct effect of a fuel 
economy standard, but is not labeled as one (i.e., a State tailpipe 
CO2 standard or prohibition on CO2 emissions).
    NHTSA and EPA agree that state tailpipe greenhouse gas emissions 
standards do not become Federal standards and qualify as ``other motor 
vehicle standards of the Government,'' when subject to a CAA preemption 
waiver. EPCA's legislative history supports this position.
    EPCA, as initially passed in 1975, mandated average fuel economy 
standards for passenger cars beginning with model year 1978. The law 
required the Secretary of Transportation to establish, through 
regulation, maximum feasible fuel economy standards \412\ for model 
years 1981 through 1984 with the intent to provide steady increases to 
achieve the standard established for 1985 and thereafter authorized the 
Secretary to adjust that standard.

    \412\ As is the case today, EPCA required the Secretary to 
determine ``maximum feasible average fuel economy'' after 
considering technological feasibility, economic practicability, the 
effect of other Federal motor vehicle standards on fuel economy, and 
the need of the Nation to conserve energy. 15 U.S.C. 2002(e) 
(recodified July 5, 1994).

    For the statutorily-established standards for model years 1978-
1980, EPCA provided each manufacturer with the right to petition for 
changes in the standards applicable to that manufacturer. A petitioning 
manufacturer had the burden of demonstrating a ``Federal fuel economy 
standards reduction'' was likely to exist for that manufacturer in one 
or more of those model years and that it had made reasonable technology 
choices. ``Federal standards,'' for that limited purpose, included not 
only safety standards, noise emission standards, property loss 
reduction standards, and emission standards issued under various 
Federal statutes, but also ``emissions standards applicable by reason 
of section 209(b) of [the CAA].'' \413\ (Emphasis added). Critically, 
all definitions, processes, and required findings regarding a Federal 
fuel economy standards reduction were located within a single self-
contained subsection of 15 U.S.C. 2002 that applied only to model years 

    \413\ Section 202 of the CAA (42 U.S.C. 7521) requires EPA to 
prescribe air pollutant emission standards for new vehicles; Section 
209 of the CAA (42 U.S.C. 7543) preempts state emissions standards 
but allows California to apply for a waiver of such preemption.
    \414\ As originally enacted as part of Public Law 94-163, that 
subsection was designated as section 502(d) of the Motor Vehicle 
Information and Cost Savings Act.

    In 1994, Congress recodified EPCA. As part of this recodification, 
the CAFE provisions were moved to Title 49 of the United States Code. 
In doing so, unnecessary provisions were deleted. Specifically, the 
recodification eliminated subsection (d). The House report on the 
recodification declared that the subdivision was ``executed,'' and 
described its purpose as ``[p]rovid[ing] for modification of average 
fuel economy standards for model years 1978, 1979, and 1980.'' \415\ It 
is generally presumed, when Congress includes text in one section and 
not in another, that Congress knew what it was doing and made the 
decision deliberately.

    \415\ H.R. Rep. No. 103-180, at 583-584, tbl. 2A.

    NHTSA has previously considered the impact of California's Low 
Emission Vehicle standards in establishing fuel economy standards and 
occasionally has done so under the ``other standards'' sections.\416\ 
During the 2012 rulemaking, NHTSA sought comment on the appropriateness 
of considering California's tailpipe GHG emission standards in this 
section and concluded that doing so was unnecessary.\417\ In light of 
the legislative history discussed above, however, NHTSA now determines 
that this was not appropriate. Notwithstanding the improper 
categorization of such discussions, NHTSA may consider elements not 
specifically designated as factors to be considered under EPCA, given 
the breadth of such factors as technological feasibility and economic 
practicability, and such consideration was appropriate.\418\

    \416\ See, e.g., 68 FR 16896, 71 FR 17643.
    \417\ See 77 FR 62669.
    \418\ See, e.g., discussion in Center for Automotive Safety v. 
National Highway Traffic Safety Administration, et al., 793 F.2d. 
1322 (D.C. Cir. 1986) at 1338, et seq., providing that NHTSA may 
consider consumer demand in establishing standards, but not ``to 
such an extent that it ignored the overarching goal of fuel 
conservation. At the other extreme, a standard with harsh economic 
consequences for the auto industry also would represent an 
unreasonable balancing of EPCA's policies.''

(4) The Need of the United States To Conserve Energy
    ``The need of the United States to conserve energy'' means ``the 
consumer cost, national balance of payments, environmental, and foreign 
policy implications of our need for large quantities of petroleum, 
especially imported petroleum.'' \419\

    \419\ 42 FR 63184, 63188 (Dec. 15, 1977).

(i) Consumer Costs and Fuel Prices
    Fuel for vehicles costs money for vehicle owners and operators. 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

[[Page 43211]]

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 new standards, and they inform NHTSA about the ``consumer 
cost . . . of our need for large quantities of petroleum.'' In this 
proposal, NHTSA's analysis relies on fuel price projections from the 
U.S. Energy Information Administration's (EIA) Annual Energy Outlook 
(AEO) for 2017. Federal government agencies generally use EIA's price 
projections in their assessment of future energy-related policies.
(ii) National Balance of Payments
    Historically, the need of the United States to conserve energy has 
included consideration of the ``national balance of payments'' 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.\420\ As recently as 2009, nearly half the U.S. 
trade deficit was driven by petroleum,\421\ yet this concern has 
largely laid fallow in more recent CAFE actions, arguably in part 
because other factors besides petroleum consumption have since played a 
bigger role in the U.S. trade deficit. Given significant recent 
increases in U.S. oil production and corresponding decreases in oil 
imports, this concern seems likely to remain fallow for the foreseeable 
future.\422\ Increasingly, changes in the price of fuel have come to 
represent transfers between domestic consumers of fuel and domestic 
producers of petroleum rather than gains or losses to foreign entities. 
Some commenters have lately raised concerns about potential economic 
consequences for automaker and supplier operations in the U.S. due to 
disparities between CAFE standards at home and their counterpart fuel 
economy/efficiency and GHG standards abroad. NHTSA finds these concerns 
more relevant to technological feasibility and economic practicability 
than to the national balance of payments. Moreover, to the extent that 
an automaker decides to globalize a vehicle platform to meet more 
stringent standards in other countries, that automaker would comply 
with United States's standards and additionally generate 
overcompensation credits that it can save for future years if facing 
compliance concerns,or sell to other automakers. While CAFE standards 
are set at maximum feasible rates, efforts of manufacturers to exceed 
those standards are rewarded not only with additional credits but a 
market advantage in that consumers who place a large weight on fuel 
savings will find such vehicles that much more attractive.

    \420\ 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.''
    \421\ See Today in Energy: Recent improvements in petroleum 
trade balance mitigate U.S. trade deficit, U.S. Energy Information 
Administration (July 21, 2014), https://www.eia.gov/todayinenergy/detail.php?id=17191.
    \422\ For an illustration of recent increases in U.S. 
production, see, e.g., U.S. crude oil and liquid fuels production, 
Short-Term Energy Outlook, U.S. Energy Information Administration 
(June 2018), https://www.eia.gov/outlooks/steo/images/fig13.png. 
While it could be argued that reducing oil consumption frees up more 
domestically-produced oil for exports, and thereby raises U.S. GDP, 
that is neither the focus of the CAFE program nor consistent with 
Congress' original intent in EPCA. EIA's Annual Energy Outlook (AEO) 
series provides midterm forecasts of production, exports, and 
imports of petroleum products, and is available at https://www.eia.gov/outlooks/aeo/.

(iii) Environmental Implications
    Higher fleet fuel economy can reduce U.S. emissions of various 
pollutants by reducing the amount of oil that is produced and refined 
for the U.S. vehicle fleet but can also 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 results in lower emissions 
of CO2, the main GHG emitted as a result of refining, 
distribution, and use of transportation fuels. Reducing fuel 
consumption directly reduces CO2 emissions because the 
primary source of transportation-related CO2 emissions is 
fuel combustion in internal combustion engines.
    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,\423\ 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 notices and prepared 
its first environmental assessment addressing that subject.\424\ It 
cited concerns about climate change as one of its reasons for limiting 
the extent of its reduction of the CAFE standard for MY 1989 passenger 
cars.\425\ Since then, NHTSA has considered the effects of reducing 
tailpipe emissions of CO2 in its fuel economy rulemakings 
pursuant to the need of the United States to conserve energy by 
reducing petroleum consumption.

    \423\ 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).
    \424\ 53 FR 33080, 33096 (Aug. 29, 1988).
    \425\ 53 FR 39275, 39302 (Oct. 6, 1988).

