TECHNICAL SUPPORT DOCUMENT:

ENERGY EFFICIENCY PROGRAM

FOR CONSUMER PRODUCTS AND 

COMMERCIAL AND INDUSTRIAL EQUIPMENT:

ASHRAE Equipment

June 2015

U.S. Department of Energy

Assistant Secretary

Office of Energy Efficiency and Renewable Energy

Building Technologies Program

Appliances and Commercial Equipment Standards

Washington, DC 20585
This Document was prepared for the Department of Energy

by staff members of

Navigant Consulting, Inc.

and

Lawrence Berkeley National Laboratory
1-i

CHAPTER 1. INTRODUCTION

TABLE OF CONTENTS

1.1 PURPOSE OF THE DOCUMENT ................................................................................. 1-1

1.2 PROCESS FOR AMENDED ENERGY CONSERVATION STANDARDS FOR 

ASHRAE EQUIPMENT ................................................................................................. 1-1

1.3 STRUCTURE OF THIS DOCUMENT .......................................................................... 1-3
1-1

CHAPTER 1. INTRODUCTION

1.1 PURPOSE OF THE DOCUMENT

This technical support document (TSD) is a stand-alone report that documents the 

technical analyses and results supporting the information presented in the Final Rule for 

establishing amended energy conservation standards for certain commercial and industrial 

equipment covered by the American National Standards Institute (ANSI)/ American Society of 

Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE)/ Illuminating Engineering 

Society of North America (IESNA) Standard 90.1 2013 Energy Standard for Buildings Except 

Low-Rise Residential Buildings (ASHRAE Standard 90.1 2013).

1.2 PROCESS FOR AMENDED ENERGY CONSERVATION STANDARDS FOR 

ASHRAE EQUIPMENT

Title III of the Energy Policy and Conservation Act (EPCA), Pub. L. 94-163, as 

amended, sets forth a variety of provisions concerning energy efficiency. Part Ca of Title III 

created the energy conservation program for “Certain Industrial Equipment.” (42 U.S.C. 6311-

6317). Of particular relevance for this rulemaking is 42 U.S.C 6313(a)(6) which directs DOE, in 

the event that ASHRAE Standard 90.1 is amended for a certain type of commercial and 

industrial equipment, to adopt that efficiency level unless clear and convincing evidence supports 

a determination that adopting a more stringent level would produce significant additional energy 

savings and is technologically feasible and economically justified. 

On October 9, 2013, American Society of Heating, Refrigeration and Air-Conditioning 

Engineers (ASHRAE) officially released the ANSI/ASHRAE/IESNA Standard 90.1-2013

(hereinafter referred to as ASHRAE Standard 90.1-2013), which addressed efficiency levels for 

certain categories of commercial heating, ventilating, and air-conditioning (HVAC) and water 

heating equipment covered by EPCA. The new ASHRAE Standard 90.1 revised the efficiency 

levels of the previous ASHRAE Standard 90.1-2010 for certain equipment, including small 

three-phase commercial air-cooled air conditioners (single package only) and heat pumps (single 

package and split system) less than 65,000 Btu/h; water-source heat pumps; oil-fired storage 

water heaters; single package vertical units; and packaged terminal air conditioners. 

Pursuant to the EISA 2007 amendments to EPCA, under 42 U.S.C. 6313(a)(6)(C), the 

agency must periodically review its already-established energy conservation standards for 

ASHRAE equipment. In December 2012, this provision was further amended by the American 

Energy Manufacturing Technical Corrections Act (AEMTCA) to clarify that DOE’s periodic 

 a This Part was subsequently redesignated as Part A-1 for editorial reasons after Part C of Title III of EPCA was 

repealed by Pub. L. 109-58.
1-2

review of ASHRAE equipment must occur “[e]very six years.” (42 U.S.C. 6313(a)(6)(C)(i))

DOE was triggered by ASHRAE for 3-phase air-cooled single package air conditioners less than 

65,000 Btu/h, but not the split system variant, even though both types of units were included in a 

more comprehensive DOE equipment class. DOE is acting to prevent confusion by proposing to 

re-create separate product classes for the two types of systems, and DOE has decided to now 

consider amended standards for 3-phase air-cooled split system air conditioners less than 65,000 

Btu/h under this 6-year lookback authority. 

EPCA also requires DOE to amend the test procedures for certain ASHRAE equipment, 

which manufacturers are required to use in order to certify compliance with energy conservation 

standards mandated under EPCA, to be consistent with the amended industry test procedure in 

ASHRAE Standard 90.1 unless clear and convincing evidence suggests that the test procedure 

does not reflect the energy efficiency, energy use, and estimated operating costs of the equipment 

and is unduly burdensome to conduct (42 U.S.C 6314(a)(2)-(4)). Additionally, EISA 2007 

amended EPCA to require that at least once every 7 years, DOE must conduct an evaluation of 

the test procedures for all covered equipment and either amend test procedures (if the Secretary 

determines that amended test procedures would more accurately or fully comply with the 

requirements of 42 U.S.C. 6314(a)(2)-(3)) or publish notice in the Federal Register of any 

determination not to amend a test procedure. (42 U.S.C. 6314(a)(1)(A))

DOE has already amended its test procedures to adopt most changes in industry test 

procedures specified by ASHRAE 90.1-2013 that affect DOE’s test procedures. However, DOE 

is proposing to incorporate by reference ANSI (American National Standards Institute) Z21.47-

2012, Standard for Gas-Fired Central Furnace, for its commercial warm-air furnace test 

procedure as specified by ASHRAE 90.1-2013. In addition, DOE is proposing to update the 

citations and incorporations by reference in DOE’s regulations for commercial warm-air 

furnaces to the most recent version of ASHRAE 103-2007, Method of Testing for Annual Fuel 

Utilization Efficiency of Residential Central Furnaces and Boiler, under the 7 year look back.

On April 11, 2014, DOE published a notice of data availability (April 2014 NODA) and 

request for public comment in the Federal Register as a preliminary step pursuant to EPCA’s 

requirements for DOE to consider amended energy conservation standards for certain types of 

commercial equipment covered by ASHRAE Standard 90.1. 79 FR 20114. Specifically, the April 

2014 NODA presented for public comment DOE’s analysis of the potential energy savings 

estimates for amended national energy conservation standards for types of commercial 

equipment based on: (1) the modified efficiency levels contained within ASHRAE Standard 

90.1-2013; and (2) more-stringent efficiency levels. DOE has described these analyses and 

preliminary conclusions and sought input from interested parties, including the submission of 

data and other relevant information.
1-3

After receiving public comment on the April 2014 NODA, DOE revised its analysis for 

certain of the ASHRAE equipment under review (i.e., small three-phase commercial air-cooled 

air conditioners and heat pumps with less than 65,000 Btu/h cooling capacity and water-source 

heat pumps with less than 135,000 Btu/h cooling capacity). For oil-fired storage water heaters, 

DOE determined that the potential for energy savings from adopting more-stringent levels than 

the ASHRAE Standard 90.1 levels was not significant, and, thus, DOE is proposing to adopt the 

ASHRAE Standard 90.1 levels without further analysis. For single package vertical units and 

packaged terminal air conditioners, DOE is performing economic analyses in separate 

rulemakings that were previously ongoing.b

On January 8, 2015, DOE published a notice of proposed rulemaking (January 2015 

NOPR) and request for public comment in the Federal Register. 80 FR 1172. In the January 

2015 NOPR, DOE addressed, in detail, the comments received in earlier stages of the 

rulemaking, and proposed to adopt energy conservation standards for small three-phase 

commercial air-cooled air conditioners and heat pumps with less than 65,000 Btu/h cooling 

capacity and water-source heat pumps with less than 135,000 Btu/h cooling capacity, that were 

the same as the levels in ASHRAE 90.1-2013. In the NODA, DOE did not conduct analysis on 

split system air conditioners because ASHRAE did not propose more stringent standards for that 

equipment. After reviewing comments in response to the NODA, DOE decided to review split 

system air conditioners under the 6 year look back in the NOPR.

DOE held a public meeting on February 6, 2015 to discuss the methodologies and results 

of the analyses set forth in the NOPR. Interested parties provided comments on the analysis. Key 

comments from stakeholders included: (1) support of DOE’s proposal to adopt the efficiency 

levels in ASHRAE 90.1-2013, (2) adopting higher standards for water-source heat pumps 

between 17,000 btu/h and 65,000 btu/h, and (3) revising the energy use analysis to account for 

oversizing of equipment. 

The assumptions and analysis results for small three-phase commercial air cooled air 

conditioners and heat pumps and water-source heat pumps are presented in this Final Rule TSD. 

A list of the analyses and the corresponding chapter number is presented in the next section.

1.3 STRUCTURE OF THIS DOCUMENT

This Final Rule TSD outlines the analytical approaches used in this rulemaking and 

presents the analysis results. The TSD consists of 11 chapters with appendices:

 b See Packaged Terminal Air Conditioners and Heat Pumps Standards Rulemaking webpage:

www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/64 and Single Package Vertical Air 

Conditioners and Heat Pumps Standards Rulemaking webpage: 

www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx?ruleid=107.
1-4

Chapter 1 Introduction: Provides an overview of the process to amend energy 

conservation standards for ASHRAE equipment and outlines the 

structure of the document. 

Chapter 2 Market Assessment: Characterizes the market for small three-phase 

commercial air-cooled air conditioners and heat pumps, and watersource heat pumps.

Chapter 3 Engineering Analysis: Discusses the methods used for developing the 

manufacturer cost-efficiency relationship for small three-phase 

commercial air-cooled air conditioners and heat pumps, and watersource heat pumps.

Chapter 4 Energy Use Analysis: Discusses the process used for generating energy 

use estimates for small three-phase commercial air-cooled air 

conditioners and heat pumps, and water-source heat pumps at the 

considered efficiency levels.

Chapter 5 Markups for Product Price Determination: Discusses the methods used 

for establishing markups for converting manufacturer prices to customer 

equipment prices

Appendix 5A Detailed Data for Equipment Price Markups

Chapter 6 Life-Cycle Cost and Payback Period Analysis: Discusses the economic 

effects of standards on commercial customers and users of the 

equipment and compares the LCC and PBP of equipment with and 

without higher efficiency standards.

Chapter 7 Shipments: Discusses the methods used for projecting shipments with 

and without higher efficiency standards.

Chapter 8 National Impact Analysis: Discusses the methods used for projecting 

national energy savings and national economic impacts based on annual 

equipment shipments and estimates of future equipment efficiency 

distributions in the absence and presence of higher energy efficiency 

standards.

Appendix 8A Full Fuel Cycle
1-5

Appendix 8B National Net Present Value of Customer Benefits Using Alternative 

Equipment Price Projections and Economic Growth Scenarios

Chapter 9 Emissions Analysis: Discusses the environmental effect of amended 

energy conservation standards, including the impact on emissions of six 

pollutants or greenhouse gases: sulfur dioxide (SO2), nitrogen oxides 

(NOx), carbon dioxide (CO2), mercury (Hg), methane (CH4), and 

nitrous oxide (N2O).

Chapter 10 Monetization of Emission Reductions Benefits: Discusses the 

monetization of reductions in CO2 and NOX emissions.

Appendix 10A Social Cost of Carbon for Regulatory Impact Analysis Under Executive 

Order 12866

Appendix 10B Technical Update of Social Cost of Carbon for Regulatory Impact 

Analysis Under Executive Order 12866

Chapter 11 Utility Impact Analysis: Discusses the effects of standards on the 

installed generation capacity of electric utilities
2-i

CHAPTER 2. MARKET ASSESSMENT

TABLE OF CONTENTS 

2.1 BACKGROUND ............................................................................................................. 2-1

2.1.1Three-Phase Air-Cooled Commercial Air-Conditioning and Heating Equipment....... 2-1

2.1.2 Water-Source Heat Pumps................................................................................... 2-2

2.1.3 Federal Energy Conservation Standards.............................................................. 2-2

2.1.4 ASHRAE Standard 90.1-2013 Efficiency Levels................................................ 2-3

2.2 MARKET ASSESSMENT.............................................................................................. 2-4

2.2.1 Trade Associations............................................................................................... 2-4

2.2.2 Manufacturers...................................................................................................... 2-5

2.2.3 Market Performance Data .................................................................................... 2-7

2.2.3.1 Small Three-Phase Commercial Air-Cooled Air Conditioners and 

Heat Pumps ................................................................................................ 2-7

2.2.3.2 Water-Source Heat Pumps....................................................................... 2-12

REFERENCES .......................................................................................................................... 2-17

LIST OF TABLES 

Table 2.1.1 Current Federal Energy Conservation Standards for Small Three-Phase 

Commercial Air-Cooled Air-Conditioning and Heating Equipment and 

Water-Source Heat Pumps............................................................................. 2-3

Table 2.1.2 Equipment Classes with Updated Efficiency Levels in ASHRAE 

Standard 90.1-2013........................................................................................ 2-4

Table 2.2.1 Manufacturers of Small Three-Phase Commercial Air-Cooled Air 

Conditioners and Heat Pumps <65,000 Btu/h................................................ 2-6

Table 2.2.2 Manufacturers of Commercial Water-Source Heat Pumps ........................... 2-7

LIST OF FIGURES 

Figure 2.2.1 SEER Histogram of Small Three-Phase Commercial Air-Cooled 

Single Package Air Conditioners <65,000 Btu/h........................................... 2-8

Figure 2.2.2 SEER Histogram of Small Three-Phase Commercial Air-Cooled Split 

System Air Conditioners < 65,000 Btu/h....................................................... 2-9

Figure 2.2.3 SEER Histogram of Small Three-Phase Commercial Air-Cooled 

Single Package Heat Pumps <65,000 Btu/h ................................................ 2-10

Figure 2.2.4 HSPF Histogram of Small Three-Phase Commercial Air-Cooled 

Single Package Heat Pumps <65,000 Btu/h ................................................ 2-10

Figure 2.2.5 SEER Histogram of Small Three-Phase Commercial Air-Cooled Split 

System Heat Pumps <65,000 Btu/h ............................................................. 2-11
2-ii

Figure 2.2.6 HSPF Histogram of Small Three-Phase Commercial Air-Cooled Split 

System Heat Pumps <65,000 Btu/h ............................................................. 2-12

Figure 2.2.7 EER Histogram of Water-Source Heat Pumps <17,000 Btu/h.................... 2-13

Figure 2.2.8 COP Histogram of Water-Source Heat Pumps <17,000 Btu/h.................... 2-13

Figure 2.2.9 EER Histogram of Water-Source Heat Pumps ≥17,000 – <65,000 

Btu/h............................................................................................................. 2-14

Figure 2.2.10 COP Histogram of Water-Source Heat Pumps ≥17,000 – <65,000 

Btu/h............................................................................................................. 2-15

Figure 2.2.11 EER Histogram of Water-Source Heat Pumps ≥65,000 Btu/h –

<135,00 Btu/h .............................................................................................. 2-16

Figure 2.2.12 COP Histogram of Water-Source Heat Pumps ≥65,000 Btu/h –

<135,00 Btu/h .............................................................................................. 2-16
2-1

CHAPTER 2. MARKET ASSESSMENT

2.1 BACKGROUND

The American Society of Heating, Refrigeration, and Air-Conditioning Engineers 

(ASHRAE) released an updated version of the American National Standards Institute 

(ANSI)/ASHRAE/Illuminating Engineering Society (IES) Standard 90.1 (hereinafter referred to 

as ASHRAE Standard 90.1-2013) on October 9, 2013. ASHRAE Standard 90.1-2013 included 

increased efficiency levels for small three-phase commercial air-cooled air-conditioning and 

heating equipment with less than 65,000 Btu/h cooling capacity and water-source heat pumps

(triggering a review by the U.S. Department of Energy (DOE) of the minimum standards for 

these equipment classes).

2.1.1 Three-Phase Air-Cooled Commercial Air-Conditioning and Heating Equipment

Three-phase air-cooled air conditioners and heat pumps are part of a subset of 

commercial package air-conditioning and heating equipment. The Energy Policy and 

Conservation Act (EPCA) defines “commercial package air conditioning and heating equipment” 

as “air-cooled, water-cooled, evaporatively-cooled, or water-source (not including ground watersource) electrically operated, unitary central air conditioners and central air conditioning heat 

pumps for commercial application.” (42 U.S.C. 6311(8)(A)) 

Air conditioners use a refrigeration cycle to cool an indoor space by moving heat from 

the cold location to a hotter location by means of evaporating and condensing a fluid, typically a 

refrigerant. An air conditioner typically has four major components: a compressor, condenser

coil, expansion valve, and evaporator coil. The compressor turns the refrigerant from a low 

pressure gas into a high pressure gas, which raises the temperature of the gas. At the next step, 

the condenser coil convects the heat from the hot refrigerant, which condenses into a liquid, still 

at a high temperature and high pressure. This liquid refrigerant then goes through an expansion 

valve that lowers its pressure and temperature before entering the evaporator coil. The evaporator

coil cools the indoor air by transferring heat from the indoor air to the low temperature 

refrigerant. Heat pumps can reverse the refrigeration cycle to draw heat from cooler external air 

and release it into the indoor space to heat it.

The Federal energy conservation standards for air-cooled air conditioners and heat pumps 

are differentiated based on the cooling capacity (i.e., small, large, or very large). For small 

equipment, there is an additional disaggregation into: (1) equipment less than 65,000 Btu/h and 

(2) equipment greater than or equal to 65,000 Btu/h and less than 135,000 Btu/h. ASHRAE 

Standard 90.1-2013 also differentiates the equipment that is less than 65,000 Btu/h into split 

system and single package subcategories. In the past, DOE has followed the same 

disaggregation. However, when the Energy Independence and Security Act (EISA) of 2007 

increased the efficiency levels to identical levels across single package and split system 

equipment, effective in 2008, DOE combined the equipment classes in the Code of Federal 

Regulations (CFR), resulting in only two equipment classes, one for air conditioners and one for 

heat pumps. Because ASHRAE has increased the standard for only single package air 

conditioners, and has increased the heating seasonal performance factor (HSPF) level to a more 
2-2

stringent level for split system heat pumps than for single package heat pumps, and DOE is 

obligated to adopt, at a minimum, the increased level in ASHRAE 90.1-2013 for that equipment 

class, DOE re-created separate equipment classes for single package and split system equipment 

in the overall equipment classes of small commercial package air conditioners and heat pumps 

(three-phase air-cooled) less than 65,000 Btu/h. DOE reviewed standards for single package 

equipment under the ASHRAE trigger (42 U.S.C 6313(a)(6)(A)) and split system equipment 

under the 6 year look back provision (42 U.S.C. 6313(a)(6)(C)(i)), as described in Chapter 1.

2.1.2 Water-Source Heat Pumps

Water source heat pumps are also part of a subset of commercial package airconditioning and heating equipment as defined by EPCA. However, EPCA does not further 

define this equipment.

According to the ASHRAE Handbook, “A water-source heat pump is a single-package 

reverse-cycle heat pump that uses water as the heat source for heating and as the heat sink for 

cooling. The main components are a compressor, refrigerant-to-water heat exchanger, 

refrigerant-to-air heat exchanger, refrigerant expansion devices, and refrigerant reversing valve. 

....A water-loop heat pump uses a circulating water loop as the heat source and heat sink. When 

loop water temperature exceeds a certain level during cooling, a cooling tower dissipates heat 

from the water loop into the atmosphere. When loop water temperature drops below a prescribed 

level during heating, heat is added to the circulating loop water, usually with a boiler.”

1

Decentralized systems using multiple water source heat pumps on a shared circulating water loop 

are common. Water-loop heat pumps systems are popular in multi-room buildings such as office 

buildings, where heat gains can be redistributed from interior to perimeter zones. Water-source 

heat pumps are manufactured in both horizontal and vertical configurations.1

EPCA and ASHRAE Standard 90.1-2013 both further divide this equipment into three 

categories based on cooling capacity ranges: (1) <17,000 Btu/h; (2) ≥17,000 and <65,000 Btu/h; 

and (3) ≥65,000 and <135,000 Btu/h. 

ASHRAE Standard 90.1-2013 changed the name of this equipment class from “watersource” to “water to air, water loop.” DOE believes this to be an editorial change only and that 

this new nomenclature refers to the same water-source heat pump equipment covered by Federal 

energy conservation standards. ASHRAE also changed the descriptor for this equipment from 

COP to COPH. DOE believes this is also an editorial change to clarify the difference between 

COP for refrigeration and COP for heat pumps.

2.1.3 Federal Energy Conservation Standards

On January 12, 2001, DOE published in the Federal Register a final rule promulgating 

the current standards for all three equipment classes of water source heat pumps relevant to this 

rulemaking. 66 FR 3336. This final rule adopted the energy conservation standards set by 

ASHRAE Standard 90.1-1999. 

The existing energy conservation standards for all classes of air-cooled three-phase air 

conditioners and heat pumps less than 65,000 Btu/h were established by the EISA 2007 
2-3

amendments to EPCA. These standards were codified by DOE in a Technical Amendment 

published in the Federal Register on March 23, 2009. 74 FR 12058. 

Table 2.1.1 shows the current Federal energy conservation levels for equipment classes 

where ASHRAE Standard 90.1-2013 increased the standard levels in comparison to the Federal 

levels. 

Table 2.1.1 Current Federal Energy Conservation Standards for Small Three-Phase 

Commercial Air-Cooled Air-Conditioning and Heating Equipment and 

Water-Source Heat Pumps

Equipment Type Cooling Capacity Subcategory Federal Standard

Small Commercial 

Packaged AirConditioning and Heating 

Equipment, Air-cooled 3-

Phase

<65,000 Btu/h

AC 13.0 SEER

HP 13.0 SEER

7.7 HSPF

Small Commercial 

Packaged Air 

Conditioning and Heating 

Equipment, Water Source

<17,000 Btu/h HP 11.2 EER

4.2 COP

≥17,000 Btu/h and <65,000 Btu/h HP 12.0 EER

4.2 COP

≥65,000 Btu/h and <135,000 Btu/h HP 12.0 EER

4.2 COP

2.1.4 ASHRAE Standard 90.1-2013 Efficiency Levels

Table 2.1.2 shows the ASHRAE Standard 90.1-2013 efficiency levels for the updated 

equipment classes along with the currently applicable Federal minimum efficiency standards for 

comparison.

ASHRAE Standard 90.1-2013 raised the efficiency levels in comparison to the current 

Federal minimum energy conservation standards for three equipment classes of small threephase commercial air-cooled air-conditioners and heat pumps and three equipment classes of 

water-source heat pump. DOE assessed the current market for each equipment class. DOE also 

assessed the current market for split system air conditioners even though ASHRAE Standard 

90.1-2013 did not raise the efficiency level for that equipment. The market assessment for all 

equipment classes is described in the sections that follow.
2-4

Table 2.1.2 Equipment Classes with Updated Efficiency Levels in ASHRAE Standard 

90.1-2013

ASHRAE Equipment Type Size Category Subcategory ASHRAE Standard 

90.1-2013

Federal 

Minimum 

Energy 

Conservation 

Standards

Air conditioners, air-cooled <65,000 Btu/h

Split System 13.0 SEER* 13.0 SEER

Single package 14.0 SEER 

(as of 1/1/2015) 13.0 SEER

Heat pumps, air-cooled <65,000 Btu/h

Split System

14.0 SEER 

8.2 HSPF 

(as of 1/1/2015)

13.0 SEER

7.7 HSPF

Single package

14.0 SEER 

8.0 HSPF 

(as of 1/1/2015)

13.0 SEER

7.7 HSPF

Heat pumps, water to air, 

water loop

<17,000 Btu/h N/A 12.2 EER

4.3 COP

11.2 EER

4.2 COP

≥17,000 and 

<65,000Btu/h N/A 13.0 EER4.3 COP

12.0 EER

4.2 COP

≥65,000 and 

<135,000Btu/h N/A 13.0 EER4.3 COP

12.0 EER

4.2 COP

*This efficiency level was not updated in ASHRAE Standard 90.1-2013 but is included here as it is part of the same 

equipment class in the current Federal standards.

2.2 MARKET ASSESSMENT

The following market assessment identifies the manufacturer trade associations and 

domestic and international manufacturers of small three-phase commercial air conditioners and 

heat pumps, and water-source heat pumps. The market assessment also summarizes the relevant 

market performance data for each equipment class where such data are available.

2.2.1 Trade Associations

DOE researched various trade groups who represent manufacturers, distributors, and 

installers of the various types of equipment being analyzed in this rulemaking. The AirConditioning, Heating, and Refrigeration Institute (AHRI) is one of the largest trade associations 

for manufacturers of space-heating, cooling, and water-heating equipment, representing more 

than 90% of the residential and commercial air-conditioning, space-heating, water-heating, and 

commercial refrigeration equipment manufactured in the United States.

2 AHRI also develops and 

publishes test procedures standards for residential and commercial heating, ventilating, and airconditioning (HVAC) equipment and coordinates with the International Organization for 

Standardization (ISO) to help harmonize U.S. standards with international standards, if feasible. 

AHRI also maintains the AHRI Directory of Certified Product Performance that lists all the 

products and equipment that have been certified by AHRI. 
2-5

Heating, Air-conditioning and Refrigeration Distributors International (HARDI) is a trade 

association that represents over 450 wholesale heating, ventilating, air-conditioning, and 

refrigeration (HVACR) companies plus over 300 manufacturing associates and nearly 140 

manufacturing representatives. HARDI estimates that 80% of the revenue of HVACR systems 

goes through its members.3

Air Conditioning Contractors of America (ACCA) is another trade association whose 

members include over 4,000 contractors and 60,000 professionals. ACCA provides contractors 

technical, legal, and market resources, helping to promote good practices and to keep buildings 

safe, clean, and affordable.4

2.2.2 Manufacturers

DOE identified 23 parent companies (comprising 61 manufacturers) of small three-phase 

air-cooled air conditioners and heat pumps and 18 parent companies (comprising 21

manufacturers) of water-source heat pumps. The manufacturers for each equipment class are 

listed in Table 2.2.1and Table 2.2.2.
2-6

Table 2.2.1 Manufacturers of Small Three-Phase Commercial Air-Cooled Air 

Conditioners and Heat Pumps <65,000 Btu/h

Aaon, Inc. Eair Llc Johnson Controls

• Coleman

• Evcon 

• Fraser

• Guardian

• Johnson Controls

• Luxaire 

• York

Nordyne

• Broan 

• Frigidaire 

• Gibson 

• Kelvinator 

• Mammoth, Inc.

• Maytag 

• Miller

• Nordyne 

• Nutone 

• Philco 

• Tappan

• Westinghouse

Addison Ecotemp Kenmore Rheem Sales Company, Inc

• Rheem Manufacturing

• United Refrigeration Inc –

Thermal Zone

Aerosys Gd Midea Heating & 

Ventilating Equipment 

Co., Ltd.

Klimaire Products Inc. United Cool Air

Air Green Corporation Heat Controller, Inc. Lennox International

• Airease

• Armstrong Air 

Conditioning, Inc

• Concord

• Ducane

• Lennox Industries, Inc.

United Technologies 

Corporation

• Airquest

• Arcoaire 

• Bryant Heating And 

Cooling Systems

• Carrier Air Conditioning

• Comfortmaker

• Day & Night

• Grandaire

• Heil 

• Icp Commercial

• Keeprite 

• Maratherm 

• Payne Heating And 

Cooling 

• Tempstar

Bard Manufacturing 

Company

Ingersoll-Rand

• American Standard

• Trane

Msh Legacy – Maintenance 

Supply Headquarters LP

Xenon

Daikin Industries

• AAF International

• Daikin

• Daikin Americas

• Goodman 

Manufacturing

Innovair Zamil Air Conditioners & 

Home Appliances Co. 

(L.L.C.) – Zamil Industrial
2-7

Table 2.2.2 Manufacturers of Commercial Water-Source Heat Pumps

Addison

Bard Manufacturing Company

Climate Master, Inc.

Coldflo Inc.

Cold Point Corporation

Daikin

• McQuay International

Enertech Global,Llc.

Robert Bosch GMBH

• Fhp Manufacturing

First Operations Lp, Dba First Co.

Geomaster, Llc.

Geosmart Energy Inc.

Ingersoll-Rand

• American Standard

• Trane

Johnson Controls, Inc.

• Johnson Controls

• York

Rheem Manufacturing

United Cool Air

United Technologies Corporation

Carrier Air Conditioning

Waterfurnace International, Inc.

• Geostar

• Waterfurnace

The Whalen Company

2.2.3 Market Performance Data

For each equipment class analyzed, DOE gathered market data to characterize the 

efficiency and performance of models currently on the market. DOE reviewed data for small 

three-phase commercial air-cooled air conditioners and heat pumps, and water-source heat 

pumps currently on the market by examining the AHRI Directory of Certified Product 

Performance.

5

2.2.3.1 Small Three-Phase Commercial Air-Cooled Air Conditioners and Heat 

Pumps

Single Package Air Conditioners. Small three-phase commercial air-cooled single 

package air conditioners have a cooling capacity of less than 65,000 Btu/h and include the 
2-8

condenser and evaporator in the same cabinet. The Federal energy conservation standard for this 

equipment is 13.0 SEER (seasonal energy efficiency ratio) (10 CFR 431.97). ASHRAE Standard

90.1-2013 changes the efficiency rating to 14.0 SEER as of January 1, 2015. Figure 2.2.1 shows 

the distribution of SEER values for this equipment. The average SEER value for this equipment 

class is 14.3. Of the models identified by DOE, 1,450 (45 percent of the total models) have

SEERs rated below the ASHRAE Standard 90.1-2013 levels. 

Figure 2.2.1 SEER Histogram of Small Three-Phase Commercial Air-Cooled Single 

Package Air Conditioners <65,000 Btu/h

Split System Air Conditioners. Small three-phase commercial air-cooled split system

air conditioners have a cooling capacity of less than 65,000 Btu/h. The outdoor cabinet contains

the condenser and the indoor cabinet contains the evaporator. The Federal energy conservation 

standard for this equipment is 13.0 SEER (seasonal energy efficiency ratio) (10 CFR 431.97). 

ASHRAE Standard 90.1-2013 did not change the efficiency rating for this equipment. Figure 

2.2.2 shows the distribution of SEER values for this equipment. The average SEER value for 

this equipment class is 13.85. 

0

200

400

600

800

1000

1200

13 13.1 -

13.5

13.6 -

14

14.1 -

14.5

15.6 -

15

15.1 -

15.5

15.6 -

16

16.1 -

16.5

16.6 -

17

17.1 -

17.5

17.6 -

18

18.1 -

18.5

18.6 -

19.15

Frequency

SEER
2-9

Figure 2.2.2 SEER Histogram of Small Three-Phase Commercial Air-Cooled Split System 

Air Conditioners < 65,000 Btu/h

Single Package Heat Pumps. Small three-phase commercial air-cooled single package 

heat pumps have a cooling capacity of less than 65,000 Btu/h and include the condenser and 

evaporator in the same cabinet. The Federal energy conservation standard for this equipment is 

13.0 SEER and 7.7 HSPF (10 CFR 431.97). ASHRAE Standard 90.1-2013 changes the 

efficiency ratings to 14.0 SEER and 8.0 HSPF as of January 1, 2015. Figure 2.2.3 and Figure 

2.2.4 show the distributions of SEER and HSPF values for this equipment. The average SEER 

value for this equipment class is 14.0. Of the models identified by DOE, 653 (54 percent of the 

total models) have SEERs rated below the ASHRAE Standard 90.1-2013 levels. The average 

HSPF value for this equipment class is 7.9. Of the models identified by DOE, 632 (52% of the 

total models) have HSPFs rated below the ASHRAE Standard 90.1-2013 levels.

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

13 13.1 -

13.5

13.6 -

14

14.1 -

14.5

14.6 -

15

15.1 -

15.5

15.6 -

16

16.1 -

16.5

16.6 -

17

17.1 -

17.5

17.6 -

18

18.1 -

18.5

18.6 -

19.15

Frequency

SEER
2-10

Figure 2.2.3 SEER Histogram of Small Three-Phase Commercial Air-Cooled Single 

Package Heat Pumps <65,000 Btu/h

Figure 2.2.4 HSPF Histogram of Small Three-Phase Commercial Air-Cooled Single 

Package Heat Pumps <65,000 Btu/h

Split System Heat Pumps. Small three-phase commercial air-cooled split system heat 

pumps have a cooling capacity of less than 65,000 Btu/h. The outdoor cabinet contains the 

compressor and condenser, while the indoor cabinet contains the evaporator. The Federal energy 

conservation standard for this equipment is 13.0 SEER and 7.7 HSPF (10 CFR 431.97). 

0

50

100

150

200

250

300

350

400

13 13. 1 - 14 14.1 - 15 15.1 - 16 16.1 - 17 17.1 - 18.05

Frequency

SEER

0

100

200

300

400

500

600

700

7.7 7.75 - 8 8.05 - 8.3 8.35 - 8.6 8.65 - 8.9 8.95 - 9.2 9.25 - 9.3

Frequency

HSPF
2-11

ASHRAE Standard 90.1-2013 changes the efficiency ratings to 14.0 SEER and 8.2 HSPF as of 

January 1, 2015. Figure 2.2.5 and Figure 2.2.6 show the distributions of SEER and HSPF values 

for this equipment. The average SEER value for this equipment class is 13.7. Of the models 

identified by DOE, 30,009 (64 percent of the total models) have SEERs rated below the 

ASHRAE Standard 90.1-2013 levels. The average HSPF for this equipment class is 7.9. Of the 

models identified by DOE, 36,902 (79% of the total models) have HSPFs rated below the 

ASHRAE Standard 90.1-2013 levels.

Figure 2.2.5 SEER Histogram of Small Three-Phase Commercial Air-Cooled Split System 

Heat Pumps <65,000 Btu/h

0

2000

4000

6000

8000

10000

12000

14000

16000

13 13.1 - 13.5 13.6 - 14 14.1 - 14.5 14.6 - 15 15.1 - 15.5 15.6 - 16.2

Frequency

SEER
2-12

Figure 2.2.6 HSPF Histogram of Small Three-Phase Commercial Air-Cooled Split System 

Heat Pumps <65,000 Btu/h

2.2.3.2 Water-Source Heat Pumps

Water-Source Heat Pumps <17,000 Btu/h. Small water-source heat pumps are 

individual packaged units that transfer heat using a water loop. The Federal energy conservation 

standards for water-source heat pumps with a cooling capacity less than 17,000 Btu/h are 11.2 

EER (energy efficiency ratio) and 4.2 COP (coefficient of performance) (10 CFR 431.97). 

ASHRAE Standard 90.1-2013 changes the efficiency ratings to 12.0 EER and 4.3 COP. Figure 

2.2.7 and Figure 2.2.8 show the distribution of EERs and COPs for this equipment. The average 

EER value for this equipment class is 13.8. Of the models identified by DOE, 34 (six percent of 

the total models) have EERs rated below the ASHRAE Standard 90.1-2013 levels. The average 

COP value for this equipment class is 4.7. Of the models identified by DOE, 30 (five percent of 

the total models) have COPs rated below the ASHRAE Standard 90.1-2013 levels.

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

7.7 7.75 -

7.95

8.0 - 8.2 8.25 -

8.45

8.5 - 8.7 8.75 -

8.95

9.0 - 9.2 9.25 -

9.45

9.5 - 9.7

Frequency

HSPF
2-13

Figure 2.2.7 EER Histogram of Water-Source Heat Pumps <17,000 Btu/h

Figure 2.2.8 COP Histogram of Water-Source Heat Pumps <17,000 Btu/h

Water-Source Heat Pumps ≥17,000 – <65,000 Btu/h. The Federal energy conservation 

standards for water-source heat pumps with a cooling capacity greater than or equal to 17,000 

0

20

40

60

80

100

120

140

160

180

11.2 11.25 - 12.2 12.3 - 13.2 13.3 - 14.2 14.3 - 15.2 15.3 - 16.2 16.3 - 17.2 17.3 - 18.1

Frequency

EER

0

10

20

30

40

50

60

70

80

Frequency

COP
2-14

Btu/h and less than 65,000 Btu/h are 12.0 EER and 4.2 COP (10 CFR 431.97). ASHRAE 

Standard 90.1-2013 changes the efficiency ratings to 13.0 EER and 4.3 COP. Figure 2.2.9 and 

Figure 2.2.10 show the distribution of EERs and COPs for this equipment. The average EER 

value for this equipment class is 15.2. Of the models identified by DOE, 72 (two percent of the 

total models) have EERs rated below the ASHRAE Standard 90.1-2013 levels. The average COP 

value for this equipment class is 4.9. Of the models identified by DOE, 133 (four percent of the 

total models) have COPs rated below the ASHRAE Standard 90.1-2013 levels.

Figure 2.2.9 EER Histogram of Water-Source Heat Pumps ≥17,000 – <65,000 Btu/h

0

100

200

300

400

500

600

700

800

900

12 12.1 -

13

13.1 -

14

14.1 -

15

15.1 -

16

16.1 -

17

17.1 -

18

18.1 -

19

19.1-

20

20.1 -

21

21.1 -

21.6

Frequency

EER
2-15

Figure 2.2.10 COP Histogram of Water-Source Heat Pumps ≥17,000 – <65,000 Btu/h

Water-Source Heat Pumps ≥65,000 Btu/h – <135,000 Btu/h. The Federal energy 

conservation standards for water-source heat pumps with a cooling capacity greater than or equal 

to 65,000 Btu/h and less than 135,000 Btu/h are 12.0 EER and 4.2 COP (10 CFR 431.97). 

ASHRAE Standard 90.1-2013 changes the efficiency ratings to 13.0 EER and 4.3 COP. Figure 

2.2.11 and Figure 2.2.12 show the distribution of EERs and COPs for this equipment. The 

average EER value for this equipment class is 14.7. Of the models identified by DOE, five (one

percent of the total models) have EERs rated below the ASHRAE Standard 90.1-2013 levels. 

The average COP value for this equipment class is 4.8. Of the models identified by DOE, two

(0.5 percent of the total models) have COPs rated below the ASHRAE Standard 90.1-2013

levels.

0

50

100

150

200

250

300

350

400

450

Frequency

COP
2-16

Figure 2.2.11 EER Histogram of Water-Source Heat Pumps ≥65,000 Btu/h – <135,00 Btu/h

Figure 2.2.12 COP Histogram of Water-Source Heat Pumps ≥65,000 Btu/h – <135,00 Btu/h

0

20

40

60

80

100

120

140

160

12 12.1 - 13 13.1 - 14 14.1 - 15 15.1 - 16 16.1 - 17 17.1 - 17.2

Frequency

EER

0

20

40

60

80

100

120

4.2 4.21 -

4.30

4.31 -

4.40

4.41 -

4.50

4.51 -

4.60

4.61 -

4.70

4.71 -

4.80

4.81-

4.90

4.91 -

5.00

5.01 -

5.10

5.11 -

5.20

5.21 -

5.30

5.31 -

5.40

5.41 -

5.50

5.51 -

5.60

Frequency

COP
2-17

REFERENCES

1 2012 ASHRAE Handbook, Heating, Ventilating, and Air-Conditioning Systems and 

Equipment. ASHRAE, Atlanta, GA. Chapters 9 and 49.

2 Air-Conditioning, Heating, and Refrigeration Institute Website. About Us. 2013. (Last accessed 

February 10, 2014.) <http://ahrinet.org/site/318/About-Us>

3 Heating, Air-conditioning & Refrigeration Distributors International Website. About HARDI. 

2014. (Last accessed February 10, 2014.) <www.hardinet.org/about-hardi-0>

4 Air Conditioning Contractors of America Website. About ACCA. 2014. (Last accessed 

February 10, 2014.) <www.acca.org/acca>

5 AHRI Directory of Certified Product Performance. 2013. (Last accessed November 11, 2013.) 

<www.ahridirectory.org>
3-i

CHAPTER 3. ENGINEERING ANALYSIS

TABLE OF CONTENTS

3.1 INTRODUCTION ........................................................................................................... 3-1

3.2 EQUIPMENT CLASSES ANALYZED.......................................................................... 3-1

3.3 EFFICIENCY LEVELS................................................................................................... 3-2

3.3.1 Baseline Units.......................................................................................................... 3-2

3.3.2 Identification of Efficiency Levels for Analysis...................................................... 3-3

3.4 METHODOLOGY OVERVIEW .................................................................................... 3-5

3.4.1 Physical Teardown Analysis.................................................................................... 3-7

3.4.2 Catalog Teardown Analysis..................................................................................... 3-7

3.4.3 Selection of Units..................................................................................................... 3-7

3.4.4 Generation of Bill of Materials and Cost Model ..................................................... 3-7

3.4.5 Manufacturer Markup .............................................................................................. 3-9

3.4.6 Shipping Costs for Small Three-Phase Commercial Air-Cooled Air Conditioners 

and Heat Pumps ................................................................................................................. 3-10

3.4.7 Shipping Costs for Water-Source Heat Pumps...................................................... 3-12

3.5 ENGINEERING ANALYSIS RESULTS...................................................................... 3-12

3.5.1 Unit Operating Weight for Small Three-Phase Commercial Air-Cooled Air 

Conditioners and Heat Pump Results ................................................................................ 3-12

3.5.2 Cost-Efficiency Results for Small Three-Phase Commercial Air Conditioners and 

Heat Pump Results............................................................................................................. 3-13

3.5.3 Unit Operating Weight for Water-Source Heat Pumps ......................................... 3-15

3.5.4 Cost-Efficiency Results for Water-Source Heat Pumps........................................ 3-15

LIST OF TABLES

Table 3.2.1 Equipment Classes and ASHRAE 90.1-2013 Efficiency Levels for Small 

Three-Phase Commercial Air-Cooled Air Conditioners and Heat Pumps 

<65,000 Btu/h ...................................................................................................... 3-2

Table 3.3.1 Baseline Efficiency Levels for Small Three-Phase Commercial AirCooled Air Conditioners and Heat Pumps <65,000 Btu/h................................... 3-3

Table 3.3.2 Baseline EER Efficiency Levels for Water-Source Heat Pumps......................... 3-3

Table 3.3.3 Efficiency Levels for Small Three-Phase Commercial Air-Cooled Air 

Conditioners and Heat Pumps <65,000 Btu/h...................................................... 3-4

Table 3.3.4 Efficiency Levels for Engineering Analysis of Water-Source Heat Pumps........ 3-5

Table 3.4.1 Major Manufacturing Processes .......................................................................... 3-8

Table 3.4.2 Manufacturer Shipping Costs for Three-Ton Split-System Three-Phase 

Commercial Air-Cooled Air Conditioners......................................................... 3-11

Table 3.4.3 Manufacturer Shipping Costs for Three-Ton Single-Package Three-Phase 

Commercial Air-Cooled Air Conditioners......................................................... 3-11
3-ii

Table 3.4.4 Manufacturer Shipping Costs for Three-Ton Split-System Three-Phase 

Commercial Air-Cooled Heat Pumps................................................................ 3-11

Table 3.4.5 Manufacturer Shipping Costs for Three-Ton Single-Package Three-Phase 

Commercial Air-Cooled Heat Pumps................................................................ 3-12

Table 3.4.6 Manufacturer Shipping Costs for Water-Source Heat Pumps........................... 3-12

Table 3.5.1 Weight-Efficiency Data for Three-Ton Three-Phase Commercial AirCooled Air Conditioners and Heat Pumps......................................................... 3-13

Table 3.5.2 Manufacturer Production Costs for Three-Ton Split-System Three-Phase 

Commercial Air-Cooled Air Conditioners......................................................... 3-14

Table 3.5.3 Manufacturer Production Costs for Three-Ton Single-Package ThreePhase Commercial Air-Cooled Air Conditioners.............................................. 3-14

Table 3.5.4 Manufacturer Production Costs for Three-Ton Split-System Three-Phase 

Commercial Air-Cooled Heat Pumps................................................................ 3-14

Table 3.5.5 Manufacturer Production Costs for Three-Ton Single-Package ThreePhase Commercial Air-Cooled Heat Pumps...................................................... 3-15

Table 3.5.6 Weight-Efficiency Data for Water-Source Heat Pumps.................................... 3-15

Table 3.5.7 Manufacturer Production Cost-Efficiency Data for Water-Source Heat 

Pumps................................................................................................................. 3-16

LIST OF FIGURES

Figure 3.4.1 Manufacturing Cost Assessment Stages................................................................. 3-8
3-1

CHAPTER 3. ENGINEERING ANALYSIS

3.1 INTRODUCTION

The engineering analysis establishes the relationship between the cost and efficiency of a 

piece of equipment DOE is evaluating for potential amended energy conservation standards. This 

relationship serves as the basis for cost-benefit calculations for individual consumers and the 

Nation. The engineering analysis identifies representative baseline equipment, which is the 

starting point for analyzing possible energy efficiency improvements. For covered ASHRAE 

equipment, DOE sets the baseline at the ASHRAE Standard 90.1 efficiency level, because by 

statute, DOE cannot adopt any level below the revised ASHRAE level. The engineering analysis 

then identifies higher efficiency levels and the incremental increase in product cost associated 

with achieving higher efficiency levels. After identifying the baseline models and cost of 

achieving increased efficiency, DOE estimates the additional costs to the customer through an 

analysis of contractor costs and markups, and uses that information in the downstream analyses 

to examine the costs and benefits associated with increased equipment efficiency. The 

downstream analyses include: the building energy-use and end-use load characterization (chapter 

4), life-cycle cost (LCC) and payback period (PBP) (chapter 6), shipment analysis (chapter 7), 

national impact analysis (chapter 8), emissions analysis (chapter 9), and utility impact analysis 

(chapter 11).

3.2 EQUIPMENT CLASSES ANALYZED

ASHRAE 90.1-2013 separates three-phase commercial air-cooled single-package and 

split-system air conditioners and heat pumps with a capacity less than 65,000 Btu/h into four 

equipment classes. Table 3.2.1 summarizes the four equipment classes and the efficiency levels

required by ASHRAE 90.1-2013. All four equipment classes are currently at the DOE standard 

level of 13.0 SEER.
3-2

Table 3.2.1 Equipment Classes and ASHRAE 90.1-2013 Efficiency Levels for Small 

Three-Phase Commercial Air-Cooled Air Conditioners and Heat Pumps 

<65,000 Btu/h 

Equipment 

Class Equipment Type Cooling Capacity

SubCategoryHeating 

Type Minimum Efficiency

1

Single-Package Three-Phase 

Commercial Air-Cooled Air 

Conditioning and Heating 

Equipment (Air-Cooled)

<65,000 Btu/h

AC All

13.0 SEER 

(before 1/1/2015)

14.0 SEER 

(as of 1/1/2015)

2 HP All

13.0 SEER and 7.7 HSPF

(before 1/1/2015)

14.0 SEER and 8.0 HSPF

(as of 1/1/2015)

3 Split-System Three-Phase 

Commercial Air-Cooled Air 

Conditioning and Heating 

Equipment (Air-Cooled)

<65,000 Btu/h

AC All 13.0 SEER

4 HP All

13.0 SEER and 7.7 HSPF

(before 1/1/2015)

14.0 SEER and 8.2 HSPF

(as of 1/1/2015)

ASHRAE 90.1-2013 also separates water-source heat pumps into three equipment classes 

based on cooling capacity. These three equipment classes are unchanged from those defined in

EPCA: (1) <17,000 Btu/h; (2) ≥17,000 and <65,000 Btu/h; and (3) ≥65,000 and <135,000 Btu/h.

3.3 EFFICIENCY LEVELS

3.3.1 Baseline Units 

DOE selected baseline efficiency levels as reference points for each equipment class, 

against which it measured changes resulting from potential amended energy conservation 

standards. DOE defined the baseline efficiency levels in the engineering analysis, as reference 

points to compare the technology, energy savings, and cost of equipment with higher energy 

efficiency levels. Typically, units at the baseline efficiency level just meet Federal energy 

conservation standards and provide basic consumer utility. However, EPCA requires that DOE 

must adopt either the ASHRAE Standard 90.1-2013 levels or more-stringent levels. Therefore, 

because the ASHRAE Standard 90.1-2013 levels were the lowest levels that DOE could adopt, 

DOE used those levels as the reference points against which more-stringent levels were 

evaluated. The current baseline and ASHRAE efficiency levels for each equipment class of small 

three-phase commercial air-cooled air conditioners and heat pumps <65,000 Btu/h are presented 

below in Table 3.3.1.
3-3

Table 3.3.1 Baseline Efficiency Levels for Small Three-Phase Commercial Air-Cooled 

Air Conditioners and Heat Pumps <65,000 Btu/h

DOE assigned the baseline units for water-source heat pumps as those just meeting the 

current federal standard. However, EPCA requires that DOE must adopt either the ASHRAE 

Standard 90.1-2013 levels or more stringent levels. Therefore, because the ASHRAE Standard 

90.1-2013 levels were the lowest levels that DOE could adopt, DOE used those levels as the 

baseline efficiency level for the purpose of assessing economic impacts of raising the standards 

above these required levels. Table 3.3.2 shows the baseline efficiency level for each water-source 

heat pump equipment class.

Table 3.3.2 Baseline EER Efficiency Levels for Water-Source Heat Pumps

Water-Source Heat 

Pumps

<17,000 Btu/h

Water-Source Heat 

Pumps

≥17,000 and 

<65,000 Btu/h

Water-Source Heat 

Pumps

≥65,000 and 

<135,000 Btu/h

Efficiency Level (EER)

Baseline - Current 

Federal Standard 11.2 12.0 12.0

Baseline -

ASHRAE Level 12.2 13.0 13.0

3.3.2 Identification of Efficiency Levels for Analysis 

DOE established intermediate energy efficiency levels for each equipment class. The 

intermediate efficiency levels are representative of efficiency levels along the technology paths 

SplitSystem ACSinglePackage ACSplitSystem HPSinglePackage HP

SEER

Baseline – Federal 

Standard 13.0 13.0 13.0 13.0

Baseline –

ASHRAE 

Standard

13.0 14.0 14.0 14.0

HSPF

Baseline – Federal 

Standard - - 7.7 7.7

Baseline –

ASHRAE 

Standard

- - 8.2 8.0
3-4

that manufacturers commonly use to maintain cost effective designs while increasing energy 

efficiency. DOE reviewed AHRI’s Directory of Certified Product Performance, manufacturer 

catalogs, and other publicly available literature to determine the most common efficiency levels 

for each representative equipment class. Then, DOE established intermediate energy efficiency 

levels for each of the equipment classes that are representative of efficiencies that are typically 

available on the market. 

For small three-phase commercial air-cooled air conditioners and heat pumps, DOE 

analyzed several efficiency levels and obtained incremental cost data for the four equipment

classes. Table 3.3.3 presents the efficiency levels for each equipment class. As part of the 

engineering analysis, DOE considered up to six efficiency levels beyond the baseline for each 

equipment class. DOE derived the “max-tech” level from the market maximum in the AHRI 

Certified Directory, as of November 2013. The highest available efficiency level for split-system 

heat pumps was 16.2, compared to 18.05 for single-package heat pumps. DOE believes that splitsystem heat pumps are capable of reaching the same efficiency level as single-package units 

because the technologies to increase efficiency can be employed in both equipment classes. As a 

result, the analyzed “max-tech” level for single-package and split-system heat pumps was 18.05. 

The “max-tech” level for single-package air conditioners was 19.15.1 DOE rounded the “maxtech” levels to integer values of 18 for split-system and single-package heat pumps, and 19 for 

split-system and single-package air conditioners, respectively. The impact of this rounding is 

minimal, and it ensures consideration of efficiency levels that are whole-number values of 

SEER.

Table 3.3.3 Efficiency Levels for Small Three-Phase Commercial Air-Cooled Air 

Conditioners and Heat Pumps <65,000 Btu/h

SplitSystem AC Split-System HP SinglePackage AC Single-Package HP

Efficiency Level SEER SEER HSPF SEER SEER HSPF

Baseline –

Federal Standard 13 13 7.7 13 13 7.7

0 – ASHRAE 

90.1 14 14 8.2 14 14 8

1 15 15 8.5 15 15 8.4

2 16 16 8.7 16 16 8.8

3 17 17 9 17 17 8.9

4 18 18 9.2 18 18 9.1

5 - Max Tech 19 - - 19 - -

For water-source heat pumps, five efficiency levels were chosen for the <17,000 Btu/h and 

17,000-65,0000 Btu/h classes, but only four efficiency levels were chosen for the 65,000-

135,000 Btu/h equipment class due to a small number of units on the market. The chosen levels 

range from the baseline to the max-tech level, which represents the most efficient unit currently 

 

1 www.gpo.gov/fdsys/pkg/FR-2014-04-11/pdf/2014-08214.pdf
3-5

on the market for each equipment class. Only EER levels for water-loop applications were 

considered in the analysis. The efficiency levels selected for analysis for each capacity class are 

shown below in Table 3.3.4.

Table 3.3.4 Efficiency Levels for Engineering Analysis of Water-Source Heat Pumps

Water-Source Heat 

Pumps

<17,000 Btu/h

Water-Source Heat 

Pumps

≥17,000 and 

<65,000 Btu/h

Water-Source Heat 

Pumps

≥65,000 and 

<135,000 Btu/h

Efficiency Level (EER)

Baseline - Current 

Federal Standard 11.2 12.0 12.0

Baseline -

ASHRAE Level (0) 12.2 13.0 13.0

Efficiency Level 1 13.0 14.6 14.0

Efficiency Level 2 14.0 16.6 15.0

Efficiency Level 3 15.7 18.0 16.0

Efficiency Level 4 16.5 19.2 -

Efficiency Level 5 

– “Max-Tech” – 18.1 21.6 17.2

3.4 METHODOLOGY OVERVIEW

DOE typically structures its engineering analysis around one of three methodologies: (1) 

the design-option approach, which calculates the incremental costs of adding specific design 

options to a baseline model; (2) the efficiency-level approach, which calculates the relative costs 

of achieving increases in energy efficiency levels without regard to the particular design options 

used to achieve such increases; and/or (3) the reverse-engineering or cost-assessment approach, 

which involves a “bottom-up” manufacturing cost assessment based on a detailed bill of 

materials (BOM) derived from tear-downs of the product being analyzed. DOE also uses a 

supplementary analysis called catalog teardowns for units that have not been physically torn 

down. Catalog teardowns combine published manufacturer product literature with physical 

teardown data to determine the manufacturer production cost (MPC). Section 3.4.2 provides 

more detail about catalog teardowns. For this analysis, DOE used a combination of the 

efficiency-level and the cost-assessment approach. DOE used the efficiency-level approach to 

identify incremental improvements in efficiency for each equipment class and the costassessment approach to develop a cost for each efficiency level. 

For small three-phase commercial air conditioners and heat pumps, the efficiency levels 

that DOE considered in the engineering analysis were representative of units produced by 

manufacturers at the time the engineering analysis was developed. DOE relied on data reported 

in the AHRI Directory of Certified Product Performance to select representative efficiency 
3-6

levels. 

DOE generated a bill of materials for each representative product that it disassembled. 

DOE did this for multiple manufacturers’ products that span a range of efficiency levels for the 

key equipment classes analyzed in this rulemaking. The BOMs describe the products in detail, 

providing the costs to manufacture and assemble the product. DOE also conducted catalog 

teardowns to supplement the information obtained directly from physical teardowns. 

Subsequently, DOE calculated manufacturer production cost (MPC) for each product, based on 

the detailed BOM data. DOE developed the cost-efficiency curves which describe the 

relationship between calculated cost and efficiency level (based on rated efficiency) of the 

analyzed products. 

DOE developed cost-efficiency curves for small three-phase commercial air-conditioning 

and heat pump equipment with a representative capacity of three tons. DOE based the costefficiency relationship for small three-phase commercial central air conditioners and heat pumps 

on reverse-engineering and catalog teardowns conducted for the 2011 Direct Final rule (DFR)2

for single-phase central air conditioners and heat pumps. Through analyzing manufacturing 

specification sheets, DOE did not find significant differences between single-phase and threephase products, aside from the use of three-phase compressors in three-phase units. DOE also 

found that manufacturers use model numbers interchangeably between single-phase and threephase products. To supplement the 2011 DFR data (29 physical teardowns and 12 catalog 

teardowns), DOE completed one physical teardown and seven catalog teardowns. 

For water-source heat pumps, the efficiency levels that DOE considered in the 

engineering analysis were representative of units currently produced by manufacturers at the 

time the engineering analysis was developed. DOE relied on data reported in the AHRI Directory 

of Certified Product Performance to select representative efficiency levels. After identifying 

representative efficiency levels, a catalog teardown or “virtual teardown” approach was used to 

estimate product costs at each level, in which published manufacturer catalog data and 

supplementary component data were used to estimate the major physical differences between 

water-source heat pumps and commercial heating and cooling products with similar components 

that were previously disassembled. This approach was used instead of physical teardowns due to 

time constraints on the analysis imposed by EPCA. 

DOE generated a cost model for water-source heat pumps based on other previously 

disassembled heating and cooling equipment, manufacturer, and component data. This cost 

model generated manufacturer production costs (MPCs) and product weights for water-source 

heat pumps at each capacity and efficiency level. These MPCs were used to generate costefficiency curves for each equipment class. DOE calculated the manufacturer selling price 

(MSP) by applying a manufacturer markup to the MSP and adding the estimated shipping costs.

 

2 www1.eere.energy.gov/buildings/appliance_standards/pdfs/cacfurn_dfr.pdf
3-7

3.4.1 Physical Teardown Analysis

As part of the reverse engineering analyses for central air conditioners and heat pumps, 

DOE conducted physical teardowns to develop a BOM to evaluate key design features (e.g., heat 

exchangers, compressors, fan and fan motors, control strategies, etc.) and determine the 

corresponding MPC of each unit. DOE selected units for physical teardowns based on the 

methodology described in Section 3.4.3. Units selected for physical teardowns were 

disassembled, and each part was characterized according to weight, manufacturing process, 

dimensions, material, and quantity.

3.4.2 Catalog Teardown Analysis

DOE used the catalog teardown method when physical teardowns could not be

completed. Catalog teardowns use published manufacturer literature combined with physical 

teardown data to estimate the MPC of the catalog unit. DOE gathered the information from the 

catalog teardown analysis to adapt a BOM from a similar unit that was developed using the

reverse engineering methodology. 

For water-source heat pumps, DOE used the catalog teardown analysis to identify the 

design pathways manufacturers typically use to increase the EER of units within each capacity 

class. DOE calculated the MPC for products spanning the full range of efficiencies from the 

baseline to the maximum technology available at various levels. DOE determined the costeffectiveness of each technology by comparing the increase in MPC to the increase in energy 

efficiency. DOE selected units for catalog teardowns based on the methodology described in 

Section 3.4.3.

3.4.3 Selection of Units

DOE considered the following criteria when selecting units for physical and catalog 

teardowns:

• The selected products should span the full range of efficiency levels for each equipment class 

under consideration.

• The selected products should come from manufacturers with large market shares.

• The selected products should have non-efficiency-related features that are the same as, or 

similar to, features of other equipment in the same class and at the same efficiency level.

3.4.4 Generation of Bill of Materials and Cost Model 

The BOM developed from physical and catalog teardown analysis incorporates all 

materials, components, and value-added steps a manufacturer takes to make a product. The cost 

model uses the BOM to determine the MPC of a product by calculating the costs of raw 

materials, purchased parts, and labor required to fabricate and assemble the product. Every part 

that makes up a product is cataloged and every value-added step to make and assemble it in the 
3-8

manufacturing facility is captured. Table 3.4.1 describes some of these manufacturing processes. 

The cost model also calculates the required equipment, space needs, and other overhead costs 

such as insurance to run and maintain a factory. Thus, the cost model recreates the factory 

electronically. 

Table 3.4.1 Major Manufacturing Processes

Fabrication Finishing Assembly/Joining Quality Control

Fixturing Washing Adhesive Bonding Inspecting & Testing

Stamping/Pressing Powder Coating Spot Welding

Turret Punching De-burring Seam Welding

Brake Forming Polishing Brazing

Cutting and Shearing

Tube Forming

To help inform the cost model inputs, DOE collected information on labor rates, tooling 

costs, raw material prices, and other factors. For purchased parts, the cost model estimates the 

purchase price based on volume-variable price quotations and detailed discussions with 

manufacturers and component suppliers. For fabricated parts, the prices of raw metal materials 

(e.g., tube, sheet metal) are based on 5-year averages. The cost of transforming the intermediate 

materials into finished parts is estimated based on current industry pricing. The cost model 

outputs the MPCs and operating weights, which were used for generating cost-efficiency curves 

and downstream economic analysis. Figure 3.4.1 shows the three major steps in generating 

manufacturing cost.

Figure 3.4.1 Manufacturing Cost Assessment Stages

For water-source heat pumps, commercial heating and cooling equipment which operate 

similar to water-source heat pumps were previously physically torn down, and a bill of materials 

(BOM) was derived from multiple sources. This BOM was adapted for water-source heat pumps 

by combining features from indoor air handlers used in heat pump applications and condenser 

systems that use a water coil as a heat exchanger. Features such as multiple compressors, 

multiple sealed systems, etc. were also accounted for in the BOM by configuring a flexible 

piping, sensor, and control sub-system.

Besides scaling indoor heat exchangers as a function of published manufacturer data, the 

model for water-source heat pumps also allows for the substitution of various capacity and 
3-9

efficiency-related components (compressors, motors, thermal expansion valves, etc.), scaling of 

pipe sizes as a function of sealed system capacity, as well as scaling the sheet metal to match 

published specifications from manufacturers. The flexibility of the model in turn allows a wide 

range of capacities to be examined as relevant inputs can be modified easily. DOE subsequently 

conducted a ‘‘virtual” or catalog teardown analysis, using published manufacturer catalogs and 

supplementary component data to estimate the MPCs of water-source heat pumps. 

For supplementary virtual teardowns, DOE gathered product data such as dimensions, 

weight, and design features from publicly-available information, such as manufacturer catalogs. 

DOE obtained information and data not typically found in catalogs and brochures, such as fan 

motor details, heat exchanger specifications, or assembly details, from the physical teardowns of 

a similar product and/or through estimates based on industry knowledge. The teardown analysis 

for this engineering analysis included over 60 different water-source heat pumps from multiple 

manufacturers. The cost model used the BOM and the inputs to calculate multiple cost-efficiency 

curves that detailed the cost of increasing energy efficiency across multiple equipment 

capacities.

3.4.5 Manufacturer Markup

DOE applies a non-production cost multiplier (the manufacturer markup) to the full MPC 

to account for corporate non-production costs and profit. The resulting manufacturer selling price

(MSP) is the price at which the manufacturer can recover all production and nonproduction costs 

and earn a profit. To meet new or amended energy conservation standards, manufacturers often 

introduce design changes to their product lines that result in increased manufacturer production 

costs. Depending on the competitive environment for these particular products, some or all of the 

increased production costs may be passed from manufacturers to retailers and eventually to 

customers in the form of higher purchase prices. As production costs increase, manufacturers 

typically incur additional overhead. The MSP should be high enough to recover the full cost of 

the product (i.e., full production and non-production costs), and yield a profit. The manufacturer 

markup has an important bearing on profitability. A high markup under a standards scenario 

suggests manufacturers can pass through the increased variable costs and some of the capital and 

product conversion costs (the one-time expenditures). A low markup suggests that manufacturers 

will not be able to recover as much of the necessary investment in plant and equipment.

The manufacturer markup is based on the 2011 Direct Final Rule (DFR)3 for single-phase 

central air conditioners and heat pumps. In the 2011 DFR analysis, DOE used U.S. Security and 

Exchange Commission (SEC) 10-K reports from publicly owned residential cooling and heating 

product companies to estimate manufacturer markups. The SEC form 10-K, filed by companies 

annually, provides a comprehensive overview of the company’s business and financial 

conditions. The 10-K report includes the company’s revenues along with the direct and indirect 

costs. The income statement section of the 10-K often lists the figures necessary for calculating 

the manufacturer markup – net sales, cost of sales, and gross profit. DOE first calculated a five 

 

3 www1.eere.energy.gov/buildings/appliance_standards/pdfs/cacfurn_dfr.pdf
3-10

year average markup for each company. After calculating the markup for each manufacturer with 

the 10-K data, DOE then calculated a single market-share weighted manufacturer markup to 

represent the industry. The markup was further calibrated based on feedback received during 

manufacturer interviews. DOE used a manufacturer markup of 1.303 for small three-phase 

commercial air conditioners and heat pumps <65,000 Btu/h.

For water-source heat pumps, DOE used the same manufacturer markup of 1.30 that was 

developed in the 2011 Direct Final Rule (DFR)4 and used for small three-phase commercial airconditioners and heat pumps.

3.4.6 Shipping Costs for Small Three-Phase Commercial Air-Cooled Air Conditioners 

and Heat Pumps

Manufacturers of HVAC products typically pay for freight to the first step in the 

distribution chain. Freight is not a manufacturing cost, but because it is a substantial cost 

incurred by the manufacturer, DOE accounted for shipping costs separately from the nonproduction costs that comprise the manufacturer markup. DOE calculated the MSP by 

multiplying the MPC (from the cost model) by the manufacturer markup and adding shipping 

costs. More specifically, DOE calculated shipping costs based on a typical 53-foot straight frame 

trailer with a storage volume of 4,240 cubic feet. DOE examined the sizes of representative units

and determined the number of units that would fit in a trailer. 

For small three-phase commercial air-cooled air conditioners and heat pumps, DOE 

assumed that units would be double-stacked. During the 2011 and 2014 physical teardowns,

DOE observed that the stacking capabilities of certain units were specified on the equipment 

packaging. DOE assumed all units can be double-stacked since there was no information 

provided on the equipment packaging to suggest otherwise. The shipping costs for small threephase commercial air-cooled air conditioners and heat pumps <65,000 Btu/h are shown in Table 

3.4.2 through Table 3.4.5. 

 

4 www1.eere.energy.gov/buildings/appliance_standards/pdfs/cacfurn_dfr.pdf
3-11

Table 3.4.2 Manufacturer Shipping Costs for Three-Ton Split-System Three-Phase 

Commercial Air-Cooled Air Conditioners

SEER

Split-System AC

Shipping Cost

(2014$)

13 $23

14 $23

15 $25

16 $34

17 $34

18 $36

19 $37

Table 3.4.3 Manufacturer Shipping Costs for Three-Ton Single-Package Three-Phase 

Commercial Air-Cooled Air Conditioners

SEER

Single-Package AC

Shipping Cost

(2014$)

13 $29

14 $31

15 $33

16 $39

17 $39

18 $39

19 $85

Table 3.4.4 Manufacturer Shipping Costs for Three-Ton Split-System Three-Phase 

Commercial Air-Cooled Heat Pumps

SEER

Split-System HP

Shipping Cost

(2014$)

13 $23

14 $23

15 $25

16 $35

17 $46

18 $58
3-12

Table 3.4.5 Manufacturer Shipping Costs for Three-Ton Single-Package Three-Phase 

Commercial Air-Cooled Heat Pumps

SEER

Single-Package HP

Shipping Cost

(2014$)

13 $29

14 $31

15 $39

16 $39

17 $39

18 $78

3.4.7 Shipping Costs for Water-Source Heat Pumps 

For water-source heat pumps, DOE assumed all units in the <17,000 Btu/h and 17,000-

65,000 Btu/h equipment classes could be double-stacked in shipping, but not the units in the 

65,000-135,000 Btu/h class because of their higher weights. The shipping costs calculated for 

water-source heat pumps are shown below in Table 3.4.6.

Table 3.4.6 Manufacturer Shipping Costs for Water-Source Heat Pumps

<17,000 Btu/h ≥17,000 and <65,000 

Btu/h

≥65,000 and 

<135,000 Btu/h

EER

Shipping 

Cost 

(2014$)

EER

Shipping 

Cost 

(2014$)

EER

Shipping 

Cost 

(2014$)

ASHRAE – Level 0 12.2 $10 13 $17 13 $78

Efficiency Level 1 13.0 $11 14.6 $17 14 $85

Efficiency Level 2 14.0 $15 16.6 $18 15 $94

Efficiency Level 3 15.7 $16 18 $18 16 $69

Efficiency Level 4 16.5 $16 19.2 $18 17.2 $78

Efficiency Level 5 18.1 $17 21.6 $21 - -

3.5 ENGINEERING ANALYSIS RESULTS

3.5.1 Unit Operating Weight for Small Three-Phase Commercial Air-Cooled Air 

Conditioners and Heat Pump Results 

Representative system weights for units at all SEER levels were used in the downstream 

analyses to estimate installation costs. DOE used the weight-efficiency relationships developed 

for the 2011 DFR, which were based on operating weights reported in manufacturer specification 
3-13

sheets. For each of the four equipment classes, data were collected across a number of 

manufacturers and a range of SEER levels. The system weights as listed by the manufacturers 

were plotted against rated SEER, and a best fit curve was applied to the data. From the curve fit, 

typical unit weights were estimated for applicable SEER levels. Applying this methodology 

allowed for a single unit weight corresponding to a given SEER value to be used in each 

equipment class. For SEER levels not covered in the 2011 DFR, DOE extrapolated the operating 

unit weights based on the curve fits. DOE’s calculated weight-efficiency relationships for small 

three-phase commercial air conditioners and heat pumps <65,000 Btu/h are shown in Table 

3.5.1.

Table 3.5.1 Weight-Efficiency Data for Three-Ton Three-Phase Commercial Air-Cooled 

Air Conditioners and Heat Pumps 

Operating Weight (lbs)

SEER Split-System 

AC

Single-Package 

AC

Split-System 

HP

Single-Package 

HP

13 323 341 358 379

14 346 361 393 411

15 367 382 426 444

16 387 402 456 476

17 405 422 485 508

18 422 443 512 541

19 439 463 - -

3.5.2 Cost-Efficiency Results for Small Three-Phase Commercial Air Conditioners and 

Heat Pump Results

DOE’s calculated MPCs for small three-phase commercial air conditioners and heat 

pumps <65,000 Btu/h are shown in Table 3.5.2 through Table 3.5.5. DOE used the costefficiency curves from the engineering analysis as an input for the LCC and other downstream 

analyses. 
3-14

Table 3.5.2 Manufacturer Production Costs for Three-Ton Split-System Three-Phase 

Commercial Air-Cooled Air Conditioners 

SEER MPC (2014$)

13 $855 

14 $937 

15 $1,023 

16 $1,115 

17 $1,212 

18 $1,316 

19 $1,427 

Table 3.5.3 Manufacturer Production Costs for Three-Ton Single-Package Three-Phase 

Commercial Air-Cooled Air Conditioners 

SEER MPC (2014$)

13 $1,003 

14 $1,122 

15 $1,241 

16 $1,361 

17 $1,480 

18 $1,599 

19 $1,719 

Table 3.5.4 Manufacturer Production Costs for Three-Ton Split-System Three-Phase

Commercial Air-Cooled Heat Pumps 

SEER HSPF MPC (2014$)

13 7.7 $1,068 

14 8.2 $1,154 

15 8.5 $1,244 

16 8.7 $1,377 

17 9.0 $1,486 

18 9.2 $1,601 
3-15

Table 3.5.5 Manufacturer Production Costs for Three-Ton Single-Package Three-Phase 

Commercial Air-Cooled Heat Pumps

SEER HSPF MPC (2014$)

13 7.7 $1,239

14 8.0 $1,372

15 8.4 $1,504

16 8.8 $1,637

17 8.9 $1,769

18 9.1 $1,902

3.5.3 Unit Operating Weight for Water-Source Heat Pumps 

The unit operating weights were calculated from the cost model spreadsheet. The inputs 

for these calculations were the same as those for the cost calculations described above in Section 

3.4. Representative unit weights for units at all EER levels were required as an input for the 

downstream analyses to estimate instillation costs. For each equipment class, unit weights for 

models examined in the catalog teardown analysis were plotted against rated cooling EER, and a 

best fit curve was applied to the data. From this curve fit, average unit weights were estimated 

for each examined EER level. DOE’s calculated weight-efficiency relationships for water-source 

heat pumps are shown in Table 3.5.6. 

