TECHNICAL SUPPORT DOCUMENT: ENERGY CONSERVATION PROGRAM FOR 

CONSUMER PRODUCTS: ENERGY CONSERVATION STANDARDS FOR HEARTH 

PRODUCTS

January 30, 2015

U.S. Department of Energy

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 the staff members of

Navigant Consulting, Inc.

and

Lawrence Berkeley National Laboratory
CHAPTER 1. INTRODUCTION

TABLE OF CONTENTS

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

1.2 SUMMARY OF THE NATIONAL BENEFITS............................................................. 1-1

1.3 OVERVIEW OF STANDARDS FOR HEARTH PRODUCTS ..................................... 1-6

1.4 PROCESS FOR SETTING ENERGY CONSERVATION STANDARDS ................... 1-7

1.5 STRUCTURE OF THE DOCUMENT............................................................................ 1-9

LIST OF TABLES

Table 1.2.1 Summary of National Economic Benefits and Costs of Proposed Pilot 

Energy Conservation Standards for Hearth Products (Trial Standard Level 

1)* ........................................................................................................................ 1-3

Table 1.2.2 Annualized Benefits and Costs of Proposed Energy Conservation 

Standards for Hearth Products (Trial Standard Level1) ...................................... 1-5

Table 1.4.1 Analyses Under the Process Rule ..........................Error! Bookmark not defined.

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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 notice of proposed rulemaking

(NOPR) for hearth products. 

1.2 SUMMARY OF THE NATIONAL BENEFITS

The U.S. Department of Energy’s (DOE) analyses indicate that the proposed energy 

conservation standard to disallow the use of continuously burning pilots (i.e., standing pilots) for 

hearth products would save a significant amount of energy. The lifetime energy savings for 

hearth products purchased in the 30-year period that begins in the first full year of compliance 

with new standards (2021–2050) amount to .69 quadsa of full-fuel-cycle energy. This represents 

a savings of about 77 percent relative to the energy use of the ignition systems in the base case, 

i.e., without the proposed energy conservation standards.

The cumulative net present value (NPV) of total consumer costs and savings for the 

proposed hearth products energy conservation standard ranges from $1.03 billion to $3.12 billion 

at 7-percent and 3-percent discount rates, respectively. This NPV expresses the estimated total 

value of future operating-cost savings minus the estimated increased product costs for hearth 

products purchased in 2021–2050.

In addition, the proposed hearth products pilot standard would have significant 

environmental benefits. The energy savings described above are expected to result in cumulative 

full-fuel cycle emission reductions of 37.0 million metric tons (Mt)b of carbon dioxide (CO2),

486 thousand tons of methane (CH4), .01 thousand tons of nitrous oxide (N2O), .26 thousand tons 

of sulfur dioxide (SO2), 125 thousand tons of nitrogen oxides (NOx), and 0.01 tons of mercury 

(Hg).c

 The cumulative reduction in CO2 emissions through 2030 amounts to 11.1 Mt.

The value of the CO2 reductions is calculated using a range of values per metric ton of 

CO2 (otherwise known as the social cost of carbon, or SCC) developed by a recent federal 

interagency process.d

 The derivation of the SCC values is discussed in chapter 14 of the NOPR 

a A quad is equal to 1015 British thermal units (Btu). b A metric ton is equivalent to 1.1 short tons. Results for emissions other than CO2 are presented in short tons. c DOE calculated emissions reductions relative to the Annual Energy Outlook 2013 (AEO 2013) Reference case, 

which generally represents current legislation and environmental regulations for which implementing regulations 

were available as of December 31, 2012. d Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866,

Interagency Working Group on Social Cost of Carbon, U. S. Government (May 2013; revised November 2013) 

(Available at: http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-social-cost-ofcarbon-for-regulator-impact-analysis.pdf).

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TSD. Using discount rates appropriate for each set of SCC values, DOE estimates the present 

monetary value of the CO2 emissions reduction is between $0.2 billion and $3.4 billion, with the 

value of $1.1 billion using the central SCC case represented by $40.5 per ton in 2015. 

Additionally, DOE estimates the present monetary value of the NOx emissions reduction to be 

$0.06 billion to $0.15 billion at 7-percent and 3-percent discount rates, respectively.e

Table 1.2.1 summarizes the national economic benefits and costs expected to result from 

the proposed pilot standards for hearth products. 

e DOE is currently investigating valuation of avoided Hg and SO2 emissions.

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Table 1.2.1 Summary of National Economic Benefits and Costs of Proposed Pilot

Energy Conservation Standards for Hearth Products (Trial Standard 

Level 1)*

Category

Present 

Value

Billion 2013$

Discount 

Rate

%

Benefits

Consumer Operating Cost Savings

1.5 7

4.1 3

CO2 reduction monetized value ($12.0/t case)** 0.2 5

CO2 reduction monetized value ($40.5/t case)** 1.1 3

CO2 deduction monetized value ($62.4/t case)** 1.8 2.5

CO2 reduction monetized value ($119/t case)** 3.4 3

NOx reduction monetized value (at $2,684/ton)**

0.1 7

0.1 3

Total benefits†

2.7 7

5.4 3

Costs

Consumer incremental installed costs

0. 5 7

1.0 3

Total Net Benefits

Including emissionsreduction monetized value† 2.2 7

4.4 3

* This table presents the costs and benefits associated with hearth products shipped in 2021−2050. These results 

include benefits to consumers which accrue after 2050 from the products purchased in 2021−2050. The results 

account for the incremental variable and fixed costs incurred by manufacturers due to the standard, some of which 

may be incurred in preparation for the rule.

** The CO2 values represent global monetized values of the SCC, in 2013$, in 2015 under several scenarios of the 

updated SCC values. The first three cases use the averages of SCC distributions calculated using 5%, 3%, and 2.5% 

discount rates, respectively. The fourth case represents the 95th percentile of the SCC distribution calculated using a 

3% discount rate. The SCC time series used by DOE incorporate an escalation factor. The value for NOx is the 

average of the low and high values used in DOE’s analysis.

† Total benefits for both the 3% and 7% cases are derived using the series corresponding to average SCC with a 3-

percent discount rate ($40.5/t in 2015).

The benefits and costs of the proposed energy conservation standards for hearth products 

sold in 2021–2050, can also be expressed in terms of annualized values. The annualized

monetary values are the sum of: (1) the annualized national economic value of the benefits from 

consumer operation of products that meet the proposed new or amended standards (consisting 

primarily of operating cost savings from using less energy, minus increases in product purchase 

and installation costs, which is another way of representing consumer NPV), and (2) the 

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annualized monetary value of the benefits of emission reductions, including CO2 emission 

reductions.f

Although combining the values of operating savings and CO2 emission reductions 

provides a useful perspective, two issues should be considered. First, the national operating 

savings are domestic U.S. consumer monetary savings that occur as a result of market 

transactions, whereas the value of CO2 reductions is based on a global value. Second, the 

assessments of operating cost savings and CO2 savings are performed with different methods that

use different timeframes for analysis. The national operating cost savings is measured for the 

lifetime of hearth products shipped in 2021–2050. The SCC values, on the other hand, reflect 

the present value of some future climate-related impacts resulting from the emission of one ton 

of carbon dioxide in each year. These impacts continue well beyond 2100.

Estimates of annualized benefits and costs of the proposed pilot standards are shown in

Table 1.2.2. The results under the primary estimate are as follows. Using a 7-percent discount 

rate for benefits and costs other than CO2 reduction (for which DOE used a 3-percent discount 

rate along with the average SCC series that uses a 3-percent discount rate ($40.5 per ton in 

2015)), cost of the hearth standards proposed in today’s rule is $61.1 million per year in 

increased equipment costs, while the estimated benefits are $186 million per year in reduced 

equipment operating costs, $67 million in CO2 reductions, and $7.0 million in reduced NOx

emissions. In this case, the net benefit would amount to $199 million per year. Using a 3-

percent discount rate for all benefits and costs and the average SCC series that uses a 3-percent 

discount rate ($40.5 per ton in 2015), the estimated cost of the hearth products standards 

proposed in today’s rule is $61.2 million per year in increased equipment costs, while the 

estimated benefits are $251 million per year in reduced equipment operating costs, $67 million in 

CO2 reductions, and $9.0 million in reduced NOx emissions. In this case, the net benefit would 

amount to $266 million per year.

f DOE used a two-step calculation process to convert the time-series of costs and benefits into annualized values.

First, DOE calculated a present value in 2014, the year used for discounting the NPV of total consumer costs and 

savings, for the time-series of costs and benefits using discount rates of 3 and 7 percent for all costs and benefits 

except for the value of CO2 reductions. For the latter, DOE used a range of discount rates, as shown in Table I.7.

From the present value, DOE then calculated the fixed annual payment over a 30-year period (2021 through 2050) 

that yields the same present value. The fixed annual payment is the annualized value. Although DOE calculated 

annualized values, this does not imply that the time-series of cost and benefits from which the annualized values 

were determined is a steady stream of payments.

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Table 1.2.2 Annualized Benefits and Costs of Proposed Energy Conservation 

Standards for Hearth Products (Trial Standard Level1)

Discount Rate

%

Primary 

Estimate*

Low Net 

Benefits 

Estimate*

High Net 

Benefits 

Estimate*

million 2013$/year

Benefits

Consumer operating 

cost savings

7 186 175 195

3 251 235 265

CO2 reduction 

monetized value 

($12.0/t case)**

5 20 20 20

CO2 reduction 

monetized value 

($40.5/t case)**

3 67 67 67

CO2 reduction 

monetized value 

($62.4/t case)**

2.5 98 98 98

CO2 reduction 

monetized value 

($119/t case)**

3 207 207 207

NOx reduction 

monetized value (at 

$2,684/ton)

7 7.00 7.00 7.00

3 8.99 8.99 8.99

Total benefits†

7 plus CO2 range 212 to 400 202 to 389 222 to 410

7 260 249 269

3 plus CO2 range 280 to 468 264 to 452 294 to 482

3 327 311 341

Costs

Consumer 

incremental installed 

costs

7 61.1 61.1 61.1

3 61.2 61.2 61.2

Net benefits

Total†

7 plus CO2 range 151 to 399 141 to 328 161 to 349

7 199 188 208

3 plus CO2 range 219 to 407 203 to 390 233 to 420

3 266 250 280

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* This table presents the annualized costs and benefits associated with hearth products shipped in 2021-–2050. 

These results include benefits to consumers which accrue after 2050 from the products purchased in 2021-2050. 

The results account for the incremental variable and fixed costs incurred by manufacturers due to the standard, some 

of which may be incurred in preparation for the rule. The primary, low benefits, and high benefits estimates utilize 

projections of energy prices from the AEO 2014 Reference case, Low Estimate, and High estimate, respectively. 

Incremental product costs are the same for each Estimate.

** The CO2 values represent global monetized values of the SCC, in 2013$, in 2015 under several scenarios of the 

updated SCC values. The first three cases use the averages of SCC distributions calculated using 5%, 3%, and 2.5% 

discount rates, respectively. The fourth case represents the 95th percentile of the SCC distribution calculated using a 

3% discount rate. The SCC time series used by DOE incorporate an escalation factor. The value for NOx is the 

average of high and low values found in the literature. 

† Total benefits for both the 3% and 7% cases are derived using the series corresponding to the average SCC with a 

3-percent discount rate ($40.5/t in 2015). In the rows labeled “7% plus CO2 range” and “3% plus CO2 range,” the 

operating cost and NOx benefits are calculated using the labeled discount rate, and those values are added to the full 

range of CO2 values.

The Energy Policy and Conservation Act of 1975 (EPCA), as amended, requires the U.S. 

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

feasible and economically justified, and would achieve the maximum improvement in energy 

efficiency. See 42 U.S.C. 6295(o)(2)(B). DOE has tentatively concluded that the proposed 

standards represent the maximum improvement in energy efficiency that is technologically 

feasible and economically justified, and would result in the significant conservation of energy. 

DOE further notes that for the products covered by this proposal, products achieving these 

standard levels (i.e. hearth products that do not use a standing pilot ignition system) are already 

commercially available. Based on the analyses described previously, DOE has tentatively 

concluded that the benefits of the proposed standards to the Nation (energy savings, positive 

NPV of consumer benefits, consumer LCC savings, and emission reductions) would outweigh 

the burdens (loss of INPV for manufacturers and LCC increases for some consumers). 

Based on consideration of the public comments DOE receives in response to this NOPR

and related information collected and analyzed during the course of this rulemaking effort, DOE 

may adopt the standard proposed in this notice, or some combination of options that incorporate 

the proposed standards in part.

1.3 OVERVIEW OF STANDARDS FOR HEARTH PRODUCTS

 There are currently no federal energy conservation standards for hearth products. 

 

On December 31, 2013, DOE published a notice of proposed determination of coverage to 

classify hearth products as covered products under EPCA. 78 FR 79638. In the proposed 

determination of coverage, DOE presented its preliminary findings relating to the energy use of 

hearth products to determine whether they could be classified as a type of covered product under 

the requirements of 42 U.S.C. 6292(b)(1)(A) and (B), and whether they would meet the criteria 

for DOE to prescribe an energy conservation standard under 42 U.S.C. 6295(l)(1)(A)-(D). (See 

section II.A of the NOPDfor a discussion of these statutory criteria.) DOE also proposed to 

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define a “hearth product” as “a gas-fired appliance that simulates a solid-fueled fireplace or 

presents a flame pattern (for aesthetics or other purpose) and that may provide space heating 

directly to the space in which it is installed.” 78 FR 79638, 79640 (Dec. 31, 2013). The 

proposed determination is still pending, but as discussed in section IV.A of the NOPR, DOE is 

using that proposed definition to delineate the scope of this proposed rulemaking. In addition, 

DOE has considered some of the comments submitted in response to the proposed coverage 

determination, which are relevant to the development of proposed energy conservation standards 

for hearth products and addresses those comments as applicable in this proposed rulemaking.

1.4 PROCESS FOR SETTING ENERGY CONSERVATION STANDARDS

Under the Energy Policy and Conservation Act of 1975 (EPCA), as amended, when DOE 

is studying new or amended standards, it must consider, to the greatest extent practicable, the 

following seven factors (42 U.S.C. 6295 (o)(2)(B)(i)):

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

products; 

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

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 products 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.

Other statutory requirements are set forth in 42 U.S.C. 6295 (o)(1)–(2)(A), (2)(B)(ii)–

(iii), and (3)–(4) and 42 U.S.C. 6316(e).

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 each stage of the rulemaking.

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Before DOE determines whether to adopt a proposed energy conservation standard, it 

must first solicit comments on the proposed standard. (42 U.S.C. 6295(p)(2)) Any new or 

amended standard must be designed to achieve the maximum improvement in energy efficiency

and be technologically feasible and economically justified. (42 U.S.C. 6295(o)(2)(A)) 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. (42 U.S.C. 6295 (o)(2)(B)(i))

The energy conservation standards rulemaking process usually involves three formal 

public notices, which DOE publishes in the Federal Register. The first of the rulemaking notices 

is typically a notice of public meeting (NOPM), which is designed to publicly vet the models and 

tools used in the rulemaking and to facilitate public participation before the NOPR stage. The 

second notice is usually the NOPR, which presents a discussion of: (1) comments received in 

response to the NOPM analyses; (2) analyses of the impacts of potential amended energy 

conservation standards on consumers, manufacturers, and the Nation; (3) DOE’s weighting of 

these impacts of amended energy conservation standards; and (4) the proposed energy 

conservation standards for each product. The third notice is usually the final rule, which presents 

a discussion of: (1) the comments received in response to the NOPR; (2) the revised analyses; (3)

DOE’s weighting of these impacts; (4) the amended energy conservation standards DOE is 

adopting for each product; and (5) the effective dates of the amended energy conservation 

standards. However, due to the close proximity between this NOPR and prior rulemaking 

activities for some hearth products, this energy conservation standards rulemaking did not 

include a publication of a notice of public meeting (NOPM) or preliminary analysis. Instead, the 

NOPR analysis included all analyses typically included in the preliminary analysis. Table 1.4.1

shows the analyses that typically occur during each phase of the rulemaking process. 

 

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Table 1.4.1 Rulemaking Analysis Stages

Preliminary Analyses* NOPR Final Rule

Market and technology assessment Revised analyses Revised NOPR analyses

Screening analysis Life-cycle cost sub-group 

analysis

Engineering analysis Manufacturer impact analysis

Energy use determination Utility impact analysis

Markups for equipment price 

determination

Emissions analysis

Life-cycle cost and payback period 

analyses

Employment impact analysis

Shipments analysis Regulatory impact analysis

National impact analysis

Preliminary manufacturer impact analysis

* The analyses typically performed for the preliminary analysis were performed at the NOPR stage for this 

rulemaking.

1.5 STRUCTURE OF THE DOCUMENT

This document outlines the analytical approaches used in this rulemaking. This document 

consists of 17 chapters and 15 appendices.

Chapter 1 Introduction: provides an overview of this rulemaking and outlines the 

structure of the document.

Chapter 2 Analytical Framework: describes an overview of the rulemaking 

process, methodology, analytical tools, and relationships among the 

various analyses.

Chapter 3 Market and Technology Assessment: characterizes the market for the 

considered products and the technologies available for increasing 

product efficiency.

Chapter 4 Screening Analysis: identifies all the design options that improve 

efficiency of the considered products 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 manufacturer production cost and increased 

product efficiency.

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

for converting manufacturer prices to customer product costs.

Chapter 7 Energy Use Analysis: discusses the process used for generating energyuse estimates for the considered products as a function of standard 

levels.

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

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

the products and compares the LCC and PBP of products with and 

without higher efficiency standards.

Chapter 9 Shipments Analysis: estimates shipments of the products 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 consumer costs and savings expected 

to result from specific, potential energy conservation standards.

Chapter 11 Consumer Subgroup Analysis: discusses the effects of standards on 

subgroups of hearth products consumers and compares the LCC and 

PBP of products with and without higher efficiency standards for these 

consumers.

Chapter 12 Manufacturer Impact Analysis: discusses the effects of amended 

standards on the finances and profitability of manufacturers.

Chapter 13 Emissions Impact Analysis: discusses the effects of standards on airborne emissions, including the impact of emissions of six pollutants or 

greenhouse gases: sulfur dioxide (SO2), nitrogen oxides (NOx), carbon 

dioxide (CO2), mercury (Hg), methane (CH4), and nitrous oxide (N2O).

Chapter 14 Monetization of Emission Reduction Benefits: discusses the 

monetization of reductions in CO2 and NOx emissions.

Chapter 15 Utility Impact Analysis: discusses the effects of standards on the 

installed generation capacity of electric utilities. 

Chapter 16 Employment Impact Analysis: discusses the effects of standards on 

national employment. 

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Chapter 17 Regulatory Impact Analysis: discusses the present regulatory actions as 

well as the impact of non-regulatory alternatives to setting energy 

conservation standards.

Appendix 6A Detailed Data for Equipment Price Markups

Appendix 7A Building Variables

Appendix 7B Mapping of Weather Station Data to RECS Households

Appendix 8A User Instructions for Life-Cycle Cost Analysis Spreadsheet for Hearth 

Products

Appendix 8B Uncertainty and Variability in the LCC Analysis

Appendix 8C Energy Price Calculations for Hearth Products

Appendix 8D Distributions Used for Discount Rates

Appendix 8E Life-Cycle Cost Analysis Using Alternative Economic Growth 

Scenarios for Hearth Products

Appendix 9A Relative Price Elasticity of Demand for Appliances

Appendix 10A User Instructions National Impact Analysis Spreadsheet Model

Appendix 10B Full-Fuel-Cycle Multipliers

Appendix 10C National Impact Analysis Using Alternative Economic Growth 

Scenarios for Hearth Products

Appendix 12A Government Regulatory Impact Model 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 Definition and Scope of Coverage....................................................................... 2-6

2.3.2 Product Classes.................................................................................................... 2-6

2.3.3 Market Assessment .............................................................................................. 2-7

2.3.4 Technology Assessment....................................................................................... 2-7

2.4 SCREENING ANALYSIS .................................................................................. 2-7

2.5 ENGINEERING ANALYSIS.............................................................................. 2-8

2.6 MARKUPS ANALYSIS ..................................................................................... 2-9

2.7 ENERGY USE ANALYSIS.............................................................................. 2-10

2.8 LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSES ...................... 2-11

2.9 SHIPMENTS ANALYSIS ................................................................................ 2-12

2.10 NATIONAL IMPACT ANALYSIS.................................................................. 2-13

2.10.1 National Energy Savings Analysis..................................................................... 2-14

2.10.2 Net Present Value Analysis ............................................................................... 2-14

2.11 CONSUMER SUBGROUP ANALYSIS .......................................................... 2-15

2.12 MANUACTURER IMPACT ANALYSIS........................................................ 2-15

2.13 EMISSIONS IMPACT ANALYSIS ................................................................. 2-16

2.14 MONETIZING REDUCED CO2 AND OTHER EMISSIONS........................ 2-18

2.15 UTILITY IMPACT ANALYSIS....................................................................... 2-19

2.16 EMPLOYMENT IMPACT ANALYSIS........................................................... 2-20

2.17 REGULATORY IMPACT ANALYSIS ........................................................... 2-21

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CHAPTER 2. ANALYTICAL FRAMEWORK

2.1 INTRODUCTION

The Energy Policy and Conservation Act (EPCA), as amended (42 USC 6291 et.

seq.), requires that when prescribing new or amended energy conservation standards for 

covered products, the U.S. Department of Energy (DOE) must promulgate standards that 

achieve the maximum improvements in energy efficiency that are technologically feasible 

and economically justified. (42 U.S.C. 6295(o)(2)(A)) This chapter provides a description 

of the analytical framework that DOE is using to evaluate amended energy conservation 

standards for hearth products. This chapter sets forth the methodology, analytical tools, 

and relationships among the various analyses that are part of this rulemaking.

Figure 2.1.1 summarizes the analytical components of the standards-setting 

process. The focus of this figure is the center column, identified as “Analyses.” The 

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

rulemaking process, and how the analyses relate to each other. Key inputs are the types of 

data and information that the analyses require. Some key inputs exist in public databases; 

DOE collects other inputs from interested parties or persons with special knowledge. Key 

outputs are analytical results that feed directly into the standards-setting process. Arrows 

connecting analyses show types of information that feed from one analysis to another.

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Figure 2.1.1 Flow Diagram of Analyses for the Rulemaking Processa

a Note: This rulemaking did not include a preliminary analysis, and all analyses typically performed for the 

preliminary analysis were performed at the NOPR stage of the rulemaking. 

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In this technical support document (TSD), DOE presents results of the following 

analyses, which were performed for the development of this NOPR for hearth products:

• A market and technology assessment to characterize the relevant products, their 

markets, and technology options for reducing their energy consumption.

• A screening analysis to review technology options and determine if they are

technologically feasible; are practicable to manufacture, install, and service; would 

adversely affect product utility or product availability; or would have adverse 

impacts on health and safety.

• An engineering analysis to develop the cost difference between the manufacturer 

selling price (MSP) of standing pilot and electronic ignition systems.

• A markups analysis to develop distribution channel markups that relate the MSP to 

the cost to the consumer.

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

products in a representative set of users.

• Life-cycle cost (LCC) and payback period (PBP) analyses to calculate the savings 

in operating costs at the consumer level throughout the life of the covered products 

compared with any increase in the installed cost for the products likely to result 

directly from imposition of a standard.

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

calculate the national impacts of standards on energy, net present value (NPV), and 

future manufacturer cash flows.

• A national impact analysis (NIA) to assess the aggregate impacts at the national 

level of potential energy conservation standards for the considered products, as 

measured by the NPV of total consumer economic impacts and the national energy 

savings (NES).

• An LCC subgroup analysis to evaluate variations in consumer characteristics that 

might cause a standard to disproportionately affect particular consumer

subpopulations.

• A manufacturer impact analysis (MIA) to assess the potential impact of energy 

conservation standards on manufacturers’ capital conversion expenditures, 

marketing costs, shipments, and research and development costs. 

• A utility impact analysis to estimate the effects of potential standards on electric, 

gas, or oil utilities.

• An employment impact analysis to assess the aggregate impacts on national 

employment.

• An emissions analysis to assess the impacts of amended energy conservation 

standards on the environment.

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• An emissions monetization to assess the benefits associated with emissions 

reductions.

• A regulatory impact analysis to examine major alternatives to amended energy 

conservation standards that potentially could achieve substantially the same 

regulatory goal at a lower cost.

2.2 BACKGROUND

Amendments to Title III of EPCA have given DOE the authority to set forth 

various provisions designed to improve energy efficiency for residential products. In 

addition to specifying a list of covered residential and commercial products, EPCA 

contains provisions that enable the Secretary of Energy to classify additional types of 

consumer products as covered products. DOE previously published a proposed 

determination of coverage to classify hearth products as covered consumer products under 

the provisions outlined in EPCA. There are currently no federal energy conservation 

standards for hearth products.

On December 31, 2013, DOE published a notice of proposed determination 

(NOPD) of coverage to classify hearth products as covered products under EPCA. 78 FR 

79638. In that proposed determination of coverage, DOE presented its preliminary 

findings relating to the energy use of hearth products to determine whether they could be 

classified as a type of covered product under the requirements of 42 U.S.C. 6292(b)(1)(A) 

and (B), and whether they would meet the criteria for DOE to prescribe an energy 

conservation standard under 42 U.S.C. 6295(l)(1)(A)-(D). (See section II.A of the NOPR

for a discussion of these statutory criteria.) DOE also proposed to define a “hearth 

product” as “a gas-fired appliance that simulates a solid-fueled fireplace or presents a 

flame pattern (for aesthetics or other purpose) and that may provide space heating directly 

to the space in which it is installed.” 78 FR 79638, 79640 (Dec. 31, 2013). The proposed 

determination is still pending, but as discussed in section IV.A of the NOPR, DOE is 

using the proposed definition to delineate the scope of this NOPR. In addition, DOE has 

considered some of the comments submitted in response to the proposed coverage 

determination, which are relevant to the development of proposed energy conservation 

standards for hearth products and addresses those comments as applicable in this NOPR.

The following sections provide a general description of the different analytical 

components of the rulemaking analytical framework. DOE has used the most reliable and 

accurate data available at the time of each analysis in this rulemaking. DOE welcomes and 

will consider any submissions of additional data during the rulemaking process.

2.3 MARKET AND TECHNOLOGY ASSESSMENT

When DOE begins an energy conservation standards rulemaking, it develops 

information that provides an overall picture of the market for the products considered, 

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including the nature of the products, market characteristics, and industry structure. This 

activity consists of both quantitative and qualitative efforts based primarily on publicly

available information. The market assessment examines manufacturers, trade associations, 

and the quantities and types of products offered for sale.

DOE recognizes that there may be limited public information on national 

shipments, manufacturing costs, channels of distribution, and manufacturer market shares 

of hearth products. This type of data is an important input for analyses that determine if 

energy conservation standards are economically justified and will result in significant 

energy savings. Therefore, DOE encourages interested parties to submit data that will 

improve DOE’s understanding of the hearth products market. These data may be provided 

under a confidentiality agreement with DOE’s contractor responsible for this part of the 

rulemaking analysis, Navigant Consulting, Inc. (NCI). As in other rulemakings, NCI 

works with confidential data provided by manufacturers and other organizations in 

preparing aggregated results for DOE’s analysis. These aggregated results do not divulge 

the sensitive, individual raw data, but enable other interested parties to comment on the 

aggregated dataset.

Alternatively, interested parties may submit confidential data to DOE, indicating in 

writing which data should remain confidential. Interested parties must submit confidential 

information to DOE according to the procedures outlined in 10 CFR 1004.11. Pursuant to

10 CFR 1004.11, any person submitting information that he or she believes to be 

confidential and exempt by law from public disclosure should submit two copies. One 

copy of the document shall include all the information believed to be confidential, and the 

other copy shall have the information believed to be confidential deleted. DOE will make 

its own determination about the confidential status of the information and treat it 

accordingly.b

DOE reviewed relevant literature and interviewed manufacturers to develop an 

overall picture of the hearth products industry in the United States. Industry publications 

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

information, including: (1) manufacturers and their approximate market shares; (2) 

shipments by product type; (3) product information; and (4) industry trends. 

The analyses developed as part of the market and technology assessment are

described in chapter 3 of this TSD.

b Factors that DOE considers when evaluating requests to treat submitted information as confidential 

include: (1) a description of the items; (2) whether and why such items are customarily treated as 

confidential within the industry; (3) whether the information is generally known by or available from other 

public sources; (4) whether the information has previously been made available to others without obligation 

concerning its confidentiality; (5) an explanation of the competitive injury to the submitting person which 

would result from public disclosure; (6) a date after which such information might lose its confidential 

character; and (7) why disclosure of the information would be contrary to the public interest.

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2.3.1 Definition and Scope of Coverage

There is currently no statutory definition of hearth products as hearth products are

not covered consumer products. In the December 2013 NOPD, DOE proposed to adopt a 

definition of “hearth product” that was used to define the scope of this rulemaking. In the 

December 2013 NOPD, DOE suggested several common hearth product types that would 

be covered under the proposed definition, including vented decorative hearth products, 

vented heater hearth products, vented gas logs, gas stoves, outdoor hearth products, and 

ventless hearth products. DOE used the definition proposed in the December 2013 NOPD 

(as stated above) to determine the scope of this proposed rulemaking.

As described in section III.B of the NOPR, DOE has tentatively determined to 

focus the current rulemaking on reducing standby mode energy consumption. Whereas an 

energy conservation standard could fractionally reduce the energy use of hearth products 

in active mode, DOE found that all standby mode fossil fuel consumption could be 

eliminated with a prescriptive design requirement.

In some instances, hearth products may have standby mode and/or off mode 

electrical energy consumption. However, DOE has tentatively determined that the standby 

and off mode electrical energy consumption of the ignition module is de minimis, and did 

not analyze energy conservation standards to regulate electrical standby mode and off 

mode energy consumption.c

2.3.2 Product Classes

As described in III.C of the NOPR, DOE has tentatively determined not to 

establish separate product classes for a standby mode energy conservation standard. The 

criteria for separation into different product classes are: (1) energy source and (2) capacity 

or other performance-related features such as those that provide utility to the consumer or 

others deemed appropriate by the Secretary that would justify the establishment of a 

separate energy conservation standard. (42 U.S.C. 6295 (q) and 6316(a)) DOE found 

substantial similarity in the function, components used, and energy use of hearth products 

with regard to standby mode. By reviewing manufacturer product literature, conducting 

teardown analyses, and interviewing manufacturers, DOE found that the same or similar 

ignition system components, including manual, millivolt, and electronic gas control 

valves, pilot assemblies, and electronic control modules for electronic ignitions, were used 

across a wide range of hearth products. DOE has tentatively determined should standing 

pilot ignitions be disallowed, most hearth products styles will switch to similar electronic 

ignition components. See section III.C of the NOPR for further detail regarding DOE’s 

tentative determination that product classes do not need to be established for hearth 

products in light of the focus on standby energy mode.

c See section III.I of the NOPR for more details regarding the tentative determination that standby electrical 

consumption for hearth products is de minimis.

