[Federal Register Volume 87, Number 76 (Wednesday, April 20, 2022)]
[Rules and Regulations]
[Pages 23421-23431]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2022-08427]



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 Rules and Regulations
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  Federal Register / Vol. 87, No. 76 / Wednesday, April 20, 2022 / 
Rules and Regulations  

[[Page 23421]]



DEPARTMENT OF ENERGY

10 CFR Part 431

[EERE-2013-BT-STD-0030]
RIN 1904-AD01


Energy Conservation Program: Energy Conservation Standards for 
Commercial Packaged Boilers; Response to United States Court of Appeals 
for the District of Columbia Circuit Remand in American Public Gas 
Association v. United States Department of Energy

AGENCY: Office of Energy Efficiency and Renewable Energy, Department of 
Energy.

ACTION: Final rule; supplemental response to comments.

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SUMMARY: On January 10, 2020, a final rule amending energy conservation 
standards for commercial packaged boilers was published in the Federal 
Register. The American Public Gas Association, Air-conditioning, 
Heating, and Refrigeration Institute, and Spire Inc. filed petitions 
for review of the final rule in the United States Courts of Appeals for 
the District of Columbia Circuit (``D.C. Circuit''), Fourth Circuit, 
and Eight Circuit, respectively. These petitions were consolidated in 
the D.C. Circuit. In its January 18, 2022, opinion, the D.C. Circuit 
remanded the final rule to the Department of Energy (``DOE'') to 
supplement its responses to the following three issues raised during 
the public comment period: The random assignment of boilers to 
buildings, forecasted fuel prices, and estimated burner operating 
hours. This document provides additional explanation regarding these 
three issues.

DATES: This supplemental response to comments document is effective 
April 20, 2022. The effective date of the final rule was March 10, 
2020. Compliance with the amended standards established for commercial 
packaged boilers in that final rule is required on and after January 
10, 2023.

ADDRESSES: Docket: The docket for this activity, which includes Federal 
Register notices, comments, and other supporting documents/materials, 
is available for review at www.regulations.gov. All documents in the 
docket are listed in the www.regulations.gov index. However, some 
documents listed in the index, such as those containing information 
that is exempt from public disclosure, may not be publicly available.
    The docket web page can be found at www.regulations.gov/docket/EERE-2013-BT-STD-0030. The docket web page contains instructions on how 
to access all documents, including public comments, in the docket.

FOR FURTHER INFORMATION CONTACT: Ms. Julia Hegarty, U.S. Department of 
Energy, Office of Energy Efficiency and Renewable Energy, Building 
Technologies Office, EE-5B, 1000 Independence Avenue SW, Washington, DC 
20585-0121. Telephone: (240) 597-6737. Email: [email protected].
    Mr. Pete Cochran, U.S. Department of Energy, Office of the General 
Counsel, GC-33, 1000 Independence Avenue SW, Washington, DC 20585-0121. 
Telephone: (202) 586-9496. Email: [email protected].
    For further information on how to review the docket, contact the 
Appliance and Equipment Standards Program staff at (202) 287-1445 or by 
email: [email protected].

SUPPLEMENTARY INFORMATION:

Table of Contents

I. Overview
II. Background
III. Supplemental Response to Comments
    A. Random Assignment of Boiler Efficiency to Buildings
    B. Fuel Prices
    C. Burner Operating Hours
IV. Procedural Issues and Regulatory Review

I. Overview

    In its January 18, 2022, opinion, the United States Court of 
Appeals for the District of Columbia Circuit remanded to the Department 
of Energy (``DOE'') the final rule, Energy Conservation Program: Energy 
Conservation Standards for Commercial Packaged Boilers, EERE-2013-BT-
STD-0030. See American Public Gas Association v. United States 
Department of Energy, No. 20-1068 (Jan. 18, 2022), 2022 WL 151923. In 
its opinion, the court determined that DOE failed to provide meaningful 
responses to comments with respect to three distinct issues related to 
the modeling used during the rulemaking proceeding: (1) The random 
assignment of boilers to buildings; (2) forecasted fuel prices; and (3) 
estimated burner operating hours. As a result, the court concluded that 
DOE failed to adequately explain why the rule satisfies the applicable 
clear and convincing evidence standard. To afford DOE the opportunity 
to cure these ``failures to explain,'' the court remanded the final 
rule to DOE for the agency to take appropriate remedial action within 
90 days. In this document, DOE provides further explanation addressing 
the three issues the court identified.

II. Background

    The American Society of Heating, Refrigerating, and Air-
Conditioning Engineers (``ASHRAE'') Standard 90.1 (ASHRAE Standard 
90.1), ``Energy Standard for Buildings Except Low-Rise Residential 
Buildings,'' sets industry energy efficiency levels for, among other 
things, commercial packaged boilers (``CPBs''). The Energy Policy and 
Conservation Act (``EPCA'') directs that if ASHRAE amends Standard 
90.1, DOE must adopt amended standards at the new ASHRAE efficiency 
level, unless DOE determines, supported by clear and convincing 
evidence, that adoption of a more stringent level would produce 
significant additional conservation of energy and would be 
technologically feasible and economically justified. (42 U.S.C. 
6313(a)(6)(A)(ii)) Under EPCA, DOE must also review energy efficiency 
standards for CPBs every six years and determine, based on clear and 
convincing evidence, whether adoption of a more stringent standard 
would result in significant additional conservation of energy and is 
technologically feasible and economically justified. (42 U.S.C. 
6313(a)(6)(C)) In determining whether a proposed standard is 
economically justified, EPCA requires DOE to consider the following 
seven factors: (1) Economic impacts on manufacturers and consumers; (2) 
changes in total installation and operating costs for the covered 
product, i.e., life-cycle costs; (3) total energy savings; (4) any 
likely

[[Page 23422]]

decrease in a product's utility or performance; (5) impacts on 
competition as determined by the Attorney General; (6) need for 
national energy conversation; and (7) other factors DOE considers 
relevant. (42 U.S.C. 6313(a)(6)(B)(ii))
    As ASHRAE has not amended the standards for CPBs since 2007,\1\ DOE 
initiated the required 6-year lookback review in 2013.\2\ DOE proposed 
amended standards for CPBs in a notice of proposed rulemaking published 
on March 24, 2016. 81 FR 15836. Subsequently, DOE issued a final rule 
amending standards for CPBs that was published on January 10, 2020. 85 
FR 1592 (``January 2020 Final Rule'').
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    \1\ DOE adopted the 2007 ASHRAE standards in a final rule 
published on July 22, 2009. 74 FR 36312.
    \2\ DOE initiated the rulemaking process with a preliminary 
framework document that was published on September 3, 2013. 78 FR 
54197.
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III. Supplemental Response to Comments

    In response to the remand in American Public Gas Association v. 
United States Department of Energy, the following discussion 
supplements the January 2020 Final Rule explanation of and response to 
comments regarding the assignment of boiler efficiencies to buildings, 
forecasted fuel prices, and estimated burner operating hours. The 
following discussion provides additional detail of the analyses 
presented in the final technical support document (``TSD'') 
accompanying the January 2020 Final Rule.

