TECHNICAL SUPPORT DOCUMENT:

ENERGY EFFICIENCY PROGRAM

FOR COMMERCIAL AND INDUSTRIAL 

EQUIPMENT:

PACKAGED TERMINAL AIR CONDITIONERS AND HEAT PUMPS

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

Ernest Orlando Lawrence Berkeley National Laboratory
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CHAPTER 1. INTRODUCTION

TABLE OF CONTENTS

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

1.2 OVERVIEW OF ENERGY CONSERVATION STANDARDS ....................................... 1

1.3 OVERVIEW OF PTAC AND PTHP STANDARDS ........................................................ 3

1.3.1 Framework and Analysis Methodology .................................................................. 3

1.4 STRUCTURE OF THE DOCUMENT ............................................................................... 5

LIST OF TABLES

Table 1.4.1. PTAC and PTHP Analyses* ....................................................................................... 4
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CHAPTER 1. INTRODUCTION

1.1 PURPOSE OF THE DOCUMENT

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

analyses and results supporting the information presented in the final rule for packaged terminal

air conditioners (PTACs) and packaged terminal heat pumps (PTHPs). This final rule TSD 

complements the life-cycle cost (LCC), payback period (PBP), and national impact analysis 

(NIA) spreadsheets posted on the U.S. Department of Energy’s (DOE’s) website at:

http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/64 

1.2 OVERVIEW OF ENERGY CONSERVATION STANDARDS

Title III, Part Ca of the Energy Policy and Conservation Act of 1975 (“EPCA” or “the 

Act”), Pub. L. 94-163 (42 U.S.C. 6311-6317, as codified), added by Pub. L. 95-619, Title IV, 

§441(a), established the Energy Conservation Program for Certain Industrial Equipment, which

includes the PTAC and PTHP equipment that are the subject of this TSD.

b

 In general, this 

program addresses the energy efficiency of certain types of commercial and industrial 

equipment. Relevant provisions of the Act include definitions (42 U.S.C. 6311), energy 

conservation standards (42 U.S.C. 6313), test procedures (42 U.S.C. 6314), labelling provisions 

(42 U.S.C. 6315), and the authority to require information and reports from manufacturers (42 

U.S.C. 6316). EPCA contains mandatory energy conservation standards for commercial heating, 

air-conditioning, and water-heating equipment. (42 U.S.C. 6313(a)) Specifically, the statute sets 

standards for small, large, and very large commercial package air-conditioning and heating 

equipment, PTACs and PTHPs, warm-air furnaces, packaged boilers, storage water heaters, 

instantaneous water heaters, and unfired hot water storage tanks. Id. 

Section 5(b) of the American Energy Manufacturing Technical Corrections Act of 2012 

(Pub. L. No. 112-210 (Dec. 18, 2012) (AEMTCA) amended Section 342(a)(6) of EPCA. Among 

other things, AEMTCA modified the manner in which DOE must amend the energy efficiency 

standards for certain types of commercial and industrial equipment. DOE is typically obligated 

either to adopt those standards developed by the American Society of Heating, Refrigerating, and 

Air-Conditioning Engineers (ASHRAE) – or to adopt levels more stringent than the ASHRAE 

levels if there is clear and convincing evidence in support of doing so (42 U.S.C. 6313(a)(6)(A)). 

AEMTCA added to this process a requirement that DOE initiate a rulemaking to consider 

amending the standards for any covered equipment as to which more than 6 years has elapsed 

since the issuance of the most recent final rule establishing or amending a standard for the 

equipment as of the date of AEMTCA’s enactment, December 18, 2012. (42 U.S.C. 

6313(a)(6)(C)(vi)) 

a For editorial reasons, upon codification in the U.S. Code, Part C was re-designated Part A-1. b All references to EPCA in this document refer to the statute as amended through the American Energy 

Manufacturing Technical Corrections Act of 2012, Pub. L. 112-210 (Dec. 18, 2012).
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Under EPCA, when DOE is studying new or amended standards, it must consider, to the 

greatest extent practicable, the following seven factors:

1) the economic impact of the standard on the manufacturers and consumers of the 

equipment subject to the standard; 

2) the savings in operating costs throughout the estimated average life of the equipment 

compared to any increases in the initial cost or maintenance expense; 

3) the total projected amount of energy savings likely to result directly from the imposition 

of the standard; 

4) any lessening of the utility or the performance of the covered equipment likely to result 

from the imposition of the standard; 

5) the impact of any lessening of competition, as determined in writing by the Attorney 

General, that is likely to result from the imposition of the standard; 

6) the need for national energy conservation; and 

7) other factors the Secretary considers relevant. (42 U.S.C. 6313(a)(6)(B))

Other statutory requirements are set forth in 42 U.S.C. 6313(a)(6)(A)–(C).

DOE considers interested party participation to be a very important part of the process for 

setting energy conservation standards. Through formal public notifications (i.e., Federal Register

notices), DOE actively encourages the participation and interaction of all interested parties 

during the comment period in each stage of the rulemaking. Beginning with the framework 

document and during subsequent comment periods, interactions among interested parties provide 

a balanced discussion of the information that is required for the standards rulemaking.

Before DOE determines whether or not to adopt a proposed energy conservation 

standard, it must first solicit comments on the proposed standard. Any new or amended standard 

must be designed to achieve significant additional conservation of energy and be technologically 

feasible and economically justified. To determine whether economic justification exists, DOE 

must review comments on the proposal and determine that the benefits of the proposed standard 

exceed its burdens to the greatest extent practicable, weighing the seven factors listed above. 

The energy conservation standards rulemaking process involves two formal public 

notices, which DOE publishes in the Federal Register. The first notice is the NOPR, which 

presents the analyses of the impacts of potential amended energy conservation standards on 

customers, manufacturers, and the Nation; DOE’s weighting of these impacts of amended energy 

conservation standards; and the proposed energy conservation standards for the equipment. The 

second notice is the final rule, which presents a discussion of the comments received in response 

to the NOPR; the revised analyses; DOE’s weighting of these impacts; the amended energy 
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conservation standards DOE is adopting for each equipment class; and the effective dates of the 

amended energy conservation standards.

1.3 OVERVIEW OF PTAC AND PTHP STANDARDS

EPCA sets standards for small, large, and very large commercial package airconditioning and heating equipment, PTACs and PTHPs, warm-air furnaces, packaged boilers, 

storage water heaters, instantaneous water heaters, and unfired hot water storage tanks. (42 

U.S.C. 6313(a)) Section 340 of EPCA defines a “packaged terminal air conditioner” as “a wall 

sleeve and a separate unencased combination of heating and cooling assemblies specified by the 

builder and intended for mounting through the wall. It includes a prime source of refrigeration, 

separable outdoor louvers, forced ventilation, and heating availability by builder's choice of hot 

water, steam, or electricity.” (42 U.S.C. 6311(10)(A)) EPCA defines a “packaged terminal heat 

pump” as “a packaged terminal air conditioner that utilizes reverse cycle refrigeration as its 

prime heat source and should have supplementary heat source available to builders with the 

choice of hot water, steam, or electric resistant heat.” (42 U.S.C. 6311(10)(B)) 

DOE most recently issued amended standards for PTACs and PTHPs on October 7, 2008, 

which codified amended standards for PTACs and PTHPs and divided PTACs and PTHPs into 

two equipment classes – standard size and non-standard size. 73 FR 58772 (Oct. 7, 2008) On 

October 29, 2010, ASHRAE released ASHRAE Standard 90.1-2010 (formally the American 

National Standards Institute (ANSI)/ASHRAE/Illuminating Engineering Society of North 

America (IES) Standard 90.1-2010), which increased the efficiency levels for standard size 

PTACs and PTHPs to be equal to DOE standards, effective as of October 8, 2012. Hence, DOE 

did not consider revision of PTAC and PTHP standards at that time.

1.3.1 Framework and Analysis Methodology

DOE began this rulemaking by analyzing amended standards consistent with the 

procedures defined under 42 U.S.C. 6313(a)(6)(C). However, before DOE could finalize the

NOPR for this rulemaking, ASHRAE acted on October 9, 2013 to adopt ANSI/ASHRAE/IES 

Standard 90.1-2013, and this revision contained amended standards PTACs at levels above the 

current Federal standards, thereby triggering DOE’s statutory obligation under 42 U.S.C. 

6313(a)(6)(A) to promulgate an amended uniform national standard at those levels unless DOE 

determines that there is clear and convincing evidence supporting the adoption of energy 

conservation standards more stringent than the ASHRAE levels. Consequently, DOE prepared 

an analysis of the energy savings potential of amended standards at the ANSI/ASHRAE/IES 

Standard 90.1-2013 levels (as required by 42 U.S.C. 6313(a)(6)(A)(i)) and updated the 

accompanying analyses to reflect appropriate statutory provision, timelines, and compliance 

dates.

On February 22, 2013, DOE published a notice of public meeting and availability of the 

framework document regarding energy conservation standards for packaged terminal air 

conditioners and heat pumps standards. 78 FR 12252. This notice is available at: 

http://www.regulations.gov/#!documentDetail;D=EERE-2012-BT-STD-0029-0001. 
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DOE held a public meeting on March 18, 2013 (“March 2013 framework public 

meeting”) to discuss procedural and analytical approaches to the rulemaking and to inform 

interested parties and facilitate their involvement in the rulemaking process. The public meeting 

sought input on DOE’s planned analytical approach and identified several issues of particular 

interest to DOE for this rulemaking proceeding. 

Table 1.3.1 lists the analyses conducted throughout the rulemaking process. 

Table 1.3.1. PTAC and PTHP Analyses* 

Preliminary Analyses NOPR Final Rule

Market and technology 

assessment

Life-cycle cost sub-group analysis Revised NOPR analyses

Screening analysis Manufacturer impact analysis

Engineering analysis Utility impact analysis

Energy use determination Emissions analysis

Markups for equipment price 

determination

Employment impact analysis

Life-cycle cost and payback 

period analysis

Regulatory impact analysis

Shipments analysis

National impact analysis

* In the current rulemaking, DOE conducted the analyses listed under Preliminary Analyses as part of the NOPR

analysis.

After the March 2013 framework public meeting, as part of the information gathering and 

sharing process for the manufacturer impact analysis (MIA), DOE organized and held interviews 

with manufacturers of PTACs and PTHPs as part of the engineering analysis. DOE had four 

objectives for these interviews: (1) solicit manufacturer feedback on the draft inputs to the 

engineering analysis; (2) solicit feedback on topics related to the manufacturer impact analysis; 

(3) provide an opportunity to express manufacturers’ concerns to DOE; and (4) foster 

cooperation between manufacturers and DOE. DOE incorporated the information gathered 

during the engineering interviews with manufacturers into its engineering analysis (Chapter 5 of 

the final rule TSD) and the manufacturer impact analysis (Chapter 12 of the final rule TSD). 

On September 16, 2014, DOE published a notice of public rulemaking (“September 2014 

NOPR”) in the Federal Register. 79 FR 55538 (September 16, 2014). In the September 2014 

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

proposed amended energy conservation standards for PTACs and PTHPs that were more 

stringent that the levels in ASHRAE 90.1-2013. In conjunction with the September 2014 NOPR, 

DOE also published on its website the complete TSD for the proposed rule, which incorporated 

the analyses DOE conducted and technical documentation for each analysis. Also published on 

DOE’s website were the engineering analysis spreadsheets, the LCC spreadsheet, and the 

national impact analysis standard spreadsheet. These materials are available at:

http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/64 
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In the September 2014 NOPR, DOE identified seven issues on which it was particularly 

interested in receiving comments and views of interested parties: alternate refrigerants, 

distribution channels, shipments data, efficiency trends, conversion costs, direct employment 

levels, and effects on small businesses. 79 FR 55538 at 55599-55600. After the publication of 

the September 2014 NOPR, DOE received written comments on these and other issues. DOE 

also held a public meeting in Washington, DC, on October 29, 2014, to discuss and receive 

comments regarding the tools and methods DOE used in the NOPR analysis, as well as the 

results of the analysis. DOE also invited written comments and announced the availability of a 

NOPR analysis technical support document (NOPR TSD). The NOPR TSD is available at: 

http://www.regulations.gov/#!documentDetail;D=EERE-2012-BT-STD-0029-0021 

At the public meeting held on October 29, 2014, DOE presented the methodologies and 

results of the analyses set forth in the NOPR TSD. Interested parties provided comments. Key 

issues raised by stakeholders included: (1) the use of the ASHRAE 90.1-2013 levels as the 

analytical baseline; (2) the proportion of units that would require redesign to meet the standard 

levels proposed in the September 2014 NOPR; (3) the cumulative burden on manufacturers of 

redesigning to the amended ASHRAE levels and then redesigning to a more stringent Federal 

standard; and (4) the assumptions about PTAC and PTHP operations used in the energy use 

analysis.

DOE refined the NOPR analyses based on stakeholder comments for the final rule TSD. 

1.4 STRUCTURE OF THE DOCUMENT

This final rule TSD outlines the analytical approaches used in this rulemaking. The TSD 

consists of seventeen chapters and appendices.

Chapter 1 Introduction: provides an overview of the appliance standards program 

and how it applies to the PTAC and PTHP rulemaking, and outlines the 

structure of the document. 

Chapter 2 Analytical Framework: describes the rulemaking process.

Chapter 3 Market and Technology Assessment: characterizes the PTAC and PTHP 

market and the technologies available for increasing equipment efficiency. 

Chapter 4 Screening Analysis: identifies design options that improve efficiency of 

the covered equipment and determines which technology options are 

viable for consideration in the engineering analysis. 

Chapter 5 Engineering Analysis: discusses the methods used for developing the 

relationship between increased manufacturer price and increased 

efficiency.

Chapter 6 Markups Analysis: discusses the methods used for establishing markups 

for converting manufacturer prices to customer equipment costs.
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Chapter 7 Energy Use Analysis: discusses the process used for generating energyuse estimates for the covered equipment as a function of standard levels.

Chapter 8 Life-Cycle Cost and Payback Period Analysis: discusses the methods 

used to analyze effects of standards on individual customers and users of 

the equipment and compares the LCC and PBP of equipment with and 

without higher efficiency standards.

Chapter 9 Shipments Analysis: estimates shipments of the equipment over the 30-

year analysis period that is used in performing the national impact analysis 

(NIA), including how shipments may vary under alternative standard 

levels.

Chapter 10 National Impact Analysis: assesses the national energy savings, and the 

national net present value of total customer costs and savings, expected to 

result from specific, potential energy conservation standards.

Chapter 11 Life-Cycle Cost Subgroup Analysis: discusses the effects of potential 

standards on different subgroups of customers.

Chapter 12 Manufacturer Impact Analysis: discusses the effects of amended energy 

conservation standards on the finances and profitability of equipment 

manufacturers.

Chapter 13 Emissions Impact Analysis: discusses the effects of standards on three 

pollutants—sulfur dioxide (SO2), nitrogen oxides (NOx), and mercury—as 

well as CO2 emissions.

Chapter 14 Monetization of Emissions Reductions Benefits: discusses the basis for 

estimated monetary values used for the reduced emissions of CO2 and 

NOx that are expected to result from each of the TSLs considered.

Chapter 15 Utility Impact Analysis: discusses selected effects of potential standards 

on electric utilities.

Chapter 16 Employment Impact Analysis: discusses the effects of amended energy 

conservation standards on national employment. 

Chapter 17 Regulatory Impact Analysis: discusses the impact of non-regulatory 

alternatives to efficiency standards.

Appendix 6A Detailed Data for Equipment Price Markups

Appendix 7A Detailed Unit Energy Consumption Data

Appendix 10A Full-Fuel-Cycle Multipliers
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Appendix 10B NIA Sensitivity Analysis for Alternative Product Price Trend 

Scenarios

Appendix 12A Manufacturer Impact Analysis Interview Guide

Appendix 12B Government Regulatory Impact Model (GRIM) Overview

Appendix 14A Social Cost of Carbon for Regulatory Impact Analysis Under 

Executive Order 12866

Appendix 14B Technical Update of Social Cost of Carbon for Regulatory Impact 

Analysis Under Executive Order 12866

Appendix 17A Regulatory Impact Analysis: Supporting Materials
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CHAPTER 2. ANALYTICAL FRAMEWORK

TABLE OF CONTENTS

2.1 INTRODUCTION ........................................................................................................... 2-1

2.2 BACKGROUND ............................................................................................................. 2-4

2.3 MARKET AND TECHNOLOGY ASSESSMENT........................................................ 2-4

2.3.1 Market Assessment .............................................................................................. 2-4

2.3.2 Technology Assessment....................................................................................... 2-4

2.4 SCREENING ANALYSIS .............................................................................................. 2-5

2.5 ENGINEERING ANALYSIS.......................................................................................... 2-5

2.6 MARKUPS ANALYSIS ................................................................................................. 2-6

2.7 ENERGY USE ANALYSIS............................................................................................ 2-7

2.8 LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS ..................................... 2-7

2.9 SHIPMENTS ANALYSIS .............................................................................................. 2-8

2.10 NATIONAL IMPACT ANALYSIS................................................................................ 2-8

2.10.1 National Energy Savings...................................................................................... 2-9

2.10.2 Net Present Value ................................................................................................ 2-9

2.10.3 Forecasted Efficiencies...................................................................................... 2-10

2.11 CUSTOMER SUB-GROUP ANALYSIS ..................................................................... 2-10

2.12 MANUFACTURER IMPACT ANALYSIS.................................................................. 2-10

2.13 EMISSIONS ANALYSIS.............................................................................................. 2-11

2.14 MONETIZING CARBON DIOXIDE AND OTHER EMISSIONS

REDUCTIONS .......................................................................................................................... 2-11

2.15 UTILITY IMPACT ANALYSIS................................................................................... 2-13

2.16 EMPLOYMENT IMPACT ANALYSIS....................................................................... 2-13

2.17 REGULATORY IMPACT ANALYSIS ....................................................................... 2-14

REFERENCES .......................................................................................................................... 2-15

LIST OF FIGURES

Figure 2.1.1 Analyses for PTAC and PTHP Energy Conservation Standards........................ 2-2
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CHAPTER 2. ANALYTICAL FRAMEWORK

2.1 INTRODUCTION

Section 6313(a)(6)(A)(ii) of 42 United Standards Code (U.S.C.), requires the U.S. 

Department of Energy (DOE) to establish energy conservation standards that are technologically 

feasible and economically justified and will result in significant energy conservation. This 

chapter describes the general analytical framework that DOE uses in developing such standards, 

as well as aspects specific to the analysis of standards for packaged terminal air conditioner 

(PTAC) and packaged terminal heat pump (PTHP) equipment. The analytical framework 

summarizes the methodologies, analytical tools, and relationships among the various analyses 

that are part of a standards rulemaking. 

Figure 2.1.1 summarizes the analytical components of the standards-setting process. The 

column labeled Approaches lists the methodologies DOE uses to perform the various steps and 

analyses in the process. The primary focus of the figure is the column labeled Analyses. The 

columns labeled Key Inputs and Key Outputs show how the analyses fit into the rulemaking 

process and how they relate to each other. Key inputs are the types of data and information that 

the analyses require. Some key inputs can be found in public databases; other inputs DOE 

collects from interested parties or experts having specialized knowledge. Key outputs are the 

analytical results that feed directly into the standards-setting process. Lines with arrows 

connecting analyses show the types of information that feed from one analysis to another.
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Figure 2.1.1 Analyses for PTAC and PTHP Energy Conservation 

Standards
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The analyses that DOE performed in developing this final rule include:

• A market and technology assessment to characterize the relevant equipment markets 

and technology options, including prototype designs;

• A screening analysis to review each technology option and determine whether it is 

technologically feasible; is practical to manufacture, install, and service; would 

adversely affect equipment utility or availability; or would have adverse impacts on 

health and safety;

• An engineering analysis to develop cost-efficiency relationships for equipment 

designs that passed the screening analysis; the manufacturer’s production costs 

(material, labor, and factory overhead) for achieving increased efficiency are 

evaluated;

• An energy use analysis to determine the annual energy use of the considered 

equipment at specific efficiency levels;

• A markups analysis to convert the manufacturer costs estimated from the engineering 

analysis to customer prices, which are then used in the life-cycle cost (LCC) and 

payback period analysis (PBP) and in the manufacturer impact analysis.;

• A life-cycle cost and payback period analysis to calculate, at the customer level, the 

discounted savings in operating costs (minus maintenance and repair costs) 

throughout the estimated average life of the covered equipment, compared to any 

increase in the installed cost of the equipment likely to result directly from the 

imposition of the standard;

• A shipments analysis to forecast equipment shipments, which then are used to 

calculate the national impacts of standards on energy consumption and costs, net 

present value (NPV), and future manufacturer cash flows;

• A national impact analysis to assess the aggregate impacts nationwide of the NPV of 

total customer LCC and national energy savings (NES);

• A customer sub-group analysis to evaluate variations in customer characteristics that 

might cause a standard to affect the LCC for particular customer sub-populations 

differently than for the overall population;

• A manufacturer impact analysis to estimate the financial impacts of standards on 

manufacturers and to calculate effects on competition, employment, and 

manufacturing capacity;

• An employment impact analysis to assess the aggregate effects of standards on 

national employment;

• A utility impact analysis to estimate the effects of proposed standards on the 

generation capacity and electricity generation of electric utilities;

• An emissions analysis to estimate the effects of amended energy conservation 

standards on emissions of carbon dioxide (CO2), sulfur dioxide (SO2), nitrogen oxides 

(NOX), and mercury (Hg);

• A monetization of emission reduction benefits resulting from reduced emissions 

associated with potential amended standards; and 

• A regulatory impact analysis to evaluate alternatives to proposed amended energy 

conservation standards that could achieve substantially the same regulatory goal.
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2.2 BACKGROUND

DOE developed the analytical framework pertaining to PTAC and PTHP equipment in 

the Rulemaking Framework for Packaged Terminal Air Conditioners and Packaged Terminal 

Heat Pumps (February 22, 2013). DOE announced the availability of the Framework document 

in a notice of public meeting and availability of a Framework document published in the Federal 

Register on February 22, 2013. 78 FR 12252

DOE presented the analytical approach to interested parties during a public meeting held 

on March 18, 2013.1 DOE used comments gathered during the Framework public meeting as 

well as additional information to conduct analyses culminating in the publication of a Notice of 

Proposed Rulemaking for PTAC and PTHP equipment2 on September 16, 2014. 79 FR 55538.

DOE also held an associated public meeting for the NOPR stage in Washington, D.C. on October 

29, 2014. After gathering stakeholder comments through the public meeting and open comment 

period, DOE revised and updated its analysis for today’s final rule. 

The following sections provide a brief overview of the different analytical approaches of 

this rulemaking analysis plan. DOE has used the most reliable data available at the time of each 

analysis in this rulemaking. DOE has also considered the submissions of additional data from 

interested parties during the rulemaking process.

2.3 MARKET AND TECHNOLOGY ASSESSMENT

The market and technology assessment characterizes the relevant equipment markets and 

technology options, including prototype designs, for considered equipment. Chapter 3 of this 

TSD describes in detail the market and technology assessment for PTAC and PTHP equipment.

2.3.1 Market Assessment

When initiating a standards rulemaking, DOE develops information on the present and 

past industry structure and market characteristics for the equipment being studied. This activity 

assesses the industry and equipment, both quantitatively and qualitatively, based on publicly 

available information. As such, for the considered equipment, DOE addressed (1) manufacturer 

market share and characteristics; (2) current regulatory and non-regulatory initiatives for 

improving equipment efficiency; and (3) trends in equipment characteristics and retail markets. 

This information serves as resource material throughout the rulemaking.

DOE reviewed equipment literature and interviewed manufacturers to develop an overall 

picture of the market for PTAC and PTHP equipment in the United States. Industry publications 

and trade journals, government agencies, and trade organizations provided the bulk of this 

information. The appropriate sections of this TSD, particularly chapter 3, describe the resulting 

information as DOE used it in the analysis.

2.3.2 Technology Assessment

DOE typically uses information relating to current and past technology options and 

prototype designs to determine what technologies manufacturers use to attain higher 
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performance levels. In consultation with interested parties, DOE develops a list of technologies 

for consideration. Initially, the list encompasses all the technologies DOE believes are 

technologically feasible. DOE developed a list of technologically feasible design options through 

consultation with manufacturers of components and systems, from trade publications and 

technical papers. Since many options for improving equipment efficiency are available in 

existing units, equipment literature and direct examination of equipment provided additional 

information. 

2.4 SCREENING ANALYSIS

As described in section 2.3.2, DOE develops an initial list of efficiency enhancement 

options from the technologies identified as technologically feasible. Then DOE, in consultation 

with interested parties, reviews the list to determine whether those options (1) are technologically 

feasible; (2) are practicable to manufacture, install, and service; (3) have an adverse impact on 

equipment utility or availability; or (4) have adverse impacts on health and safety. In addition, 

DOE removes from the list any technology options for which energy consumption data are 

lacking as well as any for which the energy consumption cannot be measured adequately by 

DOE test procedures. Chapter 4 of this TSD describes the screening analysis. In the engineering 

analysis, DOE further considers the efficiency-enhancement options that it did not eliminate in 

the screening analysis.

2.5 ENGINEERING ANALYSIS

In the engineering analysis (chapter 5 of this TSD), DOE evaluates a range of equipment 

efficiency levels and their associated manufacturing costs in order to establish the relationship 

between the cost and the efficiency of PTAC and PTHP equipment. This relationship serves as 

the basis for cost/benefit calculations related to individual customers, manufacturers, and the 

nation. Chapter 5 discusses the equipment classes DOE analyzed, the representative baseline 

units, the incremental efficiency levels, and the methodology DOE used to develop 

manufacturing costs, the methodology it used to develop the energy consumption model, the 

cost-efficiency curves, and the effect of efficiency improvements on the covered equipment.

The purpose of the engineering analysis is to estimate the incremental manufacturing 

production costs associated with increasing equipment efficiency above the level of the baseline 

model in each equipment class. The engineering analysis generally considers technologies not 

eliminated in the screening analysis, although certain technologies were not analyzed because 

they offered negligible incremental improvements to efficiency. DOE considers the remaining 

technologies, designated “design options,” in developing cost-efficiency curves, which are used 

for the LCC and PBP analysis. For each equipment class, DOE selected efficiency levels and 

obtained incremental cost data at each level.

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 
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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 derived from teardowns of the equipment being analyzed. A supplementary method 

called a catalog teardown uses published manufacturer catalogs and supplementary component 

data to estimate the major physical differences between a piece of equipment that has been 

physically disassembled and another piece of similar equipment for which catalog data is 

available to determine the cost of the latter equipment.

DOE conducted the engineering analyses using the efficiency-level approach and the

reverse-engineering approach. DOE designated a baseline efficiency level equivalent to the 

minimum efficiency allowed by energy conservation standards. DOE set the baseline level 

equivalent to the ANSI/ASHRAE/IES Standard 90.1-2013 minimum for PTAC and PTHP 

equipment, since DOE is required to, at a minimum, adopt the ASHRAE levels as the Federal 

standard. (42 U.S.C. (a)(6)(A)(ii)(I)). DOE set efficiency levels at incremental steps above the 

baseline up to the maximum efficiency level that is technologically feasible using current 

technologies. 

To estimate the manufacturing production costs for equipment at each efficiency level, 

DOE reverse engineered a set of PTAC and PTHP equipment that was specifically selected to 

represent the range of efficiency levels. This reverse engineering involved the disassembly of 

units, analysis of the materials and manufacturing processes, and development of a spreadsheet 

cost model based on a clear and consistent manufacturing cost assessment methodology. DOE 

built a detailed cost assessment model that accurately estimates the manufacturing production 

cost (MPC) associated with producing a specific piece of equipment. The cost model reports 

those costs in aggregated form to maintain confidentiality of the data. 

The reverse engineering analysis provides an estimated MPC for each PTAC and PTHP 

unit considered in the analysis. DOE used the least squares method to develop cost-efficiency 

equations for PTAC and PTHP equipment at different capacity levels. These cost-efficiency 

equations predict the MPC of a given unit based on its capacity and its rated efficiency. DOE 

used the cost-efficiency equations to estimate the incremental cost increases associated with each 

efficiency level used in the analysis. This production cost information is an input to the markups 

analysis.

2.6 MARKUPS ANALYSIS

DOE uses manufacturer-to-customer markups to convert the manufacturer selling price 

(MSP) estimates from the engineering analysis to customer prices, which are then used in the 

LCC and PBP analysis and in the manufacturer impact analysis. Retail prices are necessary for 

the baseline efficiency level and all other efficiency levels under consideration (see chapter 6 of 

this TSD). To develop markups, DOE identified distribution channels (i.e., how the equipment is

distributed from the manufacturer to the customer). After identifying appropriate distribution 

channels, DOE utilized economic census data from the U.S. Census Bureau and input from the 

industry to define how equipment is marked up from the manufacturer to the customer.
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2.7 ENERGY USE ANALYSIS

The energy use analysis (chapter 7 of this TSD) provides estimates of the annual unit 

energy consumption (UEC) of PTAC and PTHP equipment at the considered equipment classes 

and efficiency levels. The annual UECs are used in subsequent analyses including the LCC, 

PBP, and National Energy Savings (NES). In the 2008 rulemaking for PTAC and PTHP 

equipment, DOE used whole-building simulations to determine annual UEC data by cooling 

capacity and efficiency rating.3 In the current rulemaking, DOE used the data for those 

equipment classes and efficiency levels that are the same as the 2008 rulemaking, and adjusted 

the data for those equipment classes and efficiency levels that are different from the 2008 

rulemaking. Chapter 7 describes the methodology used to adjust the unit energy consumption of

PTAC and PTHP equipment for the current rulemaking. As part of the energy use 

characterization, DOE made certain engineering assumptions regarding equipment application, 

including how the equipment is operated and under what conditions, and documented these 

assumptions in chapter 7 of the TSD. 

2.8 LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

New or amended energy conservation standards for equipment result in a change in 

operating costs—usually a decrease—and a change in customer price—usually an increase. DOE 

analyzed the net effect of new standards on customers (chapter 8 of this TSD) by evaluating the 

net LCC using the cost-efficiency relationship derived in the engineering analysis, as well as the 

energy costs derived from the energy use analysis. Inputs to the LCC calculation include the 

installed cost to the customer (customer price plus installation cost); operating expenses (energy 

expenses and maintenance costs); the lifetime of the equipment; and a discount rate.

Equipment with efficiency higher than baseline typically has a higher installed cost and 

lower operating cost relative to baseline equipment. The payback period is the estimated amount 

of time (in years) it takes customers to recover the increased total installed cost (including 

equipment and installation costs) of a more efficient type of equipment through lower operating 

costs. DOE calculates the PBP by dividing the change in total installed cost (normally higher) 

due to a standard by the change in annual operating cost (normally lower) that results from the 

standard.

DOE conducted the LCC and PBP analyses for the PTAC and PTHP equipment classes 

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 analyses by incorporating uncertainty and variability considerations in 

certain of the key parameters as discussed below. Inputs to the LCC and PBP analysis are 

categorized as: (1) inputs for establishing the total installed cost and (2) inputs for calculating the 

operating expense. Results of the LCC and PBP analyses were applied to other equipment 

classes through linear scaling of the results by the cooling capacity of the equipment class.

The equipment costs faced by purchasers of PTAC and PTHP equipment are derived 

from the MSPs estimated in the markups analysis. To forecast equipment costs into the future, 
2-8

DOE chose to apply a constant price trend (2013 levels) for each efficiency level in each 

equipment class for the NOPR. DOE reviewed the Energy Information Administration (EIA)’s 

energy price data to establish electricity prices for commercial consumers. DOE used EIA’s 

Annual Energy Outlook 2014 (AEO 2014) as the default source of projections of future energy 

prices for its LCC and PBP analysis.4

DOE developed discount rates for customers based on the cost of capital, which is 

commonly used to estimate the present value of cash flows to be derived from a typical company 

project or investment. DOE estimated the cost of capital of companies that purchase PTAC and 

PTHP equipment. The types of companies that DOE used are large hotel/motel chains, 

independent hotel/motel, assisted living/health care, and small office. More details regarding 

DOE’s estimates of customer discount rates are provided in chapter 8 of the TSD.

DOE considered maintenance, repair, and installation costs for the equipment covered in 

this rulemaking. For PTACs, DOE utilized estimates of annual maintenance cost from the 2008 

rulemaking for PTAC and PTHP equipment; the values were adjusted to current material and 

labor rates. For PTHPs, DOE scaled the adjusted estimate of PTAC maintenance costs with the 

ratio of PTHP to PTAC annualized maintenance costs. Repair costs are associated with repairing 

or replacing components that have failed. DOE utilized manufacturer- and vendor-provider 

extended warranty price data to estimate annual repair costs. DOE assumed that any routine or 

minor repairs are included in the annualized maintenance costs. Repair costs were linearly scaled 

by cooling capacity to apply to all equipment classes.

DOE established average equipment lifetimes for use in the LCC and subsequent 

analyses by using data from the previous rulemaking, stakeholder comments from this current 

rulemaking, and stakeholder comments from the ASHRAE Standard 90.1-2013 Notice of Data 

Availability.5

2.9 SHIPMENTS ANALYSIS

Forecasts of shipments are required to calculate the national impacts of standards on 

energy consumption, NPV, and future manufacturer cash flows. DOE used historical data as the 

basis for projecting future shipments of PTAC and PTHP equipment (Chapter 9 of this TSD). 

Historical shipments data are used to build up an equipment stock and also to calibrate the 

shipments model. Based off the equipment stock and calibrated model, DOE calculated 

shipments intended for new construction and replacement applications. The sum of new 

construction and replacement shipments is the total shipments.

2.10 NATIONAL IMPACT ANALYSIS

The national impact analysis, described in chapter 10 of this TSD, assesses the NPV of 

total customer costs and benefits. DOE determined both the NPV and the national energy savings 

(NES) for the efficiency levels established for PTAC and PTHP equipment. To make the 

analysis more accessible and transparent to all interested parties, DOE used a spreadsheet model 

to calculate the energy savings and the national commercial customer costs and savings from 
2-9

each TSL. The NIA calculations are based on the annual energy consumption and total installed 

cost data from the energy use analysis and the LCC analysis. In the NIA, DOE forecasted the 

lifetime energy savings, energy cost savings, equipment costs, and NPV of commercial customer 

benefits for each equipment class over the lifetime of PTAC equipment sold during the 30 year 

analysis period.

2.10.1 National Energy Savings 

The inputs for determining NES are (1) annual energy consumption per unit, (2) 

shipments, (3) equipment stock, (4) national energy consumption, and (5) site-to-source 

conversion factors. DOE calculated the national energy consumption by multiplying the number 

of units, or stock, of equipment (by vintage) by the unit energy consumption (also by vintage).1

DOE calculated national annual energy savings as the difference between national energy 

consumption in the base case (without new efficiency standards) and in each higher-efficiency 

standards case. The analysis included estimated energy savings by fuel type used for generating 

electricity. DOE estimates energy consumption and savings based on site energy, then converts 

the electricity consumption and savings into source energy. Cumulative energy savings are the 

sum of the annual NES throughout the forecast period.

The stock of PTAC and PTHP equipment is dependent on annual shipments and the 

lifetime of the equipment. DOE conducted shipments projections under the baseline efficiency 

levels and new standard levels and equipment efficiency trends. DOE’s shipments model 

presumed that shipments of new PTACs and PTHPs were driven by growth in commercial floor 

space for building types using equipment as well as necessary stock replacements.

2.10.2 Net Present Value 

The inputs for determining NPV are (1) total annual installed cost, (2) total annual 

operating cost savings, (3) discount factor, (4) present value of costs, and (5) present value of 

savings. DOE calculated NPV as the difference between total operating-cost savings (including 

electricity, repair, and maintenance cost savings) and increases in total installed costs (including 

equipment price and installation cost). DOE calculated savings over the life of the equipment, 

accounting for differences in yearly energy rates, and used a discount factor to discount future 

costs and savings to the present.

DOE calculated increases in total installed costs as the product of the difference in the 

total installed cost between the baseline efficiency level and new standard levels (i.e., once the 

amended energy conservation standard takes effect) and the annual shipments in the standards 

case. Because costs of the more-efficient equipment bought in the standard cases are higher than 

those of equipment bought in the base case, price increases appear as negative values in the 

NPV.

DOE expressed operating cost savings as decreases in operating costs associated with the 

lower energy consumption of equipment bought under the new standards compared to the 

 1 Vintage represents the age of the equipment.
2-10

baseline efficiency level. Total operating-cost savings are the product of savings per unit and the 

number of units of each vintage surviving in a particular year.

2.10.3 Forecasted Efficiencies

Several of the inputs for determining NES (e.g., annual energy consumption per unit) and 

NPV (e.g., total annual installed cost and total annual operating cost savings) depend on the 

efficiency of the equipment. Thus, DOE forecasted efficiencies for the base case and standards 

cases. The forecasted efficiencies specify the annual average shipment-weighted equipment 

efficiencies for future years.

DOE based historical shipment-weighted average efficiency trends for PTAC and PTHP 

equipment on limited PTAC and PTHP efficiency data. Once DOE established historical 

efficiency trends, it estimated future trends of equipment efficiency, and in turn, annual energy 

consumption by extrapolating from the historical trend.

DOE based its standards-case forecasts (i.e., forecasts of efficiency trends after standards 

take effect) on the use of a roll-up efficiency scenario and parallel growth trend. Under a roll-up 

scenario, all equipment at energy efficiency levels below a prospective standard are moved or 

rolled-up to the minimum efficiency level allowed under the new standard. The distribution of 

equipment at new standard levels is unaffected (i.e., this equipment remains at its pre-standard 

efficiency levels). The roll-up efficiency scenario dictates how DOE determined efficiency 

distributions in the first year a new standard takes effect, but does not define how equipment 

efficiency will be distributed in the future. Under the parallel growth trend, DOE assumes that 

the standards case efficiency trend parallels the base case efficiency trend. In other words, the 

initial jump in shipment-weighted efficiency that occurs when the standard first becomes 

effective is maintained throughout the forecast.

2.11 CUSTOMER SUB-GROUP ANALYSIS

The customer sub-group analysis evaluates the potential impacts of new or amended 

standards on commercial customers, DOE evaluates impacts on identifiable groups (i.e., 

subgroups) of customers that may be disproportionately affected by a national standard.

DOE evaluated impacts on a subgroup consisting of independently-operating lodging businesses 

using the LCC and PBP spreadsheet model. To the extent possible, it utilized inputs appropriate 

for this subgroup. Chapter 11 of this TSD describes the sub-group analysis for customers of 

PTAC and PTHP equipment.

2.12 MANUFACTURER IMPACT ANALYSIS

The manufacturer impact analysis (MIA) assesses the impacts of new energy efficiency 

standards on manufacturers of PTAC and PTHP equipment. Potential impacts include financial 

effects, both quantitative and qualitative, that might lead to changes in the manufacturing 

practices for the equipment. DOE identifies those potential impacts through interviews with 

manufacturers and other interested parties.
2-11

As described in chapter 12 of this TSD, DOE conducted the MIA for the covered 

equipment in three phases, and further tailored the analytical framework based on stakeholder 

comments. In Phase I, DOE conducted structured, detailed interviews with a representative 

cross-section of manufacturers and prepared a profile of the PTAC and PTHP industry. In Phase 

II, DOE prepared an industry cash-flow analysis to quantify the potential impacts of an amended 

energy conservation standard on manufacturers of PTACs and PTHPs. In Phase III, DOE 

evaluated subgroups of manufacturers that may be disproportionately impacted by amended 

energy conservation standards or that may not be represented accurately by the average cost 

assumptions used to develop the industry cash-flow analysis. 

2.13 EMISSIONS ANALYSIS

In the emissions analysis, DOE estimated the reduction in power sector emissions of 

carbon dioxide (CO2), nitrogen oxides (NOx), sulfur dioxide (SO2), and mercury (Hg) from 

potential energy conservation standards for PTAC and PTHP equipment. In addition, DOE 

estimates emissions impacts in production activities (extracting, processing, and transporting 

fuels) that provide the energy inputs to power plants. These are referred to as “upstream” 

emissions. Together, these emissions account for the full-fuel-cycle (FFC). 

As described in chapter 13 of this TSD, the primary environmental effects of the 

standards will be reduced power plant emissions resulting from reduced consumption of 

electricity. Emissions reductions associated with new standards for PTAC and PTHP equipment

pertain primarily to CO2, NOX, and Hg. After estimating emissions reductions, DOE monetized 

the benefits associated with those reductions, as summarized below.

2.14 MONETIZING CARBON DIOXIDE AND OTHER EMISSIONS

REDUCTIONS

DOE estimated the monetary benefits likely to result from the reduced emissions of CO2 

and NOX that are expected to result from each potential standard level. The monetization of the 

benefits of emissions reductions is described in chapter 14 of this TSD. DOE is aware of 

multiple agency efforts to determine the range of values appropriate to evaluating the potential 

economic benefits of reduced Hg emissions, and DOE is evaluating how to appropriately 

monetize avoided SO2 and Hg emissions in energy conservation standards rulemakings. For this 

rulemaking, DOE did not monetize estimated SO2 and Hg reductions.

To carry out this analysis, DOE used a variant of the EIA’s National Energy Modeling 

System (NEMS). NEMS is a large, multi-sector, partial-equilibrium model of the U.S. energy 

sector that EIA has developed throughout the past decade, primarily to help in preparing the 

AEO. NEMS, which is available in the public domain, produces a widely recognized baseline 

energy forecast for the United States, currently through 2040. Typical NEMS outputs include 

forecasts of electricity sales, electricity price, and avoided electric generating capacity. DOE uses 

a variant of NEMS known as NEMS-BT to provide key inputs to its analysis.2

 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 2000, DOE/EIA-
2-12

Carbon Dioxide

In order to estimate the monetary value of benefits resulting from reduced CO2

emissions, DOE used the most current values for the social cost of carbon (SCC) developed 

and/or agreed to by an interagency work group and adjusted to 2014$ using the implicit price 

deflator for gross domestic product (GDP) from the Bureau of Economic Analysis. The SCC is 

intended to serve as a monetary measure of the incremental damage resulting from greenhouse 

gas (GHG) emissions, including, but not limited to, net loss of agricultural productivity, human 

health effects, property damage from sea level rise, and changes in ecosystem services. With full 

regard for the limits of both quantification and monetization, the SCC can be used to provide 

estimates of the social benefits of reductions in GHG emissions.

At the time of this analysis, the most recent interagency estimates of the potential global

benefits resulting from reduced CO2 emissions in 2015, expressed in 2014$, were $12.2, $41.2, 

$63.4, and $121 per metric ton avoided.3 Those 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 DOE 

gives preference to consideration of the global benefits of reduced CO2 emissions. To calculate a 

present value of the range of monetary values, DOE discounted each of the four SCC values 

using the discount rate that was used to obtain the SCC value in that case. Those values are 

subject to change as the scientific and economic knowledge continues to evolve rapidly 

regarding the contribution of CO2 and other GHGs to changes in the future global climate and 

the potential resulting damages to the world economy.

In the absence of any Federal regulation of power plant emissions of CO2, a DOE 

standard is likely to result in reductions of these emissions. The CO2 emission reductions likely 

to result from a standard were estimated using NEMS-BT and national energy savings estimates 

drawn from the NIA spreadsheet model. The net benefit of the standard is the difference between 

emissions estimated by NEMS-BT at each standard level considered and the AEO reference case. 

NEMS-BT tracks CO2 emissions using a detailed module that provides results that include broad 

coverage of all sectors and interactive effects.

Nitrogen Oxides

DOE also estimated the cumulative monetary value of the economic benefits associated 

with NOx emissions reductions anticipated to result from amended standards for PTACs and 

PTHPs. Estimates of monetary value for reducing NOx from stationary sources range from $483

 

0581(2000), March 2000. EIA approves use of the name NEMS to describe only an official version of the model 

without any modification to code or data. Because DOE's analysis entails minor code modifications, and the 

model is run under various policy scenarios that are variations on EIA assumptions, DOE refers to the model as 

NEMS-BT (BT stands for DOE’s Building Technologies Program, under whose aegis this work is performed). 

NEMS-BT previously was called NEMS-BRS.

3 The interagency group selected four sets of SCC values for use in regulatory analyses. Three sets of values are 

based on the average SCC from the three IAMs, at discount rates of 2.5, 3, and 5 percent. The fourth set, which 

represents the 95th percentile SCC estimate across all three models at a 3-percent discount rate, was included to 

represent higher than expected impacts from temperature change further out in the tails of the SCC distribution.
2-13

to $4,969 per ton in 2014$.4 DOE calculated monetary benefits using a medium value for NOx

emissions of $2,727 per short ton (in 2014$), and real discount rates of 3 percent and 7 percent.

DOE has taken into account how amended energy conservation standards would reduce 

site NOx emissions nationwide and increase power sector NOx emissions in those 22 States not 

affected by the Clean Air Interstate Rule (CAIR; 70 FR 25162 (May 12, 2005)).

5 DOE estimated 

the monetized value of net NOx emissions reductions resulting from each of the efficiency levels

considered based on estimates found in the relevant scientific literature.

2.15 UTILITY IMPACT ANALYSIS

The utility impact analysis (chapter 15 of this TSD) assesses the effects of higher 

efficiency standards on electric utility industries. DOE uses NEMS-BT to provide key inputs to 

its analysis. DOE conducted the utility impact analysis as a scenario departing from the latest 

AEO reference case. In other words, DOE modeled the energy savings from amended energy 

conservation standards using NEMS-BT to generate forecasts that deviate from the AEO

reference case. Chapter 15 of the NOPR TSD describes the utility impact analysis in further 

detail.

2.16 EMPLOYMENT IMPACT ANALYSIS

Energy conservation standards can affect employment both directly and indirectly. Direct 

employment effects are changes, resulting from the imposition of new standards, in the number 

of employees at the plants that produce covered equipment and at affiliated distribution and 

service companies. DOE evaluates direct employment impacts in the MIA. Indirect employment 

impacts may result if the imposition of standards causes expenditures to shift between goods (the 

substitution effect) and/or create changes in income and overall expenditure levels (the income 

effect). 

As discussed in chapter 16 of this TSD, DOE investigated indirect employment impacts 

for PTAC and PTHP energy conservation standards using the Impact of Sector Energy 

Technologies (ImSET) model developed by Pacific Northwest National Laboratory (PNNL). 

PNNL developed the ImSET model for DOE’s Office of Planning, Budget, and Analysis. The 

model estimates the employment and income effects of energy-saving technologies in buildings, 

industry, and transportation. In comparison with simple economic multiplier approaches, ImSET 

 4

 For additional information, refer to U.S. Office of Management and Budget (OMB)–Office of Information and 

Regulatory Affairs. 2006 Report to Congress on the Costs and Benefits of Federal Regulations and Unfunded 

Mandates on State, Local, and Tribal Entities. Washington, D.C.

5 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 CAIR 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 the caps, so DOE estimated NOx emissions reductions from the standards considered in this NOPR for 

these States.
2-14

allows for more complete and automated analysis of the economic effects of energy conservation 

investments.

2.17 REGULATORY IMPACT ANALYSIS

Pursuant to Executive Order 12866 (“Regulatory Planning and Review,” October 4, 

1993. 58 FR 51735), DOE prepared a regulatory impact analysis (RIA), which was subject to 

review under the Executive Order by the Office of Information and Regulatory Affairs at the 

Office of Management and Budget. The RIA, described in chapter 17 of this TSD, evaluated the 

ability of non-regulatory alternatives to standards to achieve significant energy savings at a 

reasonable cost, and compared the effectiveness of each to the effectiveness of the new 

standards.

DOE recognizes that voluntary or other non-regulatory efforts by manufacturers, utilities, 

and other interested parties can result in substantial improvements to energy efficiency or 

reductions in energy consumption. DOE considered the likely effects of non-regulatory 

initiatives on equipment energy use, customer utility, and LCC. Although DOE based its 

assessment on the documented effects of similar initiatives to date, it also considered information 

regarding the effects current initiatives might have in the future.
2-15

REFERENCES

1. U.S. Department of Energy – Office of Energy Efficiency and Renewable Energy Building 

Technologies Program. Rulemaking Framework for Packaged Terminal Air Conditioners 

and Packaged Terminal Heat Pumps. 2013. Washington, D.C. 

<www.regulations.gov/#!documentDetail;D=EERE-2012-BT-STD-0029-0002>

2. U.S. Department of Energy – Office of Energy Efficiency and Renewable Energy. Notice 

of Proposed Rulemaking and Public Meeting: Energy Conservation Program: Energy 

Conservation Standards for Packaged Terminal Air Conditioners and Packaged Terminal 

Heat Pumps. September 2014. Washington, D.C. 

<www.regulations.gov/#!documentDetail;D=EERE-2012-BT-STD-0029-0017>

3. U.S. Department of Energy – Office of Energy Efficiency and Renewable Energy, 

Energy Conservation Program for Commercial and Industrial Equipment: Packaged 

Terminal Air Conditioner and Packaged Terminal Heat Pump Energy Conservation 

Standards., 2007. (Last accessed June, 2014.) 

<www.regulations.gov/#!docketDetail;D=EERE-2007-BT-STD-0012> 

4. U.S. Department of Energy – Energy Information Administration, Annual Energy 

Outlook 2014 with Projections to 2040, 2014. Washington, DC. 

<www.eia.gov/forecasts/aeo/pdf/0383%282014%29.pdf>

5. U.S. Department of Energy – Office of Energy Efficiency and Renewable Energy, 

Energy Conservation Program for Certain Industrial Equipment: Energy Conservation 

Standards for Commercial Heating, Air-Conditioning, and Water-Heating Equipment: 

Notice of Data Availability, 2014. Washington, DC. <www.gpo.gov/fdsys/pkg/FR-2014-

04-11/html/2014-08214.htm>
3-i

CHAPTER 3. MARKET AND TECHNOLOGY ASSESSMENT

TABLE OF CONTENTS

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

3.2 EQUIPMENT DEFINITIONS......................................................................................... 3-1

3.3 EQUIPMENT CLASSES ................................................................................................ 3-2

3.4 ENERGY USE METRIC AND EQUIPMENT TEST PROCEDURES ......................... 3-4

3.5 MANUFACTURER TRADE GROUPS ......................................................................... 3-4

3.6 MANUFACTURER INFORMATION ........................................................................... 3-5

3.6.1 Manufacturers and Market Shares....................................................................... 3-5

3.6.2 Small Businesses.................................................................................................. 3-7

3.7 REGULATORY PROGRAMS ....................................................................................... 3-8

3.7.1 Current Federal Energy Conservation Standards................................................. 3-8

3.7.2 ASHRAE Energy Conservation Standards for PTACs and PTHPs.................... 3-9

3.7.3 State Energy Conservation Standards................................................................ 3-10

3.7.4 Canadian Energy Conservation Standards......................................................... 3-10

3.7.5 Mexican Energy Conservation Standards.......................................................... 3-11

3.8 VOLUNTARY PROGRAMS........................................................................................ 3-11

3.8.1 ENERGY STAR................................................................................................ 3-11

3.8.2 Rebate Programs................................................................................................ 3-12

3.9 SHIPMENTS ................................................................................................................. 3-13

3.9.1 Unit Shipments................................................................................................... 3-13

3.9.2 Equipment Lifetime ........................................................................................... 3-13

3.10 MARKET CHARACTERIZATION ............................................................................. 3-14

3.11 TECHNOLOGY ASSESSMENT.................................................................................. 3-17

3.11.1 Baseline Equipment Components and Operation .............................................. 3-17

3.11.2 Technology Options........................................................................................... 3-17

3.11.3 Equipment Efficiency Levels............................................................................. 3-18

LIST OF TABLES

Table 3.3.1 Existing Federal Equipment Classes for PTACs and PTHPs.................................. 3-3

Table 3.6.1 Large Manufacturers of Standard Size PTACs and PTHPs .................................... 3-5

Table 3.6.2 Other Manufacturers of Standard Size PTACs and PTHPs..................................... 3-6

Table 3.6.3 PTAC and PTHP Manufacturers: AHRI Members ................................................. 3-7

Table 3.6.4 PTAC and PTHP Manufacturers: Non-AHRI Members with AHRI-Certified 

Equipment.................................................................................................................................... 3-7

Table 3.7.1 Current Federal Energy Conservation Standards for PTACS and PTHPs.............. 3-9

Table 3.7.2 ANSI/ASHRAE/IES Standard 90.1-2013 Energy Efficiency Levels for PTACs and 

PTHPs........................................................................................................................................ 3-10

Table 3.7.3 Canadian Energy Conservation Standards for PTACS and PTHPs ...................... 3-11

Table 3.9.1 AHRI Estimated Shipment Data for PTAC and PTHP Industry (Standard and NonStandard).................................................................................................................................... 3-13
3-ii

Table 3.11.1 Potential Technology Options for Improving Energy Efficiency of PTACs and 

PTHPs........................................................................................................................................ 3-18

LIST OF FIGURES

Figure 3.2.1 Number of Certified Standard Size PTAC Models by Cooling Capacity – 2013 

AHRI Directory and Manufacturer Catalogs............................................................................. 3-14

Figure 3.2.2 Number of Certified Standard Size PTHP Models by Cooling Capacity – 2013 

AHRI Directory and Manufacturer Catalogs............................................................................. 3-15

Figure 3.2.3 Non-Standard Size PTAC Models by Cooling Capacity – 2013 AHRI Directory 

and Manufacturer Catalogs........................................................................................................ 3-16

Figure 3.2.4 Non-Standard Size PTHP Models by Cooling Capacity – 2013 AHRI Directory 

and Manufacturer Catalogs........................................................................................................ 3-16

Figure 3.2.5 Standard Size PTAC EER versus Cooling Capacity – 2013 AHRI Directory..... 3-19

Figure 3.2.6 Standard Size PTHP EER versus Cooling Capacity – 2013 AHRI Directory ..... 3-19

Figure 3.2.7 Non-Standard Size PTAC EER versus Cooling Capacity – 2013 AHRI Directory 

and Manufacturers’ Catalogs..................................................................................................... 3-20

Figure 3.2.8 Non-Standard Size PTHP EER versus Cooling Capacity – 2013 AHRI Directory 

and Manufacturers’ Catalogs..................................................................................................... 3-20

Figure 3.2.9 Standard Size PTHP COP versus Cooling Capacity – 2013 AHRI Directory..... 3-21

Figure 3.2.10 Non-Standard Size PTHP COP versus Cooling Capacity – 2013 AHRI Directory 

and Other Publicly Available Data ............................................................................................ 3-21
3-1

CHAPTER 3. MARKET AND TECHNOLOGY ASSESSMENT

3.1 INTRODUCTION

This chapter details the market and technology assessment that the U.S. Department of 

Energy (DOE) has conducted in support of the ongoing energy conservation standards 

rulemaking for packaged terminal air conditioners (PTACs) and packaged terminal heat pumps 

(PTHPs). The goal of the assessment is to develop a qualitative and quantitative characterization 

of the PTAC and PTHP industry and market structure based on publicly available information 

and data and information submitted by manufacturers and other stakeholders. Publicly available 

information includes the equipment certification directory from the Air-Conditioning, Heating,

and Refrigeration Institutea (AHRI), as well as Current Industry Reports (CIR) from the U.S. 

Census Bureau. 

This chapter first defines the PTAC and PTHP equipment that is the subject of this 

rulemaking (section 3.2), divides the equipment into equipment classes (3.3), and describes the 

metrics and test procedures that are used to characterize PTAC and PTHP equipment (3.4). 

Next, the chapter defines the equipment producers by discussing trade groups (3.5), 

manufacturers and market shares (3.6), and the regulatory programs (3.7) and voluntary 

programs (3.8) to which they may adhere. Then the chapter describes the market by discussing 

shipment data (3.9) and characterizing the market for different equipment types (3.10). The 

chapter closes with a technology assessment (3.11) that presents a preliminary list of 

technologies (referred to as technology options) that may improve the energy efficiency of 

PTACs and PTHPs.

3.2 EQUIPMENT DEFINITIONS

Section 340 of the Energy Policy and Conservation Act (EPCA) defines a “packaged 

terminal air conditioner” as “a wall sleeve and a separate unencased combination of heating and 

cooling assemblies specified by the builder and intended for mounting through the wall. It 

includes a prime source of refrigeration, separable outdoor louvers, forced ventilation, and 

heating availability by builder's choice of hot water, steam, or electricity.” (42 U.S.C. 

6311(10)(A)) EPCA defines a “packaged terminal heat pump” as “a packaged terminal air 

 a The Air-Conditioning, Heating, and Refrigeration Institute (AHRI), formerly referred to as ARI, is the trade 

association representing PTAC and PTHP manufacturers. 
3-2

conditioner that utilizes reverse cycle refrigeration as its prime heat source and should have 

supplementary heat source available to builders with the choice of hot water, steam, or electric 

resistant heat.” (42 U.S.C. 6311(10)(B)) DOE codified these definitions in 10 CFR 431.92 in a 

final rule issued October 21, 2004. 69 FR 61970.

PTACs and PTHPs are self-contained heating and air-conditioning units encased inside a 

sleeve specifically designed to go through the exterior building wall. The basic design of a 

PTAC is comprised of a compressor, an evaporator, a condenser, a fan, and an enclosure. Basic 

PTHPs feature additional items to those found in PTACs, such as more sophisticated metering 

devices, a reversing valve, and more sophisticated controls. All manufacturers offer PTACs and 

PTHPs with supplemental heating, with some offering a variety of add-on options. PTACs and 

PTHPs are installed by insertion into the wall sleeve and connection to an electrical outlet. They 

are primarily used to provide space conditioning for commercial facilities such as hotels and 

motels, assisted living facilities, hospitals, apartments, dormitories, schools, and offices.

There is a wide variety of wall sleeve sizes found in different buildings. These wall 

sleeve sizes are market driven (i.e., the applications or facilities where the PTACs or PTHPs are 

installed is what determines the “market standard” wall sleeve dimension) and require 

manufacturers to offer various PTACs and PTHPs that can fit into various wall sleeve 

dimensions. For new units, the industry has standardized the wall sleeve dimension for PTACs 

and PTHPs in buildings over the past 25 years to be 16 inches high by 42 inches wide. Units that 

have a wall sleeve dimension of 16 inches high or greater by 42 inches wide or greater are 

considered “standard size” equipment and all other units are considered “non-standard size” 

equipment. In contrast, the industry does not have a common wall sleeve dimension that is 

typical for all older existing facilities. These facilities, such as high-rise buildings found in large 

cities, typically use non-standard size equipment. In these installations, altering the existing wall 

sleeve opening to accommodate the more efficient, standard size equipment could include 

extensive structural changes to the building, could be very costly, and is therefore rarely done. 

3.3 EQUIPMENT CLASSES

When evaluating and establishing energy conservation standards, DOE divides covered 

equipment into equipment classes by the type of energy used or by capacity or other 

performance-related features that justify a different standard. In making a determination whether 

a performance-related feature justifies a different standard, DOE considers such factors as the 

utility to the customer of the feature and other factors DOE determines are appropriate.

The current equipment classes as established in the final rule issued on October 7, 2008, 

divide PTAC and PTHP equipment into twelve equipment classes. 73 FR 58772 (October 7, 

2008) Equipment classes are based on whether the equipment is an air conditioner or heat pump, 

the equipment’s cooling capacity, and the equipment’s wall sleeve dimensions. There are two 

categories of wall sleeve dimensions: “standard size” with wall sleeve dimensions greater than or 

equal to 16 inches high or greater than or equal to 42 inches wide; and “non-standard size” with 

wall sleeve dimensions less than 16 inches high or less than 42 inches wide. Table 3.3.1 shows 

the current equipment class structure.
3-3

Table 3.3.1 Existing Federal Equipment Classes for PTACs and PTHPs

Equipment Class

Equipment Category Cooling Capacity

PTAC

Standard Size*

<7,000 Btu/h

≥7,000 Btu/h and 

≤15,000 Btu/h

>15,000 Btu/h

Non-Standard Size**

<7,000 Btu/h

≥7,000 Btu/h and 

≤15,000 Btu/h

>15,000 Btu/h

PTHP

Standard Size*

<7,000 Btu/h

≥7,000 Btu/h and 

≤15,000 Btu/h

>15,000 Btu/h

Non-Standard Size**

<7,000 Btu/h

≥7,000 Btu/h and 

≤15,000 Btu/h

>15,000 Btu/h * Standard size refers to PTAC or PTHP equipment with wall sleeve dimensions having an external wall opening 

greater than or equal to 16 inches high or greater than or equal to 42 inches wide, and a cross-sectional area greater 

than or equal to 670 square inches.

** Non-standard size refers to PTAC or PTHP equipment with existing wall sleeve dimensions having an external 

wall opening of less than 16 inches high or less than 42 inches wide, and a cross-sectional area less than 670 square 

inches.

DOE is not considering amended energy conservation standards for non-standard size 

PTAC and PTHP equipment in this rulemaking, because the non-standard size equipment classes 

represent a small and declining portion of the market, and because of a lack of adequate 

information to analyze non-standard size units. The shipments analysis conducted for the 2008 

final rule projected that shipments of non-standard size PTACs and PTHPs would decline from 

about 30,000 units in 2012 (6.6% of the entire PTAC and PTHP market) to about 16,000 units in 

2042 (2.4% of the entire PTAC and PTHP market).b

 

 b See DOE’s discussion regarding shipment projections for standard and non-standard PTAC and PTHP equipment 

and the results of shipment projections in the technical support document for the 2008 PTAC and PTHP energy 

conservation standard at: http://www.regulations.gov/#!documentDetail;D=EERE-2007-BT-STD-0012-0032

(Chapter 10, Section 10.5).
3-4

Some manufacturers have introduced PTACs on the market that incorporate a ventilation 

system attachment that takes in make-up air and provides supplemental conditioning for this 

make-up air: dehumidification when outdoor humidity levels are high and also electric resistance 

heating when outdoor temperature is low. DOE believes that PTAC and PTHP units with add-on 

or integrated dehumidification systems currently meet the definition of PTACs and PTHPs, 

respectively. Thus, models with add-on or integrated dehumidification systems should be tested 

using the current test procedure and should meet the current energy conservation standards. 

Currently, the DOE test procedure does not require that the dehumidification module on such 

models be energized during testing, so the energy use of the dehumidification system would not 

be measured or accounted for in the EER metric. If DOE considers future amendments to the 

test procedure to account for energy consumed by the dehumidification systems, then DOE could 

consider designating a separate equipment class for such equipment at that time.

3.4 ENERGY USE METRIC AND EQUIPMENT TEST PROCEDURES

The energy conservation standards for PTACs and PTHPs are represented in terms of the 

energy efficiency ratio (EER) and the coefficient of performance (COP) as defined by the AHRI 

Standard 310/380-2014 Standard for Packaged Terminal Air-Conditioners and Heat Pumps

(AHRI 310/380-2014) test procedure. EER is defined as “the ratio of the produced cooling effect 

of an air conditioner or heat pump to its net work input, expressed in Btu/watt-hour.” COP is 

defined as “the ratio of the produced cooling effect of an air conditioner or heat pump (or its 

produced heating effect, depending on the mode of operation) to its net work input, when both 

the cooling (or heating) effect and the net work input are expressed in identical units of 

measurement.” DOE has incorporated these definitions and test procedures into its regulations at 

10 CFR Part 431.92 and 10 CFR Part 431.96, respectively.

3.5 MANUFACTURER TRADE GROUPS

DOE identified the Air-Conditioning, Heating, and Refrigeration Institute (AHRI) as the 

only trade group that supports, or has an interest in, the PTAC and PTHP industry. Formed in 

1953, AHRI, previously known as ARI, is the national trade association representing 

manufacturers of more than 90 percent of North American produced central air-conditioning and 

commercial refrigeration equipment. ARI and the Gas Appliance Manufacturers Association 

(GAMA) merged to become AHRI on January 1, 2008. 

AHRI develops and publishes technical standards for residential and commercial airconditioning, heating, and refrigeration equipment using rating criteria and procedures for 

measuring and certifying equipment performance. The current Federal test procedure for PTACs 

and PTHPs incorporates by reference an AHRI standard, AHRI 310/380-2004. AHRI has 

developed a certification program that a number of manufacturers in the PTAC and PTHP 

industry have used to certify their equipment. Manufacturers certify their own equipment by 

providing AHRI with test data. Through the AHRI certification program, AHRI evaluates test 

data, determines if equipment conforms to AHRI 310/380-2004, and verifies that manufacturerreported ratings are accurate. AHRI also maintains the Directory of Certified Product 

Performance, which is a database of equipment ratings for all manufacturers who elect to 
3-5

participate in the program. DOE used AHRI’s certification data, retrieved from the 2014 AHRI 

Directory of Certified Product Performance, in the engineering analysis (chapter 5 of this 

Technical Support Document (TSD)).

3.6 MANUFACTURER INFORMATION

The following section details information regarding manufacturers of PTAC and PTHP 

equipment, including estimated market shares (section 3.6.1) and small businesses (section 

3.6.2).

3.6.1 Manufacturers and Market Shares

DOE identified three large manufacturers of standard size PTAC and PTHP equipment

that hold approximately 80 percent of the standard size market in terms of shipments. Table 

3.6.1 shows these manufacturers. 

Table 3.6.1 Large Manufacturers of Standard Size PTACs and PTHPs

General Electric (GE) Company

Goodman Manufacturing Companyc

Friedrich Air Conditioning Company

Ten other manufacturers, listed in Table 3.6.2, hold the remaining 20 percent of the standard size 

PTAC and PTHP market.

 c Goodman Manufacturing Company brands its PTAC and PTHP equipment under the Amana name, a trademark of 

the Maytag Corporation. More information about the company can be found at http://www.amana-ptac.com. 
3-6

Table 3.6.2 Other Manufacturers of Standard Size PTACs and PTHPs

Daikin Appliedd

E-Air, LLC

ECR Internationale

Electrolux Home Products, Inc.

Fedders Islandaire, Inc.

GREE Electric Appliances of Zhuhai

Haier America

Heat Controller, Inc.

LG Electronics

YMGI Group, LLC

DOE estimated market share data for standard size PTAC and PTHP manufacturers using 

publicly available data including the Securities and Exchange Commission (SEC) 10-K reports 

filed by publicly owned manufacturers and from stakeholder input. Market share data has been

aggregated for this report to avoid disclosing confidential company data.

The standard size PTAC and PTHP market differs from the non-standard size PTAC and 

PTHP industry in that several of the manufacturers of standard size units are domestically owned 

with manufacturing facilities located outside of the United States. (In contrast, most non-standard 

size PTAC and PTHP production occurs in the United States.) Currently there is only one major 

manufacturer of standard size PTAC and PTHP equipment manufacturing equipment in the 

United States. Several foreign-owned companies have recently entered the U.S. market for 

standard-sized PTACs and PTHPs.

DOE identified three major manufacturers of non-standard size PTAC and PTHP 

equipment: Daikin Applied, ECR International, and Fedders Corporationf

. These three 

manufacturers share the majority of the non-standard size PTAC and PTHP market. Other 

manufacturers of non-standard size units include: Air-Con International, Cold Point Corporation, 

Comitale National Inc., E-Air LLC, Evergreen LLC, Heat Controller Inc., Ice Air LLC, 

 d Daikin Applied (formally McQuay International) is a subsidiary of Daikin Industries, Ltd. More information about 

the company can be found at http://www.daikinapplied.com.

e ECR International brands its PTAC and PTHP equipment under the RetroAire brand name. More information 

about the company can be found at http://www.retroaire.com. 

f Fedders Corporation brands its non-standard PTAC and PTHP equipment under the Fedders Islandaire brand name. 

More information about the company can be found at http://www.islandaire.com.
3-7

International Refrigeration Products, Prem Sales LLC, Simon-Aire Inc., and YMGI Group LLC. 

Market share data for non-standard size PTAC and PTHP manufacturers was estimated using 

publicly available data including the SEC 10-K reports filed by publicly owned manufacturers. 

Table 3.6.3 shows current AHRI members that manufacture PTACs and PTHPs, with 

parent companies shown in parentheses, if applicable. These member companies offer equipment 

certified under AHRI’s PTAC and PTHP certification program. 

Table 3.6.3 PTAC and PTHP Manufacturers: AHRI Members

Daikin Applied (Daikin Industries) EAIR, LLC

ECR International, Inc. Friedrich Air Conditioning Company

Goodman Manufacturing Company GREE Electric Appliances of Zhuhai

Haier America LG Electronics

Source: These PTAC and PTHP manufacturers were listed as of July 2014 at 

http://www.ahrinet.org/site/661/About-Us/AHRI-Members

Manufacturers are able to certify their equipment under AHRI’s PTAC and PTHP 

certification program without being members of AHRI. The companies that are not AHRI 

members use AHRI and ASHRAE test procedures and standards to rate the performance of their 

equipment. Table 3.6.4 shows a list of manufacturers that certify PTACs and PTHPs under 

AHRI’s PTAC and PTHP certification program but are not members of AHRI.

Table 3.6.4 PTAC and PTHP Manufacturers: Non-AHRI Members with AHRI-Certified 

Equipment

Air-Con International Cold Point Corporation

Comitale National, Inc. Electrolux Home Products, Inc.

Evergreen, LLC Fedders Islandaire, Inc.

General Electric (GE) Company Heat Controller, Inc.

Ice Air, LLC YMGI Group, LLC

3.6.2 Small Businesses

DOE considered the possibility that energy conservation standards for PTACs and 

PTHPs could adversely affect small businesses. For manufacturers of PTACs and PTHPs, the 

Small Business Administration (SBA) has set a size threshold, which defines those entities 

classified as “small businesses.” DOE used the SBA’s small business size standards to determine 

whether any small entities would be subject to the requirements of the rule. 65 FR 30836, 30848 

(May 15, 2000), as amended at 65 FR 53533, 53544 (Sept. 5, 2000) and codified at 13 CFR part 

121. The size standards are listed by North American Industry Classification System (NAICS) 

code and industry description and are published by the SBA. Manufacturing of PTACs and 

PTHPs is classified under NAICS 333415, “Air-Conditioning and Warm Air Heating Equipment 

and Commercial and Industrial Refrigeration Equipment Manufacturing.” The SBA sets a 

threshold of 750 employees or less for an entity to be considered as a small business for this 

category.
3-8

DOE studied the potential impacts on these small businesses in detail during the 

manufacturer impact analysis, which was conducted as a part of the notice of proposed 

rulemaking (NOPR) analysis. DOE initially identified 22 companies that sell PTAC and PTHP 

equipment that would be affected by today’s proposal. Of these 22 companies, DOE identified 

12 as small businesses. Of the 12 small businesses contacted, DOE was able to reach and 

discuss potential standards with two. DOE also obtained information about small businesses and 

potential impacts on small businesses while interviewing large manufacturers.

Within the PTAC and PTHP industry, DOE did not identify any small businesses that are 

original equipment manufacturers (OEMs) of equipment covered under this rulemaking. Rather, 

small businesses tend to import, rebrand, and distribute PTACs and PTHPs manufactured 

overseas, primarily in China. Some small businesses identified are original equipment 

manufacturers of non-standard size PTACs and PTHPs; however, non-standard equipment are 

not impacted by this rulemaking and therefore are not considered in this small business subgroup 

analysis. Chapter 12 of this TSD contains more details regarding the manufacturer impact 

analysis.

3.7 REGULATORY PROGRAMS

The following section details current regulatory programs mandating energy conservation 

standards for PTACs and PTHPs. Section 3.7.1 discusses current Federal energy conservation 

standards; section 3.7.2 discusses ASHRAE’s energy conservation standards; and section 3.7.3

provides an overview of existing State standards. Sections 3.7.4 and 3.7.5 review standards in 

both Canada and Mexico that may affect the companies servicing the North American market.

3.7.1 Current Federal Energy Conservation Standards

For PTAC and PTHP equipment, the last final rule issued by DOE was on October 7, 

2008, which codified the amended standards and separated PTAC and PTHP equipment classes 

into sub-categories of standard size equipment and non-standard size equipment. 73 FR 58772 

The current standards are shown in Table 3.7.1.
3-9

Table 3.7.1 Current Federal Energy Conservation Standards for PTACS and PTHPs

Equipment Class

Efficiency Level Compliance 

Date Equipment

Type

SubCategoryCooling 

Capacity

PTAC

Standard 

Size*

<7,000 Btu/h EER = 11.7 Oct. 8, 2012

≥7,000 Btu/h and 

≤15,000 Btu/h EER = 13.8 – (0.300 x Cap†

) Oct. 8, 2012

>15,000 Btu/h EER = 9.3 Oct. 8, 2012

NonStandard 

Size**

<7,000 Btu/h EER = 9.4 Oct. 7, 2010

≥7,000 Btu/h and 

≤15,000 Btu/h EER = 10.9 – (0.213 x Cap†

) Oct. 7, 2010

>15,000 Btu/h EER = 7.7 Oct. 7, 2010

PTHP

Standard 

Size*

<7,000 Btu/h EER = 11.9

COP = 3.3

Oct. 8, 2012

≥7,000 Btu/h and 

≤15,000 Btu/h

EER = 14.0 – (0.300 x Cap†)

COP = 3.7 – (0.052 x Cap†)

Oct. 8, 2012

>15,000 Btu/h EER = 9.5

COP = 2.9

Oct. 8, 2012

NonStandard 

Size**

<7,000 Btu/h EER = 9.3

COP = 2.7

Oct. 7, 2010

≥7,000 Btu/h and 

≤15,000 Btu/h

EER = 10.8 – (0.213 x Cap†)

COP = 2.9 – (0.026 x Cap†)

Oct. 7, 2010

>15,000 Btu/h EER = 7.6

COP = 2.5

Oct. 7, 2010

* Standard size refers to PTAC or PTHP equipment with wall sleeve dimensions greater than or equal to 16 inches 

high, or greater than or equal to 42 inches wide.

** Non-standard size refers to PTAC or PTHP equipment with wall sleeve dimensions less than 16 inches high and 

less than 42 inches wide. † Cap means cooling capacity in kBtu/h at 95oF outdoor dry-bulb temperature.

3.7.2 ASHRAE Energy Conservation Standards for PTACs and PTHPs

On October 9, 2013, ASHRAE adopted ANSI/ASHRAE/IES Standard 90.1-2013, which 

increased ASHRAE efficiency standards for standard size PTAC equipment to be equal to 

efficiency standards for standard size PTHP equipment. Table 3.7.2 shows the efficiency levels 

in ANSI/ASHRAE/IES Standard 90.1-2013 for PTACs and PTHPs.
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Table 3.7.2 ANSI/ASHRAE/IES Standard 90.1-2013 Energy Efficiency Levels for PTACs 

and PTHPs

Equipment Class Minimum Efficiency Equipment Category Cooling Capacity

PTAC

Standard 

Size*

<7,000 Btu/h EER = 11.9

≥7,000 Btu/h and 

≤15,000 Btu/h EER = 14.0 – (0.300 x Cap/1000†

)

>15,000 Btu/h EER = 9.5

NonStandard 

Size**

<7,000 Btu/h EER = 9.4

≥7,000 Btu/h and 

≤15,000 Btu/h EER = 10.9 – (0.213 x Cap/1000†

)

>15,000 Btu/h EER = 7.7

PTHP

Standard 

Size*

<7,000 Btu/h EER = 11.9

COP = 3.3

≥7,000 Btu/h and 

≤15,000 Btu/h

EER = 14.0 – (0.300 x Cap/1000†)

COP = 3.7 – (0.052 x Cap/1000†)

>15,000 Btu/h EER = 9.5

COP = 2.9

NonStandard 

Size**

<7,000 Btu/h EER = 9.3

COP = 2.7

≥7,000 Btu/h and 

≤15,000 Btu/h

EER = 10.8 – (0.213 x Cap/1000†)

COP = 2.9 – (0.026 x Cap/1000†)

>15,000 Btu/h EER = 7.6

COP = 2.5

* Standard size refers to PTAC or PTHP equipment with wall sleeve dimensions greater than or equal to 16 inches 

high, or greater than or equal to 42 inches wide.

** Non-standard size refers to PTAC or PTHP equipment with wall sleeve dimensions less than 16 inches high and 

less than 42 inches wide. 

† Cap means cooling capacity in Btu/h at 95oF outdoor dry-bulb temperature.

3.7.3 State Energy Conservation Standards

DOE recognizes that pursuant to EPCA, states may petition to have more stringent energy 

conservation standards than those codified into law by DOE (see 42 U.S.C. 6297(d)). DOE has 

not yet granted a petition to any state to establish more stringent energy conservation standards 

than the levels established by EPCA for PTACs and PTHPs.

3.7.4 Canadian Energy Conservation Standards

The Natural Resources Canada Office of Energy Efficiency regulation mandates 

minimum energy conservation standards for PTACs and PTHPs. These standards apply to 

factory-assembled PTACs and PTHPs intended for use in residential, commercial, and industrial 

heating and cooling systems.1

 The current standards went into effect November 15, 2006, and 

are shown in Table 3.7.3. The Canadian energy conservation standards for standard size PTACs 

and PTHPs are less stringent than the current U.S. standards for standard size PTACs and 

PTHPs. The Canadian energy conservation standards for non-standard size PTACs and PTHPs 

are identical to the current U.S. standards for non-standard size PTACs and PTHPs.
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Table 3.7.3 Canadian Energy Conservation Standards for PTACS and PTHPs

Equipment Type Minimum Efficiency Ratio

PTAC EER New Construction (cooling)* 12.5 - (0.213 x cap/293.1)†

(12.5 - (0.213 x cap/1000))

PTAC EER Replacement (cooling) 10.9 - (0.213 x cap/293.1)

(10.9 - (0.213 x cap/1000))

PTHP EER New Construction (cooling) 12.3 - (0.213 x cap/293.1)

(12.3 - (0.213 x cap/1000))

PTHP EER Replacement (cooling) 10.8 - (0.213 x cap/293.1)

(10.8 - (0.213 x cap/1000))

PTHP COP New Construction (heating)** 3.2 - (0.026 x cap/293.1)

(3.2 - (0.026 x cap/1000))

PTHP COP Replacement†† (heating) 2.9 - (0.026 x cap/293.1)

(2.9 - (0.026 x cap/1000))

*EER = Energy efficiency ratio – a ratio calculated by dividing the cooling capacity in Btu per hour by the power 

input in watts at any given set of rating conditions

**COP =Coefficient of performance – a ratio for both the cooling and heating modes calculated by dividing the 

capacity expressed in watts by the power input in watts, excluding any supplementary heat

†cap = The rated cooling capacity in watts (upper formula) or Btu/h (lower formula).

††Replacement units are to be labeled according to the requirements of CAN/CSA C744-04

3.7.5 Mexican Energy Conservation Standards

Although Mexico has minimum energy conservation standards for air conditioners and 

heat pumps in general, it currently does not have minimum energy conservation standards for 

PTACs and PTHPs. 

3.8 VOLUNTARY PROGRAMS

3.8.1 ENERGY STAR

ENERGY STAR,g a voluntary labeling program backed by the U.S. Environmental 

Protection Agency (EPA) and DOE, identifies energy-efficient products through a qualification 

process. To qualify, a product must exceed Federal minimum standards by a specified amount, 

or, if no Federal standard exists, must exhibit selected energy saving features. The ENERGY 

STAR program recognizes the top quartile of products on the market, meaning that 

 g More information regarding the ENERGY STAR program is at www.energystar.gov. 
3-12

approximately 25 percent of equipment on the market meet or exceed the ENERGY STAR 

levels. PTACs do not qualify for ENERGY STAR under the room air conditioner criteria, and 

there are currently no plans to develop ENERGY STAR criteria for PTACs and PTHPs.2

3.8.2 Rebate Programs 

DOE has identified and reviewed various local utility rebate programs. These include the 

Platte River Power Authority (PRPA), Xcel Energy Cooling Efficiency Rebate Program, 

Modesto Irrigation District MPower Business Rebate Program, Shakopee Public Utilities Energy 

Efficiency Incentive Program, CPS Energy Savers Commercial Rebate Program, and the 

Southern California Edison (SCE) Multifamily Energy Efficiency Rebate Program.

Platte River Power Authority (PRPA), an organization that generates and delivers

electricity to its owner communities of Estes Park, Fort Collins, Longmont and Loveland, 

Colorado, offers customers cash rebates for upgrading standard and non-standard size PTACs 

and PTHPs to energy-efficient units through its Efficiency Works Rebate Program. The rebate 

includes all cooling capacities of PTACs that achieve or exceed 11.0 EER.3

Xcel Energy promotes the installation of energy-efficient equipment through the Cooling 

Efficiency Rebate Program. Rebates are available to the utility’s commercial customers. Under 

this program, commercial business with PTACs can receive a base payment of $65 per ton for 

units rated at 11.0 EER and $5 per ton for every incremental increase of 0.1 EER above base 

requirements.4

Modesto Irrigation District’s MPower Business Rebate Program offers commercial, 

industrial, and agricultural customers cash rebates for the purchase and installation of energyefficient standard and non-standard size PTACs and PTHPs. Cash rebates of $75 per unit are 

available for units that meet minimum efficiency requirements, which vary by capacity. Units 

with capacity less than or equal to 7,000 Btu/h must meet a minimum 11.29 EER; units with 

capacity between 7,000 and 24,000 Btu/h must meet a minimum 10.27 EER.5

Shakopee Public Utilities promotes installation of energy-efficient equipment through the 

Commercial and Industrial Energy Efficiency Rebate Program. Rebates are available to the 

utility’s commercial customers. Under this program, commercial and industrial businesses 

installing PTACs and PTHPs can receive a rebate of $45 per ton of capacity for units that meet 

or exceed the minimum cooling efficiency, which is calculated using the equation below.6

Minimum Efficiency (EER) = 12.8 − �0.213 ×

Cooling Capacity ( Btu

h )

1000 �

CPS Energy, the Nation’s largest municipally owned energy company, offers rebates for 

energy-efficient PTACs and PTHPs through the CPS Energy Savers Commercial Rebate 

Program. Rebates only apply to building improvement or retrofit projects and are not available 

for new construction projects. The rebate amounts are separated by two tiers of efficiency. A 

rebate of $65 per ton of cooling capacity is available for PTACs and PTHPs with EER of 11.5 or 

greater (and COP of 4.9 or greater for PTHPs). A rebate of $150 per ton of cooling capacity is 
3-13

available for PTACs and PTHPs with EER of 12.5 or greater (and COP of 5.9 or greater for 

PTHPs).7

SCE offers rebates to business customers for standard and non-standard size PTAC and 

PTHP equipment through its Multifamily Energy Efficiency Rebate Program. The program 

provides a fixed $150 rebate for PTACs and PTHPs that have EER at least 20 percent above 

California’s appliance efficiency regulations, also known as Title 20. The rebate is valid for all 

units with cooling capacity below 24,000 Btu/h.8 Under the California Code of Regulations, 

Title 20, Section 1605.1 b(2), PTACs and PTHPs energy conservation standards are calculated 

using the equations below, and are less stringent than current Federal PTAC and PTHP energy 

conservation standards.9

Minimum Efficiency (EER) = 10.0 − (0.00016 × Cooling Capacity (in Btu/h))

Minimum Efficiency (COP) = 1.3 + [0.16(10.0 − 0.00016 × Cooling Capacity (in Btu/h)]

3.9 SHIPMENTS

Information about annual equipment shipment trends allows DOE to estimate the impacts 

of energy conservation standards on the PTAC and PTHP industry. Using data from AHRI 

estimates, DOE examined unit shipments and value of shipments for PTACs and PTHPs. More 

information about shipments for PTACs and PTHPs can be found in the shipments analysis 

section (chapter 9) of the TSD.