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2.3.3 Market Assessment

As part of the market and technology assessment, DOE developed information that 

provides an overall picture of the market for the products considered, including the nature 

of the products, market characteristics, and industry structure. DOE collected quantitative 

and qualitative information, primarily from publicly available sources. The market 

assessment examined manufacturers, trade associations, and the quantities and types of 

products sold and offered for sale. DOE reviewed relevant literature and interviewed 

manufacturers to develop an overall picture of the hearth products industry in the United 

States. Industry publications, government agencies, and trade organizations provided the 

bulk of the information, including: (1) manufacturers and their estimated market shares; 

(2) shipments by product type; (3) product information; and (4) industry trends. The 

analyses developed as part of the market assessment are described in chapter 3 of this 

TSD. 

2.3.4 Technology Assessment

DOE typically uses information relating to existing and past technology options 

and prototype designs as inputs to determine what technologies manufacturers use to 

attain higher performance levels. In consultation with interested parties, DOE develops a 

list of technologies for consideration. Initially, these technologies encompass all those 

DOE believes are technologically feasible. 

DOE developed its list of technologically feasible design options for the 

considered products through consultation with manufacturers of components and systems. 

Product literature and direct examination provided additional information. The 

technologies examined in the technology assessment are described in detail in chapter 3 of 

this TSD.

2.4 SCREENING ANALYSIS

The purpose of the screening analysis is to evaluate the technologies identified in 

the technology assessment to determine which technologies to consider further and which 

technologies to screen out. DOE consulted with industry, technical experts, and other 

interested parties in developing a list of energy-saving technologies for the technology 

assessment. DOE then applied the screening criteria to determine which technologies were 

unsuitable for further consideration in this rulemaking. Chapter 4 of this TSD, the 

screening analysis, contains details about DOE’s screening criteria.

As presented in further detail below, the screening analysis examines whether 

various technologies: (1) are technologically feasible; (2) are practicable to manufacture, 

install, and service; (3) have an adverse impact on product utility or availability; and (4) 

have adverse impacts on health and safety. In consultation with interested parties, DOE 

reviewed the list of hearth products technologies according to these criteria. In the 

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engineering analysis, DOE further considers the efficiency-enhancement technologies that 

it did not eliminate in the screening analysis.

1. Technological feasibility. DOE screens out technologies that are not 

incorporated in commercially available products or working prototypes.

2. Practicability to manufacture, install, and service. If DOE determines that mass 

production of a technology in commercial products and reliable installation and 

servicing of the technology cannot be achieved on the scale necessary to serve 

the relevant market by the time of the compliance date of the standard, it will 

not consider that technology further.

3. Adverse impacts on product or equipment utility or availability. If DOE 

determines a technology has a significant adverse impact on the utility of the 

product for significant consumer subgroups or results in the unavailability of 

any covered product type with performance characteristics (including 

reliability), features, size, capacities, and volumes that are substantially the 

same as products generally available in the United States at the time, it will not 

consider that technology further.

4. Adverse impacts on health or safety. If DOE determines that a technology will 

have significant adverse impacts on health or safety, it will not consider that 

technology further.

Chapter 4 contains additional details about the screening analysis and the 

justification for screening out certain technologies.

2.5 ENGINEERING ANALYSIS

The engineering analysis (chapter 5 of this TSD) establishes the difference in

manufacturing production cost between standing pilot and electronic ignition systems.

This cost difference serves as the basis for cost-benefit calculations in terms of individual 

consumers, manufacturers, and the nation. Chapter 5 discusses the representative units

analyzed, methodology used to develop manufacturing production costs and manufacturer 

markups, and results of the analysis. To determine the cost to consumers of hearth 

products with different types of ignition systems, DOE estimated manufacturing costs, 

markups in the distribution chain, installation costs, and maintenance costs product 

teardowns and manufacturer interviews.

In the engineering analysis, DOE evaluated the different ignition systems found in 

hearth products and the associated manufacturing costs. The purpose of the analysis is to 

estimate the incremental increase to selling prices that would result from disallowing the 

use of standing pilot ignition systems and replacing them with electronic ignition systems.

The engineering analysis considers the electronic ignition system since it was not 

eliminated in the screening analysis. Certain other technologies were not analyzed due to

2-8
other reasons, such as negligible incremental efficiency improvements, lack of 

information on efficiency improvement, or inapplicability to certain hearth categories 

(e.g., gas log sets). DOE considers the remaining technologies (i.e. the electronic ignition 

system) in developing the cost-efficiency relationship, which is subsequently used for the 

LCC and PBP analyses.

DOE typically structures its engineering analysis around one of three 

methodologies: (1) the design-option approach, which calculates the incremental costs of 

adding specific design options to a baseline model; (2) the efficiency-level approach, 

which calculates the relative costs of achieving increases in energy efficiency levels 

without regard to the particular design options used to achieve such increases; and/or (3) 

the reverse-engineering or cost-assessment approach, which involves a “bottom-up” 

manufacturing cost assessment based on a detailed bill of materials derived from teardowns of the product being analyzed.

For this analysis, DOE conducted the engineering analysis for hearth products using 

a combination of the design option and cost-assessment approaches. DOE selected for 

teardown hearth models that represent a range of hearth configurations (e.g., vented 

fireplaces, vented fireplace inserts, unvented fireplace inserts, vented gas log sets, 

unvented gas log sets). In light of the analytical focus on standby mode energy 

consumption, DOE examined the implementation of an electronic ignition system as a 

design option and chose representative models for analysis that would allow a direct 

comparison between standing pilot and electronic ignition systems. DOE gathered 

information using reverse-engineering methodologies, product information from 

manufacturer catalogs and manuals, and discussions with manufacturers and other experts 

on hearth products.

DOE generated bills of materials (BOMs) by disassembling products representing 

a range of hearth configurations, including vented and unvented fireplaces, inserts, and 

stoves, vented and unvented gas log sets, and outdoor products. The BOMs describe each

unit analyzed in detail, including all manufacturing steps required to make and/or 

assemble each part. Subsequently, DOE developed a cost model that converted the BOMs 

into manufacturer production costs (MPCs). By applying derived manufacturer markups to 

the MPCs, DOE calculated the manufacturer selling prices. 

More information regarding the engineering analysis can be found in chapter 5 of 

the TSD.

2.6 MARKUPS ANALYSIS

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

price estimates in the engineering analysis to customer prices, which include markups 

throughout the distribution chain (wholesalers, retailers, etc.) These are then used in the 

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

2-9
necessary for the baseline efficiency level (standing pilot) and the energy conservation 

standard case being considered (electronic ignition system). 

Before developing markups, DOE defines key market participants and identifies 

distribution channels. Generally, the hearth products distribution chain includes five

market participants: (1) manufacturer; (2) wholesaler; (3) mechanical contractors; (4) 

general contractors; and (5) consumers. For the markups analysis, DOE used two types of 

distribution channels to describe how most hearth products pass from the manufacturer to 

the consumer: (1) replacement/new owner market; and (2) new construction. These 

channels are explained in detail in chapter 6 of this TSD.

After defining the participants and channels, DOE develops baseline and 

incremental markups to transform the manufacturer selling price into a consumer product 

price. DOE uses the baseline markups, which cover all of a distributor’s costs, to 

determine the sales price of baseline models. Incremental markups are coefficients that 

DOE applies to the incremental cost of models of electronic ignition systems. Because 

companies mark up the price at each point in the distribution channel, both baseline and 

incremental markups are dependent on the particular distribution channel. 

2.7 ENERGY USE ANALYSIS

The purpose of the energy use analysis is to determine the annual energy 

consumption of hearth products used in representative U.S. single-family homes and

multi-family residences. Additionally, the energy use analysis assesses the energy savings 

potential of increased hearth product efficiency. DOE estimated the annual energy 

consumption of residential hearth products across a range of climate zones. The annual 

energy consumption includes the natural gas, liquid petroleum gas, oil fuel, and/or 

electricity use by the hearth products. The annual energy consumption of hearth products

is used in subsequent analyses, including the LCC and PBP analyses and the national 

impact analysis. 

DOE used Residential Energy Consumption Survey (RECS) 2009 data and 

weather data from NOAA to estimate weather-normalized energy use. The RECS 2009 

data provide information on the home characteristics, as well as heating energy use in each 

household. The survey includes household information such as the physical characteristics 

of housing units, household demographics, information about other heating and cooling 

products installed in the household, fuel types used, energy consumption and 

expenditures, and other relevant data.

To estimate the annual energy consumption of hearth products meeting higher 

efficiency levels, DOE calculated the heating load based on the RECS estimates of the 

annual energy consumption of the hearth products for each household.

Chapter 7 of this TSD details the energy use analysis methodology.

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2.8 LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSES

In determining whether an energy conservation standard is economically justified, 

DOE considers the economic impact of potential standards on consumers. The effect of 

new or amended standards on individual consumers usually includes a reduction in 

operating cost and an increase in purchase cost. DOE used the following two metrics to 

measure consumer impacts:

• Life-cycle cost (LCC) is the total consumer cost of an appliance or product, 

generally over the life of the appliance or product, including purchase and 

operating costs. The latter consist of 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 a more energy-efficient product through 

reduced operating costs.

DOE analyzed the net effect of potential hearth products standards on consumers 

by determining the LCC and PBP using engineering performance data, energy-use data, 

and markup data. Inputs to the LCC calculation include the installed cost to the consumer 

(purchase price plus installation cost), operating expenses (energy expenses, repair costs,

and maintenance costs), the lifetime of the product, and a discount rate. Inputs to the 

payback period calculation include the installed cost to the consumer and first-year 

operating costs.

DOE generated LCC and PBP results as probability distributions using a 

simulation approach 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 analysis produces a range of LCC and PBP results that allow

DOE to identify the fraction of customers achieving LCC savings or incurring net cost at 

the considered efficiency levels.

DOE examined expected maintenance, repair, and installation costs for the 

products covered in this rulemaking. DOE used the most recent RS Means Facilities 

Maintenance & Repair Cost Datad to develop appropriate repair and maintenance costs 

for baseline units (standing pilot models) and units including electronic ignition systems.

DOE concluded that while repair costs are likely to increase in proportion to the initial 

size and cost of hearth products, maintenance costs for more-efficient products were not 

likely to be significantly higher than those for baseline units.

DOE estimated all the installation costs associated with installing a hearth product

in a new housing unit or as a replacement for an existing hearth product using RS Means 

d RS Means, 2013 Facilities Maintenance & Repair Cost Data (Available at: 

http://rsmeans.reedconstructiondata.com/60303.aspx) (Last accessed April 10, 2013).

2-11
2013 Residential Cost Data,

e manufacturer literature, and information from expert 

consultants. This includes any additional costs, such as venting modifications that would 

be required to install various hearth products.

Based on a search of industry studies and literature, DOE concluded that hearth 

products typically have an approximate average lifetime of 15 years.

DOE used discount rates to determine the present value of lifetime operating 

expenses. The discount rate used in the LCC analysis represents the rate from an 

individual consumer’s perspective.f Much of the data used for determining consumer 

discount rates comes from the Federal Reserve Board’s triennial Survey of Consumer 

Finances.g

To estimate the share of consumers affected by a standard, DOE’s LCC and PBP 

analyses consider the projected distribution (i.e., market shares) of products with 

electronic ignition systems that consumers will purchase in the first compliance year under 

the base case (the case without amended energy conservation standards disallowing use of 

standing pilots).

Chapter 8 of this TSD provides more details on the LCC and PBP analyses 

methodology.

2.9 SHIPMENTS ANALYSIS

DOE used forecasts of product shipments to calculate the national impacts of 

standards on energy use, NPV, and future manufacturer cash flows. DOE developed these

shipment forecasts based on an analysis of key market drivers for each product. 

DOE estimated hearth product shipments by projecting shipments in three market 

segments: (1) replacements; (2) new housing; and (3) new owners in buildings that did not 

previously have a hearth product. DOE also considered whether standards that disallow 

standing pilot ignitions would have an impact on hearth product shipments.

To project hearth product replacement shipments, DOE developed retirement 

functions for hearth products from the lifetime estimates and applied them to the existing 

products in the housing stock. The existing stock of products is tracked by vintage and 

developed from historical shipments data.

To project shipments to the new housing market, DOE utilized a forecast of new 

housing construction and historic saturation rates of hearth product types in new housing. 

e RS Means Company Inc., RS Means Residential Cost Data. 32nd Annual Edition ed. 2013: Kingston, MA f The consumer discount rate differs from the discount rates used in the national impact analysis, which are 

intended to represent the rate of return on capital in the U.S. economy, as well as the societal rate of return 

on private consumption.

g Available at www.federalreserve.gov/econresdata/scf/scfindex.htm.

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DOE used the Annual Energy Outlook (AEO) 2014 for forecasts of new housing. Hearth 

product saturation rates in new housing are provided by the U.S. Census Bureau’s 

Characteristics of New Housing.

h

See chapter 9 of this TSD for more details regarding the projection of hearth 

product shipments.

Because the standards-case projections take into account the increase in purchase 

price and the decrease in operating costs caused by amended standards, projected 

shipments for a standards case typically deviate from those for the base case. Because 

purchase price tends to have a larger impact than operating cost on appliance purchase 

decisions, standards-case projections typically show a decrease in product shipments 

relative to the base case.

Consistent with economic theory, it is reasonable to expect that standards that 

result in higher hearth product prices will have some dampening effect on sales. To 

estimate the impact of the projected price increase for the considered efficiency levels,

DOE modeled developed a price elasticity model. This approach gives some weight to the 

operating cost savings from higher efficiency products. The impact of higher hearth 

products prices is expressed as a percentage drop in market share for each year during the 

analysis period.

2.10 NATIONAL IMPACT ANALYSIS

The NIA assesses the NES and the NPV from a national perspective of total 

consumer costs and savings expected to result from new energy conservation standards.

DOE determined the NPV and NES for the standard level considered (disallowing 

standing pilot ignitions) for the hearth products analyzed. To make the analysis more 

accessible and transparent to all interested parties, DOE prepared a Microsoft Excel 

spreadsheet that uses typical values (as opposed to probability distributions) as inputs. To 

assess the effect of input uncertainty on NES and NPV results, DOE has developed its 

spreadsheet model to conduct sensitivity analyses by running scenarios on specific input 

variables. 

Analyzing impacts of potential energy conservation standards for hearth products

requires comparing projections of U.S. energy consumption with new energy conservation 

standards against projections of energy consumption without new standards. The forecasts 

include projections of annual product shipments, the annual energy consumption of new 

products, and the purchase price of new products.

A key component of DOE’s NIA is the energy efficiencies forecasted over time for 

the base case (without new standards) and the standards case (disallowing standing pilot 

use). The forecasted efficiencies represent the annual shipment-weighted energy efficiency 

h Available at: http://www.census.gov/const/www/charindex.html.

2-13
of the products under consideration during the forecast period (i.e., from the assumed 

compliance date of a new standard to 30 years after compliance is required). 

DOE developed a distribution of standing pilot and electronic ignition systems in the 

base-case for 2021 (the assumed compliance date for new standards). In the standards 

case, all hearths were assumed to include an electronic ignition system for 2021. DOE 

assumed: (1) products in the base case that would have their standing pilot ignition 

systems replaced with an electronic ignition system to meet the new standard; and (2) 

products that are match lit or already include an electronic ignition system would not be 

affected.

Chapter 10 of this TSD provides additional details on the national impact analysis.

2.10.1 National Energy Savings Analysis

The inputs for determining the national energy savings for each product analyzed 

are: (1) annual energy consumption per unit; (2) shipments; (3) product stock; (4) national 

energy consumption; and (5) site-to-source conversion factors. DOE calculated the 

national energy consumption by multiplying the number of units (stock) of each product 

(by vintage or age) by the unit energy consumption (also by vintage). Vintage represents 

the age of the product. DOE calculated annual NES based on the difference in national 

energy consumption for the base case and for the standard case (disallowing standing 

pilots). DOE estimated energy consumption and savings based on site energy and 

converted the electricity consumption and savings to source (primary) energy using annual 

conversion factors derived from the most recent version of the National Energy Modeling 

System (NEMS). Cumulative energy savings are the sum of the NES for each year over 

the timeframe of the analysis.

DOE has historically presented NES in terms of primary energy savings. DOE 

published a Statement of Policy regarding its intent to incorporate full-fuel-cycle (FFC) 

metrics into its analyses, and outlining a proposed approach. DOE stated that it intends to 

calculate FFC energy and emission impacts by applying conversion factors generated by 

the greenhouse gases, regulated emissions, and energy use in transportation (GREET) 

model to the NEMS-based results currently used by DOE. 76 FR 51281 (Aug. 18, 2011).

Additionally, DOE will review alternative approaches to estimating these factors and may 

decide to use a model other than GREET to estimate the FFC energy and emission impacts 

in any particular future appliance energy conservation standards rulemaking. For this 

analysis, DOE calculated FFC energy savings using the NEMS-based methodology 

described in appendix 10-B of this TSD. Chapter 10 of this TSD presents both the primary 

NES and the FFC energy savings for the analyzed efficiency levels.

2.10.2 Net Present Value Analysis

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

savings in operating costs; (3) a discount factor to calculate the present value of costs and 

savings; (4) present value of costs; and (5) present value of savings. DOE determined the

2-14
net savings for each year as the difference between the base case and standards case in 

terms of total savings in operating costs versus total increases in installed costs. DOE 

calculated savings over the lifetime of products shipped in the forecast period. DOE 

calculated NPV as the difference between the present value of operating cost savings and 

the present value of total installed costs. DOE used a discount factor based on real 

discount rates of 3 and 7 percent to discount future costs and savings to present values.

For the NPV analysis, DOE calculates increases in total installed costs as the 

difference in total installed cost between the base case and standards case (i.e., once the 

standards take effect). Because the products bought in the standards case usually cost 

more than products bought in the base case (due to the presence of electronic ignition), 

cost increases appear as negative values in the NPV.

DOE expresses savings in operating costs as decreases associated with the lower 

energy consumption of products bought in the standards case compared to the base case.

Total savings in operating costs are the product of savings per unit and the number of units 

of each vintage that survive in a given year.

DOE used the AEO 2014 as the source of projections for future energy prices.

DOE estimates the NPV of consumer benefits using both a 3-percent and a 7-

percent real discount rate. DOE uses these discount rates in accordance with guidance 

provided by the Office of Management and Budget (OMB) to federal agencies on the 

development of regulatory analysis. (OMB Circular A-4 (Sept. 17, 2003), section E, 

“Identifying and Measuring Benefits and Costs”)

2.11 CONSUMER SUBGROUP ANALYSIS

In analyzing the potential impacts of new standards on consumers, DOE evaluates 

the potential impact of new standards on identifiable groups of consumers (i.e., 

subgroups), such as small businesses, that may be disproportionately affected by a 

national standard. Accordingly, DOE evaluated impacts on low-income households and

senior-only households using the LCC and PBP spreadsheet model, using inputs 

appropriate to these subgroups to the extent possible. The hearth products subgroup 

analysis is discussed in detail in chapter 11 of this TSD.

2.12 MANUACTURER IMPACT ANALYSIS

The MIA assesses the impacts of new energy conservation standards on 

manufacturers of the considered product. Potential impacts include financial effects, both 

quantitative and qualitative, that might lead to changes in the manufacturing practices for 

these products. DOE identified these potential impacts through interviews with 

manufacturers and other interested parties. 

2-15
DOE conducted the MIA in three phases. In Phase I, DOE created an industry 

profile to characterize the industry, and conducted a preliminary MIA to identify 

important issues that required consideration. In Phase II, DOE prepared an industry cash 

flow model and an interview questionnaire to guide subsequent discussions. In Phase III, 

DOE interviewed manufacturers, and the impacts of standards were assessed both 

quantitatively and qualitatively. Industry and subgroup cash flow and NPV were assessed 

through use of the Government Regulatory Impact Model (GRIM). Then impacts on 

competition, manufacturing capacity, employment, and cumulative regulatory burden 

were assessed based on manufacturer interview feedback and discussions. DOE discusses 

its findings from the MIA in chapter 12 of this TSD. 

2.13 EMISSIONS IMPACT 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 the considered products. In addition, 

DOE estimated 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 FFC. In accordance with 

DOE’s FFC Statement of Policy (76 FR 51282 (Aug. 18, 2011)), the FFC analysis 

includes impacts on emissions of methane and nitrous oxide, both of which are recognized 

as greenhouse gases.

DOE primarily conducted the emissions analysis using emissions factors for CO2

and most of the other gases derived from data in the latest version of AEO. Combustion 

emissions of CH4 and N2O were estimated using emissions intensity factors published by 

the Environmental Protection Agency (EPA) GHG Emissions Factors Hub.

i

The Energy Information Administration (EIA) prepares the AEO using NEMS.

Each annual version of NEMS incorporates the projected impacts of existing air quality 

regulations on emissions. This discussion refers to AEO 2014, which generally represents 

current legislation and environmental regulations, including recent government actions, 

for which implementing regulations were available as of December 31, 2013.

Because the on-site operation of hearth products requires use of fossil fuels and 

results in emissions of CO2, NOX, and SO2 at the sites where these appliances are used, 

DOE also accounted for the reduction in these site emissions and the associated upstream 

emissions due to potential standards.

SO2 emissions from affected electric generating units (EGUs) are subject to 

nationwide and regional emissions cap and trading programs. Title IV of the Clean Air 

Act sets an annual emissions cap on SO2 for affected EGUs in the 48 contiguous states 

and the District of Columbia (D.C.). SO2 emissions from 28 eastern states and D.C. were 

i

http://www.epa.gov/climateleadership/guidance/ghg-emissions.html

2-16
also limited under the Clean Air Interstate Rule (CAIR), which created an allowancebased trading program 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 it remained in effect.j

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.k The court ordered EPA to continue administering CAIR. AEO 

2014 assumes that CAIR remains a binding regulation through 2040.l

The attainment of emissions caps is typically flexible among 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 adoption 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 conservation 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 hydrochloric acid 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 2013 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 be far below the cap that would 

be 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, energy conservation standards will 

reduce SO2 emissions in 2016 and beyond.

j

See North Carolina v. EPA, 550 F.3d 1176 (D.C. Cir. 2008); North Carolina v. EPA, 531 F.3d 896 (D.C. 

Cir. 2008).

k See EME Homer City Generation, LP v. EPA, 696 F.3d 7, 38 (D.C. Cir. 2012).

l

On April 29, 2014, the U.S. Supreme Court reversed the judgment of the D.C. Circuit. and remanded the 

case for further proceedings consistent with the Supreme Court's opinion. The Supreme Court held in part 

that EPA's methodology for quantifying emissions that must be eliminated in certain states due to their 

impacts in other downwind states was based on a permissible, workable, and equitable interpretation of the 

Clean Air Act provision that provides statutory authority for CSAPR. See EPA v. EME Homer City 

Generation, No 12-1182, slip op. at 32 (U.S. April 29, 2014). Because DOE is using emissions factors based 

on AEO 2013, the analysis assumes that CAIR, not CSAPR, is the regulation in force. The difference 

between CAIR and CSAPR is not relevant for the purpose of DOE's analysis of SO2 emissions.

2-17
CAIR established a cap on NOx emissions in eastern states and the District of 

Columbia. Energy conservation standards are expected to have little or no physical effect 

on these 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 potential standards in the states where emissions are not capped.

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 reduction using emissions factors based 

on AEO 2014, which incorporates the MATS.

Power plants may emit particulates from the smoke stack, which are known as 

direct particulate matter (PM) emissions. NEMS does not account for direct PM emissions 

from power plants. DOE is investigating the possibility of using other methods to estimate 

reduction in PM emissions due to standards. The great majority of ambient PM associated 

with power plants is in the form of secondary sulfates and nitrates, which are produced at 

a significant distance from power plants by complex atmospheric chemical reactions that 

often involve the gaseous emissions of power plants, mainly SO2 and NOx. The monetary 

benefits that DOE estimated for reductions in SO2 and NOx emissions resulting from 

standards are in fact primarily related to the health benefits of reduced ambient PM. 

Further detail is provided in chapter 13 of the TSD.

2.14 MONETIZING REDUCED CO2 AND OTHER EMISSIONS

DOE considered the estimated monetary benefits likely to result from the reduced 

emissions of CO2 and NOx that are expected to result from each of the standard levels 

considered.

To estimate the monetary value of benefits resulting from reduced emissions of 

CO2, DOE used the most current SCC values developed and/or agreed to by an 

interagency process. The SCC is intended to be a monetary measure of the incremental 

damage resulting from GHG emissions, including, but not limited to, net agricultural 

productivity loss, human health effects, property damage from sea level rise, and changes 

in ecosystem services. Any effort to quantify and to monetize the harms associated with 

climate change will raise serious questions of science, economics, and ethics. But 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. 

2-18
The Interagency Working Group on Social Cost of Carbon released in 2013 an 

update of its previous report.m The most recent estimates of the SCC in 2015, expressed in 

2013$, are $12.0, $40.5, $62.4, and $119 per metric ton of CO2 avoided. For emissions 

reductions that occur in later years, these values grow 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 reducing CO2 emissions. 

DOE multiplied the CO2 emissions reduction estimated for each year by the SCC 

value for that year in each of the four cases. To calculate a present value of the stream of 

monetary values, DOE discounted the values in each of the four cases using the discount 

rates that had been used to obtain the SCC values in each case.

DOE recognizes that scientific and economic knowledge continues to evolve 

rapidly as to the contribution of CO2 and other GHG to changes in the future global 

climate and the potential resulting damages to the world economy. Thus, these values are 

subject to change. 

DOE also estimated the potential monetary benefit of reduced NOx emissions 

resulting from the standard levels it considers. Estimates of monetary value for reducing 

NOx from stationary sources range from $476 to $4,893 per ton in 2013$.

n DOE 

calculated monetary benefits using a medium value for NOx emissions of $2,684 per short 

ton (2013$), and real discount rates of 3 percent and 7 percent.

DOE is investigating appropriate valuation of Hg and SO2 emissions. DOE has not 

monetized estimates of SO2 and Hg reduction in this rulemaking.

Further detail on the emissions monetization is provided in chapter 14 of this TSD.

2.15 UTILITY IMPACT ANALYSIS

In the utility impact analysis, DOE analyzes the changes in electric installed 

capacity and generation that result for each trial standard level (TSL). DOE uses a variant 

of NEMS, referred to as NEMS-BT,o to account for these impacts. NEMS-BT has several 

m Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 

12866. Interagency Working Group on Social Cost of Carbon, United States Government; revised 

November 2013.

http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-social-cost-of-carbon-forregulator-impact-analysis.pdf n U.S. Office of Management and Budget, 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, DC. o DOE/EIA approves use of the name NEMS to describe only an official version of the model without any 

modification to code or data. Because this analysis entails some minor code modifications and the model is 

run under various policy scenarios that are variations on DOE/EIA assumptions, DOE refers to it by the 

2-19
advantages that have led to its use in the analysis of energy conservation standards. 

NEMS-BT uses a set of assumptions that are well known and fairly transparent, due to the 

exposure and scrutiny each AEO receives. In addition, the comprehensiveness of NEMSBT permits the modeling of interactions among the various energy supply and demand 

sectors.

The utility impact analysis is a comparison between the NEMS-BT model results 

for the base case and standard cases. The utility impact analysis reports the changes in 

installed capacity and generation that result from each standard level by plant type. DOE 

models the anticipated energy savings impacts from potential amended energy 

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

reference case.

Further detail is provided in chapter 15 of this TSD.

2.16 EMPLOYMENT IMPACT ANALYSIS

The adoption of energy conservation standards can affect employment both 

directly and indirectly. Direct employment impacts are changes in the number of 

employees at the plants that produce the covered products. DOE evaluates direct 

employment impacts in the MIA.

Indirect employment impacts may result from expenditures shifting between goods 

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

effect) that occur due to standards. DOE defines indirect employment impacts from 

standards as net jobs eliminated or created in the general economy as a result of increased 

spending driven by increased product prices and reduced spending on energy.

Indirect employment impacts are investigated in the employment impact analysis 

using the Pacific Northwest National Laboratory’s “Impact of Sector Energy 

Technologies” (ImSET) model.p The ImSET model was developed for DOE’s Office of 

Planning, Budget, and Analysis to estimate the employment and income effects of energysaving technologies in buildings, industry, and transportation. Compared with simple 

economic multiplier approaches, ImSET allows for more complete and automated analysis 

of the economic impacts of energy conservation investments. Further detail is provided in 

chapter 16 of this TSD.

name NEMS-BT (BT is DOE’s Building Technologies Program, under whose aegis this work has been 

performed). 

p M.J. Scott, O.V. Livingston, P.J. Balducci, J.M. Roop, and R.W. Schultz, ImSET 3.1: Impact of Sector 

Energy Technologies, PNNL-18412, Pacific Northwest National Laboratory (2009) (Available at: 

www.pnl.gov/main/publications/external/technical_reports/PNNL-18412.pdf).

2-20

 
2.17 REGULATORY IMPACT ANALYSIS

DOE prepared a regulatory impact analysis (RIA) pursuant to Executive Order 

12866, Regulatory Planning and Review, 58 FR 51735, October 4, 1993, which is subject 

to review by the Office of Information and Regulatory Affairs at the Office of 

Management and Budget. The RIA addresses the potential for non-regulatory approaches 

to supplant or augment energy conservation standards in order to improve the energy 

efficiency or reduce the energy consumption of the product covered under this 

rulemaking.

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

utilities, and other interested parties can substantially affect energy efficiency or reduce 

energy consumption. DOE bases its assessment on the actual impacts of any such 

initiatives to date, but also considers information presented by interested parties regarding 

the impacts existing initiatives might have in the future. Further detail is provided in 

chapter 17 of this TSD.

2-21
CHAPTER 3. MARKET AND TECHNOLOGY ASSESSMENT

TABLE OF CONTENTS

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

3.1.1 Description of Products............................................................................... 3-1

3.1.2 Definitions................................................................................................... 3-2

3.1.3 Product Classes........................................................................................... 3-3

3.1.4 Test Procedures........................................................................................... 3-3

3.2 MARKET ASSESSMENT..................................................................................... 3-4

3.2.1 Trade Associations...................................................................................... 3-4

3.2.1.1 Hearth, Patio and Barbecue Association...................................... 3-4

3.2.1.2 Air-Conditioning, Heating, and Refrigeration Institute............... 3-5

3.2.2 Manufacturer Information........................................................................... 3-5

3.2.2.1 Manufacturers.............................................................................. 3-5

3.2.2.2 Small Business Impacts ............................................................... 3-6

3.2.3 Distribution Channels................................................................................. 3-7

3.2.4 Regulatory Programs .................................................................................. 3-8

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

3.2.4.2 State Energy Conservation Standards.......................................... 3-8

3.2.4.3 Canadian Standards Association.................................................. 3-9

3.2.5 Voluntary Programs.................................................................................... 3-9

3.2.6 Historical Shipments................................................................................... 3-9

3.2.6.1 New Home Starts......................................................................... 3-9

3.2.6.2 Shipments................................................................................... 3-11

3.2.7 Industry Cost Structure ............................................................................. 3-13

3.2.8 Equipment Lifetime .................................................................................. 3-13

3.2.9 Market Performance Data ......................................................................... 3-14

3.3 TECHNOLOGY ASSESSMENT......................................................................... 3-14

3.3.1 Baseline Equipment Components and Operation ..................................... 3-15

3.3.2 Technology Options.................................................................................. 3-17

3.3.3 Technology Options That Do Not Affect Energy Use.............................. 3-19

LIST OF TABLES

Table 3.1.1 Hearth Product Groups for DOE Analysis ................................................... 3-3

Table 3.2.2 List of Sample Hearth Industry Standards.................................................... 3-8

Table 3.2.3 Sample Rebate Programs.............................................................................. 3-9

Table 3.2.4 HPBA Product Categories and Relative Market Shares............................. 3-12

Table 3.2.5 DOE Market Shares for Hearth Product Groups........................................ 3-13

Table 3.2.6 Industry Cost Structure ............................................................................... 3-13

Table 3.2.7 Average Market Share of Ignition Types by Analysis Group .................... 3-14

3-i
LIST OF FIGURES

Figure 3.2.1 New Privately Owned Single-Family and Multi-Family Housing Unit Starts 

from 2005 to 2013.......................................................................................................... 3-10

Figure 3.2.2 HPBA Shipments Data .............................................................................. 3-11

3-ii
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) conducted in support of the energy conservation standards 

rulemaking for hearth products.