A. Random Assignment of Boiler Efficiency to Buildings

    DOE's initial response to stakeholders regarding the assignment of 
boiler efficiencies to buildings in the Monte Carlo model used to 
calculate life-cycle cost (``LCC'') changes is in section IV.F.11 of 
the January 2020 Final Rule. 85 FR 1592, 1637-1638.
    The LCC calculates, at the consumer level, the discounted savings 
in operating costs (less maintenance and repair costs) throughout the 
estimated life of the covered equipment, compared to any increase in 
the installed cost for the equipment likely to result directly from the 
imposition of the standard. In conducting the LCC analysis, DOE first 
forecasts equipment shipments in the absence of new or amended 
standards (``no-new-standards case''), including the distribution of 
equipment efficiency across all consumers. To estimate the impact that 
new or amended standards would have on LCC (and energy savings), DOE 
then uses a ``roll-up'' scenario, which takes into consideration the 
same market failures as in the no-new-standards scenario, as discussed 
further below, to determine what changes will occur under the new 
standards. A roll-up scenario assumes that equipment efficiencies in 
the no-new-standards case, which do not meet the standard level under 
consideration, would ``roll up'' to the lowest efficiency required to 
meet the new efficiency standard level. For example, the January 2020 
Final Rule established a minimum thermal efficiency of 84 percent for 
small gas-fired hot water CPBs (the product class with the largest 
number of shipments). But DOE estimates that in 2020 approximately 81.3 
percent of the market for small gas-fired hot water CPBs already meets 
this minimum thermal efficiency.\3\ As a result, DOE's analysis rolls 
up only the remaining 18.7 percent of the market, comprised of the 
least-efficient CPBs available, to the new minimum thermal efficiency 
of 84%. This roll-up in efficiencies results in the projected LCC and 
energy savings from the amended standard by forcing the less than 20% 
segment of the market that purchases lower efficiency CPBs to purchase 
a more-efficient, minimally compliant CPB. Consumers already purchasing 
higher efficiency equipment, more than 80% of the market in this 
example, are not impacted by a new or amended standard set at a lower 
efficiency level and, as a result, do not account for any of the LCC or 
energy savings projected to result from the amended rule.
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    \3\ See appendix 8H of the final rule TSD.
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    To conduct its LCC analysis, DOE has developed spreadsheet models 
combined with a commercially available program (i.e., Crystal Ball). 
This allows DOE to explicitly model both the uncertainty and the 
variability in the inputs to the model using Monte Carlo simulation and 
probability distributions. The LCC results are displayed as 
distributions of impacts compared to the baseline conditions. Results 
are based on 10,000 samples per Monte Carlo simulation run.
    As discussed in the January 2020 Final Rule \4\ and the 
accompanying TSD,\5\ to develop the no-new-standards case, DOE 
assembled data on the share of models in each equipment class, 
separated by draft type,\6\ based on the Air-Conditioning, Heating and 
Refrigeration Institute (``AHRI'') certification directory and on 
shipments data submitted by AHRI for small gas-fired hot water 
(``SGHW'') and large gas-fired hot water (``LGHW'') equipment classes 
broken down by efficiency. DOE utilized these data to develop the no-
new-standards case efficiency distribution for each CPB equipment 
class. The efficiency distribution developed by DOE for each product 
class resulted in a shipment-weighted average efficiency that was 
consistent with the shipment-weighted values submitted by AHRI. This 
efficiency distribution was then used in assigning the efficiencies of 
installed CPBs under the no-new standards case.
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    \4\ 85 FR 1592, 1635-1636.
    \5\ See section 8.2.2.9 of chapter 8 of the final rule TSD, and 
appendix 8H of the final rule TSD.
    \6\ The regulations for commercial packaged boilers prior to the 
January 2020 Final Rule listed 10 equipment classes with 
corresponding energy efficiency standards for each. 10 CFR 431.87; 
January 2019 edition. These equipment classes were based on (1) size 
(rated input), (2) heating media (hot water or steam), and (3) type 
of fuel used (oil or gas). Commercial packaged boilers are further 
classified according to draft type (i.e., the means by which 
combustion gases are moved through the unit's stack.).
---------------------------------------------------------------------------

    To conduct the Monte Carlo simulation for the LCC analysis of a 
given product class in which the efficiencies of installed models are 
forecast over the analysis period, DOE developed a building sample from 
the Energy Information Administration's (``EIA'') 2012 Commercial 
Building Energy Consumption Survey (``CBECS 2012'') \7\ and the 2009 
Residential Energy Consumption Survey (``RECS 2009'').\8\ CBECS is a 
national sample survey that collects information on the stock of U.S. 
commercial buildings, including their energy-related building 
characteristics and energy usage data (consumption and expenditures). 
Commercial buildings include all buildings in which at least half of 
the floorspace is used for a purpose that is not residential, 
industrial, or agricultural. Similarly, RECS is a nationally 
representative sample of housing units that collects energy 
characteristics on the housing unit, usage patterns, and household 
demographics. This information is combined with data from energy 
suppliers to these homes to estimate energy costs and usage for 
heating, cooling, appliances and other end uses.
---------------------------------------------------------------------------

    \7\ EIA, 2012 Commercial Building Energy Consumption Survey, 
www.eia.gov/consumption/commercial/ (Last accessed January 20, 
2022).
    \8\ EIA, 2009 Residential Energy Consumption Survey, 
www.eia.gov/consumption/residential/ (Last accessed January 20, 
2022).
---------------------------------------------------------------------------

    Each building in the sample was then assigned a boiler efficiency 
sampled from the no-new-standards case efficiency distribution for the 
appropriate equipment class. DOE was not able to assign a CPB 
efficiency to a building in the no-new-standards case based on building 
characteristics, since CBECS 2012 and RECS 2009 did not provide enough 
information to distinguish installed boilers by

[[Page 23423]]

application type, distribution system, or return water temperature, and 
there were no shipments data disaggregating boiler efficiency by region 
or other criteria. The efficiency of a boiler was assigned based on the 
forecasted efficiency distribution (which is constrained by the 
shipment and model data collected by DOE and submitted by AHRI) and 
accounts for consumers that are already purchasing efficient CPBs.\9\
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    \9\ Appendix 8H of the final rule TSD shows the no-new-standards 
case efficiency distributions for all product classes.
---------------------------------------------------------------------------

    For example, as previously discussed, the January 2020 Final Rule 
established a minimum thermal efficiency of 84 percent for small gas-
fired hot water CPBs (the product class with the largest number of 
shipments), but DOE estimates that in 2020 approximately 81.3 percent 
of the market for small gas-fired hot water CPBs already meets this 
minimum thermal efficiency and thus will not be impacted by the final 
rule. The assignment of CPB efficiency in the LCC accounts for this 
distribution (e.g., as models with at least an 84 percent efficiency 
represent approximately 81.3 percent of the market, there was an 81.3-
percent chance that a building would be assigned a boiler with an 84 
percent efficiency or higher).
    As noted in the January 2020 Final Rule, AHRI and Burnham Holdings 
commented that the random assignment of no-new-standards case 
efficiencies (sampled from the developed efficiency distribution) in 
the LCC model is not correct, as this inherently assumes that the 
purchasers do not pay attention to costs and benefits in a world 
without standards. 85 FR 1592, 1637-1638. Instead, AHRI proposed an 
alternate approach that assigned the highest boiler efficiencies to 
scenarios involving the shortest payback periods. 85 FR 1592, 1637. In 
other words, AHRI assumed there were no market failures affecting 
consumer boiler purchases.
    While DOE acknowledges that economic factors may play a role when 
building owners or builders decide on what type of boiler to install, 
assignment of boiler efficiency for a given installation, based solely 
on economic measures such as life-cycle cost or simple payback period, 
most likely would not fully and accurately reflect actual real-world 
installations. There are a number of commercial sector market failures 
discussed in the economics literature, including a number of case 
studies, that illustrate how purchasing decisions with respect to 
energy efficiency are likely to not be completely correlated with 
energy use, as described below. DOE noted some of these market failures 
affecting purchasing decisions in sections IV.F.11 and VI.A of the 
January 2020 Final Rule, such as information asymmetry and the high 
costs of gathering and analyzing relevant information, the misaligned 
incentives between building owners (or landlords) and building 
operators, and the external benefits of improved energy efficiency 
(such as climate and health benefits) not captured by users of the 
equipment. 85 FR 1592, 1638, 1676. DOE also noted these same market 
failures in the March 2016 notice of proposed rulemaking. 81 FR 15836, 
15913. The following discussion further expands on these market 
failures impacting the commercial sector and supplements DOE's 
discussion from the January 2020 Final Rule. Additionally, DOE has 
since become aware of several case studies and sources of data specific 
to the commercial packaged boiler market that support DOE's conclusion 
regarding the existence of market failures and DOE's assignment of 
boiler efficiency in the no-new-standards case. These case studies and 
sources of data further supplement and expand upon DOE's conclusion in 
the January 2020 Final Rule that an assignment of boiler efficiency 
based solely on calculated payback, without consideration of these 
market failures, ``reflects an overly optimistic and unrealistic 
working market'' and ``may unreasonably bias the results.'' 85 FR 1592, 
1637.
    There are several market failures or barriers that affect energy 
decisions generally. Some of those that affect the commercial sector 
specifically are detailed below. However, more generally, there are 
several behavioral factors that can influence the purchasing decisions 
of complicated multi-attribute products, such as boilers. For example, 
consumers (or decision makers in an organization) are highly influenced 
by choice architecture, defined as the framing of the decision, the 
surrounding circumstances of the purchase, the alternatives available, 
and how they're presented for any given choice scenario.\10\ The same 
consumer or decision maker may make different choices depending on the 
characteristics of the decision context (e.g., the timing of the 
purchase, competing demands for funds), which have nothing to do with 
the characteristics of the alternatives themselves or their prices. 
Consumers or decision makers also face a variety of other behavioral 
phenomena including loss aversion, sensitivity to information salience, 
and other forms of bounded rationality.\11\ Thaler, who won the Nobel 
Prize in Economics in 2017 for his contributions to behavioral 
economics, and Sunstein point out that these behavioral factors are 
strongest when the decisions are complex and infrequent, when feedback 
on the decision is muted and slow, and when there is a high degree of 
information asymmetry.\12\ These characteristics describe almost all 
purchasing situations of appliances and equipment, including CPBs. The 
installation of a new or replacement CPB in a commercial building is a 
complex, technical decision involving many actors and is done very 
infrequently, as evidenced by the CPB mean lifetime of nearly 25 years. 
85 FR 1592, 1634. Additionally, it would take at least one full heating 
season for any impacts on operating costs to be fully apparent. 
Further, if the purchaser of the CPB is not the entity paying the 
energy costs (e.g., a building owner and tenant), there may be little 
to no feedback on the purchase. These behavioral factors are in 
addition to the more specific market failures described as follows.
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    \10\ Thaler, R.H., Sunstein, C.R., and Balz, J.P. (2014). 
``Choice Architecture'' in The Behavioral Foundations of Public 
Policy, Eldar Shafir (ed).
    \11\ Thaler, R.H., and Bernartzi, S. (2004). ``Save More 
Tomorrow: Using Behavioral Economics in Increase Employee Savings,'' 
Journal of Political Economy 112(1), S164-S187. See also Klemick, 
H., et al. (2015) ``Heavy-Duty Trucking and the Energy Efficiency 
Paradox: Evidence from Focus Groups and Interviews,'' Transportation 
Research Part A: Policy & Practice, 77, 154-166. (providing evidence 
that loss aversion and other market failures can affect otherwise 
profit-maximizing firms).
    \12\ Thaler, R.H., and Sunstein, C.R. (2008). Nudge: Improving 
Decisions on Health, Wealth, and Happiness. New Haven, CT: Yale 
University Press.
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    It is often assumed that because commercial and industrial 
customers are businesses that have trained or experienced individuals 
making decisions regarding investments in cost-saving measures, some of 
the commonly observed market failures present in the general population 
of residential customers should not be as prevalent in a commercial 
setting. However, there are many characteristics of organizational 
structure and historic circumstance in commercial settings that can 
lead to underinvestment in energy efficiency.
    First, a recognized problem in commercial settings is the 
principal-agent problem, where the building owner (or building 
developer) selects the equipment and the tenant (or subsequent building 
owner) pays for energy costs.13 14 Indeed, more than a

