This section of the FEDERAL REGISTER
contains regulatory documents having generalapplicability and legal effect, most of whichare keyed to and codified in the Code ofFederal Regulations, which is published under50 titles pursuant to 44 U.S.C. 1510.
The Code of Federal Regulations is sold by
the Superintendent of Documents.Rules and RegulationsFederal Register23421 
Vol. 87, No. 76 
Wednesday, April 20, 2022 
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. 
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: Julia.Hegarty@ee.doe.gov. 
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: Peter.Cochran@hq.doe.gov. 
For further information on how to 
review the docket, contact the Appliance and Equipment Standards Program staff at (202) 287–1445 or by email: ApplianceStandardsQuestions@
ee.doe.gov. 
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 
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1DOE adopted the 2007 ASHRAE standards in a 
final rule published on July 22, 2009. 74 FR 36312. 
2DOE initiated the rulemaking process with a 
preliminary framework document that was 
published on September 3, 2013. 78 FR 54197. 3See appendix 8H of the final rule TSD. 
485 FR 1592, 1635–1636. 
5See section 8.2.2.9 of chapter 8 of the final rule 
TSD, and appendix 8H of the final rule TSD. 
6The 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.). 
7EIA, 2012 Commercial Building Energy 
Consumption Survey, www.eia.gov/consumption/ commercial/ (Last accessed January 20, 2022). 
8EIA, 2009 Residential Energy Consumption 
Survey, www.eia.gov/consumption/residential/ 
(Last accessed January 20, 2022). 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,
1DOE 
initiated the required 6-year lookback review in 2013.
2DOE 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’’). 
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.
3As 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. 
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
4and the accompanying 
TSD,5to develop the no-new-standards 
case, DOE assembled data on the share of models in each equipment class, separated by draft type,
6based 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. 
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’’)
7and the 2009 Residential 
Energy Consumption Survey (‘‘RECS 2009’’).
8CBECS 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. 
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 
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9Appendix 8H of the final rule TSD shows the 
no-new-standards case efficiency distributions for 
all product classes. 10Thaler, R.H., Sunstein, C.R., and Balz, J.P. 
(2014). ‘‘Choice Architecture’’ in The Behavioral Foundations of Public Policy, Eldar Shafir (ed). 11Thaler, 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). 
12Thaler, R.H., and Sunstein, C.R. (2008). Nudge: 
Improving Decisions on Health, Wealth, and Happiness. New Haven, CT: Yale University Press. 
13Vernon, 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. 
14Blum, H. and Sathaye, J. (2010). ‘‘Quantitative 
Analysis of the Principal-Agent Problem in 
Continued 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 
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.
10The 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.
11Thaler, 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.
12These 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. 
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 14Indeed, more than a 
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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). 
15Prindle, 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). 
16Bushee, 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). 
17International 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) 
18DeCanio, 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. 
19Rohdin, 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. 
20Lovins, 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). 
21Fazzari, 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. 
22Mills, 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. 
23Reed, 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). 
24Cooremans, C. (2012). ‘‘Investment in energy 
efficiency: do the characteristics of investments matter?’’ Energy Efficiency, 5(4), 497–518. 
25Lovins 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). 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.
15Additionally, 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.
16Decision-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 
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.
19U.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 
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.
22There 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,
23which 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.
24This 
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).
25Another 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 
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26Blumstein, 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). 
27A 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. 
28DeCanio 1994, op. cit. 
29DeCanio, S.J. (1998). ‘‘The Efficiency Paradox: 
Bureaucratic and Organizational Barriers to Profitable Energy-Saving Investments,’’ Energy Policy, 26(5), 441–454. 
30Andersen, 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. 
31Prindle 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. 
32Klemick, 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. 
33de 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). 
34EIA, Annual Energy Outlook, www.eia.gov/ 
outlooks/aeo/ (Last accessed January 25, 2022). 
35For further details, see: www.eia.gov/outlooks/ 
aeo/assumptions/pdf/commercial.pdf (Last 
accessed January 25, 2022). 36DOE 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. 
37Prior 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). 
38The 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. inefficiently underinvest in new 
technologies.26 
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.
28The 
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.
29The 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.