3.9.1 Unit Shipments

Until 2010, the U.S. Census Bureau published an annual Current Industrial Report (CIR), 

which provided annual unit shipments and value of shipments for various industries including 

the PTAC and PTHP industry. However, the CIR has not published shipments data for PTACs 

or PTHPs since before 2008, due to data disclosure issues and to termination of the CIR series.10 

Table 3.9.1 presents the total shipments estimated by AHRI of the PTAC and PTHP 

industry from 2003-2012. The AHRI data shows a decrease in shipments between the 2003-07 

period and the 2008-12 period.

Table 3.9.1 AHRI Estimated Shipment Data for PTAC and PTHP Industry (Standard and 

Non-Standard)

Years Total Shipments Over All Years (Thousands of Units)

PTAC PTHP

2008-2012 1,105.9 986.0

2003-2007 1,352.3 1,068.8

3.9.2 Equipment Lifetime 

DOE reviewed available literature and consulted with manufacturers in order to establish 

typical equipment lifetimes. The literature and experts consulted offered a wide range of typical 
3-14

equipment lifetimes. Individuals with previous experience in manufacturing or distribution of 

PTACs and PTHPs suggested a typical lifetime of 5 to 10 years. Some experts suggested that the 

lifetime could be even lower because of the daily or continuous use of the equipment and neglect 

of maintenance such as cleaning the heat exchangers or replacing the air filters. In addition, the 

equipment is typically replaced about every 5 years for cosmetic reasons during remodeling in 

lodging applications. The 2000 Screening Analysis for EPACT-Covered Commercial Heating, 

Ventilating and Air-Conditioning and Water-Heating Equipment report (commonly referred to as

the 2000 Screening Analysis) used a 15-year lifetime for PTACs and PTHPs based on data from 

ASHRAE’s 1995 Handbook of HVAC Applications.

11 In the NOPR analysis for this rulemaking, 

DOE assumed the equipment lifetime for PTACs and PTHPs to be 10 years. In response to 

stakeholder input, DOE revised the analysis after the NOPR using an average equipment lifetime 

of 8 years. More information about PTAC and PTHP equipment lifetime is available in the lifecycle cost and payback period analyses section (chapter 8) of this TSD. 

3.10 MARKET CHARACTERIZATION

DOE combined information from the 2013 AHRI Directory of Certified Product 

Performance (2013 AHRI directory) with other publicly available data from manufacturer 

catalogs of PTACs and PTHPs to develop an understanding of the industry.12 The database 

contains information such as manufacturer name, model number, cooling capacity, EER, COP

where applicable, heating capacity where applicable, and wall sleeve dimensions. To maintain 

consistency in the analysis, DOE divided the data into standard and non-standard size 

classifications in the database based on DOE’s equipment classes. Figure 3.2.1 and Figure 3.2.2

show the distribution of standard size PTACs and PTHPs respectively in the 2013 AHRI 

directory.

Figure 3.2.1 Number of Certified Standard Size PTAC Models by Cooling Capacity 

– 2013 AHRI Directory and Manufacturer Catalogs

0

5

10

15

20

25

30

35

40

6,8007,2007,6008,0008,4008,8009,2009,600

10,00010,40010,80011,20011,60012,00012,40012,80013,20013,60014,00014,40014,80015,20015,60016,00016,40016,800

Number of Certified Models

Cooling Capacity (Btu/h)
3-15

Figure 3.2.2 Number of Certified Standard Size PTHP Models by Cooling Capacity 

– 2013 AHRI Directory and Manufacturer Catalogs

The standard size PTAC models listed in the 2013 AHRI directory and manufacturers’ 

catalogs may be grouped into several clusters of cooling capacity. These range from 6,800 Btu/h 

to 7,800 Btu/h; 8,600 Btu/h to 9,800 Btu/h; 11,200 Btu/h to 12,800 Btu/h; and 14,000 Btu/h to 

15,100 Btu/h. Standard size PTHPs may be similarly clustered, with cluster ranges from 7,000 

Btu/h to 7,800 Btu/h; 8,800 Btu/h to 9,800 Btu/h; 11,200 Btu/h to 12,800 Btu/h; and 14,000 

Btu/h to 15,000 Btu/h.

Figure 3.2.3 and Figure 3.2.4 show the distribution of non-standard size PTACs and 

PTHPs respectively from the 2013 AHRI directory and other manufacturer catalogs.

0

5

10

15

20

25

30

6,8007,2007,6008,0008,4008,8009,2009,600

10,00010,40010,80011,20011,60012,00012,40012,80013,20013,60014,00014,40014,80015,20015,60016,00016,40016,800

Number of Certified Models

Cooling Capacity (Btu/h)
3-16

Figure 3.2.3 Non-Standard Size PTAC Models by Cooling Capacity 

– 2013 AHRI Directory and Manufacturer Catalogs

Figure 3.2.4 Non-Standard Size PTHP Models by Cooling Capacity 

– 2013 AHRI Directory and Manufacturer Catalogs

The non-standard size PTAC models listed in the 2013 AHRI directory and 

manufacturers’ catalogs may be grouped into several clusters of cooling capacity. These range 

from 7,000 Btu/h to 7,800 Btu/h; 8,800 Btu/h to 9,800 Btu/h; 11,200 Btu/h to 12,800 Btu/h; and 

14,000 Btu/h to 15,000 Btu/h.

0

5

10

15

20

25

30

35

40

6,8007,2007,6008,0008,4008,8009,2009,600

10,00010,40010,80011,20011,60012,00012,40012,80013,20013,60014,00014,40014,80015,20015,60016,00016,40016,800

Number of Certified Models

Cooling Capacity (Btu/h)

0

5

10

15

20

25

30

6,8007,2007,6008,0008,4008,8009,2009,600

10,00010,40010,80011,20011,60012,00012,40012,80013,20013,60014,00014,40014,80015,20015,60016,00016,40016,800

Number of Certified Models

Cooling Capacity (Btu/h)
3-17

3.11 TECHNOLOGY ASSESSMENT

This section provides a technology assessment for PTAC and PTHP equipment. The 

purpose of the technology assessment is to develop a preliminary list of technologies that could 

potentially be used to improve the efficiency of PTACs and PTHPs. The following assessment 

provides descriptions of technologies and designs that apply to all equipment classes of PTACs 

and PTHPs.

Contained in this technology assessment are details about equipment characteristics and 

operation (section 3.11.1), an examination of possible technological improvements (section 

3.11.2), and a characterization of the equipment efficiency levels currently commercially 

available (section 3.11.3).

3.11.1 Baseline Equipment Components and Operation

The baseline PTAC is an air conditioner that incorporates a complete air-cooled 

refrigeration and air-handling system in an individual package. Each PTAC has a self-contained, 

direct-expansion cooling system and packaged control including electromechanical function 

switches. Models may feature various heating options (electric, hot water, or steam). The basic 

PTAC cooling system is composed of a compressor (typically a rotary compressor), evaporator, 

condenser, and motorized fan. Other components of the PTAC include a thermostat, outer 

casing, and wall sleeve. Manufacturers typically differentiate high-efficiency models from basic 

models by installing any combination of the following: a higher efficiency compressor, digital 

controls with energy savings settings, automatic fan controls, higher efficiency fan motors, 

multiple fans, or a more efficient heat exchanger.

3.11.2 Technology Options

DOE used information about existing and past technology options and prototype designs 

to help identify technologies that manufacturers could use to improve the efficiency of PTACs 

and PTHPs. This assessment provides the technical background and structure on which DOE 

bases its screening analysis (chapter 4 of this TSD) and engineering analyses (chapter 5). In 

surveying PTAC and PTHP technology options, DOE considered a wide assortment of 

equipment literature, information derived from the teardown analysis, information derived from 

the stakeholder interviews, and the previous DOE energy conservation standards rulemaking for 

air-conditioning products and equipment. 

Table 3.11.1 lists all of the potential technology options considered, including options 

listed in the Framework Document and options suggested in stakeholder comments, for 

improving energy efficiency of PTACs and PTHPs.
3-18

Table 3.11.1 Potential Technology Options for Improving Energy Efficiency of PTACs and 

PTHPs

Compressor Improvements

• Scroll Compressors

• Variable-speed Compressors

• Higher Efficiency Compressors

Complex Control Boards

Condenser and evaporator fan and fan motor improvements:

• Higher Efficiency Fan Motors

• Clutched Motor Fans

Microchannel Heat Exchangers

Rifled Interior Heat Exchanger Tube Walls

Increased Heat Exchanger Area

Hydrophobic Material Treatment of Heat Exchangers

Re-circuiting Heat Exchanger Coils

Improved Air Flow and Fan Design

Heat Pipes

Corrosion Protection

Thermostatic Expansion Valve

Alternate Refrigerants (such as HCFC-32)

3.11.3 Equipment Efficiency Levels

Using a list of PTAC and PTHP models assembled from the 2013 AHRI Directory and 

manufacturer catalogues, DOE examined the relationship between EER and cooling capacity for 

PTACs and PTHPs of both standard and non-standard size categories. Figure 3.2.5 and Figure 

3.2.6 show the relationship between EER and cooling capacity for certified models of standard 

size PTACs and PTHPs, respectively, listed in the 2013 AHRI Directory. These figures also 

identify the current Federal energy conservation standards (ECS), and the ANSI/ASHRAE/IES 

Standard 90.1-2013 efficiency levels. 
3-19

Figure 3.2.5 Standard Size PTAC EER versus Cooling Capacity – 2013 AHRI Directory

As shown in Figure 3.2.5, the EER of standard size PTACs generally decreases as 

cooling capacity increases. All of the certified standard size PTAC units are above Federal 

minimum efficiency levels, while close to 80 percent are at or above ANSI/ASHRAE/IES

Standard 90.1-2013 efficiency levels. 

Figure 3.2.6 Standard Size PTHP EER versus Cooling Capacity – 2013 AHRI Directory

As with PTACs, Figure 3.2.6 shows the EER of standard size PTHPs decreases as 

cooling capacity increases. All of the certified standard size PTHP units are above Federal 

8.0

9.0

10.0

11.0

12.0

13.0

14.0

6,000 8,000 10,000 12,000 14,000 16,000 18,000

EER (Btu/Wh)

Cooling Capacity (Btu/h)

2012 Standard Size PTAC ECS

ANSI/ASHRAE/IES 90.1-2013

8.0

9.0

10.0

11.0

12.0

13.0

14.0

6,000 8,000 10,000 12,000 14,000 16,000 18,000

EER (Btu/Wh)

Cooling Capacity (Btu/h)

2012 Standard Size PTHP ECS

ANSI/ASHRAE/IES 90.1-2013
3-20

minimum efficiency levels, which are equivalent to ANSI/ASHRAE/IES 90.1-2013 efficiency 

levels. 

Figure 3.2.7 and Figure 3.2.8 demonstrate the relationship between EER and cooling 

capacity for non-standard size PTACs and PTHPs respectively. These figures also identify the 

current Federal ECS and the ANSI/ASHRAE/IES 90.1-2013 efficiency levels for non-standard 

size equipment.

Figure 3.2.7 Non-Standard Size PTAC EER versus Cooling Capacity – 2013 AHRI 

Directory and Manufacturers’ Catalogs

Figure 3.2.8 Non-Standard Size PTHP EER versus Cooling Capacity – 2013 AHRI 

Directory and Manufacturers’ Catalogs

7.0

8.0

9.0

10.0

11.0

12.0

13.0

 6,000 8,000 10,000 12,000 14,000 16,000 18,000

EER (Btu/Wh)

Cooling Capacity (Btu/h)

2012 Non-Standard Size PTAC ECS

ANSI/ASHRAE/IES 90.1-2013

7.0

8.0

9.0

10.0

11.0

12.0

13.0

 6,000 8,000 10,000 12,000 14,000 16,000 18,000

EER (Btu/Wh)

Cooling Capacity (Btu/h)

2012 Non-Standard Size PTHP ECS

ANSI/ASHRAE/IES 90.1-2013
3-21

Similar to standard size PTACs, the EER of a non-standard size PTAC decreases as 

cooling capacity increases. All of the reviewed non-standard size PTAC units excepts one model 

are rated at or above Federal minimum efficiency levels, which are equivalent to 

ANSI/ASHRAE/IES 90.1-2013 efficiency levels. All of the reviewed non-standard size PTHP 

units are rated at or above Federal minimum and ANSI/ASHRAE/IES 90.1-2013 efficiency 

levels.

DOE also examined the relationship between COP and cooling capacity for standard size 

and non-standard size PTHPs, shown in Figure 3.2.9 and Figure 3.2.10, respectively. These 

figures also identify the current Federal ECS and the ANSI/ASHRAE/IES Standard 90.1-2013 

efficiency levels for standard size and non-standard size equipment.

Figure 3.2.9 Standard Size PTHP COP versus Cooling Capacity – 2013 AHRI Directory

Figure 3.2.10 Non-Standard Size PTHP COP versus Cooling Capacity – 2013 AHRI 

Directory and Other Publicly Available Data

2.0

2.5

3.0

3.5

4.0

4.5

6,000 8,000 10,000 12,000 14,000 16,000 18,000

COP (no unit)

Cooling Capacity (Btu/h)

2012 Standard Size PTHP ECS

ANSI/ASHRAE/IES 90.1-2013

2.0

2.5

3.0

3.5

4.0

4.5

 6,000 8,000 10,000 12,000 14,000 16,000 18,000

COP (no unit)

Cooling Capacity (Btu/h)

2012 Non-Standard Size PTHP ECS

ANSI/ASHRAE/IES 90.1-2013
3-22

As with EER, COP tends to decrease with cooling capacity. COP levels for all of the 

reviewed standard size PTHP units are above Federal efficiency levels, which are equivalent to 

ANSI/ASHRAE/IES 90.1-2013 efficiency levels. COP levels for all of the reviewed nonstandard size PTHP units meet or exceed the Federal minimum and ANSI/ASHRAE/IES 90.1-

2013 efficiency levels.
3-23

REFERENCES

1 Canada Natural Resources-Office of Energy Efficiency. Packaged Terminal Air 

Conditioners and Heat Pumps – Proposed Minimum EER for PTAC/HP November 2006. 

December 17, 2013. http://www.nrcan.gc.ca/node/6923 (Last accessed 3/17/15)

2 ENERGY STAR. Are Packaged Terminal Air Conditioners (PTACs) and Packaged 

Terminal Heat Pumps (PTHPs) ENERGY STAR qualified? 2011. 

http://energystar.supportportal.com/link/portal/23002/23018/Article/13998/ArePackaged-Terminal-Air-Conditioners-PTACs-and-Packaged-Terminal-Heat-PumpsPTHPs-ENERGY-STAR-qualified. (Last accessed 3/17/15)

3 Efficiency Works. Efficiency Works Rebate Application. 2015. 

http://www.efficiencyworks.co/files/xls/ewapp.xls. (Last accessed 3/17/15)

4 Xcel Energy. Cooling Efficiency 2014 Cooling Equipment Rebate Application. 2014. 

https://www.xcelenergy.com/staticfiles/xe/Marketing/Files/CO-Bus-Cooling-EquipmentRebate-Application-2014.pdf. (Last accessed 3/17/15)

5 Modesto Irrigation District. MPower Business 2014 Express Rebate Catalog. 2013. 

http://www.mid.org/rebates/commercial/documents/CommCatalog_002.pdf. (Last 

accessed 3/17/15)

6 Shakopee Public Utilities. 2014 Efficient Heating & Cooling: Rebate Requirements & 

Claim Sheet. 2013. http://spucweb.com/wp-content/uploads/2014-Heating-andCooling-Commercial.pdf. (Last accessed 3/17/15)

7 CPS Energy. Commercial HVAC Rebate Matrix. 2013. 

http://www.cpsenergysavers.com/docs/commercial-documents/commercial-hvacvariable-refrigerant-flow-ac-matrix.pdf. (Last accessed 3/17/15)

8 Southern California Edision. Multifamily Energy Efficiency Rebate Program. January 1, 

2013. https://www.sce.com/wps/wcm/connect/94cded9e-f114-41ee-b833-

46beec00f0ee/2013-2014+SCE+MFEER+Program+Application.pdf?MOD=AJPERES. 

(Last accessed 3/17/15)
3-24

 9 State of California Energy Commission. California Code of Regulations: Title 20. Public 

Utilities and Energy. 2013. http://www.energy.ca.gov/2014publications/CEC-140-2014-

002/CEC-140-2014-002.pdf. (Last accessed 3/17/15)

10 “Table 2. Quantity and Value of Shipments of Refrigeration, Air-Conditioning, and 

Warm Air Heating Equipment: 2010 and 2009.” 2009 - 2010 Current Industrial Reports 

(CIR). U.S. Census Bureau, June 2014.

11 U.S. Department of Energy-Office of Energy Efficiency and Renewable Energy. Energy 

Conservation Program for Consumer Products: Screening Analysis for EPACT-Covered 

Commercial HVAC and Water-Heating Equipment Screening Analysis. April 2000.

12 Air-Conditioning, Heating, and Refrigeration Institute. AHRI Directory of Certified 

Performance. 2013.

https://www.ahridirectory.org/ahridirectory/pages/ptac/defaultSearch.aspx. (Last 

accessed 7/19/14)
4-i

CHAPTER 4. SCREENING ANALYSIS

TABLE OF CONTENTS

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

4.2 SCREENED-OUT TECHNOLOGY OPTIONS ............................................................. 4-1

4.2.1 Scroll Compressors.............................................................................................. 4-2

4.2.2 Heat Pipes ............................................................................................................ 4-2

4.2.3 Alternate Refrigerants.......................................................................................... 4-2

4.3 OTHER TECHNOLOGIES NOT CONSIDERED IN THE ENGINEERING 

ANALYSIS.................................................................................................................................. 4-4

4.3.1 Re-Circuiting Heat Exchanger Coils ................................................................... 4-4

4.3.2 Rifled Interior Tube Walls................................................................................... 4-4

4.3.3 Microchannel Heat Exchangers........................................................................... 4-4

4.3.4 Complex Control Boards ..................................................................................... 4-5

4.3.5 Corrosion Protection ............................................................................................ 4-5

4.3.6 Hydrophobic Material Treatment of Heat Exchangers........................................ 4-5

4.3.7 Thermal Expansion Valves.................................................................................. 4-5

4.4 REMAINING TECHNOLOGIES ................................................................................... 4-6

4.4.1 Higher Efficiency Compressors........................................................................... 4-6

4.4.2 Higher Efficiency Fan Motors............................................................................. 4-6

4.4.3 Increased Heat Exchanger Area........................................................................... 4-7

4.4.4 Improved Air Flow and Fan Design .................................................................... 4-7

LIST OF TABLES

Table 4.4.1 Retained Design Options for PTAC and PTHP............................................ 4-6
4-1

CHAPTER 4. SCREENING ANALYSIS

4.1 INTRODUCTION

This chapter describes the screening analysis that the U.S. Department of Energy (DOE) 

has performed in support of the energy conservation standards rulemaking for packaged terminal 

air conditioners (PTACs) and packaged terminal heat pumps (PTHPs). 

In the market and technology assessment (chapter 3 of this technical support document 

(TSD)), DOE presented an initial list of technologies that have the potential to reduce the energy 

consumption of PTACs and PTHPs. The goal of the screening analysis is to screen out 

technologies that will not be considered further in the rulemaking analyses. DOE evaluated the 

technologies identified in the market and technology assessment pursuant to the criteria set out in 

the Energy Policy and Conservation Act (EPCA). (42 United States Code (U.S.C.) 6291-6317):

(1) Technological feasibility. Technologies incorporated in commercial equipment or in 

working prototypes will be considered technologically feasible.

(2) Practicability to manufacture, install, and service. If mass production of a 

technology in commercial equipment and reliable installation and servicing of the technology 

could be achieved on the scale necessary to serve the relevant market at the time of the effective 

date of the standard, then that technology will be considered practicable to manufacture, install, 

and service.

(3) Impacts on equipment utility to customers. If DOE determines that a technology 

will have significant adverse impact on the utility of the equipment to significant subgroups of 

consumers, or result in the unavailability of any covered equipment type with performance 

characteristics (including reliability), features, sizes, capacities, and volumes that are 

substantially the same as equipment generally available in the United States at the time, DOE 

will not consider it further. 

(4) Adverse impacts on health or safety. If DOE determines that a technology will 

have significant adverse impacts on health or safety, DOE will not consider it further.

If a particular technology fails to meet one or more of the four criteria, it will be screened 

out. The rationale for either screening out or retaining each technology option is detailed in the 

following sections.

4.2 SCREENED-OUT TECHNOLOGY OPTIONS

The following section details the specific technology options that were screened out prior 

to the engineering analysis, along with the rationale for elimination.
4-2

4.2.1 Scroll Compressors

Scroll compressors compress gas between two spirals, one fixed and one rotating. In 

some capacities and applications, scroll compressors may operate at higher efficiencies than the 

rotary compressors typically used in PTAC and PTHP applications. Though scroll compressors 

are less common in the capacity range associated with PTAC and PTHP equipment (6,000 to 

15,000 Btu/h), several companies manufacture scroll compressors from 9,000 Btu/h and up. 

However, DOE is not aware of scroll compressor models at these lower capacities that would fit 

in a PTAC cabinet and that are more efficient than the same capacity of rotary compressor. The 

rotary compressors found in reverse engineering of PTACs and PTHPs in the 15,000 Btu/h class 

had efficiency ratings from 9.8 to 10.6 EER. By comparison, scroll compressors of similar 

capacity are rated from 7.2 EER to 11.0 EER, but most are too tall to fit in a 16” standard-size 

PTAC cabinet.

As a result, DOE does not believe at this time that the use of scroll compressors would 

improve the efficiency of PTAC and PTHP units, given the size and capacity constraints of these 

units. For this reason, DOE did not consider scroll compressors further in the NOPR analyses.

4.2.2 Heat Pipes

Under humid ambient conditions, using heat pipes to pre-treat the entering air from the 

conditioned space can improve the evaporator heat exchanger performance. Heat pipes increase 

the latent cooling capacity (i.e., moisture removal) of an air-conditioner. They do this by 

transferring heat from the air entering the evaporator to the air leaving the evaporator. This 

allows the evaporator air exit temperature to be significantly lower. Since the maximum possible 

moisture content of air increases with increasing temperature, this also means that the reducedtemperature air at the evaporator exit would have lower moisture content. The temperature of the 

air is then warmed by the post-evaporator portion of the heat pipe. Heat pipes generally shift 

some of the cooling capacity of the equipment from reduction of air temperature to reduction of 

humidity, but do not increase the cooling capacity of an evaporator. They impose additional 

pressure drop that the indoor fan must overcome, thus they do not improve EER of the 

equipment. Therefore, DOE screened out heat pipes as a design option for improving the energy 

efficiency of PTACs and PTHPs.

4.2.3 Alternate Refrigerants

Nearly all PTAC and PTHP equipment is designed with R-410A as the refrigerant. The 

Environmental Protection Agency’s (EPA’s) Significant New Alternatives Policy (SNAP) 

Program evaluates and regulates substitutes for the ozone-depleting chemicals (such as air 

conditioning refrigerants) that are being phased out under the stratospheric ozone protection 

provisions of the Clean Air Act (CAA).

1 (42 U.S.C. 7401 et seq.) The EPA’s SNAP Program 

currently lists acceptable alternatives for refrigerant used in the Household and Light 

Commercial Air Conditioning class of equipment (which includes PTAC and PTHP equipment). 

 

1 Additional information regarding EPA’s SNAP Program is available online at: http://www.epa.gov/ozone/snap/.
4-3

On July 9, 2014, the EPA issued a notice of proposed rulemaking proposing to list three 

flammable refrigerants (HFC-32 (R-32), Propane (R-290), and R-441A) as new acceptable 

substitutes, subject to use conditions, for refrigerant in the Household and Light Commercial Air 

Conditioning class of equipment. 79 FR 38811 (July 9, 2014). On April 10, 2015, the EPA 

published its final rule that allows the use of R-32, R-290, and R-441A in limited amounts in 

PTAC and PTHP applications. 80 FR 19454 (April 10, 2015) 

DOE considered the possibility of using the alternative refrigerants that EPA approved 

for limited use in PTAC and PTHP applications. The EPA’s final rule limits the maximum 

design charge amount of the alternative refrigerants in PTAC and PTHP applications. For 

instance, for a PTAC or PTHP with cooling capacity of 9,000 Btu/h, the EPA rule imposes a 

maximum design charge of 140 grams of R-290 or 160 grams of R-441A. 80 FR at 19500 (April 

10, 2015) In comparison, DOE reverse engineered eleven units with cooling capacities around 

9,000 Btu/h and found that these units had refrigerant charges ranging from 600 grams to 950 

grams and all units used refrigerant R-410A. The refrigerant charges currently used in current 

PTAC and PTHP designs far exceed the maximum charges that are allowed for alternative 

refrigerants under EPA’s final rule. DOE acknowledges that it might be possible to incorporate 

the new refrigerants under consideration into PTAC designs through the use of microchannel 

heat exchangers or tube and fin heat exchangers with smaller tube diameters than what is 

currently on the market. However, DOE has not seen evidence that such designs are 

technologically feasible. Therefore, DOE did not further consider the R-290 and R-441A 

substitutes proposed by EPA. 

DOE is aware of initial research with drop-in applications (where an alternate refrigerant 

replaces the existing refrigerant in a system that is optimized for the existing refrigerant) using 

R-32 in place of R-410A in a residential ducted split-system application. Initial research shows 

that, in this application, R-32 had a higher capacity and similar efficiency as R-410A, but its 

discharge temperatures and pressures were significantly higher.2 This suggests that R-32 might 

show efficiency comparable to R-410A in PTAC and PTHP applications, and the research is 

inconclusive regarding whether R-32 will reduce energy use and/or by how much. DOE is not 

aware of test results from the use of alternate refrigerants in PTAC- or PTHP-specific 

applications that have been optimized for alternate refrigerants.

DOE is not aware of any SNAP-approved refrigerants, or any refrigerants that have been 

proposed for SNAP approval, that are known to enable better efficiency than R-410A for PTAC 

and PTHP equipment. Hence, DOE did not consider alternate refrigerants for further analysis.

 

2 This research was published in the journal ASHRAE Transactions, at:

Biswas, Auvi; Barve, Atharva; Cremaschi, Lorenzo (2013). “An Experimental Study of the Performance of New 

Low Global Warming Potential (LGWP) Refrigerants at Extreme High Temperature Ambient Conditions in 

Residential AC Ducted Split Systems,” ASHRAE Transactions. 119(1), special section p1.
4-4

4.3 OTHER TECHNOLOGIES NOT CONSIDERED IN THE ENGINEERING 

ANALYSIS

Typically, energy-saving technologies that pass the screening analysis are evaluated in 

the engineering analysis. However, some technologies are not included in the analysis for other 

reasons, including: (1) available data suggest that the efficiency benefits of the technology are 

negligible; (2) data are not available to evaluate the energy efficiency characteristics of the 

technology; or (3) the test procedure and EER or COP metric would not measure the energy 

impact of these technologies. Accordingly, DOE eliminated the following technologies from 

consideration in the engineering analysis based upon these three additional considerations.

4.3.1 Re-Circuiting Heat Exchanger Coils

Manufacturers of PTAC and PTHP heat exchangers may improve the heat transfer 

efficiency across the heat exchanger by rearranging the refrigerant’s path through the various 

tubes inside the heat exchanger. Manufacturers can rearrange the refrigerant path by “recircuiting” the heat exchanger, either by splitting the refrigerant path into new circuits or rerouting the existing circuits. One objective of re-circuiting is to optimally pair air and refrigerant 

at every location in the heat exchanger. DOE believes that PTACs are a very mature industry and 

that engineers have already optimized the number of circuits for heat transfer. Thus, DOE 

believes that the efficiency benefits of the technology are negligible and DOE has eliminated 

heat exchanger re-circuiting as a potential avenue for efficiency improvement.

4.3.2 Rifled Interior Tube Walls

Heat exchangers using rifled interior tube walls (also known as “microgrooves”) to 

enhance energy efficiency by improving heat transfer across the heat exchanger. With this 

technology, the internal face of heat exchanger tubes is rifled with small grooves that increase 

the interior surface area of the tube and induce turbulence in the refrigerant flow. Having 

observed that microgroove technology was used in the majority of baseline units disassembled in 

the engineering analysis, DOE believes that microgroove technology is currently being used in 

baseline equipment today. Thus, DOE believes that the efficiency benefits of the technology are 

negligible and DOE has eliminated rifled interior tube walls as a potential avenue for efficiency 

improvement.

4.3.3 Microchannel Heat Exchangers

Microchannel heat exchangers in air conditioning applications are heat exchangers in 

which refrigerant fluid flows in confinements with typical hydraulic diameter of less than one 

millimeter. Microchannels may improve unit efficiency by improving the efficiency of heat 

transfer between refrigerant and air across the heat exchanger. However, microchannel heat 

exchangers are currently in the development stage for PTAC and PTHP applications and are not 

proven for consistent, field installed equipment performance. DOE notes that the engineering 

analysis was based on efficiency levels and, because units with microchannels are not 

commercially available, DOE cannot estimate the increased manufacturing costs associated with 

whatever efficiency gains such units may offer.
4-5

DOE is aware that Zess, Inc. Industries is developing an integrated microchannel 

refrigeration system for applications in PTAC units. Zess, Inc. Industries has indicated that this 

application may achieve efficiencies as high as 15 EER in PTACs. DOE requested more 

information from Zess, Inc. Industries regarding prototype units and test results. 

At this point, DOE does not have information regarding these prototype tests that would 

allow assessment of the efficiency improvements associated with the specific microchannel 

technology and/or the costs associated with its implementation. DOE eliminated microchannel 

heat exchanges from the NOPR analysis because data are not available to evaluate the energy 

efficiency characteristics of the technology.

4.3.4 Complex Control Boards

Digital energy management control interfaces can reduce annual energy consumption of 

PTACs or PTHPs by optimizing the operation of the equipment under varying operating 

conditions. For example, they may allow operation managers in hotels to remotely turn off or 

change temperature set points of units throughout a building. Although this technology can 

reduce peak energy demand and also reduce overall energy consumption throughout the year, it 

does not increase the EER under the AHRI 310/380-2014 test procedure. The test procedure 

requires that units be tested at steady state test conditions and DOE believes that complex control 

boards do not help steady state performance in PTAC and PTHP applications.

 DOE eliminated complex control boards as an efficiency option because the test 

procedure and EER or COP metric would not measure the energy impact of these technologies.

4.3.5 Corrosion Protection

Corrosion protection materials used in PTACs and PTHPs protect the equipment and 

prolong its use when it is exposed to chemically harsh operating conditions. DOE believes that 

corrosion protection has a negative impact on steady state operation to some degree, but that 

corrosion protection may help improve the overall unit performance over several years of 

operation. Although it is beneficial for units in harsh environments to be corrosion protected, 

corrosion protection does not improve the EER as measured by the test procedure. Therefore, 

DOE did not consider this technology in the engineering analysis.

4.3.6 Hydrophobic Material Treatment of Heat Exchangers

Material treatment of heat exchangers (also known as “plasma treatment”) allows the 

condensate that forms on the fins to be repelled and drained faster than on non-treated heat 

exchangers. Hydrophobic treatments are used to reduce mineral build up and corrosion on heat 

exchanger fins, to improve long-term performance of the unit. Although enhanced long term 

performance is beneficial, this treatment is not shown to improve the EER as per the test 

procedure.

4.3.7 Thermal Expansion Valves

Thermal expansion valves (TXVs) control the flow of refrigerant into the evaporator 

based on a temperature feedback. DOE notes that thermal expansion valves (TXVs) would not 
4-6

improve the energy efficiency of PTACs or PTHPs, because there is only one condition for 

which the fixed-orifice expansion device can be optimized. DOE has insufficient information to 

know whether testing at multiple conditions would make sufficient efficiency improvement to 

justify the increased test time. Therefore, DOE did not consider this technology in the 

engineering analysis.

4.4 REMAINING TECHNOLOGIES

Table 4.4.1 lists the technologies that were retained by DOE and subsequently designated 

as design options. Each of these technologies will be evaluated further in the subsequent 

engineering analysis.

Table 4.4.1 Retained Design Options for PTAC and PTHP

Higher Efficiency Compressors

Higher Efficiency Fan Motors

Increased Heat Exchanger Area

Improved Air Flow and Fan Design

The remaining technology options in Table 4.4.1 are briefly described below.

4.4.1 Higher Efficiency Compressors

Manufacturers can improve the energy efficiency of PTAC and PTHP units by 

incorporating more efficient components, such as high efficiency compressors, into their designs. 

DOE observed in reverse engineering analysis that PTAC and PTHP manufacturers use several 

different compressor models with a wide range of efficiency ratings. During the reverse 

engineering analysis conducted as part of the engineering analysis, DOE conducted efficiency 

testing and observed the compressors that were used in nineteen test units. For the representative 

capacity of 9,000 Btu/h, DOE examined compressors in ten test units and observed that the 

compressor efficiency ratings ranged from 9.7 EER to 10.9 EER. DOE observed most of the test 

units at 9,000 Btu/h used a compressor rated at 10.1 EER, but that the test unit with the highest 

tested efficiency used a compressor rated at 10.9 EER. For the representative capacity of 15,000 

Btu/h, DOE examined compressors in nine test units and observed that the compressor efficiency 

ratings ranged from 9.8 EER to 10.5 EER. DOE observed that the test units at 15,000 Btu/h had 

an average compressor efficiency rating of 10.1 EER, but that the test unit with the highest tested 

efficiency used a compressor rated at 10.5 EER. Efficiency test results and compressor 

observations are included in chapter 5 of this TSD.

4.4.2 Higher Efficiency Fan Motors

Manufacturers of baseline PTACs and PTHPs use permanent split capacitor (PSC) fan 

motors due to their modest cost, compact design, and durability. More efficient PSC motor 

designs applicable to PTACs and PTHPs are an ongoing industry challenge, and there been no 
4-7

substantial gain in efficiency in recent years. PSC manufacturers can improve efficiency by 

increasing the surface area of rotors, although the overall size of the PSC motor would increase 

in that case. PTACs and PTHPs have size constraints that do not allow an increase in motor size 

to a level which would have a significant impact on energy efficiency. DOE believes any further 

gains in PSC fan motor efficiency will be difficult to achieve, and has thus eliminated 

improvement of PSC fan motors as a potential avenue for efficiency improvement. 

Besides PSC-based fan motors, PTAC and PTHP original equipment manufacturers 

(OEMs) can replace PSC motors with permanent magnet (PM) motors. PM motors typically 

offer higher efficiencies than PSC-based fan motors, but these improvements come with 

increased costs for the motor unit and control hardware. Several manufacturers use PM motors in 

their higher-efficiency PTAC and PTHP models.

4.4.3 Increased Heat Exchanger Area

Manufacturers of PTACs and PTHPs increase unit efficiency by increasing heat 

exchanger size, either through elongating the face of the heat exchanger or increasing the number 

of heat exchanger tube rows. Standard-size PTACs are dimensionally constrained and, because 

of these constraints on unit size, there are limits to the efficiency gains that may be had by 

increasing heat exchanger size. At least one manufacturer has incorporated bent heat exchanger 

coils to increase the heat exchanger face area while remaining inside the standard size unit 

constraints. In its reverse engineering analysis, DOE observed at least three test units that 

contained a bent heat exchanger. DOE based its analysis on the measured performance of these 

units (one of which performed at the max-tech efficiency level). The measured performance of 

these units includes the impact of additional pressure drop associated with the bent heat 

exchangers.

4.4.4 Improved Air Flow and Fan Design

Manufacturers of PTACs and PTHPs currently use several techniques to shape and direct 

airflow inside PTAC and PTHP units. Manufacturers may improve unit efficiency by optimizing 

air paths and fan blade designs, and by selecting appropriate fan and motor combinations so that 

the fan’s operational efficiency in the unit matches the fan’s peak efficiency exactly.
5-i

CHAPTER 5. ENGINEERING ANALYSIS

TABLE OF CONTENTS

5.1 INTRODUCTION ........................................................................................................... 5-1

5.2 EQUIPMENT CLASSES ANALYZED.......................................................................... 5-2

5.3 ENERGY EFFICIENCY METRICS............................................................................... 5-5

5.4 EFFICIENCY LEVELS................................................................................................... 5-5

5.4.1 Representative Cooling Capacities...................................................................... 5-5

5.4.2 Baseline Units...................................................................................................... 5-6

5.4.3 Incremental Efficiency Levels............................................................................. 5-7

5.5 METHODOLOGY OVERVIEW.................................................................................... 5-9

5.5.1 Manufacturer Interviews.................................................................................... 5-10

5.5.2 Equipment Teardown......................................................................................... 5-10

5.5.2.1 Selection of Units................................................................................... 5-11

5.5.2.2 Generation of Bills of Materials ............................................................ 5-11

5.5.2.3 Cost Structure of the Spreadsheet Models............................................. 5-12

5.5.3 Shipping Costs................................................................................................... 5-13

5.5.4 Equipment Testing............................................................................................. 5-13

5.6 ANALYSIS AND RESULTS........................................................................................ 5-13

5.6.1 Manufacturer Interviews.................................................................................... 5-14

5.6.2 Equipment Testing............................................................................................. 5-14

5.6.3 Equipment Teardown......................................................................................... 5-18

5.6.4 Cost-Efficiency Results ..................................................................................... 5-21

LIST OF TABLES

Table 5.2.1 Equipment Classes for PTACs and PTHPs............................................................. 5-3

Table 5.2.2 Equipment Classes Covered by this Rulemaking.................................................... 5-4

Table 5.4.1 Baseline Efficiency Levels ....................................................................................... 5-7

Table 5.4.2 Incremental Efficiency Levels.................................................................................. 5-9

Table 5.5.1 Major Manufacturing Processes ............................................................................ 5-12

Table 5.6.1 PTAC and PTHP Test Units at 9,000 Btu/h Capacity ........................................... 5-15

Table 5.6.2 PTAC and PTHP Test Units at 15,000 Btu/h Capacity ......................................... 5-16

Table 5.6.3 Test Results of Selected PTAC and PTHP Units .................................................. 5-17

Table 5.6.4. Cost-Efficiency Relationship for PTACs at 9,000 Btu/h Capacity ...................... 5-24

Table 5.6.5. Cost-Efficiency Relationship for PTHPs at 9,000 Btu/h Capacity....................... 5-24

Table 5.6.6. Cost-Efficiency Relationship for PTACs at 15,000 Btu/h Capacity .................... 5-25

Table 5.6.7. Cost-Efficiency Relationship for PTACs at 15,000 Btu/h Capacity .................... 5-25

LIST OF FIGURES

Figure 5.4.1 COP versus EER for Standard Size PTHPs with 9,000 Btu/h Cooling Capacity from 

2013 AHRI directory............................................................................................. 5-8
5-ii

Figure 5.4.2 COP versus EER for Standard Size PTHPs with 15,000 Btu/h Cooling Capacity 

from 2013 AHRI directory.................................................................................... 5-8

Figure 5.5.1 Manufacturing Cost Assessment Stages................................................................ 5-12

Figure 5.6.1 Baseline 9,000 Btu/h PTAC Full Production Cost Distribution............................ 5-19

Figure 5.6.2 Baseline 9,000 Btu/h PTAC Materials Cost Distribution ..................................... 5-19

Figure 5.6.3 Baseline 15,000 Btu/h PTAC Full Production Cost Distribution.......................... 5-20

Figure 5.6.4 Baseline 15,000 Btu/h PTAC Materials Cost Distribution ................................... 5-20

Figure 5.6.5 Manufacturer Production Cost (2013$) versus Efficiency for Standard Size PTACs 

with a Cooling Capacity of 9,000 Btu/h.............................................................. 5-21

Figure 5.6.6 Manufacturer Production Cost (2013$) versus Efficiency for Standard Size PTHPs 

with a Cooling Capacity of 9,000 Btu/h.............................................................. 5-22

Figure 5.6.7 Manufacturer Production Cost (2013$) versus Efficiency for Standard Size PTACs 

with a Cooling Capacity of 15,000 Btu/h............................................................ 5-22

Figure 5.6.8 Manufacturer Production Cost (2013$) versus Efficiency for Standard Size PTHPs 

with a Cooling Capacity of 15,000 Btu/h............................................................ 5-23

Figure 5.6.9 Manufacturer Production Cost (2013$) versus Efficiency for Standard Size PTACs 

and PTHPs with Cooling Capacities of 9,000 Btu/h and 15,000 Btu/h .............. 5-23
5-1

CHAPTER 5. ENGINEERING ANALYSIS

5.1 INTRODUCTION

After conducting the screening analysis, the U.S. Department of Energy (DOE) 

performed an engineering analysis based on the remaining design options. The 

engineering analysis consists of estimating the energy consumption and costs of 

producing equipment at various levels of increased efficiency. This section provides an 

overview of the engineering analysis (section 5.1), discusses equipment classes (section 

5.2), describes the efficiency metrics used for this equipment (section 5.3), establishes 

baseline unit specifications (section 5.4.2), discusses incremental efficiency levels 

(section 5.4.3), explains the methodology used during data gathering (section 5.5) and 

discusses the analysis and results (section 5.6).

The primary inputs to the engineering analysis are baseline information from the 

market and technology assessment (chapter 3 of this technical support document (TSD)) 

and technology options from the screening analysis (chapter 4). Additional inputs 

include laboratory testing and reverse-engineering of representative equipment, and 

manufacturer interviews. The primary output of the engineering analysis is a set of costefficiency curves. In the subsequent markups analysis (chapter 6), DOE determined 

consumer (i.e., equipment purchaser) prices by applying distribution markups, sales tax 

and contractor markups. After applying these markups and an assumed installation cost, 

the consumer prices serve as the input to the building energy-use and end-use load 

characterization (chapter 7) and the life-cycle cost (LCC) and payback period (PBP) 

analyses (chapter 8).

DOE typically structures its engineering analysis around one of three 

methodologies. These are: (1) the design-option approach, which calculates the 

incremental costs of adding specific design options to a baseline model that will improve 

its efficiency (i.e., lower its energy use); (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 provides “bottom-up” 

manufacturing cost assessments for achieving various levels of increased efficiency, 

based on teardown analyses (or physical teardowns) providing detailed data on costs for 

parts and material, labor, shipping/packaging, and investment for models that operate at 

particular efficiency levels. A supplementary method called a catalog teardown uses 

published manufacturer catalogs and supplementary component data to estimate the 

major physical differences between a piece of equipment that has been physically 

disassembled and another piece of similar equipment for which catalog data are available 

to determine the cost of the latter equipment. The methodology selected for the 

engineering analysis depends on the product, the design options under study, and any 

historical data upon which DOE can draw.

To establish the industry cost-efficiency curves for PTAC and PTHP equipment, 

the DOE used a combination of the efficiency-level approach and the reverse engineering 
5-2

approach. DOE designated a baseline efficiency level that is equivalent to the minimum 

efficiency allowed by energy conservation standards.a DOE set efficiency levels at 

incremental steps above the baseline up to the maximum efficiency level that is 

technologically feasible using current technologies. 

To estimate the manufacturing production costs (MPCs) for equipment at each 

efficiency level, DOE reverse engineered a set of PTAC and PTHP equipment 

specifically selected to represent the range of efficiency levels. This reverse engineering 

involved the disassembly of units, analysis of the materials and manufacturing processes, 

and development of a spreadsheet cost model based on a clear and consistent 

manufacturing cost assessment methodology. DOE built a detailed cost assessment model 

that accurately estimates the MPC associated with producing each specific piece of 

equipment. This chapter reports the cost model results in aggregated form to maintain 

confidentiality of the data.

DOE notes that the combined efficiency level and cost-assessment approach does 

not separately evaluate the effects of individual design options and does not prescribe a 

particular set of design options for manufacturers to improve unit efficiency. Instead, it 

selects units spanning a range of efficiency levels, estimates MPCs for those units, and 

constructs a cost curve to define the relationship between energy efficiency and MPC.

5.2 EQUIPMENT CLASSES ANALYZED

The current Federal energy conservation standards (ECS), shown in Table 5 of 10 

CFR Part 431.97, divide PTACs and PTHPs into twelve equipment classes based on 

whether the equipment is an air conditioner or heat pump, the cooling capacity, and the 

equipment’s wall sleeve dimensions, which fall into two categories:

• Standard size (PTAC or PTHP equipment with wall sleeve dimensions greater 

than or equal to 16 inches high, or greater than or equal to 42 inches wide)

• Non-standard size (PTAC or PTHP equipment with wall sleeve dimensions less 

than 16 inches high and less than 42 inches wide)

 

a DOE set the baseline level equivalent to the ANSI/ASHRAE/IES Standard 90.1-2013 minimum for PTAC 

and PTHP equipment, since DOE is required to, at a minimum, adopt the ASHRAE levels as the Federal 

standard. (42 U.S.C. (a)(6)(A)(ii)(I)).
5-3

The twelve equipment classes for PTACs and PTHPs are listed in Table 5.2.1, and 

correspond to the classes contained in ANSI/ASHRAE/IES Standard 90.1-2013.

Table 5.2.1 Equipment Classes for PTACs and PTHPs

Equipment Class

Equipment Category Cooling Capacity

PTAC

Standard Size*

<7,000 Btu/h

≥7,000 Btu/h and ≤15,000 Btu/h

>15,000 Btu/h

Non-Standard Size**

<7,000 Btu/h

≥7,000 Btu/h and ≤15,000 Btu/h

>15,000 Btu/h

PTHP

Standard Size*

<7,000 Btu/h

≥7,000 Btu/h and ≤15,000 Btu/h

>15,000 Btu/h

Non-Standard Size**

<7,000 Btu/h

≥7,000 Btu/h and ≤15,000 Btu/h

>15,000 Btu/h * Standard size refers to PTAC or PTHP equipment with wall sleeve dimensions greater than or equal to 16 

inches high, or greater than or equal to 42 inches wide.

** Wall sleeve dimensions less than 16 inches high and less than 42 inches wide.

DOE is not considering amended energy conservation standards for non-standard 

size PTAC and PTHP equipment in this rulemaking, because the non-standard size 

equipment classes represent a small and declining portion of the market, and because of a 

lack of adequate information to analyze non-standard size units. The shipments analysis 

conducted for the 2008 final rule projected that shipments of non-standard size PTACs 

and PTHPs would decline from about 30,000 units in 2012 (6.6% of the entire PTAC and 

PTHP market) to about 16,000 units in 2042 (2.4% of the entire PTAC and PTHP 

market).

b

 

b See DOE’s discussion regarding shipment projections for standard and non-standard PTAC and PTHP 

equipment and the results of shipment projections in TSD for the 2008 PTAC and PTHP energy 

conservation standard rulemaking at: 

http://www.regulations.gov/#!documentDetail;D=EERE-2007-BT-STD-0012-0032 (Chapter 10, Section 

10.5).
5-4

For the purposes of this rulemaking, DOE analyzed the six standard size 

equipment classes for PTACs and PTHPs, presented in Table 5.2.2.

Table 5.2.2 Equipment Classes Covered by this Rulemaking

Equipment Class

Equipment Category Cooling Capacity

PTAC Standard Size*

<7,000 Btu/h

≥7,000 Btu/h and ≤15,000 Btu/h

>15,000 Btu/h

PTHP Standard Size*

<7,000 Btu/h

≥7,000 Btu/h and ≤15,000 Btu/h

>15,000 Btu/h * Standard size refers to PTAC or PTHP equipment with wall sleeve dimensions greater than or equal to 16 

inches high, or greater than or equal to 42 inches wide.

The current Federal energy conservation standards and the efficiency levels 

specified by ANSI/ASHRAE/IES Standard 90.1-2013 for PTAC and PTHP equipment

are a function of the equipment’s cooling capacity. Both standards have equations to 

calculate the efficiency levels for PTACs and PTHPs in the cooling capacity range 

between 7,000 Btu/h and 15,000 Btu/h for each equipment class. For equipment with 

cooling capacities below 7,000 Btu/h and above 15,000 Btu/h, current Federal energy 

conservation standards and ANSI/ASHRAE/IES Standard 90.1-2013 maintain a constant 

efficiency level equal to the value of the efficiency equation at either 7,000 Btu/h or 

15,000 Btu/h cooling capacity (e.g., the efficiency level for 6,000 Btu/h cooling capacity 

equipment equals the efficiency level for 7,000 Btu/h cooling capacity equipment, and 

the efficiency level for 16,000 Btu/h cooling capacity equipment equals the efficiency 

level for 15,000 Btu/h cooling capacity equipment). 

For the engineering analysis, DOE examined specific cooling capacities for 

standard size PTACs and PTHPs, which are referred to as “representative cooling 

capacities.” Interviews with manufacturers indicated that the majority of PTAC and 

PTHP shipments are in the classes with cooling capacity between 7,000 Btu/h to 15,000 

Btu/h. According to the certification data provided by the 2013 AHRI Directory of 

Certified Performance (2013 AHRI Directory), over 90 percent of standard-size PTAC 

and PTHP models available on the market are within the 7,000 Btu/h to 15,000 Btu/h 

cooling capacity range.1

 DOE focused its analysis on equipment with cooling capacities

in the range between 7,000 Btu/h and 15,000 Btu/h. 
5-5

5.3 ENERGY EFFICIENCY METRICS

The current energy conservation standards for PTACs and PTHPs are based on 

energy efficiency ratio (EER) for cooling efficiency and on coefficient of performance 

(COP) for PTHP heating efficiency. 10 CFR 431.97(c). The current Federal test 

procedure for PTACs and PTHPs incorporates by reference AHRI 310/380-2014 

Standard for Packaged Terminal Air-Conditioners and Heat Pumps.

c This standard 

defines EER as “the ratio of the produced cooling effect of an air conditioner or heat 

pump to its net work input, expressed in Btu/watt-hour.” COP is defined by AHRI 

310/380-2014 as “the ratio of the produced cooling effect of an air conditioner or heat 

pump (or its produced heating effect, depending on the mode of operation) to its net work 

input, when both the cooling (or heating) effect and the net work input are expressed in 

identical units of measurement.”

In conducting the engineering analysis, DOE only considered technologies and 

techniques that improve the EER and COP of PTAC and PTHP equipment. As 

mentioned in the screening analysis (chapter 4 of this TSD), there are some technology

options and techniques that could reduce the annual energy consumption of the system,

but that have little effect on EER. DOE did not consider technology options that have 

negligible effects on EER and COP.

5.4 EFFICIENCY LEVELS

5.4.1 Representative Cooling Capacities

Because there are large variations in equipment cooling capacity and performance 

in the standard size PTAC and PTHP equipment classes, DOE analyzed some of the 

cooling capacities individually. DOE selected representative cooling capacities and 

analyzed specific equipment to provide information representative of the entire 

equipment class. The representative cooling capacities allowed DOE to establish 

baseline units that are used throughout the rulemaking analyses. 

For standard-size PTAC and PTHP equipment classes, DOE identified two

representative cooling capacities: 9,000 Btu/h and 15,000 Btu/h. Both representative 

cooling capacities fall within the ≥7,000 and ≤15,000 Btu/h cooling capacity range. DOE 

selected the representative cooling capacity of 9,000 Btu/h because this capacity had the 

 

c DOE has incorporated by reference AHRI Standard 310/380-2014 as the DOE test procedure at 10 CFR 

431.97.
5-6

highest number of standard-size PTAC and PTHP models listed in the 2013 AHRI 

Directory. Chapter 3 of this TSD presents the distribution of models in the 2013 AHRI 

Directory at different capacity levels for standard-size PTAC and PTHP equipment. 

DOE selected the representative cooling capacity of 15,000 Btu/h because, according to 

several manufacturers interviewed, the 15,000 Btu/h cooling capacity represents the 

greatest technical hurdles for efficiency improvement, considering the size constraints of 

standard-size PTACs and PTHPs. DOE believes that these representative cooling capacities 

capacities of 9,000 and 15,000 Btu/h accurately represent the markets for PTAC and PTHP 

equipment.

DOE used the analytical results for the two representative cooling capacities to 

examine the slope of the energy-efficiency equation (i.e., EER as a function of cooling 

capacity). Chapter 9 of this TSD contains more details explaining how DOE extrapolates 

the amended energy conservation standards for the representative cooling capacities to 

the entire range of cooling capacities.

5.4.2 Baseline Units

DOE selected baseline units as reference points for each equipment class, against 

which DOE measured changes resulting from potential energy conservation standards. 

The baseline unit in each equipment class represents the basic characteristics of 

equipment in that class. Typically, a baseline unit is a unit that just meets current required 

energy conservation standards and provides basic consumer utility. DOE used the 

baseline units in the engineering analysis and the life-cycle-cost and payback-period 

analysis. 

The baseline efficiency levels for each equipment class are presented below in 

Table 5.4.1. These levels represent the ANSI/ASHRAE/IES Standard 90.1-2013 

minimums for PTAC and PTHP equipment. These levels are used as the Baselines for 

PTAC and PTHP equipment since DOE is required to, at a minimum, adopt the 

ASHRAE levels as the Federal standard. (42 U.S.C. (a)(6)(A)(ii)(I)). The Baseline 

efficiency level is 1.8% higher than current Federal standard for PTAC equipment, but is

equal to current Federal standard for PTHP equipment. 
5-7

Table 5.4.1 Baseline Efficiency Levels

Equipment 

Type

Equipment 

Class Baseline Efficiency Equation Cooling Capacity

Baseline 

Efficiency 

Level

PTAC Standard 

Size EER = 14.0 – (0.300 x Cap†

)

9,000 Btu/h 11.3 EER

15,000 Btu/h 9.5 EER

PTHP Standard 

Size

EER = 14.0 – (0.300 x Cap†

)

COP = 3.7 – (0.052 x Cap†

)

9,000 Btu/h 11.3 EER

3.2 COP

15,000 Btu/h 9.5 EER

2.9 COP

† Cap means cooling capacity in thousand Btu/h at 95oF outdoor dry-bulb temperature.

5.4.3 Incremental Efficiency Levels

For the equipment classes presented in section 5.2, DOE analyzed several 

efficiency levels and obtained incremental cost data at each of these levels. DOE 

considered five efficiency levels beyond the baseline efficiency level for each equipment 

class. DOE selected these levels based on a review of the efficiency levels of available 

equipment. DOE indicated in the framework document for this rulemaking that it 

planned to consider a maximum efficiency level 18.2% higher than the baseline level.

d

The rated efficiencies of PTACs listed in the AHRI Directory extend up to 17.5% above 

the ANSI/ASHRAE/IES Standard 90.1-2013 baseline efficiency level. However, based 

on testing of individual units conducted for this rulemaking, DOE only considered 

efficiencies up to 16.2% above the baseline level. Accordingly, DOE revised the 

maximum efficiency level for this analysis from 18.2% to 16.2% above the baseline 

level.

For the PTHP equipment classes, DOE based heating efficiency levels on the 

variation of COP with EER, as discussed in the framework document for this rulemaking. 

78 FR 12252. DOE evaluated AHRI data for PTHP equipment to develop the 

relationship between COP and EER and used this relationship to establish the COP 

efficiency levels. Figure 5.4.1 and Figure 5.4.2 below show the COP and EER data and 

best-fit linear relationships for standard size PTHP units with 9,000 Btu/h or 15,000 

Btu/h cooling capacity results.

 

d Because DOE published the framework document before the publication of ASHRAE 90.1-2013, the 

framework document described the proposed max tech level as being 20% higher than the current Federal 

PTAC ECS.
5-8

Figure 5.4.1 COP versus EER for Standard Size PTHPs with 9,000 Btu/h Cooling 

Capacity from 2013 AHRI directory

Figure 5.4.2 COP versus EER for Standard Size PTHPs with 15,000 Btu/h Cooling 

Capacity from 2013 AHRI directory

COP = 0.241*(EER) + 0.480

2.0

2.2

2.4

2.6

2.8

3.0

3.2

3.4

3.6

3.8

4.0

8 9 10 11 12 13 14

COP (no unit)

EER (Btu/Wh)

COP = 0.261*(EER) + 0.374

2.0

2.2

2.4

2.6

2.8

3.0

3.2

3.4

3.6

3.8

4.0

8 9 10 11 12 13 14

COP (no unit)

EER (Btu/Wh)
5-9

DOE established COP efficiency levels for PTHPs at 9,000 and 15,000 Btu/h 

using the EER values corresponding to the incremental cooling performance efficiency 

levels and the linear equations developed for the COP/EER relationships that are shown 

in the figures. The resulting incremental COP efficiency levels are shown for PTHP 

equipment in Table 5.4.2. 

Table 5.4.2 presents the efficiency levels for each equipment class.

Table 5.4.2 Incremental Efficiency Levels

Equipment 

Type

Cooling 

Capacity

Efficiency 

Metric

Efficiency Levels (Percentages relative to Baseline)

Current 

Federal 

PTAC 

ECS*

EL1, 

Baseline**

EL2, 

2.2%

EL3, 

6.2%

EL4, 

10.2%

EL5, 

14.2%

EL6, 

16.2% 

(MaxTech)

PTAC, 

Standard Size

7,000-

15,000 

Btu/h

EER

13.8 -

(0.300 x 

Cap†

)

14.0 -

(0.300 x 

Cap†

)

14.4 -

(0.312 x 

Cap†

)

14.9 -

(0.324 x 

Cap†

)

15.5 -

(0.336 x 

Cap†

)

16.0 -

(0.348 x 

Cap†

)

16.3 -

(0.354 x

Cap†

)

9,000 

Btu/h EER 11.1 EER 11.3 EER 11.5 EER 12.0 EER 12.4 EER 12.9 EER 13.1 EER

15,000 

Btu/h EER 9.3 EER 9.5 EER 9.7 EER 10.0 EER 10.4 EER 10.8 EER 11.0 EER

Equipment 

Type

Cooling 

Capacity

Efficiency 

Metric Baseline** EL1, 2.2% 

EL2, 

6.2%

EL3, 

10.2%

EL4, 

14.2%

EL5, 

16.2% 

(MaxTech)

PTHP, 

Standard 

Size

7,000-

15,000 

Btu/h

EER

14.0 -

(0.300 x 

Cap†

)

14.4 -

(0.312 x 

Cap†

)

14.9 -

(0.324 x 

Cap†

)

15.5 -

(0.336 x 

Cap†

)

16.0 -

(0.348 x 

Cap†

)

16.3 -

(0.354 x

Cap†

)

7,000-

15,000 

Btu/h

COP 3.7 - (0.052 

x Cap†)

3.8 -

(0.058 x 

Cap†

)

4.0 - (0.064 

x Cap†)

4.1 - (0.068 

x Cap†)

4.2 -

(0.070 x 

Cap†

)

4.3 - (0.073 

x Cap†

)

9,000 

Btu/h

EER

COP

11.3 EER

3.2 COP

11.5 EER

3.3 COP

12.0 EER

3.4 COP

12.4 EER

3.5 COP

12.9 EER

3.6 COP

13.1 EER

3.6 COP

15,000 

Btu/h

EER

COP

9.5 EER

2.9 COP

9.7 EER

2.9 COP

10.0 EER

3.0 COP

10.4 EER

3.1 COP

10.8 EER

3.2 COP

11.0 EER

3.2 COP

* This level represents the current Federal minimum for PTAC equipment.

** This level represents the ANSI/ASHRAE/IES Standard 90.1-2013 minimum for PTAC and PTHP 

equipment. This level is used as the Baseline for PTAC and PTHP equipment since DOE is required to, at a 

minimum, adopt the ASHRAE levels as the Federal standard. (42 U.S.C. (a)(6)(A)(ii)(I)). DOE notes that 

the Baseline level is 1.8% higher than current Federal ECS for PTAC equipment, but is equivalent to 

current Federal ECS for PTHP equipment. For PTAC equipment, the Baseline level is also termed EL1.

† Cap means cooling capacity in thousand Btu/h at 95oF outdoor dry-bulb temperature.

5.5 METHODOLOGY OVERVIEW

In order to develop the cost-efficiency relationships for PTACs and PTHPs, DOE 

conducted testing and performed detailed equipment teardowns and reverse-engineering 

analyses on a sample of models spanning a range of rated efficiencies. DOE 
5-10

supplemented these analyses by conducting interviews with equipment manufacturers to 

gain a better understanding of the design options and costs associated with achieving 

higher efficiency levels.

5.5.1 Manufacturer Interviews

Throughout the rulemaking process, DOE seeks feedback and insight from 

stakeholders to improve the information used in the analyses. For the engineering 

analysis, DOE conducted confidential interviews with manufacturers of PTACs and 

PTHPs to develop a deeper understanding of the various combinations of technologies 

used to increase equipment efficiency and their associated manufacturing costs. DOE 

considered all the information manufacturers provided when refining the cost model. 

DOE incorporated confidential information (i.e., equipment and manufacturing process 

figures) into the analysis in the form of averages to avoid disclosing sensitive information 

about individual manufacturers’ equipment or manufacturing processes.

Before these interviews, DOE provided manufacturers with an engineering 

information package that included a spreadsheet with preliminary assumptions, estimates, 

and cost-efficiency curves, and a list of possible questions to be asked during the 

interview. DOE asked manufacturers to provide feedback if the data was representative 

of the market and to supply any data that could improve DOE’s estimates and 

assumptions. DOE’s questions included the following:

• Which design features affecting energy use are generally incorporated into 

“baseline” PTACs and PTHPs?

• What are the costs of attaining the individual efficiency levels selected? How 

do these efficiency levels correspond to the various design options listed? 

What other design options do you use to attain the various efficiency levels?

• Do the industry MPCs calculated by DOE represent your firm’s costs for 

manufacturing standard and size PTAC and PTHP?

DOE also requested information on a number of other factors that affect 

manufacturing cost and the incremental cost associated with attaining higher efficiency 

levels.

5.5.2 Equipment Teardown 

Other than obtaining detailed manufacturing costs directly from a manufacturer, the 

most accurate method for determining the production cost of a piece of equipment is to 

disassemble the equipment piece-by-piece and estimate the material and labor cost of 

each component using a process commonly called a physical teardown.

To calculate the manufacturing costs of PTACs and PTHPs at different efficiency 

levels, DOE disassembled multiple units into their component parts and used cost 

modeling techniques to estimate the cost of materials, labor, and capital required to 
5-11

fabricate and assemble the components into a complete piece of equipment. The 

teardown methodology is described in detail in section 5.5.2.1 through section 5.5.2.3.

5.5.2.1 Selection of Units

DOE selected several standard-size PTAC and PTHP units to represent the 

market, and used these for teardown in the engineering analysis. The selected equipment 

exhibited the following five characteristics:

1. The selected equipment, taken together, cover the full range of efficiency 

levels considered in the analysis.

2. The selected equipment has cooling capacity corresponding to one of the 

selected representative cooling capacities (9,000 Btu/h or 15,000 Btu/h).

3. When possible, DOE selected one lower-efficiency unit and one higherefficiency unit from the same manufacturer. When possible, those units 

shared similar characteristics (e.g., both from the same product line).

4. The equipment tended to be from manufacturers with relatively large shares of 

the PTAC and PTHP markets, and thus was representative of typical design 

approaches.

5. The selected equipment included base units with few, if any, equipment

features or options that add cost without affecting equipment efficiency.

5.5.2.2 Generation of Bills of Materials

The end result of each teardown is a structured bill of materials (BOM). 

Structured BOMs describe each equipment part and its relationship to the other parts, in 

the estimated order of assembly. The BOMs describe each fabrication and assembly 

operation in detail, including the type of equipment needed (e.g., stamping presses, 

injection molding machines, spot-welders, etc.) and the process cycle times. The result is 

a thorough and explicit model of the production process, which includes space, conveyor, 

and equipment requirements by planned production level.

The BOMs incorporate all materials, components, and fasteners, classified as 

either raw materials or purchased parts and assemblies. The classification into raw 

materials or purchased parts is based on DOE’s previous industry experience, recent 

information in trade publications, and discussions with high- and low-volume original 

equipment manufacturers (OEMs). 

For purchased parts, the purchase price is an estimate based on volume-variable 

price quotations and detailed discussions with suppliers. For fabricated parts, the price of 

intermediate materials (e.g., tube, sheet metal) and the cost of transforming them into 

finished parts are an estimate based on current industry pricing. DOE shared major 

assumptions and estimates with manufacturers during the engineering manufacturer 

interviews to gain feedback on the analysis, its methodology, and preliminary results.
5-12

The cost of raw materials is determined using prices for copper, steel and 

aluminum from the American Metals Market.2 Because DOE is using a 5-year average 

in material prices from 2009–2013, these price increases are normalized, which better 

represents long-term material prices.

5.5.2.3 Cost Structure of the Spreadsheet Models

The manufacturing cost assessment methodology used is a detailed, componentfocused technique for rigorously calculating the manufacturing cost of a product (direct 

materials, direct labor and some overhead costs.) Figure 5.5.1 shows the three major steps 

in generating the manufacturing cost.

Figure 5.5.1 Manufacturing Cost Assessment Stages

The first step in the manufacturing cost assessment is the creation of a complete 

and structured BOM from the disassembly of the units selected for teardown. The units 

are dismantled, and each part is characterized according to weight, manufacturing 

processes used, dimensions, material, and quantity. The BOM incorporates all materials, 

components, and fasteners with estimates of raw material costs and purchased part costs. 

Assumptions on the sourcing of parts and in-house fabrication are based on industry 

experience, information in trade publications, and discussions with manufacturers. 

Interviews and plant visits are also conducted with manufacturers to ensure accuracy on 

methodology and pricing.

Following the development of a detailed BOM, the major manufacturing 

processes are identified and developed for the spreadsheet model. These processes are 

listed in Table 5.5.1.

Table 5.5.1 Major Manufacturing Processes

Fabrication Finishing Assembly/Joining Quality Control

Injection Molding 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

Fabrication process cycle times are estimated and entered into the BOM. For this 

analysis, $9.10 per hour was used as the average fully-burdened labor rate based on 

typical annual wages and benefits of industry employees. Labor rates for manufacturers 

of standard size PTACs and PTHPs are based on the weighted averages of foreign and 
5-13

domestic manufacturers. Certain large manufacturers of PTACs and PTHPs make their 

equipment in foreign factories, where labor rates are significantly less than domestic 

rates. Foreign labor rates are based on engineering manufacturer interviews, internal 

expertise, and industry literature research. In the final step of the cost assessment, 

assembly times and associated direct labor costs are estimated. Once the cost estimate for 

each teardown unit is finalized, a detailed summary is prepared for relevant components, 

subassemblies and processes. The BOM thus details all aspects of unit costs. 

Design options used in units subject to teardown are noted in the summary sheet 

of each cost model and are cost-estimated individually. Thus, various implementations of 

design options can be accommodated, ranging from assemblies that are entirely 

purchased to units that are made entirely from raw materials. Hybrid assemblies, 

consisting of purchased parts and parts made on site are thus also accommodated. 

5.5.3 Shipping Costs

In addition to the MPC, DOE also considered the cost to ship the unit from the 

manufacturing facility to the first point on the distribution chain. In calculating the 

shipping costs, DOE first gathered estimates of the cost to ship a standard-size shipping 

container of manufactured equipment from China to the United States, and the cost to 

ship the equipment an average distance in the United States. The bulk of PTAC and 

PTHP shipments originate from factories in China, but one major manufacturer of 

standard size PTACs and PTHPs produces units in the United States. Using the relative 

market shares for standard size equipment manufacturers, DOE constructed a weighted 

average shipping cost to account for units produced domestically and overseas. DOE 

then used the representative unit sizes to calculate a volume for each unit. Along with the 

dimensions of a shipping container, DOE used this cost and volume information to 

develop an average shipping cost for each equipment class analyzed.

5.5.4 Equipment Testing

DOE conducted equipment testing to verify the energy use of equipment and to 

develop a better understanding of the potential efficiency improvements associated with 

various design options and to develop disaggregated efficiency data. DOE contracted 

with qualified third-party test laboratories to test the capacity, EER, and COP of the 

equipment using the DOE test procedures for PTACs and PTHPs (see 10 CFR 431.96).

5.6 ANALYSIS AND RESULTS

DOE conducted the engineering analysis using the efficiency-level approach and 

the reverse-engineering approach, analyzing two specific cooling capacities to represent 

the range of standard-size PTACs and PTHPs available on the market. As discussed in 

section 5.6.1, DOE selected representative capacities of 9,000 Btu/h and 15,000 Btu/h, , 
5-14

5.6.1 Manufacturer Interviews

As discussed in section 5.5.1, DOE conducted confidential interviews with PTAC 

and PTHP manufacturers. From the manufacturers interviewed, DOE collected a general 

impression of PTAC and PTHP equipment on the market. Interviewed manufacturers 

indicated that all of the equipment covered in this rulemaking currently use rotary 

compressors, round-tube-and-fin heat exchangers, and R-410A refrigerant. 

Manufacturers employ different design strategies in terms of compressor 

selection, indoor and outdoor fan motor selection, and heat exchanger design. 

Manufacturers interviewed noted that selecting higher efficiency motors and compressors 

can increase system efficiency. Manufacturers also commented that increasing the size of 

the heat exchangers in a unit increases efficiency, though this use of this option is limited 

in larger capacity models (such as 15,000 Btu/h models) because of size constraints. 

Manufacturers noted that standard-size PTACs and PTHPs are limited in terms of case 

size because the units are typically installed in wall openings of 16 inches high by 42 

inches wide. These wall opening dimensions were standardized in buildings over the past 

25 years. DOE understood from manufacturers that altering existing wall sleeve openings

could include extensive structural changes to a building, could be very costly, and is 

therefore rarely done. 

5.6.2 Equipment Testing

DOE conducted a market survey of PTAC and PTHP models and their features. 

DOE selected six 9,000 Btu/h and five 15,000 Btu/h PTAC models for testing and reverse 

engineering. The models were selected to develop a representative sample of the market 

at different efficiency levels. DOE selected the units based on the efficiency data 

available in the AHRI certification database. Where feasible, DOE selected models for 

reverse engineering with low and high capacities from a given manufacturer that are built 

on the same platform (i.e., with the same basic design and construction). DOE also 

selected five 9,000 Btu/h and four 15,000 Btu/h PTHP models to evaluate the differences 

between PTAC and PTHP units. Details about the key features of the tested units are 

presented in Table 5.6.1 and Table 5.6.2. DOE notes that the 9,000 Btu/h test unit with 

the highest tested efficiency (Test Unit 7) used DC fan motors and a compressor rated at 

10.9 EER; the 15,000 Btu/h test units with the highest tested efficiency rating (Test Unit 

16 & 20) used DC fan motors and a compressor rated at 10.5 EER. 
5-15

Table 5.6.1 PTAC and PTHP Test Units at 9,000 Btu/h Capacity

Feature

Test Unit Description

PTAC PTHP

Test 

Unit 1

Test 

Unit 2

Test 

Unit 3

Test 

Unit 4

Test 

Unit 5

Test 

Unit 6

Test 

Unit 7

Test 

Unit 8

Test 

Unit 9

Test 

Unit 10

Test 

Unit 11

Rated Cooling 

Capacity (Btu/h) 9,000 9,000 9,000 9,700 9,500 9,700 9,500 9,400 9,000 9,000 9,000

Rated EER (Btu/Wh) 11.3 11.5 11.3 12.1 12.7 12.1 12.9 12.7 11.3 11.3 11.5

Rated COP (unitless) - - - - - - 3.6 3.8 3.3 3.3 3.4

Outdoor 

Coil

Face Area 

(ft^2) 2.67 2.67 1.85 2.67 2.48 2.67 2.50 2.55 1.85 2.67 2.67

Fin Pitch 19 23 21 19 21 19 20 19 21 19 23

Tube Rows 3 2 2 2 3 2 3 3 2 3 2

Tube OD 

(in.) 0.200 0.200 0.250 0.250 0.200 0.250 0.250 0.250 0.250 0.200 0.200

Outdoor 

Fan

Motor

Type PSC PSC PSC PSC BLDC PSC BLDC BLDC PSC PSC PSC

Power (hp) 0.054 0.040 0.028 0.037 0.067 0.037 0.067 0.060 0.028 0.054 0.040

Indoor 

Coil

Face Area 

(ft^2) 2.08 1.93 2.34 1.87 1.88 1.87 1.88 1.87 2.34 2.08 1.93

Fin Pitch 18 18 18 19 19 19 19 21 18 18 18

Tube Rows 2 2 3 2 2 2 2 2 3 2 2

Tube OD 

(in.) 0.313 0.375 0.313 0.250 0.375 0.250 0.375 0.250 0.313 0.313 0.375

Indoor 

Fan

Motor

Type PSC PSC PSC PSC BLDC PSC BLDC BLDC PSC PSC PSC

Power (hp) 0.028 0.020 0.054 0.042 0.040 0.042 0.040 0.048 0.028 0.028 0.020

Compressor*

Capacity 

(Btu/h) 7,438 7,895 7,895 7,895 7,900 7,895 8,700 7,895 Not 

recorded

7,438 7,895

Efficiency 

(EER) 10.1 10.1 10.1 10.1 9.7 10.1 10.9 10.1 10.1 10.1

*All test units were observed to have a single-speed rotary compressor using refrigerant R-410a.
5-16

Table 5.6.2 PTAC and PTHP Test Units at 15,000 Btu/h Capacity

Feature

Test Unit Description

PTAC PTHP

Test 

Unit 12

Test 

Unit 13

Test 

Unit 14

Test 

Unit 15

Test 

Unit 16

Test 

Unit 17

Test 

Unit 18

Test 

Unit 19

Test 

Unit 20

Rated Cooling Capacity

(Btu/h) 15,000 14,400 15,000 15,000 14,200 15,100 14,400 15,000 14,200

Rated EER (Btu/Wh) 10.0 10.5 9.8 10.0 9.7 11.2 10.8 9.8 9.7

Rated COP (unitless) - - - - - 3.1 3.3 2.9 3.0

Outdoor 

Coil

Face Area 

(ft^2) 2.53 2.44 2.41 2.53 2.56 2.48 2.55 2.41 2.56

Fin Pitch 24 19 18 24 20 20 19 18 20

Tube Rows 2 3 3 2 2 3 3 3 2

Tube OD (in.) 0.200 0.250 0.325 0.200 0.375 0.250 0.250 0.325 0.375

Outdoor 

Fan

Motor

Type PSC PSC PSC PSC PSC BLDC BLDC PSC PSC

Power (hp) 0.094 0.060 0.088 0.094 0.094 0.067 0.060 0.088 0.094

Indoor 

Coil

Face Area 

(ft^2) 2.08 1.87 1.85 2.08 1.79 1.83 1.87 1.85 1.79

Fin Pitch 18 19 19 18 18 19 21 19 18

Tube Rows 2 2 3 2 3 2 3 3 3

Tube OD (in.) 0.375 0.250 0.313 0.375 0.313 0.375 0.250 0.313 0.313

Indoor 

Fan

Motor

Type PSC PSC PSC PSC PSC BLDC BLDC PSC PSC

Power (hp) 0.034 0.042 0.030 0.034 0.034 0.040 0.048 0.031 0.034

Compressor*

Capacity 

(Btu/h) 14,505 13,529 14,758 14,505 14,481 14,505 13,529 14,758 14,481

Efficiency 

(EER) 10.3 9.8 9.9 10.3 10.5 10.3 9.8 9.9 10.5

*All test units were observed to have a single-speed rotary compressor using refrigerant R-410a.
5-17

DOE conducted testing on each unit according to the current Federal test 

procedure for PTACs and PTHPs. Table 5.6.3 shows the test results of selected PTAC 

and PTHP units. DOE observed that the maximum deviation of tested and rated cooling 

capacity was +8.9 percent, and the maximum deviation of tested EER and rated EER was 

-7.1 percent.

Table 5.6.3 Test Results of Selected PTAC and PTHP Units

Parameter

Test 

Unit Type

Rated 

Cooling 

Capacity 

(Btu/h)

Rated 

EER 

(Btu/Wh)

Rated 

COP 

(unitless)

Tested 

Cooling 

Capacity 

(Btu/h)

Tested 

EER 

(Btu/Wh)

Tested 

COP 

(unitless)

1 PTAC 9,000 11.3 - 8,280 10.7 -

2 PTAC 9,000 11.5 - 8,849 10.9 -

3 PTAC 9,000 11.3 - 8,831 11.1 -

4 PTAC 9,700 12.1 - 9,537 12.0 -

5 PTAC 9,500 12.7 - 9,654 12.4 -

6 PTAC 9,700 12.1 - 9,737 12.3 -

7 PTHP 9,500 12.9 3.6 9,691 12.4 3.5

8 PTHP 9,400 12.7 3.8 9,673 12.9 3.6

9 PTHP 9,000 11.3 3.3 9,798 11.6 3.5

10 PTHP 9,000 11.3 3.3 9,298 11.5 3.3

11 PTHP 9,000 11.5 3.4 9,541 11.7 3.5

12 PTAC 15,000 10.0 - 15,037 10.0 -

13 PTAC 14,400 10.5 - 14,450 10.5 -

14 PTAC 15,000 9.8 - 15,635 10.3 -

15 PTAC 15,000 10.0 - 15,088 10.0 -

16 PTAC 15,100 11.2 3.1 14,871 10.4 -

17 PTHP 14,200 9.7 3.0 14,785 9.9 3.0

18 PTHP 14,400 10.8 3.3 14,590 11.0 3.3

19 PTHP 15,000 9.8 2.9 15,394 10.3 2.9

20 PTHP 15,100 11.2 3.1 * * *

* Testing on Test Unit 20 failed repeatedly due to frosting on the outdoor heat exchanger.

No test data is available for this unit.
5-18

5.6.3 Equipment Teardown

As part of the reverse engineering analyses, DOE conducted physical teardowns

on each test unite to develop a manufacturing cost model and to evaluate and identify 

design details of key components (e.g., heat exchangers, compressors, fans and fan 

motors, control strategies, etc.) and the corresponding manufacturing cost of each unit.

Based upon product teardowns, DOE developed the following baseline production 

cost distributions and materials cost distributions for typical PTACs, shown in Figure 

5.6.1 through Figure 5.6.4. Production cost distributions include raw material, purchased 

parts, labor (assembly, fabrication, supervision, and indirect labor), depreciation 

(equipment, tooling, and building depreciation), and other overhead (indirect process, 

maintenance, utility, property tax, and insurance) costs. 

 

e For test units #6, 9, 11, 12, 15, 17, and 20, DOE conducted a partial teardown, examining key components 

(heat exchangers, compressors, fans/motors, controls, etc.) and basic design construction without fully 

disassembling the unit.
5-19

Figure 5.6.1 Baseline 9,000 Btu/h PTAC Full Production Cost Distribution

Figure 5.6.2 Baseline 9,000 Btu/h PTAC Materials Cost Distribution

Labor

6%

Depreciation

5%

Other Overhead

7%

Raw Materials

34%

Purchased Parts

48%

Total Estimated MPC for Baseline 

9,000 Btu/h PTAC: $359.62

Packaging

5%

Final Assembly

21%

Outdoor HX

8%

Outdoor Fan Assy

5%

Indoor HX

12%

Indoor Fan Assy

5%

Controls

22%

Sealed System

22%
5-20

Figure 5.6.3 Baseline 15,000 Btu/h PTAC Full Production Cost Distribution

Figure 5.6.4 Baseline 15,000 Btu/h PTAC Materials Cost Distribution

Labor

5%

Depreciation

5%

Other Overhead

6% Raw Materials

29%

Purchased Parts

55%

Total Estimated MPC for Baseline 

15,000 Btu/h PTAC: $428.99

Packaging

5%

Final Assembly

22%

Outdoor HX

8%

Outdoor Fan 

Assy

Indoor HX 7%

9%

Indoor Fan Assy

6%

Controls

14%

Sealed System

29%
5-21

5.6.4 Cost-Efficiency Results

The reverse engineering analysis provides MPC data and tested efficiency data for 

the selected set of PTAC and PTHP units. DOE assembled this data into four groups, 

organized by cooling capacity (9,000 Btu/h and 15,000 Btu/h) and equipment type 

(PTAC and PTHP). DOE used the least squares method to fit second-order polynomial 

curves to the cost-efficiency data for each group. Figure 5.6.5 through Figure 5.6.8 show 

the four cost-efficiency curves in the form of EER versus MPC. These four cost curves 

are pictured together in Figure 5.6.9.

Figure 5.6.5 Manufacturer Production Cost (2013$) versus Efficiency for Standard 

Size PTACs with a Cooling Capacity of 9,000 Btu/h

MPC = 1.78*(EER)2 - 22.40*(EER) + 389.15

$300

$325

$350

$375

$400

$425

$450

$475

$500

9 10 11 12 13 14

Manufacturer Production Cost (2013$) 

System EER (Btu/Wh)

Standard Size PTAC, 9,000 Btu/h
5-22

Figure 5.6.6 Manufacturer Production Cost (2013$) versus Efficiency for Standard 

Size PTHPs with a Cooling Capacity of 9,000 Btu/h

Figure 5.6.7 Manufacturer Production Cost (2013$) versus Efficiency for Standard

Size PTACs with a Cooling Capacity of 15,000 Btu/h

MPC = 1.78*(EER)2 - 22.40*(EER) + 427.62

$300

$325

$350

$375

$400

$425

$450

$475

$500

9 10 11 12 13 14

Manufacturer Production Cost (2013$) 

System EER (Btu/Wh)

Standard Size PTHP, 9,000 Btu/h

MPC = 12.19*(EER)2 - 208.96*(EER) + 1280.9

 $300

 $325

 $350

 $375

 $400

 $425

 $450

 $475

 $500

9 10 11 12 13 14

Manufacturer Production Cost (2013$)

System EER (Btu/Wh)

Standard Size PTAC, 15,000 Btu/h
5-23

Figure 5.6.8 Manufacturer Production Cost (2013$) versus Efficiency for Standard 

Size PTHPs with a Cooling Capacity of 15,000 Btu/h

Figure 5.6.9 Manufacturer Production Cost (2013$) versus Efficiency for Standard 

Size PTACs and PTHPs with Cooling Capacities of 9,000 Btu/h and 

15,000 Btu/h

MPC = 12.19*(EER)2 - 208.96*(EER) + 1318.1

 $300

 $325

 $350

 $375

 $400

 $425

 $450

 $475

 $500

9 10 11 12 13 14

Manufacturer Production Cost (2013$)

System EER (Btu/Wh)

Standard Size PTHP, 15,000 Btu/h

 $300

 $320

 $340

 $360

 $380

 $400

 $420

 $440

 $460

 $480

 $500

9 10 11 12 13 14

Manufacturer Production Cost (2013$)

EER (Btu/Wh)

Standard Size PTAC, 15,000 Btu/h

Standard Size PTAC, 9,000 Btu/h

Standard Size PTHP, 9,000 Btu/h

Standard Size PTHP, 15,000 Btu/h
5-24

The results show that the cost-efficiency curves are nonlinear. As efficiency 

increases, manufacturing becomes more difficult and more costly for manufacturers. A 

faster increase in the curve (i.e., a steeper slope) is evident for the standard size PTACs 

and PTHPs with 15,000 Btu/h cooling capacity compared to the standard size PTACs and 

PTHPs with 9,000 Btu/h cooling capacity. The increase in capacity between 9,000 Btu/h 

and 15,000 Btu/h causes an increase in the baseline unit MPC. Comparing otherwiseidentical PTAC and PTHP models (i.e., models with similar casing, controls, and fans, 

but where the PTHP has reverse cycle capability), DOE estimates that the typical PTHP 

unit has an additional $37-$38 of production costs compared with an otherwise-identical 

PTAC unit.