[[Page 23424]]

quarter of commercial buildings with a boiler in the CBECS 2012 sample 
are occupied at least in part by a tenant, not the building owner 
(indicating that, in DOE's experience, the building owner likely is not 
responsible for paying energy costs). Additionally, some commercial 
buildings have multiple tenants. There are other similar misaligned 
incentives embedded in the organizational structure within a given firm 
or business that can impact the choice of a CPB. For example, if one 
department or individual within an organization is responsible for 
capital expenditures (and therefore equipment selection) while a 
separate department or individual is responsible for paying the energy 
bills, a market failure similar to the principal-agent problem can 
result.\15\ Additionally, managers may have other responsibilities and 
often have other incentives besides operating cost minimization, such 
as satisfying shareholder expectations, which can sometimes be focused 
on short-term returns.\16\ Decision-making related to commercial 
buildings is highly complex and involves gathering information from and 
for a variety of different market actors. It is common to see 
conflicting goals across various actors within the same organization as 
well as information asymmetries between market actors in the energy 
efficiency context in commercial building construction.\17\
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    \13\ Vernon, D., and Meier, A. (2012). ``Identification and 
quantification of principal-agent problems affecting energy 
efficiency investments and use decisions in the trucking industry,'' 
Energy Policy, 49, 266-273.
    \14\ Blum, H. and Sathaye, J. (2010). ``Quantitative Analysis of 
the Principal-Agent Problem in Commercial Buildings in the U.S.: 
Focus on Central Space Heating and Cooling,'' Lawrence Berkeley 
National Laboratory, LBNL-3557E. (Available at: escholarship.org/uc/item/6p1525mg) (Last accessed January 20, 2022).
    \15\ Prindle, B., Sathaye, J., Murtishaw, S., Crossley, D., 
Watt, G., Hughes, J., and de Visser, E. (2007). ``Quantifying the 
effects of market failures in the end-use of energy,'' Final Draft 
Report Prepared for International Energy Agency. (Available from 
International Energy Agency, Head of Publications Service, 9 rue de 
la Federation, 75739 Paris, Cedex 15 France).
    \16\ Bushee, B.J. (1998). ``The influence of institutional 
investors on myopic R&D investment behavior,'' Accounting Review, 
305-333.
    DeCanio, S.J. (1993). ``Barriers Within Firms to Energy 
Efficient Investments,'' Energy Policy, 21(9), 906-914. (explaining 
the connection between short-termism and underinvestment in energy 
efficiency).
    \17\ International Energy Agency (IEA). (2007). Mind the Gap: 
Quantifying Principal-Agent Problems in Energy Efficiency. OECD Pub. 
(Available at: www.iea.org/reports/mind-the-gap) (Last accessed 
January 20, 2022)
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    Second, the nature of the organizational structure and design can 
influence priorities for capital budgeting, resulting in choices that 
do not necessarily maximize profitability.\18\ Even factors as simple 
as unmotivated staff or lack of priority-setting and/or a lack of a 
long-term energy strategy can have a sizable effect on the likelihood 
that an energy efficient investment will be undertaken.\19\ U.S. tax 
rules for commercial buildings may incentivize lower capital 
expenditures, since capital costs must be depreciated over many years, 
whereas operating costs can be fully deducted from taxable income or 
passed through directly to building tenants.\20\
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    \18\ DeCanio, S.J. (1994). ``Agency and control problems in US 
corporations: the case of energy-efficient investment projects,'' 
Journal of the Economics of Business, 1(1), 105-124.
    Stole, L.A., and Zwiebel, J. (1996). ``Organizational design and 
technology choice under intrafirm bargaining,'' The American 
Economic Review, 195-222.
    \19\ Rohdin, P., and Thollander, P. (2006). ``Barriers to and 
driving forces for energy efficiency in the non-energy intensive 
manufacturing industry in Sweden,'' Energy, 31(12), 1836-1844.
    Takahashi, M and Asano, H (2007). ``Energy Use Affected by 
Principal-Agent Problem in Japanese Commercial Office Space 
Leasing,'' In Quantifying the Effects of Market Failures in the End-
Use of Energy. American Council for an Energy-Efficient Economy. 
February 2007.
    Visser, E and Harmelink, M (2007). ``The Case of Energy Use in 
Commercial Offices in the Netherlands,'' In Quantifying the Effects 
of Market Failures in the End-Use of Energy. American Council for an 
Energy-Efficient Economy. February 2007.
    Bjorndalen, J. and Bugge, J. (2007). ``Market Barriers Related 
to Commercial Office Space Leasing in Norway,'' In Quantifying the 
Effects of Market Failures in the End-Use of Energy. American 
Council for an Energy-Efficient Economy. February 2007.
    Schleich, J. (2009). ``Barriers to energy efficiency: A 
comparison across the German commercial and services sector,'' 
Ecological Economics, 68(7), 2150-2159.
    Muthulingam, S., et al. (2013). ``Energy Efficiency in Small and 
Medium-Sized Manufacturing Firms,'' Manufacturing & Service 
Operations Management, 15(4), 596-612. (Finding that manager 
inattention contributed to the non-adoption of energy efficiency 
initiatives).
    Boyd, G.A., Curtis, E.M. (2014). ``Evidence of an `energy 
management gap' in US manufacturing: Spillovers from firm management 
practices to energy efficiency,'' Journal of Environmental Economics 
and Management, 68(3), 463-479.
    \20\ Lovins, A. (1992). Energy-Efficient Buildings: 
Institutional Barriers and Opportunities. (Available at: rmi.org/insight/energy-efficient-buildings-institutional-barriers-and-opportunities/) (Last accessed January 20, 2022).
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    Third, there are asymmetric information and other potential market 
failures in financial markets in general, which can affect decisions by 
firms with regard to their choice among alternative investment options, 
with energy efficiency being one such option.\21\ Asymmetric 
information in financial markets is particularly pronounced with regard 
to energy efficiency investments.\22\ There is a dearth of information 
about risk and volatility related to energy efficiency investments, and 
energy efficiency investment metrics may not be as visible to 
investment managers,\23\ which can bias firms towards more certain or 
familiar options. This market failure results not because the returns 
from energy efficiency as an investment are inherently riskier, but 
because information about the risk itself tends not to be available in 
the same way it is for other types of investment, like stocks or bonds. 
In some cases energy efficiency is not a formal investment category 
used by financial managers, and if there is a formal category for 
energy efficiency within the investment portfolio options assessed by 
financial managers, they are seen as weakly strategic and not seen as 
likely to increase competitive advantage.\24\ This information 
asymmetry extends to commercial investors, lenders, and real-estate 
financing, which is biased against new and perhaps unfamiliar 
technology (even though it may be economically beneficial).\25\ Another 
market failure known as the first-mover disadvantage can exacerbate 
this bias against adopting new technologies, as the successful 
integration of new technology in a particular context by one actor 
generates information about cost-savings, and other actors in the 
market can then benefit from that information by following suit; yet 
because the first to adopt a new technology bears the risk but cannot 
keep to themselves all the informational benefits, firms may