This chapter consists of the market assessment and the technology assessment. 

The goal of the market assessment is to develop a qualitative and quantitative 

characterization of hearth industry and market structures based on publicly available data 

and other information that DOE received directly from manufacturers and other 

interested parties. The market and technology assessment addresses manufacturer 

characteristics and market shares, existing regulatory and non-regulatory efficiency 

improvement initiatives, product classes, and trends in product markets and 

characteristics. DOE performs the technology assessment to develop a preliminary list of 

technologies (referred to as technology options) that could be used to improve the 

efficiency or reduce the fuel consumption of hearth products.

3.1.1 Description of Products

Gas-fired hearth products present a visible flame that typically provides aesthetic 

appeal and may also provide supplemental space heating. Gas-fired hearth products are 

often designed to simulate wood-burning hearth products, but come in a variety of 

designs, including “modern” styles consisting of only a flame pattern, or a flame 

surrounded by glass, stones, or other media. Consumers of gas-fired hearth products may 

select a product for décor, ambiance, heat, or some combination thereof. Hearth product 

consumers are typically homeowners or contractors purchasing a hearth product for 

installation in a new home or for replacement or upgrade in an existing home. Some 

consumers purchase a gas-fired hearth product to retrofit a wood-burning fireplace. 

Hearth products covered by this rulemaking use natural gas or propane, can be 

vented or unvented, and may be designed for indoor or outdoor use. Hearth products take 

a variety of forms, including but not limited to fireplaces, fireplace inserts, freestanding 

stoves, and gas log sets. Outdoor hearths may take the form of a fireplace, fire pit or patio 

heater. Section 3.3.1 describes these hearth products and the components that typically 

comprise them.

There are three general types of ignition types for hearth products: match lit, 

constant burning or “standing” pilot, and electronic ignition. Match-lit burners do not use 

a pilot-type ignition system. The user manually turns on the gas valve and lights gas 

flowing to the main burner, typically with a match although sometimes a piezo-electric 

spark igniter is provided. DOE notes that match-lit hearth products would be unaffected 

by the proposed prescriptive requirement disallowing use of a constant burning pilot (see 

the notice of proposed rulemaking for further details on the proposal).

3-1
The most common form of electronic ignition system found in hearth products is 

the intermittent pilot ignition. Both standing pilot and intermittent pilot ignitions use a 

pilot light to ignite the main burner. In both ignition systems, a small pilot light is first lit 

and proven before gas can flow to the main burner. A “constant burning pilot” system is 

so called because when gas flow to the main burner is discontinued, the pilot light 

continues to consume fuel unless it is extinguished by the user. In such systems, the pilot 

is not designed to extinguish automatically, and in many cases the user will leave the 

pilot on either year ’round or during an entire heating season. In an intermittent pilot 

ignition, the pilot light is only lit when there is a call for heat, and the pilot light is 

automatically extinguished after the burner is turned off. In order to ignite the pilot light, 

these systems require an outside power source, often supplied by either a battery or an 

electrical connection.

3.1.2 Definitions

Currently, there is no statutory definition of hearth products, and hearth products 

are not covered consumer products under the Energy Policy and Conservation Act et seq. 

(EPCA). In December 2013, DOE published a notice of proposed determination (NOPD) 

that proposed a definition of hearth products for coverage. 78 FR 79638. In the December 

2013 NOPD, DOE proposed the following definition of hearth product:

Hearth product means a gas-fired appliance that simulates a solid-fueled fireplace or 

presents a flame pattern (for aesthetics or other purpose) and that may provide space 

heating directly to the space in which it is installed. 78 FR 79638, 79640.

DOE tentatively determined that, according to the proposed definition, hearth 

products would meet the relevant statutory criteria so as to justify coverage as a consumer 

product under EPCA, and provided the relevant justifications in the December 2013 

NOPD. Specifically, DOE estimated that hearth products exceeded the 100 kilowatt -hour

average household annual energy consumption threshold established by EPCA to define

product coverage. 78 FR 79638. Also in the December 2013 NOPD, DOE suggested 

several common styles of hearth products that would be covered under the proposed 

definition, including vented decorative hearth products, vented heater hearth products, 

vented gas logs, gas stoves, outdoor hearth products, and ventless hearth products. DOE 

used the definition proposed in the December 2013 NOPD (as stated above) for 

determining the scope of the analysis contained in this technical support document 

(TSD). If the proposed determination process results in coverage of hearth products, this 

rulemaking process would form the basis for establishing energy conservation standards

for them.

3-2
3.1.3 Product Classes

The criteria for separation of a product type into different classes are the type of 

energy used, capacity, and other performance-related features, such as those that provide 

utility to the consumer or others deemed appropriate by the Secretary that would justify 

the establishment of a separate energy conservation standard (42 U.S.C. 6295(q)). For 

hearth products, DOE has tentatively determined that product classes do not need to be 

established for a prescriptive requirement for hearth products to disallow the use of 

continuously burning pilots, as discussed in section III.C of the notice of proposed 

rulemaking (NOPR).

Although DOE tentatively determined that product classes do not need to be 

established, DOE acknowledges the wide variety of hearth products styles available.

Accordingly DOE also recognizes that the impacts of the proposed prescriptive 

requirement could depend on the types of hearth products a manufacturer produces. For 

instance, the impact of disallowing constant burning pilots on the manufacturer of vented 

fireplaces may differ from the impact on a gas log set or outdoor patio heater 

manufacturer. Additionally, DOE received information during manufacturer interviews 

and from the Hearth, Patio and Barbecue Association (HPBA) that often pertained to 

particular groups of hearth products rather than all products. To assess the differences in 

impact according to type of hearths produced and to provide the most accurate analysis 

possible using available information, DOE opted to maintain some level of 

disaggregation by hearth product type in its analysis of the hearth industry. 

The analysis examined the five hearth product groups shown in Table 3.1.1.

Based on information from manufacturers obtained during interviews, data provided by 

HPBA (described further in section 3.2.6.2), and the research presented in this TSD, these 

hearth product groups adequately capture the differences among hearth product styles and 

configurations. Section 3.3.1 and chapter 5 of this TSD provide descriptions of the hearth 

products found in each of these analysis groups.

Table 3.1.1 Hearth Product Groups for DOE Analysis

Product Analysis Group Example Products

Vented Fireplaces/Inserts/Stoves Vented fireplaces, vented fireplace 

inserts, vented freestanding stoves

Unvented Fireplaces/Inserts/Stoves Unvented fireplaces, unvented fireplace 

inserts, unvented freestanding stoves

Vented Gas Log Sets Vented gas log sets for installation in 

existing masonry fireplace

Unvented Gas Log Sets Unvented gas logs for installation in 

existing masonry fireplace

Outdoor Products

Outdoor fireplaces, outdoor fireplace 

inserts, outdoor fire pits, outdoor gas 

lamps, patio heaters

3.1.4 Test Procedures

3-3
Currently, there is no statutory definition of hearth products, and hearth products 

are not covered consumer products under EPCA. Accordingly, there is no DOE test 

procedure for measuring the energy efficiency or consumption of hearth products. 

EPCA states, in relevant part, that an amended or new standard may not be 

adopted if a test procedure has not been established for the relevant product type or class 

(42 U.S.C. 6295(o)(3)(A)) However, later sections of EPCA acknowledge that DOE may 

establish prescriptive design requirements that by nature would not require a test 

procedure. For determining compliance with standards, EPCA requires use of the test 

procedures and criteria prescribed in 42 U.S.C. 6293, except for design standards. (42 

U.S.C. 6295(s)) EPCA also states that a test procedure need not be prescribed if one 

cannot be designed to reasonably measure energy efficiency, energy use, water use, or 

annual operating cost, and not be unduly burdensome to conduct. (42 U.S.C. 6293(d)(1))

EPCA requires that a determination be published in the Federal Register providing 

justification for such case. Id.

Because the NOPR proposes to adopt a prescriptive design requirement for hearth 

products, in the NOPR DOE tentatively concluded that a test procedure is unnecessary, 

and thus, DOE is not developing a test method for these products. See section III.D of the 

NOPR for more information regarding test procedures for hearth products.

3.2 MARKET ASSESSMENT

The following market assessment identifies the manufacturer trade associations,

domestic and international manufacturers of products, and regulatory and non-regulatory 

programs. The market assessment also provides historical shipment data, describes the 

cost structure for the hearth industry, and summarizes relevant market performance data 

for each product type.

3.2.1 Trade Associations

DOE recognizes the importance of trade groups in disseminating information and 

providing growth to the industry they support. To gain insight into hearth industry, DOE 

researched various associations available to manufacturers, suppliers, and users of hearth 

products. DOE also used the member lists of these groups to construct a database of 

domestic manufacturers.

DOE identified two trade groups that support or have an interest in the hearth 

industry: the Air-Conditioning, Heating, and Refrigeration Institute (AHRI) and the 

HPBA.

3.2.1.1 Hearth, Patio and Barbecue Association

HPBA is an international trade association that promotes the hearth industry 

through marketing and education, administers surveys and compiles statistics, and serves 

its members through government relations and advocacy. HPBA has approximately 2,700 

members1 including “manufacturers, retailers, distributors, manufacturers’

3-4
representatives, service and installation firms, and other companies and individuals, all 

having business interests in and related to the hearth, patio, and barbecue products 

industries.”2

3.2.1.2 Air-Conditioning, Heating, and Refrigeration Institute

AHRIa is a national trade association representing manufacturers of air 

conditioning, heating, ventilation and commercial refrigeration equipment and 

components. AHRI was established in January 2008, when the Air-Conditioning and 

Refrigeration Institute (ARI) merged with the Gas Appliance Manufacturers Association 

(GAMA). AHRI has more than 300 member companies that account for more than 90 

percent of the residential and commercial air-conditioning, space heating, water heating, 

and commercial refrigeration equipment manufactured and sold in North America.

3

AHRI serves many functions, including advocating for the heating, ventilation, airconditioning and refrigeration (HVACR) industry; certifying product performance; 

developing performance standards for equipment; compiling statistical reports of industry 

data; sponsoring HVACR research programs; and supporting HVACR technician

education programs.

4

AHRI maintains the AHRI Efficiency Certification Program. AHRI also 

maintains on its website a database of products and equipment tested under its 

certification program. While the directory lists a “vented hearth heater” product type in 

its direct heating equipment directory, no products are listed under that category.

3.2.2 Manufacturer Information

The following section provides information about manufacturers of hearth 

products, potential small business impacts, and product distribution channels. 

3.2.2.1 Manufacturers

DOE identified 77 domestic manufacturers of hearth products as well as 13 

foreign-owned manufacturers of gas hearth products sold in the United States. The 

majority of the domestic market is controlled by six manufacturers: Hearth and Home 

Technologies, Innovative Hearth Products, Vermont Castings Group, Travis Industries, 

Regency, and Napoleon. 

Table 3.2.1 lists all identified manufacturers of products potentially affected by 

this rulemaking. Domestic small business manufacturers, defined as having 500 

employees or fewer, are noted in the table. 

Table 3.2.1 Hearth Product Manufacturers

Acucraft Fireplace 

Systems**

Fire Features (Colombo 

Construction Corp)** O.W. Lee**

a For more information, please visit www.ahrinet.org.

3-5

 
Adobelite LLC** Firegear LLC** Ortal Heating Solutions**

American Fireglass** Firetaiment Inc** Outdoor Entertainment 

Designs**

American Gas Log, LLC** Formation Creation Inc.** Pacific Energy Fireplace*

American Heating 

Technologies** GHP Group, Inc** Portland Willamette**

Appalachian Stove** Golden Blount, Inc.** Pride Family Brands**

Archgard Industries Ltd.* Hargrove Manufacturing** Procom

Architectural Pottery** Hearth & Home 

Technologies Pyrotek, Inc.

Big Woods Hearth 

Products** Hearth Innovations** Rasmussen Gas Logs & Grills**

Blaze King Industries** Hearth Products Controls 

Co.** Raw Urth Design**

Blue Rhino HearthStone Quality Home 

Heating Products, Inc.** Regency

Blue Rooster Company, 

The** Heatmaster, Inc.** Robert H. Peterson Company**

Bobe Water & Fire** Infrared dynamics** Sherwood Industries Ltd.*

Bond Manufacturing** Innovative Hearth Products Sierra Products Inc**

Buck Stove Corporation** IronHaus** Skytech Products Group**

Burley Appliances Limited* J.A. Roby * Solus Décor*

Cal Flame** Jotul* Spark Modern Fires**

California Outdoor 

Concepts** Kingsman Fireplaces* Steelog Artistic Metals**

Camp Chef** Kozy Heat** Stone Forest, Inc.**

Cast Classics Lava Heat** Sun Star Heaters**

Cooke Furniture** Louisville Tin and Stove 

Co.** Superior Radiant Products*

Crown Verity* Lynx** Sure Heat Manufacturing**

Designing Fire** Malm Fireplace** Thelin**

Diamond Fire Glass** Mason-Lite** Travis Industries, Inc.

Eiklor Flames Inc.** Mendota Hearth Products Tropitone

Empire Comfort Systems** Modern Home Products 

Corporation** Valor Fireplaces*

Empire Pre-Cast** Moderustic Fire** Vermont Castings

Enerco Group, Inc** Montigo** Warming Trends, LLC**

Fire On Glass** Napoleon Fireplaces* William Smith**

Fireboulder** Nibe Stoves* World Marketing of 

America, Inc.**

*Foreign-owned

**Small business (500 employees or fewer)

3.2.2.2 Small Business Impacts

3-6
Small businesses may be disproportionately affected by the promulgation of 

energy conservation standards for hearth products. The Small Business Administration 

(SBA) defines small business manufacturing enterprises for residential hearth products as 

those having 500 employees or fewer.5 SBA lists small business size standards for 

industries as they are described in the North American Industry Classification System 

(NAICS). The size standard for an industry establishes the largest size that a for-profit 

entity can be while still qualifying as a small business for federal government programs. 

These size standards are generally expressed in terms of the average annual receipts or 

the average employment of a firm. Hearth manufacturing is classified under NAICS 

335228, “Other Major Household Appliance Manufacturing,” and under NAICS 333414, 

“Heating Equipment (except warm air furnaces) Manufacturing.” The size standard is 

500 employees or fewer for both NAICS codes.

DOE identified 66 domestic small business manufacturers of hearth products 

covered by this rulemaking (denoted in Table 3.2.1 above). DOE studied the potential 

impacts on these small businesses as a part of the manufacturer impact analysis (chapter 

12 of this TSD). 

3.2.3 Distribution Channels

Analysis of the distribution channels of products covered by this rulemaking is an 

important facet of the market assessment. DOE gathered information from publicly 

available sources and manufacturer interviews regarding the distribution channels for 

hearth products. DOE uses distribution channel markups (e.g., manufacturer markups, 

retailer markups, distributor markups, contractor markups) and sales taxes (where 

appropriate) to convert the manufacturer production cost estimates from the engineering 

analysis to consumer prices, which are then used in the LCC and PBP analyses and in the 

manufacturer impact analysis. The markups are multipliers that are applied to the 

purchase cost at each stage in the distribution channel for hearth products. Before 

developing markups, DOE defines key market participants and identifies distribution 

channels.

DOE characterized two distribution channels to describe how hearth products pass 

from the manufacturer to consumers: (1) replacement market and (2) new construction.

The replacement market channel is characterized as follows:

Manufacturer  Wholesaler  Mechanical contractor  Consumer

The new construction distribution channel is characterized as follows:

Manufacturer  Wholesaler  Mechanical contractor  General 

contractor  Consumer

Chapter 6 of the NOPR TSD provides further detail on the estimation of markups.

3-7
3.2.4 Regulatory Programs

The following section details current regulatory programs mandating energy 

conservation standards for hearth products. Section 3.2.4.1 discusses current federal 

energy conservation standards, and section 3.2.4.2 provides an overview of existing state 

standards. Sections 3.2.4.3 reviews standards in Canada that may affect companies 

servicing the domestic market. No energy conservation standards have been implemented 

for gas hearth products in Mexico.6

3.2.4.1 Current Federal Energy Conservation Standards

As described in section 3.1.1, there are currently no energy conservation standards 

for hearth products as hearth products are not currently covered products. In the 

December 2013 NOPD, DOE proposed a definition of coverage for hearth products.

Should the December 2013 NOPD result in coverage of hearth products, this rulemaking 

would establish energy conservation standards, if it is determined that such standards 

would meet the requirements of EPCA.

b

3.2.4.2 State Energy Conservation Standards

DOE notes that state and local jurisdictions may require certification to certain 

safety standards. A sample list of these standards is provided in Table 3.2.2. However, 

none of the heater standards set forth a minimum efficiency requirement, and none of the 

standards establish a design requirement intended to reduce fuel consumption. DOE is 

not aware of any state requirements that regulate the energy use or energy efficiency of 

hearth products.

Table 3.2.2 List of Sample Hearth Industry Standards

ANSI Z21.50 Vented Gas Fireplaces

ANSI Z21.60 Decorative gas appliances for installation in solid-fuel 

burning fireplaces

ANSI Z21.84 Standard for manually lighted, natural gas, decorative 

gas appliances for installation in solid-fuel burning fireplaces

ANSI Z21.88 Vented gas fireplace heaters

ANSI Z21.11.1 Gas-fired room heaters, volume II, unvented room 

heaters

RGA #2-72 Standard for Decorative Log Sets for Installation in 

Wood-Burning Fireplaces

b Any new or amended standard for a covered product must be designed to achieve the maximum 

improvement in energy efficiency that is technologically feasible and economically justified. (42 U.S.C. 

6295(o)(2)(A) and (3)(B))

3-8

 
3.2.4.3 Canadian Standards Association

The Canadian Standards Association (CSA) is an independent standards-setting 

agency that establishes test procedures and efficiency standards that are typically adopted 

by the Canadian government.

Canada has product classes for vented gas fireplaces and fireplace heaters. Vented 

gas fireplaces are primarily used for aesthetic purposes, whereas vented gas fireplace 

heaters are designed to provide heat to the space where they are installed. Canada has not 

issued energy conservation standards for either of these products classes.7

Since September 2003, all gas fireplaces sold in Canada must be tested and 

certified to the Canadian test standard CSA P.4.1-02 under the EnerGuide program. This 

standard determines a fireplace efficiency rating that is used in certification and labeling.8

3.2.5 Voluntary Programs

DOE reviewed voluntary programs promoting energy efficient gas hearth 

products in the United States. Hearth products are not currently covered under the 

ENERGY STAR® appliance program9 DOE’s Federal Energy Management Program.10

Several utilities offer rebate programs for gas fireplaces. A sample of these 

programs is listed in Table 3.2.3.

Table 3.2.3 Sample Rebate Programs 

State Utility Product Requirement Value 

Washington Puget Sound Energy11 Gas Fireplaces

Must meet minimum fireplace 

efficiency (CSA P.4.1-02 rating) of 

70% and have electronic ignition

$200

Minnesota CenterPoint Energy12 Gas Fireplaces, 

Inserts, Stoves

Must have electronic ignition $75

Utah, 

Wyoming, 

Idaho

City Water Light and 

Power13

Gas Fireplaces 

(Direct-Vent)

Must have minimum heater 

efficiency annual fuel utilization 

efficiency 70%, must be direct-vent, 

thermostatically controlled, include 

blower, and electronic ignition

$200

3.2.6 Historical Shipments

Annual product shipment trend data are an important aspect of the market 

assessment and development of the standards rulemaking. Such data are used in the 

shipments analysis (chapter 9 of this TSD). The number of unit shipments is expected to 

follow a trend similar to that of new home starts. This relationship is further detailed in 

chapter 9 of this TSD.

3.2.6.1 New Home Starts

Figure 3.2.1 presents the total number of new single-family and multifamily 

housing units started in the United States from 2005 to 2013. Between 2005 and 2009, 

3-9
total housing starts decreased by 73 percent. Since 2009, total housing starts have 

increased slightly, and as of 2013, were at 45 percent of 2005 total housing starts.14 

Certain hearth product types, specifically indoor fireplaces, are more strongly tied 

to housing starts as these products are more often purchased and installed at the time of 

new home construction. Inserts and gas logs are less directly tied to housing starts as 

these products are typically purchased for remodeling or upgrading existing homes with 

wood-burning fireplaces. However, remodeling existing homes can be assumed to 

correlate with the overall state of the economy, an indicator of which is housing unit 

starts. The relationship between housing starts and hearth product shipments is discussed 

further in chapter 9 of this TSD.

Figure 3.2.1 New Privately Owned Single-Family and Multi-Family Housing Unit 

Starts from 2005 to 201315

0.00

500.00

1,000.00

1,500.00

2,000.00

2,500.00

2004 2006 2008 2010 2012 2014

Housing Starts (thousands)

3-10
3.2.6.2 Shipments

Information about annual equipment shipment trends allows DOE to estimate the 

impacts of energy conservation standards on the hearth products industry. DOE has 

examined unit shipments and value of shipments for various hearth products using data 

provided by HPBA.

Data for the total number of gas hearth appliances shipped are available on HPBA’s 

website for the years 1998 to 2013.

16 These total shipments include all types of gas hearth 

products shipped by member manufacturers, including fireplaces, inserts, stoves, 

fireboxes, and gas logs, among other types. HPBA also provided DOE with additional 

data for the rulemaking process, 17 including ranges of market shares for each of nine 

hearth product styles from 2005 to 2013, and the total annual shipments for these nine 

categories. HPBA did not provide the relative market shares for each year, but rather 

provided a range of market shares over the corresponding period. Figure 3.2.2 shows the 

total shipments data from the HPBA website and those provided by HPBA from 2005 to 

2014. 

Figure 3.2.2 HPBA Shipments Data

Table 3.2.4 shows the product categories and market shares provided to DOE by 

HPBA. 

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

2004 2006 2008 2010 2012 2014

Total Shipments

Provided by HPBA - Total

for 9 Hearth Categories

Provided"

Total Gas Hearths from

HPBA Website

3-11
Table 3.2.4 HPBA Product Categories and Relative Market Shares17

HPBA Product Category Shipments, 2005-

2013

%

Vented Fireplace 40 58

Unvented Fireplace 2 6

Vented Stove 3 5

Unvented Stove 0 1

Vented Fireplace Insert 4 10

Unvented Fireplace Insert 0 0.3

Vented Gas Logs 3 10

Unvented Gas Logs 16 23

Outdoor Fireplaces 0.4 24*

*HPBA subsequently clarified via email that the range 4% to 9% was typical for outdoor fireplaces over 

the period 2010-2013.

DOE confirmed with HPBA that the difference between the total shipments 

available on the HPBA website and the total shipments provided for the nine categories is 

due primarily to fireboxes and patio heaters. HPBA was unable to provide additional data 

for patio heaters. HPBA also clarified that the outdoor fireplaces category included 

shipments for fire pits, and that a substantial portion of other outdoor products are from 

non-HPBA manufacturers and are therefore not captured in these data. 

As described in section 3.1.3, DOE elected to use five hearth product groups for 

analysis. The shipment percentage ranges provided by HPBA were aggregated into the 

five product groups for the rulemaking analysis. In order to derive a set of five average 

shipment ratios for its product groups, DOE started by taking the midpoint of the ranges 

provided and proportionally adjusting so that the total of the resulting percentages 

equaled 100 percent. DOE used the 4-9 percent range for outdoor fireplaces because

HPBA acknowledged that the range initially provided was larger than would be expected 

in a typical year. 

To determine a useful and accurate market share for outdoor products and in light 

of HPBA’s suggestions, DOE deemed it necessary to capture non-HPBA shipments for 

this product group. Using a product listing database compiled for the rulemaking

(discussed in section 3.2.9), DOE found that there were 12 percent as many HPBA patio 

heater models listed compared to the number of all other outdoor product models listed

for HPBA members. DOE used this ratio as a proxy to estimate the number of patio 

heater shipments from HPBA members. For patio heaters, DOE assumed that non-HPBA 

shipments were three times those of HPBA members. For all other outdoor products, 

DOE assumed that the number of non-HPBA shipments was equal to the HPBA 

shipments. These additional shipments estimates were added to the average number of 

shipments from 2010 to2013, and the shares for each of the five hearth product groups 

were then recalculated. These values are provided in Table 3.2.5.

3-12
Table 3.2.5 DOE Market Shares for Hearth Product Groups

Product Group Market Share

%*

Vented Fireplace/Insert/Stove 56.2

Unvented Fireplace/Insert/Stove 4.4

Vented Gas Log Sets 6.1

Unvented Gas Log Sets 18.3

Outdoor 15.0

*These shipment weights include match-lit hearth products. These products are later removed becase they 

would be unaffected by a proposed rule disallowing standing pilot ignitions. See chapter 9 of this TSD for 

more information regarding the shipments analysis.

3.2.7 Industry Cost Structure

DOE developed the hearth industry cost structure using publicly available 

information (e.g., Securities and Exchange Commission 10-K reports,18 corporate annual 

reports, the U.S. Census Bureau’s 2011 Annual Survey of Manufacturers19) as well as

data obtained directly from manufacturers through interviews. Table 3.2.6presents key 

industry financial metrics, each of which is estimated as a percentage of total revenue. 

The manufacturer impact analysis (chapter 12 of this TSD) includes a more detailed 

discussion of the industry cost structure and the potential financial impacts of an energy 

conservation standard. 

Table 3.2.5 Industry Cost Structure 

Parameter

Percent of Total 

Revenue

%

Working Capital 2.2

Net Property, Plant, and 

Equipment 10.8

Selling, General and 

Administrative Expenses 25.0

Research and Development 2.3

Depreciation 2.1

Capital Expenditures 2.1

3.2.8 Equipment Lifetime 

DOE reviewed available literature and consulted with manufacturers to establish 

typical equipment lifetimes. (See the life-cycle cost analysis, chapter 8 of this TSD, for 

additional details and sources used to determine the typical equipment lifetimes.) DOE 

combined these sources to develop an average estimated lifetime of the equipment 

covered by this rulemaking. DOE tentatively determined the average lifetime of hearth 

products to be 15 years.

3-13
Chapter 8 of the TSD provides more information about hearth product lifetimes.

3.2.9 Market Performance Data

As previously mentioned, DOE compiled a product listing database by conducting 

a review of product literature. Fields captured in the database include the hearth product 

type, fuel type, and ignition type. For gas log sets, DOE found this type of data 

aggregation was not useful, as model numbers are not used to reflect all possible 

permutations of burners, log styles, and ignition types. 

In conjunction with manufacturer estimates provided during the interview 

process, DOE also used the product listing database to estimate the portion of shipments 

that use each of the three general ignition types by product group. These estimates are 

shown in Table 3.2.7.

Table 3.2.6 Average Market Share of Ignition Types by Analysis Group

Product Group Match 

Lit

%

Standi

ng

Pilot

%

Intermi

ttent 

Pilot 

Ignition

%

Source

Vented 

Fireplace/Insert/Stove

5 40 55 Manufacturer Interviews

Unvented 

Fireplace/Insert/Stove

0* 12 88 Product Listing Database

Vented Gas Log Sets 50* 44 6 Manufacturer Interviews

Unvented Gas Log Sets 0 94 6 Manufacturer Interviews

Outdoor 50** 26 24 Product Listing Database

*DOE assumed that there are no shipments of unvented match-lit hearth products because the industry 

safety standard ANSI Z21.11.2 requires the use of an oxygen depletion sensor and therefore a safety pilot.

**The product listing database showed that the number of match-lit outdoor models was lower, however 

DOE assumed that 50% of outdoor hearth products are match lit.

DOE found that efficiency data for heater-rated hearth products are inconsistent 

and frequently not fully specified. Thermal efficiency, annual fuel utilization efficiency 

(AFUE), and fireplace efficiency (CSA P.4.1-02) are used inconsistently throughout the 

industry, and efficiency ratings in product literature often do not specify which metric is 

used or the test procedure by which the rating is obtained. 

3.3 TECHNOLOGY ASSESSMENT

The purpose of the technology assessment is to develop a list of technology 

options manufacturers can use reduce the energy consumption of hearth products. The 

following assessment provides descriptions of those technology options that apply to all 

hearth product groups or specific hearth product groups.

3-14
In preparation for the screening and engineering analyses, DOE identified several 

possible technology options for improving the energy efficiency or reducing the energy 

consumption of hearth products. These options provide insight into the design 

improvements typically used to reduce the gas consumption of hearth products.

3.3.1 Baseline Equipment Components and Operation

DOE typically defines the baseline model as a product having an efficiency that 

just meets the existing federal energy conservation standards. DOE also typically defines 

baseline models as having commonly available features. The baseline models for hearth 

products serve as a reference points for measuring changes resulting from energy 

conservation standards. 

In the case of hearth products, there are no federal energy conservation standards.

For all hearth product groups, DOE assumes the baseline model uses a constant burning 

pilot ignition system. Because the inclusion of a constant burning pilot ignition would 

lead to the highest possible standby mode energy use, this ignition type represents the 

baseline for comparison to other technologies that would reduce standby mode energy 

use. Hearth products that are match lit were not considered baseline as these consume no 

energy during standby mode. 

Furthermore, DOE’s selection of baseline models focused primarily on the types 

of ignitions typically employed in each hearth product group because the standby mode 

energy use is mostly attributable to the ignition type (see section III.B of the NOPR for 

rationale). As noted in section 3.1.3, DOE has tentatively concluded that within each 

hearth product group and for each ignition type (standing pilot and electronic ignition) 

the primary ignition components (gas valve, pilot assembly, and control module for 

electronic ignition models) are largely interchangeable. The paragraphs below describe 

the baseline model for each hearth product group analyzed.

Vented Fireplace/Insert/Stove. A vented gas fireplace simulates a wood-burning 

fireplace and may be installed into a wall or other framing structure. An insert is designed 

for installation into an existing solid fuel burning fireplace. A stove is a free-standing unit

that does not require installation into a surrounding structure. Vented units include pipes 

that carry combustion gases out of the building. 