As stated above, the cost efficiency results from the engineering analysis are an 

input to subsequent LCC analyses that determine the customer price of PTACs and 

PTHPs (see chapter 8, life-cycle cost and payback period analyses). For these inputs, 

DOE used the cost-efficiency curves above to calculate the MPCs at each efficiency level 

considered. DOE used the curve-fit equations presented in Figure 5.6.5 through Figure 

5.6.8 to calculate the estimated MPC for each of the efficiency levels for PTACs and 

PTHPs. These results are shown in Table 5.6.4 through Table 5.6.7. 

Table 5.6.4. Cost-Efficiency Relationship for PTACs at 9,000 Btu/h Capacity

Efficiency Level

(Percentages relative

to 2012 PTAC ECS)

Manufacturer 

Production Cost (2013$)

Incremental Manufacturer 

Production Cost (2013$)

Incremental Shipping 

Cost (2013$)

Baseline, EL1, 1.8% $359.62 - -

EL2, 4% $363.50 $3.88 -

EL3, 8% $367.55 $7.93 -

EL4, 12% $376.19 $16.57 -

EL5, 16% $385.52 $25.91 -

EL6, 18% (Max-Tech) $395.56 $35.94 -

Table 5.6.5. Cost-Efficiency Relationship for PTHPs at 9,000 Btu/h Capacity

Efficiency Level

(Percentages relative

to 2012 PTAC ECS)

Manufacturer 

Production Cost (2013$)

Incremental Manufacturer 

Production Cost (2013$)

Incremental Shipping 

Cost (2013$)

Baseline, 1.8% $398.09 - -

EL1, 4% $401.97 $3.88 -

EL2, 8% $406.02 $7.93 -

EL3, 12% $414.66 $16.57 -

EL4, 16% $423.99 $25.91 -

EL5, 18% (Max-Tech) $434.03 $35.94 -
5-25

Table 5.6.6. Cost-Efficiency Relationship for PTACs at 15,000 Btu/h Capacity

Efficiency Level

(Percentages relative

to 2012 PTAC ECS)

Manufacturer 

Production Cost (2013$)

Incremental Manufacturer 

Production Cost (2013$)

Incremental Shipping 

Cost (2013$)

Baseline, EL1, 1.8% $391.78 - -

EL2, 4% $395.51 $3.73 -

EL3, 8% $400.08 $8.29 -

EL4, 12% $411.75 $19.96 -

EL5, 16% $426.79 $35.00 -

EL6, 18% (Max-Tech) $445.20 $53.42 -

Table 5.6.7. Cost-Efficiency Relationship for PTHPs at 15,000 Btu/h Capacity

Efficiency Level

(Percentages relative

to 2012 PTAC ECS)

Manufacturer 

Production Cost (2013$)

Incremental Manufacturer 

Production Cost (2013$)

Incremental Shipping 

Cost (2013$)

Baseline $428.99 - -

EL1, 4% $432.72 $3.73 -

EL2, 8% $437.29 $8.29 -

EL3, 12% $448.96 $19.96 -

EL4, 16% $464.00 $35.00 -

EL5, 18% (Max-Tech) $482.41 $53.42 -
5-26

REFERENCES

1 Air-Conditioning, Heating, and Refrigeration Institute. Directory of Certified Product 

Performance. Available online at: 

https://www.ahridirectory.org/ahridirectory/pages/home.aspx (Last accessed July 

2014)

2

 American Metals Market. Available online at http://www.amm.com/. (Last accessed 

December 2012)
CHAPTER 6. MARKUPS ANALYSIS

TABLE OF CONTENTS

6.1 INTRODUCTION ........................................................................................................... 6-1

6.1.1 Distribution Channels.......................................................................................... 6-1

6.2 MARKUP CALCULATION METHODOLOGY........................................................... 6-3

6.3 APPROACH FOR CALCULATING OVERALL MARKUP ........................................ 6-3

6.3.1 Baseline Markups: New Construction ................................................................. 6-3

6.3.2 Baseline Markups Replacement........................................................................... 6-4

6.3.3 Incremental Markups: New Construction............................................................ 6-5

6.3.4 Incremental Markups: Replacement .................................................................... 6-6

6.3.5 Approach for Calculating Overall Markup of the National Accounts 

Distribution Channel............................................................................................ 6-8

6.4 APPROACH FOR CALCULATING MANUFACTURER MARKUP........................ 6-10

6.5 APPROACH FOR CALCULATING WHOLESALER AND CONTRACTOR 

MARKUPS .................................................................................................................... 6-10

6.5.1 Key Data Sources and Assumptions of the Markups Methodology.................. 6-10

6.5.2 Approach for Wholesaler Markups.................................................................... 6-12

6.5.3 Approach for Mechanical Contractor Markups................................................. 6-13

6.5.4 Approach for General Contractor Markups....................................................... 6-14

6.6 DERIVATION OF MARKUPS .................................................................................... 6-15

6.6.1 Base Case Manufacturer Markups..................................................................... 6-15

6.6.2 Wholesaler Markups.......................................................................................... 6-16

6.6.3 Mechanical Contractor Markups........................................................................ 6-17

6.6.3.1 Aggregate Baseline and Incremental Markups for Mechanical 

Contractors............................................................................................... 6-17

6.6.3.2 Baseline Markups for Mechanical Contractors in the Replacement 

and New Construction Markets................................................................ 6-18

6.6.4 Estimation of General Contractor Markups....................................................... 6-19

6.7 SALES TAX .................................................................................................................. 6-20

6.8 OVERALL MARKUPS................................................................................................. 6-21

6.8.1 Estimation of National Account Markups ......................................................... 6-22

REFERENCES .......................................................................................................................... 6-23

LIST OF TABLES

Table 6.1.1 Shares of Market by Distribution Channel .................................................... 6-3

Table 6.6.1 Manufacturer Markups by Equipment Type................................................ 6-16

Table 6.6.2 National Wholesaler Expenses and Markups .............................................. 6-16

Table 6.6.3 Baseline and Incremental Markups, All Mechanical Contractors (2007 

Economic Census data)................................................................................ 6-18

Table 6.6.4 Baseline Markups for the Replacement and New Construction 

Markets, Mechanical Contractors (ACCA data).......................................... 6-19

Table 6.6.5 General Contractor Expenses and Markups................................................. 6-20

6-i
Table 6.7.1 Average Sales Tax Rates by Census Division and Large State ................... 6-21

Table 6.8.1 Summary of Overall Markups ..................................................................... 6-21

Table 6.8.2 National Account Markups.......................................................................... 6-22

LIST OF FIGURES

Figure 6.1.1 Distribution Channels for PTACs and PTHPs in New Construction 

Applications................................................................................................... 6-2

Figure 6.1.2 Distribution Channels for PTACs and PTHPs in Replacement 

Applications................................................................................................... 6-2

6-ii
CHAPTER 6. MARKUPS ANALYSIS

6.1 INTRODUCTION

To carry out the life-cycle cost (LCC) calculations described in Chapter 8 of this 

technical support document (TSD), the U.S. Department of Energy (DOE) needed to determine 

the cost to the commercial consumer of a baseline packaged terminal air conditioner (PTAC) or 

packaged terminal heat pump (PTHP), and the cost of the more-efficient unit the consumer

would purchase under the standards. However, the commercial consumer price of such units is 

not generally known. What is known is the manufacturer’s price for both baseline equipment and 

the more-efficient equipment. By applying a multiplier called a “markup” to the manufacturer’s 

price, DOE was able to estimate the commercial consumer’s price. This chapter describes how 

DOE derived such markups.

The equipment price to the commercial consumer depends on how the consumer

purchases the equipment. The Department defines two primary types of distribution channels to 

describe how the equipment passes from the manufacturer to the consumer: (1) in the first type 

of distribution channel, the manufacturer sells the equipment to a wholesaler, who in turn may 

sell it to a mechanical contractor, who in turn may sell it (and its installation) to a general 

contractor, who in turn sells it to the consumer; (2) in the second type of distribution channel, the 

manufacturer sells the equipment directly to the consumer through a national account. The 

Department has further subdivided the distribution channels for new and replacement equipment.

For wholesalers and contractors, DOE estimated a baseline markup and an incremental 

markup. DOE defined 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 manufacturing selling price of higher efficiency equipment to the 

consumer purchase price for the same higher efficiency 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 6.1.1.

6.1.1 Distribution Channels

The appropriate markups for determining consumer equipment prices depend on the type 

of distribution channels through which equipment moves 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. 

There are two primary types of distribution channels describing the way most equipment 

passes from the manufacturer to the consumer, one involving distributors and contractors and 

one from manufacturer directly to consumer via national accounts. Within these two primary 

channels, DOE distinguishes between new and replacement applications; as each application has 

a different mechanical contractor markup.

6-1
Distribution channels for new construction applications are shown in Figure 6.1.1. In the 

first new construction 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 

turns sells it to the consumer and performs the installation. In the second new construction 

distribution channel, the manufacturer sells the equipment to a wholesaler, who in turn sells the 

equipment to the consumer. In the third new construction distribution channel, the manufacturer 

sells the equipment directly to the consumer through a national account.

Channel 1 Channel 2 Channel 3

Manufacturer Manufacturer

Manufacturer

(through 

national 

accounts)

Wholesaler

Wholesaler

Mechanical 

Contractor

General 

Contractor

Consumer Consumer Consumer

Figure 6.1.1 Distribution Channels for PTACs and PTHPs in New Construction 

Applications

Distribution channels for replacement applications are shown in Figure 6.1.2. In the first 

replacement 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 turns 

sells it to the consumer and performs the installation. In the second, the manufacturer sells the 

equipment to a wholesaler, who in turn sells the equipment to the consumer. In the third, the 

manufacturer sells the equipment to a wholesaler, who in turn sells it to a mechanical contractor,

who sells it to the consumer and performs the installation.

Channel 1 Channel 2 Channel 3

Manufacturer Manufacturer Manufacturer

Wholesaler

Wholesaler

Wholesaler

Mechanical 

Contractor Mechanical 

Contractor General 

Contractor

Consumer Consumer Consumer

Figure 6.1.2 Distribution Channels for PTACs and PTHPs in Replacement Applications

Table 6.1.1 lists each distribution channel’s share of the full PTAC and PTHP market.

6-2
Table 6.1.1 Shares of Market by Distribution Channel

Distribution Channel New Replacement

Wholesaler-Consumer 30% 15%

Wholesaler-Mech Contractor-End User 0% 25%

Wholesaler-Mech Contractor-General Contractor-Consumer 38% 60%

National Account 32% 0%

Total 100% 100%

6.2 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 

(CGS). The gross margin includes company profits and the expenses of companies in the 

distribution channel, which include overhead costs (sales, general, and administration), research 

and development (R&D) and interest expenses, depreciation, and taxes. In order for sales of 

equipment to contribute positively to company cash flow, the equipment’s markup must be 

greater than the corporate gross margin. Equipment commands lower or higher markups, 

depending on company expenses associated with the equipment and the degree of market 

competition. 

Equipment manufacturers sell most of their equipment directly to wholesalers. 

Wholesalers sell to contractors or consumers at the wholesale price. Wholesalers absorb shortterm 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 

equipment, and most install the equipment they sell. 

In addition to the wholesaler and mechanical contractor markups, the general contractor 

adds a markup. In retrofit installations, sales tax applies to the final consumer cost. 

6.3 APPROACH FOR CALCULATING OVERALL MARKUP

6.3.1 Baseline Markups: New Construction

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 equipment sold under existing market conditions (i.e., without new energy conservation 

standards). The following equation shows how DOE used baseline markups to determine the 

product price for baseline models in the first new construction distribution channel.

6-3
CCCCCCBBBBBBBB = CCCCCCCCMMMMMM × �MMMMMMMMMM × MMMMWWWWWWWW BBBBBBBB × MMMMMMMM MMMMMM MM BBBBBBBB × MMMM BBBBBBBB�

 = CCCCCCCCMMMMMM × MMMM OO _BBBBBBBB_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_1 = baseline overall markup.

The following equation shows how DOE used baseline markups to determine the product price 

for baseline models in the second new construction distribution channel.

CCCCCCBBBBBBBB = CCCCCCCCMMMMMM × �MMMMMMMMMM × MMMMWWWWWWWW BBBBBBBB �

 = CCCCCCCCMMMMMM × MMMM OO _BBBBBBBB_2

Where:

CPPBASE = consumer product price for baseline models,

COSTMFG = manufacturer cost for baseline models,

MUMFG = manufacturer markup,

MUWHOLE_BASE = baseline wholesaler markup, and

MUOVERALL_BASE_2 = baseline overall markup.

6.3.2 Baseline Markups Replacement

The following equation shows how DOE used baseline markups to determine the product 

price for baseline models in the first replacement distribution channel.

CCCCCCBBBBBBBB = CCCCCCCCMMMMMM × �MMMMMMMMMM × MMMMWWWWWWWW BBBBBBBB × MMMMMMMM MMMMMM MM BBBBBBBB × MMMM BBBBBBBB�

× TTTT

 = CCCCCCCCMMMMMM × MMMM OO _BBBBBBBB_1 × TTTT

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,

MUGCONTRACT_BASE = baseline general contractor markup, 

TAXSALES = sales tax, and

MUOVERALL_BASE_1 = baseline overall markup.

6-4
The following equation shows how DOE used baseline markups to determine the product 

price for baseline models in the second replacement distribution channel.

CCCCCCBBBBBBBB = CCCCCCCCMMMMMM × �MMMMMMMMMM × MMMMWWWWWWWW BBBBBBBB � × TTTT

= CCCCCCCCMMMMMM × MMMM OO _BBBBBBBB_2 × TTTT

Where:

CPPBASE = consumer product price for baseline models,

COSTMFG = manufacturer cost for baseline models,

MUMFG = manufacturer markup,

MUWHOLE_BASE = baseline wholesaler markup,

TAXSALES = sales tax, and

MUOVERALL_BASE_2 = baseline overall markup.

The following equation shows how DOE used baseline markups to determine the product 

price for baseline models in the third replacement distribution channel.

CCCCCCBBBBBBBB = CCCCCCCCMMMMMM × �MMMMMMMMMM × MMMMWWWWWWWW BBBBBBBB × MMMMMMMM MMMMMM MM BBBBBBBB� × TTTT

= CCCCCCCCMMMMMM × MMMM OO _BBBBBBBB_3 × TTTT

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_3 = baseline overall markup.

6.3.3 Incremental Markups: New Construction

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 equipment 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.

6-5
CCCCCC = CCCCCCBBBBBBBB + CCCCCC MMMMMM_II

× �MMMMMMMMMM × MMMMWWWWWWWW _II × MMMMMMMM MMMMMM MMMM_II × MMMM _II �

= CCCCCCCCMMMMMM + CCCCCC MMMMMM_II × MM OO _II _1

Where:

CPPEFF = consumer product price for more-efficient models,

CPPBASE = consumer product price for baseline models,

COSTMFG_INCR = incremental manufacturer cost,

MUMFG = manufacturer markup,

MUWHOLE_INCR = incremental wholesaler markup,

MUMCONTRACT_INCR = incremental mechanical contractor markup,

MUGCONTRACT_INCR = incremental general contractor markup, and

MUOVERALL_INCR_1= incremental overall markup.

The following equation shows how DOE used the overall incremental markup to 

determine the product price for more energy-efficient models in the second new construction 

distribution channel.

CCCCCC = CCCC BBBBBBBB + CCCCCCCCMMMMMM_II × �MMMMMMMMMM × MMMMWWWWWWWW _II �

= CCCC BBBBBBBB + CCCCCCCCMMMMMM_II × MMMM OO _II _2

Where:

CPPEFF = consumer product price for more-efficient models,

CPPBASE = consumer product price for baseline models,

COSTMFG_INCR = incremental manufacturer cost,

MUMFG = manufacturer markup,

MUWHOLE_INCR = incremental wholesaler markup, and

MUOVERALL_INCR_2= incremental overall markup.

6.3.4 Incremental Markups: Replacement

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 = CCCC BBBBBBBB + CCCCCCCCMMMMMM_II

× �MMMMMMMMMM × MMMMWWWWWWWW _II × MMMMMMMM MMMMMM MMMM_II

× MMMM _II � × TTTT

= CCCC BBBBBBBB + CCCCCCCCMMMMMM_II × MMMM OO _II _1 × TTTT

Where:

CPPEFF = consumer product price for more-efficient models,

6-6
CPPBASE = consumer product price for baseline models,

COSTMFG_INCR = incremental manufacturer cost,

MUMFG = manufacturer markup,

MUWHOLE_INCR = incremental wholesaler markup,

MUMCONTRACT_INCR = incremental mechanical contractor markup,

MUGCONTRACT_INCR = incremental general contractor markup,

TAXSALES = sales tax, and

MUOVERALL_INCR_1= incremental overall markup.

The following equation shows how DOE used the overall incremental markup to 

determine the product price for more energy-efficient models in the second replacement 

distribution channel.

CCCCCC = CCCC BBBBBBBB + CCCCCCCCMMMMMM_II × �MMMMMMMMMM × MMMMWWWWWWWW _II � × TTTT

= CCCCCCCCMMMMMM_II × MMMM OO _II _2 × TTTT

Where:

CPPEFF = consumer product price for more-efficient models,

CPPBASE = consumer product price for baseline models,

COSTMFG_INCR = incremental manufacturer cost,

MUMFG = manufacturer markup,

MUWHOLE_INCR = incremental wholesaler markup,

TAXSALES = sales tax, and

MUOVERALL_INCR_2= incremental overall markup.

The following equation shows how DOE used the overall incremental markup to 

determine the product price for more energy-efficient models in the third replacement 

distribution channel.

CCCCCC = CCCC BBBBBBBB + CCCCCCCCMMMMMM_II × �MMMMMMMMMM × MMMMWWWWWWWW _II × MMMMMMMM MMMMMM MMMM_II �

× TTTT

= CCCCCCCCMMMMMM_II × MMMM OO _II _3 × TTTT

Where:

CPPEFF = consumer product price for more-efficient models,

CPPBASE = consumer product 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_3= incremental overall markup.

6-7
6.3.5 Approach for Calculating Overall Markup of the National Accounts Distribution 

Channel

Equipment purchased through national accounts is an exception to the usual distribution 

of heating, ventilating, and air conditioning (HVAC) equipment to end users. Large commercial 

consumers of HVAC equipment use national accounts to circumvent the typical chain of 

distribution, thereby allowing them to negotiate equipment prices directly with the manufacturer. 

Due to the large volume of equipment purchased, large commercial consumers, such as national 

retail chains, are able to purchase equipment directly from the manufacturer at significantly 

lower prices than could be obtained through the typical distribution chain.

To capture the price savings realized from equipment purchased through national 

accounts, DOE derived a “national account” markup, assuming that the resulting equipment price 

increase was one half of that realized from a typical chain of distribution. In other words, if the 

price increase resulting from the product of the wholesale, mechanical contractor, and general 

contractor markups is $100, the “national account” markup is such that the price increase is onehalf of that, or $50. The Department based the use of a “national account” markup that is onehalf of that realized from a typical chain of distribution on the assumption that the resulting 

“national account” equipment price must fall somewhere between the manufacturer price (i.e., a 

markup of 1.0) and the commercial consumer price under a typical chain of distribution. Because 

DOE did not know typical values for the actual “national account” equipment price, it chose a 

value of one-half.

The estimates of national account markups are arrived at through a weighted average of

the overall markups for the other new construction distribution channels. Of all PTAC and 

PTHP equipment used in new construction, 30 percent are sold directly from wholesaler to end 

user, 38 percent involve mechanical and general contractors, and the remaining 32 percent are 

sold through national accounts. 

The national account baseline markups for new construction are calculated using the 

following equations:

MMMMWW WW_BBBBBBBB = σσ1 × MMMM OO _BBBBBBBB_1 + σσ2 × MMMM OO _BBBBBBBB_2

Where:

MUWTD_BASE = weighted average baseline markup of typical distribution channels,

MUOVERALL_BASE_1 = overall baseline markup for distribution channel 1 (wholesaler to 

end user),

MUOVERALL_BASE_2 = overall baseline markup for distribution channel 2 (involving 

contractors),

σσ1 = share of sales through distribution channel 1, and

σσ2 = share of sales through distribution channel 2.

The half of the overall baseline markup proportion above cost is then calculated as 

follows:

6-8
ρρ _MMMM_BBBBBBBB = MMMMWW WW_BBBBBBBB − 1

2

Where:

ρNA_MU_BASE = national account baseline markup proportion above cost.

MUWTD_BASE = weighted average baseline markup of typical distribution channels.

Adding 1 to the national account baseline markup proportion above cost, DOE arrives at 

the estimated national account baseline markup:

MMMMBBBBBBBB_ = 1 + ρρ _MMMM_BBBBBBBB

Where:

MUBASE_NA= national account baseline markup, and

ρNA_MU_BASE = national account baseline markup proportion above cost.

The national account incremental markups for new construction are calculated using the 

following equations:

MMMMWW WW_II = σσ1 × MM OO _II _1+σσ2 × MMMM OO _II _2

Where:

MUWTD_INCR = weighted average incremental markup of typical distribution channels.

MUOVERALL_INCR_1 = overall incremental markup for distribution channel 1,

MUOVERALL_INCR_2 = overall incremental markup for distribution channel 2, 

σσ1 = share of sales through distribution channel 1, and

σσ2 = share of sales through distribution channel 2.

ρρ _MMMM_II = MMMMWWWWWW_IIIIIIII − 1

2

Where:

ρNA_MU_INCR = national account incremental markup proportion above cost.

MUWTD_INCR = weighted average incremental markup of typical distribution 

channels. 

Adding 1 to the national account incremental markup proportion above cost, DOE arrives 

at the estimated national account incremental markup:

MMMMII _ = 1 + ρρ _MMMM_II

Where:

6-9
MUINCR_NA= national account incremental markup, and

ρNA_MU_INCR = national account incremental markup proportion above cost.

6.4 APPROACH FOR CALCULATING 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

CC MMMMMM

Where:

MUMFG = manufacturer markup,

CGSMFG = manufacturer cost of goods sold, and

GMMFG = manufacturer gross margin.

6.5 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 equipment may not be 

strictly proportional to the purchase price of the equipment. Using the available data, DOE has

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 equipment 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 business’ 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.

6.5.1 Key Data Sources and Assumptions of the Markups Methodology

DOE derived the wholesaler and contractor markups from three key assumptions about 

the costs associated with PTAC and PTHP equipment. 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 commercial building construction 

industry. DOE obtained the firm income statements from the Heating, Air-conditioning & 

6-10
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 U.S. Economic Census to develop general contractor markups in the same form as 

the income statement data for wholesalers and mechanical contractors.3, 4 Additionally, DOE 

used 2007 Economic Census data to supplement the income statement data obtained from 

ACCA. 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:

1. The firm income statements faithfully represent the industry average for the various costs 

incurred by firms distributing and installing HVAC equipment including commercial air 

conditioners.

2. 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).

3. 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, 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 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 equipment. 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 subsequently. 

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 

a Wholesalers and mechanical contractors to which these reports refer handle multiple commodity lines, including 

residential and commercial air-conditioners.

6-11

 
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 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; an 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

6.5.2 Approach for Wholesaler Markups

Using these assumptions, DOE developed baseline and incremental markups for 

wholesalers using the firm income statement from the (HARDI 2010 Profit Report). (See 

Appendix 6-A.) 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

Where:

MUWHOLE_BASE = baseline wholesaler markup, 

CGSWHOLE = wholesaler cost of goods sold,

GMWHOLE = wholesaler gross margin, 

IVCWHOLE = wholesaler invariant costs, and

VCWHOLE = wholesaler variant costs.

Incremental markups relate the change in the manufacturer sales price of more energyefficient models, or those equipment 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 sales price (i.e., variant costs, VC). DOE

calculated the incremental markup for wholesalers using the following equation:

MMMMWWWWWWWW _II = CCCCCCWWWWWWWW + WWWWWWWW

CCCCCCWWWWWWWW

Where:

MUWHOLE_INCR = incremental wholesaler markup,

CGSWHOLE = wholesaler cost of goods sold, and

6-12
VCWHOLE= wholesaler variant costs.

These data are provided for seven regions, which are defined similarly to Census 

Divisions. Each state is assigned the baseline and incremental wholesaler markup of the region 

to which it is assigned by HARDI.

6.5.3 Approach for Mechanical Contractor Markups

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 commercial air conditioners, DOE collected 

financial data from ACCA and from the Plumbing, Heating and Air-Conditioning Contractors

(NAICS 23822) data series of the 2007 Economic Census. This data series provides limited data 

at the state level and highly detailed national aggregate data. As the Census data is the most 

recent, DOE relies on it as the primary source for this analysis, using the greater detail of the 

ACCA data to refine the estimates.

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 the state and national levels. DOE calculated national average and state-level estimates of

the baseline markup for mechanical contractors and general contractors using the following 

equation:

MMMMBBBBBBBB = PP + MM + SS

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 to the wholesaler markup, DOE estimated the national average incremental 

mechanical contractor markup by only considering those costs that scale with a change in the 

manufacturer sales price (variant costs, VC) for higher efficiency equipment. Necessary data to 

perform the incremental markups calculation were not available at the state level. As stated in 

section 6.5.1, 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

Where:

6-13
MUCONTRACT_INCR = incremental contractor markup,

CGSCONTRACT = contractor cost of goods sold, and

VCCONTRACT= contractor variant costs.

ACCA financial data provide gross margin (GM) as percent of sales for several market 

subcategories of mechanical contractor (e.g., residential and commercial, new construction and 

replacement, small and large companies, etc.). In this analysis, DOE distinguishes between 

replacement and new construction applications. From the ACCA data, baseline mechanical 

markups are estimated for replacement, new construction, and the overall average of all 

mechanical contractors. The mechanical contractor markups estimated from the 2007 Economic 

Census data were scaled by applying the following factors developed from the ACCA data:

MMMMMMMMMM_ = SSSS MMMMMM_ (%)

SSSS MMMMMM_ (%) − MMMMMM_ (%)

MMMM _MMMMMM = SSSS _MMMMMM (%)

SSSS _MMMMMM (%) − _MMMMMM (%)

MMMM MM MM MM MM = MMMMMMMMMM_

MMMM _MMMMMM

Where:

MKT_MODIFIER = factor used to disaggregate replacement and new construction 

mechanical contractor applications from the 2007 Economic 

Census data for the overall market,

SALESMKT_SEGMENT = sales of the relevant market segment (i.e., replacement or new 

construction),

SALESFULL_MKT = sales of the overall mechanical contractor industry,

GMMKT_SEGMENT = gross margin of the relevant market segment,

GMFULL_MKT = gross margin of the overall mechanical contractor industry,

MUMKT_SEGMENT = baseline markup of the relevant market segment, and

MUFULL_MKT = baseline markup of the overall mechanical contractor industry.

DOE estimated state-level incremental mechanical contractor markups by calculating the 

ratio of the national average incremental markup to the national average baseline markup. This 

ratio was then used to scale the state-level baseline markups, arriving at a state-level estimate of 

incremental mechanical contractor markups.

6.5.4 Approach for General Contractor Markups

To estimate general contractor markups, DOE collected data from the Commercial and 

Institutional Building Construction series from the 2007 Economic Census (NAICS 236220). As 

for mechanical contractors, 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 the state and national levels. DOE estimated the baseline 

markup for general contractors using the following equation:

6-14
MMMMBBBBBBBB = PP + MM + SS

Where:

MUBASE = baseline general contractor markup,

VCONSTRUCT = value of construction,

PAY = payroll for construction workers,

MATCOST = cost of materials, and

SUBCOST = cost of subcontracted work.

DOE estimated the national average incremental general contractor markups by 

considering only those costs that scale with a change in the manufacturer sales price (variant 

costs, VC) for higher efficiency equipment. Necessary data to perform the incremental markups 

calculation was not available at the state level. As stated in section 6.5.1, 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 general contractor markups using the following equation: 

MMMM _II = CCCCCC +

CCCCCC

Where:

MUCONTRACT_INCR = incremental contractor markup,

CGSCONTRACT = contractor cost of goods sold, and

VCCONTRACT= contractor variant costs.

DOE estimated state-level incremental general contractor markups by calculating the 

ratio of the national average incremental markup to the national average baseline markup. This 

ratio was then used to scale the state-level baseline markups, arriving at a state-level estimate of 

incremental general contractor markups.

6.6 DERIVATION OF MARKUPS

6.6.1 Base Case Manufacturer Markups

DOE developed a set of base case manufacturer markups using data developed as part of 

the 2008 Final Rule for PTACs and PTHPs (73 FR 58772). DOE then solicited feedback on its 

markup estimates during confidential manufacturer interviews. Based on this content and 

manufacturer comments, DOE calculated and applied an average baseline markup of 1.27 for all 

equipment classes analyzed. This markup includes selling, general and administrative expenses 

(SG&A), research and development (R&D) expenses, interest, and profit. 

6-15
Table 6.6.1 Manufacturer Markups by Equipment Type

Equipment Type Cooling Capacity (Btu/h) Baseline Markup

PTAC

<7,000 Btu/h

1.27

≥7,000 Btu/h and ≤15,000 Btu/h

>15,000 Btu/h

PTHP

<7,000 Btu/h

≥7,000 Btu/h and ≤15,000 Btu/h

>15,000 Btu/h

6.6.2 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, by HARDI region, in Appendix 6-A,

Detailed Data for Equipment Price Markups. Table 6.6.2 summarizes HARDI wholesaler data, 

aggregated to the national level, 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 6.6.2 National 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 

6-16
$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 6.6.2 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 6.5, DOE believes that the labor and occupancy costs are invariant

with respect to MSP, while other operating costs and profit are variant with respect to MSP. In 

this case, for a $1.00 increase in the MSP, the wholesale price will increase in line with changes 

in the categories “other operating costs” and “operating profit”; these approximately amount to

$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 6-A for cost 

details.

These data are provided for seven regions, which are defined similarly to Census 

Divisions. Each state is assigned the baseline and incremental wholesaler markup of the region 

to which it is assigned by HARDI.

6.6.3 Mechanical Contractor Markups 

6.6.3.1 Aggregate Baseline and Incremental Markups for Mechanical 

Contractors

DOE derived national average markups for mechanical contractors from U.S. Census 

Bureau data. The 2007 U.S. Census data for mechanical contractors 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 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. Table 6.6.3 summarizes the national 

average expenses for general contractors as expenses per dollar sales revenue in the first data 

column. (Appendix 6-A contains the full set of data.) The process used to calculate markups 

from the table values is analogous to the process explained in detail in section 6.6.2.

6-17
Table 6.6.3 Baseline and Incremental Markups, All Mechanical Contractors (2007 

Economic Census data)

Descriptions

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 and benefits 0.18 0.26

Occupancy Expense: Rent, maintenance, and utilities 0.02 0.03

Other Operating Expenses: Depreciation, advertising, and 

insurance.

0.08 0.12

Operating Profit 0.04 0.06

Wholesaler Baseline Markup (MUWHOLE BASE) 1.48

Incremental Markup (MUWHOLE INCR) 1.18

Incremental Markup as % of Baseline Markup 80%

Source: U.S. Census Bureau. 2007 Economic Census, Plumbing, Heating, and Air-Conditioning Contractors. Sector 

23: 238220. Construction: Industry Series: Preliminary Detailed Statistics for Establishments: 2007.

The markups derived for mechanical contractors are the national average values for all 

contractors, including large and small contractors serving the replacement and new construction 

markets. The results of this calculation are subsequently scaled based on state-level data also 

obtained from the 2007 U.S. Census Geographic Area Series for mechanical contractors.3 The 

following section describes how DOE further derived the baseline and incremental markups for 

two distinct categories of mechanical contractors: (1) contractors in the replacement market and

(2) contractors in the new construction market.

6.6.3.2 Baseline Markups for Mechanical Contractors in the Replacement and 

New Construction Markets

The ACCA Financial Analysis for the HVACR Contracting Industry report is used to 

disaggregate markups for the replacement and new construction markets. ACCA financial data 

provided only gross margin data, which allows for the determination of only the baseline markup 

for the two types of contractors, as the gross margin is the sum of all contractor labor and 

operating expenses plus profit. 

Table 6.6.4 summarizes the gross margin and resulting national baseline markup data for 

mechanical contractors that serve the replacement and new construction markets. 

6-18
Table 6.6.4 Baseline Markups for the Replacement and New Construction Markets, 

Mechanical Contractors (ACCA data)

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 6.6.4, 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 6.6.3. These percentage differences are applied to the 

previously calculated state-level incremental and baseline markups based on U.S. Census data to 

estimate separate national and state-level markups for the two categories of mechanical 

contractor of interest. 

6.6.4 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 the construction of commercial and institutional buildings, 

including new construction work, additions, alterations, and repairs. 

The 2007 U.S. Census data for the commercial 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 commercial 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. The results of 

this calculation are subsequently scaled based on state-level data also obtained from the 2007 

U.S. Census. Similar to the data for wholesalers, Table 6.6.5 summarizes the national average 

expenses for general contractors as expenses per dollar sales revenue in the first data column.

6-19
(Appendix 6-A contains the full set of data.) The process used to calculate markups from the 

table values is analogous to the process explained in detail in section 6.6.2.

Table 6.6.5 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 1.00

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 and Institutional Building Construction). 

Construction: Industry Series: Preliminary Detailed Statistics for Establishments: 2007.

6.7 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 state, county, and city rates. DOE then 

derived population-weighted average tax values for each Census division and large state, as 

shown in Table 6.7.1. 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 state can be found in 

Appendix 6-A.

6-20
Table 6.7.1 Average Sales Tax Rates by Census Division and Large State

Census Division/State Tax Rate (2014)

New England 5.68%

Mid Atlantic 7.47%

East North Central 6.90%

West North Central 7.09%

South Atlantic 6.47%

East South Central 8.01%

West South Central 8.13%

Mountain 6.44%

Pacific 7.65%

New York 8.40%

California 8.45%

Texas 7.90%

Florida 6.65%

U.S. Average 7.15%

Source: The Sales Tax Clearinghouse at https://thestc.com/STRates.stm (Accessed February 2014)

6.8 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 (Table 6.8.1). 

Table 6.8.1 Summary of Overall Markups 

Distribution Channel 

Segment

Replacement New Construction

Markup Incremental 

Markup Markup Incremental Markup

Manufacturer 1.27 1.27

Wholesaler 1.36 1.10 1.36 1.10

Mechanical Contractor 1.52 1.22 1.43 1.15

General Contractor 1.34 1.22 1.34 1.22

Sales tax (replacements) 1.07 -

Overall Markup 3.76 1.75 3.31 1.54

6-21
6.8.1 Estimation of National Account Markups 

DOE derived markups for national accounts for new construction through a weighting of 

the overall markups estimated for the other new construction distribution channels. To capture

the price savings realized from equipment purchased through national accounts, DOE applies a 

national account markup one half of that of a typical chain of distribution, as described in section 

6.3.5 (Table 6.8.2).

Table 6.8.2 National Account Markups

Market Markup Incremental

Markup

New Construction 1.49 1.16

6-22
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 Mnufacturing. 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.) 

<http://www.quickmba.com/econ/micro/indcon.shtml>

13. Pindyck, R. and D. Rubinfeld, Microeconomics. 2000. Prentice Hall.

6-23
14. The Sales Tax Clearinghouse, Aggregate Rates: Weighted Averages that Include County 

and City Rates, <https://thestc.com/STRates.stm>

6-24
CHAPTER 7. ENERGY USE ANALYSIS

TABLE OF CONTENTS

7.1 INTRODUCTION ........................................................................................................... 7-1

7.2 EQUIPMENT ENERGY USE SAVINGS DETERMINATION .................................... 7-1

7.2.1 Splitting Heating and Cooling Energy................................................................. 7-1

7.2.2 Adjusting for Climate Change ............................................................................. 7-2

7.2.3 Adjusting for Capacity and Energy Efficiency Ratio .......................................... 7-4

7.3 PTAC AND PTHP EQUIPMENT ENERGY USE RESULTS....................................... 7-5

REFERENCES ............................................................................................................................ 7-9

LIST OF TABLES

Table 7.3.1 Annual Unit Energy Use by Representative Cooling Capacity and 

Efficiency Level for PTACs .......................................................................... 7-6

Table 7.3.2 Annual Unit Energy Use by Representative Cooling Capacity and 

Efficiency Level for PTHPs........................................................................... 7-6

Table 7.3.3 Annual Unit Energy Use by Equipment Class and Efficiency Level for 

PTACs............................................................................................................ 7-7

Table 7.3.4 Annual Unit Energy Use by Equipment Class and Efficiency Level for 

PTHPs............................................................................................................ 7-8

LIST OF FIGURES

Figure 7.2.1 Comparison of Cooling Degree Days by State for TMY3 and TMY2......... 7-3

7-i
CHAPTER 7. ENERGY USE ANALYSIS

7.1 INTRODUCTION 

Whole-building simulations from the 2008 packaged terminal air conditioning (PTAC) 

and packaged terminal heat pump (PTHP) equipment rulemaking provided annual unit energy 

consumption (UEC) data by cooling capacity and energy efficiency ratio (EER).

1 These data are 

used for those equipment classes and efficiency levels that are the same in this rulemaking and 

adjusted for those that are different. This chapter describes the methodology used to adjust the 

unit energy consumption of PTAC and PTHP equipment for this rulemaking.

7.2 EQUIPMENT ENERGY USE SAVINGS DETERMINATION

Whole-building simulations in the previous rulemaking were performed on PTACs and 

PTHPs for four equipment classes, six efficiency levels, and 51 locations (the U.S. States and the 

District of Columbia). The four equipment classes consist of combinations of PTAC and PTHP 

equipment and of representative cooling capacities, namely 9,000 Btu/h and 12,000 Btu/h. As 

whole-building simulations are an excellent approach to obtain UEC data, the 1,224 annual 

UECs data points were leveraged for this rulemaking.

The current rulemaking considers a set of capacities and efficiency levels that differs 

from that in the previous rulemaking. In order to make relevant the UECs, three transformations 

were performed: splitting heating and cooling energy, adjusting for climate change, and adjusting 

for capacity and EER.

7.2.1 Splitting Heating and Cooling Energy

The first transformation prepares the UEC for the following two transformations. As the 

total annual UECs, UUUUCCtttttttttt, capture both a cooling and heating energy portion, the UEC was 

split into UUUUCCcc cccccc and UUUUCChee . The U.S. Department of Energy (DOE) is aware that 

PTACs and PTHPs have slight differences in function and performance. However, PTACs and 

PTHPs have a similar functionality other than a reversing valve. In addition, insufficient data 

exist highlighting substantial differences in cooling energy consumption. For these reasons, DOE 

assumed that the UEC of a PTAC is equal to the energy expended during a PTHP’s operation in 

cooling mode, assuming that the equipment has the same efficiency level and capacity, as shown 

in Eq. 7.1. The remaining energy of a PTHP, which is the difference between the total UEC and 

7-1
the cooling energy, as shown in Eq. 7.2 , is the heating energy:

UUUUCCPPPPPPPP = UUUUCCPPPPPPPP_cc cccccc Eq. 7.1

UUUUCCtttttttttt = UUUUCChee + UUUUCCcc cccccc Eq. 7.2

It was assumed that the fan energy during periods of cooling was included with the cooling 

energy and the fan energy during periods of heating was included with the heating energy. It was 

also assumed that the fan energy during periods of cooling scaled with the cooling energy and 

the fan energy during periods of heating scaled with the heating energy.

7.2.2 Adjusting for Climate Change

The second transformation adjusts for the projected impact of global warming on hotter 

summer days and warmer winter days. An adjustment to the unit cooling energy consumption 

and unit heating energy consumption was made based on the change in cooling degree days 

(CDD) and heating degree days (HDD) from an older TMY2 (Typical Meteorological Year) 

weather dataset to a newer TMY3 weather dataset, which was updated in 2008.

2,3 A comparison 

of the two datasets showed that total annual CDD have increased by 5 percent and total annual 

HDD have increased by 2 percent at all locations used in this analysis, as shown in Figure 7.2.1. 

7-2
Figure 7.2.1 Comparison of Cooling Degree Days by State for TMY3 and TMY2

The figure shows the cooling degree days (CDD) relative to 65 °F computed from the 

TMY3 data vs. the CDD for TMY2. The calculation is done separately for day time (8 am to 

8pm) and night time hours. Each point corresponds to a state; in cases where there are multiple 

stations in a single state the data are averaged. As the TMY3 data are considered to be more 

representative of current weather, the total annual CDD increase and total annual HDD increase

were assigned to the weather multipliers, WWcc and WWh, for cooling and heating, respectively, and 

were applied to the separated cooling and heating UECs in the manner as shown in Eq.7.3 and 

Eq.7.4 :

UUUUCCcooling_weather = (1 + WWcc) ∙ UUUUCCcooling Eq.7.3

UUUUCCheating_weather = (1 + WWh) ∙ UUUUCCheating Eq.7.4

7-3
7.2.3 Adjusting for Capacity and Energy Efficiency Ratio 

The third transformation adjusts UECs where cooling capacity and/or EER considered in 

the current rulemaking (target) differ from cooling capacity and/or EER considered in the 

previous rulemaking (source). Where only cooling capacity differed, DOE performed a linear 

interpolation on UUUUCCcooling_weather across a constant EER value; this operation is highlighted in Eq. 

7.5. Where only EER differed, DOE performed a linear interpolation on UUUUCCcooling_weather across a 

constant cooling capacity value; this operation is highlighted in Eq.7.6. Where both cooling 

capacity and EER differed, DOE performed a linear interpolation on UUUUCCcooling_weather across both 

variables, one at a time, one after another, using the equations as already set forth in Eq. 7.5 and 

Eq.7.6. The same transformation was performed for heating energy, UUUUCCheating_weather, as shown 

in Eq. 7.7, where COP considered in the current rulemaking differ from COP considered in the 

previous rulemaking.

UUUU target = UUUUCCsource1 + (UUUUCCsource2 − UUUUCCsource1) �

Capacitytarget-Capacitysource1

Capacitysource2-Capacitysource1

��

cc cccc cc EEEEEE

Eq.7.5

Where:

UUUU target = interpolated unit energy consumption at the target cooling 

capacity,

UUUUCCsource1= unit energy consumption at the source cooling capacity, for the 

first point of the interpolation,

UECsource2= unit energy consumption at the source cooling capacity, for the 

second point of the interpolation,

Capacitytarget= target cooling capacity,

Capacitysource1= source cooling capacity for the first point of the interpolation, and

Capacitysource2= source cooling capacity for the second point of the interpolation.

7-4
UUUU target = UUUUCCsource1 + (UUUUCCsource2 − UUUUCCsource1) � EEEE target−EEEE source1

EEEE source2−EEEE source1

��

cc cccc cc cc cccccc cc cc cccc cc

Eq.7.6

Where:

UUUU target = interpolated unit energy consumption at the target cooling 

capacity,

UUUUCCsource1= unit energy consumption at the source cooling capacity, for the 

first point of the interpolation,

UUUU source2= unit energy consumption at the source cooling capacity, for the 

second point of the interpolation,

EERtarget= target EER,

EEsource1= source EER for the first point of the interpolation, and

EEsource2= source EER for the second point of the interpolation.

UUUU tttttt tt =

UUUUCC ss cc 1 + (UUUUCC ss 2 − UUUUCC ss 1) � CCPPtttttttttttt− CCPPssssss ss 1

CCPPssssss ss 2− CCPPssssss ss 1

��

cc cccc cc cc cccccc cc cc cccc cc

Eq.7.7

Where:

UUUU tttttt tt = interpolated unit energy consumption at the target cooling 

capacity,

UUUUCC ss 1= unit energy consumption at the source cooling capacity, for the 

first point of the interpolation,

UUUUCC ss 2= unit energy consumption at the source cooling capacity, for the 

second point of the interpolation,

CCCCCCtttttt tt= target COP,

CCCCCC ss 1= source COP for the first point of the interpolation, and

CCCC ss 2= source COP for the second point of the interpolation.

7.3 PTAC AND PTHP EQUIPMENT ENERGY USE RESULTS

DOE summed the cooling energy and heating energy, upon completion of the 

transformations, to give the total UEC for the representative cooling capacities, which are 

7-5
described in Chapter 5. The U.S. average UECs are provided by cooling capacity and EER in

Table 7.3.3 for PTACs and Table 7.3.4 for PTHPs and are further disaggregated by location in 

Appendix 7-A. UECs are inputs into the LCC and PBP analysis as described in Chapter 8.

Table 7.3.1 Annual Unit Energy Use by Representative Cooling Capacity and Efficiency 

Level for PTACs

Efficiency Level*

9,000 Btu/h 15,000 Btu/h

EER UEC (kWh) EER UEC 

(kWh)

Federal Minimum 11.1 1078 9.3 1688

Baseline/EL 1 11.3 1066 9.5 1671

EL 2 11.5 1050 9.7 1657

EL 3 12.0 1022 10.0 1625

EL 4 12.4 994 10.4 1594

EL 5 12.9 966 10.8 1563

EL 6 13.1 952 11.0 1547

*The Federal Minimum efficiency level represents the efficiency level of the current federal energy conservation standards for 

PTAC equipment. Efficiency level 1 is the baseline level for PTAC equipment, and is the ANSI/ASHRAE/IES Standard 90.1-

2013 minimum for PTAC equipment.

Table 7.3.2 Annual Unit Energy Use by Representative Cooling Capacity and Efficiency 

Level for PTHPs

Efficiency 

Level*

9,000 Btu/h 15,000 Btu/h

EER

COP

UEC 

(kWh)

EER

COP

UEC 

(kWh)

Baseline 11.3

3.2 1985 9.52.9 2847

EL 1 11.5

3.3 1956 9.72.9 2829

EL 2 12.0

3.4 1918 10.03.0 2778

EL 3 12.4

3.5 1879 10.43.1 2727

EL 4 12.9

3.6 1841 10.83.2 2676

EL 5 13.1

3.6 1822 11.03.2 2651

*The baseline efficiency level represents the efficiency level of the current federal energy conservation standards for PTHP 

equipment, which is the same as the ANSI/ASHRAE/IES Standard 90.1-2013 minimum efficiency for PTHP equipment.

7-6
The impact of amended energy efficiency standards also are determined on a national 

level, as described in the NIA in Chapter 10. As standards are determined with equipment classes 

instead of representative cooling capacities, DOE calculated UECs for each equipment classes. 

DOE used the UEC of the 9,000 Btu/h representative cooling capacity unit to represent the 

≥7,000 Btu/h and ≤15,000 Btu/h cooling capacity equipment class, and the UEC of the 15,000 

Btu/h representative cooling capacity unit to represent the >15,000 Btu/h cooling capacity 

equipment class. DOE extrapolated UECs from the 9,000 Btu/h and 15,000 Btu/h representative 

cooling capacity for the UEC of the <7,000 Btu/h cooling capacity equipment class. The U.S. 

average UECs are provided by cooling capacity and EER in Table 7.3.3 for PTACs and Table 

7.3.4 for PTHPs.

Table 7.3.3 Annual Unit Energy Use by Equipment Class and Efficiency Level for 

PTACs

Efficiency Level*

<7,000 Btu/h ≥7,000 Btu/h and ≤15,000 

Btu/h >15,000 Btu/h

EER UEC

(kWh) EER UEC (kWh) EER UEC

(kWh)

Federal Minimum 11.7 892 11.1 1078 9.3 1688

Baseline/EL 1 11.9 881 11.3 1066 9.5 1671

EL 2 12.2 866 11.5 1050 9.7 1657

EL 3 12.6 839 12.0 1022 10.0 1625

EL 4 13.1 813 12.4 994 10.4 1594

EL 5 13.6 786 12.9 966 10.8 1563

EL 6 13.8 773 13.1 952 11.0 1547

*The Federal Minimum efficiency level represents the efficiency level of the current federal energy conservation standards for 

PTAC equipment. Efficiency level 1 is the baseline level for PTAC equipment, and is the ANSI/ASHRAE/IES Standard 90.1-

2013 minimum for PTAC equipment.

7-7
Table 7.3.4 Annual Unit Energy Use by Equipment Class and Efficiency Level for 

PTHPs

Efficiency

Level*

>7,000 Btu/h ≥7,000 Btu/h and 

≤15,000 Btu/h >15,000 Btu/h

EER

COP

UEC

(kWh)

EER

COP

UEC

(kWh)

EER

COP

UEC

(kWh)

Baseline 11.9

3.3 1728 11.33.2 1985 9.52.9 2847

EL 1 12.2

3.5 1708 11.53.3 1956 9.72.9 2829

EL 2 12.6

3.5 1674 12.03.4 1918 10.03.0 2778

EL 3 13.1

3.6 1639 12.43.5 1879 10.43.1 2727

EL 4 13.6

3.7 1605 12.93.6 1841 10.83.2 2676

EL 5 13.8

3.8 1588 13.13.6 1822 11.03.2 2651

*The baseline efficiency level represents the efficiency level of the current federal energy conservation standards for PTHP 

equipment, which is the same as the ANSI/ASHRAE/IES Standard 90.1-2013 minimum efficiency for PTHP equipment.

7-8
REFERENCES

1. U.S. Department of Energy – Office of Energy Efficiency and Renewable Energy, 

Energy Conservation Program for Commercial and Industrial Equipment: Packaged 

Terminal Air Conditioner and Packaged Terminal Heat Pump Energy Conservation 

Standards., 2007. (Last accessed June, 2014.) 

<www.regulations.gov/#!docketDetail;D=EERE-2007-BT-STD-0012>

2. National Renewable Energy Laboratory, National Solar Radiation Data Base 1961-1990: 

Typical Meterological Year 2, (Last accessed June, 2014.) 

<http://rredc.nrel.gov/solar/old_data/nsrdb/1961-1990/tmy2/>

3. National Renewable Energy Laboratory, National Solar Radiation Data Base 1991-2005 

Update: Typical Meterological Year 3, (Last accessed June, 2014.) 

<http://rredc.nrel.gov/solar/old_data/nsrdb/1991-2005/tmy3/>

7-9
8-i

CHAPTER 8. LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

TABLE OF CONTENTS

8.1 INTRODUCTION ........................................................................................................... 8-1

8.1.1 General Approach for Life-Cycle Cost and Payback Period Analysis................ 8-1

8.1.2 Overview of Life-Cycle Cost and Payback Period Analysis Inputs.................... 8-2

8.1.3 Use of Per Unit Annual Energy Consumption Data ............................................ 8-5

8.2 LIFE-CYCLE COST ANALYSIS INPUTS.................................................................... 8-5

8.2.1 Total Installed Cost Inputs................................................................................... 8-6

8.2.1.1 Manufacturer Costs.................................................................................... 8-6

8.2.1.2 Markups...................................................................................................... 8-7

8.2.1.3 Total Consumer Price................................................................................. 8-8

8.2.1.4 Future Equipment Prices............................................................................ 8-9

8.2.1.5 Installation Cost........................................................................................ 8-11

8.2.1.6 Total Installed Cost .................................................................................. 8-13

8.2.2 Operating Cost Inputs........................................................................................ 8-14

8.2.2.1 Annual Energy Use Savings..................................................................... 8-14

8.2.2.2 Energy Prices............................................................................................ 8-14

8.2.2.3 Energy Price Trends................................................................................. 8-16

8.2.2.4 Repair Cost............................................................................................... 8-17

8.2.2.5 Maintenance Cost..................................................................................... 8-18

8.2.2.6 Lifetime .................................................................................................... 8-18

8.2.2.7 Discount Rates.......................................................................................... 8-20

8.2.2.8 Compliance Year of Standard .................................................................. 8-22

8.2.2.9 Base Case Distribution of Efficiency Levels ........................................... 8-23

8.3 PAYBACK PERIOD INPUTS...................................................................................... 8-23

8.4 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS...................................... 8-24

8.4.1 Packaged Terminal Air Conditioners – 9,000 Btu/h.......................................... 8-25

8.4.2 Packaged Terminal Air Conditioners – 15,000 Btu/h Cooling Capacity........... 8-28

8.4.3 Packaged Terminal Heat Pumps – 9,000 Btu/h Cooling Capacity .................... 8-31

8.4.4 Packaged Terminal Heat Pumps – 15,000 Btu/h Cooling Capacity .................. 8-34

8.4.5 Alternative Base Case Analysis......................................................................... 8-36

8.4.6 Rebuttable Payback Period ................................................................................ 8-37

8.4.6.1 Inputs........................................................................................................ 8-38

8.4.6.2 Results...................................................................................................... 8-38

REFERENCES ......................................................................................................................... 8-40

LIST OF TABLES

Table 8.1.1 Summary of Inputs and Key Assumptions Used in the LCC and PBP 

Analysis.......................................................................................................... 8-4

Table 8.2.1 Manufacturer Production Costs for PTACs by Efficiency Level 

(2014$)........................................................................................................... 8-7

Table 8.2.2 Manufacturer Production Cost for PTHPs by Efficiency Level (2014$) ...... 8-7

Table 8.2.3 Summary of Average Markups...................................................................... 8-8

Table 8.2.4 Summary of National Accounts Markups...................................................... 8-8
8-ii

Table 8.2.5 Overall Markup for Packaged Terminal Air Conditioners and Heat 

Pumps............................................................................................................. 8-8

Table 8.2.6 Average Consumer Price for Packaged Terminal Air Conditioners and 

Heat Pumps (2014$) ...................................................................................... 8-9

Table 8.2.7 Installation Costs for Baseline Packaged Terminal Air Conditioners 

and Heat Pumps ........................................................................................... 8-12

Table 8.2.8 National Average Installation Cost for Packaged Terminal Air 

Conditioners and Heat Pumps...................................................................... 8-12

Table 8.2.9 Installation Cost Indices by State (National Value = 100.0) ....................... 8-13

Table 8.2.10 Average Total Installed Cost for Packaged Terminal Air Conditioners 

and Heat Pumps (2014$).............................................................................. 8-13

Table 8.2.11 Marginal Tariff-Based Prices by Region (2014 Cents) ............................... 8-15

Table 8.2.12 Commercial Electricity Prices (2014 Cents/kWh)....................................... 8-16

Table 8.2.13 Annual Repair Cost by Efficiency Level (2014$) ....................................... 8-18

Table 8.2.14 Risk-free rate and equity risk premium, 2011-2013 .................................... 8-21

Table 8.2.15 Weighted-Average Cost of Capital for Analyzed Sectors........................... 8-22

Table 8.2.16 Base Case Efficiency Market Shares in 2019 for Packaged Terminal 

Air Conditioners and 2018 for Packaged Terminal Heat Pumps................. 8-23

Table 8.4.1 Packaged Terminal Air Conditioners – 9,000 Btu/h Cooling Capacity: 

Life-Cycle Cost and Payback Period Results .............................................. 8-25

Table 8.4.2 Packaged Terminal Air Conditioners – 15,000 Btu/h Cooling 

Capacity: Life-Cycle Cost and Payback Period Results.............................. 8-28

Table 8.4.3 Packaged Terminal Heat Pumps – 9,000 Btu/h Cooling Capacity .............. 8-31

Table 8.4.4 Packaged Terminal Heat Pumps – 15,000 Btu/h Cooling Capacity ............ 8-34

Table 8.4.5 Alternative Base Case Efficiency Market Shares in 2019 for PTACs 

and 2018 for PTHPs..................................................................................... 8-36

Table 8.4.6 Alternative Base Case Results..................................................................... 8-37

Table 8.4.7 Rebuttable-Presumption Payback Periods (years) for Packaged 

Terminal Air Conditioners........................................................................... 8-39

Table 8.4.8 Rebuttable-Presumption Payback Periods (years) for Packaged 

Terminal Heat Pumps .................................................................................. 8-39

LIST OF FIGURES

Figure 8.1.1 Flow Diagram of Inputs for the Determination of LCC and PBP ................. 8-4

Figure 8.2.1 Historical Nominal and Deflated Producer Price Indexes for 

Miscellaneous Refrigeration and Air-Conditioning Equipment.................. 8-10

Figure 8.2.2 Historical Deflated Producer Price Indexes for Copper Smelting, Steel 

Mills Manufacturing and All Other Miscellaneous Refrigeration and 

Air-Conditioning Equipment ....................................................................... 8-11

Figure 8.2.3 AEO 2014 electricity price projections by region (2014 cents) .................. 8-17

Figure 8.4.1 Range of Life-Cycle Cost Savings for Packaged Terminal Air 

Conditioners – 9,000 Btu/h Cooling Capacity............................................. 8-27

Figure 8.4.2 Range of Payback Periods for Packaged Terminal Air Conditioners –

9,000 Btu/h Cooling Capacity...................................................................... 8-27
8-iii

Figure 8.4.3 Range of Life-Cycle Cost Savings for Packaged Terminal Air 

Conditioners – 15,000 Btu/h Cooling Capacity........................................... 8-29

Figure 8.4.4 Range of Payback Periods for Packaged Terminal Air Conditioners –

15,000 Btu/h Cooling Capacity.................................................................... 8-30

Figure 8.4.5 Range of Life-Cycle Cost Savings for Packaged Terminal Heat Pumps 

– 9,000 Btu/h Cooling Capacity................................................................... 8-32

Figure 8.4.6 Range of Payback Periods for Packaged Terminal Heat Pumps – 9,000 

Btu/h Cooling Capacity................................................................................ 8-33

Figure 8.4.7 Range of Life-Cycle Cost Savings for Packaged Terminal Heat Pumps 

– 15,000 Btu/h Cooling Capacity................................................................. 8-35

Figure 8.4.8 Range of Payback Periods for Packaged Terminal Heat Pumps –

15,000 Btu/h Cooling Capacity.................................................................... 8-36
8-1

CHAPTER 8. LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

8.1 INTRODUCTION

The effect of amended standards on individual consumers 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 consumers.

• Life-cycle cost (LCC) is the total consumer 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 appliance or product.

• Payback period (PBP) measures the amount of time it takes consumers to recover the 

assumed higher purchase price of more energy-efficient products 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 8.1.1.

Inputs to the LCC and PBP analysis of packaged terminal air conditioners (PTACs) and 

packaged terminal heat pumps (PTHPs) are discussed in sections 8.2 and 8.3, respectively. 

Results for each metric are presented in section 8.4. Key variables and calculations are presented 

for each metric. The calculations discussed here were performed with a series of Microsoft Excel

spreadsheets that are accessible over the Internet 

(www1.eere.energy.gov/buildings/appliance_standards/buildings/appliance_standards/product.as

px/productid/77).

8.1.1 General Approach for Life-Cycle Cost and Payback Period Analysis

PTAC and PTHP equipment usage patterns and purchase costs are unique, so variability 

and uncertainty are analyzed by performing the LCC and PBP calculations detailed here for 

prototypical commercial buildings across various U.S. locations. The results are expressed as the 

number of PTAC and PTHP equipment consumers experiencing economic impacts of different 

magnitudes. The LCC and PBP model was developed using Microsoft Excel spreadsheets 

combined with Crystal Ball. The LCC and PBP analysis explicitly models both the uncertainty 

and the variability in the model’s inputs using Monte Carlo simulation and probability 

distributions.

These inputs include estimated energy use for each PTAC and PTHP unit, as described in 

the energy use analysis in Chapter 7. Energy use is sensitive to climate and therefore varies by 

location within the United States. Aside from energy use, other important inputs influencing the 
8-2

LCC and PBP analysis include energy prices, installation costs, equipment distribution markups, 

and sales taxes. 

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 consumers 

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 and PBP results are displayed as distributions of impacts compared to a base 

case. The base case efficiency distribution is defined as a mix of efficiency levels reflecting the 

expected distribution of efficiency levels by equipment class. 

8.1.2 Overview of Life-Cycle Cost and Payback Period Analysis Inputs

The LCC is the total consumer 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 consumer 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 consumer

price. 

• Installation cost: Installation cost is the cost to the consumer of installing the equipment. 

The installation cost represents all costs required to install the equipment but does not 
8-3

include the marked-up consumer equipment price. The installation cost includes labor, 

overhead, and any miscellaneous materials and parts. 

The primary inputs for calculating the operating cost are:

• Equipment energy consumption and power demand: The equipment energy consumption 

is the site energy use associated with providing space conditioning to the building. The 

power demand is the maximum power requirement of the equipment (more commonly 

known as the peak demand) for a specific period of time. Typically, electric utilities 

measure the peak demand for each month. DOE calculated both the energy consumption 

and peak demand based on hourly whole-building simulations.

• Electricity Prices: Average and marginal electricity prices for commercial buildings are 

determined using a tariff-based analysis. Marginal prices are used to estimate operating 

costs for the baseline and 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 PTAC and PTHP equipment are retired from service.

• Discount rate: The rate at which future costs and savings are discounted to establish their 

present value.
8-4

Figure 8.1.1 graphically depicts the relationships between the installed cost and operating 

cost inputs for the calculation of the LCC and PBP.

Figure 8.1.1 Flow Diagram of Inputs for the Determination of LCC and PBP

Table 8.1.1 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 8.1.1 Summary of Inputs and Key Assumptions Used in the LCC and PBP 

Analysis

Inputs Description

Affecting Installed Costs

Equipment Price

Derived MSP for PTAC and PTHP equipment at different input 

capacities (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 consumer equipment 

price (manufacturer cost multiplied by the various markups plus sales 

tax) plus the installation cost. 

Affecting Operating Costs

Annual Energy Use See Chapter 7. 

Energy Efficiency The energy efficiency ratio (EER) is the efficiency descriptor for PTAC 

Manufacturer Selling 

Price (Base)

Manufacturer Selling 

Price (Standard i=1...n)

Wholesaler/Distributor 

Markup

Contractor Markups

Sales Tax

Installation Costs

Equipment Prices

Total Installed 

Cost (Std i – Base)

Electricity Consumption

Electricity Prices

Annual Energy Costs

Annual Maintenance 

Costs

Repair Costs

Annual Operating 

Cost (Std i –-Base)

Discount Rate

Lifetimes

Payback 

Period

Lifecycle 

Cost

Lifetime 

Operating Cost 

(Std I – Base)
8-5

Inputs Description

and PTHP equipment. Whole-building simulations were used to 

determine the annual energy consumption associated with a particular 

standard level.

Electricity Prices

Costs were calculated for generalized buildings from summer and winter 

marginal and average electricity prices using tariff data and CBECS 1992 

and 1995 monthly electricity consumption and demand. Prices were 

escalated by the AEO 2014 forecasts to update tariff prices to 2014 and 

to estimate future electricity prices. Escalation was performed at the 

census division level.

Maintenance Cost

The cost associated with maintaining the operation of the equipment 

(e.g., cleaning heat exchanger coils) was also obtained from RS Means.3

Annual maintenance cost does not change as a function of MSP.

Repair Cost

Repair costs for the first five years are calculated assuming the 

equipment has a standard 5-year warranty, which covers materials and 

labor for the refrigeration system. After the warranty period, repair costs 

are calculated using RS Means for labor and materials. DOE annualized 

the total repair costs over the lifetime of the equipment.

Affecting Present Value of Annual Operating Cost Savings

Equipment Lifetime

Adjusted the Weibull parameters from the 2014 NOPR to reduce the 

median lifetime to 8 years. DOE determined that service life was a better 

measurement of equipment lifetime than “time to failure” because many 

PTACs and PTHPs are replaced during major renovations at large hotels, 

which take place approximately every 7 years.

Discount Rate

Mean real discount rates ranging from 4.14 percent to 7.83 percent for 

various classes of commercial consumers, calculated using financial data 

from Damodaran Online. Probability distributions are used for the 

discount rates.

All of the inputs depicted in and summarized in Table 8.1.1 are discussed in sections 8.2 and 8.3.

8.1.3 Use of Per Unit Annual Energy Consumption Data

As detailed in Chapter 7, DOE used unit energy consumption (UEC) data obtained from 

the 2008 Energy Conservation Standards Rulemaking for PTAC and PTHP equipment. To 

account for differences in cooling capacity and/or EER from the previous rulemaking, the UEC 

(cooling) for each equipment class and each efficiency level were linearly scaled from existing to 

the considered cooling capacity and/or EER.

8.2 LIFE-CYCLE COST ANALYSIS INPUTS

Life-cycle cost is the total consumer cost over the life of a piece of equipment, including 

purchase cost and operating costs (which are composed of energy costs, maintenance costs, and 
8-6

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:

Eq. 8.1

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 the effective date of the energy conservation standards. 

8.2.1 Total Installed Cost Inputs

The total installed cost to the consumer is defined by the following equation:

Eq. 8.2

Where:

IC = installed cost,

EQP = equipment price ($) (i.e., consumer 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 consumer

purchases the equipment. As discussed in Chapter 6, DOE defined distribution channels for new 

and replacement units to describe how the equipment passes from the manufacturer to the 

consumer. 

The remainder of this section provides information about the variables DOE used to 

calculate the total installed cost for PTACs and PTHPs.

8.2.1.1 Manufacturer Costs

DOE developed the manufacturer costs for PTACs and PTHPs as described in the 

engineering analysis (Chapter 5). The manufacturer costs at each efficiency level for the 

representative units in each equipment class are shown in Table 8.2.1 and Table 8.2.2. DOE 

∑=

= + +

N

t

t

t LCC IC OC r

1

/(1 )

IC = EQP + INST
8-7

determined that the shipping costs from the manufacturer is $19.82 per unit and does not vary by 

equipment class or efficiency level. 

Table 8.2.1 Manufacturer Production Costs for PTACs by Efficiency Level (2014$)

Efficiency Level*

Standard Size PTAC 9,000 Btu/h Standard Size PTAC (15,000 Btu/h)

Total Cost Incremental Cost** Total Cost Incremental Cost**

Federal Minimum $365.00 ($3.54) $397.65 ($4.10)

Baseline/EL 1 $368.55 $0.00 $401.75 $0.00 

EL 2 $373.05 $4.51 $406.07 $4.32 

EL 3 $381.82 $13.27 $417.91 $16.17 

EL 4 $391.30 $22.75 $433.18 $31.43 

EL 5 $401.48 $32.94 $451.87 $50.12 

EL 6 $406.84 $38.30 $462.50 $60.75 

*The Federal Minimum efficiency level represents the efficiency level of the current federal energy conservation standards for PTAC equipment. 

Efficiency level 1 is the baseline level for PTAC equipment, and is the ANSI/ASHRAE/IES Standard 90.1-2013 minimum for PTAC equipment.

**Parenthesis indicate negative values

Table 8.2.2 Manufacturer Production Cost for PTHPs by Efficiency Level (2014$)

Efficiency Level*

Standard Size PTHP (9,000 Btu/h) Standard Size PTHP (15,000 Btu/h)

Total Cost Incremental Cost Total Cost Incremental Cost

Baseline $404.05 $0.00 $435.41 $0.00 

EL 1 $408.56 $4.51 $439.73 $4.32 

EL 2 $417.32 $13.27 $451.58 $16.17 

EL 3 $426.80 $22.75 $466.84 $31.43 

EL 4 $436.99 $32.94 $485.54 $50.12 

EL 5 $442.35 $38.30 $496.16 $60.75 

*The baseline efficiency level represents the efficiency level of the current federal energy conservation standards for PTHP equipment, which is 

the same as the ANSI/ASHRAE/IES Standard 90.1-2013 minimum efficiency for PTHP equipment.

8.2.1.2 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 by the baseline or 

standard-compliant manufacturer cost to arrive at the price paid by the consumer. 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 consumer price) and an incremental markup (i.e., a 

markup used to convert a standard-compliant manufacturer cost increase due to an efficiency 
8-8

increase into an incremental consumer 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 (see 

Chapter 6 of the technical support document (TSD).

Table 8.2.3 and Table 8.2.4 display baseline and incremental markups used to calculate 

consumer price from manufacturer cost; Table 8.2.3 presents the values used for the distribution 

channels involving wholesalers and contractors, while Table 8.2.4 presents the values used for 

the national accounts distribution channels. Table 8.2.5 presents the resulting overall baseline 

and incremental markups.

Table 8.2.3 Summary of Average Markups

Baseline Markup Incremental Markup

Manufacturer 1.27

Wholesaler 1.36 1.10

Mechanical Contractor 

(new construction/replacement) 1.43/1.52 1.15/1.22

General Contractor 

(new construction only) 1.34 1.22

Sales Tax (replacement only) 1.07 1.07

Table 8.2.4 Summary of National Accounts Markups

Market Baseline Markup Incremental Markup

New Construction 1.49 1.16

Table 8.2.5 Overall Markup for Packaged Terminal Air Conditioners and Heat Pumps

Equipment Class Baseline Markup Incremental Markup

All Standard Size PTAC and PHTP 

Equipment and Capacities 2.32 1.46

8.2.1.3 Total Consumer Price

DOE derived the consumer 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 for replacement equipment) and adding to it the product of the 

incremental manufacturer cost and the incremental overall markup (including the sales tax for 

replacement equipment). DOE followed the same process for 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 consumer 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 is represented by a distribution of values.
8-9

Table 8.2.6 presents the average consumer equipment price for each PTAC and PTHP 

equipment class at each efficiency level examined. The EERs and COPs associated with each 

efficiency level are found in Chapter 5.

Table 8.2.6 Average Consumer Price for Packaged Terminal Air Conditioners and Heat 

Pumps (2014$)

Equipment Class

Efficiency Level*

Federal 

Minimum

Baseline/

EL 1

EL 2 EL 3 EL 4 EL 5 EL 6

PTAC Standard Size 

(9,000 Btu/h) $1,190 $1,201 $1,210 $1,227 $1,245 $1,265 $1,276

PTAC Standard Size 

(15,000 Btu/h) $1,292 $1,304 $1,313 $1,336 $1,366 $1,403 $1,424

Equipment Class Baseline EL 1 EL 2 EL 3 EL 4 EL 5

PTHP Standard Size 

(9,000 Btu/h) $1,312 $1,320 $1,338 $1,356 $1,376 $1,387

PTHP Standard Size 

(15,000 Btu/h) $1,410 $1,418 $1,441 $1,472 $1,508 $1,529

*The Federal Minimum efficiency level represents the efficiency level of the current federal energy conservation standards for PTAC equipment. 

Efficiency level 1 is the baseline level for PTAC equipment, and is the ANSI/ASHRAE/IES Standard 90.1-2013 minimum for PTAC equipment.

The baseline efficiency level represents the efficiency level of the current federal energy conservation standards for PTHP equipment, which is 

the same as the ANSI/ASHRAE/IES Standard 90.1-2013 minimum efficiency for PTHP equipment.

8.2.1.4 Future Equipment Prices

To derive a price trend for PTACs and PTHPs, DOE obtained historical Producer Price 

Index (PPI) data for “all other miscellaneous refrigeration and air-conditioning equipment”

spanning the time period 1990-2014 from the Bureau of Labor Statistics (BLS).a DOE used PPI 

data for “all other miscellaneous refrigeration and air-conditioning equipment” as representative 

of PTAC and PTHP equipment because PPI data specific to PTAC and PTHP equipment are not 

available and this PPI is the closest match. The PPI data reflect nominal prices, adjusted for 

product quality changes. An inflation-adjusted (deflated) real 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 (see Figure 8.2.1)

 a

 Series ID PCU3334153334159; www.bls.gov/ppi/
8-10

Figure 8.2.1 Historical Nominal and Deflated Producer Price Indexes for Miscellaneous 

Refrigeration and Air-Conditioning Equipment

From 1990 to 2004, the deflated price index for all other miscellaneous refrigeration and 

air-conditioning equipment showed a slightly downward trend. Since then, the index has risen 

sharply, which is highly correlated with the rising prices of copper and steel products that go into 

PTAC and PTHP equipment (see Figure 8.2.2). The rising prices for copper and steel products 

were primarily a result of strong demand from China and other emerging economies. Given the 

slowdown in global economic activity starting in 2008, DOE believes that the extent to which the 

trends of the past couple of years will continue is very uncertain. 

Given these considerations, DOE decided to use a constant price assumption as the 

default price factor index to project future PTAC and PTHP equipment prices. . Thus, prices 

forecast for the LCC and PBP analysis are equal to the 2014 values for each efficiency level in 

each equipment class.
8-11

Figure 8.2.2 Historical Deflated Producer Price Indexes for Copper Smelting, Steel Mills 

Manufacturing and All Other Miscellaneous Refrigeration and AirConditioning Equipment

8.2.1.5 Installation Cost

The installation cost is the price to the consumer of labor and materials (other than the 

actual equipment) needed to install the PTAC and PTHP equipment. DOE derived installation 

costs from current RS Means data. RS Means provides estimates on the person-hours required to 

install PTAC and PTHP 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 packaged cabinet type air conditioners of 9,000 Btu/h and 15,000 

Btu/h cooling capacity. DOE decided that these data are also representative of installation costs 

for equipment of 9,000 Btu/h and 15,000 Btu/h cooling capacity that provides heating in addition 

to just cooling.

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 PTAC or PTHP consumer.
8-12

Table 8.2.7 summarizes the nationally representative person-hours and labor rates 

associated with the installation of 9,000 Btu/h and 15,000 Btu/h cooling capacity PTAC and 

PTHP equipment as presented in RS Means. The table provides both bare installation costs (i.e., 

costs before overhead and profit (O&P)) and installation costs including O&P. DOE decided that 

the 9,000 Btu/h and 15,000 Btu/h cooling capacity installation costs that include O&P represent

the installation costs for baseline efficient systems (i.e., 11.3 EER for 9,000 Btu/h equipment and 

9.5 EER for 15,000 Btu/h equipment). DOE weighted the RS Means cost index by state using 

2013 population estimates from the U.S. Census Bureau. The weighted average national 

installation index was 0.917, which DOE applied to the Total Labor Cost calculation. 

Table 8.2.7 Installation Costs for Baseline Packaged Terminal Air Conditioners and 

Heat Pumps

System Type*

Personhours

2013 Bare Costs Labor w/ O&P

Cost per 

Personhour 

(2012$)

Total 

Labor 

Cost 

(2012$)

Cost per 

Personhour 

(2012$)

Total 

Labor 

Cost 

(2012$)

PTAC Standard Size (9,000 Btu/h) 3.2 $50.93 $163 $76.75 $225.22

PTAC Standard Size (15,000 Btu/h) 5.3 $51.00 $272 $76.75 $375.34

PTHP Standard Size (9,000 Btu/h) 3.2 $50.93 $163 $76.75 $225.22

PTHP Standard Size (15,000 Btu/h) 5.3 $51.00 $272 $76.75 $375.34

*Description differs in RS Means for Packaged Cabinet Type Air-Conditioners (“9000 BTUH cooling, 13900 BTU heat”; and “15000 BTUH 

cooling, 13900 BTU heat”).

DOE converted the costs in Table 8.2.8 from 2012$ to 2014$, which are presented in 

Table 8.2.8: the national average total installation costs by equipment class for PTACs and 

PTHPs. The average installation costs are constant across all efficiency levels within each 

equipment class. The total includes O&P, which is calculated using labor markups from RS 

Means. For efficiency levels above the baseline, the installation costs do not vary with equipment 

weight.

Table 8.2.8 National Average Installation Cost for Packaged Terminal Air Conditioners 

and Heat Pumps

Equipment Class Installation Cost (2014$)

PTAC Standard Size (9,000 Btu/h) $232.00

PTAC Standard Size (15,000 Btu/h) $386.64

PTHP Standard Size (9,000 Btu/h) $232.00

PTHP Standard Size (15,000 Btu/h) $386.64

Table 8.2.9 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 8.2.8. To arrive at an average index for each state, DOE weighted the city 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 (with Pacific divided into north and south) and calculated a weighted-average 

index for each region in the same manner.
8-13

Table 8.2.9 Installation Cost Indices by State (National Value = 100.0)

State Index State Index State Index

Alabama 50.8 Kentucky 80.9 North Dakota 58.4

Alaska 104.8 Louisiana 59.7 Ohio 91.9

Arizona 79.7 Maine 62.7 Oklahoma 55.6

Arkansas 55.5 Maryland 83.3 Oregon 99.5

California 121.0 Massachusetts 119.0 Pennsylvania 113.9

Colorado 79.0 Michigan 100.7 Rhode Island 108.3

Connecticut 114.7 Minnesota 113.5 South Carolina 37.5

D.C 113.4 Mississippi 51.9 South Dakota 42.0

Delaware 94.2 Missouri 95.8 Tennessee 71.3

Florida 68.7 Montana 70.3 Texas 56.3

Georgia 66.1 Nebraska 77.3 Utah 71.3

Hawaii 109.7 Nevada 100.3 Vermont 70.5

Idaho 68.8 New Hampshire 77.8 Virginia 65.7

Illinois 127.5 New Jersey 125.5 Washington 104.2

Indiana 81.5 New Mexico 68.2 West Virginia 84.8

Iowa 78.8 New York 161.2 Wisconsin 95.2

Kansas 69.8 North Carolina 36.3 Wyoming 54.3

8.2.1.6 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 8.2.10 presents the average total installed cost for each 

equipment class at each efficiency level examined. 

Table 8.2.10 Average Total Installed Cost for Packaged Terminal Air Conditioners and 

Heat Pumps (2014$)

Equipment Class

Efficiency Level*

Federal 

Minimum

Baseline/

EL 1**

EL 2 EL 3 EL 4 EL 5 EL 6

PTAC Standard Size 

(9,000 Btu/h) $1,422 $1,433 $1,442 $1,459 $1,477 $1,497 $1,508

PTAC Standard Size 

(15,000 Btu/h) $1,678 $1,691 $1,700 $1,723 $1,753 $1,790 $1,811
8-14

Equipment Class Baseline EL 1 EL 2 EL 3 EL 4 EL 5

PTHP Standard Size 

(9,000 Btu/h) $1,544 $1,552 $1,570 $1,588 $1,608 $1,619

PTHP Standard Size 

(15,000 Btu/h) $1,796 $1,805 $1,828 $1,858 $1,895 $1,916

*The Federal Minimum efficiency level represents the efficiency level of the current federal energy conservation standards for PTAC equipment. 

Efficiency level 1 is the baseline level for PTAC equipment, and is the ANSI/ASHRAE/IES Standard 90.1-2013 minimum for PTAC equipment.

The baseline efficiency level represents the efficiency level of the current federal energy conservation standards for PTHP equipment, which is 

the same as the ANSI/ASHRAE/IES Standard 90.1-2013 minimum efficiency for PTHP equipment.

**Since higher efficiency levels for PTAC equipment are calculated against efficiency level 1 as the baseline, only the baseline markup is applied 

to efficiency level 1. 

8.2.2 Operating Cost Inputs

DOE defined the operating cost by the following equation:

OC = EC+ MC 

Eq. 8.3

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 cost for PTACs and PTHPs. The annual energy costs of the equipment are 

computed from energy consumption per unit for the baseline and standard-compliant cases 

(efficiency level 2, 3, and so forth), 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. 

8.2.2.1 Annual Energy Use Savings

For each equipment class, DOE calculated the annual energy use savings for each PTAC 

and PTHP at each efficiency level by taking the difference between a considered efficiency level 

and the baseline efficiency level. 

8.2.2.2 Energy Prices

Tariff data were used to develop marginal and average prices for each member of a

generalized building sample. The approach uses tariff data that have been processed into 

commercial building marginal and average electricity prices. Tariff data provide a means to 

calculate the monthly consumer bill given the monthly electricity consumption (kWh) and peak 

demand (kW). The CBECS 1992 and CBECS 1995 commercial building surveys provide 

monthly baseline electricity consumption and demand for a large sample of buildings, 
8-15

approximately 5300 in total.4,5 These monthly values for each building in this generalized 

building sample were used to (1) calculate monthly bills and (2) calculate monthly marginal 

consumption and demand prices. The average electricity price is defined as the total electricity 

bill divided by total electricity consumption. Two marginal prices are defined, one for electricity 

demand (in $/kW) and one for electricity consumption (in $/kWh). These marginal prices are 

calculated by applying a 5% decrement to the baseline demand or consumption and recalculating 

the electricity bill. 

This procedure provides an average electricity price, and marginal consumption and 

demand prices, for each building in the generalized building sample and for each month. The 

monthly variation was reduced to seasonal variation by taking a simple average of the monthly 

data for the summer months (defined as May through September) and for the winter months. The 

average and marginal prices for each building were converted to average prices for the entire 

building sample using a weighted average across region, vintage, building activity and building 

size for each of the buildings. The prices defined in this way were converted to 2014 prices and 

2014 dollars using scaling factors taken from the AEO.

PTACs and PTHPs are commercial air conditioners and typically consume peak 

electricity; therefore, electricity costs were calculated using the annual marginal price per kWh, 

as shown in Table 8.2.11. For each efficiency level, the operating cost savings are calculated by 

multiplying the electricity consumption savings (relative to the baseline) times the annual 

marginal consumption price.

Table 8.2.11 Marginal Tariff-Based Prices by Region (2014 Cents)

Region

Marginal Consumption

Price (¢/kWh)

Annual Summer Winter

New England 10.11 10.22 9.93

Mid-Atlantic 9.71 10.04 9.43

ESC 6.58 6.67 6.48

WSC 5.80 6.14 5.54

South Atlantic 7.37 7.34 7.36

ESC 6.00 5.85 6.08

WSC 6.15 6.75 5.45

Mountain 7.46 7.46 7.45

Pacific – WA,OR 5.77 5.76 5.78

Pacific – CA 12.74 12.95 12.34

US Average 7.69 7.86 7.48
8-16

8.2.2.3 Energy Price Trends

The tariff-based prices were updated to 2014 using the commercial electricity price series 

published in the AEO as an index. AEO prices for commercial electricity are presented by region 

in Table 8.2.12. The table also shows the scaling factor used to convert the tariff data year (2004) 

to current prices. The national average price increase in constant dollars since the tariff data year 

is 3.3percent.