[[Page 23425]]

inefficiently underinvest in new technologies.\26\
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    \21\ Fazzari, S.M., Hubbard, R.G., Petersen, B.C., Blinder, 
A.S., and Poterba, J.M. (1988). ``Financing constraints and 
corporate investment,'' Brookings Papers on Economic Activity, 
1988(1), 141-206.
    Cummins, J.G., Hassett, K.A., Hubbard, R.G., Hall, R.E., and 
Caballero, R.J. (1994). ``A reconsideration of investment behavior 
using tax reforms as natural experiments,'' Brookings Papers on 
Economic Activity, 1994(2), 1-74.
    DeCanio, S.J., and Watkins, W.E. (1998). ``Investment in energy 
efficiency: do the characteristics of firms matter?'' Review of 
Economics and Statistics, 80(1), 95-107.
    Hubbard R.G. and Kashyap A. (1992). ``Internal Net Worth and the 
Investment Process: An Application to U.S. Agriculture,'' Journal of 
Political Economy, 100, 506-534.
    \22\ Mills, E., Kromer, S., Weiss, G., and Mathew, P.A. (2006). 
``From volatility to value: analysing and managing financial and 
performance risk in energy savings projects,'' Energy Policy, 34(2), 
188-199.
    Jollands, N., Waide, P., Ellis, M., Onoda, T., Laustsen, J., 
Tanaka, K., and Meier, A. (2010). ``The 25 IEA energy efficiency 
policy recommendations to the G8 Gleneagles Plan of Action,'' Energy 
Policy, 38(11), 6409-6418.
    \23\ Reed, J.H., Johnson, K., Riggert, J., and Oh, A.D. (2004). 
``Who plays and who decides: The structure and operation of the 
commercial building market,'' U.S. Department of Energy Office of 
Building Technology, State and Community Programs. (Available at: 
www1.eere.energy.gov/buildings/publications/pdfs/commercial_initiative/who_plays_who_decides.pdf) (Last accessed 
January 20, 2022).
    \24\ Cooremans, C. (2012). ``Investment in energy efficiency: do 
the characteristics of investments matter?'' Energy Efficiency, 
5(4), 497-518.
    \25\ Lovins 1992, op. cit.
    The Atmospheric Fund. (2017). Money on the table: Why investors 
miss out on the energy efficiency market. (Available at: taf.ca/
publications/money-table-investors-energy-efficiency-market/) (Last 
accessed January 20, 2022).
    \26\ Blumstein, C. and Taylor, M. (2013). Rethinking the Energy-
Efficiency Gap: Producers, Intermediaries, and Innovation. Energy 
Institute at Haas Working Paper 243. (Available at: 
haas.berkeley.edu/wp-content/uploads/WP243.pdf) (Last accessed April 
6, 2022).
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    In sum, the commercial and industrial sectors face many market 
failures that can result in an under-investment in energy efficiency. 
This means that discount rates implied by hurdle rates \27\ and 
required payback periods of many firms are higher than the appropriate 
cost of capital for the investment.\28\ The preceding arguments for the 
existence of market failures in the commercial and industrial sectors 
are corroborated by empirical evidence. One study in particular showed 
evidence of substantial gains in energy efficiency that could have been 
achieved without negative repercussions on profitability, but the 
investments had not been undertaken by firms.\29\ The study found that 
multiple organizational and institutional factors caused firms to 
require shorter payback periods and higher returns than the cost of 
capital for alternative investments of similar risk. Another study 
demonstrated similar results with firms requiring very short payback 
periods of 1-2 years in order to adopt energy-saving projects, implying 
hurdle rates of 50 to 100 percent, despite the potential economic 
benefits.\30\ A number of other case studies similarly demonstrate the 
existence of market failures preventing the adoption of energy-
efficient technologies in a variety of commercial sectors around the 
world, including office buildings,\31\ supermarkets,\32\ and the 
electric motor market.\33\
---------------------------------------------------------------------------

    \27\ A hurdle rate is the minimum rate of return on a project or 
investment required by an organization or investor. It is determined 
by assessing capital costs, operating costs, and an estimate of 
risks and opportunities.
    \28\ DeCanio 1994, op. cit.
    \29\ DeCanio, S.J. (1998). ``The Efficiency Paradox: 
Bureaucratic and Organizational Barriers to Profitable Energy-Saving 
Investments,'' Energy Policy, 26(5), 441-454.
    \30\ Andersen, S.T., and Newell, R.G. (2004). ``Information 
programs for technology adoption: the case of energy-efficiency 
audits,'' Resource and Energy Economics, 26, 27-50.
    \31\ Prindle 2007, op. cit.
    Howarth, R.B., Haddad, B.M., and Paton, B. (2000). ``The 
economics of energy efficiency: insights from voluntary 
participation programs,'' Energy Policy, 28, 477-486.
    \32\ Klemick, H., Kopits, E., Wolverton, A. (2017). ``Potential 
Barriers to Improving Energy Efficiency in Commercial Buildings: The 
Case of Supermarket Refrigeration,'' Journal of Benefit-Cost 
Analysis, 8(1), 115-145.
    \33\ de Almeida, E.L.F. (1998). ``Energy efficiency and the 
limits of market forces: The example of the electric motor market in 
France'', Energy Policy, 26(8), 643-653.
    Xenergy, Inc. (1998). United States Industrial Electric Motor 
Systems Market Opportunity Assessment. (Available at: 
www.energy.gov/sites/default/files/2014/04/f15/mtrmkt.pdf) (Last 
accessed January 20, 2022).
---------------------------------------------------------------------------

    The existence of market failures in the commercial and industrial 
sectors is well supported by the economics literature and by a number 
of case studies. If DOE developed an efficiency distribution that 
assigned boiler efficiency in the no-new-standards case solely 
according to energy use or economic considerations such as life-cycle 
cost or payback period, the resulting distribution of efficiencies 
within the building sample would not reflect any of the market failures 
or behavioral factors above. DOE thus concludes such a distribution 
would not be representative of the CPB market. Further, even if a 
specific building/organization is not subject to the market failures 
above, the purchasing decision of CPB efficiency can be highly complex 
and influenced by a number of factors not captured by the building 
characteristics available in the CBECS or RECS samples. These factors 
can lead to building owners choosing a CPB efficiency that deviates 
from the efficiency predicted using only energy use or economic 
considerations such as life-cycle cost or payback period (as calculated 
using the information from CBECS 2012 or RECS 2009).
    DOE notes that EIA's Annual Energy Outlook \34\ (``AEO'') is 
another energy use model that implicitly includes market failures in 
the commercial sector. In particular, the commercial demand module \35\ 
includes behavioral rules regarding capital purchases such that in 
replacement and retrofit decisions, there is a strong bias in favor of 
equipment of the same technology (e.g., boiler efficiency) despite the 
potential economic benefit of choosing other technology options. 
Additionally, the module assumes a distribution of time preferences 
regarding current versus future expenditures. For space heating, 
approximately half of the total commercial floorspace is assigned one 
of the two highest time preference premiums. This translates into very 
high discount rates (and hurdle rates) and represents floorspace for 
which equipment with the lowest capital cost will almost always be 
purchased without consideration of operating costs. DOE's assumptions 
regarding market failures are therefore consistent with other prominent 
energy consumption models.
---------------------------------------------------------------------------

    \34\ EIA, Annual Energy Outlook, www.eia.gov/outlooks/aeo/ (Last 
accessed January 25, 2022).
    \35\ For further details, see: www.eia.gov/outlooks/aeo/assumptions/pdf/commercial.pdf (Last accessed January 25, 2022).
---------------------------------------------------------------------------

    Although the January 2020 rulemaking record sufficiently supports 
DOE's approach, DOE conducted an additional search after the January 
2020 Final Rule was issued for documentation of actual recent gas-fired 
commercial hot water boiler installations that included efficiency 
details, to further supplement DOE's conclusions that market failures 
cause consumers to base purchasing decisions on factors other than 
minimizing payback periods.\36\ This additional documentation, as 
discussed in more detail below, further reinforces the validity of 
DOE's approach to assigning boiler efficiencies in the January 2020 
Final Rule.
---------------------------------------------------------------------------

    \36\ DOE issued the January 2020 Final Rule in December 2016. In 
accordance with the error correction process in 10 CFR 430.5, DOE 
did not immediately submit the rule to the Federal Register for 
publication in order to allow the public and DOE the opportunity to 
identify any errors in the regulatory text. Following litigation in 
the Ninth Circuit, see Natural Res. Def. Council, Inc. v. Perry, 940 
F.3d 1072 (9th Cir. 2019), the Department submitted the rule that 
was issued in December 2016 to the Federal Register for publication 
in December 2019. The rule was subsequently published on January 10, 
2020.
---------------------------------------------------------------------------

    First, DOE obtained data from the Federal Energy Management Program 
(``FEMP'') \37\ on commercial gas-fired hot water boiler installations 
in government buildings from 2000 to 2013. DOE divided the data into 
the same North and Rest of Country regions \38\ as considered in the 
2007 residential furnace final rule. 72 FR 65136, 65146-65147 (Nov. 19, 
2007).