(iv) 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.\426\ Higher U.S. consumption of crude 
oil or refined petroleum products increases the magnitude of these 
external economic costs, thus increasing the true economic cost of 
supplying transportation fuels above the resource costs of producing 
them. Conversely, reducing U.S. consumption of crude oil or refined 
petroleum products (by reducing motor fuel use) can reduce these 
external costs.

    \426\ While the U.S. maintains a military presence in certain 
parts of the world to help secure global access to petroleum 
supplies, that is neither the primary nor the sole mission of U.S. 
forces overseas. Additionally, the scale of oil consumption 
reductions associated with CAFE standards would be insufficient to 
alter any existing military missions focused on ensuring the safe 
and expedient production and transportation of oil around the globe. 
See Chapter 7 of the PRIA for more information on this topic.

    While these costs are considerations, the United States has 
significantly increased oil production capabilities in

[[Page 43212]]

recent years to the extent that the U.S. is currently producing enough 
oil to satisfy nearly all of its energy needs and is projected to 
continue to do so or become a net energy exporter. This has added new 
stable supply to the global oil market and reduced the urgency of the 
U.S. to conserve energy. We discuss this issue in more detail below.
(5) 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.\427\ As discussed further in Section 
X.B.1.c) below, 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 fuel vehicles nor the availability of 
dedicated alternative fuel 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 fuel economy 
level than they actually achieve.

    \427\ 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, 
higher standards would appear less costly and therefore more feasible, 
which would thus tend to require manufacturers to use those 
flexibilities in order to meet higher standards. By keeping NHTSA from 
including them in our stringency determination, the provision ensures 
that these statutory credits remain true compliance flexibilities.
    Additionally, for non-statutory incentives that NHTSA developed by 
regulation, NHTSA does not consider these subject to the EPCA 
prohibition on considering flexibilities, either. EPCA 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 economy improvement values, NHTSA has 
considered those technologies as available in the analysis. Thus, 
today's analysis includes assumptions about manufacturers' use of those 
technologies, as detailed in Section X.B.1.c)(4)
(f) EPCA/EISA Requirements That No Longer Apply Post-2020
    Congress amended EPCA through EISA to add two requirements not yet 
discussed in this section relevant to determination of CAFE standards 
during the years between MY 2011 and MY 2020 but not beyond. First, 
Congress stated that, regardless of NHTSA's determination of what 
levels of standards would be maximum feasible, standards must be set at 
levels high enough to ensure that the combined U.S. passenger car and 
light truck fleet achieves an average fuel economy level of not less 
than 35 mpg no later than MY 2020.\428\ And second, between MYs 2011 
and 2020, the standards must ``increase ratably'' in each model 
year.\429\ Neither of these requirements apply after MY 2020, so given 
that this rulemaking concerns the standards for MY 2021 and after, they 
are not relevant to this rulemaking.

    \428\ 49 U.S.C. 32902(b)(2)(A).
    \429\ 49 U.S.C. 32902(b)(2)(C).

(g) Other Considerations in Determining Maximum Feasible Standards
    NHTSA has historically considered the potential for adverse safety 
consequences in setting CAFE standards. This practice has been 
consistently approved in case law. 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 (June 30, 1977)). The 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 fuel economy 
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). Thus, in evaluating what levels of stringency would 
result in maximum feasible standards, NHTSA assesses the potential 
safety impacts and considers them in balancing the statutory 
considerations and to determine the maximum feasible level of the 
    The attribute-based standards that Congress requires NHTSA to set 
help to mitigate the negative safety effects of the historical ``flat'' 
standards originally required in EPCA, in recent rulemakings, NHTSA 
limited the consideration of mass reduction in lower weight vehicles in 
its analysis, which impacted the resulting assessment of potential 
adverse safety effects. That analytical approach did not reflect, 
however, the likelihood that automakers may pursue the most cost 
effective means of improving fuel efficiency to comply with CAFE 
requirements. For this rulemaking, the modeling does not limit the 
amount of mass reduction that is applied to any segment but rather 
considers that automakers may apply mass reduction based upon cost-
effectiveness, similar to most other technologies. NHTSA does not, of 
course, mandate the use of any particular technology by manufacturers 
in meeting the standards. The current proposal, like the Draft TAR, 
also considers the safety effect associated with the additional vehicle 
miles traveled due to the rebound effect.
    In this rulemaking, NHTSA is considering the effect of additional 
expenses in fuel savings technology on the affordability of vehicles--
the likelihood that increased standards will result in consumers being 
priced out of the new vehicle market and choosing to keep their 
existing vehicle or purchase a used vehicle. Since new vehicles are 
significantly safer than used vehicles, slowing fleet turnover to newer 
vehicles results in older and less safe vehicles remaining on the roads 
longer. This significantly affects the safety of the United States 
light duty fleet, as described more fully in Section 0 above and in 
Chapter 11 of the PRIA accompanying this proposal. Furthermore, as fuel 
economy standards become more stringent, and more fuel efficient 
vehicles are introduced into the fleet, fueling costs are reduced. This 
results in consumers driving more miles, which results in more crashes 
and increased highway fatalities.
2. Administrative Procedure Act
    To be upheld under the ``arbitrary and capricious'' standard of 
judicial review in the APA, an agency rule must be rational, based on 
consideration of the relevant factors, and within the scope of

[[Page 43213]]

the authority delegated to the agency by the 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.'' 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, 467 U.S. 837 (1984). 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,'' id. at 843. 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.'' Id.
    If an agency's interpretation differs from the one that it has 
previously adopted, the agency need not demonstrate that the prior 
position was wrong or even less desirable. Rather, the agency would 
need only to demonstrate that its new position is consistent with the 
statute and supported by the record and acknowledge that this is a 
departure from past positions. The Supreme Court emphasized this in FCC 
v. Fox Television, 556 U.S. 502 (2009). When an agency changes course 
from earlier regulations, ``the requirement that an agency provide a 
reasoned explanation for its action would ordinarily demand that it 
display awareness that it is changing position,'' but ``need not 
demonstrate to a court's satisfaction that the reasons for the new 
policy are better than the reasons for the old one; it 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.'' \430\ The APA also 
requires that agencies provide notice and comment to the public when 
proposing regulations,\431\ as we are doing today.

    \430\ Ibid., 1181.
    \431\ 5 U.S.C. 553.

3. 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.\432\ To accomplish that 
purpose, NEPA requires an agency to compare the potential environmental 
impacts of its proposed action to those of a reasonable range of 

    \432\ NEPA is codified at 42 U.S.C. 4321-47.

    To explore the environmental consequences of this proposed rule in 
depth, NHTSA has prepared a Draft Environmental Impact Statement 
(``DEIS''). 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.'' \433\

    \433\ 40 CFR 1502.1.

    NEPA is ``a procedural statute that mandates a process rather than 
a particular result.'' Stewart Park & Reserve Coal., Inc. v. Slater, 
352 F.3d 545, 557 (2d Cir. 2003). The agency's overall EIS-related 
obligation is to ``take a `hard look' at the environmental consequences 
before taking a major action.'' Baltimore Gas & Elec. Co. v. Natural 
Resources Defense Council, Inc., 462 U.S. 87, 97 (1983). Significantly, 
``[i]f the adverse environmental effects of the proposed action are 
adequately identified and evaluated, the agency is not constrained by 
NEPA from deciding that other values outweigh the environmental 
costs.'' Robertson v. Methow Valley Citizens Council, 490 U.S. 332, 350 
    The agency must identify the ``environmentally preferable'' 
alternative but need not adopt it. ``Congress in enacting NEPA . . . 
did not require agencies to elevate environmental concerns over other 
appropriate considerations.'' Baltimore Gas & Elec. Co. v. Natural 
Resources Defense Council, Inc., 462 U.S. 87, 97 (1983). Instead, NEPA 
requires an agency to develop alternatives to the proposed action in 
preparing an EIS. 42 U.S.C. 4322(2)(C)(iii). The statute does not 
command the agency to favor an environmentally preferable course of 
action, only that it make its decision to proceed with the action after 
taking a hard look at the environmental consequences.
    We seek comment on the DEIS associated with this NPRM.
4. Evaluating the EPCA Factors and Other Considerations To Arrive at 
the Proposed Standards
    NHTSA well recognizes that the decision it proposes to make in 
today's NPRM is different from the one made in the 2012 final rule that 
established standards for MY 2021 and identified ``augural'' standard 
levels for MYs 2022-2025. Not only do we believe that the facts before 
us have changed, but we believe that those facts have changed 
sufficiently that the balancing of the EPCA factors and other 
considerations must also change. The standards we are proposing today 
reflect that balancing.
    The overarching purpose of EPCA is energy conservation; that fact 
remains the same. Examining that phrasing afresh, Merriam-Webster 
states that to ``conserve'' means, in relevant part, ``to keep in a 
safe or sound state; especially, to avoid wasteful or destructive use 
of.'' \434\ This is consistent with our understanding of Congress' 
original intent for the CAFE program: To raise fleet-wide fuel economy 
levels in response to the Arab oil embargo in the 1970s and protect the 
country from further gasoline price shocks and supply shortages. Those 
price shocks, while they were occurring, were disruptive to the U.S. 
economy and significantly affected consumers' daily lives. Congress 
therefore sought to keep U.S. energy consumption in a safe and sound 
state for the sake of consumers and the economy and avoid such shocks 
in the future.

