This section of the FEDERAL REGISTER

contains regulatory documents having general

applicability and legal effect, most of which

are keyed to and codified in the Code of

Federal Regulations, which is published under

50 titles pursuant to 44 U.S.C. 1510.

The Code of Federal Regulations is sold by

the Superintendent of Documents.

Rules and Regulations Federal Register

23421 

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/EERE2013-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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23422 Federal Register / Vol. 87, No. 76 / Wednesday, April 20, 2022 / Rules and Regulations 

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. 

3See appendix 8H of the final rule TSD. 

4 85 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 2007,1 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’’). 

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 nonew-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 nonew-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. 

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 gasfired hot water (‘‘SGHW’’) and large gasfired hot water (‘‘LGHW’’) equipment 

classes broken down by efficiency. DOE 

utilized these data to develop the nonew-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’’) 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. 

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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Federal Register / Vol. 87, No. 76 / Wednesday, April 20, 2022 / Rules and Regulations 23423 

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. 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. 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 gasfired 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. 

It is often assumed that because 

commercial and industrial customers 

are businesses that have trained or 

experienced individuals making 

decisions regarding investments in costsaving 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 principalagent 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 


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23424 Federal Register / Vol. 87, No. 76 / Wednesday, April 20, 2022 / Rules and Regulations 

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 shorttermism 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) 

18 DeCanio, S.J. (1994). ‘‘Agency and control 

problems in US corporations: the case of energyefficient 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-buildingsinstitutional-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.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

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

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 realestate 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 


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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. 

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. 

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. 

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. 

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). 

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. 

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 noncondensing 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.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

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 lifecycle 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. 

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. 

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). 


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39FEMP gas-fired hot water boiler building data 

(Available at: www.regulations.gov/document/ 

EERE-2013-BT-STD-0030-0101). 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. 

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. 

43 DNV–GL. (2017). Gas Boiler Market 

Characterization Study Phase II—Final Report. 

(Available at: ma-eeac.org/wp-content/uploads/GasBoiler-Market-Characterization-Study-Phase-IIFinal-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/minnesotamultifamily-rental-characterization-study.pdf) (Last 

accessed January 20, 2022). 

45The final report and all data files are available 

at: neea.org/data/commercial-building-stockassessments (Last accessed January 25, 2022). The 

data file specific to boilers is hydronic_systemsboilers.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, noncondensing boilers, which are generally 

less efficient, are not uncommon (47 

percent of buildings had a noncondensing 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 noncondensing 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 40 indicate that there are 

many installations of gas-fired noncondensing 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. 

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. 

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, weathernormalized 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. 

TABLE III.1—NUMBER OF BUILDINGS * IN CBSA BY BOILER 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—NUMBER OF BUILDINGS * IN CBSA BY BOILER 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 shipmentweighted 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 noneconomic 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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23428 Federal Register / Vol. 87, No. 76 / Wednesday, April 20, 2022 / Rules and Regulations 

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/EERE2014-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.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. 

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. 

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 nonresidential 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-bystate 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

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. 

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 


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23430 Federal Register / Vol. 87, No. 76 / Wednesday, April 20, 2022 / Rules and Regulations 

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.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. 

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. 

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. 

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

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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