[[Page 23426]]

One might expect that highly efficient condensing boilers would be more 
common in colder climates. However, these data show that in warm 
climates in the Rest of Country states, including California, Texas, 
Oklahoma, Hawaii, and others, condensing boilers, which are generally 
more efficient, were typically installed (95 percent of buildings had a 
condensing boiler installation out of 60 buildings, with one building 
installing both condensing and non-condensing boilers). In contrast, in 
colder climates in the North, including West Virginia, New Jersey, 
Washington, and others, non-condensing boilers, which are generally 
less efficient, are not uncommon (47 percent of buildings had a non-
condensing boiler installation out of 19 buildings).\39\ DOE 
acknowledges that condensing fractions are likely higher for the 
buildings in the FEMP data during this time period compared to other 
commercial buildings due to Federal mandates and management goals 
related to energy efficiency and conservation. DOE also acknowledges 
the small sample size of buildings with CPB installations obtained from 
FEMP. However, using economic criteria based on energy use or payback 
period alone, one might not predict that non-condensing gas-fired 
boilers would be more likely installed in colder climates. These real-
world installations are indicative of complex decision-making.
---------------------------------------------------------------------------

    \37\ Prior to 2014, FEMP had separate minimum energy efficiency 
designations for condensing and non-condensing gas-fired commercial 
hot water boilers, meaning that under Federal requirements for 
procuring energy efficient equipment the initial decision of whether 
to install a condensing or non-condensing unit was left to the 
Federal agency. (Available at web.archive.org/web/20130114025912/http://www1.eere.energy.gov:80/femp/technologies/eep_boilers.html) 
(Last accessed January 20, 2022). Since 2014, FEMP mandates 
condensing gas-fired commercial hot water boilers, except when an 
agency demonstrates that selecting the FEMP designated efficiency 
level may not be cost effective. (Available at: energy.gov/eere/femp/federal-energy-management-program) (Last accessed January 20, 
2022).
    \38\ The Northern region comprises states with population-
weighted heating degree days (HDD) equal to or greater than 5,000. 
This includes Alaska, Colorado, Connecticut, Idaho, Illinois, 
Indiana, Iowa, Kansas, Maine, Massachusetts, Michigan, Minnesota, 
Missouri, Montana, Nebraska, New Hampshire, New Jersey, New York, 
North Dakota, Ohio, Oregon, Pennsylvania, Rhode Island, South 
Dakota, Utah, Vermont, Washington, West Virginia, Wisconsin, and 
Wyoming. Rest of Country region comprises states with population-
weighted HDD less than 5,000. This includes Alabama, Arizona, 
Arkansas, California, Delaware, Florida, Georgia, Hawaii, Kentucky, 
Louisiana, Maryland, Mississippi, Nevada, New Mexico, North 
Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and 
the District of Columbia.
    \39\ FEMP gas-fired hot water boiler building data (Available 
at: www.regulations.gov/document/EERE-2013-BT-STD-0030-0101).
---------------------------------------------------------------------------

    DOE also gathered recent installation data and case studies for 
areas within the North region that demonstrate a significant fraction 
of installations are for non-condensing commercial boilers. Data on 
building permits from Milwaukee \40\ indicate that there are many 
installations of gas-fired non-condensing hot water boilers in a very 
cold climate (46 percent of buildings had a non-condensing boiler 
installed out of 50 remodeled buildings).41 42 In a study in 
Massachusetts, interviewed manufacturers stated that they expect the 
market for non-condensing boilers to persist for some replacement 
situations.\43\ In a study of 105 multifamily buildings in Minnesota 
(ranging in size from 5 units to over 50 units), 85 percent of 
buildings with a gas-fired boiler have a non-condensing gas boiler 
despite the cold climate.\44\ These studies indicate that a cold 
climate (and therefore a large heating load) does not necessarily mean 
that high-efficiency boilers will predominate. Additionally, in the 
case of an emergency replacement (e.g., a boiler failing in the middle 
of winter), buildings are likely to adopt a familiar ``like-for-like'' 
replacement with the same technology. If the existing technology is 
non-condensing, then these emergency replacements are likely to be non-
condensing as well, even in a cold climate.
---------------------------------------------------------------------------

    \40\ DOE examined building permit data from several 
jurisdictions in different states, however only the City of 
Milwaukee data contained the necessary information to determine 
boiler efficiency for individual permits.
    \41\ City of Milwaukee Land Management System. Boiler New Permit 
(10/24/2016-08/11/2017). (Available at: aca-prod.accela.com/MILWAUKEE/Default.aspx) (Last accessed January 20, 2022).
    \42\ Boiler model data was used to determine efficiency and 
type.
    \43\ DNV-GL. (2017). Gas Boiler Market Characterization Study 
Phase II--Final Report. (Available at: ma-eeac.org/wp-content/uploads/Gas-Boiler-Market-Characterization-Study-Phase-II-Final-Report.pdf) (Last accessed January 20, 2022).
    \44\ Minnesota Department of Commerce. (2013). Minnesota 
Multifamily Rental Characterization Study. (Available at: 
slipstreaminc.org/sites/default/files/documents/research/minnesota-multifamily-rental-characterization-study.pdf) (Last accessed 
January 20, 2022).
---------------------------------------------------------------------------

    Finally, DOE also examined the data available in Northwest Energy 
Efficiency Alliance's 2019 Commercial Building Stock Assessment 
``CBSA''), published in May 2020.\45\ The CBSA is a regional study 
characterizing the energy consumption and building characteristics of 
commercial buildings throughout the Northwest region of the country. 
The study consists of detailed site visits to 932 commercial buildings 
across 12 building types and includes on-site assessments, building 
staff interviews, and utility submission of energy consumption data. 
The rated boiler efficiency is a key variable captured by CBSA, with 
efficiencies of installed boilers ranging from below 80 percent to 97 
percent. For gas-fired hot water boilers, an efficiency of 85 percent 
and below is generally considered to be non-condensing.
---------------------------------------------------------------------------

    \45\ The final report and all data files are available at: 
neea.org/data/commercial-building-stock-assessments (Last accessed 
January 25, 2022). The data file specific to boilers is 
hydronic_systems-boilers.xlsx.
---------------------------------------------------------------------------

    DOE specifically examined the subset of buildings with gas-fired, 
mechanical draft, hot water boilers whose function includes space 
heating. DOE limited the subset of buildings to those with a boiler 
input capacity equal to or greater than 300,000 Btu/h to match the CPB 
equipment class definitions. Building characteristics include the 
conditioned floor area and the annual, weather-normalized gas 
consumption in therms \46\ (i.e., normalized to the weather in a 
typical year). Some buildings have multiple identical boilers staged 
together into one system (with a boiler system input capacity equal to 
the sum of each individual boiler's input capacity).\47\ Some buildings 
are served by multiple boiler systems, likely servicing different 
sections of the building. In these cases, the conditioned floor area 
and facility gas consumption were split evenly among the number of 
boiler systems for ease of comparison. In total this subset represents 
53 boiler systems, although not every building includes a complete set 
of data. Table III.1 shows the number of boiler systems above and below 
a rated efficiency of 86 percent, across a number of different 
characteristics. For each characteristic, the sample is approximately 
divided into two similarly sized subsets, with an additional subset 
showing the extreme end of the distribution.
---------------------------------------------------------------------------

    \46\ One therm is equal to 100,000 BTUs.
    \47\ Staging multiple boilers together may be desired in order 
to provide redundancy, or to manage average and peak heating loads.