30A 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,
31supermarkets,32and 
the electric motor market.33 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
35includes 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. 
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.
36This 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. 
First, DOE obtained data from the 
Federal Energy Management Program (‘‘FEMP’’)
37on 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
38as considered in the 2007 
residential furnace final rule. 72 FR 65136, 65146–65147 (Nov. 19, 2007). 
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39FEMP gas-fired hot water boiler building data 
(Available at: www.regulations.gov/document/ 
EERE-2013-BT-STD-0030-0101). 
40DOE 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. 
41City 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). 42Boiler model data was used to determine 
efficiency and type. 
43DNV–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). 
44Minnesota 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). 
45The 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. 
46One therm is equal to 100,000 BTUs. 
47Staging multiple boilers together may be 
desired in order to provide redundancy, or to 
manage average and peak heating loads. 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).
39DOE 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. 
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
40indicate 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 42In a 
study in Massachusetts, interviewed manufacturers stated that they expect the market for non-condensing boilers to persist for some replacement situations.
43In 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. 
Finally, DOE also examined the data 
available in Northwest Energy Efficiency Alliance’s 2019 Commercial Building Stock Assessment ‘‘CBSA’’), published in May 2020.
45The 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. 
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).
47Some 
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. 
TABLE III.1—N UMBER OF BUILDINGS * INCBSA BYBOILER EFFICIENCY ACROSS SELECTED CHARACTERISTICS  
Rated efficiency 
below 86 percent Rated efficiency 
at 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 
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48The 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. TABLE III.1—N UMBER OF BUILDINGS * INCBSA BYBOILER EFFICIENCY ACROSS SELECTED CHARACTERISTICS — 
Continued 
Rated efficiency 
below 86 percent Rated efficiency 
at or above 
86 percent 
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 
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 
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49See 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. 
50Form 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). 51See 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). 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.
49A 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. 
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).
51DOE 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. 
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 
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52Figure 7.3.1 in chapter 7 of the final rule TSD 
provides an overview of the energy use 
methodology. 
53See 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). 
54See equation 7.9 and section 7.3.3 of the final 
rule TSD. 
55The 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. 
56See 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. 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.
52BOHs 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.
53BOHs 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 
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.
55However, 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. 
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.
56BOHs are constrained in 
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57Table 7B.2.8 in appendix 7B of the final rule 
TSD displays the distribution of BOHs for each CPB 
equipment class. 58The 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. 
59See table 7.4.1 in chapter 7 of the final rule 
TSD. 
60See section 8.2.2.2 in chapter 8 of the final rule 
TSD. 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.
57For 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. 
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.
58Adjustments 
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. 
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’’).
59Assuming 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,
60this 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. 
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 
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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 
DEPARTMENT OF TRANSPORTATION 
Federal Aviation Administration 
14 CFR Part 97 
[Docket No. 31423; Amdt. No. 4004] 
Standard Instrument Approach 
Procedures, and Takeoff Minimums and Obstacle Departure Procedures; Miscellaneous Amendments 
AGENCY : Federal Aviation 
Administration (FAA), DOT. 
ACTION : Final rule. 
SUMMARY : This rule amends, suspends, 
or removes Standard Instrument Approach Procedures (SIAPs) and associated Takeoff Minimums and Obstacle Departure Procedures for operations at certain airports. These regulatory actions are needed because of the adoption of new or revised criteria, or because of changes occurring in the National Airspace System, such as the commissioning of new navigational facilities, adding new obstacles, or changing air traffic requirements. These changes are designed to provide for the safe and efficient use of the navigable airspace and to promote safe flight operations under instrument flight rules at the affected airports. 
DATES : This rule is effective April 20, 
2022. The compliance date for each SIAP, associated Takeoff Minimums, and ODP is specified in the amendatory provisions. 
The incorporation by reference of 
certain publications listed in the regulations is approved by the Director of the Federal Register as of April 20, 2022. 
ADDRESSES : Availability of matter 
incorporated by reference in the amendment is as follows: 
For Examination 
1. U.S. Department of Transportation, 
Docket Ops–M30, 1200 New Jersey Avenue SE, West Bldg., Ground Floor, 
Washington, DC 20590–0001; 
2. The FAA Air Traffic Organization 
Service Area in which the affected airport is located; 
3. The office of Aeronautical 
Information Services, 6500 South MacArthur Blvd., Oklahoma City, OK 73169 or, 
4. The National Archives and Records 
Administration (NARA). 