    \434\ ``Conserve,'' Merriam-Webster, available at https://www.merriam-webster.com/dictionary/conserve (last visited June 25, 

    Today, the conditions that led both to those price shocks and to 
U.S. energy vulnerability overall have changed significantly. In the 
late 1970s, the U.S. was a major oil importer and changes (intentional 
or not) in the global oil supply had massive domestic consequences, as 
Congress saw. While oil consumption exceeded domestic production for 
many years after that, net energy imports peaked in 2005, and since 
then, oil imports have declined while exports have increased.
    The relationship between the U.S. and the global oil market has 
changed for two principal reasons. The first reason is that the U.S. 
now consumes a significantly smaller share of global oil production 
than it did in the 1970s. At the time of the Arab oil embargo, the U.S. 
consumed about 17 million barrels per day of the globe's approximately 
55 million barrels per day.\435\ While OPEC (particularly Saudi Arabia) 
still has the ability to influence global oil prices by imposing 
discretionary supply restrictions, the greater diversity of both 
suppliers and consumers since the 1970s has reduced the degree to which

[[Page 43214]]

a single actor (or small collection of actors) can impact the welfare 
of individual consumers. Oil is a fungible global commodity, though 
there are limits to the substitutability of different types of crude 
for a given application. The global oil market can, to a large extent, 
compensate for any producer that chooses not to sell to a given buyer 
by shifting other supply toward that buyer. And while regional 
proximity, comparability of crude oil, and foreign policy 
considerations can make some transactions more or less attractive, as 
long as exporters have a vested interest in preserving the stability 
(both in terms of price and supply) of the global oil market, 
coordinated, large-scale actions (like the multi-nation sanctions 
against Iran in recent years) would be required to impose costs or 
welfare losses on one specific player in the global market. As a 
corollary to the small rise in U.S. petroleum consumption over the last 
few decades, the oil intensity of U.S. GDP has continued to decline 
since the Arab oil embargo, suggesting that U.S. GDP is less 
susceptible to increases in global petroleum prices (sudden or 
otherwise) than it was at the time of EPCA's passage or when these 
policies were last considered in 2012. While the U.S. still has a 
higher energy intensity of GDP than some other developed nations, our 
energy intensity has been declining since 1950 (shrinking by about 60% 
since 1950 and almost 30% between 1990 and 2015).\436\

    \435\ Short-Term Energy Outlook, U.S. Energy Information 
Administration (June 2018), available at https://www.eia.gov/outlooks/steo/pdf/steo_full.pdf.
    \436\ Today in Energy: Global energy intensity continues to 
decline, U.S. Energy Information Administration (July 12, 106), 

    The second factor that has changed the United States' relationship 
to the global oil market is the changing U.S. reliance on imported oil 
over the last decade. U.S. domestic oil production began rising in 2009 
with more cost-effective drilling and production technologies.\437\ 
Domestic oil production became more cost-effective for two basic 
reasons. First, technology improved: The use of horizontal drilling in 
conjunction with hydraulic fracturing has greatly expanded the ability 
of producers to profitably recover natural gas and oil from low-
permeability geologic plays--particularly, shale plays--and 
consequently, oil production from shale plays has grown rapidly in 
recent years.\438\ And second, rising global oil prices themselves made 
using those technologies more feasible. As a hypothetical example, if 
it costs $79 per barrel to extract oil from a shale play, when the 
market price for that oil is $60 per barrel, it is not worth the 
producer's cost to extract the oil; when the market price is $80 per 
barrel, it becomes cost-effective.

    \437\ Energy Explained, U.S. Energy Information Administration, 
https://www.eia.gov/energyexplained/index.cfm (last visited June 25, 
    \438\ Review of Emerging Resources: U.S. Shale Gas and Shale Oil 
Plays, U.S. Energy Information Administration (July 8, 2011), 
https://www.eia.gov/analysis/studies/usshalegas/. Practical 
application of horizontal drilling to oil production began in the 
early 1980s, by which time the advent of improved downhole drilling 
motors and the invention of other necessary supporting equipment, 
materials, and technologies (particularly, downhole telemetry 
equipment) had brought some applications within the realm of 
commercial viability. EIA's AEO 2018 also projects that by the early 
2040s, tight oil production will account for nearly 70% of total 
U.S. production, up from 54% of the U.S. total in 2017. See also, 
Tight oil remains the leading source of future U.S. crude oil 
production, U.S. Energy Information Administration (Feb. 22, 2018), 

    Recent analysis further suggests that the U.S. oil supply response 
to a rise in global prices is much larger now due to the shale 
revolution, as compared to what it was when U.S. production depended 
entirely on conventional wells. Unconventional wells may be not only 
capable of producing more oil over time but also may be capable of 
responding faster to price shocks. One 2017 study concluded that ``The 
long-run price responsiveness of supply is about 6 times larger for 
tight oil on a per well basis, and about 9 times larger when also 
accounting for the rise in unconventional-directed drilling.'' That 
same study further found that ``Given a price rise to $80 per barrel, 
U.S. oil production could rise by 0.5 million barrels per day in 6 
months, 1.2 million in 1 year, 2 million in 2 years, and 3 million in 5 
years.'' \439\ Some analysts suggest that shale drillers can respond 
more quickly to market conditions because, unlike conventional 
drillers, they do not need to spend years looking for new deposits, 
because there are simply so many shale oil wells being drilled, and 
because they are more productive (although their supply may be 
exhausted more quickly than a conventional well, the sheer numbers 
appear likely to make up for that concern).\440\ Some commenters 
disagree and suggest that the best deposits are already known and 
tapped.\441\ Other commenters raise the possibility that even if the 
most productive deposits are already tapped, any rises in global oil 
prices should spur technology development that improves output of less 
productive deposits.\442\ Moreover, even if U.S. production increases 
more slowly than, for example, EIA currently estimates, all increases 
in U.S. production help to temper global prices and the risk of oil 
shocks because they reduce the influence of other producing countries 
who might experience supply interruptions due to geopolitical 
instability or deliberately reduce supply in an effort to raise 

    \439\ Newell, R. G. & Prest, B.C. The Unconventional Oil Supply 
Boom: Aggregate Price Response from Microdata, Working Paper 23973, 
National Bureau of Economic Research (Oct. 2017), available at 
http://www.nber.org/papers/w23973 (last visited June 25, 2018).
    \440\ Ip, G. America's Emerging Petro Economy Flips the Impact 
of Oil, Wall Street Journal (Feb. 21, 2018), available at https://www.wsj.com/articles/americas-emerging-petro-economy-flips-the-impact-of-oil-1519209000 (last visited June 25, 2018).
    \441\ Olson, B. Shale Trailblazer Turns Skeptic on Soaring U.S. 
Oil Production, Wall Street Journal (Mar. 5, 2018), available at 
    \442\ LeBlanc, R. In the Sweet Spot: The Key to Shale, Wall 
Street Journal (Mar. 6, 2018), available at http://partners.wsj.com/ceraweek/connection/sweet-spot-key-shale/.
    \443\ Alessi, C. & Sider, A. U.S. Oil Output Expected to Surpass 
Saudi Arabia, Rivaling Russia for Top Spot, Wall Street Journal 
(Jan. 19, 2018), available at https://www.wsj.com/articles/u-s-crude-production-expected-to-surpass-saudi-arabia-in-2018-1516352405.