 Table III.1--Number of Buildings * in CBSA by Boiler Efficiency Across
                        Selected Characteristics
------------------------------------------------------------------------
                                Rated efficiency     Rated efficiency at
                                below 86 percent     or above 86 percent
------------------------------------------------------------------------
                conditioned floor area per boiler system
------------------------------------------------------------------------
<70,000 sq ft...............                     9                    14
>=70,000 sq ft..............                    13                    14
>=100,000 sq ft.............                     5                     6
------------------------------------------------------------------------

[[Page 23427]]

 
                      boiler system input capacity
------------------------------------------------------------------------
<2,500,000 Btu/h............                    10                    17
>=2,500,000 Btu/h...........                    14                    12
>=5,000,000 Btu/h...........                     8                     6
------------------------------------------------------------------------
  annual, weather-normalized facility gas consumption per boiler system
------------------------------------------------------------------------
<35,000 therms..............                    12                    14
>=35,000 therms.............                    11                    14
>=100,000 therms............                     6                     6
------------------------------------------------------------------------
* Buildings with a gas-fired, hot water, mechanical draft boiler whose
  function includes space heating and with an input capacity equal to or
  greater than 300,000 Bth/h.

    Across each characteristic, there is a lack of any strong 
correlation with the efficiency of the existing boiler system. 
Buildings with boilers servicing a larger conditioned floor area do not 
preferentially have higher efficiency boilers. The same is true for 
buildings with higher capacity boilers installed, and for buildings 
with higher annual gas consumption. Additionally, neither the buildings 
with the largest conditioned floor area, the buildings with the largest 
capacity boilers, nor the buildings with the highest annual weather-
normalized gas consumption have a systematic preference for high 
efficiency boilers. Without the consideration of potential market 
failures, one would expect a correlation with boiler efficiency.\48\
---------------------------------------------------------------------------

    \48\ The 2019 CBSA also includes 7 buildings with a gas-fired, 
hot water, natural draft boiler system; 24 buildings with a gas-
fired steam boiler system; and 5 buildings with an oil-fired, hot 
water boiler system. Of the 24 buildings with steam boilers, only 3 
have boiler efficiencies greater than 85 percent. Only 1 building 
has a higher efficiency oil-fired boiler.
---------------------------------------------------------------------------

    These examples indicate that CPB purchasing decisions are most 
likely subject to several market failures. These decisions can be 
complex and are not always made based on total building energy use, 
life-cycle cost, or payback period estimates. The data show that 
condensing and non-condensing boilers are installed in a variety of 
building types and that the building characteristics do not correlate 
strongly with the existing boiler efficiency.
    For these reasons, DOE selected a random assignment of CPB boiler 
efficiency (sampled from the developed efficiency distribution, which 
is consistent with the overall shipment-weighted efficiency data 
submitted by AHRI) as a more appropriate representation of the market 
than if that assignment was based on energy use or payback period only. 
DOE acknowledges that a random sampling from a distribution of boiler 
efficiency is an approximation of what takes place in the commercial 
boiler market. However, given the factors discussed in the preceding 
paragraphs, DOE explains that an approach that relied only on apparent 
cost-effectiveness criteria using the information available in the 
CBECS or RECS samples would lead to a more unrepresentative estimate of 
the potential impact on the CPB market from an energy conservation 
standard compared to DOE's current approach.
    At the present time, there are insufficient data to analyze site-
specific economics that take into account a multitude of technical and 
other non-economic decision-making criteria in the analyses, as well as 
model the effects of various market failures, on a building-by-building 
level. In the absence of such a model and the necessary supporting 
data, DOE concludes that using a random assignment sampled from the 
developed efficiency distributions (consistent with stakeholder-
submitted data) is a reasonable approach, one that simulates behavior 
in the CPB market, where market failures result in purchasing decisions 
not being perfectly aligned with economic interests, more realistically 
than relying only on apparent cost-effectiveness criteria derived from 
the limited information in CBECS or RECS. DOE further emphasizes that 
its approach does not assume that all purchasers of CPBs make 
economically irrational decisions (i.e., the lack of a correlation is 
not the same as a negative correlation). As part of the random 
assignment, some buildings with large heating loads will be assigned 
higher efficiency CPBs, and some buildings with particularly low 
heating loads will be assigned baseline CPBs, which aligns with the 
available data. By using this approach, DOE acknowledges the 
uncertainty inherent in the data and minimizes any bias in the analysis 
by using random assignment, as opposed to assuming certain market 
conditions that are unsupported given the available evidence.
    Finally, even if DOE were to assume the random assignment approach 
produced some overstatement of the economic benefits of the new 
standards--because one were to conclude that even with all of those 
market failures there may be more strictly rational purchasers in the 
market than the random distribution accounts for--for all of the 
reasons discussed above any such overstatement would be small and would 
not alter DOE's conclusion that the revised standards are economically 
justified. That is particularly clear given that DOE considers numerous 
factors in addition to any savings to consumers. For instance, the 
January 2020 Final Rule is expected to result in cumulative emission 
reductions of 16 million metric tons of carbon dioxide and 41 thousand 
tons of nitrogen oxides, among other pollutants. The present monetized 
value of the nitrogen oxide emissions reduction, for example, is 
estimated to be $35 million at a 7-percent discount rate and $99 
million at a 3-percent discount rate. 85 FR 1592, 1597. There are also 
many significant unquantified benefits from the Rule, including 
additional environmental and public health benefits. When considering 
these benefits together with the other statutory factors listed in 42 
U.S.C. 6313(a)(6)(B)(ii), DOE has an abiding conviction that its 
determination that the benefits of the standard exceed its burdens, 
i.e., the standard is economically justified, is highly probable to be 
true. As a result, DOE

[[Page 23428]]

found clear and convincing evidence that the standard was economically 
justified.

B. Fuel Prices

    DOE clarifies its response to stakeholders in section IV.F.4 of the 
January 2020 Final Rule regarding the estimation of energy prices in 
the LCC analysis. 85 FR 1592, 1631-32.
    As described in the January 2020 Final Rule and final rule TSD, DOE 
developed marginal energy prices (electricity, natural gas, and fuel 
oil) for use in the LCC analysis.\49\ A marginal energy price reflects 
the cost or benefit of adding or subtracting one additional unit of 
energy consumption. The starting point for the estimation of marginal 
energy prices is with publicly available average energy prices 
published by the EIA in various publications (Form 826 data, natural 
gas prices, and State Energy Data System).\50\ These data are 
disaggregated by state and by month and can be aggregated into the same 
reportable domains used in RECS and census divisions used in CBECS. The 
price data by month allow DOE to separately estimate winter (heating 
season) and non-winter (cooling season) energy prices. The detailed 
breakdown of these average energy prices by fuel type, region, and 
month is available in appendix 8C of the final rule TSD.
---------------------------------------------------------------------------

    \49\ See section IV.F.4 of the January 2020 Final Rule, sections 
8.2.2.2 and 8.2.2.3 of chapter 8 of the final rule TSD, and appendix 
8C of the final rule TSD.
    \50\ Form EIA-826 is now Form EIA-861M. Available at: 
www.eia.gov/electricity/data/eia861m/ (Last accessed January 25, 
2022).
    Natural gas prices available at: www.eia.gov/naturalgas/ (Last 
accessed January 25, 2022).
    State Energy Data System available at: www.eia.gov/state/seds/ 
(Last accessed January 25, 2022).
---------------------------------------------------------------------------