For this product group, the typical constant burning pilot ignition system included 

a millivolt gas control valve and a pilot assembly. The millivolt valve uses the pilot light 

to generate a small voltage potential, allowing use with a remote control or thermostat to 

open the gas valve without requiring an additional source of power. The pilot assembly 

includes a gas line connecting the gas valve to the pilot, a thermocouple, and a 

thermopile. The thermocouple is heated by the pilot and used to prove that the pilot is 

burning gas, while the thermopile provides the voltage potential for use with a remote 

control or thermostat.

Unvented Fireplace/Insert/Stove. Unvented fireplaces, inserts, and stoves are 

similar to those found in the vented group, but are not designed to vent combustion gases.

3-15
Rather, these products are designed to safely allow the byproducts of combustion into the 

dwelling. The ignition systems on these products differ from their vented counterparts in 

their use of an oxygen depletion sensor (ODS). The ODS is a precision pilot light and 

calibrated thermocouple that shuts the gas control valve when the oxygen in the room is 

below a threshold. These components are included with the pilot assembly. 

Vented Gas Log Sets. Vented gas log sets are designed for installation directly 

into existing solid fuel burning fireplaces. These products are open flame devices and do 

not include the metal heat shield or enclosure found in fireplaces, inserts, and stoves. Gas 

log sets are meant as a replacement for wood as a fuel source in existing fireplaces, and 

are therefore subject to the physical space constraints of the existing fireplace.

Additionally, they rely on the existing chimney or ventilation system to remove the 

combustion byproducts from the building. Vented gas log sets typically consist of a 

burner, a grate, and ceramic or cement imitation logs. 

In jurisdictions that do not require a safety pilot ignition system, the burner is

commonly lit manually with a match or lighter. These types of gas log sets do not 

maintain a constant burning pilot and are therefore unaffected by the proposed 

prescriptive requirement contained in the NOPR.

In jurisdictions that require a safety pilot specifically or certification to a safety 

standard that requires one, manufacturers may offer or include a manual safety pilot 

system, a millivolt pilot system, or an electronic ignition system. Manual gas valves are 

typically less expensive and smaller in size than millivolt gas valves or gas valves for 

electronic ignition systems. DOE selected the manual safety pilot system as the baseline 

model due to the space and cost constraints consumers of these products often face.

Unvented Gas Log Sets. Unvented gas log sets are similar to their vented 

counterparts except that the burner is designed so that the byproducts of combustion enter 

the occupied space rather than being vented outside of the building. Unvented gas log 

sets also incorporate an ODS for safe operation.

Outdoor Products. Outdoor products may take several forms. DOE identified 

fireplaces, fire pits or burners, and patio heaters as the most prominent styles of outdoor 

hearth products. Outdoor fireplaces are similar in design to indoor fireplaces, but are 

specially designed with materials that make them suitable for installation outdoors. Fire 

pits and burners are open flame devices that do not incorporate heat shielding or an 

enclosure. These products are comprised of a burner and an ignition system consisting of 

a gas valve and pilot. 

Patio heaters may come in two styles, radiant and pyramid. Radiant patio heaters 

provide primarily radiant heat to an outdoor space. A steel emitter screen that surrounds 

the combustion zone glows when heated and provides radiant heat to its surroundings.

These patio heaters typically consist of a base or stand, a gas connection and regulator, a 

post, and a head unit, which contains the gas valve and pilot assembly, a burner, the 

emitter screen, and a reflector shield. These patio heaters may use natural gas or propane; 

in the case of propane, the base or stand is often large enough to store a propane tank.

3-16
Pyramid style patio heaters create a very large flame through a glass tube. These 

heaters consist of a base, gas connections and regulator, gas valve and pilot assembly, 

burner, glass tube, and reflector.

3.3.2 Technology Options

Air-to-Fuel Ratio. The mixture of air and fuel for combustion determines key 

flame aspects for hearth products, in particular the flame color, height, and heat output 

from the hearth product. As described previously, gas-fired hearth products are often 

designed to simulate the burning of wood. In order to achieve flame characteristics that 

mimic wood-burning flames, gas-fired hearth products utilize a “rich” mixture, that is,

the ratio of air to fuel is low. For many natural gas products, primary air is in fact not premixed, and what is burned is nearly 100 percent natural gas. This results in a tall yellow 

flame. For propane products, air is pre-mixed with fuel prior to combustion. Optimizing 

the air-to-fuel ratio would improve the active mode energy use of hearth products, but 

may reduce the flame aesthetic appeal. Manufacturers indicated during interviews that 

becasue the aesthetic appeal of the flame must be maintained, there would be no room to 

reduce the fuel consumption by adjusting the air-to-fuel ratio.

Burner port design. Gas burners for hearth products typically comprise tubes 

with holes or slots through which the gas exits and combusts. The holes or slots are 

designed with particular sizes and patterns in order to achieve the desired flame pattern or 

aesthetic. While the primary objective of optimizing gas burner ports is to achieve the 

desired flame pattern, the ports could also be optimized to deliver an acceptable flame 

aesthetic while reducing the amount of fuel consumed. This design would have the 

potential to improve the active mode energy use of hearth products, but may reduce 

aesthetic appeal. DOE is not aware of any products on the market using advanced burner 

port designs as a means of reducing energy consumption. During manufacturer 

interviews, most manufacturers stated that they expected that adjusting the burner port 

design would insignificantly reduce fuel consumption .

Simulated log design. Many gas hearth products incorporate cement, fiber, or 

ceramic logs that are designed to simulate the look of wood logs. The log shapes are 

optimized in conjunction with the burner design. The combination of the burner design 

and log shape, size, and placement results in the overall aesthetic for the product.

Additionally, logs must be designed in conjunction with the burner to ensure that flames 

do not impinge on the logs themselves, as this causes the flame to cool and form soot. For 

contemporary style hearth products, simulated logs may be replaced with other shapes or 

materials or may be removed entirely.

For products that incorporate simulated logs or other objects, the combination of 

the logs or objects with the flame pattern (burner design) results in the complete design.

A key design objective for this log and flame pattern combination is to fill the firebox 

into which they are installed as much as possible. For a given size, the logs could 

therefore be optimized to reduce the amount of fuel needed while sufficiently filling the 

3-17
firebox. This design option would potentially improve the active mode energy efficiency

of hearth products. However, DOE is not aware of any product designs on the market that 

have proven to be more efficient methods for arranging the simulated log. Manufacturer

indicated during interviews that insignificant reductions in energy use would be achieved 

by adjusting the simulated log design.

Pan burner media/bead or glowing ember type. Many hearth products include an 

ember material that glows and radiates when heated. In pan type burners, sand is used to 

cover the burner and results in a flame pattern. In contemporary hearth products and in 

outdoor fire pits, glass beads may be used in place of simulated logs for effect. These 

media could potentially be selected to produce a satisfactory flame pattern while reducing 

the required gas consumption. This design option would reduce the active mode energy 

use of hearth products. During manufacturer interviews, DOE inquired about the use of 

media, beads, or glowing embers for reducing fuel consumption while providing an 

adequate aesthetic flame. Manufacturers indicated that this technology would not result 

in measurable energy savings. Additionally, this technology would only apply to the 

subset of hearth products that use media, glass, or other beads, or glowing embers.

Reflective walls and/or other components in firebox/combustion zone. For 

hearth products that include a firebox or other enclosure, the interior walls could 

potentially be painted with a reflective coating. This could potentially give the illusion of 

more or taller flames, thereby reducing the amount of fuel required to achieve a 

satisfactory aesthetic. This design option would only apply to fireplaces, inserts, and 

stoves, as other hearth products do not incorporate a firebox. This design option may

reduce the amount of fuel used in active mode. Manufacturers indicated during 

interviews that reflective walls would not substantially reduce the fuel needed to produce 

an aesthetic flame, and that the reflective coating would only serve as a supplemental 

aesthetic effect. Also, this technology would only apply to the subset of hearth products 

that include an enclosure surrounding the flame.

Air circulating fan. Air circulating fans improve the efficiency of heater hearth 

products by increasing the air flow rate through the heat exchanger section of a fireplace, 

insert, or stove. For vented products, this results in more heat being provided to the 

occupied space rather than lost through the vent. An air circulating fan would only be an 

option for vented fireplaces, inserts, and stoves. These hearth products already 

incorporate the heat exchanger for which the circulating fan would be of use, and many 

are already available with circulating fans as an option. A circulating fan would not be of 

use to or feasible for products such as gas logs, outdoor fire pits, and patio heaters 

because these products do not incorporate a heat exchanger and do not have sufficient 

cabinetry or enclosure to house the fan. A circulating fan would not improve the 

efficiency of an unvented hearth product, as all of the heat from an unvented hearth 

already remains in the occupied space. A circulating fan would primarily improve the 

heating efficiency during the active mode for those hearth products for which a fan could 

be integrated, namely fireplaces, inserts, and stoves. It would not substantially affect the 

standby mode energy use of hearth products.

3-18
Condensing heat exchanger. For hearth products that provide space heating, the 

combustion gases can be passed through a larger or improved heat exchanger to extract as 

much heat as possible. Such a heat exchange can remove sufficient energy from the 

combustion gases to reduce the combustion gas temperature below its condensing point. 

This type of heater is typically termed a condensing heater and is highly efficient at 

converting the energy in combustion gases into space heating. 

The flue-gas condensate is often acidic and corrosive. Therefore, special 

corrosion-resistant heat exchangers and vent linings are required for safe and reliable 

operation. Corrosion due to condensation of combustion gases limits the heating 

efficiency of a hearth product with a standard flue and vent system. Using corrosionresistant heat exchangers or sidewall venting, and lining the vent/masonry systems with 

corrosion-resistant material can extend the heating efficiency.

Condensing systems require some means to collect and drain the condensate that 

develops within the heat exchangers. Condensing systems can be designed to use 

secondary heat exchangers and air circulation fans as well. This technology would only 

apply to hearth products designed to provide space heating and only improves heating

efficiency during active mode operation. 

Electronic Ignition. As shown in Table 3.2.7, a substantial portion of the hearth 

industry uses standing pilot ignition systems. For hearth products, DOE found that there 

are primarily two types of electronic ignition systems that do not rely on a continuously 

burning pilot: intermittent pilot ignition and hot surface (or wire) ignition. The 

intermittent pilot ignition is a device that generates a spark to light a pilot which in turn 

lights the main burner. The pilot then automatically extinguishes after the main burner is 

lit. The hot surface or hot wire ignition system lights the main burner directly via a 

sufficiently hot surface. 

Both hot surface and intermittent pilot electronic ignitions require an outside 

source of electricity to operate. The gas valve for each electronic ignition system 

typically only consumes power during a call for heat. The control module, which houses 

the electronic circuitry required to control the entire ignition system, typically only 

consumes power during a call for heat. Finally, the electronic thermostat, if present,

draws power continuously (regardless of whether heat is needed). These power 

requirements in total are typically much less than the pilot burn rate.

An electronic ignition would eliminate most standby mode energy consumption 

since these ignitions do not rely on a continuously burning pilot light. This design option 

would not affect active mode energy use.

3.3.3 Technology Options That Do Not Affect Energy Use

After reviewing the technology options, DOE found that the circulating fan, 

condensing heat exchanger, and electronic ignition may substantially reduce energy 

consumption of hearth products. DOE understands that altering the air-to-fuel ratio, 

burner port design, simulated log design, burner pan media, bead, or ember type, and 

3-19
reflective combustion chamber walls in order to achieve fuel savings would result in 

negatively impacting the design aesthetic of hearth products. When maintaining an 

aesthetic flame, these technologies would not be means for achieving significant fuel 

savings. During manufacturer interviews, DOE found that regulations focused on or 

requiring any of these five technology options would hinder manufacturers’ ability to 

innovate and produce aesthetic products. However, DOE does not discourage 

manufacturers from using these design options or strategies since they may reduce annual 

energy consumption.

3-20
REFERENCES

1 HPBA. Join HPBA. Last accessed August 2014 

<http://www.hpba.org/members/members/join-hpba>.

2 HPBA. HPBA Overview. Last accessed August 2014. 

<www.hpba.org/index.php?id=46>.

3 AHRI. About Us. Last accessed January 2015.

<http://www.ahrinet.org/site/318/About-Us>.

4 Ibid.

5 U.S. Census Bureau, 2012 NAICS Definitions. Last accessed August 2014. 

<

http://www.census.gov/eos/www/naics/2012NAICS/2012_Definition_File.pdf>.

6 Comisión Nacional Para El Uso Eficiente De La Energía. Last Accessed August 

2014. 

<http://www.conuee.gob.mx/wb/CONAE/CONA_1002_nom_publicadas_vigen>.

7 National Resources Canada. Last accessed August 8, 2014. 

<http://www.nrcan.gc.ca/energy/regulations-codes-standards/products/6865>.

8 Ibid.

9 ENERGY STAR All Certified Products. Last accessed August 2014. 

<http://www.energystar.gov/certified-products/certified-products>.

10 Federal Energy Management Program. Covered Product Categories. Last 

accessed August 2014. < http://energy.gov/eere/femp/covered-productcategories>.

11 Puget Sound Energy. Fireplace Rebate. Last accessed August 2014. 

<https://pse.com/savingsandenergycenter/ForHomes/Pages/FireplaceRebate.aspx>.

12 CenterPoint Energy 2014 Fireplace Rebates. Last accessed August 2014. 

<http://www.centerpointenergy.com/services/naturalgas/residential/efficiencyreba

tesandprograms/fireplaceandhearthrebates/MN/>.

13 THERMWISE High-Efficiency Gas Fireplace Rebates. Last accessed August 

2014. < http://www.thermwise.com/home/Firesidechat.php >

14 U.S. Census. New Privately Owned Housing Units Started. Last accessed August 

2015. <https://www.census.gov/construction/nrc/pdf/startsan.pdf>.

3-21
15 Ibid.

16 HPBA U.S. Hearth Statistics. Last accessed August 2014. 

<http://www.hpba.org/statistics/hpba-us-hearth-statistics>.

17 HPBA Hearth Product Categories, Market Shares, and Total Shipments Data.

Submitted on August 18, 2014.

18. U.S. Securities and Exchange Commission. Annual 10-K Reports. Various Years. 

<http://www.sec.gov/>

19. U.S. Census Bureau, Annual Survey of Manufacturers: General Statistics: 

Statistics for Industry Groups and Industries (2010)

<http://www.census.gov/manufacturing/asm/index.html>.

3-22

 
CHAPTER 4. SCREENING ANALYSIS

TABLE OF CONTENTS

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

4.2 TECHNOLOGY OPTIONS ............................................................................................ 4-2

4.3 SCREENED OUT TECHNOLOGIES ............................................................................ 4-2

4.4 REMAINING TECHNOLOGIES ................................................................................... 4-2
CHAPTER 4. SCREENING ANALYSIS

4.1 INTRODUCTION

This chapter discusses the screening analysis conducted by the U.S. Department of 

Energy (DOE) of the technology options identified in the market and technology assessment for 

hearth products (chapter 3 of this technical support document; TSD). In the market and 

technology assessment, DOE presented an initial list of technology options that can be used to

improve the energy efficiency and/or reduce energy consumption of hearth products. The goal of 

the screening analysis is to identify any technology options that will be eliminated from further 

consideration in the rulemaking analyses. 

The candidate technology options are assessed based on information gathered during 

DOE research, as well as inputs from interested parties. Technology options that are judged to be 

viable approaches for improving energy efficiency or reducing annual energy consumption are 

retained as inputs to the subsequent engineering analysis. Technology options that are not 

incorporated in commercially available products or in working prototypes, that fail to meet 

certain criteria pertaining to practicability to manufacture, install, and service, that have adverse

impacts on product utility or availability, or have adverse impacts on health or safety will be 

eliminated from consideration, in accordance with 10 CFR 430, subpart C, appendix A, section 

4(a)(4)(i-iv).

DOE uses the following four screening criteria to determine which technology options 

are suitable for further consideration in an energy conservation standards rulemaking: 

1. Technological feasibility. DOE will consider technologies incorporated in 

commercially available products or in working prototypes to be technologically feasible.

2. Practicability to manufacture, install, and service. If mass production and reliable 

installation and servicing of a technology in commercial products could be achieved on the scale 

necessary to serve the relevant market at the time the standard comes into effect, then DOE will 

consider that technology practicable to manufacture, install, and service.

3. Adverse impacts on product utility or product availability. If DOE determines a 

technology would have a significant adverse impact on the utility of the product to significant 

subgroups of consumers, or would result in the unavailability of any covered product type with 

performance characteristics (including reliability), features, sizes, capacities, and volumes that 

are substantially the same as products generally available in the United States at the time, it will 

not consider this technology further.

4. Adverse impacts on health or safety. If DOE determines that a technology will have 

significant adverse impacts on health or safety, it will not consider this technology further.

4-1
4.2 TECHNOLOGY OPTIONS

As described in chapter 3 of this TSD, DOE developed a list of technology options 

manufacturers could use to reduce the energy consumption of hearth products. The following 

technology options were considered in the technology assessment.

• Air-to-fuel ratio

• Burner port design

• Simulated log design

• Pan burner media/bead type

• Reflective walls and/or other components inside combustion zone

• Air circulating fan

• Electronic ignition

• Condensing heat exchanger

Several of these technologies were not considered further in the screening analysis (see 

chapter 3 of this TSD for details). The air circulating fan, condensing heat exchanger, and 

electronic ignition were considered as part of the screening analysis because DOE concluded that

these three options may substantially the reduce energy consumption of hearth products.

4.3 SCREENED OUT TECHNOLOGIES

DOE has tentatively concluded that these three technologies, namely the air circulating fan, 

the condensing heat exchanger, and the electronic ignition, would not be screened out by any of 

the four screening criteria listed above. These technologies are currently commercially available 

for hearth products and do not result in adverse impacts on health or safety. Therefore, they do 

not fail the first, second or fourth screening criteria. With regard to impact on product utility and

availability, DOE notes that an electronic ignition provides the same functionality as a millivolt 

standing pilot gas valve, specifically the ability to be used with a remote control or thermostat. 

DOE has also tentatively determined that electronic ignition components are available for a wide 

range of gas-fired equipment beyond hearth products, and that the ability of hearth manufacturers 

to comply with the standard will not be restricted for lack of available components.

4.4 REMAINING TECHNOLOGIES

DOE passed the circulating fan, condensing heat exchanger, and electronic ignition to the 

engineering analysis for further consideration. See chapter 5 of this TSD for the results of that 

analysis.

4-2
CHAPTER 5 ENGINEERING ANALYSIS

TABLE OF CONTENTS

CHAPTER 5 ENGINEERING ANALYSIS............................................................5-i

5.1 INTRODUCTION ............................................................................................... 5-1

5.2 PRODUCT CLASSES......................................................................................... 5-2

5.2.1 Product Groups Analyzed........................................................................ 5-2

5.3 DESIGN OPTIONS............................................................................................. 5-3

5.3.1 Baseline Model ........................................................................................ 5-4

5.3.2 Electronic Ignition ................................................................................... 5-5

5.4 METHODOLOGY .............................................................................................. 5-5

5.5 TEARDOWN ANALYSIS.................................................................................. 5-6

5.5.1 Selection of Units..................................................................................... 5-6

5.5.2 Baseline Units.......................................................................................... 5-7

5.6 COST MODEL.................................................................................................... 5-7

5.6.1 Generation of Bills of Materials .............................................................. 5-7

5.6.2 Cost Structure of the Spreadsheet Models............................................... 5-7

5.6.3 Cost Model and Definitions..................................................................... 5-8

5.6.4 Cost Model Assumptions Overview ........................................................ 5-9

5.6.5 Manufacturing Production Cost............................................................. 5-12

5.7 MPC BREAKDOWN........................................................................................ 5-13

5.8 MANUFACTURER MARKUP ........................................................................ 5-13

5.9 ENGINEERING ANALYSIS SUMMARY OF RESULTS.............................. 5-14

5.10 REFERENCES .................................................................................................. 5-16

LIST OF TABLES

Table 5.2.1 Hearth Product Analysis Groups .................................................................. 5-3

Table 5.3.1 Gas Control Valve Types Used for Constant Burning Pilot Units in 

Engineering Analysis....................................................................................................... 5-4

Table 5.3.2 Electronic Ignition Types Used in Engineering Analysis............................. 5-5

Table 5.6.1 Cost Model In-House Manufacturing Operation Assumptions.................. 5-10

Table 5.6.3 Five-Year Material Prices (2009 to 2014).................................................. 5-12

Table 5.7.1 Total Product Manufacturer Production Cost Breakdown for Hearth Product 

Groups............................................................................................................................ 5-13

Table 5.9.1 Baseline Full Assembly Manufacturer Production Costs and Incremental 

Electronic Ignition System Costs for Hearth Products.................................................. 5-15

LIST OF FIGURES

Figure 5.6.1 Full Production Costs................................................................................ 5-12

5-i
CHAPTER 5. ENGINEERING ANALYSIS

5.1 INTRODUCTION

The U.S. Department of Energy (DOE) performed an engineering analysis to 

establish the relationship between manufacturer production cost (MPC) and reduced 

energy consumption in hearth products. Specifically, DOE analyzed the change in MPC 

resulting from implementation of electronic ignition systems that would replace a 

constant burning pilot. The change in MPC resulting from this requirement serves as the 

basis for cost-benefit calculations in terms of individual consumers, manufacturers, and 

the nation. This chapter provides an overview of the engineering analysis (section 5.1), 

discusses product classes (section 5.2), establishes baseline unit specifications (section

5.5.2), discusses incremental efficiency levels (section 5.3), explains the methodology 

used during data gathering (section 5.4), and discusses the analysis and results (section

5.9). DOE completed a separate engineering analysis for each of the hearth product 

groups identified for analysis: vented fireplaces/inserts/stoves, unvented 

fireplaces/inserts/stoves, vented gas log sets, unvented gas log sets, and outdoor hearth 

products.

The primary inputs of the engineering analysis are baseline information from the 

market and technology assessment (MTA; chapter 3 of this TSD) and the technologies

that are passed through the screening analysis (chapter 4 of this TSD). Additional inputs 

include cost data derived from the physical teardown analysis and interviews with 

manufacturers. The primary output of the engineering analysis is the estimated change in 

MPC associated with reduced energy consumption from disallowing constant burning 

pilots by hearth product group.

DOE typically structures its engineering analysis around one of three 

methodologies: (1) the design-option approach, which calculates the incremental cost of 

adding specific design options to the baseline model; (2) the efficiency-level approach, 

which calculates the relative costs of achieving increases in energy efficiency levels 

without regard to the particular design options used to achieve such increases; and/or (3) 

the reverse engineering cost-assessment approach, which involves a “bottom-up” 

manufacturing cost assessment based on a detailed bill of materials derived from 

teardowns of products being analyzed. Deciding which methodology to use for the 

engineering analysis depends on the product, the technologies under study, and any 

historical data DOE can draw upon.

To establish the industry incremental cost associated with implementing an 

electronic ignition system, DOE used both the design-option approach and the costassessment approach. After identifying hearth products that represent a cross section of 

the market, DOE gathered additional information using reverse engineering 

methodologies, product information from manufacturer catalogs, and discussions with 

experts and manufacturers of hearth products. DOE generated bills of materials (BOMs) 

5-1
by disassembling manufacturers’ products. The BOMs describe the product in detail, 

including all manufacturing steps required to make and/or assemble each part.

Subsequently, DOE developed a cost model that converted the BOMs into MPCs. By 

applying derived manufacturer markups to the MPCs, DOE calculated the manufacturer 

selling prices (MSPs) and developed the incremental MPCs associated with the 

prescriptive requirement.

In a subsequent life-cycle cost analysis (chapter 8), DOE used the MPCs and 

MSPs to determine consumer prices for hearth products by applying the appropriate 

distribution channel markups. 

5.2 PRODUCT CLASSES

As described in section III.C of the NOPR, DOE has tentatively determined not to

establish product classes for a standby mode energy conservation standard. The criteria 

for separation into different product classes are: (1) energy source and (2) capacity or 

other performance-related features, such as those that provide utility to the consumer, or 

others deemed appropriate by the Secretary that would justify the establishment of a 

separate energy conservation standard. (42 U.S.C. 6295 (q) and 6316(a)) DOE found 

substantial similarity in the function, components used, and energy use of hearth products 

with regard to ignition systems and their standby mode energy consumption. By 

reviewing manufacturer product literature, DOE found that the same or similar ignition 

system components, including manual, millivolt, and electronic gas control valves; pilot 

assemblies; and electronic control modules for electronic ignitions; were used across a 

wide range of hearth products. DOE has tentatively determined that if standing pilot 

ignitions are ultimately disallowed, most hearth products styles currently using standing 

pilot ignitions will switch to similar electronic ignition components. However, 

recognizing the need to account for differences in product design and manufacturing 

production volumes, DOE analyzed several specific subgroups of hearths separately, to 

determine the impacts individually. This is explained in more detail in section 5.2.1.

5.2.1 Product Groups Analyzed

While DOE has tentatively concluded that product classes are not necessary to 

distinguish hearth products as a result of focusing on standby mode operation (see section 

III.C of the NOPR), DOE acknowledges that the impact on manufacturers and consumers 

could differ depending on the hearth product. To investigate these differences, DOE 

selected five hearth product groups for analysis based on product literature review and 

manufacturer interviews.

 Unvented hearth products consume air from the occupied space for combustion,

and release all byproducts of combustion into the occupied space. For safety, unvented 

ignition systems on indoor hearth products use an oxygen depletion sensor that closes the 

gas control valve when oxygen in the room drops below a threshold. This sensor consists 

of a precision pilot light and thermocouple connected to the gas control valve. The pilot 

light heats the thermocouple; as the oxygen in the room decreases, the pilot flame “lifts” 

5-2
farther from the thermocouple to seek out sufficient oxygen for combustion. This cools 

the thermocouple, and once the thermocouple cools to a certain temperature 

(corresponding to an unsafe room oxygen level) the gas control valve closes, preventing 

further depletion of oxygen in the room. To account for this difference in ignition system 

components, DOE separated unvented from vented products.

DOE also acknowledges differences between gas log sets and other indoor hearth 

products. In terms of standing ignition systems, DOE tentatively determined that manual 

safety pilot valves were more likely to be found on gas log sets, rather than using larger, 

more expensive millivolt valves. Gas log sets also lack the enclosure and integrated heat 

shielding found in fireplaces, inserts, and stoves that would more readily house and 

protect the additional components necessary for ignition systems of any kind. DOE 

therefore used separate analysis groups for gas log sets.

Outdoor fireplaces often use similar ignition system components to indoor hearth 

products. However, DOE found that certain types of outdoor hearth products, such as 

patio heaters and fire pits, may use different ignition components than indoor hearth 

products. For example, patio heaters do not typically use millivolt gas valves, and fire 

pits often use hot wire igniters to ignite the pilot light. For these reasons, DOE separated 

outdoor hearth products as well.

Table 5.2.1 presents the product groups used for analysis.

Table 5.2.1 Hearth Product Analysis Groups

Vented Fireplaces, Inserts, and Stoves

Unvented Fireplaces, Inserts, and Stoves

Vented Gas Log Sets

Unvented Gas Logs Sets

Outdoor Hearth Products

5.3 DESIGN OPTIONS

For each of the hearth product groups listed in Table 5.2.1, DOE analyzed 

representative models and estimated the manufacturer production costs. As described in 

the market and technology assessment (chapter 3 of this TSD) and the screening analysis 

(chapter 4 of this TSD), additional design options for hearth products were considered.

Because this rulemaking has focused on standby mode energy consumption for the 

reasons described in section III.B of the NOPR, DOE did not consider technologies

affecting active mode energy consumption in the engineering analysis (i.e., condensing 

heat exchanger and air circulating blower). Rather, DOE focused its analysis on the 

impacts of removing the standing pilot ignition system and replacing it with a system that 

does not use a continuously burning pilot.

5-3
The following subsections describe the models selected for each product group

for comparison between the baseline (standing pilot) and design option (electronic 

ignition).

5.3.1 Baseline Model

DOE selected baseline units (standing pilot; i.e., without new energy standards) as 

reference points for each product group, against which DOE measured changes resulting 

from potential energy conservation standards. The baseline units in each hearth product 

group use a constant burning pilot ignition system to allow for comparison to the 

electronic ignition design option. DOE used the baseline units in the engineering analysis 

and the life-cycle-cost and payback-period analyses. DOE determined the increase in 

MPC associated with switching to an electronic ignition. DOE notes that less expensive 

ignition systems are available–namely match lit burner –but those systems would not be 

affected by this rulemaking because they do not employ a continuously burning pilot 

light. DOE did not consider match lit burners as a viable alternative to standing pilot 

ignition systems because many municipalities and product safety standards require an 

ignition system be present for gas-fired hearth products. However, DOE notes that for 

cases where match lit burners are permissible, the MPC for the product would be lower 

for the match lit hearth product than for those with standing pilot ignition systems.

DOE considered that there are two main standing pilot valve types: manual and 

millivolt. The manual valve requires the user to manually open and close the valve and is 

therefore smaller, simpler, and cheaper. The millivolt gas valve uses a thermopile to 

generate a voltage difference such that the valve can be coupled with additional control 

systems, for example, a remote control or thermostat. Because gas log sets are subject to 

physical space constraints that fireplaces, inserts, and stoves are not, DOE selected gas 

log sets with manual valves as representative of gas log sets with standing pilots. DOE 

selected models with millivolt gas valves as being representative of the fireplace, insert,

and stove vented and unvented categories as millivolt gas valves are common for these 

products.

Table 5.3.1 Gas Control Valve Types Used for Constant Burning Pilot Units in 

Engineering Analysis

Hearth Product Group Standing Pilot 

Valve

Vented Fireplaces, Inserts, and 

Stoves

Millivolt

Unvented Fireplaces, Inserts, and 

Stoves

Millivolt

Vented Gas Log Sets Manual

Unvented Gas Logs Sets Manual

Outdoor Manual

The market baseline units identified in the engineering analysis represent the cost 

of the typical products on the market that utilize a constant burning pilot. DOE used these 

5-4
baseline models for the subsequent analyses (e.g., life-cycle cost (LCC), payback period 

(PBP), national impact analysis (NIA), manufacturer impact analysis (MIA).

5.3.2 Electronic Ignition

The results of the screening analysis (chapter 4) are used as inputs to the 

engineering analysis. As described above in section 5.3, three technologies passed the 

screening analysis (condensing heat exchanger, air circulating blower, and electronic 

ignition), only the electronic ignition is being considered in the engineering analysis due 

to the rulemaking focus on standby energy use.

DOE selected models for each hearth product group that represented typical 

implementations of electronic ignitions. Based on product literature review and 

confirmation during manufacturer interviews, DOE determined that the intermittent pilot 

(IPI) is by far the most common type of electronic ignition used for hearth products. DOE 

also found that in some outdoor products with high-input capacities, a hot wire igniter 

(HWI) is used to light the pilot intermittently rather than a spark igniter. DOE therefore 

considered both types of units for its outdoor product analysis.