    These changes in U.S. oil intensity, production, and capacity 
cannot entirely insulate consumers from the effects of price shocks at 
the gas pump, because although domestic production may be able to 
satisfy domestic energy demand, we cannot predict whether domestically 
produced oil will be distributed domestically or more broadly to the 
global market. But it appears that domestic supply may dampen the 
magnitude, frequency, and duration of price shocks. As global per-
barrel oil prices rise, U.S. production is now much better able to (and 
does) ramp up in response, pulling those prices back down. 
Corresponding per-gallon gas prices may not fall overnight,\444\ but it 
is foreseeable that they could moderate over time and likely respond 
faster than prior to the shale revolution. EIA's Annual Energy Outlook 
for 2018 acknowledges uncertainty regarding these new oil sources but 
projects that while retail prices of gasoline and diesel will increase 
between 2018 and 2050, annual average gasoline prices would not exceed 
$4/gallon (in real dollars) during that timeframe under EIA's 

[[Page 43215]]

case'' projection.\445\ The International Energy Agency (IEA)'s Oil 
2018 report suggests some concern that excessive focus on investing in 
U.S. shale oil production may increase price volatility after 2023 if 
investment is not applied more broadly but also states that U.S. shale 
oil is capable of and expected to respond quickly to rising prices in 
the future, and that American influence on global oil markets is 
expected to continue to rise.\446\ From the supply side, it is possible 
that the oil market conditions that created the price shocks in the 
1970s may no longer exist.

    \444\ To be clear, the fact that the risk of gasoline price 
shocks may now be lower than in the past is different from arguing 
that gasoline prices will never rise again at all. The Energy 
Information Administration tracks and reports on pump prices around 
the country, and we refer readers to their website for the most up-
to-date information. EIA projects under its ``reference case'' 
assumptions that the structural changes in the oil market will keep 
prices below $4/gallon through 2050. Prices will foreseeably 
continue to rise and fall with supply and demand changes; the 
relevant question for the need of the U.S. to conserve energy is not 
whether there will be any movement in prices but whether that 
movement is likely to be sudden and large.
    \445\ Annual Energy Outlook 2018, U.S. Energy Information 
Administration (Feb. 6, 2018) at 57, 58, available at https://www.eia.gov/outlooks/aeo/pdf/AEO2018.pdf. The U.S. Energy 
Information Administration (EIA) is the statistical and analytical 
agency within the U.S. Department of Energy (DOE). EIA is the 
nation's premier source of energy information and every fuel economy 
rulemaking since 2002 (and every joint CAFE and CO2 
rulemaking since 2009) has applied fuel price projections from EIA's 
Annual Energy Outlook (AEO). AEO projections, documentation, and 
underlying data and estimates are available at https://www.eia.gov/outlooks/aeo/.
    \446\ See Oil 2018: Analysis and Forecasts to 2023 Executive 
Summary, International Energy Agency (2018), available at http://www.iea.org/Textbase/npsum/oil2018MRSsum.pdf (last visited June 25, 
2018). See also Kent, S. & Puko, T. U.S. Will Be the World's Largest 
Oil Producer by 2023, Says IEA, Wall Street Journal (Mar. 5, 2018), 
available at https://www.wsj.com/articles/u-s-will-be-the-worlds-largest-oil-producer-by-2023-says-iea-1520236810 (reporting on 
remarks at the 2018 CERAWeek energy conference by IEA Executive 
Director Fatih Birol).

    Regardless of changes in the oil supply market, on the demand side, 
conditions are also significantly different from the 1970s. If gasoline 
prices increase suddenly and dramatically, in today's market American 
consumers have more options for fuel-efficient new vehicles. Fuel-
efficient vehicles were available to purchasers in the 1970s, but they 
were generally small entry-level vehicles with features that did not 
meet the needs and preferences of many consumers. Today, most U.S. 
households maintain a household vehicle fleet that serves a variety of 
purposes and represents a variety of fuel efficiency levels. 
Manufacturers have responded to fuel economy standards and to consumer 
demand over the last decade to offer a wide array of fuel-efficient 
vehicles in different segments and with a wide range of features. A 
household may now respond to short-term increases in fuel price by 
shifting vehicle miles traveled within their household fleet away from 
less-efficient vehicles and toward models with higher fuel economy. A 
similar option existed in the 1970s, though not as widely as today, and 
vehicle owners in 2018 do not have to sacrifice as much utility as 
owners did in the 1970s when making fuel-efficiency trade-offs within 
their household fleets (or when replacing household vehicles at the 
time of purchase). On a longer-term basis, if oil prices rise, 
consumers have more options to invest in additional fuel economy when 
purchasing new vehicles than at any other time in history.
    Global oil demand conditions are also different than in previous 
years. Countries that had very small markets for new light-duty 
vehicles in the 1970s are now driving global production as their 
economies improve and growing numbers of middle-class consumers are 
able to purchase vehicles for personal use. The global increase in 
drivers inevitably affects global oil demand, which affects oil prices. 
However, these changes generally occur gradually over time, unlike a 
disruption that causes a gasoline price shock. Market growth happens 
relatively gradually and is subject to many different factors. Oil 
supply markets likely have time to adjust to increases in demand from 
higher vehicle sales in countries like China and India, and in fact, 
those increases in demand may temper global prices by keeping 
production increasing more steadily than if demand was less certain; 
clear demand rewards increased production and encourages additional 
resource development over time. It therefore seems unlikely that growth 
in these vehicle markets could lead to gasoline price shocks. Moreover, 
even as these vehicle markets grow, it is possible that these and other 
vehicle markets may be moving away from petroleum usage under the 
direction of their governments.\447\ If this occurs, global oil 
production will fall in response to reduced global demand, but latent 
production capacity would exist to offset the impacts of unexpected 
supply interruptions and maintain a level of global production that is 
accessible to petroleum consumers. This, too, would seem likely to 
reduce the risk of gasoline price shocks.

    \447\ Lynes, M. Plug-in electric vehicles: future market 
conditions and adoption rates, U.S. Energy Information 
Administration (Oct. 23, 2017), https://www.eia.gov/outlooks/ieo/pev.php.

    Considering all of the above factors, if gasoline price shocks are 
no longer as much of a threat as they were when EPCA was originally 
passed, it seems reasonable to consider what the need of the United 
States to conserve oil is today and going forward. Looking to the 
discussion above on what factors are relevant to the need of the United 
States to conserve oil, one may conclude that the U.S. is no longer as 
dependent upon petroleum as the engine of economic prosperity as it was 
when EPCA was passed. The national balance of payments considerations 
are likely drastically less important than they were in the 1970s, at 
least in terms of oil imports and vehicle fuel economy. Foreign policy 
considerations appear to have shifted along with the supply shifts also 
discussed above.
    Whether and how environmental considerations create a need for CAFE 
standards is, perhaps, more complicated. As discussed earlier in this 
document, carbon dioxide is a direct byproduct of the combustion of 
carbon-based fuels in vehicle engines.\448\ Many argue that it is 
likely that human activities, especially emissions of greenhouse gases 
like carbon dioxide, contribute to the observed climate warming since 
the mid-20th century.\449\ Even taking that premise as given, it is 
reasonable to ask whether rapid ongoing increases in CAFE stringency 
(or even, for that matter, electric vehicle mandates) can sufficiently 
address climate change to merit their costs. To ``conserve,'' again, 
means ``to avoid wasteful or destructive use of.''

    \448\ Depending on the energy source, it may also be a byproduct 
of consumption of electricity by vehicles.
    \449\ Climate Science Special Report: Fourth National Climate 
Assessment, Volume I (Wuebbles, D.J. et al., eds. 2017), available 
at https://science2017.globalchange.gov/ (last accessed Feb. 23, 

    Some commenters have argued essentially that any petroleum use is 
destructive because it all adds incrementally to climate change. They 
argue that as CAFE standards increase, petroleum use will decrease; 
therefore CAFE standard stringency should increase as rapidly as 
possible. Other commenters, recognizing that economic practicability is 
also relevant, have argued essentially that because more stringent CAFE 
standards produce less CO2 emissions, NHTSA should simply 
set CAFE standards to increase at the most rapid of the alternative 
rates that NHTSA cannot prove is economically impracticable. The 
question here, again, is whether the additional fuel saved (and 
CO2 emissions avoided) by more rapidly increasing CAFE 
standards better satisfies the U.S.'s need to avoid destructive or 
wasteful use of energy than more moderate approaches that more 
appropiately balance other statutory considerations.
    In the context of climate change, NHTSA believes it is hard to say 
that increasing CAFE standards is necessary to avoid destructive or 
wasteful use of energy as compared to somewhat-less-rapidly-increasing 
CAFE standards. The most stringent of the regulatory