    EIA data additionally provides historical monthly energy 
consumption and total energy expenditures by state. By analyzing how 
total expenditures change with changes in energy consumption, DOE can 
estimate seasonal marginal energy price factors. These changes in 
expenditures are due to the marginal changes in energy consumption and 
exclude, for example, fixed costs, connection fees, and other 
surcharges. In a regression of total expenditures versus total energy 
consumption, the slope represents the marginal price. DOE used a 10-
year average across the same regional divisions in either RECS or CBECS 
to determine seasonal marginal price factors in order to transform the 
average energy prices into marginal energy prices. The detailed 
breakdown of these marginal energy price factors by fuel type and 
region, for both winter and non-winter months, is available in appendix 
8C of the final rule TSD.
    These detailed estimates of marginal energy prices are then used in 
the LCC and NIA analyses. To project energy prices in future years, DOE 
relied on energy price projections from EIA's AEO to develop energy 
price indices over time and scaled marginal prices accordingly.
    In response to the notice of proposed rulemaking published prior to 
the January 2020 Final Rule, DOE received comments on marginal energy 
prices and, in particular, on the accuracy of the marginal rates paid 
by larger load consumers. DOE noted that the Gas Associations (American 
Gas Association, American Public Gas Association) commented that the 
analysis should adjust the energy price calculation methodology using 
marginal prices to use a tariff-based approach to make the analysis 
more robust. Spire commented that DOE used erroneous utility marginal 
energy pricing and forecasts in its analysis resulting in overstated 
benefits, and that consumers with large loads do not pay the same 
marginal rates as an average commercial consumer. PG&E agreed with 
Spire that larger consumers pay less for utilities. And AHRI commented 
that the marginal gas rates do not accurately reflect what larger 
consumers pay. 85 FR 1592, 1632. DOE further acknowledged comments from 
Spire asserting that EIA data is completely inaccurate for its largest 
consumers and that transport rates are typically used, and from Phoenix 
Energy Management stating that the largest consumers also hedge gas 
prices by buying and selling futures and commenting that it is 
extremely difficult to figure out what the true cost of the energy is. 
Id.
    Regarding the usage of EIA data and comparisons to tariff data, DOE 
emphasizes that the EIA data provide complete coverage of all utilities 
and all customers, including larger commercial and industrial utility 
customers that may have discounted energy prices. The actual rates paid 
by individual customers are captured and reflected in the EIA data and 
are averaged over all customers in a state. DOE has previously compared 
these two approaches for determining marginal energy price factors in 
the residential sector. In a September 2016 supplemental notice of 
proposed rulemaking for residential furnaces, DOE compared its marginal 
natural gas price approach using EIA data with marginal natural gas 
price factors determined from residential tariffs submitted by 
stakeholders. 81 FR 65719, 65784 (Sept. 23, 2016). The submitted 
tariffs represented only a small subset of utilities and states and 
were not nationally representative, but DOE found that its marginal 
price factors were generally comparable to those computed from the 
tariff data (averaging across rate tiers).\51\ DOE noted that a full 
tariff-based analysis would require information on each household's 
total baseline gas consumption (to establish which rate tier is 
applicable) and how many customers are served by a utility on a given 
tariff. These data were not available in the public domain. By relying 
on EIA data, DOE noted, its marginal price factors represented all 
utilities and all states, averaging over all customers, and was 
therefore ``more representative of a large group of consumers with 
diverse baseline gas usage levels than an approach that uses only 
tariffs.'' 81 FR 65719, 65784. While the above comparative analysis was 
conducted for residential consumers, the general conclusions regarding 
the accuracy of EIA data relative to tariff data remain the same for 
commercial consumers. DOE uses EIA data for determining both 
residential and commercial electricity prices and the nature of the 
data is the same for both sectors. DOE further notes that not all 
operators of CPBs are larger load utility customers. As reflected in 
the building sample derived from CBECS 2012 and RECS 2009 data, there 
are a range of buildings with varying characteristics, including multi-
family residential buildings, that operate CPBs. The buildings in the 
LCC sample have varying heating load, square footage, and boiler 
capacity. Operators of CPBs are varied, some large and some smaller, 
and thus the determination of the applicable marginal energy price 
should reflect the average operator of CPBs.
---------------------------------------------------------------------------

    \51\ See appendix 8E of the TSD for the 2016 supplemental notice 
of proposed rulemaking for residential furnaces for a direct 
comparison, available at: www.regulations.gov/document/EERE-2014-BT-STD-0031-0217 (Last accessed January 25, 2022).
---------------------------------------------------------------------------

    DOE's approach is based on the largest, most comprehensive, most 
granular national data sets on commercial energy prices that are 
publicly available from EIA. The data from EIA are the highest quality 
energy price data available to DOE. The resulting estimated marginal 
energy prices do represent an average across all commercial customers 
in a given region (state or group of states for RECS, census division 
for CBECS). Some customers may have a lower marginal energy price, 
while others may have a higher marginal energy price. With respect to 
large customers who may pay a lower

[[Page 23429]]

energy price, no tariffs were submitted to DOE during the rulemaking 
for analysis. Tariffs for individual non-residential customers can be 
very complex and generally depend on both total energy use and peak 
demand (especially for electricity). These tariffs vary significantly 
from one utility to another. While DOE was unable to identify data to 
provide a basis for determining a potentially lower price for larger 
commercial and industrial utility customers, either on a state-by-state 
basis or in a nationally representative manner, the historic data on 
which DOE did rely includes such discounts. The EIA data include both 
large non-residential customers with a potentially lower rate as well 
as more typical non-residential customers with a potentially higher 
rate. Thus, to the extent larger consumers of energy pay lower marginal 
rates, those lower rates are already incorporated into the EIA data, 
which would drive down EIA's marginal rates for all consumers. If DOE 
were to adjust downward the marginal energy price for a small subset of 
individual customers in the LCC Monte Carlo sample as suggested by 
commenters, it would also have to adjust upward the marginal energy 
price for all other customers in the sample to maintain the same 
marginal energy price averaged over all customers. Even assuming DOE 
could accomplish those adjustments in a reliable or accurate way, this 
upward adjustment in marginal energy price would affect the majority of 
buildings in the LCC sample. Operational cost savings would therefore 
both decrease and increase for different buildings in the LCC sample, 
yielding substantially the same overall average LCC savings result as 
DOE's current estimate.
    In summary, DOE's current approach utilizes an estimate of marginal 
energy prices and captures the impact of actual utility rates paid by 
all customers, including those that enjoy lower marginal rates for 
whatever reason, in an aggregated fashion. Adjustments to this 
methodology are unlikely to change the average LCC results and 
therefore the conclusions of the January 2020 Final Rule are 
insensitive to this issue.

C. Burner Operating Hours

    DOE clarifies its response to stakeholders in section IV.F.11 of 
the January 2020 Final Rule regarding the estimation of burner 
operating hours (``BOHs'') in the LCC analysis. 85 FR 1592, 1637.
    BOHs are used to estimate energy consumption of elements other than 
the heating element (e.g., electronic controls, fans). The BOHs are not 
used to estimate the amount of fuel consumed to meet a heating load but 
are the result of a separate heating load estimation and an assumed CPB 
capacity. Instead, heating load and the efficiency of the CPB are used 
to determine fuel consumption. As a result, CPBs with the same 
efficiency level, but different capacities will have different BOHs in 
meeting the same heating load. For example, in meeting a specific 
heating load a CPB with a lower capacity will have higher BOHs than a 
similarly efficient CPB with a higher capacity. The lower capacity CPB 
will burn fuel at a lower rate so it will need to be on longer to meet 
the heating load as compared to a larger capacity CPB, which will burn 
fuel at a higher rate. While the hours of operation differ between the 
CPBs of different capacities, the amount of fuel burned is the same 
(i.e., the heating load and unit efficiency, not hours of operation, 
dictate fuel consumption). BOHs are therefore not a crucial component 
of determining operating costs in the LCC analysis. Operating costs are 
dominated by fuel consumption to meet the heating load, which as 
described in further detail below, is not dependent on any assumptions 
regarding BOHs.
    A full discussion of boiler energy use and the determination of 
BOHs is available in chapter 7 and appendix 7B of the final rule 
TSD.\52\ BOHs represent the amount of time the burner operates at full 
load. BOHs are not a primary input parameter separately estimated by 
DOE, but rather a derived quantity that is largely determined from the 
space heating fuel consumption reported in CBECS 2012 or RECS 2009. As 
described previously, CBECS and RECS are large, nationally 
representative surveys and the energy consumption and expenditure 
estimates are derived directly from utility billing data. CBECS and 
RECS data are the most robust energy consumption data for space heating 
available to DOE. CBECS and RECS form the basis of the LCC Monte Carlo 
sample for CPBs and both CBECS and RECS report space heating fuel 
consumption for each building in the surveys (determined from utility 
bill data). DOE estimated each building's heating load from this 
reported fuel consumption, coupled with estimates of the historical 
boiler efficiency, building shell efficiency, and adjustments for 
average climate conditions in each region.\53\ BOHs are then calculated 
using the building heating load and the efficiency of the CPB of that 
building. BOHs are utilized to estimate auxiliary electricity 
consumption for the circulating pump, draft inducer (if applicable), 
igniter, and standby power.\54\
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    \52\ Figure 7.3.1 in chapter 7 of the final rule TSD provides an 
overview of the energy use methodology.
    \53\ See equation 7.4 in the final rule TSD. Equation 7.5 shows 
the adjustment to average climate conditions. See appendix 7B for 
the derivation of existing boiler efficiency in 2012 and 2009 (the 
sample years for CBECS and RECS).
    \54\ See equation 7.9 and section 7.3.3 of the final rule TSD.
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    In the January 2020 Final Rule DOE included comments from AHRI in 
which AHRI posited that either due to DOE's sizing assumption and/or 
due to the use of the CBECS energy use data in the sample itself, the 
energy use model produced excessively high operating hours in some 
instances and that these distort the economic results; and that AHRI's 
consultant suggested that a more logical approach for estimating may be 
to use directly measured data or estimated load data. 85 FR 1592, 1637.
    As discussed, DOE derived the BOHs from CBECS and RECS data. BOH 
values are determined from building heating loads, which are themselves 
derived from reported fuel consumption data taken form large, 
nationally representative surveys. DOE therefore has a high degree of 
confidence in the resulting building heating loads. The presence of 
high BOHs in some instances is not an indication of an error, but due 
to the representative boiler capacity assigned in that instance.\55\ 
However, the building heating load and resulting fuel consumption are 
fixed and these are the primary determinant of operating costs. 
Furthermore, adjusting the BOHs downward in some instances would 
require adjusting upward the BOHs in other instances to maintain the 
same average capacity, yielding the substantially the same overall 
average LCC results.
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    \55\ The engineering analysis and all downstream analyses 
utilize a representative capacity (or rated input) that aligns with 
the highest number of shipments. Using a representative capacity 
allows DOE to analyze certain equipment characteristics as a proxy 
for that equipment class. See section 5.2.1 in chapter 5 of the 
final rule TSD.
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    Once each building's heating load is determined, DOE can estimate 
BOHs in both the no-new-standards case and all potential standards 
cases using the assigned boiler efficiency, boiler capacity, and the 
number of boilers assigned to each building, with adjustments made for 
estimated return water temperatures and part load operation.\56\ BOHs 
are constrained in