Table 3.5.6 Weight-Efficiency Data for Water-Source Heat Pumps

<17,000 Btu/h ≥17,000 and <65,000 

Btu/h

≥65,000 and 

<135,000 Btu/h

EER Weight (lbs) EER Weight (lbs) EER Weight (lbs)

ASHRAE – Level 0 12.2 131 13 272 13 737

Efficiency Level 1 13.0 141 14.6 290 14 811

Efficiency Level 2 14.0 154 16.6 313 15 885

Efficiency Level 3 15.7 177 18 329 16 959

Efficiency Level 4 16.5 187 19.2 343 17.2 1048

Efficiency Level 5 18.1 208 21.6 371 - -

3.5.4 Cost-Efficiency Results for Water-Source Heat Pumps

DOE calculated the manufacturer production costs (MPCs) for water-source heat pumps 

in the same manner described above for unit operating weights, using outputs from the cost 

model from catalog teardown analysis and a trend line analysis. DOE used the cost-efficiency 

curves from the engineering analysis as an input for downstream economic analyses.

Representative MPCs for each EER level of each of the three capacity classes are shown below 

in Table 3.5.7. DOE did not estimate costs of units at the current federal standard—these were 
3-16

not required for the analysis, as EPCA requires DOE to compare the costs and benefits of higherefficiency levels to the level in ASHRAE 90.1.

Table 3.5.7 Manufacturer Production Cost-Efficiency Data for Water-Source Heat 

Pumps

<17,000 Btu/h ≥17,000 and <65,000 

Btu/h

≥65,000 and 

<135,000 Btu/h

EER MPC 

(2014$) EER MPC (2014$) EER MPC (2014$)

ASHRAE – Level 0 12.2 $860 13 $1,346 13 $3,274 

Efficiency Level 1 13.0 $904 14.6 $1,463 14 $3,660 

Efficiency Level 2 14.0 $960 16.6 $1,609 15 $4,045 

Efficiency Level 3 15.7 $1,053 18 $1,711 16 $4,431 

Efficiency Level 4 16.5 $1,097 19.2 $1,798 17.2 $4,893 

Efficiency Level 5 18.1 $1,185 21.6 $1,974 - -
4-i

CHAPTER 4. ENERGY USE ANALYSIS

TABLE OF CONTENTS 

4.1 INTRODUCTION ........................................................................................................... 4-1

4.2 BASE NATIONAL AVERAGE UEC DATA ................................................................ 4-1

4.2.1 Air-Cooled Air Conditioners ............................................................................... 4-1

4.2.1.1 Cooling UEC.............................................................................................. 4-1

4.2.1.2 Heating UEC .............................................................................................. 4-3

4.2.2 Water Source Heat Pumps................................................................................... 4-4

4.2.2.1 Cooling UEC.............................................................................................. 4-4

4.2.2.2 Heating UEC .............................................................................................. 4-5

4.2.2.3 UEC Adjustment ........................................................................................ 4-7

4.3 VARIABILITY BY BUILDING TYPE AND CENSUS DIVISION............................. 4-8

4.4 RESULTS ...................................................................................................................... 4-10

REFERENCES .......................................................................................................................... 4-13

LIST OF TABLES 

Table 4.2.1 Cooling UECs for Air-Cooled Single-Phase Air Conditioners <65,000 

Btu/h............................................................................................................... 4-2

Table 4.2.2 Cooling UECs for Air-Cooled Single-Phase Heat Pumps <65,000 

Btu/h............................................................................................................... 4-3

Table 4.2.3 Heating UECs for Air-Cooled Single-Phase Heat Pumps <65,000 

Btu/h............................................................................................................... 4-4

Table 4.2.4 2000 Cooling UECs for Water Source Heat Pumps...................................... 4-5

Table 4.2.5 Commercial Unitary Heat Pump (≥65,000 - <135,000 Btu/h) Heating 

Energy Use Parameters.................................................................................. 4-6

Table 4.2.6 Water-Source Heat Pump COP to EER Relationship.................................... 4-6

Table 4.2.7 Heating UECs for Water Source Heat Pumps ............................................... 4-7

Table 4.2.8 Factors Used to Represent Relative Changes in Required Cooling 

Energy............................................................................................................ 4-7

Table 4.2.9 Factors Used to Represent Relative Changes in Required Heating 

Energy............................................................................................................ 4-8

Table 4.3.1 Distribution of Small Commercial Air-Cooled Air-Conditioning and 

Heating Equipment by Building Type ........................................................... 4-8

Table 4.3.2 Distribution of Water-Source Heat Pumps by Building Type ....................... 4-8

Table 4.3.3 Distribution of Building Types by Census Division: Small Commercial 

Air-Cooled Air-Conditioning and Heating Equipment.................................. 4-9

Table 4.3.4 Distribution of Building Types by Census Division: Water-Source 

Heat Pumps.................................................................................................... 4-9

Table 4.4.1 Cooling UECs (kWh/year) for Small, Air-Cooled, 3-Phase AirConditioning and Heating Equipment less than 65,000 Btu/h..................... 4-11
4-ii

Table 4.4.2 Cooling UECs for Water -Source Heat Pumps............................................ 4-11

Table 4.4.3 Heating UECs for Water-Source Heat Pumps............................................. 4-12
4-1

CHAPTER 4. ENERGY USE ANALYSIS

4.1 INTRODUCTION

This chapter describes the energy use analysis for small, air-cooled, 3-phase airconditioning and heat pump equipment less than 65,000 Btu/h and water source heat pumps with 

cooling capacities less than 17,000 Btu/h, between 17,000 and 65,000 Btu/h, and between 65,000 

and 135,000 Btu/h. DOE analyzed the energy savings consumers would realize from the 

standards set forth by the American Society of Heating, Refrigeration, and Air-Conditioning 

Engineers (ASHRAE) Standard 90.1-2013 for specific equipment classes, as well as for higher 

energy efficiency levels. 

4.2 BASE NATIONAL AVERAGE UEC DATA

4.2.1 Air-Cooled Air Conditioners

4.2.1.1 Cooling UEC

To estimate the UEC for each class of small commercial packaged air-conditioning and 

heating equipment less than 65,000 Btu/h (air-cooled, three-phase), DOE began with the cooling 

UECs for single-phase equipment installed in commercial buildings as presented in the national 

impact analysis associated with the 2011 direct final rule (DFR) for residential central air 

conditioners and heat pumps.

1 Table 4.2.1 and Table 4.2.2 show these cooling UECs for the 

United States. In the 2011 DFR analysis, the cooling UECs for split and single package systems 

were very similar, but UECs were available for higher efficiency levels for split systems than for 

single package equipment. As a result, DOE used the 2011 UECs for split systems for all 

equipment classes analyzed for this Final Rule, including both split and single package systems. 

UECs were directly available for each of the air conditioner and heat pump efficiency levels 

analyzed for this Final Rule.
4-2

Table 4.2.1 Cooling UECs for Air-Cooled Single-Phase Air Conditioners <65,000 Btu/h

Electric Commercial

Cooling U.S. (kWh/yr)

SEER Split Packaged

13.0 2702.7 2716.0

13.5 2658.8 2669.1

14.0 2614.9 2622.2

14.5 2529.9 2539.3

15.0 2445.0 2456.4

15.5 2360.0 2373.5

16.0 2275.1 2290.6

16.5 2246.6 2260.5

17.0 2218.1 -

18.0 2161.2 -

19.0 2119.3 -

20.0 2077.5 -

21.0 2035.6 -

22.0 1993.7 -

23.0 1978.3 -

24.5 1955.1 -

Source: U.S. Department of Energy – Office of Energy Efficiency and Renewable Energy. Central Air Conditioners 

National Impact Analysis.1

Note: UECs for new units were used, and these values were very similar to the UECs for replacement units.
4-3

Table 4.2.2 Cooling UECs for Air-Cooled Single-Phase Heat Pumps <65,000 Btu/h

Electric Commercial Cooling 

U.S. (kWh/yr)

SEER Split Packaged

13.0 2741.4 2710.6

13.5 2701.4 2670.1

14.0 2661.3 2629.5

14.5 2582.1 2552.1

15.0 2502.8 2474.7

15.5 2423.5 2397.3

16.0 2344.2 2319.8

16.5 2310.8 2286.3

17.0 2277.4 -

18.0 2210.5 -

19.0 2161.5 -

20.0 2112.5 -

21.0 2063.5 -

22.0 2045.3 -

Source: U.S. Department of Energy – Office of Energy Efficiency and Renewable Energy. Central Air Conditioners 

National Impact Analysis.1

4.2.1.2 Heating UEC

Although ASHRAE 90.1-2013 increased the HSPF levels for this equipment, DOE did 

not include heating UECs in this analysis. Table 4.2.3 shows that for commercial installations in 

the 2011 analysis, the heating UECs did not scale proportionally with HSPF. Based on these 

data, DOE has determined that increasing HSPF for small commercial air conditioners will likely 

not result in significant energy savings. 
4-4

Table 4.2.3 Heating UECs for Air-Cooled Single-Phase Heat Pumps <65,000 Btu/h

SEER

HSPF Electric Commercial

Heating U.S. (kWh/yr)

Split Packaged Split Packaged

13.0 8.0 7.7 509.5 359.7

13.5 8.1 7.9 508.5 359.0

14.0 8.3 8.1 507.5 358.3

14.5 8.4 8.3 510.4 359.9

15.0 8.5 8.4 513.3 361.6

15.5 8.6 8.6 516.2 363.2

16.0 8.7 8.8 519.0 364.8

16.5 8.8 9.0 515.5 361.9

17.0 9.0 - 512.0 -

18.0 9.2 - 505.0 -

19.0 9.4 - 499.8 -

20.0 9.7 - 494.6 -

21.0 9.9 - 489.5 -

22.0 9.9 - 487.5 -

Source: U.S. Department of Energy – Office of Energy Efficiency and Renewable Energy. Central Air Conditioners 

National Impact Analysis.1

4.2.2 Water Source Heat Pumps

4.2.2.1 Cooling UEC

To estimate the cooling UEC for each class of water source heat pump, DOE began with 

the data provided for water source heat pumps published in Appendix D of the 2000 Screening 

Analysis for the Energy Policy Act of 2005 (EPACT).2 Table 4.2.4 shows the input capacity and 

national average full load equivalent operating hours (FLEOH) for each equipment class and 

efficiency level. The UEC equals the input capacity times the FLEOH.
4-5

Table 4.2.4 2000 Cooling UECs for Water Source Heat Pumps

WSHP 

<17,000 

Btu/h

EER 9.3 11.2 12.5 13.1 14 15.8

Input Capacity (kW) 1.29 1.071 0.96 0.916 0.857 0.759

National Avg FLEOH 1,622.5 1,622.5 1,622.5 1,622.5 1,622.5 1,622.5

UEC (kwh/year) 2,093 1,738 1,558 1,486 1,390 1,231

WSHP 

≥17,000 -

<65,000 

Btu/h

EER 9.3 12 12.5 13.1 14 15.2

Input Capacity (kW) 3.871 3 2.88 2.748 2.571 2.368

National Avg FLEOH 1,622.5 1,622.5 1,622.5 1,622.5 1,622.5 1,622.5

UEC (kwh/year) 6,281 4,868 4,673 4,459 4,171 3,842

WSHP 

≥65,000 -

<135,000 

Btu/h

EER 10.5 12 12.5 13 14

Input Capacity (kW) 8.571 7.5 7.2 6.923 6.429

National Avg FLEOH 1,537.1 1,537.1 1,537.1 1,537.1 1,537.1

UEC (kwh/year) 13,174 11,528 11,067 10,641 9,882

Source: U.S. Department of Energy – Office of Energy Efficiency and Renewable Energy. Screening Analysis for 

EPACT-Covered Commercial HVAC and Water-Heating Equipment. Volume 1 – Main Report. 2000.

Where identical efficiency levels were available, DOE used the input capacity and 

FLEOH directly from the screening analysis. For additional efficiency levels, DOE used the 

same FLEOH (as it does not differ by efficiency level) and scaled the input capacities based on 

the ratio of EER, as was done in the original analysis and shown in the following equation:

IIIIIIIIIIIIIIIIIIIIIIIIIIBB = EEEEEEAA

EEEEEEBB

xx IIIIIIIIIIIIIIIIIIIIIIIIIIAA

Eq. 4.1

DOE then multiplied the FLEOH by the scaled input capacity to calculate the UEC for each 

additional efficiency level. 

4.2.2.2 Heating UEC

The 2000 screening analysis did not estimate heating UECs for water source heat pumps.

DOE requested data on this in the April 2014 Notice of Data Availability (NODA) (79 FR 

20114) but did not receive any. In order to characterize heating-side performance, DOE relied on 

an analysis of the Commercial Buildings Energy Consumption Survey (CBECS) 2003 data for 

air source heat pumps at larger capacities than two of the water source heat pump equipment 

classes in this rulemaking.3 DOE assumed that this estimate was sufficiently representative of the 

heating energy use for all three classes of water source heat pumps.

In the air-source heat pump analysis, DOE identified a representative building set 

consisting of 117 CBECS 2003 records. From this sub-sample of buildings, DOE determined a 

weighted-average annual heating energy consumption of 1.37 kWh/sq.ft. Because CBECS 

provides no information on equipment efficiency, DOE assumed that this energy use value 
4-6

pertained to heat pumps with a heating efficiency, or coefficient of performance (COP), of 2.9. A 

heating efficiency of 2.9 COP corresponds to the minimum heating efficiency standard for airsource heat pumps in 2003, a value which DOE feels is representative of the heat pump stock 

characterized by CBECS. 

DOE used the energy consumption per square foot value derived from CBECS and the 

tons per square foot value developed for commercial unitary air conditioners greater than or 

equal to 65,000 Btu/h and less than 135,000 Btu/h to calculate an annual energy use per ton 

value for a 2.9 COP unit (Table 4.2.5). Because equipment energy use is a function of 

efficiency, DOE assumed that the annual heating energy consumption of a unit scales in direct 

proportion to the COP. Therefore, DOE multiplied the annual energy use per ton values by the 

representative tons for a given equipment class (1 ton, 3 tons, and 7.5 tons), and adjusted it by 

the ratio of 2.9 COP to the COP for that efficiency level. The COPs for each efficiency level 

were developed based on a relationship between EER and COP of units in the AHRI databasea

(Table 4.2.6). Table 4.2.7 shows the heating-side UEC values for water source heat pumps. 

Table 4.2.5 Commercial Unitary Heat Pump (≥65,000 - <135,000 Btu/h) Heating Energy 

Use Parameters

Tons/SF 0.00260

Annual Energy Use/ SF 

(2.9 COP) 1.37

Annual Energy Use per Ton 

(2.9 COP) 527

Representative Tons 7.5

Table 4.2.6 Water-Source Heat Pump COP to EER Relationship

Equipment Class Slope Intercept

WSHP <17,000 

Btu/h 0.2136 1.781

WSHP ≥17,000 -

<65,000 Btu/h 0.2354 1.368

WSHP ≥65,000 -

<135,000 Btu/h 0.1434 2.667

 a

 www.ahridirectory.org/ahridirectory/pages/homeM.aspx
4-7

Table 4.2.7 Heating UECs for Water Source Heat Pumps

WSHP 

<17,000 

Btu/h

EER N/A 11.2 12.2 13.0 14.0 15.7 16.5 18.1

COP 2.9 4.2 4.3 4.6 4.8 5.1 5.3 5.6

Heating UEC

(kWh/year) 527 364 355 335 320 298 288 271

WSHP

≥17,000 -

<65,000 

Btu/h

EER N/A 12.0 13.0 14.6 16.6 18.0 19.2 21.6

COP 2.9 4.2 4.3 4.8 5.3 5.6 5.9 6.5

Heating UEC

(kWh/year) 1581 1092 1066 954 869 818 779 711

WSHP 

≥65,000 -

<135,000 

Btu/h

EER N/A 12.0 13.0 14.0 15.0 16.0 17.2 N/A

COP 2.9 4.2 4.3 4.7 4.8 5.0 5.1 N/A

Heating UEC

(kWh/year) 3953 2729 2666 2452 2379 2310 2233 N/A

4.2.2.3 UEC Adjustment

The cooling UECs for water source heat pumps are based on a study from 2000 and the 

heating UECs from the building stock in 2003. Over time, changes in building shell 

characteristics and internal loads can lead to a change in the energy required to meet a given 

cooling or heating load. The National Energy Modeling System (NEMS) commercial demand 

module accounts for these trends by adjusting the cooling and heating energy use by factors that 

are a function of region and building activity. These factors can be either less than one indicating 

a relative decrease in energy consumption, or positive, indicating an increase. DOE used the 

factors from the version of NEMS underlying AEO 20144 to adjust the cooling and heating 

energy use from the values developed in 2000 and 2003, respectively, to the start of the analysis 

period, 2020. To do this, DOE took the ratio of the factors in 2020 and 2004. Factors prior to 

2004 were not available, so DOE assumed the 2004 factor was representative of earlier years as 

well. Ideally, DOE would have applied scalars based on building vintages but because DOE did 

not have a breakdown of the buildings by vintage represented by the available UECs, DOE 

applied the ratio of the 2020 and 2004 factors to the average UEC. The resulting factors make 

relatively modest adjustments to the UEC, as shown in Table 4.2.8 and Table 4.2.9.

Table 4.2.8 Factors Used to Represent Relative Changes in Required Cooling Energy 

Building Type Census Division

1 2 3 4 5 6 7 8 9

Office 0.96 0.95 0.96 0.96 0.93 0.94 0.92 0.94 0.90

Education 0.96 0.85 0.96 0.94 0.92 0.88 0.9 0.94 0.89

Lodging 0.97 0.96 0.98 0.98 0.94 0.93 0.93 0.95 0.96

Residential 0.97 0.96 0.98 0.98 0.94 0.93 0.93 0.95 0.96

Healthcare 0.96 0.95 0.96 0.95 0.94 0.95 0.95 0.97 0.91

Source: NEMS commercial demand module.
4-8

Table 4.2.9 Factors Used to Represent Relative Changes in Required Heating Energy 

Building Type Census Division

1 2 3 4 5 6 7 8 9

Office 0.96 0.94 0.96 0.96 0.93 0.92 0.90 0.95 0.80

Education 0.95 0.92 0.94 0.92 0.92 0.87 0.91 0.93 0.84

Lodging 0.95 0.94 0.95 0.95 0.95 0.93 0.94 0.97 0.94

Residential 0.96 0.94 0.95 0.95 0.95 0.92 0.93 0.96 0.83

Healthcare 0.95 0.94 0.95 0.94 0.94 0.93 0.94 0.98 0.86

Source: NEMS commercial demand module.

4.3 VARIABILITY BY BUILDING TYPE AND CENSUS DIVISION

DOE incorporates uncertainty and variability considerations in certain of the key 

parameters for the LCC analysis. In order to reflect variability of the base national average UEC 

estimates, DOE created distributions of UECs by building type and Census Division.

DOE determined that small, air-cooled, 3-phase air-conditioning and heat pump 

equipment less than 65,000 Btu/h are used in the office and retail sectors and that water-source 

heat pumps are used in the office, lodging, education, multi-family housing, and healthcare 

sectors. Table 4.3.1 and Table 4.3.2 show the distributions of small, air-cooled air conditioning 

and heating equipment and water source heat pumps by building type.3,5 Table 4.3.3 and Table 

4.3.4 show the distributions of building types by census division.3

Table 4.3.1 Distribution of Small Commercial Air-Cooled Air-Conditioning and Heating 

Equipment by Building Type

Building Type Market Share

Office 47%

Retail 53%

Source: CBECS 2003.

Table 4.3.2 Distribution of Water-Source Heat Pumps by Building Type

Building Type Market Share

Offices 26%

Public 30%

Lodging 5%

Property owners 29%

Medical services 10%

Source: Adjusted from market shares for a broader set of building types identified in the 2014 CUAC NOPR.
4-9

Table 4.3.3 Distribution of Building Types by Census Division: Small Commercial AirCooled Air-Conditioning and Heating Equipment

Census Division Building Type

Office Retail

New England 4% 5%

Middle Atlantic 19% 11%

East North Central 18% 14%

West North Central 7% 7%

South Atlantic 17% 22%

East South Central 4% 6%

West South Central 11% 14%

Mountain 6% 7%

Pacific 14% 13%

Source: CBECS 2003.

Table 4.3.4 Distribution of Building Types by Census Division: Water-Source Heat 

Pumps

Census Division Building Type

Office Education Lodging Multifamily Healthcare

New England 4% 3% 7% 7% 3%

Middle Atlantic 19% 14% 16% 20% 16%

East North Central 18% 20% 11% 15% 17%

West North Central 7% 6% 12% 5% 6%

South Atlantic 17% 25% 18% 17% 25%

East South Central 4% 3% 7% 3% 7%

West South Central 11% 12% 8% 10% 12%

Mountain 6% 6% 9% 4% 5%

Pacific 14% 10% 13% 19% 9%

Source: CBECS 2003.

In order to develop the distribution of UECs, DOE used estimates of Full Load 

Equivalent Operating Hours (FLEOH) provided in the 2000 Screening Analysis for commercial 

air conditioners and heat pumps in 7 building types and 11 cities across the United States.2

The Screening Analysis had FLEOHs for five of the building types but did not have 

FLEOHs for multi-family and healthcare. DOE determined that the lodging FLEOHs would be 

representative for multi-family and the office FLEOHs would be representative for healthcare. 

DOE used the total floor space by Census Division from the 2003 Commercial Building Energy 

Consumption Survey (CBECS) to determine the distribution of building types.3 DOE then 

mapped the 11 cities to their respective Census Divisions. The Mountain and Pacific Census 

Divisions had multiple cities which represented different climate zones within the Census 

Division. DOE weighted the FLEOHs by the population of that Census Division which lives in 

the climate zone represented by each city.6,7 The mid-Atlantic Census Division did not have a 
4-10

representative city in the screening analysis, so DOE used the same FLEOHs as New England 

(whose representative city is Providence) for the Middle Atlantic. 

DOE normalized the distributions of FLEOHs to the national average UECs used in this 

analysis to create distributions of UECs by building type and Census Division.

4.4 RESULTS

Table 4.4.1 through Table 4.4.3 show the national average UECs by equipment class and 

efficiency level that were used as inputs to the LCC analysis. These include the UEC adjustments 

made to the data for water-source heat pumps to account for changes in cooling and heating 

loads over time. These UECs do not include the effects of random sampling that takes into 

account variability by building type and Census Division. This variability is accounted for in the 

LCC outputs, which are used as inputs to the NIA. As a result, the UECs used in the NIA are not 

exactly the same as those shown here.
4-11

Table 4.4.1 Cooling UECs (kWh/year) for Small, Air-Cooled, 3-Phase Air-Conditioning 

and Heating Equipment less than 65,000 Btu/h

SEER Split AC Package AC Split HP Package HP

13 2,703 2,703 2,741 2,741

14 2,615 2,615 2,661 2,661

15 2,445 2,445 2,503 2,503

16 2,275 2,275 2,344 2,344

17 2,218 2,218 2,277 2,277

18 2,161 2,161 2,211 2,211

19 2,119 2,119 N/A N/A

Table 4.4.2 Cooling UECs for Water -Source Heat Pumps

<17,000 Btu/h ≥17,000 and <65,000 Btu/h ≥65,000 and <135,000 Btu/h

EER UEC 

(kWh/year) EER UEC (kWh/year) EER UEC (kWh/year)

11.2 1,631 12.0 4,568 12.0 10,819

12.2 1,497 13.0 4,217 13.0 9,987

13.0 1,405 14.6 3,755 14.0 9,274

14.0 1,305 16.6 3,302 15.0 8,655

15.7 1,163 18.0 3,045 16.0 8,114

16.5 1,107 19.2 2,855 17.2 7,548

18.1 1,009 21.6 2,538 N/A N/A
4-12

Table 4.4.3 Heating UECs for Water-Source Heat Pumps

<17,000 Btu/h ≥17,000 and <65,000 Btu/h ≥65,000 and <135,000 Btu/h

COP UEC 

(kWh/year) COP UEC (kWh/year) COP UEC (kWh/year)

4.2 336 4.2 1,007 4.2 2,517

4.3 328 4.3 983 4.3 2,458

4.5 309 4.8 880 4.7 2,261

4.8 295 5.4 801 4.8 2,194

5.3 274 5.8 754 5.0 2,130

5.5 266 6.1 718 5.1 2,059

6.0 250 6.8 655 N/A N/A
4-13

REFERENCES

1 U.S. Department of Energy – Office of Energy Efficiency and Renewable Energy. 2011-10-

31 Energy Conservation Program: Energy Conservation Standards for Residential Furnaces 

and Residential Central Air Conditioners and Heat Pumps

<www.regulations.gov/#!documentDetail;D=EERE-2011-BT-STD-0011-0058>

2 U.S. Department of Energy – Office of Energy Efficiency and Renewable Energy. Screening 

Analysis for EPACT-Covered Commercial HVAC and Water-Heating Equipment. Volume 1 

– Main Report. 2000. <www.regulations.gov/#!documentDetail;D=EERE-2006-STD-0098-

0015>

3 U.S. Department of Energy-Energy Information Administration, Commercial Buildings 

Energy Consumption Survey. 2003. <www.eia.gov/consumption/commercial/index.cfm>

4 U.S. Energy Information Administration, Commercial Demand Module of the National 

Energy Modeling System: Model Documentation 2014. 2014. 

<www.eia.gov/forecasts/aeo/nems/documentation/commercial/pdf/m066(2014).pdf>

5 U.S. Department of Energy. 2014. Technical Support Document: Energy Efficiency Program 

for Commercial and Industrial Equipment: Small, Large, and Very Large Commercial 

Package Air Conditioning and Heating Equipment. Washington, D.C. 

<www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/59>

6 U.S. Department of Energy–Energy Information Administration, Commercial Buildings 

Energy Consumption Survey – Maps. 2003. <www.eia.gov/consumption/commercial/censusmaps.cfm>

7 U.S. Census Bureau – American Fact Finder. Annual Estimates of the Resident Population 

by Sex, Race, and Hispanic Origin: April 1, 2010 to July 1, 2013. 

<www.census.gov/popest/data/counties/asrh/2013/index.html>
5-i 

CHAPTER 5. MARKUPS FOR PRODUCT PRICE DETERMINATION

TABLE OF CONTENTS 

5.1 INTRODUCTION ........................................................................................................... 5-1

5.1.1 Distribution Channels .......................................................................................... 5-2

5.2 MARKUP CALCULATION METHODOLOGY ........................................................... 5-3

5.3 APPROACH FOR CALCULATING OVERALL MARKUP ........................................ 5-4

5.3.1 Baseline Markup .................................................................................................. 5-4

5.3.2 Incremental Markups ........................................................................................... 5-5

5.4 APPROACH FOR CALUCLATING MANUFACTURER MARKUP .......................... 5-6

5.5 APPROACH FOR CALCULATING WHOLESALER AND CONTRACTOR 

MARKUPS ...................................................................................................................... 5-6

5.5.1 Key Data and Assumptions of the Markups Methodology .................................. 5-7

5.5.2 Approach for Wholesaler Markup ....................................................................... 5-8

5.5.3 Approach for Mechanical and General Contractor Markup ................................ 5-9

5.6 DERIVATION OF MARKUPS .................................................................................... 5-10

5.6.1 Manufacturer Markups....................................................................................... 5-10

5.6.2 Wholesaler Markups .......................................................................................... 5-10

5.6.3 Mechanical Contractor Markups........................................................................ 5-12

5.6.3.1 Aggregate Baseline Markups for Mechanical Contractors ...................... 5-12

5.6.3.2 Baseline Markups for Mechanical Contractors in the Replacement 

and New Construction Markets ................................................................ 5-13

5.6.3.3 Baseline Markups for Small and Large Mechanical Contractors............. 5-13

5.6.3.4 Baseline Markups by Disaggregated Mechanical Contractor Type ......... 5-14

5.6.3.5 Incremental Markups by Mechanical Contractor Type ............................ 5-15

5.6.4 Estimation of General Contractor Markups ....................................................... 5-16

5.7 SALES TAX .................................................................................................................. 5-18

5.8 OVERALL MARKUPS................................................................................................. 5-18

REFERENCES .......................................................................................................................... 5-20

LIST OF TABLES 

Table 5.1.1 Shares of Market by Distribution Channel .................................................... 5-3

Table 5.6.1 Wholesaler Expenses and Markups ............................................................. 5-11

Table 5.6.2 Baseline Markup, All Mechanical Contractors ............................................ 5-12

Table 5.6.3 Baseline Markups for the Replacement and New Construction 

Markets, All Mechanical Contractors .......................................................... 5-13

Table 5.6.4 Baseline Markups for Small and Large Mechanical Contractors ................ 5-14

Table 5.6.5 Baseline Markups by Mechanical Contractor Type ..................................... 5-15

Table 5.6.6 Mechanical Contractor Disaggregated Expenses and Markups Based 

on 2007 U.S. Census Bureau Data ............................................................... 5-15

Table 5.6.7 Incremental Markups by Mechanical Contractor Type ............................... 5-16
5-ii

Table 5.6.8 General Contractor Expenses and Markups ................................................. 5-17

Table 5.7.1 Average Sales Tax Rates by Census Division and Large State ................... 5-18

Table 5.8.1 Summary of Overall Markups (3-phase, air cooled, air conditioning 

and heat pumps <65,000 Btu/h and water source heat pumps) ................... 5-19

LIST OF FIGURES 

Figure 5.1.1 Distribution Channels for New Construction Applications ........................... 5-2

Figure 5.1.2 Distribution Channels for Replacement Applications ................................... 5-3
5-1 

CHAPTER 5. MARKUPS FOR PRODUCT PRICE DETERMINATION

5.1 INTRODUCTION

To carry out the life-cycle cost (LCC) calculations described in Chapter 6, DOE needed 

to determine the cost to the commercial consumer of baseline air-cooled, air-conditioners and

heat pumps, and water source heat pumps, along with the cost of the more-efficient units the 

consumer would purchase under potential standards. However, the commercial consumer price 

of such units is not generally known. By applying a multiplier called a “markup” to the known 

manufacturer’s price, DOE was able to estimate the commercial consumer’s price. DOE based 

the markups for small commercial air-cooled air-conditioning and heating equipment and watersource heat pumps on the analysis conducted for commercial unitary air conditioners and heat 

pumps (CUAC/CUHP). This chapter describes how DOE derived those markups and the 

modifications made for today's Final Rule. 

The equipment price to the commercial consumer depends on how the consumer

purchases the equipment. DOE defines two primary types of distribution channels to describe 

how air-conditioners and heat pumps generally pass from the manufacturer to the consumer: (1) 

in the first distribution channel, the manufacturer sells the equipment to a wholesaler, who in 

turn sells it to a mechanical contractor, who in turn sells it to a general contractor, who in turn 

sells it to the consumer; (2) in the second distribution channel, the manufacturer sells the 

equipment directly to the consumer through a national account. DOE has further subdivided the 

first distribution channel by mechanical contractor size (as measured in annual revenues). 

DOE did not use the national accounts distribution channel in this rulemaking. National 

accounts are used by large commercial consumers of HVAC equipment, such as national retail 

chains. Due to the large volume of equipment purchased, the large commercial consumers can 

buy directly from the manufacturer at lower prices than would be obtained in the typical 

distribution chain. Small, air-cooled, 3-phase air-conditioning and heat pump equipment less 

than 65,000 Btu/h and water source heat pumps with cooling capacities less than 135,000 Btu/h 

do not fit the profile of equipment which would be used by a large, national chain. Therefore, 

DOE assumed all equipment uses a small or large mechanical contractor distribution channel to 

determine the markups in this rulemaking. 

For wholesalers and contractors, DOE estimated a baseline markup and an incremental 

markup. DOE defines a baseline markup as a multiplier that converts the manufacturing selling 

price of equipment with baseline efficiency to the consumer purchase price for the equipment at 

the same baseline efficiency level. An incremental markup is defined as the multiplier to convert 

the incremental increase in manufacturer selling price of higher efficiency equipment to the 

consumer purchase price for the same equipment. Because companies mark up the price to cover 

business cost and profit margin at each step in the distribution channel, both baseline and 

incremental markups are dependent on the particular distribution channel, as described in section

5.1.1. 
5-2 

5.1.1 Distribution Channels

The appropriate markups for determining consumer equipment prices depend on the type 

of distribution channels through which products move from manufacturers to purchasers. At each 

point in the distribution channel, companies mark up the price of the equipment to cover their 

business costs and profit margin. In this analysis, DOE considered a primary distribution 

channel involving distributors and contractors. Within this primary channel, DOE distinguishes 

between new and replacement applications, as only new construction applications are expected 

to include a general contractor. DOE also distinguishes between small and large mechanical 

contractors. 

The distribution channel for new construction applications is shown in 

Figure 5.1.1. In the new construction distribution channel, the manufacturer sells the 

equipment to a wholesaler, who in turn sells it to either a small or large mechanical contractor, 

who in turn sells it to a general contractor, who in turns sells it to the consumer and performs the 

installation. 

 

Figure 5.1.1 Distribution Channels for New Construction Applications

Distribution channels for replacement applications are shown in 

Figure 5.1.2. In the replacement distribution channel, the manufacturer sells the 

equipment to a wholesaler, who in turn sells it to either a small or large mechanical contractor, 

who in turns sells it to the consumer and performs the installation. 

Wholesaler

Small 

Mechanical 

Contractor

General 

Contractor

Consumer

Manufacturer

Large

Mechanical 

Contractor

General 

Contractor

Consumer
5-3 

Figure 5.1.2 Distribution Channels for Replacement Applications

 Based on information that equipment manufacturers provided in the commercial unitary 

air conditioner and heat pump rulemaking, end-use commercial consumers purchase 50 percent 

of equipment through small mechanical contractors, 32.5 percent through large mechanical 

contractors, and the remaining 17.5 percent through national accounts. DOE excluded national 

accounts from this analysis, therefore of the contractor distribution channel, small contractors 

represent 61 percent of sales and large contractors represent 39 percent of sales. In addition, 20 

percent of commercial air-conditioning equipment is purchased for the new construction market 

while the remaining 80 percent is assumed to serve the replacement market. DOE used this same 

distribution for the equipment in today's Final Rule. Table 5.1.1 lists each distribution channel’s 

share of the full commercial air conditioner market for the original CUAC/CUHP analysis and 

today's analysis.

Table 5.1.1 Shares of Market by Distribution Channel

Distribution Channel

CUAC/CUHP 

Share of 

Market

Small 3-Phase 

AC/HP and 

WSHP Share of 

Market

New Construction, Small Mechanical Contractor 10.0% 12.2%

New Construction, Large Mechanical Contractor 6.5% 7.8%

New Construction, National Account 3.5% N/A

Replacement, Small Mechanical Contractor 40.0% 48.8%

Replacement, Large Mechanical Contractor 26.0% 31.2%

Replacement, National Account 14.0% N/A

5.2 MARKUP CALCULATION METHODOLOGY

At each point in the distribution channel, companies mark up the price of the equipment 

to cover their business costs and profit margin. In financial statements, gross margin is the 

difference between the company revenue and the company cost of sales or cost of goods sold 

Wholesaler

Small 

Mechanical 

Contractor

Consumer

Manufacturer

Large

Mechanical 

Contractor

Consumer
5-4 

(CGS). The gross margin includes the expenses of companies in the distribution channel—

including overhead costs (sales, general, and administration); research and development (R&D) 

and interest expenses; depreciation and taxes—and company profits. In order for sales of a 

product to contribute positively to company cash flow, the product’s markup must be greater 

than the corporate gross margin. Products command lower or higher markups, depending on 

company expenses associated with the product and the degree of market competition. 

Equipment manufacturers sell most of their products directly to wholesalers. 

Wholesalers sell to contractors at the wholesale price. Wholesalers absorb short-term imbalances 

in supply and demand, allowing manufacturers to operate more efficiently and satisfying 

consumer needs for fast deliveries. In addition, wholesalers are important sources for parts. 

Most contractors compete at the local level. Many carry more than one brand of air conditioner, 

and most install the products they sell. 

In addition to the wholesaler and mechanical contractor markups, in new construction the 

general contractor adds a markup. In retrofit installations, sales tax applies to the final consumer 

cost. 

5.3 APPROACH FOR CALCULATING OVERALL MARKUP

5.3.1 Baseline Markup

DOE used the overall baseline markup to estimate the consumer product price of baseline 

models, given the manufacturer cost of the baseline models. DOE considers baseline models to 

be product sold under existing market conditions (i.e., without new energy conservation 

standards). The following equation shows how DOE used the overall baseline markup to 

determine the product price for baseline models in the first new construction distribution 

channel.

CCCCCCBBBBBBBB = CCCCCCCCMMMMMM

× �MMMMMMMMMM × MM WWWWWWWW _BBBBBBBB × MMMMMMMM MMMMMM MMMM_BBBBBBBB × MMMM _BBBBBBBB�

= CCCCCCCCMMMMMM × MMMM OO _BBBBBBBB

Eq. 5.1 

Where:

CPPBASE = Consumer product price for baseline models, 

COSTMFG = Manufacturer cost for baseline models, 

MUMFG = Manufacturer markup, 

MUWHOLE_BASE = Baseline wholesaler markup,

MUMCONTRACT_BASE = Baseline mechanical contractor markup,

MUMGENERAL_BASE = Baseline general contractor markup, and

MUOVERALL_BASE = Baseline overall markup. 
5-5 

 The following equation shows how DOE used the overall baseline markup to determine 

the product price for baseline models in the first replacement distribution channel. 

CCCCCCBBBBBBBB = CCCCCCCCMMMMMM × �MMMMMMMMMM × MMMMWWWWWWWW _BBBBBBBB × MM MMMM MMMMMM MMMM_BBBBBBBB × TTTT �

= CCCCCCCCMMMMMM × MMMM OO _BBBBBBBB

Eq. 5.2 

Where:

CPPBASE = Consumer product price for baseline models,

COSTMFG = Manufacturer cost for baseline models, 

MUMFG = Manufacturer markup, 

MUWHOLE_BASE = Baseline wholesaler markup,

MUMCONTRACT_BASE = Baseline mechanical contractor markup,

TAXSALES = Sales tax, and

MUOVERALL_BASE = Baseline overall markup.

5.3.2 Incremental Markups

Similarly, DOE used the overall incremental markup to estimate changes in the consumer 

product price, given changes in the manufacturer cost from the baseline model cost resulting 

from an energy conservation standard to raise product energy efficiency. The total consumer 

product price for more energy-efficient models is composed of two components: the consumer 

product price of the baseline model and the change in consumer product price associated with the 

increase in manufacturer cost to meet the new energy conservation standard. The following 

equation shows how DOE used the overall incremental markup to determine the product price 

for more energy-efficient models in the first new construction distribution channel. 

CCCCCC = CCCCCCBBBBBBBB + CCCCCCCCMMMM _II _

× �MMMMMMMMMM × MMMMWWWWWWWW _II × MMMMMMMM MMMMMM MMMM_II × MMMM _II �

= CCCCCCBBBBBBBB + CCCCCCCCMMMMMM_II × MMMM OO _II

Eq. 5.3 

Where:

CPPEFF = Consumer product price for more-efficient models, 

CPPBASE = Consumer equipment price for baseline models,

COSTMFG_INCR = Incremental manufacturer cost, 

MUMFG = Manufacturer markup, 

MUWHOLE_INCR = Incremental wholesaler markup,

MUMCONTRACT_INCR = Incremental mechanical contractor markup,

MUMGENERAL_INCR = Incremental general contractor markup, and

MUOVERALL_INCR= Incremental overall markup.
5-6 

 The following equation shows how DOE used the overall incremental markup to 

determine the product price for more energy-efficient models in the first replacement distribution 

channel.

CCCCCC = CCCCCCBBBBBBBB + CCCCCCCCMMMMMM_II

× �MMMMMMMMMM × MMMMWWWWWWWW _II × MMMMMMMM MMMMMM MMMM_II × TTTT �

= CCCCCCBBBBBBBB + CCCCCCCCMMMMMM_II × MMMM OO _II

Eq. 5.4 

Where:

CPPEFF = Consumer product price for more-efficient models, 

CPPBASE = Consumer equipment price for baseline models,

COSTMFG_INCR = Incremental manufacturer cost, 

MUMFG = Manufacturer markup, 

MUWHOLE_INCR = Incremental wholesaler markup,

MUMCONTRACT_INCR = Incremental mechanical contractor markup,

TAXSALES = Sales tax, and

MUOVERALL_INCR= Incremental overall markup.

5.4 APPROACH FOR CALUCLATING MANUFACTURER MARKUP

DOE uses manufacturer markups to transform a manufacturer’s product cost into a 

manufacturer sales price. Using the CGS and gross margin, the manufacturer markup can be 

calculated as follows:

MMMMMMMMMM = CCCCCCMMMMMM + MMMMMM

CCCCCCMMMMMM

Eq. 5.5 

Where:

MUMFG = Manufacturer markup, 

CGSMFG = Manufacturer cost of goods sold, and 

GMMFG = Manufacturer gross margin. 

5.5 APPROACH FOR CALCULATING WHOLESALER AND CONTRACTOR 

MARKUPS

DOE examined the manner in which markups change by efficiency level and other 

factors for wholesalers and contractors. DOE determined that markups are neither fixed-dollar, 

nor proportional to all direct costs, which means that the selling price of a product may not be 

strictly proportional to the purchase price of the equipment. Using the available data, DOE has 
5-7 

found measurable differences between incremental markups on direct equipment costs and the 

average aggregate markup on direct business costs. Additionally, DOE discovered significant 

differences between average and incremental markups for heating, ventilation, air-conditioning 

and refrigeration (HVACR) wholesalers and for HVAC contractors. 

The main reason that the selling price of a product may not be strictly proportional to the 

purchase price of the equipment is that businesses incur a wide variety of costs. When the 

purchase price of equipment and materials increases, only a fraction of the business expenses 

increase, while the remainder of the businesses’ expenses stays relatively constant. Certain 

business expenses are uncorrelated with the cost of equipment or cost of goods. For example, if 

the unit price of an air conditioner increases by 30 percent, it is unlikely that the cost of 

secretarial support in an administrative office will increase by 30 percent also.

5.5.1 Key Data and Assumptions of the Markups Methodology

DOE derived the wholesaler and contractor markups from three key assumptions about 

the costs associated with furnaces and central air conditioners and heat pumps. DOE based the 

wholesaler and mechanical contractor markups on firm-level income statement data, while it 

based the general contractor markups on U.S. Census Bureau data for the residential building 

construction industry. DOE obtained the firm income statements from the Heating, Airconditioning & Refrigeration Distributors International (HARDI) 2010 Profit Planning Report 

and from the Air Conditioning Contractors of America (ACCA).1 2 HARDI and ACCA are trade 

associations representing wholesalers and mechanical contractors, respectively. DOE used the 

financial data from the 2007 US Census of Business to develop general contractor markups in 

the same form as the income statement data for wholesalers and mechanical contractors.3 4 These 

income statements break down the components of all costs incurred by firms that supply and 

install air conditioning equipment.a

 

The key assumptions used to estimate markups using these financial data are: 

 

• The firm income statements faithfully represent the various average costs incurred by 

firms distributing and installing HVAC products including commercial air conditioners.

• These costs can be divided into two categories: 1) costs that vary in proportion to the 

manufacturer selling price (MSP) of commercial air conditioners (variant costs); and 2) 

costs that do not vary with the MSP commercial air conditioners (invariant costs).

• Overall, commercial air-conditioner wholesaler and contractor prices vary in proportion 

to commercial air-conditioner wholesaler and contractor costs included in the income 

statements.

 In support of the first assumption, the income statements itemize firm costs into a 

number of expense categories, including direct costs to purchase or install the equipment, 

 

a Wholesalers and mechanical contractors to which these reports refer handle multiple commodity lines, including 

residential and commercial air-conditioners.
5-8 

operating labor and occupancy costs, and other operating costs and profit. Although wholesalers 

and contractors tend to handle multiple commodity lines, including room air conditioners, 

furnaces, central air conditioners and heat pumps, and boilers, the data provide the most accurate 

available indication of the expenses associated with commercial air conditioners.

 Information obtained from the trade literature, and from selected heating, ventilating, and 

air-conditioning (HVAC) wholesalers, contractors, and consultants, tends to support the second 

assumption; this information indicates that wholesale and contractor markups vary according to 

the quantity of labor and materials used to distribute and install appliances In its analysis, DOE 

assumes a division of costs between those that do not scale with the manufacturer price (labor 

and occupancy expenses), and those that do (operating expenses and profit). This division of 

costs led to the estimate of wholesale and contractor markups described below. 

In support of the third assumption, the HVAC wholesaler and contractor industry is 

competitive, and consumer demand for commercial heating and air conditioning is inelastic, i.e., 

the demand is not expected to decrease significantly with an increase in price of equipment. The 

large number of HVAC firms listed in the 2007 Census indicates the competitive nature of the 

market. For example, there are more than 700 HVAC manufacturers,5

 300 wholesalers of heat 

pumps and air-conditioning equipment,6 more than 170,000 general residential contractors, 

36,000 commercial and institutional building contractors,7,8 and 91,000 HVAC contractors listed 

in the 2007 Census.9 Additionally, the 2007 Census estimated that the four firm concentration 

ratio (FFCR) for the HVAC wholesale sector is 29.7%.10 The FFCR is the market share of the 

four largest firms in the industry; FFCR under 40% represents a competitive industry.11,12 

Following standard economic theory, competitive firms facing inelastic demand either set prices 

in line with costs or quickly go out of business.13

5.5.2 Approach for Wholesaler Markup

Using the above assumptions, DOE developed baseline and incremental markups for 

wholesalers using the firm income statement from the Heating, Air-conditioning & Refrigeration 

Distributors International (HARDI) 2010 Profit Report (Appendix 5A.1). DOE used the baseline 

markups, which cover all of the wholesaler’s costs (both invariant costs and variant costs), to 

determine the sales price of baseline models. Here variant costs were defined as costs that vary in 

proportion to the change in MSP induced by increased efficiency standards; in contrast, invariant 

costs were defined as costs that do not vary in proportion to the change in MSP due to increased 

efficiency standards. The baseline markup relates the manufacturer sales price to the wholesaler 

sales price. DOE calculated the baseline markup for wholesalers using the following equation: 

MMMMWWWWWWWW _BBBBBBBB = CCCCCCWWWWWWWW + WWWWWWWW

CCCCCCWWWWWWWW

= CCCCCCWWWWWWWW + (IIII WWWWWWWW + WWWWWWWW )

CCCCCCWWWWWWWW

Eq. 5.6 

Where:

MUWHOLE_BASE = Baseline wholesaler markup, 

CGSWHOLE = Wholesaler cost of goods sold, 
5-9 

GMWHOLE = Wholesaler gross margin, 

IVCWHOLE = Wholesaler invariant costs, and 

VCWHOLE = Wholesaler variant costs.

Incremental markups are coefficients that relate the change in the manufacturer sales 

price of more energy-efficient models, or those products that meet the requirements of new 

energy conservation standards, to the change in the wholesaler sales price. Incremental markups 

cover only those costs that scale with a change in the manufacturer’s sales price (i.e., variant 

costs, VC). DOE calculated the incremental markup for wholesalers using the following 

equation: 

MMMMWWWWWWWW _II = CCCCCCWWWWWWWW + WWWWWWWW

CCCCCCWWWWWWWW

Eq. 5.7 

Where:

MUWHOLE_INCR = Incremental wholesaler markup,

CGSWHOLE = Wholesaler cost of goods sold, and 

VCWHOLE= Wholesaler variant costs.

5.5.3 Approach for Mechanical and General Contractor Markup

Similar financial data to that used to estimate wholesaler markups are also available for 

mechanical contractors and general contractors from ACCA and the 2007 Economic Census.2, 9

To estimate mechanical contractor markups for furnaces and central air conditioners and heat 

pumps, DOE collected financial data from ACCA and from the Plumbing and HVAC 

Contractors (NAICS 23822) series from the 2007 Economic Census. To estimate general 

contractor markups, DOE collected data from the Commercial and Institutional Building 

Construction series from the 2007 Economic Census (NAICS 236220). 

ACCA financial data provides gross margin (GM) as percent of sales for the mechanical 

contractor industry; therefore, the mechanical contractor baseline markup is derived using the 

following equation: 

MMMMMMMM MMMMMM MMMM_BBBBBBBB = SSSS (%)

SSSS (%) − (%)

Eq. 5.8 

 The 2007 Economic Census data include the number of establishments, payroll for 

construction workers, value of construction, cost of materials, and cost of subcontracted work at 

both state and national level. DOE calculated another estimate of the baseline markup for 

mechanical contractors and general contractors using the following equation: 
5-10 

MMMMBBBBBBBB = CC

PP + MM + SS

Eq. 5.9 

Where:

MUBASE = Baseline mechanical contractor or general contractor markup,

VCONSTRUCT = Value of construction, 

PAY = Payroll for construction workers, 

MATCOST = Cost of materials, and

SUBCOST = Cost of subcontracted work. 

Analogously, DOE estimated the incremental mechanical contractor and general 

contractor markups by only considering those costs that scale with a change in the 

manufacturer’s sales price (variant costs, VC) for higher energy efficiency products. As stated in 

Section 5.5.1, DOE assumes a division of costs between those that do not scale with the 

manufacturer price (labor and occupancy expenses), and those that do (other operating expenses 

and profit). Hence, DOE categorized the 2007 Economic Census cost data in each major cost 

category and estimated incremental contractor markups using the following equation: 

MMMM _II = CCCCCC +

CCCCCC

Eq. 5.10 

Where:

MUCONTRACT_INCR = Incremental contractor markup,

CGSCONTRACT = Contractor cost of goods sold, and 

VCCONTRACT= Contractor variant costs. 

5.6 DERIVATION OF MARKUPS

5.6.1 Manufacturer Markups

For this rulemaking, DOE relied on the manufacturer markups developed for central air 

conditioners and heat pumps, which is a markup of 1.30.b

 DOE applied this markup to all 

equipment classes.

5.6.2 Wholesaler Markups

Wholesalers reported median data in a confidential survey that HARDI conducted across 

its members. In the survey, HARDI itemized revenues and costs into categories, including direct 

equipment expenses (cost of goods sold), labor expenses, occupancy expenses, other operating 

expenses, and profit. DOE presents these data in full in appendix 5A.1, Detailed Data for 

 

b www1.eere.energy.gov/buildings/appliance_standards/pdfs/cacfurn_dfr.pdf
5-11 

Equipment Price Markups. Table 5.6.1 summarizes them in terms of cost-per-dollar sales 

revenue in the first data column and in terms of cost-per-dollar of cost of goods sold in the 

second column. 

Table 5.6.1 Wholesaler Expenses and Markups

Descriptions

Per Dollar

Sales Revenue

$

Per Dollar Cost 

of Goods 

$

Direct Cost of Equipment Sales: Cost of goods sold 0.737 1.000

Labor Expenses: Salaries and benefits 0.151 0.205

Occupancy Expense: Rent, maintenance, and utilities 0.036 0.049

Other Operating Expenses: Depreciation, advertising, and 

insurance. 0.055 0.075 

Operating Profit 0.020 0.027

Baseline Revenue: Baseline revenue earned per dollar cost of goods sold 1.357

Wholesaler Baseline Markup (MUWHOLE BASE) 1.357

Incremental Revenue: Increased revenue per dollar increase cost of goods 

sold 1.103 

Incremental Markup (MUWHOLE INCR) 1.103

Source: Heating, Air Conditioning & Refrigeration Distributors International. 2012. 2012 Profit 

Report (2010 Data). 

In this case, direct equipment expenses (cost of goods sold) represent about $0.74 per 

dollar sales revenue, so for every $1 wholesalers take in as sales revenue, $0.74 is used to pay 

the direct equipment costs. Labor expenses represent $0.15 per dollar sales revenue, occupancy 

expenses represent $0.04, other operating expenses represent $0.06, and profit accounts for $0.02 

per dollar sales revenue.

DOE converted the expenses per dollar sales into expenses per dollar cost of goods sold, 

by dividing each figure in the first data column by $0.74 (i.e., cost of goods sold per dollar of 

sales revenue). For every $1.00 the wholesaler spends on equipment costs, the wholesaler spends 

$0.205 to cover labor costs, $0.049 to cover occupancy expenses, $0.075 for other operating 

expenses, and $0.027 in profits. This totals to $1.357 in sales revenue earned for every $1.00 

spent on equipment costs. Therefore, the wholesaler baseline markup (MUWHOLE BASE) is 1.357 

($1.357 ÷ $1.00). 

DOE also used the data in column two to estimate the incremental markups. The 

incremental markup depends on which of the costs in Table 5.6.1 are variant or invariant with 

respect to MSP. For example, for a $1.00 increase in the MSP, if all of the other costs scale with 

the MSP (i.e., all costs are variant), the increase in wholesale price will be $1.357, implying that 

the incremental markup is 1.357, or the same as the baseline markup. At the other extreme, if no 

other costs are variant, then a $1.00 increase in the MSP will lead to a $1.00 increase in the 

wholesale price, for an incremental markup of 1.0. 

As stated in section 5.5 DOE believes that the labor and occupancy costs are invariant 

with respect to MSP, while other operating costs and profit are variant. In this case, for a $1.00 
5-12 

increase in the MSP, the wholesale price will increase in line with changes in the categories 

“other operating costs” and “operating profit”; these are approximately of $0.067, which when 

divided by $0.737 cents in cost of goods sold yields an increase of $0.103, giving a wholesaler 

incremental markup (MUWHOLE INCR) of 1.103. See appendix 5.A for cost details. 

5.6.3 Mechanical Contractor Markups

5.6.3.1 Aggregate Baseline Markups for Mechanical Contractors

The HVAC contractors, represented here as mechanical contractors, reported median cost 

data in an ACCA financial analysis of the HVAC industry. These data are shown in Table 5.6.2

Table 5.6.2 Baseline Markup, All Mechanical Contractors

Description

Contractor Expenses or Revenue

Per Dollar

Sales Revenue

$

Per Dollar 

Cost of Goods 

$

Direct Cost of Equipment Sales: Cost of goods sold 0.73 1.00

Gross Margin: Labor, occupancy, operating expenses, 

and profit 0.27 0.37 

Revenue: Baseline revenue earned per dollar cost of goods 1.37

Baseline Markup (MUMECH CONT BASE) 1.37

Source: Air Conditioning Contractors of America. 2005. Financial Analysis for the HVACR Contracting Industry.

Data reported in this analysis are not as detailed as those provided by HARDI and the 

U.S. Census, containing only cost of goods sold and gross margin. Since the gross margin is the 

sum of labor, occupancy expenses, operating expenses, and profit, the data are sufficient to 

estimate the baseline markup. The first data column in Table 5.6.2 provides the cost of goods 

sold and gross margin, expressed as dollars per dollar of sales revenue. The only important 

difference is that the direct cost of equipment sales in this table includes installation costs as well 

as the cost of goods sold. As shown in the table, the direct cost of sales represents about $0.73 

per dollar sales revenue to the contractor and the gross margin is $0.27 per dollar sales revenue. 

DOE converted these expenses per dollar sales into revenue per dollar cost of goods sold 

by dividing each figure in the first data column by $0.73. For every $1.00 the mechanical 

contractor spends on equipment costs, the mechanical contractor earns $1.00 in sales revenue to 

cover the equipment cost, and $0.37 in sales revenue to cover the other costs. This totals $1.37 in 

sales revenue earned for every $1.00 spent on equipment costs, for a baseline markup (MUMECH 

CONT BASE) of 1.37 for mechanical contractors. This is the beginning point for the determination 

of mechanical contractor markups. 

The baseline markup derived above for mechanical contractors is the average value for 

all contractors, including large and small contractors serving the replacement and new 

construction markets. The following sections describe how DOE further derived the baseline and 

incremental markups for four distinct categories of mechanical contractors: (1) small contractors 

in the replacement market; (2) small contractors in the new construction market; (3) large 

contractors in the replacement market; and (4) large contractors in the new construction market. 
5-13 

5.6.3.2 Baseline Markups for Mechanical Contractors in the Replacement and 

New Construction Markets

As presented above for all mechanical contractors in aggregate , the ACCA financial data 

provided only gross margin data, which allows for the determination of only the baseline markup 

for the four types of contractor, since the gross margin is the sum of all contractor labor and 

operating expenses plus profit. 

Table 5.6.3 summarizes the gross margin and resulting baseline markup data for all 

mechanical contractors that serve the replacement and new construction markets. Note that while 

this data is disaggregated by the market served, it aggregates mechanical contractors of all sizes.

Table 5.6.3 Baseline Markups for the Replacement and New Construction Markets, All 

Mechanical Contractors

Description

Contractor Expenses or Revenue by Market Type

Replacement New Construction

Per Dollar

Sales 

Revenue

$

Per Dollar 

Cost of 

Goods 

$

Per Dollar

Sales 

Revenue

$

Per Dollar 

Cost of 

Goods 

$

Direct Cost of Equipment Sales: 

Cost of goods sold 0.703 1.000 0.745 1.000 

Gross Margin: Labor, occupancy, 

operating expenses, and profit 0.297 0.422 0.255 0.342 

Baseline Markup (MUMECH CONT 

BASE): Revenue per dollar cost of 

goods

NA 1.42 NA 1.34 

% Difference from Aggregate 

Mechanical Contractor Baseline 

MU

NA 3.6% NA -2.2% 

Source: Air Conditioning Contractors of America. 2005. Financial Analysis for the HVACR Contracting Industry.

Using the baseline markup data for replacement and new construction contractors from 

Table 5.6.3, DOE calculated that the baseline markups for the replacement and new markets are 

3.6 percent higher and 2.2 percent lower, respectively, than that for all mechanical contractors 

serving all markets shown in Table 5.6.2. These percentage differences are later used, along 

with those defined in section 5.6.3.3 to estimate separate markups for the four categories of 

mechanical contractor of interest.

5.6.3.3 Baseline Markups for Small and Large Mechanical Contractors

ACCA provides data for contracting businesses earning annual revenues in each of the 

four following categories: (1) below one million dollars; (2) from one to two million dollars; (3) 

from two to five million dollars; and (4) over five million dollars. DOE aggregated the data into 

two categories: contractors earning annual revenue below two million dollars and contractors 

earning annual revenue above two million dollars. The values reported for small contractors are 
5-14 

the average of the categories: (1) below one million dollars, and (2) from one to two million 

dollars. The values reported for large contractors are the average of the categories: (3) from two 

to five million dollars, and (4) over five million dollars. 

Table 5.6.4 Baseline Markups for Small and Large Mechanical Contractors

Description

Contractor Expenses or Revenue by Market Type

Small ( < $2 million) Large ( > $2 million)

Per Dollar

Sales 

Revenue

$

Per Dollar 

Cost of 

Goods 

$

Per Dollar

Sales 

Revenue

$

Per Dollar 

Cost of 

Goods 

$

Direct Cost of Equipment Sales: 

Cost of goods sold 0.653 1.000 0.753 1.000 

Gross Margin: Labor, occupancy, 

operating expenses, and profit 0.347 0.532 0.247 0.328 

Baseline Markup (MUMECH CONT 

BASE): Revenue per dollar cost of 

goods

NA 1.53 NA 1.33 

% Difference from Aggregate 

Mechanical Contractor Baseline 

MU

NA 11.6% NA -3.2% 

Source: Air Conditioning Contractors of America. 2005. Financial Analysis for the HVACR Contracting Industry.

Using the baseline markup data for small and large contractors from Table 5.6.4, DOE 

calculated that the baseline markups for small and large are 11.6 percent higher and 3.2 percent 

lower, respectively, than that for all mechanical contractors serving all markets shown in Table 

5.6.2. 

5.6.3.4 Baseline Markups by Disaggregated Mechanical Contractor Type 

Using the percentage difference from the aggregate mechanical contractor baseline 

markup (Table 5.6.2), DOE then estimated the baseline markup for each of the four mechanical 

contractor types: (1) small contractors in the replacement market; (2) small contractors in the 

new construction market; (3) large contractors in the replacement market; and (4) large 

contractors in the new construction market. The baseline markup for each mechanical contractor 

type is estimated using the following equation:

MMMMMMMM MMMMMM MMMM_BBBBBBBB = MMMM + MMMM × μμ SS + MMMM × μμMM MMMM MM

Eq. 5.11 

 

Where:

MUAGGREGATE = Baseline markup for aggregate of all mechanical contractors,

μSIZE = Size modifier (small or large contractor percentage difference from 

aggregate of all contractors’ markup), 
5-15 

μMARKET = Market modifier (replacement or new construction contractor percentage 

difference from aggregate of all contractors’ markup), and, 

MUMCONTRACT_BASE = Baseline markup for specific mechanical contractor type.

Table 5.6.5 Baseline Markups by Mechanical Contractor Type

Mechanical Contractor Type Size 

Modifier

Market 

Modifier

Baseline 

Size Market Markup

Small Replacement 11.6% 3.6% 1.58

New Construction 11.6% -2.2% 1.50

Large Replacement -3.2% 3.6% 1.38

New Construction -3.2% -2.2% 1.30

5.6.3.5 Incremental Markups by Mechanical Contractor Type

Calculation of the incremental markups for each mechanical contractor type required 

separation of the invariant and variant costs. Since this was not possible using the ACCA data, 

DOE used more detailed 2007 U.S. Census Bureau data for all plumbing, heating, and airconditioning contractors. The U.S. Census Bureau data were used to calculate the baseline and 

incremental markups as shown in Table 5.6.6 . Appendix 5A.2 contains the full set of data used 

in this calculation.

Table 5.6.6 Mechanical Contractor Disaggregated Expenses and Markups Based on 2007 

U.S. Census Bureau Data

Description

Contractor Expenses or Revenue

Per Dollar

Sales Revenue

$

Per Dollar 

Cost of Goods 

$

Direct Cost of Equipment Sales: Cost of goods sold 0.68 1.00

Labor Expenses: Salaries (indirect) and benefits 0.17 0.26

Occupancy Expense: Rent, maintenance, and utilities 0.02 0.03

Other Operating Expenses: Depreciation, advertising, and 

insurance. 0.04 0.06 

Net Profit Before Taxes 0.09 0.13

Baseline Markup (MUMECH BASE): Revenue per dollar cost of goods 1.47

Incremental Markup (MUMECH INCR): Increased revenue per dollar increase cost 

of goods sold 1.18 

Percent difference between Baseline and Incremental Markup 20.0%

Source: U.S. Census Bureau. 2007. Plumbing, Heating, and Air-Conditioning Contractors. Sector 23: 238220. 

Construction: Industry Series, Preliminary Detailed Statistics for Establishments, 2007.
5-16 

Using the data in Table 5.6.6, the incremental markup is 20 percent lower than the 

baseline markup. By applying this 20 percent differential between baseline and incremental 

markup to the baseline markups estimated for the four specific mechanical contractor types, 

DOE estimates the incremental markup for each type (Table 5.6.7).

Table 5.6.7 Incremental Markups by Mechanical Contractor Type

Mechanical Contractor Type Incremental 

Size Market Markup

Small Replacement 1.26

New Construction 1.20

Large Replacement 1.10

New Construction 1.04

5.6.4 Estimation of General Contractor Markups

DOE derived markups for general contractors from U.S. Census Bureau data for the 

commercial building construction sector. The commercial construction sector includes 

establishments primarily engaged in construction work, including new construction work, 

additions, alterations, and repairs. 

The U.S. Census Bureau data for the construction sector include detailed statistics for 

establishments with payrolls, similar to the data reported by HARDI for wholesalers. The 

primary difference is that the U.S. Census Bureau reports itemized revenues and expenses in 

total dollars for the construction industry as a whole, rather than in typical values for an average 

or representative business. Because of this, DOE assumed that the total dollar values that the 

U.S. Census Bureau reported, once converted to a percentage basis, represented revenues and 

expenses for an average or typical contracting business. Similar to the data for wholesalers, 

Table 5.6.8 summarizes the expenses for general contractors as expenses per dollar sales revenue 

in the first data column (Appendix 5A.3 contains the full set of data). 
5-17 

Table 5.6.8 General Contractor Expenses and Markups

Description

Wholesale Firm Expenses or 

Revenue

Per Dollar

Sales Revenue

$

Per Dollar 

Cost of Goods 

$

Direct Cost of Equipment Sales: Cost of goods sold 0.76 1

Labor Expenses: Salaries (indirect) and benefits 0.08 0.10

Occupancy Expense: Rent, maintenance, and utilities 0.01 0.01

Other Operating Expenses: Depreciation, advertising, and 

insurance. 0.03 0.04 

Net Profit Before Taxes 0.12 0.15

Baseline Markup (MUGEN CONT BASE): Revenue per dollar cost of goods 1.31

Incremental Markup (MUGEN CONT INCR): Increased revenue per dollar increase 

cost of goods sold 1.19 

Source: U.S. Census Bureau. 2007. Sector 236220 (Commercial Building Construction). Construction: Industry 

Series: Preliminary Detailed Statistics for Establishments: 2007.

As shown in the first column, the direct cost of sales represents about $0.76 per dollar 

sales revenue to the general contractor. Labor expenses represent $0.08 per dollar sales revenue, 

occupancy expenses represent $0.01 per dollar sales revenue, other operating expenses represent 

$0.03, and profit makes up $0.12 per dollar sales revenue.

DOE converted these expenses per dollar sales into revenue per dollar cost of goods sold, 

by dividing each figure in the first data column by $0.76. The data in column two show that, for 

every $1.00 the general contractor spends on equipment costs, the general contractor earns $1.00 

in sales revenue to cover the equipment cost, $0.10 to cover labor costs, $0.01 to cover 

occupancy expenses, $0.04 for other operating expenses, and $0.15 in profits. This totals to 

$1.31 in sales revenue earned for every $1.00 spent on equipment costs. Thus, the general 

contractor baseline markup (MUGCONTRACT_ BASE) is 1.31. 

DOE was also able to use the data in column two of Table 5.6.8 to estimate the 

incremental markups, after classifying the costs as either invariant or variant. At one extreme, if 

all of the other costs scale with the equipment price (i.e., all costs are variant), the increase in 

general contractor price will be $1.48, implying that the incremental markup is 1.48, or the same 

as the baseline markup. At the other extreme, if none of the other costs are variant, then a $1.00 

increase in the equipment price will lead to a $1.00 increase in the general contractor price, for 

an incremental markup of 1.0. DOE believes the labor and occupancy costs are invariant, while 

the other operating costs and profit to scale with the equipment price (i.e., are variant). In this 

case, for a $1.00 increase in the equipment price, the general contractor price will increase by 

$1.19, giving a general contractor incremental markup (MUGCONTRACT_INCR) of 1.19. 
5-18 

5.7 SALES TAX

The sales tax represents state and local sales taxes that are applied to the consumer price 

of the equipment. The sales tax is a multiplicative factor that increases the consumer equipment 

price.

DOE derived state and local taxes from data provided by the Sales Tax Clearinghouse.14

These data represent weighted averages that include county and city rates. DOE then derived 

population-weighted average tax values for each Census division and large state, as shown in 

Table 5.7.1 below. The average sales tax presented in the bottom row of the table is the weighted 

average accounting for commercial building floor space with packaged cooling by Census 

division, calculated using CBECS 2003. Detailed sales tax data by each state can be found in 

Appendix 5A.4. 

Table 5.7.1 Average Sales Tax Rates by Census Division and Large State 

Census Division/State Tax Rate

New England 6.91%

Mid Atlantic 8.01%

East North Central 7.47%

West North Central 6.44%

South Atlantic 5.68%

East South Central 7.65%

West South Central 6.50%

Mountain 7.09%

Pacific 8.13%

New York 8.40%

California 8.45%

Texas 7.90%

Florida 6.65%

Average 7.16%

Source: The Sales Tax Clearinghouse at https://thestc.com/STRates.stm (Accessed March, 2014)

5.8 OVERALL MARKUPS

The overall markup for each distribution channel is the product of the appropriate 

markups, as well as sales tax in the case of replacement applications. These values are provided 

in Table 5.8.1. 
5-19 

Table 5.8.1 Summary of Overall Markups (3-phase, air cooled, air conditioning and heat 

pumps <65,000 Btu/h and water source heat pumps)

Distribution Channel 

Segment

Replacement New Construction

Baseline 

Markup

Incremental 

Markup

Baseline 

Markup

Incremental 

Markup

Manufacturer 1.30 1.30 1.30 1.30

Wholesaler 1.36 1.10 1.36 1.10

Mechanical Contractor 

(large/small) 1.38 / 1.58 1.10 / 1.26 1.30 / 1.50 1.04 / 1.20 

General Contractor 

(new construction only) -- -- 1.31 1.20 

Sales tax 

(replacements only) 1.07 1.07 -- --

Overall Markup

(large/small contractor) 2.60 / 2.99 1.70 / 1.94 3.00 / 3.47 1.78 / 2.06 
5-20 

REFERENCES

1

 Heating Air Conditioning & Refrigeration Distributors International, 2010 Profit Report, 2010. 

2

 Air Conditioning Contractors of America, 2005 Financial Analysis for the HVACR Contracting 

Industry, 2005. 

3

 U.S. Census Bureau, Data set for Sector 23, EC0723A1, Construction: Geographic Area Series, 

Detailed Statistics for Establishments, 2007. 

4

 U.S. Census Bureau, Data set for Sector 23: EC0723I1, Construction: Industry Series, 

Preliminary Detailed Statistics for Establishments: 2007. 2007.