[[Page 43216]]

alternatives considered in the 2012 final rule and FRIA (under much 
more optimistic assumptions about technology effectiveness), which 
would have required a seven percent average annual fleetwide increase 
in fuel economy for MYs 2017-2025 compared to MY 2016 standards, was 
forecast to only decrease global temperatures in 2100 by 0.02 [deg]C in 
2100. Under NHTSA's current proposal, we anticipate that global 
temperatures would increase by 0.003 [deg]C in 2100 compared to the 
augural standards. As reported in NHTSA's Draft EIS, compared to the 
average global mean surface temperature for 1986-2005, global surface 
temperatures are still forecast to increase by 3.484-3.487 [deg]C, 
depending on the alternative. Because the impacts of any standards are 
small, and in fact several-orders-of-magnitude smaller, as compared to 
the overall forecast increases, this makes it hard for NHTSA to 
conclude that the climate change effects potentially attributable to 
the additional energy used, even over the full lifetimes of the 
vehicles in question, is ``destructive or wasteful'' enough that the 
``need of the U.S. to conserve energy'' requires NHTSA to place an 
outsized emphasis on this consideration as opposed to others.\450\

    \450\ The question of whether or how rapidly to increase CAFE 
stringency is different from the question of whether to set CAFE 
standards at all. Massachusetts v. EPA, 549 U.S. 497 (2007) 
(``Agencies, like legislatures, do not generally resolve massive 
problems in one fell regulatory swoop.'')

    Consumer costs are the remaining issue considered in the context of 
the need of the U.S. to conserve energy. NHTSA has argued in the past, 
somewhat paternalistically, that CAFE standards help to solve 
consumers' ``myopia'' about the value of fuel savings they could 
receive, when buying a new vehicle if they chose a more fuel-efficient 
model. There has been extensive debate over how much consumers do (and/
or should) value fuel savings and fuel economy as an attribute in new 
vehicles, and that debate is addressed in Section II.E. For purposes of 
considering the need of the U.S. to conserve energy, the question of 
consumer costs may be closer to whether U.S. consumers so need to save 
money on fuel that they must be required to save substantially more 
fuel (through purchasing a new vehicle made more fuel-efficient by more 
stringent CAFE standards) than they would otherwise choose.
    Again, when EPCA originally passed, Congress was trying to protect 
U.S. consumers from the negative effects of another gasoline price 
shock. It appears much more likely today that oil prices will rise only 
moderately in the future and that price shocks are less likely. 
Accordingly, it is reasonable to believe that U.S. consumers value 
future fuel savings accurately and choose new vehicles based on that 
view. This is particularly true, since Federal law requires that new 
vehicles be posted with a window sticker providing estimated costs or 
savings over a five year period compared to average new vehicles.\451\ 
Even if consumers do not explicitly think to themselves ``this new car 
will save me $5,000 in fuel costs over its lifetime compared to that 
other new car,'' gradual and relatively predictable fuel price 
increases in the foreseeable future allow consumers to roughly estimate 
the comparative value of fuel savings among vehicles and choose the 
amount of fuel savings that they want, in light of the other vehicle 
attributes they value. It seems, then, that consumer cost as an element 
of the need of the U.S. to conserve energy is also less urgent in the 
context of the structural changes in oil markets over the last several 

    \451\ 49 CFR 575.401; 40 CFR 600.302-12.

    Given the discussion above, NHTSA tentatively concludes that the 
need of the U.S. to conserve energy may no longer function as assumed 
in previous considerations of what CAFE standards would be maximum 
feasible. The overall risks associated with the need of the U.S. to 
conserve oil have entered a new paradigm with the risks substantially 
lower today and projected into the future than when CAFE standards were 
first issued and in the recent past. The effectiveness of CAFE 
standards in reducing the demand for fuel combined with the increase in 
domestic oil production have contributed significantly to the current 
situation and outlook for the near- and mid-term future. The world has 
changed, and the need of the U.S. to conserve energy, at least in the 
context of the CAFE program, has also changed.
    Of the other factors under 32902(g), the changes are perhaps less 
significant. We continue to believe that technological feasibility, per 
se, is not limiting during this rulemaking time frame. The technologies 
considered in this analysis either are already in commercial production 
or likely will be by MY 2021--some at great expense. Based on our 
analysis, all of the alternatives appear as though they could narrowly 
be considered technologically feasible, in that they could be achieved 
based on the existence or the projected future existence of 
technologies that could be incorporated on future vehicles. Any of the 
alternatives could thus be achieved on a technical basis alone but only 
if the level of resources that might be required to implement the 
technologies is not considered. However, as discussed above, we no 
longer view the need of the U.S. to conserve energy as nearly infinite, 
which means that it no longer combines with boundless technological 
feasibility to quickly push stringency upward.
    The effect of other motor vehicle standards of the Government on 
fuel economy is similarly not limiting during this rulemaking time 
frame. As discussed above, the analysis projects that safety standards 
will add some mass to new vehicles during this time frame and accounts 
for Tier 3 compliance in estimates of technology effectiveness, but 
neither of these things appear likely to make it significantly harder 
for industry to comply with more stringent CAFE standards. In terms of 
EPA's GHG standards, as also discussed above, NHTSA and EPA's 
coordination in this proposal should make the two sets of standards 
similarly binding, although differences in compliance provisions remain 
such that which standards are more binding will vary somewhat between 
manufacturers and over time.
    The remaining factor to consider is economic practicability. NHTSA 
has typically defined economic practicability, as discussed above, as 
whether a given CAFE standard is ``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.'' As part of that definition, NHTSA 
looks at a variety of elements that can lead to adverse economic 
consequences. All of the alternatives considered today arguably raise 
economic practicability issues. NHTSA believes there could be potential 
for unreasonable elimination of consumer choice, loss of U.S. jobs, and 
a number of adverse economic consequences under nearly all if not all 
of the regulatory alternatives considered today.
    If a potential CAFE standard requires manufacturers to add 
technology to new vehicles that consumers do not want, or to skip 
adding technology to new vehicles that consumers do want, it would seem 
to present issues with elimination of consumer choice. Depending on the 
extent and expense of required fuel saving technology, that elimination 
of consumer choice could be unreasonable.
    When deciding on which new vehicle to purchase, American consumers

[[Page 43217]]

generally tend not to be interested in better fuel economy above other 
attributes, particularly when gasoline prices are low.\452\ 
Manufacturers have repeatedly indicated to the agencies that new 
vehicle buyers are only willing to pay for fuel economy-improving 
technology if it pays back within the first two to three years of 
vehicle ownership.\453\ NHTSA has therefore incorporated this 
assumption (of willingness to pay for technology that pays back within 
30 months) into today's analysis. As a result, NHTSA's analysis finds 
that the most cost-effective technology is applied with or without CAFE 
(or CO2) standards, diminishing somewhat the incremental 
cost-effectiveness of new CAFE standards.

    \452\ See, e.g., Comment by Global Automakers, Docket ID NHTSA-
2016-0068-0062 (citing a 2014 study by Strategic Vision that found 
that ``. . . generally, customers as a whole place a higher priority 
on handling and ride than fuel economy.'').
    \453\ This is supported by the 2015 NAS study, which found that 
consumers seek to recoup added upfront purchasing costs within two 
or three years. See 2015 NAS Report, at pg. 317.

    Consumers not being interested in better fuel economy can take two 
forms: First, it can dampen sales of vehicles with the additional 
technology required to meet the standards, and second, it can increase 
sales of vehicles that do not help manufacturers meet the standards 
(such as vehicles that fall significantly short of their fuel economy 
targets, due to higher levels of performance (e.g., larger, less 
efficient engines) or other features). Over the last several years, 
despite record sales overall, most manufacturers have been managing 
their CAFE compliance obligations through use of credits,\454\ because 
many consumers have chosen to buy vehicles that do not improve 
manufacturers' compliance positions.

    \454\ See CAFE Public Information Center, National Highway 
Traffic Safety Administration, https://one.nhtsa.gov/cafe_pic/CAFE_PIC_Mfr_LIVE.html (last visited June 25, 2018). Readers can 
examine achieved versus required fuel economy by model year and by 
individual manufacturer or by entire fleets. When a manufacturer's 
achieved fuel economy falls short of required fuel economy but the 
manufacturer has not paid civil penalties, the manufacturer is using 
credits somehow to make up the shortfall.

    Consumer decisions to purchase relatively low-fuel economy vehicles 
might seem irrational if gasoline prices were expected to rebound in 
the future, but current indicators suggest this is not particularly 
likely. Although we know of no clear ``tipping point'' for gasoline 
prices at which American consumers suddenly become more interested in 
fuel economy over other attributes, In addition, EIA's latest AEO 2018 
suggests, based on current assumptions, that per-gallon prices are 
likely to stay under $4 through 2050.\455\ It therefore seems unlikely 
that consumer preferences are going to change dramatically in the 
foreseeable future and certainly not within the time frame of the 
standards covered by this proposal.