[[Page 23430]]

the model to be, at most, 5,840 hours per year (two thirds of a year), 
although the vast majority of boilers have BOHs that are significantly 
lower than this maximum value.\57\ For all but one product class, the 
median BOHs are below 1,000 hours. For context, 1,000 hours of 
operation represents approximately 8-9 hours per day for 4 months or 5-
6 hours per day for 6 months. These median values are not unreasonable 
expectations for when the burner is on during the winter heating season 
in a commercial building, depending on the local climate. Furthermore, 
some commercial buildings may require heating for longer periods during 
the day during winter, including possibly 24 hours a day (e.g., 
hospitals). BOHs of over 2000 hours represent one end of the 
distribution and only apply to a subset of buildings where heating 
loads are driven higher by climate, size, age, etc.; similarly, some 
buildings have BOHs under 500 hours, representing the other end of the 
distribution. Given that the median BOHs derived from the estimated 
building heating loads represent reasonable operating conditions, DOE 
therefore has no reason to suspect the building heating loads derived 
from CBECS and RECS are erroneous.
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    \56\ See equation 7.3 in the final rule TSD. See appendix 7B for 
a detailed discussion of adjustments made for return water 
temperature and part-load operation.
    \57\ Table 7B.2.8 in appendix 7B of the final rule TSD displays 
the distribution of BOHs for each CPB equipment class.
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    BOHs are inversely related to the number of boilers and overall 
boiler capacity assigned to each building. This means that in a 
building with multiple boilers, each individual boiler has fewer BOHs 
to meet the building heating load compared to another building with a 
similar building heating load with only a single boiler at the same 
capacity. The same is also true when comparing two single boilers of 
different capacity; the higher capacity boiler will have lower BOHs to 
meet the same building heating load. Larger capacity CPBs are typically 
installed in buildings with larger heating loads, but these loads are 
not necessarily proportional to the increase in CPB capacity. 
Therefore, it is not unusual for the larger capacity CPB equipment 
classes to have lower median BOHs in some instances.
    Because BOHs are a derived quantity and not a primary input 
parameter, the estimated fuel consumption of each building in the LCC 
sample would be the same regardless of the assigned boiler capacity and 
number of boilers in a given building. BOHs do not affect the fuel 
consumption of the sample building. The annual fuel consumption in the 
no-new-standards and standards cases is largely set by the building 
heating load determined from CBECS or RECS, coupled with the assigned 
boiler efficiency. There may be individual buildings in the LCC sample 
at the extreme ends of the distribution with high or low BOHs due to 
the assigned boiler capacity. If, in the field, a larger capacity 
boiler (or multiple boilers) with the same efficiency were installed 
instead in that building, BOHs would go down but overall fuel 
consumption would remain the same to match the building heating load. 
Similarly, at the low end of the distribution, if a lower capacity 
boiler were installed in the field instead, BOHs would increase but 
fuel consumption would remain the same. The only impact of changes to 
BOHs would be with electricity consumption. Electricity consumption 
while the boiler is on would decrease with decreasing BOHs and increase 
with increasing BOHs; however, electricity consumption is a minor 
component of overall operating costs.\58\ Adjustments to these BOHs at 
either end of the distribution would yield an overall average LCC 
savings result substantially the same as DOE's current estimate. In 
summary, higher and lower capacities may be present in the field (with 
correspondingly lower and higher BOHs), however the net result of any 
adjustments would be a minimal impact to average LCC savings and the 
percentage of negatively impacted consumers.
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    \58\ The number of standby hours would increase with decreasing 
BOHs. Total standby electricity consumption (for those CPBs with 
standby power) would therefore increase, however this represents an 
even smaller fraction of total operating costs and would have a 
negligible impact on LCC results.
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    As an illustration of the small impact of electricity consumption 
adjustments, a small gas-fired hot water CPB at a thermal efficiency of 
84 percent with a typical heating load has an estimated average annual 
fuel use of 863.7 million Btus per year (``MMBtu/yr'') and an estimated 
average annual electricity consumption of 683.5 kilowatt-hours per year 
(``kWh/yr'').\59\ Assuming this CPB is in New England, with a 
commercial natural gas price of $10.56/MMBtu and a commercial 
electricity price of $0.15/kWh,\60\ this results in an annual operating 
cost of $9,121 for natural gas and $103 for electricity. The 
electricity consumption of the auxiliary equipment and standby power 
accounts for approximately 1 percent of total energy costs. The 
difference in electricity consumption between efficiency levels is an 
even smaller fraction, compared to the difference in natural gas 
consumption between efficiency levels. Changes to BOHs both upward and 
downward would have a negligible impact on overall LCC savings results 
given that the fuel consumption is the dominant factor and it is 
determined by the heating load and assigned boiler efficiency. 
Therefore, the conclusions of the January 2020 Final Rule are 
insensitive to adjustments to BOHs.
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    \59\ See table 7.4.1 in chapter 7 of the final rule TSD.
    \60\ See section 8.2.2.2 in chapter 8 of the final rule TSD.
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IV. Procedural Issues and Regulatory Review

    DOE has concluded that the determinations made pursuant to the 
various procedural requirements applicable to the January 2020 Final 
Rule remain unchanged for this supplemental response to comments. These 
determinations are set forth in the January 2020 Final Rule. 85 FR 
1592, 1676-1681. Because the rule was remanded without vacatur for 
further explanation, DOE was able to provide this explanation without 
opening another notice and comment period. See Chamber of Commerce v. 
SEC, 443 F.3d 890, 900 (D.C. Cir. 2006).
    In the alternative, however, DOE finds that, pursuant to the 
Administrative Procedure Act, 5 U.S.C. 553(b), there is good cause to 
not issue a separate notice to solicit public comment on the 
supplemental responses to comments contained in this document. This 
document does not change the determinations made by DOE in the January 
2020 Final Rule, but is a supplement to that final rule, which already 
went through notice and comment. This document provides further 
explanation to the response to comments already provided. In addition, 
this supplement to the January 2020 Final Rule is issued pursuant to a 
court order directing DOE to provide supplemental responses to certain 
comments within 90 days. Issuing a separate notice to solicit public 
comment during that time period would be impracticable, unnecessary, 
and contrary to the public interest.

Signing Authority

    This document of the Department of Energy was signed on April 14, 
2022, by Kelly J. Speakes-Backman, Principal Deputy Assistant Secretary 
for Energy Efficiency and Renewable Energy, pursuant to delegated 
authority from the Secretary of Energy. That document with the original 
signature and date is maintained by DOE. For administrative purposes 
only, and in compliance with requirements of the Office of the Federal 
Register, the undersigned DOE Federal Register Liaison Officer has been

[[Page 23431]]

authorized to sign and submit the document in electronic format for 
publication, as an official document of the Department of Energy. This 
administrative process in no way alters the legal effect of this 
document upon publication in the Federal Register.

    Signed in Washington, DC, on April 15, 2022.
Treena V. Garrett,
Federal Register Liaison Officer, U.S. Department of Energy.
[FR Doc. 2022-08427 Filed 4-19-22; 8:45 am]
BILLING CODE 6450-01-P