5 U.S. Census Bureau, Manufacturing Industry Series: Detailed Statistics for the United States: 

2007. Air-Conditioning and Warm Air Heating Equipment and Commercial and Industrial 

Refrigeration Equipment Manufacturing. Data set for Sector 31: EC073111 2007. 

6 U.S. Census Bureau, Wholesale Trade: Industry Series: Preliminary Summary Statistics for the 

United States: 2007. HVAC Equipment Merchant Wholesaler. Data set for Sector 42: 

EC0742111, 2007. 

7

 U.S. Census Bureau, Construction: Industry Series: Preliminary Detailed Statistics for 

Establishments: 2007. Commercial and Institutional Building Construction. , 2007. 

8

 U.S. Census Bureau, Construction: Industry Series: Preliminary Detailed Statistics for 

Establishments: 2007. New Single-Family General Contractors, New Multifamily Housing 

Construction (Except Operative Builders), New Housing Operative Builders Residential., 

2007. 

9

 U.S. Census Bureau, Plumbing, Heating, and Air-Conditioning Contractors. Sector 23: 

238220. Construction: Industry Series, Preliminary Detailed Statistics for Establishments, 

2007., 2007. 

10 U.S. Census Bureau, Wholesale Trade: Subject Series: Estab and Firm Size: Summary 

Statistics by Concentration of Largest Firms for the United States: 2007. Data set for Sector 

42: EC074373. 2007. 

11 University of Maryland University College, Note on Industry Structure, 2002. (Last accessed 

February, 2013.) <http://info.umuc.edu/mba/public/AMBA607/IndustryStructure.html

12 QuickMBA, Industry Concentration, 2010. (Last accessed February, 2013.) 

<www.quickmba.com/econ/micro/indcon.shtml> 

13 Pindyck, R. and D. Rubinfeld, Microeconomics. 2000. Prentice Hall. 

14 The Sales Tax Clearinghouse, <https://thestc.com/STRates.stm> 
5A-i

APPENDIX 5A. DETAILED DATA FOR EQUIPMENT PRICE MARKUPS

TABLE OF CONTENTS

5A.1 DETAILED WHOLESALER COST DATA ............................................................... 5A-1

5A.2 DETAILED MECHANICAL CONTRACTOR DATA............................................... 5A-2

5A.3 DETAILED GENERAL CONTRACTOR COST DATA............................................ 5A-3

5A.4 STATE SALES TAX RATES...................................................................................... 5A-5

REFERENCES ......................................................................................................................... 5A-6

LIST OF TABLES

Table 5A.1.1 Disaggregated Costs and Expenses for Wholesalers.................................. 5A-1

Table 5A.2.1 Mechanical Contractor Expenses and Markups Used To Scale the 

Incremental Markups.................................................................................. 5A-3

Table 5A.3.1 General Contractor Expenses and Markups................................................ 5A-4

Table 5A.4.1 State Sales Tax Rates (Percent) .................................................................. 5A-5
5A-1

APPENDIX 5A. DETAILED DATA FOR EQUIPMENT PRICE MARKUPS

5A.1 DETAILED WHOLESALER COST DATA

Based on data provided by the Heating Air-conditioning & Refrigeration Distributors 

International (HARDI), Table 5.6.1 of chapter 5 shows wholesaler revenues and costs in 

aggregated form.1 Table 5A.1.1 below provides the complete breakdown of costs and expenses. 

The column labeled “Scaling” in Table 5A.1.1 indicates which expenses the U.S. Department of 

Energy (DOE) assumed to scale with only the baseline markup and which with both the baseline 

and incremental markups. As described in chapter 5, section 5.5, only those expenses that scale 

with both baseline and incremental costs are marked up when there is an incremental change in 

equipment costs.

Table 5A.1.1 Disaggregated Costs and Expenses for Wholesalers

Item

Percent of Revenue

% Scaling

Cost of Goods Sold 73.7

Gross Margin 26.3

Payroll Expenses 15.1 Baseline

Executive Salaries & Bonuses 1.7

Branch Manager Salaries and Commissions 1.5

Sales Executive Salaries & Commissions 0.5

Outside Sales Salaries & Commissions 2.1

Inside/Counter Sales/Wages 2.8

Purchasing Salaries/Wages 0.4

Credit Salaries/Wages 0.2

IT Salaries/Wages 0.1

Warehouse Salaries/Wages 1.4

Accounting 0.5

Delivery Salaries/Wages 0.7

All Other Salaries/Wages & Bonuses 0.8

Payroll Taxes 1.0

Group Insurance 1.1

Benefit Plans 0.3

Occupancy Expenses 3.6 Baseline

Utilities: Heat, Light, Power, Water 0.4

Telephone 0.3

Building Repairs & Maintenance 0.2

Rent or Ownership in Real Estate 2.7
5A-2

Table 5A.1.1 Continued Disaggregated Costs and Expenses for Wholesalers

Other Operating Expenses 5.5 Baseline & Incremental

Advertising & Promotion 0.9

Insurance (business liability & casualty) 0.2

Depreciation 0.4

Vehicle Expenses 1.4

Personal Property Taxes/Licenses 0.1

Collection Expenses 0.3

Bad Debt Losses 0.3

All Other Operating Expenses 1.7

Total Operating Expenses 24.2

Operating Profit 2.1 Baseline & Incremental

Other Income 0.4

Interest Expense 0.5

Other Non-operating Expenses 0.0

Profit Before Taxes 2.0

Source: Heating, Air-conditioning & Refrigeration Distributors International. 2012. 2012Profit Report (2010 data).

Note: The wholesaler costs and expenses are percentage values as opposed to the per-dollar of sales revenue values shown in Table 6.6.1.

5A.2 DETAILED MECHANICAL CONTRACTOR DATA

Tables 5.6.2, 5.6.3, and 5.6.4 of chapter 5 provide mechanical contractor revenues and 

costs in aggregated form by ‘Cost of Goods Sold’ and ‘Gross Margin.’ The tables are based on 

data in the 2005 edition of Financial Analysis for the HVACR Contracting Industry, published by 

the Air Conditioning Contractors of America (ACCA).2

 The ACCA report did not provide a 

more disaggregate tabulation of these costs and expenses. The gross margin category was 

assumed to scale only with the baseline markup.

The more disaggregated breakdown of costs used to scale the incremental markup are 

shown in Table 5A.2.1, presented in both dollar value and percentage terms from the 2007 

Economic Census.3 As the ACCA data were used to calculate the baseline markup, in Table 

5A.2.1 only the categories in the ‘Scaling’ column that scaled with both the baseline and 

incremental markups are marked when there is an incremental change in equipment costs.
5A-3

Table 5A.2.1 Mechanical Contractor Expenses and Markups Used To Scale the Incremental 

Markups

Item

Dollar Value

$1000

Percentage

% Scaling

Total Cost of Equipment Sales 107,144,428 67.80

Cost of materials, components, and supplies 59,023,964 37.35

Payroll, construction workers 31,373,558 19.85

Cost of construction work subcontracted out to others 13,646,192 8.64

Cost of selected power, fuels, and lubricants 3,100,714 1.96

Gross Margin 50,895,129 32.20

Payroll Expenses 27,626,376 17.48 Baseline

Fringe benefits, all employees 13,585,040 8.60

Payroll, other employees 14,041,336 8.89

Occupancy Expenses

Rental cost for machinery, equipment, and buildings + 

Cost of repairs to buildings and other structures + 

Purchased communication services

3,436,208 2.17 Baseline

Other Operating Expenses 6,165,776 3.90 Baseline & 

Depreciation charges during year Incremental 2,297,550 1.50

Computers + Insurance and other business services + 

Advertising and Promotions + Taxes and License Fees

3,868,226 2.40

Net Profit Before Income Taxes 13,666,769 8.60 Baseline & 

Incremental

Source: U.S. Census Bureau. 2007. Plumbing, Heating, and Air-Conditioning Contractors: 2007. Sector 23: 238220. Construction: Geographic 

Area Series. Detailed Statistics for Establishments: 2007.

Note: Mechanical contractor costs and expenses are first presented as total dollar values and then converted to percentage values.

5A.3 DETAILED GENERAL CONTRACTOR COST DATA

Based on U.S. Department of Census data, Table 5.6.8 of chapter 5 shows general 

contractor revenues and costs in aggregated form. Table 5A.3.1 below shows the complete 

breakdown of costs and expenses provided in the 2007 Economic Census.

4 The column labeled 

“Scaling” in Table 5A.4.1 indicates which expenses DOE assumed to scale with only the 

baseline markup and which scaled with both the baseline and incremental markups. Only those 

expenses that scale with baseline and incremental costs are marked up when there is an 

incremental change in equipment costs.
5A-4

Table 5A.3.1 General Contractor Expenses and Markups

Item

Dollar Value 

$1000

Percentage 

% Scaling

Total Cost of Equipment Sales 250,657,006 76.24

Total payroll, construction workers wages 16,449,830 5.00

Cost of materials, components, and supplies 74,148,280 22.55

Cost of construction work subcontracted out to others 157,873,840 48.02

Total cost of selected power, fuels, and lubricants 2,185,056 0.66

Gross Margin 78,113,967 23.76

Payroll Expenses 25,948,454 7.89

Baseline Total payroll, other employees wages 16,652,791 5.07

Total fringe benefits 8,666,079 2.64

Temporary staff and leased employee expenses 629,584 0.19

Occupancy Expenses 3,301,046 1.00

Baseline

Rental costs of machinery and equipment 1,403,979 0.43

Rental costs of buildings 1,045,163 0.32

Communication services 385,109 0.12

Cost of repair to machinery and equipment 466,795 0.14

Other Operating Expenses 10,770,620 3.28

Baseline & 

Incremental

Purchased professional and technical services 1,121,644 0.34

Data processing and other purchased computer services 127,031 0.04

Expensed computer hardware and other equipment 219,601 0.07

Expensed purchases of software 67,977 0.02

Advertising and promotion services 290,239 0.09

All other expenses 6,321,197 1.92

Refuse removal (including hazardous waste) services 233,831 0.07

Taxes and license fees 807,872 0.25

Total depreciation ($1,000) 1,581,228 0.48

Net Profit Before Income Taxes 38,093,847 11.59 Baseline & 

Incremental

Source: U.S. Census Bureau. 2007. Commercial and Institutional Building Construction. Sector 236220. Construction, Industry Series, Preliminary 

Detailed Statistics for Establishments: 2007.

Note: General contractor costs and expenses are first presented as total dollar values and then converted to percentage values. 
5A-5

5A.4 STATE SALES TAX RATES

Table 5A.4.1 State Sales Tax Rates (Percent)

State

Combined 

State and Local 

Tax Rate State

Combined 

State and 

Local Tax Rate State

Combined 

State and 

Local Tax Rate

Alabama 8.55% Louisiana 8.75% Ohio 6.80%

Alaska 1.35% Maine 5.00% Oklahoma 8.35%

Arizona 8.15% Maryland 6.00% Oregon 0.00%

Arkansas 8.35% Massachusetts 6.25% Pennsylvania 6.40%

California 8.40% Michigan 6.00% Rhode Island 7.00%

Colorado 6.15% Minnesota 7.20% South Carolina 7.10%

Connecticut 6.35% Mississippi 7.00% South Dakota 5.40%

Delaware 0.00% Missouri 6.60% Tennessee 9.45%

Florida 6.65% Montana 0.00% Texas 7.95%

Georgia 7.10% Nebraska 6.00% Utah 6.70%

Hawaii 4.40% Nevada 7.85% Vermont 6.05%

Idaho 6.05% New Hampshire 0.00% Virginia 5.00%

Illinois 8.05% New Jersey 6.95% Washington 8.90%

Indiana 7.00% New Mexico 6.60% West Virginia 6.05%

Iowa 6.85% New York 8.40% Wisconsin 5.45%

Kansas 8.00% North Carolina 6.85% Wyoming 5.35%

Kentucky 6.00% North Dakota 5.90%

Source: The Sales Tax Clearinghouse at https://thestc.com/STRates.stm (Accessed on February 2013)
5A-6

REFERENCES

1. Heating Air Conditioning & Refrigeration Distributors International, 2012 Profit Report, 

2012. <www.hardinet.org/Profit-Report>

2. Air Conditioning Contractors of America, 2005 Financial Analysis for the HVACR 

Contracting Industry, 2005. 

3. U.S. Census Bureau, Plumbing, Heating, and Air-Conditioning Contractors. Sector 23: 

238220. Construction: Industry Series, Preliminary Detailed Statistics for 

Establishments, 2007., 2007. 

4. U.S. Census Bureau, Construction: Industry Series: Preliminary Detailed Statistics for 

Establishments: 2007. Commercial and Institutional Building Construction. , 2007. 
6-i

CHAPTER 6. LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

TABLE OF CONTENTS

6.1 INTRODUCTION ........................................................................................................... 6-1

6.1.1 General Approach for Life-Cycle Cost and Payback Period Analysis................ 6-1

6.1.2 Overview of Life-Cycle Cost and Payback Period Analysis Inputs.................... 6-2

6.2 LIFE CYCLE COST ANALYSIS INPUTS.................................................................... 6-4

6.2.1 Total Installed Cost Inputs................................................................................... 6-5

6.2.1.1 Manufacturer Costs.................................................................................... 6-6

6.2.1.2 Markups...................................................................................................... 6-6

6.2.1.3 Total Customer Price.................................................................................. 6-6

6.2.1.4 Future Equipment Prices............................................................................ 6-7

6.2.1.5 Installation Cost.......................................................................................... 6-9

6.2.1.6 Total Installed Cost .................................................................................. 6-13

6.2.2 Operating Cost Inputs........................................................................................ 6-14

6.2.2.1 Annual Energy Use Savings..................................................................... 6-14

6.2.2.2 Energy Prices............................................................................................ 6-14

6.2.2.3 Energy Price Trends................................................................................. 6-15

6.2.2.4 Repair Cost............................................................................................... 6-16

6.2.2.5 Maintenance Cost..................................................................................... 6-18

6.2.2.6 Lifetime .................................................................................................... 6-20

6.2.2.7 Discount Rates.......................................................................................... 6-21

6.2.2.8 Compliance Date of Standard .................................................................. 6-24

6.2.2.9 Base Case Distribution of Efficiency Levels ........................................... 6-25

6.3 PAYBACK PERIOD INPUTS...................................................................................... 6-26

6.4 LCC AND PBP RESULTS............................................................................................ 6-27

REFERENCES .......................................................................................................................... 6-35

LIST OF TABLES 

Table 6.1.1 Summary of Inputs and Key Assumptions Used in the LCC and PBP 

Analysis.......................................................................................................... 6-3

Table 6.2.1 Summary of Average Markups...................................................................... 6-6

Table 6.2.2 Overall Markup for All Equipment Classes................................................... 6-6

Table 6.2.3 Average Customer Price for Small Commercial Air-cooled Air 

Conditioning and Heating Equipment (2014$).............................................. 6-7

Table 6.2.4 Average Customer Price for Water-Source Heat Pumps (2014$) ................. 6-7

Table 6.2.5 Installation Costs for Baseline Small, 3-phase Air-Cooled Air 

Conditioners and Heat Pumps <65,000 Btu/h.............................................. 6-10

Table 6.2.6 Installation Costs for Baseline Water-Source Heat Pumps.......................... 6-10
6-ii

Table 6.2.7 National Average Installation Costs for Small, 3-phase Air-Cooled Air 

Conditioners and Heat Pumps <65,000 Btu/h (2014$)................................ 6-11

Table 6.2.8 National Average Installation Costs for Water Source Heat Pumps 

(2014$)......................................................................................................... 6-11

Table 6.2.9 Installation Cost Indices by State (National Value = 100.0) ....................... 6-12

Table 6.2.10 Installation Cost Indices by Region (National Value = 100.0).................... 6-13

Table 6.2.11 Average Total Installed Cost for Small, 3-phase Air-Cooled Air 

Conditioners and Heat Pumps <65,000 Btu/h (2014$)................................ 6-13

Table 6.2.12 Average Total Installed Cost for Water-Source Heat Pumps (2014$) ........ 6-13

Table 6.2.13 Average and Marginal Tariff-based Prices by Region ................................ 6-15

Table 6.2.14 Repair Costs for 3-Phase, Air-Cooled, Air Conditioners < 65,000 

Btu/h............................................................................................................. 6-17

Table 6.2.15 RS Means Costs Used to Derive Repair Costs for 3-Phase, Air-Cooled 

Heat Pumps < 65,000 Btu/h and Water-Source Heat Pumps ...................... 6-17

Table 6.2.16 Total Repair Costs for 3-Phase, Air-Cooled, Air Conditioning and 

Heating Equipment < 65,000 Btu/h (2014$) ............................................... 6-18

Table 6.2.17 Total Repair Costs for Water Source Heat Pumps (2014$)......................... 6-18

Table 6.2.18 Annual Repair Costs for 3-Phase, Air-Cooled, Air Conditioning and 

Heating Equipment < 65,000 Btu/h (2014$) ............................................... 6-18

Table 6.2.19 Annual Repair Costs for Water Source Heat Pumps (2014$)...................... 6-18

Table 6.2.20 Maintenance Costs for Air-Cooled Air Conditioning and Heating 

Equipment.................................................................................................... 6-20

Table 6.2.21 Risk Free Rate and Equity Risk Premium (2011-2013) .............................. 6-22

Table 6.2.22 Weighted Average Cost of Capital for Sectors that Purchase 3-phase 

Air-Cooled, Air Conditioning and Heating Equipment............................... 6-23

Table 6.2.23 Weighted Average Cost of Capital for Sectors that Purchase WaterSource Heat Pumps...................................................................................... 6-23

Table 6.2.24 Base Case Efficiency Market Shares in 2020 for 3-phase Air-Cooled, 

Air Conditioning and Heating Equipment ................................................... 6-25

Table 6.2.25 Base Case Efficiency Market Shares in 2020 for Water-Source Heat 

Pumps........................................................................................................... 6-26

Table 6.4.1 Life Cycle and Payback Period Results for 3-phase, Air-Cooled, SplitSystem Air Conditioners <65,000 Btu/h...................................................... 6-27

Table 6.4.2 LCC Savings Relative to the Base-Case Efficiency Distribution for 3-

Phase Air-Cooled Split-System Air Conditioners <65,000 Btu/h ............... 6-28

Table 6.4.3 Life Cycle and Payback Period Results for 3-phase, Air-Cooled, 

Package Air Conditioners <65,000 Btu/h .................................................... 6-28

Table 6.4.4 LCC Savings Relative to the Base-Case Efficiency Distribution for 3-

Phase Air-Cooled Single-Package Air Conditioners <65,000 Btu/h........... 6-29

Table 6.4.5 Life Cycle and Payback Period Results for 3-phase, Air-Cooled, Split 

System Heat Pumps <65,000 Btu/h ............................................................. 6-29

Table 6.4.6 LCC Savings Relative to the Base-Case Efficiency Distribution for 3-

Phase Air-Cooled Split System Heat Pumps <65,000 Btu/h ....................... 6-30

Table 6.4.7 Life Cycle and Payback Period Results for 3-phase, Air-Cooled, 

Package Heat Pumps <65,000 Btu/h............................................................ 6-30
6-iii

Table 6.4.8 LCC Savings Relative to the Base-Case Efficiency Distribution for 3-

Phase Air-Cooled Package Heat Pumps <65,000 Btu/h .............................. 6-31

Table 6.4.9 Life Cycle and Payback Period Results for Water Source Heat Pumps 

<17,000 Btu/h .............................................................................................. 6-31

Table 6.4.10 LCC Savings Relative to the Base-Case Efficiency Distribution for 

Water Source Heat Pumps <17,000 Btu/h .................................................. 6-32

Table 6.4.11 Life Cycle and Payback Period Results for Water Source Heat Pumps 

≥17,000 to <65,000 Btu/h ............................................................................ 6-32

Table 6.4.12 LCC Savings Relative to the Base-Case Efficiency Distribution for 

Water Source Heat Pumps ≥17,000 to <65,000 Btu/h................................. 6-33

Table 6.4.13 Life Cycle and Payback Period Results for Water Source Heat Pumps 

≥65,000 to <135,000 Btu/h .......................................................................... 6-33

Table 6.4.14 LCC Savings Relative to the Base-Case Efficiency Distribution for 

Water Source Heat Pumps ≥65,000 to <135,000 Btu/h............................... 6-34

LIST OF FIGURES

Figure 6.2.1 Historical Nominal and Deflated Producer Price Indexes for Unitary 

Air Conditioners except Heat Pumps............................................................. 6-8

Figure 6.2.2 Historical Nominal and Deflated Producer Price Indexes for All other 

Miscellaneous Refrigeration and Air Conditioning Equipment .................... 6-9

Figure 6.2.3 AEO 2014 Electricity Price Projections by Census Division ...................... 6-16
6-1

CHAPTER 6. LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

6.1 INTRODUCTION

The effect of amended standards on individual customers usually includes a reduction in 

operating cost and an increase in purchase cost. This chapter describes two metrics used in the 

analysis to determine the economic impact of standards on individual commercial customers.

• Life-cycle cost (LCC) is the total customer cost over the life of an appliance or product, 

including purchase costs and operating costs (which in turn include maintenance, repair, 

and energy costs). Future operating costs are discounted to the time of purchase and 

summed over the lifetime of the equipment.

• Payback period (PBP) measures the amount of time it takes customers to recover the 

assumed higher purchase price of more energy-efficient equipment through reduced 

operating costs.

The U.S. Department of Energy (DOE) conducted the LCC and PBP analysis using a 

spreadsheet model developed in Microsoft Excel. When combined with Crystal Ball (a 

commercially available software program), the LCC and PBP model generates a Monte Carlo 

simulation to perform the analysis by incorporating uncertainty and variability considerations in 

certain of the key parameters as discussed further in section 6.1.1.

Inputs to the LCC and PBP analysis of 3-phase, air-cooled air conditioners, 3-phase, aircooled heat pumps, and water-source heat pumps are discussed in sections 6.2 and 6.3

respectively. Results for each metric are presented in section 6.4. Key variables and calculations

are presented for each metric. The calculations discussed here were performed with a series of 

Microsoft Excel spreadsheets. 

6.1.1 General Approach for Life-Cycle Cost and Payback Period Analysis

In recognition of the fact that the energy use of three-phase, air-cooled air conditioning 

and heating equipment and water-source heat pumps varies by building type and climate, the 

LCC and PBP calculations analyze energy use based on the distribution of end-users

(commercial buildings types) by census division. The results are expressed as the unit energy 

consumption for each equipment type. The LCC and PBP model was developed using Microsoft 

Excel spreadsheets combined with Crystal Ball. 

The LCC analysis used the estimated energy use for each equipment type as described in 

the energy use analysis in chapter 4. Energy use is sensitive to climate and therefore varies by 

location within the United States. Aside from energy use, other important factors influencing the 

LCC and PBP analysis include energy prices, installation costs, equipment distribution markups, 

and sales taxes. The LCC spreadsheets explicitly modeled both the uncertainty and the variability 

in the model’s inputs. 
6-2

As mentioned previously, DOE generated LCC and PBP results as probability 

distributions using a simulation based on Monte Carlo analysis methods, in which certain key 

inputs to the analysis consist of probability distributions rather than single-point values. 

Therefore, the outcomes of the Monte Carlo analysis can also be expressed as probability 

distributions. As a result, the Monte Carlo analysis produces a range of LCC and PBP results. A 

distinct advantage of this type of approach is that DOE can identify the percentage of customers 

achieving LCC savings or attaining certain PBP values due to an increased efficiency level, in 

addition to the average LCC savings or average PBP for that efficiency level.

The LCC results are displayed as distributions of impacts compared to a base case. The 

base case efficiency distribution is for 2019 for split system air conditioners and 2020 for the 

other equipment classes and is defined as a mix of efficiency levels reflecting the expected 

distribution of efficiency levels by equipment class. 

6.1.2 Overview of Life-Cycle Cost and Payback Period Analysis Inputs

The LCC is the total customer cost over the life of the equipment, including purchase 

price (including retail markups, sales taxes, and installation costs) and operating cost (including 

repair costs, maintenance costs, and energy cost). Future operating costs are discounted to the 

time of purchase and summed over the lifetime of the equipment. The PBP is the increase in 

purchase cost of a higher efficiency unit divided by the change in annual operating cost of the 

unit. It represents the number of years that it will take the customer to recover the increased 

purchase cost through decreased operating costs. In the calculation of PBP, future costs are not 

discounted.

Inputs to the LCC and PBP analysis are categorized as: (1) inputs for establishing the 

purchase cost, otherwise known as the total installed cost; and (2) inputs for calculating the 

operating cost (i.e., energy, maintenance, and repair costs).

The primary inputs for establishing the total installed cost are:

• Baseline manufacturer selling price: The baseline manufacturer selling price (MSP) is 

the price charged by the manufacturer to a wholesaler for equipment meeting existing 

minimum efficiency (or baseline) standards. The MSP includes a markup that converts 

the cost of production (i.e., the manufacturer cost) to a MSP.

• Standard-level manufacturer selling price increase: The standard-level MSP is the 

incremental change in MSP associated with producing equipment at each of the higher 

standard levels. 

• Markups and sales tax: Markups and sales tax are the wholesaler and contractor margins 

and state and local retail sales taxes associated with converting the MSP to a customer 

price. 

• Installation cost: Installation cost is the cost to the customer of installing the equipment. 

The installation cost represents all costs required to install the equipment but does not 

include the marked-up customer equipment price. The installation cost includes labor, 

overhead, and any miscellaneous materials and parts. 
6-3

The primary inputs for calculating the operating cost are:

• Equipment energy consumption: The equipment energy consumption is the site energy 

use associated with providing space conditioning to the building. 

• Electricity Prices: Average and marginal electricity prices for commercial buildings are 

determined using a tariff-based analysis. The average electricity price is used to estimate 

operating costs for the baseline, and marginal prices are used to value the operating cost 

savings at higher efficiency levels.

• Electricity price trends: The Energy Information Administration’s (EIA’s) Annual 

Energy Outlook 2014 (AEO 2014)

1 is used to project electricity prices into the future. 

• Maintenance costs: The labor and material costs associated with maintaining the 

operation of the equipment.

• Repair costs: The labor and material costs associated with repairing or replacing 

components that have failed.

• Lifetime: The age at which the equipment is retired from service.

• Discount rate: The rate at which future costs and savings are discounted to establish their 

present value.

Table 6.1.1 provides descriptions of the various inputs to the calculation of the LCC and 

PBP. As noted earlier, most of the inputs are characterized by probability distributions that 

capture variability in the input variables. 

Table 6.1.1 Summary of Inputs and Key Assumptions Used in the LCC and PBP 

Analysis

Inputs Description

Affecting Installed Costs

Equipment Price

Derived MSP for equipment (from the engineering analysis) and 

multiplied by wholesaler markups and contractor markups plus sales tax 

(from markups analysis). Used the probability distribution for the 

different markups to describe their variability. 

Installation Cost

Includes installation labor derived from RS Means.2 Overhead and 

materials costs and profits are assumed to be included in the contractor’s 

markup. Thus, the total installed cost equals the customer equipment 

price (manufacturer cost multiplied by the various markups plus sales 

tax) plus the installation cost. 

Affecting Operating Costs

Annual Energy Use See chapter 4. 

Energy Efficiency

Seasonal energy efficiency ratio (SEER) is the cooling performance 

descriptor for small commercial air-cooled air conditioners and heat 

pumps. Energy efficiency ratio (EER) is the efficiency descriptor for 

cooling performance of water-source heat pumps. Coefficient of 

performance (COP) is the performance descriptor for heating 
6-4

Inputs Description

performance of water-source heat pumps. Unit energy consumption data 

is used to determine the annual energy consumption associated with a 

particular standard level.

Electricity Prices

Average and marginal electricity prices for commercial buildings are 

determined using a tariff-based analysis from a representative sample of 

electric utilities. The average electricity price is used to estimate 

operating costs for the baseline, and marginal prices are used to value the 

operating cost savings at higher efficiency levels.

Maintenance Cost

Maintenance costs came from RS Means.3 The cost associated with 

maintaining the operation of the equipment (e.g., cleaning heat exchanger 

coils). Annual maintenance cost does not change as a function of MSP.

Repair Cost

Repair costs were derived from RS Means.3 In order to make the repair 

costs into an annual cost, DOE annualized the present value of the repair 

cost (which takes place in year 10) and applied them over the lifetime of 

the equipment. 

Affecting Present Value of Annual Operating Cost Savings

Equipment Lifetime DOE used Weibull distributions from the 2011 DFR to determine the 

lifetimes of each equipment class in this rulemaking. 

Discount Rate

Mean real discount rates ranging from 6.57 percent to 7.21 percent for 

various classes of commercial customers of three-phase air-cooled air 

conditioning and heating equipment less than 65,000 Btu/h. Mean real 

discount rates ranging from 3.74 percent to 5.57 percent for water-source 

heat pumps. Discount rates were calculated using financial data from 

Damodaran Online. Probability distributions are used for the discount 

rates.

Compliance Date 

2020 (4 years after expected publication of the final rule) for all 

equipment except Split System AC which has a compliance date of 2019

(3 years after expected publication of the final rule). 

All of the inputs summarized in Table 6.1.1 are discussed in sections 6.2 and 6.3.

6.2 LIFE CYCLE COST ANALYSIS INPUTS

Life-cycle cost is the total customer cost over the life of a piece of equipment, including 

purchase cost and operating costs (which are composed of energy costs, maintenance costs, and 

repair costs). Future operating costs are discounted to the time of purchase and summed over the 

lifetime of the equipment. Life-cycle cost is defined by the following equation:
6-5

Eq. 6.1

Where:

LCC = life-cycle cost ($),

IC = total installed cost ($),

∑ = sum over the lifetime, from year 1 to year N, 

 where N = lifetime of equipment (years),

OC = operating cost ($),

r = discount rate, and

t = year for which operating cost is being determined.

DOE expresses all the costs in 2014$. Total installed cost, operating cost, lifetime, and 

discount rate are discussed in the following sections. In the LCC analysis, the year of equipment

purchase is assumed to be 2020 the expected compliance date of energy conservation standards 

higher than the ASHRAE level for the triggered equipment classes, or 2019 for split system air 

conditioners.

6.2.1 Total Installed Cost Inputs

The total installed cost to the customer is defined by the following equation:

IIII = EEEEEE + IIIIIIII

Eq. 6.2

Where:

EQP = equipment price ($) (i.e., customer price for the equipment only), and

INST = installation cost ($) (i.e., the cost for labor and materials).

The equipment price is based on the distribution channel through which the customer 

purchases the equipment. As discussed in chapter 5, DOE defined one major distribution channel 

for new units to describe how the equipment passes from the manufacturer to the customer: the 

manufacturer sells the equipment to a wholesaler or distributor, who sells to a mechanical 

contractor hired by a general contractor. The general contractor purchases and installs the 

equipment on behalf of the customer and adds its markup to the mechanical contractor’s price. 

Replacement equipment follows the same distribution channel, except that there is no general 

contractor. Instead, the mechanical contractor takes on the general contractor’s function.

The remainder of this section provides information about the variables DOE used to 

calculate the total installed cost

∑=

= + +

N

t

t

t LCC IC OC r

1

/(1 )
6-6

6.2.1.1 Manufacturer Costs

DOE developed the manufacturer costs as described in chapter 3, Engineering Analysis. 

6.2.1.2 Markups

For a given distribution channel, the overall markup is the value determined by 

multiplying all the associated markups and the applicable sales tax together to arrive at a single 

overall distribution chain markup value. The overall markup is multiplied times the baseline or 

standard-compliant manufacturer cost to arrive at the price paid by the customer. Because there 

are baseline and incremental markups associated with the wholesaler and mechanical and general 

contractors, the overall markup is also divided into a baseline markup (i.e., a markup used to 

convert the baseline manufacturer price into a customer price) and an incremental markup (i.e., a 

markup used to convert a standard-compliant manufacturer cost increase due to an efficiency 

increase into an incremental customer price). Markups can differ depending on whether the 

equipment is being purchased for a new construction installation or is being purchased to replace 

existing equipment. DOE developed the overall baseline markups and incremental markups for 

both new construction and replacement applications as a part of the markups analysis (chapter 5

of the TSD).

Table 6.2.1 displays baseline and incremental markups used to calculate customer price 

from manufacturer cost. Because the relative importance of new construction and replacements 

in total shipments varies among the equipment classes, the total markup varies as well (Table 

6.2.2).

Table 6.2.1 Summary of Average Markups

Baseline Markup Incremental Markup

Manufacturer 1.30 1.30

Wholesaler 1.36 1.10

Mechanical Contractor 

(new 

construction/replacement)

1.42/1.50 1.14/1.20

General Contractor 

(new construction only) 1.31 1.19

Sales Tax (replacement only) 1.07 1.07

Table 6.2.2 Overall Markup for All Equipment Classes

Baseline Markup Incremental Markup

2.929 1.867

6.2.1.3 Total Customer Price

DOE derived the customer equipment price for the efficiency levels above the baseline 

by taking the product of the baseline manufacturer cost and the baseline overall markup 

(including the sales tax) and adding to it the product of the incremental manufacturer cost and the 

incremental overall markup (including the sales tax). DOE followed the same process for 
6-7

shipping costs but in this case the manufacturer markup was not included in the overall markup. 

DOE then added the marked-up shipping cost to the customer equipment price. Markups and the 

sales tax all can take on a variety of values, depending on location, so the resulting total installed 

cost for a particular efficiency level (EL) is represented by a distribution of values. Table 6.2.3

and Table 6.2.4 present the average customer equipment price for each equipment class at each 

efficiency level examined. 

Table 6.2.3 Average Customer Price for Small Commercial Air-cooled Air Conditioning 

and Heating Equipment (2014$)

Equipment 

Class

Federal 

Standard

Baseline

ASHRAE 

Baseline/ 

EL0

EL 1 EL 2 EL 3 EL 4 EL 5

Split AC $2,555 $2,709 $2,872 $3,057 $3,239 $3,436 $3,645

Package AC N/A $3,358 $3,584 $3,814 $4,037 $4,260 $4,549

Split HP N/A $3,433 $3,602 $3,864 $4,068 $4,286 N/A

Package HP N/A $4,089 $4,347 $4,595 $4,842 $5,146 N/A

Table 6.2.4 Average Customer Price for Water-Source Heat Pumps (2014$)

Equipment Class ASHRAE 

Baseline EL 1 EL 2 EL 3 EL 4 EL 5

Water Source HP <17,000 

Btu/h $2,544 $2,627 $2,735 $2,911 $2,993 $3,160

Water Source HP ≥17,000 

to <65,000 Btu/h $3,981 $4,199 $4,473 $4,664 $4,827 $5,159

Water Source HP ≥65,000 

to 135,000 Btu/h $9,766 $10,497 $11,229 $11,914 $12,790 N/A

6.2.1.4 Future Equipment Prices

To develop an equipment price trend for small commercial air-cooled air conditioners 

<65,000 Btu/h, DOE obtained an inflation-adjusted index of the producer price index (PPI) for 

“unitary air-conditioners, except air source heat pumps” from 1978 to 2013.

4 Although the PPI 

index shown in Figure 6.2.1 shows a long-term declining trend, data for the last decade have 

shown a flat-to-slightly rising trend. Given the uncertainty as to which of the trends will prevail 

in coming years, DOE chose to apply a constant price trend (2013 levels) for the Final Rule. 

Because PPI data for air-source heat pumps was not available for very many years, DOE applied 

the same constant price trend to small commercial air-cooled heat pumps. 
6-8

Figure 6.2.1 Historical Nominal and Deflated Producer Price Indexes for Unitary Air 

Conditioners except Heat Pumps

To develop an equipment price trend for water-source heat pumps, DOE derived an 

inflation-adjusted index of the producer price index (PPI) for “all other miscellaneous 

refrigeration and air-conditioning equipment” from 1990-2013.5 Although the inflation-adjusted 

index shown in Figure 6.2.2 shows a declining trend from 1990 to 2004, data since 2008 have 

shown a flat-to-slightly rising trend. Given the uncertainty as to which of the trends will prevail 

in coming years, DOE chose to apply a constant price trend (2013 levels) for each water-source 

heat pump equipment class. 

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

180.0

197819801982198419861988199019921994199619982000200220042006200820102012

Deflated PPI (2013=1)

Nomianl PPI (1982=100)

Unitary air-conditioners, except air source heat pumps Nominal PPI

Unitary air-conditioners, except air source heat pumps Deflated PPI
6-9

Figure 6.2.2 Historical Nominal and Deflated Producer Price Indexes for All other 

Miscellaneous Refrigeration and Air Conditioning Equipment

6.2.1.5 Installation Cost

The installation cost is the price to the customer of labor and materials (other than the 

actual equipment) needed to install equipment. DOE derived installation costs from 2013 RS 

Means data.2

 RS Means provides estimates on the person-hours required to install HVAC 

equipment and the labor rates associated with the type of crew required to install the equipment. 

DOE calculated the installation cost by multiplying the number of person-hours by the 

corresponding labor rate. RS Means provides specific person-hour and labor rate data for the 

installation of 3-ton air conditioners, 3-ton air to air heat pumps, and 1-, 3-, and 7.5-ton watersource heat pumps. DOE decided that data for 3-ton, single zone air conditioners and 3-ton air to 

air split system heat pumps are representative of installation costs for 3 phase, air-cooled air 

conditioners and heat pumps less than 65,000 Btu/h. DOE determined the 1-, 3-, and 7.5- ton 

water- source heat pumps were representative of the market for water-source heat pumps less 

than 135,000 Btu/h.

Labor rates vary significantly among regions, and the RS Means data provide the 

necessary information to capture this regional variability. RS Means provides cost indices that 

reflect the labor rates for 656 cities in the United States. Several cities in all 50 states of the 

United States and the District of Columbia are identified in the RS Means data. DOE 

incorporated these cost indices into the analysis to vary the installation cost depending on the 

location of the customer.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

180.0

200.0

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

Deflated PPI (2013=1)

Nomianl PPI (1989=100)

All other miscellaneous refrigeration & air-conditioning equipment Nominal PPI

All other miscellaneous refrigeration & air-conditioning equipment Defalted PPI
6-10

Within a given equipment class, DOE chose to vary installation costs in direct proportion 

to the weight of the equipment. The weight of the equipment in each equipment class and 

efficiency level was determined through the engineering analysis.

Table 6.2.5 and Table 6.2.6 summarize the nationally representative person-hours and 

labor rates associated with the installation of 3-ton air conditioners, 3-ton air to air heat pumps, 

and 1-, 3-, and 7.5- ton water-source heat pumps as presented in RS Means 2013.

Table 6.2.5 and Table 6.2.6 provide both bare installation costs (i.e., costs before 

overhead and profit (O&P)) and installation costs including O&P. DOE decided that the 

installation costs that include O&P represent the installation costs for baseline efficient systems 

(i.e., 13.0 SEER for small, 3-phase air-cooled air conditioners and heat pumps <65,000 Btu/h,, 

11.2 EER for water-source heat pumps less than 17,000 Btu/h, and 12.0 EER for water-source 

heat pumps of greater than or equal to 17,000 to less than 65,000 Btu/h and greater than or equal 

to 65,000 and less than 135,000 Btu/h).

Table 6.2.5 Installation Costs for Baseline Small, 3-phase Air-Cooled Air Conditioners 

and Heat Pumps <65,000 Btu/h

System Type*

Person

-hours

2013 Bare Costs Labor w/ O&P

Cost per 

Personhour 

(2014$)

Total 

Labor 

Cost 

(2014$)

Cost per 

Personhour 

(2014$)

Total 

Labor 

Cost 

(2014$)

Rooftop Air Conditioners, single 

zone, electric cool only, 3 ton 

cooling

18.2 $52 $942 $78 $1,417

Air-Source Heat Pumps, air to air, 

split system, 3 ton cooling 13.3 $52 $690 $78 $1,038

Air-Source Heat Pumps, air to air, 

single package, 3 ton cooling 13.3 $52 $690 $78 $1,038

Table 6.2.6 Installation Costs for Baseline Water-Source Heat Pumps

System Type*

Person

-hours

2013 Bare Costs Labor w/ O&P

Cost per 

Personhour 

(2014$)

Total 

Labor 

Cost 

(2014$)

Cost per 

Personhour 

(2014$)

Total 

Labor 

Cost 

(2014$)

Water-Source Heat Pumps, watersource to air, single package, 1 ton 

cooling

8 $52 $414 $78 $623

Water-Source Heat Pumps, watersource to air, single package, 3 ton 

cooling

11.4 $52 $591 $78 $890

Water-Source Heat Pumps, watersource to air, single package, 7.5 

ton cooling

26.6 $52 $1,379 $78 $2,076
6-11

Table 6.2.7 and Table 6.2.8 show the national average total installation costs by 

equipment class and efficiency level. The total includes overhead and profit (O&P), which is 

calculated using labor markups from RS Means. For efficiency levels above the baseline, the 

installation costs vary in direct proportion to equipment weight.

Table 6.2.7 National Average Installation Costs for Small, 3-phase Air-Cooled Air 

Conditioners and Heat Pumps <65,000 Btu/h (2014$)

Equipment Class

Efficiency Level

Federal 

Baseline

ASHRAE 

Baseline/ 

EL0

EL 1 EL 2 EL 3 EL 4 EL 5

Split AC

Weight (lbs) 323 346 367 387 405 422 439

Installation 

Cost (2014$) $1,417 $1,518 $1,611 $1,698 $1,777 $1,852 $1,926

Package 

AC

Weight (lbs) 341 361 382 402 422 443 463

Installation 

Cost (2014$) $1,417 $1,501 $1,588 $1,671 $1,754 $1,841 $1,925

Split HP

Weight (lbs) 358 393 426 456 485 512

Installation 

Cost (2014$) $1,038 $1,140 $1,235 $1,322 $1,407 $1,485

Package 

HP

Weight (lbs) 379 411 444 476 508 541

Installation 

Cost (2014$) $1,038 $1,126 $1,216 $1,304 $1,392 $1,482

Table 6.2.8 National Average Installation Costs for Water Source Heat Pumps (2014$)

Equipment Class

Efficiency Level

Federal 

Baseline

ASHRAE 

Baseline EL 1 EL 2 EL 3 EL 4 EL 5

<17,000 

Btu/h

Weight (lbs) 118 131 141 154 177 187 208

Installation 

Cost $623 $693 $749 $819 $937 $993 $1,105

≥17,000 to 

<65,000 

Btu/h

Weight (lbs) 260 272 290 313 329 343 371

Installation 

Cost $890 $930 $993 $1,072 $1,127 $1,175 $1,270

≥65,000 to 

<135,000 

Btu/h

Weight (lbs) 663 737 811 885 959 1048

Installation 

Cost $2,076 $2,309 $2,540 $2,772 $3,004 $3,282

Table 6.2.9 summarizes the cost indices for installations in each of the 50 States of the 

U.S., plus the District of Columbia, used to vary the nationally representative installation costs in

Table 6.2.7 and Table 6.2.8. To arrive at an average index for each state, DOE weighted the city 
6-12

indices in each state by their population. It used population estimates for the year 2010 from the 

U.S. Census Bureau to calculate a weighted-average index for each state. DOE then assigned 

each state to a census division and calculated a weighted-average index for each region in the 

same manner (Table 6.2.10).

Table 6.2.9 Installation Cost Indices by State (National Value = 100.0)

State Index State Index State Index

Alabama 50.1 Kentucky 83.6 North Dakota 58.6

Alaska 107.4 Louisiana 62.5 Ohio 93.2

Arizona 79.5 Maine 75.9 Oklahoma 66.4

Arkansas 58.9 Maryland 83.8 Oregon 99.6

California 124.9 Massachusetts 120.6 Pennsylvania 116.4

Colorado 78.8 Michigan 101.4 Rhode Island 112.0

Connecticut 119.8 Minnesota 112.9 South Carolina 52.3

D.C 95.3 Mississippi 52.4 South Dakota 41.4

Delaware 119.5 Missouri 99.2 Tennessee 73.2

Florida 68.0 Montana 73.2 Texas 56.8

Georgia 68.9 Nebraska 80.5 Utah 70.8

Hawaii 112.9 Nevada 100.3 Vermont 75.1

Idaho 75.6 New Hampshire 83.0 Virginia 73.1

Illinois 130.6 New Jersey 126.5 Washington 107.2

Indiana 81.9 New Mexico 70.5 West Virginia 88.6

Iowa 80.5 New York 163.8 Wisconsin 97.7

Kansas 73.9 North Carolina 55.7 Wyoming 67.2
6-13

Table 6.2.10 Installation Cost Indices by Region (National Value = 100.0)

Region Index

New England 110.3

Mid Atlantic 141.0

East North Central 104.3

West North Central 91.2

South Atlantic 68.7

East South Central 66.3

West South Central 58.7

Mountain 79.1

Pacific 120.4

6.2.1.6 Total Installed Cost

The total installed cost is the sum of the equipment price and the installation cost. MSPs, 

markups, and sales taxes all can take on a variety of values, depending on location, so the 

resulting total installed cost for a particular efficiency level will not be a single-point value, but 

rather a distribution of values. Table 6.2.11 and Table 6.2.12 present the average total installed 

cost for each equipment class at each efficiency level examined. 

Table 6.2.11 Average Total Installed Cost for Small, 3-phase Air-Cooled Air Conditioners 

and Heat Pumps <65,000 Btu/h (2014$)

Equipment 

Class

Efficiency Level

Federal 

Baseline

ASHRAE 

Baseline/ EL0 EL 1 EL 2 EL 3 EL 4 EL 5

Split AC $3,973 $4,227 $4,483 $4,756 $5,017 $5,288 $5,571

Package AC N/A $4,858 $5,171 $5,485 $5,791 $6,101 $6,473

Split HP N/A $4,573 $4,838 $5,187 $5,474 $5,771 N/A

Package HP N/A $5,214 $5,563 $5,899 $6,234 $6,628 N/A

Table 6.2.12 Average Total Installed Cost for Water-Source Heat Pumps (2014$)

Equipment Class

Efficiency Level

ASHRA

E 

Baseline

EL 1 EL 2 EL 3 EL 4 EL 5

<17,000 Btu/h $3,237 $3,376 $3,554 $3,849 $3,986 $4,264

≥17,000 to <65,000 

Btu/h $4,911 $5,192 $5,545 $5,791 $6,002 $6,429

≥65,000 to 

<135,000 Btu/h $12,075 $13,037 $14,001 $14,918 $16,073 N/A
6-14

6.2.2 Operating Cost Inputs

DOE defined the operating cost by the following equation:

OC = EC + MC

Eq. 6.3

Where:

OC = operating cost ($), 

EC = energy cost associated with operating the equipment ($), and

MC = annual maintenance cost for maintaining equipment operation ($).

The remainder of this section provides information about the variables that DOE used to 

calculate the operating costs by equipment class and efficiency level. The annual energy costs of 

the equipment are computed from energy consumption per unit for the baseline and standardcompliant cases (efficiency level 2, 3, and so on), combined with the energy prices. Equipment 

lifetime, discount rate, and compliance date of the standard are required for determining the 

operating cost and for establishing the operating cost present value. 

6.2.2.1 Annual Energy Use Savings

For each equipment class, DOE calculated the annual energy use savings for each product

at each efficiency level by taking the difference between a considered efficiency level and the 

baseline efficiency level.

6.2.2.2 Energy Prices

In this analysis, DOE used electricity prices ($/kWh) by census division which came 

from the 2014 Central Unitary Air Conditioner Notice of Proposed Rulemaking.

6 The prices by 

census division were in 2012$ in the CUAC rulemaking and were scaled to 2014$ for this

analysis. Electricity cost savings at efficiency levels above the ASHRAE baseline were 

calculated using the annual marginal price per kWh, as shown in Table 6.2.13. For each 

efficiency level, the operating costs savings are calculated by multiplying the reduction in 

electricity consumption (in kWh) by the annual marginal price.
6-15

Table 6.2.13 Average and Marginal Tariff-based Prices by Region

Census Division Average Marginal

2014 $/kWh

New England $0.16 $0.10

Mid-Atlantic $0.16 $0.10

East North Central $0.13 $0.07

West North Central $0.08 $0.06

South Atlantic $0.11 $0.07

East South Central $0.09 $0.06

West South Central $0.08 $0.06

Mountain $0.11 $0.08

Pacific $0.15 $0.11

US Average $0.12 $0.08

6.2.2.3 Energy Price Trends

DOE projected future electricity prices using trends in national-average commercial 

electricity price from AEO 2014, as shown in Figure 6.2.3. The US average trend is shown as a 

heavy dashed line. DOE used AEO 2014 Reference Case scenariosa for the nine census 

divisions. DOE applied the projected energy price for each of the nine census divisions based on 

the distribution of buildings across divisions, as discussed in Chapter 4.

 a The reference case is a business-as-usual estimate, given known market, demographic, and technological trends.
6-16

Figure 6.2.3 AEO 2014 Electricity Price Projections by Census Division 

6.2.2.4 Repair Cost

Repair costs are associated with repairing or replacing components that have failed. DOE 

utilized RS Means to find the repair costs associated with small commercial air-cooled airconditioning and heating equipment, and water-source heat pumps. For air-cooled air 

conditioners, DOE used the repair costs for a 3 ton, single zone rooftop unit. For air-cooled heat 

pumps, DOE took the repair costs for 1.5 ton, 5 ton, and 10 ton air to air heat pumps and linearly 

scaled the repair costs to derive a 3 ton repair cost. Because specific water-source heat pump data 

were not available, for water-source heat pumps, DOE also used the repair costs for 1.5 ton, 5 

ton, and 10 ton air to air heat pumps and linearly scaled the repair costs to derive costs for 1 ton, 

3 ton, and 7.5 ton water-source heat pumps. DOE assumed the air-source costs would be 

representative of the water-source costs.

RS Means breaks down repair costs into two components, material and labor. DOE 

assumed the repair costs in RS Means represented the current baseline efficiency level. DOE 

then scaled the material portion of the repair costs by the percentage increase in manufacturer 

production costs at each efficiency level. 

0.06

0.08

0.10

0.12

0.14

0.16

0.18

201120132015201720192021202320252027202920312033203520372039

Electricity Price ($/kwh)

New England

Mid-Atlantic

East North Central

West North Central

South Atlantic

East South Central

West South Central

Mountain

Pacific

National
6-17

Based on information in RS Means, DOE assumed the repair would take place in the 10th 

year of the equipment life. In order to make the repair costs into an annual value, DOE 

annualized the present value of the repair costs in year 10 over the lifetime of the equipment. 

Table 6.2.14 shows the repair costs used for air-cooled air conditioners, and Table 6.2.15

shows the repair costs for air to air heat pumps which were used to derive the repair costs for airsource heat pumps and water-source heat pumps. Table 6.2.16 shows one-time repair costs for 

small commercial air-cooled equipment, and Table 6.2.17 shows one-time repair costs for watersource heat pumps. Table 6.2.18 shows the annualized repair costs for small commercial aircooled equipment, and Table 6.2.19 shows the annualized repair costs for water-source heat 

pumps. 

Table 6.2.14 Repair Costs for 3-Phase, Air-Cooled, Air Conditioners < 65,000 Btu/h

System 

Type

Annual 

Personhours

Bare Costs Labor and Materials w/O&P

Mat’l

($2014)

Cost 

per 

Personhour

($2014)

Annual 

Labor 

Cost

($2014)

Annual 

Total 

Cost

($2014)

Mat’l

($2014)

Cost 

per 

Personhour

($2014)

Annual 

Labor 

Cost

($2014)

Annual 

Total 

Cost

($2014)

Single 

zone 

rooftop 

unit, 3 

ton

18 $781 $58 $1,041 $1,822 $1,474 $87 $1,544 $3,018

Table 6.2.15 RS Means Costs Used to Derive Repair Costs for 3-Phase, Air-Cooled Heat 

Pumps < 65,000 Btu/h and Water-Source Heat Pumps

System 

Type

Annual 

Personhours

Bare Costs Labor and Materials w/O&P

Mat’l 

($2014)

Cost 

per 

Personhour 

($2014)

Annual 

Labor 

Cost

($2014)

Annual 

Total 

Cost 

($2014)

Mat’l

($2014)

Cost 

per 

Personhour

($2014)

Annual 

Labor 

Cost

($2014)

Annual 

Total 

Cost

($2014)

1.5 ton 

air to 

air HP

16 $612 $57 $900 $1,512 $726 $87 $1,355 $2,081 

5 ton 

air to 

air HP

18 $968 $57 $1,016 $1,984 $1,691 $87 $1,530 $3,221 

10 ton 

air to 

air HP

24 $1,994 $52 $1,228 $3,222 $3,607 $78 $1,848 $5,455 
6-18

Table 6.2.16 Total Repair Costs for 3-Phase, Air-Cooled, Air Conditioning and Heating 

Equipment < 65,000 Btu/h (2014$)

Equipment Class

Federal 

Baseline

ASHRAE 

Baseline/ 

EL 0

EL 1 EL 2 EL 3 EL 4 EL 5

Split AC $3,018 $3,159 $3,308 $3,467 $3,636 $3,815 $4,007

Package AC $3,018 $3,193 $3,193 $3,369 $3,545 $3,721 $3,897

Split HP $2,585 $2,673 $2,676 $2,811 $2,922 $3,040 N/A

Package HP $2,585 $2,701 $2,701 $2,817 $2,933 $3,049 N/A

Table 6.2.17 Total Repair Costs for Water Source Heat Pumps (2014$)

Equipment Class Federal 

Baseline

ASHRAE 

Baseline/ 

EL 0

EL 1 EL 2 EL 3 EL 4 EL 5

<17,000 Btu/h $1,784 $1,827 $1,818 $1,861 $1,934 $1,968 $2,036

≥17,000 to <65,000 Btu/h $2,585 $2,647 $2,713 $2,874 $3,052 $3,237 $3,529

≥65,000 to <135,000 

Btu/h $4,387 $4,726 $4,726 $5,066 $5,406 $5,813 N/A

Table 6.2.18 Annual Repair Costs for 3-Phase, Air-Cooled, Air Conditioning and Heating 

Equipment < 65,000 Btu/h (2014$)

Equipment Class

Federal 

Baseline

ASHRAE 

Baseline/ 

EL 0

EL 1 EL 2 EL 3 EL 4 EL 5

Split AC $148 $155 $162 $170 $178 $187 $196

Package AC N/A $157 $157 $165 $174 $182 $191

Split HP N/A $143 $143 $150 $156 $162 N/A

Package HP N/A $144 $144 $150 $157 $163 N/A

Table 6.2.19 Annual Repair Costs for Water Source Heat Pumps (2014$)

Equipment Class

ASHRAE 

Baseline EL 1 EL 2 EL 3 EL 4 EL 5

<17,000 Btu/h $93 $92 $95 $98 $100 $104

≥17,000 to <65,000 Btu/h $135 $138 $146 $155 $165 $179

≥65,000 to <135,000 Btu/h $240 $240 $258 $275 $296 N/A

6.2.2.5 Maintenance Cost

The maintenance cost is the routine cost to the customer of maintaining equipment operation. 

DOE took annualized maintenance costs for air conditioners and heat pumps from data in RS 

Means Online, which provides estimates on the person-hours, labor rates, and materials required 

to maintain air-conditioning equipment. DOE took annualized maintenance costs for CUACs 
6-19

from data in RS Means Online, which provides estimates on the person-hours, labor rates, and 

materials required to maintain CUAC equipment. RS Means specifies the following 11 actions 

that constitute required annual maintenance:

• Check with operating or area personnel for deficiencies.

• Check tensions, condition, and alignment of belts; adjust as necessary.

• Lubricate shaft and motor bearings.

• Replace air filters.

• Clean electrical wiring and connections; tighten loose connections.

• Clean coils, evaporator drain pan, blowers, fan motors, and drain piping as required.

• Perform operational check of unit; make adjustments on controls and other components 

as required.

• During operation of unit, check refrigerant pressure; add refrigerant as necessary.

• Check compressor oil level; add oil as required.

• Clean area around unit.

• Fill out maintenance checklist and report deficiencies.

DOE calculated the annualized maintenance cost by multiplying the number of personhours by the corresponding labor rate and adding to it the associated materials costs. RS Means

provides specific cost data for the maintenance of rooftop air conditioners ranging in cooling 

capacity from 3 through 24 tons as well as for air-cooled heat pumps up to 5 tons and over 5 

tons. RS Means did not provide maintenance costs specific to water-source heat pumps. DOE 

took the 3 through 24 ton rooftop air conditioner data to be representative of maintenance costs 

for the small commercial air-cooled air conditioner equipment classes. DOE took the up to 5 ton

air-cooled heat pump data to be representative of maintenance costs for the small commercial 

air-cooled heat pump equipment classes and the water-source heat pump classes less than 65,000 

Btu/h. DOE took the over 5 ton air-cooled heat pump data to be representative of the watersource heat pump class greater than or equal to 65,000 Btu/h and less than 135,000 Btu/h.

Because data were not available to indicate how maintenance costs vary with equipment 

efficiency, the Department decided to use costs that stay constant as equipment efficiency 

increases.

Table 6.2.20 summarizes the nationally representative annualized maintenance costs for 

the relevant equipment as presented in RS Means Online. Table 6.2.20 provides both bare 

maintenance costs (i.e., costs before O&P) and maintenance costs including O&P. DOE uses 

maintenance costs including O&P in its analysis. 
6-20

Table 6.2.20 Maintenance Costs for Air-Cooled Air Conditioning and Heating Equipment

System Type

Annual 

Personhours

Bare Costs Labor and Materials w/O&P

Mat’l 

($2014)

Cost 

per 

Person

-hour 

($2014)

Annual 

Labor 

Cost 

($2014)

Annual 

Total 

Cost 

($2014)

Mat’l 

($2014)

Cost 

per 

Person

-hour 

($2014)

Annual 

Labor 

Cost 

($2014)

Annual 

Total 

Cost 

($2014)

Packaged unit, 

air-cooled, 3-tons 

through 24-tons

2.4 $75 $57 $138 $213 $94 $87 $208 $302

Heat pump, air 

cooled, up to 5 ton 3.2 $45 $57 $184 $229 $56 $87 $277 $334

Heat pump, air 

cooled, over 5 ton 3.6 $75 $57 $206 $281 $94 $87 $310 $404

6.2.2.6 Lifetime

DOE defines lifetime as the age when equipment is retired from service. In the April 

2014 Notice of Data Availability (NODA), DOE identified the average lifetime as 15 years for 3-

phase, air-cooled air conditioning and heating equipment <65,000 Btu/ and 19 years for watersource heat pumps. (79 FR 20114) For this Final Rule, DOE uses a "survival function" to more 

accurately represent equipment lifetime. DOE used the survival functions in the form of Weibull 

distributions presented in the 2011 DFR for residential central air conditioners and heat pumps. 

The Weibull distribution is a probability distribution function commonly used to measure 

failure rates.4 Its form is similar to an exponential distribution, which would model a fixed 

failure rate, except that it allows for a failure rate that changes over time in a particular fashion. 

The cumulative distribution takes the form:

 for x > θ and P(x) = 1 for x ≤ θ,

Eq. 6.4

Where:

P(x) = probability that the appliance is still in use at age x,

x = appliance age,

α = the scale parameter, which is the decay length in an exponential distribution,

β = the shape parameter, which determines the way in which the failure rate changes 

in time, and

θ = the delay parameter, which allows for a delay before any failures occur.

The 2011 DFR include Weibull distributions for both air conditioners and heat pumps. 

The air conditioner function uses an alpha of 20.29, a beta of 1.89, and a theta of 1. This resulted 

e x

P x

β

α

θ 



 



 − −

( ) =
6-21

in a mean lifetime of 19.0 years and a median lifetime of 17.7 years. This is a longer lifetime 

than the 15 years estimated in the NODA, which was for larger commercial equipment. DOE 

believes that the estimated lifetime from the 2011 DFR for single-phase air conditioners used in 

commercial settings is more representative of the lifetime for similar three-phase air conditioners

than is the estimate for larger equipment used in the NODA.

The heat pump function used an alpha of 17.04, a beta of 1.64, and a theta of 1. This 

resulted in mean lifetime of 16.24 years and a median lifetime of 14.63 years.

DOE also applied the air conditioner function to water-source heat pumps as they are 

estimated to have a mean lifetime of 19 years and DOE does not expect the shape of the function 

to be significantly different.

6.2.2.7 Discount Rates

The discount rate is the rate at which future expenditures and savings are discounted to 

establish their present value. The cost of capital is commonly used to estimate the present value 

of cash flows to be derived from a typical company project or investment. Following financial 

theory, the finance cost of raising funds for expenditure can be interpreted as: (1) the financial 

cost of any debt incurred to maintain equipment, or (2) the opportunity cost of any equity used to 

maintain equipment. Similarly, the value of cost savings can be interpreted as: (1) the cost 

avoided by decreasing debt, or (2) the return earned from increasing equity holdings. Most 

companies use both debt and equity capital to fund investments, so their cost of capital is the 

weighted average of the cost to the firm of equity and debt financing.7 DOE defines the discount 

rate as the weighted average cost of capital (WACC), calculated as the weighted average of the 

cost of equity financing and the cost of debt financing, as estimated from financial data for 

publicly traded firms in the sectors that purchase commercial air conditioners.

Damodaran Online is a widely used source of information about company debt and 

equity financing for most types of firms, and was the primary source of data for this analysis.8

Companies included in the Damondaran Online database were assigned to the categories listed 

below for 3-phase air-cooled air conditioning and heating equipment:

• Small Office

• Small Retail

For water-source heat pumps, companies were assigned to the following categories: 

• Property owners

• Medical services

• Lodging

• Offices

• Public

DOE estimated the cost of equity using the capital asset pricing model (CAPM).9 The 

CAPM assumes that the cost of equity (ke) for a particular company is proportional to the 
6-22

systematic risk faced by that company, where high risk is associated with a high cost of equity 

and low risk is associated with a low cost of equity. The systematic risk facing a firm is 

determined by several variables: the risk coefficient of the firm (β), the expected return on riskfree assets (Rf), and the equity risk premium (ERP). The risk coefficient of the firm indicates the 

risk associated with that firm relative to the price variability in the stock market. The expected 

return on risk-free assets is defined by the yield on long-term government bonds. The ERP

represents the difference between the expected stock market return and the risk-free rate. The 

cost of equity financing is estimated using the following equation, where the variables are 

defined as above:

kkee = RRff + (ββ × EE EE)

Eq. 6.5

Where:

Ke = cost of equity,

Rf = expected return on risk-free assets,

β = risk coefficient of the firm, and

ERP = equity risk premium.

Several parameters of the cost of capital equations can vary substantially over time, and 

therefore the estimates can vary with the time period over which data is selected and the

technical details of the data averaging method. For guidance on the time period for selecting and 

averaging data for key parameters and the averaging method, DOE used Federal Reserve 

methodologies for calculating these parameters. In its use of the CAPM, the Federal Reserve 

uses a forty-year period for calculating discount rate averages, utilizes the gross domestic 

product price deflator for estimating inflation, and considers the best method for determining the 

risk free rate as one where “the time horizon of the investor is matched with the term of the riskfree security.”10

By taking a forty-year geometric average of Federal Reserve data on annual nominal 

returns for 10-year Treasury bills, DOE found for this analysis the following risk free rates for 

2011-2013 (Table 6.2.21). DOE also estimated the ERP by calculating the difference between 

risk free rate and stock market return for the same time period.11

Table 6.2.21 Risk Free Rate and Equity Risk Premium (2011-2013)

Year Risk free rate (%) ERP (%)

2011 6.61% 2.94%

2012 6.41% 3.99%

2013 6.24% 5.30%

The cost of debt financing (kd) is the interest rate paid on money borrowed by a company. 

The cost of debt is estimated by adding a risk adjustment factor (Ra) to the risk-free rate. This 

risk adjustment factor depends on the variability of stock returns represented by standard 

deviations in stock prices. So for firm i, the cost of debt financing is:
6-23

kkdddd = RRff + RRaa

Eq. 6.6

Where:

kdi = cost of debt financing for firm, i,

Rf = expected return on risk-free assets, and

Rai = risk adjustment factor to risk-free rate for firm, i. 

DOE estimates the WACC using the following equation:

e e d wd WACC = k ×w + k ×

Eq. 6.7

Where:

WACC = weighted average cost of capital,

we = proportion of equity financing, and

wd = proportion of debt financing.

By adjusting for the influence of inflation, DOE estimates the real weighted average cost 

of capital, or discount rate, for each company. DOE then aggregates the company real weighted 

average costs of capital to estimate the discount rate for each of the ownership types in the 

analysis. An overall average is estimated by weighting each ownership type’s discount rate by its 

estimated share of the equipment market. The values are presented in

Table 6.2.22 for small commercial air-cooled air conditioners and heat pumps and Table 

6.2.23 for water-source heat pumps. While WACC values for any category may trend higher or 

lower over substantial periods of time, these values represent a private sector cost of capital that 

is averaged over major business cycles. 

Table 6.2.22 Weighted Average Cost of Capital for Sectors that Purchase 3-phase AirCooled, Air Conditioning and Heating Equipment

Sector

Discount 

Rate

Standard 

Deviation

Market 

Share

Small Office 6.57% 2.09% 47%

Small Retail 7.21% 2.06% 53%

Source: Damodaran Online Data Page: Costs of Capital by Industry Sector, 2011,2012,2013.

Table 6.2.23 Weighted Average Cost of Capital for Sectors that Purchase Water-Source 

Heat Pumps

Sector

Discount 

Rate

Standard 

Deviation

Market 

Share

Offices 5.13% 2.05% 26%

Public 3.74% 1.43% 30%

Lodging 5.57% 1.87% 5%

Property owners 5.15% 1.20% 29%

Medical services 4.73% 1.66% 10%

Source: Damodaran Online Data Page: Costs of Capital by Industry Sector, 2011,2012,2013.
6-24

6.2.2.8 Compliance Date of Standard

Generally, covered equipment to which a new or amended energy conservation standard 

applies must comply with the standard if such equipment is manufactured or imported on or after 

a specified date. In this Final Rule, DOE is evaluating whether more-stringent efficiency levels 

than those in ASHRAE Standard 90.1-2013 would be technologically feasible, economically 

justified, and result in a significant additional amount of energy savings. If DOE were to propose 

a rule prescribing energy conservation standards at the efficiency levels contained in ASHRAE 

Standard 90.1-2013 for the triggered equipment classes, EPCA states that compliance with any 

such standards shall be required on or after a date which is two or three years (depending on 

equipment size) after the compliance date of the applicable minimum energy efficiency 

requirement in the amended ASHRAE/IES standard. (42 U.S.C. 6313(a)(6)(D)) Given the 

equipment size at issue here, DOE has applied the two-year implementation period to determine 

the compliance date of any energy conservation standard equal to the efficiency levels specified 

by ASHRAE Standard 90.1-2013 proposed by this rulemaking. Thus, if DOE decides to adopt 

the efficiency levels in ASHRAE Standard 90.1-2013, the compliance date of the rulemaking 

would be dependent upon the date specified in ASHRAE Standard 90.1-2013 or its publication 

date, if none is specified. In this case, the rule would apply to small commercial air-cooled air 

conditioners and heat pumps less than 65,000 Btu/h manufactured on or after January 1, 2017, 

which is two years after the date specified in ASHRAE Standard 90.1-2013. The rule would 

apply to water-source heat pumps manufactured on or after October 9, 2015, which is two years 

after the publication date of ASHRAE Standard 90.1-2013.

If DOE were to propose a rule prescribing energy conservation standards more stringent 

than the efficiency levels contained in ASHRAE Standard 90.1-2013, EPCA states that 

compliance with any such standards is required for products manufactured on or after a date 

which is four years after the date the final rule is published in the Federal Register. (42 U.S.C. 

6313(a)(6)(D)) DOE has applied this 4-year implementation period to determine the compliance 

date for any energy conservation standard more stringent than the efficiency levels specified by 

ASHRAE Standard 90.1-2013 that might be prescribed at the final rule stage for the equipment

classes triggered by ASHRAE. Thus, for equipment for which DOE might adopt a level more 

stringent than the ASHRAE efficiency levels, the rule would apply to products manufactured on 

or after a date four years from the date of publication of the final rule, which the statute requires 

to be completed by April 9, 2016 (thereby resulting in a compliance date no later than April 9, 

2020).b

Economic justification is not required for DOE to adopt the efficiency levels in ASHRAE 

90.1-2013, as DOE is statutorily required to, at a minimum, adopt those levels. Therefore, DOE 

did not perform an LCC analysis on the ASHRAE Standard 90.1-2013 levels. And therefore, for 

purposes of the LCC analysis, DOE used 2020 as the first year of compliance with amended 

standards.

 b Since ASHRAE published ASHRAE Standard 90.1-2013 on October 9, 2013, EPCA requires that DOE publish a 

final rule adopting more-stringent standards than those in ASHRAE Standard 90.1-2013, if warranted, within 30 

months of ASHRAE action (i.e., by April 2016). Thus, four years from April 2016 would be April 2020, which 

would be the anticipated compliance date for DOE adoption of more-stringent standards.
6-25

For split system air-cooled air conditioners, which DOE evaluated under the 6 year look 

back, DOE applied a different compliance date. Specifically, EPCA states that amended 

standards prescribed under this subsection shall apply to products manufactured after a date that 

is the later of—(I) the date that is 3 years after publication of the final rule establishing a new 

standard; or (II) the date that is 6 years after the effective date of the current standard for a 

covered product. (42 U.S.C. 6313(a)(6)(C)(iv)) Because DOE must publish a final rule by April 

9, 2016, in the case that it adopts standards higher than those in ASHRAE 90.1 for the other 

equipment classes, DOE projected that the date under clause (I) would be April 2019, which is 

later than the date under clause (II). For purposes of its analysis, DOE used 2019 as the first year 

of compliance with amended standards.

6.2.2.9 Base Case Distribution of Efficiency Levels

DOE created base case efficiency distributions for the compliance year based on model 

availability in the AHRI certified directory. DOE bundled the efficiency levels into "efficiency 

ranges" and determined the percentage of models within each range. DOE applied the 

percentages of models within each efficiency range to the total unit shipments for a given 

equipment class to estimate the distribution of shipments within the base case.

DOE has estimated a base-case efficiency trend of approximately 1 SEER every 35 years, 

based on the EER trend from 2012 to 2035 found in the Commercial Unitary Air Conditioner 

Advance Notice of Proposed Rulemaking.

12 DOE applied this efficiency trend to estimate the 

base case efficiency distribution in the year in which compliance is expected for any standards 

adopted higher than the ASHRAE level.c Table 6.2.24 and Table 6.2.25 show the distributions.