    \455\ As noted elsewhere in this proposal, the agencies based 
analysis on AEO 2017 projections of, for instance, fuel prices, as 
it was the best available information at the time the analysis was 
conducted. As such, where possible, the agency incorporated latest 
AEO 2018 projections into the discussion, in effort to re-confirm no 
discernible impact to analysis results or to provide the best 
possible information for the discussion.

    Thus, if manufacturers are not currently able to sell higher-fuel 
economy vehicles without heavy subsidization, particularly HEVs, PHEVs, 
and EVs, it seems unlikely that their ability to do so will improve 
unless consumer preferences change or fuel prices rise significantly, 
either of which seem unlikely. Today's analysis indicates, perhaps 
predictably, that electrification rates must increase as stringency 
increases among the options the agencies are considering.

[[Page 43218]]


[[Page 43219]]


[[Page 43220]]


[[Page 43221]]


    Manufacturers have commented to the agencies that ``Although 
automakers are offering more of these models every year, with improved 
technology and options, sales of these vehicles are not growing,'' 
noting that even for hybrid

[[Page 43222]]

vehicles, which require no adaptation by consumers (for example, to 
range limits or refueling by charging), sales ``have declined from a 
peak of a 3.1 percent share of the market (in 2013) to . . . 1.8 
percent [in 2016].'' \456\ The same source further stated that this 
decline was despite the technology being available in the market for 
more than 15 years, and that in 2016, ``close to 75 percent of the 
people who have traded in a hybrid or electric car to a dealer have 
replaced it with a conventional (non-hybrid) gasoline-powered car.'' 
\457\ While some consumers continue to seek out hybrid and electric 
vehicles, then, many other consumers seem uninterested in them, even 
given the generous incentives and subsidies often available for 
consumers in the form of tax credits, government rebates, High 
Occupancy Vehicle Lane access, preferred and/or subsidized parking, 
among others. Despite this broad ongoing lack of consumer interest, a 
number of manufacturers nonetheless continue to increase their 
offerings of these vehicles. At best, this trend seems economically 
inefficient; more concerningly for economic practicability, it seems 
likely to impact consumer choice (as discussed further below) in ways 
that could weigh heavily on sales, jobs, and consumers themselves. We 
seek comment on this issue.

    \456\ Comment by Global Automakers, Docket ID NHTSA-2016-0068-
0062, citing IHS Global New Vehicle Registration Data for 2013, 
2015, and January-June 2016.
    \457\ Id. at B-6 and B-7, citing Matt Richtel, American Drivers 
Regain Appetite for Gas Guzzlers, New York Times (June 24, 2016), 

    If the evidence indicates that hybrid sales are declining as 
gasoline prices remain low, it seems reasonable to conclude that 
consumers will not choose to buy more of them going forward as gasoline 
prices are forecast to remain low. This is consistent with the analysis 
discussed in Section II.E, that even while some consumers may be 
willing to pay between $2,000 and $3,000 more for vehicles with 
electrified technologies, that incremental willingness-to-pay falls 
well short of the additional costs projected for HEVs, PHEVs, and EVs. 
This trend may well extend beyond electrification technologies to other 
technologies. When costs for fuel economy-improving technology exceed 
the fuel savings, consumers may very well be unwilling to pay the full 
cost for vehicles with higher fuel economy that would be increasingly 
needed as to comply as the stringency of the alternatives increases.
    If consumers are not willing to pay the full cost for vehicles with 
higher fuel economy, it seems reasonably foreseeable that they will 
consider vehicles made more expensive by higher CAFE standards to be 
not ``available'' to them to purchase--or put more simply, that they 
will be turned off by more expensive vehicles with technologies they do 
not want, and seek instead to purchase cheaper vehicles without that 
technology (or with different technologies, such as those that improve 
performance or safety). Manufacturers have long cross-subsidized 
vehicle models in their lineups in order to recoup costs in cases where 
they do not believe they can pass the full costs of development and 
production forward as price increases for the vehicle model in 
question. Given that this cross-subsidization is ongoing, however, and 
possibly deepening as manufacturers have had to meet increasingly 
stringent CAFE standards over the past several years, it is unclear how 
much additional distribution of costs could be supported by the market. 
Certainly, if CAFE standards continue to increase in stringency as 
gasoline prices stay relatively low and consumer willingness to pay for 
significant additional fuel economy improvements remains 
correspondingly low, then additional cross-subsidization of products to 
try to ease those products into consumer acceptance seems likely to 
impair consumer choice, insofar as the vehicles they want to buy will 
cost more and may have technology for which they are unwilling to pay. 
Models that have historically been able to bear higher percentages of 
the cross-subsidization burden may not be able to bear much more--a 
pickup truck buyer, for example, may eventually decide to purchase a 
used vehicle, another type of vehicle, or a pickup made by a different 
manufacturer rather than pay the extra cost that the manufacturer is 
trying to recoup from higher-fuel economy vehicles that had to be 
artificially discounted to be sold. We seek comment on the effect of 
fuel economy standards on cross-subsidization across models.
    Moreover, assuming that manufacturers try to pass the costs of 
those technologies on to consumers in the form of higher new vehicle 
prices, rather than absorbing them and hurting profitability, this can 
affect consumers' ability to afford new vehicles. The analysis assumes 
that the increased cost of meeting standards is passed on to consumers 
through higher new vehicle prices, and looks at those increases as a 
one-time payment. In the context of, for example, a $30,000 new 
vehicle, another $2,000 may not seem significant to some readers. Yet 
manufacturers and dealers have repeatedly commented to NHTSA that the 
overall price of the vehicle is less relevant to the majority of 
consumers than the monthly payment amount, which is a significant 
factor in consumers' ability to purchase or lease a new vehicle. 
Amortizing a $2,000 price increase over, for example, 48 months may 
also not seem like a large amount to some readers, even accounting for 
interest payments. Yet the corresponding up-front and monthly costs may 
pose a challenge to low-income or credit-challenged purchasers. As 
discussed previously, such increased costs will price many consumers 
out of the market--leaving them to continue driving an older, less 
safe, less efficient, and more polluting vehicle, or purchasing another 
used vehicle that would likewise be less safe, less efficient, and more 
polluting than an equivalent new vehicle.
    For example, the average MY 2025 prices estimated here under the 
baseline and proposed CAFE standards are about $34,800 and $32,750, 
respectively (and $34,500 and $32,550 under the baseline and proposed 
GHG standards). The buyer of a new MY 2025 vehicle might thus avoid the 
following purchase and first-year ownership costs under the proposed 

[[Page 43223]]


    While the buyer of the average vehicle would also purchase somewhat 
more fuel under the proposed standards, this difference might average 
only five gallons per month during the first year of ownership.\462\ 
Some purchasers may consider it more important to avoid these very 
certain (e.g., being reflected in signed contracts) cost savings than 
the comparatively uncertain (because, e.g., some owners drive 
considerably less than others, and may purchase fuel in small 
increments as needed) fuel savings. For some low-income purchasers or 
credit-challenged purchasers, the cost savings may make the difference 
between being able or not to purchase the desired vehicle. As vehicles 
get more expensive in response to higher CAFE standards, it will get 
more and more difficult for manufacturers and dealers to continue 
creating loan terms that both keep monthly payments low and do not 
result in consumers still owing significant amounts of money on the 
vehicle by the time they can be expected to be ready for a new vehicle.