For information on the availability of 
this material at NARA, email fr.inspection@nara.gov or go to: https:// 
www.archives.gov/federal-register/cfr/ 
ibr-locations.html. 
Availability 
All SIAPs and Takeoff Minimums and 
ODPs are available online free of charge. 
Visit the National Flight Data Center online at nfdc.faa.gov to register. 
Additionally, individual SIAP and Takeoff Minimums and ODP copies may be obtained from the FAA Air Traffic Organization Service Area in which the affected airport is located. 
FOR FURTHER INFORMATION CONTACT : 
Thomas J. Nichols, Flight Procedures and Airspace Group, Flight Technologies and Procedures Division, Flight Standards Service, Federal Aviation Administration. Mailing Address: FAA Mike Monroney Aeronautical Center, Flight Procedures and Airspace Group, 6500 South MacArthur Blvd., Registry Bldg 29 Room 104, Oklahoma City, OK 73169. Telephone: (405) 954–4164. 
SUPPLEMENTARY INFORMATION : This rule 
amends 14 CFR part 97 by amending the referenced SIAPs. The complete regulatory description of each SIAP is listed on the appropriate FAA Form 8260, as modified by the National Flight Data Center (NFDC)/Permanent Notice to Airmen (P–NOTAM), and is incorporated by reference under 5 U.S.C. 552(a), 1 CFR part 51, and 14 CFR 97.20. The large number of SIAPs, their complex nature, and the need for a special format make their verbatim publication in the Federal Register expensive and impractical. Further, airmen do not use the regulatory text of the SIAPs, but refer to their graphic depiction on charts printed by publishers of aeronautical materials. Thus, the advantages of incorporation by reference are realized and publication of the complete description of each SIAP contained on FAA form documents is unnecessary. This amendment provides the affected CFR sections, and specifies the SIAPs and Takeoff Minimums and ODPs with their applicable effective dates. This amendment also identifies the airport and its location, the procedure and the amendment number. 
Availability and Summary of Material 
Incorporated by Reference 
The material incorporated by 
reference is publicly available as listed in the 
ADDRESSES section. 
The material incorporated by 
reference describes SIAPs, Takeoff Minimums and ODPs as identified in the amendatory language for part 97 of this final rule. 
The Rule 
This amendment to 14 CFR part 97 is 
effective upon publication of each 
separate SIAP and Takeoff Minimums and ODP as amended in the transmittal. For safety and timeliness of change considerations, this amendment incorporates only specific changes contained for each SIAP and Takeoff Minimums and ODP as modified by FDC permanent NOTAMs. 
The SIAPs and Takeoff Minimums 
and ODPs, as modified by FDC permanent NOTAM, and contained in this amendment are based on criteria contained in the U.S. Standard for Terminal Instrument Procedures (TERPS). In developing these changes to SIAPs and Takeoff Minimums and ODPs, the TERPS criteria were applied only to specific conditions existing at the affected airports. All SIAP amendments in this rule have been previously issued by the FAA in a FDC NOTAM as an emergency action of immediate flight safety relating directly to published aeronautical charts. 
The circumstances that created the 
need for these SIAP and Takeoff Minimums and ODP amendments require making them effective in less than 30 days. 
Because of the close and immediate 
relationship between these SIAPs, Takeoff Minimums and ODPs, and safety in air commerce, I find that notice and public procedure under 5 U.S.C. 553(b) are impracticable and contrary to the public interest and, where applicable, under 5 U.S.C. 553(d), good cause exists for making these SIAPs effective in less than 30 days. 
The FAA has determined that this 
regulation only involves an established body of technical regulations for which frequent and routine amendments are necessary to keep them operationally current. It, therefore—(1) is not a ‘‘significant regulatory action’’ under Executive Order 12866; (2) is not a ‘‘significant rule’’ under DOT regulatory Policies and Procedures (44 FR 11034; February 26, 1979); and (3) does not warrant preparation of a regulatory evaluation as the anticipated impact is 
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