Table 6.2.24 Base Case Efficiency Market Shares in the compliance year for 3-phase AirCooled, Air Conditioning and Heating Equipment

Split System AC Single Package AC Split System HP Single Package HP

SEER Market 

Share SEER Market Share SEER Market Share SEER Market Share

13.0 26% N/A N/A N/A N/A N/A N/A

14.0 50% 14.0 52% 14.0 80% 14.0 69%

15.0 22% 15.0 30% 15.0 19% 15.0 21%

16.0 2% 16.0 7% 16.0 1% 16.0 9%

17.0 0% 17.0 4% 17.0 0% 17.0 1%

18.0 0% 18.0 7% 18.0 0% 18.0 1%

19.0 0% 19.0 0%

Note: The compliance year is 2019 for Split System AC and 2020 for Single Package AC, Split System HP, and 

Single Package HP.

 c Chapter 8 contains additional information about development of efficiency distributions for the ASHRAE level and 

use of the base-case efficiency trend.
6-26

Table 6.2.25 Base Case Efficiency Market Shares in 2020 for Water-Source Heat Pumps

WSHP

<17,000 Btu/h

WSHP

≥17,000 to

<65,000 Btu/h

WSHP

≥65,000 to

<135,000 Btu/h

EER Market 

Share EER Market Share EER Market Share

12.2 0.7% 13.0 7.6% 13.0 0.0%

13.0 49.7% 14.6 55.1% 14.0 29.8%

14.0 22.0% 16.6 25.0% 15.0 48.5%

15.7 20.5% 18.0 8.9% 16.0 20.1%

16.5 4.9% 19.2 2.5% 17.2 1.7%

18.1 2.3% 21.6 1.0%

6.3 PAYBACK PERIOD INPUTS

The PBP is the amount of time it takes the customer to recover the assumed higher 

purchase cost of more energy-efficient equipment as a result of lower operating costs. 

Numerically, the PBP is the ratio of the increase in purchase cost (i.e., from a less efficient 

design to a more efficient design) to the decrease in first year annual operating expenditures. 

The equation for PBP is:

PBP =∆IC/∆OC

Eq. 6.8

Where:

PBP = payback period in years,

∆ IC = difference in the total installed cost between the more efficient standard-level 

equipment (efficiency levels 2, 3, etc.) and the baseline efficiency equipment, and

∆OC = difference in first year annual operating costs.

Payback periods are expressed in years. Payback periods can be greater than the life of 

the equipment if the increased total installed cost of the more efficient equipment is not 

recovered fast enough in reduced operating costs.

The data inputs to PBP are the total installed cost of the equipment to the customer for 

each efficiency level and the annual (first year) operating costs for each efficiency level. The 

inputs to the total installed cost are the equipment price and the installation cost. The inputs to 

the operating costs are the annual energy cost and the annual maintenance cost (or, in the case of 

rebuttable PBP, only the annual energy cost). The PBP uses the same inputs as the LCC analysis, 

except that electricity price trends and discount rates are not required. Since the PBP is a 

“simple” payback, the required electricity cost is only for the year in which a new efficiency
6-27

standard is to take effect—in this case, 2019 for split system air conditioners and 2020 for all 

other equipment classes.

6.4 LCC AND PBP RESULTS

This section presents the results of the LCC and PBP analysis. As discussed previously, 

DOE used probability distributions to characterize the uncertainty in many of the inputs to the 

analysis. DOE used Monte Carlo simulation to perform the LCC calculations. LCC and PBP 

calculations were performed 10,000 times, sampling from the probability distributions that DOE 

developed to characterize many of the inputs to the analysis. 

For each set of customers using the equipment in each equipment class, DOE calculated 

the average LCC and LCC savings and the simple PBP for each efficiency level. DOE calculated 

simple payback relative to the baseline equipment. DOE calculated LCC savings relative to the 

base-case efficiency distribution in the compliance year. Based on the Monte Carlo simulations 

that DOE performed, for each standard level DOE also calculated the share of customers 

receiving a net LCC cost. 

Table 6.4.1 through Table 6.4.14 show the LCC and PBP results for each equipment 

class. In general, the average LCC savings are negative for nearly all equipment classes and 

efficiency levels, with the exception of water-source heat pumps greater than or equal to 17,000 

Btu/h and less than 65,000 Btu/h, is has positive LCC savings for the first three efficiency levels. 

Table 6.4.1 Life Cycle and Payback Period Results for 3-phase, Air-Cooled, Split-System 

Air Conditioners <65,000 Btu/h

Efficiency 

Level

Average Costs

2014$ Simple 

Payback

years

Average 

Lifetime

years Installed 

Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

Federal 

Baseline $3,901 $776 $7,532 $11,433 N/A 19

0 $4,150 $773 $7,497 $11,647 68 19

1 $4,401 $766 $7,433 $11,834 49 19

2 $4,670 $760 $7,373 $12,043 47 19

3 $4,927 $763 $7,409 $12,335 80 19

4 $5,194 $768 $7,449 $12,643 148 19

5 $5,474 $774 $7,507 $12,981 560 19

Note: The results for each efficiency level are calculated assuming that all customers use equipment with that 

efficiency level. The PBP is measured relative to the baseline equipment. 
6-28

Table 6.4.2 LCC Savings Relative to the Base-Case Efficiency Distribution for 3-Phase 

Air-Cooled Split-System Air Conditioners <65,000 Btu/h

Efficiency Level

Life-Cycle Cost Savings

% of Customers that 

Experience Average Savings*

Net Cost 2014$

0 26% ($56)

1 75% ($198)

2 97% ($402)

3 100% ($695)

4 100% ($1,002)

5 100% ($1,341)

* The calculation includes households with zero LCC savings (no impact).

Table 6.4.3 Life Cycle and Payback Period Results for 3-phase, Air-Cooled, Package Air 

Conditioners <65,000 Btu/h

Efficiency 

Level

Average Costs

2014$ Simple 

Payback

years

Average 

Lifetime

years Installed 

Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

ASHRAE 

Baseline $4,781 $772 $7,516 $12,297 N/A 19

1 $5,090 $758 $7,381 $12,471 22 19

2 $5,400 $753 $7,329 $12,729 32 19

3 $5,702 $757 $7,368 $13,070 61 19

4 $6,007 $761 $7,407 $13,414 110 19

5 $6,375 $766 $7,457 $13,833 270 19

Note: The results for each efficiency level are calculated assuming that all customers use equipment with that 

efficiency level. The PBP is measured relative to the baseline equipment. 
6-29

Table 6.4.4 LCC Savings Relative to the Base-Case Efficiency Distribution for 3-Phase 

Air-Cooled Single-Package Air Conditioners <65,000 Btu/h

Efficiency Level

Life-Cycle Cost Savings

% of Customers that 

Experience Average Savings*

Net Cost 2014$

1 49% ($89)

2 81% ($299)

3 89% ($602)

4 93% ($922)

5 100% ($1,340)

* The calculation includes households with zero LCC savings (no impact).

Table 6.4.5 Life Cycle and Payback Period Results for 3-phase, Air-Cooled, Split System 

Heat Pumps <65,000 Btu/h

Efficiency 

Level

Average Costs

2014$ Simple 

Payback

years

Average 

Lifetime

years Installed 

Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

ASHRAE 

Baseline $4,513 $796 $7,070 $11,584 N/A 16

1 $4,774 $783 $6,957 $11,731 20 16

2 $5,118 $777 $6,906 $12,024 33 16

3 $5,401 $778 $6,911 $12,312 49 16

4 $5,694 $778 $6,918 $12,612 69 16

Note: The results for each efficiency level are calculated assuming that all customers use equipment with that 

efficiency level. The PBP is measured relative to the baseline equipment. 
6-30

Table 6.4.6 LCC Savings Relative to the Base-Case Efficiency Distribution for 3-Phase 

Air-Cooled Split System Heat Pumps <65,000 Btu/h

Efficiency Level

Life-Cycle Cost Savings

% of Customers that 

Experience Average Savings*

Net Cost 2014$

1 75% ($118)

2 99% ($410)

3 100% ($697)

4 100% ($997)

* The calculation includes households with zero LCC savings (no impact).

Table 6.4.7 Life Cycle and Payback Period Results for 3-phase, Air-Cooled, Package 

Heat Pumps <65,000 Btu/h

Efficiency 

Level

Average Costs

2014$ Simple 

Payback

years

Average 

Lifetime

years Installed 

Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

ASHRAE 

Baseline $5,155 $797 $7,084 $12,239 N/A 16

1 $5,499 $784 $6,969 $12,468 27 16

2 $5,830 $777 $6,909 $12,739 34 16

3 $6,161 $778 $6,916 $13,077 53 16

4 $6,550 $779 $6,923 $13,473 77 16

Note: The results for each efficiency level are calculated assuming that all customers use equipment with that 

efficiency level. The PBP is measured relative to the baseline equipment. 
6-31

Table 6.4.8 LCC Savings Relative to the Base-Case Efficiency Distribution for 3-Phase 

Air-Cooled Package Heat Pumps <65,000 Btu/h

Efficiency Level

Life-Cycle Cost Savings

% of Customers that 

Experience Average Savings*

Net Cost 2014$

1 68% ($158)

2 90% ($402)

3 99% ($735)

4 99% ($1,128)

* The calculation includes households with zero LCC savings (no impact).

Table 6.4.9 Life Cycle and Payback Period Results for Water Source Heat Pumps 

<17,000 Btu/h

Efficiency 

Level

Average Costs

2014$ Simple 

Payback

years

Average 

Lifetime

years Installed 

Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

ASHRAE 

Baseline $3,216 $654 $7,692 $10,908 -- 19

1 $3,354 $645 $7,578 $10,932 14 19

2 $3,530 $638 $7,492 $11,022 19 19

3 $3,822 $628 $7,377 $11,199 23 19

4 $3,958 $624 $7,334 $11,292 25 19

5 $4,233 $618 $7,263 $11,496 28 19

Note: The results for each efficiency level are calculated assuming that all customers use equipment with that 

efficiency level. The PBP is measured relative to the baseline equipment. 
6-32

Table 6.4.10 LCC Savings Relative to the Base-Case Efficiency Distribution for Water 

Source Heat Pumps <17,000 Btu/h

Efficiency Level

Life-Cycle Cost Savings

% of Customers that 

Experience Average Savings*

Net Cost 2014$

1 0% ($0)

2 46% ($46)

3 68% ($175)

4 89% ($262)

5 95% ($462)

* The calculation includes households with zero LCC savings (no impact).

Table 6.4.11 Life Cycle and Payback Period Results for Water Source Heat Pumps 

≥17,000 to <65,000 Btu/h

Efficiency 

Level

Average Costs

2014$ Simple 

Payback

years

Average 

Lifetime

years Installed 

Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

ASHRAE 

Baseline $4,882 $1,118 $13,169 $18,052 -- 19

1 $5,162 $1,075 $12,655 $17,817 6 19

2 $5,513 $1,039 $12,232 $17,745 8 19

3 $5,758 $1,023 $12,041 $17,799 9 19

4 $5,968 $1,013 $11,930 $17,898 10 19

5 $6,392 $997 $11,732 $18,124 12 19

Note: The results for each efficiency level are calculated assuming that all customers use equipment with that 

efficiency level. The PBP is measured relative to the baseline equipment. 
6-33

Table 6.4.12 LCC Savings Relative to the Base-Case Efficiency Distribution for Water 

Source Heat Pumps ≥17,000 to <65,000 Btu/h

Efficiency Level

Life-Cycle Cost Savings

% of Customers that 

Experience Average Savings*

Net Cost 2014$

1 2% $19

2 29% $64

3 52% $17

4 66% ($78)

5 76% ($303)

* The calculation includes households with zero LCC savings (no impact).

Table 6.4.13 Life Cycle and Payback Period Results for Water Source Heat Pumps 

≥65,000 to <135,000 Btu/h

Efficiency 

Level

Average Costs

2014$ Simple 

Payback

years

Average 

Lifetime

years Installed 

Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

ASHRAE 

Baseline $12,005 $2,202 $25,958 $37,963 -- 19

1 $12,961 $2,126 $25,065 $38,026 13 19

2 $13,919 $2,087 $24,599 $38,518 17 19

3 $14,830 $2,054 $24,213 $39,042 19 19

4 $15,977 $2,022 $23,834 $39,811 22 19

Note: The results for each efficiency level are calculated assuming that all customers use equipment with that 

efficiency level. The PBP is measured relative to the baseline equipment. 
6-34

Table 6.4.14 LCC Savings Relative to the Base-Case Efficiency Distribution for Water 

Source Heat Pumps ≥65,000 to <135,000 Btu/h

Efficiency 

Level

Life-Cycle Cost Savings

% of Customers that 

Experience Average Savings*

Net Cost 2014$

1 0%** $0**

2 27% ($148)

3 72% ($560)

4 93% ($1,315)

* The calculation includes households with zero LCC savings (no impact).

**The base case efficiency distribution has 0% market share at the ASHRAE baseline; therefore there are 

no savings for EL1.
6-35

REFERENCES 

 1 U.S. Department of Energy-Energy Information Administration, Annual Energy Outlook 2014, 

April, 2014. Washington, DC. Report No. DOE/EIA-0383(2014). 

www.eia.gov/forecasts/aeo/pdf/0383%282014%29.pdf

2 RS Means Mechanical Cost Data 2013. Reed Construction Data, LLC. 2012.

3 RS Means Facilities Maintenance & Repair Cost Data 2013. Reed Construction Data, LLC. 

2012

4 U.S. Department of Labor-Bureau of Labor Statistics. Unitary air-conditioners, except air 

source. Series ID: PCU333415333415E. http://data.bls.gov/cgi-bin/srgate. Accessed: 

6/6/2013.

5 U.S. Department of Labor-Bureau of Labor Statistics. All other miscellaneous refrigeration and 

air conditioning equipment. Series PCU3334153334159; www.bls.gov/ppi/

6 U.S. Department of Energy. 2014. Technical Support Document: Energy Efficiency Program 

for Commercial and Industrial Equipment: Small, Large, and Very Large Commercial 

Package Air Conditioning and Heating Equipment. Washington, D.C. <

<www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/59>

7 Modigliani, F. and M. Miller, The Cost of Capital, Corporations Finance and the Theory of 

Investment. American Economic Review, 1958. 48(3): pp. 261-297 

<www.jstor.org/stable/1809766>

8 Damodaran Online, Data Page: Costs of Capital by Industry Sector, 2011, 2012, 2013.

Damodaran. (Last accessed February, 2014.) <http://pages.stern.nyu.edu/~adamodar/>

9 Ibbotson Associates, Cost of Capital 2001 Yearbook. 2001. Chicago, IL.

10 Board of Governors of the Federal Reserve System, Federal Reserve Bank Services Private 

Sector Adjustment Factor. 2005: Washington, DC.

11 Damodaran Online, Data Page: Historical Returns on Stocks, Bonds and Bills-United States, 

2013. Damodaran. (Last accessed February, 2014.) <http://pages.stern.nyu.edu/~adamodar/>

12 U.S. Department of Energy. 2004. Technical Support Document: Energy Efficiency Program 

for Commercial and Industrial Equipment: Commercial Unitary Air Conditioners and Heat 

Pumps. Washington, D.C. 

<www1.eere.energy.gov/buildings/appliance_standards/commercial/cuac_tsd_060904.html>
7-i

CHAPTER 7. SHIPMENTS

TABLE OF CONTENTS

7.1 INTRODUCTION ........................................................................................................... 7-1

7.2 INPUTS AND METHODOLOGY.................................................................................. 7-1

7.2.1 Commercial Air-cooled Air Conditioners and Heat Pumps................................ 7-1

7.2.2 Water Source Heat Pumps................................................................................... 7-4

7.3 BASE CASE SHIPMENTS............................................................................................. 7-6

7.4 STANDARDS CASE SHIPMENTS ............................................................................... 7-8

REFERENCES .......................................................................................................................... 7-10

LIST OF TABLES

Table 7.2.1 Historical Shipments of Three-Phase Air-Cooled Air Conditioners and 

Heat Pumps <65,000 Btu/h............................................................................ 7-1

Table 7.2.2 Census Shipments of Air-Cooled Equipment <65,000 Btu/h (Including 

Both Single- and Three-Phase Equipment..................................................... 7-2

Table 7.2.3 Year Over Year Percentage Growth of Small, Air-Cooled Air 

Conditioners and Heat Pumps from 2005-2014 (AHRI)............................... 7-4

Table 7.2.4 Census Shipments of Water Source Heat Pumps .......................................... 7-5

Table 7.2.5 Total Shipments of Water-Source Heat Pumps by Equipment Class............ 7-6

Table 7.3.1 Projected Shipments of Air-Cooled Three-Phase Equipment <65,000 

Btu/h (2011-2046).......................................................................................... 7-7

Table 7.3.2 Projected WSHP Shipments by Equipment Class (2011-2045) .................... 7-8

LIST OF FIGURES

Figure 7.2.1 Air-Cooled Equipment Shipping Trends 1999-2010..................................... 7-3

Figure 7.2.2 Water Source Heat Pump Shipment Trends 1990-2010................................ 7-6
7-1

CHAPTER 7. SHIPMENTS

7.1 INTRODUCTION

Estimates of equipment shipments are a necessary input for calculating national energy 

savings (NES) and net present value (NPV), which are required to justify potential new or 

amended energy efficiency standards. Shipments also are a necessary input to the manufacturer 

impact analysis. This chapter describes DOE’s method and results of projecting annual 

shipments for small 3-phase air-cooled air conditioners and heat pumps as well as water source 

heat pumps.

The shipments models are part of the National Impact Analysis models and are prepared 

as Microsoft Excel spreadsheets that are accessible on the Internet 

(www.eere.energy.gov/buildings/appliance_standards/). 

7.2 INPUTS AND METHODOLOGY

This section outlines the inputs and methodology used to develop the shipments model 

for small, 3-phase air-cooled air conditioning and heat pump equipment and water source heat 

pumps. 

7.2.1 Commercial Air-cooled Air Conditioners and Heat Pumps

DOE estimated shipments of three-phase air-cooled equipment less than 65,000 Btu/h by 

equipment class for the year 1999 as part of the 2000 Screening Analysis for the Energy Policy 

Act (EPACT).

1 Table 7.2.1 shows these data. DOE used U.S. Census data for similar equipment, 

including both single- and three-phase, from 1999 to 2010,2 shown in Table 7.2.2, to extrapolate 

DOE’s 1999 estimated shipments data from 2000 to 2010. For each year from 2000 to 2010, 

DOE multiplied the 1999 shipments by the indexed value for the relevant year from the Census 

data. 

Table 7.2.1 Historical Shipments of Three-Phase Air-Cooled Air Conditioners and Heat 

Pumps <65,000 Btu/h

Equipment Class 1999

Split System AC 91,598

Single Package AC 213,728

Split System HP 11,903

Single Package HP 27,773

Source: U.S. Department of Energy – Office of Energy Efficiency and Renewable Energy. Screening Analysis for 

EPACT-Covered Commercial HVAC and Water-Heating Equipment. Volume 1 – Main Report. 2000. 
7-2

Table 7.2.2 Census Shipments of Air-Cooled Equipment <65,000 Btu/h (Including Both 

Single- and Three-Phase Equipment

Split System AC Single Package AC Split System HP Single Package HP

Year Shipments

Index 

(to 

1999)

Shipments

Index 

(to 

1999)

Shipments

Index 

(to 

1999)

Shipments

Index 

(to 

1999)

1999 4,255,509 1.00 666,314 1.00 1,011,525 1 202,608 1.00

2000 4,181,538 0.98 686,448 1.03 1,041,287 1.03 204,324 1.01

2001 3,818,875 0.90 611,636 0.92 1,159,308 1.15 220,716 1.09

2002 4,033,357 0.95 553,728 0.83 1,159,507 1.15 180,521 0.89

2003 4,393,942 1.03 602,282 0.90 1,345,671 1.33 205,301 1.01

2004 4,988,640 1.17 614,937 0.92 1,634,521 1.62 214,886 1.06

2005 5,908,762 1.39 607,981 0.91 1,849,512 1.83 236,189 1.17

2006 4,456,324 1.05 575,916 0.86 1,873,980 1.85 232,762 1.15

2007 3,890,171 0.91 551,675 0.83 1,578,409 1.56 244,256 1.21

2008 3,534,140 0.83 453,055 0.68 1,530,443 1.51 197,024 0.97

2009 3,342,768 0.79 364,616 0.55 1,581,241 1.56 169,317 0.84

2010 3,235,467 0.76 330,965 0.50 1,571,177 1.55 172,816 0.85

Source: U.S.Census Bureau. Current Industrial Reports for Refrigeration, Air Conditioning, and Warm Air Heating 

Equipment, MA333M.

The census data showed a declining trend in shipments for Split System AC, Single 

Package AC and Single Package HP equipment classes and a slightly increasing trend for Split 

System HP, as shown in Figure 7.2.1. For the April 2014 Notice of Data Availability, DOE 

applied these trends to the single package and split system heat pumps and used a constant value 

for single package air conditioners based on the average value from 1999 to 2010.a (79 FR 

20114) However, in response to comments on the NODA, DOE reviewed the inputs available for 

its shipments projections. Because the Census data end in 2010, DOE cannot use that data to 

determine whether shipments continue to decline past 2010. Therefore, DOE reviewed AHRI’s 

monthly shipments data for the broader category of central air conditioners and heat pumps3 to 

determine more recent trends. 

 a DOE did not analyze split system AC in the NODA.
7-3

Figure 7.2.1 Air-Cooled Equipment Shipping Trends 1999-2010

Table 7.2.3 shows the annual percentage growth in shipments of small air conditioners 

and heat pumps from 2005 to 2014 as reported by AHRI. DOE found that the average annual 

growth rate from 2005 to 2010 was -12% for air conditioners and -4% for heat pumps. However, 

the average annual growth rate from 2010 to 2014 was 7% for air conditioners and 8% for heat 

pumps. These data indicate that the decline in shipments through 2010 has stopped and has in 

fact begun to reverse. Therefore, DOE used the AHRI reported growth rates from 2010 to 2011 

(10% for air conditioners and 1% for heat pumps) to scale its projected 2010 shipments to 2011.

In order to project shipments into the future, DOE relied on Annual Energy Outlook 

(AEO) 2014 forecasts for commercial floor space from 2011 through 2040.4 DOE assumed that 

shipments of small commercial air conditioners and heat pumps would be related to the growth 

of commercial floor space. DOE used this projection, with an average annual growth rate of 1%, 

to project shipments for each of the four equipment classes through 2040. For years beyond 

2040, DOE also applied an average annual growth rate of 1%. 

y = -8738.8x + 2E+07

R2 = 0.7655

y = -185.13x + 399431

R2 = 0.0397

y = 720.37x - 1E+06

R2 = 0.5542

y = 1E+133x-38.79

R2 = 0.1709

 -

 50,000

 100,000

 150,000

 200,000

 250,000

1998 2000 2002 2004 2006 2008 2010 2012

CUAC <65 single

CUHP <65 single

CUHP <65 split

CUAC <65 split
7-4

Table 7.2.3 Year Over Year Percentage Growth of Small, Air-Cooled Air Conditioners 

and Heat Pumps from 2005-2014 (AHRI)

Year Central Air Conditioners Central Heat Pumps

2005 17% 13%

2006 -23% -1%

2007 -9% -10%

2008 -12% -2%

2009 -11% -12%

2010 -3% 6%

2011 10% 1%

2012 5% -4%

2013 7% 16%

2014 

(May) 6% 17%

Source: AHRI. 2012. HVACR & Water Heating Industry Statistical Profile. See also AHRI Monthly Shipments 

December 2013 and May 2014 releases. 

7.2.2 Water Source Heat Pumps

DOE based its shipments analysis for water-source heat pumps on data from the U.S. 

Census. The U.S. Census published historical (1980, 1983-1994, 1997-2006, and 2008-2010) 

water-source heat pump shipment data.

2 Table 7.2.4 exhibits the complete available time series.

DOE analyzed data from the years 1990-2010 to establish a trend from which to project 

shipments beyond 2010. DOE used a linear trend, as shown in Figure 7.2.2. Because the Census 

data do not distinguish between equipment capacities, DOE used the shipments data by 

equipment class provided by AHRI in 1999, and published in the 2000 Screening Analysis for 

EPACT1, to distribute the total water-source heat pump shipments to individual equipment 

classes (Table 7.2.5).
7-5

Table 7.2.4 Census Shipments of Water Source Heat Pumps 

Year Product Code: 333415E181

1980 48,783

1981 N/A

1982 N/A

1983 69,280

1984 86,165

1985 119,164

1986 103,970

1987 142,033

1988 149,795

1989 157,080

1990 139,864

1991 111,746

1992 99,236

1993 105,159

1994 99,321

1995 N/A

1996 N/A

1997 112,052

1998 120,080

1999 120,545

2000 133,654

2001 130,351

2002 114,336

2003 123,974

2004 135,769

2005 141,410

2006 202,234

2007 N/A

2008 223,636

2009 180,101

2010 170,788

Source: U.S.Census Bureau. Current Industrial Reports for Refrigeration, Air Conditioning, and Warm Air Heating 

Equipment, MA333M.
7-6

Figure 7.2.2 Water Source Heat Pump Shipment Trends 1990-2010

Table 7.2.5 Total Shipments of Water-Source Heat Pumps by Equipment Class 

Equipment Class 1999 Percent

<17,000 Btu/h 41,000 31%

≥17,000 - <65,000 Btu/h 86,000 65%

≥65,000 -135,000 Btu/h 5,000 4%

Source: U.S. Department of Energy – Office of Energy Efficiency and Renewable Energy. Screening Analysis for 

EPACT-Covered Commercial HVAC and Water-Heating Equipment. Volume 1 – Main Report. 2000.

7.3 BASE CASE SHIPMENTS

Table 7.3.1 and Table 7.3.2 show the resulting projected shipments for 3-phase, aircooled air conditioning and heat pump equipment and water source heat pumps, respectively.

y = 4135.8x - 8E+06

R2 = 0.5483

0

50000

100000

150000

200000

250000

1985 1990 1995 2000 2005 2010 2015
7-7

Table 7.3.1 Projected Shipments of Air-Cooled Three-Phase Equipment <65,000 Btu/h 

(2011-2048)

Year Split System AC Single Package AC Split System HP Single Package HP

2011 76,270 116,264 18,669 23,921

2012 76,855 117,157 18,813 24,104

2013 77,333 117,884 18,930 24,254

2014 77,878 118,715 19,063 24,425

2015 78,536 119,719 19,224 24,632

2016 79,307 120,894 19,413 24,873

2017 80,210 122,271 19,634 25,157

2018 81,187 123,759 19,873 25,463

2019 82,199 125,302 20,121 25,780

2020 83,175 126,790 20,360 26,086

2021 84,127 128,242 20,593 26,385

2022 85,053 129,653 20,819 26,675

2023 85,945 131,013 21,038 26,955

2024 86,806 132,325 21,248 27,225

2025 87,651 133,613 21,455 27,490

2026 88,471 134,864 21,656 27,748

2027 89,269 136,081 21,851 27,998

2028 90,053 137,275 22,043 28,244

2029 90,830 138,460 22,234 28,487

2030 91,610 139,649 22,424 28,732

2031 92,414 140,874 22,621 28,984

2032 93,279 142,193 22,833 29,256

2033 94,195 143,590 23,057 29,543

2034 95,154 145,051 23,292 29,844

2035 96,170 146,600 23,541 30,162

2036 97,255 148,253 23,806 30,502

2037 98,357 149,933 24,076 30,848

2038 99,458 151,612 24,345 31,193

2039 100,541 153,262 24,610 31,533

2040 101,593 154,867 24,868 31,863

2041 102,603 156,405 25,115 32,180

2042 103,622 157,959 25,365 32,499

2043 104,652 159,529 25,617 32,822

2044 105,692 161,114 25,871 33,148

2045 106,742 162,715 26,128 33,478

2046 107,802 164,332 26,388 33,810

2047 108,874 N/A N/A N/A

2048 109,955 N/A N/A N/A
7-8

Table 7.3.2 Projected WSHP Shipments by Equipment Class (2011-2045)

Year Total <17,000 Btu/h ≥17,000 - <65,000 

Btu/h

≥65,000 -135,000 

Btu/h

2011 181,937 56,511 118,535 6,892

2012 186,073 57,795 121,229 7,048

2013 190,209 59,080 123,924 7,205

2014 194,344 60,365 126,618 7,362

2015 198,480 61,649 129,313 7,518

2016 202,616 62,934 132,007 7,675

2017 206,752 64,218 134,702 7,832

2018 210,887 65,503 137,396 7,988

2019 215,023 66,788 140,091 8,145

2020 219,159 68,072 142,785 8,301

2021 223,295 69,357 145,480 8,458

2022 227,430 70,641 148,174 8,615

2023 231,566 71,926 150,869 8,771

2024 235,702 73,210 153,563 8,928

2025 239,838 74,495 156,258 9,085

2026 243,974 75,780 158,952 9,241

2027 248,109 77,064 161,647 9,398

2028 252,245 78,349 164,342 9,555

2029 256,381 79,633 167,036 9,711

2030 260,517 80,918 169,731 9,868

2031 264,652 82,203 172,425 10,025

2032 268,788 83,487 175,120 10,181

2033 272,924 84,772 177,814 10,338

2034 277,060 86,056 180,509 10,495

2035 281,195 87,341 183,203 10,651

2036 285,331 88,626 185,898 10,808

2037 289,467 89,910 188,592 10,965

2038 293,603 91,195 191,287 11,121

2039 297,739 92,479 193,981 11,278

2040 301,874 93,764 196,676 11,435

2041 306,010 95,049 199,370 11,591

2042 310,146 96,333 202,065 11,748

2043 314,282 97,618 204,759 11,905

2044 318,417 98,902 207,454 12,061

2045 322,553 100,187 210,148 12,218

7.4 STANDARDS CASE SHIPMENTS

As equipment purchase price and repair costs increase with efficiency, DOE recognizes 

that higher first costs and repair costs can result in a drop in shipments. However, DOE had no 

basis for estimating the elasticity of shipments for small commercial air-cooled air conditioners 

and heat pumps less than 65,000 Btu/h or water-source heat pumps as a function of first costs, 

repair costs, or operating costs. In addition, because air-cooled air conditioners are likely the 
7-9

lowest-cost option for air conditioning small office and retail applications, DOE believes it is 

unlikely that air-cooled shipments would change as a result of higher first costs and repair costs. 

In contrast, because water-source heat pumps are often installed for their higher efficiency as 

compared to air-cooled equipment, DOE believes it is unlikely that water-source shipments 

would change as a result of higher first costs and repair costs. Therefore, DOE presumed that the 

shipments projection does not change with higher standard levels. 
7-10

REFERENCES

1 U.S. Department of Energy – Office of Energy Efficiency and Renewable Energy. Screening 

Analysis for EPACT-Covered Commercial HVAC and Water-Heating Equipment. Volume 1 

– Main Report. 2000. <www.regulations.gov/#!documentDetail;D=EERE-2006-STD-0098-

0015>

2 U.S.Census Bureau. Current Industrial Reports for Refrigeration, Air Conditioning, and 

Warm Air Heating Equipment, MA333M. 

<www.census.gov/manufacturing/cir/historical_data/ma333m/index.html>

3 AHRI. 2012. HVACR & Water Heating Industry Statistical Profile. See also AHRI Monthly 

Shipments: <ahrinet.org/site/498/Resources/Statistics/Monthly-Shipments>; especially 

December 2013 release: 

<ahrinet.org/App_Content/ahri/files/Statistics/Monthly%20Shipments/2013/December2013.

pdf>; May 2014 release: 

<ahrinet.org/App_Content/ahri/files/Statistics/Monthly%20Shipments/2014/May2014.pdf>.

4 U.S. Department of Energy-Energy Information Administration, Annual Energy Outlook 

2014, April, 2014. Washington, DC. Report No. DOE/EIA-0383(2014). 

<www.eia.gov/forecasts/aeo/pdf/0383%282014%29.pdf.>
8-i

CHAPTER 8. NATIONAL IMPACT ANALYSIS

TABLE OF CONTENTS

8.1 INTRODUCTION ........................................................................................................... 8-1

8.2 NATIONAL ENERGY SAVINGS ................................................................................. 8-1

8.2.1 Definition ............................................................................................................. 8-1

8.2.2 NES Inputs........................................................................................................... 8-5

8.2.2.1 Shipments................................................................................................... 8-5

8.2.2.2 Equipment Lifetime.................................................................................... 8-5

8.2.2.3 National Annual Energy Consumption ...................................................... 8-5

8.2.2.4 Site-to-Power-Plant Energy Use Factor ................................................... 8-16

8.2.2.5 Full-Fuel-Cycle Energy Factors............................................................... 8-17

8.2.2.6 Compliance Dates and Analysis Period ................................................... 8-17

8.3 NET PRESENT VALUE OF CUSTOMER BENEFITS .............................................. 8-19

8.3.1 Net Present Value Definition............................................................................. 8-19

8.3.2 Inputs to Calculation.......................................................................................... 8-20

8.3.2.1 Total Annual Installed Cost...................................................................... 8-21

8.3.2.2 Equipment Price Projection...................................................................... 8-21

8.3.2.3 Total Annual Operating Cost ................................................................... 8-22

8.3.2.4 Discount Factor ........................................................................................ 8-23

8.3.2.5 Present Value of Increased Installed Costs.............................................. 8-23

8.3.2.6 Present Value of Savings.......................................................................... 8-24

8.4 NES AND NPV RESULTS........................................................................................... 8-24

8.4.1 National Energy Savings.................................................................................... 8-24

8.4.2 Annual Costs and Savings ................................................................................. 8-25

8.4.3 Net Present Value Results.................................................................................. 8-26

8.4.4 Summary............................................................................................................ 8-28

REFERENCES .......................................................................................................................... 8-29

LIST OF TABLES

Table 8.2.1 Efficiency Distributions for the Base and Standards Cases for 

Commercial Split-System Air Conditioners <65,000 Btu/h.......................... 8-6

Table 8.2.2 Efficiency Distributions for the Base and Standards Cases for 

Commercial Package Air Conditioners <65,000 Btu/h ................................. 8-6

Table 8.2.3 Efficiency Distributions for the Base and Standards Cases for 

Commercial Split-System Heat Pumps <65,000 Btu/h.................................. 8-7

Table 8.2.4 Efficiency Distributions for the Base and Standards Cases for 

Commercial Package Heat Pumps <65,000 Btu/h......................................... 8-7

Table 8.2.5 Efficiency Distributions for the Base and Standards Cases for WaterSource Heat Pumps <17,000 Btu/h................................................................ 8-8
8-ii

Table 8.2.6 Efficiency Distributions for the Base and Standards Cases for WaterSource Heat Pumps ≥17,000 to ≤65,000 Btu/h.............................................. 8-8

Table 8.2.7 Efficiency Distributions for the Base and Standards Cases for WaterSource Heat Pumps ≥65,000 to ≤135,000 Btu/h............................................ 8-9

Table 8.2.8 Full-Fuel-Cycle Energy Multipliers (Based on AEO 2014) ........................ 8-17

Table 8.3.1 Average Total Installed Costs for 3-phase, Air-Cooled, Air 

Conditioning and Heating Equipment < 65,000 Btu/h ................................ 8-21

Table 8.3.2 Average Total Installed Costs for Water-Source Heat Pumps..................... 8-21

Table 8.3.3 Average Annual Electricity Costs in the Compliance Year for 3-phase, 

Air-Cooled, Air Conditioning and Heating Equipment < 65,000 Btu/h 

(2014$)......................................................................................................... 8-22

Table 8.3.4 Average Annual Electricity Costs in 2020 for Water-Source Heat 

Pumps (2014$)............................................................................................. 8-22

Table 8.4.1 Cumulative Primary Energy Savings for 3-phase, Air-Cooled, Air 

Conditioning and Heating Equipment < 65,000 Btu/h (quads) ................... 8-24

Table 8.4.2 Full Fuel Cycle Energy Savings for 3-phase, Air-Cooled, Air 

Conditioning and Heating Equipment < 65,000 Btu/h (quads) ................... 8-25

Table 8.4.3 Cumulative Primary Energy Savings for Water-Source Heat Pumps 

(quads).......................................................................................................... 8-25

Table 8.4.4 Full Fuel Cycle Energy Savings for Water-Source Heat Pumps (quads).... 8-25

Table 8.4.5 Cumulative Net Present Value for 3-phase, Air-Cooled Air 

Conditioning and Heating Equipment <65,000 Btu/h (7% Discount 

Rate)............................................................................................................. 8-27

Table 8.4.6 Cumulative Net Present Value for 3-phase, Air-Cooled Air 

Conditioning and Heating Equipment <65,000 Btu/h (3% Discount 

Rate)............................................................................................................. 8-27

Table 8.4.7 Cumulative Net Present Value for Water-Source Heat Pumps (7% 

Discount Rate) ............................................................................................. 8-27

Table 8.4.8 Summary of Cumulative Net Present Value for Water-Source Heat 

Pumps (3% Discount Rate).......................................................................... 8-28

LIST OF FIGURES

Figure 8.2.1 Flow Diagram of National Energy Savings Calculation................................ 8-2

Figure 8.2.2 Efficiency Trends for Small, Commercial Air-Cooled Split-System Air 

Conditioners <65,000 Btu/h......................................................................... 8-10

Figure 8.2.3 Efficiency Trends for Small, Commercial Air-Cooled Single Package 

Air Conditioners <65,000 Btu/h .................................................................. 8-11

Figure 8.2.4 Efficiency Trends for Small, Commercial Air-Cooled Split System 

Heat Pumps <65,000 Btu/h.......................................................................... 8-12

Figure 8.2.5 Efficiency Trends for Small, Commercial Air-Cooled Single Package 

Heat Pumps <65,000 Btu/h.......................................................................... 8-13

Figure 8.2.6 Efficiency Trends for Water-Source Heat Pumps <17,000 Btu/h ............... 8-14

Figure 8.2.7 Efficiency Trends for Water-Source Heat Pumps ≥17,000 to ≤65,000 

Btu/h............................................................................................................. 8-15
8-iii

Figure 8.2.8 Efficiency Trends for Water-Source Heat Pumps ≥65,000 to ≤135,000 

Btu/h............................................................................................................. 8-16

Figure 8.4.1 3-phase, Air-Cooled, Single Package Air Conditioners <65,000 Btu/h: 

Installed Costs, Operating Cost Savings, and Net Savings; EL 1................ 8-26

Figure 8.4.2 Cumulative NES and NPV Results for 3-Phase, Air-Cooled Air 

Conditioning and Heating Equipment <65,000 Btu/h at a Seven 

Percent Discount Rate.................................................................................. 8-28
8-1

CHAPTER 8. NATIONAL IMPACT ANALYSIS

8.1 INTRODUCTION

This chapter describes the method the U.S. Department of Energy (DOE) used to 

estimate the impacts on national energy consumption of efficiency levels for both small 

commercial air-cooled air-conditioning and heating equipment less than 65,000 Btu/h and watersource heat pumps. DOE evaluated the following impacts: (1) national energy savings (NES and 

NWS) attributable to each efficiency level, (2) monetary value of energy savings for customers 

of the equipment, (3) increased total installed cost of the equipment because of standards, and (4) 

the net present value (NPV) of energy savings (i.e., the difference between the savings in 

operating costs and the increase in total installed costs).

DOE examined various standard levels for each class of equipment, including the 

ASHRAE Standard 90.1-2013 efficiency level, the max-tech level (the highest efficiency level of 

equipment currently on the market), and several levels between. The base case for the ASHRAE

level consists of equipment at or above the current Federal energy conservation standard. For 

levels higher than ASHRAE Standard 90.1-2013, the base case consists of equipment at or above

the ASHRAE level. Because DOE by statute may not adopt standards at efficiency levels less 

stringent than those specified in ASHRAE Standard 90.1-2013, it reports all energy and 

economic impacts in this analysis as potential impacts compared with adoption of the ASHRAE 

Standard 90.1-2013 levels. Note that for split system air conditioners, for which ASHRAE did 

not raise the efficiency level, the ASHRAE 90.1-2013 levels are equivalent to the current Federal 

energy conservation standard.

DOE performed all calculations using a Microsoft Excel® spreadsheet, which is 

accessible on the Internet (www.regulations.gov/#!docketDetail;D=EERE-2014-BT-STD-0015).

8.2 NATIONAL ENERGY SAVINGS

DOE calculated the national energy savings (NES) associated with the difference 

between the base case and each potential standards case for small commercial air-cooled airconditioning and heating equipment less than 65,000 Btu/h and water-source heat pumps. DOE 

calculated cumulative energy savings throughout the lifetime of equipment shipped during the 

projection period, from 2017 to 2046 for air-cooled equipment other than split system air 

conditioners, and from 2016 to 2045 for water-source heat pumps. For both types of equipment, 

in standards cases above the ASHRAE level energy savings do not begin accruing until 2020.

For split system air conditioners, DOE used a projection period of 2019 to 2048 for all cases.

8.2.1 Definition

DOE calculates annual NES as the difference between two projections: a base case 

(without new standards) and a standards case (with new standards). Figure 8.2.1 illustrates the 

inputs, intermediate values, and outputs of the NES calculation. The base case and standards case 

distributions are discussed in section 8.2.2.1.
8-2

NES Inputs

Calculated Values

NES Output

Historical Shipments

Equip. Class Market 

Shares

Equipment Lifetime

Baseline Unit Energy 

Consumption

Unit Energy 

Consumption (at 

each EER/SEER/Et 

under 

consideration)

Site to Primary and 

Upstream Energy 

Conversion

Analysis Period

Base Case UEC 

Market Shares

Standards Case UEC 

Market Shares

Projected 

Shipments

Projected 

Shipments by 

Equip. Class

Base Case Unit 

Energy 

Consumption

Standards Case 

Unit Energy 

Consumption

Annual 

Energy 

Savings

National 

Energy 

Savings

Figure 8.2.1 Flow Diagram of National Energy Savings Calculation
8-3

For each equipment class, DOE calculated the total energy savings using a stock 

accounting method. DOE projected shipments into the future based on available information for 

each equipment class. The number of new shipments in any given year is assumed to remain in 

the commercial building stock based on a survival function, with a certain percentage of 

shipments dropping out of the stock over time until there are not any left. In this way, the total 

stock, or number of surviving units, in any given year is the sum of that year’s shipments plus the 

shipments remaining from previous years up to the maximum lifetime of the equipment.

The annual base case total energy use in any given year is the sum product of the 

shipments for that year and those surviving from previous years and the market weighted average 

base unit energy consumption (UECa

) in each relevant year. 

AAAAAAAAAAAA BB BBBB CC TTTTTT EE EEEEEE UU yyyyyyyy=nn

= � SS SSSSSSSS SSh iiii yyyyyyyy × BB BBBB UU yyyyyyyy

nn

yyyyyyyy=nn− mm mm llllll llll −1

Eq. 8.1

As currently modeled, the base UEC remains constant throughout time. As a result, in the 

previous calculation, BB BBBB UU yyyyyyyycould be moved outside the summation terms as BB BBBB UU .

The annual standards case total energy use in any given year is the sum product of the 

shipments for that year and those surviving from previous years and the market weighted average 

standards case UECb in each relevant year (multiplied by primary and upstream conversion

factors). 

AAAAAAAAAAAA SS SS CC TTTTTT EE EEEEEE UU yyyyyyyy=nn

= � SS SSSSSSSS SSh iiii yyyyyyyy × SS SS UU yyyyyyyy

nn

yyyyyyyy=nn−mm mm llllll llll −1

Eq. 8.2

Prior to the analysis period, the Standards Case UEC is equal to the Base UEC, as 

standards have not yet become effective. In the earlier part of the analysis period, energy savings 

accrue from only the newer units in the building stock. Later in the analysis period, all units in 

the building stock will have the standards UEC (as they were all shipped within the analysis 

period) and will be providing energy savings.

 a As will be discussed later, the market weighted average base UEC is based on the distribution of efficiencies under 

the current federal standards (base case). It includes equipment both meeting and exceeding the current federal 

minimum efficiency. It is not the UEC for a product at a specified EER or SEER. b The market weighted average standards UEC is based on the distribution of efficiencies under a given amended 

federal standards scenario. It may include equipment both meeting and exceeding the given amended federal 

standards level (with the exception of the Max Tech scenario, in which it is assumed that no equipment exceeds the 

amended federal standards level). It is not the UEC for a product at a specified EER or SEER.
8-4

For water-source heat pumps, energy savings for heating based on COP are also taken 

into account: 

AAAAAAAAAAAA BB BBBB CC TTTTTT EE EEEEEE UU yyyyyyyy=nn

= � SS SSSSSS SSh iiii yyyyyyyy × (BB BBBB UU yyyyyyyy

nn

yyyyyyyy=nn−(MM LL −1)

+ BB BBBB UU _h yyyyyyyy)

Eq. 8.3

AAAAAAAAAAAA SS SS SS CC TTTTTT EE EEEEEE UU yyyyyyyy=nn

= � SS SSSSSSSS SSh iiii yyyyyyyy × (SS SS UU yyyyyyyy + SS SS UU _h yyyyyyyy)

nn

yyyyyyyy=nn−(MM LL −1)

Eq. 8.4

For all equipment, the annual energy savings is the difference between the base case total 

energy use and the standards case total energy use.

AAAAAAAAAAAA EE EEEEEE SS SSSS yyyyyyyy=nn

= AAAAAAAAAAAA BB BBBB CC TTTTTT EE EEEEEE UU yyyyyyyy=nn

− AAAAAAAAAAAA SS SS SS CC TTTTTT EE EEEEEE UU yyyyyyyy=nn

Eq. 8.5

DOE calculated the cumulative energy savings over the analysis period by summing the 

annual energy savings for each year in the analysis period. The analysis period differs by 

equipment, as discussed in the next section.

CC EE EEEEEE SS SSSS = � AAAAAAAAAA EE EEEEEE SS SSSS yyyyyyyy

+29

yyyyyyyy=

Eq. 8.6

For the ASHRAE standard, the cumulative energy savings are calculated as above. For 

higher efficiency levels (except for split system air conditioners), DOE uses the market-weighted 

average UEC under the ASHRAE case (ASHRAE UEC) instead of the Base UEC to calculate 

the annual total energy use to which the annual standards case total energy use is compared. 
8-5

8.2.2 NES Inputs

The inputs for calculating national energy savings are:

• shipments,

• equipment lifetime,

• annual energy consumption for the Nation (AEC), and

• power plant primary energy use factor (src_conv) and full-fuel-cycle (FFC) multiplier.

8.2.2.1 Shipments

An extensive description of the methodology for conducting and generating the 

shipments projections can be found in Chapter 9.

8.2.2.2 Equipment Lifetime

For this analysis, DOE used the equipment lifetimes and survival functions as discussed 

in Chapter 6.

8.2.2.3 National Annual Energy Consumption

The national annual energy consumption is the product of the per-unit annual energy 

consumption and the stocks of units of each vintage. This approach accounts for differences in 

UEC from year to year. 

DOE uses a market- or shipment-weighted average annual UEC in its calculation of the 

national AEC. This takes into account that the markets include shipments of equipment with a 

broad range of efficiencies, as was shown in chapter 2. To assess the impacts on the market from 

the introduction of higher standards, DOE calculates impacts based on efficiency-distribution 

weighted annual UEC. Chapter 6 described how DOE estimated base case efficiency 

distributions for 2020 (or 2019 for split system air conditioners), the year that compliance with 

any higher standard would be required. 

For each standard level analyzed, DOE has assumed that all equipment shipped in the 

base case with efficiencies below the standard level become shipments at that standard level in 

the standard-level shipment distributions, starting with the assumed compliance date for 

amended standards (i.e., 2015 or 2017 for the ASHRAE level and 2019 or 2020 for standards 

higher than ASHRAE 90.1-2013). Shipments in the base-case distribution that are above the 

efficiency defined by the standard level are assumed to be unaffected. DOE terms this a “rollup” 

scenario, because base-case efficiency shipments below the proposed standard level “roll up” to 

meet the standard.

The tables below present the efficiency distributions for the base case and standards cases 

for 3-phase, air-cooled, air conditioners and heat pumps and water-source heat pumps by 

equipment class. Each standards case refers to a standard at the corresponding efficiency level. 
8-6

Table 8.2.1 Efficiency Distributions for the Base and Standards Cases for Commercial 

Split-System Air Conditioners <65,000 Btu/h

SEER

Market Share

Base 

Year 

(2012)

Base Case 

(2019)

Standards Case (2019)

EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

13.0 41% 26% 0% 0% 0% 0% 0% 0%

14.0 35% 50% 76% 0% 0% 0% 0% 0%

15.0 22% 22% 22% 98% 0% 0% 0% 0%

16.0 2% 2% 2% 2% 100% 0% 0% 0%

17.0 0% 0% 0% 0% 0% 100% 0% 0%

18.0 0% 0% 0% 0% 0% 0% 100% 0%

19.0 0% 0% 0% 0% 0% 0% 0% 100%

Market 

Weighted 

Average 

UEC

2,605 2,590 2,569 2,440 2,274 2,217 2,160 2,118

Table 8.2.2 Efficiency Distributions for the Base and Standards Cases for Commercial 

Package Air Conditioners <65,000 Btu/h

SEER

Market Share

Base 

Year 

(2012)

ASHRAE 

(2017)

ASHRAE

Base Case

(2020)

Standards Case (2020)

EL 1 EL 2 EL 3 EL 4 EL 5

13.0 42% 0% 0% 0% 0% 0% 0% 0%

14.0 18% 60% 52% 0% 0% 0% 0% 0%

15.0 22% 22% 30% 82% 0% 0% 0% 0%

16.0 7% 7% 7% 7% 89% 0% 0% 0%

17.0 4% 4% 4% 4% 4% 93% 0% 0%

18.0 7% 7% 7% 7% 7% 7% 100% 0%

19.0 0% 0% 0% 0% 0% 0% 0% 100%

Market 

Weighted 

Average 

UEC

2,542 2,505 2,491 2,403 2,264 2,213 2,160 2,118
8-7

Table 8.2.3 Efficiency Distributions for the Base and Standards Cases for Commercial 

Split-System Heat Pumps <65,000 Btu/h

SEER

Market Share

Base 

Year 

(2012)

ASHRAE 

(2017)

ASHRAE 

Base Case

(2020)

Standards Case (2020)

EL 1 EL 2 EL 3 EL 4 EL 5

13.0 52% 0% 0% 0% 0% 0% 0% 0%

14.0 37% 89% 80% 0% 0% 0% 0% 0%

15.0 11% 11% 19% 99% 0% 0% 0% 0%

16.0 1% 1% 1% 1% 100% 0% 0% 0%

17.0 0% 0% 0% 0% 0% 100% 0% 0%

18.0 0% 0% 0% 0% 0% 0% 100% 100%

Market 

Weighted 

Average 

UEC

2,682 2,641 2,627 2,500 2,343 2,276 2,209 2,209

Table 8.2.4 Efficiency Distributions for the Base and Standards Cases for Commercial 

Package Heat Pumps <65,000 Btu/h

SEER

Market Share

Base 

Year 

(2012)

ASHRAE 

(2017)

ASHRAE 

Base Case

(2020)

Standards Case (2020)

EL 1 EL 2 EL 3 EL 4 EL 5

13.0 42% 0% 0% 0% 0% 0% 0% 0%

14.0 36% 78% 69% 0% 0% 0% 0% 0%

15.0 12% 12% 21% 90% 0% 0% 0% 0%

16.0 9% 9% 9% 9% 99% 0% 0% 0%

17.0 1% 1% 1% 1% 1% 99% 0% 0%

18.0 1% 1% 1% 1% 1% 1% 100% 100%

Market 

Weighted 

Average 

UEC

2,641 2,608 2,594 2,485 2,342 2,276 2,209 2,209
8-8

Table 8.2.5 Efficiency Distributions for the Base and Standards Cases for Water-Source 

Heat Pumps <17,000 Btu/h

EER

Market Share

Base 

Year 

(2012)

ASHRAE 

(2016)

ASHRAE 

Base Case

(2020)

Standards Case (2020)

EL 1 EL 2 EL 3 EL 4 EL 5

11.2 1% 0% 0% 0% 0% 0% 0% 0%

12.2 20% 15% 1% 0% 0% 0% 0% 0%

13.0 29% 35% 50% 50% 0% 0% 0% 0%

14.0 22% 22% 22% 22% 72% 0% 0% 0%

15.7 20% 20% 20% 20% 20% 93% 0% 0%

16.5 5% 5% 5% 5% 5% 5% 98% 0%

18.1 2% 2% 2% 2% 2% 2% 2% 100%

Market 

Weighted 

Average 

UEC

1,630 1,622 1,606 1,606 1,548 1,431 1,370 1,259

Table 8.2.6 Efficiency Distributions for the Base and Standards Cases for Water-Source 

Heat Pumps ≥17,000 to ≤65,000 Btu/h

EER

Market Share

Base Year 

(2012)

ASHRAE 

(2016)

ASHRAE 

Base Case

(2020)

Standards Case (2020)

EL 1 EL 2 EL 3 EL 4

12.0 0% 0% 0% 0% 0% 0% 0%

13.0 18% 15% 8% 0% 0% 0% 0%

14.6 45% 48% 55% 0% 0% 0% 0%

16.6 25% 25% 25% 88% 0% 0% 0%

18.0 9% 9% 9% 9% 97% 0% 0%

19.2 2% 2% 2% 2% 2% 99% 0%

21.6 1% 1% 1% 1% 1% 1% 100%

Market 

Weighted 

Average 

UEC

4,491 4,471 4,430 4,055 3,788 3,569 3,193
8-9

Table 8.2.7 Efficiency Distributions for the Base and Standards Cases for Water-Source 

Heat Pumps ≥65,000 to ≤135,000 Btu/h

EER

Market Share

Base 

Year 

(2012)

ASHRAE 

(2016)

ASHRAE 

Base Case

(2020)

Standards Case (2020)

EL 1 EL 2 EL 3 EL 4 EL 5

12.0 0% 0% 0% 0% 0% 0% 0% 0%

13.0 13% 7% 0% 0% 0% 0% 0% 0%

14.0 21% 27% 30% 30% 0% 0% 0% 0%

15.0 44% 44% 48% 48% 78% 0% 0% 0%

16.0 20% 20% 20% 20% 20% 98% 0% 0%

17.2 2% 2% 2% 2% 2% 2% 100% 100%

Market 

Weighted 

Average 

UEC

11,058 11,006 10,911 10,911 10,707 10,234 9,607 9,607

To estimate a trend post-2020, DOE applied the same rate of growth in efficiency as it 

did to get from 2013 to 2020, which is also the same rate of growth used in the 2004 rulemaking 

for commercial unitary air conditioners, which is approximately 1 EER every 35 years.1 Figure 

8.2.2 through Figure 8.2.8 show the trend in market-weighted efficiency for each equipment 

class.
8-10

Figure 8.2.2 Efficiency Trends for Small, Commercial Air-Cooled Split-System Air 

Conditioners <65,000 Btu/h

10.00

11.00

12.00

13.00

14.00

15.00

16.00

17.00

18.00

19.00

20.00

2012 2017 2022 2027 2032 2037 2042

Shipment Weighted EER

ASHRAE EL 1 EL 2 EL 3 EL 4 EL 5
8-11

Figure 8.2.3 Efficiency Trends for Small, Commercial Air-Cooled Single Package Air 

Conditioners <65,000 Btu/h

10.00

11.00

12.00

13.00

14.00

15.00

16.00

17.00

18.00

19.00

20.00

2012 2017 2022 2027 2032 2037 2042

Shipment Weighted EER

ASHRAE EL 1 EL 2 EL 3 EL 4 EL 5
8-12

Figure 8.2.4 Efficiency Trends for Small, Commercial Air-Cooled Split System Heat 

Pumps <65,000 Btu/h

10

11

12

13

14

15

16

17

18

19

2012 2017 2022 2027 2032 2037 2042

Shipment Weighted EER

ASHRAE EL 1 EL 2 EL 3 EL 4
8-13

Figure 8.2.5 Efficiency Trends for Small, Commercial Air-Cooled Single Package Heat 

Pumps <65,000 Btu/h

10

11

12

13

14

15

16

17

18

19

2012 2017 2022 2027 2032 2037 2042

Shipment Weighted EER

ASHRAE EL 1 EL 2 EL 3 EL 4
8-14

Figure 8.2.6 Efficiency Trends for Water-Source Heat Pumps <17,000 Btu/h

Note: There is a minimal difference between ASHRAE and EL 1 as nearly all equipment sold after 2020 is projected 

to be at or above EL 1. 

10

11

12

13

14

15

16

17

18

19

2012 2017 2022 2027 2032 2037 2042

Shipment Weighted EER

ASHRAE EL 1 EL 2 EL 3 EL 4 EL 5
8-15

Figure 8.2.7 Efficiency Trends for Water-Source Heat Pumps ≥17,000 to ≤65,000 Btu/h

10

12

14

16

18

20

22

24

2012 2017 2022 2027 2032 2037 2042

ASHRAE EL 1 EL 2 EL 3 EL 4 EL 5
8-16

Figure 8.2.8 Efficiency Trends for Water-Source Heat Pumps ≥65,000 to ≤135,000 Btu/h

Note: There is no change in Shipment Weighted EER between ASHRAE and EL 1 because there is no market share 

projected below EL 1 after 2020. 

8.2.2.4 Site-to-Power-Plant Energy Use Factor

In determining national annual energy consumption, DOE initially calculated the annual 

energy consumption at the site (for electricity, the energy in kWh consumed at the household. 

DOE then applied a conversion factor to site energy consumption to account for losses associated 

with the generation, transmission, and distribution of electricity. This multiplicative site-topower-plant conversion factor converts site energy consumption into primary energy 

consumption, expressed in quadrillion Btus (quads). 

13.5

14

14.5

15

15.5

16

16.5

17

17.5

2012 2017 2022 2027 2032 2037 2042

Shipment Weighted EER

ASHRAE EL 1 EL 2 EL 3 EL 4
8-17

DOE used annual site-to-power-plant conversion factors based on the version of the 

National Energy Modeling System (NEMS)c that corresponds to DOE’s Energy Information 

Administration’s (EIA’s) Annual Energy Outlook 2014 (AEO2014). 2 The factors are marginal 

values, which represent the response of the system to an incremental decrease in consumption. 

For electricity, the conversion factors change over time in response to projected changes in 

generation sources (that is, the types of power plants projected to provide electricity to the 

Nation). The value AEO2014 reported for 2040 (the last year available in AEO2014) was 

extrapolated through the end of the projection period (2046).

8.2.2.5 Full-Fuel-Cycle Energy Factors

The full-fuel-cycle (FFC) includes point-of-use (site) energy; the energy losses associated 

with generation, transmission, and distribution of electricity; and the energy consumed in 

extracting, processing, and transporting or distributing primary fuels. To estimate the FFC by 

including the energy consumed in extracting, processing, and transporting or distributing primary 

fuels, which we refer to as “upstream” activities, DOE developed FFC multipliersd using the data 

and projections generated by NEMS and used for AEO2014. The AEO2014 provides extensive 

information about the energy system, including projections of future oil, natural gas, and coal 

supplies; energy use for oil and gas field and refinery operations; and fuel consumption and 

emissions related to electric power production. This information can be used to define a set of 

parameters that represent the energy intensity of energy production.

Table 8.2.8 shows the FFC energy multipliers used for selected years. The method used 

to calculate FFC energy multipliers is described in appendix 8A.

Table 8.2.8 Full-Fuel-Cycle Energy Multipliers (Based on AEO 2014)

Energy Source 2020 2025 2030 2035 2040

Electricity

(power plant energy use) 1.044 1.045 1.046 1.047 1.047

8.2.2.6 Compliance Dates and Analysis Period

Generally, covered equipment to which a new or amended energy conservation standard 

applies must comply with the standard if such equipment is manufactured or imported on or after 

a specified date. In this Final Rule, DOE is evaluating whether more-stringent efficiency levels 

than those in ASHRAE Standard 90.1-2013 would be technologically feasible, economically 

justified, and result in a significant additional amount of energy savings. If DOE were to propose 

 c For more information on NEMS, refer to the U.S. Department of Energy, Energy Information Administration 

documentation. A useful summary is National Energy Modeling System: An Overview 2000, DOE/EIA0581(2000), March 2000. EIA approves use of the name NEMS to describe only an official version of the model 

with no modification to code or data. Because this analysis entails minor code modifications, and the model is run 

under policy scenarios that are variations on EIA assumptions, DOE refers to the model as NEMS-BT (BT is 

DOE’s Building Technologies Program, under whose aegis this work was performed). NEMS-BT previously was 

called NEMS-BRS. d FFC multipliers discussed in this chapter relate to the upstream part of the FFC process.
8-18

a rule prescribing energy conservation standards at the efficiency levels contained in ASHRAE 

Standard 90.1-2013 for the triggered equipment classes, EPCA states that compliance with any 

such standards shall be required on or after a date which is two or three years (depending on 

equipment size) after the compliance date of the applicable minimum energy efficiency 

requirement in the amended ASHRAE/IES standard. (42 U.S.C. 6313(a)(6)(D)) Given the 

equipment size at issue here, DOE has applied the two-year implementation period to determine 

the compliance date of any energy conservation standard equal to the efficiency levels specified 

by ASHRAE Standard 90.1-2013 proposed by this rulemaking. Thus, if DOE decides to adopt 

the efficiency levels in ASHRAE Standard 90.1-2013, the compliance date of the rulemaking 

would be dependent upon the date specified in ASHRAE Standard 90.1-2013 or its publication 

date, if none is specified. In this case, the rule would apply to small commercial air-cooled air 

conditioners and heat pumps less than 65,000 Btu/h manufactured on or after January 1, 2017, 

which is two years after the date specified in ASHRAE Standard 90.1-2013. The rule would 

apply to water-source heat pumps manufactured on or after October 9, 2015, which is two years 

after the publication date of ASHRAE Standard 90.1-2013.

If DOE were to propose a rule prescribing energy conservation standards more stringent 

than the efficiency levels contained in ASHRAE Standard 90.1-2013, EPCA states that 

compliance with any such standards is required for products manufactured on or after a date 

which is four years after the date the final rule is published in the Federal Register. (42 U.S.C. 

6313(a)(6)(D)) DOE has applied this 4-year implementation period to determine the compliance 

date for any energy conservation standard more stringent than the efficiency levels specified by 

ASHRAE Standard 90.1-2013 that might be prescribed at the final rule stage for the equipment 

classes triggered by ASHRAE. Thus, for equipment for which DOE might adopt a level more 

stringent than the ASHRAE efficiency levels, the rule would apply to products manufactured on 

or after a date four years from the date of publication of the final rule, which the statute requires

to be completed by April 9, 2016 (thereby resulting in a compliance date no later than April 9, 

2020).e

For split system air-cooled air conditioners, which DOE evaluated under the 6 year look 

back, DOE applied a different compliance date. Specifically, EPCA states that amended 

standards prescribed under this subsection shall apply to products manufactured after a date that 

is the later of—(I) the date that is 3 years after publication of the final rule establishing a new 

standard; or (II) the date that is 6 years after the effective date of the current standard for a 

covered product. (42 U.S.C. 6313(a)(6)(C)(iv)) Because DOE must publish a final rule by April 

9, 2016, in the case that it adopts standards higher than those in ASHRAE 90.1 for the other 

equipment classes, DOE projected that the date under clause (I) would be April 2019, which is 

later than the date under clause (II). For purposes of its analysis, DOE used 2019 as the first year 

of compliance with amended standards.

 e Since ASHRAE published ASHRAE Standard 90.1-2013 on October 9, 2013, EPCA requires that DOE publish a 

final rule adopting more-stringent standards than those in ASHRAE Standard 90.1-2013, if warranted, within 30 

months of ASHRAE action (i.e., by April 2016). Thus, four years from April 2016 would be April 2020, which 

would be the anticipated compliance date for DOE adoption of more-stringent standards.
8-19

A 30-year analysis period is used for the NES, extending from the compliance date based 

on adoption of the levels in ASHRAE Standard 90.1-2013. However, energy savings accrue until 

the last unit purchased at the end of the 30-year analysis period is retired from service. For small 

commercial air-cooled air conditioners and heat pumps less than 65,000 Btu/h except split 

system air conditioners, the analysis period extends from 2017 to 2046; for water-source heat 

pumps, the analysis period extends from 2015 to 2044. The time period for energy savings for 

standards going higher than ASHRAE Standard 90.1-2013 is from 2020 to 2046 or 2044, based 

on the equipment classes described. This approach accounts for any lost energy savings from a 

delay incurred by DOE’s not adopting ASHRAE efficiency levels, but instead adopting 

standards at efficiency levels higher than those specified in ASHRAE Standard 90.1-2013. For 

split system air conditioners, which were not triggered by ASHRAE, energy savings accrue 

during the lifetime of equipment shipped in the period 2019 to 2048 in all cases.

8.3 NET PRESENT VALUE OF CUSTOMER BENEFITS

For equipment triggered by ASHRAE, DOE considers the ASHRAE level as the base 

minimum standard that it could set under Federal law. In the NIA, DOE establishes the NPV for 

going above the ASHRAE base case, but does not need to determine the NPV of adoption of that 

minimum base case. DOE only looked at subsequent higher standard levels to determine whether 

each higher level, when examined independently, has a positive NPV compared with the 

ASHRAE base case. For split system air conditioners, which were not triggered by ASHRAE, 

DOE determined the NPV for all levels.

8.3.1 Net Present Value Definition

The NPV is the value in the present of a time-series of costs and savings. The NPV is 

described by the equation: 

NPV = PVS - PVC

Eq. 8.7

Where:

PVS = present value of savings in operating cost (including costs for energy, repair, and 

maintenance); and 

PVC = present value of increase in total installed cost (including costs for equipment, 

extended repairs, and installation). 

DOE determined the PVS and PVC according to the following expressions.

Eq. 8.8

Eq. 8.9

PVS =∑OCSy × DFy

PVC = ∑TICy × DFy
8-20

DOE calculated the total annual savings in operating cost by multiplying the number or 

stock of a given equipment (by vintage) by its per-unit operating cost savings (also by vintage). 

DOE calculated the total annual increase in installed cost by multiplying the number or stock of a 

given equipment (by vintage) by its per-unit total installed cost increase (also by vintage). Total 

annual savings in operating cost and increases in installed cost are calculated using the following 

equations.

Eq. 8.10

Eq. 8.11

Where:

OCS = total annual savings in operating cost each year summed over vintages of 

the equipment stock, STOCKV;

TIC = total annual increase in installed cost each year summed over vintages of 

the equipment stock, STOCKV;

DF = discount factor in each year;

STOCKV = stock of equipment (units) of vintage V that survive in the year for which 

DOE calculated annual energy consumption;

UOCSV = annual per-unit savings in operating cost;

UTICV = annual total per-unit increase in installed cost;

V = year in which the equipment was purchased as a new unit; and 

y = year in the projection.

DOE determined the PVC for each year from the compliance date of the standard until 

2044 (for water-source heat pumps), 2046 (for small commercial air conditioners and heat pumps

other than split system air conditioners), or 2048 (for split system air conditioners). DOE 

determined the PVS for each year from the compliance date of the standard until the year when 

units purchased in the last year retire. DOE calculated costs and savings as the difference 

between each standards case and the base case. 

DOE calculated a discount factor from the discount rate and the number of years between 

the “present” (2014, the year to which the sum is being discounted) and the year in which the 

costs and savings occur. The NPV is the sum over time of the discounted net savings.

8.3.2 Inputs to Calculation

Listed below are the inputs to DOE’s calculation of the NPV of costs and savings.

• total annual installed cost,

• total annual operating cost savings,

• discount factor,

OCS y = ∑STOCKV ×UOCSV

TICy = ∑STOCKV ×UTICV
8-21

• present value of total annual increases in installed cost,

• present value of savings.

8.3.2.1 Total Annual Installed Cost

The increase in the total annual installed cost is equal to the annual change in the per-unit

total installed cost (difference between base case and standards case) multiplied by the shipments 

projected in the standards case.f The total installed cost includes both the equipment cost and the 

installation cost. Table 8.3.1 and Table 8.3.2 provide average total installed cost values by 

efficiency level for all equipment classes.

Table 8.3.1 Average Total Installed Costs for 3-phase, Air-Cooled, Air Conditioning and 

Heating Equipment < 65,000 Btu/h

Equipment Class Federal 

Baseline

ASHRAE 

Baseline/ 

EL 0

EL 1 EL 2 EL 3 EL 4 EL 5

Split AC $3,901 $4,150 $4,401 $4,670 $4,927 $5,194 $5,474

Package AC N/A $4,781 $5,090 $5,400 $5,702 $6,007 $6,375

Split HP N/A $4,513 $4,774 $5,118 $5,401 $5,694 N/A

Package HP N/A $5,155 $5,499 $5,830 $6,161 $6,550 N/A

Table 8.3.2 Average Total Installed Costs for Water-Source Heat Pumps

Equipment Class

ASHRAE 

Baseline/ 

EL 0

EL 1 EL 2 EL 3 EL 4 EL 5

<17,000 Btu/h $3,216 $3,354 $3,530 $3,822 $3,958 $4,233

≥17,000 to ≤65,000 Btu/h $4,882 $5,162 $5,513 $5,758 $5,968 $6,392

≥65,000 to ≤135,000 Btu/h $12,005 $12,961 $13,919 $14,830 $15,977 N/A

As discussed in section 8.2.2.2, DOE developed base case and standards case projections 

of market share by efficiency level. DOE multiplied the market share by efficiency level in each 

year by the values in Table 8.3.1 and Table 8.3.2 to calculate annual shipment-weighted average 

total installed costs.

8.3.2.2 Equipment Price Projection

As discussed in chapter 6, DOE used a constant price assumption for the default 

projection in the NIA. In order to investigate the impact of different equipment price forecasts on 

 f For this rulemaking, the base case and standards case shipments are identical. 
8-22

the NPV for the considered efficiency levels, DOE also considered two alternative price trends

for each equipment type.

For small commercial air-cooled air conditioners and heat pumps, DOE used an 

exponential fit on the deflated price index for unitary air-conditioners from 1978-2006 (low price 

case) and from 2007-2013 (high price case). For water-source heat pumps, DOE used an 

exponential fit on the deflated price index for all other miscellaneous refrigeration and airconditioning equipment, and the other is based on the deflator for industrial equipment that was 

forecasted for AEO2014. Details on how these alternative price trends were developed are in 

Appendix 10B, which also presents the results of the sensitivity analysis.

8.3.2.3 Total Annual Operating Cost

The annual operating cost savings are equal to the change in the annual operating costs 

(difference between base case and standards case) per unit multiplied by the shipments projected 

in the standards case. The annual operating cost includes electricity and repair/maintenance 

costs.