    \458\ Using down payment assumption of $4,056. See Press 
Release, Edmunds, New Vehicle Prices Climb to All-Time High in 
December (Jan. 3, 2018), https://www.edmunds.com/about/press/new-vehicle-prices-climb-to-all-time-high-in-december.html.
    \459\ Using average rate of 5.46% (discussed above in Section 
    \460\ Using average rate of 4.25% (discussed above in Section 
    \461\ Using average rate of 1.83% (discussed above in Section 
    \462\ Based on estimated sales volumes and average fuel 
consumption discussed below in Section VI, and on average vehicle 
survival and mileage accumulation rates (discussed above in Section 
II.E) indicating that the average vehicle delivers about 11% of it 
lifetime service (i.e., distance driven) during the first year of 

    Over the last decade, as vehicle sales have rebounded in the wake 
of the recession, historically low interest rates and increases in the 
average duration of financing terms have helped manufacturers and 
dealers keep consumers' monthly payments low. These trends (low 
interest rates and longer loan periods), along with pent-up demand for 
new vehicles, have helped keep vehicle sales high. As interest rates 
have increased, and most predict will continue to rise, monthly 
payments will foreseeably increase, and the ability to offset such 
increases by extending finance terms to account for increased finance 
charges and vehicle prices due to CAFE standards is limited by the fact 
that doing so increases the amount of time before consumers will have 
positive equity in their vehicles (and able to trade in the vehicle for 
a newer model). This reduces the mechanisms that manufacturers, captive 
finance companies, dealers, and independent lenders have in order to 
maintain sales at comparable levels. In other words, if vehicle sales 
have not already hit the breaking point, they may be close.\463\ The 
agencies seek comment on the impact that increased prices, interest 
rates, and financing terms are likely to have on the new vehicle 

    \463\ See, e.g., Comment by Global Automakers, Docket ID NHTSA-
2016-0068-0062, at 10 (``Current sales are a poor predictor of 
future sales. Many of the macroeconomic factors that have 
contributed to the current boom may not exist six to nine years into 
the future [i.e., during the mid-2020s]. The low interest loans and 
extended time loans that are now readily available may not be 
available then. The automotive industry is a cyclical business, and 
it appears to be near the top of a cycle now.'')


[[Page 43224]]


    The increasing risk that manufacturers and dealers will hit a wall 
in their ability to keep monthly payments low may fall 
disproportionately on new and low-income buyers. To build on the 
discussion above, manufacturers often purposely cross-subsidize the 
prices of entry-level vehicles to keep monthly payments low and attract 
new and young consumers to their brand. Higher CAFE standard stringency 
leads to higher costs for technology across manufacturers' fleets, 
meaning that more and more cross-subsidization becomes necessary to 
maintain affordability for entry-level vehicle purchasers. While this 
is clearly an economic issue for industry, it may also slow fleet-wide 
improvement in vehicle characteristics like safety--both in terms of 
manufacturers having to divert resources to adding technology to 
vehicles that consumers do not want and then figuring out how to get 
consumers to buy them and in terms of new vehicles potentially becoming 
unaffordable for certain groups of consumers, meaning that they must 
either defer new vehicle purchases or turn to the used vehicle market, 
where levels of safety may not be comparable. We seek comment on these 
    Alternatively, rather than or in addition to continuing to cross-
subsidize fuel economy improvements that consumers are unwilling to pay 
for directly, manufacturers may choose to try to improve their 
compliance position under higher CAFE standards by restricting sales of 
certain vehicle models or options. If consumers tend to want the 6-
cylinder engine version of a vehicle rather than the 4-cylinder 
version, for example, the manufacturer may choose to make fewer 6-
cylinders available. This solution, if chosen, would directly impact 
consumer choice. It seems increasingly likely that this solution could 
be chosen as CAFE stringency increases.
    In terms of risks to employment, today's analysis focuses on 
employment as a function of estimated changes in vehicle price in 
response to different levels of standards and assumes that all cost 
increases to vehicle models are passed forward to consumers in the form 
of price increases for that vehicle model. As Section VII.C on today's 
sales and employment analysis indicates, the sales function of the CAFE 
model appears fairly accurate at predicting sales trends but does not 
presume that sales are particularly responsive to changes in vehicle 
price. We are concerned, however, that the sales model as it currently 
functions may miss two key points about potential future sales and 
employment effects.
    First, the analysis does not account for the risk discussed above 
that manufacturers and dealers may not be able to continue keeping 
monthly new vehicle payments low, for a variety of reasons. Interest 
rates and inflation may rise; further lengthening loan terms may not be 
practical as they increase the period of time that the purchaser has 
negative equity (which has secondary impacts described above). While 
these may be not-entirely-negative things for the economy as a whole, 
they would create negative pressure on vehicle sales or employment 
associated with those sales.
    Second, as the cost of compliance increases with CAFE stringency, 
it is possible that manufacturers may shift

[[Page 43225]]

production overseas to locations where labor is cheaper. The CAFE 
program contains no mandates with regard to where vehicles are 
manufactured and arguably disincentivizes domestic production of 
passenger cars through the minimum domestic passenger car standard. If 
it becomes substantially more expensive for manufacturers to meet their 
CAFE obligations, they may seek to cut costs wherever they can, which 
could include layoffs or changing production locations.
    There may be other adverse economic consequences besides those 
discussed above. If manufacturers seek to avoid losing sales by 
absorbing the additional costs of meeting higher CAFE standards, it is 
foreseeable that absorbing those costs would hurt company profits. If 
manufacturers choose that approach year after year to avoid losing 
market share, continued falling profits would lead to negative earnings 
reports and risks to companies' long-term viability. Thus, even if 
sales levels are maintained despite higher standards, it seems possible 
that industry could face adverse economic consequences.
    More broadly, when gasoline prices stay relatively low (as they are 
expected to remain through the lifetime of nearly all vehicles covered 
by the rulemaking time frame), higher stringency standards are 
increasingly less cost-beneficial. As shown and discussed in Section 
VII.C, the analysis of consumer impacts shows that consumers recoup 
only a portion of the costs associated with increasing stringency under 
all of the alternatives. The fuel savings resulting from each of the 
alternatives is substantially less than the costs associated with the 
alternative, meaning that net savings for consumers improves as 
stringency decreases. Figure V-2 below illustrates this trend.\464\

    \464\ For the reader's reference, Alternatives 3 and 7 phase out 
A/C and off-cycle procedures, while the other alternatives leave 
those procedures unchanged. Phasing out these procedures increases 
compliance costs and reduces net savings relative to leaving the 
procedures unchanged, net savings to consumer with seven percent 
discount rate.

    We recognize that this is a significantly different analytical 
result from the 2012 final rule, which showed the opposite trend. Using 
the projections available to the agencies for the 2012 rulemaking, all 
of the alternatives considered in that rulemaking were projected to 
have net savings to consumers and to society overall, and those net 
savings improved as stringency increased. Put simply, the result is 
different today from what it was in 2012 because the facts and the 
analysis are also different. While the differences in the facts and the 
analysis are described extensively in Section II above and in the PRIA 
accompanying this proposal, a few noteworthy ones include:

     In 2012, we assumed in the main analysis that 
manufacturers would add no more technology than needed for 
compliance, while today's analysis assumes logically that 
manufacturers will add technologies that pay for themselves within 
2.5 years, consistent with manufacturer information on payback 
     In 2012, we measured impacts of the post-2017 standards 
relative to compliance with pre-2017 standards, which meant that a 
lot of cost-effective technology attributable to the 2017-2020 
standards was ``counted'' toward the 2025 standards.
     In 2012, we used analysis fleets based on 2008 or 2010 
technology. Today's analysis uses a 2016-based analysis fleet.

    These three points above mean that, overall, the current analysis 
fleet reflects the application of much additional technology than the 
2012-final-rule analysis fleet reflected. When technology is used by 
the analysis fleet, it is ``unavailable'' to be used again for 
compliance with future standards because the same technology cannot be 
used twice (once by a manufacturer for its own reasons and then again 
by the model to simulate manufacturer responses to higher standards). 
Some of this would happen necessarily in an updated rulemaking because 
a later-in-time analysis fleet inevitably includes more technology; in 
this particular case, 2016 happened to be a somewhat technology-heavy 
year, and 2008 and 2010 (the fleets used in 2012) arguably did not 
reflect the state of technology in 2012 well.
    Furthermore, readers should note the following changes:

[[Page 43226]]

     Estimates of effectiveness and cost are different for a 
number of technologies, as discussed in Section II above and in 
Chapter 6 of the PRIA, and indirect costs are determined using the 
RPE rather than the ICM;
     Fuel prices forecasts are considerably lower in AEO 
2017 than they were in AEO 2012;
     The current analysis uses a rebound effect value of 20% 
instead of 10%;
     The current analysis newly accounts for price impacts 
on fleet turnover;
     The social cost of carbon is different and accounts 
only for domestic (not international) impacts;
     The current analysis does not attempt to purposely 
limit the appearance of potential safety effects, and the value of a 
statistical life is higher than in 2012.

    All of these changes, together, mean that the standards under any 
of the regulatory alternatives (compared to the preferred alternative) 
are more expensive and have lower benefits than if they had been 
calculated using the inputs and assumptions of the 2012 analysis. This, 
in turn, helps lead the agency to a different conclusion about what 
standards might be maximum feasible in the model years covered by the 
rulemaking. NHTSA has thus both relied on new facts and circumstances 
in developing today's proposal and reasonably rejected prior facts and 
analyses relied on in the 2012 final rule.\465\

    \465\ See Fox v. FCC, 556 U.S. at 514-515; see also NAHB v. EPA, 
682 F.3d 1032 (D.C. Cir. 2012).