Table 8.2.12 Commercial Electricity Prices (2014 Cents/kWh)

 Year 

Region 2009 2010 2011 2012 Scaling Factor

New England 16.50 15.86 15.12 14.50 1.033

Middle Atlantic 14.50 15.01 14.43 14.08 0.964

East North Central 9.97 10.06 10.01 10.16 1.086

West North Central 8.05 8.47 8.70 8.20 1.101

South Atlantic 10.41 10.01 10.01 9.57 1.105

East South Central 10.04 10.06 10.35 8.85 1.046

West South Central 9.68 9.46 9.04 7.66 0.772

Mountain 9.25 9.43 9.37 8.85 1.029

Pacific (North) 12.68 12.48 12.28 12.04 0.957

U.S. 10.99 10.93 10.75 10.21 1.033

Source: AEO 2009 - 2012

DOE projected future electricity prices using trends in average commercial electricity 

price from EIA's AEO 2014, as shown in Figure 8.2.3. The chart shows constant dollar prices by 

region for the AEO forecast period, 2011 to 2040. The U.S. average trend is shown as a heavy 

dashed line. DOE used AEO 2014 Reference Case scenariosb for the nine Census divisions. 

DOE applied the projected energy price for each of the nine Census divisions to each building in 

the sample based on the building’s location.

 b The reference case is a business-as-usual estimate, given known market, demographic, and technological trends. 
8-17

Figure 8.2.3 AEO 2014 electricity price projections by region (2014 cents)

8.2.2.4 Repair Cost

Repair costs are associated with repairing or replacing components that have failed. DOE 

assumed that all PTAC and PTHP equipment have a one-year warranty which would cover all 

repairs and a 5 year limited warranty which would cover repairs of the refrigeration system 

(labor for non-refrigeration system repairs would be paid by the owner in years 2-5). After year 

5, the owner bears the full cost of a repair. The total repair cost is the expected value of the sum 

of the repair cost in the warranty period and in the post-warranty period from different failures 

rates in each of those periods, which DOE assumed to have a 1 percent chance of failure per year 

in years 1-5 and a 19 percent chance of failure per year in years 6-10, respectively.

6

DOE calculated the cost of a repair in the warranty period and the post-warranty period, 

by multiplying estimated component failure rates with the relevant labor and material costs for 

each component, which was based on RS Means.3 The costs in years 2-5 only represent labor 

costs for non-refrigeration system component repairs, and the costs in years 6-10 represent the 

total labor and materials cost of a repair for the entire system. DOE then determined the present 

value of the total repair cost for each year and annualized it. Next, DOE averaged the annualized 

values to create two annual values, one for the warranty period and another for the post-warranty 

period. The annual repair costs after year 6 were scaled with equipment price by efficiency level 

to account for higher material costs for higher efficiency equipment (materials covered under 

warranty received no scaling in years 1-5). Finally, DOE applied the failure rate associated with 

each year to determine a weighted average annual repair cost which are shown in Table 8.2.13.
8-18

Table 8.2.13 Annual Repair Cost by Efficiency Level (2014$)

Equipment Class

Efficiency Level*

Federal 

Minimum

Baseline/

EL 1

EL 2 EL 3 EL 4 EL 5 EL 6

PTAC Standard Size 

(9,000 Btu/h) $69.99 $70.29 $70.67 $71.42 $72.22 $73.09 $73.54

PTAC Standard Size 

(15,000 Btu/h) $72.08 $72.39 $72.79 $73.55 $74.38 $75.27 $75.74

Equipment Class Baseline EL 1 EL 2 EL 3 EL 4 EL 5

PTHP Standard Size 

(9,000 Btu/h) $74.69 $75.06 $75.79 $76.57 $77.41 $77.85

PTHP Standard Size 

(15,000 Btu/h) $77.09 $77.47 $78.22 $79.03 $79.90 $80.36

*The Federal Minimum efficiency level represents the efficiency level of the current federal energy conservation standards for PTAC equipment. 

Efficiency level 1 is the baseline level for PTAC equipment, and is the ANSI/ASHRAE/IES Standard 90.1-2013 minimum for PTAC equipment.

The baseline efficiency level represents the efficiency level of the current federal energy conservation standards for PTHP equipment, which is 

the same as the ANSI/ASHRAE/IES Standard 90.1-2013 minimum efficiency for PTHP equipment.

8.2.2.5 Maintenance Cost

The maintenance cost is the routine cost to the consumer of maintaining equipment 

operation. DOE calculated the annualized maintenance costs for PTACs based off the figure of 

$50 that was used in the 2008 PTAC and PTHP rulemaking.7 The figure was adjusted for 

inflation to arrive at $55.56 in annual maintenance costs at the baseline efficiency level. The 

annualized maintenance costs for PTHP were derived from the annualized maintenance costs for 

PTACs based on RS Means data for both PTACs and PTHPs.3 The percentage difference in 

maintenance costs was applied to the PTAC maintenance costs to arrive at the maintenance cost 

of $62.62 for PTHPs. 

8.2.2.6 Lifetime

Equipment lifetime is the age at which the equipment is retired from service. In the 

September 2014 NOPR, DOE used a median equipment lifetime of 10 years with a maximum 

lifetime of 20 years. AHRI questioned DOE's use of "time-to-failure" instead of "service life" 

and thereby urged DOE to recalibrate the Weibull distribution to have a mean of 5 years and a 

maximum of 12 years.8 SCS commented that many hotel chains remodel their rooms and replace 

PTAC/PTHP equipment every seven to ten years.9 SCS believes that DOE is using a longer 

equipment lifetime than is applicable in real world use.

9
8-19

Large hotels account for 70 percent of the market for PTAC and PTHP equipment. Given 

stakeholder comments and data asserting that large hotels undergo major renovations 

approximately every 7 yearsc, DOE revised the Weibull distribution to reflect such an industry 

practice. DOE assumed that large hotels would replace their PTAC and PTHP equipment every 7

years, and that the other 30 percent of the market (independent hotels, offices, and medical 

services) would replace their PTAC or PTHP every 10 years. DOE calculated the weighted 

average median lifetime for the total market for PTACs and PTHPs (7.9 years) and assumed it 

would represent the median lifetime. DOE rounded the median lifetime up to 8 years and then 

adjusted the scale and shape factor of the Weibull function so that the mean and median lifetimes 

would be 8 years. DOE took the same approach for maximum lifetime, using a maximum of 14 

years for large hotels (double the median value) and 20 years for independent hotels, offices, and 

medical services, which provided a weighted average maximum lifetime of 15.8 years. The 

parameters for the Weibull distribution are provided below. 

The Weibull distribution is a probability distribution function commonly used to measure 

failure rates.10 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:

Eq. 8.4

for x > θ and P(x) = 1 for x ≤ θ, 

Where:

P(x) = probability that the equipment is still in use at age x,

x = equipment 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 

over time, and

θ = the delay parameter, which allows for a delay before any failures occur.

For the subject analysis, DOE developed a Weibull distribution with an alpha of 9.0 and a 

beta of 3.0, resulting in a mean lifetime of 8, a median lifetime of 8 and a maximum lifetime of 

15 years (15 years is the 99th percentile of the Weibull distribution) years. DOE assumed that the 

lifetime is the same at different efficiency levels and therefore used the same lifetime distribution 

for each PTAC and PTHP equipment class. 

 c McDaniel, K.C., Senie, Stephen R. Why Hotels Will Fail: Source of Distress in the Current Market. American 

College of Real Estate Lawyers. www.acrel.org/Documents/Seminars/a002127.htm#_ftn1

e x

P x

β

α

θ 



 



 − −

( ) =
8-20

8.2.2.7 Discount Rates

The commercial discount rate is the rate at which future operating costs are discounted to 

establish their present value in the LCC analysis. The discount rate value is applied in the LCC 

to future year energy costs and non-energy operations and maintenance costs to calculate the 

estimated net life-cycle cost of products of various efficiency levels and life-cycle cost savings as 

compared to the baseline for a representative sample of commercial end users.

DOE’s method views the purchase of higher efficiency equipment as an investment that 

yields a stream of energy cost savings. DOE derived the discount rates for the LCC analysis by 

estimating the cost of capital for companies that purchase PTACs and PTHPs. The weightedaverage cost of capital (WACC) is commonly used to estimate the present value of cash flows to 

be derived from a typical company project or investment. 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, as estimated from financial data for publicly traded firms 

in the sectors that purchase PTACs and PTHPs.

11

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.12

Companies included in the Damodaran Online database were assigned to the aggregate 

categories listed below:

• Office, 

• Hotel,

• Medical Services.

DOE estimated the cost of equity using the capital asset pricing model (CAPM).13 The 

CAPM assumes that the cost of equity (ke) for a particular company is proportional to the 

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:

k R ( ERP) e = f + β ×

Eq. 8.5

Where:

ke = cost of equity,

Rf = expected return on risk-free assets,

β = risk coefficient of the firm, and
8-21

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.”14

By taking a 40-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 8.2.14).15 DOE also estimated the ERP by calculating the difference between the 

risk-free rate and stock market return for the same time period.12

Table 8.2.14 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:

kdi = Rf + Rai

Eq. 8.6

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. 8.7

Where:
8-22

WACC = weighted-average cost of capital,

ke = cost of equity,

we = proportion of equity financing, 

kd = cost of debt financing for firm, 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 weightedaverage costs of capital to estimate the discount rate for each of the eight broader ownership 

types in the PTAC-PTHP analysis. An overall average is estimated by weighting each 

ownership type’s discount rate by its estimated share of the PTAC and PTHP markets. These 

values are presented in Table 8.2.15. 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 8.2.15 Weighted-Average Cost of Capital for Analyzed Sectors

Sector Discount Rate Standard Deviation Market Share

Office 4.57% 2.0% 5%

Large Hotel / Motel 

Chain 6.31% 2.0% 70%

Independent Hotel / 

Motel 7.83% 3.2% 10%

Medical Services 4.14% 1.5% 15%

Average Discount Rate: 6.05% - -

Source: Damodaran Online Data Page: Costs of Capital by Industry Sector, 2011, 2011, 2013.

8.2.2.8 Compliance Year of Standard

DOE calculated the LCC and PBP for all consumers as if each would purchase a new 

piece of equipment in the year that compliance with amended standards is required. The Energy 

Policy and Conservation Act (EPCA), as amended, requires DOE to consider amending the 

existing Federal energy conservation standard for certain types of listed commercial and 

industrial equipment, including packaged terminal air conditioners and heat pumps, each time the 

American Society of Heating, Refrigerating and Air- Conditioning Engineers (ASHRAE) 

Standard 90.1, Energy Standard for Buildings Except Low-Rise Residential Buildings, is 

amended with respect to such equipment. (42 U.S.C. 6313(a)(6)(A)) If DOE does indeed adopt 

a uniform national standard more stringent than the amended ASHRAE Standard 90.1,

equipment must comply with the efficiency level contained therein if they are manufactured on 

or after a date which is four years after the date such rule is published in the Federal Register. (42 

U.S.C 6313(a)(6)(D)) At the time of preparation of the Final Rule analysis, the expected 

publication year was 2015.
8-23

8.2.2.9 Base Case Distribution of Efficiency Levels

Market share and efficiency level data for PTACs and PTHPs were obtained from a 2013 

dataset from the Air-Conditioning, Heating, and Refrigeration Institute (AHRI) Directory of 

Certified Product Performance. DOE used an efficiency trend to establish the efficiency 

distribution for 2019. DOE applied the trend from 2012 to 2035 that was used in the commercial 

unitary air conditioner Advance Notice of Proposed Rulemaking (ANOPR), which estimated an 

increase of approximately 1 EER every 35 years.d Table 8.2.16 presents the estimated base case 

efficiency market shares for each PTAC and PTHP representative cooling capacity, as described 

in Chapter 5.

Table 8.2.16 Base Case Efficiency Market Shares in 2019 for Packaged Terminal Air 

Conditioners and 2018 for Packaged Terminal Heat Pumps

PTAC – 9,000 Btu/h PTAC – 15,000 Btu/h PTHP – 9,000 Btu/h PTHP – 15,000 Btu/h

IEER Market Share IEER Market Share IEER Market Share IEER Market Share

11.1 0.0% 9.3 0.0%

11.3 43.6% 9.5 25.8% 11.3 52.5% 9.5 63.1%

11.5 24.3% 9.7 34.8% 11.5 8.9% 9.7 0.0%

12.0 29.5% 10.0 34.7% 12.0 26.1% 10.0 28.4%

12.4 2.1% 10.4 2.7% 12.4 12.4% 10.4 7.2%

12.9 0.5% 10.8 1.4% 12.9 0.0% 10.8 1.4%

13.1 0.0% 11.0 0.7% 13.1 0.0% 11.0 0.0%

8.3 PAYBACK PERIOD INPUTS

The PBP is the amount of time it takes the consumer 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. 8.8

Where:

PBP = payback period in years,

 

d

 See DOE’s technical support document underlying DOE’s July 29, 2004 ANOPR. 69 FR 45460 (Available at: 

www.regulations.gov/#!documentDetail;D=EERE-2006-STD-0103-0078). 
8-24

∆IC = difference in the total installed cost between the more efficient standard-level 

equipment (efficiency levels 2, 3, and so on) 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.

DOE also calculates a rebuttable PBP, which is the time it takes the consumer to recover 

the assumed higher purchase cost of more energy-efficient equipment as a result of lower energy 

costs. Numerically, the rebuttable 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 annual energy expenditures—

that is, the difference in first year annual energy cost as calculated based on the DOE test 

procedure. The calculation excludes maintenance costs. 

The data inputs to PBP are the total installed cost of the equipment to the consumer 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

standard is to take effect.

8.4 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS

This section presents the results of the LCC and PBP analysis for PTACs and PTHPs. As 

discussed previously, DOE’s approach to the LCC analysis relied on 1,224 unit energy 

consumption values from the previous rulemaking. DOE also 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 for the consumers in the sample. 

LCC and PBP calculations were performed 10,000 times on the sample of consumers 

established for each equipment class. Each LCC and PBP calculation was performed on a single 

consumer selected from the sample. A consumer was selected based on its weight (i.e., how 

representative that particular consumer was of other consumers in the distribution). Each LCC 

and PBP calculation also sampled from the probability distributions that DOE developed to 

characterize many of the inputs to the analysis. 

For each set of sample consumers using the equipment in each equipment class, DOE 

calculated the average LCC and LCC savings and the median PBP for each trial standard level 

(TSL). DOE calculated LCC savings and PBPs relative to the base-case products that it assigned 

to the sample consumers. For some consumers, DOE assigned a base-case product that is more 

efficient than the baseline. For that reason, the average LCC impacts are not equal to the 

difference between the LCC of a specific standard level and the LCC of the baseline product. 

The calculation of average LCC savings includes consumers with zero LCC savings (no impact 
8-25

from a standard). DOE considered a consumer to receive no effect at a given standard level if 

DOE assigned it base-case equipment having the same or higher efficiency than the standard 

level. DOE calculated the median PBP values by excluding the consumers that are not impacted 

by a standard at a given efficiency level.

For each TSL, DOE also calculated the share of consumers experiencing a net LCC 

benefit, a net LCC cost, and no effect. DOE considered a consumer to receive no effect at a 

given standard level if DOE assigned it base-case equipment having the same or higher 

efficiency than the standard level. 

For this final rule, DOE also repeated the LCC and PBP calculations for an alternative 

base case where the mandatory efficiency level is the Federal minimum, for informational 

purposes.

8.4.1 Packaged Terminal Air Conditioners – 9,000 Btu/h

Table 8.4.1summarizes the LCC and PBP results for PTACs of 9,000 Btu/h cooling capacity.

Table 8.4.1 Packaged Terminal Air Conditioners – 9,000 Btu/h Cooling Capacity: LifeCycle Cost and Payback Period Results

Efficiency 

Level

Average Life-Cycle Costs 

(2014$)* Life-Cycle Cost Savings** Median 

Payback 

Period 

(years)†

Installed 

Cost

Operating 

Cost LCC

Consumers Showing 

(%)

Avg. 

Savings

Net No 

Impact

Net 

Benefit

(2014$)

Cost

EL 1, 

Baseline $1,433 $1,314 $2,746 - - - - -

EL 2 $1,442 $1,308 $2,750 39% 57% 4% -$1.45 10.4

EL 3 $1,459 $1,298 $2,757 64% 32% 4% -$6.56 11.1

EL 4 $1,477 $1,289 $2,767 93% 2% 4% -$15.60 12.1

EL 5 $1,497 $1,280 $2,778 96% 0% 3% -$26.57 12.9

EL 6 $1,508 $1,276 $2,784 97% 0% 3% -$32.74 13.3

*The average discounted LCC for each efficiency level is calculated assuming that all purchases are for products only with that efficiency level. 

This allows the LCCs for each efficiency level to be compared under the same conditions. 

**The LCC savings for each efficiency level are calculated relative to the base case efficiency distribution. The calculation includes 

establishments with zero LCC savings (no impact).

†The median payback period is calculated only for affected establishments. Establishments with no impact have an undefined payback period, 

and are therefore not included in calculating the median PBP.

Figure 8.4.1 and Figure 8.4.2 show the range of LCC savings and PBPs for all of the 

efficiency levels considered for PTACs with outputs of 9,000 Btu/h cooling capacity. For each 

standard level, the top and the bottom of the box indicate the 75th and 25th percentiles, 

respectively. The bar at the middle of the box indicates the median; 50 percent of the consumers 

have LCC savings or PBP above this value. The horizontal lines above and below each box 
8-26

indicate the 95th and 5th percentiles, respectively. The small box shows the average LCC 

savings or PBP for each standard level.
8-27

Figure 8.4.1 Range of Life-Cycle Cost Savings for Packaged Terminal Air Conditioners –

9,000 Btu/h Cooling Capacity

Figure 8.4.2 Range of Payback Periods for Packaged Terminal Air Conditioners – 9,000 

Btu/h Cooling Capacity

-60

-50

-40

-30

-20

-10

0

10

Level 1 Level 2 Level 3 Level 4 Level 5

Average LCC Savings (2014$)

Efficiency Level

PTAC - 9,000 Btu/h Range of LCC Savings

Average; Median; Box 25%-75%; Whisker 5%-95%

median

average

0

5

10

15

20

25

Level 1 Level 2 Level 3 Level 4 Level 5

Average PBB (years)

Efficiency Level

PTAC - 9,000 Btu/h Average Payback Period

Average; Median; Box 25%-75%; Whisker 5%-95%

median

average
8-28

8.4.2 Packaged Terminal Air Conditioners – 15,000 Btu/h Cooling Capacity

Table 8.4.2 summarizes the LCC and PBP results for PTACs of 15,000 Btu/h cooling capacity.

Table 8.4.2 Packaged Terminal Air Conditioners – 15,000 Btu/h Cooling Capacity: LifeCycle Cost and Payback Period Results

Efficiency

Level

Average Life-Cycle Costs 

(2014$)* Life-Cycle Cost Savings**

Median 

Payback 

Period 

(years)†

Installed 

Cost

Operating 

Cost LCC

Consumers Showing (%) Avg. 

Savings

Net No 

Impact

Net 

Benefit

(2014$)

Cost

EL 1, 

Baseline $1,691 $1,635 $3,326 - - - - -

EL 2 $1,700 $1,630 $3,330 23% 75% 2% -$0.88 11.1

EL 3 $1,723 $1,619 $3,342 59% 39% 2% -$8.17 13.2

EL 4 $1,753 $1,608 $3,361 95% 5% 1% -$26.43 15.7

EL 5 $1,790 $1,597 $3,387 98% 2% 0% -$52.16 18.0

EL 6 $1,811 $1,592 $3,403 99% 1% 0% -$67.89 19.2

*The average discounted LCC for each efficiency level is calculated assuming that all purchases are for products only with that efficiency level. 

This allows the LCCs for each efficiency level to be compared under the same conditions. 

**The LCC savings for each efficiency level are calculated relative to the base case efficiency distribution. The calculation includes 

establishments with zero LCC savings (no impact).

†The median payback period is calculated only for affected consumers. Consumers with no impact have an undefined payback period and are 

therefore not included in calculating the median PBP.

Figure 8.4.3 and Figure 8.4.4 show the range of LCC savings and PBPs for all of the 

efficiency levels considered for PTACs with output of 15,000 Btu/h cooling capacity. For each 

standard level, the top and the bottom of the box indicate the 75th and 25th percentiles, 

respectively. The bar at the middle of the box indicates the median; 50 percent of the consumers 

have LCC savings or PBP above this value. The horizontal lines above and below each box 

indicate the 95th and 5th percentiles, respectively. The small box shows the average LCC 

savings or PBP for each standard level.
8-29

Figure 8.4.3 Range of Life-Cycle Cost Savings for Packaged Terminal Air Conditioners –

15,000 Btu/h Cooling Capacity

-120

-100

-80

-60

-40

-20

0

20

Level 1 Level 2 Level 3 Level 4 Level 5

Average LCC Savings (2014$)

Efficiency Level

PTAC - 15,000 Btu/h Range of LCC Savings

Average; Median; Box 25%-75%; Whisker 5%-95%

median

average
8-30

Figure 8.4.4 Range of Payback Periods for Packaged Terminal Air Conditioners – 15,000 

Btu/h Cooling Capacity

0

5

10

15

20

25

30

35

40

Level 1 Level 2 Level 3 Level 4 Level 5

Average PBB (years)

Efficiency Level

PTAC - 15,000 Btu/h Average Payback Period

Average; Median; Box 25%-75%; Whisker 5%-95%

median

average
8-31

8.4.3 Packaged Terminal Heat Pumps – 9,000 Btu/h Cooling Capacity

Table 8.4.3 summarizes the LCC and PBP results for PTHPs of 9,000 Btu/h cooling capacity.

Table 8.4.3 Packaged Terminal Heat Pumps – 9,000 Btu/h Cooling Capacity

Efficiency

Level

Average Life-Cycle Costs 

(2014$)* Life-Cycle Cost Savings** Median 

Payback 

Period 

(years)†

Installed 

Cost

Operating 

Cost LCC

Consumers Showing 

(%)

Avg. 

Savings

Net No 

Impact

Net 

Benefit

(2014$)

Cost

EL 1 $1,552 $1,835 $3,388 12% 47% 41% $2.14 4.5

EL 2 $1,570 $1,820 $3,390 33% 38% 30% $0.74 6.2

EL 3 $1,588 $1,806 $3,394 60% 12% 28% -$2.87 7.2

EL 4 $1,608 $1,792 $3,400 75% 0% 25% -$8.70 7.9

EL 5 $1,619 $1,785 $3,404 77% 0% 23% -$12.27 8.2

*The average discounted LCC for each efficiency level is calculated assuming that all purchases are for products only with that efficiency level. 

This allows the LCCs for each efficiency level to be compared under the same conditions. 

**The LCC savings for each efficiency level are calculated relative to the base case efficiency distribution. The calculation includes 

establishments with zero LCC savings (no impact).

†The median payback period is calculated only for affected consumers. Consumers with no impact have an undefined payback period and are 

therefore not included in calculating the median PBP.
8-32

Figure 8.4.5 and Figure 8.4.6 show the range of LCC savings and PBPs for all of the 

efficiency levels considered for PTHPs with outputs of 9,000 Btu/h cooling capacity. For each 

standard level, the top and the bottom of the box indicate the 75th and 25th percentiles, 

respectively. The bar at the middle of the box indicates the median; 50 percent of the consumers 

have LCC savings or PBP above this value. The horizontal lines above and below each box 

indicate the 95th and 5th percentiles, respectively. The small box shows the average LCC 

savings or PBP for each standard level.

Figure 8.4.5 Range of Life-Cycle Cost Savings for Packaged Terminal Heat Pumps –

9,000 Btu/h Cooling Capacity

-50

-40

-30

-20

-10

0

10

20

30

40

Level 1 Level 2 Level 3 Level 4 Level 5

Average LCC Savings (2014$)

Efficiency Level

PTHP 9,000 Btu/h Range of LCC Savings

Average; Median; Box 25%-75%; Whisker 5%-95%

median

average
8-33

Figure 8.4.6 Range of Payback Periods for Packaged Terminal Heat Pumps – 9,000 Btu/h 

Cooling Capacity

0

2

4

6

8

10

12

14

Level 1 Level 2 Level 3 Level 4 Level 5

Average PBB (years)

Efficiency Level

PTHP - 9,000 Btu/h Average Payback Period

Average; Median; Box 25%-75%; Whisker 5%-95%

median

average
8-34

8.4.4 Packaged Terminal Heat Pumps – 15,000 Btu/h Cooling Capacity

Table 8.4.4 summarizes the LCC and PBP results for PTHPs of 15,000 Btu/h cooling capacity.

Table 8.4.4 Packaged Terminal Heat Pumps – 15,000 Btu/h Cooling Capacity

Efficiency 

Level

Average Life-Cycle Costs 

(2014$)* Life-Cycle Cost Savings** Median 

Payback 

Period 

(years)†

Installed 

Cost

Operating 

Cost LCC

Consumers Showing 

(%)

Avg. 

Savings

Net No 

Impact

Net 

Benefit

(2014$)

Cost

EL 1 $1,805 $2,295 $4,100 47% 38% 15% -$1.04 8.2

EL 2 $1,828 $2,274 $4,102 42% 38% 20% -$2.27 7.4

EL 3 $1,858 $2,253 $4,111 73% 9% 18% -$10.50 8.3

EL 4 $1,895 $2,232 $4,127 87% 2% 12% -$26.41 9.5

EL 5 $1,916 $2,222 $4,138 91% 0% 9% -$37.17 10.0

*The average discounted LCC for each efficiency level is calculated assuming that all purchases are for products only with that efficiency level. 

This allows the LCCs for each efficiency level to be compared under the same conditions. 

**The LCC savings for each efficiency level are calculated relative to the base case efficiency distribution. The calculation includes 

establishments with zero LCC savings (no impact).

†The median payback period is calculated only for affected consumers. Consumers with no impact have an undefined payback period and are 

therefore not included in calculating the median PBP.
8-35

Figure 8.4.7 and Figure 8.4.8 show the range of LCC savings and PBPs for all of the 

efficiency levels considered for PTHPs with output of 15,000 Btu/h cooling capacity. For each 

standard level, the top and the bottom of the box indicate the 75th and 25th percentiles, 

respectively. The bar at the middle of the box indicates the median; 50 percent of the consumers 

have LCC savings or PBP above this value. The horizontal lines above and below each box 

indicate the 95th and 5th percentiles, respectively. The small box shows the average LCC 

savings or PBP for each standard level. 

Figure 8.4.7 Range of Life-Cycle Cost Savings for Packaged Terminal Heat Pumps –

15,000 Btu/h Cooling Capacity

-100

-80

-60

-40

-20

0

20

40

Level 1 Level 2 Level 3 Level 4 Level 5

Average LCC Savings (2011$)

Efficiency Level

PTHP 15,000 Btu/h Range of LCC Savings

Average; Median; Box 25%-75%; Whisker 5%-95%

median

average
8-36

Figure 8.4.8 Range of Payback Periods for Packaged Terminal Heat Pumps – 15,000 

Btu/h Cooling Capacity

8.4.5 Alternative Base Case Analysis

The alternative base case analysis was based off a scenario where the mandatory efficiency level 

is the Federal minimum. Table 8.4.5 shows the efficiency distribution against which LCC and 

PBP results were calculated. The results of these calculations are shown in Table 8.4.6.

Table 8.4.5 Alternative Base Case Efficiency Market Shares in 2019 for PTACs and 2018 

for PTHPs

PTAC – 9,000 Btu/h PTAC – 15,000 Btu/h PTHP – 9,000 Btu/h PTHP – 15,000 Btu/h

EER Market Share EER Market Share EER Market Share EER Market Share

11.1 3.4% 9.3 10.9%

11.3 40.3% 9.5 14.9% 11.3 52.5% 9.5 63.1%

11.5 24.3% 9.7 34.8% 11.5 8.9% 9.7 0.0%

12.0 29.5% 10.0 34.7% 12.0 26.1% 10.0 28.4%

12.4 2.1% 10.4 2.7% 12.4 12.4% 10.4 7.2%

12.9 0.5% 10.8 1.4% 12.9 0.0% 10.8 1.4%

13.1 0.0% 11.0 0.7% 13.1 0.0% 11.0 0.0%

0

2

4

6

8

10

12

14

16

18

Level 1 Level 2 Level 3 Level 4 Level 5

Average PBB (years)

Efficiency Level

PTHP - 15,000 Btu/h Average Payback Period

Average; Median; Box 25%-75%; Whisker 5%-95%

median

average
8-37

Table 8.4.6 Alternative Base Case Results 

Trial 

Standard 

Level

Average Life-Cycle Costs (2014$)* Life-Cycle Cost Savings**

Median 

Payback 

Period 

(years)†

Installed 

Cost

Operating 

Cost LCC

Customers Showing (%) Avg. 

Savings

Net No 

Impact

Net 

Benefit

(2014$)

Cost

PTAC 

<12,000 

Btu/h

1 $1,437 $1,308 $2,746 39% 57% 4% -$2.48 10.1

2 $1,455 $1,298 $2,753 63% 32% 5% -$9.01 10.7

3 $1,473 $1,289 $2,762 92% 2% 6% -$21.93 11.4

4 $1,493 $1,280 $2,773 95% 0% 4% -$33.30 12.1

5 $1,504 $1,276 $2,780 96% 0% 4% -$39.63 12.5

PTAC 

≥12,000 

Btu/h

1 $1,695 $1,630 $3,325 21% 75% 4% -$1.21 9.1

2 $1,718 $1,619 $3,337 57% 39% 4% -$10.24 11.2

3 $1,748 $1,608 $3,356 93% 5% 2% -$34.40 13.3

4 $1,785 $1,597 $3,382 97% 2% 1% -$61.07 15.5

5 $1,806 $1,592 $3,398 99% 1% 0% -$77.77 16.7

PTHP 

<12,000 

Btu/h

1 $1,552 $1,835 $3,388 12% 47% 41% $2.14 4.5

2 $1,570 $1,820 $3,390 33% 38% 30% $0.74 6.2

3 $1,588 $1,806 $3,394 60% 12% 28% -$2.87 7.2

4 $1,608 $1,792 $3,400 75% 0% 25% -$8.70 7.9

5 $1,619 $1,785 $3,404 77% 0% 23% -$12.27 8.2

PTHP 

≥12,000 

Btu/h

1 $1,805 $2,295 $4,100 47% 38% 15% -$1.04 8.2

2 $1,828 $2,274 $4,102 42% 38% 20% -$2.27 7.4

3 $1,858 $2,253 $4,111 73% 9% 18% -$10.50 8.3

4 $1,895 $2,232 $4,127 87% 2% 12% -$26.41 9.5

5 $1,916 $2,222 $4,138 91% 0% 9% -$37.17 10.0

*The average discounted LCC for each TSL is calculated assuming that all purchases are for equipment only with 

that CSL. This allows the LCCs for each TSL to be compared under the same conditions. 

**The LCC savings for each TSL are calculated relative to the base case efficiency distribution. The calculation 

includes customers with zero LCC savings (no impact).

†

The median payback period is calculated only for affected customers. Customers with no impact have an undefined 

payback period and are therefore not included in calculating the median PBP.

8.4.6 Rebuttable Payback Period

DOE presents rebuttable PBPs to provide the legally established rebuttable presumption 

that an energy efficiency standard is economically justified if the additional equipment costs 

attributed to the standard are less than three times the value of the first-year energy cost savings. 

(42 U.S.C. §6295(o)(2)(B)(iii))

The basic equation for rebuttable PBP is the same as that shown for the PBP in section

8.3. Unlike the analyses described in section 8.3, however, the rebuttable PBP is not based on the 

use of probability distributions, but on discrete single-point values. For example, whereas DOE 

uses a probability distribution of energy prices in the distributional PBP analysis, it uses only the

national average energy price to determine the rebuttable PBP.
8-38

8.4.6.1 Inputs

Inputs for the rebuttable PBP differ from the distribution PBP in that the calculation uses 

discrete values, rather than distributions. Note that for the calculation of distribution PBP, 

because inputs for the determination of total installed cost were based on single-point values, 

only the variability and/or uncertainty in the inputs for determining operating cost contributed to 

variability in the distribution PBPs. The following summarizes the single-point values that DOE 

used in determining the rebuttable PBP: 

• Manufacturing costs, markups, sales taxes, and installation costs were all based on the 

single-point values used in the distributional LCC and PBP analysis.

• Energy prices were based on national average values for the year that new standards will 

take effect.

• An average discount rate or lifetime is not required in the rebuttable PBP calculation.

• The annual energy consumption uses the single-point per unit values, determined from 

the whole-building simulations as discussed in Chapter 7.

8.4.6.2 Results

DOE calculated rebuttable PBPs for each standard level relative to the distribution of 

equipment energy efficiencies estimated for the base case. Table 8.4.7 and Table 8.4.8 present 

the rebuttable PBPs for PTACs and PTHPs.
8-39

Table 8.4.7 Rebuttable-Presumption Payback Periods (years) for Packaged Terminal Air 

Conditioners

PTACs - 9,000 Btu/h PTACs - 15,000 Btu/h

Efficiency Level PBP (years) Efficiency Level PBP (years)

EL 1, Baseline - EL 1, Baseline -

EL 2 6.4 EL 2 6.5

EL 3 6.7 EL 3 7.7

EL 4 6.9 EL 4 8.9

EL 5 7.2 EL 5 10.1

EL 6 7.4 EL 6 10.7

Table 8.4.8 Rebuttable-Presumption Payback Periods (years) for Packaged Terminal 

Heat Pumps

PTHPs - 9,000 Btu/h PTHPs - 15,000 Btu/h

Efficiency Level PBP (years) Efficiency Level PBP (years)

EL 1 3.4 EL 1 5.4

EL 2 4.4 EL 2 5.3

EL 3 4.8 EL 3 5.9

EL 4 5.1 EL 4 6.6

EL 5 5.3 EL 5 6.9
8-40

REFERENCES

1. U.S. Department of Energy-Energy Information Administration, Annual Energy Outlook 

2014 with Projections to 2040, 2014. Washington, DC. <www.eia.gov/forecasts/aeo/>

2. RS Means, Mechanical Cost Data, 36st Annual Edition, 2013. Kingston, MA. 

<www.rsmeansonline.com>

3. RS Means Company Inc., RS Means Facilities Maintenance & Repair Cost Data. 2013. 

Kingston, MA.

4. U.S. Energy Information Agency, Commercial Buildings Energy Consumption Survey, 1995. 

(Last accessed August, 2013.) 

<www.eia.gov/consumption/commercial/data/1995/index.cfm?view=microdata

5. U.S. Energy Information Agency, Commercial Buildings Energy Consumption Survey, 1992. 

(Last accessed August, 2013.) 

<www.eia.gov/consumption/commercial/data/1992/index.cfm?view=microdata>

6. Mullen, Jim. PT Warranty and Repairs: Prepared for Lawrence Berkeley National 

Laboratory. EER Consulting, LLC. 2015. 

7. U.S. Department of Energy – Office of Energy Efficiency and Renewable Energy, Energy 

Conservation Program for Commercial and Industrial Equipment: Packaged Terminal Air 

Conditioner and Packaged Terminal Heat Pump Energy Conservation Standards, 2008. 

Washington, DC. <www.regulations.gov/#!documentDetail;D=EERE-2007-BT-STD-0012-

0002>

8. Air-Conditioning, Heating & Refrigeration Institute. Energy Conservation Standards for 

Packaged Terminal Air Conditioners and Packaged Terminal Heat Pump [Docket Number 

EERE-2012-BT-STD-0029], Notice of Proposed Rulemaking Comment #0035. 2014. 

www.regulations.gov/#!docketBrowser;rpp=25;po=0;dct=PS;D=EERE-2012-BT-STD-0029

9. Southern Company. Energy Conservation Standards for Packaged Terminal Air Conditioners 

and Packaged Terminal Heat Pump [Docket Number EERE-2012-BT-STD-0029], Notice of 

Proposed Rulemaking Comment #0037. 2014.

www.regulations.gov/#!docketBrowser;rpp=25;po=0;dct=PS;D=EERE-2012-BT-STD-0029

10. National Institute of Standards and Technology, NIST/SEMATECH e-Handbook of 

Statistical Methods. www.itl.nist.gov/div898/handbook/

11. Modigliani, F. and M. Miller, The Cost of Capital, Corporation Finance and the Theory of 

Investment. American Economic Review, 1958. June: pp. 261-297

12. Damodaran Online Data Page, The Data Page: Historical Returns on Stocks, Bonds and Bills 

- United States, 2013. Damodaran. (Last accessed June, 2014.) 

<http://pages.stern.nyu.edu/~adamodar/>
8-41

13. Ibbotsons Associates, Cost of Capital 2001 Yearbook, Data Through March 2001, 2001. 

Chicago, IL.

14. The Federal Reserve Board, Federal Reserve Bank Services Private Sector Adjustment 

Factor, 2005. Washington, DC.

15. The Federal Reserve Board, Federal Reserve Statistical Release, Selected Interest Rates, 

H.15 Historical Data, CDs (secondary market), 6-month, 2000. (Last accessed March 15, 

2001.) <www.federalreserve.gov/releases/h15/data.htm>
CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION ...............................................................................................................1

9.2 METHODOLOGY ..............................................................................................................1

9.2.1 Mathematical Formulation of the Shipments Model ...............................................2

9.2.2 Historical Shipments................................................................................................2

9.2.3 Stock Events.............................................................................................................2

9.2.3.1 Replacement Equipment ................................................................................3

9.2.3.2 New Equipment..............................................................................................3

9.3 MODEL INPUTS ................................................................................................................4

9.3.1 New Construction ....................................................................................................4

9.3.2 Replacements...........................................................................................................6

9.4 RESULTS ............................................................................................................................7

REFERENCES ..............................................................................................................................10

LIST OF TABLES

Table 9.3.1 Historical and Projected New Construction (million sq ft) for the 

Shipments Model for PTAC and PTHP Equipment* ........................................5

Table 9.3.2 Historical PTAC and PTHP Shipments with New Construction Floor 

Space Values Used to Calculate Saturation*.....................................................6

Table 9.4.1 PTAC and PTHP Equipment Class Shipment Distribution...............................7

Table 9.4.2 PTAC Efficiency Distribution in 2014 ..............................................................9

Table 9.4.3 PTHP Efficiency Distribution in 2014...............................................................9

LIST OF FIGURES

Figure 9.3.1 PTAC and PTHP Equipment Survival Function ...............................................6

Figure 9.4.1 Historical and Projected Shipments by Equipment Class..................................8

Figure 9.4.2 Projected New Construction and Replacement Shipments................................8

9-i
CHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION

The U.S. Department of Energy (DOE) analyzes shipments of affected equipment as part 

of every rulemaking regarding new or amended energy efficiency standards for appliances or 

commercial and industrial equipment. Estimates of equipment shipments are a necessary input 

for calculating national energy savings (NES) and net present value (NPV) of the investment, 

which are required to justify potential new or amended energy efficiency standards. Shipments 

also are a necessary input to the manufacturer impact analysis (MIA). This chapter describes 

DOE’s method and results of projecting annual shipments for packaged terminal air conditioners 

(PTACs) and packaged terminal heat pumps (PTHPs). 

To project shipments of PTAC and PTHP equipment, DOE considered specific factors 

that drive equipment shipments. DOE developed shipments forecasts by accounting for: (1) the 

growth in the building stock of hotel/motel, healthcare, and small office buildings that are the 

primary end users of PTACs and PTHPs; (2) market segments; (3) equipment failure; and (4) 

equipment age. 

The shipments models are prepared as Microsoft Excel spreadsheets that are accessible 

on the Internet (www.eere.energy.gov/buildings/appliance_standards/). The rest of this chapter 

explains the shipments model in more detail. Section 9.2 describes the methodology that 

underlies development of the model; section 9.3 describes the input data used to develop the 

shipments model; and section 9.4 discusses the results in terms of base-case and standards-case 

shipments.

9.2 METHODOLOGY

This section outlines the methodology used to develop the shipments model that provides 

projections of shipments for PTAC and PTHP equipment classes defined in Chapter 5. First, a 

brief summary of the basic framework is provided in the following section.

PTAC and PTHP shipments are driven primarily by two market segments: new 

construction and replacements. For new construction shipments, DOE combined new 

construction floor space forecasts with a constant market saturation of the equipment in new 

construction floor space. DOE estimated replacement shipments using an equipment retirement 

function that it developed based on equipment lifetimes. DOE designed its shipments model by 

developing a single shipments model for all PTACs and PTHPs and then disaggregating the 

shipments into six equipment classes. The shipments model assumes that, in each year, each 

existing PTAC or PTHP either ages by one year or breaks down, and that equipment that breaks 

down is replaced. In addition, new equipment can be shipped into new commercial building floor 

space.

9-1
9.2.1 Mathematical Formulation of the Shipments Model 

DOE determines the yearly stock based on replacement and new shipments of PTAC and 

PTHP equipment. The total stock of equipment U of an age a in a given year t is accounted for 

and is modified by the shipments of new stock in a given year, Uship(t, a), as described below.

UU(tt + 1, aa) = UU(tt, aa) + UUUUhiiii(tt, 0)| for each tt and for each aa

Eq. 9.1

Where:

U(t,a) = total stock of equipment of an age a in a given year t,

Uship(t,0) = total shipments of new stock,

a = age of stock in years, 

t = year. 

By definition, the age of the equipment is zero in the year that it is shipped, so Uship(t, a) =

Uship(t, 0). The total stock of equipment is initialized for the year 2013 after the initial stock is 

built up, as described below.

UU(2013, aa) = UU0(2013, aa)

Eq. 9.2

Where: 

U0(2013,a) = total stock of equipment for year after based on the build-up of the initial 

stock.

9.2.2 Historical Shipments

DOE received confidential historical shipments from AHRI for the years 2005-2012. In 

addition, AHRI provided 1998-2004 data that were published in the 2008 PTAC and PTHP 

rulemaking.1 The average of the historical shipments (1998-2012) was used as the constant value 

for shipments for the years 1993-2012 to build up the initial stock for 2013.

9.2.3 Stock Events 

In the transition from year t to year (t+1), two things could happen to the equipment 

stock: 

• existing equipment could break or be removed during a building renovation and be 

replaced, or 

• the stock could simply age by one year. 

Such stock events as shipments for replacement equipment and shipments for new equipment are 

modeled into the shipments of new stock Uship(t):

9-2
UUUUhiiii(tt) = UURR(tt) + UUUU(tt)

Eq. 9.3

Where:

Uship(t,0) = total shipments of new stock in year t

UR(t) = units replaced in year t

UN(t) = the number of units going into new buildings in year t 

In the model, early replacements (i.e., existing equipment that is replaced before the end of its 

useful life) are not considered, and all broken equipment are assumed to be replaced. The 

following sections present the equations used to represent each possible event.

9.2.3.1 Replacement Equipment

DOE determined the probability that equipment of average age a from the stock of existing units 

U will break, UB(t), and replaced on a one-to-one basis, UR(t) using a function PB(a), based on a 

Weibull statistical distribution of retirements with a 8-year average life and maximum life of 15

years. The inputs for the Weibull distribution that attains these lifetime characteristics are a scale 

parameter of 9.0 and shape parameter of 3.0. These probabilities do not depend on the model 

year t. DOE defines the quantities of replaced equipment as

UU (tt) = UUUU(tt) = � PP (aa) ∙ UU(tt, aa)

aa=1

aaaaaa aa

Eq. 9.4

Where:

UR(t) = units replaced,

UB(t) = units broken,

PB(a) = probability that stock of existing units will break,

U(t, a) = total stock of equipment of an age a in a given year t,

a = average age of stock in years, 

t = year.

All broken units are assumed to be replaced.

9.2.3.2 New Equipment

New PTAC and PTHP equipment will be purchased to replace the units described above. In 

addition, new equipment will be purchased to install in newly constructed buildings. Available 

information suggests that the purchase of new equipment that would go into new buildings is 

driven by the rate of construction of hotels/motels, health care facilities, and office floor space.

9-3
The number of PTAC and PTHP units intended for new buildings is:

UUUU(tt) = AA0 x NNNN(tt)

Eq. 9.5

Where: 

UN(t) = the number of units going into new buildings in year t,

A0 = the market saturation that accounts for the number of units per new commercial 

floor space,

NFS(t) = the projected new commercial floor space.

DOE has limited information on the variation in the market saturation of PTAC and PTHP 

equipment by building type or over time. Therefore, in the model, the purchase of new 

equipment is driven by the construction of new floor space, and broken or removed equipment is 

replaced on a one for one basis.

9.3 MODEL INPUTS

As described in Eq. 9.3, the market for PTACs and PTHPs comprises replacement units 

for equipment that has been retired and units for new construction. The following sections 

discuss both the new construction and replacement markets in further detail. 

9.3.1 New Construction

To develop shipments to new construction, DOE combined new construction floor space

forecasts with a constant market saturation of the equipment in new construction. DOE used new 

construction floor space forecasts for the healthcare, lodging, and small office sectors from the

Energy Information Administration (EIA)’s Annual Energy Outlook 2014 (AEO2014) for 2013–

2040.2 Table 9.3.1 presents these forecasts. The data for 2041 through 2048 are based on an 

extrapolation of the trend from 2030 through 2040.

9-4
Table 9.3.1 Historical and Projected New Construction (million sq ft) for the Shipments 

Model for PTAC and PTHP Equipment*

Year Healthcare Lodging Small Office Year Healthcare Lodging Small Office

2013 66 147 97 2031 71 161 149

2014 66 139 112 2032 71 169 160

2015 67 144 137 2033 72 184 175

2016 67 149 167 2034 72 192 193

2017 68 163 191 2035 72 207 211

2018 67 164 209 2036 74 215 225

2019 66 171 222 2037 74 214 230

2020 65 176 227 2038 75 207 228

2021 63 179 228 2039 76 198 218

2022 62 182 224 2040 76 188 207

2023 61 187 217 2041 77 185 198

2024 61 186 209 2042 78 183 189

2025 63 181 199 2043 78 180 181

2026 65 172 187 2044 79 177 173

2027 67 165 173 2045 80 174 165

2028 69 155 160 2046 80 171 158

2029 70 151 149 2047 81 169 151

2030 71 150 146 2048 82 166 145

*Source: AEO2014; data for 2041-2048 are extrapolated.

To derive the saturation of PTACs and PTHPs (combined) in new construction, DOE 

used data on shipments to new construction and commercial new construction floor space 

provided in the 2008 rulemaking, as shown in Table 9.3.2. DOE divided the new construction 

shipments by the total new construction floor space and used this saturation as the constant 

saturation for the analysis period. 

9-5
Table 9.3.2 Historical PTAC and PTHP Shipments with New Construction Floor Space 

Values Used to Calculate Saturation*

Year

Health care 

(million s.f.)

Lodging 

(million s.f.)

Small Office 

(million s.f.)

Total 

(million 

s.f.)

New 

Construction 

Shipments

Saturation 

(units/

million s.f.)

2000 68 172 179 419 66,407 6,315

*Source: DOE 2008 PTAC and PTHP Energy Conservation Standards Rulemaking.

For new commercial buildings acquiring equipment, shipments are estimated by 

multiplying new construction floor space in each projection year by the saturation value. 

9.3.2 Replacements

To determine shipments to the replacement market, DOE used an accounting method that 

tracks the total stock of units by vintage. DOE estimated a stock of PTACs and PTHPs by using 

the average of the available historical shipments (1998-2012) as the constant value for shipments 

for the years 1993-2012. Over time, some units break and are removed from the stock, triggering 

the one-for-one shipment of a replacement unit. Depending on the vintage, a certain percentage 

of units will fail and need to be replaced. All PTACs and PTHPs are presumed to be replaced by 

a unit of the same equipment class and capacity, but of an age of zero. To estimate how long a 

unit will function before failing, DOE used a survival function based on the distributions of 

equipment lifetime. The survival function that DOE used had an average lifetime of 8 years and

a maximum lifetime of 15 years. Further discussion of equipment lifetime is located in Chapter 

8. Figure 9.3.1 shows the survival function DOE used to estimate replacement shipments. 

Figure 9.3.1 PTAC and PTHP Equipment Survival Function

9-6
9.4 RESULTS

For each year in the analysis period, DOE distributed total PTAC and PTHP shipments

into the individual equipment classes using the average shares of the 1998-2004 shipments by 

equipment class provided by AHRI for the previous rulemaking. Since the data did not 

perceptibly indicate an increasing or decreasing trend, DOE assumed the distribution to persist 

throughout the projection years. Table 9.4.1 shows the distribution of PTAC and PTHP 

shipments by equipment class. Since the market shares are not expected to change with amended 

standards, the results apply for the base case and standards cases.

Table 9.4.1 PTAC and PTHP Equipment Class Shipment Distribution

PTAC PTHP

Total

<7,000 

Btu/h

≥7,000 -

≤15,000 

Btu/h

>15,000 

Btu/h

<7,000 

Btu/h

≥7,000 -

≤15,000 

Btu/h

>15,000 

Btu/h

1998-2004 

Average 

Shipments

13,340 212,844 16,026 7,945 174,538 14,057 438,750

Percent 3% 48.5% 3.7% 1.8% 39.8% 3.2% 100%

Figure 9.4.1 shows the projected shipments of PTACs and PTHPs by equipment class 

and the historical shipments (ending in 2048 for PTAC and 2047 for PTHP) DOE used to 

develop the initial stock that drove the replacement shipment projection. 

Figure 9.4.2 shows total shipments (aggregated from individual equipment classes) 

broken into new construction and replacement shipments. This figure starts in 2013, as historical 

shipments are not disaggregated into new construction and replacements.

DOE assumed that projected shipments do not change with higher efficiency levels. DOE 

expects that most consumers would rather replace than repair failed equipment, given the price 

of repair (as discussed in Chapter 8) and the benefits of new equipment (operating life extension 

and extended warranty). DOE also assumed that the distribution of the efficiencies of shipments 

is constant over time. 

9-7
Figure 9.4.1 Historical and Projected Shipments by Equipment Class

Figure 9.4.2 Projected New Construction and Replacement Shipments

 -

 50,000

 100,000

 150,000

 200,000

 250,000

 300,000

 350,000

 400,000

 450,000

1993199519971999200120032005200720092011201320152017201920212023202520272029203120332035203720392041204320452047

Shipments

Year

PTAC ≥ 7000 to ≤ 15000 Btu/h PTAC ≥ 15000 Btu/h PTHP < 7000 Btu/h

PTHP ≥ 7000 to ≤ 15000 Btu/h PTHP ≥ 15000 Btu/h PTAC < 7000 Btu/h

 -

 100,000

 200,000

 300,000

 400,000

 500,000

 600,000

 700,000

 800,000

201320152017201920212023202520272029203120332035203720392041204320452047

Shipments

Year

New Construction Shipments Replacement Shipments

9-8
For the current year, DOE distributed shipments by efficiency level based on the market 

availability of models by equipment efficiency. DOE was not able to obtain shipments data of 

PTAC and PTHP equipment by efficiency as this information was not available, so it used the 

number of models in a certified model product directory as a proxy for market availability. DOE 

obtained PTAC and PTHP model and equipment efficiency data from the AHRI Directory of 

Certified Product Performance3 and determined and assigned the number of units that fell within 

a range of efficiencies based on the baseline- and standards-level efficiencies described in 

Chapter 5. DOE obtained a total of 346 non-discontinued PTAC models and 230 nondiscontinued PTHP models; their distribution of efficiencies is reported in Table 9.4.2 and Table 

9.4.3.

Table 9.4.2 PTAC Efficiency Distribution in 2014

 Equipment class

Efficiency level* <7,000 Btu/h ≥7,000 - ≤15,000 Btu/h >15,000 Btu/h

Federal Minimum 0% 8% 100%

Baseline/EL 1 0% 46% 0%

EL 2 100% 39% 0%

EL 3 0% 3% 0%

EL 4 0% 2% 0%

EL 5 0% 1% 0%

EL 6 0% 0% 0%

*The Federal Minimum efficiency level represents the efficiency level of the current federal energy conservation standards for PTAC equipment. 

Efficiency level 1 is the baseline level for PTAC equipment, and is the ANSI/ASHRAE/IES Standard 90.1-2013 minimum for PTAC equipment.

Table 9.4.3 PTHP Efficiency Distribution in 2014

 Equipment class

Efficiency level* <7,000 Btu/h ≥7,000 - ≤15,000 Btu/h >15,000 Btu/h

Baseline 100% 78% 100%

EL 1 0% 6% 0%

EL 2 0% 6% 0%

EL 3 0% 9% 0%

EL 4 0% 1% 0%

EL 5 0% 0% 0%

*The baseline efficiency level represents the efficiency level of the current federal energy conservation standards for PTHP equipment, which is 

the same as the ANSI/ASHRAE/IES Standard 90.1-2013 minimum efficiency for PTHP equipment.

9-9
REFERENCES

1. U.S. Department of Energy – Office of Energy Efficiency and Renewable Energy, 

Energy Conservation Program for Commercial and Industrial Equipment: Packaged 

Terminal Air Conditioner and Packaged Terminal Heat Pump Energy Conservation 

Standards., 2007. (Last accessed June, 2014.) 

<www.regulations.gov/#!docketDetail;D=EERE-2007-BT-STD-0012>

2. U.S. Department of Energy/Energy Information Administration, Annual Energy Outlook 

2014, 2013. Washington, D.C. Report No. DOE/EIA0383.

3. Air Conditioning Heating and Refrigeration Institute, AHRI Directory of Certified 

Product Performance, 2013. (Last accessed November 5, 2013.) 

<www.ahridirectory.org/ahridirectory/pages/home.aspx>

9-10
10-i

CHAPTER 10. NATIONAL IMPACT ANALYSIS

TABLE OF CONTENTS

10.1 INTRODUCTION ......................................................................................................... 10-1

10.2 BASE AND STANDARDS CASE EFFICIENCIES .................................................... 10-1

10.3 NATIONAL ENERGY SAVINGS ............................................................................... 10-4

10.3.1 Definition ........................................................................................................... 10-4

10.3.2 NES Inputs......................................................................................................... 10-5

10.3.2.1 Annual Energy Consumption per Unit..................................................... 10-6

10.3.2.2 Shipments................................................................................................. 10-6

10.3.2.3 Equipment Stock ...................................................................................... 10-6

10.3.2.4 National Annual Energy Consumption .................................................... 10-7

10.3.2.5 Site to Primary Energy Conversion Factor .............................................. 10-7

10.3.2.6 Full-Fuel-Cycle Energy Factors............................................................... 10-8

10.4 NET PRESENT VALUE OF CUSTOMER BENEFITS .............................................. 10-9

10.4.1 Net Present Value Definition............................................................................. 10-9

10.4.2 Inputs to Calculation........................................................................................ 10-10

10.4.2.1 Total Annual Installed Cost.................................................................... 10-11

10.4.2.2 Total Annual Operating Cost ................................................................. 10-11

10.4.2.3 Discount Factor ...................................................................................... 10-13

10.4.2.4 Present Value of Increased Installed Costs............................................ 10-13

10.4.2.5 Present Value of Savings........................................................................ 10-14

10.5 TRIAL STANDARD LEVELS ................................................................................... 10-14

10.6 NES AND NPV RESULTS......................................................................................... 10-14

10.6.1 National Energy Savings.................................................................................. 10-14

10.6.2 Annual Costs and Savings ............................................................................... 10-15

10.6.3 Net Present Value Results................................................................................ 10-19

10.6.4 Alternative Base Case Analysis....................................................................... 10-22

REFERENCES ........................................................................................................................ 10-24

LIST OF TABLES

Table 10.2.1 Efficiency Distributions for the Base and Standards Cases in 2019 for 

the PTAC <7,000 Btu/h Equipment Class................................................... 10-2

Table 10.2.2 Efficiency Distributions for the Base and Standards Cases in 2019 for 

the PTAC 7,000 – 15,000 Btu/h Equipment Class...................................... 10-2

Table 10.2.3 Efficiency Distributions for the Base and Standards Cases in 2019 for 

the PTAC ≥15,000 Btu/h Equipment Class................................................. 10-2

Table 10.2.4 Efficiency Distributions for the Base and Standards Cases in 2018 for 

the PTHP <7,000 Btu/h Equipment Class ................................................... 10-3

Table 10.2.5 Efficiency Distributions for the Base and Standards Cases in 2018 for 

the PTHP 7,000 – 15,000 Btu/h Equipment Class....................................... 10-3
10-ii

Table 10.2.6 Efficiency Distributions for the Base and Standards Cases in 2018 for 

the PTHP ≥15,000 Btu/h Equipment Class.................................................. 10-3

Table 10.3.1 Full-Fuel-Cycle Energy Multipliers (Based on AEO 2014)........................ 10-9

Table 10.4.1 Average Total Installed Costs for PTACs in 2019 and PTHPs in 2018 

(2014$)....................................................................................................... 10-11

Table 10.4.2 Average Annual Electricity Costs for PTAC in 2019 and for PTHP in 

2018 (2014$).............................................................................................. 10-12

Table 10.4.3 Average Repair Cost Per Unit for PTACs in 2019 and PTHPs in 2018 

(2014$)....................................................................................................... 10-13

Table 10.5.1 Trial Standard Levels for PTACs and PTHPs.......................................... 10-14

Table 10.6.1 Cumulative Primary Energy Savings for PTAC and PTHP equipment 

(quads)........................................................................................................ 10-15

Table 10.6.2 Full-Fuel-Cycle National Energy Savings for PTAC and PTHP (quads).. 10-15

Table 10.6.3 Cumulative NPV Results for PTAC and PTHP, 7% Discount Rate 

(millions 2014$)......................................................................................... 10-20

Table 10.6.4 Cumulative NPV Results for PTAC and PTHP, 3% Discount Rate 

(millions 2014$)......................................................................................... 10-21

Table 10.6.5 Cumulative Primary Energy Savings for PTAC and PTHP Equipment 

Compared to the Alternative Base Case (quads) ....................................... 10-22

Table 10.6.6 Full-Fuel-Cycle National Energy Savings for PTAC and PTHP 

Equipment Compared to the Alternative Base Case (quads)..................... 10-22

Table 10.6.7 Cumulative NPV Results for PTAC and PTHP Equipment Compared 

to the Alternative Base Case, 7% Discount Rate (millions 2014$)........... 10-23

Table 10.6.8 Cumulative NPV Results for PTAC and PTHP Equipment Compared 

to the Alternative Base Case, 3% Discount Rate (millions 2014$)........... 10-23

LIST OF FIGURES

Figure 10.2.1 Efficiency Trends for the PTAC 7,000 – 15,000 Btu/h Equipment 

Class............................................................................................................. 10-4

Figure 10.3.1 Site-to-Power Plant Energy Use Factors...................................................... 10-8

Figure 10.6.1 Packaged Terminal Air Conditioners 7,000 Btu/h – 15, 000 Btu/h: 

Incremental Equipment Costs, Operating Cost Savings, and Net 

Savings; TSL 2........................................................................................... 10-16

Figure 10.6.2 Packaged Terminal Air Conditioners ≥15,000 Btu/h: Incremental 

Equipment Costs, Operating Cost Savings, and Net Savings; TSL 2........ 10-17

Figure 10.6.3 Packaged Terminal Heat Pumps <7,000 Btu/h: Incremental Equipment 

Costs, Operating Cost Savings, and Net Savings; TSL 2 .......................... 10-18

Figure 10.6.4 Packaged Terminal Heat Pumps 7,000 Btu/h – 15, 000 Btu/h: Installed 

Costs, Operating Cost Savings, and Net Savings; TSL 2 .......................... 10-18

Figure 10.6.5 Packaged Terminal Heat Pumps ≥15,000 Btu/h: Incremental 

Equipment Costs, Operating Cost Savings, and Net Savings; TSL 2........ 10-19
10-iii

Figure 10.6.6 Present Value of Annual Costs and Benefits for all PTACs and PTHPs, 

7% Discount Rate at TSL 2 ....................................................................... 10-20

Figure 10.6.7 TSL 2: Present Value of Annual Costs and Benefits for all PTACs and 

PTHPs, 3% Discount Rate ......................................................................... 10-21
10-1

CHAPTER 10. NATIONAL IMPACT ANALYSIS

10.1 INTRODUCTION

This chapter describes the method for estimating the quantity and net value to customers

of future national energy savings (NES) from possible trial standard levels (TSLs). Results 

described here include: (1) national energy consumption and savings, (2) monetary value of 

operating cost savings to the Nation due to standards, (3) increased total installed costs to the 

Nation due to standards, and (4) the net present value (NPV) of operating cost savings (the 

difference between value of operating cost savings and increased total installed costs).

DOE performed all calculations using a Microsoft Excel® spreadsheet, which is 

accessible on the Internet

(www1.eere.energy.gov/buildings/appliance_standards/buildings/appliance_standards/product.as

px/productid/77). 

Chapter 9 provides a detailed description of the shipments model that DOE used to 

project future shipments of PTAC and PTHP equipment. It includes estimates of efficiency 

market shares in the base case and the considered standards cases, as well as estimates of the 

impact of standards on the distribution of shipment efficiencies.

10.2 BASE AND STANDARDS CASE EFFICIENCIES 

For each equipment class, DOE developed a distribution of base case efficiencies in the 

compliance year for PTAC and PTHP equipment as described in Chapter 8. In each standards 

case, DOE assumed a “roll-up” scenario to establish the efficiency distribution. Equipment

efficiencies in the base case that did not meet the standard under consideration “roll up” to meet 

the new standard level. All efficiency shares in the base case that were above the standard under 

consideration would not be affected.

The tables below present the efficiency distributions for the base case and standards cases 

for each PTAC and PTHP equipment class. Each standards case refers to a standard at the 

corresponding efficiency level. For example, standards case 1 refers to the case with a standard 

at an efficiency level one level above the baseline: for PTAC this represents EL 2, for PTHP this 

represents EL 1.
10-2

Table 10.2.1 Efficiency Distributions for the Base and Standards Cases in 2019 for the 

PTAC <7,000 Btu/h Equipment Class

Efficiency Level*

Market Share

Base Case Standards Case

1 2 3 4 5

Federal Minimum 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Baseline/EL 1 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

EL 2 63.5% 63.5% 0.0% 0.0% 0.0% 0.0%

EL 3 36.5% 36.5% 100.0% 0.0% 0.0% 0.0%

EL 4 0.0% 0.0% 0.0% 100.0% 0.0% 0.0%

EL 5 0.0% 0.0% 0.0% 0.0% 100.0% 0.0%

EL 6 0.0% 0.0% 0.0% 0.0% 0.0% 100.0%

*The Federal Minimum efficiency level represents the efficiency level of the current federal energy conservation standards for 

PTAC equipment. Efficiency level 1 is the baseline level for PTAC equipment, and is the ANSI/ASHRAE/IES Standard 90.1-

2013 minimum for PTAC equipment.

Table 10.2.2 Efficiency Distributions for the Base and Standards Cases in 2019 for the 

PTAC 7,000 – 15,000 Btu/h Equipment Class

Efficiency Level*

Market Share

Base Case

Standards Case

1 2 3 4 5

Federal Minimum 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Baseline/EL 1 37.9% 0.0% 0.0% 0.0% 0.0% 0.0%

EL 2 29.0% 68.7% 0.0% 0.0% 0.0% 0.0%

EL 3 29.3% 27.6% 96.3% 0.0% 0.0% 0.0%

EL 4 2.5% 2.5% 2.5% 98.8% 0.0% 0.0%

EL 5 0.9% 0.9% 0.9% 0.9% 99.7% 0.0%

EL 6 0.3% 0.3% 0.3% 0.3% 0.3% 100.0%

*The Federal Minimum efficiency level represents the efficiency level of the current federal energy conservation standards for 

PTAC equipment. Efficiency level 1 is the baseline level for PTAC equipment, and is the ANSI/ASHRAE/IES Standard 90.1-

2013 minimum for PTAC equipment.

Table 10.2.3 Efficiency Distributions for the Base and Standards Cases in 2019 for the 

PTAC ≥15,000 Btu/h Equipment Class

Efficiency Level*

Market Share

Base Case

Standards Case

1 2 3 4 5

Federal Minimum 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Baseline/EL 1 65.1% 0.0% 0.0% 0.0% 0.0% 0.0%

EL 2 17.4% 87.4% 0.0% 0.0% 0.0% 0.0%

EL 3 17.5% 12.6% 100.0% 0.0% 0.0% 0.0%

EL 4 0.0% 0.0% 0.0% 100.0% 0.0% 0.0%

EL 5 0.0% 0.0% 0.0% 0.0% 100.0% 0.0%

EL 6 0.0% 0.0% 0.0% 0.0% 0.0% 100.0%

*The Federal Minimum efficiency level represents the efficiency level of the current federal energy conservation standards for 

PTAC equipment. Efficiency level 1 is the baseline level for PTAC equipment, and is the ANSI/ASHRAE/IES Standard 90.1-

2013 minimum for PTAC equipment.
10-3

Table 10.2.4 Efficiency Distributions for the Base and Standards Cases in 2018 for the 

PTHP <7,000 Btu/h Equipment Class

Efficiency Level*

Market Share

Base Case

Standards Case

1 2 3 4 5

Baseline 71.8% 0.0% 0.0% 0.0% 0.0% 0.0%

EL 1 13.8% 88.3% 0.0% 0.0% 0.0% 0.0%

EL 2 14.4% 11.7% 100.0% 0.0% 0.0% 0.0%

EL 3 0.0% 0.0% 0.0% 100.0% 0.0% 0.0%

EL 4 0.0% 0.0% 0.0% 0.0% 100.0% 0.0%

EL 5 0.0% 0.0% 0.0% 0.0% 0.0% 100.0%

*The baseline efficiency level represents the efficiency level of the current federal energy conservation standards for PTHP 

equipment, which is the same as the ANSI/ASHRAE/IES Standard 90.1-2013 minimum efficiency for PTHP equipment.

Table 10.2.5 Efficiency Distributions for the Base and Standards Cases in 2018 for the 

PTHP 7,000 – 15,000 Btu/h Equipment Class

Efficiency Level*

Market Share

Base Case

Standards Case

1 2 3 4 5

Baseline 55.9% 0.0% 0.0% 0.0% 0.0% 0.0%

EL 1 8.2% 68.0% 0.0% 0.0% 0.0% 0.0%

EL 2 25.9% 22.0% 90.0% 0.0% 0.0% 0.0%

EL 3 9.5% 9.5% 9.5% 99.5% 0.0% 0.0%

EL 4 0.5% 0.5% 0.5% 0.5% 100.0% 0.0%

EL 5 0.0% 0.0% 0.0% 0.0% 0.0% 100.0%

*The baseline efficiency level represents the efficiency level of the current federal energy conservation standards for PTHP 

equipment, which is the same as the ANSI/ASHRAE/IES Standard 90.1-2013 minimum efficiency for PTHP equipment.

Table 10.2.6 Efficiency Distributions for the Base and Standards Cases in 2018 for the 

PTHP ≥15,000 Btu/h Equipment Class

Efficiency Level*

Market Share

Base Case

Standards Case

1 2 3 4 5

Baseline 71.8% 0.0% 0.0% 0.0% 0.0% 0.0%

EL 1 2.8% 79.6% 0.0% 0.0% 0.0% 0.0%

EL 2 25.4% 20.4% 100.0% 0.0% 0.0% 0.0%

EL 3 0.0% 0.0% 0.0% 100.0% 0.0% 0.0%

EL 4 0.0% 0.0% 0.0% 0.0% 100.0% 0.0%

EL 5 0.0% 0.0% 0.0% 0.0% 0.0% 100.0%

*The baseline efficiency level represents the efficiency level of the current federal energy conservation standards for PTHP 

equipment, which is the same as the ANSI/ASHRAE/IES Standard 90.1-2013 minimum efficiency for PTHP equipment.

Technological improvement in equipment and historical shifts to higher energy efficiency 

suggest that the distribution of efficiencies do not remain constant. DOE therefore used an
10-4

efficiency trend to establish the efficiency distribution, as described in chapter 8, and to project 

the efficiency distribution for the years after the compliance year for the standard case 

shipments. Figure 10.2.1 illustrates the trend in market-weighted efficiency for the base case and 

standards cases for the PTAC 7,000 – 15,000 Btu/h cooling capacity equipment class.

Figure 10.2.1 Efficiency Trends for the PTAC 7,000 – 15,000 Btu/h Equipment Class 

10.3 NATIONAL ENERGY SAVINGS

10.3.1 Definition

DOE calculates annual NES as the difference between two projections: a base case 

(without new standards) and a standards case (with new standards). The standards cases also take 

into account the energy use of units repaired rather than replaced.

The calculation of annual nation energy savings (NESy) are represented by the following 

expressions.

Cumulative energy savings are the sum of each annual NES over the lifetime of 

equipment shipped or repaired in the period that extends from a standard’s assumed compliance 

date for 30 years. This calculation is represented by the following equations for:

11.0

11.5

12.0

12.5

13.0

13.5

20132015201720192021202320252027202920312033203520372039204120432045

Basecase

Rollup to EL1

Rollup to EL2

Rollup to EL3

Rollup to EL4

Rollup to EL5

Rollup to EL6

NES y = AECnatl−base AECnatl−std

_
10-5

DOE calculated AEC by multiplying the number or stock of a given piece of equipment 

(by vintage) by its unit energy consumption (also by vintage). The calculation of the national and 

each regional AEC is represented by the following equation:

Where:

AEC = annual energy consumption each year for the Nation in quadrillion 

British thermal units (Btus)—quads—summed over vintages of the 

equipment stock, STOCKV;

NESy = national annual energy savings (quads);

NEScum = national cumulative energy savings (quads);

STOCKV = stock of equipment (millions of units) of vintage V that survive in 

the year for which DOE calculated annual energy consumption;

UECV = annual energy consumption per unit in kilowatt-hours (kWh); 

electricity consumption is converted from site energy to power 

plant energy (quads) by applying a time-dependent conversion 

factor;

natl = designates the quantity corresponding to the Nation;

base = designates the quantity corresponding to the base case;

std = designates the quantity corresponding to the standards case;

y = year in the projection; and

cum = cumulative over the projection period; and

V = year in which the equipment was purchased as a new unit.

The stock of PTAC and PTHP equipment is dependent on annual shipments, repairs, and 

the lifetime of the equipment. As described in Chapter 9, DOE conducted shipments projections

under the base case and standards cases. DOE determined that the shipment projections under the 

standards cases were lower than those in the base case projection, due to the higher installed cost 

of the more-efficient equipment, which would cause some customers to repair rather than replace

equipment. These so-called extended repairs are higher in the standards cases.

10.3.2 NES Inputs 

The inputs for calculating national energy savings are:

• average annual energy consumption per unit (UEC),

• shipments and extended repairs,

• equipment stock (STOCKV),

• annual energy consumption for the Nation (AEC), and

• power plant primary energy use factor (src_conv).

NEScum = ∑NES y

= ∑ V ×UECV AEC STOCK
10-6

10.3.2.1 Annual Energy Consumption per Unit 

The annual energy consumption per unit (UEC) is the site energy consumed by a PTAC

or PTHP unit per year. The annual energy consumption is directly tied to the efficiency of the 

unit. Thus, knowing the efficiency of a PTAC or PTHP unit enables a determination of the 

corresponding annual energy consumption.

For PTACs, as detailed in chapter 7, DOE used unit energy consumption data obtained 

from the 2008 Energy Conservation Standards Rulemaking for PTAC equipment. To account for 

differences in cooling capacity and/or EER from the previous rulemaking, the UEC (cooling) for 

each equipment class and each efficiency level were linearly scaled from source to target cooling 

capacity and/or EER. 

For PTHP, also detailed in chapter 7, DOE used unit energy consumption data obtained 

from the 2008 Energy Conservation Standards Rulemaking for PHTP equipment. To account for 

differences in cooling capacity and/or EER from the previous rulemaking, the UECs (cooling 

potion) for each equipment class and each efficiency level were linearly scaled from source to 

target cooling capacity and/or EER. To account for differences in COP from the previous 

rulemaking, the UECs (heating potion) for each equipment class and each efficiency level were 

linearly scaled from source to target cooling capacity and/or COP.

DOE adjusted UECs to account for changes in climate based on analyses of weather 

databases. DOE determined annual projected market shares by efficiency level that, in turn, 

when multiplied by annual energy consumption values by efficiency level, enabled a 

determination of shipment-weighted annual energy consumption values.

10.3.2.2 Shipments

DOE projected shipments for the base case and all standards cases (see Chapter 9). 

Several factors, including total installed costs, operating cost, and equipment lifetime, all impact 

projected shipments. Of the above factors, total installed costs were the primary driver in 

projecting the impact of standards on shipments. As noted earlier, the increased installed cost of 

more-efficient equipment causes some customers to forego equipment purchases. Consequently, 

shipments projected under the standards cases are lower than those projected under the base case. 

An extensive description of the methodology for conducting and generating the shipments 

projections can be found in Chapter 9.

10.3.2.3 Equipment Stock

The PTAC and PTHP stock in a given year is the number of PTAC and PTHP units 

shipped from earlier years that survive in the given year. The NIA model keeps track of the 

PTAC and PTHP equipment shipped each year. DOE assumed that PTAC and PTHP equipment 

have an increasing probability of retiring as they age. The probability of survival as a function of 

years-since-purchase is the survival function. Lifetimes range from 1 to approximately 20 years, 

with an average lifetime of 10 years (see Chapters 8 and 9 for further details). 
10-7

10.3.2.4 National Annual Energy Consumption

The national energy consumption is the product of the per-unit PTAC and PTHP annual 

energy consumption and the number of PTAC and PTHP units of each vintage and efficiency. 

This approach accounts for differences in unit energy consumption from year to year.

For each equipment class, DOE calculated the total national site (i.e., the energy 

consumed at the household or establishment) annual energy consumption (AEC). Annual energy 

consumption is the product of the AEC per unit [also termed the unit energy consumption 

(UEC)] and the number of units of each vintage and efficiency. This method accounts for 

differences in UEC from year to year. 

In determining national annual energy consumption, DOE initially calculated the annual 

energy consumption at the site (i.e., electricity in kWh consumed by the PTAC and PTHP unit 

within the building it is serving). DOE then calculated primary energy consumption from site 

energy consumption by applying a conversion factor to account for losses associated with the 

generation, transmission, and distribution of electricity.

10.3.2.5 Site to Primary Energy Conversion Factor

DOE calculates primary energy savings (power plant consumption) from site energy 

savings by applying a factor to account for losses associated with the generation, transmission, 

and distribution of electricity. DOE derived annual average site-to-power plant factors based on 

the version of the National Energy Modeling System (NEMS) that corresponds to Energy 

Information Administration (EIA) Annual Energy Outlook 2014 (AEO 2014)

1

. The factors 

change over time in response to projected changes in the types of power plants projected to 

provide electricity to the country. Figure 10.3.1 shows the site-to-power plant factors from 2019

to the end of the projection period. For years after 2040 (the last year in the AEO), DOE held the 

factors constant at the 2040 value.
10-8

Figure 10.3.1 Site-to-Power Plant Energy Use Factors

10.3.2.6 Full-Fuel-Cycle Energy Factors

The full-fuel-cycle (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. To complete the fullfuel-cycle by encompassing the energy consumed in extracting, processing, and transporting or 

distributing primary fuels, which we refer to as “upstream” activities, DOE developed FFC 

multipliers a using the data and projections generated by the NEMS used for AEO 2014. The 

AEO provides extensive information about the energy system, including projections of future oil, 

natural gas and coal supply, 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 representing the energy intensity of energy production.

Table 10.3.1 shows the FFC energy multipliers used for PTACs and PTHPs for selected 

years. The method used to calculate FFC energy multipliers is described in appendix 10-A.

 a FFC multipliers discussed in this chapter relate to the upstream part of the FFC process.

7.600

8.100

8.600

9.100

9.600

10.100

10.600

11.100

2019202120232025202720292031203320352037203920412043204520472049205120532055205720592061206320652067

Source mn Btu/Site MWh
10-9

Table 10.3.1 Full-Fuel-Cycle Energy Multipliers (Based on AEO 2014)

2019 2020 2025 2030 2035 2040

Electricity

(power plant energy use)

1.043 1.044 1.045 1.046 1.047 1.047

Natural Gas (site) 1.108 1.109 1.111 1.113 1.114 1.114

Petroleum Fuels (site) 1.176 1.176 1.176 1.174 1.172 1.170

10.4 NET PRESENT VALUE OF CUSTOMER BENEFITS

10.4.1 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: 

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.

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 or extended repair cost

(also by vintage). Total annual savings in operating cost and increases in installed cost and 

extended repair cost are calculated using the following equations.

Where:

NPV PVS PVC _ =

PVS =∑OCSy × DFy

PVC = ∑TICy × DFy

OCS y = ∑STOCKV ×UOCSV

TICy = ∑STOCKV ×UTICV
10-10

OCS = total annual savings in operating cost each year summed over vintages of 

the equipment stock, STOCKV;

TIC = total annual increase in installed cost and extended repair cost each year 

summed over vintages of the equipment stock, STOCKV;

DF = discount factor in each year;

STOCKV = stock of equipment (millions of 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 and extended repair cost;

V = year in which the equipment was purchased as a new unit or repaired; and 

y = year in the projection.

DOE determined the PVC for each year from the compliance date of the standard until 

the end of the analysis period, which is 2048 for PTAC and 2047 for PTHP. DOE determined the 

PVS for each year from the compliance date of the standard until the year when units purchased 

or repaired at the end of the analysis period 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.

10.4.2 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 costs,

• discount factor (DF),

• present value of total annual increases in installed cost (TIC),

• present value of savings (PVS).

The total annual increase in installed cost is equal to the annual change in the total perunit installed cost (difference between base case and standards case) multiplied by the shipments 

projected for each standards case. As with calculating energy savings, DOE did not use base-case 

shipments to calculate total annual installed costs for all of the equipment classes. DOE used the 

projected shipments and stock for each standards case to calculate costs.

The annual operating cost includes energy and maintenance costs. The total annual 

savings in operating cost are equal to the change in the annual operating costs (difference 

between base case and standards case) per unit multiplied by the shipments projected for each 

candidate standard level. As with calculating total annual installed costs, DOE did not use basecase shipments to calculate savings in operating cost. 
10-11

10.4.2.1 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. The total installed cost includes both the equipment cost and the 

installation cost. Table 10.4.1 provides average total installed cost values by efficiency level for 

all PTAC and PTHP equipment classes.

Table 10.4.1 Average Total Installed Costs for PTACs in 2019 and PTHPs in 2018 (2014$)

Equipment Class

Efficiency Level*

Federal 

Minimum

Baseline/

EL 1

EL 2 EL 3 EL 4 EL 5 EL 6

PTAC <7,000 Btu/h $1,313 $1,324 $1,333 $1,351 $1,372 $1,395 $1,406

PTAC 7,000 – 15,000 

Btu/h

$1,422 $1,433 $1,442 $1,459 $1,477 $1,497 $1,508

PTAC ≥15,000 Btu/h $1,678 $1,691 $1,700 $1,723 $1,753 $1,790 $1,811

Equipment Class Baseline EL 1 EL 2 EL 3 EL 4 EL 5

PTHP <7,000 Btu/h $1,431 $1,440 $1,458 $1,479 $1,502 $1,514

PTHP 7,000 – 15,000 

Btu/h

$1,544 $1,552 $1,570 $1,588 $1,608 $1,619

PTHP ≥15,000 Btu/h $1,796 $1,805 $1,828 $1,858 $1,895 $1,916

*The Federal Minimum efficiency level represents the efficiency level of the current federal energy conservation standards for PTAC equipment. 

Efficiency level 1 is the baseline level for PTAC equipment, and is the ANSI/ASHRAE/IES Standard 90.1-2013 minimum for PTAC equipment.

The baseline efficiency level represents the efficiency level of the current federal energy conservation standards for PTHP equipment, which is 

the same as the ANSI/ASHRAE/IES Standard 90.1-2013 minimum efficiency for PTHP equipment.

As discussed in section 10.2, 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 10.4.1 to calculate annual shipment-weighted average total installed 

costs.

10.4.2.2 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 7, DOE calculated annual electricity costs using data from the 2008 

rulemaking which provided unit energy consumption for PTACs and PTHPs. Chapter 8 

describes how DOE calculated annual electricity prices for PTAC and PTHP equipment. 

Table 10.4.2 provides weighted-average annual electricity expense values for each

efficiency level and each PTAC and PTHP equipment class.
10-12

Table 10.4.2 Average Annual Electricity Costs for PTAC in 2019 and for PTHP in 2018 

(2014$)

Equipment Class

Efficiency Level*

Federal 

Minimum

Baseline/

EL 1

EL 2 EL 3 EL 4 EL 5 EL 6

PTAC <7,000 Btu/h $80 $79 $77 $75 $73 $70 $69

PTAC 7,000 – 15,000 

Btu/h

$96 $95 $94 $91 $89 $86 $85

PTAC ≥15,000 Btu/h $151 $150 $148 $145 $143 $140 $138

Equipment Class Baseline EL 1 EL 2 EL 3 EL 4 EL 5

PTHP <7,000 Btu/h $152 $150 $147 $144 $141 $139

PTHP 7,000 – 15,000 

Btu/h

$174 $172 $168 $165 $162 $160

PTHP ≥15,000 Btu/h $250 $249 $244 $240 $235 $233

*The Federal Minimum efficiency level represents the efficiency level of the current federal energy conservation standards for PTAC equipment. 

Efficiency level 1 is the baseline level for PTAC equipment, and is the ANSI/ASHRAE/IES Standard 90.1-2013 minimum for PTAC equipment.

The baseline efficiency level represents the efficiency level of the current federal energy conservation standards for PTHP equipment, which is 

the same as the ANSI/ASHRAE/IES Standard 90.1-2013 minimum efficiency for PTHP equipment.

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 values 

in Table 10.4.2 to calculate annual shipment-weighted average annual electricity costs. DOE 

then applied electricity price trends from AEO 2014 to scale the electricity expenses moving 

forward.

Annual Maintenance Costs

DOE determined the average annual maintenance costs to be $56 for PTACs and $62 for PTHPs. 

Because maintenance costs do not change with efficiency, annual maintenance costs do not 

factor into the determination of the total operating cost savings.

Annual Repair Costs

Since annualized warranty costs offer a proxy price for the price of a repair, DOE determined the 

average annual repair cost for PTACs and PTHPs by dividing the total cost of various 

manufacturer- and third-party-provided warranties for a unit by the duration of the warranty. 