Annual Electricity Cost Savings

As explained in chapter 4, DOE used unit energy consumption data for the energy use of 

each product at different efficiency levels. Chapter 6 describes how DOE calculated annual 

electricity prices. For each efficiency level, the electricity cost savings are calculated by 

multiplying the reduction in electricity consumption (in kWh) by the annual marginal electricity 

price. Table 8.3.3 and Table 8.3.4 show the electricity costs in 2020 by equipment type and TSL. 

Table 8.3.3 Average Annual Electricity Costs in the Compliance Year for 3-phase, AirCooled, Air Conditioning and Heating Equipment < 65,000 Btu/h (2014$)

Equipment Class Federal 

Baseline ASHRAE EL 1 EL 2 EL 3 EL 4 EL 5

Split AC $312 $302 $288 $275 $270 $266 $263

Package AC N/A $302 $288 $275 $270 $266 $263

Split HP N/A $307 $295 $282 $277 $272 N/A

Package HP N/A $307 $295 $282 $277 $272 N/A

Note: 2019 is the compliance year for Split AC; 2020 is the compliance year for the other classes.

Table 8.3.4 Average Annual Electricity Costs in 2020 for Water-Source Heat Pumps 

(2014$)

Equipment Class ASHRAE EL 1 EL 2 EL 3 EL 4 EL 5

<17,000 Btu/h $219 $210 $201 $188 $183 $174

≥17,000 to ≤65,000 Btu/h $625 $580 $537 $513 $495 $465

≥65,000 to ≤135,000 Btu/h $1,497 $1,424 $1,369 $1,321 $1,270 N/A
8-23

As with the total installed cost data, DOE developed projected annual electricity expenses 

based on the annual projections of market share by efficiency level specified in the base case and 

standards cases. DOE multiplied the market share by efficiency level in each year by the 

electricity costs specific to each efficiency level to calculate annual shipment-weighted average 

electricity costs. DOE then applied electricity price trends from AEO 2014 to scale the electricity 

expenses moving forward.

Annual Maintenance Costs

As discussed in chapter 6, DOE determined that the maintenance costs are $302 for 3-

phase, air cooled air conditioners, $334 for 3-phase, air-cooled heat pumps, $334 for watersource heat pumps less than 17,000 Btu/h and between 17,000 and 65,000 Btu/h, and $404 for 

water-source heat pumps between 65,000 and 135,000 Btu/h. As the maintenance costs do not 

vary efficiency level, they have no impact on operating cost savings. 

Annual Repair Costs

DOE based average annual repair costs on a base repair cost, which increased with the 

value of the equipment in 2014$ (see chapter 6). Using the efficiency distribution of the stock at 

each standard level analyzed, DOE developed a weighted average repair cost for the stock based 

on the distribution at each standard level. 

8.3.2.4 Discount Factor

DOE multiplied monetary values in future years by a discount factor to determine the 

present value. The discount factor (DF) is described by the equation:

Eq. 8.12

Where: 

r = discount rate, 

y = year of the monetary value, and 

yp = year in which the present value is being determined. 

Although DOE used consumer discount rates to determine the life-cycle cost of

equipment (chapter 8), it used national discount rates to calculate national NPV. DOE estimated 

NPV using both a 3-percent and a 7-percent real discount rate, in accordance with the Office of 

Management and Budget’s guidance to Federal agencies on the development of regulatory 

analysis, particularly section E therein: Identifying and Measuring Benefits and Costs.

3 DOE 

defined the present year as 2014.

8.3.2.5 Present Value of Increased Installed Costs

The present value of increased installed cost is the difference between installation cost in 

each standards case and the base case discounted to the present and summed throughout the 

period over which DOE is considering the installation of units (from the compliance date of 

( y yp ) _

( r )

DF

1+

1 =
8-24

standards, 2020 (or 2019 for split system air conditioners), through 2044, 2046, or 2048,

depending on equipment class). DOE calculated annual increases in installed cost as the 

difference in total installed cost for new equipment purchased each year between the base case 

and each standards case.

8.3.2.6 Present Value of Savings

The present value of annual savings in operating cost is the difference between the base 

case and each standards case discounted to the present and summed throughout the period from 

the compliance date, 2020 (or 2019 for split system air conditioners), to the time when the last 

unit installed in 2044, 2046, or 2048 is retired from service.

Savings represent decreases in operating cost (including electricity, maintenance, and 

repair cost) associated with the more energy efficient equipment purchased in each standards 

case compared to the base case. Total annual savings in operating cost are the savings per unit 

multiplied by the number of units of each vintage that survive in a particular year. 

8.4 NES AND NPV RESULTS

8.4.1 National Energy Savings

DOE based the inputs to the NIA model on weighted-average values, yielding results that 

are discrete point values rather than a distribution of values as in the LCC analysis.

Table 8.4.1 Cumulative Primary Energy Savings for 3-phase, Air-Cooled, Air 

Conditioning and Heating Equipment < 65,000 Btu/h (quads)

Equipment Class ASHRAE EL 1 EL 2 EL 3 EL 4 EL 5

AC Split 0.02 0.08 0.13 0.16 0.18 0.19

AC Single 0.04 0.05 0.11 0.15 0.18 0.19

HP Split 0.01 0.01 0.02 0.03 0.03 N/A

HP Single 0.01 0.01 0.02 0.03 0.04 N/A

Note: The potential energy savings for efficiency levels more stringent than those specified by ASHRAE Standard 

90.1-2013 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-2013

standards were adopted.
8-25

Table 8.4.2 Full Fuel Cycle Energy Savings for 3-phase, Air-Cooled, Air Conditioning 

and Heating Equipment < 65,000 Btu/h (quads)

Equipment Class ASHRAE EL 1 EL 2 EL 3 EL 4 EL 5

AC Split 0.02 0.08 0.14 0.17 0.19 0.20

AC Single 0.04 0.06 0.12 0.15 0.18 0.20

HP Split 0.01 0.01 0.02 0.03 0.03 N/A

HP Single 0.01 0.01 0.02 0.03 0.04 N/A

Note: The potential energy savings for efficiency levels more stringent than those specified by ASHRAE Standard 

90.1-2013 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-2013

standards were adopted.

Table 8.4.3 Cumulative Primary Energy Savings for Water-Source Heat Pumps (quads)

Equipment Class ASHRAE EL 1 EL 2 EL 3 EL 4 EL 5

<17,000 Btu/h -

* 0.00 0.02 0.06 0.08 0.11

≥17,000 to ≤65,000 Btu/h -

* 0.02 0.26 0.45 0.60 0.83

≥65,000 to ≤135,000 Btu/h -

* -* 0.01 0.03 0.05 0.05

Note: The potential energy savings for efficiency levels more stringent than those specified by ASHRAE Standard 

90.1-2013 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-2013

standards were adopted.

*

There are no savings for this level because there is no market share projected below this level in the base case.

Table 8.4.4 Full Fuel Cycle Energy Savings for Water-Source Heat Pumps (quads)

Equipment Class ASHRAE EL 1 EL 2 EL 3 EL 4 EL 5

<17,000 Btu/h -

* 0.00 0.02 0.06 0.08 0.11

≥17,000 to ≤65,000 Btu/h -

* 0.03 0.27 0.47 0.63 0.87

≥65,000 to ≤135,000 Btu/h -

* -* 0.01 0.03 0.05 0.05

Note: The potential energy savings for efficiency levels more stringent than those specified by ASHRAE Standard 

90.1-2013 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-2013

standards were adopted.

*

There are no savings for this level because there is no market share projected below this level in the base case.

8.4.2 Annual Costs and Savings

As an example, Figure 8.4.1 shows the changes over time of the non-discounted annual 

total installed cost increases and the non-discounted operating cost savings for 3-phase, aircooled single-package air conditioners <65,000 Btu/h at efficiency level 1. The total net annual 

impact is the discounted value of the difference between annual equipment purchases and annual 

operating costs at a 7-percent discount rate. The annual equipment price change is the increase in 

equipment price for equipment purchased each year over the period from 2020 to 2046. The 

annual operating savings is the savings in operating costs for equipment purchased, and which 

have not been retired, for each year over the time period from 2020 to 2080. The NPV (the 
8-26

discounted total net annual impact) is the difference between the cumulative annual discounted 

savings and the cumulative annual discounted costs.

Figure 8.4.1 3-phase, Air-Cooled, Single Package Air Conditioners <65,000 Btu/h: 

Installed Costs, Operating Cost Savings, and Net Savings; EL 1

Figure 8.4.1 shows that annual operating cost savings are growing faster than the 

increased equipment price costs through 2035, after which annual operating cost savings begin to 

stabilize while increased equipment price costs continue to rise. After 2046, no additional 3-

phase, air-cooled, packaged air conditioner sales are included in the analysis; however the annual 

energy savings continues to accrue until the last of the 3-phase, air-cooled, packaged air 

conditioners installed in 2046 are removed from the stock.

8.4.3 Net Present Value Results

The following section provides NPV results for the different efficiency levels. Results are 

cumulative and are shown as discounted values in dollar terms. 

Table 8.4.5 through show Table 8.4.8 show the NPV results for each equipment class

based on a seven-percent or a three-percent discount rate.

DOE also developed sensitivity analyses using an increasing equipment price trend and a 

decreasing equipment price trend coupled with AEO2014 Low Economic Growth and High

-25

-15

-5

5

15

25

35

20202023202620292032203520382041204420472050205320562059206220652068207120742077208020832086208920922095

Millions of 2014$

Incremental Equipment Cost

Operating Cost Savings

Net Savings
8-27

Economic Growth cases, respectively. These equipment trends and AEO Economic Growth case 

are described in appendix 8B, which also presents NPV results based on these alternative 

sensitivities.

Table 8.4.5 Cumulative Net Present Value for 3-phase, Air-Cooled Air Conditioning and 

Heating Equipment <65,000 Btu/h (Billions of 2014$ - 7% Discount Rate)

Equipment Class ASHRAE EL 1 EL 2 EL 3 EL 4 EL 5

AC Split ($0.05) ($0.18) ($0.38) ($0.66) ($0.95) ($1.17)

AC Single N/A* ($0.14) ($0.43) ($0.82) ($1.25) ($1.63)

HP Split N/A* ($0.03) ($0.09) ($0.15) ($0.19) N/A**

HP Single N/A* ($0.04) ($0.11) ($0.20) ($0.28) N/A**

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1-2013 were 

calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-2013 standards were adopted. * Economic analysis was not conducted for the ASHRAE levels (Efficiency Level 0). ** The max-tech level for this equipment class is efficiency level 4.

Table 8.4.6 Cumulative Net Present Value for 3-phase, Air-Cooled Air Conditioning and 

Heating Equipment <65,000 Btu/h (Billions of 2014$ - 3% Discount Rate)

Equipment Class ASHRAE EL 1 EL 2 EL 3 EL 4 EL 5

AC Split ($0.07) ($0.27) ($0.64) ($1.15) ($1.71) ($2.09)

AC Single N/A* ($0.21) ($0.74) ($1.47) ($2.30) ($2.96)

HP Split N/A* ($0.05) ($0.15) ($0.26) ($0.33) N/A**

HP Single N/A* ($0.07) ($0.19) ($0.35) ($0.48) N/A**

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1-2013 were 

calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-2013 standards were adopted. * Economic analysis was not conducted for the ASHRAE levels (Efficiency Level 0). ** The max-tech level for this equipment class is efficiency level 4.

Table 8.4.7 Cumulative Net Present Value for Water-Source Heat Pumps (Billions of 

2014$ - 7% Discount Rate)

Equipment Class EL 1 EL 2 EL 3 EL 4 EL 5

<17,000 Btu/h ($0.00) ($0.04) ($0.14) ($0.21) ($0.33)

≥17,000 to ≤65,000 Btu/h $0.01 $0.00 ($0.11) ($0.27) ($0.59)

≥65,000 to ≤135,000 Btu/h -

* ($0.01) ($0.06) ($0.11) N/A**

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1-2013 were 

calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-2013 standards were adopted. *

There are no savings for EL1 because there is no market share projected below this level in the base case. ** The max-tech level for this equipment class is efficiency level 4.
8-28

Table 8.4.8 Summary of Cumulative Net Present Value for Water-Source Heat Pumps 

(Billions of 2014$ - 3% Discount Rate)

Equipment Class EL 1 EL 2 EL 3 EL 4 EL 5

<17,000 Btu/h ($0.00) ($0.05) ($0.20) ($0.30) ($0.49)

≥17,000 to ≤65,000 Btu/h $0.03 $0.26 $0.21 $0.03 ($0.37)

≥65,000 to ≤135,000 Btu/h -

* ($0.02) ($0.08) ($0.15) N/A**

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1-2013 were 

calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-2013 standards were adopted. *

There are no results for EL1 because there is no market share projected below this level in the base case. ** The max-tech level for this equipment class is efficiency level 4.

8.4.4 Summary

Figure 8.4.2 shows the relationship of NPV results to NES results by efficiency level for 

an example equipment class. 

Figure 8.4.2 Cumulative NES and NPV Results for 3-Phase, Air-Cooled Air Conditioning 

and Heating Equipment <65,000 Btu/h at a Seven Percent Discount Rate

-3.5000

-3.0000

-2.5000

-2.0000

-1.5000

-1.0000

-0.5000

0.0000

0.0000

0.0500

0.1000

0.1500

0.2000

0.2500

0.3000

0.3500

0.4000

0.4500

0.5000

EL 1 EL 2 EL 3 EL 4 EL 5

NPV (Billions of 2014$)

Cumulative Energy Savings (Quads)

NES

NPV
8-29

REFERENCES

1 U.S. Department of Energy. 2004. Technical Support Document: Energy Efficiency Program 

for Commercial and Industrial Equipment: Commercial Unitary Air Conditioners and Heat 

Pumps. Washington, D.C. 

www1.eere.energy.gov/buildings/appliance_standards/commercial/cuac_tsd_060904.html

2 U.S. Department of Energy-Energy Information Administration, Annual Energy Outlook 

2014, April, 2014. Washington, DC. Report No. DOE/EIA-0383(2014). 

www.eia.gov/forecasts/aeo/pdf/0383%282014%29.pdf.

3 U.S. Office of Management and Budget, Circular A-4: Regulatory Analysis, September 17, 

2003. www.whitehouse.gov/omb/circulars_a004_a-4/
8A-i

APPENDIX 8A. FULL FUEL CYCLE

TABLE OF CONTENTS 

8A.1 INTRODUCTION ........................................................................................................ 8A-1

8A.2 METHODOLOGY ....................................................................................................... 8A-1

8A.3 ENERGY MULTIPLIERS FOR THE FULL FUEL CYCLE...................................... 8A-3

REFERENCES ......................................................................................................................... 8A-4

LIST OF TABLES 

Table 8A.2.1 Dependence of FFC Parameters on AEO Inputs......................................... 8A-3

Table 8A.3.1 Energy Multipliers for the Full Fuel Cycle (Based on AEO2014).............. 8A-3
8A-1

APPENDIX 8A. FULL FUEL CYCLE

8A.1 INTRODUCTION

This appendix summarizes the methods the U.S. Department of Energy (DOE) used to 

calculate the full-fuel-cycle (FFC) energy savings estimated from potential standards for 3-phase, 

air-cooled air conditioning and heating equipment and water source heat pumps. The FFC 

measure includes point-of-use (site) energy; the energy losses associated with generation, 

transmission, and distribution of electricity; and the energy consumed in extracting, processing, 

and transporting or distributing primary fuels. DOE’s method of analysis previously 

encompassed only site energy and the energy lost through generation, transmission, and 

distribution of electricity. In 2011 DOE announced its intention, based on recommendations from 

the National Academy of Sciences, to use FFC measures of energy use and emissions when 

analyzing proposed energy conservation standards.0F

1 This appendix summarizes the methods 

DOE used to incorporate impacts of the full fuel cycle into the analysis.

This analysis uses several terms to describe aspects of energy use. The physical sources 

of energy are primary fuels such as coal, natural gas, or liquid fuel. Primary energy is equal to 

the heat content (British thermal units [Btu]) of the primary fuel used to produce an end-use 

service. Site energy use is defined as the energy consumed at the point of use in a house or 

establishment. When natural gas or petroleum fuels are consumed at the site (for example in an 

on-site furnace), site energy is identical to primary energy, with both equal to the heat content of 

the primary fuel consumed.

For electricity generated by an off-site power plant, site energy is measured in kilowatthours (kWh). In such a case the primary energy is equal to the quads (quadrillion Btu) of primary 

energy required to generate and deliver electricity to the site. For the FFC analysis, upstream 

energy use is defined as the energy consumed in extracting, processing, and transporting or 

distributing primary fuels. FFC energy use is the sum of primary plus upstream energy use. 

Both primary fuels and electricity are used in upstream activities. The treatment of 

electricity in full-fuel-cycle analysis must distinguish between electricity generated by fossil 

fuels and uranium and electricity generated from renewable sources (wind, solar, and hydro). For 

the former, the upstream fuel cycle relates to the amount of fuel consumed at the power plant. 

There is no upstream component for the latter, because no fuel per se is used.

8A.2 METHODOLOGY

The mathematical approach to determining FCC is discussed in Coughlin (2012).1F

2 Details 

on analyzing the fuel production chain are presented in Coughlin (2013).2F

3 The methods used to 

calculate FFC energy use are summarized here. When all energy quantities are normalized to the 

same units, FFC energy use can be represented as the product of the primary energy use and an 

FFC multiplier. Mathematically the FFC multiplier is a function of a set of parameters that 

represent the energy intensity and material losses at each stage of energy production. Those 

parameters depend only on physical data, so the calculations require no assumptions about prices 
8A-2

or other economic factors. Although the parameter values often differ by geographic region, this 

analysis utilizes national averages. 

The fuel cycle parameters are defined as follows.

• ax is the quantity of fuel x burned per unit of electricity produced, on average, for grid

electricity. The calculation of ax includes a factor to account for losses incurred through 

the transmission and distribution systems. 

• by is the amount of grid electricity used in producing fuel y, in MWh per physical unit of 

fuel y.

• cxy is the amount of fuel x consumed in producing one unit of fuel y.

• qx is the heat content of fuel x (MBtu/physical unit). 

• zx(s) is the emissions intensity for fuel x (mass of pollutant s per physical unit of x 

produced).

All the parameters are calculated as functions of an annual time step; hence, when 

evaluating the effects of potential new standards, a time series of annual values is used to 

estimate the FFC energy and emissions savings in each year of the analysis period and 

cumulatively. Fossil fuel quantities are converted to energy units using the heat content factor qx. 

To convert electricity in kWh to primary energy units, on-site electricity consumption is 

multiplied by the site-to-power-plant energy use factor, described in chapter 10. The site-topower-plant energy use factor is defined as the ratio of the total primary energy consumption by 

the electric power sector (in quads) divided by the total electricity generated each year.

The FFC multiplier is denoted μ (mu). A separate multiplier is calculated for each fuel 

used on site. Also calculated is a multiplier for electricity that reflects the fuel mix used in its 

generation. The multipliers are dimensionless numbers applied to primary energy savings to 

obtain the FFC energy savings. The upstream component of the energy savings is proportional to 

(μ-1). The fuel type is denoted by a subscript on the multiplier μ.

When DOE estimates energy savings attributable to appliance standards, the method for 

performing the full-fuel-cycle analysis utilizes data and projections published in the Annual 

Energy Outlook (AEO); in the case of this rulemaking, the AEO2014.3F

4 Table 8A.2.1 summarizes 

the AEO2014 data used as inputs to the calculation of various parameters. The column titled 

"AEO Table" gives the name of the table that provided the reference data.
8A-3

Table 8A.2.1 Dependence of FFC Parameters on AEO Inputs

Parameter(s) Fuel(s) AEO Table Variables

qx All Conversion factors MMBtu per physical unit

ax All

Electricity supply, disposition, 

prices, and emissions Generation by fuel type

Energy consumption by sector 

and source

Electric energy consumption 

by the power sector

bc, cnc, cpc Coal

Coal production by region and 

type

Coal production by type and 

sulfur content

bp, cnp, cpp Petroleum

Refining industry energy 

consumption Refining-only energy use

Liquid fuels supply and 

disposition Crude supply by source

International liquids supply and 

disposition Crude oil imports

Oil and gas supply Domestic crude oil production

cnn Natural gas

Oil and gas supply U.S. dry gas production

Natural gas supply, disposition, 

and prices Pipeline, lease, and plant fuel

zx All

Electricity supply, disposition, 

prices, and emissions Power sector emissions

The AEO2014 does not provide all the information needed to estimate total energy use in 

the fuel production chain. Coughlin (2013) describes the additional data sources needed to 

complete the analysis. The time dependence in the FFC multipliers for dishwashers, however, 

arises exclusively from variables taken from the AEO2014.

8A.3 ENERGY MULTIPLIERS FOR THE FULL FUEL CYCLE

FFC energy multipliers for selected years are presented in Table 8A.3.1. The 2040 value 

was held constant for the analysis period beyond 2040, which is the last year in the AEO2014

projection. The multiplier for electricity reflects the shares of various primary fuels in total 

electricity generation throughout the forecast period. 

Table 8A.3.1 Energy Multipliers for the Full Fuel Cycle (Based on AEO2014)

2020 2025 2030 2035 2040

Electricity 1.044 1.045 1.046 1.047 1.047

Natural gas 1.109 1.111 1.113 1.114 1.114

Petroleum fuels 1.176 1.176 1.174 1.172 1.170
8A-4

REFERENCES 

1 U.S. Department of Energy–Office of Energy Efficiency and Renewable Energy. Energy 

Conservation Program for Consumer Products and Certain Commercial and Industrial 

Equipment. Statement of Policy for Adopting Full Fuel Cycle Analyses into Energy 

Conservation Standards Program. Federal Register. August 18, 2011. Vol. 76, no. 160: 

pp. 51281–51289.

2 Coughlin, K. A Mathematical Analysis of Full Fuel Cycle Energy Use. Energy. 2012. 37(1): 

pp. 698–708. (Last accessed June 22, 2014.) 

<www.sciencedirect.com/science/article/pii/S0360544211006803>

3 Coughlin, K. Projections of Full-Fuel-Cycle Energy and Emissions Metrics. 2013. Lawrence 

Berkeley National Laboratory. Report No. LBNL-6025E. 

4 U.S. Energy Information Administration. Annual Energy Outlook 2014 with Projections to 

2040. April 2014. DOE/EIA-0383(2014). Washington, D.C. (Last accessed June 22, 

2014.) <www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf >
8B-i

APPENDIX 8B. NATIONAL NET PRESENT VALUE OF CUSTOMER BENEFITS 

USING ALTERNATIVE EQUIPMENT PRICE PROJECTIONS AND ECONOMIC 

GROWTH SCENARIOS

TABLE OF CONTENTS

8B.1 INTRODUCTION .........................................................................................................8B-1

8B.2 DESCRIPTION OF ALTERNATIVE EQUIPMENT PRICE PROJECTIONS...........8B-1

8B.2.1 Decreasing Price Scenario .................................................................................8B-2

8B.2.2 Increasing Price Scenario...................................................................................8B-3

8B.2.3 Summary............................................................................................................8B-6

8B.3 DESCRIPTION OF HIGH AND LOW ECONOMIC SCENARIOS ...........................8B-8

8B.4 NET PRESENT VALUE RESULTS USING ALTERNATIVE EQUIPMENT 

PRICE TRENDS............................................................................................................8B-9

REFERENCES ........................................................................................................................8B-15

LIST OF TABLES

Table 8B.2.1 Small Commercial Air-Cooled Air Conditioning and Heating 

Equipment Price Trend Sensitivities............................................................8B-6

Table 8B.2.2 Water-Source Heat Pump Price Trend Sensitivities....................................8B-6

Table 8B.4.1 Cumulative NPV Results for 3-phase, Air-Cooled, Air Conditioning 

and Heating Equipment Using Reference Equipment Price Forecast 

and AEO Reference Case Scenario, 3% Discount Rate (billion 2014$) .....8B-9

Table 8B.4.2 Cumulative NPV Results for 3-phase, Air-Cooled, Air Conditioning 

and Heating Equipment Using Reference Equipment Price Forecast 

and AEO Reference Case Scenario, 7% Discount Rate (billion 2014$) ...8B-10

Table 8B.4.3 Cumulative NPV Results 3-phase, Air-Cooled, Air Conditioning and 

Heating Equipment Using Alternative Equipment Price Forecast 

(Decreasing Trend) and AEO High Economic Growth Case Scenario, 

3% Discount Rate (billion 2014$) .............................................................8B-10

Table 8B.4.4 Cumulative NPV Results for 3-phase, Air-Cooled, Air Conditioning 

and Heating Equipment Using Alternative Equipment Price Forecast 

(Decreasing Trend) and AEO High Economic Growth Case Scenario, 

7% Discount Rate (billion 2014$) .............................................................8B-11

Table 8B.4.5 Cumulative NPV Results for 3-phase, Air-Cooled, Air Conditioning 

and Heating Equipment Using Alternative Equipment Price Forecast 

(Increasing Trend) and AEO Low Economic Growth Case Scenario, 

3% Discount Rate (billion 2014$) .............................................................8B-11

Table 8B.4.6 Cumulative NPV Results for 3-phase, Air-Cooled, Air Conditioning 

and Heating Equipment Using Alternative Equipment Price Forecast 

(Increasing Trend) and AEO Low Economic Growth Case Scenario, 

7% Discount Rate (billion 2014$) .............................................................8B-12
8B-ii

Table 8B.4.7 Cumulative NPV Results for Water Source Heat Pumps Equipment 

Using Reference Equipment Price Forecast and AEO Reference Case 

Scenario, 3% Discount Rate (billion 2014$) .............................................8B-12

Table 8B.4.8 Cumulative NPV Results for Water Source Heat Pumps Equipment 

Using Reference Equipment Price Forecast and AEO Reference Case 

Scenario, 7% Discount Rate (billion 2014$) .............................................8B-13

Table 8B.4.9 Cumulative NPV for Water Source Heat Pumps Using Alternative 

Equipment Price Forecast (Decreasing Trend) and AEO High 

Economic Growth Case Scenario, 3% Discount Rate (billion 2014$)......8B-13

Table 8B.4.10 Cumulative NPV Results for Water Source Heat Pumps Using 

Alternative Equipment Price Forecast (Decreasing Trend) and AEO 

High Economic Growth Case Scenario, 7% Discount Rate (billion 

2014$) ........................................................................................................8B-13

Table 8B.4.11 Cumulative NPV Results for Water Source Heat Pumps Using 

Alternative Equipment Price Forecast (Increasing Trend) and AEO 

Low Economic Growth Case Scenario, 3% Discount Rate (billion 

2014$) ........................................................................................................8B-14

Table 8B.4.12 Cumulative NPV Results for Water Source Heat Pumps Using 

Alternative Equipment Price Forecast (Increasing Trend) and AEO 

Low Economic Growth Case Scenario, 7% Discount Rate (billion 

2014$) ........................................................................................................8B-14

LIST OF FIGURES

Figure 8B.2.1 Deflated Unitary Air-Conditioners PPI versus Year, with Exponential 

Fit from 1978 to 2006 ..................................................................................8B-3

Figure 8B.2.2 Deflated Unitary Air-Conditioners PPI versus Year, with Exponential 

Fit from 2007 to 2013 ..................................................................................8B-4

Figure 8B.2.3 Deflated All Other Miscellaneous Refrigeration and Air Conditioning 

Equipment PPI versus Year, with Exponential Fit from 2001 to 2013 .......8B-5

Figure 8B.2.4 Small Commercial Air-Cooled Air Conditioning and Heating 

Equipment Price Trend Indexes...................................................................8B-7

Figure 8B.2.5 Water-Source Heat Pump Price Trend Indexes...........................................8B-7

Figure 8B.3.1 Electricity Price Projections for Reference Case, High, and Low 

Economic Growth Scenarios........................................................................8B-8
8B-1

APPENDIX 8B. NATIONAL NET PRESENT VALUE OF CUSTOMER BENEFITS

USING ALTERNATIVE EQUIPMENT PRICE PROJECTIONS AND ECONOMIC 

GROWTH SCENARIOS

8B.1 INTRODUCTION

This appendix presents National Impact Analysis (NIA) results using alternative 

equipment price projections and economic growth scenarios. As will be discussed below, DOE 

developed increasing and decreasing equipment price projections. In addition, alternative 

economic growth scenarios were utilized based on the High Economic Growth case and the Low 

Economic Growth case from Energy Information Administration’s (EIA’s) Annual Energy 

Outlook 2014 (AEO2014).1 DOE developed low and high NPV scenarios based on the 

combination of equipment price projections and economic growth scenarios. DOE based the low 

NPV scenario on the combination of an increasing equipment price projection and the Low 

Economic Growth case, while the high NPV scenario was based on the combination of a 

decreasing equipment price projection and the High Economic Growth case. Results are 

presented for the above two combinations at the end of this appendix.

8B.2 DESCRIPTION OF ALTERNATIVE EQUIPMENT PRICE 

PROJECTIONS

DOE used a constant price assumption for the default projection in the NIA described in 

Chapter 8. In order to investigate the impact of different equipment price projections on the net 

present value (NPV) for the considered efficiency levels, DOE also considered two alternative 

price trends for a sensitivity analysis. This appendix describes the alternative price trends and 

compares NPV results for these scenarios with the default projection. 

In recent rulemakings for several residential products, DOE has used the experience 

curve method to derive learning rates to project future prices. In the experience curve method, 

the real cost of production is related to the cumulative production, or experience, with a 

manufactured product. That experience usually is measured in terms of cumulative production. 

As experience (production) accumulates, the cost of producing the next unit decreases. The 

percentage reduction in cost that occurs with each doubling of cumulative production is known 

as the learning rate. A recent report from Lawrence Berkeley National Laboratory by Taylor and 

Fujita provides an overview of some of the major findings of the academic literature on learning 

curves, and describes the application of a component-based learning curve approach (by the 

Environmental Protection Agency and the National Highway Transportation Safety 

Administration) and a price-based learning curve approach (by DOE) in regulatory impact 

assessment.2

For some commercial and industrial equipment, there are insufficient data to apply a

price-based learning curve approach, particularly with respect to cumulative production. In such 

cases, DOE used a constant price assumption for the default forecast in the NIA, but made use of 

price indexes that are relevant for the equipment in question to derive alternative price trends for 
8B-2

sensitivity analysis.a DOE is using this approach for small commercial air-cooled air 

conditioning and heating equipment as well as for water-source heat pumps.

 

DOE considered two alternative price trends for a sensitivity analysis. For small 

commercial air-cooled air conditioning and heating equipment, one assumes decreasing prices 

used an exponential fit on the deflated price index for “unitary air-conditioners, except heat 

pumps” during the period of 1978 to 2006; the other assumes rising prices and is based on the 

same deflated price index between 2007 and 2013. For water-source heat pumps, one assumes 

decreasing prices based on the deflator for industrial equipment that was forecasted for 

AEO2014; the other assumes rising prices and is based on an exponential fit on the deflated price 

index for all other miscellaneous refrigeration and air-conditioning equipment during the period 

of 2001 to 2013.

8B.2.1 Decreasing Price Scenario 

Small Commercial Air-Cooled Equipment

For the decreasing price scenario, DOE used an inflation-adjusted “unitary airconditioners, except heat pumps” Producer Price Index (PPI) spanning the time period of 1978-

2006 from the Bureau of Labor Statistics’ (BLS) to fit an exponential model with year as the 

explanatory variable.b The PPI during this period of time showed a continuing downward trend, 

so the exponential fit based on this part of the historical PPI represents the decreasing price 

scenario of future price projection. 

The PPI data reflect nominal prices, adjusted for equipment quality changes. An 

inflation-adjusted (deflated) price index for “unitary air-conditioners, except heat pumps” was 

calculated by dividing the PPI series by the Gross Domestic Product Chained Price Index. In 

this case, the exponential function takes the form of:

YY = aa ∙ eebbbb

where Y is the small commercial air-cooled air conditioning and heating equipment price index, 

X is the time variable, a is the constant and b is the slope parameter of the time variable. 

To estimate these exponential parameters, a least-square fit was performed on the 

inflation-adjusted small commercial air-cooled air conditioning and heating equipment price 

index versus year from 1978 to 2006. See Figure 8B.2.1.

 a See appendix 10C of the final rule TSD for distribution transformers. 

www.regulations.gov/#!documentDetail;D=EERE-2010-BT-STD-0048-0760 b Series ID PCU333415333415E; www.bls.gov/ppi/
8B-3

Figure 8B.2.1 Deflated Unitary Air-Conditioners PPI versus Year, with Exponential Fit 

from 1978 to 2006

The regression performed as an exponential trend line fit results in an R-square of 0.95, 

which indicates a good fit to the data. The final estimated exponential function is:

YY = 3.29 × 1011 ∙ ee(−0.0133)

DOE then derived a price factor index for this scenario, renormalized with 2013 equal to 

1, to project prices in each future year in the analysis period considered in the NIA. The index 

value in a given year is a function of the exponential parameter and year.

Water-Source Heat Pumps

For water-source heat pumps, DOE examined a forecast based on the “chained price 

index—industrial equipment” that was forecasted for AEO2014 out to 2040. This index is the 

most disaggregated category that includes water-source heat pumps. To develop an inflationadjusted index, DOE normalized the above index with the “chained price index—gross domestic 

product” forecasted for AEO2014. To extend the price index beyond 2040, DOE used the 

average annual price growth rate in 2031 to 2040. 

8B.2.2 Increasing Price Scenario 

Small Commercial Air-Cooled Equipment

DOE used an inflation-adjusted “unitary air-conditioners, except heat pumps” Producer 

Price Index (PPI) spanning the time period of 2007-2013 from the Bureau of Labor Statistics’ 

(BLS) to fit an exponential model with year as the explanatory variable. The PPI during this 

period of time shows a continuing upward trend, so the exponential fit based on this part of the 
8B-4

historical PPI represents the increasing price scenario of future price projection. The PPI data 

reflect nominal prices, adjusted for equipment quality changes. An inflation-adjusted (deflated) 

price index for “unitary air-conditioners, except heat pumps” was calculated by dividing the PPI 

series by the Gross Domestic Product Chained Price Index. In this case, the exponential function 

takes the form of:

YY = aa ∙ eebbbb

where Y is the small commercial air-cooled air conditioning and heating equipment price index, 

X is the time variable, a is the constant and b is the slope parameter of the time variable. 

To estimate these exponential parameters, a least-square fit was performed on the 

inflation-adjusted small commercial air-cooled air conditioning and heating equipment price 

index versus year from 2007 to 2013. See Figure 8B.2.2.

Figure 8B.2.2 Deflated Unitary Air-Conditioners PPI versus Year, with Exponential Fit

from 2007 to 2013

The regression performed as an exponential trend line fit results in an R-square of 0.30. 

The final estimated exponential function is:

YY = 1.66 × 10(−4) ∙ ee0.0043

DOE then derived a price factor index for this scenario, renormalized with 2013 equal to 

1, to project prices in each future year in the analysis period considered in the NIA. The index 

value in a given year is a function of the exponential parameter and year.
8B-5

Water-Source Heat Pumps

DOE used an inflation-adjusted “all other miscellaneous refrigeration and air 

conditioning equipment” Producer Price Index (PPI) spanning the time period of 2001-2013 

from the Bureau of Labor Statistics’ (BLS) to fit an exponential model with year as the 

explanatory variable. The PPI during this period of time shows a continuing upward trend, so the 

exponential fit based on this part of the historical PPI represents the increasing price scenario of 

future price projection. The PPI data reflect nominal prices, adjusted for equipment quality 

changes. An inflation-adjusted (deflated) price index for “all other miscellaneous refrigeration 

and air conditioning equipment” was calculated by dividing the PPI series by the Gross Domestic 

Product Chained Price Index. In this case, the exponential function takes the same form 

described above for small commercial air-cooled air conditioning and heating equipment.

To estimate the exponential parameters, a least-square fit was performed on the inflationadjusted water-source heat pump price index versus year from 2001 to 2013. See Figure 8B.2.3.

Figure 8B.2.3 Deflated All Other Miscellaneous Refrigeration and Air Conditioning 

Equipment PPI versus Year, with Exponential Fit from 2001 to 2013

The regression performed as an exponential trend line fit results in an R-square of 0.75. 

The final estimated exponential function is:

YY = 8.12 × 10(−13) ∙ ee0.0138

DOE then derived a price factor index for this scenario, renormalized with 2013 equal to 

1, to project prices in each future year in the analysis period considered in the NIA. The index 

value in a given year is a function of the exponential parameter and year.
8B-6

8B.2.3 Summary

Table 8B.2.1 and Table 8B.2.2 show the summaries of the average annual rates of change 

for the equipment price index in each scenario. Figure 8B.2.4 and Figure 8B.2.5 show the 

resulting price trends.

Table 8B.2.1 Small Commercial Air-Cooled Air Conditioning and Heating Equipment 

Price Trend Sensitivities

Sensitivity Price Trend Average Annual Rate 

of Change %

Medium (Default) Constant Price Projection 0.0

Decreasing Price 

Scenario

Exponential Fit using data from 1978 to 2006 -1.32

Increasing Price 

Scenario

Exponential Fit using data from 2007 to 2013 0.43

Table 8B.2.2 Water-Source Heat Pump Price Trend Sensitivities

Sensitivity Price Trend Average Annual Rate 

of Change %

Medium (Default) Constant Price Projection 0.0

Decreasing Price 

Scenario

AEO2014-- “chained price index—industrial 

equipment” -0.39

Increasing Price 

Scenario

Exponential Fit using data from 2001 to 2013 1.39
8B-7

Figure 8B.2.4 Small Commercial Air-Cooled Air Conditioning and Heating Equipment

Price Trend Indexes

Figure 8B.2.5 Water-Source Heat Pump Price Trend Indexes
8B-8

8B.3 DESCRIPTION OF HIGH AND LOW ECONOMIC SCENARIOS

To generate NIA results reported in chapter 8, DOE uses the Reference case energy price 

projections from AEO2014. The reference case is a business-as-usual estimate, given known 

market, demographic, and technological trends. For AEO2014, EIA explored the impacts of 

alternative assumptions in other scenarios with different macroeconomic growth rates, world oil 

prices, rates of technology progress, and policy changes. 

To reflect uncertainty in the projection of U.S. economic growth, EIA’s AEO2014 uses 

High and Low Economic Growth scenarios to project the possible impacts of alternative 

economic growth assumptions on energy markets.3

 

Figure 8B.3.1shows electricity price projections based on the different AEO 2014

scenarios.

Figure 8B.3.1 Electricity Price Projections for Reference Case, High, and Low Economic

Growth Scenarios
8B-9

8B.4 NET PRESENT VALUE RESULTS USING ALTERNATIVE EQUIPMENT 

PRICE TRENDS

This section presents the NPV results for low and high scenarios using the alternative 

equipment price projections in combination with the alternative economic growth scenarios. 

DOE based the low NPV scenario on the combination of an increasing equipment price 

projection and the AEO Low Economic Growth case, while the high NPV scenario was based on 

the combination of a decreasing equipment price projection and the AEO High Economic 

Growth case. Results are presented below for each equipment class. 

Table 8B.4.1 Cumulative NPV Results for 3-phase, Air-Cooled, Air Conditioning and

Heating Equipment Using Reference Equipment Price Forecast and AEO 

Reference Case Scenario, 3% Discount Rate (billion 2014$)

Equipment Class Efficiency Level

0 1 2 3 4 5

Split System Air 

Conditioners <65,000 Btu/h (0.07) (0.27) (0.64) (1.15) (1.71) (2.09)

Packaged Air Conditioners 

<65,000 Btu/h N/A* (0.21) (0.74) (1.47) (2.30) (2.96)

Split System Heat Pumps 

<65,000 Btu/h N/A* (0.05) (0.15) (0.26) (0.33) N/A**

Packaged Heat Pumps 

<65,000 Btu/h N/A* (0.07) (0.19) (0.35) (0.48) N/A**

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1-2013 

were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-2013 standards were 

adopted.

* Economic analysis was not conducted for the ASHRAE levels (Efficiency Level 0). ** The max-tech level for this equipment class is efficiency level 4.
8B-10

Table 8B.4.2 Cumulative NPV Results for 3-phase, Air-Cooled, Air Conditioning and 

Heating Equipment Using Reference Equipment Price Forecast and AEO 

Reference Case Scenario, 7% Discount Rate (billion 2014$)

Equipment Class Efficiency Level

0 1 2 3 4 5

Split System Air 

Conditioners <65,000 Btu/h (0.05) (0.18) (0.38) (0.66) (0.95) (1.17)

Packaged Air Conditioners 

<65,000 Btu/h N/A* (0.14) (0.43) (0.82) (1.25) (1.63)

Split System Heat Pumps 

<65,000 Btu/h N/A* (0.03) (0.09) (0.15) (0.19) N/A**

Packaged Heat Pumps 

<65,000 Btu/h N/A* (0.04) (0.11) (0.20) (0.28) N/A**

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1-2013 

were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-2013 standards were 

adopted.

* Economic analysis was not conducted for the ASHRAE levels (Efficiency Level 0). ** The max-tech level for this equipment class is efficiency level 4.

Table 8B.4.3 Cumulative NPV Results 3-phase, Air-Cooled, Air Conditioning and 

Heating Equipment Using Alternative Equipment Price Forecast 

(Decreasing Trend) and AEO High Economic Growth Case Scenario, 3% 

Discount Rate (billion 2014$)

Equipment Class Efficiency Level

0 1 2 3 4 5

Split System Air 

Conditioners <65,000 Btu/h (0.05) (0.19) (0.47) (0.91) (1.39) (1.73)

Packaged Air Conditioners 

<65,000 Btu/h N/A* (0.14) (0.56) (1.18) (1.88) (2.46)

Split System Heat Pumps 

<65,000 Btu/h N/A* (0.03) (0.11) (0.20) (0.26) N/A**

Packaged Heat Pumps 

<65,000 Btu/h N/A* (0.05) (0.14) (0.28) (0.38) N/A**

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1-2013 

were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-2013 standards were 

adopted.

* Economic analysis was not conducted for the ASHRAE levels (Efficiency Level 0). ** The max-tech level for this equipment class is efficiency level 4.
8B-11

Table 8B.4.4 Cumulative NPV Results for 3-phase, Air-Cooled, Air Conditioning and 

Heating Equipment Using Alternative Equipment Price Forecast 

(Decreasing Trend) and AEO High Economic Growth Case Scenario, 7% 

Discount Rate (billion 2014$)

Equipment Class Efficiency Level

0 1 2 3 4 5

Split System Air 

Conditioners <65,000 Btu/h (0.04) (0.14) (0.31) (0.55) (0.80) (1.00)

Packaged Air Conditioners 

<65,000 Btu/h N/A* (0.10) (0.34) (0.68) (1.05) (1.39)

Split System Heat Pumps 

<65,000 Btu/h N/A* (0.02) (0.07) (0.12) (0.16) N/A**

Packaged Heat Pumps 

<65,000 Btu/h N/A* (0.03) (0.09) (0.17) (0.23) N/A**

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1-2013 

were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-2013 standards were 

adopted.

* Economic analysis was not conducted for the ASHRAE levels (Efficiency Level 0). ** The max-tech level for this equipment class is efficiency level 4.

Table 8B.4.5 Cumulative NPV Results for 3-phase, Air-Cooled, Air Conditioning and 

Heating Equipment Using Alternative Equipment Price Forecast 

(Increasing Trend) and AEO Low Economic Growth Case Scenario, 3% 

Discount Rate (billion 2014$)

Equipment Class Efficiency Level

0 1 2 3 4 5

Split System Air 

Conditioners <65,000 Btu/h (0.08) (0.31) (0.71) (1.26) (1.86) (2.25)

Packaged Air Conditioners 

<65,000 Btu/h N/A* (0.24) (0.82) (1.59) (2.48) (3.17)

Split System Heat Pumps 

<65,000 Btu/h N/A* (0.06) (0.17) (0.28) (0.36) N/A**

Packaged Heat Pumps 

<65,000 Btu/h N/A* (0.08) (0.21) (0.39) (0.52) N/A**

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1-2013 

were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-2013 standards were 

adopted.

* Economic analysis was not conducted for the ASHRAE levels (Efficiency Level 0). ** The max-tech level for this equipment class is efficiency level 4.
8B-12

Table 8B.4.6 Cumulative NPV Results for 3-phase, Air-Cooled, Air Conditioning and 

Heating Equipment Using Alternative Equipment Price Forecast 

(Increasing Trend) and AEO Low Economic Growth Case Scenario, 7% 

Discount Rate (billion 2014$)

Equipment Class Efficiency Level

0 1 2 3 4 5

Split System Air 

Conditioners <65,000 Btu/h (0.05) (0.19) (0.42) (0.70) (1.01) (1.24)

Packaged Air Conditioners 

<65,000 Btu/h N/A* (0.15) (0.47) (0.87) (1.33) (1.73)

Split System Heat Pumps 

<65,000 Btu/h N/A* (0.03) (0.10) (0.16) (0.21) N/A**

Packaged Heat Pumps 

<65,000 Btu/h N/A* (0.05) (0.12) (0.22) (0.29) N/A**

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1-2013 

were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-2013 standards were 

adopted.

* Economic analysis was not conducted for the ASHRAE levels (Efficiency Level 0). ** The max-tech level for this equipment class is efficiency level 4.

Table 8B.4.7 Cumulative NPV Results for Water Source Heat Pumps Equipment Using 

Reference Equipment Price Forecast and AEO Reference Case Scenario, 

3% Discount Rate (billion 2014$)

Equipment Class Efficiency Level

1 2 3 4 5

<17,000 Btu/h (0.00) (0.05) (0.20) (0.30) (0.49)

≥17,000 to ≤65,000 Btu/h 0.03 0.26 0.21 0.03 (0.37)

≥65,000 to ≤135,000 Btu/h -

* (0.02) (0.08) (0.15) N/A**

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1-2013 

were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-2013 standards were 

adopted.

* There are no savings for EL1 because there is no market share projected below this level in the base case. ** The max-tech level for this equipment class is efficiency level 4.
8B-13

Table 8B.4.8 Cumulative NPV Results for Water Source Heat Pumps Equipment Using 

Reference Equipment Price Forecast and AEO Reference Case Scenario, 

7% Discount Rate (billion 2014$)

Equipment Class Efficiency Level

1 2 3 4 5

<17,000 Btu/h (0.00) (0.04) (0.14) (0.21) (0.33)

≥17,000 to ≤65,000 Btu/h 0.01 0.00 (0.11) (0.27) (0.59)

≥65,000 to ≤135,000 Btu/h -

* (0.01) (0.06) (0.11) N/A**

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1-2013 

were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-2013 standards were 

adopted.

* There are no savings for EL1 because there is no market share projected below this level in the base case. ** The max-tech level for this equipment class is efficiency level 4.

Table 8B.4.9 Cumulative NPV for Water Source Heat Pumps Using Alternative 

Equipment Price Forecast (Decreasing Trend) and AEO High Economic 

Growth Case Scenario, 3% Discount Rate (billion 2014$)

Equipment Class Efficiency Level

1 2 3 4 5

<17,000 Btu/h (0.00) (0.04) (0.17) (0.26) (0.43)

≥17,000 to ≤65,000 Btu/h 0.04 0.35 0.38 0.26 (0.03)

≥65,000 to ≤135,000 Btu/h -

* (0.02) (0.06) (0.13) N/A**

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1-2013 

were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-2013 standards were 

adopted.

* There are no savings for EL1 because there is no market share projected below this level in the base case.

** The max-tech level for this equipment class is efficiency level 4.

Table 8B.4.10 Cumulative NPV Results for Water Source Heat Pumps Using Alternative 

Equipment Price Forecast (Decreasing Trend) and AEO High Economic 

Growth Case Scenario, 7% Discount Rate (billion 2014$)

Equipment Class Efficiency Level

1 2 3 4 5

<17,000 Btu/h (0.00) (0.04) (0.13) (0.19) (0.30)

≥17,000 to ≤65,000 Btu/h 0.01 0.04 (0.04) (0.18) (0.46)

≥65,000 to ≤135,000 Btu/h -

* (0.01) (0.05) (0.10) N/A**

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1-2013 

were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-2013 standards were 

adopted.

* There are no savings for EL1 because there is no market share projected below this level in the base case.
8B-14

** The max-tech level for this equipment class is efficiency level 4.

Table 8B.4.11 Cumulative NPV Results for Water Source Heat Pumps Using Alternative 

Equipment Price Forecast (Increasing Trend) and AEO Low Economic 

Growth Case Scenario, 3% Discount Rate (billion 2014$)

Equipment Class Efficiency Level

1 2 3 4 5

<17,000 Btu/h (0.00) (0.08) (0.28) (0.42) (0.65)

≥17,000 to ≤65,000 Btu/h 0.02 0.05 (0.17) (0.51) (1.17)

≥65,000 to ≤135,000 Btu/h -

* (0.03) (0.12) (0.22) N/A**

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1-2013 

were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-2013 standards were 

adopted.

* There are no savings for EL1 because there is no market share projected below this level in the base case.

** The max-tech level for this equipment class is efficiency level 4.

Table 8B.4.12 Cumulative NPV Results for Water Source Heat Pumps Using Alternative 

Equipment Price Forecast (Increasing Trend) and AEO Low Economic 

Growth Case Scenario, 7% Discount Rate (billion 2014$)

Equipment Class Efficiency Level

1 2 3 4 5

<17,000 Btu/h (0.00) (0.05) (0.18) (0.26) (0.41)

≥17,000 to ≤65,000 Btu/h (0.00) (0.09) (0.28) (0.51) (0.96)

≥65,000 to ≤135,000 Btu/h -

* (0.02) (0.07) (0.14) N/A**

Notes: Numbers in parentheses indicate negative NPV.

The net present value for efficiency levels more stringent than those specified by ASHRAE Standard 90.1-2013 

were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-2013 standards were 

adopted.

* There are no savings for EL1 because there is no market share projected below this level in the base case. ** The max-tech level for this equipment class is efficiency level 4.
8B-15

REFERENCES

1 Energy Information Administration, Annual Energy Outlook 2014 with Projections to 2040, 

2014. Washington, DC. www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf

2 Taylor, Margaret, and Sydny K. Fujita. Accounting for Technological Change in Regulatory 

Impact Analyses: The Learning Curve Technique. Berkeley: Lawrence Berkeley National 

Laboratory, 2013. LBNL-6195E

3 Energy Information Administration, Macroeconomic Activity Module for Annual Energy 

Outlook 2014, 2014. Washington, DC. 

<www.eia.gov/forecasts/aeo/assumptions/pdf/macroeconomic.pdf>
9-i

CHAPTER 9. EMISSIONS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION ........................................................................................................... 9-1

9.2 AIR QUALITY REGULATIONS AND EMISSIONS IMPACTS................................. 9-2

9.3 POWER SECTOR AND SITE EMISSIONS FACTORS............................................... 9-3

9.4 UPSTREAM FACTORS ................................................................................................. 9-4

9.5 EMISSIONS IMPACT RESULTS.................................................................................. 9-5

9.5.1 Small Three-Phase Air-Cooled Air Conditioners and Heat Pumps <65,000 

Btu/h..................................................................................................................... 9-5

9.5.2 Water-Source Heat Pumps................................................................................. 9-10

REFERENCES .......................................................................................................................... 9-14

LIST OF TABLES

Table 9.3.1 Power Plant Emissions Factors (Small Commercial Air-Cooled Air 

Conditioners and Heat Pumps) ...................................................................... 9-4

Table 9.3.2 Power Plant Emissions Factors (Water-Source Heat Pumps)........................ 9-4

Table 9.4.1 Electricity Upstream Emissions Factors........................................................ 9-5

Table 9.5.1 Cumulative Emissions Reduction for Potential Standard for Small 

Three-Phase Air-Cooled Air Conditioners and Heat Pumps <65,000 

Btu/h............................................................................................................... 9-6

Table 9.5.2 Cumulative Emissions Reduction for Potential Standard for WaterSource Heat Pumps...................................................................................... 9-10

LIST OF FIGURES

Figure 9.5.1 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: 

CO2 Total Emissions Reduction.................................................................... 9-7

Figure 9.5.2 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: SO2 

Total Emissions Reduction ............................................................................ 9-7

Figure 9.5.3 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: 

NOx Total Emissions Reduction ................................................................... 9-8

Figure 9.5.4 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: Hg 

Total Emissions Reduction ............................................................................ 9-8

Figure 9.5.5 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: 

N2O Total Emissions Reduction ................................................................... 9-9

Figure 9.5.6 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: 

CH4 Total Emissions Reduction.................................................................... 9-9

Figure 9.5.7 Water-Source Heat Pumps: CO2 Total Emissions Reduction..................... 9-11

Figure 9.5.8 Water-Source Heat Pumps: SO2 Total Emissions Reduction ..................... 9-11
9-ii

Figure 9.5.9 Water-Source Heat Pumps: NOx Total Emissions Reduction..................... 9-12

Figure 9.5.10 Water-Source Heat Pumps: Hg Total Emissions Reduction........................ 9-12

Figure 9.5.11 Water-Source Heat Pumps: N2O Total Emissions Reduction..................... 9-13

Figure 9.5.12 Water-Source Heat Pumps: CH4 Total Emissions Reduction..................... 9-13
9-1

CHAPTER 9. EMISSIONS ANALYSIS

9.1 INTRODUCTION

The emissions analysis consists of two components. The first component estimates the 

effect of potential energy conservation standards on power sector and site combustion emissions 

of carbon dioxide (CO2), nitrogen oxides (NOX), sulfur dioxide (SO2) and mercury (Hg). The 

second component estimates the impacts of a potential standard on emissions of two additional 

greenhouse gases, methane (CH4) and nitrous oxide (N2O), as well as the reductions to emissions 

of all species due to “upstream” activities in the fuel production chain. These upstream activities 

comprise extraction, processing, and transporting fuels to the site of combustion. The associated 

emissions are referred to as upstream emissions. Together, these emissions account for the fullfuel-cycle (FFC), in accordance with DOE’s FFC Statement of Policy. 76 FR 51282 (Aug. 18, 

2011). 

The analysis of power sector emissions uses marginal emissions intensity factors 

calculated by DOE. As of 2014, DOE is using a new methodology based on results published for 

the Annual Energy Outlook 2014 (AEO 2014) reference case and a set of side cases that 

implement a variety of efficiency-related policies.

1 The new methodology is described in chapter 

11 and in the report “Utility Sector Impacts of Reduced Electricity Demand” (Coughlin, 2014).2

Site emissions of CO2 and NOX are estimated using emissions intensity factors from a 

publication of the Environmental Protection Agency (EPA).

3

Combustion emissions of CH4 and N2O are estimated using emissions intensity factors 

published by the EPA, GHG Emissions Factors Hub.a The FFC upstream emissions are estimated 

based on the methodology developed by Coughlin (2013).4 The upstream emissions include both 

emissions from fuel combustion during extraction, processing and transportation of fuel, and 

“fugitive” emissions (direct leakage to the atmosphere) of CH4 and CO2.

The emissions intensity factors are expressed in terms of physical units per MWh or 

MMBtu of site energy savings. Total emissions reductions are estimated using the energy 

savings calculated in the national impact analysis (chapter 8).

For CH4 and N2O, DOE also presents results in terms of units of carbon dioxide 

equivalent (CO2e). Gases are converted to CO2e by multiplying the physical units by the gas

global warming potential (GWP) over a 100 year time horizon. Based on the Fifth Assessment 

Report of the Intergovernmental Panel on Climate Change,5 DOE used GWP values of 28 for 

CH4 and 265 for N2O.b

 a www.epa.gov/climateleadership/inventory/ghg-emissions.html b The values are without inclusion of climate-carbon feedbacks in response to emissions of the indicated non-CO2 

gases.
9-2

9.2 AIR QUALITY REGULATIONS AND EMISSIONS IMPACTS

Each annual version of the AEO incorporates the projected impacts of existing air quality 

regulations on emissions. AEO 2014 generally represents current Federal and State legislation 

and final implementation regulations in place as of the end of October 2013.

SO2 emissions from affected electric generating units (EGUs) are subject to nationwide 

and regional emissions cap and trading programs. Title IV of the Clean Air Act sets an annual 

emissions cap on SO2 for affected EGUs in the 48 contiguous states and the District of Columbia 

(D.C.). SO2 emissions from 28 eastern states and D.C. were also limited under the Clean Air 

Interstate Rule (CAIR), which created an allowance-based trading program that that operates 

along with the Title IV program in those States and D.C. 70 FR 25162 (May 12, 2005). CAIR 

was remanded to EPA by the U.S. Court of Appeals for the District of Columbia Circuit (D.C. 

Circuit) but parts of it remained in effect. On July 6, 2011 EPA issued a replacement for CAIR, 

the Cross-State Air Pollution Rule (CSAPR). 76 FR 48208 (August 8, 2011). On August 21, 

2012, the D.C. Circuit issued a decision to vacate CSAPR. See EME Homer City Generation, LP 

v. EPA, 696 F.3d 7, 38 (D.C. Cir. 2012). The court ordered EPA to continue administering 

CAIR. The AEO 2014 emissions factors used for the present analysis assume that CAIR remains 

a binding regulation through 2040. c

The attainment of emissions caps is typically flexible among affected Electric Generating 

Units (EGUs) and is enforced through the use of emissions allowances and tradable permits. 

Under existing EPA regulations, any excess SO2 emissions allowances resulting from the lower 

electricity demand caused by the imposition of an efficiency standard could be used to permit 

offsetting increases in SO2 emissions by any regulated EGU. In past rulemakings, DOE 

recognized that there was uncertainty about the effects of efficiency standards on SO2 emissions 

covered by the existing cap-and-trade system, but it concluded that no reductions in power sector 

emissions would occur for SO2 as a result of standards.

Beginning in 2016, however, SO2 emissions will fall as a result of the Mercury and Air 

Toxics Standards (MATS) for power plants. 77 FR 9304 (Feb. 16, 2012). In the final MATS 

rule, EPA established a standard for hydrogen chloride as a surrogate for acid gas hazardous air 

pollutants (HAP), and also established a standard for SO2 (a non-HAP acid gas) as an alternative 

equivalent surrogate standard for acid gas HAP. The same controls are used to reduce HAP and 

non-HAP acid gas; thus, SO2 emissions will be reduced as a result of the control technologies 

installed on coal-fired power plants to comply with the MATS requirements for acid gas. AEO 

2014 assumes that, in order to continue operating, coal plants must have either flue gas

 c On April 29, 2014, the U.S. Supreme Court reversed the judgment of the D.C. Circuit and remanded the case for 

further proceedings consistent with the Supreme Court's opinion. The Supreme Court held in part that EPA's 

methodology for quantifying emissions that must be eliminated in certain states due to their impacts in other 

downwind states was based on a permissible, workable, and equitable interpretation of the Clean Air Act provision 

that provides statutory authority for CSAPR. See EPA v. EME Homer City Generation, No 12-1182, slip op. at 32 

(U.S. April 29, 2014). Because DOE is using emissions factors based on AEO 2014, the analysis assumes that 

CAIR, not CSAPR, is the regulation in force. The difference between CAIR and CSAPR is not relevant for the 

purpose of DOE's analysis of SO2 emissions.
9-3

desulfurization or dry sorbent injection systems installed by 2016. Both technologies, which are 

used to reduce acid gas emissions, also reduce SO2 emissions. Under the MATS, emissions will 

be far below the cap established by CAIR, so it is unlikely that excess SO2 emissions allowances 

resulting from the lower electricity demand would be needed or used to permit offsetting 

increases in SO2 emissions by any regulated EGU. Therefore, DOE believes that efficiency 

standards will reduce SO2 emissions in 2016 and beyond.

CAIR established a cap on NOx emissions in 28 eastern States and the District of 

Columbia. Energy conservation standards are expected to have little effect on NOx emissions in 

those States covered by CSAPR because excess NOx emissions allowances resulting from the 

lower electricity demand could be used to permit offsetting increases in NOx emissions. 

However, standards would be expected to reduce NOx emissions in the States not affected by 

CAIR, so DOE estimated NOx emissions reductions from potential standards for those States.

The MATS limit mercury emissions from power plants, but they do not include 

emissions caps and, as such, DOE’s energy conservation standards would likely reduce Hg 

emissions. DOE estimated mercury emissions reductions using emissions factors based on AEO 

2014, which incorporates the MATS.

9.3 POWER SECTOR AND SITE EMISSIONS FACTORS 

The analysis of power sector emissions uses marginal emissions intensity factors derived 

from analysis of the AEO 2014 reference and a number of side cases incorporating enhanced 

equipment efficiencies. To model the impact of a standard, DOE calculates factors that relate a

unit reduction to annual site electricity demand for a given end use to corresponding reductions 

to installed capacity by fuel type, fuel use for generation, and power sector emissions. Details on 

the approach used may be found in Coughlin (2014).

Table 9.3.1 and Table 9.3.2 present the average power plant emissions factors for 

selected years. These power plant emissions factors are derived from the emissions factors of the 

plant types used to supply electricity to commercial buildings for space cooling and space 

heating. The average factors for each year take into account the projected shares of each of the 

sources in total electricity generation. For mall commercial air-cooled air-conditioning and 

heating equipment, the emissions factors refer to the power plants that supply electricity for 

space cooling. For water-source heat pumps, the factors are calculated as the weighted average of 

emissions factors of plants that supply electricity for space cooling (80%) and space heating 

(20%).
9-4

Table 9.3.1 Power Plant Emissions Factors (Small Commercial Air-Cooled Air 

Conditioners and Heat Pumps)

Unit* 2020 2025 2030 2035 2040

CO2 kg/MWh 695 620 560 514 472

SO2 g/MWh 649 507 426 357 319

NOx g/MWh 536 450 394 348 316

Hg g/MWh 0.00200 0.00156 0.00132 0.00110 0.00098

N2O g/MWh 7.2 7.1 6.9 6.6 6.4

CH4 g/MWh 50 49 48 46 45

* Refers to site electricity savings.

Table 9.3.2 Power Plant Emissions Factors (Water-Source Heat Pumps)

Unit* 2020 2025 2030 2035 2040

CO2 kg/MWh 705 628 567 519 477

SO2 g/MWh 671 524 441 369 330

NOx g/MWh 549 460 402 355 322

Hg g/MWh 0.00207 0.00162 0.00136 0.00114 0.00102

N2O g/MWh 7.2 7.1 6.9 6.6 6.4

CH4 g/MWh 50 49 48 46 45

* Refers to site electricity savings.

9.4 UPSTREAM FACTORS 

The upstream emissions accounting uses the same approach as the upstream energy 

accounting described in appendix 8A. See also Coughlin (2013) and Coughlin (2014). When 

demand for a particular fuel is reduced, there is a corresponding reduction in the emissions from 

combustion of that fuel at either the building site or the power plant. The associated reduction in 

energy use for upstream activities leads to further reductions in emissions. These upstream 

emissions are defined to include the combustion emissions from the fuel used upstream, the 

fugitive emissions associated with the fuel used upstream, and the fugitive emissions associated 

with the fuel used on site. 

Fugitive emissions of CO2 occur during oil and gas production, but are small relative to 

combustion emissions. They comprise about 2.5 percent of total CO2 emissions for natural gas 

and 1.7 percent for petroleum fuels. Fugitive emissions of methane occur during oil, gas and coal 

production. Combustion emissions of CH4 are very small, while fugitive emissions (particularly 

for gas production) may be relatively large. Hence, fugitive emissions make up over 99 percent

of total methane emissions for natural gas, about 95 percent for coal, and 93 percent for 

petroleum fuels. 
9-5

Upstream emissions factors account for both fugitive emissions and combustion 

emissions in extraction, processing, and transport of primary fuels. Fugitive emissions factors for 

methane from coal mining and natural gas production were estimated based on a review of recent 

studies compiled by Burnham (2011).6 This review includes estimates of the difference between 

fugitive emissions factors for conventional production of natural vs. unconventional (shale or 

tight gas). These estimates rely in turn on data gathered by EPA under new GHG reporting 

requirements for the petroleum and natural gas industries.7, 8 As more data are made available, 

DOE will continue to update these estimated emissions factors.

For ease of application in its analysis, DOE developed all of the emissions factors using 

site (point of use) energy savings in the denominator. Table 9.4.1 presents the electricity 

upstream emissions factors for selected years. These were used to estimate the emissions 

associated with the decreased electricity use. The caps that apply to power sector NOX emissions 

do not apply to upstream combustion sources.

Table 9.4.1 Electricity Upstream Emissions Factors

Unit 2020 2025 2030 2035 2040

CO2 kg/MWh 29.1 29.4 29.7 29.9 29.8

SO2 g/MWh 5.0 5.1 4.9 4.7 4.6

NOx g/MWh 368 375 382 387 387

Hg g/MWh 0.000012 0.000011 0.000011 0.000010 0.000010

N2O g/MWh 0.252 0.247 0.241 0.234 0.228

CH4 g/MWh 2149 2195 2216 2248 2255

* Refers to site electricity savings.

9.5 EMISSIONS IMPACT RESULTS

9.5.1 Small Three-Phase Air-Cooled Air Conditioners and Heat Pumps <65,000 Btu/h

Table 9.5.1 presents the estimated cumulative emissions reductions for the lifetime of 

equipment sold in 2017-2046 for the ASHRAE level and 2020-2046 for each additional 

efficiency level for the equipment classes triggered by ASHRAE, and 2019-2048 for split system 

air conditioners. Negative values indicate that emissions increase.
9-6

Table 9.5.1 Cumulative Emissions Reduction for Potential Standard for Small ThreePhase Air-Cooled Air Conditioners and Heat Pumps <65,000 Btu/h

Efficiency Level

ASHRAE/

0 1 2 3 4 5

Power Sector Emissions

 CO2 (million metric tons) 3.7 8.9 16.8 20.8 24.3 25.9

 SO2 (thousand tons) 2.9 6.9 13.0 16.1 18.8 20.1

 NOX (thousand tons) 2.8 6.7 12.6 15.6 18.2 19.4

 Hg (tons) 0.01 0.02 0.04 0.05 0.06 0.06

 N2O (thousand tons) 0.05 0.13 0.24 0.30 0.35 0.37

 CH4 (thousand tons) 0.38 0.90 1.69 2.10 2.45 2.61

Upstream Emissions

 CO2 (million metric tons) 0.22 0.54 1.00 1.24 1.45 1.54

 SO2 (thousand tons) 0.04 0.09 0.17 0.22 0.25 0.27

 NOX (thousand tons) 3.2 7.6 14.3 17.7 20.7 22.0

 Hg (tons) 0.0001 0.0002 0.0004 0.0005 0.0006 0.0006

 N2O (thousand tons) 0.002 0.005 0.009 0.011 0.012 0.013

 CH4 (thousand tons) 19 45 83 103 121 128

Total FFC Emissions

 CO2 (million metric tons) 4.0 9.5 17.8 22.1 25.8 27.4

 SO2 (thousand tons) 2.9 7.0 13.2 16.4 19.1 20.3

 NOX (thousand tons) 6.0 14.3 26.8 33.4 38.9 41.4

 Hg (tons) 0.01 0.02 0.04 0.05 0.06 0.06

 N2O (thousand tons) 0.06 0.13 0.25 0.31 0.36 0.39

 CH4 (thousand tons) 19 45 85 105 123 131

Note: The potential emissions reduction for efficiency levels more stringent than those specified by ASHRAE 

Standard 90.1-2013 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-

2013 standards were adopted.

Figure 9.5.1 through Figure 9.5.6 show the annual reductions for total emissions for each 

type of emission from each efficiency level. The reductions reflect the lifetime impacts of 

equipment sold in 2017-2046 for the ASHRAE level and 2020-2046 for each additional 

efficiency level for the equipment classes triggered by ASHRAE, and 2019-2048 for split system 

air conditioners.
9-7

Figure 9.5.1 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: CO2 Total 

Emissions Reduction

Figure 9.5.2 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: SO2 Total 

Emissions Reduction
9-8

Figure 9.5.3 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: NOx Total 

Emissions Reduction

Figure 9.5.4 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: Hg Total 

Emissions Reduction
9-9

Figure 9.5.5 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: N2O Total 

Emissions Reduction

Figure 9.5.6 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: CH4 Total 

Emissions Reduction
9-10

9.5.2 Water-Source Heat Pumps

Table 9.5.2 presents the estimated cumulative emissions reductions for the lifetime of 

equipment sold in 2016-2045 for the ASHRAE level and 2020-2045 for each additional 

efficiency level. Negative values indicate that emissions increase.

Table 9.5.2 Cumulative Emissions Reduction for Potential Standard for Water-Source 

Heat Pumps

Efficiency Level

ASHRAE/

0* 1 2 3 4 5

Power Sector Emissions

 CO2 (million metric tons) - 1.4 16.3 30.5 41.5 56.7

 SO2 (thousand tons) - 1.1 12.9 24.1 32.9 44.9

 NOX (thousand tons) - 1.1 12.3 23.1 31.4 42.9

 Hg (tons) - 0.003 0.040 0.074 0.101 0.139

 N2O (thousand tons) - 0.02 0.23 0.44 0.60 0.81

 CH4 (thousand tons) - 0.14 1.63 3.06 4.16 5.68

Upstream Emissions

 CO2 (million metric tons) - 0.08 0.97 1.81 2.47 3.36

 SO2 (thousand tons) - 0.01 0.17 0.32 0.43 0.59

 NOX (thousand tons) - 1.2 13.8 25.9 35.2 48.0

 Hg (tons) - 0.00003 0.00037 0.00070 0.00095 0.00129

 N2O (thousand tons) - 0.001 0.008 0.016 0.021 0.029

 CH4 (thousand tons) - 7.0 80.4 150.7 205.0 279.6

Total FFC Emissions

 CO2 (million metric tons) - 1.5 17.3 32.3 44.0 60.1

 SO2 (thousand tons) - 1.1 13.1 24.5 33.3 45.5

 NOX (thousand tons) - 2.3 26.1 48.9 66.6 90.9

 Hg (tons) - 0.004 0.040 0.075 0.102 0.140

 N2O (thousand tons) - 0.02 0.24 0.45 0.62 0.84

 CH4 (thousand tons) - 7.2 82.0 153.8 209.1 285.3

Note: The potential emissions reduction for efficiency levels more stringent than those specified by ASHRAE 

Standard 90.1-2013 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-

2013 standards were adopted. *

There are no reductions for the ASHRAE level because there is no market share projected at the federal baseline in 

the base case.