    By directing NHTSA to determine maximum feasible standards by 
considering the four factors, Congress recognized that ``maximum 
feasible'' may change over time as the agency assessed the relative 
importance of each factor.\466\ If one factor appears to be more 
important than the others in the time frame to be covered by the 
standards, it makes sense to give it more weight in the agency's 
determination of maximum feasible standards for those model years. If 
no factor appears to be particularly paramount, it makes sense to 
determine maximum feasible standards by more generally weighing each 
factor, as long as EPCA's direction to establish maximum feasible 
standards continues to be fulfilled in a manner that does not undermine 
energy conservation.

    \466\ If this were not accurate, it seems illogical that 
Congress would have, at various times, set specific mpg goals for 
the CAFE program (e.g., 35 mpg by 2020).

    NHTSA tentatively concludes that proposing CAFE standards that hold 
the MY 2020 curves for passenger cars and light trucks constant through 
MY 2026 would be the maximum feasible standards for those fleets and 
would fulfill EPCA's overarching purpose of energy conservation in 
light of the facts before the agency today and as we expect them to be 
in the rulemaking time frame. In the 2012 final rule that established 
CAFE standards for MYs 2017-2021, and presented augural CAFE standards 
for MYs 2022-2025, NHTSA stated that ``maximum feasible standards would 
be represented by the mpg levels that we could require of the industry 
before we reach a tipping point that presents risk of significantly 
adverse economic consequences.'' \467\ However, the context of that 
rulemaking was meaningfully different from the current context. At that 
time, NHTSA understood the need of the U.S. to conserve energy as 
necessarily pushing the agency toward setting stricter and stricter 
standards. Combining a then-paramount need of the U.S. to conserve 
energy with the perception that technological feasibility should no 
longer be seen as an important limiting factor, NHTSA then concluded 
that only significant economic harm would be a basis for controlling 
the pace at which CAFE stringency increased over time.

    \467\ 77 FR 62624, 63039 (Oct. 15, 2012).

    Today, the relative importance of the need of the U.S. to conserve 
energy has changed when compared to the beginning of the CAFE program 
and a great deal even since the 2012 rulemaking. As discussed above, 
the effectiveness of CAFE standards in reducing the demand for fuel 
combined with the increase in domestic oil production have contributed 
significantly to the current situation and outlook for the near- and 
mid-term future. The world has changed, and the need of the U.S. to 
conserve energy may no longer disproportionately outhweigh other 
statutorily-mandated considerations such as economic practicability--
even when considering fuel savings from potentially more-stringent 
    Thus, while more stringent standards may be possible, insofar as 
production-ready technology exists that the industry could physically 
employ to reach higher standards, it is not clear that higher standards 
are now economically practicable in light of current U.S. consumer 
needs to conserve energy. While vehicles can be built with advanced 
fuel economy-improving technology, this does not mean that consumers 
will buy the new vehicles that might be required to include such 
technology; that industry could continue to subsidize their production 
and sale; or that adverse economic consequences would not result from 
doing so. The effect of other motor vehicle standards of the Government 
is minimal when the two agencies regulating the same aspects of vehicle 
performance are working together to develop those regulations. 
Therefore, NHTSA views the determination of maximum feasible standards 
as a question of the appropriateness of standards given that their 
need--either from the societal-benefits perspective in terms of risk 
associated with gasoline price shocks or other related catastrophes, or 
from the private-benefits perspective in terms of consumer willingness 
to purchase new vehicles with expensive technologies that may allow 
them to save money on future fuel purchases--seems likely to remain low 
for the foreseeable future.
    When determining the maximum feasible standards, and in particular 
the economic practicability of higher standards, we also note that the 
proposed standards have the most positive effect on on-road safety as 
compared to the alternatives considered. The analysis indicates that, 
compared to the baseline standards defining the No-Action alternative, 
any regulatory alternatives under consideration would improve overall 
highway safety. Some of this estimated reduction is attributable to 
vehicles, themselves, being generally safer if they do not apply as 
much mass reduction to passenger cars as might be applied under the 
baseline standards. Additionally, the analysis estimates that the 
alternatives to the baseline standards would cause the fleet to turn 
over to newer and safer vehicles, which will also be more fuel 
efficient than the vehicles being replaced, more quickly than otherwise 
anticipated. Furthermore, the analysis estimates that the alternatives 
to the baseline standard would involve reduced overall demand for 
highway travel. As discussed above in Section II.F, and in Chapter 11 
of the accompanying PRIA, most of the estimated overall improvement in 
highway safety from this proposal is attributable to reduced travel 
demand (attributable to the rebound effect) and accelerated turnover to 
safer vehicles. The trend in these results is clear, with the less 
stringent alternatives producing the greatest estimated improvement in 
highway safety and the proposed standards producing the most favorable 
outcomes from a highway safety perspective. These considerations 
bolster our determination that the proposed standards are maximum 
feasible based upon current and projected technology for the model 
years in question.
    Standards that retain the MY 2020 curves through MY 2026 will save 

[[Page 43227]]

beyond what the market would achieve on its own for vehicles 
manufactured during the rulemaking time frame and will result in the 
highest net benefits both for consumers and for society. Such standards 
would avoid the risks identified in the discussion of economic 
practicability for more stringent standards and are consistent with the 
relatively lower need of the United States to conserve energy and the 
impact that has on consumer choice. Moreover, as the fuel economy of 
the new vehicle fleet improves over time, the marginal benefits of 
continued improvements diminish, making the consumer willingness to 
bear them and the economic practicability of them diminish. It is much 
more expensive, and saves much less fuel, for a vehicle to improve from 
40 to 50 mpg, than for a vehicle to improve from 15 to 20 mpg.\468\ If 
obtaining the marginal benefits of new cars and their fuel economy 
technologies becomes too expensive for consumers, some consumers will 
choose to drive less efficient used vehicles longer.

    \468\ As the base level of fuel economy improves, there are 
fewer gallons to be saved from improving further. A typical 
assumption is that vehicles are driven 15,000 miles per year. A 
vehicle that improves from 30 mpg to 40 mpg reduces its annual fuel 
consumption from 500 gallons/year to 375 gallons/year at 15,000 
miles/year or by 125 gallons. A vehicle that improves from 15 mpg to 
20 mpg, on the other hand, reduces its annual fuel consumption from 
1,000 gallons/year to 750 gallons/year--twice as much as the first 
example, even though the mpg improvement is only half as large. 
Going from 40 to 50 mpg would save only 75 gallons/year at 15,000 
miles/year. If fuel prices are high, the value of those gallons may 
be sufficient to offset the cost of improving further, but (1) EIA 
does not currently anticipate particularly high fuel prices in the 
foreseeable future, and (2) as the baseline level of fuel economy 
continues to increase, the marginal cost of the next gallon saved 
similarly increases with the cost of the technologies required to 
meet the savings.

    NHTSA recognizes that the Ninth Circuit has previously held that 
NHTSA must consider whether a ``backstop'' is necessary for the CAFE 
standards based on the EPCA factors in 49 U.S.C. 32902(f), given that 
the overarching purpose of EPCA is energy conservation.\469\ NHTSA and 
EPA discussed the concept of backstops in the context of the modern 
CAFE program (as opposed to the CAFE program at issue in the Ninth 
Circuit decision) in the 2010 final rule establishing CAFE and GHG 
standards for MYs 2012-2016. In that document, the agencies explained 
that even if the statute did not preclude a backstop beyond what was 
already provided for in the minimum domestic passenger car CAFE 
standard and in the ``flat'' portions of the footprint curves at the 
larger-footprint end, designing an appropriate backstop was likely to 
be fairly complex and likely to undermine Congress' objective in 
requiring attribute-based standards. See, particularly, 75 FR at 25369-
70 (May 7, 2010).

    \469\ CBD v. NHTSA, 508 F.3d 508, 537 (9th Cir. 2007), opinion 
vacated and superseded on denial of reh'g, 538 F.3d 1172 (9th Cir. 

    As in 2010, NHTSA believes that additional backstop standards are 
not necessary. The current proposal is based on the agency's best 
current understanding of the need of the U.S. to conserve energy now 
and going forward, in light of changed circumstances and balanced 
against the other EPCA factors. We seek comment on how an additional 
backstop standard might be constructed that addresses the concerns 
raised in the 2010 final