Table 5.3.2 Electronic Ignition Types Used in Engineering Analysis

Hearth Product Group EIS

Vented Fireplaces, Inserts, and 

Stoves

IPI

Unvented Fireplaces, Inserts, and 

Stoves

IPI

Vented Gas Log Sets IPI

Unvented Gas Logs Sets IPI

Outdoor IPI, HWI

5.4 METHODOLOGY

This section describes the analytical methodology used in the engineering 

analysis.

DOE first identified units with standing pilot ignitions and with electronic 

ignitions to represent each hearth product group. DOE gathered the information from the 

physical teardown analysis to create a BOM using reverse engineering methods (see 

section 5.5). DOE calculated the MPC for both the standing pilot model and the 

electronic ignition model for each product type.

During the preparation of the MPCs, DOE held interviews with manufacturers to 

gain insight into hearth product industry and request comments regarding its cost 

estimations for various components, particularly those associated with the ignition 

systems. DOE used the information gathered from these interviews to refine assumptions 

5-5
in the cost model. Next, DOE converted the MPCs into MSPs (see section 5.8) using 

publicly available industry financial data, in addition to manufacturers’ feedback.

5.5 TEARDOWN ANALYSIS

To assemble BOMs and calculate the manufacturing costs of the different 

components in hearth products, DOE disassembled multiple units into their components 

and estimated the material and labor cost of each individual component. This process is 

referred to as a “physical teardown.”

5.5.1 Selection of Units

Because the engineering analysis is designed to assess the difference in MPC 

between baseline (standing pilot) and the proposed design option (electronic ignition), 

DOE was primarily concerned with selecting units for teardown that represented typical 

implementations of standing pilot ignition systems and electronic ignition systems. DOE 

determined the typical characteristics of hearth products based on the market analysis 

done for the market and technology assessment (chapter 3 of this TSD). In order to 

provide direct comparison between standing pilot and electronic ignition costs, DOE 

selected models within each hearth product group from the same manufacturer and within 

the same product line.

To compare only the cost difference between standing pilot and electronic 

ignition models in each product group, DOE considered only those subassemblies 

necessary to constitute the baseline unit and both ignition types. Items that were not 

common between ignition types and necessary to determining the cost difference for the 

ignition system only were excluded. For example, remote controls and accompanying 

remote receivers may be found on electronic ignition systems but not with manual safety 

pilot ignition systems. Because the goal of the analysis is to directly compare the change 

in MPC related to the ignition system only, remote controls and remote receivers were 

not included, as they are often included as a premium feature but are not required for 

implementing an electronic ignition. If, in the case of electronic ignition systems, the 

electronic control module for the intermittent pilot ignition system was found to also 

perform remote receiver functions, the components associated with the remote receiver 

functions were not considered in the analysis.

Using the data gathered from the physical teardowns, DOE characterized each 

component according to its weight, dimensions, material, quantity, and the manufacturing 

processes used to fabricate and assemble it. DOE collected additional component 

information during the manufacturer interviews.

DOE did not explicitly identify the model number or manufacturer of the units it 

tore down because this could expose sensitive information about individual 

manufacturers’ products. 

5-6
5.5.2 Baseline Units

DOE selected baseline units for the teardown analysis as a comparison for more 

efficient designs. Typically, DOE defines baseline products as those with energy 

efficiencies equal to the current federal energy conservation standards, and which are 

representative of the minimum technology and lowest costing product that a 

manufacturer can produce that provides basic functionality and utility to the consumer. In 

this case, because there is currently no federal energy standard for hearth products, DOE 

selected as baseline units those with continuously burning pilot designs. DOE then used 

the baseline units as reference points to compare the technology and cost of products with 

electronic ignition systems. The characteristics of the baseline units are described further 

in section 5.3.1.

5.6 COST MODEL 

5.6.1 Generation of Bills of Materials

The end result of each teardown is a structured BOM. DOE developed structured 

BOMs for each of the teardowns. Structured BOMs describe each product part and its 

relationship to the other parts in the estimated order in which manufacturers assembled 

them. The BOMs describe each fabrication and assembly operation in detail, including 

the type of equipment needed (e.g., presses, drills), the process cycle times, and the labor 

associated with each manufacturing step. 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 classifications into raw 

materials or purchased parts were based on DOE’s previous industry experience, recent 

information in trade publications, and discussions with high- and low-volume original 

equipment manufacturers (OEMs). DOE also visited manufacturing plants to reinforce its 

understanding of the industry’s current manufacturing practices for hearth products.

For purchased parts, the purchase price is estimated based on volume-variable 

price quotations and detailed discussions with manufacturers and component suppliers.

For fabricated parts, the prices of “raw” materials (e.g., tube, sheet metal) are estimated 

on the basis of 5-year averages (see section 5.6.4.4). The cost of transforming the 

intermediate materials into finished parts is estimated based on current industry pricing.

5.6.2 Cost Structure of the Spreadsheet Models

The last step was to convert the BOM information into dollar values. To perform 

this task, DOE collected information on labor rates, tooling costs, raw material prices, 

and other factors. DOE assumed values for these parameters using internal expertise and 

confidential information available to DOE contractors. Although most of the assumptions 

are manufacturer specific and cannot be revealed, section 5.6.4.3 provides a discussion of 

the values used for each assumption.

5-7
In summary, DOE assigned costs of labor, materials, and overhead to each part 

whether purchased or produced in house. DOE then aggregated single-part costs into 

major assemblies (e.g., ignition assembly, controls, burner, packaging.) and summarized 

these costs in a worksheet. During interviews with manufacturers, DOE showed key 

estimates from the cost model and asked for feedback. Because this engineering analysis 

is focused on ignition components, DOE was particularly interested in feedback 

regarding purchase prices for key ignition components. DOE considered any information 

manufacturers gave that was relevant to the cost model and incorporated it into the 

analysis as appropriate. 

5.6.3 Cost Model and Definitions

Once DOE disassembled selected units, gathered information from manufacturer 

catalogs on additional products, and identified technologies, DOE created an appropriate 

manufacturing cost model that could translate physical information into MPCs. The cost 

model is based on production activities and divides factory costs into the following 

categories:

• Materials: Purchased parts (e.g.,. gas valves, pilot assemblies ), raw materials 

(e.g., cold rolled steel), and indirect materials that are used for processing and 

fabrication.

• Labor: Fabrication, assembly, indirect, and supervisor labor. Fabrication and 

assembly labor cost are burdened with benefits and supervisory costs.

• Overhead: Equipment, tooling, and building depreciation, as well as utilities, 

equipment and tooling maintenance, insurance, and property taxes.

5.6.3.1 Cost Definitions

Because there are many different accounting systems and methods to monitor 

costs, DOE defined the above terms as follows:

• Direct material: Purchased parts (outsourced) plus manufactured parts (made in

house from raw materials).

• Indirect material: Material used during manufacturing (e.g., welding rods, 

adhesives).

• Fabrication labor: Labor associated with in-house piece manufacturing.

• Assembly labor: Labor associated with final assembly.

• Indirect labor: Labor costs that scaled with fabrication and assembly labor. This 

included the cost of technicians, manufacturing engineering support, stocking, 

etc., that were assigned on a span basis.

• Equipment and plant depreciation: Money allocated to pay for initial equipment 

installation and replacement as the production equipment wears out.

• Tooling depreciation: Cost for initial tooling (including non-recurring engineering 

and debugging of the tools) and tooling replacement as it wears out.

• Building depreciation: Money allocated to pay for the building space and the 

conveyors that feed and/or make up the assembly line.

5-8
• Utilities: Electricity, gas, telephones, etc.

• Maintenance: Annual money spent on maintaining tooling and equipment.

• Insurance: Appropriated as a function of unit cost.

• Property Tax: Appropriated as a function as unit cost.

5.6.4 Cost Model Assumptions Overview

In converting physical information about the product into cost information, DOE 

reconstructed manufacturing processes for each component using internal expertise and 

knowledge of the methods used by the industry. DOE used assumptions regarding the 

manufacturing process parameters (e.g., equipment use, labor rates, tooling depreciation, 

and cost of purchased raw materials) to determine the value of each component. DOE 

then summed the values of the components into assembly costs and, finally, the total 

product cost. The product cost included the material, labor, and overhead costs associated 

with the manufacturing facility. The labor costs included fabrication, assembly, indirect,

direct, and supervisor labor rates, including the associated overhead. The labor costs were 

determined by the type of product manufactured at the factory. Overhead costs included 

equipment depreciation, tooling depreciation, building depreciation, utilities, equipment, 

tooling maintenance, insurance, property, and taxes.

Using the information gathered during manufacturer interviews, DOE updated

the cost model to address manufacturer comments, particularly with respect to purchased 

parts for ignition assemblies. These changes involved updating component and material 

pricing and production volumes. DOE used a continuous refinement process to update 

information. Changes to the cost model were made immediately after interviews so that 

refined data could be presented to the next manufacturer. Positive feedback from 

manufacturers presented with refined data confirmed the accuracy of the changes. 

The next sections discuss specific assumptions about outsourcing, factory 

parameters, production volumes, and material prices. When the assumptions are 

manufacturer specific, they are presented as industry averages to prevent disclosure of 

confidential information.

5.6.4.1 Fabrication Estimates

DOE characterized parts based on whether manufacturers purchased them from 

outside suppliers or fabricated them in house. For purchased parts, DOE estimated the 

purchase price. For fabricated parts, DOE estimated the price of raw materials (e.g., tube, 

sheet metal) and the cost of transforming them into finished parts. Whenever possible, 

DOE obtained price quotes directly from the manufacturers’ suppliers.

DOE based the manufacturing operations assumptions on internal expertise, 

interviews with manufacturers, and manufacturing facilities site visits. The major 

manufacturer processes identified and developed for the spreadsheet model are listed in 

Table 5.6.1.

5-9
Table 5.6.3 Cost Model In-House Manufacturing Operation Assumptions

Fabrication Finishing Assembly/Joining Quality Control

Fixturing Painting Adhesive Bonding Inspecting and Testing

Stamping/Pressing Powder Coating Spot Welding

Turret Punch Seam Welding

Tube Forming Packaging

Brake Forming Clinching

Cutting and Shearing

Hand Bending

Drilling

Concrete

5.6.4.2 Production Volumes Assumptions

A manufacturer’s production volumes vary depending on several factors, 

including market share, the type of product produced (e.g., fireplace, gas log set), and if 

the manufacturer produces other similar products (e.g., direct heating equipment,

barbeque grills ). For hearth products, DOE based production volume assumptions on 

data provided by the Hearth, Patio, and Barbecue Association (HPBA) and information 

obtained during manufacturer interviews.

The data provided by HPBA included ranges of the market share for nine product 

categories. DOE used these data to determine average market sizes for each of its product 

groups used in its analysis. Based on these weights, the historical shipments data also 

provided by HPBA, the number of manufacturers identified for each product group, and 

feedback from manufacturers, DOE selected representative production volumes. The 

production volumes are presented in Table 5.6.2

5.6.4.3 Factory Parameters Assumptions

DOE used information gathered from publicly available literature and analysis of 

common industry practices to formulate factory parameters for each type of 

manufacturer. Table 5.6.2 lists DOE’s assumptions for manufacturers of hearth products.

5-10
Table 5.6.4 Hearth Product Production Volumes and Factory Parameter 

Assumptions

Parameter

Plant

Capacity 

units /year

Actual 

Annual 

Production 

Volume 

units/year

Fabrication 

Labor 

Wages 

$/hr

Fringe 

Benefits 

Ratio

Estimate

Vented 

Fireplaces, 

Inserts, 

Stoves

12,000 10,000 16 50%

Unvented 

Fireplaces, 

Inserts, 

Stoves

2,400 2,000 16 50%

Vented Gas 

Log Sets 2,400 2,000 16 50%

Unvented 

Gas Log 

Sets

6,000 5,000 16 50%

Outdoor 3,600 3,000 16 50%

The main difference among the assumptions for all hearth products is the 

production volumes. Labor rates were assumed to be the same across hearth products.

Approximate labor rates are based on published labor rates for the hearth industry from 

the U.S. Department of Labor.

1

5.6.4.4 Material Prices Assumptions

DOE determined the cost of raw materials using publicly available information 

such as the American Metals Market,2 interviews with manufacturers, and direct 

discussions with material suppliers. Common metals used in the fabrication of hearth

products include plain cold rolled steel (CRS), CRS tubing, and stainless steel. To 

account for fluctuations, DOE used a 5-year average of metal prices from the Bureau of 

Labor Statistics Producer Price Indices (PPIs) spanning 2009 to 2014 with an adjustment 

to 2014$.3 DOE used the PPIs for copper rolling, drawing, and extruding and steel mill 

products, and made the adjustments to 2014$ using the gross domestic product implicit 

price deflator.4 DOE also used a 5-year average in material prices from 2009 to 2014 to 

normalize changes to some extent to better represent long-term material price averages.

Table 5.6.3 shows the 5-year average metal prices DOE used for the analysis.

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Table 5.6.5 Five-Year Material Prices (2009 to 2014)

Metals Cost $/lb

2014$

Plain Cold Rolled Steel 0.432

Aluminized CRS 0.528

CRS Tube 0.803

CRS Wire 1.148

Aluminized CRS Tube 0.867

Stainless Steel 1.507

Stainless Steel Tube 2.377

Brass (Sand Cast) 2.111

Concrete 0.028

5.6.5 Manufacturing Production Cost

Once the cost estimate for each teardown unit was finalized, a detailed summary 

was prepared for relevant components, subassemblies, and processes. The BOM thus 

details all aspects of unit costs. DOE totaled the cost of materials, labor, and direct 

overhead used to manufacture a product to calculate the MPC.a Figure 5.6.1 shows the 

general breakdown of costs associated with manufacturing a product.

Figure 5.6.1 Full Production Costs 

a When viewed from the company-wide perspective, the sum of all material, labor, and overhead costs 

equals the company’s sales cost, also referred to as the cost of goods sold (COGS).

5-12

 
The full cost of product is broken down into two main costs, the full production 

cost or MPC, and the non-production cost. The non-production cost is equal to the 

manufacturer markup minus profits.

Following the development of the MPCs, DOE reviewed its major cost estimates 

by conducting interviews with hearth products manufacturers. DOE presented the data 

and estimates for purchased ignition components to manufacturers who provided 

feedback and validation. DOE used a continuous refinement process by incorporating 

each manufacturer’s feedback before each set of interviews. As a result, DOE developed 

MPCs for use in the engineering analysis and subsequent analyses.

5.7 MPC BREAKDOWN

After DOE incorporated all of the assumptions into the cost model, the different 

production cost percentages were calculated. The product cost percentages are used to

validate the assumptions by comparing them to manufacturers’ actual financial data 

published in annual reports, where possible, along with feedback from manufacturers 

during interviews. DOE also used these figures in the MIA (see chapter 12 of this TSD).

DOE calculated the average product cost percentages by product group due to the 

variations in production volumes, fabrication and assembly costs, and other assumptions 

that affect the calculation of a unit’s total MPC. Table 5.7.1 shows the different

percentages for the production costs that make up the total product MPC.

Table 5.7.1 Total Product Manufacturer Production Cost Breakdown for Hearth 

Product Groups

Percentage Cost Breakdown by Hearth Product Group

(Baseline)

Materials

%

Labor

%

Depreciation

%

Overhead

%

Vented Fireplaces, 

Inserts, and Stoves 43.4% 22.4% 22.1% 12.1%

Unvented Fireplaces, 

Inserts, and Stoves 38.2% 25.0% 24.8% 12.0%

Vented Gas Log Sets 29.5% 23.5% 32.8% 14.3%

Unvented Gas Log Sets 38.9% 28.9% 21.2% 11.0%

Outdoor Products 55.6% 15.8% 18.7% 9.9%

5.8 MANUFACTURER MARKUP

DOE applies a manufacturer markup to calculated MPCs in order to estimate 

MSPs. The manufacturer markup is a multiplier that covers both non-production costs

(e.g., selling, general and administrative expenses, research and development , interest 

expenses) and profit. The manufacturer markup can be thought of as: 

5-13
Markup = Gross Profit______

Cost of Goods Sold (COGS)

Where:

Gross Profit = Revenue – COGS

The manufacturer markup is distinct from, and greater than, a manufacturer profit 

margin, or gross margin percentage, which is calculated as:

Gross Margin Percentage = Gross Profit

Revenue

For the hearth industry, DOE estimated a set of base case manufacturer markups 

using data developed as part of the 2010 Energy Conservation Standard for Residential 

Water Heaters, Direct Heating Equipment, and Pool Heaters (75 FR 20112). DOE then 

solicited feedback on its markup estimates during confidential manufacturer interviews. 

Based on manufacturer feedback, DOE calculated and applied an average baseline 

markup of 1.45 percent for all gas hearth products. The MSPs estimated using this 

markup reflect the price at which manufacturers recover both production and nonproduction costs and earn a profit. Additional markups across the distribution chain (e.g., 

distributors, retailers, contractors) account for the final consumer price. 

For further discussion of manufacturer markups, see the manufacturer impact analysis 

(chapter 12 of this TSD). For further discussion of final consumer prices, see the lifecycle cost analysis and payback period analysis (chapter 8 of this TSD), and the national 

impact analysis (chapter 10 of this TSD).

5.9 ENGINEERING ANALYSIS SUMMARY OF RESULTS

Throughout the rulemaking process, the results from the engineering analysis are 

used in the LCC analysis to determine consumer prices for hearth products. The 

manufacturer production costs for the baseline assembly by product group are listed in

Table 5.9.1. DOE also determined the incremental cost to each product group associated 

with the switch to the electronic ignition design; these are also presented in Table 5.9.1. 

5-14
Table 5.9.2 Baseline Full Assembly Manufacturer Production Costs and 

Incremental Electronic Ignition System Costs for Hearth Products

Hearth Product Analysis Group

Baseline 

MPC

$

Incremental 

Cost for EIS 

$

Vented Fireplaces, Inserts, and Stoves 322 28

Unvented Fireplaces, Inserts, and Stoves 281 32

Vented Gas Log Sets 190 70

Unvented Gas Log Sets 208 56

Outdoor Products 210 55

5-15
5.10 REFERENCES

1 U.S. Department of Labor, Bureau of Labor Statistics, Wages by Area and 

Occupation. Last accessed August 2014. <http://www.bls.gov/bls/blswage.htm>. 

2 American Metals Market. Last accessed June 2014. <http://www.amm.com>.

3 U.S. Department of Labor, Bureau of Labor Statistics, Producer Price Indices.

Last accessed June 2014. <http://www.bls.gov/ppi>. 

4 U.S. Department of Commerce, Bureau Economic Analysis, Gross Domestic 

Product Implicit Price Deflator. Last accessed June 2014. 

<http://www.bea.gov/iTable/iTable.cfm?reqid=9&step=3&isuri=1&903=13#reqid=9&ste

p=3&isuri=1&903=13>.

5-16

 
6-i

CHAPTER 6. MARKUPS ANALYSIS

TABLE OF CONTENTS

6.1 INTRODUCTION ........................................................................................................... 6-1

6.2 DISTRIBUTION CHANNELS ....................................................................................... 6-1

6.3 APPROACH FOR MANUFACTURER MARKUP ....................................................... 6-3

6.4 APPROACH FOR WHOLESALER AND CONTRACTOR MARKUPS ..................... 6-3

6.4.1 Wholesaler Markup........................................................................................ 6-4

6.4.2 Mechanical and General Contractor Markups............................................... 6-5

6.5 DERIVATION OF MARKUPS ...................................................................................... 6-6

6.5.1 Manufacturer Markup .................................................................................... 6-6

6.5.2 Wholesaler Markup........................................................................................ 6-7

6.5.3 Mechanical Contractor Markups.................................................................... 6-8

6.5.3.1 Aggregate Markups for Mechanical Contractors........................................... 6-8

6.5.3.2 Markups for Mechanical Contractors in the Replacement and New 

Construction Markets..................................................................................... 6-9

6.5.4 General Contractor Markups........................................................................ 6-11

6.6 DERIVATION OF CENSUS REGIONS MARKUPS.................................................. 6-13

6.6.1 Estimation of Wholesaler Markups ............................................................. 6-13

6.6.2 Estimation of Mechanical Contractor Markups........................................... 6-14

6.6.3 Estimation of General Contractor Markups................................................. 6-16

6.7 SALES TAX .................................................................................................................. 6-18

6.7 OVERALL MARKUPS................................................................................................. 6-20

LIST OF TABLES

Table 6.5.1 Wholesaler Expenses and Markups..................................................................... 6-7

Table 6.5.2 Mechanical Contractor Expenses and Markups Based on Census Bureau 

Data ...................................................................................................................... 6-8

Table 6.5.3 Baseline Markup, All Mechanical Contractors.................................................. 6-10

Table 6.5.4 Baseline Markups for the Replacement and New Construction Markets, 

All Mechanical Contractors............................................................................... 6-10

Table 6.5.5 Markups for the Replacement and New Construction Markets......................... 6-11

Table 6.5.6 Residential Building General Contractor Expenses and Markups..................... 6-12

Table 6.6.1 Wholesaler Markups for Hearth Products.......................................................... 6-14

Table 6.6.2 Mechanical Contractor Markups Weighted by Housing Projections in 

2021 for Hearth Products................................................................................... 6-16

Table 6.6.3 General Contractor Markups Weighted by Housing Projections in 2021 for 

Hearth Products.................................................................................................. 6-18

Table 6.6.4 Average Sales Tax Rates by RECS 2009 Region.............................................. 6-20

Table 6.7.2 Markups for Hearth Products............................................................................. 6-22

Table 6.7.3 Summary of Overall Markup by Hearth Product Class..................................... 6-22
6-ii

LIST OF FIGURES

Figure 6.2.1 Distribution Channels for Hearth Products.......................................................... 6-2
6-1

CHAPTER 6. MARKUPS ANALYSIS

6.1 INTRODUCTION

To carry out its analyses, the U.S. Department of Energy (DOE) needed to determine the 

cost to the consumer of baseline products and the cost of more efficient units the consumer 

would purchase under new energy conservation standards. DOE calculated such costs based on 

engineering estimates of manufacturing product costs (see chapter 5) plus appropriate markups 

for the various distribution channels for hearth products. 

Generally, companies mark up the price of a product to cover their business costs and

profit margin. In financial statements, the gross margin is the difference between the company 

revenue and the company cost of sales or cost of goods sold (CGS). The gross margin takes 

account of the expenses of companies in the distribution channel, including overhead costs 

(sales, general, and administration); research and development (R&D) and interest expenses; 

depreciation; and taxes—and company profits. In order for sales of a product to contribute 

positively to company cash flow, the product’s markup must be greater than the corporate gross 

margin. Products command lower or higher markups, depending on company expenses 

associated with the product and the degree of market competition. 

For wholesalers and contractors, DOE estimated a baseline markup and an incremental 

markup. DOE defines a baseline markup as a multiplier that converts the manufacturer selling 

price (MSP) of equipment with baseline efficiency to the consumer purchase price for the 

equipment at the same baseline efficiency level. An incremental markup is defined as the 

multiplier to convert the incremental increase in manufacturer selling price of higher efficiency 

equipment to the consumer purchase price for the same equipment. Because companies mark up 

the price at each point in the distribution channel, both baseline and incremental markups are 

dependent on the distribution channel, as described in section 6.2. 

The components used to produce a hearth product are usually purchased by hearth 

product manufacturers who install the ignition device in a hearth product. From this point, the 

ignition devices are passed along the distribution channels as part of hearth products. Essentially, 

various markups applied to these products by different market participants are also the markups 

applied to hearth product ignition devices, whose manufacturing costs account for a portion of 

the total manufacturing costs of the finished products. Therefore, DOE developed the markup 

analysis for hearth product ignition devices based on hearth products.

6.2 DISTRIBUTION CHANNELS

The appropriate markups for determining the consumer product price depend on the type 

of distribution channel through which products move from manufacturers to purchasers. At each 

point in the distribution channel, companies mark up the price of the product to cover their 

business costs and profit margin.
6-2

There are two primary distribution channels describing the way most products pass from 

the manufacturer to the consumer, one applying to hearth products installed in replacement 

markets or by new owners and the other applying to hearth products that are installed in new 

construction. For replacement applications, most sales go through wholesalers to mechanical 

contractors, and then to consumers. The new construction distribution channel includes an 

additional link in the chain—the general contractor. Thus, DOE defined two distribution 

channels for the purposes of estimating markups for hearth products, as shown in Figure 6.2.1.

Replacement and New Owner:

New Construction:

Figure 6.2.1 Distribution Channels for Hearth Products

Based on information provided from manufacturer interviews, there is another possible 

distribution channel that includes a retail store instead of a wholesaler. In this case, the 

manufacturer sells the equipment to a retailer, who in turn sells it to a mechanical contractor, 

who in turn sells it to the consumer. However, DOE does not have enough information at this 

point to make a separate markup estimation for this distribution channel. DOE assumed that the 

retailer markup is similar to the wholesaler markup. DOE is also aware that there may be two 

additional distribution channels for hearth products: (1) an online distribution channel where 

manufacturers sell the products to online retailers who in turn sell them directly to consumers, 

and (2) a rebranding distribution channel where wholesalers or retailers negotiate good pricing 

from the hearth product manufacturer based on high volumes and have the product customized to 

carry their name, and then send it through their normal distribution channel to the contractors. 

The former one mainly applies to the do-it-yourself (DIY) installation representing around two 

percent of the total HVAC shipments, which implies an even smaller fraction of the total hearth 

product shipments. For the latter one, DOE assumes that it would have the same overall markups 

as the conventional distribution channels. Although manufacturers may have lower margin, 

wholesalers and retailers would redistribute the profit throughout the distribution channel to have 

the final retail price comparable with products sold through conventional distribution channels. 

Due to the reasons mentioned above, DOE did not consider them in this analysis.

Manufacturer Wholesaler Mechanical

Contractor Consumer

Wholesaler Mechanical 

Contractor

General 

Contractor Manufacturer Consumer
6-3

6.3 APPROACH FOR MANUFACTURER MARKUP

DOE uses manufacturer markups to transform a manufacturer’s product cost into a 

manufacturer sales price. Detailed methodology to derive manufacturer markups were described 

in chapter 5 (Engineering Analysis).

6.4 APPROACH FOR WHOLESALER AND CONTRACTOR MARKUPS

DOE examined the manner in which wholesaler and contractor markups may change in 

response to changes in hearth product ignition system efficiency and other factors. Using the 

available data, DOE estimated that there are differences between incremental markups on 

incremental equipment costs of higher efficiency products and the baseline markup on direct 

business costs of products with baseline efficiency.

DOE derived the wholesaler and contractor markups from three key assumptions about 

the costs associated with hearth products. DOE based the wholesaler and mechanical contractor 

markups on firm-level income statement data, and based the general contractor markups on U.S. 

Census Bureau data for the residential building construction industry. DOE obtained the firm 

income statements from the Heating, Air-conditioning & Refrigeration Distributors International 

(HARDI) 2013 Profit Report and from the Air Conditioning Contractors of America (ACCA)

2005 Financial Analysis.1, 2 HARDI and ACCA are trade associations representing wholesalers 

and mechanical contractors, respectively. DOE used the financial data from the 2007 U.S. 

Census of Business for developing general contractor markups in the same form as the income 

statement data for wholesalers and mechanical contractors. These income statements break down 

the components of all costs incurred by firms that supply and install heating and air-conditioning 

equipment.a The key assumptions used to estimate markups using these financial data are:

1. The firm income statements faithfully represent the various average costs incurred by 

firms distributing and installing hearth products.

2. These costs can be divided into two categories: 1) costs that vary in proportion to the 

MSP of hearth products (variant costs); and 2) costs that do not vary with the MSP of 

hearth products (invariant costs).

3. Overall, wholesale and contractor prices for hearth products vary in proportion to the 

wholesaler and contractor costs for hearth products 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 

 a Wholesalers and mechanical contractors to which these reports refer handle multiple commodity lines.
6-4

conditioners and heat pumps, and boilers, the data provide the most accurate available indication 

of the expenses associated with hearth products.

Information obtained from the trade literature, 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 appliances. In the following discussion, 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).

In support of the third assumption, the HVAC wholesaler and contractor industry is 

competitive, and consumer demand for heating and air conditioning is inelastic, i.e., the demand 

is not expected to decrease significantly with an increase in the price of equipment. The large 

number of HVAC firms listed in the 2007 Census indicates the competitive nature of the market. 

For example, there are more than 700 HVAC manufacturers,3 5,300 wholesalers of heat pumps 

and air-conditioning equipment,4 more than 170,000 general residential contractors, and 91,000 

HVAC contractors5 listed in the 2007 Census. Following standard economic theory, competitive 

firms facing inelastic demand either set prices in line with costs or quickly go out of business.6

DOE concluded that markups for more-efficient products are unlikely to be proportional 

to all direct costs. When the wholesaler’s purchase price of equipment increases, for example, 

only a fraction of a business’ expenses increases, while the remainder may stay relatively 

constant. For example, if the unit price of a hearth product unit increases by 30 percent due to 

improved efficiency of the ignition system, it is unlikely that the cost of secretarial support in an 

administrative office will increase by 30 percent also. Therefore, DOE assumed that incremental 

markups cover only those costs that scale with a change in the MSP (variant costs).

6.4.1 Wholesaler Markup 

Using the above assumptions, DOE developed baseline and incremental markups for 

wholesalers using the firm income statement from the HARDI 2013 Profit Report (appendix 6A). 

The baseline markups cover all of the wholesaler’s costs (both invariant costs and variant costs). 

Here, variant costs were defined as costs that likely vary in proportion to the change in MSP 

induced by increased efficiency standards; in contrast, invariant costs were defined as costs that 

are unlikely to vary in proportion to the change in MSP due to increased efficiency standards. 

DOE calculated the baseline markup for wholesalers using the following equation:

WHOLE

WHOLE WHOLE WHOLE

WHOLE

WHOLE WHOLE

BASE CGS

CGS IVC VC

CGS

CGS GM MU+ ( + ) = + =

Eq. 6.1

Where:

MUBASE = baseline wholesaler markup, 
6-5

CGSWHOLE = wholesaler cost of goods sold,

GMWHOLE = wholesaler gross margin, 

IVCWHOLE = wholesaler invariant costs, and

VCWHOLE = wholesaler variant costs.

Incremental markups are coefficients that relate the change in the MSP of more energyefficient models, or those products that meet the requirements of new energy conservation 

standards, to the change in the wholesaler sales price. Incremental markups cover only those 

costs that scale with a change in the MSP (variant costs, VC). DOE calculated the incremental 

markup (MUINCR) for wholesalers using the following equation:

WHOLE

WHOLE WHOLE

INCR CGS

CGS VC MU + =

Eq. 6.2

Where:

MUINCR = incremental wholesaler markup,

CGSWHOLE = wholesaler cost of goods sold, and

VCWHOLE= wholesaler variant costs.

6.4.2 Mechanical and General Contractor Markups 

The type of financial data used to estimate markups for wholesalers is also available for 

mechanical contractors and general contractors from the 2007 Economic Census and ACCA 

2005 Financial Analysis. To estimate mechanical contractor markups for hearth products, DOE 

collected financial data from the Plumbing and HVAC Contractors (NAICS 23822) series from 

the 2007 Economic Census and from ACCA 2005 Financial Analysis. To estimate general 

contractor markups for hearth products, DOE collected data from the Residential Building 

Construction series from the 2007 Economic Census, which is the aggregation of New SingleFamily General Contractors (NAICS 236115), New Multifamily Housing Construction (NAICS 

236116), New Housing Operative Builders (NAICS 236117), and Residential Remodelers

(NAICS 236118).