Table 10.4.3 provides repair cost values by efficiency level for the PTAC and PTHP equipment 

classes. 
10-13

Table 10.4.3 Average Repair Cost Per Unit for PTACs in 2019 and PTHPs in 2018 (2014$)

Equipment Class

Efficiency Level*

Federal 

Minimum

Baseline/

EL 1

EL 2 EL 3 EL 4 EL 5 EL 6

PTAC <7,000 Btu/h $69 $70 $70 $71 $72 $72 $73

PTAC 7,000 – 15,000 

Btu/h

$70 $70 $71 $71 $72 $73 $74

PTAC ≥15,000 Btu/h $72 $72 $73 $74 $74 $75 $76

Equipment Class Baseline EL 1 EL 2 EL 3 EL 4 EL 5

PTHP <7,000 Btu/h $62 $62 $62 $62 $62 $62

PTHP 7,000 – 15,000 

Btu/h

$75 $75 $76 $77 $77 $78

PTHP ≥15,000 Btu/h $77 $77 $78 $79 $80 $80

*The Federal Minimum efficiency level represents the efficiency level of the current federal energy conservation standards for PTAC equipment. 

Efficiency level 1 is the baseline level for PTAC equipment, and is the ANSI/ASHRAE/IES Standard 90.1-2013 minimum for PTAC equipment.

The baseline efficiency level represents the efficiency level of the current federal energy conservation standards for PTHP equipment, which is 

the same as the ANSI/ASHRAE/IES Standard 90.1-2013 minimum efficiency for PTHP equipment.

10.4.2.3 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:

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 PTAC 

equipment (chapter 8), it used national discount rates to calculate national NPV. DOE estimated 

NPV using both a 3% and a 7% 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.

2 DOE defined the 

present year as 2014.

10.4.2.4 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.. DOE calculated annual increases 

( y yp ) _

( r )

DF

1+

1 =
10-14

in installed cost as the difference in total installed cost for new equipment purchased each year

between the base case and each standards case. DOE also calculated the repair costs for units that 

are repaired rather than replaced in the standards cases.

10.4.2.5 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, to the time when the last unit is retired from service.

Savings represent decreases in operating cost (including electricity and maintenance) 

associated with the more energy efficient equipment purchased in each standards case compared 

to the base case. Savings are reduced by the energy costs associated with units that are repaired 

rather than replaced in the standards cases. 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. 

10.5 TRIAL STANDARD LEVELS

DOE developed TSLs that combine efficiency levels for each equipment class. Table 

10.5.1 presents the efficiency levels for each equipment class in each TSL. 

Table 10.5.1 Trial Standard Levels for PTACs and PTHPs

Equipment Class ASHRAE TSL 1 TSL 2 TSL 3 TSL 4TSL 5

Efficiency Level

PTAC <7,000 Btu/h 1 2 3 4 5 6

PTAC 7,000 – 15,000 Btu/h 1 2 3 4 5 6

PTAC ≥15,000 Btu/h 1 2 3 4 5 6

PTHP <7,000 Btu/h 0 1 2 3 4 5

PTHP 7,000 – 15,000 Btu/h 0 1 2 3 4 5

PTHP ≥15,000 Btu/h 0 1 2 3 4 5

10.6 NES AND NPV RESULTS

10.6.1 National Energy Savings 

The following section provides NES results for the trial standard levels considered for 

PTAC and PTHP equipment. 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 10.6.1 and Table 10.6.2 show the NES results for the TSLs that DOE analyzed. 

Each of the standards cases represented by TSLs was compared to the base case represented by 

the ASHRAE level. NES for the ASHRAE level were determined by comparing to a base case 
10-15

represented by the Federal minimum efficiency levels of current energy conservation standards. 

To demonstrate the relative share of the energy savings due to each equipment class, DOE

disaggregated the results by PTAC and PTHP equipment class.

Table 10.6.1 Cumulative Primary Energy Savings for PTAC and PTHP equipment 

(quads) 

Equipment Class** ASHRAE*

Trial Standard Level

1 2 3 4 5

PTAC <7,000 Btu/h 0.000 0.000 0.001 0.002 0.003 0.003

PTAC 7,000 – 15,000 Btu/h 0.000 0.004 0.020 0.041 0.052 0.053

PTAC ≥15,000 Btu/h 0.001 0.000 0.002 0.004 0.005 0.005

PTHP <7,000 Btu/h - 0.000 0.001 0.002 0.003 0.003

PTHP 7,000 – 15,000 Btu/h - 0.007 0.024 0.046 0.058 0.060

PTHP ≥15,000 Btu/h - 0.001 0.003 0.005 0.006 0.006

Total – All Classes 0.001 0.013 0.052 0.100 0.127 0.130

*Cells that have “-“ have zero energy savings because efficiency remains the same at this level.

**Energy savings of 0.000 have energy savings but cannot be shown due to rounding.

Table 10.6.2 Full-Fuel-Cycle National Energy Savings for PTAC and PTHP (quads)

Equipment Class** ASHRAE*

Trial Standard Level

1 2 3 4 5

PTAC <7,000 Btu/h 0.000 0.000 0.001 0.002 0.003 0.003

PTAC 7,000 – 15,000 Btu/h 0.000 0.004 0.020 0.042 0.053 0.054

PTAC ≥15,000 Btu/h 0.001 0.000 0.002 0.004 0.005 0.005

PTHP <7,000 Btu/h - 0.000 0.001 0.002 0.003 0.003

PTHP 7,000 – 15,000 Btu/h - 0.007 0.025 0.047 0.059 0.061

PTHP ≥15,000 Btu/h - 0.001 0.003 0.005 0.006 0.007

Total – All Classes 0.001 0.014 0.052 0.102 0.129 0.133

*Cells that have “-“ have zero energy savings because efficiency remains the same at this level.

**Energy savings of 0.000 have energy savings but cannot be shown due to rounding.

10.6.2 Annual Costs and Savings

As a prelude to providing the NPVs for each trial standard level in each equipment class, 

this section presents the annual total installed cost increases and annual operating cost savings 

for each product class at the national level at TSL 2 as a means to illustrate the inputs for the 

calculation of NPV.

Figure 10.6.1 to Figure 10.6.5 show the changes over time of the non-discounted annual 

total equipment cost increases and the non-discounted operating cost savings for each product 
10-16

class at TSL 2.

b The figures also show the net annual impact, which is the difference between the 

savings and costs for each year. The annual incremental equipment cost is the increase in the 

total installed cost for equipment purchased each year. The annual operating cost savings is the 

savings in operating costs for equipment that has been purchased, and has not been retired, for 

each year until all purchased equipment has been retired. The NPV is the difference between the 

cumulative annual discounted savings and the cumulative annual discounted costs.

Figure 10.6.1 Packaged Terminal Air Conditioners 7,000 Btu/h – 15, 000 Btu/h: 

Incremental Equipment Costs, Operating Cost Savings, and Net Savings; 

TSL 2

 b Note that the annual costs and savings for the PTAC <7,000 Btu/h cooling capacity equipment class are excluded 

as they have no market share at TSL 2.

-1

-0.5

0

0.5

1

1.5

201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045204620472048

Millions of Dollars

Incremental Equipment

Cost

Operating Cost Savings

Net Savings
10-17

Figure 10.6.2 Packaged Terminal Air Conditioners ≥15,000 Btu/h: Incremental 

Equipment Costs, Operating Cost Savings, and Net Savings; TSL 2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045204620472048

Incremental Equipment Cost

Operating Cost Savings

Net Savings
10-18

Figure 10.6.3 Packaged Terminal Heat Pumps <7,000 Btu/h: Incremental Equipment

Costs, Operating Cost Savings, and Net Savings; TSL 2

Figure 10.6.4 Packaged Terminal Heat Pumps 7,000 Btu/h – 15, 000 Btu/h: Installed 

Costs, Operating Cost Savings, and Net Savings; TSL 2

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044204520462047

Millions of Dollars

Incremental Equipment Cost

Operating Cost Savings

Net Savings

-1

-0.5

0

0.5

1

1.5

2

2.5

201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044204520462047

Millions of Dollars

Incremental Equipment Cost

Operating Cost Savings

Net Savings
10-19

Figure 10.6.5 Packaged Terminal Heat Pumps ≥15,000 Btu/h: Incremental Equipment 

Costs, Operating Cost Savings, and Net Savings; TSL 2

10.6.3 Net Present Value Results

The following section provides NPV results for the trial standard levels considered for 

PTAC and PTHP equipment. Results are cumulative and are shown as discounted values in 

dollar terms. 

The present value of increased total installed costs is the total installed cost increase (i.e., 

the difference between the standards case and base case), discounted to the present, and summed 

over the period of shipments forecasts. The results presented here assume no change in 

equipment prices during the forecast period. NPV results are not presented for the ASHRAE 

levels, unlike NES results, because DOE receives no benefit from economic savings when 

adopting a mandatory efficiency level, which is the efficiency level set forth in ASHRAE 

Standard 90.1-2013 for PTAC equipment.

Savings are decreases in operating costs associated with the higher energy efficiency of 

PTAC and PTHP equipment purchased in the standards case compared to the base case. Total 

operating cost savings are the savings per unit multiplied by the number of units of each vintage 

(i.e., the year of manufacture) surviving in a particular year. The operating cost includes energy 

consumed and maintenance and repair costs incurred until the last unit is retired from service.

Table 10.6.3 and Table 10.6.4 show the NPV results for PTAC and PTHP equipment at 

each TSL, based on a seven-percent discount rate. DOE based all results on electricity price 

forecasts from the AEO2014 Reference Case. Table 10.6.4 and Figure 10.6.6 provide the NPV 

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044204520462047

Millions of Dollars

Incremental Equipment

Cost

Operating Cost Savings
10-20

results based on a three-percent discount rate and electricity price forecasts from the AEO2014

Reference Case. 

DOE also developed sensitivity analyses using an increasing product price trend and a 

decreasing product price trend coupled with AEO2014 Low Economic Growth and High

Economic Growth cases, respectively. These product trends and AEO Economic Growth case are 

described in appendix 10-B, which also presents NPV results based on these alternative 

sensitivities.

Table 10.6.3 Cumulative NPV Results for PTAC and PTHP, 7% Discount Rate (millions 

2014$)

Equipment Class Trial Standard Level

1 2 3 4 5

PTAC <7,000 Btu/h 0.0 -1.0 -3.0 -4.6 -5.0

PTAC 7,000 – 15,000 Btu/h -2.7 -14.9 -35.6 -53.9 -57.6

PTAC ≥15,000 Btu/h -0.5 -3.3 -7.5 -10.9 -11.6

PTHP <7,000 Btu/h -0.1 -0.5 -1.1 -1.8 -1.9

PTHP 7,000 – 15,000 Btu/h 3.3 2.9 -0.7 -6.3 -7.7

PTHP ≥15,000 Btu/h -0.1 -0.6 -2.2 -3.9 -4.3

Total – All Classes -0.1 -17.3 -50.2 -81.4 -88.1

Figure 10.6.6 Present Value of Annual Costs and Benefits for all PTACs and PTHPs, 

7% Discount Rate at TSL 2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045204620472048

Millions of Dollars

Incremental Equip Cost

Operating Cost Savings

Net Savings
10-21

Table 10.6.4 Cumulative NPV Results for PTAC and PTHP, 3% Discount Rate (millions 

2014$)

Product Class Trial Standard Level

1 2 3 4 5

PTAC <7,000 Btu/h 0.0 -1.6 -4.6 -6.9 -7.2

PTAC 7,000 – 15,000 Btu/h -3.0 -18.2 -46.2 -68.7 -72.7

PTAC ≥15,000 Btu/h -0.9 -5.7 -12.7 -17.5 -18.3

PTHP <7,000 Btu/h 0.1 -0.1 -0.8 -1.4 -1.5

PTHP 7,000 – 15,000 Btu/h 9.4 19.1 25.7 25.7 25.1

PTHP ≥15,000 Btu/h 0.3 0.5 -1.1 -2.8 -3.1

Total – All Classes 5.9 -6.0 -39.7 -71.5 -77.7

Figure 10.6.7 TSL 2: Present Value of Annual Costs and Benefits for all PTACs and 

PTHPs, 3% Discount Rate

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

2018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045204620472048

Millions of Dollars

Series1 Series2 Series3
10-22

10.6.4 Alternative Base Case Analysis

The alternative base case analysis was based off a scenario where the mandatory 

efficiency level is the Federal minimum. Chapter 8 presents the alternative base case efficiency 

market shares in the compliance year for PTACs and PTHPs. The calculations for NES and NPV 

were performed against this alternative base case and the cumulative primary energy savings, 

FFC national energy savings, and NPV results at the 7% and 3% discount rates are shown in 

Table 10.6.5 to Table 10.6.8, for informational purposes.

Table 10.6.5 Cumulative Primary Energy Savings for PTAC and PTHP Equipment 

Compared to the Alternative Base Case (quads)

Product Class** ASHRAE*

Trial Standard Level

1 2 3 4 5

PTAC <7,000 Btu/h 0.000 0.000 0.001 0.002 0.003 0.003

PTAC 7,000 – 15,000 Btu/h 0.000 0.004 0.020 0.041 0.052 0.053

PTAC ≥15,000 Btu/h 0.001 0.001 0.003 0.005 0.005 0.006

PTHP <7,000 Btu/h - 0.000 0.001 0.002 0.003 0.003

PTHP 7,000 – 15,000 Btu/h - 0.007 0.024 0.046 0.058 0.060

PTHP ≥15,000 Btu/h - 0.001 0.003 0.005 0.006 0.006

Total – All Classes 0.001 0.014 0.052 0.101 0.128 0.131

*Cells that have “-“ have zero energy savings because efficiency remains the same at this level.

**Energy savings of 0.000 have energy savings but cannot be shown due to rounding.

Table 10.6.6 Full-Fuel-Cycle National Energy Savings for PTAC and PTHP Equipment 

Compared to the Alternative Base Case (quads)

Product Class** ASHRAE*

Trial Standard Level

1 2 3 4 5

PTAC <7,000 Btu/h 0.000 0.000 0.001 0.002 0.003 0.003

PTAC 7,000 – 15,000 Btu/h 0.000 0.004 0.020 0.042 0.053 0.054

PTAC ≥15,000 Btu/h 0.001 0.001 0.003 0.005 0.005 0.006

PTHP <7,000 Btu/h - 0.000 0.001 0.002 0.003 0.003

PTHP 7,000 – 15,000 Btu/h - 0.007 0.025 0.047 0.059 0.061

PTHP ≥15,000 Btu/h - 0.001 0.003 0.005 0.006 0.007

Total – All Classes 0.001 0.014 0.053 0.103 0.130 0.133

*Cells that have “-“ have zero energy savings because efficiency remains the same at this level.

**Energy savings of 0.000 have energy savings but cannot be shown due to rounding.
10-23

Table 10.6.7 Cumulative NPV Results for PTAC and PTHP Equipment Compared to the 

Alternative Base Case, 7% Discount Rate (millions 2014$)

Product Class Trial Standard Level

1 2 3 4 5

PTAC <7,000 Btu/h 0.0 -1.0 -3.0 -4.6 -5.0

PTAC 7,000 – 15,000 Btu/h -2.9 -15.1 -35.8 -54.0 -57.8

PTAC ≥15,000 Btu/h -1.5 -4.3 -8.5 -11.9 -12.6

PTHP <7,000 Btu/h -0.1 -0.5 -1.1 -1.8 -1.9

PTHP 7,000 – 15,000 Btu/h 3.3 2.9 -0.7 -6.3 -7.7

PTHP ≥15,000 Btu/h -0.1 -0.6 -2.2 -3.9 -4.3

Total – All Classes -1.3 -18.5 -51.3 -82.6 -89.3

Table 10.6.8 Cumulative NPV Results for PTAC and PTHP Equipment Compared to the 

Alternative Base Case, 3% Discount Rate (millions 2014$)

Product Class Trial Standard Level

1 2 3 4 5

PTAC <7,000 Btu/h 0.0 -1.6 -4.6 -6.9 -7.2

PTAC 7,000 – 15,000 Btu/h -3.2 -18.4 -46.5 -68.9 -72.9

PTAC ≥15,000 Btu/h -2.4 -7.2 -14.1 -18.9 -19.7

PTHP <7,000 Btu/h 0.1 -0.1 -0.8 -1.4 -1.5

PTHP 7,000 – 15,000 Btu/h 9.4 19.1 25.7 25.7 25.1

PTHP ≥15,000 Btu/h 0.3 0.5 -1.1 -2.8 -3.1

Total – All Classes 4.2 -7.7 -41.3 -73.2 -79.4
10-24

REFERENCES

1. Energy Information Administration, Annual Energy Outlook 2012 with Projections to 

2040, 2014. U.S. Department of Energy. <http://www.eia.gov/forecasts/aeo/>

2. U.S. Office of Management and Budget (OMB), Circular A-4: Regulatory Analysis, 

2003. (Posted September 17, 2003) (Last accessed April, 2013.) 

<www.whitehouse.gov/sites/default/files/omb/assets/regulatory_matters_pdf/a-4.pdf>
CHAPTER 11. CONSUMER SUBGROUP ANALYSIS

TABLE OF CONTENTS

11.1 INTRODUCTION ...............................................................................................................1

11.2 SUBGROUPS DEFINITION ..............................................................................................1

11.2.1 Commercial Consumer Subgroup............................................................................1

11.2.2 Small Business Commercial Discount Rate ............................................................1

11.2.3 Life-Cycle Cost and Payback Period Results for Small Business Subgroup...........2

LIST OF TABLES

Table 11.2.1 Discount Rate Difference between Small Company and Sector 

Average ..............................................................................................................2

Table 11.2.2 PTAC 9,000 Btu/h Units: LCC Results Comparison between Small 

Business and Subgroup and All Buildings.........................................................3

Table 11.2.3 PTAC 15,000 Btu/h Units: LCC Results Comparison Between Small 

Business and Subgroup and All Buildings.........................................................3

Table 11.2.4 PTHP 9,000 Btu/h Units: LCC Results Comparison Between Small 

Business and Subgroup and All Buildings.........................................................4

Table 11.2.5 PTHP 15,000 Btu/h Units: LCC Results Comparison Between Small 

Business and Subgroup and All Buildings.........................................................4

Table 11.2.6 PTAC 9,000 Btu/h Units: PBP Results Comparison Between Small 

Business Subgroup and All Buildings ...............................................................5

Table 11.2.7 PTAC 15,000 Btu/h Units: PBP Results Comparison Between Small 

Business Subgroup and All Buildings ...............................................................5

Table 11.2.8 PTHP 9,000 Btu/h Units: PBP Results Comparison Between Small 

Business Subgroup and All Buildings ...............................................................6

Table 11.2.9 PTHP 15,000 Btu/h Units: PBP Results Comparison Between Small 

Business Subgroup and All Buildings ...............................................................6

11-i
CHAPTER 11. CONSUMER SUBGROUP ANALYSIS

11.1 INTRODUCTION

The subgroup analysis evaluates impacts on any identifiable groups of commercial 

consumers of packaged terminal air conditioners (PTACs) and packaged terminal heat pumps 

(PTHPs) who may be disproportionately affected by a national energy-efficiency standard. The 

U.S. Department of Energy (DOE) accomplished this, in part, by analyzing the LCC and 

payback period (PBPs) for those commercial consumers that fall into specific subgroup.

DOE determined the impact on commercial consumer subgroup using the LCC 

spreadsheet model. The standard LCC and PBP analysis (described in Chapter 8) includes 

various types of commercial buildings that use PTAC equipment. The LCC spreadsheet model 

allows for the identification of certain subgroup of commercial consumers that can then be 

analyzed by sampling only that subgroup. (Chapter 8 explains in detail the inputs to the 

spreadsheet model used in determining the LCC and PBP.)

This chapter describes the subgroup identification in further detail and gives the results of 

the LCC and PBP analysis for the considered subgroups.

11.2 SUBGROUPS DEFINITION

11.2.1 Commercial Consumer Subgroup

DOE identified small businesses as a subgroup that possibly could be disproportionately 

affected by PTAC efficiency standards. DOE was concerned that increases in the purchase price 

of equipment could have negative impacts on small businesses (i.e., those with low annual 

revenues).

The U.S. Small Business Administration (SBA) established size standards to define small 

businesses for types of economic activity, or industry, under the North American Industry 

Classification System (NAICS)1

. The SBA defines a small business by either its annual receipts 

(i.e., revenues) or its number of employees. In the case of traveler accommodations, firms with 

annual revenues of $7 million or less are categorized as small businesses. Generally, chain hotels 

do not meet this criterion, but a substantial portion of independent hotels do. Based on data 

reported by Ducker Worldwide, DOE established that independent hotels are the primary small

business subgroup, representing approximately 10% of the total building sample.2

11.2.2 Small Business Commercial Discount Rate

The capital asset pricing model (CAPM) underestimates the cost of capital for small 

companies. In CAPM, the risk premium β is used to account for the higher returns associated 

with greater risk. However, for small companies, particularly very small companies, historic 

returns have been significantly higher than the CAPM equation predicts. This additional return 

11-1
can be accounted for by adding a size premium to the cost of equity for small firms, as shown in 

Eq. 11.1:

ke = Rf + (β × ERP)+ S

Eq. 11.1

Where:

ke = cost of equity,

Rf = expected return on risk-free assets,

β = risk coefficient of the firm, 

ERP = equity risk premium, and

S = size premium.

DOE obtained size premium data from Ibbotson Associates’ Stocks, Bonds, Bills, and 

Inflation 2009 Yearbook.

3 For the period of 1926-2008, the average size premium for the smallest 

companies in all industries is 5.81%, implying that on average, historic performance of small 

companies has been 5.81% higher than the CAPM estimate of the small company cost of equity.

a

DOE calculated the real weighted-average cost of capital (as described in Chapter 8) 

using the cost of equity including a size premium for small companies instead of the CAPM cost 

of equity.4 Table 11.2.1 presents DOE’s estimates of the discount rates for entire sectors, small 

companies specifically, and the small company discount rate premium. 

To estimate the impact of standards specifically on small businesses, the small company 

discount rates for each sector were used in the LCC and PBP analysis instead of the sector 

average discount rates.

Table 11.2.1 Discount Rate Difference between Small Company and Sector Average 

Hotels

Discount Rate

Average Standard

Deviation

Small Company 

Discount Rate Premium

Entire Sector 6.05% 2.82%

1.76%

Small Companies 7.81% 3.10%

11.2.3 Life-Cycle Cost and Payback Period Results for Small Business Subgroup

Table 11.2.2 to Table 11.2.5 summarize the LCC results for the small business subgroup

for each of the PTAC and PTHP equipment classes, and compare them to the results for the total 

sample of buildings used in the overall LCC analysis. Table 11.2.6 to Table 11.2.9 summarize 

a In this calculation, small companies are defined as companies with market capitalization of less than or equal to 

$218.53 million, the Ibbotson Associates’ definition of Decile 10 companies.

11-2

 
the PBP results. Results are provided by trial standard level (TSL). Note that this is likely an 

overestimate of the impact on small businesses; not all independent hotels qualify as small 

businesses. 

As is evident from the LCC and PBP results, the effect of higher PTAC and PTHP

standards on small businesses is similar to the effect on the full sample of commercial 

consumers. Thus, small businesses are not substantially disadvantaged by increased PTAC and 

PTHP equipment standards, as compared to the general population of commercial consumers.

Table 11.2.2 PTAC 9,000 Btu/h Units: LCC Results Comparison between Small Business 

and Subgroup and All Buildings

Efficiency 

Level*

Small Businesses All Buildings

Mean LCC

Mean 

Increase in 

LCC from 

Baseline

Percent of 

Units with 

LCC 

Savings

Mean LCC

Mean 

Increase in 

LCC from 

Baseline

Percent of 

Units with 

LCC 

Savings

Baseline/EL 1 $2,651 - - $2,746 - -

EL 2 $2,659 $4 3% $2,750 $3 4%

EL 3 $2,667 $11 3% $2,757 $10 4%

EL 4 $2,677 $17 3% $2,767 $16 4%

EL 5 $2,689 $29 2% $2,778 $27 3%

EL 6 $2,695 $35 2% $2,784 $33 3%

*The Federal Minimum efficiency level represents the efficiency level of the current federal energy conservation standards for PTAC equipment. 

Efficiency level 1 is the baseline level for PTAC equipment, and is the ANSI/ASHRAE/IES Standard 90.1-2013 minimum for PTAC equipment.

Table 11.2.3 PTAC 15,000 Btu/h Units: LCC Results Comparison Between Small Business 

and Subgroup and All Buildings

Efficiency 

Level*

Small Businesses All Buildings

Mean LCC

Mean 

Increase in 

LCC from 

Baseline

Percent of 

Units with 

LCC 

Savings

Mean LCC

Mean 

Increase in 

LCC from 

Baseline

Percent of 

Units with 

LCC 

Savings

Baseline/EL 1 $3,207 - - $3,326 - -

EL 2 $3,216 $4 1% $3,330 $3 2%

EL 3 $3,229 $14 0% $3,342 $13 2%

EL 4 $3,249 $29 0% $3,361 $28 1%

EL 5 $3,276 $55 0% $3,387 $53 0%

EL 6 $3,292 $71 1% $3,403 $68 0%

*The Federal Minimum efficiency level represents the efficiency level of the current federal energy conservation standards for PTAC equipment. 

Efficiency level 1 is the baseline level for PTAC equipment, and is the ANSI/ASHRAE/IES Standard 90.1-2013 minimum for PTAC equipment.

11-3
Table 11.2.4 PTHP 9,000 Btu/h Units: LCC Results Comparison Between Small Business 

and Subgroup and All Buildings

Efficiency 

Level*

Small Businesses All Buildings

Mean 

LCC

Mean 

Increase/(Decrease)

in LCC from 

Baseline**

Percent of 

Units with 

LCC 

Savings

Mean 

LCC

Mean 

Increase/(Decrease) 

in LCC from 

Baseline**

Percent of 

Units 

with LCC 

Savings

Baseline $3,263 - - $3,392 - -

EL 1 $3,260 ($3) 39% $3,388 ($4) 41%

EL 2 $3,264 $1 25% $3,390 ($1) 30%

EL 3 $3,269 $6 22% $3,394 $3 28%

EL 4 $3,276 $12 19% $3,400 $9 25%

EL 5 $3,280 $16 17% $3,403 $12 23%

*The baseline efficiency level represents the efficiency level of the current federal energy conservation standards for PTHP equipment, which is 

the same as the ANSI/ASHRAE/IES Standard 90.1-2013 minimum efficiency for PTHP equipment.

**Parentheses indicate negative values

Table 11.2.5 PTHP 15,000 Btu/h Units: LCC Results Comparison Between Small Business 

and Subgroup and All Buildings

Efficiency 

Level*

Small Businesses All Buildings

Mean 

LCC

Mean Increase in 

LCC from Baseline

Percent of 

Units with 

LCC 

Savings

Mean 

LCC

Mean Increase in 

LCC from Baseline

Percent of 

Units with 

LCC 

Savings

Baseline $3,938 - - $4,098 - -

EL 1 $3,940 $2 11% $4,100 $2 15%

EL 2 $3,944 $6 15% $4,102 $4 20%

EL 3 $3,954 $14 13% $4,111 $12 18%

EL 4 $3,972 $31 8% $4,127 $27 12%

EL 5 $3,983 $42 6% $4,138 $37 9%

*The baseline efficiency level represents the efficiency level of the current federal energy conservation standards for PTHP equipment, which is 

the same as the ANSI/ASHRAE/IES Standard 90.1-2013 minimum efficiency for PTHP equipment.

11-4
Table 11.2.6 PTAC 9,000 Btu/h Units: PBP Results Comparison Between Small Business 

Subgroup and All Buildings

Efficiency Level*

Small Businesses All Buildings

Mean PBP (years) Median PBP (years) Mean PBP (years) Median PBP (years)

Baseline/EL 1 - - - -

EL 2 10.7 10.4 10.7 10.4

EL 3 11.5 11.1 11.5 11.1

EL 4 12.6 12.1 12.6 12.1

EL 5 13.4 12.9 13.4 12.9

EL 6 13.8 13.3 13.8 13.3

*The Federal Minimum efficiency level represents the efficiency level of the current federal energy conservation standards for PTAC equipment. 

Efficiency level 1 is the baseline level for PTAC equipment, and is the ANSI/ASHRAE/IES Standard 90.1-2013 minimum for PTAC equipment.

Table 11.2.7 PTAC 15,000 Btu/h Units: PBP Results Comparison Between Small Business 

Subgroup and All Buildings

Efficiency Level*

Small Businesses All Buildings

Mean PBP (years) Median PBP (years) Mean PBP (years) Median PBP (years)

Baseline/EL 1 - - - -

EL 2 11.4 11.1 11.4 11.1

EL 3 13.3 13.2 13.3 13.2

EL 4 16.6 15.7 16.6 15.7

EL 5 19.1 18.0 19.1 18.0

EL 6 20.5 19.2 20.5 19.2

*The Federal Minimum efficiency level represents the efficiency level of the current federal energy conservation standards for PTAC equipment. 

Efficiency level 1 is the baseline level for PTAC equipment, and is the ANSI/ASHRAE/IES Standard 90.1-2013 minimum for PTAC equipment.

11-5
Table 11.2.8 PTHP 9,000 Btu/h Units: PBP Results Comparison Between Small Business 

Subgroup and All Buildings

Efficiency Level*

Small Businesses All Buildings

Mean PBP (years) Median PBP (years) Mean PBP (years) Median PBP (years)

Baseline - - - -

EL 1 4.4 4.4 4.4 4.4

EL 2 6.3 6.1 6.3 6.1

EL 3 7.4 7.1 7.4 7.1

EL 4 8.1 7.7 8.1 7.7

EL 5 8.3 8.0 8.3 8.0

*The baseline efficiency level represents the efficiency level of the current federal energy conservation standards for PTHP equipment, which is 

the same as the ANSI/ASHRAE/IES Standard 90.1-2013 minimum efficiency for PTHP equipment.

Table 11.2.9 PTHP 15,000 Btu/h Units: PBP Results Comparison Between Small Business 

Subgroup and All Buildings

Efficiency Level*

Small Businesses All Buildings

Mean PBP (years) Median PBP (years) Mean PBP (years) Median PBP (years)

Baseline - - - -

EL 1 8.2 8.0 8.2 8.0

EL 2 7.3 7.2 7.3 7.2

EL 3 8.5 8.2 8.5 8.2

EL 4 9.7 9.3 9.7 9.3

EL 5 10.3 9.9 10.3 9.9

*The baseline efficiency level represents the efficiency level of the current federal energy conservation standards for PTHP equipment, which is 

the same as the ANSI/ASHRAE/IES Standard 90.1-2013 minimum efficiency for PTHP equipment.

11-6
REFERENCES

1. Title 13, Code of Federal Regulations, Chapter I-Small Business Administration, Part 

121-Small Business Administration, Subpart A-Size Eligibility Provisions and Standards. 

2005. 

2. Ducker Worldwide, 2000 U.S. Market for Residential and Specialty Air Conditioning: 

PTAC (Packaged Terminal Air Conditioning), March 2001. Ducker Industrial Standards. 

Bloomfield Hills, Michigan 48301. 

<http://www1.eere.energy.gov/buildings/appliance_standards/commercial/pdfs/ptac_pthp

_tsd/chapter_10.pdf>

3. Ibbotson Associates, SBBI Valuation Edition 2009 Yearbook. 2009. Chicago, IL.

4. Damodaran Online, The Data Page: Cost of Capital by Industry Sector.

2012<http://pages.stern.nyu.edu/~adamodar>

11-7
12-i

CHAPTER 12. MANUFACTURER IMPACT ANALYSIS

TABLE OF CONTENTS

12.1 INTRODUCTION ......................................................................................................... 12-1

12.2 METHODOLOGY ........................................................................................................ 12-1

12.2.1 Phase I: Industry Profile..................................................................................... 12-1

12.2.1.1 Manufacturer Interviews .......................................................................... 12-2

12.2.2 Phase II: Industry Cash-Flow Analysis.............................................................. 12-2

12.2.3 Phase III: Manufacturer Subgroup Analysis...................................................... 12-3

12.2.3.1 Manufacturers with Production Assets Subgroup.................................... 12-3

12.2.3.2 Small Business Manufacturer Subgroup .................................................. 12-4

12.2.4 Manufacturing Capacity Impact ........................................................................ 12-4

12.2.5 Employment Impact........................................................................................... 12-5

12.2.6 Cumulative Regulatory Burden ......................................................................... 12-5

12.3 GRIM INPUTS AND ASSUMPTIONS........................................................................ 12-5

12.3.1 Overview of the Government Regulatory Impact Model (GRIM).................... 12-5

12.3.2 Sources for GRIM Inputs................................................................................... 12-6

12.1.1.1 2008 Final Rule for PTACs and PTHPs ..................................................... 12-6

12.3.2.1 Shipment Model ....................................................................................... 12-7

12.3.2.2 Engineering Analysis ............................................................................... 12-7

12.3.2.3 Manufacturer Interviews .......................................................................... 12-7

12.3.3 Trial Standard Levels (TSLs)............................................................................. 12-7

12.3.4 NIA Shipments................................................................................................... 12-8

12.3.5 Production Costs................................................................................................ 12-9

12.3.6 Conversion Costs............................................................................................. 12-10

12.3.6.1 Capital Conversion Costs....................................................................... 12-10

12.3.6.2 Product Conversion Costs...................................................................... 12-11

12.3.7 Markup Scenarios ............................................................................................ 12-12

12.3.7.1 Preservation of Gross Margin Percentage Scenario............................... 12-12

12.3.7.2 Preservation of Operating Profit Scenario ............................................. 12-13

12.4 INDUSTRY FINANCIAL IMPACTS ........................................................................ 12-13

12.4.1 Introduction...................................................................................................... 12-13

12.4.2 PTAC and PTHP Industry Financial Impacts.................................................. 12-14

12.5 IMPACTS ON SUBGROUPS OF MANUFACTURERS .......................................... 12-18

12.5.1 Impacts on Manufacturers with Production Assets ......................................... 12-18

12.5.2 Impacts on Small Business Manufacturers...................................................... 12-20

12.6 OTHER IMPACTS...................................................................................................... 12-22

12.6.1 Employment..................................................................................................... 12-22

12.6.1.1 Methodology .......................................................................................... 12-22

12.6.1.2 Direct Employment Impacts................................................................... 12-22

12.6.2 Production Capacity......................................................................................... 12-23

12.6.3 Cumulative Regulatory Burden ....................................................................... 12-24

12.7 CONCLUSION............................................................................................................ 12-26

12.7.1 Conclusions for PTACs and PTHPs MIA ....................................................... 12-27

REFERENCES ........................................................................................................................ 12-30
12-ii

LIST OF TABLES

Table 12.2.1 SBA and NAICS Classification of Small Businesses Potentially 

Affected by This Rulemaking...................................................................... 12-4

Table 12.3.1 GRIM Financial Parameters for PTACs and PTHPs................................... 12-7

Table 12.3.2 Trial Standard Levels for Analysis of PTACs and PTHPs.......................... 12-8

Table 12.3.3 Manufacturer Production Cost Breakdown (2014$) for PTACs, < 

7,000 Btu/h Capacity ................................................................................... 12-9

Table 12.3.4 Manufacturer Production Cost Breakdown (2014$) for PTACs, ≥ 

7,000 Btu/h and ≤ 15,000 Btu/h Capacity.................................................. 12-10

Table 12.3.5 Manufacturer Production Cost Breakdown (2014$) for PTACs, > 

15,000 Btu/h Capacity ............................................................................... 12-10

Table 12.3.6 Industry Cumulative Capital Conversion Costs (2014$ Millions) ............ 12-11

Table 12.3.7 Industry Cumulative Product Conversion Costs (2014$ Millions)............ 12-12

Table 12.4.1 ASHRAE Baseline: Preservation of Gross Margin Percentage Scenario 

Changes in INPV for PTACs and PTHPs.................................................. 12-14

Table 12.4.2 ASHRAE Baseline: Preservation of Operating Profit Scenario 

Changes in INPV for PTACs and PTHPs.................................................. 12-15

Table 12.4.3 EPCA Baseline: Preservation of Gross Margin Percentage Scenario 

Changes in INPV for PTACs and PTHPs.................................................. 12-16

Table 12.4.4 EPCA Baseline: Preservation of Operating Profit Scenario Changes in 

INPV for PTACs and PTHPs..................................................................... 12-16

Table 12.5.1 Manufacturer Impact Analysis Results for the Subgroup of PTAC and 

PTHP Manufacturers with Production Assets, Gross Margin 

Percentage Markup Scenario ..................................................................... 12-19

Table 12.5.2 Manufacturer Impact Analysis Results for the Subgroup of PTAC and 

PTHP Manufacturers with Production Assets, Preservation of 

Operating Profit Markup Scenario............................................................. 12-20

Table 12.6.1 Potential Changes in the Total Number of Production Workers in the 

PTAC and PTHP Industry in 2019 ............................................................ 12-23

Table 12.6.2 Compliance Dates and Expected Conversion Expenses of Federal 

Energy Conservation Standards Affecting PTAC and PTHP 

Manufacturers............................................................................................ 12-25

Table 12.7.1 Manufacturer Impact Analysis Results for PTACs and PTHPs, Gross 

Margin Percentage Markup Scenario*....................................................... 12-27

Table 12.7.2 Manufacturer Impact Analysis Results for PTACs and PTHPs, 

Preservation of Operating Profit Markup Scenario* ................................. 12-28

LIST OF FIGURES

Figure 12.3.1 Using the GRIM to Calculate Cash Flow .................................................... 12-6

Figure 12.4.1 ASHRAE Baseline: Annual Industry Net Cash Flows under 

Preservation of Gross Margin Percentage Markup Scenario (in 

2014$M)..................................................................................................... 12-15

Figure 12.4.2 ASHRAE Baseline: Annual Industry Net Cash Flows under 

Preservation of Operating Profit Markup Scenario (in 2014$M).............. 12-16
12-iii

Figure 12.4.3 EPCA Baseline: Annual Industry Net Cash Flows under Preservation 

of Gross Margin Percentage Markup Scenario (in 2014$M)..................... 12-17

Figure 12.4.4 EPCA Baseline: Annual Industry Net Cash Flows under Preservation 

of Operating Profit Markup Scenario (in 2014$M)................................... 12-18
12-1

CHAPTER 12. MANUFACTURER IMPACT ANALYSIS

12.1 INTRODUCTION

In determining whether a standard is economically justified, the U.S. Department of 

Energy (DOE) is required to consider “the economic impact of the standard on the manufacturers 

and on the customers of the products subject to such a standard.” (42 U.S.C. 6313(a)(6)(B)(i)) 

The law also calls for an assessment of the impact of any lessening of competition as determined 

in writing by the Attorney General. Id. DOE conducted a manufacturer impact analysis (MIA) to 

estimate the financial impact of amended energy conservation standards on manufacturers of

packaged terminal air conditioners (PTACs) and packaged terminal heat pumps (PTHPs), and 

assessed the impact of such standards on direct employment and manufacturing capacity. 

The MIA has both quantitative and qualitative aspects. The quantitative part of the MIA 

primarily relies on the Government Regulatory Impact Model (GRIM), an industry cash-flow 

model adapted for each equipment type in this rulemaking. The GRIM inputs include 

information on industry cost structure, shipments, and pricing strategies. The GRIM’s key output 

is the industry net present value (INPV). The model estimates the financial impact of more 

stringent energy conservation standards for each equipment type by comparing changes in INPV 

between a base case and the various trial standard levels (TSLs) in the standards case. The 

qualitative part of the MIA addresses equipment characteristics, manufacturer characteristics, 

market and equipment trends, as well as the impact of standards on subgroups of manufacturers. 

12.2 METHODOLOGY

DOE conducted the MIA in three phases. Phase I, “Industry Profile,” consisted of 

preparing a characterization of the PTAC and PTHP industry, including data on sales volumes, 

pricing, employment, and financial structure. As part of this phase, DOE conducted interviews 

with a broad cross-section of PTAC and PTHP manufacturers to gather information on the 

industry as well as the potential impacts of amended energy conservation standards. In Phase II, 

“Industry Cash Flow Analysis,” DOE used the Government Regulatory Impact Model (GRIM)

to assess the potential impacts of amended energy conservation standards on manufacturers. 

DOE used financial inputs derived from a combination of sources including manufacturer 

interviews conducted in Phase I as well as public sources of information. In Phase III, “Subgroup 

Impact Analysis,” DOE developed additional analyses for subgroups that required special 

consideration and incorporated qualitative data from interviews into its analysis.

12.2.1 Phase I: Industry Profile

In Phase I of the MIA, DOE prepared a profile of the PTAC and PTHP industry. DOE 

developed its industry profile using a combination of sources, including: public information, 

such as Securities and Exchange Commission (SEC) 10-K reports,

1 Standard & Poor’s (S&P) 

stock reports,2 market research tools (e.g., Hoovers3

), corporate annual reports, and the U.S. 

Census Bureau’s 2011 Annual Survey of Manufacturers (ASM)4

; information obtained through 

DOE’s engineering analysis, life-cycle cost analysis, and market and technology assessment 

prepared for this rulemaking; financial analysis performed as part of the 2008 energy 
12-2

conservation standards final rule for PTACs and PTHPs; and information obtained directly from 

manufacturers through interviews.

The industry profile includes: (1) further detail on the overall market and equipment

characteristics; (2) estimated manufacturer market shares; (3) financial parameters such as net 

plant, property, and equipment; selling, general and administrative (SG&A) expenses; cost of 

goods sold; and (4) trends in the number of firms, market, and equipment characteristics. 

12.2.1.1 Manufacturer Interviews

During Phase I of the MIA, DOE interviewed manufacturers to gather information on the 

effects of amended energy conservation standards on revenues and finances, direct employment, 

capital assets, and industry competitiveness. Before the interviews, DOE distributed an interview 

guide to interviewees. The interview guide provided a starting point for identifying relevant 

issues and impacts of amended energy conservation standards on individual manufacturers or 

subgroups of manufacturers. Most of the information received from these meetings is protected 

by non-disclosure agreements and resides with DOE’s contractors. The MIA interview topics 

included: (1) key issues to this rulemaking; (2) engineering analysis; (3) company overview and 

organizational characteristics; (4) manufacturer markups and profitability; (5) shipping costs; (6) 

industry projections; (7) financial parameters; (8) conversion costs; (9) cumulative regulatory 

burden; (10) direct employment impact assessment; (11) capacity, exports, foreign competition, 

and outsourcing; (12) consolidation; and (13) impacts on small businesses.

The interview process provides an opportunity for manufacturers to express their views on 

important issues privately, allowing confidential or sensitive information to be considered in the 

rulemaking process. DOE sought to obtain feedback from industry on the approaches used in the 

GRIM and to isolate key issues and concerns. DOE used these interviews to tailor the GRIM to 

reflect financial characteristics unique to the PTAC and PTHP industry. Interviews were 

scheduled well in advance to provide every opportunity for key individuals to be available for 

comment. Although a written response to the questionnaire was acceptable, DOE sought 

interactive interviews, which help clarify responses and identify additional issues. The resulting 

information provides valuable inputs to the GRIM.

12.2.2 Phase II: Industry Cash-Flow Analysis 

Phase II focused on the financial impacts of potential amended energy conservation standards 

on manufacturers of PTAC and PTHP equipment. In general, energy conservation standards can 

affect manufacturer cash flow in three distinct ways: (1) create a need for increased investment;

(2) raise production costs per unit; and (3) alter revenue due to higher per-unit prices and/or

possible changes in sales volumes. To quantify these impacts, DOE used the GRIM to perform a 

cash-flow analysis for the PTAC and PTHP industry. In performing these analyses, DOE used 

the financial values derived during Phase I and the shipment scenarios used in the national 

impact analysis (NIA). 

The GRIM uses several factors to determine a series of annual cash flows from the 

announcement year of amended energy conservation standards until 30 years after the standards’ 

compliance date. These factors include annual expected revenues, costs of goods sold, SG&A, 

taxes, and capital expenditures related to the amended standards. Inputs to the GRIM include 
12-3

manufacturer production costs, markup assumptions, and shipments forecasts developed in other 

analyses. DOE derived the manufacturing costs from the engineering analysis and information 

provided by the industry. It estimated typical manufacturer markups from public financial reports 

and interviews with manufacturers. DOE developed alternative markup scenarios based on 

discussions with manufacturers. DOE’s shipments analysis, presented in Chapter 9 of the 

technical support document (TSD), provided the basis for the shipment projections in the GRIM. 

The financial parameters were developed using publicly available manufacturer data and were 

revised with information submitted confidentially during manufacturer interviews. The GRIM 

results are compared to base-case projections for the industry. The financial impact of amended 

energy conservation standards is the difference between the discounted annual cash flows in the 

base case and standards case at each TSL.

The results of the industry cash-flow analysis are presented in section 12.4.

12.2.3 Phase III: Manufacturer Subgroup Analysis

For its GRIM analysis, DOE presented impacts on the PTAC and PTHP industry as a 

whole. However, using average cost assumptions to develop an industry cash-flow estimate may 

not adequately assess differential impacts of amended energy conservation standards among 

manufacturer subgroups. For example, small manufacturers, niche players, or manufacturers 

exhibiting a cost structure that largely differs from the industry average could be more negatively 

affected. To address this possible impact, DOE used the results of the industry characterization 

analysis in Phase I to group manufacturers that exhibit similar characteristics. 

During the interviews, DOE discussed the potential subgroups and subgroup members it 

identified for the analysis. DOE asked manufacturers and other interested parties to suggest what 

subgroups or characteristics are the most appropriate to analyze. As described in section 12.4, 

DOE analyzes the industry impacts on PTAC and PTHP equipment manufacturers as a whole 

because most of the equipment classes represent the same market served by the same 

manufacturers. However, as discussed below, DOE identified two manufacturer subgroups that 

could be disproportionately impacted by amended energy conservation standards and therefore 

warranted a separate impact analysis: (1) manufacturers with production assets; and (2) small 

businesses.

12.2.3.1 Manufacturers with Production Assets Subgroup

DOE initially identified 22 companies that sell PTAC and PTHP equipment in the U.S. 

Among U.S. companies, however, few own production assets; rather, they import and distribute 

PTACs and PTHPs manufactured overseas, primarily in China. DOE identified a subgroup of 

three U.S. manufacturers that own production assets. These companies own tooling and 

manufacturing assets in the US or in foreign countries. Together, these three manufacturers 

account for approximately 80 percent of the domestic PTAC and PTHP market. Because 

manufacturers with production assets will incur different costs to comply with amended energy 

conservation standards compared to their competitors who do not own production assets, DOE 

conducted a separate subgroup analysis to evaluate the potential impacts of amended energy 

conservation standards on manufacturers with production assets. DOE reports the potential 

impact of this rulemaking on the subgroup of manufacturers with production assets in section 

12.5.1.
12-4

12.2.3.2 Small Business Manufacturer Subgroup

DOE investigated whether small business manufacturers should be analyzed as a 

manufacturer subgroup. DOE used the Small Business Administration (SBA) small business size 

standards effective on January 1, 2012, as amended, and the North American Industry 

Classification System (NAICS) code, presented in Table 12.2.1, to determine whether any small 

entities would be affected by the rulemaking.5 For the equipment classes under review, the SBA 

bases its small business definition on the total number of employees for a business, its 

subsidiaries, and its parent companies. An aggregated business entity with fewer employees than 

the listed limit is considered a small business.

Table 12.2.1 SBA and NAICS Classification of Small Businesses Potentially Affected by 

This Rulemaking

Industry Description Revenue Limit Employee Limit NAICS

Air-Conditioning and Warm Air 

Heating Equipment and Commercial 

and Industrial Refrigeration 

Equipment Manufacturing

N/A 750 333415

DOE used publicly available and proprietary information to identify potential small 

businesses. DOE’s research involved industry trade association membership directories, product 

databases (e.g., AHRI Directory), individual company websites, and market research tools (e.g.,

Hoovers.com) to create a list of companies that manufacture or sell equipment covered by this 

rulemaking. DOE also asked stakeholders and industry representatives if they were aware of any 

other small manufacturers during manufacturer interviews. DOE screened out companies that did 

not offer equipment covered by this rulemaking, did not meet the definition of a “small 

business,” or are foreign owned and operated.

Based on this analysis DOE identified 12 small businesses that sell PTAC and PTHP 

equipment affected by this rulemaking. DOE reports the potential impact of this rulemaking on 

small businesses in section 12.5.2.

12.2.4 Manufacturing Capacity Impact

One significant outcome of amended energy conservation standards could be the 

obsolescence of existing manufacturing assets, including tooling and investment. The 

manufacturer interview guides have a series of questions to help identify impacts of amended 

standards on manufacturing capacity, specifically capacity utilization and plant location 

decisions in the United States, with and without amended standards; the ability of manufacturers 

to upgrade or remodel existing facilities to accommodate the new requirements; the nature and 

value of any stranded assets; and estimates for any one-time changes to existing plant, property, 

and equipment (PPE). DOE’s estimates of the one-time capital changes and stranded assets affect 

the cash flow estimates in the GRIM. These estimates can be found in section 12.3.6. DOE’s 

discussion of the capacity impact can be found in section 12.6.2.
12-5

12.2.5 Employment Impact 

The impact of amended energy conservation standards on employment is an important 

consideration in the rulemaking process. To assess how domestic direct employment patterns 

might be affected, the interviews explored current employment trends in the PTAC and PTHP

industry. The interviews also solicited manufacturer views on changes in employment patterns 

that may result from more stringent standards. The employment impacts section of the interview 

guide focused on current employment levels associated with manufacturers at each production 

facility, expected future employment levels with and without amended energy conservation 

standards, and differences in workforce skills and issues related to the retraining of employees. 

The employment impacts are reported in section 12.6.1.

12.2.6 Cumulative Regulatory Burden

DOE seeks to mitigate the overlapping effects on manufacturers due to amended energy 

conservation standards and other regulatory actions affecting the same products. DOE analyzed 

the impact on manufacturers of multiple, product-specific regulatory actions. Based on research 

and discussions with manufacturers, DOE identified regulations that impact other products made 

by manufacturers of PTACs and PTHPs. Discussion of the cumulative regulatory burden can be 

found in section 12.6.3. 

12.3 GRIM INPUTS AND ASSUMPTIONS

The Government Regulatory Impact Model (GRIM) serves as the main tool for assessing 

the impacts on industry due to amended energy conservation standards. DOE relies on several 

sources to obtain inputs for the GRIM. Data and assumptions from these sources are then fed 

into an accounting model that calculates the industry cash flow both with and without amended 

energy conservation standards.

12.3.1 Overview of the Government Regulatory Impact Model (GRIM)

The basic structure of the GRIM, illustrated in Figure 12.3.1, is an annual cash flow 

analysis that uses manufacturer production costs, manufacturer selling prices, shipments, and 

industry financial information as inputs, and accepts a set of regulatory conditions such as 

changes in costs, investments, and associated margins. The GRIM spreadsheet uses a number of 

inputs to arrive at a series of annual cash flows, beginning with the base year of the analysis, 

2015, and continuing for a 30-year period that begins in the compliance year for each equipment 

class. The model calculates the INPV by summing the stream of annual discounted cash flows 

during this period and adding a discounted terminal value.6
12-6

Figure 12.3.1 Using the GRIM to Calculate Cash Flow

The GRIM projects cash flows using standard accounting principles and compares 

changes in INPV between the base case and the standard-case scenario induced by amended 

energy conservation standards. The difference in INPV between the base case and the standard 

case represents the estimated financial impact of the amended energy conservation standard on 

manufacturers. Appendix 12A provides more technical details and user information for the 

GRIM.

DOE presents MIA results relative to a base case that uses efficiency levels specified by 

ASHRAE Standard 90.1-2013 as the baseline efficiencies for PTACs and PTHPs. Consequently, 

when comparing the INPV impacts of the GRIM model, the baseline efficiency is greater than 

the current federal minimum efficiency requirements.

For this TSD, DOE also repeats the INPV and cash flow calculations relative to an 

alternative base case, where the baseline efficiency levels are equal to the current federal 

minimums, which were set in 2008 (73 FR 58772). This alternative scenario is referred to as the 

“EPCA Baseline.”

12.3.2 Sources for GRIM Inputs

The GRIM uses several different sources of data to determine industry cash flows. 

Sources include corporate annual reports, company profiles, census data, credit ratings, the 

shipments model, the engineering analysis, and manufacturer interviews.

12.1.1.1 2008 Final Rule for PTACs and PTHPs 

The 2008 Final Rule for PTACs and PTHPs (73 FR 58772) provided many of the initial 

financial inputs to the GRIM. As part of the 2008 Final Rule, DOE derived a series of financial 

parameters for the industry based on a review of corporate annual reports, company profiles, 

credit ratings, and manufacturer interviews. DOE used these parameters as a starting point for 

analysis under the current rulemaking. Drawing on feedback obtained during manufacturer 
12-7

interviews conducted in Phase I of this rulemaking, DOE then revised its estimated financial 

parameters to better reflect the current PTAC and PTHP industry. Table 12.3.1 presents the 

revised financial parameters used as inputs to the GRIM. The values indicated have been 

weighted to reflect manufacturers’ respective market shares. 

Table 12.3.1 GRIM Financial Parameters for PTACs and PTHPs 

Parameter Revised Estimate

Tax Rate (% of Taxable Income) 34%

Discount Rate 8.5%

Working Capital (% of Revenue) 7%

Net Property, Plant, and Equipment (% of Revenues) 15%

SG&A (% of Revenue) 15%

R&D (% of Revenues) 3%

Depreciation (% of Revenues) 5%

Capital Expenditures (% of Revenues) 5%

12.3.2.1 Shipment Model

The GRIM used shipment projections derived from DOE’s shipments model in the 

national impact analysis (NIA). Chapter 9 of the TSD describes the methodology and analytical 

model DOE used to forecast shipments.

12.3.2.2 Engineering Analysis 

During the engineering analysis, DOE used a manufacturing cost model to develop 

manufacturing production cost (MPC) estimates. The analysis provided the labor, materials, 

overhead, and total production costs for different design options for PTACs and PTHPs. The 

engineering analysis also estimated a manufacturer markup and a shipping cost to provide the 

manufacturer selling price (MSP) for design options. 

12.3.2.3 Manufacturer Interviews

During the course of the MIA, DOE conducted interviews with a representative crosssection of manufacturers. During these discussions, DOE obtained information to determine and 

verify GRIM input assumptions. Key topics discussed during the interviews and reflected in the 

GRIM include:

• Capital conversion costs (one-time investments in PPE);

• Product conversion costs (one-time investments in research, product development, 

testing, and marketing);

• Equipment cost structure;

• Industry financial parameters;

• Possible profitability impacts.

12.3.3 Trial Standard Levels (TSLs)

DOE developed a number of efficiency levels (ELs) for each equipment class. Trial 

Standard Levels (TSLs) were then developed by selecting likely groupings of efficiency levels 
12-8

for all equipment classes. Each TSL includes combinations of efficiency levels for PTACs and 

PTHPs of different cooling capacity. 

In this rulemaking, each TSL represents a percentage increase in efficiency relative to the 

current federal minimum efficiency standard. For both PTACs and PTHPs, TSLs 1, 2, 3, 4, and 5 

represent respective increases of 4 percent, 8 percent, 12 percent, 16 percent, and 18 percent 

above the current federal minimum efficiency standard for PTACs of a specified cooling 

capacity. Whereas the current federal standard specifies different minimum efficiencies for 

PTACs and PTHPs of equivalent cooling capacity, DOE has structured TSLs in the present 

rulemaking to align efficiency standards for PTACs and PTHPs of the same cooling capacity. 

Table 12.3.2 presents the TSLs used for energy efficiency analysis in the GRIM.

Table 12.3.2 Trial Standard Levels for Analysis of PTACs and PTHPs

Equipment 

Type

Cooling 

Capacity 

(Btu/h)

Baseline TSL 

1

TSL 

2

TSL 

3

TSL 

4

TSL 

5

PTAC

<7,000 Btu/h EL 1 EL 2 EL 3 EL 4 EL 5 EL 6

≥7,000 Btu/h 

and ≤15,000 

Btu/h

EL 1 EL 2 EL 3 EL 4 EL 5 EL 6

>15,000 

Btu/h EL 1 EL 2 EL 3 EL 4 EL 5 EL 6

PTHP

<7,000 Btu/h

Current 

Federal 

Minimum

EL 1 EL 2 EL 3 EL 4 EL 5

≥7,000 Btu/h 

and ≤15,000 

Btu/h

Current 

Federal 

Minimum

EL 1 EL 2 EL 3 EL 4 EL 5

>15,000 

Btu/h

Current 

Federal 

Minimum

EL 1 EL 2 EL 3 EL 4 EL 5

The PTAC baseline efficiency level varies from the current federal minimum in order to 

align with efficiency standards established by the American Society of Heating, Refrigerating, 

and Air-Conditioning Engineers (ASHRAE). In October 2013, ASHRAE published

ANSI/ASHRAE/IES Standard 90.1-2013, which amended efficiency standards for PTACs, 

increasing them to 1.8 percent above the current federal minimum. This rulemaking for PTACs 

is considered an ASHRAE trigger. As such, the baseline for the PTACs analysis is the minimum 

efficiency level established under the ASHRAE amendment. The baseline analyzed for PTHPs 

remains the current federal minimum, as PTHP standards were not modified under the ASHRAE 

amendment. Beyond baseline, as described above, each TSL represents a percentage increase in 

efficiency for both PTACs and PTHPs relative to the current federal minimum.

12.3.4 NIA Shipments

The GRIM estimates manufacturer revenues based on total-unit-shipment forecasts and 
12-9

the distribution of these values by efficiency level. Changes in the efficiency mix at each 

standard level are a key driver of manufacturer finances. For this analysis, the GRIM applied the 

NIA shipments forecasts. 

As part of the shipments forecasts, DOE estimated the base-case shipment distribution by 

efficiency level for each of the six PTAC and PTHP equipment classes. In the standards case, the 

shipments analysis assumes a roll-up scenario, where all shipments in the base case that do not 

meet the standard would instead ship at the new standard level. The key assumptions and 

methodology used to forecast shipments can be found in Chapter 9 of the TSD.

12.3.5 Production Costs

Changes in production costs affect revenues and gross profits. Products that are more 

efficient typically cost more to produce than baseline products (as shown in Chapter 5 of the 

TSD). For the MIA, DOE used the MPCs derived in the engineering analysis. 

The engineering analysis developed MPCs for representative PTAC units at each of the 

three capacity categories used to define equipment classes: <7,000 Btu/h; ≥7,000 Btu/h and ≤ 

15,000 Btu/h; and >15,000 Btu/h. The NIA shipments estimated the number of PTAC and PTHP 

units shipped at each equipment class. The GRIM, in turn, used the MPCs from the engineering 

analysis and the NIA shipments to calculate shipment-weighted average MPCs for each 

equipment class. Additionally, the GRIM relied on the engineering analysis to determine labor, 

materials, overhead, and depreciation percentages that constitute the full MPC.

To calculate baseline MSP, DOE followed a two-step process. First, DOE derived MPCs 

from the engineering and tear down analyses. Second, DOE applied a manufacturer markup, 

which varies with the markup scenario (discussed in detail in section 12.3.7). 

Table 12.3.3 through Table 12.3.5 show the production cost estimates used in the GRIM 

for each analyzed equipment class. A flat markup of 1.27 was applied to all equipment classes.

Table 12.3.3 Manufacturer Production Cost Breakdown (2014$) for PTACs, < 7,000 Btu/h 

Capacity

Materials Labor Depreciation Overhead MPC Markup MSP

Baseline $281.38 $22.61 $19.62 $24.37 $347.98 1.27 $441.94

EL 1 $282.79 $23.35 $20.65 $24.72 $351.52 1.27 $446.44

EL 2 $285.33 $24.04 $21.61 $25.06 $356.03 1.27 $452.16

EL 3 $290.63 $25.21 $23.26 $25.69 $364.80 1.27 $463.29

EL 4 $297.69 $26.13 $24.58 $26.28 $374.68 1.27 $475.84

EL 5 $305.68 $26.80 $25.57 $26.81 $384.87 1.27 $488.78

EL 6 $310.19 $27.04 $25.94 $27.05 $390.23 1.27 $495.59
12-10

Table 12.3.4 Manufacturer Production Cost Breakdown (2014$) for PTACs, ≥ 7,000 Btu/h 

and ≤ 15,000 Btu/h Capacity

Materials Labor Depreciation Overhead MPC Markup MSP

Baseline $298.40 $22.61 $19.62 $24.37 $365.00 1.27 $463.55

EL 1 $300.21 $23.35 $20.65 $24.72 $368.94 1.27 $468.55

EL 2 $302.35 $24.04 $21.61 $25.06 $373.05 1.27 $473.78

EL 3 $307.65 $25.21 $23.26 $25.69 $381.82 1.27 $484.91

EL 4 $314.30 $26.13 $24.58 $26.28 $391.30 1.27 $496.94

EL 5 $322.30 $26.80 $25.57 $26.81 $401.48 1.27 $509.88

EL 6 $326.81 $27.04 $25.94 $27.05 $406.84 1.27 $516.69

Table 12.3.5 Manufacturer Production Cost Breakdown (2014$) for PTACs, > 15,000 

Btu/h Capacity

Materials Labor Depreciation Overhead MPC Markup MSP

Baseline $331.40 $19.79 $20.70 $25.76 $397.65 1.27 $505.01

EL 1 $332.23 $21.30 $21.23 $26.68 $401.43 1.27 $509.82

EL 2 $334.05 $22.72 $21.82 $27.48 $406.07 1.27 $515.71

EL 3 $340.68 $25.30 $23.17 $28.76 $417.91 1.27 $530.75

EL 4 $351.28 $27.54 $24.75 $29.61 $433.18 1.27 $550.14

EL 5 $365.85 $29.43 $26.58 $30.01 $451.87 1.27 $573.87

EL 6 $374.63 $30.25 $27.58 $30.05 $462.50 1.27 $587.37

12.3.6 Conversion Costs

Amended energy conservation standards typically cause manufacturers to incur one-time 

conversion costs to bring their production facilities and equipment designs into compliance with 

new regulations. For the MIA, DOE classified these one-time conversion costs into two major 

groups: capital conversion costs and product conversion costs. Capital conversion costs are onetime investments in plant, property, and equipment to adapt or change existing production 

facilities in order to fabricate and assemble new equipment designs that comply with amended 

energy conservation standards. Product conversion costs are one-time investments in research, 

development, testing, marketing and other costs to make equipment designs comply with 

amended energy conservation standards. DOE based its estimates of the conversion costs for 

each efficiency level on information obtained from manufacturer interviews and the design 

pathways analyzed in the engineering analysis. 

12.3.6.1 Capital Conversion Costs

To estimate the level of capital conversion costs manufacturers would likely incur to 

comply with amended energy conservation standards, DOE relied on information obtained 

through manufacturer interviews as well as the engineering analysis. Table 12.3.6 presents 

estimated capital conversion costs at each TSL. The estimates are cumulative and reflect capital 

conversion costs anticipated across equipment classes and manufacturers in order to achieve 

compliance with each TSL analyzed.
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Table 12.3.6 Industry Cumulative Capital Conversion Costs (2014$ Millions)

TSL Capital Conversion 

Costs (2014$ Millions)

TSL 1 $2.3

TSL 2 $2.9

TSL 3 $7.2

TSL 4 $7.2

TSL 5 $7.5

At TSL 1, manufacturers indicated that converting PTAC equipment lines to comply with 

amended standards would require minimal capital conversion costs whereas converting PTHPs 

would require more substantial investment. In particular, converting PTHP lines to meet TSL 1 

would require some manufacturers to implement new coil fabrication systems. This accounts for 

the majority of expected capital conversion costs at TSL 1, which DOE estimates at $2.3 million 

for all equipment classes. 

At TSL 2, manufacturers stated they would need to implement motor and control changes 

across PTAC and PTHP equipment classes. The additional investment would increase capital 

conversion costs for the industry to an estimated $2.9 million. 

At TSL 3, DOE expects manufacturers to require new tooling and to redesign products to 

incorporate additional coils and/or formed coils. DOE estimates capital conversion costs at this 

level to increase to $7.2 million.

At TSL 4, DOE does not expect capital conversion costs beyond those required at TSL 3. 

Accordingly, capital conversion costs remain level at $7.2 million.

At TSL 5, the engineering analysis suggests manufacturers would have to increase the fin 

density of the heat exchanger, requiring investment in new jigs to position the fins. Industry 

capital conversion costs increase to $7.5 million.

12.3.6.2 Product Conversion Costs

As with capital conversion costs, DOE relied on manufacturer interviews as well as the 

engineering analysis to evaluate product conversion costs. For manufacturers with production 

assets, DOE estimated costs related to R&D (including design engineering, technician salaries, 

and laboratory costs) as well as costs of testing, certification, etc. DOE assumed R&D costs 

ranging from $50,000 to $200,000 per platform based on the complexity of the redesign 

anticipated at each TSL. For all manufacturers (i.e., manufacturers with production assets as well 

as manufacturers that import and distribute PTACs and PTHPs manufactured overseas) DOE 

assumed a flat fee per platform required for testing and certification. DOE multiplied this fee by 

the number of platforms identified for each manufacturer in order to estimate total product 

conversion costs facing the industry. 
12-12

Table 12.3.7 presents industry-wide product conversion costs at each TSL. The estimates 

are cumulative and reflect product conversion costs anticipated across equipment classes and 

manufacturers in order to achieve compliance with each TSL analyzed.

Table 12.3.7 Industry Cumulative Product Conversion Costs (2014$ Millions)

TSL Product Conversion 

Costs (2014$ Millions)

TSL 1 $2.2

TSL 2 $4.8

TSL 3 $7.3

TSL 4 $8.6

TSL 5 $13.7

The increase in product conversion costs, which ranges from a low of $2.2 million at TSL 

1 to a high of $13.7 million at TSL 5, reflects a rise in R&D effort required to meet increasingly 

stringent efficiency standards. As noted, R&D costs will fall disproportionately on manufacturers 

with production assets. See section 12.5.1 for further analysis of financial impacts facing the 

subgroup of manufacturers with production assets. 

12.3.7 Markup Scenarios

DOE modeled multiple standards-case markup scenarios to represent uncertainty 

surrounding the potential impacts of energy conservation standards on prices and profitability. In 

the base case, DOE used the same markups applied in the engineering analysis. In the standards 

case, DOE modeled two markup scenarios to capture a range of potential impacts on 

manufacturers following implementation of amended energy conservation standards: (1) a 

preservation of gross margin percentage scenario; and (2) a preservation of operating profit

scenario. These scenarios lead to different markup values, which, when applied to the inputted 

MPCs, result in varying revenue and cash flow impacts.

12.3.7.1 Preservation of Gross Margin Percentage Scenario

Under the preservation of gross margin percentage scenario, DOE applied a single 

uniform “gross margin percentage” markup across all efficiency levels, which assumes that 

manufacturers would be able to maintain the same amount of profit as a percentage of revenues 

at all efficiency levels within an equipment class. As production costs increase with efficiency, 

this scenario implies that the absolute dollar markup will increase as well. Based on publicly

available financial information for manufacturers of PTACs and PTHPs as well as comments 

from manufacturer interviews, DOE assumed the average markup—which includes SG&A 

expenses, R&D expenses, interest, and profit—to be 1.27 for all PTAC and PTHP equipment 

classes. Because this markup scenario assumes that manufacturers would be able to maintain 

their gross margin percentage markups as production costs increase in response to an amended 

energy conservation standard, it represents a high bound to industry profitability.
12-13

12.3.7.2 Preservation of Operating Profit Scenario

In the preservation of per unit operating profit scenario, manufacturer markups are set so 

that operating profit one year after the compliance date of the amended energy conservation 

standard is the same as in the base case on a per unit basis. Under this scenario, as the costs of 

production increase under a standards case, manufacturers are generally required to reduce their 

markups to a level that maintains base-case operating profit per unit. The implicit assumption 

behind this markup scenario is that the industry can only maintain its operating profit in absolute 

dollars per unit after compliance with the new standard is required. Therefore, operating margin 

in percentage terms is reduced between the base case and standards case. DOE adjusted the 

manufacturer markups in the GRIM at each TSL to yield approximately the same earnings before 

interest and taxes in the standards case as in the base case. This markup scenario represents a low 

bound to industry profitability under an amended energy conservation standard.

12.4 INDUSTRY FINANCIAL IMPACTS

Using the inputs and scenarios described in the previous sections, the GRIM estimated 

indicators of financial impacts on the PTAC and PTHP industry. The following sections detail 

additional inputs and assumptions for the analysis of industry financial impacts. The main results 

of the MIA are also reported in this section. The MIA consists of two key financial metrics:

industry net present value (INPV) and annual cash flows.

12.4.1 Introduction

The INPV measures the industry value and is used in the MIA to compare the economic 

impacts of different TSLs in the standards case. The INPV is different from DOE’s NPV, which 

applies to the U.S. economy. The INPV is the sum of annual net cash flows over the 30-year 

analysis period discounted at the industry’s cost of capital or discount rate. The GRIM for this 

rulemaking estimates cash flows beginning in the base year of the analysis, 2015, and continuing 

for a 30-year period that begins in the compliance year for each equipment class.

In the MIA, DOE compares the INPV of the base case to that of each TSL in the 

standards case. The difference between the base case and a standards case INPV is an estimate of 

the economic impacts that implementing that particular TSL would have on the industry. In this 

final rule, DOE presents MIA results relative to a base case that uses efficiency levels specified 

by ASHRAE Standard 90.1-2013 as the baseline efficiencies for PTACs and PTHPs. 

Consequently, when comparing the INPV impacts of the GRIM model, the baseline efficiency 

for PTACs is greater than the current federal minimum efficiency requirements. However, the 

baseline efficiency for PTHPs is equivalent to the current federal minimum efficiency 

requirements, as ASHRAE Standard 90.1-2013 did not specify new efficiency levels for PTHPs.

In analyzing the financial impacts in this TSD, DOE also presents the INPV and cash 

flow calculations relative to an alternative base case, where the baseline efficiency levels are 

equal to the current federal minimums, which were set in 2008 (73 FR 58772). This alternative 

scenario is referred to as the “EPCA Baseline”.
12-14

While INPV is useful for evaluating the long-term effects of amended energy 

conservation standards, short-term changes in cash flow are also important indicators of the 

industry’s financial situation. For example, a large investment over one or two years could strain 

the industry’s access to capital. Consequently, the sharp drop in financial performance could 

cause investors to flee, even though recovery may be possible. Thus, a short-term disturbance 

can have long-term effects that the INPV cannot capture. To get an idea of the behavior of 

annual net cash flows, Figure 12.4.1 and Figure 12.4.2 below present the annual net cash flows 

over the analysis period.

Annual cash flows are discounted to the base year, 2015. After the standards 

announcement date (i.e., the publication date of the final rule), industry cash flows begin to 

decline as companies use their financial resources to prepare for the amended energy 

conservation standard. Cash flows between the announcement date and the compliance date are 

driven by the level of conversion costs and the proportion of these investments spent every year. 

The more stringent the amended energy conservation standard, the greater the impact on industry 

cash flows in the years leading up to the compliance date, as product conversion costs lower cash 

inflows from operations and capital conversion costs increase cash outflows for capital 

expenditures. 

Free cash flow in the year the amended energy conservation standards take effect is 

driven by two competing factors. In addition to capital and product conversion costs, amended 

energy conservation standards could create stranded assets, i.e., tooling and equipment that 

would have enjoyed longer use if the energy conservation standard had not made them obsolete. 

In this year, manufacturers write down the remaining book value of existing tooling and 

equipment whose value is affected by the amended energy conservation standard. This one time 

write down acts as a tax shield that alleviates decreases in cash flow from operations in the year 

of the write-down. In this year, there is also an increase in working capital that reduces cash flow 

from operations. A large increase in working capital is needed due to more costly production 

components and materials, higher inventory carrying to sell more expensive products, and higher 

accounts receivable for more expensive products. Depending on these two competing factors, 

cash flow can either be positively or negatively affected in the year the standard takes effect. 

12.4.2 PTAC and PTHP Industry Financial Impacts

Table 12.4.1 and Table 12.4.2 provide INPV estimates for PTACs and PTHPs under the 

two markup scenarios analyzed. Figure 12.4.1 and Figure 12.4.2 present annual industry net cash 

flows under the two markup scenarios. As described in section 12.3.7, the preservation of gross 

margin percentage scenario presents an upper bound to industry profitability under amended 

standards while the preservation of operating profit scenario presents a lower bound to industry 

profitability. These results are based on an ASHRAE baseline and are consistent with results 

presented in the final rule. 

Table 12.4.1 ASHRAE Baseline: Preservation of Gross Margin Percentage Scenario 

Changes in INPV for PTACs and PTHPs

Units Base 

Case

Trial Standard Level

1 2 3 4 5
12-15

INPV 2014$M 62.2 61.1 63.1 61.9 63.1 60.3

Change 

in INPV*

2014$M - (1.1) 0.8 (0.3) 0.8 (1.9)

% Change - (1.8) 1.3 (0.5) 1.4 (3.1)

* Parentheses indicate negative values.

Table 12.4.2 ASHRAE Baseline: Preservation of Operating Profit Scenario Changes in 

INPV for PTACs and PTHPs

Units Base 

Case

Trial Standard Level

1 2 3 4 5

INPV 2014$M 62.2 60.7 61.8 59.3 58.9 55.6

Change 

in INPV*

2014$M - (1.5) (0.5) (3.0) (3.4) (6.7)

% Change - (2.4) (0.8) (4.8) (5.4) (10.7)

* Parentheses indicate negative values.

At TSL 1, DOE estimates impacts on INPV to range from -$1.5 million to -$1.1 million, 

or a change of -2.4 percent to -1.8 percent. At TSL 2, DOE estimates impacts on INPV to range 

from -$0.5 million to $0.8 million, or a change in INPV of -0.8 percent to 1.3 percent. At TSL 3, 

DOE estimates impacts on INPV to range from -$3.0 million to -$0.3 million, or a change in 

INPV of -4.8 percent to -0.5 percent. At TSL 4, DOE estimates impacts on INPV to range from -

$3.4 million to $0.8 million, or a change in INPV of -5.4 percent to 1.4 percent. At TSL 5, DOE 

estimates impacts on INPV to range from -$6.7 million to -$1.9 million, or a change in INPV of -

10.7 percent to -3.1 percent. See section 12.7 below for a more detailed discussion of results.

Figure 12.4.1 ASHRAE Baseline: Annual Industry Net Cash Flows under Preservation of 

Gross Margin Percentage Markup Scenario (in 2014$M)

($4.0)

($2.0)

$0.0

$2.0

$4.0

$6.0

$8.0

2014 2019 2024 2029

Base Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5
12-16

Figure 12.4.2 ASHRAE Baseline: Annual Industry Net Cash Flows under Preservation of 

Operating Profit Markup Scenario (in 2014$M)

The following tables present alternative results based on the EPCA Baseline. Table 

12.4.3 and Table 12.4.4 present the INPV estimates for the two markup scenarios relative to the 

EPCA Baseline. Figure 12.4.3 and Figure 12.4.4 present the net annual cash flows for the two 

markup scenarios under the alternative baseline.

Table 12.4.3 EPCA Baseline: Preservation of Gross Margin Percentage Scenario Changes 

in INPV for PTACs and PTHPs

Units Base 

Case

Trial Standard Level

1 2 3 4 5

INPV 2014$M 62.1 60.7 62.5 61.3 62.4 59.5

Change 

in INPV*

2014$M - (1.4) 0.4 (0.8) 0.3 (2.6)

% Change - (2.3) 0.7 (1.3) 0.5% (4.2)

* Parentheses indicate negative values.

Table 12.4.4 EPCA Baseline: Preservation of Operating Profit Scenario Changes in INPV 

for PTACs and PTHPs

Units Base 

Case

Trial Standard Level

1 2 3 4 5

INPV 2014$M 62.1 60.3 61.2 58.6 58.2 54.7

Change 

in INPV*

2014$M - (1.8) (0.9) (3.5) (3.9) (7.4)

% Change - (2.9) (1.4) (5.6) (6.3) (11.9)

($4.0)

($2.0)

$0.0

$2.0

$4.0

$6.0

$8.0

2014 2019 2024 2029

Base Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5
12-17

* Parentheses indicate negative values.

In the EPCA baseline analysis, DOE estimated an additional set of product conversion 

costs intended to capture the cost to manufacturers of bringing PTAC equipment into compliance 

with ASHRAE Standard 90.1-2013. Based on feedback received from manufacturers during 

interviews, DOE does not expect manufacturers to undertake capital investments in order to 

comply with efficiency levels established by ASHRAE and therefore did not account for 

additional capital conversion costs. DOE estimated total industry product conversion costs of 

$0.48 million to meet amended ASHRAE standards for PTACs. DOE incorporated these costs 

into its cash flow model as one-time product conversion costs incurred in 2015, the year the 

ASHRAE standard takes effect. 

Figure 12.4.3 EPCA Baseline: Annual Industry Net Cash Flows under Preservation of 

Gross Margin Percentage Markup Scenario (in 2014$M)

($4.0)

($2.0)

$0.0

$2.0

$4.0

$6.0

$8.0

2014 2019 2024 2029

Base Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5
12-18

Figure 12.4.4 EPCA Baseline: Annual Industry Net Cash Flows under Preservation of 

Operating Profit Markup Scenario (in 2014$M)

12.5 IMPACTS ON SUBGROUPS OF MANUFACTURERS 

As discussed above, using average cost assumptions to develop an industry cash flow 

estimate is not adequate for assessing differential impacts among subgroups of manufacturers. 

Small manufacturers, niche players, or manufacturers exhibiting a cost structure that differs 

largely from the industry average could be affected differently. DOE used the results of the 

industry characterization to group manufacturers exhibiting similar characteristics. Specifically, 

DOE identified two subgroups of manufacturers for separate impact analyses: (1) manufacturers 

with production assets; and (2) small business manufacturers.

12.5.1 Impacts on Manufacturers with Production Assets 

As discussed above, DOE initially identified 22 companies that sell PTAC and PTHP 

equipment. Most U.S. companies, however, do not own production assets; rather, they import 

and distribute PTACs and PTHPs manufactured overseas, primarily in China. DOE identified a 

subgroup of three U.S.-headquartered manufacturers that own production assets. These 

companies own tooling and manufacturing assets in the U.S. or in foreign countries. Together, 

these three manufacturers account for approximately 80 percent of the domestic PTAC and 

PTHP market. Because manufacturers with production assets will incur different costs to comply 

with an amended energy conservation standard compared to their competitors who do not own 

($4.0)

($2.0)

$0.0

$2.0

$4.0

$6.0

$8.0

2014 2019 2024 2029

Base Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5
12-19

production assets, DOE conducted a separate analysis to evaluate the impact of an amended 

energy conservation standard on the subgroup of manufacturers with production assets. 

As with the overall industry analysis, DOE modeled two different markup scenarios to 

evaluate the range of cash flow impacts on manufacturers with production assets: (1) the 

preservation of gross margin percentage markup scenario; and (2) the preservation of operating 

profit markup scenario. See section 12.3.7 for a complete description of markup scenarios.

Each of the modeled scenarios results in a unique set of cash flows and corresponding 

INPV values at each TSL. In the following discussion, the INPV results refer to the difference in 

value of manufacturers with production assets between the base case and each TSL in the 

standards case. As with the overall industry analysis, the INPV for manufacturers with 

production assets is calculated as the sum of annual net cash flows over the 30-year analysis 

period, discounted at the industry’s cost of capital. 

To provide perspective on the short-run cash flow impact, DOE includes in the discussion 

of results a comparison of free cash flow between the base case and the standards case at each 

TSL in the year before amended standards would take effect. This figure provides an 

understanding of the magnitude of required conversion costs relative to the cash flow generated 

by manufacturers with production assets in the base case.

Table 12.5.1 and Table 12.5.2 present a range of results reflecting both the preservation of 

gross margin percentage markup scenario and the preservation of operating profit markup 

scenario. As discussed in section 12.3.7, the preservation of operating profit scenario accounts 

for the more severe impacts presented. 

Table 12.5.1 Manufacturer Impact Analysis Results for the Subgroup of PTAC and PTHP 

Manufacturers with Production Assets, Gross Margin Percentage Markup 

Scenario

Units Base Case Trial Standard Level*

1 2 3 4 5

INPV 2014$M 49.8 48.7 49.9 48.1 48.9 46.0

Change in INPV

2014$M - (1.1) 0.1 (1.7) (0.9) (3.8)

% Change - (2.1) 0.3 (3.4) (1.8) (7.5)

Product Conversion 

Costs 2014$M - 1.4 4.0 6.5 7.8 12.8

Capital Conversion Costs 2014$M - 2.3 2.9 7.2 7.2 7.5

Total Conversion Costs 2014$M - 3.7 6.9 13.7 15.0 20.4

Free Cash Flow**

2014$M 3.1 1.7 0.8 (1.9) (2.3) (4.0)

% Change - (43.7) (74.7) (160.1) (173.8) (228.3)

* Parentheses indicate negative values.
12-20

Table 12.5.2 Manufacturer Impact Analysis Results for the Subgroup of PTAC and PTHP 

Manufacturers with Production Assets, Preservation of Operating Profit 

Markup Scenario

Units Base Case Trial Standard Level*

1 2 3 4 5

INPV 2014$M 49.8 48.5 48.9 46.0 45.5 42.3

Change in INPV

2014$M - (1.3) (0.9) (3.8) (4.3) (7.5)

% Change - (2.7) (1.8) (7.7) (8.6) (15.1)

Product Conversion 

Costs 2014$M - 1.4 4.0 6.5 7.8 12.8

Capital Conversion Costs 2014$M - 2.3 2.9 7.2 7.2 7.5

Total Conversion Costs 2014$M - 3.7 6.9 13.7 15.0 20.4

Free Cash Flow**

2014$M 3.1 1.7 0.7 (1.9) (2.4) (4.1)

% Change - (44.2) (76.0) (162.6) (177.7) (232.6)

* Parentheses indicate negative values.

In the standards case, manufacturers with production assets would likely experience 

financial impacts more negative than those facing the industry as a whole (see section 12.4 for 

industry-wide financial impacts). These differential impacts derive primarily from the conversion 

costs manufacturers with production assets would incur in order to comply with an amended 

standard. In particular, manufacturers with production assets would face capital conversion costs 

not shared by their competitors who import and distribute PTACs and PTHPs and do not require 

tooling investments. In interviews, manufacturers with production assets indicated that higher 

standards could require significant investment in new tooling to support new coil designs. In 

addition, manufacturers with production assets would face product conversion costs in the form 

of design engineering, product development, testing, certification, marketing, and related costs. 

See section 12.3.6 for further discussion of conversion costs. However, since this rule maintains 

the standard at baseline (i.e., ASHRAE), DOE’s modeling does not show any negative financial 

impacts on industry, including manufacturers with production assets, as a direct result of the 

standard.