Figure 9.5.7 through Figure 9.5.12 show the annual reductions for total emissions for 

each type of emission from each efficiency level. The reductions reflect the lifetime impacts of 

equipment sold in 2016-2045 for the ASHRAE level and 2020-2045 for each additional 

efficiency level.
9-11

Figure 9.5.7 Water-Source Heat Pumps: CO2 Total Emissions Reduction

Figure 9.5.8 Water-Source Heat Pumps: SO2 Total Emissions Reduction
9-12

Figure 9.5.9 Water-Source Heat Pumps: NOx Total Emissions Reduction

Figure 9.5.10 Water-Source Heat Pumps: Hg Total Emissions Reduction
9-13

Figure 9.5.11 Water-Source Heat Pumps: N2O Total Emissions Reduction

Figure 9.5.12 Water-Source Heat Pumps: CH4 Total Emissions Reduction
9-14

REFERENCES

1. Energy Information Administration, Annual Energy Outlook 2014 with Projections to 

2040, 2014 Washington, DC. <www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf>

2. Coughlin, K., Utility Sector Imacts of Reduced Electricity Demand, 2014. Lawrence 

Berkeley National Laboratory. Report No. LBNL-6864E. 

3. U.S. Environmental Protection Agency, Compilation of Air Pollutant Emission Factors, 

AP-42, Fifth Edition, Volume I: Stationary Point and Area Sources. 1998. 

www.epa.gov/ttn/chief/ap42/index.html

4. Coughlin, K., Projections of Full-Fuel-Cycle Energy and Emissions Metrics, 2013. 

Lawrence Berkeley National Laboratory. Report No. LBNL-6025E. 

5. Intergovernmental Panel On Climate Change, Chapter 8. In Climate Change 2013: The 

Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report 

of the Intergovernmental Panel on Climate Change, T.F. Stocker, D. Qin, G.-K. Plattner, 

M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, 

Editor. 2013. Cambridge University Press: Cambridge, United Kingdom and New York, 

NY, USA

6. Burnham, A., J. Han, C. E. Clark, M. Wang, J. B. Dunn, I. Palou-Rivera, Life-Cycle 

Greenhouse Gas Emissions of Shale Gas, Natural Gas, Coal, and Petroleum.

Environmental Science & Technology, 2011. 46(2): pp. 619-627 

7. U.S. Environmental Protection Agency, Fugitive Emissions Reporting from the 

Petroleum and Natural Gas Industry, 2009. U.S. Environmental Protection Agency. 

Washington, DC. 

8. U.S. Environmental Protection Agency, Oil and Natural Gas Sector: Standards of 

Performance for Crude Oil and Natural Gas Production, Transmission, and Distribution, 

Background Supplemental Technical Support Document for the Final New Source 

Performance Standards, 2012. U.S. Environmental Protection Agency. Washington, DC. 
10-i

CHAPTER 10. MONETIZATION OF EMISSION REDUCTIONS BENEFITS 

TABLE OF CONTENTS

10.1 INTRODUCTION ......................................................................................................... 10-1

10.2 MONETIZING CARBON DIOXIDE EMISSIONS..................................................... 10-1

10.2.1 Social Cost of Carbon ........................................................................................ 10-1

10.2.2 Monetizing Carbon Dioxide Emissions............................................................. 10-2

10.3 DEVELOPMENT OF SOCIAL COST OF CARBON VALUES................................. 10-2

10.3.1 Current Approach and Key Assumptions.......................................................... 10-2

10.3.2 Limitations of Current Estimates....................................................................... 10-4

10.4 VALUATION OF OTHER EMISSIONS REDUCTIONS ........................................... 10-5

10.5 RESULTS ...................................................................................................................... 10-5

REFERENCES ........................................................................................................................ 10-12

LIST OF TABLES

Table 10.3.1 Annual SCC Values for 2010-2050 from 2010 Interagency Report (in 

2007$ per Metric Ton)................................................................................. 10-3

Table 10.3.2 Annual SCC Values for 2010–2050 from 2013 Interagency Update (in 

2007$ per Metric Ton of CO2).................................................................... 10-4

Table 10.5.1 Estimates of Global Present Value of CO2 Emissions Reduction under 

TSLs for Small Three-Phase Air-Cooled Air Conditioners and Heat 

Pumps <65,000 Btu/h .................................................................................. 10-6

Table 10.5.2 Estimates of Global Present Value of CO2 Emissions Reduction under 

TSLs for Water-Source Heat Pumps............................................................ 10-7

Table 10.5.3 Estimates of Domestic Present Value of CO2 Emissions Reduction 

under TSLs for Small Three-Phase Air-Cooled Air Conditioners and 

Heat Pumps <65,000 Btu/h.......................................................................... 10-8

Table 10.5.4 Estimates of Domestic Present Value of CO2 Emissions Reduction 

under TSLs for Water-Source Heat Pumps ................................................. 10-9

Table 10.5.5 Estimates of Present Value of NOX Emissions Reduction under TSLs 

for Small Three-Phase Air-Cooled Air Conditioners and Heat Pumps 

<65,000 Btu/h ............................................................................................ 10-10

Table 10.5.6 Estimates of Present Value of NOX Emissions Reduction under TSLs 

for Water-Source Heat Pumps................................................................... 10-11
10-1

CHAPTER 10. MONETIZATION OF EMISSION REDUCTIONS BENEFITS 

10.1 INTRODUCTION

As part of its assessment of the effects of potential energy conservation standards for 

small commercial air-cooled air-conditioning and heating equipment less than 65,000 Btu/h and 

water-source heat pumps, the U.S. Department of Energy (DOE) estimated the monetary benefits 

of the reduced emissions of carbon dioxide (CO2) and nitrogen oxides (NOX) that would be 

expected to result from each trial standard level (TSL) considered for this rulemaking. This 

chapter summarizes the basis for the monetary values assigned to emissions and presents the 

modeled benefits of estimated reductions. 

10.2 MONETIZING CARBON DIOXIDE EMISSIONS

One challenge for anyone attempting to calculate the monetary benefits of reduced 

emissions of CO2 is what value to assign to each unit eliminated. The value must encompass a 

broad range of physical, economic, social, and political effects. Analysts developed the concept 

of the social cost of carbon (SCC) to represent the broad cost or value associated with 

producing—or reducing—a quantifiable amount of CO2 emissions.

10.2.1 Social Cost of Carbon 

The SCC is an estimate of the monetized damages associated with an incremental 

increase in carbon emissions in a given year. The SCC is intended to include (but is not limited 

to) changes in net agricultural productivity, human health, property damages from increased 

flood risk, and the value of ecosystem services. SCC estimates are provided in dollars per metric 

ton of carbon dioxide. A value for the domestic SCC is meant to represent the damages in the 

United States resulting from a unit change in carbon dioxide emissions, whereas a global SCC is 

meant to reflect the value of damages worldwide.

Under section 1(b)(6) of Executive Order 12866,

1 agencies must, to the extent permitted 

by law, “assess both the costs and the benefits of the intended regulation and, recognizing that 

some costs and benefits are difficult to quantify, propose or adopt a regulation only upon a 

reasoned determination that the benefits of the intended regulation justify its costs.” The purpose 

of the SCC estimates required by the Executive Order is to enable agencies to incorporate the 

monetized social benefits of reducing CO2 emissions into cost-benefit analyses of regulatory 

actions. The estimates are presented with an acknowledgement of the many uncertainties 

involved and with a clear understanding that they will need updating in response to increasing 

knowledge of the science and economics of climate impacts.

As part of the interagency process that developed the SCC estimates, technical experts 

from numerous agencies met regularly to explore the technical literature in relevant fields, 

discuss key model inputs and assumptions, and consider public comments. The primary objective 

of the process was to develop a range of SCC values using a defensible set of assumptions 

regarding model inputs that was grounded in the scientific and economic literature. In this way, 
10-2

key uncertainties and model differences transparently and consistently inform the range of SCC 

estimates developed for use in the rulemaking process.

10.2.2 Monetizing Carbon Dioxide Emissions

When attempting to assess the incremental economic impacts of carbon dioxide 

emissions, the analyst faces several serious challenges. A report from the National Research 

Council2 points out that any assessment will suffer from uncertainty, speculation, and lack of 

information about (1) future emissions of greenhouse gases, (2) the effects of past and future 

emissions on the climate system, (3) the effects of changes in climate on the physical and 

biological environment, and (4) the translation of those environmental impacts into economic 

damages. As a result, any effort to quantify and monetize the harms associated with climate 

change raises serious questions of science, economics, and ethics and should be viewed as 

provisional. 

Despite the limits of both quantification and monetization, SCC estimates can be useful 

in estimating the social benefits of reducing CO2 emissions. An agency can estimate the benefits 

from reduced (or costs from increased) emissions in any future year by multiplying the change in 

emissions in that year by the SCC values appropriate for that year. Then the net present value of 

the benefits can be calculated by multiplying each of the future benefits by an appropriate 

discount factor and summing across all affected years. This approach assumes that the marginal 

damages from increased emissions are constant for small departures from the baseline emissions 

path, an approximation that is reasonable for policies that have effects on emissions that are 

small relative to cumulative global carbon dioxide emissions.

10.3 DEVELOPMENT OF SOCIAL COST OF CARBON VALUES

In 2009, an interagency process was initiated to develop a preliminary assessment of how 

best to quantify the benefits from reducing carbon dioxide emissions. To provide consistency in 

how benefits are evaluated across Federal agencies, the Administration sought to develop a 

transparent and defensible method, specifically designed for the rulemaking process, to quantify 

avoided climate change damages from reduced CO2 emissions. The interagency group did not 

undertake any original analysis. Instead, it combined SCC estimates from the literature to use as 

interim values until a more comprehensive analysis could be conducted. The outcome of the 

preliminary assessment was a set of five interim values: global SCC estimates for 2007 (in 

2006$) of $55, $33, $19, $10, and $5 per ton of CO2.

3 Those interim values represented the first 

sustained interagency effort within the U.S. government to develop an SCC for use in regulatory 

analysis. The results of that preliminary effort were presented in several proposed and final rules.

10.3.1 Current Approach and Key Assumptions

After the release of the interim values, the interagency group reconvened regularly to 

improve the SCC estimates. Specifically, the group considered public comments and further 

explored the technical literature in relevant fields. The interagency group relied on three 

integrated assessment models commonly used to estimate the SCC. The models are known by 

their acronyms of FUND, DICE, and PAGE. Those three models frequently are cited in the peer-
10-3

reviewed literature and were used in the most recent assessment of the Intergovernmental Panel 

on Climate Change. Each model was given equal weight in developing SCC values.

Each model takes a slightly different approach to calculating how increases in emissions 

produce economic damages. A key objective of the interagency process was to enable a 

consistent exploration of the three models while respecting the different approaches taken by the 

key modelers in the field. An extensive review of the literature identified three sets of input 

parameters for the models: climate sensitivity; socioeconomic and emissions trajectories; and 

discount rates. A probability distribution for climate sensitivity was specified as an input to all 

three models. In addition, the interagency group used a range of scenarios for the socioeconomic 

parameters and a range of values for the discount rate. All other model features were left 

unchanged, relying on the model developers’ best estimates and judgments.

The interagency group selected four SCC values for use in regulatory analyses. Three 

values are based on the average SCC from the three integrated assessment models, at discount 

rates of 2.5 percent, 3 percent, and 5 percent. The fourth value, which represents the 95th

percentile of the SCC estimate across all three models at a 3-percent discount rate, is included to 

represent larger-than-expected effects from temperature changes farther out in the tails of the 

SCC distribution. The values increase in real terms over time. Additionally, the interagency 

group determined that a range of values from 7 percent to 23 percent should be used to adjust the 

global SCC to calculate domestic effects, although preference is given to consideration of the 

global benefits of reducing CO2 emissions. Table 10.3.1 presents the values in the 2010 

interagency group report.

4

Table 10.3.1 Annual SCC Values for 2010-2050 from 2010 Interagency Report (in 2007$

per Metric Ton)

Year

Discount Rate (%)

5 3 2.5 3

Average Average Average 95th Percentile

2010 $4.7 $21.4 $35.1 $64.9

2015 $5.7 $23.8 $38.4 $72.8

2020 $6.8 $26.3 $41.7 $80.7

2025 $8.2 $29.6 $45.9 $90.4

2030 $9.7 $32.8 $50.0 $100.0

2035 $11.2 $36.0 $54.2 $109.7

2040 $12.7 $39.2 $58.4 $119.3

2045 $14.2 $42.1 $61.7 $127.8

2050 $15.7 $44.9 $65.0 $136.2

The SCC values used for the analysis of the effects of potential standards for hearth 

products were generated using the most recent versions of the three integrated assessment 

models that have been published in the peer-reviewed literature, as described in the 2013 update 

from the interagency working group (revised November 2013). Table 10.3.2 shows the updated 

sets of SCC estimates in 5-year increments from 2010 to 2050. The full set of annual SCC 
10-4

estimates for 2010–2050 is presented in appendix 10B of this TSD. The central value that 

emerges is the average SCC across models at a 3-percent discount rate. To capture the 

uncertainties involved in regulatory impact analysis, however, the interagency group emphasizes 

the importance of including all four sets of SCC values.

Table 10.3.2 Annual SCC Values for 2010–2050 from 2013 Interagency Update (in 2007$

per Metric Ton of CO2)

Year

Discount Rate (%)

5 3 2.5 3

Average Average Average 95th Percentile

2010 $11 $32 $51 $89

2015 $11 $37 $57 $109

2020 $12 $43 $64 $128

2025 $14 $47 $69 $143

2030 $16 $52 $75 $159

2035 $19 $56 $80 $175

2040 $21 $61 $86 $191

2045 $24 $66 $92 $206

2050 $26 $71 $97 $220

10.3.2 Limitations of Current Estimates

The interagency group recognizes that current models are imperfect and incomplete. 

Because key uncertainties remain, current SCC estimates should be treated as provisional and 

revisable. Estimates doubtless will evolve in response to improved scientific and economic 

understanding. The 2009 National Research Council report points out the tension between 

producing quantified estimates of the economic damages from an incremental ton of carbon and 

the limits of current modeling efforts. Several analytic challenges are being addressed by the 

research community, some by research programs housed in many of the Federal agencies 

participating in the interagency process. The interagency group intends to review and reconsider 

SCC estimates periodically to incorporate expanding knowledge of the science and economics of 

climate impacts, as well as improvements in modeling.

In summary, in considering the potential global benefits resulting from reduced CO2

emissions, DOE used the values from the 2013 interagency report, applying the GDP price 

deflator to adjust the values to 2014$. For the four SCC values, the values of emissions in 2015 

were $12.2, $41.2, $63.4, and $121 per metric ton avoided (values expressed in 2014$). DOE 

derived values after 2050 using the relevant growth rates for 2040–2050 in the interagency 

update.

DOE multiplied the CO2 emissions reduction estimated for each year by the SCC value 

for that year under each discount rate. To calculate a present value of the stream of monetary 
10-5

values, DOE discounted the values in each of the four cases using the same discount rate that had 

been used to obtain the SCC values in each case.

10.4 VALUATION OF OTHER EMISSIONS REDUCTIONS

DOE considered the potential monetary benefits of reduced NOX emissions attributable to 

the TSLs considered for hearth product ignition devices. As noted in chapter 9, new or amended 

energy conservation standards would reduce NOX emissions in those States that are not affected 

by emissions caps. DOE estimated the monetized value of NOX emissions reductions resulting 

from each TSL based on estimates of environmental damage found in the scientific literature. 

Estimates suggest a wide range of monetary values, from $484to $4,971 per ton (in 2014$).5

DOE calculated monetary benefits using a median value for NOX emissions of $2,727 per short 

ton (in 2014$), at real discount rates of 3 percent and 7 percent.

DOE continues to evaluate appropriate values for monetizing avoided SO2 and Hg 

emissions. DOE did not monetize those emissions for this analysis.

10.5 RESULTS

Table 10.5.1 and Table 10.5.2 present the global values of CO2 emissions reductions from 

standards for small commercial air-cooled air conditioners and heat pumps and water-source heat 

pumps for each efficiency level. 
10-6

Table 10.5.1 Estimates of Global Present Value of CO2 Emissions Reduction under TSLs 

for Small Three-Phase Air-Cooled Air Conditioners and Heat Pumps 

<65,000 Btu/h

Efficiency 

Level

SCC Case*

5% discount

rate, average*

3% discount

rate, average*

2.5% discount

rate, average*

3% discount rate,

95th percentile*

Million 2014$

Primary Energy Emissions

ASHRAE/0 $24 $115 $184 $356

1 $57 $273 $437 $846

2 $110 $521 $832 $1,613

3 $136 $646 $1,031 $1,999

4 $159 $754 $1,204 $2,334

5 $170 $804 $1,283 $2,489

Upstream Emissions

ASHRAE/0 $1 $7 $11 $21

1 $3 $16 $26 $50

2 $6 $31 $49 $95

3 $8 $38 $61 $118

4 $9 $44 $71 $138

5 $10 $47 $76 $147

Full-Fuel-Cycle Emissions

ASHRAE/0 $25 $122 $195 $377

1 $60 $289 $463 $896

2 $116 $552 $881 $1,708

3 $144 $684 $1,092 $2,117

4 $168 $799 $1,275 $2,472

5 $179 $851 $1,359 $2,635

Note: The potential emissions reduction for efficiency levels more stringent than those specified by ASHRAE 

Standard 90.1-2013 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-2013

standards were adopted.

* For each of the four cases, the corresponding global SCC value for emissions in 2015 is $12.2, $41.2, $63.4, and 

$121 per metric ton (2014$)
10-7

Table 10.5.2 Estimates of Global Present Value of CO2 Emissions Reduction under TSLs 

for Water-Source Heat Pumps

Efficiency 

Level

SCC Case*

5% discount

rate, average*

3% discount

rate, average*

2.5% discount

rate, average*

3% discount rate,

95th percentile*

Million 2014$

Primary Energy Emissions

ASHRAE/0** - - - -

1 $9 $44 $71 $137

2 $106 $504 $805 $1,560

3 $198 $943 $1,507 $2,922

4 $270 $1,285 $2,052 $3,979

5 $370 $1,758 $2,808 $5,446

Upstream Emissions

ASHRAE/0** - - - -

1 $1 $3 $4 $8

2 $6 $30 $47 $92

3 $12 $55 $89 $172

4 $16 $75 $121 $234

5 $21 $103 $165 $320

Full-Fuel-Cycle Emissions

ASHRAE/0** - - - -

1 $10 $47 $75 $145

2 $112 $533 $852 $1,652

3 $209 $999 $1,596 $3,094

4 $285 $1,360 $2,173 $4,213

5 $391 $1,862 $2,973 $5,765

Note: The potential emissions reduction for efficiency levels more stringent than those specified by ASHRAE 

Standard 90.1-2013 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-2013

standards were adopted.

* For each of the four cases, the corresponding global SCC value for emissions in 2015 is $12.2, $41.2, $63.4, and 

$121 per metric ton (2014$). **There are no reductions for the ASHRAE level because there is no market share projected at the federal baseline in 

the base case.

After calculating global values of CO2 emissions reductions for each efficiency level, 

DOE calculated domestic values as a range of from 7 percent to 23 percent of the global values. 

Results for domestic values are presented in Table 10.5.3 and Table 10.5.4.
10-8

Table 10.5.3 Estimates of Domestic Present Value of CO2 Emissions Reduction under 

TSLs for Small Three-Phase Air-Cooled Air Conditioners and Heat Pumps 

<65,000 Btu/h

Efficiency 

Level

SCC Case*

5% discount

rate, average*

3% discount

rate, average*

2.5% discount

rate, average*

3% discount rate,

95th percentile*

Million 2014$

Primary Energy Emissions

ASHRAE/0 $1.7 to $5.5 $8.1 to $26.5 $12.9 to $42.3 $24.9 to $81.9

1 $4.0 to $13.1 $19.1 to $62.8 $30.6 to $100.5 $59.2 to $194.5

2 $7.7 to $25.3 $36.5 to $119.9 $58.2 to $191.3 $112.9 to $371.0

3 $9.5 to $31.2 $45.2 to $148.5 $72.2 to $237.1 $139.9 to $459.7

4 $11.1 to $36.5 $52.8 to $173.5 $84.3 to $276.9 $163.4 to $536.8

5 $11.9 to $39.0 $56.3 to $184.9 $89.8 to $295.2 $174.2 to $572.4

Upstream Emissions

ASHRAE/0 $0.1 to $0.3 $0.5 to $1.6 $0.8 to $2.5 $1.5 to $4.9

1 $0.2 to $0.8 $1.1 to $3.7 $1.8 to $6.0 $3.5 to $11.6

2 $0.4 to $1.5 $2.1 to $7.1 $3.4 to $11.3 $6.7 to $21.9

3 $0.6 to $1.8 $2.7 to $8.8 $4.3 to $14.0 $8.3 to $27.1

4 $0.6 to $2.1 $3.1 to $10.2 $5.0 to $16.4 $9.6 to $31.7

5 $0.7 to $2.3 $3.3 to $10.9 $5.3 to $17.4 $10.3 to $33.7

Full-Fuel-Cycle Emissions

ASHRAE/0 $1.8 to $5.8 $8.5 to $28.0 $13.6 to $44.8 $26.4 to $86.7

1 $4.2 to $13.9 $20.3 to $66.6 $32.4 to $106.5 $62.7 to $206.0

2 $8.1 to $26.7 $38.6 to $126.9 $61.7 to $202.6 $119.6 to $392.8

3 $10.1 to $33.1 $47.9 to $157.3 $76.4 to $251.2 $148.2 to $486.9

4 $11.8 to $38.6 $55.9 to $183.7 $89.3 to $293.3 $173.0 to $568.5

5 $12.6 to $41.3 $59.6 to $195.8 $95.1 to $312.6 $184.5 to $606.2

Note: The potential emissions reduction for efficiency levels more stringent than those specified by ASHRAE 

Standard 90.1-2013 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-2013

standards were adopted.

* For each of the four cases, the corresponding SCC value for emissions in 2015 is 12.2, $41.2, $63.4, and $121 per 

metric ton (2014$).
10-9

Table 10.5.4 Estimates of Domestic Present Value of CO2 Emissions Reduction under 

TSLs for Water-Source Heat Pumps

Efficiency 

Level

SCC Case*

5% discount

rate, average*

3% discount

rate, average*

2.5% discount

rate, average*

3% discount rate,

95th percentile*

Million 2014$

Primary Energy Emissions

ASHRAE/0** - - - -

1 $0.6 to $2.1 $3.1 to $10.2 $4.9 to $16.2 $9.6 to $31.5

2 $7.4 to $24.3 $35.3 to $115.8 $56.3 to $185.0 $109.2 to $358.8

3 $13.8 to $45.5 $66.0 to $217.0 $105.5 to $346.6 $204.5 to $672.1

4 $18.9 to $62.0 $89.9 to $295.5 $143.6 to $472.0 $278.5 to $915.1

5 $25.9 to $85.0 $123.1 to $404.4 $196.5 to $645.8 $381.2 to $1,252.5

Upstream Emissions

ASHRAE/0** - - - -

1 $0.0 to $0.1 $0.2 to $0.6 $0.3 to $1.0 $0.6 to $1.8

2 $0.4 to $1.4 $2.1 to $6.8 $3.3 to $10.9 $6.4 to $21.1

3 $0.8 to $2.6 $3.9 to $12.8 $6.2 to $20.4 $12.0 to $39.5

4 $1.1 to $3.6 $5.3 to $17.4 $8.5 to $27.8 $16.4 to $53.8

5 $1.5 to $4.9 $7.2 to $23.7 $11.6 to $38.0 $22.4 to $73.5

Full-Fuel-Cycle Emissions

ASHRAE/0** - - - -

1 $0.7 to $2.3 $3.3 to $10.8 $5.2 to $17.2 $10.1 to $33.3

2 $7.8 to $25.7 $37.3 to $122.6 $59.6 to $195.9 $115.6 to $379.9

3 $14.7 to $48.2 $69.9 to $229.7 $111.7 to $367.1 $216.6 to $711.6

4 $20.0 to $65.6 $95.2 to $312.8 $152.1 to $499.7 $294.9 to $968.9

5 $27.4 to $90.0 $130.3 to $428.1 $208.1 to $683.7 $403.6 to $1,326.0

Note: The potential emissions reduction for efficiency levels more stringent than those specified by ASHRAE 

Standard 90.1-2013 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-2013

standards were adopted.

* For each of the four cases, the corresponding SCC value for emissions in 2015 is 12.2, $41.2, $63.4, and $121 per 

metric ton (2014$). **There are no reductions for the ASHRAE level because there is no market share projected at the federal baseline in 

the base case.

Table 10.5.5 and Table 10.5.6 present the present-values of the cumulative NOX

emissions reductions from standards for small commercial air-cooled air conditioners and heat 

pumps and water-source heat pumps for each efficiency level. Monetary values are calculated 

using the average dollar-per-ton values assigned to NOX emissions at 7-percent and 3-percent 

discount rates.
10-10

Table 10.5.5 Estimates of Present Value of NOX Emissions Reduction under TSLs for 

Small Three-Phase Air-Cooled Air Conditioners and Heat Pumps <65,000 

Btu/h

Efficiency 

Level

3% Discount 

Rate

7% Discount 

Rate

million 2014$

Primary Energy Emissions

ASHRAE/

0 $3.5 $1.5

1 $8.2 $3.5

2 $16 $7.0

3 $20 $8.6

4 $23 $10

5 $25 $11

Upstream Emissions

ASHRAE/

0 $3.8 $1.5

1 $9.0 $3.6

2 $17 $7.2

3 $22 $8.9

4 $25 $10

5 $27 $11

Full-Fuel-Cycle Emissions

ASHRAE/

0 $7.3 $3.0

1 $17 $7.1

2 $33 $14

3 $41 $17

4 $48 $20

5 $51 $22

Note: The potential emissions reduction for efficiency levels more stringent than those specified by ASHRAE 

Standard 90.1-2013 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-2013

standards were adopted.
10-11

Table 10.5.6 Estimates of Present Value of NOX Emissions Reduction under TSLs for 

Water-Source Heat Pumps

Efficiency 

Level

3% Discount 

Rate

7% Discount 

Rate

million 2014$

Primary Energy Emissions

ASHRAE/

0* - -

1 $1.4 $0.6

2 $15 $6.6

3 $29 $12

4 $39 $17

5 $54 $23

Upstream Emissions

ASHRAE/

0* - -

1 $1.5 $0.6

2 $17 $6.7

3 $31 $13

4 $42 $17

5 $58 $24

Full-Fuel-Cycle Emissions

ASHRAE/

0* - -

1 $2.8 $1.2

2 $32 $13

3 $60 $25

4 $82 $34

5 $112 $47

Note: The potential emissions reduction for efficiency levels more stringent than those specified by ASHRAE 

Standard 90.1-2013 were calculated relative to the efficiency levels that would result if ASHRAE Standard 90.1-2013

standards were adopted. *

There are no reductions for the ASHRAE level because there is no market share projected at the federal baseline in 

the base case.
10-12

REFERENCES

1. Presidential Documents--Executive Order 12866--Regulatory Planning and Review, 

October 4, 1993. <www.archives.gov/federal-register/executive-orders/pdf/12866.pdf>

2. National Research Council, Hidden Costs of Energy: Unpriced Consequences of Energy 

Production and Use, 2010. National Academies Press. Washington, DC. 

<www.nap.edu/catalog.php?record_id=12794>

3. Interagency Working Group on Social Cost of Carbon, Social Cost of Carbon for 

Regulatory Impact Analysis under Executive Order 12866, February, February 2010. 

United States Government. Washington, D.C. 

<www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/Social-Cost-ofCarbon-for-RIA.pdf>

4. Interagency Working Group on Social Cost of Carbon, Technical Update of the Social 

Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, May 

2013; revised November 2013. United States Government. Washington, D.C. 

<www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-socialcost-of-carbon-for-regulator-impact-analysis.pdf>

5. U.S. Office of Management and Budget (OMB), 2006 Report to Congress on the Costs 

and Benefits of Federal Regulations and Unfunded Mandates on State, Local, and Tribal 

Entities, 2006. Washington, DC. 

<www.whitehouse.gov/sites/default/files/omb/assets/omb/inforeg/2006_cb/2006_cb_fina

l_report.pdf>
10A-i

APPENDIX 10A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT 

ANALYSIS UNDER EXECUTIVE ORDER 12866

TABLE OF CONTENTS

10A.1 EXECUTIVE SUMMARY .................................................................................... 10A-1

10A.2 MONETIZING CARBON DIOXIDE EMISSIONS.............................................. 10A-2

10A.3 SOCIAL COST OF CARBON VALUES USED IN PAST REGULATORY 

ANALYSES............................................................................................................................ 10A-4

10A.4 APPROACH AND KEY ASSUMPTIONS ........................................................... 10A-5

10A.4.1 Integrated Assessment Models ........................................................................... 10A-5

10A.4.2 Global versus Domestic Measures of SCC....................................................... 10A-11

10A.4.3 Valuing Non-CO2 Emissions ........................................................................... 10A-13

10A.4.4 Equilibrium Climate Sensitivity ....................................................................... 10A-13

10A.4.5 Socioeconomic and Emissions Trajectories...................................................... 10A-16

10A.4.6 Discount Rate.................................................................................................... 10A-19

10A.5 REVISED SCC ESTIMATES .............................................................................. 10A-26

10A.6 LIMITATIONS OF THE ANALYSIS ................................................................. 10A-32

10A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPACTS AND DAMAGE 

FUNCTIONS ........................................................................................................................ 10A-34

10A.8 CONCLUSION..................................................................................................... 10A-37

REFERENCES ..................................................................................................................... 10A-38

10A.9 ANNEX................................................................................................................. 10A-43

10A.9.1 Other (non-CO2) gases...................................................................................... 10A-44

10A.9.2 Extrapolating Emissions Projections to 2300 ................................................... 10A-46

LIST OF TABLES

Table 10A.1.1 Social Cost of CO2, 2010 – 2050 (in 2007 dollars) .................................. 10A-2

Table 10A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity 

Distributions............................................................................................ 10A-14

Table 10A.4.2 Socioeconomic and Emissions Projections from Select EMF-22 

Reference Scenarios................................................................................ 10A-18

Table 10A.5.1 Disaggregated Social Cost of CO2 Values by Model, Socioeconomic 

Trajectory, and Discount Rate for 2010 (in 2007 dollars)...................... 10A-29

Table 10A.5.2 Social Cost of CO2, 2010 – 2050 (in 2007 dollars) ................................ 10A-31

Table 10A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates 

between 2010 and 2050 .......................................................................... 10A-31

Table 10A.7.1 Probabilities of Various Tipping Points from Expert Elicitation............ 10A-35

Table 10A.9.1 Annual SCC Values: 2010–2050 (in 2007 dollars) ................................ 10A-43

Table 10A.9.2 2010 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton 

CO2) ........................................................................................................ 10A-54

Table 10A.9.3 2010 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton 

CO2) ........................................................................................................ 10A-55
10A-ii

Table 10A.9.4 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton 

CO2) ........................................................................................................ 10A-56

Table 10A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates............. 10A-57

LIST OF FIGURES

Figure 10A.4.1 Annual Consumption Loss as a Fraction of Global GDP in 2100 Due 

to an Increase in Annual Global Temperature in the DICE, FUND, and 

PAGE models.......................................................................................... 10A-10

Figure 10A.4.2 Annual Consumption Loss for Lower Temperature Changes in DICE, 

FUND, and PAGE .................................................................................. 10A-11

Figure 10A.4.3 Estimates of the Probability Density Function for Equilibrium Climate 

Sensitivity (°C) ....................................................................................... 10A-16

Figure 10A.5.1 Level of Global GDP across EMF Scenarios ......................................... 10A-30

Figure 10A.9.2 Sulfur Dioxide Emission Scenarios........................................................ 10A-46

Figure 10A.9.3 Global Population, 2000-2300 (Post-2100 extrapolations assume the 

population growth rate changes linearly to reach a zero growth rate by 

2200.) ...................................................................................................... 10A-48

Figure 10A.9.4 World GDP, 2000-2300 (Post-2100 extrapolations assume GDP per 

capita growth declines linearly, reaching zero in the year 2300) ........... 10A-49

Figure 10A.9.5 Global Fossil and Industrial CO2 Emissions, 2000-2300 (Post-2100 

extrapolations assume growth rate of CO2 intensity (CO2/GDP) over 

2090-2100 is maintained through 2300)................................................. 10A-50

Figure 10A.9.6 Global Net Land Use CO2 Emissions, 2000-2300 (Post-2100 

extrapolations assume emissions decline linearly, reaching zero in the 

year 2200) ............................................................................................... 10A-51

Figure 10A.9.7 Global Non-CO2 Radiative Forcing, 2000-2300 (Post-2100 

extrapolations assume constant non-CO2 radiative forcing after 2100) . 10A-52

Figure 10A.9.8 Global CO2 Intensity (fossil & industrial CO2 emissions/GDP), 2000-

2300 (Post-2100 extrapolations assume decline in CO2/GDP growth 

rate over 2090-2100 is maintained through 2300).................................. 10A-53

Figure 10A.9.9 Histogram of Global SCC Estimates in 2010 (2007$/ton CO2), by 

discount rate ............................................................................................ 10A-56
10A-1

APPENDIX 10A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT 

ANALYSIS UNDER EXECUTIVE ORDER 12866a

10A.1 EXECUTIVE SUMMARY

Under Executive Order 12866, agencies are required, to the extent permitted by law, “to 

assess both the costs and the benefits of the intended regulation and, recognizing that some costs 

and benefits are difficult to quantify, propose or adopt a regulation only upon a reasoned 

determination that the benefits of the intended regulation justify its costs.” The purpose of the 

“social cost of carbon” (SCC) estimates presented here is to allow agencies to incorporate the 

social benefits of reducing carbon dioxide (CO2) emissions into cost-benefit analyses of 

regulatory actions that have small, or “marginal,” impacts on cumulative global emissions. The 

estimates are presented with an acknowledgement of the many uncertainties involved and with a 

clear understanding that they should be updated over time to reflect increasing knowledge of the 

science and economics of climate impacts.

The SCC is an estimate of the monetized damages associated with an incremental 

increase in carbon emissions in a given year. It is intended to include (but is not limited to) 

changes in net agricultural productivity, human health, property damages from increased flood 

risk, and the value of ecosystem services due to climate change. 

This document presents a summary of the interagency process that developed these SCC 

estimates. Technical experts from numerous agencies met on a regular basis to consider public 

comments, explore the technical literature in relevant fields, and discuss key model inputs and 

assumptions. The main objective of this process was to develop a range of SCC values using a 

defensible set of input assumptions grounded in the existing scientific and economic literatures. 

In this way, key uncertainties and model differences transparently and consistently inform the 

range of SCC estimates used in the rulemaking process. 

 

a Prepared by Interagency Working Group on Social Cost of Carbon, United States Government.

With participation by:

Council of Economic Advisers 

Council on Environmental Quality 

Department of Agriculture

Department of Commerce

Department of Energy

Department of Transportation

Environmental Protection Agency

National Economic Council

Office of Energy and Climate Change

Office of Management and Budget

Office of Science and Technology Policy 

Department of the Treasury
10A-2

The interagency group selected four SCC values for use in regulatory analyses. Three 

values are based on the average SCC from three integrated assessment models, at discount rates 

of 2.5, 3, and 5 percent. The fourth value, which represents the 95th percentile SCC estimate 

across all three models at a 3 percent discount rate, is included to represent higher-than-expected 

impacts from temperature change further out in the tails of the SCC distribution.

Table 10A.1.1 Social Cost of CO2, 2010 – 2050 (in 2007 dollars)

Discount Rate

5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

10A.2 MONETIZING CARBON DIOXIDE EMISSIONS

The SCC is an estimate of the monetized damages associated with an incremental 

increase in carbon emissions in a given year. We report estimates of the SCC in dollars per 

metric ton of carbon dioxide throughout this document.b

 

When attempting to assess the incremental economic impacts of carbon dioxide 

emissions, the analyst faces a number of serious challenges. A recent report from the National 

Academies of Science (NRC 2009) points out that any assessment will suffer from uncertainty, 

speculation, and lack of information about (1) future emissions of greenhouse gases, (2) the 

effects of past and future emissions on the climate system, (3) the impact of changes in climate 

on the physical and biological environment, and (4) the translation of these environmental 

impacts into economic damages. As a result, any effort to quantify and monetize the harms 

associated with climate change will raise serious questions of science, economics, and ethics and 

should be viewed as provisional. 

Despite the serious limits of both quantification and monetization, SCC estimates can be 

useful in estimating the social benefits of reducing carbon dioxide emissions. Under Executive 

 b In this document, we present all values of the SCC as the cost per metric ton of CO2 emissions. Alternatively, one 

could report the SCC as the cost per metric ton of carbon emissions. The multiplier for translating between mass of 

CO2 and the mass of carbon is 3.67 (the molecular weight of CO2 divided by the molecular weight of carbon = 44/12 

= 3.67). 
10A-3

Order 12866, agencies are required, to the extent permitted by law, “to assess both the costs and 

the benefits of the intended regulation and, recognizing that some costs and benefits are difficult 

to quantify, propose or adopt a regulation only upon a reasoned determination that the benefits of 

the intended regulation justify its costs.” The purpose of the SCC estimates presented here is to 

make it possible for agencies to incorporate the social benefits from reducing carbon dioxide 

emissions into cost-benefit analyses of regulatory actions that have small, or “marginal,” impacts 

on cumulative global emissions. Most federal regulatory actions can be expected to have 

marginal impacts on global emissions. 

For such policies, the benefits from reduced (or costs from increased) emissions in any 

future year can be estimated by multiplying the change in emissions in that year by the SCC 

value appropriate for that year. The net present value of the benefits can then be calculated by 

multiplying each of these future benefits by an appropriate discount factor and summing across 

all affected years. This approach assumes that the marginal damages from increased emissions 

are constant for small departures from the baseline emissions path, an approximation that is 

reasonable for policies that have effects on emissions that are small relative to cumulative global 

carbon dioxide emissions. For policies that have a large (non-marginal) impact on global 

cumulative emissions, there is a separate question of whether the SCC is an appropriate tool for 

calculating the benefits of reduced emissions; we do not attempt to answer that question here.

An interagency group convened on a regular basis to consider public comments, explore 

the technical literature in relevant fields, and discuss key inputs and assumptions in order to 

generate SCC estimates. Agencies that actively participated in the interagency process include 

the Environmental Protection Agency, and the Departments of Agriculture, Commerce, Energy, 

Transportation, and Treasury. This process was convened by the Council of Economic Advisers 

and the Office of Management and Budget, with active participation and regular input from the 

Council on Environmental Quality, National Economic Council, Office of Energy and Climate 

Change, and Office of Science and Technology Policy. The main objective of this process was 

to develop a range of SCC values using a defensible set of input assumptions that are grounded 

in the existing literature. In this way, key uncertainties and model differences can more 

transparently and consistently inform the range of SCC estimates used in the rulemaking process. 

The interagency group selected four SCC estimates for use in regulatory analyses. For 

2010, these estimates are $5, $21, $35, and $65 (in 2007 dollars). The first three estimates are 

based on the average SCC across models and socioeconomic and emissions scenarios at the 5, 3, 

and 2.5 percent discount rates, respectively. The fourth value is included to represent the higherthan-expected impacts from temperature change further out in the tails of the SCC distribution. 

For this purpose, we use the SCC value for the 95th percentile at a 3 percent discount rate. The 

central value is the average SCC across models at the 3 percent discount rate. For purposes of 

capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance 

and value of considering the full range. These SCC estimates also grow over time. For instance, 

the central value increases to $24 per ton of CO2 in 2015 and $26 per ton of CO2 in 2020. See 

section 16-A.5 for the full range of annual SCC estimates from 2010 to 2050.

It is important to emphasize that the interagency process is committed to updating these 

estimates as the science and economic understanding of climate change and its impacts on 
10A-4

society improves over time. Specifically, we have set a preliminary goal of revisiting the SCC 

values within two years or at such time as substantially updated models become available, and to 

continue to support research in this area. In the meantime, we will continue to explore the issues 

raised in this document and consider public comments as part of the ongoing interagency 

process.

10A.3 SOCIAL COST OF CARBON VALUES USED IN PAST REGULATORY 

ANALYSES

To date, economic analyses for Federal regulations have used a wide range of values to 

estimate the benefits associated with reducing carbon dioxide emissions. In the final model year 

2011 CAFE rule, the Department of Transportation (DOT) used both a “domestic” SCC value of 

$2 per ton of CO2 and a “global” SCC value of $33 per ton of CO2 for 2007 emission reductions 

(in 2007 dollars), increasing both values at 2.4 percent per year. It also included a sensitivity 

analysis at $80 per ton of CO2. A domestic SCC value is meant to reflect the value of damages 

in the United States resulting from a unit change in carbon dioxide emissions, while a global 

SCC value is meant to reflect the value of damages worldwide. 

A 2008 regulation proposed by DOT assumed a domestic SCC value of $7 per ton CO2

(in 2006 dollars) for 2011 emission reductions (with a range of $0-$14 for sensitivity analysis), 

also increasing at 2.4 percent per year. A regulation finalized by DOE in October of 2008 used a 

domestic SCC range of $0 to $20 per ton CO2 for 2007 emission reductions (in 2007 dollars). In 

addition, EPA’s 2008 Advance Notice of Proposed Rulemaking for Greenhouse Gases identified 

what it described as “very preliminary” SCC estimates subject to revision. EPA’s global mean 

values were $68 and $40 per ton CO2 for discount rates of approximately 2 percent and 3 

percent, respectively (in 2006 dollars for 2007 emissions).

In 2009, an interagency process was initiated to offer a preliminary assessment of how 

best to quantify the benefits from reducing carbon dioxide emissions. To ensure consistency in 

how benefits are evaluated across agencies, the Administration sought to develop a transparent 

and defensible method, specifically designed for the rulemaking process, to quantify avoided 

climate change damages from reduced CO2 emissions. The interagency group did not undertake 

any original analysis. Instead, it combined SCC estimates from the existing literature to use as 

interim values until a more comprehensive analysis could be conducted. 

The outcome of the preliminary assessment by the interagency group was a set of five 

interim values: global SCC estimates for 2007 (in 2006 dollars) of $55, $33, $19, $10, and $5 per 

ton of CO2. The $33 and $5 values represented model-weighted means of the published 

estimates produced from the most recently available versions of three integrated assessment 

models—DICE, PAGE, and FUND—at approximately 3 and 5 percent discount rates. The $55 

and $10 values were derived by adjusting the published estimates for uncertainty in the discount 

rate (using factors developed by Newell and Pizer (2003)) at 3 and 5 percent discount rates, 

respectively. The $19 value was chosen as a central value between the $5 and $33 per ton 

estimates. All of these values were assumed to increase at 3 percent annually to represent growth 

in incremental damages over time as the magnitude of climate change increases.
10A-5

These interim values represent the first sustained interagency effort within the U.S. 

government to develop an SCC for use in regulatory analysis. The results of this preliminary 

effort were presented in several proposed and final rules and were offered for public comment in 

connection with proposed rules, including the joint EPA-DOT fuel economy and CO2 tailpipe 

emission proposed rules.

10A.4 APPROACH AND KEY ASSUMPTIONS

Since the release of the interim values, the interagency group has reconvened on a regular 

basis to generate improved SCC estimates. Specifically, the group has considered public 

comments and further explored the technical literature in relevant fields. This section details the 

several choices and assumptions that underlie the resulting estimates of the SCC. 

It is important to recognize that a number of key uncertainties remain, and that current 

SCC estimates should be treated as provisional and revisable, since they will evolve with 

improved scientific and economic understanding. The interagency group also recognizes that the 

existing models are imperfect and incomplete. The National Academy of Science (2009) points 

out that there is tension between the goal of producing quantified estimates of the economic 

damages from an incremental ton of carbon and the limits of existing efforts to model these 

effects. Throughout this document, we highlight a number of concerns and problems that should 

be addressed by the research community, including research programs housed in many of the 

agencies participating in the interagency process to estimate the SCC. 

The U.S. Government will periodically review and reconsider estimates of the SCC used 

for cost-benefit analyses to reflect increasing knowledge of the science and economics of climate 

impacts, as well as improvements in modeling. In this context, statements recognizing the 

limitations of the analysis and calling for further research take on exceptional significance. The 

interagency group offers the new SCC values with all due humility about the uncertainties 

embedded in them and with a sincere promise to continue work to improve them.

10A.4.1 Integrated Assessment Models 

We rely on three integrated assessment models (IAMs) commonly used to estimate the 

SCC: the FUND, DICE, and PAGE models.c

 These models are frequently cited in the peerreviewed literature and used in the IPCC assessment. Each model is given equal weight in the 

SCC values developed through this process, bearing in mind their different limitations (discussed 

below).

 c The DICE (Dynamic Integrated Climate and Economy) model by William Nordhaus evolved from a series of 

energy models and was first presented in 1990 (Nordhaus and Boyer 2000, Nordhaus 2008). The PAGE (Policy 

Analysis of the Greenhouse Effect) model was developed by Chris Hope in 1991 for use by European decisionmakers in assessing the marginal impact of carbon emissions (Hope 2006, Hope 2008). The FUND (Climate 

Framework for Uncertainty, Negotiation, and Distribution) model, developed by Richard Tol in the early 1990s, 

originally to study international capital transfers in climate policy. is now widely used to study climate impacts (e.g., 

Tol 2002a, Tol 2002b, Anthoff et al. 2009, Tol 2009).
10A-6

 

These models are useful because they combine climate processes, economic growth, and 

feedbacks between the climate and the global economy into a single modeling framework. At 

the same time, they gain this advantage at the expense of a more detailed representation of the 

underlying climatic and economic systems. DICE, PAGE, and FUND all take stylized, reducedform approaches (see NRC 2009 for a more detailed discussion; see Nordhaus 2008 on the 

possible advantages of this approach). Other IAMs may better reflect the complexity of the 

science in their modeling frameworks but do not link physical impacts to economic damages. 

There is currently a limited amount of research linking climate impacts to economic damages, 

which makes this exercise even more difficult. Underlying the three IAMs selected for this 

exercise are a number of simplifying assumptions and judgments reflecting the various modelers’ 

best attempts to synthesize the available scientific and economic research characterizing these 

relationships.

The three IAMs translate emissions into changes in atmospheric greenhouse 

concentrations, atmospheric concentrations into changes in temperature, and changes in 

temperature into economic damages. The emissions projections used in the models are based on 

specified socioeconomic (GDP and population) pathways. These emissions are translated into 

concentrations using the carbon cycle built into each model, and concentrations are translated 

into warming based on each model’s simplified representation of the climate and a key 

parameter, climate sensitivity. Each model uses a different approach to translate warming into 

damages. Finally, transforming the stream of economic damages over time into a single value 

requires judgments about how to discount them.

Each model takes a slightly different approach to model how changes in emissions result 

in changes in economic damages. In PAGE, for example, the consumption-equivalent damages 

in each period are calculated as a fraction of GDP, depending on the temperature in that period 

relative to the pre-industrial average temperature in each region. In FUND, damages in each 

period also depend on the rate of temperature change from the prior period. In DICE, 

temperature affects both consumption and investment. We describe each model in greater detail 

here. In a later section, we discuss key gaps in how the models account for various scientific and 

economic processes (e.g. the probability of catastrophe, and the ability to adapt to climate change 

and the physical changes it causes).

The parameters and assumptions embedded in the three models vary widely. A key 

objective of the interagency process was to enable a consistent exploration of the three models 

while respecting the different approaches to quantifying damages taken by the key modelers in 

the field. An extensive review of the literature was conducted to select three sets of input 

parameters for these models: climate sensitivity, socioeconomic and emissions trajectories, and 

discount rates. A probability distribution for climate sensitivity was specified as an input into all 

three models. In addition, the interagency group used a range of scenarios for the socioeconomic

parameters and a range of values for the discount rate. All other model features were left 

unchanged, relying on the model developers’ best estimates and judgments. In DICE, these 

parameters are handled deterministically and represented by fixed constants; in PAGE, most 

parameters are represented by probability distributions. FUND was also run in a mode in which 

parameters were treated probabilistically.
10A-7

The sensitivity of the results to other aspects of the models (e.g. the carbon cycle or 

damage function) is also important to explore in the context of future revisions to the SCC but 

has not been incorporated into these estimates. Areas for future research are highlighted at the 

end of this document.

The DICE Model

The DICE model is an optimal growth model based on a global production function with 

an extra stock variable (atmospheric carbon dioxide concentrations). Emission reductions are 

treated as analogous to investment in “natural capital.” By investing in natural capital today 

through reductions in emissions—implying reduced consumption—harmful effects of climate 

change can be avoided and future consumption thereby increased. 

For purposes of estimating the SCC, carbon dioxide emissions are a function of global 

GDP and the carbon intensity of economic output, with the latter declining over time due to 

technological progress. The DICE damage function links global average temperature to the 

overall impact on the world economy. It varies quadratically with temperature change to capture 

the more rapid increase in damages expected to occur under more extreme climate change, and is 

calibrated to include the effects of warming on the production of market and nonmarket goods 

and services. It incorporates impacts on agriculture, coastal areas (due to sea level rise), “other 

vulnerable market sectors” (based primarily on changes in energy use), human health (based on 

climate-related diseases, such as malaria and dengue fever, and pollution), non-market amenities 

(based on outdoor recreation), and human settlements and ecosystems. The DICE damage 

function also includes the expected value of damages associated with low probability, high 

impact “catastrophic” climate change. This last component is calibrated based on a survey of 

experts (Nordhaus 1994). The expected value of these impacts is then added to the other market 

and non-market impacts mentioned above.

No structural components of the DICE model represent adaptation explicitly, though it is 

included implicitly through the choice of studies used to calibrate the aggregate damage function. 

For example, its agricultural impact estimates assume that farmers can adjust land use decisions 

in response to changing climate conditions, and its health impact estimates assume 

improvements in healthcare over time. In addition, the small impacts on forestry, water systems, 

construction, fisheries, and outdoor recreation imply optimistic and costless adaptation in these 

sectors (Nordhaus and Boyer, 2000; Warren et al., 2006). Costs of resettlement due to sea level 

rise are incorporated into damage estimates, but their magnitude is not clearly reported. 

Mastrandrea’s (2009) review concludes that “in general, DICE assumes very effective 

adaptation, and largely ignores adaptation costs."

Note that the damage function in DICE has a somewhat different meaning from the 

damage functions in FUND and PAGE. Because GDP is endogenous in DICE and because 

damages in a given year reduce investment in that year, damages propagate forward in time and 
10A-8

reduce GDP in future years. In contrast, GDP is exogenous in FUND and PAGE, so damages in 

any given year do not propagate forward.d

The PAGE Model

PAGE2002 (version 1.4epm) treats GDP growth as exogenous. It divides impacts into 

economic, non-economic, and catastrophic categories and calculates these impacts separately for 

eight geographic regions. Damages in each region are expressed as a fraction of output, where 

the fraction lost depends on the temperature change in each region. Damages are expressed as 

power functions of temperature change. The exponents of the damage function are the same in 

all regions but are treated as uncertain, with values ranging from 1 to 3 (instead of being fixed at 

2 as in DICE).

PAGE2002 includes the consequences of catastrophic events in a separate damage subfunction. Unlike DICE, PAGE2002 models these events probabilistically. The probability of a 

“discontinuity” (i.e., a catastrophic event) is assumed to increase with temperature above a 

specified threshold. The threshold temperature, the rate at which the probability of experiencing 

a discontinuity increases above the threshold and the magnitude of the resulting catastrophe are 

all modeled probabilistically.

Adaptation is explicitly included in PAGE. Impacts are assumed to occur for temperature 

increases above some tolerable level (2°C for developed countries and 0°C for developing 

countries for economic impacts, and 0°C for all regions for non-economic impacts), but 

adaptation is assumed to reduce these impacts. Default values in PAGE2002 assume that the 

developed countries can ultimately eliminate up to 90 percent of all economic impacts beyond 

the tolerable 2°C increase and that developing countries can eventually eliminate 50 percent of 

their economic impacts. All regions are assumed to be able to mitigate 25 percent of the noneconomic impacts through adaptation (Hope 2006). 

The FUND Model

Like PAGE, the FUND model treats GDP growth as exogenous. It includes separately 

calibrated damage functions for eight market and nonmarket sectors: agriculture, forestry, water, 

energy (based on heating and cooling demand), sea level rise (based on the value of land lost and 

the cost of protection), ecosystems, human health (diarrhea, vector-borne diseases, and 

cardiovascular and respiratory mortality), and extreme weather. Each impact sector has a 

different functional form, and is calculated separately for sixteen geographic regions. In some 

impact sectors, the fraction of output lost or gained due to climate change depends not only on 

 d Using the default assumptions in DICE 2007, this effect generates an approximately 25 percent increase in the 

SCC relative to damages calculated by fixing GDP. In DICE2007, the time path of GDP is 

endogenous. Specifically, the path of GDP depends on the rate of saving and level of abatement in each period 

chosen by the optimizing representative agent in the model. We made two modifications to DICE to make it 

consistent with EMF GDP trajectories (see next section): we assumed a fixed rate of savings of 20%, and we recalibrated the exogenous path of total factor productivity so that DICE would produce GDP projections in the 

absence of warming that exactly matched the EMF scenarios.
10A-9

the absolute temperature change but also on the rate of temperature change and level of regional 

income.e In the forestry and agricultural sectors, economic damages also depend on CO2

concentrations.

Tol (2009) discusses impacts not included in FUND, noting that many are likely to have a 

relatively small effect on damage estimates (both positive and negative). However, he 

characterizes several omitted impacts as “big unknowns”: for instance, extreme climate 

scenarios, biodiversity loss, and effects on economic development and political violence. With 

regard to potentially catastrophic events, he notes, “Exactly what would cause these sorts of 

changes or what effects they would have are not well-understood, although the chance of any one 

of them happening seems low. But they do have the potential to happen relatively quickly, and if 

they did, the costs could be substantial. Only a few studies of climate change have examined 

these issues.”

Adaptation is included both implicitly and explicitly in FUND. Explicit adaptation is 

seen in the agriculture and sea level rise sectors. Implicit adaptation is included in sectors such 

as energy and human health, where wealthier populations are assumed to be less vulnerable to 

climate impacts. For example, the damages to agriculture are the sum of three effects: (1) those 

due to the rate of temperature change (damages are always positive); (2) those due to the level of 

temperature change (damages can be positive or negative depending on region and temperature); 

and (3) those from CO2 fertilization (damages are generally negative but diminishing to zero). 

Adaptation is incorporated into FUND by allowing damages to be smaller if climate 

change happens more slowly. The combined effect of CO2 fertilization in the agricultural sector, 

positive impacts to some regions from higher temperatures, and sufficiently slow increases in 

temperature across these sectors can result in negative economic damages from climate change.

Damage Functions

To generate revised SCC values, we rely on the IAM modelers’ current best judgments of 

how to represent the effects of climate change (represented by the increase in global-average 

surface temperature) on the consumption-equivalent value of both market and non-market goods 

(represented as a fraction of global GDP). We recognize that these representations are 

incomplete and highly uncertain. But given the paucity of data linking the physical impacts to 

economic damages, we were not able to identify a better way to translate changes in climate into 

net economic damages, short of launching our own research program. 

The damage functions for the three IAMs are presented in Figure 10A.4.1 and Figure 

10A.4.2, using the modeler’s default scenarios and mean input assumptions. There are 

significant differences between the three models both at lower (Figure 10A.4.2) and higher 

(Figure 10A.4.1) increases in global-average temperature. 

 

e In the deterministic version of FUND, the majority of damages are attributable to increased air conditioning 

demand, while reduced cold stress in Europe, North America, and Central and East Asia results in health benefits in 

those regions at low to moderate levels of warming (Warren et al., 2006).
10A-10

Figure 10A.4.1 Annual Consumption Loss as a Fraction of Global GDP in 

2100 Due to an Increase in Annual Global Temperature in the 

DICE, FUND, and PAGE modelsf

The lack of agreement among the models at lower temperature increases is underscored 

by the fact that the damages from FUND are well below the 5th percentile estimated by PAGE, 

while the damages estimated by DICE are roughly equal to the 95th percentile estimated by 

PAGE. This is significant because at higher discount rates we expect that a greater proportion of 

the SCC value is due to damages in years with lower temperature increases. For example, when 

the discount rate is 2.5 percent, about 45 percent of the 2010 SCC value in DICE is due to 

damages that occur in years when the temperature is less than or equal to 3 °C. This increases to 

approximately 55 percent and 80 percent at discount rates of 3 and 5 percent, respectively.

These differences underscore the need for a thorough review of damage functions—in 

particular, how the models incorporate adaptation, technological change, and catastrophic 

damages. Gaps in the literature make modifying these aspects of the models challenging, which 

highlights the need for additional research. As knowledge improves, the Federal government is 

 f The x-axis represents increases in annual, rather than equilibrium, temperature, while the y-axis represents the 

annual stream of benefits as a share of global GDP. Each specific combination of climate sensitivity, 

socioeconomic, and emissions parameters will produce a different realization of damages for each IAM. The 

damage functions represented in Figures 1A and 1B are the outcome of default assumptions. For instance, under 

alternate assumptions, the damages from FUND may cross from negative to positive at less than or greater than 3 

°C.
10A-11

committed to exploring how these (and other) models can be modified to incorporate more 

accurate estimates of damages.

Figure 10A.4.2 Annual Consumption Loss for Lower Temperature Changes in 

DICE, FUND, and PAGE

10A.4.2 Global versus Domestic Measures of SCC

Because of the distinctive nature of the climate change problem, we center our current 

attention on a global measure of SCC. This approach is the same as that taken for the interim 

values, but it otherwise represents a departure from past practices, which tended to put greater 

emphasis on a domestic measure of SCC (limited to impacts of climate change experienced 

within U.S. borders). As a matter of law, consideration of both global and domestic values is 

generally permissible; the relevant statutory provisions are usually ambiguous and allow 

selection of either measure.g

Global SCC

Under current OMB guidance contained in Circular A-4, analysis of economically 

significant proposed and final regulations from the domestic perspective is required, while 

analysis from the international perspective is optional. However, the climate change problem is 

highly unusual in at least two respects. First, it involves a global externality: emissions of most 

greenhouse gases contribute to damages around the world even when they are emitted in the 

 g It is true that federal statutes are presumed not to have extraterritorial effect, in part to ensure that the laws of the 

United States respect the interests of foreign sovereigns. But use of a global measure for the SCC does not give 

extraterritorial effect to federal law and hence does not intrude on such interests.
10A-12

United States. Consequently, to address the global nature of the problem, the SCC must 

incorporate the full (global) damages caused by GHG emissions. Second, climate change 

presents a problem that the United States alone cannot solve. Even if the United States were to 

reduce its greenhouse gas emissions to zero, that step would be far from enough to avoid 

substantial climate change. Other countries would also need to take action to reduce emissions if 

significant changes in the global climate are to be avoided. Emphasizing the need for a global 

solution to a global problem, the United States has been actively involved in seeking 

international agreements to reduce emissions and in encouraging other nations, including 

emerging major economies, to take significant steps to reduce emissions. When these 

considerations are taken as a whole, the interagency group concluded that a global measure of 

the benefits from reducing U.S. emissions is preferable. 

When quantifying the damages associated with a change in emissions, a number of 

analysts (e.g., Anthoff, et al. 2009a) employ “equity weighting” to aggregate changes in 

consumption across regions. This weighting takes into account the relative reductions in wealth 

in different regions of the world. A per-capita loss of $500 in GDP, for instance, is weighted 

more heavily in a country with a per-capita GDP of $2,000 than in one with a per-capita GDP of 

$40,000. The main argument for this approach is that a loss of $500 in a poor country causes a 

greater reduction in utility or welfare than does the same loss in a wealthy nation. 

Notwithstanding the theoretical claims on behalf of equity weighting, the interagency group 

concluded that this approach would not be appropriate for estimating a SCC value used in 

domestic regulatory analysis.h

 For this reason, the group concluded that using the global (rather 

than domestic) value, without equity weighting, is the appropriate approach.

Domestic SCC

As an empirical matter, the development of a domestic SCC is greatly complicated by the 

relatively few region- or country-specific estimates of the SCC in the literature. One potential 

source of estimates comes from the FUND model. The resulting estimates suggest that the ratio 

of domestic to global benefits of emission reductions varies with key parameter 

assumptions. For example, with a 2.5 or 3 percent discount rate, the U.S. benefit is about 7-10 

percent of the global benefit, on average, across the scenarios analyzed. Alternatively, if the 

fraction of GDP lost due to climate change is assumed to be similar across countries, the 

domestic benefit would be proportional to the U.S. share of global GDP, which is currently about 

23 percent.i

On the basis of this evidence, the interagency workgroup determined that a range of 

values from 7 to 23 percent should be used to adjust the global SCC to calculate domestic 

 h It is plausible that a loss of $X inflicts more serious harm on a poor nation than on a wealthy one, but development 

of the appropriate "equity weight" is challenging. Emissions reductions also impose costs, and hence a full account 

would have to consider that a given cost of emissions reductions imposes a greater utility or welfare loss on a poor 

nation than on a wealthy one. Even if equity weighting—for both the costs and benefits of emissions reductions—is 

appropriate when considering the utility or welfare effects of international action, the interagency group concluded

that it should not be used in developing an SCC for use in regulatory policy at this time. 

i

Based on 2008 GDP (in current US dollars) from the World Bank Development Indicators Report.
10A-13

effects. Reported domestic values should use this range. It is recognized that these values are 

approximate, provisional, and highly speculative. There is no a priori reason why domestic 

benefits should be a constant fraction of net global damages over time. Further, FUND does not 

account for how damages in other regions could affect the United States (e.g., global migration, 

economic and political destabilization). If more accurate methods for calculating the domestic 

SCC become available, the Federal government will examine these to determine whether to 

update its approach.

10A.4.3 Valuing Non-CO2 Emissions

While CO2 is the most prevalent greenhouse gas emitted into the atmosphere, the U.S. 

included five other greenhouse gases in its recent endangerment finding: methane, nitrous oxide, 

hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride. The climate impact of these 

gases is commonly discussed in terms of their 100-year global warming potential (GWP). GWP 

measures the ability of different gases to trap heat in the atmosphere (i.e., radiative forcing per 

unit of mass) over a particular timeframe relative to CO2. However, because these gases differ in 

both radiative forcing and atmospheric lifetimes, their relative damages are not constant over 

time. For example, because methane has a short lifetime, its impacts occur primarily in the near 

term and thus are not discounted as heavily as those caused by longer-lived gases. Impacts other 

than temperature change also vary across gases in ways that are not captured by GWP. For 

instance, CO2 emissions, unlike methane and other greenhouse gases, contribute to ocean 

acidification. Likewise, damages from methane emissions are not offset by the positive effect of 

CO2 fertilization. Thus, transforming gases into CO2-equivalents using GWP, and then 

multiplying the carbon-equivalents by the SCC, would not result in accurate estimates of the 

social costs of non-CO2 gases. 

In light of these limitations, and the significant contributions of non-CO2 emissions to 

climate change, further research is required to link non-CO2 emissions to economic impacts. 

Such work would feed into efforts to develop a monetized value of reductions in non-CO2

greenhouse gas emissions. As part of ongoing work to further improve the SCC estimates, the 

interagency group hopes to develop methods to value these other greenhouse gases. The goal is 

to develop these estimates by the time we issue revised SCC estimates for carbon dioxide 

emissions. 

10A.4.4 Equilibrium Climate Sensitivity

Equilibrium climate sensitivity (ECS) is a key input parameter for the DICE, PAGE, and 

FUND models.j

 It is defined as the long-term increase in the annual global-average surface 

temperature from a doubling of atmospheric CO2 concentration relative to pre-industrial levels 

(or stabilization at a concentration of approximately 550 parts per million (ppm)). Uncertainties 

in this important parameter have received substantial attention in the peer-reviewed literature.

 j

The equilibrium climate sensitivity includes the response of the climate system to increased greenhouse gas 

concentrations over the short to medium term (up to 100-200 years), but it does not include long-term feedback 

effects due to possible large-scale changes in ice sheets or the biosphere, which occur on a time scale of many 

hundreds to thousands of years (e.g. Hansen et al. 2007).
10A-14

The most authoritative statement about equilibrium climate sensitivity appears in the 

Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC):

Basing our assessment on a combination of several independent lines of evidence...including 

observed climate change and the strength of known feedbacks simulated in [global climate 

models], we conclude that the global mean equilibrium warming for doubling CO2, or 

‘equilibrium climate sensitivity’, is likely to lie in the range 2 °C to 4.5 °C, with a most likely 

value of about 3 °C. Equilibrium climate sensitivity is very likely larger than 1.5 °C. k

 

For fundamental physical reasons as well as data limitations, values substantially higher 

than 4.5 °C still cannot be excluded, but agreement with observations and proxy data is 

generally worse for those high values than for values in the 2 °C to 4.5 °C range. (Meehl et 

al., 2007, p 799)

After consulting with several lead authors of this chapter of the IPCC report, the 

interagency workgroup selected four candidate probability distributions and calibrated them to 

be consistent with the above statement: Roe and Baker (2007), log-normal, gamma, and Weibull. 

Table 10A.4.1 included below gives summary statistics for the four calibrated distributions.

Table 10A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity Distributions

Roe & Baker Log-normal Gamma Weibull

Pr(ECS < 1.5°C) 0.013 0.050 0.070 0.102

Pr(2°C < ECS < 4.5°C) 0.667 0.667 0.667 0.667

5th percentile 1.72 1.49 1.37 1.13

10th percentile 1.91 1.74 1.65 1.48

Mode 2.34 2.52 2.65 2.90

Median (50th percentile) 3.00 3.00 3.00 3.00

Mean 3.50 3.28 3.19 3.07

90th percentile 5.86 5.14 4.93 4.69

95th percentile 7.14 5.97 5.59 5.17

 

k This is in accord with the judgment that it “is likely to lie in the range 2 °C to 4.5 °C” and the IPCC definition of 

“likely” as greater than 66 percent probability (Le Treut et al.2007). “Very likely” indicates a greater than 90 percent 

probability.
10A-15

Each distribution was calibrated by applying three constraints from the IPCC:

(1) a median equal to 3°C, to reflect the judgment of “a most likely value of about 3 °C”;l

(2) two-thirds probability that the equilibrium climate sensitivity lies between 2 and 4.5 °C; 

and

(3) zero probability that it is less than 0°C or greater than 10°C (see Hegerl et al. 2006, p. 

721).

We selected the calibrated Roe and Baker distribution from the four candidates for two 

reasons. First, the Roe and Baker distribution is the only one of the four that is based on a 

theoretical understanding of the response of the climate system to increased greenhouse gas 

concentrations (Roe and Baker 2007, Roe 2008). In contrast, the other three distributions are 

mathematical functions that are arbitrarily chosen based on simplicity, convenience, and general 

shape. The Roe and Baker distribution results from three assumptions about climate response: 

(1) absent feedback effects, the equilibrium climate sensitivity is equal to 1.2 °C; (2) feedback 

factors are proportional to the change in surface temperature; and (3) uncertainties in feedback 

factors are normally distributed. There is widespread agreement on the first point and the second 

and third points are common assumptions. 

Second, the calibrated Roe and Baker distribution better reflects the IPCC judgment that 

“values substantially higher than 4.5°C still cannot be excluded.” Although the IPCC made no 

quantitative judgment, the 95th percentile of the calibrated Roe & Baker distribution (7.1 °C) is 

much closer to the mean and the median (7.2 °C) of the 95th percentiles of 21 previous studies 

summarized by Newbold and Daigneault (2009). It is also closer to the mean (7.5 °C) and 

median (7.9 °C) of the nine truncated distributions examined by the IPCC (Hegerl, et al., 2006) 

than are the 95th percentiles of the three other calibrated distributions (5.2-6.0 °C).

Finally, we note the IPCC judgment that the equilibrium climate sensitivity “is very 

likely larger than 1.5°C.” Although the calibrated Roe & Baker distribution, for which the 

probability of equilibrium climate sensitivity being greater than 1.5 °C is almost 99 percent, is 

not inconsistent with the IPCC definition of “very likely” as “greater than 90 percent 

probability,” it reflects a greater degree of certainty about very low values of ECS than was 

expressed by the IPCC. 

 l

Strictly speaking, “most likely” refers to the mode of a distribution rather than the median, but common usage 

would allow the mode, median, or mean to serve as candidates for the central or “most likely” value and the IPCC 

report is not specific on this point. For the distributions we considered, the median was between the mode and the 

mean. For the Roe and Baker distribution, setting the median equal to 3°C, rather than the mode or mean, gave a 95th

percentile that is more consistent with IPCC judgments and the literature. For example, setting the mean and mode 

equal to 3°C produced 95th percentiles of 5.6 and 8.6 °C, respectively, which are in the lower and upper end of the 

range in the literature. Finally, the median is closer to 3°C than is the mode for the truncated distributions selected 

by the IPCC (Hegerl, et al., 2006); the average median is 3.1 °C and the average mode is 2.3 °C, which is most 

consistent with a Roe and Baker distribution with the median set equal to 3 °C.
10A-16

Figure 10A.4.3 Estimates of the Probability Density Function for Equilibrium 

Climate Sensitivity (°C)

To show how the calibrated Roe and Baker distribution compares to different estimates 

of the probability distribution function of equilibrium climate sensitivity in the empirical 

literature, Figure 10A.4.3 (above) overlays it on Figure 9.20 from the IPCC Fourth Assessment 

Report. These functions are scaled to integrate to unity between 0 °C and 10 °C. The horizontal 

bars show the respective 5 percent to 95 percent ranges; dots indicate the median estimate.m

10A.4.5 Socioeconomic and Emissions Trajectories

Another key issue considered by the interagency group is how to select the set of 

socioeconomic and emissions parameters for use in PAGE, DICE, and FUND. Socioeconomic

pathways are closely tied to climate damages because, all else equal, more and wealthier people 

tend to emit more greenhouse gases and also have a higher (absolute) willingness to pay to avoid 

climate disruptions. For this reason, we consider how to model several input parameters in 

tandem: GDP, population, CO2 emissions, and non-CO2 radiative forcing. A wide variety of 

scenarios have been developed and used for climate change policy simulations (e.g., SRES 2000, 

CCSP 2007, EMF 2009). In determining which scenarios are appropriate for inclusion, we 

aimed to select scenarios that span most of the plausible ranges of outcomes for these variables. 

 m The estimates based on instrumental data are from Andronova and Schlesinger (2001), Forest et al. (2002; dashed 

line, anthropogenic forcings only), Forest et al. (2006; solid line, anthropogenic and natural forcings), Gregory et al. 