ACCA financial data provide GM as percent of sales for the mechanical contractor 

industry. For mechanical contractors, the baseline markup can be derived from the ACCA data 

with the following equation:

(%) (%)

(%)

Sales GM

Sales MUBASE − =

Eq. 6.3

The U.S. Census data include the number of establishments, payroll for construction 

workers, value of construction, cost of materials, and cost of subcontracted work at both state 
6-6

and national levels. DOE calculated the baseline markup for mechanical contractors and general 

contractors using the following equation:

Pay MatCost SubCost

V MU CONSTRUCT

BASE

+ + =

Eq. 6.4

Where:

MUBASE = baseline mechanical contractor or general contractor markup,

VCONSTRUCT = value of construction,

Pay = payroll for construction workers,

MatCost = cost of materials, and

SubCost = cost of subcontracted work.

Analogously, DOE estimated the incremental mechanical contractor and general 

contractor markups by only marking up those costs that scale with a change in the MSP (variant 

costs, VC) for more energy-efficient products. As stated above, 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 Census cost data 

in each major cost category and estimated markups using the following equation: 

CONT

CONT CONT

INCR CGS

CGS VC MU + =

Eq. 6.5

Where:

MUINCR = incremental contractor markup,

CGSCONT = contractor cost of goods sold, and

VCCONT = contractor variant costs.

6.5 DERIVATION OF MARKUPS

6.5.1 Manufacturer Markup

The methodology DOE used to determine the manufacturer markup for hearth products is 

similar to the methodology described in chapter 5 of the Residential Furnace and Central AirConditioning products Direct Final Rule technical support document (TSD).b DOE used U.S. 

Security and Exchange Commission (SEC) 10-K reports from publicly owned hearth product

 b The TSD for the direct final rule for HVAC products is available at the following website: 

www.regulations.gov/#!documentDetail;D=EERE-2011-BT-STD-0011-0012.
6-7

manufacturing companies to estimate manufacturer markups. The estimated manufacturer 

markup for hearth products is 1.42.

6.5.2 Wholesaler Markup

Wholesalers reported median data in a confidential survey that HARDI conducted of 

member firms. In the survey, HARDI itemized revenues and costs into cost categories, including 

direct equipment expenses (cost of goods sold), labor expenses, occupancy expenses, other 

operating expenses, and profit. DOE presents these data in full in appendix 6A. Table 6.5.1

summarizes them at the national aggregated level as cost-per-dollar sales revenue in the first data 

column. These wholesaler markups are applicable to hearth products. 

Table 6.5.1 Wholesaler Expenses and Markups

Descriptions

Per Dollar

Sales Revenue

$

Per Dollar Cost 

of Goods

$

Direct Cost of Equipment Sales: Cost of goods sold 0.739 1.000

Labor Expenses: Salaries and benefits 0.151 0.204

Occupancy Expense: Rent, maintenance, and utilities 0.035 0.047

Other Operating Expenses: Depreciation, advertising, and 

insurance.

0.052 0.070

Operating Profit 0.023 0.031

Wholesaler Baseline Markup (MUWHOLE BASE) 1.353

Incremental Markup (MUWHOLE INCR) 1.101

Source: Heating, Air Conditioning & Refrigeration Distributors International. 2013. 2013 Profit Report (2012Data).

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 $0.05, 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). The data in column two show that, for every $1.00 the wholesaler spends on 

equipment costs, the wholesaler allocates $0.204 to cover labor costs, $0.047 to cover occupancy 

expenses, $0.070 for other operating expenses, and $0.031 in profits. This totals to $1.353 in 

sales revenue earned for every $1.00 spent on equipment costs. Therefore, the wholesaler 

baseline markup (MUWHOLE BASE) is 1.353 ($1.353 ÷ $1.00). 

DOE used the data in column two to estimate the incremental markup. The incremental 

markup depends on which of the costs in Table 6.5.1 are variant and which are invariant with 

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 $1.353, implying that the 

incremental markup is 1.353, or the same as the baseline markup. At the other extreme, if none 
6-8

of the 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. DOE believes that the labor and 

occupancy costs will be invariant and that the other operating costs and profit will scale with the 

MSP (i.e., be variant). In this case, for a $1.00 increase in the MSP, the wholesale price will 

increase to match changes in ”other” operating costs and operating profit of $0.075, which when 

divided by 73.9 cents in cost of goods sold yields an increase of $0.101, giving a wholesaler 

incremental markup (MUWHOLE INCR) of 1.101. See appendix 6A for cost details.

6.5.3 Mechanical Contractor Markups 

6.5.3.1 Aggregate Markups for Mechanical Contractors

The 2007 Economic Census provides Geographic Area Series for the Plumbing and 

HVAC Contractors (NAICS 23822) sector, which contains national average sales and cost data,

including value of construction, cost of subcontract work, cost of materials, and payroll for 

construction workers. It also provides the cost breakdown of gross margin, including labor 

expenses, occupancy expenses, other operating expenses, and profit. The gross margin provided 

by the U.S. Census is disaggregated enough that DOE was able to determine the invariant (labor 

and occupancy expenses) and variant (other operating expenses and profits) costs for this 

particular sector. By using the equation mentioned above, baseline and incremental markups 

were estimated. The markup results representing the plumbing and HVAC contractor industry at 

the national aggregated level are presented in Table 6.5.2. (Appendix 6A contains the full set of 

data.)

Table 6.5.2 Mechanical Contractor Expenses and Markups Based on Census Bureau 

Data

Description

Mechanical Contractor Expenses 

or Revenue

Per Dollar

Sales Revenue

$

Per Dollar 

Cost of Goods

$

Direct Cost of Equipment Sales: Cost of goods sold 0.68 1.00

Labor Expenses: Salaries (indirect) and benefits 0.18 0.26

Occupancy Expense: Rent, maintenance, and utilities 0.02 0.03

Other Operating Expenses: Depreciation, advertising, 

and insurance.

0.08 0.12

Net Profit Before Taxes 0.04 0.06

Baseline Markup (MUMECH BASE): Revenue per dollar cost of goods 1.48

Incremental Markup (MUMECH INCR): Increased revenue per dollar 

increase in cost of goods sold

1.18

Source: U.S. Census Bureau. 2007. Plumbing, Heating, and Air-Conditioning Contractors. Sector 23: 238220. 

Construction: Industry Series, Preliminary Detailed Statistics for Establishments, 2007.
6-9

The first data column in Table 6.5.2 provides the cost of goods sold and a list of gross 

margin components as expenses per dollar of sales revenue. As shown in the table, the direct cost 

of sales represents about $0.68 per dollar sales revenue to the mechanical contractor, and the 

gross margin totals $0.32 per dollar sales revenue. DOE converted these expenses per dollar 

sales into revenue per dollar cost of goods sold by dividing each figure in the first data column 

by $0.68. For every $1.00 the mechanical contractor spends on equipment costs, the mechanical 

contractor earns $1.00 in sales revenue to cover the equipment cost and $0.48 to cover the other 

costs. This totals $1.48 in sales revenue earned for every $1.00 spent on equipment costs. This is 

equivalent to a baseline markup (MUMECH CONT BASE) of 1.48 for mechanical contractors. 

DOE used the data in column two in Table 6.5.2 to estimate the incremental markups, 

after classifying the costs as either invariant or variant. At one extreme, if all of the other costs 

scale with the equipment price (i.e., all costs are variant), the increase in general contractor price 

will be $1.48, implying that the incremental markup is 1.48 or the same as the baseline markup. 

At the other extreme, if none of the other costs are variant, then a $1.00 increase in the 

equipment price will lead to a $1.00 increase in the general contractor price, for an incremental 

markup of 1.0. DOE believes the labor and occupancy costs are invariant and the other operating 

costs and profit scale with the equipment price (i.e., are variant). In this case, for a $1.00 increase 

in the equipment price, the general contractor price will increase by $1.18, giving a general 

contractor incremental markup (MUMECH CONT INCR) of 1.18.

6.5.3.2 Markups for Mechanical Contractors in the Replacement and New 

Construction Markets

DOE derived the baseline and incremental markups for both replacement and new 

construction markets using the 2007 Economic Census industrial cost data7 supplemented with 

the most recent ACCA 2005 financial data.2 The 2007 Economic Census provides sufficient 

detailed cost breakdown for the Plumbing and HVAC Contractors (NAICS 23822) sector so that 

DOE was able to estimate baseline and incremental markups for mechanical contractors. 

However, the 2007 Economic Census does not separate the mechanical contractor market into 

replacement and new construction markets. In order to calculate markups for these two markets, 

DOE utilized 2005 ACCA financial data, which reports gross margin data for the entire 

mechanical contractor market and for both the replacement and new construction markets. 

The HVAC contractors, defined here as mechanical contractors, reported median cost 

data in an ACCA 2005 financial analysis of the HVAC industry. These data are shown in Table 

6.5.3.
6-10

Table 6.5.3 Baseline Markup, All Mechanical Contractors

Description

Contractor Expenses or Revenue

Per Dollar

Sales Revenue

$

Per Dollar 

Cost of Goods

$

Direct Cost of Equipment Sales: Cost of goods sold 0.7286 1.00

Gross Margin: Labor, occupancy, operating 

expenses, and profit

0.2714 0.372

Revenue: Baseline revenue earned per dollar cost of goods 1.372

Baseline Markup (MUMECH CONT BASE) 1.372

Source: Air Conditioning Contractors of America. 2005. Financial Analysis for the HVACR Contracting Industry.

Table 6.5.4 summarizes the gross margin and resulting baseline markup data for all 

mechanical contractors that serve the replacement and new construction markets. 

Table 6.5.4 Baseline Markups for the Replacement and New Construction Markets, All 

Mechanical Contractors

Description

Contractor Expenses or Revenue by Market Type

Replacement New Construction

Per Dollar

Sales 

Revenue

$

Per Dollar 

Cost of 

Goods

$

Per Dollar

Sales 

Revenue

$

Per Dollar 

Cost of 

Goods

$

Direct Cost of Equipment Sales: 

Cost of goods sold 0.7031 1.000 0.745 1.000

Gross Margin: Labor, occupancy, 

operating expenses, and profit 0.2969 0.422 0.255 0.342

Baseline Markup (MUMECH 

CONT BASE): Revenue per 

dollar cost of goods

NA 1.422 NA 1.342

% Difference from Aggregate 

Mechanical Contractor Baseline 

MU

NA 3.63% NA -2.20%

Source: Air Conditioning Contractors of America. 2005. Financial Analysis for the HVACR Contracting Industry.

Using the baseline markup data from Table 6.5.4 and results from Table 6.5.3, DOE 

calculated that the baseline markups for the replacement and new construction markets are 3.63

percent higher and 2.20 percent lower, respectively, than for all mechanical contractors serving 

all markets.

The markup deviations (i.e., 3.63 percent higher and 2.20 percent lower for the 

replacement and new construction markets, respectively) derived for all mechanical contractors 

were then applied to the baseline markup of 1.48 and the incremental markup of 1.18 estimated 

for the Plumbing and HVAC Contractors (NAICS 23822) sector in Table 6.5.2. DOE assumed 
6-11

that this deviation applies equally to the baseline and incremental markups calculated from the 

2007 Economic Census. The results of the baseline and incremental markups for the replacement 

and new construction markets served by mechanical contractors are shown in Table 6.5.5.

Table 6.5.5 Markups for the Replacement and New Construction Markets

Baseline Markup Incremental Markup

Replacement Market 1.53 1.22

New Construction Market 1.44 1.16

6.5.4 General Contractor Markups 

DOE derived markups for general contractors from U.S. Census Bureau data for the 

residential building construction sector to reflect application of hearth products.

8 The residential

construction sector includes establishments primarily engaged in construction work, including 

new construction work, additions, alterations, and repairs of residential buildings.

9 The U.S. 

Census Bureau data for the construction sector include detailed statistics for establishments with 

payrolls, similar to the data reported by HARDI for wholesalers. The primary difference is that 

the U.S. Census Bureau reports itemized revenues and expenses for the construction industry as a 

whole in total dollars 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, represent revenues and expenses for an average or typical 

contracting business. Similar to the data for wholesalers, Table 6.5.6 summarizes the expenses 

for general contractors in residential building construction at the national aggregated level as 

expenses per dollar sales revenue in the first data column. (Appendix 6A contains the full set of 

data.)
6-12

Table 6.5.6 Residential Building General Contractor Expenses and Markups

Description

General Contractor Expenses 

or Revenue

Per Dollar

Sales Revenue

$

Per Dollar 

Cost of Goods

$

Direct Cost of Equipment Sales: Cost of goods sold 0.68 1.00

Labor Expenses: Salaries (indirect) and benefits 0.08 0.12

Occupancy Expense: Rent, maintenance, and utilities 0.01 0.01

Other Operating Expenses: Depreciation, advertising, and 

insurance.

0.06 0.09

Net Profit Before Taxes 0.17 0.25

Baseline Markup (MUGEN CONT BASE): Revenue per dollar cost of goods 1.47

Incremental Markup (MUGEN CONT INCR): Increased revenue per dollar 

increase in cost of goods sold

1.34

Source: U.S. Census Bureau. 2007. Residential Building Construction. Sector 23: 236115-236118. Construction: 

Industry Series: Preliminary Detailed Statistics for Establishments: 2007.

As shown in the first column, the direct cost of sales represents about $0.68 per dollar 

sales revenue to the general contractor. Labor expenses represent $0.08 per dollar sales revenue, 

occupancy expenses represent $0.01 per dollar sales revenue, other operating expenses represent 

$0.03, and profit makes up $0.20 per dollar sales revenue.

DOE converted these expenses per dollar sales into revenue per dollar cost of goods sold, 

by dividing each figure in the first data column by $0.68. The data in column two show that, for 

every $1.00 the general contractor spends on equipment costs, the general contractor earns $1.00 

in sales revenue to cover the equipment cost, $0.12 to cover labor costs, $0.01 to cover 

occupancy expenses, $0.09 for other operating expenses, and $0.25 in profits. This totals to 

$1.47 in sales revenue earned for every $1.00 spent on equipment costs. Thus, the general 

contractor baseline markup (MUGEN CONT BASE) is 1.47.

DOE used the data in column two in Table 6.5.6 to estimate the incremental markups, 

after classifying the costs as either invariant or variant. At one extreme, if all of the other costs 

scale with the equipment price (i.e., all costs are variant), the increase in general contractor price 

will be $1.47, implying that the incremental markup is 1.47, or the same as the baseline markup. 

At the other extreme, if none of the other costs are variant, then a $1.00 increase in the 

equipment price will lead to a $1.00 increase in the general contractor price, for an incremental 

markup of 1.0. DOE believes the labor and occupancy costs are invariant and the other operating 

costs and profit scale with the equipment price (i.e., are variant). In this case, for a $1.00 increase 

in the equipment price, the general contractor price will increase by $1.34, giving a general 

contractor incremental markup (MUGEN CONT INCR) of 1.34.
6-13

6.6 DERIVATION OF CENSUS REGIONS MARKUPS

In this analysis, DOE assumed a market saturation rate for hearth products that varies by 

geographical region defined by the 2009 Residential Energy Consumption Survey (RECS

2009),

10 based on the housing projections for the year 2021. Therefore, regional markups were 

calculated for hearth products.

Wholesalers and mechanical and general contractors in the hearth products industry

were divided into the 30 regionsc provided by RECS 2009. Regional baseline and incremental 

markups were derived using the region/state level data from the 2013 HARDI Profit Report and 

the 2007 Economic Census. 

6.6.1 Estimation of Wholesaler Markups

Based on the regional income statement from the 2013 HARDI Profit Report, DOE 

estimated baseline and incremental markups for the seven HARDI regions (Northeastern, MidAtlantic, Southwestern, Great Lakes, Central, Southwestern, and Western) using the 

methodology shown in section 6.4.1. Next, each state in each region was assigned the HARDI 

regional baseline and incremental markups for the region to which it belongs. Then, DOE 

assigned all states to one of the 30 RECS 2009 regions used in the analysis and then calculated 

2021 housing projections-weighted baseline and incremental markup averages for each region. 

The results are summarized in Table 6.6.1.

 c RECS 2009 provides 27 regions (also called reportable domains). The 27th region includes Oregon, Washington, 

Alaska, and Hawaii. DOE subdivided Alaska and Hawaii into separate regions (28 and 29, respectively) based on 

cooling and heating degree days. In addition, West Virginia, which is in RECS region 14, was disaggregated into 

region 30 based on cooling and heating degree days. See appendix 7B for more details.
6-14

Table 6.6.1 Wholesaler Markups for Hearth Products

RECS 

Regions State(s) Baseline MU

Incremental 

MU

1 Connecticut, Maine, New Hampshire, Rhode Island, Vermont 1.366 1.072

2 Massachusetts 1.366 1.072

3 New York 1.366 1.072

4 New Jersey 1.355 1.092

5 Pennsylvania 1.354 1.095

6 Illinois 1.364 1.115

7 Indiana, Ohio 1.353 1.097

8 Michigan 1.353 1.097

9 Wisconsin 1.364 1.115

10 Iowa, Minnesota, North Dakota, South Dakota 1.364 1.115

11 Kansas, Nebraska 1.364 1.115

12 Missouri 1.364 1.115

13 Virginia 1.355 1.092

14 Delaware, District of Columbia, Maryland 1.355 1.092

15 Georgia 1.330 1.097

16 North Carolina, South Carolina 1.330 1.097

17 Florida 1.330 1.097

18 Alabama, Kentucky, Mississippi 1.338 1.097

19 Tennessee 1.330 1.097

20 Arkansas, Louisiana, Oklahoma 1.348 1.112

21 Texas 1.348 1.112

22 Colorado 1.364 1.115

23 Idaho, Montana, Utah, Wyoming 1.400 1.110

24 Arizona 1.404 1.110

25 Nevada, New Mexico 1.380 1.111

26 California 1.404 1.110

27 Oregon, Washington 1.404 1.110

28 Alaska 1.404 1.110

29 Hawaii 1.404 1.110

30 West Virginia 1.353 1.097

6.6.2 Estimation of Mechanical Contractor Markups

The 2007 Economic Census provides Geographic Area Series for the Plumbing and 

HVAC Contractors (NAICS 23822) sector, which contains state-level sale and cost data,

including value of construction, cost of subcontract work, cost of materials, and payroll for 

construction workers. By using the equations in section 6.4.2, DOE was able to estimate baseline 

markups for each state. Because the Census does not provide more disaggregated cost data, DOE 

was not able to differentiate between invariant and variant cost. 
6-15

Alternatively, DOE calculated the national baseline and incremental markups (Table 

6.6.2) and found that the incremental markup is around 20 percent lower than the baseline 

markups. DOE further derived the state-level incremental markups by applying this ratio to the 

baseline markup in each state, assuming that this deviation applies equally to all states. 

(Appendix 6A contains the full set of data.)

To estimate the baseline and incremental markups for both replacement and new 

construction markets for each state, DOE applied the markup deviations (i.e., 3.6 percent higher 

and 2.2 percent lower for the replacement and new construction markets, respectively) derived in 

section 6.5.3.2 to the statewide baseline and incremental markups. DOE assumed that this 

deviation of replacement and new construction markets applies equally to the baseline and 

incremental markups. 

Lastly, DOE divided all states among the 30 RECS 2009 regions and then calculated 

average baseline and incremental markups for mechanical contractors weighted by housing 

projections in 2021 for each region, as shown in Table 6.6.2.
6-16

Table 6.6.2 Mechanical Contractor Markups Weighted by Housing Projections in 2021 

for Hearth Products

6.6.3 Estimation of General Contractor Markups

To derive regional general contractor markups for hearth products from the 2007 

RECS 

Regions State(s) Replacement 

Baseline MU

Replacement 

Incremental 

MU

New 

Construction 

Baseline MU

New 

Construction 

Incremental 

MU

1 Connecticut, Maine, New 

Hampshire, Rhode Island, Vermont 1.557 1.246 1.449 1.159

2 Massachusetts 1.538 1.231 1.431 1.145

3 New York 1.600 1.280 1.488 1.191

4 New Jersey 1.583 1.267 1.473 1.178

5 Pennsylvania 1.479 1.183 1.375 1.100

6 Illinois 1.577 1.262 1.467 1.173

7 Indiana, Ohio 1.563 1.250 1.453 1.163

8 Michigan 1.530 1.224 1.423 1.138

9 Wisconsin 1.510 1.208 1.404 1.123

10 Iowa, Minnesota, North Dakota, 

South Dakota 1.531 1.224 1.423 1.139

11 Kansas, Nebraska 1.460 1.168 1.358 1.086

12 Missouri 1.479 1.183 1.376 1.101

13 Virginia 1.557 1.246 1.448 1.158

14 Delaware, District of Columbia, 

Maryland 1.491 1.193 1.386 1.109

15 Georgia 1.474 1.179 1.371 1.096

16 North Carolina, South Carolina 1.501 1.201 1.396 1.117

17 Florida 1.512 1.210 1.407 1.125

18 Alabama, Kentucky, Mississippi 1.526 1.220 1.419 1.135

19 Tennessee 1.477 1.182 1.374 1.099

20 Arkansas, Louisiana, Oklahoma 1.541 1.233 1.434 1.147

21 Texas 1.498 1.198 1.393 1.115

22 Colorado 1.531 1.225 1.424 1.139

23 Idaho, Montana, Utah, Wyoming 1.491 1.193 1.387 1.110

24 Arizona 1.580 1.264 1.470 1.176

25 Nevada, New Mexico 1.537 1.230 1.430 1.144

26 California 1.607 1.286 1.495 1.196

27 Oregon, Washington 1.579 1.263 1.469 1.175

28 Alaska 1.766 1.413 1.642 1.314

29 Hawaii 1.835 1.468 1.707 1.366

30 West Virginia 1.528 1.222 1.421 1.137
6-17

Economic Census, DOE combined four Geographic Area Series: (1) New Single-Family General 

Contractors (NAICS 236115), (2) New Multifamily Housing Construction (NAICS 236116), (3)

New Housing Operative Builders (NAICS 236117), and (4) Residential Remodelers (NAICS 

236118). 

Each series consists of statewide cost data required to calculate baseline markups for each 

state, as illustrated in section 6.4.2. Although there is only a new construction (no replacement) 

channel for general contractors, the same technique shown for mechanical contractors can still be 

employed to estimate regional baseline and incremental markups. First, DOE estimated the 

statewide incremental markups by applying the ratio of national baseline and incremental 

markups (i.e., the national incremental markup is around 8.84 percent lower than the national 

baseline markup) to the baseline markups for each state. Lastly, DOE divided all states among 

the 30 RECS regions; then calculated average baseline and incremental markups for general 

contractors weighted by 2021 housing projections for each region. The final results are 

summarized in Table 6.6.3 (Appendix 6A contains the full set of data.)
6-18

Table 6.6.3 General Contractor Markups Weighted by Housing Projections in 2021 for 

Hearth Products

RECS 

Regions State(s) Baseline MU

Incremental 

MU

1 Connecticut, Maine, New Hampshire, Rhode Island, Vermont 1.404 1.278

2 Massachusetts 1.343 1.222

3 New York 1.393 1.267

4 New Jersey 1.503 1.368

5 Pennsylvania 1.362 1.239

6 Illinois 1.589 1.446

7 Indiana, Ohio 1.378 1.254

8 Michigan 1.537 1.399

9 Wisconsin 1.340 1.219

10 Iowa, Minnesota, North Dakota, South Dakota 1.368 1.244

11 Kansas, Nebraska 1.351 1.229

12 Missouri 1.325 1.206

13 Virginia 1.450 1.320

14 Delaware, District of Columbia, Maryland 1.419 1.291

15 Georgia 1.428 1.300

16 North Carolina, South Carolina 1.390 1.265

17 Florida 1.528 1.391

18 Alabama, Kentucky, Mississippi 1.355 1.233

19 Tennessee 1.353 1.231

20 Arkansas, Louisiana, Oklahoma 1.372 1.248

21 Texas 1.499 1.364

22 Colorado 1.499 1.364

23 Idaho, Montana, Utah, Wyoming 1.303 1.186

24 Arizona 1.707 1.553

25 Nevada, New Mexico 1.637 1.490

26 California 1.717 1.562

27 Oregon, Washington 1.465 1.333

28 Alaska 1.854 1.687

29 Hawaii 1.417 1.289

30 West Virginia 1.545 1.406

6.7 SALES TAX

The sales tax represents state and local sales taxes that are applied to the consumer price 

of the product. The sales tax is a multiplicative factor that increases the consumer product price. 

DOE only applied the sales tax to the consumer price of the products in the replacement market, 

not the new construction market. The common practice for selling larger residential appliances 

like hearth products in the new construction market is that general contractors (or builders) bear 
6-19

the added sales tax for product, in addition to the cost of product, and then mark up the entire 

cost in the final listing price to consumers. Therefore, no additional sales tax is necessary to 

calculate the consumer product price for the new construction market.

DOE derived state and local taxes from data provided by the Sales Tax Clearinghouse.11

These data represent weighted averages that include county and city rates. DOE then derived 

average tax values weighted by housing projections in 2021 for each RECS 2009 region to match 

the regional markups for wholesalers and mechanical and general contractors, as shown in Table 

6.6.4. Detailed sales tax data by each state can be found in appendix 6A.
6-20

Table 6.6.4 Average Sales Tax Rates by RECS 2009 Region

RECS 

Regions State(s) Fraction of Housing Projections in 2021 %

Tax Rate

(2014) %

1 Connecticut, Maine, New Hampshire, Rhode Island, 

Vermont 0.17 5.16

2 Massachusetts 0.69 6.25

3 New York 2.96 8.40

4 New Jersey 1.87 6.95

5 Pennsylvania 3.38 6.40

6 Illinois 6.24 8.05

7 Indiana, Ohio 5.00 7.06

8 Michigan 5.06 6.00

9 Wisconsin 3.17 5.45

10 Iowa, Minnesota, North Dakota, South Dakota 1.84 6.83

11 Kansas, Nebraska 1.29 7.12

12 Missouri 3.44 7.45

13 Virginia 1.85 5.60

14 Delaware, District of Columbia, Maryland 2.03 5.19

15 Georgia 4.49 7.05

16 North Carolina, South Carolina 1.91 7.00

17 Florida 0.74 6.65

18 Alabama, Kentucky, Mississippi 1.22 7.27

19 Tennessee 1.92 9.45

20 Arkansas, Louisiana, Oklahoma 1.77 8.65

21 Texas 9.88 7.90

22 Colorado 2.19 6.05

23 Idaho, Montana, Utah, Wyoming 0.90 5.12

24 Arizona 1.18 7.15

25 Nevada, New Mexico 0.81 7.31

26 California 10.52 8.45

27 Oregon, Washington 1.65 5.72

28 Alaska 0.07 1.30

29 Hawaii 0.03 4.40

30 West Virginia 0.37 6.10

2021 Housing Projection-Weighted National Average 7.13

6.7 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.
6-21

DOE used the overall baseline markup to estimate the consumer product price of baseline 

models, given the manufacturer cost of the baseline models. As stated above, DOE considers 

baseline models to be product sold under existing market conditions (i.e., without new energy 

conservation standards). The following equation shows how DOE used the overall baseline 

markup to determine the product price for baseline models.

( ) CPPBASE = COSTMFG × MU MFG × MUBASE ×TaxSALES = COSTMFG × MUOVERALL _ BASE

Eq. 6.6

Where:

CPPBASE = consumer product price for baseline models,

COSTMFG = manufacturer cost for baseline models,

MUMFG = manufacturer markup,

MUBASE = baseline replacement or new home channel markup,

TaxSALES = sales tax (replacement applications only), and

MUOVERALL_BASE = baseline overall markup.

Similarly, DOE used the overall incremental markup to estimate changes in the consumer 

product price, given changes in the manufacturer cost above the baseline model cost resulting 

from an energy conservation standard to raise product energy efficiency. The total consumer 

product price for higher 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 consumer 

product price for higher energy efficient models (i.e., models meeting new energy conservation

standards).

( )

BASE MFG OVERALL INCR

STD MFG OVERALL BASE MFG MFG INCR SALES

CPP COST MU

CPP COST MU COST MU MU Tax

_

_

= + D ×

= × + D × × ×

Eq. 6.7

Where:

CPPSTD = consumer product price for models meeting new energy conservation standards,

CPPBASE = consumer product price for baseline models, 

COSTMFG = manufacturer cost for baseline models,

ΔCOSTMFG = change in manufacturer cost for more energy-efficient models,

MUMFG = manufacturer markup,

MUINCR = incremental replacement or new home channel markup,

TaxSALES = sales tax (replacement applications only),

MUOVERALL_BASE = baseline overall markup (product of manufacturer markup, baseline 

replacement or new home channel markup, and sales tax), and

MUOVERALL_INCR = incremental overall markup.
6-22

National weighted-average baseline and incremental markups for each market participant 

are summarized in Table 6.7.2 for hearth products. These values represent the weighted average 

markups based on the state-level markup values and 2021 housing projections by state. Based on

hearth product shipment forecasts for the year 2021 (see chapter 9), DOE estimated that 25

percent of hearth products go to new construction and 75 percent go to the replacement market. 

By weighting the markups by the market shares for each type of hearth product and market, 

overall markups are listed in Table 6.7.3.

Table 6.7.2 Markups for Hearth Products

Replacement New Construction

Baseline Incremental Baseline Incremental

Manufacturer 1.45 1.45

Wholesaler 1.36 1.10 1.36 1.10

Mechanical Contractor 1.53 1.23 1.43 1.14

General Contractor - 1.46 1.33

Sales Tax 1.07 -

Total 3.25 2.10 4.11 2.43

Table 6.7.3 Summary of Overall Markup by Hearth Product Class

Product Class Baseline Markup Incremental Markup

Hearth Products 3.43 2.17
6-23

REFERENCES

1. Heating, Air Conditioning & Refrigeration Distributors International 2013 Profit Report, 

2013. <www.hardinet.org/>

2. Air Conditioning Contractors of America (ACCA), Financial Analysis for the HVACR 

Contracting Industry: 2005, 2005. <www.acca.org/store/>

3. U.S. Census Bureau, Manufacturing Industry Series: Detailed Statistics for the United 

States: 2007. Air-Conditioning and Warm Air Heating Equipment and Commercial and 

Industrial Refrigeration Equipment Manufacturing. Data set for Sector 31: EC073111, 

2007. <www.census.gov/econ/>

4. 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. <www.census.gov/econ/>

5. U.S. Census Bureau, Data set for Sector 23, EC0723A1: 238220 (Plumbing, Heating and 

Air-Conditioning Contractors), Construction: Geographic Area Series, Detailed 

Statistics for Establishments, 2007. <www.census.gov/econ/>

6. Pindyck, R. S. and D. L. Rubinfeld, Microeconomics. 5th ed. 2000. Prentice Hall: New 

Jersey.