12.5.2 Impacts on Small Business Manufacturers

DOE conducted a more focused inquiry of the companies that could be small business 

manufacturers of products covered by this rulemaking. For “Air-Conditioning and Warm Air 

Heating Equipment and Commercial and Industrial Refrigeration Equipment Manufacturing,”

the Small Business Administration (SBA) has set a size threshold of 750 employees or less for an 

entity to be considered a small business for this category. During its market survey, DOE used all 

available public information to identify potential small manufacturers. DOE’s research involved 

industry trade association membership directories (e.g., AHRI), product databases, individual 
12-21

company websites, and market research tools (e.g., Hoovers.com) to create a comprehensive list 

of companies that manufacture or sell products covered by this rulemaking. DOE also asked 

stakeholders and industry representatives if they were aware of any other small manufacturers 

during manufacturer interviews and at previous DOE public meetings. DOE reviewed publicly 

available data and contacted companies on its list, as necessary, to determine whether they met 

the SBA’s definition of a small business manufacturer of covered PTAC and PTHP products. 

DOE screened out companies that did not offer products affected by this rulemaking, did not 

meet the definition of a “small business,” or are foreign owned and operated.

DOE identified 22 companies that sell PTAC and PTHP equipment that would be 

affected by today’s proposal. Of these 22 companies, DOE identified 12 as small businesses. 

DOE contacted the identified small businesses to invite them to take part in a manufacturer 

impact analysis interview. Of the 12 small businesses contacted, DOE was able to reach and 

discuss potential standards with two. DOE also obtained information about small businesses and 

potential impacts on small businesses while interviewing large manufacturers.

Within the PTAC and PTHP industry, no small business identified is an original equipment 

manufacturer of standard-size equipment affected by this rulemaking. Rather, small businesses 

tend to import, rebrand, and distribute PTACs and PTHPs manufactured overseas, primarily in 

China. Some small businesses identified are original equipment manufacturers of non-standard 

size PTACs and PTHPs; however, non-standard equipment is not impacted by this rulemaking 

and therefore is not considered in this small business subgroup analysis. 

Because small businesses import and distribute, rather than directly manufacture, covered 

equipment, they would not be expected to incur capital conversion costs in order to comply with 

amended energy conservation standards nor would they be expected to incur product conversion 

costs related to engineering and redesign of equipment. Small businesses could potentially incur 

product conversion costs related to testing and certification of products that undergo redesign by 

original equipment manufacturers in order to comply with amended standards.

However, in this final rule, DOE is adopting amended energy conservation standards for 

PTACs equivalent to those set forth in ASHRAE Standard 90.1-2013. In line with ASHRAE 

Standard 90.1-2013, DOE is not amending energy conservation standards for PTHPs. DOE is 

required to adopt minimum efficiency standards either equivalent to or more stringent than those 

set forth by ASHRAE. Therefore, at the proposed level, no regulatory alternatives are available. 

Since this rule adopts the ASHRAE baseline as the standards level, DOE’s modeling does not 

show any negative financial impacts on industry, including small manufacturers, as a direct 

result of the standard.

DOE provides additional analysis in section VI.B of the final rule, Review under the 

Regulatory Flexibility Act.
12-22

12.6 OTHER IMPACTS 

12.6.1 Employment

12.6.1.1 Methodology

To quantitatively assess the impacts of energy conservation standards on employment, 

DOE used the GRIM to estimate the domestic labor expenditures and number of employees in 

the base case and at each TSL from 2015 through the end of the analysis period. DOE used 

statistical data from the U.S. Census Bureau’s 2011 Annual Survey of Manufacturers (ASM), the 

results of the engineering analysis, and interviews with manufacturers to determine the inputs 

necessary to calculate industry-wide labor expenditures and domestic employment levels. Labor 

expenditures related to manufacturing of the equipment are a function of the labor intensity of 

the product, the sales volume, and an assumption that wages remain fixed in real terms over 

time. The total labor expenditures in each year are calculated by multiplying the MPCs by the 

labor percentage of MPCs. 

The total labor expenditures in the GRIM were then converted to domestic production 

employment levels by dividing production labor expenditures by the annual payment per 

production worker (production worker hours times the labor rate found in the U.S. Census 

Bureau’s 2011 ASM). The production worker estimates in this section cover workers up to the 

line-supervisor level who are directly involved in fabricating and assembling equipment within 

the original equipment manufacturer facility. Workers performing services that are closely 

associated with production operations, such as materials handling tasks using forklifts, are also 

included as production labor. DOE’s estimates only account for production workers who 

manufacture the specific products covered by this rulemaking.

To estimate an upper bound to employment change, DOE assumes all domestic 

manufacturers would choose to continue producing products in the U.S. and would not move 

production to foreign countries. To estimate a lower bound to employment, DOE estimates the 

maximum portion of the industry that would choose to leave the industry or relocate production 

overseas rather than make the necessary conversions at domestic production facilities.

12.6.1.2 Direct Employment Impacts

DOE estimates that 50 percent of standard-size PTAC and PTHP units are manufactured 

domestically. In the absence of amended energy conservation standards, DOE estimates that the 

PTAC and PTHP industry would employ 175 domestic production workers in 2019.

Table 12.6.1 shows the range of impacts of potential amended energy conservation 

standards on U.S. production workers of PTACs and PTHPs. The potential changes to direct 

employment presented suggest that the PTAC and PTHP industry could experience anything 

from a slight gain in domestic direct employment to a loss of all domestic direct employment. 

Since this rule maintains the standard at baseline (i.e., ASHRAE), DOE does not expect any loss 

in domestic direct employment. 
12-23

Table 12.6.1 Potential Changes in the Total Number of Production Workers in the PTAC 

and PTHP Industry in 2019

Trial Standard Level

Base 

Case** 1 2 3 4 5

Potential Changes 

in Domestic 

Production 

Workers in 2019*

-

(175) (175) (175) (175) (175)

to to to to to 

4 10 17 22 24

* Parentheses indicate negative values.

**Base Case assumes 175 domestic production workers in the PTAC and PTHP industry in 2019.

The upper end of the range estimates the maximum increase in the number of domestic 

production workers in the PTAC and PTHP industry after implementation of an amended energy 

conservation standard. It assumes manufacturers would continue to produce the same scope of 

covered products within the United States and would require some additional labor to produce 

more efficient products. 

The lower end of the range represents the maximum decrease in total number of U.S. 

production workers that could result from an amended energy conservation standard. During 

interviews, manufacturers stated their concerns about increasing offshore competition entering 

the market. If the cost of complying with amended standards significantly erodes the profitability 

of domestic manufacturers relative to their competitors who manufacture and/or import PTACs 

and PTHPs from overseas, manufacturers with domestic production could decide to exit the 

PTAC and PTHP market and/or shift their production facilities offshore. The lower bound of 

direct employment impacts therefore assumes domestic production of PTACs and PTHPs ceases, 

as domestic manufacturers either exit the market or shift production overseas in search of 

reduced manufacturing costs. 

The direct employment impacts discussed here do not include indirect employment 

impacts on the broader U.S. economy, which are documented in Chapter 16 of the TSD.

12.6.2 Production Capacity

According to PTAC and PTHP manufacturers interviewed, amended energy conservation 

standards would not significantly constrain manufacturing production capacity. Among 

manufacturers with production assets, some indicated that higher energy conservation standards 

could reduce sales volumes, thereby resulting in excess capacity. Among importers and 

distributors, amended energy conservation standards would not likely impact production 

capacity. Since this rule maintains the standard at baseline (i.e., ASHRAE), DOE does not expect 

any change in production capacity.
12-24

12.6.3 Cumulative Regulatory Burden 

While any one regulation may not impose a significant burden on manufacturers, the 

combined effects of several impending regulations may have serious consequences for some 

manufacturers, groups of manufacturers, or an entire industry. Assessing the impact of a single 

regulation may overlook this cumulative regulatory burden. Multiple regulations affecting the 

same manufacturer can strain profits and can lead companies to abandon equipment lines or 

markets with lower expected future returns than competing products. For these reasons, DOE 

conducts an analysis of cumulative regulatory burden as part of its rulemakings pertaining to 

equipment efficiency.

Companies that produce a wide range of regulated products may be faced with more 

capital and product development expenditures than competitors with a narrower scope of 

products. Regulatory burdens can prompt companies to exit the market or reduce their equipment 

offerings, potentially reducing competition. Smaller companies in particular can be affected by 

regulatory costs since these companies have lower sales volumes over which they can amortize 

the costs of meeting new regulations. A proposed standard is not economically justified if it 

contributes to an unacceptable level of cumulative regulatory burden. 

For the cumulative regulatory burden analysis, DOE looks at other regulations that could 

affect PTAC and PTHP manufacturers that will take effect approximately three years before or 

after the 2017 compliance date of this final rule. In interviews, manufacturers cited federal 

regulations on equipment other than PTACs and PTHPs that contribute to their cumulative 

regulatory burden. The compliance years and expected industry conversion costs of relevant 

amended energy conservation standards are presented in Table 12.6.2.
12-25

Table 12.6.2 Compliance Dates and Expected Conversion Expenses of Federal Energy 

Conservation Standards Affecting PTAC and PTHP Manufacturers

Federal Energy Conservation 

Standards

Approximate 

Compliance Date

Estimated Total 

Industry Conversion 

Expense

2011 Room Air Conditioners

76 FR 22454 (April 21, 2011);

76 FR 52854 (August 24, 2011)

2014 $171M (2009$)

2007 Residential Furnaces & Boilers

72 FR 65136 (Nov. 19, 2007) 2015 $88M (2006$)*

2011 Residential Furnaces

76 FR 37408 (June 27, 2011);

76 FR 67037 (Oct. 31, 2011)

2015 $2.5M (2009$)**

2011 Residential Central Air 

Conditioners and Heat Pumps

76 FR 37408 (June 27, 2011);

76 FR 67037 (Oct. 31, 2011)

2015 $26.0M (2009$)**

2010 Gas Fired and Electric Storage 

Water Heaters

75 FR 20112 (April 16, 2010)

2015 $95.4M (2009$)

Dishwashers*** 2018 TBD

Commercial Packaged AirConditioning and Heating 

Equipment***

79 FR 58948 (September 30, 2014)

2018 $226.4M 

(2013$)

Commercial Warm-Air Furnaces*** 2018 $19.9M

(2013$)

Furnace Fans

79 FR 38129 (July 3, 2014) 2019 $40.6M (2013$)

Miscellaneous Residential 

Refrigeration*** 2019 TBD

Single Packaged Vertical Units***

79 FR 78614 (December 30, 2014) 2019 $16.1M(2013$)

Commercial Water Heaters*** 2019 TBD

Commercial Packaged Boilers*** 2020 TBD

* Conversion expenses for manufacturers of oil-fired furnaces and gas-fired and oil-fired boilers associated with the November 2007 final rule for 

residential furnaces and boilers are excluded from this figure. The 2011 direct final rule for residential furnaces sets a higher standard and earlier 

compliance date for oil-fired furnaces than the 2007 final rule. As a result, manufacturers will be required design to the 2011 direct final rule 

standard. The conversion costs associated with the 2011 direct final rule are listed separately in this table. EISA 2007 legislated higher standards 

and earlier compliance dates for residential boilers than were in the November 2007 final rule. As a result, gas-fired and oil-fired boiler 

manufacturers were required to design to the EISA 2007 standard beginning in 2012. The conversion costs listed for residential gas-fired and oilfired boilers in the November 2007 residential furnaces and boilers final rule analysis are not included in this figure.

**Estimated industry conversion expense and approximate compliance date reflect a court-ordered April 24, 2014 remand of the residential nonweatherized and mobile home gas furnaces standards set in the 2011 Energy Conservation Standards for Residential Furnaces and Residential 
12-26

Central Air Conditioners and Heat Pumps. The costs associated with this rule reflect implementation of the amended standards for the remaining 

furnace product classes (i.e., oil-fired furnaces).

***The final rule for this energy conservation standard has not been published. The compliance date and analysis of conversion costs have not 

been finalized at this time. (If a value is provided for total industry conversion expense, this value represents an estimate from the NOPR.)

Additionally, manufacturers cited increasing ENERGY STAR standards for room air 

conditioners and ductless heating and cooling systems as a source of regulatory burden. 

However, DOE does not consider ENERGY STAR in its presentation of cumulative regulatory 

burden because ENERGY STAR is a voluntary program and is not federally mandated. DOE 

also notes that it does not consider proposed legislation in its cumulative regulatory burden 

analysis because the impacts of such legislation would be speculative.

Manufacturers also cited the U.S. EPA Significant New Alternatives Policy (SNAP) 

Program as a source of regulatory burden. The SNAP Program evaluates and regulates

substitutes for ozone-depleting chemicals (such as air conditioning refrigerants) that are being 

phased out under the stratospheric ozone protection provisions of the Clean Air Act. On July 9, 

2014, the EPA issued a notice of proposed rulemaking proposing to list three flammable 

refrigerants (HFC-32 (R-32), Propane (R-290), and R-441A) as new acceptable substitutes, 

subject to use conditions, for refrigerant in the Household and Light Commercial Air 

Conditioning class of equipment. 79 FR 38811 (July 9, 2014). On February 27, 2015, the EPA 

finalized its proposed rule, and the final rule allows the use of R-32, R-290, and R-441A in 

limited amounts in PTAC and PTHP applications.a DOE notes that the EPA has not proposed 

delisting R-410A for use in new production in the Household and Light Commercial Air 

Conditioning class of equipment (which includes PTAC and PTHP equipment). DOE also notes 

that the use of alternate refrigerants by manufacturers of PTACs and PTHPs would not be 

required as a direct result of this rule. Furthermore, there is no requirement (nor any proposal to 

adopt requirements) mandating the use of alternate refrigerants at this time. Hence, alternate 

refrigerants were not considered in this analysis.

12.7 CONCLUSION

The following section summarizes the range of financial impacts DOE believes PTAC 

and PTHP manufacturers are likely to experience as a result of amended energy conservation 

standards. DOE also notes that while these scenarios bound the range of most plausible impacts 

on manufacturers, there potentially could be circumstances that cause manufacturers to 

experience impacts outside this range. 

Each TSL analyzed in this rulemaking represents a percentage increase in efficiency 

above current federal minimum efficiency standards. Specifically, TSLs 1, 2, 3, 4, and 5 

represent respective increases of 4 percent, 8 percent, 12 percent, 16 percent, and 18 percent 

 a The pre-publication version of the final rule is available from the EPA at: 

www.epa.gov/ozone/snap/download/SAN_5745-SNAP_Low_GWP_Refrigerants_FRM_Signature_Version-signed2-27-2015.pdf
12-27

above the current federal minimum efficiency standard for PTACs of a specified cooling 

capacity. See section 12.3.3 for further discussion of the TSLs. 

12.7.1 Conclusions for PTACs and PTHPs MIA

Table 12.7.1 presents a range of results reflecting both the preservation of gross margin 

percentage markup scenario and the preservation of operating profit markup scenario. As 

explained in section 12.3.7, the preservation of operating profit scenario accounts for the more 

severe impacts presented. Estimated conversion costs do not vary with the markup scenario.

Table 12.7.1 Manufacturer Impact Analysis Results for PTACs and PTHPs, Gross 

Margin Percentage Markup Scenario*

Units Base Case Trial Standard Level*

1 2 3 4 5

INPV 2014$M 62.2 61.1 63.1 61.9 63.1 60.3

Change in INPV

2014$M - (1.1) 0.8 (0.3) 0.8 (1.9)

% Change - (1.8) 1.3 (0.5) 1.4 (3.1)

Product Conversion 

Costs 2014$M - 2.2 4.8 7.3 8.6 13.7

Capital Conversion Costs 2014$M - 2.3 2.9 7.2 7.2 7.5

Total Conversion Costs 2014$M - 4.5 7.7 14.5 15.8 21.2

Free Cash Flow **

2014$M 3.9 2.3 1.4 (1.3) (1.7) (3.4)

% Change - (40.6) (64.9) (133.2) (144.5) (188.5)

* Parentheses indicate negative values.
12-28

Table 12.7.2 Manufacturer Impact Analysis Results for PTACs and PTHPs, Preservation 

of Operating Profit Markup Scenario*

Units Base Case Trial Standard Level*

1 2 3 4 5

INPV 2014$M 62.2 60.7 61.8 59.3 58.9 55.6

Change in INPV

2014$M - (1.5) (0.5) (3.0) (3.4) (6.7)

% Change - (2.4) (0.8) (4.8) (5.4) (10.7)

Product Conversion 

Costs 2014$M - 2.2 4.8 7.3 8.6 13.7

Capital Conversion Costs 2014$M - 2.3 2.9 7.2 7.2 7.5

Total Conversion Costs 2014$M - 4.5 7.7 14.5 15.8 21.2

Free Cash Flow **

2014$M 3.9 2.3 1.3 (1.4) (1.9) (3.6)

% Change - (41.1) (66.2) (135.6) (148.3) (192.8)

* Parentheses indicate negative values.

TSL 1 represents a 4 percent increase above current federal minimum efficiency 

standards for PTACs. At TSL 1, DOE estimates the impacts on INPV to range from -$1.5 

million to -$1.1 million, or a change of -2.4 percent to -1.8 percent. Industry free cash flow is 

estimated to decrease by as much as $1.6 million, in the preservation of operating profit markup 

scenario, or a change of 41.1 percent compared to the base-case value of $3.9 million in the year 

before the compliance date (2018). At TSL 1, DOE estimates industry conversion costs of $4.5 

million.

TSL 2 represents an 8 percent increase above current federal minimum efficiency 

standards for PTACs. At TSL 2, DOE estimates impacts on INPV to range from -$0.5 million to 

$0.8 million, or a change in INPV of -0.8 percent to 1.3 percent. At this level, industry free cash 

flow is estimated to decrease by as much as $2.6 million, in the preservation of operating profit 

markup scenario, or a change of 66.2 percent compared to the base-case value of $3.9 million in 

the year before the compliance date (2018). DOE expects conversion costs at this level to 

increase to $7.7 million, reflecting the need for additional motor and control changes as well as a 

more significant R&D and testing burden. The INPV impacts at TSL 2 are slightly less severe 

than those at TSL 1 due to the interplay of conversion costs, manufacturer selling prices, and 

shipments. Specifically, the anticipated increase in per-unit purchase price at this level combined 

with steady shipments is expected to dampen the effects of conversion costs on INPV. 

TSL 3 represents a 12 percent increase above current federal minimum efficiency 

standards for PTACs. At TSL 3, DOE estimates impacts on INPV to range from -$3.0 million to 

-$0.3 million, or a change in INPV of -4.8 percent to -0.5 percent. At this level, industry free 

cash flow is estimated to decrease by as much as $5.2 million, in the preservation of operating 

profit markup scenario, or a change of 135.6 percent compared to the base-case value of $3.9 
12-29

million in the year before the compliance date (2018). DOE estimates conversion costs at TSL 3 

would increase to $14.5 million, nearly double the expected conversion costs at TSL 2. 

Anticipated conversion costs at this level include investing in new tooling and redesigning 

equipment to incorporate additional coils and/or formed coils.

TSL 4 represents a 16 percent increase above current federal minimum efficiency 

standards for PTACs. At TSL 4, DOE estimates impacts on INPV to range from -$3.4 million to 

$0.8 million, or a change in INPV of -5.4 percent to 1.4 percent. At this level, industry free cash 

flow is estimated to decrease by as much as $5.7 million, in the preservation of operating profit 

markup scenario, or a change of 148.3 percent compared to the base-case value of $3.9 million in 

the year before the compliance date (2018). DOE estimates conversion costs at TSL 4 would 

increase to $15.8 million. At this level, however, DOE does not anticipate capital conversion 

costs beyond those required at TSL 3. Rather, product conversion costs account for the full 

increase. Similar to TSL 2, the INPV impacts at TSL 4 are slightly less severe than those at TSL 

3 due to the interplay of conversion costs, manufacturer selling prices, and shipments. The 

anticipated increase in per-unit purchase price at this level combined with steady shipments is 

expected to dampen the effects of conversion costs on INPV.

TSL 5 represents the use of max-tech design options for each equipment class. At this 

level, DOE estimates impacts on INPV to range from -$6.7 million to -$1.9 million, or a change 

in INPV of -10.7 percent to -3.1 percent. Industry free cash flow is estimated to decrease by as 

much as $7.5 million, in the preservation of operating profit markup scenario, or a change of 

192.8 percent compared to the base-case value of $3.9 million in the year before the compliance 

date (2018). At this level, DOE estimates conversion costs would increase to $21.2 million.
12-30

REFERENCES

1. U.S. Securities and Exchange Commission. Annual 10-K Reports. Various Years. 

<www.sec.gov>

2. Standard and Poor’s Financial Services LLC. Company Credit Ratings, Various Companies. 

<www2.standardandpoors.com>

3. Hoovers Inc. Company Profiles. Various Companies. <www.hoovers.com/>

4. U.S.Census Bureau, Annual Survey of Manufacturers: General Statistics: Statistics for 

Industry Groups and Industries (2010) (Available at: 

<www.census.gov/manufacturing/asm/index.html>) 

5. U.S. Small Business Association. Table of Small Business Size Standards. 2012. 

<www.sba.gov/content/table-small-business-size-standards>.

6. McKinsey & Company, Inc. Valuation: Measuring and Managing the Value of Companies, 

3rd Edition, Copeland, Koller, Murrin. New York: John Wiley & Sons, 2000.
13-i

CHAPTER 13. EMISSIONS IMPACT ANALYSIS

TABLE OF CONTENTS

13.1 INTRODUCTION ......................................................................................................... 13-1

13.2 AIR QUALITY REGULATIONS AND EMISSIONS IMPACTS............................... 13-1

13.3 POWER SECTOR AND SITE EMISSIONS FACTORS............................................. 13-3

13.4 UPSTREAM EMISSIONS FACTORS......................................................................... 13-4

13.5 EMISSIONS IMPACT RESULTS................................................................................ 13-5

REFERENCES ........................................................................................................................ 13-10

LIST OF TABLES

Table 13.3.1 Power Plant Emissions Factors.................................................................... 13-3

Table 13.4.1 Electricity Upstream Emissions Factors...................................................... 13-4

Table 13.5.1 Cumulative Emissions Reduction for Potential Standard for Packaged 

Terminal Air Conditioners and Packaged Terminal Heat Pumps................ 13-5

Table 13.5.2 Cumulative Emissions Reduction Estimated for Packaged Terminal 

Air Conditioners and Packaged Terminal Heat Pumps Trial Standard 

Levels Compared to the Alternative Base Case........................................... 13-9

LIST OF FIGURES

Figure 13.5.1 CO2 Total Emissions Reduction .................................................................. 13-6

Figure 13.5.2 SO2 Total Emissions Reduction................................................................... 13-6

Figure 13.5.3 NOx Total Emissions Reduction .................................................................. 13-7

Figure 13.5.4 Hg Total Emissions Reduction .................................................................... 13-7

Figure 13.5.5 N2O Total Emissions Reduction .................................................................. 13-8

Figure 13.5.6 CH4 Total Emissions Reduction .................................................................. 13-8
13-1

CHAPTER 13. EMISSIONS IMPACT ANALYSIS

13.1 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 

15 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 10).

13.2 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 

 a www.epa.gov/climateleadership/documents/resources/mfgrfg.pdf
13-2

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. 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.b On 

October 23, 2014, the D.C. Circuit lifted the stay of CSAPR. c Pursuant to this action, CSAPR 

went into effect (and CAIR ceased to be in effect) as of January 1, 2015. 

Because AEO 2014 was prepared prior to the Supreme Court's opinion, it assumed that 

CAIR remains a binding regulation through 2040. Thus, DOE’s analysis used emissions factors 

that assume that CAIR, not CSAPR, is the regulation in force. However, the difference between 

CAIR and CSAPR is not relevant for the purpose of DOE's analysis of emissions impacts from 

energy conservation standards.

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 

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 

 b See EPA v. EME Homer City Generation, 134 S.Ct. 1584, 1610 (U.S. 2014). 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. 

c See Georgia v. EPA, Order (D. C. Cir. filed October 23, 2014) (No. 11-1302), 
13-3

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.

13.3 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 13.3.1 presents 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 for space cooling and lighting in commercial buildings. The average factors for 

each year take into account the projected shares of each of the sources in total electricity 

generation. 

Table 13.3.1 Power Plant Emissions Factors

Unit* 2020 2025 2030 2035 2040

CO2 kg/MWh 708 630 569 521 478

SO2 g/MWh 679 530 446 374 334

NOx g/MWh 553 463 405 357 324

Hg g/MWh 0.00209 0.00164 0.00138 0.00115 0.00103

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.
13-4

13.4 UPSTREAM EMISSIONS FACTORS 

The upstream emissions accounting uses the same approach as the upstream energy 

accounting described in appendix 10-A. 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. 

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 13.4.1 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 13.4.1 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 0.00001 0.00001 0.00001 0.00001 0.00001

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.
13-5

13.5 EMISSIONS IMPACT RESULTS

Table 13.5.1 presents the estimated cumulative emissions reductions for the lifetime of 

products sold during the 30 year analysis period at all TSLs.

Table 13.5.1 Cumulative Emissions Reduction for Potential Standard for Packaged 

Terminal Air Conditioners and Packaged Terminal Heat Pumps

ASHRAE TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Power Sector and Site Emissions

CO2 (million metric tons) 0.049 0.788 3.04 5.90 7.57 7.80

SO2 (thousand tons) 0.041 0.651 2.50 4.85 6.28 6.50

NOX (thousand tons) 0.038 0.607 2.34 4.53 5.84 6.03

Hg (tons) 0.000 0.002 0.008 0.015 0.019 0.020

N2O (thousand tons) 0.001 0.011 0.043 0.082 0.105 0.108

CH4 (thousand tons) 0.005 0.077 0.297 0.576 0.731 0.752

Upstream Emissions

CO2 (million metric tons) 0.003 0.045 0.173 0.336 0.424 0.436

SO2 (thousand tons) 0.000 0.008 0.031 0.059 0.075 0.077

NOX (thousand tons) 0.039 0.636 2.47 4.79 6.04 6.20

Hg (tons) 0.000 0.000 0.000 0.000 0.000 0.000

N2O (thousand tons) 0.000 0.000 0.002 0.003 0.004 0.004

CH4 (thousand tons) 0.225 3.70 14.4 27.9 35.2 36.1

Total Emissions

CO2 (million metric tons) 0.052 0.833 3.21 6.24 7.99 8.24

SO2 (thousand tons) 0.042 0.658 2.53 4.91 6.36 6.58

NOX (thousand tons) 0.076 1.24 4.81 9.32 11.9 12.2

Hg (tons) 0.000 0.002 0.008 0.015 0.020 0.020

N2O (thousand tons) 0.001 0.011 0.044 0.085 0.108 0.111

CH4 (thousand tons) 0.229 3.78 14.7 28.5 35.9 36.8

Figure 13.5.1 through Figure 13.5.6 show the annual reductions for total emissions for 

each type of emission from each TSL. The reductions reflect the lifetime impacts of products 

sold during the 30 year analysis period at all TSLs.
13-6

Figure 13.5.1 CO2 Total Emissions Reduction

Figure 13.5.2 SO2 Total Emissions Reduction

0

50

100

150

200

250

300

350

2017202220272032

2037

204220472052205720622067

kt

CO2 Emissions Reduction

ASHRAE TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0

50

100

150

200

250

300

20172022202720322037204220472052205720622067

ton

SO2 Emissions Reduction

ASHRAE TSL 1 TSL 2 TSL 3 TSL 4 TSL 5
13-7

Figure 13.5.3 NOx Total Emissions Reduction

Figure 13.5.4 Hg Total Emissions Reduction

0

50

100

150

200

250

300

350

400

450

500

2017202220272032

2037

204220472052205720622067

ton

NOx Emissions Reduction

ASHRAE TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0.0000

0.0001

0.0002

0.0003

0.0004

0.0005

0.0006

0.0007

0.0008

0.0009

20172022202720322037204220472052205720622067

ton

Hg Emissions Reduction

ASHRAE TSL 1 TSL 2 TSL 3 TSL 4 TSL 5
13-8

Figure 13.5.5 N2O Total Emissions Reduction

Figure 13.5.6 CH4 Total Emissions Reduction

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

2017202220272032

2037

204220472052205720622067

ton

N2OEmissions Reduction

ASHRAE TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0

200

400

600

800

1,000

1,200

1,400

20172022202720322037204220472052205720622067

ton

CH4 Emissions Reduction

ASHRAE TSL 1 TSL 2 TSL 3 TSL 4 TSL 5
13-9

As described in Chapter 8, the alternative base case analysis was based off a scenario 

where the mandatory efficiency level is the Federal minimum, against which DOE determined 

the impacts on emissions. The estimated cumulative emissions reduction for PTACs and PTHPs 

are shown in Table 13.5.2, for informational purposes. 

Table 13.5.2 Cumulative Emissions Reduction Estimated for Packaged Terminal Air 

Conditioners and Packaged Terminal Heat Pumps Trial Standard Levels 

Compared to the Alternative Base Case

ASHRAE 1 2 3 4 5

Primary Emissions

CO2 (million metric tons) 0.05 0.84 3.09 5.95 7.62 7.85

SO2 (thousand tons) 0.04 0.69 2.54 4.89 6.32 6.54

NOX (thousand tons) 0.04 0.64 2.37 4.57 5.87 6.07

Hg (tons) 0.00 0.00 0.01 0.02 0.02 0.02

N2O (thousand tons) 0.00 0.01 0.04 0.08 0.11 0.11

CH4 (thousand tons) 0.00 0.08 0.30 0.58 0.74 0.76

Upstream Emissions

CO2 (million metric tons) 0.00 0.05 0.18 0.34 0.43 0.44

SO2 (thousand tons) 0.00 0.01 0.03 0.06 0.08 0.08

NOX (thousand tons) 0.04 0.68 2.51 4.83 6.08 6.24

Hg (tons) 0.00 0.00 0.00 0.00 0.00 0.00

N2O (thousand tons) 0.00 0.00 0.00 0.00 0.00 0.00

CH4 (thousand tons) 0.22 3.94 14.62 28.11 35.40 36.32

Full Fuel Cycle Emissions

CO2 (million metric tons) 0.05 0.89 3.27 6.29 8.04 8.29

SO2 (thousand tons) 0.04 0.70 2.57 4.95 6.40 6.62

NOX (thousand tons) 0.08 1.32 4.88 9.40 11.95 12.30

Hg (tons) 0.00 0.00 0.01 0.02 0.02 0.02

N2O (thousand tons) 0.00 0.01 0.04 0.09 0.11 0.11

CH4 (thousand tons) 0.23 4.02 14.92 28.70 36.14 37.08

Note: Components may not sum to total due to rounding. Values appear as “0.00” due to rounding.
13-10

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 Impacts 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. 
CHAPTER 14. MONETIZATION OF EMISSION REDUCTIONS BENEFITS

TABLE OF CONTENTS 

14.1 INTRODUCTION ......................................................................................................... 14-1

14.2 MONETIZING CARBON DIOXIDE EMISSIONS ..................................................... 14-1

14.2.1 Social Cost of Carbon ........................................................................................ 14-1

14.2.2 Monetizing Carbon Dioxide Emissions ............................................................. 14-2

14.3 DEVELOPMENT OF SOCIAL COST OF CARBON VALUES ................................. 14-2

14.3.1 Current Approach and Key Assumptions .......................................................... 14-2

14.3.2 Limitations of Current Estimates ....................................................................... 14-4

14.4 VALUATION OF OTHER EMISSIONS REDUCTIONS ........................................... 14-5

14.5 RESULTS ...................................................................................................................... 14-5

REFERENCES ........................................................................................................................ 14-12

LIST OF TABLES 

Table 14.3.1 Annual SCC Values for 2010-2050 from 2010 Interagency Report (in 

2007$ per Metric Ton) ................................................................................. 14-3

Table 14.3.2 Annual SCC Values for 2010–2050 from 2013 Interagency Update (in 

2007$ per Metric Ton of CO2) ..................................................................... 14-4

Table 14.5.1 Estimates of Global Present Value of CO2 Emissions Reduction under 

TSLs for Packaged Terminal Air Conditioners and Packaged Terminal 

Heat Pumps .................................................................................................. 14-6

Table 14.5.2 Estimates of Domestic Present Value of CO2 Emissions Reduction 

under TSLs for Packaged Terminal Air Conditioners and Packaged 

Terminal Heat Pumps .................................................................................. 14-7

Table 14.5.3 Estimates of Present Value of NOX Emissions Reduction under TSLs 

for Packaged Terminal Air Conditioners and Packaged Terminal Heat 

Pumps ........................................................................................................... 14-8

Table 14.5.4 Estimates of Global Present Value of CO2 Emissions Reduction under 

Packaged Terminal Air Conditioners and Packaged Terminal Heat 

Pumps Trial Standard Levels Compared to the Alternative Base Case ....... 14-9

Table 14.5.5 Estimates of Domestic Present Value of CO2 Emissions Reduction 

under Packaged Terminal Air Conditioners and Packaged Terminal 

Heat Pumps Trial Standard Levels Compared to the Alternative Base 

Case ............................................................................................................ 14-10

Table 14.5.6 Estimates of Present Value of NOX Emissions Reduction under 

Packaged Terminal Air Conditioners and Packaged Terminal Heat 

Pumps Trial Standard Levels Compared to the Alternative Base Case ..... 14-11

14-i 
CHAPTER 14. MONETIZATION OF EMISSION REDUCTIONS BENEFITS 

14.1 INTRODUCTION

As part of its assessment of the effects of potential energy conservation standards for 

Packaged Terminal Air Conditioners and Packaged Terminal 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. 

14.2 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.

14.2.1 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, 

key uncertainties and model differences transparently and consistently inform the range of SCC 

estimates developed for use in the rulemaking process.

14-1 
14.2.2 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.

14.3 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.

14.3.1 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 peerreviewed 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.

14-2
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 14.3.1 presents the values in the 2010 

interagency group report.4

Table 14.3.1 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 14.3.2 shows the updated 

sets of SCC estimates in 5-year increments from 2010 to 2050. The full set of annual SCC 

estimates for 2010–2050 is presented in appendix 14B of this TSD. The central value that 

emerges is the average SCC across models at a 3-percent discount rate. To capture the 

14-3 
uncertainties involved in regulatory impact analysis, however, the interagency group emphasizes 

the importance of including all four sets of SCC values. 

Table 14.3.2 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

14.3.2 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 

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. 

14-4 
14.4 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 13, 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 $484 to $4,971 per ton (in 2014$).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. 

14.5 RESULTS

Table 14.5.1 presents the global values of CO2 emissions reductions for each considered 

TSL. 

14-5 
Table 14.5.1 Estimates of Global Present Value of CO2 Emissions Reduction under TSLs 

for Packaged Terminal Air Conditioners and Packaged Terminal Heat 

Pumps 

TSL

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

1 5.60 25.7 40.7 79.3

2 21.4 98.3 156 304

3 41.7 192 304 592 

4 55.2 250 396 772

5 57.3 259 409 799

Upstream Emissions

1 0.310 1.43 2.28 4.44 

2 1.19 5.54 8.81 17.1

3 2.32 10.8 17.1 33.3

4 3.02 13.8 22.0 42.8 

5 3.12 14.3 22.6 44.1

Full-Fuel-Cycle Emissions

1 5.91 27.1 43.0 83.7

2 22.5 104 165 321

3 44.0 202 321 626

4 58.2 264 418 815

5 60.5 273 432 843

* For each of the four cases, the corresponding global SCC value for emissions in 2015 is $12.2, $41.2, $63.4, and

$121 per metric ton (2014$).

After calculating global values of CO2 emissions reductions for each considered TSL, 

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 14.5.2. 

14-6 
Table 14.5.2 Estimates of Domestic Present Value of CO2 Emissions Reduction under 

TSLs for Packaged Terminal Air Conditioners and Packaged Terminal Heat 

Pumps 

TSL

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

1 0.4 to 1.3 1.8 to 5.9 2.8 to 9.4 5.5 to 18.2

2 1.5 to 4.9 6.9 to 22.6 10.9 to 35.9 21.3 to 69.9

3 2.9 to 9.6 13.4 to 44.0 21.3 to 69.9 41.5 to 136.2 

4 3.9 to 12.7 17.5 to 57.5 27.7 to 91.0 54.0 to 177.6

5 4.0 to 13.2 18.1 to 59.5 28.7 to 94.2 55.9 to 183.8

Upstream Emissions

1 0.0 to 0.1 0.1 to 0.3 0.2 to 0.5 0.3 to 1.0 

2 0.1 to 0.3 0.4 to 1.3 0.6 to 2.0 1.2 to 3.9

3 0.2 to 0.5 0.8 to 2.5 1.2 to 3.9 2.3 to 7.7

4 0.2 to 0.7 1.0 to 3.2 1.5 to 5.0 3.0 to 9.8 

5 0.2 to 0.7 1.0 to 3.3 1.6 to 5.2 3.1 to 10.1

Full-Fuel-Cycle Emissions

1 0.4 to 1.4 1.9 to 6.2 3.0 to 9.9 5.9 to 19.3

2 1.6 to 5.2 7.3 to 23.9 11.6 to 38.0 22.5 to 73.9

3 3.1 to 10.1 14.2 to 46.5 22.5 to 73.9 43.8 to 143.9

4 4.1 to 13.4 18.5 to 60.7 29.2 to 96.1 57.0 to 187.4

5 4.2 to 13.9 19.1 to 62.8 30.2 to 99.4 59.0 to 193.9

* For each of the four cases, the corresponding global SCC value for emissions in 2015 is $12.2, $41.2, $63.4, and

$121 per metric ton (2014$).

Table 14.5.3 presents the present value of cumulative NOX emissions reductions for each 

TSL. Monetary values are calculated using the average dollar-per-ton values assigned to NOX

emissions at 7-percent and 3-percent discount rates. 

14-7 
Table 14.5.3 Estimates of Present Value of NOX Emissions Reduction under TSLs for 

Packaged Terminal Air Conditioners and Packaged Terminal Heat Pumps

TSL

3% discount rate 7% discount rate

Million 2014$

Primary Energy Emissions

1 0.871 0.427 

2 3.30 1.58 

3 6.45 3.11

4 8.63 4.34

5 9.01 4.60 

Upstream Emissions

1 0.873 0.403

2 3.34 1.51

3 6.53 2.97 

4 8.56 4.07 

5 8.87 4.27

Full-Fuel-Cycle Emissions

1 1.74 0.830

2 6.64 3.10

3 13.0 6.08

4 17.2 8.42

5 17.9 8.87

As described in Chapter 8, the alternative base case analysis was based off a scenario 

where the mandatory efficiency level is the Federal minimum, against which DOE determined 

the impacts on the monetization of emissions reduction. Estimates of the present values of global 

and domestic CO2 emissions reduction and of cumulative NOX emissions reductions are shown 

in Table 14.5.4 to Table 14.5.6, for informational purposes. 

14-8 
Table 14.5.4 Estimates of Global Present Value of CO2 Emissions Reduction under 

Packaged Terminal Air Conditioners and Packaged Terminal Heat Pumps 

Trial Standard Levels Compared to the Alternative Base Case 

TSL

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 

1 5.95 27.25 43.24 84.21 

2 21.70 99.93 158.73 309.02 

3 42.04 193.10 306.57 597.15 

4 55.52 251.48 398.20 776.89 

5 57.68 260.37 412.02 803.97 

Upstream Emissions

1 0.33 1.52 2.42 4.71 

2 1.21 5.63 8.95 17.42 

3 2.34 10.86 17.27 33.62 

4 3.04 13.92 22.09 43.08 

5 3.14 14.34 22.75 44.35 

Full-Fuel-Cycle Emissions

1 6.28 28.77 45.66 88.92 

2 22.91 105.56 167.69 326.43

3 44.38 203.95 323.85 630.76 

4 58.57 265.40 420.30 819.97 

5 60.82 274.72 434.76 848.32 

* For each of the four cases, the corresponding global SCC value for emissions in 2015 is $12.2,

$41.2, $63.4, and $121 per metric ton (2014$).

14-9 
Table 14.5.5 Estimates of Domestic Present Value of CO2 Emissions Reduction under 

Packaged Terminal Air Conditioners and Packaged Terminal Heat Pumps 

Trial Standard Levels Compared to the Alternative Base Case 

TSL

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 

1 0.4 to 1.4 1.9 to 6.3 3.0 to 9.9 5.9 to 19.4 

2 1.5 to 5.0 7.0 to 23.0 11.1 to 36.5 21.6 to 71.1

3 2.9 to 9.7 13.5 to 44.4 21.5 to 70.5 41.8 to 137.3 

4 3.9 to 12.8 17.6 to 57.8 27.9 to 91.6 54.4 to 178.7

5 4.0 to 13.3 18.2 to 59.9 28.8 to 94.8 56.3 to 184.9

Upstream Emissions

1 0.0 to 0.1 0.1 to 0.4 0.2 to 0.6 0.3 to 1.1 

2 0.1 to 0.3 0.4 to 1.3 0.6 to 2.1 1.2 to 4.0 

3 0.2 to 0.5 0.8 to 2.5 1.2 to 4.0 2.4 to 7.7 

4 0.2 to 0.7 1.0 to 3.2 1.5 to 5.1 3.0 to 9.9 

5 0.2 to 0.7 1.0 to 3.3 1.6 to 5.2 3.1 to 10.2 

Full-Fuel-Cycle Emissions

1 0.4 to 1.4 2.0 to 6.6 3.2 to 10.5 6.2 to 20.5

2 1.6 to 5.3 7.4 to 24.3 11.7 to 38.6 22.9 to 75.1

3 3.1 to 10.2 14.3 to 46.9 22.7 to 74.5 44.2 to 145.1

4 4.1 to 13.5 18.6 to 61.0 29.4 to 96.7 57.4 to 188.6

5 4.3 to 14.0 19.2 to 63.2 30.4 to 100.0 59.4 to 195.1

* For each of the four cases, the corresponding global SCC value for emissions in 2015 is $12.2,

$41.2, $63.4, and $121 per metric ton (2014$).

14-10 
Table 14.5.6 Estimates of Present Value of NOX Emissions Reduction under Packaged 

Terminal Air Conditioners and Packaged Terminal Heat Pumps Trial 

Standard Levels Compared to the Alternative Base Case 

TSL 3% discount rate 7% discount rate

Million 2014$ 

Primary Energy Emissions 

1 0.92 0.45 

2 3.35 1.61 

3 6.50 3.14 

4 8.69 4.37 

5 9.07 4.62 

Upstream Emissions

1 0.93 0.43 

2 3.40 1.54 

3 6.58 3.00 

4 8.62 4.10 

5 8.92 4.30 

Full-Fuel-Cycle Emissions

1 1.85 0.88

2 6.75 3.15

3 13.08 6.13 

4 17.30 8.47 

5 17.99 8.92

14-11 
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>

14-12 
CHAPTER 15. UTILITY IMPACT ANALYSIS

TABLE OF CONTENTS

15.1 INTRODUCTION ......................................................................................................... 15-1

15.2 METHODOLOGY ........................................................................................................ 15-2

15.3 UTILITY IMPACT RESULTS ..................................................................................... 15-2

15.3.1 Installed Capacity............................................................................................... 15-2

15.3.2 Electricity Generation ........................................................................................ 15-5

15.3.3 Results Summary ............................................................................................... 15-8

REFERENCES ........................................................................................................................ 15-10

LIST OF TABLES

Table 15.3.1 Summary of Utility Impact Results............................................................. 15-9

LIST OF FIGURES

Figure 15.3.1 Total Electric Capacity Reduction............................................................... 15-3

Figure 15.3.2 Coal Capacity Reduction ............................................................................. 15-3

Figure 15.3.3 Nuclear Capacity Reduction ........................................................................ 15-4

Figure 15.3.4 Gas Combined Cycle Capacity Reduction................................................... 15-4

Figure 15.3.5 Peaking Capacity Reduction........................................................................ 15-5

Figure 15.3.6 Renewables Capacity Reduction.................................................................. 15-5

Figure 15.3.7 Total Generation Reduction......................................................................... 15-6

Figure 15.3.8 Coal Generation Reduction.......................................................................... 15-6

Figure 15.3.9 Nuclear Generation Reduction..................................................................... 15-7

Figure 15.3.10 Gas Combined Cycle Generation Reduction ............................................... 15-7

Figure 15.3.11 Peaking Generation Reduction .................................................................... 15-8

Figure 15.3.12 Renewables Generation Reduction.............................................................. 15-8

15-i
CHAPTER 15. UTILITY IMPACT ANALYSIS

15.1 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.

The methodology is described in more detail in K. Coughlin, “Utility Sector Impacts of 

Reduced Electricity Demand.”3

This chapter presents the results for Packaged Terminal Air Conditioners and Packaged 

Terminal Heat Pumps.

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

15-1

 
15.2 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. DOE represents these marginal impacts using time series of 

impact factors.

The impact factors are calculated based on output from NEMS for the AEO 2014. 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 inter-related 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 types and technologies may 

change. Technology changes lead to a change in the proportion of fuel consumption to electricity 

generated (referred to as the heat rate). 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.

DOE defined impact factors describing the change in installed capacity and fuel 

consumption per unit reduction of site electricity demand. The impact factors vary by sector and 

end-use, as well as by year. DOE multiplied the impact factors by the stream of energy savings 

calculated in the NES (chapter 10) to produce estimates of the utility impacts. For Packaged 

Terminal Air Conditioners and Packaged Terminal Heat Pumps DOE used the impact factors for 

space cooling and lighting in commercial buildings.

15.3 UTILITY IMPACT RESULTS

This section presents results of the analysis for all of the capacity types.

15.3.1 Installed Capacity

The figures in this section show the changes in U.S. electricity installed capacity that 

result for each TSL by major plant type for selected years. Units are megawatts of capacity per 

gigawatt-hour of site electricity use (MW/GWh). b Note that a negative number means an 

increase in capacity under a TSL.

b These units are equivalent to GW/TWh.

15-2

 
Figure 15.3.1 Total Electric Capacity Reduction

Figure 15.3.2 Coal Capacity Reduction

0

50

100

150

200

250

2017

20222027203220372042204720522057

20622067

MW

Total

ASHRAE TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0

5

10

15

20

25

30

35

20172022202720322037204220472052205720622067

MW

Coal

ASHRAE TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15-3
Figure 15.3.3 Nuclear Capacity Reduction

Figure 15.3.4 Gas Combined Cycle Capacity Reduction

0.0

0.5

1.0

1.5

2.0

2.5

3.0

2017

20222027203220372042204720522057

20622067

MW

Nuclear

ASHRAE TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0

10

20

30

40

50

60

70

20172022202720322037204220472052205720622067

MW

NGCC

ASHRAE TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15-4
Figure 15.3.5 Peaking Capacity Reduction

Figure 15.3.6 Renewables Capacity Reduction

15.3.2 Electricity Generation

The figures in this section show the annual change in electricity generation that result for 

each TSL by fuel type. Note that a negative number means an increase in generation under a 

TSL.

0

20

40

60

80

100

120

140

20172022202720322037

20422047

2052205720622067

MW

Peaking

ASHRAE TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0

5

10

15

20

25

20172022202720322037204220472052205720622067

MW

Renewable

ASHRAE TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15-5
Figure 15.3.7 Total Generation Reduction

Figure 15.3.8 Coal Generation Reduction

0

100

200

300

400

500

600

700

2017

20222027203220372042204720522057

20622067

GWh

Total

ASHRAE TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0

20

40

60

80

100

120

140

160

180

200

20172022202720322037204220472052205720622067

GWh

Coal

ASHRAE TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15-6
Figure 15.3.9 Nuclear Generation Reduction

Figure 15.3.10 Gas Combined Cycle Generation Reduction

0

5

10

15

20

25

2017

20222027203220372042204720522057

20622067

GWh

Nuclear

ASHRAE TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0

50

100

150

200

250

300

350

20172022202720322037204220472052205720622067

GWh

NGCC

ASHRAE TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15-7
Figure 15.3.11 Peaking Generation Reduction

Figure 15.3.12 Renewables Generation Reduction

15.3.3 Results Summary 

Table 15.3.1 presents a summary of the utility impact results for Packaged Terminal Air 

Conditioners and Packaged Terminal Heat Pumps.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

2017

20222027203220372042204720522057

20622067

GWh

Peaking

ASHRAE TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0

10

20

30

40

50

60

70

80

20172022202720322037204220472052205720622067

GWh

Renewable

ASHRAE TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15-8
Table 15.3.1 Summary of Utility Impact Results 

ASHRAE TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Installed Capacity Reduction (MW)

2020 0.420 4.10 13.6 26.4 39.9 46.0

2025 0.877 13.0 45.9 90.2 137 150

2030 1.10 18.3 68.6 136 203 211

2035 1.22 20.3 79.7 159 220 222

2040 1.38 22.4 89.5 180 219 219

Electricity Generation Reduction (GWh)

2020 1.42 13.9 46.1 89.1 135 156

2025 2.45 36.2 128 252 383 418

2030 2.93 48.8 182 362 538 561

2035 3.05 50.8 199 398 550 555

2040 3.27 53.0 212 425 519 519

15-9
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/pdf/0383(2014).pdf>

3. Coughlin, K., Utility Sector Impacts of Reduced Electricity Demand, 2014. Lawrence 

Berkeley National Laboratory. Report No. LBNL-6864E. 

15-10
CHAPTER 16. EMPLOYMENT IMPACT ANALYSIS

TABLE OF CONTENTS

16.1 INTRODUCTION ......................................................................................................... 16-1

16.2 ASSUMPTIONS............................................................................................................ 16-1

16.3 METHODOLOGY ........................................................................................................ 16-1

16.4 SHORT-TERM RESULTS............................................................................................ 16-2

16.5 LONG-TERM IMPACTS.............................................................................................. 16-3

REFERENCES .......................................................................................................................... 16-5

LIST OF TABLES

Table 16.4.1 Net National Short-Term Change in Employment (Number of Jobs)............... 16-3

16-i
CHAPTER 16. EMPLOYMENT IMPACT ANALYSIS

16.1 INTRODUCTION

The U.S. Department of Energy’s (DOE’s) employment impact analysis is designed to 

estimate indirect national job creation or elimination resulting from possible standards, due to 

reallocation of the associated expenditures for purchasing and operating PTACs/PTHPs. Job 

increases or decreases reported in this chapter are separate from the direct manufacturing sector 

employment impacts reported in chapter 12 and reflect the employment impact of efficiency 

standards on all other sectors of the economy. 

16.2 ASSUMPTIONS

DOE expects energy conservation standards to decrease energy consumption and,

therefore, to reduce energy expenditures. The savings in energy expenditures may be spent on 

new investment or not at all (i.e., they may remain “saved”). The standards may increase the 

purchase price of appliances, including the retail price plus sales tax, and increase installation 

costs. 

Using an input/output econometric model of the U.S. economy, this analysis estimated 

the short-term effect of these expenditure impacts on net economic output and employment. 

DOE intends this analysis to quantify the indirect employment impacts of these expenditure 

changes. It evaluated direct employment impacts at manufacturers’ facilities in the manufacturer 

impact analysis (see chapter 12).

DOE notes that ImSET is not a general equilibrium forecasting model and understands 

the uncertainties involved in projecting employment impacts, especially changes in the later 

years of the analysis.1 Because ImSET does not incorporate price changes, the employment 

effects predicted by ImSET would over-estimate the magnitude of actual job impacts over the 

long run for this rule. As input/output models do not allow prices to bring markets into 

equilibrium, they are best used for short-run analyses. DOE, therefore, includes a qualitative 

discussion of how labor markets are likely to respond in the longer term. In future rulemakings, 

DOE may consider the use of other modeling approaches for examining long-run employment 

impacts.

16.3 METHODOLOGY

The Department based its analysis on an input/output model of the U.S. economy that 

estimates the effects of standards on major sectors of the economy related to buildings and the

net impact of standards on jobs. The Pacific Northwest National Laboratory developed the 

model, ImSET 3.1.12 (Impact of Sector Energy Technologies), as a successor to ImBuild3, a 

special-purpose version of the IMPLAN4 national input/output model. ImSET estimates the 

employment and income effects of building energy technologies. In comparison with simple 

economic multiplier approaches, ImSET allows for a more complete and automated analysis of 

the economic impacts of energy efficiency investments in buildings.

16-1
In an input/output model, the level of employment in an economy is determined by the 

relationship of different sectors of the economy and the spending flows among them. Different 

sectors have different levels of labor intensity, and changes in the level of spending (e.g., due to 

the effects of an efficiency standard) in one sector of the economy will affect flows in other 

sectors, which affect the overall level of employment.

ImSET uses a 187-sector model of the national economy to predict the economic effects 

of residential and commercial building technologies. ImSET collects estimates of initial 

investments, energy savings, and economic activity associated with spending the savings 

resulting from standards (e.g., changes in final demand in personal consumption, business 

investment and spending, and government spending). It provides overall estimates of the change 

in national output for each input-output sector. The model applies estimates of employment and 

wage income per dollar of economic output for each sector and calculates impacts on national 

employment and wage income.

Energy efficiency technology primarily affects the U.S. economy along three spending 

pathways. First, general investment funds are diverted to sectors that manufacture, install, and 

maintain energy-efficient appliances. The increased cost of appliances leads to higher 

employment in the appliance manufacturing sectors and lower employment in other economic 

sectors. Second, commercial firm and residential spending are redirected from utilities toward 

firms that supply production inputs. Third, electric utility sector investment funds are released 

for use in other sectors of the economy. When consumers use less energy, electric utilities 

experience relative reductions in demand, which leads to reductions in utility sector investment 

and employment.

DOE also notes that the employment impacts estimated with ImSET for the entire 

economy differ from the employment impacts in the PTAC manufacturing sector estimated in 

chapter 12 using the Government Regulatory Impact Model (GRIM). The methodologies used 

and the sectors analyzed in the ImSET and GRIM models are different. 

16.4 SHORT-TERM RESULTS

The results in this section refer to impacts of PTAC/PTHP standards relative to the base 

case. DOE disaggregated the impact of standards on employment into three component effects: 

increased capital investment costs, decreased energy costs, and changes in operations and 

maintenance costs. DOE presents the summary impact.

Conceptually, one can consider the impact of the rule in its first year on three aggregate 

sectors: the PTAC production sector, the energy generation sector, and the general consumer 

good sector (as mentioned previously, ImSET’s calculations are made at a much more 

disaggregate level). By raising energy efficiency, the rule increases the purchase price of PTACs;

this increase in expenditures causes an increase in employment in this sector. At the same time, 

the improvements in energy efficiency reduce consumer expenditures on electricity. The 

16-2
reduction in electricity demand causes a reduction in employment in that sector. Finally, based 

on the net impact of increased expenditures on PTACs and reduced expenditures on electricity, 

consumer expenditures on everything else are either positively or negatively affected, increasing 

or reducing jobs in that sector accordingly. The model also captures any indirect jobs created or 

lost by changes in consumption due to changes in employment. (As more workers are hired, they 

consume more goods, generating more employment; the converse is true for workers who are 

laid off.)

Table 16.4.1 presents the modeled net employment impact from the rule in 2019, rounded 

to the nearest ten jobs. Approximately 40% of PTACs are domestically produced and 60% are 

imported or assembled from imported components. The net employment impact estimate is 

sensitive to assumptions regarding the return to the U.S. economy of money spent on imported 

PTACs. The two scenarios bounding the ranges presented in Table 16.4.1 represent situations in 

which none of the money spent on imported PTACs returns to the U.S. economy and all of the 

money spent on imported PTACs returns to the U.S. economy. The U.S. trade deficit in recent 

years suggests that between 50% and 75% of the money spent on imported PTACs is likely to 

return, with employment impacts falling within the ranges presented below.

Table 16.4.1 Net National Short-Term Change in Employment (Number of Jobs)

Trial Standard Level 2019 2024

ASHRAE (ASHRAE level: impact not modeled)

1 -10 to 0 -10 to 10

2 -60 to 0 -40 to 30

3 -130 to -10 -90 to 50

4 -220 to -20 -150 to 70

5 -260 to -20 -150 to 80

For context, the Office of Management and Budget (OMB) currently projects that the 

official unemployment rate may decline to 5.4% in 2019.

5 The unemployment rate in 2019 is 

projected to be close to “full employment.” When an economy is at full employment, any effects 

on net employment are likely to be transitory as workers change jobs, rather than enter or exit 

longer-term employment.

16.5 LONG-TERM IMPACTS

Due to the short payback period of energy efficiency improvements mandated by this 

rule, over the long term DOE expects the energy savings to consumers to increasingly dominate 

the increase in appliance costs, resulting in increased aggregate savings to consumers. As a 

result, DOE expects demand for electricity to decline over time and demand for other goods to 

increase. As the electricity generation sector is relatively capital intensive compared to the 

consumer goods sector, the net effect will be an increase in labor demand. In equilibrium, this 

should lead to upward pressure on wages and a shift in employment away from electricity 

16-3
generation towards consumer goods. Note that, in a long-run equilibrium, there is no net effect 

on total employment, because wages adjust to bring the labor market into equilibrium.

Nonetheless, even to the extent that markets are slow to adjust, DOE anticipates that net labor 

market impacts will be negligible over time due to the small magnitude of the short-term effects 

presented in Table 16.4.1. The ImSET model projections, assuming no price or wage effects 

until 2024, are included in the second column of Table 16.4.1.

16-4
REFERENCES

1. Scott, M., JM Roop, RW Schultz, DM Anderson, KA Cort, The Impact of DOE Building 

Technology Energy Efficiency Programs on U.S. Employment, Income, and Investment.

Energy Economics, 2008. 30(5): pp. 2283-2301 

2. Roop, J. M., M. J. Scott, and R. W. Schultz, ImSET: Impact of Sector Energy 

Technologies, 2005. Pacific Northwest National Laboratory. Richland, WA. Report No. 

PNNL- 15273. 

3. Scott, M. J., D. J. Hostick, and D. B. Belzer, ImBuild: Impact of Building Energy 

Efficiency Programs, April, 1998. Pacific Northwest National Laboratory. Richland, WA. 

Report No. PNNL-11884. 

4. Minnesota IMPLAN Group Inc., IMPLAN Professional: User's Guide, Analysis Guide, 

Data Guide, 1997. Stillwater, MN. 

5. Office of Management and Budget, Fiscal Year 2013 Mid-Session Review: Budget of the 

U.S. Government, 

<www.whitehouse.gov/sites/default/files/omb/budget/fy2013/assets/13msr.pdf>

16-5
17-i

CHAPTER 17. REGULATORY IMPACT ANALYSIS: ASSESSMENT OF 

ALTERNATIVES TO NATIONAL STANDARDS

TABLE OF CONTENTS

17.1 INTRODUCTION ................................................................................................... 17-1

17.2 NON-REGULATORY POLICIES.......................................................................... 17-2

17.2.1 Methodology............................................................................................................ 17-2

17.2.2 Assumptions Regarding Non-Regulatory Policies .................................................. 17-3

17.2.3 Policy Interactions ................................................................................................... 17-4

17.3 NON-REGULATORY POLICY ASSUMPTIONS ................................................ 17-4

17.3.1 No New Regulatory Action ..................................................................................... 17-5

17.3.2 Consumer Rebates ................................................................................................... 17-5

17.3.2.1 Methodology.......................................................................................... 17-5

17.3.2.2 Analysis.................................................................................................. 17-6

17.3.3 Consumer Tax Credits........................................................................................... 17-10

17.3.4 Manufacturer Tax Credits...................................................................................... 17-12

17.3.5 Voluntary Energy Efficiency Targets.................................................................... 17-14

17.3.6 Bulk Government Purchases.................................................................................. 17-17

17.4 IMPACTS OF NON-REGULATORY ALTERNATIVES................................... 17-20

REFERENCES ........................................................................................................................ 17-23

LIST OF TABLES

Table 17.1.1 Non-Regulatory Alternatives to National Standards...................................... 17-1

Table 17.2.1 Efficiency Levels for Trial Standard Levels for Packaged Terminal Air 

Conditioner and Packaged Terminal Heat Pump Equipment Classes 

(EER) .............................................................................................................. 17-4

Table 17.3.1 Benefit/Cost Ratios Without and With Rebates for Packaged Terminal 

Air Conditioners and Packaged Terminal Heat Pumps.................................. 17-7

Table 17.3.2 Market Penetrations in the First Year of the Analysis Period* 

Attributable to Consumer Rebates for Packaged Terminal Air 

Conditioners and Packaged Terminal Heat Pumps......................................... 17-9

Table 17.3.3 Market Penetrations in the First Year of the Analysis Period* 

Attributable to Consumer Tax Credits for Packaged Terminal Air 

Conditioners and Packaged Terminal Heat Pumps....................................... 17-12

Table 17.3.4 Market Penetrations in the First Year of the Analysis Period* 

Attributable to Manufacturer Tax Credits for Packaged Terminal Air 

Conditioners and Packaged Terminal Heat Pumps....................................... 17-14

Table 17.3.5 Estimated Market Barriers to Energy Efficient PTACs and PTHPs ............ 17-15

Table 17.3.6 Reduced Market Barriers to Energy Efficient PTACs and PTHPs .............. 17-16
17-ii

Table 17.3.7 Market Penetrations in the First Year of the Analysis Period* 

Attributable to Voluntary Energy Efficiency Targets for Packaged 

Terminal Air Conditioners and Packaged Terminal Heat Pumps................. 17-17

Table 17.3.8 Market Penetrations in the First Year of the Analysis Period* 

Attributable to Bulk Government Purchasing of Packaged Terminal Air 

Conditioners and Packaged Terminal Heat Pumps....................................... 17-19

Table 17.4.1 Impacts of Non-Regulatory Alternatives for Packaged Terminal Air 

Conditioners* (TSL ASHRAE).................................................................... 17-22

LIST OF FIGURES

Figure 17.3.1 Market Penetration Curves (TSL ASHRAE).................................................. 17-8

Figure 17.4.1 Market Penetration of Packaged Terminal Air Conditioners 7,000 –

15,000 Btu/h Meeting the Target Level in Policy Cases (TSL ASHRAE) .. 17-20

Figure 17.4.2 Market Penetration of Packaged Terminal Air Conditioners >15,000 

Btu/h Meeting the Target Level in Policy Cases (TSL ASHRAE) .............. 17-21
17-1

CHAPTER 17. REGULATORY IMPACT ANALYSIS: ASSESSMENT OF 

ALTERNATIVES TO NATIONAL STANDARDS

17.1 INTRODUCTION

The U.S. Department of Energy (DOE) has determined that the regulatory action

described in the Federal Register notice associated with this TSD constitutes an “economically 

significant regulatory action” under Executive Order (E.O.) 12866, Regulatory Planning and 

Review. 58 FR 51735 (October 4, 1993). For such actions, E.O. 12866 requires Federal agencies 

to provide “an assessment, including the underlying analysis, of costs and benefits of potentially 

effective and reasonably feasible alternatives to the planned regulation, identified by the agencies 

or the public (including improving the current regulation and reasonably viable non-regulatory 

actions), and an explanation why the planned regulatory action is preferable to the identified 

potential alternatives.” 58 FR 51735, 51741. 

To conduct this analysis, DOE used an integrated National Impact Analysis (NIA)-RIA

model built on the NIA model discussed in Chapter 10 for its analysis. 

DOE identified five non-regulatory policy alternatives that possibly could provide 

incentives for the same energy efficiency levels as the ones in the trial standard levels (TSL) for 

packaged terminal air conditioners (PTACs) and packaged terminal heat pumps (PTHPs), which

are the subject of this rulemaking. The non-regulatory policy alternatives are listed in Table 

17.1.1, which also includes the “no new regulatory action” alternative. DOE evaluated each 

alternative in terms of its ability to achieve significant energy savings at a reasonable cost, and 

compared the effectiveness of each to the effectiveness of the selected standard. 

Table 17.1.1 Non-Regulatory Alternatives to National Standards 

No New Regulatory Action

Consumer Rebates

Consumer Tax Credits

Manufacturer Tax Credits

Voluntary Energy Efficiency Targets

Bulk Government Purchases

Sections 17.2 and 17.3 discuss the analysis of five selected policies listed above (excluding the 

alternative of no new regulatory action). Section 17.4 presents the results of the policy 

alternatives.
17-2

17.2 NON-REGULATORY POLICIES

This section describes the method DOE used to analyze the energy savings and cost 

effectiveness of the non-regulatory policy alternatives for PTACs and PTHPs. This section also 

describes the assumptions underlying the analysis. 

17.2.1 Methodology 

DOE used its integrated NIA-RIA spreadsheet model to calculate the national energy 

savings (NES) and net present value (NPV) associated with each non-regulatory policy 

alternative. Chapter 10 of the technical support document (TSD) describes the NIA spreadsheet 

model. Appendix 17-A discusses the NIA-RIA integrated model approach.

DOE quantified the effect of each alternative on the purchase of equipment that meet the 

efficiency levels corresponding to the levels set for each TSL. After establishing the quantitative 

assumptions underlying each alternative, DOE appropriately revised inputs to the NIA-RIA

spreadsheet model. The primary model inputs revised were market shares of equipment meeting 

the target efficiency levels set for each TSL. The shipments of equipment for any given year 

reflect a distribution of efficiency levels. DOE assumed, for each TSL, that new energy 

efficiency standards would affect 100 percent of the shipments of equipment that did not meet 

the TSL target level in the base case,a whereas the non-regulatory policies would affect a smaller 

percentage of those shipments. DOE made certain assumptions about the percentage of 

shipments affected by each alternative policy. DOE used those percentages to calculate the 

shipment-weighted average energy consumption and costs of PTACs and PTHPs attributable to 

each policy alternative. 

Increasing the efficiency of equipment often increases its average installed cost. 

However, operating costs generally decrease because energy consumption declines. DOE 

therefore calculated an NPV for each non-regulatory alternative in the same way it did for the 

selected standards. In some policy scenarios, increases in total installed cost are mitigated by 

government rebates or tax credits. Because DOE assumed that consumers would re-pay credits

and rebates in some way (such as additional taxes), DOE did not include rebates or tax credits as 

a consumer benefit when calculating national NPV. DOE’s analysis also excluded any 

administrative costs for the non-regulatory policies; including such costs would decrease the

NPVs slightly.

The following are key measures for evaluating the impact of each alternative. 

• National Energy Savings (NES), given in quadrillion Btus (quads), describes the 

cumulative national primary energy saved over the lifetime of equipment purchased 

during the 30-year analysis period..

 

a The base case for the NIA is a market-weighted average energy efficiency calculated from units at several 

efficiency levels.
17-3

• Net Present Value (NPV), represents the value of net monetary savings in 2015, 

expressed in 2014$, from equipment purchased during the 30-year analysis period.

DOE calculated the NPV as the difference between the present value of installed 

equipment cost and operating expenditures in the base case and the present value of 

those costs in each policy case. DOE calculated operating expenses (including energy

costs) for the life of equipment.

17.2.2 Assumptions Regarding Non-Regulatory Policies

The effects of non-regulatory policies are by nature uncertain because they depend on 

program implementation, marketing efforts, and on consumers’ responses to a program. Because 

the projected effects depend on assumptions regarding the rate of consumer participation, they 

are subject to more uncertainty than are the impacts of mandatory standards, which DOE 

assumes will be met with full compliance. To increase the robustness of the analysis, DOE 

conducted a literature review regarding each non-regulatory policy and consulted with 

recognized experts to gather information on similar incentive programs that have been 

implemented in the United States. By studying experiences with the various types of programs,

DOE sought to make credible assumptions regarding potential market impacts. Section 17.3

presents the sources DOE relied on in developing assumptions about each alternative policy and 

reports DOE’s conclusions as they affected the assumptions that underlie the modeling of each 

alternative policy.

Each non-regulatory policy that DOE considered would improve the average efficiency 

of new PTACs and PTHPs relative to their base case efficiency scenario (which involves no new 

regulatory action), except for PTAC <7,000 Btu/hr at TSL ASHRAE and TSL 1, for which there 

is no market in the base case at efficiency levels below the corresponding CSLs, and for all 

PTHPs at TSL ASHRAE for which the selected standards coincide with current baseline 

efficiency levels. The analysis considered that each alternative policy would induce consumers to 

purchase units having the same efficiency level as required by the selected standards (the target 

level) set for each TSL. As opposed to the standards case, however, the policy cases may not 

lead to 100 percent market penetration of units that meet the target level.

Table 17.2.1 shows the efficiency level stipulated in the selected standards for PTACs 

and PTHPs.
17-4

Table 17.2.1 Efficiency Levels for Trial Standard Levels for Packaged Terminal Air 

Conditioner and Packaged Terminal Heat Pump Equipment Classes

(EER)

Equipment Class Trial Standard Levels

ASHRAE 1 2 3 4 5

PTAC

< 7,000 Btu/h 11.9 12.2 12.6 13.1 13.6 13.8

7,000 – 15,000 Btu/h 11.3 11.5 12.0 12.4 12.9 13.1

> 15,000 Btu/h 9.5 9.7 10.0 10.4 10.8 11.0

PTHP

< 7,000 Btu/h 11.9 12.2 12.6 13.1 13.6 13.8

7,000 – 15,000 Btu/h 11.3 11.5 12.0 12.4 12.9 13.1

> 15,000 Btu/h 9.5 9.7 10.0 10.4 10.8 11.0

DOE assumed that the effects of non-regulatory policies would last from the effective 

date of standards through the end of the analysis period.

17.2.3 Policy Interactions

DOE calculated the effects of each non-regulatory policy separately from those of the 

other policies. In practice, some policies are most effective when implemented in combination, 

such as voluntary efficiency targets implemented with consumer rebates or tax credits. However, 

DOE attempted to make conservative assumptions to avoid double-counting policy impacts. The 

resulting policy impacts are not additive; the combined effect of several or all policies cannot be 

inferred from summing their results. 

Section 17.4 presents graphs that show the market penetration estimated under each nonregulatory policy for the PTAC and PTHP equipment classes.

17.3 NON-REGULATORY POLICY ASSUMPTIONS

The following subsections describe DOE’s analysis of the impacts of the five nonregulatory policy alternatives to selected standards for PTACs and PTHPs. (Because the 

alternative of No New Regulatory Action has no energy or economic impacts, essentially 

representing the NIA base case, DOE did not perform any additional analysis for that 

alternative.) DOE developed estimates of the market penetration of high-efficiency equipment 

both with and without each of the non-regulatory policy alternatives.
17-5

17.3.1 No New Regulatory Action

The case in which no new regulatory action is taken with regard to the energy efficiency 

of PTACs and PTHPs constitutes the base case, as described in Chapter 10, National Impact 

Analysis. The base case provides the basis of comparison for all other policies. By definition, no 

new regulatory action yields zero NES and an NPV of zero dollars.

17.3.2 Consumer Rebates

DOE considered the scenario in which the Federal government would provide financial 

incentives in the form of rebates to consumers for purchasing energy-efficient appliances. This 

policy provides a consumer rebate for purchasing PTACs and PTHPs that operate at the same 

efficiency as stipulated in each TSL (target level).

17.3.2.1 Methodology

DOE based its evaluation methodology for consumer rebates on a comprehensive study 

of California’s potential for achieving energy efficiency. The study, performed by XENERGY, 

Inc.,b summarized experiences with various utility rebate programs.1 XENERGY’s analytical 

method utilized graphs, or penetration curves, that estimate the market penetration of a 

technology based on its benefit/cost (B/C) ratio. DOE consulted with experts and reviewed other 

methods of estimating the effect of consumer rebate programs on the market penetration of 

efficient technologies. The other methods, developed after the referenced XENERGY report was 

published,2, 3, 4, 5, 6, 7 used different approaches: other economic parameters (e.g., payback 

period), expert surveys, or model calibration based on specific utility program data rather than 

multi-utility data. Some models in use by energy efficiency program evaluation experts were so 

client-specific that generic relationships between economic parameters and consumer response 

could not be established.5DOE decided that the most appropriate available method for this RIA 

analysis was the XENERGY approach of penetration curves based on B/C ratio, which 

incorporates lifetime operating cost savings.

XENERGY’s model estimates market impacts induced by financial incentives based on 

the premise that two types of information diffusion drive the adoption of new technologies. 

Internal sources of information encourage consumers to purchase new equipment primarily 

through word-of-mouth from early adopters. External sources affect consumer purchase 

decisions through marketing efforts and information from outside the consumer group. Appendix 

17-A contains additional details on internal and external information diffusion.

XENERGY’s model equation accounts for the influences of both internal and external 

sources of information by superimposing the two components. Combining the two mechanisms 

for information diffusion, XENERGY’s model generates a set of penetration (or implementation) 

curves for a policy measure. XENERGY calibrated the curves based on participation data from 

 

b XENERGY is now owned by KEMA, Inc. (www.kema.com)
17-6

utility rebate programs. The curves illustrate the increased penetration (i.e., increased market 

share) of efficient equipment driven by consumer response to changes in B/C ratio induced by 

rebate programs. The penetration curves depict various diffusion patterns based on perceived 

market barriers (from no-barriers to extremely-high-barriers) to consumer purchase of highefficiency equipment. DOE adjusted the XENERGY former penetration curves based on expert 

advice founded on more recent utility program experience.5, 8

DOE modeled the effects of a consumer rebate policy for PTACs and PTHPs by 

determining, for each TSL, the increase in market penetration of equipment meeting the target 

level relative to their market penetration in the base case. It used the interpolation method 

presented in Blum et al (2011)9 to create customized penetration curves based on relationships 

between actual base case market penetrations and actual B/C ratios. To inform its estimate of 

B/C ratios provided by a rebate program DOE performed a thorough nationwide search for 

existing rebate programs for PTACs and PTHPs. It gathered data on utility or agency rebates 

throughout the nation for this equipment, and used this data to calibrate the customized 

penetration curves it developed for each equipment class so they can best reflect the market 

barrier level faced by each equipment class. Section 17.3.2.2 shows the interpolated curves used 

in the analysis.

17.3.2.2 Analysis 

For the six equipment classes it analyzed, DOE estimated the effect of increasing its B/C 

ratio via a rebate that would pay all or part of the increased installed cost of a unit that met the 

target efficiency level compared to one meeting the baseline efficiency level.c To inform its 

estimate of an appropriate rebate amount, DOE performed a thorough nationwide search for 

existing rebate programs for PTACs and PTHPs. It gathered data from a sample of utility and 

agency rebate programs that includes 37 rebates for PTACs and PTHPs initiated by 30 utilities or 

agencies in various States. (Appendix 17-A, identifies the rebate programs.) To represent the 

rebate level, DOE used the simple average of the rebate amounts in these programs. DOE 

assumed that rebates would remain in effect at the same levels throughout the forecast period.

For each of the six equipment classes, DOE first calculated the B/C ratio without a rebate 

using the difference in total installed costs (C) and lifetime operating cost savings (B) between 

the unit meeting the target level and the baseline unit. It then calculated the B/C ratio given a 

rebate for the unit meeting the target efficiency level. Because the rebate reduced the incremental 

cost, the unit receiving the rebate had a larger B/C ratio. Table 17.3.1 shows the effect of 

consumer rebates on the B/C ratio for each TSL in the first year of its corresponding analysis 

period.

 

c The baseline technology for each product class is defined in the engineering analysis, Chapter 5, as the technology 

that represents the basic characteristics of products in that class. A baseline unit typically is one that just meets 

current Federal energy conservation standards and provides basic consumer utility. 
17-7

Table 17.3.1 Benefit/Cost Ratios Without and With Rebates for Packaged Terminal 

Air Conditioners and Packaged Terminal Heat Pumps

Trial Standard Levels

PTAC ASHRAE 1 2 3 4 5

< 7,000 Btu/h

B/C Without Rebate

- -

0.8 0.8 0.8 0.8

Rebate (2014$) 37.13 39.43 41.74 42.89

B/C With Rebate 4.8 1.8 1.4 1.2

Market Barriers No-Low Hg-xHg Hg-xHg Hg-xHg

7,000 – 15,000 Btu/h

B/C Without Rebate 0.7 0.8 0.9 0.9 0.9 0.9

Rebate (2014$) 41.89 43.95 47.70 51.45 55.20 57.08

B/C With Rebate infinite infinite infinite 4.0 2.2 1.9

Market Barriers No-Low No-Low No-Low Low-Mod Mod-Hg Mod-Hg

> 15,000 Btu/h

B/C Without Rebate 0.8 0.9 0.8 0.7 0.7 0.6

Rebate (2014$) 46.93 48.74 52.67 56.60 60.52 62.49

B/C With Rebate infinite infinite infinite 2.1 1.2 1.1

Market Barriers Hg-xHg No Hg-xHg Hg-xHg Hg-xHg Hg-xHg

PTHP ASHRAE 1 2 3 4 5

< 7,000 Btu/h

B/C Without Rebate

-

1.3 1.2 1.1 1.0 1.0

Rebate (2014$) 32.91 33.14 33.37 33.60 33.71

B/C With Rebate infinite infinite 2.6 1.7 1.6

Market Barriers Low-Mod Low-Mod xHigh xHigh xHigh

7,000 – 15,000 Btu/h

B/C Without Rebate

-

2.0 1.5 1.4 1.3 1.3

Rebate (2014$) 40.95 41.32 41.70 42.07 42.26

B/C With Rebate infinite infinite 6.4 2.9 2.4

Market Barriers Low-Mod No-Low Low-Mod Mod-Hg xHigh

> 15,000 Btu/h

B/C Without Rebate

-

1.3 1.3 1.1 1.0 1.0

Rebate (2014$) 45.71 46.10 46.49 46.88 47.08

B/C With Rebate infinite infinite 3.2 1.7 1.5

Market Barriers Low-Mod No-Low xHigh xHigh xHigh

* No-Low: No-to-Low market barriers; Mod: Moderate market barriers; Low-Mod: Low-to-Moderate market 

barriers; Mod-High: Moderate-to-High market barriers; xHigh: Extremely-High market barriers; Hg-xHg: Highto-Extremely-High market barriers

DOE used these B/C ratios, along with the penetration curves it estimated for each 

equipment class at each TSL, to estimate the percentage of consumers who would purchase

PTACs and PTHPs that meet the target level both with and without a rebate incentive. Figure 

17.3.1 shows the penetration curves DOE estimated for the two equipment classes affected by 

the policy at TSL ASHRAE. The market barriers DOE calculated to represent the market 

behavior for these two equipment classes at TSL ASHRAE are indicated in Table 17.3.1.
17-8

PTAC 7,000 – 15,000 Btu/h

PTAC > 15,000 Btu/h

Figure 17.3.1 Market Penetration Curves (TSL ASHRAE)

d

For each equipment class, DOE next estimated the percent increases represented by the 

change in penetration rate shown on the corresponding penetration curve. It then added this

 d The green dots are not shown in the charts, as the B/C ratio for both equipment classes at TSL ASHRAE is infinite.
17-9

percent increase to the market share of units that meet the target level in the base case to obtain 

the market share of units that meet the target level in the rebate policy case.e

Table 17.3.2 summarizes DOE’s assumptions for PTACs and PTHPs regarding the 

market penetration of products in the start year of analysis that meet the target efficiency level at 

each TSL given a consumer rebate. 

Table 17.3.2 Market Penetrations in the First Year of the Analysis Period* 

Attributable to Consumer Rebates for Packaged Terminal Air 

Conditioners and Packaged Terminal Heat Pumps

Trial Standard Levels

PTAC ASHRAE 1 2 3 4 5

< 7,000 Btu/h

Base-Case Market Share 0.0% 69.5% 30.5% 0.0% 0.0% 0.0%

Policy Case Market Share 0.0% 69.5% 66.7% 0.2% 0.1% 0.1%

Increased Market Share 0.0% 0.0% 36.2% 0.2% 0.1% 0.1%

7,000 – 15,000 Btu/h

Base-Case Market Share 36.0% 32.8% 25.3% 2.5% 0.9% 0.3%

Policy Case Market Share 41.6% 71.0% 87.9% 20.6% 5.8% 1.6%

Increased Market Share 5.6% 38.2% 62.6% 18.2% 4.8% 1.3%

> 15,000 Btu/h

Base-Case Market Share 0.0% 38.5% 0.0% 0.0% 0.0% 0.0%

Policy Case Market Share 51.6% 100.0% 15.0% 0.3% 0.1% 0.1%

Increased Market Share 51.6% 61.5% 15.0% 0.3% 0.1% 0.1%

PTHP ASHRAE 1 2 3 4 5

< 7,000 Btu/h

Base-Case Market Share - 13.8% 14.4% 0.0% 0.0% 0.0%

Policy Case Market Share - 75.8% 69.4% 0.2% 0.1% 0.1%

Increased Market Share - 62.0% 55.0% 0.2% 0.1% 0.1%

7,000 – 15,000 Btu/h

Base-Case Market Share - 8.2% 25.9% 9.5% 0.5% 0.0%

Policy Case Market Share - 64.1% 80.6% 36.6% 2.6% 0.2%

Increased Market Share - 55.9% 54.7% 27.1% 2.1% 0.2%

> 15,000 Btu/h

Base-Case Market Share - 2.8% 25.4% 0.0% 0.0% 0.0%

Policy Case Market Share - 70.9% 82.0% 0.4% 0.1% 0.1%

Increased Market Share - 68.0% 56.6% 0.4% 0.1% 0.1%

* 2017 for TSL ASHRAE for PTACs; 2019 for the remaining PTAC TSLs; and 2018 for all TSLs for PTHPs.

DOE used the resulting annual increases in market shares as inputs to represent the rebate 

policy case scenario in its NIA-RIA model. Appendix 17-A shows the annual market share 

increases due to this policy. Section 17.4 presents the resulting market penetration trends for the 

policy case of consumer rebates for PTACs and PTHPs. 

 

e Note that the percent increases are upper bounded by the total market share below the target level in the base case.
17-10

17.3.3 Consumer Tax Credits

DOE estimated the effects of tax credits on consumer purchases based on its previous 

analysis of consumer participation in tax credits. DOE supported its approach using data from 

Oregon State’s tax credit program for energy-efficient appliances. DOE also incorporated 

previous research that disaggregated the effect of rebates and tax credits into a direct price effect, 

which derives from the savings in purchase price, and an announcement effect, which is 

independent of the amount of the incentive.

10, 11 The announcement effect derives from the 

credibility that a technology receives from being included in an incentive program, as well as 

changes in product marketing and modifications in markup and pricing. DOE assumed that the 

rebate and consumer tax credit policies would encompass both direct price effects and 

announcement effects, and that half the increase in market penetration associated with either 

policy would be due to the direct price effect and half to the announcement effect.

In estimating the effects of a tax credit on purchases of consumer products that meet new 

efficiency standards, DOE assumed the amount of the tax credit would be the same as the 

corresponding rebate amount discussed above. 

DOE estimated that fewer consumers would participate in a tax credit program than 

would take advantage of a rebate. Research has shown that the delay required for a consumer to 

receive a tax credit, plus the added time and cost in preparing the tax return, make a tax credit 

incentive less effective than a rebate received at the time of purchase. Based on previous 

analyses, DOE assumed that only 60 percent of the consumers who would take advantage of a 

rebate would take advantage of a tax credit.12

In preparing its assumptions to estimate the effects of tax credits on consumer purchases 

of PTACs and PTHPs, DOE also reviewed other tax credit programs that have been offered at 

both the Federal and State levels for energy-efficient appliances.