(2002a), Knutti et al. (2002), Frame et al. (2005), and Forster and Gregory (2006). Hegerl et al. (2006) are based on 

multiple palaeoclimatic reconstructions of north hemisphere mean temperatures over the last 700 years. Also shown 

are the 5-95 percent approximate ranges for two estimates from the last glacial maximum (dashed, Annan et al. 

2005; solid, Schneider von Deimling et al. 2006), which are based on models with different structural properties.
10A-17

To accomplish this task in a transparent way, we decided to rely on the recent Stanford 

Energy Modeling Forum exercise, EMF-22. EMF-22 uses ten well-recognized models to 

evaluate substantial, coordinated global action to meet specific stabilization targets. A key 

advantage of relying on these data is that GDP, population, and emission trajectories are 

internally consistent for each model and scenario evaluated. The EMF-22 modeling effort also is 

preferable to the IPCC SRES due to their age (SRES were developed in 1997) and the fact that 3

of 4 of the SRES scenarios are now extreme outliers in one or more variables. Although the 

EMF-22 scenarios have not undergone the same level of scrutiny as the SRES scenarios, they are 

recent, peer-reviewed, published, and publicly available.

To estimate the SCC for use in evaluating domestic policies that will have a small effect 

on global cumulative emissions, we use socioeconomic and emission trajectories that span a 

range of plausible scenarios. Five trajectories were selected from EMF-22 (see Table 10A.4.2

below). Four of these represent potential business-as-usual (BAU) growth in population, 

wealth, and emissions and are associated with CO2 (only) concentrations ranging from 612 to 

889 ppm in 2100. One represents an emissions pathway that achieves stabilization at 550 ppm 

CO2e (ii.e., CO2-only concentrations of 425 – 484 ppm or a radiative forcing of 3.7 W/m2) in 

2100, a lower-than-BAU trajectory.n Out of the 10 models included in the EMF-22 exercise, we 

selected the trajectories used by MiniCAM, MESSAGE, IMAGE, and the optimistic scenario 

from MERGE. For the BAU pathways, we used the GDP, population, and emission trajectories 

from each of these four models. For the 550 ppm CO2e scenario, we averaged the GDP, 

population, and emission trajectories implied by these same four models. 

 

n Such an emissions path would be consistent with widespread action by countries to mitigate GHG emissions, 

though it could also result from technological advances. It was chosen because it represents the most stringent case 

analyzed by the EMF-22 where all the models converge: a 550 ppm, not to exceed, full participation scenario.
10A-18

Table 10A.4.2 Socioeconomic and Emissions Projections from Select EMF-22 Reference 

Scenarios

Reference Fossil and Industrial CO2 Emissions (GtCO2/yr)

EMF – 22 Based Scenarios 2000 2010 2020 2030 2050 2100

IMAGE 26.6 31.9 36.9 40.0 45.3 60.1

MERGE Optimistic 24.6 31.5 37.6 45.1 66.5 117.9

MESSAGE 26.8 29.2 37.6 42.1 43.5 42.7

MiniCAM 26.5 31.8 38.0 45.1 57.8 80.5

550 ppm average 26.2 31.1 33.2 32.4 20.0 12.8

Reference GDP (using market exchange rates in trillion 2005$)o

EMF – 22 Based Scenarios 2000 2010 2020 2030 2050 2100

IMAGE 38.6 53.0 73.5 97.2 156.3 396.6

MERGE Optimistic 36.3 45.9 59.7 76.8 122.7 268.0

MESSAGE 38.1 52.3 69.4 91.4 153.7 334.9

MiniCAM 36.1 47.4 60.8 78.9 125.7 369.5

550 ppm average 37.1 49.6 65.6 85.5 137.4 337.9

Global Population (billions)

EMF – 22 Based Scenarios 2000 2010 2020 2030 2050 2100

IMAGE 6.1 6.9 7.6 8.2 9.0 9.1

MERGE Optimistic 6.0 6.8 7.5 8.2 9.0 9.7

MESSAGE 6.1 6.9 7.7 8.4 9.4 10.4

MiniCAM 6.0 6.8 7.5 8.1 8.8 8.7

550 ppm average 6.1 6.8 7.6 8.2 8.7 9.1

We explore how sensitive the SCC is to various assumptions about how the future will 

evolve without prejudging what is likely to occur. The interagency group considered formally 

assigning probability weights to different states of the world, but this proved challenging to do in 

an analytically rigorous way given the dearth of information on the likelihood of a full range of 

future socioeconomic pathways. 

 o While the EMF-22 models used market exchange rates (MER) to calculate global GDP, it is also possible to use 

purchasing power parity (PPP). PPP takes into account the different price levels across countries, so it more 

accurately describes relative standards of living across countries. MERs tend to make low-income countries appear 

poorer than they actually are. Because many models assume convergence in per capita income over time, use of 

MER-adjusted GDP gives rise to projections of higher economic growth in low income countries. There is an 

ongoing debate about how much this will affect estimated climate impacts. Critics of the use of MER argue that it 

leads to overstated economic growth and hence a significant upward bias in projections of greenhouse gas 

emissions, and unrealistically high future temperatures (e.g., Castles and Henderson 2003). Others argue that 

convergence of the emissions-intensity gap across countries at least partially offset the overstated income gap so that 

differences in exchange rates have less of an effect on emissions (Holtsmark and Alfsen, 2005; Tol, 2006). 

Nordhaus (2007b) argues that the ideal approach is to use superlative PPP accounts (i.e., using cross-sectional PPP 

measures for relative incomes and outputs and national accounts price and quantity indexes for time-series 

extrapolations). However, he notes that it important to keep this debate in perspective; it is by no means clear that 

exchange-rate-conversion issues are as important as uncertainties about population, technological change, or the 

many geophysical uncertainties.
10A-19

There are a number of caveats. First, EMF BAU scenarios represent the modelers’ 

judgment of the most likely pathway absent mitigation policies to reduce greenhouse gas 

emissions, rather than the wider range of possible outcomes. Nevertheless, these views of the 

most likely outcome span a wide range, from the more optimistic (e.g., abundant low-cost, lowcarbon energy) to more pessimistic (e.g., constraints on the availability of nuclear and 

renewables).p Second, the socioeconomic trajectories associated with a 550 ppm CO2e 

concentration scenario are not derived from an assessment of what policy is optimal from a 

benefit-cost standpoint. Rather, it is indicative of one possible future outcome. The emission 

trajectories underlying some BAU scenarios (e.g., MESSAGE’s 612 ppm) also are consistent 

with some modest policy action to address climate change.q We chose not to include 

socioeconomic trajectories that achieve even lower GHG concentrations at this time, given the 

difficulty many models had in converging to meet these targets.

For comparison purposes, the Energy Information Agency in its 2009 Annual Energy 

Outlook projected that global carbon dioxide emissions will grow to 30.8, 35.6, and 40.4 

gigatons in 2010, 2020, and 2030, respectively, while world GDP is projected to be $51.8, $71.0 

and $93.9 trillion (in 2005 dollars using market exchange rates) in 2010, 2020, and 2030, 

respectively. These projections are consistent with one or more EMF-22 scenarios. Likewise, 

the United Nations’ 2008 Population Prospect projects population will grow from 6.1 billion 

people in 2000 to 9.1 billion people in 2050, which is close to the population trajectories for the 

IMAGE, MiniCAM, and MERGE models.

In addition to fossil and industrial CO2 emissions, each EMF scenario provides 

projections of methane, nitrous oxide, fluorinated greenhouse gases, and net land use CO2

emissions out to 2100. These assumptions also are used in the three models while retaining the 

default radiative forcings due to other factors (e.g., aerosols and other gases). See the Annex for 

greater detail.

10A.4.6 Discount Rate

The choice of a discount rate, especially over long periods of time, raises highly 

contested and exceedingly difficult questions of science, economics, philosophy, and law. 

Although it is well understood that the discount rate has a large influence on the current value of 

future damages, there is no consensus about what rates to use in this context. Because carbon 

dioxide emissions are long-lived, subsequent damages occur over many years. In calculating the 

SCC, we first estimate the future damages to agriculture, human health, and other market and 

non-market sectors from an additional unit of carbon dioxide emitted in a particular year in terms 

of reduced consumption (or consumption equivalents) due to the impacts of elevated 

temperatures, as represented in each of the three IAMs. Then we discount the stream of future 

damages to its present value in the year when the additional unit of emissions was released using 

 p For instance, in the MESSAGE model’s reference case total primary energy production from nuclear, biomass, and 

non-biomass renewables is projected to increase from about 15 percent of total primary energy in 2000 to 54 percent 

in 2100. In comparison, the MiniCAM reference case shows 10 percent in 2000 and 21 percent in 2100. 

q For example, MiniCAM projects if all non-US OECD countries reduce CO2 emissions to 83 percent below 2005 

levels by 2050 (per the G-8 agreement) but all other countries continue along a BAU path CO2 concentrations in 

2100 would drop from 794 ppmv in its reference case to 762 ppmv.
10A-20

the selected discount rate, which is intended to reflect society's marginal rate of substitution 

between consumption in different time periods. 

For rules with both intra- and intergenerational effects, agencies traditionally employ 

constant discount rates of both 3 percent and 7 percent in accordance with OMB Circular A-4. 

As Circular A-4 acknowledges, however, the choice of discount rate for intergenerational 

problems raises distinctive problems and presents considerable challenges. After reviewing 

those challenges, Circular A-4 states, “If your rule will have important intergenerational benefits 

or costs you might consider a further sensitivity analysis using a lower but positive discount rate 

in addition to calculating net benefits using discount rates of 3 and 7 percent.” For the specific 

purpose of developing the SCC, we adapt and revise that approach here.

Arrow et al. (1996) outlined two main approaches to determine the discount rate for 

climate change analysis, which they labeled “descriptive” and “prescriptive.” The descriptive 

approach reflects a positive (non-normative) perspective based on observations of people’s 

actual choices—e.g., savings versus consumption decisions over time, and allocations of savings 

among more and less risky investments. Advocates of this approach generally call for inferring 

the discount rate from market rates of return “because of a lack of justification for choosing a 

social welfare function that is any different than what decision makers [individuals] actually use” 

(Arrow et al. 1996). 

One theoretical foundation for the cost-benefit analyses in which the social cost of carbon 

will be used—the Kaldor-Hicks potential-compensation test—also suggests that market rates 

should be used to discount future benefits and costs, because it is the market interest rate that 

would govern the returns potentially set aside today to compensate future individuals for climate 

damages that they bear (e.g., Just et al. 2004). As some have noted, the word “potentially” is an 

important qualification; there is no assurance that such returns will actually be set aside to 

provide compensation, and the very idea of compensation is difficult to define in the 

intergenerational context. On the other hand, societies provide compensation to future 

generations through investments in human capital and the resulting increase in knowledge, as 

well as infrastructure and other physical capital.

The prescriptive approach specifies a social welfare function that formalizes the 

normative judgments that the decision-maker wants explicitly to incorporate into the policy 

evaluation—e.g., how inter-personal comparisons of utility should be made, and how the welfare 

of future generations should be weighed against that of the present generation. Ramsey (1928), 

for example, has argued that it is “ethically indefensible” to apply a positive pure rate of time 

preference to discount values across generations, and many agree with this view. 

Other concerns also motivate making adjustments to descriptive discount rates. In 

particular, it has been noted that the preferences of future generations with regard to 

consumption versus environmental amenities may not be the same as those today, making the 

current market rate on consumption an inappropriate metric by which to discount future climaterelated damages. Others argue that the discount rate should be below market rates to correct for 

market distortions and uncertainties or inefficiencies in intergenerational transfers of wealth, 
10A-21

which in the Kaldor-Hicks logic are presumed to compensate future generations for damage (a 

potentially controversial assumption, as noted above) (Arrow et al. 1996, Weitzman 1999).

Further, a legitimate concern about both descriptive and prescriptive approaches is that 

they tend to obscure important heterogeneity in the population. The utility function that 

underlies the prescriptive approach assumes a representative agent with perfect foresight and no 

credit constraints. This is an artificial rendering of the real world that misses many of the 

frictions that characterize individuals’ lives and indeed the available descriptive evidence 

supports this. For instance, many individuals smooth consumption by borrowing with credit 

cards that have relatively high rates. Some are unable to access traditional credit markets and 

rely on payday lending operations or other high-cost forms of smoothing consumption. Whether 

one puts greater weight on the prescriptive or descriptive approach, the high interest rates that 

credit-constrained individuals accept suggest that some account should be given to the discount 

rates revealed by their behavior. 

We draw on both approaches but rely primarily on the descriptive approach to inform the 

choice of discount rate. With recognition of its limitations, we find this approach to be the most 

defensible and transparent given its consistency with the standard contemporary theoretical 

foundations of benefit-cost analysis and with the approach required by OMB’s existing guidance. 

The logic of this framework also suggests that market rates should be used for discounting future 

consumption-equivalent damages. Regardless of the theoretical approach used to derive the 

appropriate discount rate(s), we note the inherent conceptual and practical difficulties of 

adequately capturing consumption trade-offs over many decades or even centuries. While 

relying primarily on the descriptive approach in selecting specific discount rates, the interagency 

group has been keenly aware of the deeply normative dimensions of both the debate over 

discounting in the intergenerational context and the consequences of selecting one discount rate 

over another. 

Historically Observed Interest Rates

In a market with no distortions, the return to savings would equal the private return on 

investment, and the market rate of interest would be the appropriate choice for the social 

discount rate. In the real world risk, taxes, and other market imperfections drive a wedge 

between the risk-free rate of return on capital and the consumption rate of interest. Thus, the 

literature recognizes two conceptual discount concepts—the consumption rate of interest and the 

opportunity cost of capital. 

According to OMB’s Circular A-4, it is appropriate to use the rate of return on capital 

when a regulation is expected to displace or alter the use of capital in the private sector. In this 

case, OMB recommends Agencies use a discount rate of 7 percent. When regulation is expected 

to primarily affect private consumption—for instance, via higher prices for goods and services—

a lower discount rate of 3 percent is appropriate to reflect how private individuals trade-off 

current and future consumption. 

The interagency group examined the economics literature and concluded that the 

consumption rate of interest is the correct concept to use in evaluating the benefits and costs of a 
10A-22

marginal change in carbon emissions (see Lind 1990, Arrow et al 1996, and Arrow 2000). The 

consumption rate of interest also is appropriate when the impacts of a regulation are measured in 

consumption (-equivalent) units, as is done in the three integrated assessment models used for 

estimating the SCC. 

Individuals use a variety of savings instruments that vary with risk level, time horizon, 

and tax characteristics. The standard analytic framework used to develop intuition about the 

discount rate typically assumes a representative agent with perfect foresight and no credit 

constraints. The risk-free rate is appropriate for discounting certain future benefits or costs, but 

the benefits calculated by IAMs are uncertain. To use the risk-free rate to discount uncertain 

benefits, these benefits first must be transformed into "certainty equivalents," that is the 

maximum certain amount that we would exchange for the uncertain amount. However, the 

calculation of the certainty-equivalent requires first estimating the correlation between the 

benefits of the policy and baseline consumption.

If the IAM projections of future impacts represent expected values (not certaintyequivalent values), then the appropriate discount rate generally does not equal the risk-free rate. 

If the benefits of the policy tend to be high in those states of the world in which consumption is 

low, then the certainty-equivalent benefits will be higher than the expected benefits (and vice 

versa). Since many (though not necessarily all) of the important impacts of climate change will 

flow through market sectors such as agriculture and energy, and since willingness to pay for 

environmental protections typically increases with income, we might expect a positive (though 

not necessarily perfect) correlation between the net benefits from climate policies and market 

returns. This line of reasoning suggests that the proper discount rate would exceed the riskless 

rate. Alternatively, a negative correlation between the returns to climate policies and market 

returns would imply that a discount rate below the riskless rate is appropriate.

This discussion suggests that both the post-tax riskless and risky rates can be used to 

capture individuals’ consumption-equivalent interest rate. As a measure of the post-tax riskless 

rate, we calculate the average real return from Treasury notes over the longest time period 

available (those from Newell and Pizer 2003) and adjust for Federal taxes (the average marginal 

rate from tax years 2003 through 2006 is around 27 percent).r This calculation produces a real 

interest rate of about 2.7 percent, which is roughly consistent with Circular A-4’s 

recommendation to use 3 percent to represent the consumption rate of interest.s

 A measure of 

the post-tax risky rate for investments whose returns are positively correlated with overall equity 

 r The literature argues for a risk-free rate on government bonds as an appropriate measure of the consumption rate of 

interest. Arrow (2000) suggests that it is roughly 3-4 percent. OMB cites evidence of a 3.1 percent pre-tax rate for 

10-year Treasury notes in the A-4 guidance. Newell and Pizer (2003) find real interest rates between 3.5 and 4 

percent for 30-year Treasury securities. s The positive approach reflects how individuals make allocation choices across time, but it is important to keep in 

mind that we wish to reflect preferences for society as a whole, which generally has a longer planning horizon.
10A-23

market returns can be obtained by adjusting pre-tax rates of household returns to risky 

investments (approximately 7 percent) for taxes, which yields a real rate of roughly 5 percent.t 

The Ramsey Equation

Ramsey discounting also provides a useful framework to inform the choice of a discount 

rate. Under this approach, the analyst applies either positive or normative judgments in selecting 

values for the key parameters of the Ramsey equation: η (coefficient of relative risk aversion or 

elasticity of the marginal utility of consumption) and ρ (pure rate of time preference).u These are 

then combined with g (growth rate of per-capita consumption) to equal the interest rate at which 

future monetized damages are discounted: ρ + η∙g.v In the simplest version of the Ramsey 

model, with an optimizing representative agent with perfect foresight, what we are calling the 

“Ramsey discount rate,” ρ + η∙g, will be equal to the rate of return to capital, i.e., the market 

interest rate.

A review of the literature provides some guidance on reasonable parameter values for the 

Ramsey discounting equation, based on both prescriptive and descriptive approaches. 

• η. Most papers in the climate change literature adopt values for η in the range of 0.5 to 3 

(Weitzman cites plausible values as those ranging from 1 to 4), although not all authors 

articulate whether their choice is based on prescriptive or descriptive reasoning.w 

Dasgupta (2008) argues that η should be greater than 1 and may be as high as 3, because

η equal to 1 suggests savings rates that do not conform to observed behavior. 

 t

Cambell et al (2001) estimates that the annual real return from stocks for 1900-1995 was about 7 percent. The 

annual real rate of return for the S&P 500 from 1950 – 2008 was about 6.8 percent. In the absence of a better way to 

population-weight the tax rates, we use the middle of the 20 – 40 percent range to derive a post-tax interest rate 

(Kotlikoff and Rapson 2006).

u The parameter ρ measures the pure rate of time preference: people’s behavior reveals a preference for an 

increase in utility today versus the future. Consequently, it is standard to place a lower weight on utility in the 

future. The parameter η captures diminishing marginal utility: consumption in the future is likely to be higher than 

consumption today, so diminishing marginal utility of consumption implies that the same monetary damage will 

cause a smaller reduction of utility for wealthier individuals, either in the future or in current generations. If η = 0, 

then a one dollar increase in income is equally valuable regardless of level of income; if η = 1, then a one percent 

increase in income is equally valuable no matter the level of income; and if η > 1, then a one percent increase in 

income is less valuable to wealthier individuals. v In this case, g could be taken from the selected EMF socioeconomic scenarios or alternative assumptions about the 

rate of consumption growth.

w Empirical estimates of η span a wide range of values. A benchmark value of 2 is near the middle of the range of 

values estimated or used by Szpiro (1986), Hall and Jones (2007), Arrow (2007), Dasgupta (2006, 2008), Weitzman 

(2007, 2009), and Nordhaus (2008). However, Chetty (2006) developed a method of estimating η using data on 

labor supply behavior. He shows that existing evidence of the effects of wage changes on labor supply imposes a 

tight upper bound on the curvature of utility over wealth (CRRA < 2) with the mean implied value of 0.71 and 

concludes that the standard expected utility model cannot generate high levels of risk aversion without contradicting 

established facts about labor supply. Recent work has jointly estimated the components of the Ramsey equation. 

Evans and Sezer (2005) estimate η = 1.49 for 22 OECD countries. They also estimate ρ = 1.08 percent per year 

using data on mortality rates. Anthoff, et al. (2009b) estimate η = 1.18, and ρ = 1.4 percent. When they multiply the 

bivariate probability distributions from their work and Evans and Sezer (2005) together, they find η = 1.47, and ρ = 

1.07. 
10A-24

• ρ. With respect to the pure rate of time preference, most papers in the climate change 

literature adopt values for ρ in the range of 0 to 3 percent per year. The very low rates 

tend to follow from moral judgments involving intergenerational neutrality. Some have 

argued that to use any value other than ρ = 0 would unjustly discriminate against future 

generations (e.g., Arrow et al. 1996, Stern et al. 2006). However, even in an intergenerational setting, it may make sense to use a small positive pure rate of time 

preference because of the small probability of unforeseen cataclysmic events (Stern et al. 

2006).

• g. A commonly accepted approximation is around 2 percent per year. For the 

socioeconomic scenarios used for this exercise, the EMF models assume that g is about 

1.5-2 percent to 2100. 

Some economists and non-economists have argued for constant discount rates below 2 

percent based on the prescriptive approach. When grounded in the Ramsey framework, 

proponents of this approach have argued that a ρ of zero avoids giving preferential treatment to 

one generation over another. The choice of η has also been posed as an ethical choice linked to 

the value of an additional dollar in poorer countries compared to wealthier ones. Stern et al. 

(2006) applies this perspective through his choice of ρ = 0.1 percent per year, η = 1 and g = 1.3 

percent per year, which yields an annual discount rate of 1.4 percent. In the context of 

permanent income savings behavior, however, Stern’s assumptions suggest that individuals 

would save 93 percent of their income.x

Recently, Stern (2008) revisited the values used in Stern et al. (2006), stating that there is 

a case to be made for raising η due to the amount of weight lower values place on damages far in 

the future (over 90 percent of expected damages occur after 2200 with η = 1). Using Stern’s 

assumption that ρ = 0.1 percent, combined with a η of 1.5 to 2 and his original growth rate, 

yields a discount rate of greater than 2 percent. 

We conclude that arguments made under the prescriptive approach can be used to justify 

discount rates between roughly 1.4 and 3.1 percent. In light of concerns about the most 

appropriate value for η, we find it difficult to justify rates at the lower end of this range under the 

Ramsey framework. 

Accounting for Uncertainty in the Discount Rate

While the consumption rate of interest is an important driver of the benefits estimate, it is 

uncertain over time. Ideally, we would formally model this uncertainty, just as we do for climate 

sensitivity. Weitzman (1998, 2001) showed theoretically and Newell and Pizer (2003) and 

Groom et al. (2006) confirm empirically that discount rate uncertainty can have a large effect on 

net present values. A main result from these studies is that if there is a persistent element to the 

 x Stern (2008) argues that building in a positive rate of exogenous technical change over time reduces the implied 

savings rate and that η at or above 2 are inconsistent with observed behavior with regard to equity. (At the same 

time, adding exogenous technical change—all else equal—would increase g as well.)
10A-25

uncertainty in the discount rate (e.g., the rate follows a random walk), then it will result in an 

effective (or certainty-equivalent) discount rate that declines over time. Consequently, lower 

discount rates tend to dominate over the very long term (see Weitzman 1998, 1999, 2001; 

Newell and Pizer 2003; Groom et al. 2006; Gollier 2008; Summers and Zeckhauser 2008; and 

Gollier and Weitzman 2009). 

The proper way to model discount rate uncertainty remains an active area of research. 

Newell and Pizer (2003) employ a model of how long-term interest rates change over time to 

forecast future discount rates. Their model incorporates some of the basic features of how 

interest rates move over time, and its parameters are estimated based on historical observations 

of long-term rates. Subsequent work on this topic, most notably Groom et al. (2006), uses more 

general models of interest rate dynamics to allow for better forecasts. Specifically, the volatility 

of interest rates depends on whether rates are currently low or high and the variation in the level 

of persistence over time.

While Newell and Pizer (2003) and Groom et al (2006) attempt formally to model 

uncertainty in the discount rate, others argue for a declining scale of discount rates applied over 

time (e.g., Weitzman 2001, and the UK’s “Green Book” for regulatory analysis). This approach 

uses a higher discount rate initially, but applies a graduated scale of lower discount rates further

out in time.y

 A key question that has emerged with regard to both of these approaches is the 

trade-off between potential time inconsistency and giving greater weight to far future outcomes 

(see the EPA Science Advisory Board’s recent comments on this topic as part of its review of 

their Guidelines for Economic Analysis).z

The Discount Rates Selected for Estimating SCC

In light of disagreement in the literature on the appropriate market interest rate to use in 

this context and uncertainty about how interest rates may change over time, we use three 

discount rates to span a plausible range of certainty-equivalent constant discount rates: 2.5, 3, 

and 5 percent per year. Based on the review in the previous sections, the interagency workgroup 

determined that these three rates reflect reasonable judgments under both descriptive and 

prescriptive approaches.

The central value, 3 percent, is consistent with estimates provided in the economics 

literature and OMB’s Circular A-4 guidance for the consumption rate of interest. As previously 

mentioned, the consumption rate of interest is the correct discounting concept to use when future 

 y For instance, the UK applies a discount rate of 3.5 percent to the first 30 years; 3 percent for years 31 - 75; 2.5 

percent for years 76 - 125; 2 percent for years 126 - 200; 1.5 percent for years 201 - 300; and 1 percent after 300 

years. As a sensitivity, it recommends a discount rate of 3 percent for the first 30 years, also decreasing over time. 

z Uncertainty in future damages is distinct from uncertainty in the discount rate. Weitzman (2008) argues that 

Stern’s choice of a low discount rate was “right for the wrong reasons.” He demonstrates how the damages from a 

low probability, catastrophic event far in the future dominate the effect of the discount rate in a present value 

calculation and result in an infinite willingness-to-pay for mitigation today. Newbold and Daigneault, (2009) and 

Nordhaus (2009) find that Weitzman’s result is sensitive to the functional forms chosen for climate sensitivity, 

utility, and consumption. Summers and Zeckhauser (2008) argue that uncertainty in future damages can also work in 

the other direction by increasing the benefits of waiting to learn the appropriate level of mitigation required. 
10A-26

damages from elevated temperatures are estimated in consumption-equivalent units. Further, 3 

percent roughly corresponds to the after-tax riskless interest rate. The upper value of 5 percent is 

included to represent the possibility that climate damages are positively correlated with market 

returns. Additionally, this discount rate may be justified by the high interest rates that many 

consumers use to smooth consumption across periods.

The low value, 2.5 percent, is included to incorporate the concern that interest rates are 

highly uncertain over time. It represents the average certainty-equivalent rate using the meanreverting and random walk approaches from Newell and Pizer (2003) starting at a discount rate 

of 3 percent. Using this approach, the certainty equivalent is about 2.2 percent using the random 

walk model and 2.8 percent using the mean reverting approach.aa Without giving preference to a 

particular model, the average of the two rates is 2.5 percent. Further, a rate below the riskless 

rate would be justified if climate investments are negatively correlated with the overall market 

rate of return. Use of this lower value also responds to certain judgments using the prescriptive 

or normative approach and to ethical objections that have been raised about rates of 3 percent or 

higher.

10A.5 REVISED SCC ESTIMATES

Our general approach to estimating SCC values is to run the three integrated assessment 

models (FUND, DICE, and PAGE) using the following inputs agreed upon by the interagency 

group:

• A Roe and Baker distribution for the climate sensitivity parameter bounded between 0 

and 10 with a median of 3 °C and a cumulative probability between 2 and 4.5 °C of twothirds.

• Five sets of GDP, population, and carbon emissions trajectories based on EMF-22.

• Constant annual discount rates of 2.5, 3, and 5 percent.

Because the climate sensitivity parameter is modeled probabilistically, and because PAGE and 

FUND incorporate uncertainty in other model parameters, the final output from each model run 

is a distribution over the SCC in year t. 

For each of the IAMs, the basic computational steps for calculating the SCC in a particular year t

are:

1. Input the path of emissions, GDP, and population from the selected EMF-22 

scenarios, and the extrapolations based on these scenarios for post-2100 years.

2. Calculate the temperature effects and (consumption-equivalent) damages in each 

year resulting from the baseline path of emissions. 

 

aa Calculations done by Pizer et al. using the original simulation program from Newell and Pizer (2003).
10A-27

a. In PAGE, the consumption-equivalent damages in each period are 

calculated as a fraction of the EMF GDP forecast, depending on the 

temperature in that period relative to the pre-industrial average 

temperature in each region. 

b. In FUND, damages in each period depend on both the level and the rate of 

temperature change in that period. 

c. In DICE, temperature affects both consumption and investment, so we 

first adjust the EMF GDP paths as follows: Using the Cobb-Douglas 

production function with the DICE2007 parameters, we extract the path of 

exogenous technical change implied by the EMF GDP and population 

paths, then we recalculate the baseline GDP path taking into account 

climate damages resulting from the baseline emissions path. 

3. Add an additional unit of carbon emissions in year t. (The exact unit varies by 

model.)

4. Recalculate the temperature effects and damages expected in all years beyond t

resulting from this adjusted path of emissions, as in step 2. 

5. Subtract the damages computed in step 2 from those in step 4 in each year. 

(DICE is run in 10-year time steps, FUND in annual time steps, while the time 

steps in PAGE vary.)

6. Discount the resulting path of marginal damages back to the year of emissions 

using the agreed upon fixed discount rates.

7. Calculate the SCC as the net present value of the discounted path of damages 

computed in step 6, divided by the unit of carbon emissions used to shock the 

models in step 3. 

8. Multiply by 12/44 to convert from dollars per ton of carbon to dollars per ton of 

CO2 (2007 dollars) in DICE and FUND. (All calculations are done in tons of CO2 

in PAGE).

The steps above were repeated in each model for multiple future years to cover the time horizons 

anticipated for upcoming rulemaking analysis. To maintain consistency across the three IAMs, 

climate damages are calculated as lost consumption in each future year. 

It is important to note that each of the three models has a different default end year. The 

default time horizon is 2200 for PAGE, 2595 for DICE, and 3000 for the latest version of FUND. 

This is an issue for the multi-model approach because differences in SCC estimates may arise 

simply due to the model time horizon. Many consider 2200 too short a time horizon because it 

could miss a significant fraction of damages under certain assumptions about the growth of 

marginal damages and discounting, so each model is run here through 2300. This step required a 

small adjustment in the PAGE model only. This step also required assumptions about GDP, 

population, and greenhouse gas emission trajectories after 2100, the last year for which these 
10A-28

data are available from the EMF-22 models. (A more detailed discussion of these assumptions is 

included in the Annex.)

This exercise produces 45 separate distributions of the SCC for a given year, the product 

of 3 models, 3 discount rates, and 5 socioeconomic scenarios. This is clearly too many separate 

distributions for consideration in a regulatory impact analysis. 

To produce a range of plausible estimates that still reflects the uncertainty in the 

estimation exercise, the distributions from each of the models and scenarios are equally weighed 

and combined to produce three separate probability distributions for SCC in a given year, one for 

each assumed discount rate. These distributions are then used to define a range of point estimates 

for the global SCC. In this way, no IAM or socioeconomic scenario is given greater weight than 

another. Because the literature shows that the SCC is quite sensitive to assumptions about the 

discount rate, and because no consensus exists on the appropriate rate to use in an 

intergenerational context, we present SCCs based on the average values across models and 

socioeconomic scenarios for each discount rate. 

The interagency group selected four SCC values for use in regulatory analyses. Three 

values are based on the average SCC across models and socioeconomic and emissions scenarios 

at the 2.5, 3, and 5 percent discount rates. The fourth value is included to represent the higherthan-expected economic impacts from climate change further out in the tails of the SCC 

distribution. For this purpose, we use the SCC value for the 95th percentile at a 3 percent discount 

rate. (The full set of distributions by model and scenario combination is included in the Annex.) 

As noted above, the 3 percent discount rate is the central value, and so the central value that 

emerges is the average SCC across models at the 3 percent discount rate. For purposes of 

capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance 

and value of considering the full range.

As previously discussed, low probability, high impact events are incorporated into the 

SCC values through explicit consideration of their effects in two of the three models as well as 

the use of a probability density function for equilibrium climate sensitivity. Treating climate 

sensitivity probabilistically results in more high-temperature outcomes, which in turn lead to 

higher projections of damages. Although FUND does not include catastrophic damages (in 

contrast to the other two models), its probabilistic treatment of the equilibrium climate sensitivity 

parameter will directly affect the non-catastrophic damages that are a function of the rate of 

temperature change.

In Table 10A.5.1, we begin by presenting SCC estimates for 2010 by model, scenario, 

and discount rate to illustrate the variability in the SCC across each of these input parameters. 

As expected, higher discount rates consistently result in lower SCC values, while lower discount 

rates result in higher SCC values for each socioeconomic trajectory. It is also evident that there 

are differences in the SCC estimated across the three main models. For these estimates, FUND 

produces the lowest estimates, while PAGE generally produces the highest estimates. 
10A-29

Table 10A.5.1 Disaggregated Social Cost of CO2 Values by Model, Socioeconomic

Trajectory, and Discount Rate for 2010 (in 2007 dollars)

Discount rate: 5% 3% 2.5% 3%

Model Scenario Avg Avg Avg 95th DICE

IMAGE 10.8 35.8 54.2 70.8

MERGE 7.5 22.0 31.6 42.1

Message 9.8 29.8 43.5 58.6

MiniCAM 8.6 28.8 44.4 57.9

550 Average 8.2 24.9 37.4 50.8

PAGE

IMAGE 8.3 39.5 65.5 142.4

MERGE 5.2 22.3 34.6 82.4

Message 7.2 30.3 49.2 115.6

MiniCAM 6.4 31.8 54.7 115.4

550 Average 5.5 25.4 42.9 104.7

FUND

IMAGE -1.3 8.2 19.3 39.7

MERGE -0.3 8.0 14.8 41.3

Message -1.9 3.6 8.8 32.1

MiniCAM -0.6 10.2 22.2 42.6

550 Average -2.7 -0.2 3.0 19.4

These results are not surprising when compared to the estimates in the literature for the 

latest versions of each model. For example, adjusting the values from the literature that were 

used to develop interim SCC values to 2007 dollars for the year 2010 (assuming, as we did for 

the interim process, that SCC grows at 3 percent per year), FUND yields SCC estimates at or 

near zero for a 5 percent discount rate and around $9 per ton for a 3 percent discount rate. There 

are far fewer estimates using the latest versions of DICE and PAGE in the literature: Using 

similar adjustments to generate 2010 estimates, we calculate a SCC from DICE (based on 

Nordhaus 2008) of around $9 per ton for a 5 percent discount rate, and a SCC from PAGE 

(based on Hope 2006, 2008) close to $8 per ton for a 4 percent discount rate. Note that these 

comparisons are only approximate since the literature generally relies on Ramsey discounting, 

while we have assumed constant discount rates.bb

 bb Nordhaus (2008) runs DICE2007 with ρ = 1.5 and η = 2. The default approach in PAGE2002 (version 1.4epm) 

treats ρ and η as random parameters, specified using a triangular distribution such that the min, mode, and max = 

0.1, 1, and 2 for ρ, and 0.5, 1, and 2 for η, respectively. The FUND default value for η is 1, and Tol generates SCC 

estimates for values of ρ = 0, 1, and 3 in many recent papers (e.g. Anthoff et al. 2009). The path of per-capita 

consumption growth, g, varies over time but is treated deterministically in two of the three models. In DICE, g is 
10A-30

The SCC estimates from FUND are sensitive to differences in emissions paths but 

relatively insensitive to differences in GDP paths across scenarios, while the reverse is true for 

DICE and PAGE. This likely occurs because of several structural differences among the models. 

Specifically in DICE and PAGE, the fraction of economic output lost due to climate damages 

increases with the level of temperature alone, whereas in FUND the fractional loss also increases 

with the rate of temperature change. Furthermore, in FUND increases in income over time 

decrease vulnerability to climate change (a form of adaptation), whereas this does not occur in 

DICE and PAGE. These structural differences among the models make FUND more sensitive to 

the path of emissions and less sensitive to GDP compared to DICE and PAGE. 

Figure 10A.5.1 shows that IMAGE has the highest GDP in 2100 while MERGE 

Optimistic has the lowest. The ordering of global GDP levels in 2100 directly corresponds to the 

rank ordering of SCC for PAGE and DICE. For FUND, the correspondence is less clear, a result 

that is to be expected given its less direct relationship between its damage function and GDP.

Figure 10A.5.1 Level of Global GDP across EMF Scenarios

Table 10A.5.2 shows the four selected SCC values in five-year increments from 2010 to 

2050. Values for 2010, 2020, 2040, and 2050 are calculated by first combining all outputs 

(10,000 estimates per model run) from all scenarios and models for a given discount rate. Values 

for the years in between are calculated using a simple linear interpolation.

 

endogenous. Under Ramsey discounting, as economic growth slows in the future, the large damages from climate 

change that occur far out in the future are discounted at a lower rate than impacts that occur in the nearer term.

0

50

100

150

200

250

300

350

400

450

2000 2010 2020 2030 2050 2100

Global 

GDP

(trillions

of U.S. 

dollars)

IMAGE

MERGE Optimistic

MESSAGE

MiniCAM

3.7 W/m2 radiative forcing
10A-31

Table 10A.5.2 Social Cost of CO2, 2010 – 2050 (in 2007 dollars)

Discount 5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

The SCC increases over time because future emissions are expected to produce larger 

incremental damages as physical and economic systems become more stressed in response to 

greater climatic change. Note that this approach allows us to estimate the growth rate of the SCC 

directly using DICE, PAGE, and FUND rather than assuming a constant annual growth rate as 

was done for the interim estimates (using 3 percent). This helps to ensure that the estimates are 

internally consistent with other modeling assumptions. Table 10A.5.3 illustrates how the growth 

rate for these four SCC estimates varies over time. The full set of annual SCC estimates between 

2010 and 2050 is reported in the Annex.

Table 10A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates between 

2010 and 2050

Average Annual 

Growth Rate (%)

5% 3% 2.5% 3.0%

Avg Avg Avg 95th

2010-2020 3.6% 2.1% 1.7% 2.2%

2020-2030 3.7% 2.2% 1.8% 2.2%

2030-2040 2.7% 1.8% 1.6% 1.8%

2040-2050 2.1% 1.4% 1.1% 1.3%

While the SCC estimate grows over time, the future monetized value of emissions 

reductions in each year (the SCC in year t multiplied by the change in emissions in year t) must 

be discounted to the present to determine its total net present value for use in regulatory analysis. 

Damages from future emissions should be discounted at the same rate as that used to calculate 

the SCC estimates themselves to ensure internal consistency—i.e., future damages from climate 

change, whether they result from emissions today or emissions in a later year, should be 

discounted using the same rate. For example, climate damages in the year 2020 that are 
10A-32

calculated using a SCC based on a 5 percent discount rate also should be discounted back to the 

analysis year using a 5 percent discount rate.cc 

10A.6 LIMITATIONS OF THE ANALYSIS

As noted, any estimate of the SCC must be taken as provisional and subject to further 

refinement (and possibly significant change) in accordance with evolving scientific, economic, 

and ethical understandings. During the course of our modeling, it became apparent that there are 

several areas in particular need of additional exploration and research. These caveats, and 

additional observations in the following section, are necessary to consider when interpreting and 

applying the SCC estimates.

Incomplete treatment of non-catastrophic damages. The impacts of climate change are 

expected to be widespread, diverse, and heterogeneous. In addition, the exact magnitude of these 

impacts is uncertain because of the inherent complexity of climate processes, the economic 

behavior of current and future populations, and our inability to accurately forecast technological 

change and adaptation. Current IAMs do not assign value to all of the important physical, 

ecological, and economic impacts of climate change recognized in the climate change literature 

(some of which are discussed above) because of lack of precise information on the nature of 

damages and because the science incorporated into these models understandably lags behind the 

most recent research. Our ability to quantify and monetize impacts will undoubtedly improve 

with time. But it is also likely that even in future applications, a number of potentially significant 

damage categories will remain non-monetized. (Ocean acidification is one example of a 

potentially large damage from CO2 emissions not quantified by any of the three models. Species 

and wildlife loss is another example that is exceedingly difficult to monetize.) 

Incomplete treatment of potential catastrophic damages. There has been considerable 

recent discussion of the risk of catastrophic impacts and how best to account for extreme 

scenarios, such as the collapse of the Atlantic Meridional Overturning Circulation or the West 

Antarctic Ice Sheet, or large releases of methane from melting permafrost and warming oceans. 

Weitzman (2009) suggests that catastrophic damages are extremely large—so large, in fact, that 

the damages from a low probability, catastrophic event far in the future dominate the effect of 

the discount rate in a present value calculation and result in an infinite willingness-to-pay for 

mitigation today. However, Nordhaus (2009) concluded that the conditions under which 

Weitzman's results hold “are limited and do not apply to a wide range of potential uncertain 

scenarios." 

Using a simplified IAM, Newbold and Daigneault (2009) confirmed the potential for 

large catastrophe risk premiums but also showed that the aggregate benefit estimates can be 

highly sensitive to the shapes of both the climate sensitivity distribution and the damage function 

at high temperature changes. Pindyck (2009) also used a simplified IAM to examine high-

 

cc However, it is possible that other benefits or costs of proposed regulations unrelated to CO2 emissions will be 

discounted at rates that differ from those used to develop the SCC estimates. 
10A-33

impact, low-probability risks, using a right-skewed gamma distribution for climate sensitivity as 

well as an uncertain damage coefficient, but in most cases found only a modest risk premium. 

Given this difference in opinion, further research in this area is needed before its practical 

significance can be fully understood and a reasonable approach developed to account for such 

risks in regulatory analysis. (The next section discusses the scientific evidence on catastrophic 

impacts in greater detail.)

Uncertainty in extrapolation of damages to high temperatures: The damage functions in 

these IAMs are typically calibrated by estimating damages at moderate temperature increases 

(e.g., DICE was calibrated at 2.5 °C) and extrapolated to far higher temperatures by assuming 

that damages increase as some power of the temperature change. Hence, estimated damages are 

far more uncertain under more extreme climate change scenarios. 

Incomplete treatment of adaptation and technological change: Each of the three 

integrated assessment models used here assumes a certain degree of low- or no-cost adaptation. 

For instance, Tol assumes a great deal of adaptation in FUND, including widespread reliance on 

air conditioning; so much so, that the largest single benefit category in FUND is the reduced 

electricity costs from not having to run air conditioning as intensively (NRC 2009). 

Climate change also will increase returns on investment to develop technologies that 

allow individuals to cope with adverse climate conditions, and IAMs to do not adequately 

account for this directed technological change.dd For example, scientists may develop crops that 

are better able to withstand higher and more variable temperatures. Although DICE and FUND 

have both calibrated their agricultural sectors under the assumption that farmers will change land 

use practices in response to climate change (Mastrandrea, 2009), they do not take into account 

technological changes that lower the cost of this adaptation over time. On the other hand, the 

calibrations do not account for increases in climate variability, pests, or diseases, which could 

make adaptation more difficult than assumed by the IAMs for a given temperature change. 

Hence, models do not adequately account for potential adaptation or technical change that might 

alter the emissions pathway and resulting damages. In this respect, it is difficult to determine 

whether the incomplete treatment of adaptation and technological change in these IAMs 

understate or overstate the likely damages.

Risk aversion: A key question unanswered during this interagency process is what to 

assume about relative risk aversion with regard to high-impact outcomes. These calculations do 

not take into account the possibility that individuals may have a higher willingness to pay to 

reduce the likelihood of low-probability, high-impact damages than they do to reduce the 

likelihood of higher-probability, but lower-impact, damages with the same expected cost. (The 

inclusion of the 95th percentile estimate in the final set of SCC values was largely motivated by 

this concern.) If individuals do show such a higher willingness to pay, a further question is 

whether that fact should be taken into account for regulatory policy. Even if individuals are not 

risk-averse for such scenarios, it is possible that regulatory policy should include a degree of 

risk-aversion.

 dd However these research dollars will be diverted from whatever their next best use would have been in the absence 

of climate change (so productivity/GDP would have been still higher).
10A-34

Assuming a risk-neutral representative agent is consistent with OMB’s Circular A-4, 

which advises that the estimates of benefits and costs used in regulatory analysis are usually 

based on the average or the expected value and that “emphasis on these expected values is 

appropriate as long as society is ‘risk neutral’ with respect to the regulatory alternatives. While 

this may not always be the case, [analysts] should in general assume ‘risk neutrality’ in [their] 

analysis.” 

Nordhaus (2008) points to the need to explore the relationship between risk and income 

in the context of climate change across models and to explore the role of uncertainty regarding 

various parameters in the results. Using FUND, Anthoff et al (2009) explored the sensitivity of 

the SCC to Ramsey equation parameter assumptions based on observed behavior. They conclude

that “the assumed rate of risk aversion is at least as important as the assumed rate of time 

preference in determining the social cost of carbon.” Since Circular A-4 allows for a different 

assumption on risk preference in regulatory analysis if it is adequately justified, we plan to 

continue investigating this issue.

10A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPACTS AND 

DAMAGE FUNCTIONS

As noted above, the damage functions underlying the three IAMs used to estimate the 

SCC may not capture the economic effects of all possible adverse consequences of climate 

change and may therefore lead to underestimates of the SCC (Mastrandrea 2009). In particular, 

the models’ functional forms may not adequately capture: (1) potentially discontinuous “tipping 

point” behavior in Earth systems, (2) inter-sectoral and inter-regional interactions, including 

global security impacts of high-end warming, and (3) limited near-term substitutability between 

damage to natural systems and increased consumption. 

It is the hope of the interagency group that over time researchers and modelers will work 

to fill these gaps and that the SCC estimates used for regulatory analysis by the Federal 

government will continue to evolve with improvements in modeling. In the meantime, we 

discuss some of the available evidence.

Extrapolation of climate damages to high levels of warming

The damage functions in the models are calibrated at moderate levels of warming and 

should therefore be viewed cautiously when extrapolated to the high temperatures found in the 

upper end of the distribution. Recent science suggests that there are a number of potential 

climatic “tipping points” at which the Earth system may exhibit discontinuous behavior with 

potentially severe social and economic consequences (e.g., Lenton et al, 2008, Kriegler et al., 

2009). These tipping points include the disruption of the Indian Summer Monsoon, dieback of 

the Amazon Rainforest and boreal forests, collapse of the Greenland Ice Sheet and the West 

Antarctic Ice Sheet, reorganization of the Atlantic Meridional Overturning Circulation, 

strengthening of El Niño-Southern Oscillation, and the release of methane from melting 
10A-35

permafrost. Many of these tipping points are estimated to have thresholds between about 3 °C 

and 5 °C (Lenton et al., 2008). Probabilities of several of these tipping points were assessed 

through expert elicitation in 2005–2006 by Kriegler et al. (2009); results from this study are 

highlighted in Table 10A.7.1. Ranges of probability are averaged across core experts on each 

topic.

As previously mentioned, FUND does not include potentially catastrophic effects. DICE 

assumes a small probability of catastrophic damages that increases with increased warming, but 

the damages from these risks are incorporated as expected values (i.e., ignoring potential risk 

aversion). PAGE models catastrophic impacts in a probabilistic framework (see Figure 10A.4.1), 

so the high-end output from PAGE potentially offers the best insight into the SCC if the world 

were to experience catastrophic climate change. For instance, at the 95th percentile and a 3 

percent discount rate, the SCC estimated by PAGE across the five socioeconomic and emission 

trajectories of $113 per ton of CO2 is almost double the value estimated by DICE, $58 per ton in 

2010. We cannot evaluate how well the three models account for catastrophic or noncatastrophic impacts, but this estimate highlights the sensitivity of SCC values in the tails of the 

distribution to the assumptions made about catastrophic impacts. 

Table 10A.7.1 Probabilities of Various Tipping Points from Expert Elicitation

Possible Tipping Points

Duration before

effect is fully

realized (in years)

Additional Warming by 2100

0.5-1.5 C 1.5-3.0 C 3-5 C

Reorganization of Atlantic Meridional 

Overturning Circulation about 100 0-18% 6-39% 18-67%

Greenland Ice Sheet collapse at least 300 8-39% 33-73% 67-96%

West Antarctic Ice Sheet collapse at least 300 5-41% 10-63% 33-88%

Dieback of Amazon rainforest about 50 2-46% 14-84% 41-94%

Strengthening of El Niño-Southern Oscillation about 100 1-13% 6-32% 19-49%

Dieback of boreal forests about 50 13-43% 20-81% 34-91%

Shift in Indian Summer Monsoon about 1 Not formally assessed

Release of methane from melting permafrost Less than 100 Not formally assessed.

PAGE treats the possibility of a catastrophic event probabilistically, while DICE treats it 

deterministically (that is, by adding the expected value of the damage from a catastrophe to the 

aggregate damage function). In part, this results in different probabilities being assigned to a 

catastrophic event across the two models. For instance, PAGE places a probability near zero on a 

catastrophe at 2.5 °C warming, while DICE assumes a 4 percent probability of a catastrophe at 

2.5 °C. By comparison, Kriegler et al. (2009) estimate a probability of at least 16-36 percent of 
10A-36

crossing at least one of their primary climatic tipping points in a scenario with temperatures 

about 2-4 °C warmer than pre-Industrial levels in 2100. 

It is important to note that crossing a climatic tipping point will not necessarily lead to an 

economic catastrophe in the sense used in the IAMs. A tipping point is a critical threshold across 

which some aspect of the Earth system starts to shifts into a qualitatively different state (for 

instance, one with dramatically reduced ice sheet volumes and higher sea levels). In the IAMs, a 

catastrophe is a low-probability environmental change with high economic impact.

Failure to incorporate inter-sectoral and inter-regional interactions

The damage functions do not fully incorporate either inter-sectoral or inter-regional 

interactions. For instance, while damages to the agricultural sector are incorporated, the effects 

of changes in food supply on human health are not fully captured and depend on the modeler’s 

choice of studies used to calibrate the IAM. Likewise, the effects of climate damages in one 

region of the world on another region are not included in some of the models (FUND includes 

the effects of migration from sea level rise). These inter-regional interactions, though difficult to 

quantify, are the basis for climate-induced national and economic security concerns (e.g., 

Campbell et al., 2007; U.S. Department of Defense 2010) and are particularly worrisome at 

higher levels of warming. High-end warming scenarios, for instance, project water scarcity 

affecting 4.3-6.9 billion people by 2050, food scarcity affecting about 120 million additional 

people by 2080, and the creation of millions of climate refugees (Easterling et al., 2007; 

Campbell et al., 2007).

Imperfect substitutability of environmental amenities

Data from the geological record of past climate changes suggests that 6 °C of warming 

may have severe consequences for natural systems. For instance, during the Paleocene-Eocene 

Thermal Maximum about 55.5 million years ago, when the Earth experienced a geologically 

rapid release of carbon associated with an approximately 5 °C increase in global mean 

temperatures, the effects included shifts of about 400-900 miles in the range of plants (Wing et 

al., 2005), and dwarfing of both land mammals (Gingerich, 2006) and soil fauna (Smith et al., 

2009).

The three IAMs used here assume that it is possible to compensate for the economic 

consequences of damages to natural systems through increased consumption of non-climate 

goods, a common assumption in many economic models. In the context of climate change, 

however, it is possible that the damages to natural systems could become so great that no 

increase in consumption of non-climate goods would provide complete compensation (Levy et 

al., 2005). For instance, as water supplies become scarcer or ecosystems become more fragile 

and less bio-diverse, the services they provide may become increasingly more costly to replace. 

Uncalibrated attempts to incorporate the imperfect substitutability of such amenities into IAMs 

(Sterner and Persson, 2008) indicate that the optimal degree of emissions abatement can be 

considerably greater than is commonly recognized. 
10A-37

10A.8 CONCLUSION

The interagency group selected four SCC estimates for use in regulatory analyses. For 

2010, these estimates are $5, $21, $35, and $65 (in 2007 dollars). The first three estimates are 

based on the average SCC across models and socioeconomic and emissions scenarios at the 5, 3, 

and 2.5 percent discount rates, respectively. The fourth value is included to represent the higherthan-expected impacts from temperature change further out in the tails of the SCC distribution. 

For this purpose, we use the SCC value for the 95th percentile at a 3 percent discount rate. The 

central value is the average SCC across models at the 3 percent discount rate. For purposes of 

capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance 

and value of considering the full range. These SCC estimates also grow over time. For instance, 

the central value increases to $24 per ton of CO2 in 2015 and $26 per ton of CO2 in 2020.

We noted a number of limitations to this analysis, including the incomplete way in which 

the integrated assessment models capture catastrophic and non-catastrophic impacts, their 

incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of 

damages to high temperatures, and assumptions regarding risk aversion. The limited amount of 

research linking climate impacts to economic damages makes this modeling exercise even more 

difficult. It is the hope of the interagency group that over time researchers and modelers will 

work to fill these gaps and that the SCC estimates used for regulatory analysis by the Federal 

government will continue to evolve with improvements in modeling. 
10A-38

REFERENCES

Andronova, N., and M. Schlesinger. 2001. “Objective estimation of the probability density 

function for climate sensitivity.” J. Geophys. Res., 106(D19), 22605–22611.

Annan, J., et al., 2005. “Efficiently constraining climate sensitivity with paleoclimate 

simulations.” Scientific Online Letters on the Atmosphere, 1, 181–184.

Anthoff D, C. Hepburn, and R. Tol. 2009a. “Equity Weighting and the Marginal Damage Costs 

of Climate Change.” Ecological Economics 68:836-849.

Anthoff, D., R. Tol, and G. Yohe. 2009b. “Risk aversion, time preference, and the social cost of 

carbon.” Environmental Research Letters 4: 024002 (7pp).

Arrow, K. 2007. “Global climate change: a challenge to policy.” Economist’s Voice 4(3):Article 

2.

Arrow, K. 2000. “A Comment on Cooper.” The World Bank Research Observer. vol 15, no. 2. 

Arrow, K., et al. 1996. Benefit-Cost Analysis in Environmental, Health, and Safety Regulation: A 

Statement of Principles. Washington, D.C., AEI Press. pp. 13-14.

Arrow, K.J., et al. 1996. ”Intertemporal equity, discounting and economic efficiency,” in 

Climate Change 1995: Economic and Social Dimensions of Climate Change, Contribution of 

Working Group III to the Second Assessment Report of the Intergovernmental Panel on Climate 

Change.

Campbell, J., P. Diamond, and J. Shoven. 2001. “Estimating the Real Rate of Return on Stocks 

Over the Long Term.” Presented to the Social Security Advisory Board. August.

Campbell, K. et al. 2007. The age of consequences: The foreign policy and national security 

implications of global climate change. Center for Strategic & International Studies, 119 pp.

Castles, I. and D. Henderson. 2003. “The IPCC Emission Scenarios: An Economic-Statistical 

Critique.” Energy and Environment 14(2-3): 159-185.

Chetty, R. 2006. “A New Method of Estimating Risk Aversion.” American Economic Review

96(5): 1821–1834.

Dasgupta P. 2006. “Comments on the Stern Review’s economics of climate change.” University 

of Cambridge working paper.

Dasgupta P. 2008. “Discounting climate change.” Journal of Risk and Uncertainty 37:141-169. 

Easterling, W., et al. 2007. Climate Change 2007: Impacts, Adaptation, and Vulnerability. 

Intergovernmental Panel on Climate Change, 976 pp. 
10A-39

Evans D., and H. Sezer. 2005. “Social discount rates for member countries of the European 

Union.” J. Econ. Stud. 32 47–59.

Forest, C., et al. 2002. “Quantifying uncertainties in climate system properties with the use of 

recent observations.” Science 295, 113.

Forest, D., P. Stone, and A. Sokolov. 2006. “Estimated PDFs of climate system properties 

including natural and anthropogenic forcings.” Geophys. Res. Lett., 33, L01705. 

Forster, P., and J. Gregory. 2006. “The climate sensitivity and its components diagnosed from 

Earth radiation budget data.” J. Clim., 19, 39–52. 

Frame, D., et al. 2005. “Constraining climate forecasts: The role of prior assumptions.” Geophys. 

Res. Lett., 32, L09702.

Gingerich, P. 2006. “Environment and evolution through the Paleocene-Eocene thermal 

maximum.” Trends Ecol. Evol. 21: 246-253.

Gollier, C. 2008. “Discounting with fat-tailed economic growth.” Journal of Risk and 

Uncertainty 37:171-186.

Gollier, C. and M. Weitzman (2009). "How Should the Distant Future be Discounted When 

Discount Rates are Uncertain?" Harvard University, mimeo, Nov 2009.

Gregory, J., et al. 2002a. “An observationally based estimate of the climate sensitivity.” J. Clim., 

15(22), 3117–3121. 

Groom, B., Koundouri, P., Panipoulou, E., Pantelidis, T. 2006. “An econometric approach to 

estimating long-run discount rates.” Journal of Applied Econometrics.

Hall R, and C. Jones . 2007. “The value of life and the rise in health spending.” Quarterly 

Journal of Economics 122(1):39-72.

Hansen, J.,M. Sato, P. Kharecha, G. Russell, D. W. Lea and M. Siddall. 2007. “Climate change 

and trace gases.” Phil. Trans. Roy. Soc. A 365: 1925-1954.

Hegerl G, et al. 2007. “Understanding and attributing climate change.” in Solomon S, et al. (eds) 

Climate Change 2007: the physical science basis. Contribution of Working Group I to the 

Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: 

Cambridge University Press.

Hegerl, G., T. Crowley, W. Hyde, and D. Frame. 2006. “Constraints on climate sensitivity from 

temperature reconstructions of the past seven centuries.” Nature 440. 
10A-40

Holtsmark, B., and K. Alfsen. 2005. “PPP Correction of the IPCC Emission Scenarios – Does it 

Matter?” Climatic Change 68(1-2): 11-19.

Hope C. 2006. “The marginal impact of CO2 from PAGE2002: an integrated assessment model 

incorporating the IPCC’s five reasons for concern.” The Integrated Assessment Journal 6(1):19-

56.

Hope C. 2008. “Optimal carbon emissions and the social cost of carbon under uncertainty.” The 

Integrated Assessment Journal 8(1):107-122.

Intergovernmental Panel on Climate Change (2007). “Summary for Policymakers.” In Climate 

Change 2007: The Physical Science Basis, Contribution of Working Group I to the Fourth 

Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University 

Press.

Just, R., D. Hueth, and A. Schmitz. 2004. The Welfare Economics of Public Policy. Glos UK: 

Edward Elgar Publishing Limited.

Knutti, R., T. Stocker, F. Joos, and G. Plattner. 2002. “Constraints on radiative forcing and future 

climate change from observations and climate model ensembles.” Nature, 416, 719–723. 

Kriegler, E. et al. 2009. “Imprecise probability assessment of tipping points in the climate 

system.” Proc. Natl. Acad. Sci. 106: 5041-5046.

Kotlikoff, L. and D. Rapson. 2006. “Does It Pay, at the Margin, to Work and Save? – Measuring 

Effective Marginal Taxes on Americans’ Labor Supply and Saving.” National Bureau of 

Economic Research, Working Paper, No. 12533.

Le Treut H., et al. 2007. “Historical Overview of Climate Change.” in Solomon et al., Climate 

Change 2007.

Lenton, T., et al. 2008. “Tipping elements in the Earth’s climate system.” Proc. Natl. Acad. Sci.

105: 1786-1793.

Levy, M., et al. 2005. “Ecosystem conditions and human well-being.” In: Ecosystems and 

Human Well-being: Current State and Trends, Volume 1. [R. Hassan, R. Scholes, and N. Ash, 

eds.] Washington: Island Press. pp. 123-164.

Lind, R. 1990. “Reassessing the Government’s Discount Rate Policy in Light of New Theory 

and Data in a World Economy with a High Degree of Capital Mobility.” Journal of 

Environmental Economics and Management 18, S-8-S-28.

Mastrandre, M. 2009. “Calculating the benefits of climate policy: Examining the assumptions of 

Integrated Assessment Models.” Pew Center on Global Climate Change Working Paper, 60 pp.

Meehl, G, et al. 2007. “Global Climate Projections.” in Solomon et al., Climate Change 2007.
10A-41

National Research Council (2009). Hidden Costs of Energy: Unpriced Consequences of Energy 

Production and Use. National Academies Press.

Newbold S, Daigneault A. 2009. “Climate response uncertainty and the benefits of greenhouse 

gas emissions reductions.” Environmental and Resource Economics 44:351-377.

Newell, R., and W. Pizer. 2003. Discounting the distant future: how much do uncertain rates 

increase valuations? Journal of Environmental Economics and Management 46: 52-71.

Nordhaus, W. 1994. “Expert Opinion on Climate Change.” American Scientist 82: 45-51.

Nordhaus, W. 2007a. Accompanying notes and documentation on development of DICE-2007 

model: notes on DICE-2007.delta.v8 as of September 21, 2007.

Nordhaus, W. 2007b. “Alternative measures of output in global economic-environmental 

models: Purchasing power parity or market exchange rates?” Energy Economics 29: 349-372.

Nordhaus W. 2008. A Question of Balance: Weighing the Options on Global Warming Policies. 

New Haven, CT: Yale University Press.

Nordhaus, W. 2009. “An Analysis of the Dismal Theorem. Cowles Foundation Discussion 

Paper. No. 1686. January.

Nordhaus W., and Boyer J. 2000. Warming the World: Economic Models of Global Warming. 

Cambridge, MA: MIT Press.

Pindyck, R. 2009. “Uncertain Outcomes and Climate Change Policy.” NBER Working Paper, 

No. 15259. August.

Ramsey, F. 1928. “A Mathematical Theory of Saving.” The Economic Journal 38(152): 543-

559.

Roe, G. 2008. “Feedbacks, timescales, and seeing red.” Annual Review of Earth and Planetary 

Sciences 37:5.1-5.23. 

Roe, G., and M. Baker. 2007. “Why is climate sensitivity so unpredictable?” Science 318:629-

632.

Schneider von Deimling, T., H. Held, A. Ganopolski, and S. Rahmstorf. 2006. “Climate 

sensitivity estimated from ensemble simulations of glacial climate.” Clim. Dyn., 27, 149–163. 

Smith, J. et al. 2009. “Transient dwarfism of soil fauna during the Paleocene-Eocene Thermal 

Maximum.” Proc. Natl. Acad. Sci. 106: 17665-17660.

Stern, N., et al. (2006), Stern Review: The Economics of Climate Change, HM Treasury, London.
10A-42

Stern N. 2008. “The economics of climate change.” American Economic Review 98(2):1-37.

Sterner, T., and U. Persson. 2008. An even Sterner review: Introducing relative prices into the 

discounting debate. Rev. Env. Econ. Pol. 2: 61-76.

Summers, L., and R. Zeckhauser. 2008. “Policymaking for Prosperity.” Journal of Risk and 

Uncertainty 37: 115-140.

Szpiro, G. 1986. “Measuring Risk Aversion: An Alternative Approach.” The Review of 

Economics and Statistics 68(1): 156-9.

Tol, R. 2002a. “Estimates of the damage costs of climate change. Part I: benchmark estimates.” 

Environmental and Resource Economics 21:47-73.

Tol, R. 2002b. “Estimates of the damage costs of climate change. Part II: dynamic estimates.” 

Environmental and Resource Economics 21:135-160.

Tol, R. 2006. “Exchange Rates and Climate Change: An Application of FUND.” Climatic 

Change 75(1-2): 59-80.

Tol, R. 2009. “An analysis of mitigation as a response to climate change.” Copenhagen 

Consensus on Climate. Discussion Paper.

U.S. Department of Defense. 2010. Quadrennial Defense Review Report. February.

Warren, R., et al. 2006. “Spotlight Impact Functions in Integrated Assessment.” Tyndall Center 

for Climate Change Research, Working Paper 91.

Weitzman, M. 2009. “On modeling and interpreting the economics of catastrophic climate 

change.” Review of Economics and Statistics 91:1-19.

Weitzman, M. 2007. “A review of The Stern Review of the Economics of Climate Change.” 

Journal of Economic Literature 45:703-724.

Weitzman, M. 1999. In Portney P.R. and Weyant J.P. (eds.), Discounting and Intergenerational 

Equity, Resources for the Future, Washington, D.C.

Weitzman, M. 1998. “Why the Far-Distant Future Should Be Discounted at Its Lowest Possible 

Rate.” Journal of Environmental Economics and Management 36 (3): 201-208.

Wing, S. et al. 2005. “Transient floral change and rapid global warming at the Paleocene-Eocene 

boundary.” Science 310: 993-996.
10A-43

10A.9 ANNEX

Table 10A.9.1 Annual SCC Values: 2010–2050 (in 2007 dollars)

Discount Rate 5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2011 4.9 21.9 35.7 66.5

2012 5.1 22.4 36.4 68.1

2013 5.3 22.8 37.0 69.6

2014 5.5 23.3 37.7 71.2

2015 5.7 23.8 38.4 72.8

2016 5.9 24.3 39.0 74.4

2017 6.1 24.8 39.7 76.0

2018 6.3 25.3 40.4 77.5

2019 6.5 25.8 41.0 79.1

2020 6.8 26.3 41.7 80.7

2021 7.1 27.0 42.5 82.6

2022 7.4 27.6 43.4 84.6

2023 7.7 28.3 44.2 86.5

2024 7.9 28.9 45.0 88.4

2025 8.2 29.6 45.9 90.4

2026 8.5 30.2 46.7 92.3

2027 8.8 30.9 47.5 94.2

2028 9.1 31.5 48.4 96.2

2029 9.4 32.1 49.2 98.1

2030 9.7 32.8 50.0 100.0

2031 10.0 33.4 50.9 102.0

2032 10.3 34.1 51.7 103.9

2033 10.6 34.7 52.5 105.8

2034 10.9 35.4 53.4 107.8

2035 11.2 36.0 54.2 109.7

2036 11.5 36.7 55.0 111.6

2037 11.8 37.3 55.9 113.6

2038 12.1 37.9 56.7 115.5

2039 12.4 38.6 57.5 117.4

2040 12.7 39.2 58.4 119.3

2041 13.0 39.8 59.0 121.0

2042 13.3 40.4 59.7 122.7

2043 13.6 40.9 60.4 124.4

2044 13.9 41.5 61.0 126.1

2045 14.2 42.1 61.7 127.8

2046 14.5 42.6 62.4 129.4

2047 14.8 43.2 63.0 131.1

2048 15.1 43.8 63.7 132.8

2049 15.4 44.4 64.4 134.5

2050 15.7 44.9 65.0 136.2

This Annex provides additional technical information about the non-CO2 emission 

projections used in the modeling and the method for extrapolating emissions forecasts through 

2300 and shows the full distribution of 2010 SCC estimates by model and scenario combination. 
10A-44

10A.9.1 Other (non-CO2) gases

In addition to fossil and industrial CO2 emissions, each EMF scenario provides 

projections of methane (CH4), nitrous oxide (N2O), fluorinated gases, and net land use CO2

emissions to 2100. These assumptions are used in all three IAMs while retaining each model’s 

default radiative forcings (RF) due to other factors (e.g., aerosols and other gases). Specifically, 

to obtain the RF associated with the non-CO2 EMF emissions only, we calculated the RF 

associated with the EMF atmospheric CO2 concentrations and subtracted them from the EMF 

total RF.ee This approach respects the EMF scenarios as much as possible and at the same time 

takes account of those components not included in the EMF projections. Since each model treats 

non-CO2 gases differently (e.g., DICE lumps all other gases into one composite exogenous 

input), this approach was applied slightly differently in each of the models. 

FUND: Rather than relying on RF for these gases, the actual emissions from each 

scenario were used in FUND. The model default trajectories for CH4, N20, SF6, and the CO2

emissions from land were replaced with the EMF values. 

PAGE: PAGE models CO2, CH4, sulfur hexafluoride (SF6), and aerosols and contains an 

"excess forcing" vector that includes the RF for everything else. To include the EMF values, we 

removed the default CH4 and SF6 factorsff, decomposed the excess forcing vector, and 

constructed a new excess forcing vector that includes the EMF RF for CH4, N20, and fluorinated 

gases, as well as the model default values for aerosols and other factors. Net land use CO2

emissions were added to the fossil and industrial CO2 emissions pathway. 

DICE: DICE presents the greatest challenge because all forcing due to factors other than 

industrial CO2 emissions is embedded in an exogenous non-CO2 RF vector. To decompose this 

exogenous forcing path into EMF non-CO2 gases and other gases, we relied on the references in 

DICE2007 to the Intergovernmental Panel on Climate Change’s (IPCC) Fourth Assessment 

Report (AR4) and the discussion of aerosol forecasts in the IPCC’s Third Assessment Report 

(TAR) and in AR4, as explained below. In DICE2007, Nordhaus assumes that exogenous 

forcing from all non-CO2 sources is -0.06 W/m2 in 2005, as reported in AR4, and increases 

linearly to 0.3 W/m2 in 2105, based on GISS projections, and then stays constant after that time.

According to AR4, the RF in 2005 from CH4, N20, and halocarbons (approximately 

similar to the F-gases in the EMF-22 scenarios) was 0.48 + 0.16 + 0.34 = 0.98 W/m2 and RF 

from total aerosols was -1.2 W/m2. Thus, the -.06 W/m2 non-CO2 forcing in DICE can be 

 ee Note EMF did not provide CO2 concentrations for the IMAGE reference scenario. Thus, for this scenario, we fed 

the fossil, industrial, and land CO2 emissions into MAGICC (considered a "neutral arbiter" model, which is tuned to 

emulate the major global climate models) and the resulting CO2 concentrations were used. Note also that MERGE 

assumes a neutral biosphere so net land CO2 emissions are set to zero for all years for the MERGE Optimistic 

reference scenario, and for the MERGE component of the average 550 scenario (i.e., we add up the land use 

emissions from the other three models and divide by 4).

ff Both the model default CH4 emissions and the initial atmospheric CH4 is set to zero to avoid double counting the 

effect of past CH4 emissions.
10A-45

decomposed into: 0.98 W/m2 due to the EMF non-CO2 gases, -1.2 W/m2 due to aerosols, and the 

remainder, 0.16 W/m2, due to other residual forcing. 

For subsequent years, we calculated the DICE default RF from aerosols and other nonCO2 gases based on the following two assumptions:

(1) RF from aerosols declines linearly from 2005 to 2100 at the rate projected by the TAR 

and then stays constant thereafter; and 

(2) With respect to RF from non-CO2 gases not included in the EMF-22 scenarios, the share 

of non-aerosol RF matches the share implicit in the AR4 summary statistics cited above and 

remains constant over time. 