7. U.S. Census Bureau, Plumbing, Heating, and Air-Conditioning Contractors. Sector 23: 

238220. Construction: Industry Series, Preliminary Detailed Statistics for 

Establishments, 2007, 2007. <www.census.gov/econ/>

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, 

and Residential Remodelers, 2007. <www.census.gov/econ/>

9. U.S. Census Bureau, Construction: Industry Series: Preliminary Detailed Statistics for 

Establishments: 2007. Sector 23, EC0723I1:236200 (Commercial Building 

Construction), 2007. <www.census.gov/econ/>

10. U.S. Department of Energy: Energy Information Administration, Residential Energy 

Consumption Survey (RECS): 2009 RECS Survey Data, 2009. (Last accessed January 7, 

2015.) <www.eia.gov/consumption/residential/data/2009/>

11. Sales Tax Clearinghouse Inc., State Sales Tax Rates Along with Combined Average City 

and County Rates, 2014. (Last accessed January 10, 2014.) 

<http://thestc.com/STrates.stm>
CHAPTER 7. ENERGY USE ANALYSIS

TABLE OF CONTENTS

7.1 INTRODUCTION ........................................................................................................... 7-1

7.2 HOUSEHOLD SAMPLE ................................................................................................ 7-1

7.3 HEARTH PRODUCT IGNITION ENERGY CONSUMPTION.................................... 7-3

7.3.1 Determination of Standing Pilot Energy Consumption ................................................... 7-3

7.3.1.1 Determination of Hearth Product Main Burner Operating Hours ................. 7-6

7.3.1.2 Determination of Secondary Effects.............................................................. 7-9

7.3.2 Intermittent Pilot Ignition Electricity Consumption ...................................................... 7-11

7.4 SUMMARY OF ENERGY USE RESULTS................................................................. 7-12

LIST OF TABLES

Table 7.1.1 Hearth Product Groups Analyzed ........................................................................ 7-1

Table 7.2.1 Selection of RECS 2009 Records for Hearth Products........................................ 7-2

Table 7.3.1 Distribution of Hearth Product Standing Pilot Operation Behavior.................... 7-6

Table 7.3.2 Overall Range of Standing Pilot Light Operating Hours..................................... 7-6

Table 7.3.3 Hearth Product Standing Pilot Representative Inputs.......................................... 7-6

Table 7.3.4 Hearth Product Representative Input Capacities ................................................. 7-7

Table 7.3.5 Range of Burner Operating Hours for Hearth Products....................................... 7-9

Table 7.3.6 Hearth Product Standing Pilot Representative Secondary Effect Fraction.......... 7-9

Table 7.3.7 Main Heating Appliance Efficiencies................................................................ 7-10

Table 7.3.8 Main Cooling Appliance Efficiencies................................................................ 7-11

Table 7.3.9 Range of Intermittent Pilot Ignition Hours........................................................ 7-12

Table 7.4.1 Average Annual Energy Consumption and Savings for Hearth Products......... 7-13

7-i
CHAPTER 7. ENERGY USE ANALYSIS

7.1 INTRODUCTION

The purpose of the energy use analysis is to determine the annual energy consumption of 

hearth product ignition devices in use in the United States and to assess the energy savings 

potential in switching from standing pilot lights to intermittent pilot ignition. DOE used survey 

data, tear downs, manufacturer literature, and consultant input to establish representative energy 

consumption for each hearth product group and pilot light option (see chapter 5). DOE estimated 

the annual energy consumption of hearth product ignition systems across a range of climate 

zones, building characteristics, and heating applications.

DOE developed energy consumption estimates for the key product groups listed in Table 

7.1.1. The hearth products analyzed utilize gas for a standing pilot or electricity to power an 

intermittent pilot ignition. 

Table 7.1.1 Hearth Product Groups Analyzed

Product 

Group Description

1 Vented Fireplace (Fireplace/Insert/Stove)

2 Unvented Fireplace (Fireplace/Insert/Stove)

3 Vented Gas Logs

4 Unvented Gas Logs

5 Outdoor

DOE estimated the energy consumption of hearth products by developing a building 

sample for each of the five product groups based on the Energy Information Administration’s 

(EIA) 2009 Residential Energy Consumption Survey (RECS 2009).

1 This is the latest available 

survey for residential households.

a This sample is further described in section 7.2.

DOE used RECS 2009 reported hearth product heating energy consumption and 

household characteristics to calculate the energy use of the hearth product ignition system for

each household. RECS 2009 also provided weather data for the sample households, which was 

used to characterize the heating season for each household. In addition, DOE made adjustments 

based on historical weather data, projections of building shell efficiency and building square 

footage, and for household’s primary heating equipment type. To complete the analysis, DOE 

calculated the energy consumption of alternative (more energy-efficient) ignition systems if they 

replaced existing ignition system in each housing unit.

7.2 HOUSEHOLD SAMPLE

DOE’s calculation of the annual energy use of residentialb hearth products relied on data 

from RECS 2009. RECS 2009 included energy-related data from 12,083 housing units that 

a EIA is currently working on the 2015 version of RECS, which is not expected to be available until 2017.

b DOE did not consider hearth products in commercial applications for this analysis

7-1

 
represent almost 113.6 million households. Of these units, 712 of them contained gas fireplaces, 

representing 6.49 million households. For this analysis, DOE assumed that, on average, the 

characteristics of households with gas fireplaces do not significantly differ from those of 

households with other types of hearth products.

DOE divided the hearth product subset into further subsets that include households that 

use either vented or ventless hearth products. For vented gas fireplaces, the subset of RECS 2009

records used in the analysis met all of the following criteria:

• used a fireplace for secondary or primary space heating, 

• used a heating fuel that is natural gas or liquefied petroleum gas (LPG), and

• had a flue on the gas fireplace to the outside.

To identify households with ventless gas fireplaces, DOE used the same criteria as for 

vented gas fireplaces except for criterion (3), which was replaced with:

• did not have a flue on the gas fireplace to the outside.

DOE used the vented and ventless sample to more accurately assign hearth products to 

households. In DOE’s product groups listed in Table 7.1.1, groups 1 and 3 include only vented 

hearth products, and groups 2, 4, and 5 include only ventless hearth products. DOE assigned 

vented hearth products to the households in RECS 2009 listed as having vented gas fireplaces.

Similarly, DOE assigned ventless hearth products from groups 2, 4, and 5 to households listed as 

having ventless gas fireplaces. This sampling method takes into account that ventless hearth 

products are not allowed to be installed in some states.

The RECS 2009 weighting indicates how commonly each household configuration 

occurs in the general population in 2009. Appendix 7A presents the variables included in the 

analysis and their definitions. Table 7.2.1 lists the number of records and representative 

population of households in the RECS 2009 selected for the hearth products replacement and 

new construction samples. Based on hearth product shipment forecasts for the year 2021 (see 

chapter 9), DOE estimated that 25 percent of hearth products go to new construction and 75 

percent go to the replacement market. For the new construction sample, DOE only selected 

homes built on or after 2000 that would better represent the new construction market.

Table 7.2.1 Selection of RECS 2009 Records for Hearth Products

Product Class

Replacements Sample

(All homes in RECS2009)

New Construction Sample

(Homes built on or after 2000)

No. of 

Records

Number of 

Households

million

No. of 

Records

Number of 

Households

million

Vented Gas Fireplaces 541 4.67 182 1.56

Ventless Gas Fireplaces 171 1.83 70 0.72

7-2
7.3 HEARTH PRODUCT IGNITION ENERGY CONSUMPTION

To calculate the energy use of hearth product ignition devices in each of the product 

groups, DOE determined either the annual natural gas or LPG consumption associated with a 

standing pilot light or the annual electricity use of an intermittent pilot ignition. DOE estimated 

the input capacity and operating hours of both the existing hearth product and hearth product 

ignition device using household and hearth product characteristics. In addition, because hearth 

products (excluding outdoor units) are installed in conditioned spaces, a portion of the standing 

pilot fuel input is useful heat that slightly reduces the load of the primary heating appliance

during the heating season. Conversely, if the pilot light is left on during the cooling season, this 

heat increases the cooling load, resulting in additional electricity consumption by the primary 

cooling appliance.

To calculate the natural gas or LPG consumption of a standing pilot light, DOE

determined the standing pilot operating hours, the input capacity of the standing pilot, and the 

secondary space conditioning effects of heat from the standing pilot entering the conditioned 

space. The electrical consumption of intermittent pilot ignitions was calculated from the 

operating hours and input capacity of the intermittent pilot ignition. 

The sum of the fuel and electrical energy consumption (when applicable) represents the 

estimated annual energy use of a sampled ignition device. Additional details used for 

determining the total energy use can be found in the following sections.

The calculation used for the determination of the total annual energy use for hearth 

product ignition devices (EnergyUseTotal) is:

EEEEEEEEEEEEEEEEEETTTTTTTTTT = EEEEEEEEEEEEEEEEEEPPPP ,TTTTTTTT + SS SSSS SSSS SSSS SSSS

Eq. 7.1

Where:

EEEEEEEEEEEEEEEEEEPPPP ,TTTTTTTT = total annual fuel consumption as a result of standing pilot light operation

or the total annual electrical consumption as a result of intermittent pilot ignition operation, 

and

SS SSSS SSSS SSSS SSSS = total annual energy consumed by the primary heating or cooling 

appliance as a result of the heat input from the standing pilot into the conditioned space

(when applicable).

7.3.1 Determination of Standing Pilot Energy Consumption

DOE calculated the annual fuel consumption (EnergyUsePilot,Standing) for each standing

pilot light using the following formula:

EEEEEEEEEEEEEEEEEEPPPP ,SS SSSS SSSS = OOOOPPPP ,SS SSSS SSSS × QQPP

Eq. 7.2

7-3
Where:

OHPilot,Standing = annual standing pilot operating hours (hr/yr), and

QP = input capacity of hearth product standing pilot light (kBtu/hr).

DOE derived a range of possible operating hours from field studies.

2, 3 The operating 

hours for each household were determined based on typical behavior patterns and the 

household’s characteristics, such as heating load, length of heating season, and primary heating 

appliance. These ranges correspond to three modes of behavior:

1. Mode 1: consumers closely monitor the standing pilot light operation and only use it 

when starting the hearth product; therefore, their standing pilot operating hours are 

almost zero;

2. Mode 2: consumers leave the standing pilot light on for the entirety of the heating season

but turn it off for the remaining part of the year; or

3. Mode 3: consumers leave the standing pilot light on for the entire year.

DOE represented each of these three scenarios with a continuous distribution of standing 

pilot operating hours, not including the pilot operating hours when the burner is also operating.

These distributions are shown in Figure 7.3.1 through Figure 7.3.3. Note that Figure 7.3.3 shows

the number of hours the standing pilot light is off. 

Figure 7.3.1 Distribution of Standing Pilot Light Operating Hours for Mode 1

7-4
Figure 7.3.2 Distribution of Standing Pilot Light Operating Hours for Mode 2

Figure 7.3.3 Distribution of Hours when the Standing Pilot Light is not Operating for 

Mode 3

DOE then weighted these probability distributions by the likelihood that a consumer 

would adopt them. The likelihood was determined from a field study of fireplace standing pilot 

light gas usage.2 The study found that 30 of 68 households (44 percent) left the pilot on yearround, while 14 of 68 (21 percent) had almost no pilot light usage. These households were 

represented with the exponential distributions in Figure 7.3.1 and Figure 7.3.3. The remaining 

households are represented by a distribution of behavior when the standing pilot light is on only 

during the heating season, which DOE represented as a triangular distribution, as shown in 

Figure 7.3.2. 

7-5
For all LPG units, it was assumed that consumers do not leave the standing pilot light on 

for the entire heating season, but rather operate the pilot light proportionally to the main burner 

of the hearth product because of the high cost of LPG fuel. From the RECS 2009 household 

sample, DOE determined that 23 percent of all gas fireplaces use LPG. 

The percentages of households operating their standing pilot light in each mode are listed 

in Table 7.3.1. These probabilities are derived from a field study.

2

Table 7.3.1 Distribution of Hearth Product Standing Pilot Operation Behavior

Behavior 

Mode Description Percentage

1 Closely Monitored 40%

2 Heating Season 40%

3 Year-Round 20%

Table 7.3.2 shows the resulting range of hearth product standing pilot light operating 

hours among sample households. 

Table 7.3.2 Overall Range of Standing Pilot Light Operating Hours

Min Max Average Percentiles

5% 25% 50% 75% 95%

18.8 8760 3708 156 544 2691 7718 8760

Table 7.3.3 shows the representative pilot light inputs that DOE determined from the 

tear-down analysis (chapter 5). These values are used to calculate the fuel used by the standing 

pilot as in Eq. 7.2.

Table 7.3.3 Hearth Product Standing Pilot Representative Inputs

Hearth Product Group Input Capacity

kBtu/h

Vented Fireplace (fireplace/insert/stove) 1

Unvented Fireplace (fireplace/insert/stove) 1.2

Vented Gas Log Sets 0.7

Unvented Gas Log Sets 0.8

Outdoor 1

7.3.1.1 Determination of Hearth Product Main Burner Operating Hours

In creating the ranges of standing pilot operating hours, DOE also determined the main 

burner operating hours (BOH) of the hearth product. This allowed DOE to establish the 

minimum of the range of standing pilot operating hours (i.e., the hours that the hearth product is 

operating). To calculate the BOH for hearth products used for heating, DOE used the building 

heating load met by the hearth product (BHLHearth), and the representative main burner input. The 

7-6
BHLHearth is determined from the annual space heating energy consumption provided by RECS 

2009 for vented and unvented gas fireplaces (fireplace/insert/stove).

The BOH of the hearth product (BOHHearth) is calculated as:

BB HH HH h = BB BBHH HH h

QQIIII,HH HH h × ηHH HH h

Eq. 7.3

Where:

BHLHearth = building heating load served by a single hearth product (kBtu/yr),

QIN,Hearth = representative input capacity of the hearth product main burner (kBtu/h), and

ηHearth = average efficiency of newly installed gas hearth product.

DOE assumed that the resulting BOHHearth value for vented and unvented fireplaces 

(fireplace/insert/stove) would be similar for the other hearth product groups.

Table 7.3.4 shows the representative input capacity (QIN,Hearth) that DOE determined from 

the tear-down analysis (chapter 5). 

Table 7.3.4 Hearth Product Representative Input Capacities

Hearth Product Group Input Capacity 

kbtu/h

Vented Fireplaces (fireplace/insert/stove) 35

Unvented Fireplaces(fireplace/insert/stove) 30

Vented Gas Log Sets 35

Unvented Gas Log Sets 25

Outdoor 50

The annual BHL is the total amount of heat output from the hearth product that the house 

needs during the heating season. This includes heat from the burner. DOE determined projected 

BHLHearth in 2021 for each sampled housing unit based on the input capacity of the assigned 

existing hearth product, using the following calculation:

BB BBHH HH h = QQYYYY × ηHH HH h × HH HH HH × AA jjFF FF

Eq. 7.4

Where:

QYR = annual total space heating fuel consumption reported by RECS 2009 (kBtu/yr),

ηHearth,ex = average efficiency of existing gas hearth product, assumed to be 64 percent for vented 

and 100 percent for ventless,

HeatingLoadFraction = fraction of the total building heating load met by the hearth product, and

AdjFactor = adjustment factor (discussed below).

7-7
RECS 2009 reports space heating energy use (QYR) for each of the sampled households. 

RECS 2009 also reports the fraction of heat provided by the main heating equipment. DOE 

assumed that the remainder of the heat not provided by the main heating equipment was 

provided by the hearth product.

DOE adjusted the BHL to reflect the expectation that the houses in 2021 will have a 

somewhat different BHL than the households in the RECS 2009 hearth product sample. The 

adjustment involves multiplying the calculated BHL for each RECS 2009 household by the 

building shell efficiency indexc based on information from the National Energy Modeling 

System (NEMS) simulation associated with EIA’s Annual Energy Outlook (AEO 2014).

4 This 

factor differs for new construction and replacement buildings. The factor applied in the analysis 

is 0.91 for replacements and 0.92 for new construction. This means that households on average 

will have lower space heating load compared to households in 2009.

DOE also adjusted BHL to reflect historical average climate conditions using heating 

degree days (HDD) reported in RECS 2009 for each household and NOAA HDD data by 

region.

5 The adjustment factors are calculated using the following equation:

AA jjFF FF aa cccccccccccccc =

HHHH10yyrr aaaa

HHHHbb 2009

Eq. 7.5

Where:

HDDbldg_2009 = HDD in 2009 for the specific region where the housing unit is located, and

HDD10_yr_avg = 10-year average HDD (2004–2013) based on NOAA data for the specific region

where the housing unit is located.

The adjustment factors range from 0.91 to 1.02 and on average 0.95 for the hearth 

product household sample (i.e., 2009 was in general colder than the 10-year average).

DOE also account for future climate trends based on AEO 2014 HDD projections, which 

show a decline in HDD, leading to lower projected BHLHearth in 2021 relative to the non-climatetrend-adjusted BHLHearth values. On average, this decreases the heating load by 7.25% in 2021.

Table 7.3.5 presents the calculated hearth product burner operating hours among sample 

households. 

c The building shell efficiency index sets the heating load value at 1.00 for an average home in 2009 (by type) in 

each census division. The values listed below represent the change in heating load based on the difference in 

physical size and shell attributes for homes in the future (which takes into account physical size difference and 

efficiency gains from better insulation and windows).

7-8

 
Table 7.3.5 Range of Burner Operating Hours for Hearth Products

Min Max Average

Percentiles

5% 25% 50% 75% 95%

1.95 3518 157 21.02 54.04 106.3 194.1 443.7

7.3.1.2 Determination of Secondary Effects

DOE included the seasonal secondary effects of the operation of a standing pilot light on 

space conditioning energy use. During the heating season, a fraction of the standing pilot light 

heat input (HeatInputPilot,HeatingSeason) contributes to space heating that would otherwise be 

provided by a main heating appliance, such as a furnace. Therefore, DOE added a fraction of the 

standing pilot light energy consumption that contributes to space heating to the intermittent pilot 

fuel energy consumption. Based on the results of a field study that quantified the percentage of 

fireplace standing pilot fuel energy converted into useful heat in a home, DOE estimated the

fraction of the heat output from the hearth product standing pilot light that contributes to space 

heating for each hearth product group as listed in Table 7.3.6.

6 For the decorative vented 

fireplaces and vented gas logs, which are not intended for space heating, DOE assumed that the 

fraction of the heat transferred to the space is half of the fraction for the vented fireplaces. For 

the ventless hearth products, DOE assumed that the fraction of the heat transferred to the space is

double the fraction for the vented fireplaces. The large range in the ventless hearth products 

fraction accounts for increased heat transfer to the space due to the combustion products going 

into the room for a fraction of installations, as well as for units installed in space without a 

thermostat, where the heat input from the hearth product has no impact on the operation of the 

main heating equipment (see Table 7.3.6).

Table 7.3.6 Hearth Product Standing Pilot Representative Secondary Effect Fraction

Hearth Product Group Fraction

%

Vented Fireplace (fireplace/insert/stove) 1 to 47

Vented Fireplaces (Decorative) 0 to 23

Unvented Fireplace (fireplace/insert/stove) 2 to 94

Vented Gas Log Sets 0 to 23

Unvented Gas Log Sets 2 to 94

Outdoor Not Applicable

The operation of standing pilot lights during the cooling season contributes some heat to 

the conditioned space (HeatInputPilot,CoolingSeason) , resulting in an additional cooling load and 

increased electrical energy consumption. To account for the increased cooling energy use, DOE 

subtracted the additional electrical energy consumption of the cooling appliance from the 

electricity consumption of the intermittent pilot ignition.

Eq. 7.6 through Eq. 7.8 describe the calculation approach of the effects 

(SecondaryEffects) of the heat input of the pilot during the heating and cooling season.

7-9
HH HHHH PPPP ,HH HH = BB PPPP ,HH HH QQPPηPPPP

ηηMM

Eq. 7.6

HH HHHH PPPP ,CC = BB PPPP ,HH HH QQPPηPPPP

CC CCCC

Eq. 7.7

SS SSSS SSSS EE EE EE = PPPP ,CC − HH HHHH PPPP ,HH HH

Eq. 7.8

Where:

HH HHHH PPPP ,HH HH = annual heat input of the pilot light into the space during the 

heating season (Btu) adjusted for main heating appliance operation,

QP = as previously defined,

ηPilot = efficiency of the pilot light, as listed in Table 7.3.6,

ηMainHeat = national average efficiency of the main heating appliance, as listed in AEO 2014,

HH HHHH PPPP ,CC = annual heat input of the pilot light into the space during the 

cooling season (kWh) adjusted for main cooling appliance operation, and

COPCool = national average Coefficient of Performance of the main cooling appliance based on 

AEO 2014.

RECS 2009 contains information about several primary heating and cooling appliances.

DOE applied the AEO 2014 projections of the average efficiency ratings of these products in 

2021. The listed efficiencies for each type of main heating appliance (ηMainHeat) are listed in Table 

7.3.7. The listed efficiencies for each main cooling appliance (COPCool) are listed Table 7.3.8.

Table 7.3.7 Main Heating Appliance Efficiencies

Main Heating Appliance Description Efficiency 

Rating

Natural Gas Furnace (AFUE, %)* 80 or 92

Gas DHE (%) 64

Electric Heat (%) 98

Electric Heat Pumps (HSPF)** 8.01 or 8.52

No Primary Heating Appliance Not Applicable * In the replacement hearth market, the average stock AFUE in 2021 from AEO2014 is used, which is 55 percent at 

80-percent AFUE and 45 percent at 92-percent AFUE. In the new construction hearth market, the average shipment 

AFUE in 2021 from DOE’s 2011 Direct Final Rule7 is used, which is 43 percent at 80-percent AFUE and 57 percent 

at 92-percent AFUE. ** In replacement hearth market, the average stock HSPF in 2021 from AEO2014 is used (8.01 HSPF). In new 

construction hearth market, the average shipment HSPF in 2021 from DOE’s 2011 Direct Final Rule is used (8.52 

HSPF).

7-10
Table 7.3.8 Main Cooling Appliance Efficiencies

Main Cooling Appliance Description Efficiency

Rating

Central Air Conditions (SEER) 14.2

Electric Heat Pump (SEER) 13.7

Room Air Conditioner (EER) 11.0

No Primary Cooling Appliance Not Applicable

7.3.2 Intermittent Pilot Ignition Electricity Consumption

DOE calculated intermittent pilot ignition energy consumption with a similar 

methodology as for standing pilot lights (section 7.3.1). The annual energy consumption of the 

hearth product intermittent pilot ignition (EnergyUsePilot,Intermittent) is:

EEEEEEEEEEEEEEEEEEPPPP ,II II = OOOOPPPP ,II II × IIII

Eq. 7.9

Where:

OHPilot,Intermittent = operating hours of the intermittent pilot ignition, and 

PEIG = input capacity of the intermittent pilot ignition, conservatively determined to be 50 watts

(see chapter 5).

d

The distribution of BOH of hearth products with intermittent pilot ignitions was assumed 

to be the same as for hearth products with standing pilots, as listed in Table 7.3.2. Because hearth 

products are a secondary heating appliance similar to direct heating equipment, DOE applied the 

average on-time per cycle used in the DHE test procedure to hearth products, which is 20 

minutes.11 DOE assumed a 30-second operating time per cycle for the intermittent pilot ignition.

The operating hours of intermittent pilot ignitions (OHPilot,Intermittent) is determined by: 

OOOOPPPP ,II II = BB HH HH h

TTOO

TTCC

Eq. 7.10

Where:

BB HH HH h = hearth product burner operating hours (Eq. 7.3),

TTOO = on-time per cycle of the intermittent pilot ignition, assumed to be 30 seconds, and

TTCC = on-time per cycle of the hearth product, assumed to be 20 minutes.

Table 7.3.9 lists the resulting annual national average intermittent pilot operating hours. 

d DOE surveyed several intermittent ignition systems and found the typical electrical requirements of the largest of 

these units was approximately 24 volts, 2 amps, or 48 watts.8, 9, 10

7-11

 
Table 7.3.9 Range of Intermittent Pilot Ignition Hours

Min Max Average Percentiles

5% 25% 50% 75% 95%

0.05 87.95 3.94 0.53 1.35 2.66 4.85 11.09

7.4 SUMMARY OF ENERGY USE RESULTS

This section presents the average annual energy use and the average energy savings for 

each considered energy efficiency level (EL 1) compared to the baseline energy efficiency (EL 

0) for each hearth product ignition device.

e The LCC and PBP analysis uses the results calculated 

as the national average from all sampled households. Table 7.4.1 lists the average annual energy 

use for hearth product ignition devices and the average energy savings for each considered 

energy efficiency level compared to the baseline standing pilot light for each hearth product 

group.

e The derivation of the base case energy consumption, which represents the energy consumption without the 

standard, includes households that already include EL1 products as explained in chapter 8.

7-12

 
Table 7.4.1 Average Annual Energy Consumption and Savings for Hearth Products

EL Hearth Product 

Group

Annual Fuel Use Annual Electricity 

Consumption

Overall Energy 

Savings**

Total Savings Total Savings Savings

MMBtu/yr MMBtu/yr kWh/yr kWh/yr MMBtu/yr

0 Vented Fireplace 3.99 -- 0.000 -- --

1 Vented Fireplace 0.499 3.49 13.60 (13.60) 3.44

0 Unvented Fireplace 3.52 -- 0.000 -- --

1 Unvented Fireplace 1.30 2.22 99.38 (99.38) 1.88

0 Vented Gas Logs 3.13 -- 0.000 -- --

1 Vented Gas Logs 0.289 2.84 5.79 (5.79) 2.82

0 Unvented Gas Logs 2.29 -- 0.000 -- --

1 Unvented Gas Logs 0.924 1.36 70.61 (70.61) 1.12

0 Outdoor 3.91 -- 0.000 -- --

1 Outdoor 0.000 3.91 0.175 (0.175) 3.91

0 All Hearth Products 3.58 -- 0.000 -- --

1 All Hearth Products 0.58 3.00 28.54 (28.54) 2.90

* Parentheses () indicate negative values

** Savings include both fuel and electrical energy consumption, reported in MMBtu/yr.

7-13
REFERENCES

1. U.S. Department of Energy: Energy Information Administration, Residential Energy 

Consumption Survey (RECS): 2009 RECS Survey Data. 2009. (Last accessed January 7, 

2015.) <www.eia.gov/consumption/residential/data/2009/>

2. Hayden, A. C. S., Fireplace Pilots Take Gas Use Sky High. 1997. (Last accessed August 

8, 2014.) <http://www.homeenergy.org/show/article/nav/hvac/page/28/id/1264>

3. Menkedick, J., P. Hartford, S. Collins, S. Shumaker, and D. Wells, Hearth Products 

Meter Study (1995-1997). 1997. Gas Research Institute. Report No. GRI-97/0298. 

4. U.S. Department of Energy-Energy Information Administration, Annual Energy Outlook 

2014 with Projections to 2040. 2014. Washington, DC. (Last accessed May 2014.) 

<www.eia.gov/forecasts/aeo/>

5. National Oceanic and Atmospheric Administration, NNDC Climate Data Online. (Last 

accessed March 15, 2013.) <www7.ncdc.noaa.gov/CDO/CDODivisionalSelect.jsp>

6. Armstrong, M. M., M. C. Swinton, and F. Szadkowski, Assessment of the Impact of a 

Natural Gas Fireplace on Heating Energy Consumption and Room Temperatures at the 

Canadian Centre for Housing Technology. 2010. National Research Council Canada. 

7. U.S. Department of Energy - Energy Efficiency & Renewable Energy, Technical Support 

Document: Energy Efficiency Standards for Consumer Products: Residential Furnaces, 

Central Air Conditioners, and Heat Pumps. 2011. Washington, DC.

8. Johnson Controls, G67 Intermittent Pilot Ignition Controls Manual. (Last accessed 

August 28, 2014.) <http://www.engineeredair.com/parts/VaQdUhG16.pdf>

9. BASO Gas Products LLC, BG1600M Intermittent Pilot Ignition Controls Manual. (Last 

accessed August 28, 2014.) 

<http://www.baso.com/lib/subdomain/baso/content/products/b2a6e982af38c9e7e453490

b7ed40e45/BASO-IGN-INSBG1600M_Intermittent_Pilot_Ignition_Control_Instructions.pdf>

10. Honeywell, Honeywell Intermittent Pilot Modules S8600, S8610, S8660, S8670. (Last 

accessed August 28, 2014.) 

<http://www.hvacwebconnection.com/images/S8610Installation.pdf>

11. Department Of Energy, Appendix O to Subpart B of Part 430-Uniform Test Method For

Measuring The Energy Consumption Of Vented Home Heating Equipment.