The Energy Policy Act of 2005 (EPACT 2005) included Federal tax credits for 

consumers who purchase energy-efficient products.

13 Those tax credits were in effect in 2006 

and 2007, expired in 2008, were reinstated for 2009–2010 by the American Recovery and 

Reinvestment Act of 2009 (ARRA), extended by Congress for 2011 with some modifications, 

and expired at the end of 2011.

14, 15 The American Taxpayer Relief Act of 2012 extended, with 

some modifications, residential tax credits for air conditioners, heat pumps, furnaces, and water 

heaters placed in service between January 1, 2012 and December 31, 2013.16 DOE reviewed 

Internal Revenue Service data on the numbers of taxpayers who claimed the tax credits during 

tax years 2006 and 2007. DOE also reviewed data from an earlier Federal energy conservation 

tax credit program in place in the 1980s. However, DOE did not find data specific enough to 

PTACs and PTHPs to warrant adjusting its analysis method for the Consumer Tax Credits policy 

case. Appendix 17-A contains more information on Federal consumer tax credits. 

DOE also reviewed its previous analysis of Oregon’s tax credits for clothes washers to 

provide support for its assumptions.17 In that previous analysis, DOE compared the market 
17-11

shares of ultra-high efficiency (UHE) residential clothes washers in Oregon, which offered both 

State tax credits and utility rebates, with those in Washington State, which offered only utility 

rebates during the same period. Based on this analysis, DOE estimated that in Oregon the impact 

of tax credits was 62 percent of the impact of rebates for UHE clothes washers having equivalent

efficiency. This finding supports its original assumption that participation in a tax credit program 

would be about 60 percent of participation in a rebate program. Additional discussion of State 

tax credits for Oregon and other states is in Appendix 17-A.

DOE applied the assumed 60 percent participation described above to the penetration 

rates estimated for the rebate policy to estimate penetration rates attributable to consumer tax 

credits. In doing so, DOE incorporated the assumptions for consumer response to financial 

incentives from the penetration curves selected for PTACs and PTHPs.

Table 17.3.3 summarizes DOE’s assumptions for PTACs and PTHPs regarding the 

market penetration of units in the start year of analysis that meet the efficiency level at each TSL 

given a consumer tax credit. 
17-12

Table 17.3.3 Market Penetrations in the First Year of the Analysis Period* 

Attributable to Consumer Tax Credits for Packaged Terminal Air 

Conditioners and Packaged Terminal Heat Pumps

Trial Standard Levels

PTAC ASHRAE 1 2 3 4 5

< 7,000 Btu/h

Base-Case Market Share 0.0% 69.5% 30.5% 0.0% 0.0% 0.0%

Policy Case Market Share 0.0% 69.5% 52.3% 0.1% 0.1% 0.04%

Increased Market Share 0.0% 0.0% 21.7% 0.1% 0.1% 0.04%

7,000 – 15,000 Btu/h

Base-Case Market Share 36.0% 32.8% 25.3% 2.5% 0.9% 0.3%

Policy Case Market Share 39.3% 55.7% 62.9% 13.4% 3.8% 1.1%

Increased Market Share 3.4% 22.9% 37.5% 10.9% 2.9% 0.8%

> 15,000 Btu/h

Base-Case Market Share 0.0% 38.5% 0.0% 0.0% 0.0% 0.0%

Policy Case Market Share 30.9% 75.4% 9.0% 0.2% 0.1% 0.1%

Increased Market Share 30.9% 36.9% 9.0% 0.2% 0.1% 0.1%

PTHP ASHRAE 1 2 3 4 5

< 7,000 Btu/h

Base-Case Market Share

-

13.8% 14.4% 0.0% 0.0% 0.0%

Policy Case Market Share 51.0% 47.4% 0.1% 0.1% 0.05%

Increased Market Share 37.2% 33.0% 0.1% 0.1% 0.05%

7,000 – 15,000 Btu/h

Base-Case Market Share

-

8.2% 25.9% 9.5% 0.5% 0.0%

Policy Case Market Share 41.7% 58.7% 25.7% 1.8% 0.1%

Increased Market Share 33.6% 32.8% 16.3% 1.3% 0.1%

> 15,000 Btu/h

Base-Case Market Share

-

2.8% 25.4% 0.0% 0.0% 0.0%

Policy Case Market Share 43.7% 59.3% 0.2% 0.1% 0.05%

Increased Market Share 40.8% 34.0% 0.2% 0.1% 0.05%

* 2017 for TSL ASHRAE for PTACs; 2019 for the remaining PTAC TSLs; and 2018 for all TSLs for PTHPs.

The increased market shares attributable to consumer tax credits shown in Table 17.3.3

were used as inputs in the NIA-RIA model. Appendix 17-A shows the annual market share 

increases due to this policy. Section 17.4 presents the resulting market penetration trends for the 

policy case of consumer tax credits for PTACs and PTHPs that meet the efficiency level for each 

TSL.

17.3.4 Manufacturer Tax Credits

To analyze the potential effects of a policy that offers tax credits to manufacturers that 

produce PTACs and PTHPs that meet the target efficiency level at each TSL, DOE assumed that 

a manufacturer tax credit would lower the consumer’s purchase cost by an amount equivalent to 

that provided by the consumer rebates or tax credits described above. DOE further assumed that 

manufacturers would pass on some of their reduced costs to consumers, causing a direct price 

effect. DOE assumed that no announcement effect would occur, because the program would not 
17-13

be visible to consumers.f Because the direct price effect is approximately equivalent to the 

announcement effect,10 DOE estimated that a manufacturer tax credit would induce half the 

number of consumers assumed to take advantage of a consumer tax credit to purchase more 

efficient equipment. Thus the assumed participation rate is equal to 30 percent of the number of 

consumers who would participate in a rebate program.

DOE attempted to investigate manufacturer response to the Energy Efficient Appliance 

Credits for manufacturers mandated by EPACT 2005.18 Those manufacturer tax credits have 

been in effect for dishwashers, clothes washers and refrigerators produced beginning in 2009. 

DOE was unable to locate data from the Internal Revenue Service or other sources on 

manufacturer response to the Federal credits. Appendix 17-A presents details on Federal 

manufacturer tax credits.

DOE applied the assumption of 30 percent participation to the penetration rates predicted 

for the rebate policy to estimate the effects of a manufacturer tax credit policy. In doing so, the 

Department incorporated the assumptions for consumer response to financial incentives from the 

penetration curves calculated for PTACs and PTHPs.

Table 17.3.4 summarize DOE’s assumptions for PTACs and PTHPs regarding the market 

penetration of units in the start year of analysis that meet the efficiency level at each TSL given a 

manufacturer tax credit.

 

f Note that this is a conservative assumption, since it is possible that manufacturers or utility/agency efficiency 

programs might promote the models for which manufacturers increase production due to the tax credits, which in 

turn might induce some announcement effect. However, DOE found no data on such programs on which to base an 

estimate of the magnitude of this possible announcement effect on consumer behavior.
17-14

Table 17.3.4 Market Penetrations in the First Year of the Analysis Period* 

Attributable to Manufacturer Tax Credits for Packaged Terminal Air 

Conditioners and Packaged Terminal Heat Pumps

Trial Standard Levels

PTAC ASHRAE 1 2 3 4 5

< 7,000 Btu/h

Base-Case Market Share 0.0% 69.5% 30.5% 0.0% 0.0% 0.0%

Policy Case Market Share 0.0% 69.5% 41.4% 0.1% 0.03% 0.02%

Increased Market Share 0.0% 0.0% 10.9% 0.1% 0.03% 0.02%

7,000 – 15,000 Btu/h

Base-Case Market Share 36.0% 32.8% 25.3% 2.5% 0.9% 0.3%

Policy Case Market Share 37.7% 44.2% 44.1% 7.9% 2.4% 0.7%

Increased Market Share 1.7% 11.5% 18.8% 5.4% 1.5% 0.4%

> 15,000 Btu/h

Base-Case Market Share 0.0% 38.5% 0.0% 0.0% 0.0% 0.0%

Policy Case Market Share 15.5% 56.9% 4.5% 0.1% 0.03% 0.03%

Increased Market Share 15.5% 18.5% 4.5% 0.1% 0.03% 0.03%

PTHP ASHRAE 1 2 3 4 5

< 7,000 Btu/h

Base-Case Market Share

-

13.8% 14.4% 0.0% 0.0% 0.0%

Policy Case Market Share 32.4% 30.9% 0.1% 0.03% 0.02%

Increased Market Share 18.6% 16.5% 0.1% 0.03% 0.02%

7,000 – 15,000 Btu/h

Base-Case Market Share

-

8.2% 25.9% 9.5% 0.5% 0.0%

Policy Case Market Share 25.0% 42.3% 17.6% 1.2% 0.05%

Increased Market Share 16.8% 16.4% 8.1% 0.6% 0.05%

> 15,000 Btu/h

Base-Case Market Share

-

2.8% 25.4% 0.0% 0.0% 0.0%

Policy Case Market Share 23.3% 42.3% 0.1% 0.03% 0.02%

Increased Market Share 20.4% 17.0% 0.1% 0.03% 0.02%

* 2017 for TSL ASHRAE for PTACs; 2019 for the remaining PTAC TSLs; and 2018 for all TSLs for PTHPs.

The increased market shares attributable to a manufacturer tax credit shown in Table 

17.3.4 were used as inputs in the NIA-RIA model. Appendix 17-A shows the annual market 

share increases due to this policy. Section 17.4 presents the resulting market penetration trends 

for the policy case of manufacturer tax credits for PTACs and PTHPs.

17.3.5 Voluntary Energy Efficiency Targets 

For each equipment class, DOE assumed that voluntary energy efficiency targets would 

be achieved as manufacturers gradually stopped producing units that operated below the

efficiency level set for each TSL. DOE assumed that the impetus for phasing out production of 

low-efficiency units would be a program with impacts similar to those of the ENERGY STAR 

labeling program conducted by the Environmental Protection Agency (EPA) and DOE in 

conjunction with industry partners. The ENERGY STAR program specifies the minimum energy 

efficiencies that various products must have to receive the ENERGY STAR label. ENERGY 
17-15

STAR encourages consumers to purchase efficient products via marketing that promotes 

consumer label recognition, various incentive programs that adopt the ENERGY STAR 

specifications, and manufacturers’ promotion of their qualifying appliances. ENERGY STAR 

projects market penetration of compliant appliances and estimates the percentage of sales of 

compliant appliances that are attributable to the ENERGY STAR program. 

Researchers have analyzed the ENERGY STAR program’s effects on sales of several 

consumer products. Program efforts generally involve a combination of information 

dissemination and utility or agency rebates. The analyses have been based on State-specific data 

on percentages of shipments of various appliances that meet ENERGY STAR specifications. The 

analyses generally have concluded that the market penetration of ENERGY STAR-qualifying 

appliances is higher in regions or States where ancillary promotional programs have been 

active.

19, 20, 21

DOE believes that informational incentive programs – like ENERGY STAR, or any other 

labeling program sponsored by industry or other organizations – are likely to reduce the market 

barriers to more efficient equipment over time. Table 17.3.5 shows the estimated market barriers 

to energy efficient PTACs and PTHPs in the base case for each TSL. DOE estimates that 

voluntary energy efficiency targets could reduce these barriers to lower levels over 10 years. 

Table 17.3.6 shows the reduced market barriers to energy efficient PTACs and PTHPs for each 

TSL. DOE followed the methodology presented by Blum et al (2011)22 to evaluate the effects 

that such a reduction in market barriers have on the market penetration of each equipment class 

of PTACs and PTHPs. The methodology relies on interpolated market penetration curves to 

calculate – given a B/C ratio – how the market penetration of more efficient units increases as 

the market barrier level to those units decreases.

Table 17.3.5 Estimated Market Barriers to Energy Efficient PTACs and PTHPs

Trial Standard Levels

PTAC ASHRAE 1 2 3 4 5

< 7,000 Btu/h - - No Hg-xHg Hg-xHg Hg-xHg

7,000 – 15,000 Btu/h No No No Lo-Mod Lo-Mod Mod-Hg

> 15,000 Btu/h Hg-xHg No Hg-xHg Hg-xHg Hg-xHg Mod-Hg

PTHP ASHRAE 1 2 3 4 5

< 7,000 Btu/h - Lo-Mod No Hg-xHg Hg-xHg Hg-xHg

7,000 – 15,000 Btu/h - Lo-Mod No Lo-Mod Mod-Hg Hg-xHg

> 15,000 Btu/h - Lo-Mod No Hg-xHg Hg-xHg Hg-xHg

No-Low: No-to-Low market barriers; Mod: Moderate market barriers; Low-Mod: Low-to-Moderate market 

barriers; Mod-High: Moderate-to-High market barriers; xHigh: Extremely-High market barriers; Hg-xHg: Highto-Extremely-High market barriers
17-16

Table 17.3.6 Reduced Market Barriers to Energy Efficient PTACs and PTHPs

Trial Standard Levels

PTAC ASHRAE 1 2 3 4 5

< 7,000 Btu/h - - No High High High

7,000 – 15,000 Btu/h No No No Low Low Moderate

> 15,000 Btu/h High No High High High Moderate

PTHP ASHRAE 1 2 3 4 5

< 7,000 Btu/h - Low No High High High

7,000 – 15,000 Btu/h - Low No Low Moderate High

> 15,000 Btu/h - Low No High High High

Table 17.3.7 summarizes DOE’s assumptions for PTACs and PTHPs regarding the 

market penetration of units in the start year of analysis that meet the efficiency level at each TSL 

given voluntary energy efficiency targets.
17-17

Table 17.3.7 Market Penetrations in the First Year of the Analysis Period* 

Attributable to Voluntary Energy Efficiency Targets for Packaged 

Terminal Air Conditioners and Packaged Terminal Heat Pumps

Trial Standard Levels

PTAC ASHRAE 1 2 3 4 5

< 7,000 Btu/h

Base-Case Market Share 0.0% 69.5% 30.5% 0.0% 0.0% 0.0%

Policy Case Market Share 0.0% 69.5% 31.4% 0.03% 0.03% 0.03%

Increased Market Share 0.0% 0.0% 0.9% 0.03% 0.03% 0.03%

7,000 – 15,000 Btu/h

Base-Case Market Share 36.0% 32.8% 25.3% 2.5% 0.9% 0.31%

Policy Case Market Share 36.0% 33.3% 27.9% 4.5% 2.8% 0.36%

Increased Market Share 0.0% 0.6% 2.6% 2.0% 1.9% 0.05%

> 15,000 Btu/h

Base-Case Market Share 0.0% 38.5% 0.0% 0.0% 0.0% 0.0%

Policy Case Market Share 0.03% 38.5% 0.03% 0.03% 0.03% 0.05%

Increased Market Share 0.03% 0.0% 0.03% 0.03% 0.03% 0.05%

PTHP ASHRAE 1 2 3 4 5

< 7,000 Btu/h

Base-Case Market Share

-

13.8% 14.4% 0.0% 0.0% 0.0%

Policy Case Market Share 15.4% 17.6% 0.05% 0.05% 0.04%

Increased Market Share 1.6% 3.2% 0.05% 0.05% 0.04%

7,000 – 15,000 Btu/h

Base-Case Market Share

-

8.2% 25.9% 9.5% 0.5% 0.0%

Policy Case Market Share 14.2% 32.6% 12.4% 0.7% 0.1%

Increased Market Share 6.0% 6.7% 2.9% 0.2% 0.1%

> 15,000 Btu/h

Base-Case Market Share

-

2.8% 25.4% 0.0% 0.0% 0.0%

Policy Case Market Share 6.3% 30.9% 0.1% 0.05% 0.04%

Increased Market Share 3.5% 5.5% 0.1% 0.05% 0.04%

* 2017 for TSL ASHRAE for PTACs; 2019 for the remaining PTAC TSLs; and 2018 for all TSLs for PTHPs.

The increased market shares attributable to voluntary energy efficiency targets shown in 

Table 17.3.7 were used as inputs in the NIA-RIA model. Appendix 17-A shows the annual 

market share increases due to this policy. Section 17.4 presents the resulting market penetration 

trends for the policy case of voluntary energy efficiency targets for PTACs and PTHPs that meet 

the efficiency level for each TSL.

17.3.6 Bulk Government Purchases 

Bulk government purchases can lead to Federal, State, and local governments purchasing

large quantities of equipment that meet the target efficiency level. Combining the market 

demands of multiple public sectors also can provide a market signal to manufacturers and 

vendors that some of their largest customers seek equipment that meet an efficiency target at 

favorable prices. Such a program also can induce “market pull,” whereby manufacturers and 

vendors would achieve economies of scale for high efficiency equipment.
17-18

Most of the previous bulk government purchase (procurement) initiatives at the Federal, 

State, and municipal levels have not tracked data on numbers of purchases or degree of 

compliance with procurement specifications. In many cases, procurement programs are 

decentralized, being part of larger State or regional initiatives. DOE based its assumptions 

regarding the effects of this policy on studies the Federal Energy Management Program (FEMP) 

performed regarding the savings potential of its procurement specifications for appliances and 

other equipment. FEMP, however, does not track purchasing data, because of the complex range 

of purchasing systems, large number of vendors, and so on. States, counties, and municipalities

have demonstrated increasing interest and activity in “green purchasing." Although many of the 

programs target office equipment, the growing infrastructure for developing and applying 

efficient purchasing specifications indicates that bulk government purchase programs are 

feasible.

23, 24

DOE assumed that government agencies would administer bulk purchasing programs for 

PTACs and PTHPs. There are currently no FEMP procurement guidelines in place for PTACs 

and PTHPs.

25 DOE reviewed its own previous research on the potential for market 

transformation through bulk government purchases. Its major study analyzed several scenarios 

based on the assumption that 20 percent of Federal equipment purchases in 2000 already 

incorporated energy efficiency requirements based on FEMP guidelines. One scenario in the 

DOE report showed energy efficient purchasing ramping up during 10 years from 20 percent to 

80 percent of all Federal purchases.26 Based on this study, DOE estimated that a bulk 

government purchase program instituted within a 10-year period would result in at least 80 

percent of government-purchased PTACs and PTHPs meeting target efficiency levels.

DOE assumed that bulk government purchases would affect a subset of federal 

government owned buildings cooled by individual room air conditioners or heat pumps. 

According to the 2003 Commercial Buildings Energy Consumption Survey (CBECS 2003 )

27, 

most of the cooled floor space of this subset of buildings (61 percent for PTACs, and 82 percent 

for PTHPs) would consist of public buildings used for education, lodging, offices and nonrefrigerated warehouses. They represent about 1.8 percent and 0.3 percent of the floor space of 

all commercial buildings cooled – respectively – by individual room air conditioners and 

individual room heat pumps.

27 DOE therefore estimated that these percentages of U.S. 

commercial buildings constitute the market to which this policy would apply.

DOE estimated that starting in the first policy year, each year of a bulk government 

purchase policy would result in an increasing percent of shipments of government-purchased 

units beyond the base case that would meet target efficiency levels. DOE estimated that within 

10 years bulk government purchasing programs would result in 80 percent of the PTACs and 

PTHPs market for federal government owned commercial buildings meeting target levels. DOE 

modeled the bulk government purchase program assuming that the market share for PTACs and 

PTHPs achieved in the tenth year would be at least maintained throughout the rest of the forecast 

period.
17-19

Table 17.3.8 summarizes DOE’s assumptions for PTACs and PTHPs regarding the 

market penetration of units in the start year of analysis that meet the efficiency level at each TSL

given bulk government purchasing.

Table 17.3.8 Market Penetrations in the First Year of the Analysis Period* 

Attributable to Bulk Government Purchasing of Packaged Terminal Air 

Conditioners and Packaged Terminal Heat Pumps

Trial Standard Levels

PTAC ASHRAE 1 2 3 5 5

< 7,000 Btu/h

Base-Case Market Share 0.0% 69.5% 30.5% 0.0% 0.0% 0.0%

Policy Case Market Share 0.0% 69.5% 31.1% 0.3% 0.3% 0.3%

Increased Market Share 0.0% 0.0% 0.6% 0.3% 0.3% 0.3%

7,000 – 15,000 Btu/h

Base-Case Market Share 36.0% 32.8% 25.3% 2.5% 0.9% 0.3%

Policy Case Market Share 36.6% 33.4% 25.8% 2.8% 1.2% 0.6%

Increased Market Share 0.7% 0.6% 0.5% 0.3% 0.3% 0.3%

> 15,000 Btu/h

Base-Case Market Share 0.0% 38.5% 0.0% 0.0% 0.0% 0.0%

Policy Case Market Share 0.0% 39.2% 0.3% 0.3% 0.3% 0.3%

Increased Market Share 0.0% 0.7% 0.3% 0.3% 0.3% 0.3%

PTHP ASHRAE 1 2 3 4 5

< 7,000 Btu/h

Base-Case Market Share

-

13.8% 14.4% 0.0% 0.0% 0.0%

Policy Case Market Share 13.9% 14.4% 0.04% 0.04% 0.04%

Increased Market Share 0.0% 0.0% 0.04% 0.04% 0.04%

7,000 – 15,000 Btu/h

Base-Case Market Share

-

8.2% 25.9% 9.5% 0.53% 0.0%

Policy Case Market Share 8.2% 26.0% 9.5% 0.57% 0.04%

Increased Market Share 0.0% 0.1% 0.0% 0.04% 0.04%

> 15,000 Btu/h

Base-Case Market Share

-

2.8% 25.4% 0.0% 0.0% 0.0%

Policy Case Market Share 2.9% 25.4% 0.04% 0.04% 0.04%

Increased Market Share 0.0% 0.1% 0.04% 0.04% 0.04%

* 2017 for TSL ASHRAE for PTACs; 2019 for the remaining PTAC TSLs; and 2018 for all TSLs for PTHPs.

The increased market shares attributable to bulk government purchasing shown in Table 

17.3.8 were used as inputs in the NIA-RIA model. Appendix 17-A shows the annual market 

share increases due to this policy that DOE used as inputs to the NIA-RIA model. Section 17.4

below presents the resulting market penetration trends for the policy case of bulk government 

purchase of PTACs and PTHPs.
17-20

17.4 IMPACTS OF NON-REGULATORY ALTERNATIVES

Figure 17.4.1 and Figure 17.4.2 show the effects of TSL ASHRAE on market penetration 

of each non-regulatory policy for the two PTAC equipment classes affected by that TSL. (Notice 

that TSL ASHRAE has no effects on market penetration of PTAC <7,000 Btu/hr and all PTHP 

equipment classes). Relative to the base case, (most of) the policy cases increase the market 

shares that meet the target level until 2026, when the base case market share reaches 100 percent.

Recall the selected standards (not shown in the figures) would result in a 100-percent market 

penetration of equipment that meets the target efficiency level over the entire analysis period.

Figure 17.4.1 Market Penetration of Packaged Terminal Air Conditioners 

7,000 – 15,000 Btu/h Meeting the Target Level in Policy Cases 

(TSL ASHRAE)
17-21

Figure 17.4.2 Market Penetration of Packaged Terminal Air Conditioners 

>15,000 Btu/h Meeting the Target Level in Policy Cases (TSL 

ASHRAE)

Table 17.4.1 shows the national energy savings and net present value for five nonregulatory policies analyzed in detail for PTACs. The target level for each policy equals the 

efficiency levels selected for standards in TSL ASHRAE. The case in which no regulatory action 

is taken with regard to PTACs constitutes the base case (or "No New Regulatory Action" 

scenario), in which NES and NPV are zero by definition. For comparison, the table includes the 

impacts of the selected standards. Energy savings are given in billion British thermal units 

(quads), and economic savings in million 2014 dollars. The NPVs shown in Table 17.4.1 is

based on two discount rates, 7 percent and 3 percent.

The energy benefits from the alternative policies for PTACs range from 1.4 percent to 

21.0 percent of those from standards. NPV results for all alternative policies are negative at both 

7 and 3 percent discount rates. The policy with the highest projected cumulative energy savings 

are consumer rebates, followed by consumer tax credits, manufacturer tax credits, and voluntary 

energy efficiency targets. Bulk government purchases result in very little benefits compared to 

energy efficiency standards.
17-22

Table 17.4.1 Impacts of Non-Regulatory Alternatives for Packaged Terminal Air 

Conditioners* (TSL ASHRAE)

Policy Alternative

Primary Energy 

Savings bi BTU

Net Present Value

million 2014$

7% Discount 

Rate

3% Discount 

Rate

Consumer Rebates 65.6 -0.476 -0.447

Consumer Tax Credits 39.4 -0.286 -0.268

Manufacturer Tax Credits 19.7 -0.143 -0.134

Voluntary Energy Efficiency Targets 15.4 -0.120 -0.183

Bulk Government Purchases 4.5 -0.039 -0.040

Proposed Standards 311.7 -1.282 -1.715

*For equipment shipped during the analysis period of TSL ASHRAE (2017-2046)
17-23

REFERENCES

1. Rufo, M. and F. Coito, California's Secret Energy Surplus: The Potential for Energy 

Efficiency, 2002. XENERGY Inc. (Last accessed March, 2014.) 

<www.issuelab.org/resource/californias_secret_energy_surplus_the_potential_for_energy

_efficiency>

2. ICF International, Arizona Public Service Energy Efficient Market Potential Study, 2007. 

Fairfax, VA. 

3. Mosenthal, P., Telephone Conversation with Barbara Atkinson of LBNL, personal 

communication. Optimal Energy. January, 2008.

4. Lee, A., Telephone Conversation with Barbara Atkinson of LBNL, personal 

communication. Quantec, LLC. January, 2008.

5. Rufo, M., Telephone Conversation with Barbara Atkinson of LBNL, personal 

communication. Itron, Inc. January 2008 and March 2009.

6. Itron Inc. and KEMA Inc., California Energy Efficiency Potential Study, 2008. (Last 

accessed March, 2014.) <http://calmac.org/publications/PGE0264_Final_Report.pdf>

7. Global Energy Partners LLC, AmerenUE Demand Side Management (DSM) Market 

Potential Study, 2010. (Last accessed March, 2014.) <www.ameren.com/-

/media/missourisite/Files/Environment/Renewables/AmerenUEVolume2MarketResearchReport2.pdf>

8. Coito, F., E-mail to Barbara Atkinson of LBNL, personal communication. KEMA, Inc. 

June, 2010.

9. Blum, H., B. Atkinson, and A. Lekov, A Framework for Comparative Assessments of 

Energy Efficiency Policy Measures, May, 2011. Report No. LBNL-4749E. 

<http://escholarship.org/uc/item/4t32x6vr>

10. Lawrence Berkeley National Laboratory, Energy End-Use Forecasting: Analysis of Tax 

Credits for Efficient Equipment, 1998. LBNL Technology Evaluation Modeling and 

Assessment Group (Last accessed March, 2014.) 

<http://enduse.lbl.gov/Projects/TaxCredits.html>

11. Train, K., Customer Decision Study: Analysis of Residential Customer Equipment 

Purchase Decisions, 1994. Prepared for Southern California Edison by Cambridge 

Systematics, Pacific Consulting Services, The Technology Applications Group, and 

California Survey Research Services. 
17-24

12. U.S. Department of Energy: Office of Codes and Standards, Technical Support 

Document: Energy Efficiency Standards for Consumer Products: Refrigerators, 

Refrigerator-Freezers, and Freezers, Including Environmental Assessment and 

Regulatory Impact Analysis, November, 1995. Washington, DC. Report No. DOE/EE0064. 

13. Energy Policy Act of 2005, in 119 STAT. 594 Public Law 109-58. Section 1333, 26 USC 

25C note,. 2005. www.gpo.gov/fdsys/pkg/BILLS-109hr6pcs/pdf/BILLS-109hr6pcs.pdf

14. American Recovery and Reinvestment Act of 2009, 2009. Government Printing Office. 

(Last accessed March, 2014.) <www.gpo.gov/fdsys/pkg/BILLS-111hr1enr/pdf/BILLS111hr1enr.pdf>

15. Tax Incentives Assistance Project, Consumer Tax Incentives: Home Heating & Cooling 

Equipment., 2011. (Last accessed March, 2014.) 

<www.energytaxincentives.org/consumers/heating-cooling.php>

16. Tax Incentives Assistance Project, The Tax Incentives Assistance Project (TIAP), 2013.

(Last accessed March, 2014.) <http://energytaxincentives.org/>

17. U.S. Department of Energy - Energy Efficiency & Renewable Energy, Technical Support 

Document: Energy Efficiency Program for Consumer Products and Commercial and 

Industrial Equipment, Residential Dishwashers, Dehumidifiers, and Cooking Products, 

and Commercial Clothes Washers, 2008. Washington, DC. 

18. Tax Incentives Assistance Project, Manufacturers Incentives, (Last accessed March, 

2014.) <http://energytaxincentives.org/builders/appliances.php>

19. Feldman, S., L. Hoegfen, L.Wilson-Wright, and A. Li, Modelling the Effects of U.S. 

ENERGY STAR® Appliance Programs. In Energy Savings: What Works and Who 

Delivers?, May 30-June 4, 2005. ECEEE Summer Study Proceedings. Mandelieu La 

Napoule, France. 

<www.eceee.org/conference_proceedings/eceee/2005c/Panel_4/4228feldman/paper>

20. Rosenberg, M., The Impact of Regional Programs on the Market Share of Energy Star® 

Appliances, August 20-22, 2003. Seattle, WA. Published in the 2003 International Energy 

Program Evaluation Conference Proceedings. 

21. Titus, E. and S. Feldman, Tracking the Effectiveness of Energy-Efficient Appliance 

Programs in the U.S., August 20-22, 2003. In 2003 International Energy Program 

Evaluation Conference Proceedings. Seattle, WA. 
17-25

22. Blum, H., Atkinson B., and Lekov, A., A Framework for Comparative Assessments of 

Energy Efficiency Policy Measures, May, 2011. 

23. Smith, N., Telephone conversation with Barbara Atkinson of LBNL, personal 

communication. National Association of State Procurement Officials. April, 2008.

24. Responsible Purchasing Network, (Last accessed March, 2014.) 

<www.responsiblepurchasing.org/>

25. Federal Energy Management Program (FEMP), Covered Products Category: Light 

Commercial Heating and Cooling, <http://energy.gov/eere/femp/covered-productcategory-light-commercial-heating-and-cooling>

26. Harris, J. and F. Johnson, Potential Energy, Cost, and CO2 Savings from EnergyEfficient Government Purchase. ACEEE: Summer Study on Energy Efficiency in 

Buildings, 2000: pp. 147-166 

<www.aceee.org/files/proceedings/2000/data/papers/SS00_Panel4_Paper13.pdf>

27. U.S. Department of Energy: Energy Information Administration (eia), Commercial 

Buildings Energy Consumption Survey, (Last accessed November, 2013.) 

<www.eia.gov/consumption/commercial/data/2003/index.cfm?view=microdata>
APPENDIX 6A. DETAILED DATA FOR EQUIPMENT PRICE MARKUPS

TABLE OF CONTENTS

6A.1 DETAILED WHOLESALER COST DATA ......................................................... 6A-1

6A.2 DETAILED MECHANICAL CONTRACTOR DATA......................................... 6A-4

6A.3 DETAILED GENERAL CONTRACTOR COST DATA...................................... 6A-6

6A.4 STATE SALES TAX RATES................................................................................ 6A-8

REFERENCES ......................................................................................................................... 6A-9

LIST OF TABLES

Table 6A.1.1 Disaggregated Costs and Expenses for Wholesalers.................................. 6A-1

Table 6A.1.2 State-Level Baseline and Incremental Markups for Wholesalers............... 6A-3

Table 6A.2.1 Detailed National Mechanical Contractor Expenses .................................. 6A-4

Table 6A.2.2 State-Level Mechanical Contractor Baseline and Incremental Markups ... 6A-5

Table 6A.3.1 General Contractor Expenses and Markups................................................ 6A-6

Table 6A.3.2 State-Level General Contractor Baseline and Incremental Markups ......... 6A-7

Table 6A.4.1 State Sales Tax Rates.................................................................................. 6A-8

6A-i
APPENDIX 6A. DETAILED DATA FOR EQUIPMENT PRICE MARKUPS

6A.1 DETAILED WHOLESALER COST DATA

Based on data provided by the Heating Air-conditioning & Refrigeration Distributors 

International (HARDI), Table 6.6.1 of Chapter 6 shows wholesaler revenues and costs in 

aggregated form.1 Table 6-A.1.1 provides the complete breakdown of costs and expenses by 

HARDI region. These markups are then assigned to each state within a HARDI region. As 

described in Chapter 6, Section 6.5, only those expenses that scale with both baseline and 

incremental costs are marked up when there is an incremental change in equipment costs. State 

level baseline and incremental markups for wholesalers, as well as state populations, are 

presented in Table 6-A.1.2.

2

Table 6A.1.1 Disaggregated Costs and Expenses for Wholesalers

North 

Eastern

MidAtlanticSouth 

Eastern

Great 

Lakes Central

South 

Western Western

Number of Firms Reporting 8 11 14 18 26 11 9

Typical Sales Volume (million$) $20.77 $58.34 $34.11 $32.10 $34.27 $22.56 $14.58

Sales Change (2009-2010) 9.3% 4.4% 19.0% 8.6% 9.2% 11.8% 0.6%

Strategic Profit Model Ratios

Profit Margin 0.2% 2.3% 1.9% 2.4% 2.9% 3.6% 2.9%

Asset Turnover 2.50 3.10 2.40 2.50 2.40 2.50 2.60

Return on Assets 0.5% 7.1% 4.6% 6.0% 7.0% 9.0% 7.5%

Financial Leverage 1.6 2.7 2.3 2.1 1.8 1.7 1.4

Return on Net Worth 0.8% 19.2% 10.6% 12.6% 12.6% 15.3% 10.5%

Income Statement

Net Sales 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

 Cost of Goods Sold 73.1% 73.1% 76.4% 73.3% 73.6% 74.5% 71.6%

Gross Margin 26.9% 26.9% 23.6% 26.7% 26.4% 25.5% 28.4%

Payroll Expenses

Executive Salaries & Bonuses 2.0% 1.3% 1.8% 1.4% 2.0% 1.6% 1.8%

Branch Manager Salaries & Bonuses 1.9% 1.2% 1.0% 1.4% 1.5% 1.9% 1.7%

Sales Executive Salaries 0.8% 0.4% 0.5% 0.4% 0.6% 0.3% 0.0%

Outside Sales Salaries 2.3% 1.9% 2.5% 2.0% 2.6% 2.0% 2.1%

Inside/Counter Sales Wages 2.2% 2.8% 2.4% 2.9% 2.6% 2.8% 2.7%

Purchasing Salaries/Wages 0.7% 0.4% 0.3% 0.5% 0.4% 0.4% 0.4%

Credit Salaries/Wages 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2%

IT Salaries/Wages 0.1% 0.2% 0.0% 0.2% 0.2% 0.0% 0.1%

Warehouse Salaries/Wages 2.0% 1.3% 1.0% 1.3% 1.2% 1.2% 1.6%

Accounting Salaries/Wages 0.6% 0.5% 0.4% 0.5% 0.5% 0.5% 0.4%

Delivery Salaries/Wages 1.2% 1.0% 0.7% 0.8% 0.9% 0.3% 0.5%

All Other Salaries/Wages & Bonuses 0.8% 0.8% 0.4% 1.0% 0.6% 0.4% 0.6%

 Total Salaries, Wages, Bonuses 14.8% 12.0% 11.2% 12.6% 13.3% 11.6% 12.1%

Payroll Taxes 1.3% 1.0% 1.0% 1.1% 1.0% 1.0% 1.3%

Group Insurance 0.9% 1.1% 0.8% 1.3% 1.3% 0.9% 1.3%

Benefit Plans 0.6% 0.4% 0.2% 0.3% 0.3% 0.3% 0.5%

Total Payroll Expenses 17.6% 14.5% 13.2% 15.3% 15.9% 13.8% 15.2%

6A-1
Table 6A.1.1 Continued Disaggregated Costs and Expenses for Wholesalers

North 

Eastern

MidAtlanticSouth 

Eastern

Great 

Lakes Central

South 

Western Western

Occupancy Expenses

Utilities (heat, light, power, water) 0.5% 0.5% 0.3% 0.5% 0.4% 0.4% 0.3%

Telephone 0.3% 0.3% 0.3% 0.4% 0.3% 0.4% 0.3%

Building Repairs & Maintenance 0.3% 0.3% 0.1% 0.3% 0.3% 0.2% 0.2%

Rent or Ownership I Real Estate 2.5% 3.1% 2.1% 2.4% 2.5% 2.2% 3.5%

Total Occupancy Expenses 3.6% 4.2% 2.8% 3.6% 3.5% 3.2% 4.3%

Other Operating Expenses

Sales Expenses 0.7% 0.8% 1.1% 1.0% 0.7% 0.6% 0.7%

Insurance 0.1% 0.2% 0.2% 0.2% 0.2% 0.3% 0.2%

Depreciation 0.3% 0.6% 0.4% 0.4% 0.3% 0.2% 0.5%

Vehicle Expenses 1.8% 1.3% 1.0% 1.3% 0.9% 1.0% 1.0%

Personal Property Taxes/Licenses 0.0% 0.1% 0.2% 0.1% 0.0% 0.3% 0.1%

Collection Expenses 0.2% 0.3% 0.2% 0.4% 0.2% 0.4% 0.6%

Bad Debit Losses 0.3% 0.5% 0.3% 0.4% 0.2% 0.3% 0.5%

Data Processing 0.2% 0.1% 0.2% 0.3% 0.3% 0.3% 0.5%

Employee Training 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0%

All Other Operating Expenses 1.5% 1.8% 1.8% 1.4% 1.3% 1.6% 1.7%

Total Other Operating Expenses 5.1% 5.7% 5.5% 5.5% 4.1% 5.0% 5.8%

Total Operating Expenses 26.3% 24.4% 21.5% 24.4% 23.5% 22.0% 25.3%

Operating Profit 0.6% 2.5% 2.1% 2.3% 2.9% 3.5% 3.1%

Other Income 0.4% 0.3% 0.6% 0.8% 0.4% 0.3% 0.1%

Interest Expense 0.4% 0.4% 0.7% 0.5% 0.4% 0.2% 0.3%

Other Non-operating Expenses 0.1% 0.1% 0.0% 0.2% 0.0% 0.0% 0.0%

Profit Before Taxes & Discretionary Bonuses 0.5% 2.3% 2.0% 2.4% 2.9% 3.6% 2.9%

Owners' & Officers' Discretionary Bonus 0.1% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0%

Other Employees' Discretionary Bonus 0.2% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Profit Before Taxes 0.2% 2.3% 1.9% 2.4% 2.9% 3.6% 2.9%

Wholesaler Baseline Regional Markup 1.37 1.37 1.31 1.36 1.36 1.34 1.40

Wholesaler Incremental Regional Markup 1.08 1.11 1.10 1.11 1.10 1.11 1.12

Source: Heating, Air-conditioning & Refrigeration Distributors International. 2012. 2012 Profit Report (2010 data).

6A-2
Table 6A.1.2 State-Level Baseline and Incremental Markups for Wholesalers

State Census Division Baseline Incremental Population (2013)

Alabama East South Central 1.309 1.099 4,833,722 

Alaska Pacific 1.397 1.124 735,132 

Arizona Mountain 1.397 1.124 6,626,624 

Arkansas West South Central 1.342 1.114 2,959,373 

California Pacific 1.397 1.124 38,332,521 

Colorado Mountain 1.359 1.095 5,268,367 

Connecticut New England 1.368 1.078 3,596,080 

DC South Atlantic 1.368 1.112 925,749 

Delaware South Atlantic 1.368 1.112 646,449 

Florida South Atlantic 1.368 1.112 19,552,860 

Georgia South Atlantic 1.309 1.099 9,992,167 

Hawaii Pacific 1.309 1.099 1,404,054 

Idaho Mountain 1.397 1.124 1,612,136 

Illinois East North Central 1.397 1.124 12,882,135 

Indiana East North Central 1.359 1.095 6,570,902 

Iowa West North Central 1.364 1.106 3,090,416 

Kansas West North Central 1.359 1.095 2,893,957 

Kentucky East South Central 1.359 1.095 4,395,295 

Louisiana West South Central 1.364 1.106 4,625,470 

Maine New England 1.342 1.114 1,328,302 

Maryland South Atlantic 1.368 1.078 5,928,814 

Massachusetts New England 1.368 1.112 6,692,824 

Michigan East North Central 1.368 1.078 9,895,622 

Minnesota West North Central 1.364 1.106 5,420,380 

Mississippi East South Central 1.359 1.095 2,991,207 

Missouri West North Central 1.309 1.099 6,044,171 

Montana Mountain 1.359 1.095 1,015,165 

Nebraska West North Central 1.397 1.124 1,868,516 

Nevada Mountain 1.359 1.095 2,790,136 

New Hampshire New England 1.397 1.124 1,323,459 

New Jersey Mid Atlantic 1.368 1.078 8,899,339 

New Mexico Mountain 1.368 1.112 2,085,287 

New York Mid Atlantic 1.342 1.114 19,651,127 

North Carolina South Atlantic 1.368 1.078 9,848,060 

North Dakota West North Central 1.309 1.099 723,393 

Ohio East North Central 1.359 1.095 11,570,808 

Oklahoma West South Central 1.364 1.106 3,850,568 

Oregon Pacific 1.342 1.114 3,930,065 

Pennsylvania Mid Atlantic 1.397 1.124 12,773,801 

Rhode Island New England 1.366 1.109 1,051,511 

South Carolina South Atlantic 1.368 1.078 4,774,839 

South Dakota West North Central 1.309 1.099 844,877 

Tennessee East South Central 1.359 1.095 6,495,978 

Texas West South Central 1.309 1.099 26,448,193 

Utah Mountain 1.342 1.114 2,900,872 

Vermont New England 1.397 1.124 626,630 

Virginia South Atlantic 1.368 1.078 8,260,405 

Washington Pacific 1.368 1.112 6,971,406 

West Virginia South Atlantic 1.397 1.124 1,854,304 

Wisconsin East North Central 1.364 1.106 5,742,713 

Wyoming Mountain 1.359 1.095 582,658 

Sources: Heating, Air-conditioning & Refrigeration Distributors International. 2012. 2012 Profit Report (2010 data); U.S. 

Census Bureau, Population Division, Annual Estimates of the Population for the United States, Regions, States, and 

Puerto Rico (2013).

6A-3
6A.2 DETAILED MECHANICAL CONTRACTOR DATA

Section 6.6 of Chapter 6 provides mechanical contractor revenues and costs in aggregated form 

by ‘Cost of Goods Sold’ and ‘Gross Margin’. The more disaggregated breakdown of costs used 

to calculate the incremental markups are shown in Table 6-A.2.1, presented in both dollar value 

and percentage terms from the 2007 Economic Census.4

Table 6A.2.1 Detailed National Mechanical Contractor Expenses 

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

Fringe benefits, all employees 13,585,040 8.60 Baseline

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 12,671,194 8.02

Baseline & 

Incremental

Depreciation charges during year 2,297,550 1.45

Computers + Insurance and other business services + 

Advertising and Promotions + Taxes and License Fees 10,373,664 6.56

Net Profit Before Income Taxes 6,722,095 4.25 Baseline & 

Incremental

Source: U.S. Census Bureau. 2007. Plumbing, Heating, and Air-Conditioning Contractors: 2007. Sector 23: 238220. Detailed Statistics for 

Establishments: 2007.

Note: Mechanical contractor costs and expenses are first presented as total dollar values and then converted to percentage values.

6A-4
Table 6A.2.2 State-Level Mechanical Contractor Baseline and Incremental Markups

State Census Division

Replacement New Construction

Baseline Incremental Baseline Incremental Population (2013)

Alabama East South Central 1.476 1.182 1.393 1.116 4,833,722 

Alaska Pacific 1.741 1.394 1.643 1.315 735,132 

Arizona Mountain 1.558 1.247 1.470 1.177 6,626,624 

Arkansas West South Central 1.475 1.181 1.392 1.115 2,959,373 

California Pacific 1.584 1.268 1.495 1.197 38,332,521 

Colorado Mountain 1.509 1.208 1.424 1.140 5,268,367 

Connecticut New England 1.544 1.236 1.457 1.167 3,596,080 

DC South Atlantic 1.486 1.190 1.402 1.123 925,749 

Delaware South Atlantic 1.462 1.171 1.380 1.105 646,449 

Florida South Atlantic 1.491 1.194 1.407 1.126 19,552,860 

Georgia South Atlantic 1.453 1.163 1.371 1.098 9,992,167 

Hawaii Pacific 1.809 1.449 1.707 1.367 1,404,054 

Idaho Mountain 1.502 1.202 1.417 1.135 1,612,136 

Illinois East North Central 1.555 1.245 1.467 1.175 12,882,135 

Indiana East North Central 1.581 1.266 1.492 1.194 6,570,902 

Iowa West North Central 1.472 1.179 1.389 1.113 3,090,416 

Kansas West North Central 1.485 1.189 1.402 1.122 2,893,957 

Kentucky East South Central 1.554 1.244 1.467 1.174 4,395,295 

Louisiana West South Central 1.560 1.249 1.472 1.179 4,625,470 

Maine New England 1.514 1.212 1.428 1.144 1,328,302 

Maryland South Atlantic 1.466 1.174 1.384 1.108 5,928,814 

Massachusetts New England 1.516 1.214 1.431 1.146 6,692,824 

Michigan East North Central 1.508 1.208 1.423 1.140 9,895,622 

Minnesota West North Central 1.512 1.211 1.427 1.143 5,420,380 

Mississippi East South Central 1.475 1.181 1.392 1.114 2,991,207 

Missouri West North Central 1.458 1.167 1.376 1.102 6,044,171 

Montana Mountain 1.440 1.153 1.359 1.088 1,015,165 

Nebraska West North Central 1.368 1.095 1.291 1.034 1,868,516 

Nevada Mountain 1.496 1.198 1.412 1.131 2,790,136 

New Hampshire New England 1.515 1.213 1.429 1.145 1,323,459 

New Jersey Mid Atlantic 1.561 1.250 1.473 1.180 8,899,339 

New Mexico Mountain 1.541 1.234 1.454 1.164 2,085,287 

New York Mid Atlantic 1.578 1.263 1.489 1.192 19,651,127 

North Carolina South Atlantic 1.448 1.160 1.367 1.094 9,848,060 

North Dakota West North Central 1.455 1.165 1.373 1.100 723,393 

Ohio East North Central 1.517 1.215 1.432 1.147 11,570,808 

Oklahoma West South Central 1.506 1.206 1.421 1.138 3,850,568 

Oregon Pacific 1.574 1.260 1.485 1.189 3,930,065 

Pennsylvania Mid Atlantic 1.458 1.167 1.376 1.102 12,773,801 

Rhode Island New England 1.582 1.266 1.493 1.195 1,051,511 

South Carolina South Atlantic 1.544 1.237 1.458 1.167 4,774,839 

South Dakota West North Central 1.662 1.331 1.569 1.256 844,877 

Tennessee East South Central 1.456 1.166 1.374 1.100 6,495,978 

Texas West South Central 1.477 1.182 1.394 1.116 26,448,193 

Utah Mountain 1.455 1.165 1.373 1.099 2,900,872 

Vermont New England 1.515 1.213 1.429 1.145 626,630 

Virginia South Atlantic 1.535 1.229 1.449 1.160 8,260,405 

Washington Pacific 1.547 1.239 1.460 1.169 6,971,406 

West Virginia South Atlantic 1.506 1.206 1.422 1.138 1,854,304 

Wisconsin East North Central 1.488 1.191 1.404 1.124 5,742,713 

Wyoming Mountain 1.503 1.204 1.419 1.136 582,658 

Source: U.S. Census Bureau. 2007. Plumbing, Heating, and Air-Conditioning Contractors, Sector 23: EC0723A1: Construction: 

Geographic Area Series: Detailed Statistics for Establishments: 2007; U.S. Census Bureau, Population Division, Annual Estimates of 

the Population for the United States, Regions, States, and Puerto Rico (2013).

6A-5
6A.3 DETAILED GENERAL CONTRACTOR COST DATA

Based on U.S. Department of Census data, Table 6.6.8 of Chapter 6 shows general 

contractor revenues and costs in aggregated form. Table 6-A.3.1 shows the complete breakdown 

of costs and expenses provided in the 2007 Economic Census.

5 The column labeled “Scaling” in 

Detailed sales tax data by state can be found in Table 6-A.4.1.

Table 6-A.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.

Table 6A.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. 

Table 6-A.3.2 shows the state-level general contractor baseline and incremental markups. DOE 

estimated state-level incremental general contractor markups by calculating the ratio of the 

national average incremental markup to the national average baseline markup. This ratio was 

then used to scale the state-level baseline markups, arriving at a state-level estimate of 

incremental general contractor markups.

6A-6
Table 6A.3.2 State-Level General Contractor Baseline and Incremental Markups

State Census Division Baseline Incremental Population (2013)

Alabama East South Central 1.459 1.329 4,833,722

Alaska Pacific 1.354 1.233 735,132

Arizona Mountain 1.358 1.237 6,626,624

Arkansas West South Central 1.252 1.141 2,959,373

California Pacific 1.366 1.245 38,332,521

Colorado Mountain 1.204 1.097 5,268,367

Connecticut New England 1.499 1.365 3,596,080

DC South Atlantic 1.334 1.215 925,749

Delaware South Atlantic 1.334 1.215 646,449

Florida South Atlantic 1.368 1.246 19,552,860

Georgia South Atlantic 1.396 1.272 9,992,167

Hawaii Pacific 1.297 1.181 1,404,054

Idaho Mountain 1.191 1.085 1,612,136

Illinois East North Central 1.325 1.207 12,882,135

Indiana East North Central 1.288 1.174 6,570,902

Iowa West North Central 1.317 1.200 3,090,416

Kansas West North Central 1.214 1.106 2,893,957

Kentucky East South Central 1.326 1.208 4,395,295

Louisiana West South Central 1.457 1.327 4,625,470

Maine New England 1.254 1.142 1,328,302

Maryland South Atlantic 1.259 1.147 5,928,814

Massachusetts New England 1.349 1.229 6,692,824

Michigan East North Central 1.410 1.285 9,895,622

Minnesota West North Central 1.391 1.267 5,420,380

Mississippi East South Central 1.713 1.560 2,991,207

Missouri West North Central 1.180 1.075 6,044,171

Montana Mountain 1.263 1.151 1,015,165

Nebraska West North Central 1.276 1.162 1,868,516

Nevada Mountain 1.357 1.236 2,790,136

New Hampshire New England 1.277 1.163 1,323,459

New Jersey Mid Atlantic 1.636 1.491 8,899,339

New Mexico Mountain 1.280 1.166 2,085,287

New York Mid Atlantic 1.370 1.248 19,651,127

North Carolina South Atlantic 1.333 1.215 9,848,060

North Dakota West North Central 1.276 1.162 723,393

Ohio East North Central 1.247 1.136 11,570,808

Oklahoma West South Central 1.164 1.060 3,850,568

Oregon Pacific 1.246 1.135 3,930,065

Pennsylvania Mid Atlantic 1.345 1.225 12,773,801

Rhode Island New England 2.097 1.910 1,051,511

South Carolina South Atlantic 1.232 1.122 4,774,839

South Dakota West North Central 1.276 1.162 844,877

Tennessee East South Central 1.231 1.121 6,495,978

Texas West South Central 1.333 1.214 26,448,193

Utah Mountain 1.213 1.105 2,900,872

Vermont New England 1.218 1.109 626,630

Virginia South Atlantic 1.291 1.176 8,260,405

Washington Pacific 1.198 1.091 6,971,406

West Virginia South Atlantic 1.334 1.215 1,854,304

Wisconsin East North Central 1.263 1.151 5,742,713

Wyoming Mountain 1.232 1.122 582,658

Source: U.S. Census Bureau. 2007. Commercial and Institutional Building Construction (NAICS 236220): 

Construction: Summary Series: General Summary: Detailed Statistics for Establishments: 2007; U.S. Census 

Bureau, Population Division, Annual Estimates of the Population for the United States, Regions, States, and Puerto

Rico (2013).

6A-7
6A.4 STATE SALES TAX RATES

Detailed sales tax data by state can be found in Table 6A.4.1.

Table 6A.4.1 State Sales Tax Rates 

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 7.10%

Alaska 1.30% Maine 5.50% Oklahoma 8.35%

Arizona 7.15% Maryland 6.00% Oregon 0.00%

Arkansas 8.90% Massachusetts 6.25% Pennsylvania 6.40%

California 8.45% Michigan 6.00% Rhode Island 7.00%

Colorado 6.05% Minnesota 7.15% South Carolina 7.20%

Connecticut 6.35% Mississippi 7.00% South Dakota 5.40%

Delaware 5.75% Missouri 7.50% Tennessee 9.45%

Florida 0.00% Montana 0.00% Texas 7.90%

Georgia 6.65% Nebraska 6.00% Utah 6.70%

Hawaii 7.05% Nevada 7.85% Vermont 6.05%

Idaho 4.40% New Hampshire 0.00% Virginia 5.60%

Illinois 6.05% New Jersey 6.95% Washington 8.90%

Indiana 8.00% New Mexico 6.60% West Virginia 6.10%

Iowa 7.00% New York 8.40% Wisconsin 5.45%

Kansas 6.85% North Carolina 6.90% Wyoming 5.50%

Kentucky 7.85% North Dakota 5.90% US Average 7.15%

Source: The Sales Tax Clearinghouse at https://thestc.com/STRates.stm (Accessed on February 2014)

6A-8
REFERENCES

1. Heating Air Conditioning & Refrigeration Distributors International, 2012 Profit Report, 

2012. <www.hardinet.org/Profit-Report>

2. U.S. Census Bureau, Annual Estimates of the Population for the United States, Regions, 

States, and Puerto Rico, Population Division. 2012. 

www.census.gov/popest/data/state/totals/2012/index.html

3. Air Conditioning Contractors of America, 2005 Financial Analysis for the HVACR 

Contracting Industry, 2005. 

4. U.S. Census Bureau, Plumbing, Heating, and Air-Conditioning Contractors. Sector 23: 

238220. Construction: Industry Series, Preliminary Detailed Statistics for 

Establishments, 2007.

5. U.S. Census Bureau, Construction: Industry Series: Preliminary Detailed Statistics for 

Establishments: Commercial and Institutional Building Construction, 2007. 

6A-9
APPENDIX 7A. DETAILED UNIT ENERGY CONSUMPTION DATA

TABLE OF CONTENTS

7A.1 DETAILED UNIT ENERGY CONSUMPTION DATA....................................... 7A-1

REFERENCES ......................................................................................................................... 7A-5

LIST OF TABLES

Table 7A.1.1 Disaggregated PTAC Unit Energy Consumption by Location................... 7A-1

Table 7A.1.2 Disaggregated PTHP Unit Energy Consumption by Location ................... 7A-3

7A-i
APPENDIX 7A. DETAILED UNIT ENERGY CONSUMPTION DATA

7A.1 DETAILED UNIT ENERGY CONSUMPTION DATA

Based on the whole-building simulations provided in the previous packaged terminal air 

conditioning (PTAC) and packaged terminal heat pump (PTHP) equipment rulemaking, unit 

energy consumption (UEC) data for PTACs and PTHPs on a U.S. average basis are provided in 

Table 7.3.1 of Chapter 7.

1 For each of the 51 locations (the U.S. States and the District of 

Columbia) the UECs for the equipment classes and efficiency levels for PTACs and PTHPs are 

presented in Table 7A.1.1 and Table 7A.1.2.

Table 7A.1.1 Disaggregated PTAC Unit Energy Consumption by Location

State

PTAC Standard Size - 9,000 Btu/h PTAC Standard Size - 15,000 Btu/h

EL0 EL1 EL2 EL3 EL4 EL5 EL6 EL0 EL1 EL2 EL3 EL4 EL5 EL6

Alabama 1344 1326 1304 1264 1224 1185 1165 2043 2019 1998 1953 1909 1864 1841

Alaska 511 510 509 506 504 501 500 971 970 969 966 964 961 960

Arizona 1495 1474 1449 1403 1358 1312 1289 2218 2191 2168 2117 2066 2016 1991

Arkansas 1317 1299 1278 1240 1202 1163 1144 2013 1989 1969 1926 1882 1839 1817

California 901 892 880 859 838 816 806 1423 1411 1401 1378 1356 1333 1322

Colorado 722 716 709 696 683 671 664 1206 1199 1192 1179 1165 1151 1144

Connecticut 852 844 834 815 797 779 770 1393 1382 1373 1353 1332 1312 1302

Delaware 985 974 961 937 913 888 876 1570 1555 1543 1516 1489 1462 1448

Dist. of Col. 992 981 967 943 918 894 881 1574 1560 1547 1520 1493 1466 1452

Florida 1823 1796 1763 1703 1643 1583 1553 2693 2656 2625 2556 2488 2420 2386

Georgia 1317 1300 1278 1240 1201 1162 1143 2005 1981 1961 1918 1874 1831 1809

Hawaii 2115 2082 2042 1969 1897 1824 1788 3062 3018 2980 2898 2816 2734 2693

Idaho 694 689 682 671 659 648 642 1174 1167 1161 1149 1137 1125 1119

Illinois 879 870 860 840 821 802 792 1432 1421 1411 1390 1368 1347 1336

Indiana 804 797 788 772 757 741 733 1340 1331 1323 1305 1288 1270 1262

Iowa 889 880 869 850 830 810 800 1446 1434 1424 1402 1380 1359 1348

Kansas 1094 1081 1065 1036 1008 979 965 1714 1696 1681 1649 1617 1585 1569

Kentucky 1043 1031 1016 989 963 936 923 1641 1626 1612 1582 1552 1523 1508

Louisiana 1604 1581 1553 1503 1452 1401 1376 2399 2368 2341 2283 2225 2168 2139

Maine 700 694 688 676 665 653 647 1192 1185 1179 1166 1154 1141 1135

Maryland 997 986 972 947 923 898 886 1585 1571 1558 1530 1502 1475 1461

Massachusetts 787 780 772 756 741 725 717 1305 1296 1288 1271 1254 1237 1228

Michigan 783 776 768 752 737 722 714 1305 1296 1288 1272 1255 1239 1230

Minnesota 802 795 786 770 755 739 731 1332 1323 1315 1298 1280 1263 1254

Mississippi 1453 1433 1409 1365 1320 1276 1254 2194 2167 2144 2094 2044 1993 1968

7A-1
State

PTAC Standard Size - 9,000 Btu/h PTAC Standard Size - 15,000 Btu/h

EL0 EL1 EL2 EL3 EL4 EL5 EL6 EL0 EL1 EL2 EL3 EL4 EL5 EL6

Missouri 1111 1098 1082 1052 1023 993 979 1740 1722 1707 1673 1640 1607 1591

Montana 661 656 651 641 631 621 616 1138 1133 1128 1118 1107 1097 1091

Nebraska 946 936 924 902 879 857 846 1521 1507 1496 1471 1447 1422 1410

Nevada 1090 1078 1062 1034 1005 977 962 1682 1665 1651 1620 1589 1558 1543

New Hampshire 751 745 737 723 709 696 689 1259 1251 1244 1228 1213 1198 1190

New Jersey 921 911 900 878 857 835 824 1480 1467 1456 1433 1409 1385 1373

New Mexico 905 896 884 864 843 822 811 1439 1427 1417 1394 1372 1350 1338

New York 869 860 850 830 811 792 783 1411 1399 1389 1368 1347 1326 1316

North Carolina 1148 1134 1116 1085 1054 1022 1007 1779 1760 1744 1709 1674 1639 1621

North Dakota 731 725 718 706 693 681 674 1240 1233 1226 1213 1199 1185 1178

Ohio 991 980 967 942 918 894 881 1575 1561 1548 1521 1494 1467 1453

Oklahoma 1252 1236 1216 1181 1145 1110 1092 1924 1902 1884 1844 1804 1763 1743

Oregon 665 660 654 643 633 622 617 1132 1126 1121 1110 1099 1088 1082

Pennsylvania 888 879 868 848 828 809 799 1440 1428 1418 1396 1374 1352 1341

Rhode Island 794 787 778 762 746 730 722 1316 1307 1299 1281 1264 1246 1238

South Carolina 1284 1267 1247 1209 1172 1135 1117 1962 1939 1920 1878 1836 1794 1773

South Dakota 804 797 788 772 756 740 732 1330 1321 1313 1295 1278 1261 1252

Tennessee 1229 1214 1195 1160 1125 1090 1073 1892 1871 1853 1815 1776 1737 1717

Texas 1547 1525 1499 1450 1402 1354 1330 2319 2289 2264 2209 2154 2100 2072

Utah 816 809 799 782 765 748 740 1327 1317 1309 1291 1273 1256 1247

Vermont 745 739 731 718 704 691 684 1257 1249 1242 1228 1213 1199 1191

Virginia 1069 1056 1041 1013 985 957 943 1675 1659 1644 1613 1582 1551 1535

Washington 617 613 608 600 591 583 579 1074 1069 1065 1056 1048 1039 1034

West Virginia 867 858 847 828 809 790 781 1405 1394 1384 1363 1342 1321 1311

Wisconsin 763 757 749 735 721 707 700 1282 1273 1266 1251 1235 1220 1212

Wyoming 694 689 683 672 660 649 643 1182 1176 1170 1158 1146 1134 1128

US Average 1078 1066 1050 1022 994 966 952 1688 1671 1657 1625 1594 1563 1547

7A-2
Table 7A.1.2 Disaggregated PTHP Unit Energy Consumption by Location

State

PTHP Standard Size - 9,000 Btu/h PTHP Standard Size - 15,000 Btu/h

EL0 EL1 EL2 EL3 EL4 EL5 EL0 EL1 EL2 EL3 EL4 EL5

Alabama 1729 1697 1653 1608 1563 1541 2576 2554 2494 2433 2373 2343

Alaska 4295 4267 4240 4213 4187 4173 5431 5421 5385 5350 5314 5297

Arizona 1659 1628 1582 1537 1491 1468 2459 2436 2375 2314 2253 2222

Arkansas 1873 1839 1793 1748 1702 1679 2758 2735 2674 2612 2551 2520

California 1091 1072 1047 1022 997 984 1723 1711 1677 1642 1608 1591

Colorado 1902 1879 1854 1829 1804 1791 2718 2707 2673 2639 2605 2588

Connecticut 2184 2152 2116 2080 2044 2026 3098 3082 3034 2986 2938 2914

Delaware 2035 2002 1962 1922 1883 1863 2933 2915 2862 2809 2756 2729

Dist. of Col. 1959 1928 1890 1852 1814 1795 2852 2834 2784 2733 2682 2657

Florida 2066 2027 1969 1911 1853 1824 3004 2975 2896 2818 2739 2700

Georgia 1676 1645 1602 1559 1516 1494 2511 2489 2431 2372 2313 2284

Hawaii 2086 2045 1988 1933 1878 1851 2980 2954 2892 2833 2791 2771

Idaho 1966 1939 1910 1881 1852 1838 2826 2814 2775 2737 2699 2680

Illinois 2595 2564 2527 2491 2454 2436 3549 3532 3484 3436 3388 3364

Indiana 3383 3355 3322 3290 3258 3241 4402 4388 4345 4302 4260 4238

Iowa 2904 2875 2841 2807 2774 2757 3872 3857 3812 3767 3722 3700

Kansas 2194 2163 2123 2084 2044 2025 3112 3093 3040 2987 2934 2908

Kentucky 2024 1994 1956 1917 1879 1860 2904 2886 2835 2784 2733 2707

Louisiana 1781 1746 1695 1643 1592 1566 2661 2634 2564 2494 2425 2390

Maine 2638 2611 2582 2553 2523 2509 3586 3573 3534 3495 3456 3437

Maryland 2017 1984 1945 1906 1866 1847 2913 2895 2842 2790 2738 2711

Massachusetts 2169 2138 2104 2069 2035 2018 3071 3055 3010 2965 2919 2897

Michigan 2625 2594 2560 2526 2491 2474 3581 3566 3521 3476 3432 3409

Minnesota 3390 3365 3336 3308 3279 3264 4398 4385 4346 4308 4270 4251

Mississippi 1756 1722 1674 1626 1578 1554 2631 2607 2542 2477 2412 2379

Missouri 2277 2245 2204 2163 2122 2101 3206 3186 3131 3076 3021 2994

Montana 2593 2568 2542 2515 2488 2475 3508 3497 3462 3426 3391 3374

Nebraska 2802 2772 2737 2701 2666 2648 3756 3740 3693 3646 3599 3575

Nevada 1607 1581 1545 1510 1475 1457 2386 2369 2322 2275 2228 2205

New Hampshire 2728 2701 2672 2642 2612 2597 3689 3676 3636 3596 3556 3536

New Jersey 1927 1896 1859 1822 1786 1767 2806 2789 2740 2691 2642 2617

New Mexico 1642 1617 1586 1555 1524 1509 2425 2411 2370 2329 2288 2267

New York 2028 1998 1963 1928 1893 1875 2913 2897 2850 2804 2757 2734

North Carolina 1741 1710 1670 1629 1589 1568 2585 2565 2510 2456 2401 2374

North Dakota 3580 3557 3531 3506 3480 3467 4603 4591 4557 4522 4487 4470

Ohio 2229 2198 2160 2123 2085 2066 3133 3116 3066 3016 2966 2941

Oklahoma 1962 1930 1886 1843 1799 1778 2850 2829 2770 2712 2653 2624

7A-3
State

PTHP Standard Size - 9,000 Btu/h PTHP Standard Size - 15,000 Btu/h

EL0 EL1 EL2 EL3 EL4 EL5 EL0 EL1 EL2 EL3 EL4 EL5

Oregon 1328 1303 1276 1250 1223 1209 2079 2067 2031 1994 1957 1939

Pennsylvania 2241 2209 2172 2135 2098 2080 3153 3137 3088 3039 2990 2966

Rhode Island 2172 2141 2106 2071 2037 2019 3074 3059 3013 2967 2921 2898

South Carolina 1679 1648 1605 1562 1519 1497 2518 2497 2438 2380 2322 2293

South Dakota 3020 2995 2967 2938 2909 2894 3989 3976 3938 3899 3860 3841

Tennessee 1857 1825 1783 1740 1697 1676 2733 2712 2654 2596 2539 2510

Texas 1848 1813 1762 1712 1661 1636 2735 2709 2641 2573 2504 2470

Utah 1758 1732 1701 1670 1639 1624 2586 2572 2531 2490 2449 2429

Vermont 3137 3110 3080 3050 3020 3005 4124 4111 4071 4032 3992 3972

Virginia 1802 1771 1732 1693 1654 1634 2660 2642 2589 2536 2484 2458

Washington 1411 1384 1356 1328 1300 1286 2185 2173 2136 2098 2060 2041

West Virginia 2033 2005 1973 1940 1907 1891 2900 2886 2842 2798 2755 2733

Wisconsin 3113 3085 3054 3023 2992 2977 4103 4090 4049 4008 3967 3946

Wyoming 2520 2495 2467 2439 2412 2398 3425 3413 3376 3340 3303 3285

7A-4
REFERENCES

1. U.S. Department of Energy – Office of Energy Efficiency and Renewable Energy, 

Energy Conservation Program for Commercial and Industrial Equipment: Packaged 

Terminal Air Conditioner and Packaged Terminal Heat Pump Energy Conservation 

Standards, 2008. Washington, D.C. <www.regulations.gov/#!documentDetail;D=EERE2007-BT-STD-0012-0002>

7A-5
APPENDIX 10A. FULL-FUEL-CYCLE MULTIPLIERS

TABLE OF CONTENTS

10A.1 INTRODUCTION ................................................................................................ 10A-1

10A.2 METHODOLOGY ............................................................................................... 10A-1

10A.3 FULL-FUEL-CYCLE ENERGY MULTIPLIERS .............................................. 10A-3

REFERENCES ....................................................................................................................... 10A-4

LIST OF TABLES

Table 10A.2.1 Dependence of FFC Parameters on AEO Inputs ...................................... 10A-3

Table 10A.3.1 Full-Fuel-Cycle Energy Multipliers (Based on AEO 2014)..................... 10A-3

10A-i
APPENDIX 10A. FULL-FUEL-CYCLE MULTIPLIERS

10A.1 INTRODUCTION

This appendix summarizes the methods used to calculate full-fuel-cycle (FFC) energy 

savings expected to result from potential standards. 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. The U.S. Department of Energy’s (DOE’s) traditional approach encompassed only site 

energy and the energy losses associated with generation, transmission, and distribution of 

electricity.1 Per DOE’s 2011 Statement of Policy for Adopting Full Fuel Cycle Analyses, DOE 

now uses FFC measures of energy use and emissions in its energy conservation standards 

analyses. This appendix summarizes the methods used to incorporate the full-fuel-cycle impacts 

into the analysis.

This analysis uses several different terms to reference energy use. The physical sources of 

energy are the primary fuels such as coal, natural gas, liquid fuels, etc. Primary energy is equal to 

the heat content (Btu) of the primary fuels used to provide an end-use service. Site energy use is 

defined as the energy consumed at the point-of-use in a building or industrial process. Where 

natural gas and petroleum fuels are consumed at the site (for example in a furnace), site energy is 

identical to primary energy, with both equal to the heat content of the primary fuel consumed. 

For electricity, site energy is measured in kWh. In this case the primary energy is equal to the 

quads of primary energy required to generate and deliver the site electricity. This primary energy 

is calculated by multiplying the site kWh times the site-to-power plant energy use factor, given 

in Chapter 10. For the FFC analysis, the 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 fuel cycle analysis must distinguish between electricity generated by fossil fuels 

and uranium, and electricity generated from renewable fluxes (wind, solar and hydro). For the 

former, the upstream fuel cycle impacts are derived from the amount of fuel consumed at the 

power plant. For the latter, no fuel per se is used, so there is no upstream component.

10A.2 METHODOLOGY

The mathematical approach is discussed in the paper A Mathematical Analysis of Full 

Fuel Cycle Energy Use,

2 and details on the fuel production chain analysis are presented in the 

paper Projections of Full Fuel Cycle Energy and Emissions Metrics.

3 The text below provides a 

brief summary of the methods used to calculate FFC energy.

10A-1
When all energy quantities are normalized to the same units, the FFC energy use can be 

represented as the product of the primary energy use and an FFC multiplier. The FFC multiplier 

is defined mathematically as a function of a set of parameters representing the energy intensity 

and material losses at each production stage. These parameters depend only on physical data, so

the calculations do not require any assumptions about prices or other economic data. While in 

general these parameter values may vary by geographic region, for this analysis national 

averages are used. 

In the notation below, the indices x and y are used to indicate fuel type, with x=c for coal, 

x=g for natural gas, x=p for petroleum fuels, x=u for uranium and x=r for renewable fluxes. The 

fuel cycle parameters are:

• ax is the quantity of fuel x burned per unit of electricity output, on average, for grid 

electricity. The calculation of ax includes a factor to account for transmission and 

distribution system losses. 

• by is the amount of grid electricity used in production of 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)

The parameters are calculated as a function of time with an annual time step; hence, a 

time series of annual values is used to estimate the FFC energy and emissions savings in each 

year of the analysis period. Fossil fuel quantities are converted to energy units using the heat 

content factors 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. The site-to-power plant

energy use factor is defined as the ratio of the total primary energy consumption by the electric

power sector (in quadrillion Btu’s) divided by the total electricity generation in each year.

The FFC multiplier is denoted μ (mu). A separate multiplier is calculated for each fuel 

used on site. A multiplier is also calculated for electricity reflecting the fuel mix used in its 

generation. The multipliers are dimensionless numbers that are 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.

For DOE’s appliance standards energy savings estimates, the fuel cycle analysis 

methodology is designed to make use of data and projections published in the Annual Energy 

Outlook (AEO). Table 10-A.2.1 provides a summary of the AEO data used as inputs to the 

different parameter calculations. The AEO does not provide all the information needed to 

estimate total energy use in the fuel production chain. Reference three describes the additional 

data sources used to complete the analysis. However, the time dependence in the FFC multipliers 

arises exclusively from variables taken from the AEO. The FFC analysis for PTAC and PTHP

equipment used data from AEO 2014.

4

10A-2
Table 10A.2.1 Dependence of FFC Parameters on AEO Inputs

Parameter Fuel 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 power sector energy 

consumption

bc, cnc, cpc coal Coal Production by Region and 

Type

Production by coal 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 Crude oil domestic production

cnn

natural 

gas

Oil and Gas Supply US 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

10A.3 FULL-FUEL-CYCLE ENERGY MULTIPLIERS

FFC energy multipliers are presented in Table 10-A.3.1 for selected years. For the

analysis period beyond 2040, the last year in the AEO 2014 projection, the 2040 value was held 

constant. The multiplier for electricity reflects the shares of various primary fuels in total 

electricity generation over the forecast period.

Table 10A.3.1 Full-Fuel-Cycle Energy Multipliers (Based on AEO 2014)

2019 2020 2025 2030 2035 2040

Electricity 1.043 1.044 1.045 1.046 1.047 1.047

Natural Gas 1.108 1.109 1.111 1.113 1.114 1.114

Petroleum Fuels 1.176 1.176 1.176 1.174 1.172 1.170

10A-3
REFERENCES

1. United States - Office of the Federal Register, Federal Register, Volume 76, Number 160, 

August 18, 2011. UNT Digital Library. Washington D.C. 

2. Coughlin, K., A Mathematical Analysis of Full Fuel Cycle Energy Use. Energy, 2012. 

37(1): pp. 698-708 <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. Energy Information Administration, Annual Energy Outlook 2014 with Projections to 

2040, 2014. Washington, DC. Report No. DOE/EIA-0383. 

<www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf>

10A-4
APPENDIX 10B. NIA SENSITIVITY ANALYSIS FOR ALTERNATIVE PRODUCT

PRICE TREND SCENARIOS

TABLE OF CONTENTS

10B.1 INTRODUCTION .......................................................................................................10B-1

10B.2 ALTERNATIVE PTAC PRICE TREND SCENARIOS.............................................10B-1

10B.2.1.1 Exponential Fit (High Price Scenario) ...................................................10B-1

10B.2.1.2 Annual Energy Outlook 2014 Price Forecast (Low Price Scenario) .....10B-3

10B.2.1.3 Summary ................................................................................................10B-3

10B.3 NPV RESULTS BY ALTERNATIVE PRICE SCENARIO ......................................10B-4

REFERENCES ........................................................................................................................10B-6

LIST OF TABLES

Table 10B.2.1 Price Trend Sensitivites .............................................................................10B-3

Table 10B.3.1 Summary of Cumulative Net Present Value for PTAC and PTHP: 

Decreasing Price Scenario (Seven Percent Discount Rate).......................10B-4

Table 10B.3.2 Summary of Cumulative Net Present Value for PTAC and PTHP: 

Decreasing Price Scenario (Three Percent Discount Rate)........................10B-5

Table 10B.3.3 Summary of Cumulative Net Present Value for PTACs and PTHPs: 

Increasing Price Scenario (Seven Percent Discount Rate) ........................10B-5

Table 10B.3.4 Summary of Cumulative Net Present Value for PTACs and PTHPs: 

Increasing Price Scenario (Three Percent Discount Rate).........................10B-5

LIST OF FIGURES

Figure 10B.2.1 Relative Price of PTAC and PTHP Equipment versus Year, with 

Exponential Fit...........................................................................................10B-2

Figure 10B.2.2 PTAC Price Forecast Indexes....................................................................10B-4

10B-i
APPENDIX 10B. NIA SENSITIVITY ANALYSIS FOR ALTERNATIVE PRODUCT

PRICE TREND SCENARIOS

10B.1 INTRODUCTION

The U.S. Department of Energy (DOE) used a constant price assumption for the default 

forecast in the national impact analysis (NIA) described in Chapter 10. In order to investigate the 

impact of different product price forecasts on the consumer net present value (NPV) for the 

considered trial standard levels (TSLs) for packaged terminal air conditioners (PTACs) and 

packaged terminal heat pumps (PTHPs), 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 forecast. 

In recent rulemakings for several residential products, DOE has used the experience 

curve method to derive learning rates to forecast 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.1

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 

sensitivity analysis. This approach was used for PTACs and PTHPs.

10B.2 ALTERNATIVE PTAC PRICE TREND SCENARIOS

For PTACs and PTHPs, DOE considered two alternative price trends for sensitivity 

analysis. One of these used an exponential fit on the deflated Producer Price Index (PPI) for all 

other miscellaneous refrigeration and air-conditioning equipment, and the other is based on the 

“deflator— industrial equipment” that was forecasted for the U.S. Energy Information 

Administration’s (EIA’s) Annual Energy Outlook 2014 (AEO2014). 

10B.2.1.1 Exponential Fit (High Price Scenario)

For this scenario, DOE used an inflation-adjusted all other miscellaneous refrigeration 

and air-conditioning equipment PPI from 2001-2014 to fit an exponential model with year as the 

10B-1
explanatory variable. Spanning the time period 2001-2014, DOE obtained historical PPI data, as 

well as all other miscellaneous refrigeration and air-conditioning equipment from the Bureau of 

Labor Statistics’ (BLS).a The PPI data reflect nominal prices, adjusted for product 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. The deflated price index is now presented in 2014 dollar values. In 

this case, the exponential function takes the form of:

YY = aa ∙ eebbbb

where Y is the all other miscellaneous refrigeration and air-conditioning 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 equipment price index versus year from 2001 to 2014. See

Figure 10B.2.1 Relative Price of PTAC and PTHP Equipment versus Year, with 

Exponential Fit

The regression performed as an exponential trend line fit results in an R-square of 0.77, 

which indicates a moderate fit to the data. The final estimated exponential function is:

YY = 5.60 × 10(−13) ∙ ee0.014

a

 Series ID PCU3334153334159; www.bls.gov/ppi/

y = 5.60E-13e0.014x

R2 = 0.7665

0.00

0.20

0.40

0.60

0.80

1.00

1.20

2000 2005 2010 2015

Deflated Price Index (2014=1)

Year

Exponential Fit on Deflated PPI from 2001 

to 2014

Exponential Fit

on Deflated PPI

from 2001 -

2014

Expon.

(Exponential Fit

on Deflated PPI

from 2001 -

2014)

10B-2

 
DOE then derived a price factor index for this scenario, with 2014 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.

10B.2.1.2 Annual Energy Outlook 2014 Price Forecast (Low Price Scenario)

DOE also examined a forecast based on the “chained price index—industrial equipment” 

that was forecasted for AEO 2014 out to 2040. This index is the most disaggregated category 

that includes PTACs. To develop an inflation-adjusted index, DOE normalized the above index 

with the “chained price index—gross domestic product” forecasted for AEO 2014. To extend the 

price index beyond 2040, DOE used the average annual price growth rate in 2031 to 2040. 

10B.2.1.3 Summary

Table 10B.2.1 shows the summary of the average annual rates of changes for the product 

price index in each scenario. Figure 10B.2.2 shows the resulting price trends.

Table 10B.2.1 Price Trend Sensitivites

Sensitivity Price Trend Average Annual rate of 

change %

Medium (Default) Constant Price Projection 0.00

Low Price Scenario AEO2014— “chained price index—

industrial equipment”

-0.38

High Price Scenario Exponential Fit using data from 2001 to 

2014

1.41

10B-3
Figure 10B.2.2 PTAC Price Forecast Indexes

10B.3 NPV RESULTS BY ALTERNATIVE PRICE SCENARIO

Table 10B.3.1 Summary of Cumulative Net Present Value for PTAC and PTHP: 

Decreasing Price Scenario (Seven Percent Discount Rate)

Equipment Class Net Present Value (Million 2014$)

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

PTAC < 7,000 Btu/h 0.0 -1.0 -2.9 -4.5 -4.8

PTAC 7,000 - 15,000 Btu/h -2.5 -13.8 -33.4 -51.2 -55.0

PTAC > 15,000 Btu/h -0.5 -3.1 -7.1 -10.5 -11.2

PTHP < 7,000 Btu/h 0.0 -0.4 -1.0 -1.7 -1.8

PTHP 7,000 - 15,000 Btu/h 3.5 3.8 1.0 -4.3 -5.7

PTHP > 15,000 Btu/h 0.0 -0.4 -2.0 -3.6 -4.0

All Classes 0.5 -14.9 -45.4 -75.8 -82.6

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

201420162018202020222024202620282030203220342036203820402042204420462048

Price Factor Index (2014=1)

Year

Constant Price Index_Default

Exponential Fit using PPI

deflated with GDP (2001-

2014)_ High Price Case

AEO 2014_Low Price Case

10B-4
Table 10B.3.2 Summary of Cumulative Net Present Value for PTAC and PTHP: 

Decreasing Price Scenario (Three Percent Discount Rate)

Equipment Class Net Present Value (Million 2014$)

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

PTAC < 7,000 Btu/h 0.0 -1.5 -4.3 -6.4 -6.8

PTAC 7,000 - 15,000 Btu/h -2.4 -15.4 -40.5 -62.1 -66.1

PTAC > 15,000 Btu/h -0.8 -5.2 -11.8 -16.5 -17.3

PTHP < 7,000 Btu/h 0.1 0.0 -0.5 -1.1 -1.2

PTHP 7,000 - 15,000 Btu/h 10.0 21.4 30.1 30.6 30.1

PTHP > 15,000 Btu/h 0.4 0.9 -0.4 -2.1 -2.4

All Classes 7.4 0.2 -27.3 -57.5 -63.8

Table 10B.3.3 Summary of Cumulative Net Present Value for PTACs and PTHPs: 

Increasing Price Scenario (Seven Percent Discount Rate)

Equipment Class Net Present Value (Million 2014$)

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

PTAC < 7,000 Btu/h 0.0 -1.7 -4.6 -6.8 -7.2

PTAC 7,000 - 15,000 Btu/h -5.2 -26.9 -61.0 -86.9 -91.5

PTAC > 15,000 Btu/h -0.9 -5.2 -11.1 -15.5 -16.3

PTHP < 7,000 Btu/h -0.3 -1.1 -2.4 -3.4 -3.6

PTHP 7,000 - 15,000 Btu/h 0.4 -7.6 -21.4 -33.0 -35.1

PTHP > 15,000 Btu/h -0.4 -2.1 -5.3 -7.8 -8.3

All Classes -6.4 -44.6 -105.9 -153.4 -162.0

Table 10B.3.4 Summary of Cumulative Net Present Value for PTACs and PTHPs: 

Increasing Price Scenario (Three Percent Discount Rate)

Equipment Class Net Present Value (Million 2014$)

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

PTAC < 7,000 Btu/h 0.0 -3.0 -8.3 -11.5 -12.0

PTAC 7,000 - 15,000 Btu/h -8.7 -45.6 -103.3 -139.0 -144.2

PTAC > 15,000 Btu/h -1.8 -10.1 -20.8 -27.2 -28.1

PTHP < 7,000 Btu/h -0.3 -1.7 -3.6 -4.8 -5.0

PTHP 7,000 - 15,000 Btu/h 3.0 -4.2 -19.4 -29.7 -31.1

PTHP > 15,000 Btu/h -0.6 -3.1 -7.9 -10.8 -11.3

All Classes -8.4 -67.6 -163.3 -223.0 -231.7

10B-5
REFERENCES

1. Margaret Taylor and K. Sydny Fujita. Accounting for Technological Change in 

Regulatory Impact Analyses: The Learning Curve Technique. Lawrence Berkeley 

National Laboratory, Berkeley, CA. April 2013.