Assumption (1) means that the RF from aerosols in 2100 equals 66 percent of that in 2000, 

which is the fraction of the TAR projection of total RF from aerosols (including sulfates, black 

carbon, and organic carbon) in 2100 vs. 2000 under the A1B SRES emissions scenario. Since 

the SRES marker scenarios were not updated for the AR4, the TAR provides the most recent 

IPCC projection of aerosol forcing. We rely on the A1B projection from the TAR because it 

provides one of the lower aerosol forecasts among the SRES marker scenarios and is more 

consistent with the AR4 discussion of the post-SRES literature on aerosols: 

Aerosols have a net cooling effect and the representation of aerosol and aerosol precursor 

emissions, including sulfur dioxide, black carbon and organic carbon, has improved in the 

post-SRES scenarios. Generally, these emissions are projected to be lower than reported in 

SRES. {WGIII 3.2, TS.3, SPM}.gg

Assuming a simple linear decline in aerosols from 2000 to 2100 also is more consistent 

with the recent literature on these emissions. For example, the figure below shows that the sulfur 

dioxide emissions peak over the short term of some SRES scenarios above the upper bound 

estimates of the more recent scenarios.hh Recent scenarios project sulfur emissions to peak earlier 

and at lower levels compared to the SRES in part because of new information about present and 

planned sulfur legislation in some developing countries, such as India and China.ii

 The lowerbound projections of the recent literature have also shifted downward slightly compared to the 

SRES scenario (IPCC 2007). 

 gg AR4 Synthesis Report, p. 44, www.ipcc.ch/pdf/assessment-report/ar4/syr/ar4_syr.pdf

hh See Smith, S.J., R. Andres, E. Conception, and J. Lurz, 2004: Historical sulfur dioxide emissions, 1850-2000: 

methods and results. Joint Global Research Institute, College Park, 14 pp. ii See Carmichael, G., D. Streets, G. Calori, M. Amann, M. Jacobson, J. Hansen, and H. Ueda, 2002: Changing 

trends in sulphur emissions in Asia: implications for acid deposition, air pollution, and climate. Environmental 

Science and Technology, 36(22):4707- 4713; Streets, D., K. Jiang, X. Hu, J. Sinton, X.-Q. Zhang, D. Xu, M. 

Jacobson, and J. Hansen, 2001: Recent reductions in China’s greenhouse gas emissions. Science, 294(5548): 1835-

1837.
10A-46

With these assumptions, the DICE aerosol forcing changes from -1.2 in 2005 to -0.792 in 

2105 W/m2; forcing due to other non-CO2 gases not included in the EMF scenarios declines from 

0.160 to 0.153 W/m2

. 

.

Figure 10A.9.1 Sulfur Dioxide Emission Scenarios

Notes: Thick colored lines depict the four SRES marker scenarios and black dashed lines show the median, 5th, and 

95th percentile of the frequency distribution for the full ensemble of 40 SRES scenarios. The blue area (and the thin 

dashed lines in blue) illustrates individual scenarios and the range of Smith et al. (2004). Dotted lines indicate the 

minimum and maximum of SO2 emissions scenarios developed pre-SRES.

Source: IPCC (2007), AR4 WGIII 3.2, www.ipcc.ch/publications_and_data/ar4/wg3/en/ch3-ens3-2-2-4.html.

Although other approaches to decomposing the DICE exogenous forcing vector are 

possible, initial sensitivity analysis suggests that the differences among reasonable alternative 

approaches are likely to be minor. For example, adjusting the TAR aerosol projection above to 

assume that aerosols will be maintained at 2000 levels through 2100 reduces average SCC values 

(for 2010) by approximately 3 percent (or less than $2); assuming all aerosols are phased out by 

2100 increases average 2010 SCC values by 6-7 percent (or $0.50-$3)–depending on the 

discount rate. These differences increase slightly for SCC values in later years but are still well 

within 10 percent of each other as far out as 2050. 

Finally, as in PAGE, the EMF net land use CO2 emissions are added to the fossil and 

industrial CO2 emissions pathway. 

10A.9.2 Extrapolating Emissions Projections to 2300

To run each model through 2300 requires assumptions about GDP, population, 

greenhouse gas emissions, and radiative forcing trajectories after 2100, the last year for which 

these projections are available from the EMF-22 models. These inputs were extrapolated from 

2100 to 2300 as follows:
10A-47

1. Population growth rate declines linearly, reaching zero in the year 2200.

2. GDP/per capita growth rate declines linearly, reaching zero in the year 2300.

3. The decline in the fossil and industrial carbon intensity (CO2/GDP) growth rate over 

2090-2100 is maintained from 2100 through 2300.

4. Net land use CO2 emissions decline linearly, reaching zero in the year 2200.

5. Non-CO2 radiative forcing remains constant after 2100.

Long run stabilization of GDP per capita was viewed as a more realistic simplifying 

assumption than a linear or exponential extrapolation of the pre-2100 economic growth rate of 

each EMF scenario. This is based on the idea that increasing scarcity of natural resources and 

the degradation of environmental sinks available for assimilating pollution from economic 

production activities may eventually overtake the rate of technological progress. Thus, the 

overall rate of economic growth may slow over the very long run. The interagency group also 

considered allowing an exponential decline in the growth rate of GDP per capita. However, 

since this would require an additional assumption about how close to zero the growth rate would 

get by 2300, the group opted for the simpler and more transparent linear extrapolation to zero by 

2300. 

The population growth rate is also assumed to decline linearly, reaching zero by 2200. 

This assumption is reasonably consistent with the United Nations long run population forecast, 

which estimates global population to be fairly stable after 2150 in the medium scenario (UN 

2004).jj

 The resulting range of EMF population trajectories (figure below) also encompass the 

UN medium scenario forecasts through 2300—global population of 8.5 billion by 2200, and 9 

billion by 2300. 

Maintaining the decline in the 2090-2100 carbon intensity growth rate (i.e., CO2 per 

dollar of GDP) through 2300 assumes that technological improvements and innovations in the 

areas of energy efficiency and other carbon reducing technologies (possibly including currently 

unavailable methods) will continue to proceed at roughly the same pace that is projected to occur 

towards the end of the forecast period for each EMF scenario. This assumption implies that total 

cumulative emissions in 2300 will be between 5,000 and 12,000 GtC, which is within the range 

of the total potential global carbon stock estimated in the literature.

 

Net land use CO2 emissions are expected to stabilize in the long run, so in the absence of 

any post 2100 projections, the group assumed a linear decline to zero by 2200. Given no a priori 

reasons for assuming a long run increase or decline in non-CO2 radiative forcing, it is assumed to 

remain at the 2100 levels for each EMF scenario through 2300. 

Figures below show the paths of global population, GDP, fossil and industrial CO2

emissions, net land CO2 emissions, non-CO2 radiative forcing, and CO2 intensity (fossil and 

industrial CO2 emissions/GDP) resulting from these assumptions. 

 jj United Nations. 2004. World Population to 2300. 

www.un.org/esa/population/publications/longrange2/WorldPop2300final.pdf
10A-48

Figure 10A.9.2 Global Population, 2000-2300 (Post-2100 extrapolations 

assume the population growth rate changes linearly to reach a 

zero growth rate by 2200.)

Note: In the fifth scenario, 2000-2100 population is equal to the average of the population under 

the 550 ppm CO2e, full-participation, not-to-exceed scenarios considered by each of the four 

models. 

5.5

6.5

7.5

8.5

9.5

10.5

11.5

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Global Population [billion]

Year
10A-49

Figure 10A.9.3 World GDP, 2000-2300 (Post-2100 extrapolations assume GDP 

per capita growth declines linearly, reaching zero in the year 

2300)

Note: In the fifth scenario, 2000-2100 GDP is equal to the average of the GDP under the 550 ppm CO2e, fullparticipation, not-to-exceed scenarios considered by each of the four models. 

0

500

1000

1500

2000

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Year

World GDP [$trillion]
10A-50

Figure 10A.9.4 Global Fossil and Industrial CO2 Emissions, 2000-2300 (Post2100 extrapolations assume growth rate of CO2 intensity 

(CO2/GDP) over 2090-2100 is maintained through 2300)

Note: In the fifth scenario, 2000-2100 emissions are equal to the average of the emissions under the 550 ppm CO2e, 

full-participation, not-to-exceed scenarios considered by each of the four models. 

0

50

100

150

200

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Fossil&Industrial CO2 Emissions [GtCO2/decade]

Year
10A-51

Figure 10A.9.5 Global Net Land Use CO2 Emissions, 2000-2300 (Post-2100 

extrapolations assume emissions decline linearly, reaching zero 

in the year 2200)

kk

Note: In the fifth scenario, 2000-2100 emissions are equal to the average of the emissions under the 550 ppm CO2e, 

full-participation, not-to-exceed scenarios considered by each of the four models. 

 kk MERGE assumes a neutral biosphere so net land CO2 emissions are set to zero for all years for the MERGE 

Optimistic reference scenario, and for the MERGE component of the average 550 scenario (i.e., we add up the land 

use emissions from the other three models and divide by 4).

-3.00

-2.00

-1.00

0.00

1.00

2.00

3.00

4.00

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scneario

Extrapolations

Net land CO2 emissions [GtCO2/yr]

Year

z
10A-52

Figure 10A.9.6 Global Non-CO2 Radiative Forcing, 2000-2300 (Post-2100 

extrapolations assume constant non-CO2 radiative forcing 

after 2100)

Note: In the fifth scenario, 2000-2100 emissions are equal to the average of the emissions under the 550 ppm CO2e, 

full-participation, not-to-exceed scenarios considered by each of the four models. 

0.00

0.75

1.50

2.25

3.00

3.75

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

nonCO2 RF [W/m^2]

Year
10A-53

Figure 10A.9.7 Global CO2 Intensity (fossil & industrial CO2 emissions/GDP), 

2000-2300 (Post-2100 extrapolations assume decline in 

CO2/GDP growth rate over 2090-2100 is maintained through 

2300)

Note: In the fifth scenario, 2000-2100 emissions are equal to the average of the emissions under the 550 ppm CO2e, 

full-participation, not-to-exceed scenarios considered by each of the four models. 

0

0

0

0

0

1

1

1

1

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Fossil & Industrial CO2 intensity [GtCO2/trillion $]

Year
10A-54

Table 10A.9.2 2010 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 3.3 5.9 8.1 13.9 28.8 65.5 68.2 147.9 239.6 563.8

MERGE optimistic 1.9 3.2 4.3 7.2 14.6 34.6 36.2 79.8 124.8 288.3

Message 2.4 4.3 5.8 9.8 20.3 49.2 50.7 114.9 181.7 428.4

MiniCAM base 2.7 4.6 6.4 11.2 22.8 54.7 55.7 120.5 195.3 482.3

5th scenario 2.0 3.5 4.7 8.1 16.3 42.9 41.5 103.9 176.3 371.9

Scenario DICE

IMAGE 16.4 21.4 25 33.3 46.8 54.2 69.7 96.3 111.1 130.0

MERGE optimistic 9.7 12.6 14.9 19.7 27.9 31.6 40.7 54.5 63.5 73.3

Message 13.5 17.2 20.1 27 38.5 43.5 55.1 75.8 87.9 103.0

MiniCAM base 13.1 16.7 19.8 26.7 38.6 44.4 56.8 79.5 92.8 109.3

5th scenario 10.8 14 16.7 22.2 32 37.4 47.7 67.8 80.2 96.8

Scenario FUND

IMAGE -33.1 -18.9 -13.3 -5.5 4.1 19.3 18.7 43.5 67.1 150.7

MERGE optimistic -33.1 -14.8 -10 -3 5.9 14.8 20.4 43.9 65.4 132.9

Message -32.5 -19.8 -14.6 -7.2 1.5 8.8 13.8 33.7 52.3 119.2

MiniCAM base -31.0 -15.9 -10.7 -3.4 6 22.2 21 46.4 70.4 152.9

5th scenario -32.2 -21.6 -16.7 -9.7 -2.3 3 6.7 20.5 34.2 96.8
10A-55

Table 10A.9.3 2010 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 2.0 3.5 4.8 8.1 16.5 39.5 41.6 90.3 142.4 327.4

MERGE optimistic 1.2 2.1 2.8 4.6 9.3 22.3 22.8 51.3 82.4 190.0

Message 1.6 2.7 3.6 6.2 12.5 30.3 31 71.4 115.6 263.0

MiniCAM base 1.7 2.8 3.8 6.5 13.2 31.8 32.4 72.6 115.4 287.0

5th scenario 1.3 2.3 3.1 5 9.6 25.4 23.6 62.1 104.7 222.5

Scenario DICE

IMAGE 11.0 14.5 17.2 22.8 31.6 35.8 45.4 61.9 70.8 82.1

MERGE optimistic 7.1 9.2 10.8 14.3 19.9 22 27.9 36.9 42.1 48.8

Message 9.7 12.5 14.7 19 26.6 29.8 37.8 51.1 58.6 67.4

MiniCAM base 8.8 11.5 13.6 18 25.2 28.8 36.9 50.4 57.9 67.8

5th scenario 7.9 10.1 11.8 15.6 21.6 24.9 31.8 43.7 50.8 60.6

Scenario FUND

IMAGE -25.2 -15.3 -11.2 -5.6 0.9 8.2 10.4 25.4 39.7 90.3

MERGE optimistic -24.0 -12.4 -8.7 -3.6 2.6 8 12.2 27 41.3 85.3

Message -25.3 -16.2 -12.2 -6.8 -0.5 3.6 7.7 20.1 32.1 72.5

MiniCAM base -23.1 -12.9 -9.3 -4 2.4 10.2 12.2 27.7 42.6 93.0

5th scenario -24.1 -16.6 -13.2 -8.3 -3 -0.2 2.9 11.2 19.4 53.6
10A-56

Table 10A.9.4 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 0.5 0.8 1.1 1.8 3.5 8.3 8.5 19.5 31.4 67.2

MERGE optimistic 0.3 0.5 0.7 1.2 2.3 5.2 5.4 12.3 19.5 42.4

Message 0.4 0.7 0.9 1.6 3 7.2 7.2 17 28.2 60.8

MiniCAM base 0.3 0.6 0.8 1.4 2.7 6.4 6.6 15.9 24.9 52.6

5th scenario 0.3 0.6 0.8 1.3 2.3 5.5 5 12.9 22 48.7

Scenario DICE

IMAGE 4.2 5.4 6.2 7.6 10 10.8 13.4 16.8 18.7 21.1

MERGE optimistic 2.9 3.7 4.2 5.3 7 7.5 9.3 11.7 12.9 14.4

Message 3.9 4.9 5.5 7 9.2 9.8 12.2 15.4 17.1 18.8

MiniCAM base 3.4 4.2 4.7 6 7.9 8.6 10.7 13.5 15.1 16.9

5th scenario 3.2 4 4.6 5.7 7.6 8.2 10.2 12.8 14.3 16.0

Scenario FUND

IMAGE -11.7 -8.4 -6.9 -4.6 -2.2 -1.3 0.7 4.1 7.4 17.4

MERGE optimistic -10.6 -7.1 -5.6 -3.6 -1.3 -0.3 1.6 5.4 9.1 19.0

Message -12.2 -8.9 -7.3 -4.9 -2.5 -1.9 0.3 3.5 6.5 15.6

MiniCAM base -10.4 -7.2 -5.8 -3.8 -1.5 -0.6 1.3 4.8 8.2 18.0

5th scenario -10.9 -8.3 -7 -5 -2.9 -2.7 -0.8 1.4 3.2 9.2

Figure 10A.9.8 Histogram of Global SCC Estimates in 2010 (2007$/ton CO2), 

by discount rate 

* The distribution of SCC values ranges from -$5,192 to $66,116, but the X-axis has been 

truncated at approximately the 1st and 99th percentiles to better show the data.

0

2000

4000

6000

8000

10000

12000

14000

16000

-25-12

1

14274053667992

105118131144157170183196209222235248261274

All 2010, 5.0% All 2010, 3.0% All 2010, 2.5%
10A-57

Table 10A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates 

Discount 

Rate

Scenario

DICE PAGE FUND

5%

Mean 9 6.5 -1.3

Variance 13.1 136 70.1

Skewness 0.8 6.3 28.2

Kurtosis 0.2 72.4 1,479.00

3%

Mean 28.3 29.8 6

Variance 209.8 3,383.70 16,382.50

Skewness 1.1 8.6 128

Kurtosis 0.9 151 18,976.50

2.50%

Mean 42.2 49.3 13.6

Variance 534.9 9,546.00 #######

Skewness 1.2 8.7 149

Kurtosis 1.1 143.8 23,558.30
10B-i

APPENDIX 10B. TECHNICAL UPDATE OF SOCIAL COST OF CARBON FOR 

REGULATORY IMPACT ANALYSIS UNDER EXECUTIVE ORDER 12866

TABLE OF CONTENTS

10B.1 PREFACE............................................................................................................... 10B-1

10B.2 PURPOSE............................................................................................................... 10B-1

10B.3 SUMMARY OF MODEL UPDATES.................................................................... 10B-2

10B.3.1 Dice ..................................................................................................................... 10B-2

10B.3.1.1 Carbon Cycle Parameters............................................................................ 10B-2

10B.3.1.2 Sea Level Dynamics ................................................................................... 10B-3

10B.3.1.3 Re-calibrated Damage Function ................................................................. 10B-4

10B.3.2 Fund .................................................................................................................... 10B-4

10B.3.2.1 Space Heating ............................................................................................. 10B-5

10B.3.2.2 Sea Level Rise and Land Loss.................................................................... 10B-5

10B.3.2.3 Agriculture .................................................................................................. 10B-5

10B.3.2.4 Temperature Response Model .................................................................... 10B-6

10B.3.2.5 Methane....................................................................................................... 10B-6

10B.3.3 Page..................................................................................................................... 10B-6

10B.3.3.1 Sea Level Rise............................................................................................. 10B-7

10B.3.3.2 Revised Damage Function to Account for Saturation ................................ 10B-7

10B.3.3.3 Regional Scaling Factors ............................................................................ 10B-7

10B.3.3.4 Probability of a Discontinuity..................................................................... 10B-7

10B.3.3.5 Adaptation................................................................................................... 10B-8

10B.3.3.6 Other Noteworthy Changes......................................................................... 10B-8

10B.4 REVISED SCC ESTIMATES ................................................................................ 10B-9

10B.5 OTHER MODEL LIMITATIONS OR RESEARCH GAPS ............................... 10B-11

REFERENCES ..................................................................................................................... 10B-12

10B.6 ANNEX................................................................................................................. 10B-14

LIST OF TABLES

Table 10B.4.1 Revised Social Cost of CO2, 2010 – 2050 (in 2007 dollars per ton of 

CO2) .........................................................................................................10B-10

Table 10B.4.2 Average Annual Growth Rates of SCC Estimates between 2010 and 

2050..........................................................................................................10B-11

Table 10B.5.1 Annual SCC Values: 2010-2050 (2007$/ton CO2)..................................10B-14

Table 10B.5.2 202 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton 

CO2) .........................................................................................................10B-15

Table 10B.5.3 SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2) .................10B-15

Table 10B.5.4 2020 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton 

CO2) .........................................................................................................10B-16
10B-ii

LIST OF FIGURES

Figure 10B.4.1 Distribution of SCC Estimates for 2020 (in 2007$ per ton CO2) ............10B-10
10B-1

APPENDIX 10B. TECHNICAL UPDATE OF SOCIAL COST OF CARBON FOR 

REGULATORY IMPACT ANALYSIS UNDER EXECUTIVE ORDER 12866

10B.1 PREFACE

The following text is reproduced almost verbatim from the May 2013 report of the 

Interagency Working Group on the Social Cost of Carbon of the United States Government. 

Minor changes were made to the working group's report to make it more consistent with the rest 

of this technical support document.

10B.2 PURPOSE

The purpose of this document is to update the schedule of social cost of carbon (SCC) a

estimates from the 2010 interagency technical support document (TSD) (Interagency Working 

Group on Social Cost of Carbon 2010).1 E.O. 13563 commits the Administration to regulatory 

decision making “based on the best available science.” b Additionally, the interagency group 

recommended in 2010 that the SCC estimates be revisited on a regular basis or as model updates 

that reflect the growing body of scientific and economic knowledge become available. c

 New 

versions of the three integrated assessment models used by the U.S. government to estimate the 

SCC (DICE, FUND, and PAGE), are now available and have been published in the peer 

reviewed literature. While acknowledging the continued limitations of the approach taken by the 

interagency group in 2010 (documented in the original 2010 TSD), this document provides an 

update of the SCC estimates based solely on the latest peer-reviewed version of the models, 

replacing model versions that were developed up to ten years ago in a rapidly evolving field. It 

does not revisit other assumptions with regard to the discount rate, reference case socioeconomic 

and emission scenarios, or equilibrium climate sensitivity. Improvements in the way damages are 

modeled are confined to those that have been incorporated into the latest versions of the models 

by the developers themselves in the peer-reviewed literature. The Environmental Protection 

Agency (EPA), in collaboration with other Federal agencies such as the Department of Energy 

(DOE), continues to investigate potential improvements to the way in which economic damages 

associated with changes in CO2 emissions are quantified. 

Section 10B.3 summarizes the major updates relevant to SCC estimation that are 

contained in the new versions of the integrated assessment models released since the 2010 

interagency report. Section 10B.4 presents the updated schedule of SCC estimates for 2010 –

2050 based on these versions of the models.

 

a

 In this document, we present all values of the SCC as the cost per metric ton of CO2 emissions. Alternatively, one 

could report the SCC as the cost per metric ton of carbon emissions. The multiplier for translating between mass of 

CO2 and the mass of carbon is 3.67. b www.whitehouse.gov/sites/de5fault/files/omb/inforeg/eo12866/eo13563_01182011.pdf c See p. 1, 3, 4, 29, and 33 (Interagency Working Group on Social Cost of Carbon 2010).1
10B-2

10B.3 SUMMARY OF MODEL UPDATES

This section briefly summarizes changes integrated into the most recent versions of the 

three integrated assessment models (IAMs) used by the interagency group in 2010. We focus on 

describing those model updates that are relevant to estimating the social cost of carbon. For 

example, both the DICE and PAGE models now include an explicit representation of sea level 

rise damages. Other revisions to PAGE include: updated adaptation assumptions, revisions to 

ensure damages are constrained GDP, updated regional scaling of damages, and a revised 

treatment of potentially abrupt shifts in climate damages. In the most recent version of DICE, 

the model’s simple carbon cycle has been updated to be more consistent with a relatively more 

complex climate model. The FUND model includes updated damage functions for sea level rise 

impacts, the agricultural sector, and reduced space heating requirements, as well as changes to 

the response of temperature to the buildup of GHG concentrations and the inclusion of indirect 

effects of methane emissions. Changes made to parts of the models that are superseded by the 

interagency working group’s modeling assumptions – regarding climate sensitivity, discounting, 

and socioeconomic variables – are not discussed.

10B.3.1 DICE

Changes in the DICE model relevant for the SCC estimates developed by the interagency 

working group include: 1) updated parameter values for the carbon cycle model, 2) an explicit 

representation of sea level dynamics, and 3) a re-calibrated damage function that includes an 

explicit representation of economic damages from sea level rise. Changes were also made to 

other parts of the DICE model—including the equilibrium climate sensitivity parameter, the rate 

of change of total factor productivity, and the elasticity of the marginal utility of consumption—

but these components of DICE are superseded by the interagency working group’s assumptions 

and so will not be discussed here. More details on DICE2007 can be found in Nordhaus (2008)2

and on DICE2010 in Nordhaus (2010)3 and the associated on-line appendix containing 

supplemental information.

10B.3.1.1 Carbon Cycle Parameters

DICE uses a three-box model of carbon stocks and flows to represent the accumulation 

and transfer of carbon among the atmosphere, the shallow ocean and terrestrial biosphere, and 

the deep ocean. These parameters are “calibrated to match the carbon cycle in the Model for the 

Assessment of Greenhouse Gas Induced Climate Change (MAGICC)” (Nordhaus 2008 p 44).2 d

Carbon cycle transfer coefficient values in DICE2010 are based on re-calibration of the model to 

match the newer version of MAGICC (Nordhaus 2010 p 2).3 For example, in DICE2010 in each 

decade, 12 percent of the carbon in the atmosphere is transferred to the shallow ocean, 4.7 

percent of the carbon in the shallow ocean is transferred to the atmosphere, 94.8 percent remains 

in the shallow ocean, and 0.5 percent is transferred to the deep ocean. For comparison, in DICE 

2007, 18.9 percent of the carbon in the atmosphere is transferred to the shallow ocean each 

 d MAGICC is a simple climate model initially developed within the U.S. National Center for Atmospheric Research 

that has been used heavily by the Intergovernmental Panel on Climate Change (IPCC) to emulate projections from 

much more sophisticated state of the art earth system simulation models (Randall et al. 2007).4
10B-3

decade, 9.7 percent of the carbon in the shallow ocean is transferred to the atmosphere, 85.3 

percent remains in the shallow ocean, and 5 percent is transferred to the deep ocean.

The implication of these changes for DICE2010 is in general a weakening of the ocean as 

a carbon sink and therefore a higher concentration of carbon in the atmosphere than in 

DICE2007, for a given path of emissions. All else equal, these changes will generally increase 

the level of warming and therefore the SCC estimates in DICE2010 relative to those from 

DICE2007.

10B.3.1.2 Sea Level Dynamics

A new feature of DICE2010 is an explicit representation of the dynamics of the global 

average sea level anomaly to be used in the updated damage function (discussed below). This 

section contains a brief description of the sea level rise (SLR) module; a more detailed 

description can be found on the model developer’s website. e The average global sea level 

anomaly is modeled as the sum of four terms that represent contributions from: 1) thermal 

expansion of the oceans, 2) melting of glaciers and small ice caps, 3) melting of the Greenland 

ice sheet, and 4) melting of the Antarctic ice sheet. 

The parameters of the four components of the SLR module are calibrated to match 

consensus results from the IPCC’s Fourth Assessment Report.4,f The rise in sea level from 

thermal expansion in each time period (decade) is 2 percent of the difference between the sea 

level in the previous period and the long run equilibrium sea level, which is 0.5 meters per 

degree Celsius (°C) above the average global temperature in 1900. The rise in sea level from the 

melting of glaciers and small ice caps occurs at a rate of 0.008 meters per decade per °C above 

the average global temperature in 1900.

The contribution to sea level rise from melting of the Greenland ice sheet is more 

complex. The equilibrium contribution to SLR is 0 meters for temperature anomalies less than 1

o

C and increases linearly from 0 meters to a maximum of 7.3 meters. The contribution to SLR in 

each period is proportional to the difference between the previous period’s sea level anomaly and 

the equilibrium sea level anomaly, where the constant of proportionality increases with the 

temperature anomaly in the current period.

The contribution to SLR from the melting of the Antarctic ice sheet is -0.001 meters per 

decade when the temperature anomaly is below 3 °C and increases linearly to a maximum rate of 

0.025 meters per decade at a temperature anomaly of 6 °C.

 e Documentation on the new sea level rise module of DICE is available on William Nordhaus’ website at: 

www.econ.yale.edu/~nordhaus/homepage/documents/SLR_021910.pdf. f For a review of post-IPCC AR4 research on sea level rise, see Nicholls et al. (2011)

5 and NAS (2011).6
10B-4

10B.3.1.3 Re-calibrated Damage Function

Economic damages from climate change in the DICE model are represented by a 

fractional loss of gross economic output in each period. A portion of the remaining economic 

output in each period (net of climate change damages) is consumed and the remainder is invested 

in the physical capital stock to support future production, so each period’s climate damages will 

reduce consumption in that period and in all future periods due to the lost investment. The 

fraction of output in each period that is lost due to climate change impacts is represented as one 

minus a fraction, which is one divided by a quadratic function of the temperature anomaly, 

producing a sigmoid (“S”-shaped) function. The loss function in DICE2010 has been expanded 

by adding a quadratic function of SLR to the quadratic function of temperature. In DICE2010 the 

temperature anomaly coefficients have been recalibrated to avoid double-counting damages from 

sea level rise that were implicitly included in these parameters in DICE2007. 

The aggregate damages in DICE2010 are illustrated by Nordhaus (2010 p 3),3 who notes 

that “...damages in the uncontrolled (baseline) (i.e., reference) case ... in 2095 are $12 trillion, 

or 2.8 percent of global output, for a global temperature increase of 3.4 o

C above 1900 levels.” 

This compares to a loss of 3.2 percent of global output at 3.4 oC in DICE2007. However, in 

DICE2010 (as downloaded from the homepage of William Nordhaus), annual damages are lower 

in most of the early periods but higher in later periods of the time horizon than would be 

calculated using the DICE2007 damage function. Specifically, the percent difference between 

damages in the base run of DICE2010 and those that would be calculated using the DICE2007 

damage function starts at +7 percent in 2005, decreases to a low of -14 percent in 2065, then 

continuously increases to +20 percent by 2300 (the end of the interagency analysis time horizon), 

and to +160 percent by the end of the model time horizon in 2595. The large increases in the far 

future years of the time horizon are due to the permanence associated with damages from sea 

level rise, along with the assumption that the sea level is projected to continue to rise long after 

the global average temperature begins to decrease. The changes to the loss function generally 

decrease the interagency working group SCC estimates slightly, all else equal.

10B.3.2 FUND

FUND version 3.8 includes a number of changes over the previous version 3.5 used in 

the interagency report. Documentation supporting FUND and the model’s source code for all 

versions of the model is available from the model authors. g Notable changes, due to their impact 

on the estimates of expected SCC, are adjustments to the space heating, agriculture, and sea level 

rise damage functions in addition to changes to the temperature response function and the 

inclusion of indirect effects from methane emissions.h We discuss each of these in turn.

 g www.fund-model.org/. This report uses version 3.8 of the FUND model, which represents a modest update to the 

most recent version of the model to appear in the literature (version 3.7) (Anthoff and Tol, 2013).7 For the purpose 

of computing the SCC, the relevant changes are associated with improving consistency with IPCC AR4 by adjusting 

the atmospheric lifetimes of CH4 and N2O and incorporating the indirect forcing effects of CH4, along with making 

minor stability improvements in the sea wall construction algorithm.

h The other damage sectors (water resources, space cooling, land loss, migration, ecosystems, human health, and 

extreme weather) were not the subject of significant updates.
10B-5

10B.3.2.1 Space Heating

In FUND, the damages associated with the change in energy needs for space heating are 

based on the estimated impact due to one degree of warming. These baseline damages are scaled 

based on the forecasted temperature anomaly’s deviation from the one degree benchmark and 

adjusted for changes in vulnerability due to economic and energy efficiency growth. In FUND 

3.5, the function that scales the base year damages adjusted for vulnerability allows for the 

possibility that in some simulations the benefits associated with reduced heating needs may be an 

unbounded convex function of the temperature anomaly. In FUND 3.8, the form of the scaling 

has been modified to ensure that the function is everywhere concave, meaning that for every 

simulation there will exist an upper bound on the benefits a region may receive from reduced 

space heating needs. The new formulation approaches a value of two in the limit as the 

temperature anomaly increases, or in other words, assuming no decrease in vulnerability, the 

reduced expenditures on space heating at any level of warming will not exceed two times the 

reductions experienced at one degree of warming. Since the reduced need for space heating 

represents a benefit of climate change in the model, or a negative damage, this change will 

increase the estimated SCC. This update accounts for a significant portion of the difference in the 

expected SCC estimates reported by the two versions of the model when run probabilistically.

10B.3.2.2 Sea Level Rise and Land Loss

The FUND model explicitly includes damages associated with the inundation of dry land 

due to sea level rise. The amount of land lost within a region is dependent upon the proportion of 

the coastline being protected by adequate sea walls and the amount of sea level rise. In FUND 

3.5 the function defining the potential land lost in a given year due to sea level rise is linear in 

the rate of sea level rise for that year. This assumption implicitly assumes that all regions are 

well represented by a homogeneous coastline in length and a constant uniform slope moving 

inland. In FUND 3.8 the function defining the potential land lost has been changed to be a nonlinear function of sea level rise, thereby assuming that the slope of the shore line is not constant 

moving inland, with a positive first derivative. The effect of this change is to typically reduce the 

vulnerability of some regions to sea level rise based land loss, therefore having an effect of 

lowering the expected SCC estimate. The model has also been updated to assume that the value 

of dry land at risk of inundation is not uniform across a region but will be a decreasing function 

of protection measure, thereby implicitly assuming that the most valuable land will be protected 

first.

10B.3.2.3 Agriculture

In FUND, the damages associated with the agricultural sector are measured as 

proportional to the sector’s value. The fraction is made up of three additively separable 

components that represent the effects from carbon fertilization, the rate of temperature change, 

and the level of the temperature anomaly. In both FUND 3.5 and FUND 3.8, the fraction of the 

sector’s value lost due to the level of the temperature anomaly is modeled as a quadratic function 

with an intercept of zero. In FUND 3.5, the linear and quadratic coefficients are modeled as the 

ratio of two normal distributions. Within this specification, as draws from the distribution in the 
10B-6

denominator approached zero the share of the sector’s value “lost” approaches (+/-) infinity 

independent of the temperature anomaly itself. In FUND 3.8, the linear and quadratic 

coefficients are drawn directly from truncated normal distributions so that they remain in the 

range [0,∞) and (−∞,0], respectively, where the means for the new distributions are set equal to 

the ratio of the means from the normal distributions used in the previous version. In general the 

impact of this change has been to increase the likelihood that increases in the temperature level 

will have either larger positive or negative effects on the agricultural sector relative to the 

previous version (through eliminating simulations in which the “lost” value approached (+/-) 

infinity). The net effect of this change on the SCC estimates is difficult to predict. 

10B.3.2.4 Temperature Response Model

The temperature response model translates changes in global levels of radiative forcing 

into the current expected temperature anomaly. In FUND, a given year’s increase in the 

cumulative temperature anomaly is based on a mean reverting function where the mean equals 

the equilibrium temperature anomaly that would eventually be reached if that year’s level of 

radiative forcing were sustained. The rate of mean reversion defines the rate at which the 

transient temperature approaches the equilibrium. In FUND 3.5, the rate of temperature response 

is defined as a decreasing linear function of equilibrium climate sensitivity to capture the fact 

that the progressive heat uptake of the deep ocean causes the rate to slow at higher values of the 

equilibrium climate sensitivity. In FUND 3.8, the rate of temperature response has been updated 

to a quadratic function of the equilibrium climate sensitivity. This change reduces the sensitivity 

of the rate of temperature response to the level of the equilibrium climate sensitivity. Therefore 

in FUND 3.8, the temperature response will typically be faster than in the previous version. The 

overall effect of this change is likely to increase estimates of the SCC as higher temperatures are 

reached during the timeframe analyzed and as the same damages experienced in the previous 

version of the model are now experienced earlier and therefore discounted less.

10B.3.2.5 Methane

The IPCC notes a series of indirect effects of methane emissions, and has developed 

methods for proxying such effects when computing the global warming potential of methane 

(Forster et al. 2007).8 FUND 3.8 now includes the same methods for incorporating the indirect 

effects of methane emissions. Specifically, the average atmospheric lifetime of methane has been 

set to 12 years to account for the feedback of CH4 emissions on its own lifetime. The radiative 

forcing associated with atmospheric methane has also been increase by 40% to account for its net 

impact on ozone production and increase in stratospheric water vapor. The general effect of this 

increased radiative forcing will be to increase the estimated SCC values, where the degree to 

which this occurs will be dependent upon the relative curvature of the damage functions with 

respect to the temperature anomaly.

10B.3.3 PAGE

PAGE09 (Hope 2012)9 includes a number of changes from PAGE2002, the version used 

in the 2009 SCC interagency report. The changes that most directly affect the SCC estimates 
10B-7

include: explicitly modeling the impacts from sea level rise, revisions to the damage function to 

ensure damages are constrained by GDP, a change in the regional scaling of damages, a revised 

treatment for the probability of a discontinuity within the damage function, and revised 

assumptions on adaptation. The model also includes revisions to the carbon cycle feedback and 

the calculation of regional temperatures. More details on PAGE2009 can be found in three 

working papers (Hope 2011a, 2011b, 2011c).10, 11, 12 A description of PAGE2002 can be found 

in Hope (2006).13

10B.3.3.1 Sea Level Rise

While PAGE2002 aggregates all damages into two categories – economic and noneconomic impacts - PAGE2009 adds a third explicit category: damages from sea level rise. In the 

previous version of the model, damages from sea level rise were subsumed by the other damage 

categories. PAGE09 models damages from sea level rise as increasing less than linearly with sea 

level based on the assumption that low-lying shoreline areas will be associated with higher 

damages than current inland areas. Damages from the economic and non-economic sector were 

adjusted to account for the introduction of this new category. 

10B.3.3.2 Revised Damage Function to Account for Saturation

In PAGE09, small initial economic and non-economic benefits (negative damages) are 

modeled for small temperature increases, but all regions eventually experience positive economic 

damages from climate change, where damages are the sum of additively separable polynomial 

functions of temperature and sea level rise. Damages transition from this polynomial function to 

a logistic path once they exceed a certain proportion of remaining Gross Domestic Product 

(GDP) to ensure that damages do not exceed 100 percent of GDP. This differs from PAGE2002, 

which allowed Eastern Europe to potentially experience large benefits from temperature 

increases, and which also did not bound the possible damages that could be experienced.

10B.3.3.3 Regional Scaling Factors

As in the previous version of PAGE, the PAGE09 model calculates the damages for the 

European Union (EU) and then, assumes that damages for other regions are proportional based 

on a given scaling factor. The scaling factor in PAGE09 is based on the length of a region’s 

coastline relative to the EU (Hope 2011b).11 Because of the long coastline in the EU, other 

regions are, on average, less vulnerable than the EU for the same sea level and temperature 

increase, but all regions have a positive scaling factor. PAGE2002 based its scaling factors on 

four studies reported in the IPCC’s third assessment report, and allowed for benefits from 

temperature increase in Eastern Europe, smaller impacts in developing countries, and higher 

damages in developing countries. 

10B.3.3.4 Probability of a Discontinuity

In PAGE2002, the damages associated with a “discontinuity” were modeled as an 

expected value. That is, additional damages from an extreme event, such as extreme melting of 
10B-8

the Greenland ice sheet, were multiplied by the probability of the event occurring and added to 

the damage estimate. In PAGE09, the probability of “discontinuity” is treated as a discrete event 

for each year in the model. The damages for each model run are estimated with or without a 

discontinuity occurring, rather than as an expected value. A large‐scale discontinuity becomes 

possible when the temperature rises beyond some threshold value between 2 and 4°C. The 

probability that a discontinuity will occur beyond this threshold then increases by between 10 

and 30 percent for every 1°C rise in temperature beyond the threshold. If a discontinuity occurs, 

the EU loses an additional 5 to 25 percent of its GDP (drawn from a triangular distribution with a 

mean of 15 percent) in addition to other damages, and other regions lose an amount determined 

by the regional scaling factor. The threshold value for a possible discontinuity is lower than in 

PAGE2002, while the rate at which the probability of a discontinuity increases with the 

temperature anomaly and the damages that result from a discontinuity are both higher than in 

PAGE2002. The model assumes that only one discontinuity can occur and that the impact is 

phased in over a period of time, but once it occurs, its effect is permanent.

10B.3.3.5 Adaptation

As in PAGE2002, adaptation is available to increase the tolerable level of temperature 

change and can help mitigate any climate change impacts that still occur. In PAGE this 

adaptation is the same regardless of the temperature change or sea level rise and is therefore akin 

to what is more commonly considered a reduction in vulnerability. It is modeled by modifying 

the temperature change and sea level rise used in the damage function or by reducing the 

damages by some percentage. PAGE09 assumes a smaller decrease in vulnerability than the 

previous version of the model and assumes that it will take longer for this change in vulnerability 

to be realized. In the aggregated economic sector, at the time of full implementation, this 

adaptation will mitigate all damages up to a temperature increase of 1°C, and for temperature 

anomalies between 1°C and 3°C, it will reduce damages by 15-30 percent (depending on the 

region). However, it takes 20 years to fully implement this adaptation. In PAGE2002, adaptation 

was assumed to reduce economic sector damages up to 3°C by 50-90 percent after 20 years. 

Beyond 3°C, no adaptation is assumed to be available to mitigate the impacts of climate change. 

For the non-economic sector, in PAGE09 adaptation is available to reduce 15 percent of the 

damages due to a temperature increase between 0°C and 2°C and is assumed to take 40 years to 

fully implement, instead of 25 percent of the damages over 20 years assumed in PAGE2002. 

Similarly, adaptation is assumed to alleviate 25-50 percent of the damages from the first 0.20 to 

0.25 meters of sea level rise but is assumed to be ineffective thereafter. Hope (2011c)12 estimates 

that the less optimistic assumptions regarding the ability to offset impacts of temperature and sea 

level rise via adaptation increase the SCC by approximately 30 percent.

10B.3.3.6 Other Noteworthy Changes

Two other changes in the model are worth noting. A revised carbon cycle feedback is 

introduced to simulate decreased CO2 absorption by the terrestrial biosphere and ocean as the 

temperature rises. This feedback is linear in the average global and annual temperature anomaly 

but is capped at a maximum value. In the previous version of PAGE, an additional amount was 

added to the CO2 emissions each period to account for a decrease in ocean absorption and a loss 
10B-9

of soil carbon. Also updated is the method by which the average global and annual temperature 

anomaly is downscaled to determine annual average regional temperature anomalies to be used 

in the regional damage functions. In the previous version of PAGE, the scaling was determined 

solely based on regional difference in emissions of sulfate aerosols. In PAGE09, this regional 

temperature anomaly is further adjusted using an additive factor that is based on the average 

absolute latitude of a region relative to the area weighted average absolute latitude of the Earth’s 

landmass.

10B.4 REVISED SCC ESTIMATES

The updated versions of the three integrated assessment models were run using the same 

methodology detailed in the 2010 TSD.1 The approach along with the inputs for the 

socioeconomic emissions scenarios, equilibrium climate sensitivity distribution, and discount 

rate remains the same. This includes the five reference scenarios based on the EMF-22 modeling 

exercise, the Roe and Baker equilibrium climate sensitivity distribution calibrated to the Fourth 

Assessment Report of the IPCC, and three constant discount rates of 2.5, 3, and 5 percent.

As was previously the case, the use of three models, three discount rates, and five 

scenarios produces 45 separate distributions for the SCC. The approach laid out in the TSD 

applied equal weight to each model and socioeconomic scenario in order to reduce the 

dimensionality down to three separate distributions representative of the three discount rates. The 

interagency group selected four values from these distributions for use in regulatory analysis. 

Three values are based on the average SCC across models and socio-economic-emissions 

scenarios at the 2.5, 3, and 5 percent discount rates, respectively. The fourth value was chosen to 

represent the higher-than-expected economic impacts from climate change further out in the tails 

of the SCC distribution. For this purpose, the 95th percentile of the SCC estimates at a 3 percent 

discount rate was chosen. (A detailed set of percentiles by model and scenario combination is 

available in the Annex.) As noted in the original TSD, “the 3 percent discount rate is the central 

value, and so the central value that emerges is the average SCC across models at the 3 percent 

discount rate” (TSD, p. 25). However, for purposes of capturing the uncertainties involved in 

regulatory impact analysis, the interagency group emphasizes the importance and value of 

including all four SCC values.

Table 10B.4.1 shows the four selected SCC estimates in five year increments from 2010 

to 2050. Values for 2010, 2020, 2030, 2040, and 2050 are calculated by first combining all 

outputs (10,000 estimates per model run) from all scenarios and models for a given discount rate. 

Values for the years in between are calculated using basic linear interpolation. The full set of 

annual SCC estimates between 2010 and 2050 is reported in the Annex.
10B-10

Table 10B.4.1 Revised Social Cost of CO2, 2010 – 2050 (in 2007 dollars per ton of CO2)

Discount Rate 5.0% 3.0% 2.5% 3.0%

Year Avg Avg Avg 95th

2010 11 33 52 90

2015 12 38 58 109

2020 12 43 65 129

2025 14 48 70 144

2030 16 52 76 159

2035 19 57 81 176

2040 21 62 87 192

2045 24 66 92 206

2050 27 71 98 221

The SCC estimates using the updated versions of the models are higher than those 

reported in the TSD due to the changes to the models outlined in the previous section. Figure 

10B.4.1 illustrates where the four SCC values for 2020 fall within the full distribution for each 

discount rate based on the combined set of runs for each model and scenario (150,000 estimates 

in total for each discount rate). In general, the distributions are skewed to the right and have long 

tails. The Figure also shows that the lower the discount rate, the longer the right tail of the 

distribution.

Figure 10B.4.1 Distribution of SCC Estimates for 2020 (in 2007$ per ton CO2)

As was the case in the original TSD, the SCC increases over time because future 

emissions are expected to produce larger incremental damages as physical and economic systems 

become more stressed in response to greater climatic change. The approach taken by the 

interagency group is to allow the growth rate to be determined endogenously by the models 
10B-11

through running them for a set of perturbation years out to 2050. Table 10B.4.2 illustrates how 

the growth rate for these four SCC estimates varies over time.

Table 10B.4.2 Average Annual Growth Rates of SCC Estimates between 2010 and 2050

Average Annual 5.0% 3.0% 2.5% 3.0%

Rate (%) Avg Avg Avg 95th

2010-2020 1.2% 3.2% 2.4% 4.3%

2020-2030 3.4% 2.1% 1.7% 2.4%

2030-2040 3.0% 1.8% 1.5% 2.0%

2040-2050 2.6% 1.6% 1.3% 1.5%

The future monetized value of emission reductions in each year (the SCC in year t 

multiplied by the change in emissions in year t) must be discounted to the present to determine 

its total net present value for use in regulatory analysis. As previously discussed in the original 

TSD, damages from future emissions should be discounted at the same rate as that used to 

calculate the SCC estimates themselves to ensure internal consistency – i.e., future damages from 

climate change, whether they result from emissions today or emissions in a later year, should be 

discounted using the same rate.

10B.5 OTHER MODEL LIMITATIONS OR RESEARCH GAPS

The 2010 interagency SCC technical support report discusses a number of important 

limitations for which additional research is needed. In particular, the document highlights the 

need to improve the quantification of both non-catastrophic and catastrophic damages, the 

treatment of adaptation and technological change, and the way in which inter-regional and intersectoral linkages are modeled. It also discusses the need to more carefully assess the implications 

of risk aversion for SCC estimation as well as the inability to perfectly substitute between 

climate and non-climate goods at higher temperature increases, both of which have implications 

for the discount rate used. EPA, DOE, and other agencies continue to engage in long-term 

research work on modeling and valuation of climate impacts that we expect will inform 

improvements in SCC estimation in the future.
10B-12

REFERENCES

1. Interagency Working Group on Social Cost of Carbon, Social Cost of Carbon for 

Regulatory Impact Analysis under Executive Order 12866, February, 2010. United States 

Government. <www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/SocialCost-of-Carbon-for-RIA.pdf>

2. Nordhaus, W., A Question of Balance. 2008. Yale University Press: New Haver.

3. Nordhaus, W., Economic aspects of global warming in a post-Copenhagen environment.

Proceedings of the National Academy of Sciences, 2010. 107(26): pp. 11721-11726 

4. Randall, D. A., R.A. Wood, S. Bony, R. Colman, T. Fichefet, J. Fyfe, V. Kattsov, A. 

Pitman, J. Shukla, J. Srinivasan, R.J. Stouffer, A. Sumi and K.E. Taylor, Climate Models 

and Their Evaluation. In Climate Change 2007: The Physical Science Basis. 

Contribution of Working Group I to the Fourth Assessment Report of the 

Intergovernmental Panel on Climate Change, S. Solomon, D. Qin, M. Manning, Z. Chen, 

M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller, Editor. 2007. Cambridge University 

Press: Cambridge, United Kingdom and New York, NY, USA

5. Nicholls, R. J., N. Marinova, J.A. Lowe, S. Brown, P. Vellinga, D. de Gusmão, J. Hinkel 

and R.S.J. Tol, Sea-level rise and its possible impacts given a ‘beyond 4°C world’ in the 

twenty-first century. Phil. Trans. R. Soc. A 2011. 369(1934): pp. 161-181 

6. National Academy of Sciences, Climate Stabilization Targets: Emissions, 

Concentrations, and Impacts over Decades to Millennia. 2011. National Academies 

Press, Inc: Washington, DC.

7. Anthoff, D. and R. S. J. Tol, The uncertainty about the social cost of carbon: a 

decomposition analysis using FUND. Climatic Change, 2013(Forthcoming)

8. Forster, P., V. Ramaswamy, P. Artaxo, T. Berntsen, R. Betts, D.W. Fahey, J. Haywood, 

J. Lean, D.C. Lowe, G. Myhre, J. Nganga, R. Prinn, G. Raga, M. Schulz and R. Van 

Dorland, Changes in Atmospheric Constituents and in Radiative Forcing. In Climate 

Change 2007: The Physical Science Basis. Contribution of Working Group I to the 

Fourth Assessment Report of the Intergovernmental Panel on Climate Change S. 

Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. 

Miller Editor. 2007. Cambridge University Press: Cambridge, United Kingdom and New 

York, NY, USA

9. Hope, C., Critical issues for the calculation of the social cost of CO2: why the estimates 

from PAGE09 are higher than those from PAGE2002. Climatic Change, 

2012(Forthcoming)
10B-13

10. Hope, C., The PAGE09 Integrated Assessment Model: A Technical Description. 2011, 

Cambridge Judge Business School 

www.jbs.cam.ac.uk/fileadmin/user_upload/research/workingpapers/wp1104.pdf

11. Hope, C., The Social Cost of CO2 from the PAGE09 Model. 2011, Cambridge Judge 

Business School 

www.jbs.cam.ac.uk/fileadmin/user_upload/research/workingpapers/wp1105.pdf

12. Hope, C., New Insights from the PAGE09 Model: The Social Cost of CO2. 2011, 

Cambridge Judge Business School 

www.jbs.cam.ac.uk/fileadmin/user_upload/research/workingpapers/wp1108.pdf

13. Hope, C., The Marginal Impact of CO2 from PAGE2002: An Integrated Assessment 

Model Incorporating the IPCC’s Five Reasons for Concern. The Integrated Assessment 

Journal, 2006. 6(1): pp. 19-56 
10B-14

10B.6 ANNEX

Table 10B.6.1 Annual SCC Values: 2010-2050 (2007$/ton CO2)

Discount Rate 5.0% 3.0% 2.5% 3.0%

Year Avg Avg Avg 95th

2010 11 33 52 90

2011 11 34 54 94

2012 11 35 55 98

2013 11 36 56 102

2014 11 37 57 106

2015 12 38 58 109

2016 12 39 60 113

2017 12 40 61 117

2018 12 41 62 121

2019 12 42 63 125

2020 12 43 65 129

2021 13 44 66 132

2022 13 45 67 135

2023 13 46 68 138

2024 14 47 69 141

2025 14 48 70 144

2026 15 49 71 147

2027 15 49 72 150

2028 15 50 73 153

2029 16 51 74 156

2030 16 52 76 159

2031 17 53 77 163

2032 17 54 78 166

2033 18 55 79 169

2034 18 56 80 172

2035 19 57 81 176

2036 19 58 82 179

2037 20 59 84 182

2038 20 60 85 185

2039 21 61 86 188

2040 21 62 87 192

2041 22 63 88 195

2042 22 64 89 198

2043 23 65 90 200

2044 23 65 91 203

2045 24 66 92 206

2046 24 67 94 209

2047 25 68 95 212

2048 25 69 96 215

2049 26 70 97 218
10B-15

2050 27 71 98 221

Table 10B.6.2 202 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 6 11 15 27 58 129 139 327 515 991

MERGE 4 6 9 16 34 78 82 196 317 649

MESSAGE 4 8 11 20 42 108 107 278 483 918

MiniCAM Base 5 9 12 22 47 107 113 266 431 872

5th Scenario 2 4 6 11 25 85 68 200 387 955

Scenario DICE

IMAGE 25 31 37 47 64 72 92 123 139 161

MERGE 14 18 20 26 36 40 50 65 74 85

MESSAGE 20 24 28 37 51 58 71 95 109 221

MiniCAM Base 20 25 29 38 53 61 76 102 117 135

5th Scenario 17 22 25 33 45 52 65 91 106 126

Scenario FUND

IMAGE -17 -1 5 17 34 44 59 90 113 176

MERGE -7 2 7 16 30 35 49 72 91 146

MESSAGE -19 -4 2 12 27 32 46 70 87 135

MiniCAM Base -9 1 8 18 35 45 59 87 108 172

5th Scenario -30 -12 -5 6 19 24 35 57 72 108

Table 10B.6.3 SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 4 7 10 18 38 91 95 238 385 727

MERGE 2 4 6 11 23 56 58 142 232 481

MESSAGE 3 5 7 13 29 75 74 197 330 641

MiniCAM Base 3 5 8 14 30 73 75 184 300 623

5th Scenario 1 3 4 7 17 58 48 136 264 660

Scenario DICE

IMAGE 16 21 24 32 43 48 60 79 90 102

MERGE 10 13 15 19 25 28 35 44 50 58

MESSAGE 14 18 20 26 35 40 49 64 73 83

MiniCAM Base 13 17 20 26 35 39 49 65 73 85

5th Scenario 12 15 17 22 30 34 43 58 67 79

Scenario FUND

IMAGE -14 -3 1 9 20 25 35 54 69 111

MERGE -8 -1 3 9 18 22 31 47 60 97

MESSAGE -16 -5 -1 6 16 18 28 43 55 88

MiniCAM Base -9 -1 3 10 21 27 35 53 67 107

5th Scenario -22 -10 -5 2 10 13 20 33 42 63
10B-16

Table 10B.6.4 2020 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 1 2 2 5 10 28 27 71 123 244

MERGE 1 1 2 3 7 17 17 45 75 153

MESSAGE 1 1 2 4 9 24 22 60 106 216

MiniCAM Base 1 1 2 3 8 21 21 54 94 190

5th Scenario 0 1 1 2 5 18 14 41 78 208

Scenario DICE

IMAGE 6 8 9 11 14 15 18 22 25 27

MERGE 4 5 6 7 9 10 12 15 16 18

MESSAGE 6 7 8 10 12 13 16 20 22 25

MiniCAM Base 5 6 7 8 11 12 14 18 20 22

5th Scenario 5 6 6 8 10 11 14 17 19 21

Scenario FUND

IMAGE -9 -5 -3 -1 2 3 6 11 15 25

MERGE -6 -3 -2 0 3 4 7 12 16 27

MESSAGE -10 -6 -4 -1 2 2 5 9 13 23

MiniCAM Base -7 -3 -2 0 3 4 7 11 15 26

5th Scenario -11 -7 -5 -2 0 0 3 6 8 14
11-i

CHAPTER 11. UTILITY IMPACT ANALYSIS

TABLE OF CONTENTS

11.1 INTRODUCTION ......................................................................................................... 11-1

11.2 METHODOLOGY ........................................................................................................ 11-2

11.3 UTILITY IMPACT RESULTS ..................................................................................... 11-4

11.3.1 Small Three-Phase Air-Cooled Air Conditioners and Heat Pumps <65,000 

Btu/h................................................................................................................... 11-4

11.3.1.1 Installed Capacity..................................................................................... 11-4

11.3.1.2 Electricity Generation .............................................................................. 11-7

11.3.1.3 Results Summary.................................................................................... 11-11

11.3.2 Water-Source Heat Pumps............................................................................... 11-11

11.3.2.1 Installed Capacity................................................................................... 11-11

11.3.2.2 Electricity Generation ............................................................................ 11-14

11.3.2.3 Results Summary.................................................................................... 11-17

REFERENCES ........................................................................................................................ 11-19

LIST OF TABLES

Table 11.3.1 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: 

Summary of Utility Impact Results........................................................... 11-11

Table 11.3.2 Water-Source Heat Pumps: Summary of Utility Impact Results............... 11-18

LIST OF FIGURES

Figure 11.3.1 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: 

Total Electric Capacity Reduction............................................................... 11-4

Figure 11.3.2 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: 

Coal Capacity Reduction ............................................................................. 11-5

Figure 11.3.3 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: 

Nuclear Capacity Reduction ........................................................................ 11-5

Figure 11.3.4 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: Gas 

Combined Cycle Capacity Reduction.......................................................... 11-6

Figure 11.3.5 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: 

Peaking Capacity Reduction........................................................................ 11-6

Figure 11.3.6 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: 

Renewables Capacity Reduction.................................................................. 11-7

Figure 11.3.7 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: 

Total Generation Reduction......................................................................... 11-8

Figure 11.3.8 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: 

Coal Generation Reduction.......................................................................... 11-8
11-ii

Figure 11.3.9 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: 

Nuclear Generation Reduction..................................................................... 11-9

Figure 11.3.10 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: Gas 

Combined Cycle Generation Reduction ...................................................... 11-9

Figure 11.3.11 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: Oil 

Generation Reduction ................................................................................ 11-10

Figure 11.3.12 Small Commercial Air-Cooled Air Conditioners and Heat Pumps 

Renewables Generation Reduction............................................................ 11-10

Figure 11.3.13 Water-Source Heat Pumps: Total Electric Capacity Reduction ................ 11-12

Figure 11.3.14 Water-Source Heat Pumps: Coal Capacity Reduction............................... 11-12

Figure 11.3.15 Water-Source Heat Pumps: Nuclear Capacity Reduction ......................... 11-13

Figure 11.3.16 Water-Source Heat Pumps: Gas Combined Cycle Capacity Reduction.... 11-13

Figure 11.3.17 Water-Source Heat Pumps: Peaking Capacity Reduction ......................... 11-14

Figure 11.3.18 Water-Source Heat Pumps: Renewables Capacity Reduction................... 11-14

Figure 11.3.19 Water-Source Heat Pumps: Total Generation Reduction .......................... 11-15

Figure 11.3.20 Water-Source Heat Pumps: Coal Generation Reduction........................... 11-15

Figure 11.3.21 Water-Source Heat Pumps: Nuclear Generation Reduction...................... 11-16

Figure 11.3.22 Water-Source Heat Pumps: Gas Combined Cycle Generation 

Reduction ................................................................................................... 11-16

Figure 11.3.23 Water-Source Heat Pumps: Oil Generation Reduction ............................. 11-17

Figure 11.3.24 Water-Source Heat Pumps: Renewables Generation Reduction ............... 11-17
11-1

CHAPTER 11. UTILITY IMPACT ANALYSIS

11.1 INTRODUCTION

In the utility impact analysis, the U.S. Department of Energy (DOE) analyzes the changes 

in electric installed capacity and power generation that result for each trial standard level (TSL). 

The utility impact analysis is based on output of the DOE/Energy Information 

Administration (EIA)’s National Energy Modeling System (NEMS).a NEMS is a public domain, 

multi-sectored, partial equilibrium model of the U.S. energy sector. Each year, DOE/EIA uses 

NEMS to produce an energy forecast for the United States, the Annual Energy Outlook (AEO). 

The EIA publishes a reference case, which incorporates all existing energy-related policies at the 

time of publication, and a variety of side cases which analyze the impact of different policies, 

energy price and market trends. As of 2014, DOE is using a new methodology based on results 

published for the Annual Energy Outlook 2014 (AEO 2014) Reference case and a set of side 

cases that implement a variety of efficiency-related policies.

2

The new approach retains key aspects of DOE’s previous methodology, and provides 

some improvements:

• The assumptions used in the AEO reference case and side cases are fully 

documented and receive detailed public scrutiny.

• NEMS is updated each year, with each edition of the AEO, to reflect changes in 

energy prices, supply trends, regulations, etc.

• The comprehensiveness of NEMS permits the modeling of interactions among 

the various energy supply and demand sectors. 

• Using EIA published side cases to estimate the utility impacts enhances the 

transparency of DOE’s analysis.

• The variability in impacts estimates from one edition of AEO to the next will be 

reduced under the new approach.

On the average, however, over the full analysis period, the results from the new approach 

are comparable to results from the old approach.

 a For more information on NEMS, refer to the U.S. Department of Energy, Energy Information Administration 

documentation. A useful summary is National Energy Modeling System: An Overview.

1
11-2

11.2 METHODOLOGY 

DOE estimates the marginal impacts of reduction in energy demand on the energy supply 

sector. In principle, marginal values should provide a better estimate of the actual impact of 

energy conservation standards. 

NEMS uses predicted growth in demand for each end use to build up a projection of the 

total electric system load growth. The system load shapes are converted internally to load 

duration curves, which are then used to estimate the most cost-effective additions to capacity. 

When electricity demand deviates from the AEO reference case, in general there are three interrelated effects: the annual generation (TWh) from the stock of electric generating capacity 

changes, the total generation capacity itself (GW) may change, and the mix of capacity by fuel 

type may change. Each of these effects can vary for different types of end use. The change in 

total generating capacity is sensitive to the degree to which the end-use is peak coincident, while

the capacity mix is sensitive to the hourly load shape associated with the end use. Changes in 

generation by fuel type lead in turn to changes in total power sector emissions of SO2, NOx, Hg 

and CO2.

DOE’s new approach examines a series of AEO side cases to estimate the relationship 

between demand reductions and the marginal energy, emissions and capacity changes. The 

assumptions for each side case are documented in Appendix E of the AEO. The side cases, or 

scenarios, that incorporate significant changes to equipment efficiencies relative to the Reference 

case are: 

• 2013 Technology (leaves all technologies at 2013 efficiencies);

• Best Available Technology (highest efficiency irrespective of cost);

• High Technology (higher penetration rates for efficiency and demand management);

• Extended Policies (includes efficiency standards that are not in the reference).

Scenarios that incorporate policies that directly affect the power sector without changes 

in energy demand (for example, subsidies for renewables, or high fuel price assumptions) are not 

appropriate for this analysis. The methodology proceeds in seven steps:

1. Supply-side data on generation, capacity and emissions, and demand-side data on electricity 

use by sector and end-use, are extracted from each side case. The data are converted to 

differences relative to the AEO Reference case.

2. The changes in electricity use on the demand-side data are allocated to one of three 

categories: on-peak, shoulder, and off-peak. These categories are used in the utility sector to 

correlate end-use consumption with supply types. For each of the end-uses that are modeled 

explicitly in NEMS, load shape information is used to identify the fraction of annual 

electricity use assigned to each category. On-peak hours are defined as noon-5pm, June 

through September. Off-peak hours are nights and Sundays. All other hours are assigned to 

the shoulder period.
11-3

3. For each year and each side case, the demand-side reductions to on-peak, off-peak and 

shoulder-period electricity use are matched on the supply-side to reductions in generation by 

fuel type. The fuel types are petroleum fuels, natural gas, renewables, nuclear and coal. The 

allocation is based on the following rules:

3.1. All Petroleum-Based Generation Is Allocated To Peak Periods;

3.2. Natural Gas Generation Is Allocated To Any Remaining Peak Reduction; This Is 

Consistent With The Fact That Oil And Gas Steam Units Are Used In Nems To Meet 

Peak Demand;

3.3. Base-Load Generation (Nuclear And Coal) Is Allocated Proportionally To All Periods;

3.4. The Remaining Generation Of All Types Is Allocated To The Remaining Off-Peak And 

Shoulder Reductions Proportionally.

4. The output of step 3 defines fuel-share weights giving the fraction of energy demand in each 

load category that is met by each fuel type as a function of time. These are combined with the 

weights that define the load category shares by end-use to produce coefficients that allocate a 

marginal reduction in end-use electricity demand to each of the five fuel types.

5. A regression model is used to relate reductions in generation by fuel type to reductions in 

emissions of power sector pollutants. The model produces coefficients that define the change 

in total annual emissions of a given pollutant resulting from a unit change in total annual 

generation for each fuel type, as a function of time. These coefficients are combined with the 

weights calculated in step 4 to produce coefficients that relate emissions changes to changes 

in end-use demand.

6. A regression model is used to relate reductions in generation by fuel type to reductions in 

installed capacity. The categories used for installed capacity are the same as for generation 

except for peak: NEMS uses two peak capacity types (combustion turbine/diesel and oil and 

gas steam) which are combined here into a single “peak” category. The model produces 

coefficients that define the change in total installed capacity of a given type resulting from a 

unit change in total annual generation for the corresponding fuel type. These coefficients are 

combined with the weights calculated in step 4 to produce coefficients that relate installed 

capacity changes to changes in end-use demand, as a function of time.

7. The coefficient time-series for fuel share, pollutant emissions and capacity for the 

appropriate end use are multiplied by the stream of energy savings calculated in the NIA to 

produce estimates of the utility impacts.

This analysis ignores pumped storage, fuel cells and distributed generation, as these 

generation types are not affected by the policy changes modeled in the EIA side cases. The 

methodology is described in more detail in K. Coughlin, “Utility Sector Impacts of Electricity 

Demand Reductions”.

3
11-4

11.3 UTILITY IMPACT RESULTS

This section presents results of the analysis for all of the capacity types used to supply 

electricity to operate small commercial air-cooled air-conditioning and heating equipment less 

than 65,000 Btu/h and water-source heat pumps in commercial buildings.

11.3.1 Small Three-Phase Air-Cooled Air Conditioners and Heat Pumps <65,000 Btu/h

11.3.1.1 Installed Capacity

The figures in this section show the changes in U.S. electricity installed capacity that 

result for each efficiency level by major plant type for selected years. Note that a negative 

number means an increase in capacity under an efficiency level.

Figure 11.3.1 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: Total 

Electric Capacity Reduction
11-5

Figure 11.3.2 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: Coal 

Capacity Reduction

Figure 11.3.3 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: Nuclear 

Capacity Reduction
11-6

Figure 11.3.4 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: Gas 

Combined Cycle Capacity Reduction

Figure 11.3.5 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: Peaking

Capacity Reduction
11-7

Figure 11.3.6 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: 

Renewables Capacity Reduction

11.3.1.2 Electricity Generation

The figures in this section show the annual change in electricity generation that result for 

each efficiency level by fuel type. Note that a negative number means an increase in generation

under an efficiency level.
11-8

Figure 11.3.7 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: Total 

Generation Reduction

Figure 11.3.8 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: Coal 

Generation Reduction
11-9

Figure 11.3.9 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: Nuclear 

Generation Reduction

Figure 11.3.10 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: Gas 

Combined Cycle Generation Reduction
11-10

Figure 11.3.11 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: Oil

Generation Reduction

Figure 11.3.12 Small Commercial Air-Cooled Air Conditioners and Heat Pumps 

Renewables Generation Reduction
11-11

11.3.1.3 Results Summary 

Table 11.3.1 presents a summary of the utility impact results for small commercial aircooled air-conditioning and heating equipment less than 65,000 Btu/h.

Table 11.3.1 Small Commercial Air-Cooled Air Conditioners and Heat Pumps: Summary 

of Utility Impact Results

ASHRAE 

Level/ EL0 EL 1 EL 2 EL 3 EL 4 EL 5

Installed Capacity Reduction (MW)

2020 9.78 15.69 35.10 42.19 49.37 53.91

2025 35.65 90.44 198.97 241.48 283.41 307.82

2030 65.55 162.17 342.56 417.99 489.80 529.42

2035 96.39 231.28 467.68 574.52 672.11 722.42

2040 124.02 292.01 561.97 696.29 813.12 867.95

Electricity Generation Reduction (GWh)

2020 27.99 44.90 100.46 120.74 141.31 154.29

2025 80.39 203.96 448.70 544.56 639.11 694.16

2030 142.12 351.61 742.73 906.28 1061.98 1147.89

2035 197.52 473.91 958.30 1177.23 1377.19 1480.30

2040 242.25 570.38 1097.69 1360.06 1588.25 1695.36

11.3.2 Water-Source Heat Pumps

11.3.2.1 Installed Capacity

The figures in this section show the changes in U.S. electricity installed capacity that 

result for each efficiency level by major plant type for selected years. Note that a negative 

number means an increase in capacity under an efficiency level.
11-12

Figure 11.3.13 Water-Source Heat Pumps: Total Electric Capacity Reduction

Figure 11.3.14 Water-Source Heat Pumps: Coal Capacity Reduction
11-13

Figure 11.3.15 Water-Source Heat Pumps: Nuclear Capacity Reduction

Figure 11.3.16 Water-Source Heat Pumps: Gas Combined Cycle Capacity Reduction
11-14

Figure 11.3.17 Water-Source Heat Pumps: Peaking Capacity Reduction

Figure 11.3.18 Water-Source Heat Pumps: Renewables Capacity Reduction

11.3.2.2 Electricity Generation

The figures in this section show the annual change in electricity generation that result for 

each efficiency level by fuel type. Note that a negative number means an increase in generation

under an efficiency level.
11-15

Figure 11.3.19 Water-Source Heat Pumps: Total Generation Reduction

Figure 11.3.20 Water-Source Heat Pumps: Coal Generation Reduction
11-16

Figure 11.3.21 Water-Source Heat Pumps: Nuclear Generation Reduction

Figure 11.3.22 Water-Source Heat Pumps: Gas Combined Cycle Generation Reduction
11-17

Figure 11.3.23 Water-Source Heat Pumps: Oil Generation Reduction

Figure 11.3.24 Water-Source Heat Pumps: Renewables Generation Reduction

11.3.2.3 Results Summary 

Table 11.3.2 presents a summary of the utility impact results for water-source heat 

pumps.
11-18

Table 11.3.2 Water-Source Heat Pumps: Summary of Utility Impact Results

ASHRAE 

Level/ EL0* EL 1 EL 2 EL 3 EL 4 EL 5

Installed Capacity Reduction (MW)

2020 - 2.08 19.96 36.77 50.42 71.04

2025 - 13.45 146.57 272.27 373.37 523.72

2030 - 23.73 271.34 506.24 693.22 966.08

2035 - 34.10 394.49 738.04 1008.29 1393.85

2040 - 43.63 504.36 945.74 1288.17 1763.56

Electricity Generation Reduction (GWh)

2020 - 6.65 63.66 117.28 160.79 226.58

2025 - 34.83 379.64 705.24 967.11 1356.54

2030 - 58.74 671.65 1253.09 1715.92 2391.31

2035 - 79.48 919.62 1720.51 2350.51 3249.31

2040 - 96.60 1116.59 2093.74 2851.85 3904.29 *

There are no reductions for the ASHRAE level because there is no market share projected at the federal baseline in 

the base case.
11-19

REFERENCES

1. U.S. Department of Energy-Energy Information Administration, National Energy 

Modeling System: An Overview, 2009. Report No. DOE/EIA-0581(2009). 

<www.eia.gov/oiaf/aeo/overview/>

2. U.S. Department of Energy-Energy Information Administration, Annual Energy Outlook 

2014 with Projections to 2040, 2014. Washington, DC. <www.eia.gov/forecasts/aeo/>

3. Coughlin, K., Utility Sector Imacts of Reduced Electricity Demand, 2014. Lawrence 

Berkeley National Laboratory. Report No. LBNL-6864E.