10B-6
APPENDIX 12A MANUFACTURER IMPACT ANALYSIS INTERVIEW GUIDE

TABLE OF CONTENTS

12A.1 INTRODUCTION.................................................................................................... 12A-1

12A.2 MANUFACTURER IMPACT ANALYSIS INTERVIEW GUIDE ....................... 12A-1

12A.3 KEY ISSUES ........................................................................................................... 12A-4

12A.4 ENGINEERING....................................................................................................... 12A-4

12A.5 COMPANY OVERVIEW AND ORGANIZATIONAL CHARACTERISTICS.... 12A-9

12A.6 MARKUPS AND PROFITABILITY .................................................................... 12A-10

12A.7 SHIPPING COSTS ................................................................................................ 12A-11

12A.8 INDUSTRY PROJECTIONS ................................................................................ 12A-12

12A.9 FINANCIAL PARAMETERS............................................................................... 12A-13

12A.10 CONVERSION COSTS..................................................................................... 12A-14

12A.11 CUMULATIVE REGULATORY BURDEN .................................................... 12A-19

12A.12 DIRECT EMPLOYMENT IMPACT ASSESSMENT ...................................... 12A-20

12A.13 CAPACITY/EXPORTS / FOREIGN COMPETITION / OUTSOURCING..... 12A-21

12A.14 CONSOLIDATION............................................................................................ 12A-22

12A.15 IMPACTS ON SMALL BUSINESS.................................................................. 12A-23

LIST OF TABLES

Table 12A.2.1. Equipment Classes and Potential Efficiency Levels in EER......................... 12A-2

Table 12A.2.2. Potential Design Options ............................................................................... 12A-3

Table 12A.4.1. Heat Exchanger Cost and Fin Spacing........................................................... 12A-5

Table 12A.4.2. Compressor Type(s) Used by System Cooling Capacity............................... 12A-6

Table 12A.4.3. Baseline Compressor Costs............................................................................ 12A-6

Table 12A.4.4. Factory Parameters......................................................................................... 12A-8

Table 12A.5.1. Shipment Information (total units shipped)................................................... 12A-9

Table 12A.9.1. Financial Parameters for PTAC and PTHP Manufacturers......................... 12A-13

Table 12A.10.1. Expected Conversion Costs for PTAC and PTHP Equipment, Cooling Capacity: 

<7,000 Btu/h ......................................................................................................................... 12A-15

Table 12A.10.2. Expected Conversion Costs for PTAC and PTHP Equipment, Cooling Capacity: 

≥7,000 Btu/h and ≤15,000 Btu/h........................................................................................... 12A-16

Table 12A.10.3. Expected Conversion Costs for PTAC and PTHP Equipment, Cooling Capacity: 

>15,000 Btu/h ....................................................................................................................... 12A-17

Table 12A.11.1. Other Regulations Identified...................................................................... 12A-19

Table 12A.12.1. Manufacturing locations ............................................................................ 12A-20

12A-i
APPENDIX 12A MANUFACTURER IMPACT ANALYSIS INTERVIEW GUIDE

12A.1 INTRODUCTION

This appendix presents the interview guide that DOE used in 2013 to gather data for the 

manufacturer impact analysis. When this guide was administered, DOE assumed the baseline in 

this rulemaking would be equivalent to the PTAC and PTHP energy conservation standards set 

in the 2008 final rule. 73 FR 58772 (Oct. 7, 2008). ANSI/ASHRAE/IES 90.1-2013 was 

published after DOE concluded manufacturer interviews, and DOE set the baseline equivalent to 

ASHRAE levels in the NOPR and final rule analysis.

12A.2 MANUFACTURER IMPACT ANALYSIS INTERVIEW GUIDE

As part of the potential rulemaking process for amending energy conservation standards 

for packaged terminal air conditioners (PTACs) and packaged terminal heat pumps (PTHPs), the 

Department of Energy (DOE) is conducting a manufacturer impact analysis (MIA). In this 

analysis, DOE uses publicly available information and information provided by manufacturers 

during interviews to assess possible impacts on manufacturers due to amended energy 

conservation standards. 

This questionnaire is a part of the MIA process and is intended to inform the 

Department’s understanding of how changes in the energy conservation standard will affect 

manufacturers of PTACs and PTHPs. All information provided in response to this questionnaire 

will be treated as confidential. The questions below range from requests about specific financial 

figures for use in industry modeling to generic questions intended to solicit qualitative 

comments. Topics covered include:

1) Key Issues 

2) Engineering

3) Company Overview and Organizational Characteristics

4) Markups and Profitability

5) Shipping Costs

6) Industry Projections

7) Financial Parameters 

8) Conversion Costs

9) Cumulative Regulatory Burden

10) Direct Employment Impact Assessment

11) Capacity / Exports / Foreign Competition / Outsourcing

12) Consolidation

13) Impacts on Small Business

12A-1
The questions in this interview guide refer to the equipment classes and potential efficiency levels (ELs) in EER for each 

equipment class provided in Table 12A.2.1 as well as the design options listed in Table 12A.2.2. The baseline in Table 12A.2.1 refers to 

current efficiency standards. In the far right column of Table 12A.2.1, please fill out the percentage of your PTAC/PTHP shipments that 

fall into each equipment class. 

Table 12A.2.1. Equipment Classes and Potential Efficiency Levels in EER

Equipment 

Type

Size 

Category Cooling Capacity Baseline EL 1 EL 2 EL 3 EL 4 Max Tech

% of your 

shipments 

in this class

1

PTAC

Standard*

<7,000 Btu/h 11.7 12.17 12.64 13.10 13.57 14.04

2 ≥7,000 Btu/h and 

≤15,000 Btu/h

13.8 –

(0.300 x Cap)

14.35 –

(0.312 x Cap)

14.90 –

(0.324 x Cap)

15.46 –

(0.336 x Cap)

16.01 –

(0.348 x Cap)

16.56 –

(0.360 x Cap)

3 >15,000 Btu/h 9.3 9.67 10.04 10.42 10.79 11.16

4

NonStandard**

<7,000 Btu/h 9.4 10.15 10.90 11.66 12.41 13.16

5 ≥7,000 Btu/h and 

≤15,000 Btu/h

10.9 –

(0.213 x Cap)

11.77 –

(0.230 x Cap)

12.64 –

(0.247 x Cap)

13.52 –

(0.264 x Cap)

14.39 –

(0.281 x Cap)

15.26 –

(0.298 x Cap)

6 >15,000 Btu/h 7.7 8.32 8.93 9.55 10.16 10.78

7

PTHP

Standard*

<7,000 Btu/h 11.9 12.17 12.64 13.10 13.57 14.04

8 ≥7,000 Btu/h and 

≤15,000 Btu/h

14.0 –

(0.300 x Cap)

14.35 –

(0.312 x Cap)

14.90 –

(0.324 x Cap)

15.46 –

(0.336 x Cap)

16.01 –

(0.348 x Cap)

16.56 –

(0.360 x Cap)

9 >15,000 Btu/h 9.5 9.67 10.04 10.42 10.79 11.16

10

NonStandard**

<7,000 Btu/h 9.3 10.15 10.90 11.66 12.41 13.16

11 ≥7,000 Btu/h and 

≤15,000 Btu/h

10.8 - (0.213 

x Cap)

11.77-

(0.230 x Cap)

12.64 –

(0.247 x Cap)

13.52 –

(0.264 x Cap)

14.39 –

(0.281 x Cap)

15.26 –

(0.298 x Cap)

12 >15,000 Btu/h 7.6 8.32 8.93 9.55 10.16 10.78

* Standard refers to PTAC or PTHP equipment with wall sleeve dimensions greater than or equal to 16 inches high, or greater than or 

equal to 42 inches wide.

** Non-Standard refers to PTAC or PTHP equipment with wall sleeve dimensions less than 16 inches high and less than 42 inches wide. 

12A-2
For standard size PTACs, the ELs indicated in Table 12A.2.1 (above) are 4%, 8%, 12%, 

16%, and 20% more efficient than the baseline. For non-standard size PTACs, the ELs are 8%, 

16%, 24%, 32%, and 40% more efficient than the baseline. The ELs for PTHPs correspond to the 

ELs for PTACs of equivalent size category and cooling capacity. 

Table 12A.2.2. Potential Design Options

Design Options

Heat transfer improvements: 

• Electro-hydrodynamic enhancement1

High-efficiency motors for indoor blower and/or condenser fan

• Copper rotor motor

• Permanent split-capacitor

• Permanent magnet, electronically commutated

Alternative refrigerants

Larger heat exchangers

More efficient condenser/outdoor coil fan

• Larger condenser fan diameter

• More efficient fan blades

More efficient evaporator/indoor coil fan

• Housed backward-curved or airfoil

• Plenum fan

Microchannel heat exchangers

• Condenser (for Air Conditioners)

• Evaporator/Indoor Coil

• Outdoor Coil (for heat pumps)

Compressor Improvements

• High efficiency compressors

• Multiple- or variable-capacity compressors

Thermostatic expansion valves

Electronic expansion valves

Ambient subcoolers2

Mechanical subcoolers3

1 An EHD system uses high-voltage (>1kV), low-current, electricity from integrated electrodes to create an electric 

field and incite fluid mixing.

2 Ambient subcoolers reject refrigerant heat to the lower temperature of a surrounding medium (usually air).

3 Mechanical subcoolers use smaller, secondary vapor-compression circuits.

12A-3

 
12A.3 KEY ISSUES

DOE is interested in understanding the impact of amended energy conservation standards 

on manufacturers. This section provides an opportunity for manufacturers to identify highpriority issues that DOE should take into consideration when conducting the Manufacturer 

Impact Analysis. 

1) In general, what are the key concerns for your company regarding this potential 

rulemaking for PTACs and PTHPs?

2) For the issues identified, how significant are they for different equipment classes and/or 

efficiency levels?

12A.4 ENGINEERING

1) Which design features impacting energy use do you generally incorporate into a 

“baseline” unit in each equipment class? Describe typical key components: compressor, 

condenser/outdoor coil, evaporator/indoor coil, indoor fan, outdoor fans. 

2) Which design features impacting energy use are incorporated into products to reach 

higher efficiencies—which of these design options provide the greatest EER 

improvement, and what are their costs? How does the selection of these design options 

vary by cooling capacity? Would it be possible to obtain detailed design information for 

key representative products spanning important efficiency levels that would clearly 

illustrate these trends?

3) How does equipment with “standard sizes” differ from equipment with “non-standard 

sizes” in terms of design features and energy-efficiency opportunities? 

4) How do heat pumps differ from air conditioners in terms of achievable cooling efficiency 

levels (EER)? Is the 0.2 EER differential of the ASHRAE 90.1-2007 EER levels 

associated with coil design compromises and reversing valve pressure drop? What 

unique challenges do heat pumps encounter when trying to improve energy efficiency in 

terms of EER? Does this vary by capacity? Do design options that increase heating 

efficiency (COP) positively or negatively impact cooling efficiency (EER)?

5) Are there specific charge imbalance issues associated with heat pumps—what strategies 

are used to address these issues and do they affect efficiency?

12A-4
6) Are any of the design options listed in Table 12A.2.2 more effective in air conditioners or 

heat pumps? Higher or lower capacities? Are there any design options that are not 

technologically feasible for specific equipment classes? If so, which classes? Are there 

any important design options for improvement of EER that are not listed in the table? 

7) Do you manufacture your own heat exchangers or purchase them from another source? If 

purchased from another source, please provide cost estimates for the following coil types 

for 9000 Btu/h, and 15000 Btu/h. In addition, please provide the typical fin spacing for 

each coil type and unit size. If you manufacture them, do you purchase pre-prepared 

stock (e.g., tubing and fin stock) and what are the costs for these items?

Table 12A.4.1. Heat Exchanger Cost and Fin Spacing

Estimated Cost Fin Spacing

Coil Type 9000

Btu/h unit

15000 

Btu/h unit

9000 

Btu/h unit

15000 

Btu/h unit

Fin & Tube

8) Do any of your units utilize microchannel heat exchangers? If so, do you manufacture 

them or purchase them from another source? If you purchase them from another source, 

please provide cost estimates. If you manufacture them, do you purchase pre-prepared 

stock (e.g., tubing and fin stock), and what are the costs for these items?

9) What is the typical coil depth in number of tube rows for indoor and outdoor coils at 

baseline and higher efficiency levels for a 9000 Btu/h and 15000 Btu/h AC unit? HP?

10) What factors limit the use of increased coil area to reach higher efficiencies (e.g., 

aesthetic appeal, cost, sleeve size, etc.)? What efficiency increases can be obtained in 

terms of EER from increasing coil area before these factors come into play? 

11) Does coil efficiency change with the use of different blower motors or compressors? 

What are typical coil face velocities for indoor and outdoor coils of baseline-efficiency 

equipment and does this change significantly at higher efficiency levels? 

12) Do typical units utilize refrigerant receivers? Does this depend on capacity?

12A-5
13) What types of compressor are used in PTACs and PTHPs, and does the type of 

compressor used change based on the nominal capacity of the system? Please list the 

types and typical efficiency ranges of compressors used for each capacity range (i.e., 

<7000 Btu/h, between 7000 Btu/h and 15000 Btu/h, and >15000 Btu/h) in Table 12A.4.2. 

Compressor Type(s) Used by System Cooling Capacity. In cases where multiple 

compressor types may be used for a given capacity range, what factors affect the decision 

of which compressor type to use for a given unit? 

Table 12A.4.2. Compressor Type(s) Used by System Cooling Capacity

System Cooling Capacity Compressor Type(s) 

Used

Typical Efficiency 

Range

<7,000 Btu/h

≥7,000 Btu/h and ≤15,000 Btu/h

>15,000 Btu/h

14) How does the cost of a “baseline” compressor vary as a function of compressor type and 

nominal capacity? Please provide the estimated costs for several “baseline” compressor 

type and capacity combinations commonly used in your products. 

Table 12A.4.3. Baseline Compressor Costs

Compressor Type Nominal Capacity “Baseline” Cost

15) What type(s) of fan motors do you typically use in your products (e.g., TEAO4, TEFC5, 

ODP6, or EC7)? What factors affect the decision of which motor type to use for a given 

unit?

16) When purchasing fans, do you purchase the fan and motor separately or as a single 

4 Totally Enclosed, Air Over

5 Totally Enclosed, Fan C

6 Open Drip Proof

7 Electronically Commutated

12A-6

 
package? If purchased separately, how does shaft power affect motor cost? Please 

provide estimates of how cost varies based on shaft power for a baseline-efficiency motor 

of each motor type listed in the previous question. In addition, please provide an estimate 

of the incremental cost of purchasing a high-efficiency motor of each type. 

17) Are installation costs a function of efficiency? Maintenance costs? Repair costs? If yes, 

please characterize this relationship by providing incremental installation, maintenance, 

and/or repair cost data.

18) DOE plans to conduct a reverse engineering analysis on units of a representative capacity 

for each equipment class. DOE plans to use 9000 Btu/h and 15000 Btu/h units to 

represent medium and large PTAC sizes, respectively. Please comment on the 

representative sizes. 

19) Information gathered from analysis of common industry practices were used to formulate 

factory parameters for manufacturers. Please comment on the following factory 

parameter assumptions listed in Table 12A.4.4.

12A-7
Table 12A.4.4. Factory Parameters

Parameter Estimate Manufacturer 

Feedback

Actual Annual Production Volume (units/year) 100,000

Work Days Per Year (days) 250

Assembly Shifts Per Day (shifts) 1

Fabrication Shifts Per Day (shifts) 2

Fabrication Labor Wages ($/hr) 7

Assembly Labor Wages ($/hr) 7

Assembly Worker Hours Per Year 1687.5

Fabrication Worker Hours Per Year 3750

Length of Shift (hrs) 7.5

Units Per Day 400

Average Equipment Installation Cost (% of purchase price) 10%

Fringe Benefits Ratio 15%

Indirect to Direct Labor Ratio 33%

Average Scrap Recovery Value 30%

Building Cost ($/ft2) 100

Worker Downtime 10%

Building Life (in years) 30

Burdened Assembly Labor Wage ($/hr) 9

Burdened Fabrication Labor Wage ($/hr) 9

Supervisor Span (workers/supervisor) 10

Supervisor Wage Premium (over fabrication and assembly 

wage) 30%

12A-8
12A.5 COMPANY OVERVIEW AND ORGANIZATIONAL CHARACTERISTICS

1) Do you have a parent company and/or subsidiary? If so, please provide their name(s).

2) What is your company’s approximate market share of the PTAC/PTHP market? Does this 

vary significantly for any particular equipment class that you manufacture?

3) Please provide information on your company’s shipments of PTAC and PTHP equipment 

(# of units shipped) for the last five years:

Table 12A.5.1. Shipment Information (total units shipped)

Equipment 

Type

Size 

Category 2008 2009 2010 2011 2012

PTAC

Standard

NonStandard

PTHP

Standard

NonStandard

4) What percentage of your overall revenue is from PTAC and PTHP sales?

5) Do you manufacturer other products in the same facilities as your PTAC and PTHP 

equipment?

6) What are your product line niches and relative strengths in the PTAC and PTHP market?

12A-9
12A.6 MARKUPS AND PROFITABILITY

One of the primary objectives of the Manufacturer Impact Analysis is to assess the 

impact of amended energy conservation standards on industry profitability. In this section, DOE 

would like to understand the current markup structure of the industry and how setting an 

amended energy conservation standard would impact your company’s markup structure and 

profitability. The manufacturer markup is a multiplier applied to manufacturer production cost to 

cover per-unit research and development, selling, general, and administrative expenses, and 

profit. It is NOT a profit margin. The manufacturer production cost multiplied by the 

manufacturer markup plus the shipping costs covers all costs involved in manufacturing and 

profit for the product. 

1) DOE estimated a baseline markup of 1.29 for all equipment classes. Please comment on 

the accuracy of this figure and whether or not it may vary by equipment class. 

2) Within each equipment class, do the per-unit markups vary by efficiency level? Does the 

markup for more efficient designs differ from the markup for baseline models?

3) What factors besides efficiency affect markups in the same equipment class?

4) Would you expect amended energy conservation standards to affect your profitability? If 

so, please explain why.

5) Would amended energy conservation standards that eliminate least efficient products 

impact markups of more efficient products? 

12A-10
12A.7 SHIPPING COSTS

An amended energy conservation standard can change overall shipments by altering 

product attributes, marketing approaches, product availability, and price. Having an accurate 

estimate of these changes allows DOE to better examine impacts on profitability due to amended 

standards. DOE’s shipments model includes forecasts for the base case shipments (i.e., total 

industry shipments absent amended energy conservation standards) and the standards case 

shipments (i.e., total industry shipments with amended energy conservation standards).

1) In the PTAC and PTHP industry, which party in the supply chain typically pays to ship 

the product to the distributor warehouse?

2) If the manufacturer pays for shipping costs, is it industry practice to mark-up shipping 

costs?

3) Do any of the efficiency levels trigger substantial increases in shipping costs? If so, 

which ones and for which products? Why?

12A-11
12A.8 INDUSTRY PROJECTIONS

1) Are there any types of equipment that you expect will soon be phased out (in the absence 

of an amended standard)?

2) How do you think amended energy conservation standards will impact the sales of more 

efficient products? For example, would customers continue to buy products that exceed 

the energy conservation standard level? Would your response change for higher 

mandated efficiency levels?

3) How do you expect shipments to change for the industry as a whole as a result of 

amended standards? Why? 

4) What factors would affect customer decisions to buy new vs. used equipment? What 

percentage of equipment is purchased used?

12A-12
12A.9 FINANCIAL PARAMETERS 

Navigant Consulting, Inc. (NCI) has developed a “straw man” model of financial 

performance called the Government Regulatory Impact Model (GRIM) using publicly available 

data. This section attempts to understand how your company’s financial situation differs from 

our industry aggregate picture.

Please compare your company’s PTAC and PTHP financial parameters to the GRIM 

parameters tabulated below.

Table 12A.9.1. Financial Parameters for PTAC and PTHP Manufacturers

GRIM 

INPUT DEFINITION

INDUSTRY 

ESTIMATED

VALUE

YOUR 

ACTUAL

Income Tax 

Rate

Corporate effective income tax paid 

(percentage of earnings before taxes, EBT) 35.4%

Discount 

Rate

Weighted average cost of capital (inflationadjusted weighted average of corporate cost 

of debt and return on equity)

5.0%

Working 

Capital

Current assets less current liabilities 

(percentage of revenues) 3.5%

SG&A Selling, general, and administrative expenses 

(percentage of revenues) 14.1%

R&D Research and development expenses 

(percentage of revenues) 1.6%

Depreciation Amortization of fixed assets (percentage of 

revenues) 1.7%

Capital 

Expenditures

Outlay of cash to acquire or improve capital 

assets (percentage of revenues, not including 

acquisition or sale of business units)

1.8%

Net 

Property, 

Plant &

Equipment

Fixed assets, or long-lived assets, including 

building, machinery, and equipment less 

accumulated depreciation (percentage of 

revenues)

12.8%

1) Are the figures in Table 12A.9.1 representative of the PTAC / PTHP industry as a whole? 

If not, why?

2) Do any of the financial parameters in Table 12A.9.1 change for a particular subgroup of 

manufacturers? Please describe any differences.

12A-13
12A.10 CONVERSION COSTS

An increase in energy conservation standards may cause the industry to incur capital and 

product conversion costs to meet the amended energy conservation standard. The MIA considers 

three types of conversion expenditures:

• Capital conversion costs: One-time investments in plant, property, and equipment (PPE) 

necessitated by an amended energy conservation standard. These may be incremental 

changes to existing PPE or the replacement of existing PPE. Included are expenditures 

on buildings, equipment, and tooling.

• Product conversion costs: One-time investments in research, product development, 

testing, marketing and other costs for redesigning products necessitated by an amended 

energy conservation standard. 

• Stranded assets: Assets replaced before the end of their useful lives as a direct result of 

the change in energy conservation standard.

With a detailed understanding of the conversion costs necessitated by different standard 

levels, DOE can better model the impact of amended energy conservation standards on the 

PTAC and PTHP industry. 

1) At your manufacturing facilities, would potential amended energy conservation standards 

be difficult to implement? If so, would your company modify existing facilities or 

develop new facilities?

2) What level of conversion costs do you anticipate incurring at each efficiency level? 

Please provide dollar amounts as well as descriptions of the investment in the tables 

below. In the description column, DOE is interested in understanding the kind of changes 

that would need to be implemented in production lines and production facilities. Where 

applicable, please quantify the number and cost of new production equipment that would 

be required to meet the specified efficiency levels. 

12A-14
Table 12A.10.1. Expected Conversion Costs for PTAC and PTHP Equipment, Cooling Capacity: <7,000 Btu/h

Equipment 

Type

Size 

Category

Efficiency 

Level EER Capital Conversion Costs

Product 

Conversion Costs Description

PTAC

Standard

Baseline 11.70

EL 1 12.17

EL 2 12.64

EL 3 13.10

EL 4 13.57

Max Tech 14.04

NonStandard

Baseline 9.40

EL 1 10.15

EL 2 10.90

EL 3 11.66

EL 4 12.41

Max Tech 13.16

PTHP

Standard

Baseline 11.90

EL 1 12.17

EL 2 12.64

EL 3 13.10

EL 4 13.57

Max Tech 14.04

NonStandard

Baseline 9.30

EL 1 10.15

EL 2 10.90

EL 3 11.66

EL 4 12.41

Max Tech 13.16

12A-15
Table 12A.10.2. Expected Conversion Costs for PTAC and PTHP Equipment, Cooling Capacity: ≥7,000 Btu/h and ≤15,000 

Btu/h

Equipment 

Type

Size 

Category

Efficiency 

Level EER Capital Conversion Costs

Product 

Conversion Costs Description

PTAC

Standard

Baseline 13.8 - (0.300 x Cap)

EL 1 14.35 - (0.312 x Cap)

EL 2 14.90 - (0.324 x Cap)

EL 3 15.46 - (0.336 x Cap)

EL 4 16.01 - (0.348 x Cap)

Max Tech 16.56 - (0.360 x Cap)

NonStandard

Baseline 10.9 - (0.213 x Cap)

EL 1 11.77 - (0.230 x Cap)

EL 2 12.64 - (0.247 x Cap)

EL 3 13.52 - (0.264 x Cap)

EL 4 14.39 - (0.281 x Cap)

Max Tech 15.26 - (0.298 x Cap)

PTHP

Standard

Baseline 14.0 - (0.300 x Cap)

EL 1 14.35 - (0.312 x Cap)

EL 2 14.90 - (0.324 x Cap)

EL 3 15.46 - (0.336 x Cap)

EL 4 16.01 - (0.348 x Cap)

Max Tech 16.56 - (0.360 x Cap)

NonStandard

Baseline 10.8 - (0.213 x Cap)

EL 1 11.77 - (0.230 x Cap)

EL 2 12.64 - (0.247 x Cap)

EL 3 13.52 - (0.264 x Cap)

EL 4 14.39 - (0.281 x Cap)

Max Tech 15.26 - (0.298 x Cap)

12A-16
Table 12A.10.3. Expected Conversion Costs for PTAC and PTHP Equipment, Cooling Capacity: >15,000 Btu/h

Equipment 

Type

Size 

Category

Efficiency 

Level EER Capital Conversion Costs

Product 

Conversion Costs Description

PTAC

Standard

Baseline 9.30

EL 1 9.67

EL 2 10.04

EL 3 10.42

EL 4 10.79

Max Tech 11.16

NonStandard

Baseline 7.70

EL 1 8.32

EL 2 8.93

EL 3 9.55

EL 4 10.16

Max Tech 10.78

PTHP

Standard

Baseline 9.50

EL 1 9.67

EL 2 10.04

EL 3 10.42

EL 4 10.79

Max Tech 11.16

NonStandard

Baseline 7.60

EL 1 8.32

EL 2 8.93

EL 3 9.55

EL 4 10.16

Max Tech 10.78

12A-17
3) Please comment on any potential stranded assets that may result from an amended energy 

conservation standard.

4) For any efficiency levels that would require new production equipment, please describe 

how much downtime would be required. What impact would downtime have on your 

business? 

5) Please provide any additional qualitative information that might help DOE understand the 

type and nature of your conversion investments, including plant and tooling changes and

product development efforts required for different efficiency levels and equipment 

classes.

12A-18
12A.11 CUMULATIVE REGULATORY BURDEN

Cumulative regulatory burden refers to the burden that industry faces from overlapping 

effects of new or revised DOE standards, and/or other regulatory actions affecting the same 

product or industry.

1) Are there other recent or impending standards that PTAC and PTHP manufacturers face 

from DOE, other US federal agencies, state regulators, foreign government agencies, or 

other standard-setting bodies? If so, please identify the regulation and the corresponding 

possible effective dates for those regulations in the table below.

Table 12A.11.1. Other Regulations Identified

Regulation Effective 

Date(s) Expected Expenses / Comments

2) Under what circumstances would you be able to coordinate expenditures related to these 

other regulations with an amended energy conservation standard, thereby lessening the 

cumulative burden?

12A-19
12A.12 DIRECT EMPLOYMENT IMPACT ASSESSMENT

The impact of amended energy conservation standards on employment is an important 

consideration in the rulemaking process. This section of the interview guide seeks to explore 

current trends in PTAC and PTHP equipment employment and solicit manufacturer views on 

how domestic employment patterns might be affected by amended energy conservation 

standards.

1) Where are your production facilities located, and what type of product is manufactured at 

each location? Please provide production figures for your company’s manufacturing at 

each location by product class.

Table 12A.12.1. Manufacturing locations

Location Product Class Employees

(Production)

Employees

(Nonproduction)

Units/Year 

Produced

2) Would you expect domestic employment levels to change significantly under amended 

energy conservation standards?

3) If so, please identify particular standard levels that may trigger changes in employment. 

4) Are higher efficiency products built at different plants than lower efficiency products of 

the same equipment class?

12A-20
12A.13 CAPACITY/EXPORTS / FOREIGN COMPETITION / OUTSOURCING

Disparity between domestic and foreign energy conservation standards could impact 

exports or imports. Labor content and material changes resulting from amended energy 

conservation standards may impact sourcing decisions.

1) How would amended energy conservation standards impact your company’s 

manufacturing capacity, in both the short term and the long term?

2) Absent amended energy conservation standards, are production facilities being relocated 

to foreign countries? 

3) Would amended energy conservation standards impact your domestic vs. foreign 

manufacturing decision? 

4) What percentage of the U.S. market for PTAC and PTHP equipment is imported? Would 

amended energy conservation standards have an impact on foreign competition? 

12A-21
12A.14 CONSOLIDATION

Amended energy conservation standards can alter the competitive dynamics of the 

market. This can include prompting companies to enter or exit the market, or to merge. DOE and 

the Department of Justice are both interested in any potential reduction in competition that would 

result from an amended energy conservation standard.

1) Please comment on industry consolidation and related trends over the last 10 years.

2) How would industry competition change as a result of amended conservation standards? 

Due to amended energy conservation standards, do you expect accelerated industry 

consolidation? Please describe your expectations.

3) How would amended energy conservation standards affect your ability to compete in the 

marketplace? Would you expect your market share to change? 

4) To your knowledge, are there any niche manufacturers for which the adoption of 

amended energy conservation standards would have a particularly severe impact?

5) To your knowledge, are there any component manufacturers for which the adoption of 

amended energy conservation standards would have a particularly severe impact?

12A-22
12A.15 IMPACTS ON SMALL BUSINESS 

1) The Small Business Association (SBA) denotes a small business in the PTAC and PTHP 

industry as having less than 750 employees.8 By this definition, is your company 

considered a small business?

2) Below is a list of small business PTAC and PTHP manufacturers compiled by DOE. Are 

there any small manufacturers that should be added to (or removed from) this list? Are 

there specific manufacturers on this list that may be more severely impacted by an 

amended energy conservation standard than others?

• Air-Con International

• Cold Point Corporation

• Comitale National, Inc.

• EAIR, LLC

• ECR International

• Heat Controller, Inc.

• Ice Air LLC

• International Refrigeration 

Products

• Island Aire, Inc.

•Prem Sales, LLC

•Simon-Aire, Inc.

•YMGI Group, LLC

3) Are there any reasons that a small business might be at a disadvantage relative to a larger 

business under amended energy conservation standards? Please consider such factors as 

technical expertise, access to capital, bulk purchasing power for materials/components, 

engineering resources, and any other relevant issues.

4) To your knowledge, are there any small business manufacturers for which the adoption of 

amended energy conservation standards would have a particularly severe impact? If so, 

would small business manufacturers face different incremental impacts from amended 

energy conservation standards than the rest of the industry?

8 DOE uses the SBA small business size standards effective January 1, 2012 to determine whether a company is a 

small business. To be categorized as a small business, a PTAC/PTHP manufacturer and its affiliates may employ a 

maximum of 750 employees. The 750 employee threshold includes all employees in a business’s parent company 

and any other subsidiaries.

12A-23

 
12B-i

APPENDIX 12B. GOVERNMENT REGULATORY IMPACT MODEL (GRIM)

OVERVIEW

TABLE OF CONTENTS

12B.1 Introduction and Purpose ..............................................................................................12B-1

12B.2 Model Description.........................................................................................................12B-1

LIST OF TABLES

Table 12B.2.1 Detailed Cash Flow Example...........................................................................12B-4
12B-1

APPENDIX 12B. GOVERNMENT REGULATORY IMPACT MODEL (GRIM)

OVERVIEW

12B.1 INTRODUCTION AND PURPOSE

The purpose of the Government Regulatory Impact Model (GRIM) is to help quantify the 

impacts of energy conservation standards and other regulations on manufacturers. The basic 

mode of analysis is to estimate the change in the value of the industry or manufacturers

following a regulation or a series of regulations. The model structure also allows an analysis of 

multiple equipment types with regulations taking effect over a period of time, and of multiple 

regulations on the same equipment.

Industry net present value is defined, for the purpose of this analysis, as the discounted 

sum of industry free cash flows plus a discounted terminal value. The model calculates the actual 

cash flows by year and then determines the present value of those cash flows both without an 

energy conservation standard (i.e., the base case) and under different trial standard levels (i.e., 

the standards case).

Outputs from the model consist of summary financial metrics, graphs of major variables, 

and, when appropriate, access to the complete cash flow calculation.

12B.2 MODEL DESCRIPTION

The basic structure of the GRIM is a standard annual cash flow analysis that uses 

manufacturer selling prices, manufacturing costs, a shipments forecast, and financial parameters 

as inputs and accepts a set of regulatory conditions as changes in costs and investments. The cash 

flow analysis is separated into two major blocks: income and cash flow. The income calculation 

determines net operating profit after taxes. The cash flow calculation converts net operating 

profit after taxes into an annual cash flow by including investment and non-cash items. Below 

are definitions of listed items on the printout of the output sheet of the GRIM.

(1) Revenues: Annual revenues – computed by multiplying equipment unit prices at each 

efficiency level by the appropriate manufacturer markup.

(2) Total shipments: Total annual shipments for the industry were obtained from the National 

Impact Analysis Spreadsheet.

(3) Material: The portion of cost of goods sold (COGS) that includes materials.

(4) Labor: The portion of COGS that includes direct labor, commissions, dismissal pay, 

bonuses, vacation, sick leave, social security contributions, fringe, and assembly labor uptime.

(5) Depreciation: The portion of overhead that includes an allowance for the total amount of 

fixed assets used to produce that one unit. Annual depreciation is computed as a percentage 

of COGS. While included in overhead, the depreciation is shown as a separate line item.
12B-2

(6) Overhead: The portion of COGS that includes indirect labor, indirect material, energy use, 

maintenance, depreciation, property taxes, and insurance related to assets. While included 

in overhead, the depreciation is shown as a separate line item.

(7) Standard SG&A: Selling, general, and administrative costs are computed as a percentage of 

Revenues (1).

(8) R&D: GRIM separately accounts for ordinary research and development (R&D) as a 

percentage of Revenues (1).

(9) Product Conversion Costs: Product conversion costs are one-time investments in research, 

development, testing, marketing, and other costs focused on making equipment designs 

comply with the new energy conservation standard. The GRIM allocates these costs over 

the period between the standard’s announcement and compliance dates.

(10) Stranded Assets: In the year the standard becomes effective, a one-time write-off of 

stranded assets is accounted for.

(11) Earnings Before Interest and Taxes (EBIT): Includes profits before deductions for interest 

paid and taxes.

(12) EBIT as a Percentage of Sales (EBIT/Revenues): GRIM calculates EBIT as a percentage 

of sales to compare with the industry’s average reported in financial statements.

(13) Taxes: Taxes on EBIT (11) are calculated by multiplying the tax rate contained in Major 

Assumptions by EBIT (11).

(14) Net Operating Profits After Taxes (NOPAT): Computed by subtracting Cost of Goods 

Sold ((3) to (6)), SG&A (7), R&D (8), Product Conversion Costs (9), and Taxes (13) from 

Revenues (1).

(15) NOPAT repeated: NOPAT is repeated in the Statement of Cash Flows.

(16) Depreciation repeated: Depreciation and Stranded Assets are added back in the Statement 

of Cash Flows because they are non-cash expenses.

(17) Change in Working Capital: Change in cash tied up in accounts receivable, inventory, and 

other cash investments necessary to support operations is calculated by multiplying 

working capital (as a percentage of revenues) by the change in annual revenues. 

(18) Cash Flow from Operations: Calculated by taking NOPAT (15), adding back non-cash 

items such as Depreciation (16), and subtracting the Change in Working Capital (17).

(19) Ordinary Capital Expenditures: Ordinary investments in property, plant, and equipment to 

maintain and replace existing production assets, computed as a percentage of Revenues (1).

(20) Capital Conversion Costs: Capital conversion costs are one-time investments in property, 

plant, and equipment to adapt or change existing production facilities so that new 
12B-3

equipment designs can be fabricated and assembled under the new regulation. The GRIM 

allocates these costs over the period between the standard’s announcement and compliance 

dates.

(21) Capital Investment: Total investments in property, plant, and equipment are computed by 

adding Ordinary Capital Expenditures (19) and Capital Conversion Costs (20).

(22) Free Cash Flow: Annual cash flow from operations and investments; computed by 

subtracting Capital Investment (21) from Cash Flow from Operations (18).

(23) Terminal Value: Estimate of the continuing value of the industry after the analysis period. 

Computed by growing the Free Cash Flow at a constant rate in perpetuity.

(24) Present Value Factor: Factor used to calculate an estimate of the present value of an 

amount to be received in the future.

(25) Discounted Cash Flow: Free Cash Flows (22) multiplied by the Present Value Factor 

(24). For the end of 2048, the discounted cash flow includes the discounted Terminal Value 

(23).

(26) Industry Value thru the end of 20481

: The sum of Discounted Cash Flows (25).

 

1 For PTACs and PTHPs, the industry value calculated in the GRIM incorporates three different analysis periods. 

Since amended standards for PTACs were triggered by ASHRAE Standard 90.1-2013 while amended standards for 

PTHPs were triggered by EPCA, compliance dates and terminal years differ by case and equipment class. 
12B-4

Table 12B.2.1 Detailed Cash Flow Example
APPENDIX 14A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT 

ANALYSIS UNDER EXECUTIVE ORDER 12866

TABLE OF CONTENTS

14A.1 EXECUTIVE SUMMARY .................................................................................. 14A-1

14A.2 MONETIZING CARBON DIOXIDE EMISSIONS............................................ 14A-2

14A.3 SOCIAL COST OF CARBON VALUES USED IN PAST REGULATORY 

ANALYSES.......................................................................................................... 14A-4

14A.4 APPROACH AND KEY ASSUMPTIONS ......................................................... 14A-5

14A.4.1 Integrated Assessment Models ............................................................................. 14A-5

14A.4.2 Global versus Domestic Measures of SCC......................................................... 14A-11

14A.4.3 Valuing Non-CO2 Emissions.............................................................................. 14A-13

14A.4.4 Equilibrium Climate Sensitivity ......................................................................... 14A-13

14A.4.5 Socioeconomic and Emissions Trajectories........................................................ 14A-16

14A.4.6 Discount Rate...................................................................................................... 14A-19

14A.5 REVISED SCC ESTIMATES ............................................................................ 14A-26

14A.6 LIMITATIONS OF THE ANALYSIS ............................................................... 14A-32

14A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPACTS AND 

DAMAGE FUNCTIONS ................................................................................... 14A-34

14A.8 CONCLUSION................................................................................................... 14A-37

REFERENCES ..................................................................................................................... 14A-38

14A.9 ANNEX............................................................................................................... 14A-44

14A.9.1 Other (non-CO2) gases........................................................................................ 14A-45

14A.9.2 Extrapolating Emissions Projections to 2300 ..................................................... 14A-47

LIST OF TABLES

Table 14A.1.1 Social Cost of CO2, 2010 – 2050 (in 2007 dollars) .................................. 14A-2

Table 14A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity 

Distributions............................................................................................ 14A-14

Table 14A.4.2 Socioeconomic and Emissions Projections from Select EMF-22 

Reference Scenarios................................................................................ 14A-18

Table 14A.5.1 Disaggregated Social Cost of CO2 Values by Model, Socioeconomic 

Trajectory, and Discount Rate for 2010 (in 2007 dollars)...................... 14A-29

Table 14A.5.2 Social Cost of CO2, 2010 – 2050 (in 2007 dollars) ................................ 14A-31

Table 14A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates 

between 2010 and 2050 .......................................................................... 14A-31

Table 14A.7.1 Probabilities of Various Tipping Points from Expert Elicitation............ 14A-35

Table 14A.9.1 Annual SCC Values: 2010–2050 (in 2007 dollars) ................................ 14A-44

Table 14A.9.2 2010 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton 

CO2) ........................................................................................................ 14A-55

14A-i
Table 14A.9.3 2010 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton 

CO2) ........................................................................................................ 14A-56

Table 14A.9.4 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton 

CO2) ........................................................................................................ 14A-57

Table 14A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates............. 14A-58

LIST OF FIGURES

Figure 14A.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.......................................................................................... 14A-10

Figure 14A.4.2 Annual Consumption Loss for Lower Temperature Changes in DICE, 

FUND, and PAGE .................................................................................. 14A-11

Figure 14A.4.3 Estimates of the Probability Density Function for Equilibrium Climate 

Sensitivity (°C) ....................................................................................... 14A-16

Figure 14A.5.1 Level of Global GDP across EMF Scenarios ......................................... 14A-30

Figure 14A.9.2 Sulfur Dioxide Emission Scenarios........................................................ 14A-47

Figure 14A.9.3 Global Population, 2000-2300 (Post-2100 extrapolations assume the 

population growth rate changes linearly to reach a zero growth rate by 

2200.) ...................................................................................................... 14A-49

Figure 14A.9.4 World GDP, 2000-2300 (Post-2100 extrapolations assume GDP per 

capita growth declines linearly, reaching zero in the year 2300) ........... 14A-50

Figure 14A.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)................................................. 14A-51

Figure 14A.9.6 Global Net Land Use CO2 Emissions, 2000-2300 (Post-2100 

extrapolations assume emissions decline linearly, reaching zero in the 

year 2200) ............................................................................................... 14A-52

Figure 14A.9.7 Global Non-CO2 Radiative Forcing, 2000-2300 (Post-2100 

extrapolations assume constant non-CO2 radiative forcing after 2100) . 14A-53

Figure 14A.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).................................. 14A-54

Figure 14A.9.9 Histogram of Global SCC Estimates in 2010 (2007$/ton CO2), by 

discount rate ............................................................................................ 14A-57

14A-ii
APPENDIX 14A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT 

ANALYSIS UNDER EXECUTIVE ORDER 12866a

14A.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

14A-1

 
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 14A.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

14A.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). 

14A-2

 
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 14-A.9 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 

14A-3
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.

14A.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.

14A-4
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.

14A.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.

14A.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).

14A-5

 
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.

14A-6
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 

14A-7
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 re-calibrated 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.

14A-8

 
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 14A.4.1 and Figure 

14A.4.2, using the modeler’s default scenarios and mean input assumptions. There are significant 

differences between the three models both at lower (Figure 14A.4.2) and higher (Figure 14A.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).

14A-9

 
Figure 14A.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 

committed to exploring how these (and other) models can be modified to incorporate more 

accurate estimates of damages.

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.

14A-10

 
Figure 14A.4.2 Annual Consumption Loss for Lower Temperature Changes in 

DICE, FUND, and PAGE

14A.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 

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 

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.

14A-11

 
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 

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 

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.

14A-12

 
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.

14A.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.

14A.4.4 Equilibrium Climate Sensitivity

Equilibrium climate sensitivity (ECS) is a key input parameter for the DICE, PAGE, and 

FUND models.jIt 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.

The most authoritative statement about equilibrium climate sensitivity appears in the 

Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC):

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).

14A-13

 
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 14A.4.1 included below gives summary statistics for the four calibrated distributions.

Table 14A.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

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

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.

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.

14A-14

 
(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. 

14A-15
Figure 14A.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 14A.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

14A.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.

14A-16

 
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 14A.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 lowerthan-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.

14A-17

 
Table 14A.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.

14A-18

 
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.

14A.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.

14A-19

 
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, 

14A-20
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 

marginal change in carbon emissions (see Lind 1990, Arrow et al 1996, and Arrow 2000). The 

14A-21
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 

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

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.

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).

14A-22

 
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. 

• 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 interu 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. 

14A-23

 
generational 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 

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).

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.)

14A-24

 
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 

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.

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. 

14A-25

 
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.

14A.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.

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. 

aa Calculations done by Pizer et al. using the original simulation program from Newell and Pizer (2003).

14A-26

 
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 

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. 

14A-27
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 14A.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. 

14A-28
Table 14A.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 

14A-29

 
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 14A.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 14A.5.1 Level of Global GDP across EMF Scenarios

Table 14A.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

14A-30

 
Table 14A.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 14A.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 14A.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 

14A-31
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

14A.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 highcc 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.

14A-32

 
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).

14A-33

 
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.

14A.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 

14A-34
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 14A.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 14A.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 14A.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 

14A-35
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. 

14A-36
14A.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. 

14A-37
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14A-43
14A.9 ANNEX

Table 14A.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.

14A-44
14A.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.

14A-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.

14A-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 14A.9.2 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. 

14A.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 

14A-47
these projections are available from the EMF-22 models. These inputs were extrapolated from 

2100 to 2300 as follows:

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.

jj United Nations. 2004. World Population to 2300. 

www.un.org/esa/population/publications/longrange2/WorldPop2300final.pdf

14A-48

 
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. 

Figure 14A.9.3 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

14A-49
Figure 14A.9.4 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, full-participation, 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]

14A-50
Figure 14A.9.5 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

14A-51
Figure 14A.9.6 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

14A-52

 
Figure 14A.9.7 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

14A-53
Figure 14A.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)

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

14A-54
Table 14A.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

14A-55
Table 14A.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

14A-56
Table 14A.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 14A.9.9 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%

14A-57
Table 14A.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

14A-58
14B-i 

APPENDIX 14B. TECHNICAL UPDATE OF SOCIAL COST OF CARBON FOR 

REGULATORY IMPACT ANALYSIS UNDER EXECUTIVE ORDER 12866

TABLE OF CONTENTS

14B.1 PREFACE ....................................................................................................................14B-1

14B.2 PURPOSE ....................................................................................................................14B-1

14B.3 SUMMARY OF MODEL UPDATES.........................................................................14B-2

14B.3.1 DICE ................................................................................................................14B-2

14B.3.1.1 Sea Level Dynamics ...............................................................................14B-3

14B.3.1.2 Re-calibrated Damage Function .............................................................14B-3

14B.3.2 FUND ...............................................................................................................14B-4

14B.3.2.1 Space Heating .........................................................................................14B-4

14B.3.2.2 Sea Level Rise and Land Loss ...............................................................14B-5

14B.3.2.3 Agriculture .............................................................................................14B-5

14B.3.2.4 Temperature Response Model ................................................................14B-6

14B.3.2.5 Methane ..................................................................................................14B-6

14B.3.3 PAGE ...............................................................................................................14B-6

14B.3.3.1 Sea Level Rise ........................................................................................14B-7

14B.3.3.2 Revised Damage Function to Account for Saturation ............................14B-7

14B.3.3.3 Regional Scaling Factors ........................................................................14B-7

14B.3.3.4 Probability of a Discontinuity ................................................................14B-7

14B.3.3.5 Adaptation ..............................................................................................14B-8

14B.3.3.6 Other Noteworthy Changes ....................................................................14B-8

14B.4 REVISED SCC ESTIMATES .....................................................................................14B-8

14B.5 OTHER MODEL LIMITATIONS OR RESEARCH GAPS ....................................14B-11

REFERENCES ......................................................................................................................14B-12

14B.6 ANNEX......................................................................................................................14B-14

LIST OF TABLES

Table 14B.4.1 Revised Social Cost of CO2, 2010 – 2050 (in 2007 dollars per ton of 

CO2) ...........................................................................................................14B-9

Table 14B.4.2 Average Annual Growth Rates of SCC Estimates between 2010 and 

2050..........................................................................................................14B-10

Table 14B.6.1 Annual SCC Values: 2010-2050 (2007$/ton CO2) ..................................14B-14

Table 14B.6.2 202 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton 

CO2) .........................................................................................................14B-15

Table 14B.6.3 SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2) .................14B-15

Table 14B.6.4 2020 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton 

CO2) .........................................................................................................14B-16

LIST OF FIGURES

Figure 14B.4.1 Distribution of SCC Estimates for 2010 (in 2007$ per ton CO2) ............14B-10
14B-1 

APPENDIX 14B. TECHNICAL UPDATE OF SOCIAL COST OF CARBON FOR 

REGULATORY IMPACT ANALYSIS UNDER EXECUTIVE ORDER 12866

14B.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. 

14B.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 14B.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 14B.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/default/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
14B-2 

14B.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. 

14B.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. 

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).2d

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 

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. 

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
14B-3 

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. 

14B.3.1.1 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. 

14B.3.1.2 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 

 

e Documentation on the new sea level rise module of DICE is available on William Nordhaus’ website at: 

http://aida.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

 
14B-4 

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 oC 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.

14B.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. 

14B.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 

 

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.
14B-5 

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. 

14B.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.

14B.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 

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 
14B-6 

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. 

14B.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. 

14B.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.

14B.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 

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
14B-7 

14B.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. 

14B.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. 

14B.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. 

14B.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 

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 
14B-8 

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. 

14B.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.

14B.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 

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.

14B.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 
14B-9 

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 14B.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. 

Table 14B.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 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

 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 

14B.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. 
14B-10 

Figure 14B.4.1 Distribution of SCC Estimates for 2010 (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 

through running them for a set of perturbation years out to 2050. Table 14B.4.2 illustrates how 

the growth rate for these four SCC estimates varies over time.

Table 14B.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% 3.3% 2.4% 4.4%

2020-2030 3.4% 2.1% 1.7% 2.4%

2030-2040 3.0% 1.9% 1.5% 2.1%

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. 
14B-11 

14B.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.
14B-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. <http://www.whitehouse.gov/sites/default/files/omb/inforeg/foragencies/Social-Cost-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) 
14B-13 

10. Hope, C., The PAGE09 Integrated Assessment Model: A Technical Description. 2011, 

Cambridge Judge Business School 

http://www.jbs.cam.ac.uk/research/working_papers/2011/wp1104.pdf

11. Hope, C., The Social Cost of CO2 from the PAGE09 Model. 2011, Cambridge Judge 

Business School http://www.jbs.cam.ac.uk/research/working_papers/2011/wp1105.pdf

12. Hope, C., New Insights from the PAGE09 Model: The Social Cost of CO2. 2011, 

Cambridge Judge Business School 

http://www.jbs.cam.ac.uk/research/working_papers/2011/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 

 
14B-14 

14B.6 ANNEX

Table 14B.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 32 51 89

2011 11 33 52 93

2012 11 34 54 97

2013 11 35 55 101

2014 11 36 56 105

2015 11 37 57 109

2016 12 38 59 112

2017 12 39 60 116

2018 12 40 61 120

2019 12 42 62 124

2020 12 43 64 128

2021 12 43 65 131

2022 13 44 66 134

2023 13 45 67 137

2024 14 46 68 140

2025 14 47 69 143

2026 15 48 70 146

2027 15 49 71 149

2028 15 50 72 152

2029 16 51 73 155

2030 16 52 75 159

2031 17 52 76 162

2032 17 53 77 165

2033 18 54 78 168

2034 18 55 79 172

2035 19 56 80 175

2036 19 57 81 178

2037 20 58 83 181

2038 20 59 84 185

2039 21 60 85 188

2040 21 61 86 191

2041 22 62 87 194

2042 22 63 88 197

2043 23 64 89 200

2044 23 65 90 203

2045 24 66 92 206

2046 24 67 93 209

2047 25 68 94 211

2048 25 69 95 214

2049 26 70 96 217

2050 26 71 97 220
14B-15 

Table 14B.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 -14 -2 4 15 31 39 55 86 107 157

MERGE -6 1 6 14 27 35 46 70 87 141

MESSAGE -16 -5 1 11 24 31 43 67 83 126

MiniCAM Base -7 2 7 16 32 39 55 83 103 158

5th Scenario -29 -13 -6 4 16 21 32 53 69 103

Table 14B.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 -13 -4 0 8 18 23 33 51 65 99

MERGE -7 -1 2 8 17 21 29 45 57 95

MESSAGE -14 -6 -2 5 14 18 26 41 52 82

MiniCAM Base -7 -1 3 9 19 23 33 50 63 101

5th Scenario -22 -11 -6 1 8 11 18 31 40 62
14B-16 

Table 14B.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 -4 -1 2 3 6 10 14 24

MERGE -6 -4 -2 0 3 4 6 11 15 26

MESSAGE -10 -6 -4 -1 1 2 5 9 12 21

MiniCAM Base -7 -4 -2 0 3 4 6 11 14 25

5th Scenario -11 -7 -5 -3 0 0 3 5 7 13
APPENDIX 17A. REGULATORY IMPACT ANALYSIS: SUPPORTING MATERIALS

TABLE OF CONTENTS

17A.1 INTRODUCTION ................................................................................................ 17A-1

17A.2 MARKET SHARE ANNUAL INCREASES BY POLICY................................. 17A-2

17A.3 NIA-RIA INTEGRATED MODEL...................................................................... 17A-4

17A.4 CONSUMER REBATE POLICY MARKET PENETRATION CURVES ......... 17A-4

17A.4.1 Introduction........................................................................................................... 17A-4

17A.4.2 Adjustment of Energy Penetration Curves ........................................................... 17A-6

17A.4.3 Interpolation of Penetration Curves...................................................................... 17A-7

17A.5 CONSUMER REBATE PROGRAMS................................................................. 17A-8

17A.6 FEDERAL AND STATE TAX CREDITS......................................................... 17A-12

17A.6.1 Federal Tax Credits for Consumers.................................................................... 17A-12

17A.6.2 Federal Tax Credits for Manufacturers............................................................... 17A-13

17A.6.3 State Tax Credits................................................................................................. 17A-14

REFERENCES ..................................................................................................................... 17A-15

LIST OF TABLES

Table 17A.2.1 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Packaged Terminal Air Conditioners (7,000 – 15,000 

Btu/h) ........................................................................................................ 17A-2

Table 17A.2.2 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Packaged Terminal Air Conditioners (> 15,000 Btu/h)...... 17A-3

Table 17A.5.1 Representative Cooling Capacities and Energy Efficiencies Used to 

Calculate the Rebate Amounts for each Rebate Program......................... 17A-8

Table 17A.5.2 Representative Rebate Amounts (2014$) ................................................. 17A-9

Table 17A.5.3 Rebates for Packaged Terminal Air Conditioners and Packaged 

Terminal Heat Pumps ............................................................................. 17A-10

LIST OF FIGURES

Figure 17A.4.1 S-Curves Showing Effects of External and Internal Sources on 

Adoption of New Technologies................................................................ 17A-6

17A-i
APPENDIX 17A. REGULATORY IMPACT ANALYSIS: SUPPORTING MATERIALS 

17A.1 INTRODUCTION

This appendix contains sections discussing the following topics: 

• Projections of annual market share increases for the alternative policies;

• NIA-RIA Integrated Model;

• XENERGY penetration curves used to analyze consumer rebates, including:

o Background material,

o DOE’s adjustment of these curves for this analysis, and

o The method DOE used for interpolating the curves;

• Detailed tables of rebates offered for the considered products; and

• Background material on Federal and state tax credits for appliances.

17A-1
17A.2 MARKET SHARE ANNUAL INCREASES BY POLICY

Table 17-A.2.1 and Table 17-A.2.2 show the annual increases in market shares of the two 

packaged terminal air conditioner (PTAC) equipment classes meeting the target efficiency levels

for the selected TSL (TSL ASHRAE). DOE used these market share increases as inputs to the 

NIA-RIA spreadsheet model. The remaining PTAC and PTHP equipment classes are not 

affected by the selected TSL.

Table 17A.2.1 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Packaged Terminal Air Conditioners (7,000 – 15,000 Btu/h)

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Voluntary Energy 

Efficiency Targets

Bulk Government 

Purchases

2017 4.3% 2.6% 1.3% 0.7% 0.6%

2018 3.7% 2.2% 1.1% 3.5% 0.5%

2019 3.1% 1.9% 0.9% 3.1% 0.5%

2020 2.5% 1.5% 0.7% 2.5% 0.4%

2021 1.9% 1.1% 0.6% 1.9% 0.4%

2022 1.2% 0.7% 0.4% 1.2% 0.3%

2023 0.6% 0.4% 0.2% 0.6% 0.3%

2024 0.0% 0.0% 0.0% 0.0% 0.0%

2025 0.0% 0.0% 0.0% 0.0% 0.0%

2026 0.0% 0.0% 0.0% 0.0% 0.0%

2027 0.0% 0.0% 0.0% 0.0% 0.0%

2028 0.0% 0.0% 0.0% 0.0% 0.0%

2029 0.0% 0.0% 0.0% 0.0% 0.0%

2030 0.0% 0.0% 0.0% 0.0% 0.0%

2031 0.0% 0.0% 0.0% 0.0% 0.0%

2032 0.0% 0.0% 0.0% 0.0% 0.0%

2033 0.0% 0.0% 0.0% 0.0% 0.0%

2034 0.0% 0.0% 0.0% 0.0% 0.0%

2035 0.0% 0.0% 0.0% 0.0% 0.0%

2036 0.0% 0.0% 0.0% 0.0% 0.0%

2037 0.0% 0.0% 0.0% 0.0% 0.0%

2038 0.0% 0.0% 0.0% 0.0% 0.0%

2039 0.0% 0.0% 0.0% 0.0% 0.0%

2040 0.0% 0.0% 0.0% 0.0% 0.0%

2041 0.0% 0.0% 0.0% 0.0% 0.0%

2042 0.0% 0.0% 0.0% 0.0% 0.0%

2043 0.0% 0.0% 0.0% 0.0% 0.0%

2044 0.0% 0.0% 0.0% 0.0% 0.0%

2045 0.0% 0.0% 0.0% 0.0% 0.0%

2046 0.0% 0.0% 0.0% 0.0% 0.0%

17A-2
Table 17A.2.2 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Packaged Terminal Air Conditioners (> 15,000 Btu/h)

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Voluntary Energy 

Efficiency Targets

Bulk Government 

Purchases

2017 51.6% 30.9% 15.5% 0.0% 0.0%

2018 46.2% 27.7% 13.8% 0.0% 0.0%

2019 38.5% 23.1% 11.5% 0.1% 0.0%

2020 30.8% 18.5% 9.2% 0.1% 0.0%

2021 23.1% 13.8% 6.9% 0.1% 0.0%

2022 15.4% 9.2% 4.6% 0.1% 0.0%

2023 7.7% 4.6% 2.3% 0.1% 0.0%

2024 0.0% 0.0% 0.0% 0.0% 0.0%

2025 0.0% 0.0% 0.0% 0.0% 0.0%

2026 0.0% 0.0% 0.0% 0.0% 0.0%

2027 0.0% 0.0% 0.0% 0.0% 0.0%

2028 0.0% 0.0% 0.0% 0.0% 0.0%

2029 0.0% 0.0% 0.0% 0.0% 0.0%

2030 0.0% 0.0% 0.0% 0.0% 0.0%

2031 0.0% 0.0% 0.0% 0.0% 0.0%

2032 0.0% 0.0% 0.0% 0.0% 0.0%

2033 0.0% 0.0% 0.0% 0.0% 0.0%

2034 0.0% 0.0% 0.0% 0.0% 0.0%

2035 0.0% 0.0% 0.0% 0.0% 0.0%

2036 0.0% 0.0% 0.0% 0.0% 0.0%

2037 0.0% 0.0% 0.0% 0.0% 0.0%

2038 0.0% 0.0% 0.0% 0.0% 0.0%

2039 0.0% 0.0% 0.0% 0.0% 0.0%

2040 0.0% 0.0% 0.0% 0.0% 0.0%

2041 0.0% 0.0% 0.0% 0.0% 0.0%

2042 0.0% 0.0% 0.0% 0.0% 0.0%

2043 0.0% 0.0% 0.0% 0.0% 0.0%

2044 0.0% 0.0% 0.0% 0.0% 0.0%

2045 0.0% 0.0% 0.0% 0.0% 0.0%

2046 0.0% 0.0% 0.0% 0.0% 0.0%

17A-3
17A.3 NIA-RIA INTEGRATED MODEL

For this analysis, DOE used its integrated NIA-RIAa model approach that built on the 

NIA model discussed in Chapter 10. The resulting integrated NIA-RIA model featured both the 

NIA analysis inputs and results and the RIA inputs and had the capability to generate results for 

each of the RIA policies. A separate module produced results summaries for the tables and 

figures in the RIA document. For the RIA methodology documentation in Chapter 17, the

module created summaries of parameters calculated by the model for the consumer rebates 

policy, generated its penetration curves (discussed in Section 17-A.4.3 below), and reported 

market share impacts for the rebate and tax credit policies by equipment class. For the RIA 

results reported in Chapter 17, the module produced graphs of the market share increases 

resulting from each of the policies analyzed and created summary tables for the national energy 

savings (NES) and net present value (NPV) results. This module also generated tables of market 

share increases for each policy reported in Section 17-A.2 of this Appendix.

17A.4 CONSUMER REBATE POLICY MARKET PENETRATION CURVES

This section first discusses the theoretical basis for the market penetration curves that 

DOE used to analyze the Consumer Rebates policy. Next it discusses the adjustments it made to 

the maximum penetration rates. It then refers to the method it used to develop interpolated 

penetration curves for each specific equipment class and efficiency level in the analysis. The 

resulting curves for the PTAC and PTHP equipment classes are in Chapter 17.

17A.4.1 Introduction

XENERGY, Inc.b, developed a re-parameterized, mixed-source information diffusion 

model to estimate market impacts induced by financial incentives for purchasing energy efficient 

appliances.

1 The basic premise of the mixed-source model is that information diffusion drives 

the adoption of technology. 

Extensive economic literature describes the diffusion of new products as technologies 

evolve. Some research focuses primarily on developing analytical models of diffusion patterns

applicable to individual consumers or to technologies from competing firms.

2, 3, 4 One study 

records researchers’ attempts to investigate the factors that drive diffusion processes.

5 Because a 

new product generally has its own distinct characteristics, few studies have been able to 

conclusively develop a universally applicable model. Some key findings, however, generally are 

accepted in academia and industry. 

One accepted finding is that, regardless of their economic benefits and technological 

merits, new technologies are unlikely to be adopted by all potential users. For many products, a 

a NIA = national impact analysis; RIA = regulatory impact analysis

b XENERGY is now owned by KEMA, Inc. (www.kema.com)

17A-4

 
ceiling must be placed on the adoption rate. A second conclusion is that not all adopters purchase 

new products at the same time: some act quickly after a new product is introduced; others wait 

for the product to mature. Third, diffusion processes can be characterized approximately by 

asymmetric S-curves that depict three stages of diffusion: starting, accelerating, and decreasing 

(as the adoption ceiling is approached).

A so-called epidemic model of diffusion is used widely in marketing and social studies. 

The epidemic model assumes that (1) all consumers place identical value on the benefits of a 

new product, and (2) the cost of a new product is constant or declines monotonically over time. 

What induces a consumer to purchase a new product is information about the availability and 

benefits of the product. In other words, information diffusion drives consumers’ adoption of a 

new product.

3 The model incorporates information diffusion from both internal sources (spread 

by word of mouth from early adopters to prospective adopters) and external sources (the 

“announcement effect” produced by government agencies, institutions, or commercial 

advertising). The model incorporates both internal and external sources by combining a logistic 

function with an exponential function.4,5

The relative degree of influence from the internal and external sources determines the 

general shape of the diffusion curve for a specific product.4,5 If adoption of a product is 

influenced primarily by external sources of information (the announcement effect), for instance, 

a high rate of diffusion occurs at the beginning of the process. In this scenario, external sources 

provide immediate information exposure to a significant number of prospective adopters. In 

contrast, internal sources (such as a network of prospective adopters) are relatively small in size 

and reach, producing a more gradual exposure to prospective adopters. Graphically speaking, 

information diffusion dominated by external sources is represented by a concave curve (the 

exponential curve in Figure 17A.4.1). If adoption of a new product is influenced most strongly 

by internal sources of information, the number of adopters increases gradually, forming a convex 

curve (the logistic curve in Figure 17A.4.1).

17A-5
Figure 17A.4.1 S-Curves Showing Effects of External and Internal 

Sources on Adoption of New Technologies

17A.4.2 Adjustment of Energy Penetration Curves

In consultation with the primary authors of the 2002 XENERGY study who later 

conducted similar California studies, DOE made some adjustments to XENERGY’s original 

implementation (penetration) curves.6 The experiences with utility programs since the 

XENERGY study indicate that incentive programs have difficulty achieving penetration rates as 

high as 80 percent. Consumer response is limited by barriers created by consumer utility issues 

and other non-economic factors. DOE therefore adjusted the maximum penetration parameters 

for some of the curves from 80 percent to the following levels:

Moderate Barriers: 70%

High Barriers: 60%

Extremely High Barriers: 50%

The low barriers and no barriers curves (the latter used only when a product has a very 

high base-case-market share) remained, respectively, with 80 percent and 100 percent as their 

maximum penetration rates. For the interpolated penetration curves (discussed below), DOE set 

the no barriers and extremely high barriers curves as the upper and lower bounds, respectively, 

17A-6
for any benefit/cost ratio points higher or lower than the curves. It set another constraint such 

that the policy case market share cannot be greater than 100 percent, as might occur for products 

with high base case market shares of the target-level technology.

17A.4.3 Interpolation of Penetration Curves

As discussed above, the XENERGY penetration (implementation) curves followed a 

functional form to estimate the market implementation rate caused by energy efficiency 

measures such as consumer rebates.c The XENERGY report presents five reference market 

implementation curves that vary according to the level of market barriers to technology 

penetration.1 Such curves have been used by DOE in the Regulatory Impact Analyses for 

rulemakings for appliance energy efficiency standards to estimate market share increases in 

response to rebate programs.d They provide a framework for evaluating technology penetration, 

yet require matching the studied market to the curve that best represents it. This approximate 

matching can introduce some inaccuracy to the analysis. 

Blum et al (2011, Appendix A)7

presents an alternative approach to such evaluation: a method 

to estimate market implementation rates more accurately by performing interpolations of the 

reference curves. The referred report describes the market implementation rate function and the 

reference curves, the method to calibrate the function to a given market, and the limitations of 

the method. 

c The RIA chapter refers to these curves as penetration curves. This section, in references to the original source, uses 

the term implementation curve. d DOE has also used this method to estimate market share increases resulting from consumer tax credit and 

manufacturer tax credit programs, since the effects of tax credits on markets can be considered proportional to the 

rebate impacts. 

17A-7

 
17A.5 CONSUMER REBATE PROGRAMS 

DOE performed a search during the second quarter of 2014 for rebate programs that 

offered incentives for PTACs and PTHPs. Some organizations nationwide, comprising electric 

utilities and regional agencies, offered rebate programs for this equipment. Table 17-A.5.3

provides the organizations’ names, states, rebate amounts, and program websites. If there is more 

than one entry for an organization, it offered different rebates in different states. When an 

organization offered rebates through several utilities, it is represented only once in the table. 

Rebates were offered either on per unit or per ton basis. Most of the rebate programs 

offered a fixed amount rebate. However, some of them included an incremental rebate amount 

proportional to the equipment cooling capacity and energy efficiency. DOE calculated a 

representative rebate amount for each equipment class and TSL. The representative amount 

accounts for the equipment class cooling capacity and the efficiency level corresponding to each 

TSL. Table 17-A.5.1 shows the cooling capacities (in tons) and efficiencies (in EER) that DOE 

used to calculate specific rebate amounts for each equipment class and TSL. 

Table 17A.5.1 Representative Cooling Capacities and Energy Efficiencies Used to 

Calculate the Rebate Amounts for each Rebate Program

Equipment Class

Energy Efficiency (EER) per 

Trial Standard Levels

Representative

Cooling 

Capacity

ASHRAE (tons) 1 2 3 4 5

Packaged Terminal Air Conditioners

< 7,000 Btu/h 11.9 12.2 12.6 13.1 13.6 13.8 7

7,000 – 15,000 Btu/h 11.3 11.5 12.0 12.4 12.9 13.1 12

> 15,000 Btu/h 9.5 9.7 10.0 10.4 10.8 11.0 15

Packaged Terminal Heat Pumps

< 7,000 Btu/h 11.9 12.2 12.6 13.1 13.6 13.8 7

7,000 – 15,000 Btu/h 11.3 11.5 12.0 12.4 12.9 13.1 12

> 15,000 Btu/h 9.5 9.7 10.0 10.4 10.8 11.0 15

After calculating a rebate amount for each rebate program, DOE calculated the 

representative rebate amount for each equipment class and TSL taking the simple average of the 

rebate amounts that DOE calculated for each rebate program. Table 17-A.5.2 shows the 

representative rebate amounts DOE used for each equipment class and TSL in its analysis of the 

benefits from consumer rebates.

17A-8
Table 17A.5.2 Representative Rebate Amounts (2014$)

Equipment Class

Trial Standard Levels

ASHRAE 1 2 3 4 5

Packaged Terminal Air Conditioners

< 7,000 Btu/h 33.50 41.89 46.93 32.78 40.74 45.52

7,000 – 15,000 Btu/h 34.82 43.95 48.74 32.91 40.95 45.71

> 15,000 Btu/h 37.13 47.70 52.67 33.14 41.32 46.10

Packaged Terminal Heat Pumps

< 7,000 Btu/h 39.43 51.45 56.60 33.37 41.70 46.49

7,000 – 15,000 Btu/h 41.74 55.20 60.52 33.60 42.07 46.88

> 15,000 Btu/h 42.89 57.08 62.49 33.71 42.26 47.08

17A-9
Table 17A.5.3 Rebates for Packaged Terminal Air Conditioners and Packaged Terminal Heat Pumpse

Organization State Rebate Website

PTAC PTHP

SRP AZ $20/ton $20/ton www.savewithsrpbiz.com/rebates.aspx

Imperial Irrigation District CA $50/ton $50/ton www.iid.com/index.aspx?page=293

Modesto Irrigation District CA $75/unit $75/unit www.mid.org/rebates/commercial/default.html

Pacific Power CA $25/ton $50/ton www.pacificpower.net/bus/se/california/sc/hc.html

City of Roseville California CA $125/unit $125/unit www.roseville.ca.us/electric/business/rebates/hvac.asp

Southern California Edison CA $100/unit $100/unit www.sce.com/wps/portal/home/business/savingsincentives/express-solutions/how-to-apply/!ut/p/b1

Silicon Valley Power CA $100/ton $100/ton http:/siliconvalleypower.com/index.aspx?page=1931

SMUD CA $100/unit $100/unit www.smud.org/en/business/save-energy/rebates-incentivesfinancing/incentives-for-heating-and-cooling/expressincentives-air-conditioning.htm

City of Fort Collins CO $50/ton* $50/ton* www.fcgov.com/utilities/business/improve-efficiency/rebatesincentives/electric-efficiency/cooling-rebates

JEA FL $50/ton $50/ton www.jea.com/Manage_My_Account/Ways_to_Save/Rebate_P

rograms/InvestSmart/Heating,_Cooling_and_Air_Handling_Re

bates.aspx

TECO FL $37.50/ton $37.50/ton www.tampaelectric.com/business/saveenergy/cooling/

Cedar Falls Utilities IA $50/unit $50/unit www.cfu.net/save-energy/business-rebates.aspx

Illinois Municipal Electric Agency IL $50/ton $50/ton www.imea.org/EEProjects.aspx

Duke Energy IN $20/ton $20/ton www.duke-energy.com/indiana-business/energymanagement/cooling-incentive.asp

Kentucky Utilities Company KY $10/ton $10/ton http://lge-ku.com/saving-energy-money/commercial-rebates

Louisville Gas & Electric KY $10/ton $10/ton http://lge-ku.com/saving-energy-money/commercial-rebates

Baltimore Gas & Electric Company MD $30/unit $30/unit www.bgesmartenergy.com/business/energy-solutionsbusiness/application-forms

Baltimore Gas & Electric Company MD $40/unit $40/unit www.bgesmartenergy.com/business/energy-solutionsbusiness/application-forms

e This table is based on rebate programs DOE found to be available through an extensive internet search during the second quarter of 2014. Some of the programs 

referenced—and consequently their websites—may no longer be available by the time this document is published.

17A-10

 
Organization State Rebate Website

Baltimore Gas & Electric Company MD $50/unit $50/unit www.bgesmartenergy.com/business/energy-solutionsbusiness/application-forms

CONNEXUS ENERGY MN $10/ton** $10/ton** www.connexusenergy.com/business/rebates/cooling-and-heatpumps/

CONNEXUS ENERGY MN $10/ton† $10/ton† www.connexusenergy.com/business/rebates/cooling-and-heatpumps/

Dakota Electric Association MN $10/ton** $10/ton** www.dakotaelectric.com/business/programs/rebates_grants_an

d_loans/heating_cooling_ventilation_rebates

Dakota Electric Association MN $10/ton† $10/ton† www.dakotaelectric.com/business/programs/rebates_grants_an

d_loans/heating_cooling_ventilation_rebates

East Central Energy MN $10/ton** $10/ton** www.eastcentralenergy.com/rebatesbusiness.aspx

East Central Energy MN $10/ton † $10/ton† www.eastcentralenergy.com/rebatesbusiness.aspx

Elk River Municipal Utilities MN $10/ton** $10/ton** www.elkriverutilities.com/pages/rebate-forms

Elk River Municipal Utilities MN $10/ton† $10/ton† www.elkriverutilities.com/pages/rebate-forms

Minnesota Valley Electric Cooperative MN $10/ton** $10/ton** www.mvec.net/business/efficiency-rebates/

Minnesota Valley Electric Cooperative MN $10/ton† $10/ton† www.mvec.net/business/efficiency-rebates/

Moorhead Public Service Utility MN $35/room†

†

$30/room†† www.brightenergysolutions.org/municipalities/?category=busi

ness&state=mn&municipality=28&program=4

El Paso Electric Company NM $30/ton $30/ton www.epesaver.com/index.php/component/content/article/105-

commercial-cooling

PNM NM $20/ton $20/ton www.pnmenergyefficiency.com/Projects/Default.aspx?tabid=9

08

conEdison NY $50/ton $50/ton www.conedci.com/HVAC.aspx#electric

Dayton Power & Light OH $50/unit $50/unit www.dpandl.com/save-money/business-government/rapidrebates/hvac-rebates/#pack

AEP Ohio OH $30/ton $30/ton www.aepohio.com/save/programs/PrescriptiveProgram.aspx

FirstEnergy (MetEdison, Penelec, Penn 

Power)

PA $150/unit $150/unit www.energysavepa-business.com/index.php

AEP Appalachian Power WV $30/ton $30/ton http://aeprebates.com/commercial-program/ * incremental rebate ($5/ton per 0.1 EER above base)

** incremental rebate ($1.75/ton per 0.1 EER above base)

† incremental rebate ($3.50/ton per 0.1 EER above base)

††DOE assumed a per unit rebate amount

17A-11
17A.6 FEDERAL AND STATE TAX CREDITS

This section summarizes the Federal and State tax credits available to consumers who 

purchase energy efficient appliances. This section also describes tax credits available to 

manufacturers who produce certain energy efficient appliances.

17A.6.1 Federal Tax Credits for Consumers

EPACT 2005 included Federal tax credits for consumers who installed efficient air 

conditioners or heat pumps; gas, oil and propane furnaces and boilers; furnace fans; and/or gas, 

oil, or electric heat pump water heaters in new or existing homes.8, 9 These tax credits were in 

effect in 2006 and 2007, expired in 2008, and were reinstated for 2009–2010 by the American 

Recovery and Reinvestment Act (ARRA).10 There was a $1,500 cap on the credit per home, 

including the amount received for insulation, windows, and air and duct sealing. Congress 

extended this provision for 2011, with some modifications to eligibility requirements, and 

reductions in the cap to $500 per home. The American Taxpayer Relief Act of 2012 extended, 

with some modifications, residential tax credits for air conditioners, heat pumps, furnaces, and 

water heaters placed in service between January 1, 2012 and December 31, 2013.8, 11

The tax credit for furnace fans was $50 in 2011, after which it expired.

The importance of the Federal tax credits has been emphasized in research in the 

residential heating industry on the impacts of the relatively large credits that were available for 

HVAC (heating, ventilating, and air conditioning) equipment. In a survey of HVAC distributors 

conducted by Vermont Energy Investment Corporation, respondents indicated that the ample 

credit had had a notable impact on sales of higher-efficiency heating and cooling equipment. 

Some distributors combined the Federal tax credits with manufacturer rebates and utility 

program rebates for a greater consumer incentive. However, when the amount of the Federal tax 

credit was reduced, smaller utility rebate incentives had not induced the same levels of 

equipment sales increases. The decrease in incentive size from a $1,500 cap in 2009-2010 to a 

$500 cap in 2011, during a period when the economy continued to be sluggish, resulted in a 

decline in total sales of residential HVAC products. Distributors stated that an incentive needed 

to cover 25 to 75 percent of the incremental cost of the efficient equipment to influence 

consumer choice. The industry publication “2011 HVAC Review and Outlook” noted a decline 

in sales of air conditioning units with >14 SEER in 2011 and a return in sales of units with >16 

SEER to 2009 levels (after an increase in 2010). The large majority of distributor observed no 

impacts from the utility programs with their lower rebate amounts available in 2011. Distributors 

also commented on the advantages of the Federal tax credit being nationwide in contrast to 

utility rebate programs that target regional markets.12, 13

In an effort to evaluate the potential impact of a Federal appliance tax credit program, 

DOE reviewed Internal Revenue Service (IRS) data on the numbers of taxpayers who claimed 

the tax credits during tax years 2006 and 2007. It estimated the percentage of taxpayers who filed 

Form 5695, Residential Energy Credits.

14 It also estimated the percentage of taxpayers with 

entries under Form 5695’s section 3, Residential energy property costs, line 3b, qualified natural 

gas, propane, or oil furnace or hot water boiler. DOE reasoned that the percentage of taxpayers 

17A-12
with an entry on Line 3b could serve as a rough indication of the potential of taxpayer 

participation in a Federal tax credit program for furnaces during the initial program years. DOE 

found that of all residential taxpayers filing tax returns, 0.8 percent in 2006 and 0.6 percent in 

2007, claimed a credit for a furnace or boiler. DOE further found that the percentages of those 

filing Form 5695 for any qualifying energy property expenditure (which also included 

installation of efficient windows, doors and roofs) were 3.1 and 3.2 percent in 2006 and 2007 

respectively. 

DOE also reviewed data from an earlier Federal energy conservation tax credit program 

in place in the 1980s. While this tax credit was available from 1979 through 1985, DOE located 

data for only the first three years of the program.

15, 16, 17 For those three years - 1979, 1980, and 

1981 - the percentages of taxpayers filing Form 5695 were 6.4 percent, 5.2 percent, and 4.9 

percent. Given that the data from this earlier tax credit program were not disaggregated by type 

of energy property, this data series served only to indicate a possible trend of greater 

participation in the initial program year, followed by slightly smaller participation in subsequent 

years. However, DOE did not find detailed analysis of this program to indicate the possible 

reasons for such a trend. Also, this trend varies from the more stable trend shown in the EPAct 

2005 energy tax credit program data for its first two program years.

As discussed in Chapter 17, DOE analyzed the percentage of participation in consumer 

tax credit programs using its estimates of consumer participation in rebate programs that was 

based on benefit/cost data specific to each equipment class. Hence it was difficult to compare 

these detailed estimates to the more general data analysis described above from the existing 

Federal tax credit program, or to use the IRS data analysis in its consumer tax credit analysis.

17A.6.2 Federal Tax Credits for Manufacturers

EPACT 2005 provided Federal Energy Efficient Appliance Credits to manufacturers that 

produced high-efficiency refrigerators, clothes washers, and dishwashers in 2006 and 2007.18

The Emergency Economic Stabilization Act of 200819 amended the credits and extended them 

through 2010. The credits were extended again to 2011 with modifications in the eligibility 

requirements. Manufacturer tax credits were extended again, by the American Taxpayer Relief 

Act of 2012, for clothes washers, refrigerators, and dishwashers manufactured between January 

1, 2012 and December 31, 2013. 

Manufacturers who produce these appliances receive the credits for increasing their 

production of qualifying appliances. These credits had several efficiency tiers in 2011. For 2012-

2013, credits for the higher tiers remain but were eliminated for the lowest (least efficient) tiers 

for clothes washers and dishwashers.

11 The credit amounts applied to each unit manufactured. 

The credit to manufacturers of qualifying clothes washers, refrigerators and dishwashers was 

capped at $75 million for the period of 2008-2010. However, the most efficient refrigerator 

(30%) and clothes washer (2.2 MEF/4.5 wcf) models was not subject to the cap. The credit to 

manufacturers was capped at $25 million for 2011, with the most efficient refrigerators (35%) 

and clothes washers (2.8 MEF/3.5 WCF) exempted from this cap.

20

17A-13
17A.6.3 State Tax Credits

The states of Oregon and Montana have offered consumer tax credits for efficient 

appliances for several years, and the states of Kentucky, Michigan and Indiana began offering 

such credits in 2009. The Oregon Department of Energy (ODOE) has disaggregated data on 

taxpayer participation in credits for eligible products. (See the discussion in Chapter 17, Section 

17.3.3, on tax credit data for clothes washers). Montana’s Department of Revenue does not 

disaggregate participation data by appliance, although DOE reviewed Montana's overall 

participation trends and found them congruent with its analysis of Oregon's clothes washer tax 

credits. 

Oregon’s Residential Energy Tax Credit (RETC) was created in 1977. The Oregon 

legislature expanded the RETC program in 1997 to include residential refrigerators, clothes 

washers, and dishwashers, which significantly increased participation in the program. The 

program subsequently added credits for high-efficiency heat pump systems, air conditioners, and 

water heaters (2001); furnaces and boilers (2002); and duct/air sealing, fuel cells, heat recovery, 

and renewable energy equipment. Beginning in 2012 a Tax Credit Extension Bill (HB3672) 

eliminated refrigerators, clothes washers, dishwashers, air conditioners, and boilers from the 

RETC program, leaving credits for water heaters, furnaces, heat pumps, tankless water heaters, 

and heat pump water heaters.21, 22 Those technologies recognized by the Oregon Department of 

Energy as “premium efficiency” are eligible for tax credit of $0.60 per kWh saved in the first 

year (up to $1,500).21, 23

Montana has had an Energy Conservation Tax Credit for residential measures since 

1998.24 The tax credit covers various residential energy and water efficient products, including 

split system central air conditioning; package system central air conditioning; split system air 

source heat pumps; package system heat pumps; natural gas, propane, or oil furnaces; hot water 

boilers; advanced main air circulating fans; heat recovery ventilators; gas, oil, or propane water 

heaters; electric heat pump water heaters; low-flow showerheads and faucets; light fixtures; and 

controls. In 2002 the amount of the credit was increased from 5 percent of product costs (up to 

$150) to 25 percent (up to $500) per taxpayer. The credit can be used for products installed in 

new construction or remodeling projects. The tax credit covers only that part of the cost and 

materials that exceed established standards of construction.

17A-14
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