7-14
CHAPTER 8. LIFE-CYCLE COST AND PAYPACK 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 Residential Energy Consumption Survey in Life-Cycle Cost and Payback 

Period Analysis................................................................................................................ 8-5

8.2 LIFE-CYCLE COST ANALYSIS INPUTS.................................................................... 8-6

8.2.1 Total Installed Cost Inputs............................................................................................... 8-6

8.2.1.1 Manufacturer Costs........................................................................................ 8-7

8.2.1.2 Markups ......................................................................................................... 8-7

8.2.1.3 Total Consumer Price .................................................................................... 8-8

8.2.1.4 Future Product Prices..................................................................................... 8-9

8.2.1.5 Installation Cost ........................................................................................... 8-10

8.2.1.6 Total Installed Cost...................................................................................... 8-13

8.2.2 Operating Cost Inputs.................................................................................................... 8-13

8.2.2.1 Annual Energy Use Savings ........................................................................ 8-13

8.2.2.2 Energy Prices............................................................................................... 8-13

8.2.2.1 Maintenance Cost......................................................................................... 8-19

8.2.2.2 Repair Cost................................................................................................... 8-19

8.2.2.3 Lifetime........................................................................................................ 8-21

8.2.2.4 Discount Rates............................................................................................. 8-22

8.2.2.5 Compliance Date of Standard ...................................................................... 8-27

8.2.2.6 Base Case Distribution of Efficiency Levels............................................... 8-27

8.3 PAYBACK PERIOD INPUTS...................................................................................... 8-28

8.4 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS...................................... 8-29

8.5 REBUTTABLE PAYBACK PERIOD.......................................................................... 8-31

8.5.1 Inputs.............................................................................................................................. 8-32

8.5.2 Results............................................................................................................................ 8-32

LIST OF TABLES

Table 8.1.1 Summary of Inputs and Key Assumptions Used in the LCC and PBP 

Analysis................................................................................................................ 8-5

Table 8.2.1 Manufacturer Production Cost for Hearth Product Ignitions Systems by 

Hearth Product Group.......................................................................................... 8-7

Table 8.2.2 Summary of National Average Markups on Hearth Product Ignition 

Devices................................................................................................................. 8-8

Table 8.2.3 Overall Markup for Hearth Product Ignition Devices ......................................... 8-8

Table 8.2.4 Average Consumer Price for Hearth Product Ignition Devices (2013$)............. 8-9

Table 8.2.5 Installation Cost Household Sample Assumptions............................................ 8-10

Table 8.2.6 RS Means National Installation Labor and Material Costs (2013$).................. 8-10

8-i
Table 8.2.7 RS Means 2013 National Average Residential Labor Costs by Crew............... 8-11

Table 8.2.8 RS Means Labor Costs Markups by Trade (Residential) .................................. 8-11

Table 8.2.9 Material and Labor Cost Factors by Geographical Area (for RECS 2009 

Sample) .............................................................................................................. 8-12

Table 8.2.10 Average Installation Cost for Hearth Product Ignition Systems (2013$).......... 8-12

Table 8.2.11 Average Total Installed Cost for Hearth Product Ignition Devices (2013$) ..... 8-13

Table 8.2.12 Residential Average Monthly Natural Gas Prices for 2012 Using Monthly 

Price Factors (2013$/MMBtu)........................................................................... 8-15

Table 8.2.13 Residential Marginal Monthly Natural Gas Prices for 2012 Using Monthly 

Price Factors (2013$/MMBtu)........................................................................... 8-16

Table 8.2.14 Residential Average Electricity Prices for 2012 Using Monthly Price 

Factors (2013$/kWh) ......................................................................................... 8-17

Table 8.2.15 Residential Marginal Electricity Prices for 2012 Using Monthly Price 

Factors (2013$/kWh) ......................................................................................... 8-18

Table 8.2.16 Hearth Product Average Repair Cost Components (2013$).............................. 8-20

Table 8.2.17 RS Means 2013 National Average Residential Labor Costs by Crew............... 8-20

Table 8.2.18 RS Means Labor Costs Markups by Trade (Residential) .................................. 8-20

Table 8.2.19 Annualized Repair Cost for Hearth Product Ignition Devices (2013$)............. 8-21

Table 8.2.20 Lifetime Parameters for Hearth Products .......................................................... 8-22

Table 8.2.21 Definitions of Income Groups ........................................................................... 8-23

Table 8.2.22 Types of Household Debt and Equity by Percentage Shares (%)...................... 8-24

Table 8.2.23 Data Used to Calculate Real Effective Mortgage Rates.................................... 8-25

Table 8.2.24 Average Real Effective Interest Rates for Household Debt .............................. 8-25

Table 8.2.25 Average Nominal and Real Interest Rates for Household Equity ..................... 8-26

Table 8.2.26 Average Real Effective Discount....................................................................... 8-27

Table 8.2.27 Market Shares of Intermittent Ignition in 2021 by Product Group.................... 8-28

Table 8.2.28 Market Shares of Match-Lit Units in 2021 by Product Group .......................... 8-28

Table 8.2.29 Market Shares of Hearth Products Ignition in 2021 by Product Group............. 8-28

Table 8.4.1 Average LCC and PBP Results by Efficiency Level for Hearth Product 

Ignition Devices................................................................................................. 8-30

Table 8.4.2 Average LCC Savings Relative to the Base Case Efficiency Distribution 

for Hearth Product Ignition Devices.................................................................. 8-30

Table 8.5.1 Rebuttable Payback Period for Hearth Product Ignition Devices...................... 8-32

LIST OF FIGURES

Figure 8.1.1 Flow Diagram of Inputs for the Determination of LCC and PBP ....................... 8-4

Figure 8.2.2 Historical Deflated Producer Price Indexes for Floor and Wall Furnaces........... 8-9

Figure 8.2.3 Projected National Commercial Energy Price Factors, AEO 2014 

(Reference Case)................................................................................................ 8-19

Figure 8.2.1 Lifetime Distribution of Hearth Products (whole unit)...................................... 8-22

Figure 8.4.2 Distribution of LCC Savings for Hearth Product Ignition Devices................... 8-31

8-ii
CHAPTER 8. LIFE-CYCLE COST AND PAYPACK 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 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 hearth product ignition devices are discussed in 

sections 8.2 and 8.3, respectively. Results for each metric are presented in sections 8.4 and 8.5. 

Key variables and calculations are presented for each metric. The calculations discussed here 

were performed with a series of Microsoft Excelspreadsheets that are accessible over the Internet 

(www1.eere.energy.gov/buildings/appliance_standards/product.aspx?productid=83).

Details of the spreadsheets and instructions for using them are discussed in appendix 8A.

8.1.1 General Approach for Life-Cycle Cost and Payback Period Analysis

In recognition of the fact that each residential building using hearth products is unique, 

variability and uncertainty are analyzed by performing the LCC and PBP calculations detailed 

here for a representative sample of individual households. The results are expressed as the 

number of buildings 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 model both the uncertainty and the variability in the model’s inputs 

using Monte Carlo simulation and probability distributions (see appendix 8B).

The LCC analysis used the estimated energy use for each hearth product unit as described 

in the energy use analysis in chapter 7. Energy use of hearth products is sensitive to climate and

therefore varies by location within the United States. Aside from energy use, other important 

8-1
factors influencing the LCC and PBP analysis include energy prices, installation costs, product

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 results are displayed as distributions of impacts compared to a base case. The 

base case efficiency is for 2021 and reflects the expected distribution of efficiency levels by 

product class. The PBP results are displayed compared to the baseline efficiency level.

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 product, 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 product. The PBP is the increase in purchase cost 

of a higher efficiency product divided by the change in annual operating cost of the product. It 

represents the number of years that it will take the consumer to recover the increased purchase 

cost through decreased operating costs. In the PBP calculation, 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 product meeting existing 

minimum efficiency (or baseline) standards. The MSP includes a markup that converts 

the cost of production (i.e., the manufacturer cost) to an MSP.

• Standard-level manufacturer selling price increase: The standard-level MSP is the 

incremental change in MSP associated with producing product at each of the higher 

efficiency 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. 

8-2
• Installation cost: Installation cost is the cost to the consumer of installing the product. 

The installation cost represents all costs required to install the product but does not 

include the marked-up consumer product price. The installation cost includes labor, 

overhead, and any miscellaneous materials and parts. 

The primary inputs for calculating the operating cost are:

• Product energy consumption: The product energy consumption is the site energy use 

associated with the use of the hearth product ignition device to start the hearth product.

• Energy Prices: Electricity, natural gas, and liquid petroleum gas (LPG) prices are 

determined using average and marginal monthly energy prices.

• Electricity, LPG and natural gas price trends: The Energy Information Administration’s 

(EIA’s) Annual Energy Outlook 2014 (AEO 2014)

1 is used to forecast energy prices into 

the future. For the results presented in this chapter, DOE used the AEO 2014 Reference 

case to forecast future energy prices.

• Maintenance costs: The labor and material costs associated with maintaining the 

operation of the product.

• Repair costs: The labor and material costs associated with repairing or replacing 

components that have failed.

• Lifetime: The age at which the hearth product is retired from service.

• Discount rate: The rate at which future costs and savings are discounted to establish their 

present value.

Figure 8.1.1 graphically depicts the relationships between the installed cost and operating 

cost inputs for the calculation of the LCC and PBP. 

8-3
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. 

Manufacturer Sales 

Price Standard i= 1...n

Manufacturer Sales 

Price Standard i= 1...n

Installation Costs Installation Costs

Repair Costs Repair Costs Lifetimes Lifetimes

Annual Maintenance

Electricity 

Consumption Electricity 

Consumption

Manufacturer Base 

Price

Total Installed Cost 

(Std i –Base)

Manufacturer Base 

Price

Contractor Markups

Wholesaler/ 

Distributor Markup

Electricity Prices

Equipment Prices

Sales Tax

Manufacturer Selling 

Price (Base)

Installation Costs

Manufacturer Selling 

Price Standard i= 1...n

Electricity 

Consumption Annual Energy Costs

Annual Maintenance

Repair Costs

Annual Operating 

Cost (Std i - Base)

Payback 

Period

Discount Rate

Lifetimes

Lifetime 

Operating 

Cost (Std 

i – Base)

Lifecycle 

Cost

Annual Maintenance 

Costs

Manufacturer Sales 

Price Standard i= 1...n

Manufacturer Sales 

Price Standard i= 1...n

Installation Costs Installation Costs

Repair Costs Repair Costs Lifetimes Lifetimes

Annual Maintenance

Electricity 

Consumption Electricity 

Consumption

Manufacturer Base 

Price

Total Installed Cost 

(Std i –Base)

Manufacturer Base 

Price

Contractor Markups

Wholesaler/ 

Distributor Markup

Electricity Prices

Equipment Prices

Sales Tax

Manufacturer Selling 

Price (Base)

Installation Costs

Manufacturer Selling 

Price Standard i= 1...n

Electricity 

Consumption Annual Energy Costs

Annual Maintenance

Repair Costs

Annual Operating 

Cost (Std i - Base)

Payback 

Period

Discount Rate

Lifetimes

Lifetime 

Operating 

Cost (Std 

i – Base)

Lifecycle 

Cost

Annual Maintenance 

Costs

8-4
Table 8.1.1 Summary of Inputs and Key Assumptions Used in the LCC and PBP 

Analysis

Inputs Description

Affecting Installed Costs

Product Price

Derived from MSP for hearth product groups (from the engineering analysis, 

chapter 5) 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 Residential Cost Data 2013.

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 product 

price (manufacturer cost multiplied by the various markups plus sales tax) plus 

the installation cost. 

Affecting Operating Costs

Energy Use Determined from the input capacity and operating hours of the hearth product 

ignition system. See chapter 7.

Energy Prices

Costs were calculated for RECS 2009 households from monthly marginal 

average electricity and natural gas or LPG prices in each of 30 states and groups 

of states in RECS 2009.a Residential prices were escalated by the AEO 2014

forecasts to estimate future electricity prices. Escalation was performed at the 

census division level and aggregated to the regions used in the study.

Maintenance Cost No maintenance cost is applied in this analysis.

Repair Cost Estimated the annualized repair cost for hearth product ignition systems based 

on the cost of replacement. 

Affecting Present Value of Annual Operating Cost Savings

Product Lifetime Used the probability distribution of lifetimes developed for hearth products. 

Discount Rate

Mean real discount rates ranging from 3.57 percent to 5.12 percent for various 

classes of residential consumers based on Federal Reserve Board’s Survey of 

Consumer Finances. Probability distributions are used for the discount rates.

Date Standard 

Becomes Effective 2021 (5 years after expected publication of the final rule)

All of the inputs depicted in Figure 8.1.1 and summarized in Table 8.1.1 are discussed in 

section 8.2.

8.1.3 Use of Residential Energy Consumption Survey in Life-Cycle Cost and Payback 

Period Analysis

The LCC and PBP calculations detailed here are for a representative sample of individual 

hearth product users. As explained in chapter 7, the Energy Information Administration (EIA)’s 

2009 Residential Energy Consumption Survey (RECS 2009)

3 serves as the basis for determining 

a RECS 2009 provides 27 regions (also called reportable domains). The 27th region includes Oregon, Washington, 

Alaska, and Hawaii. DOE subdivided Alaska and Hawaii into separate regions (28 and 29, respectively) based on 

cooling and heating degree days. In addition, West Virginia, which is in RECS region 14, was disaggregated into 

region 30 based on cooling and heating degree days. See appendix 7A for more details.

8-5

 
the representative residential sample. RECS collects energy-related data for occupied primary 

housing units in the United States. RECS 2009 included data from 12,083 housing units that 

represent almost 113.6 million households. 

Appendix 7A presents the variables used and their definitions, as well as further 

information about the derivation of the household samples.

8.2 LIFE-CYCLE COST ANALYSIS INPUTS

Life-cycle cost is the total consumer cost over the life of a product, including purchase 

cost and operating costs (which are composed of energy costs, maintenance costs, and repair 

costs). Future operating costs are discounted to the time of purchase and summed over the 

lifetime of the product. 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 product (years),

OC = operating cost ($),

r = discount rate, and

t = year for which operating cost is being determined.

DOE expressed all the costs in 2013$. Total installed cost, operating cost, lifetime, and 

discount rate are discussed in the following sections. In the LCC analysis, the year of product 

purchase is assumed to be 2021, the assumed effective date of energy conservation standards for 

hearth products.

8.2.1 Total Installed Cost Inputs

The total installed cost to the consumer is defined by the following equation:

Eq. 8.2

Where:

EQP = product price ($) (i.e., consumer price for the product only), and

INST = installation cost ($) (i.e., the cost for labor and materials).

∑=

= + +

N

t

t

t LCC IC OC r

1

/(1 )

IC = EQP + INST

8-6
The product price is based on the distribution channel through which the consumer

purchases the product. As discussed in chapter 6, DOE defined two major distribution channels

for new units to describe how a hearth product passes from the manufacturer to the consumer, 

one applying to hearth products installed in replacement markets or by new owners and the other 

applying to hearth products that are installed in new construction. In the new construction 

channel, the manufacturer sells the product to a wholesaler or distributor, who sells to a 

mechanical contractor hired by a general contractor. The general contractor purchases and 

installs the product on behalf of the consumer and adds its markup to the mechanical contractor’s 

price. Replacement products follow the same distribution channel, except that there is no general 

contractor. Instead, the mechanical contractor takes on the general contractor’s function.

The remainder of this section provides information about the variables DOE used to 

calculate the total installed cost for hearth products.

8.2.1.1 Manufacturer Costs

DOE developed the manufacturer costs for hearth products ignition systems as described 

in chapter 5, Engineering Analysis. The manufacturer costs for a baseline ignition system (i.e., 

standing pilot light) for each product group and the incremental cost of an intermittent pilot

ignition system are shown in Table 8.2.1.

Table 8.2.1 Manufacturer Production Cost for Hearth Product Ignitions Systems by 

Hearth Product Group

Product 

Group Description Standing Pilot MPC

Intermittent 

Pilot 

MPC

1 Vented Fireplaces/Stoves/Inserts $49.13 $76.83

2 Unvented Fireplaces/Stoves/Inserts $69.17 $101.44

3 Vented Log Sets $30.79 $100.63

4 Unvented Log Sets $29.04 $85.46

5 Outdoor $84.83 $139.96

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 contractor, 

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 increase 

into an incremental consumer price). Markups can differ depending on whether the product is 

being purchased for a new construction installation or is being purchased to replace an existing 

8-7
product. DOE developed the overall baseline markups and incremental markups for both new 

construction and replacement applications as a part of the markups analysis (chapter 6).

Table 8.2.2 displays the average markups and their associated components weighted by 

housing projections in 2021,

16,17,18 for the baseline and incremental markups for hearth product 

ignition devices, respectively. DOE calculated the projected number of houses in 2021 by state 

by accounting for the growth in population by region from 2008 to 2012 based on historical U.S. 

census population numbers by state and the number of people per house in each state based on 

historical U.S. census population and housing numbers.

Table 8.2.2 Summary of National Average Markups on Hearth Product Ignition 

Devices

Replacement New Construction

Baseline 

Markup

Incremental 

Markup

Baseline 

Markup

Incremental 

Markup

Manufacturer 1.45 1.45

Wholesaler 1.36 1.10 1.36 1.10

Mechanical Contractor 1.53 1.23 1.43 1.14

General Contractor - 1.46 1.33

Sales Tax 1.07 -

Total Markup 3.25 2.10 4.11 2.43

Note: Components may not multiply to the overall markup due to rounding.

Table 8.2.3 presents the total markup for hearth product ignition devices based on the 

percentage of the market attributed to each distribution channel.

Table 8.2.3 Overall Markup for Hearth Product Ignition Devices

Product Class Baseline Markup Incremental Markup

Hearth Products 3.43 2.17

8.2.1.3 Total Consumer Price

DOE derived the consumer product price for the baseline product by multiplying the 

baseline manufacturer cost by the baseline overall markup (including the sales tax). For each

efficiency level above the baseline, DOE derived the consumer product price by taking baseline 

product consumer price and adding to it the product of the incremental manufacturer cost and the 

incremental overall markup (including the sales tax). Markups and sales tax 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.

Table 8.2.4 presents the average consumer product price for hearth product ignition 

devices at each efficiency level examined.

8-8
Table 8.2.4 Average Consumer Price for Hearth Product Ignition Devices (2013$)

Product Class Efficiency Level Average Consumer Price Incremental Cost

2013$ 2013$

Hearth Products Standing Pilot $166.46 -

Intermittent Pilot $248.14 $81.68

8.2.1.4 Future Product Prices

DOE examined the historical price trend of hearth products by looking at the producer 

price index (PPI) data for floor and wall furnaces from 1999-2013 from the Bureau of Labor 

Statistics’ (BLS).b The PPI for floor and wall furnaces is the most representative price index for 

hearth products, as the products in this PPI index are generally similar to hearth products. The 

PPI data reflect nominal prices, adjusted for product quality changes. The inflation-adjusted 

(deflated) price index for floor and wall furnaces is calculated by dividing the PPI series by the 

Gross Domestic Product Chained Price Index (see Figure 8.2.1).

Figure 8.2.2 Historical Deflated Producer Price Indexes for Floor and Wall Furnaces

In Figure 8.2.1, the deflated PPI for floor and wall furnaces has remained relatively

constant throughout the past decade. Even though there is a sign of downward trend beginning in 

2012, DOE does not have sufficient evidence that the downward trend will continue in the 

future. Given the uncertainty, DOE chose to apply a constant price trend (in real dollars) to the 

manufacturer selling price of hearth products.

b Floor and wall furnaces PPI series ID: PCU 3334143334147; www.bls.gov/ppi/

8-9

 
8.2.1.5 Installation Cost

The installation cost is the cost to the consumer of installing a hearth product ignition 

device. Because the ignition device is a component of the hearth product, the only installation 

costs considered in the analysis were the labor and material costs associated with electrical 

retrofits for the fraction of households for which this is necessary. DOE assumed that in new 

construction there would be no additional installation cost of the intermittent pilot compared to 

the standing pilot. DOE estimated the cost of installing a new electrical connection or installing 

electrical grounding when applicable for each sample household based the age of the household

given in RECS 2009. In addition, DOE assumed that the cost of electrical retrofits for ventless 

gas logs, which are easier to move closer to the location of the electrical outlet, is half of the cost 

applied to the other hearth product groups. Table 8.2.5 presents the assumptions DOE used to 

determine the households that require electrical retrofits.

Table 8.2.5 Installation Cost Household Sample Assumptions

Criteria Fraction of Households that Require

Electrical Outlet Electrical Grounding

Household built before 1960 50% 50%

Household built before 1990 25% 0%

Household built after 1990 10% 0%

New construction 0% 0%

Table 8.2.6 presents the national average basic installation cost based on RS Means 2013 

Residential Cost Data.2

Table 8.2.6 RS Means National Installation Labor and Material Costs (2013$)

Labor

Hours

Bare Material

Cost*

Electricity Connection (Retrofit)

Trip Charge 0.5 $0.00

Receptacle Devices (Duplex Outlet,15 

amp recpt., EMT & wire) (20' avg. runs 

and #14/2 wiring included)

1.501 $29.50

Electricity Grounding (Retrofit)

Clamp, bronze,1/2" diameter 0.25 $5.20

Bare copper wire, #14 solid 0.571 $7.80

*Does not include sales tax or RS Means markups by trade.

RS Means provides average national labor costs for different trade groups as shown in 

Table 8.2.7. Labor costs including overhead and profit (O&P) are the bare costs provided by RS 

Means multiplied by the RS Means markups by trade shown in Table 8.2.8.

8-10
Table 8.2.7 RS Means 2013 National Average Residential Labor Costs by Crew

Crew Type Crew Description Laborers 

per Crew

Cost per Labor-Hour

Bare Costs Incl. O&P*

1 Elec 1 Electrician 1 35.10 57.42

* O&P includes markups in Table 8.2.8.

Table 8.2.8 RS Means Labor Costs Markups by Trade (Residential)

Trade Workers 

Comp.

Avg. Fixed 

Overhead Overhead Profit Total

Electrician 5.7% 17.9% 30.0% 10.0% 63.6%

DOE’s analysis of installation costs accounts for regional differences in labor and 

material costs. RS Means provides material and labor cost factors for 295 cities and towns in the 

U.S. To derive average installation labor cost values by state, DOE weighted these price factors 

by housing projections in 2021. DOE used the average material and labor cost factors for costs

associated with electrical labor. Table 8.2.9 shows the final regional material and labor price 

factors used in the analysis by geographical area. 

8-11
Table 8.2.9 Material and Labor Cost Factors by Geographical Area (for RECS 2009 

Sample)

Geographical Area

Electrical

Material Labor

Connecticut, Maine, New Hampshire, Rhode 

Island, Vermont 1.01 0.97

Massachusetts 1.02 1.16

New York 1.02 1.68

New Jersey 1.02 1.37

Pennsylvania 0.96 1.25

Illinois 0.95 1.27

Indiana, Ohio 0.98 0.89

Michigan 0.97 0.99

Wisconsin 1.02 0.95

Iowa, Minnesota, North Dakota, South Dakota 1.01 0.91

Kansas, Nebraska 0.99 0.77

Missouri 1.01 0.95

Virginia 0.97 0.71

Delaware, District of Columbia, Maryland 0.98 0.97

Georgia 0.99 0.69

North Carolina, South Carolina 0.97 0.48

Florida 0.99 0.68

Alabama, Kentucky, Mississippi 0.99 0.69

Tennessee 1.00 0.63

Arkansas, Louisiana, Oklahoma 1.00 0.62

Texas 0.95 0.61

Colorado 1.01 0.84

Idaho, Montana, Utah, Wyoming 0.96 0.70

Arizona 0.98 0.66

Nevada, New Mexico 0.94 0.93

California 1.00 1.21

Oregon, Washington 1.02 0.97

Alaska 1.34 1.17

Hawaii 1.06 1.27

West Virginia 0.96 0.90

Table 8.2.10 presents the average installation cost for all hearth product ignition systems.

Table 8.2.10 Average Installation Cost for Hearth Product Ignition Systems (2013$)

Product Class Efficiency Level

Average 

Installation Cost Incremental Cost

2013$ 2013$

Hearth Products Standing Pilot $0.00 -

Intermittent Pilot $19.53 $19.53

8-12
8.2.1.6 Total Installed Cost

The total installed cost is the sum of the product price and the installation cost. Markups, 

sales taxes, and labor and material costs 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 singlepoint value, but rather a distribution of values. Table 8.2.11 presents the average total installed 

cost for hearth products ignition devices. 

Table 8.2.11 Average Total Installed Cost for Hearth Product Ignition Devices (2013$)

Product Class Efficiency Level Total Installed Cost Incremental Cost

2013$ 2013$

Hearth Products Standing Pilot $166.46 -

Intermittent Pilot $267.67 $101.21 

8.2.2 Operating Cost Inputs

DOE defined the operating cost by the following equation:

OC = EC+ RC+ MC

Eq. 8.3

Where:

OC = operating cost ($), 

EC = energy cost associated with operating the product ($),

RC = repair cost associated with component failure ($), and

MC = annual maintenance cost for maintaining product operation ($).

The remainder of this section provides information about the variables that DOE used to 

calculate the operating cost for hearth products. The annual energy costs of the product are 

computed from energy consumption per unit for the baseline and standard-compliant cases, 

combined with the energy prices. Product 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

DOE calculated the annual energy use savings for each sample household at each 

efficiency level as described in chapter 7. DOE believes that consumers will not use a hearth 

product with an intermittent pilot more frequently than a hearth product with a standing pilot 

light, and did not include a rebound effect in the energy use calculations.

8.2.2.2 Energy Prices

DOE derived average and marginal monthly energy prices for a number of geographic 

areas in the United States using the latest data from EIA and monthly energy price factors that it 

8-13
developed. Average energy prices are applied to the base case energy use, while marginal prices 

are applied to the differential energy use from the other efficiency options. DOE then assigned an 

appropriate energy price to each household in the sample, depending on its location.

Derivation of Average and Marginal Monthly Prices

Derivation of Average Annual Energy Prices using EIA data. DOE obtained the data 

for natural gas prices from EIA’s Natural Gas Navigator,

4 which includes monthly natural gas 

prices by state for residential, commercial, and industrial consumers. DOE derived 2012 annual 

electricity prices from EIA Form 826 data,

5 which includes electricity prices by state for 

residential, commercial, and industrial consumers. DOE calculated annual state electricity and 

natural gas prices by averaging monthly energy prices by state.

DOE collected 2012 average LPG prices from EIA’s 2012 State Energy Consumption, 

Price, and Expenditures Estimates (SEDS).6 SEDS includes annual LPG prices for residential, 

commercial, industrial, and transportation consumers by state.

For a RECS region with more than one state, DOE weighted each state’s average energy 

price by its number of homes in 2021. See appendix 8C for the calculated annual energy prices in 

2012.

Derivation of Average Monthly Energy Factors using EIA data. To determine monthly 

prices for use in the analysis, DOE developed monthly energy price factors for each fuel based 

on long-term price data. See appendix 8C for a description of the method. DOE multiplied the 

average 2012 annual prices by the monthly price factors for each fuel to derive prices for each 

month.

Seasonal Electricity and Natural Gas Marginal Price Factors using EIA data. Monthly 

electricity and natural gas prices were adjusted using seasonal marginal price factors to 

determine monthly marginal electricity and natural gas prices. These marginal energy prices 

were used to determine the cost to the consumer of the change in energy consumed. Because 

marginal price data is only available for electricity and natural gas, DOE only developed 

marginal monthly prices for these fuels. For LPG, DOE used average monthly prices. For a 

detailed discussion of the development of marginal energy price factors, see appendix 8C.

Table 8.2.12, Table 8.2.13, Table 8.2.14, and Table 8.2.15 show the average and marginal 

monthly natural gas and electricity prices. Average LPG prices are shown in appendix 8C.

8-14
Table 8.2.12 Residential Average Monthly Natural Gas Prices for 2012 Using Monthly

Price Factors (2013$/MMBtu)

Geographical Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Connecticut, Maine, 

New Hampshire, Rhode 

Island, Vermont

14.4 14.5 14.6 14.9 15.6 16.9 18.4 18.7 18.2 16.3 15.5 15.1

Massachusetts 12.9 12.9 12.8 13.1 12.3 12.6 13.9 14.7 14.2 12.3 13.3 13.2

New York 12.8 12.6 12.8 13.3 14.8 16.9 17.8 16.8 16.4 14.4 12.9 12.1

New Jersey 10.9 10.7 10.7 11.0 11.7 12.9 13.4 13.4 13.2 12.1 11.5 11.2

Pennsylvania 11.4 11.6 11.8 12.3 13.8 15.9 17.6 18.1 17.2 14.0 12.4 11.8

Illinois 8.1 8.2 8.2 8.7 10.6 12.2 13.1 13.3 12.3 9.7 8.6 8.1

Indiana, Ohio 11.3 11.4 11.8 12.6 14.1 16.2 17.7 17.9 16.6 13.3 11.8 11.5

Michigan 10.1 10.1 10.3 10.8 12.0 13.7 15.2 15.7 14.5 12.1 10.9 10.5

Wisconsin 9.6 9.3 9.5 9.6 9.7 11.1 11.5 11.7 10.7 8.8 9.8 9.6

Iowa, Minnesota, North 

Dakota, South Dakota 8.3 8.1 8.2 8.4 9.6 11.2 12.1 12.5 11.6 9.2 8.6 8.2

Kansas, Nebraska 9.5 9.5 9.5 10.4 11.7 13.7 14.7 15.4 15.0 12.9 10.4 9.8

Missouri 12.2 12.2 12.3 13.8 15.9 19.4 22.2 23.5 21.9 18.6 14.6 13.0

Virginia 12.1 11.6 11.5 12.8 15.1 17.6 18.9 18.6 18.6 15.1 12.4 12.0

Delaware, District of 

Columbia, Maryland 11.9 11.8 12.1 13.2 15.0 16.9 18.0 17.9 17.6 14.6 12.8 12.2

Georgia 13.4 14.2 14.9 16.5 20.5 22.8 24.0 23.8 23.1 19.9 15.3 14.3

North Carolina, South 

Carolina 12.6 12.5 12.9 13.9 15.9 18.8 19.9 20.7 19.8 16.5 13.8 13.3

Florida 15.4 15.7 16.7 17.7 19.3 20.5 21.2 21.6 21.3 20.9 19.2 17.0

Alabama, Kentucky, 

Mississippi 11.7 11.7 12.1 13.3 15.2 16.9 17.3 17.7 17.3 15.7 13.4 12.4

Tennessee 9.9 10.0 10.0 10.9 12.0 13.7 14.6 15.0 14.3 13.0 11.0 10.4

Arkansas, Louisiana, 

Oklahoma 10.6 10.5 10.8 11.9 14.1 15.6 16.6 17.1 16.6 15.5 13.1 11.2

Texas 9.5 9.5 9.8 11.2 12.9 14.4 14.8 15.2 15.1 13.6 11.2 9.9

Colorado 7.8 7.9 8.2 8.4 9.3 11.8 11.9 12.6 11.7 9.3 8.4 8.0

Idaho, Montana, Utah, 

Wyoming 8.2 8.2 8.4 8.2 8.5 9.2 10.0 10.5 9.8 8.6 8.5 8.3

Arizona 13.5 14.0 14.5 15.8 17.7 19.6 21.4 22.1 21.4 20.0 16.9 14.6

Nevada, New Mexico 8.7 8.9 9.1 9.8 11.3 13.1 12.8 13.3 12.8 11.3 9.6 8.7

California 9.2 9.1 8.8 8.8 9.2 9.6 9.7 9.6 9.3 9.4 9.0 9.0

Oregon, Washington 11.0 11.2 11.2 11.5 12.0 12.6 13.9 14.4 14.0 12.7 11.7 11.4

Alaska 8.1 8.2 8.3 8.4 8.8 9.1 9.8 9.6 8.9 8.4 8.2 8.4

Hawaii 49.6 50.7 50.8 51.0 51.8 52.3 53.6 54.9 54.8 54.5 53.7 52.6

West Virginia 10.8 10.8 10.9 11.3 12.5 15.1 16.9 16.6 15.3 12.5 11.5 11.1

United States 10.5 10.5 10.6 11.2 12.3 13.8 14.6 14.9 14.3 12.3 11.2 10.7

8-15
Table 8.2.13 Residential Marginal Monthly Natural Gas Prices for 2012 Using 

Monthly Price Factors (2013$/MMBtu)

Geographical Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Connecticut, Maine, New 

Hampshire, Rhode Island, 

Vermont

13.21 13.32 13.42 12.40 12.97 14.03 15.26 15.52 15.11 13.52 14.27 13.84

Massachusetts 13.30 13.29 13.19 11.75 10.98 11.30 12.45 13.13 12.71 11.00 13.66 13.60

New York 11.44 11.24 11.38 10.01 11.12 12.67 13.38 12.61 12.35 10.85 11.47 10.81

New Jersey 10.33 10.20 10.22 9.23 9.82 10.78 11.23 11.18 11.03 10.13 10.89 10.64

Pennsylvania 10.58 10.70 10.90 8.93 10.02 11.55 12.78 13.11 12.45 10.16 11.50 10.91

Illinois 7.93 7.96 8.01 5.91 7.17 8.26 8.88 8.99 8.33 6.58 8.40 7.91

Indiana, Ohio 10.43 10.53 10.88 9.17 10.20 11.78 12.82 12.99 12.08 9.65 10.91 10.65

Michigan 9.40 9.41 9.55 8.38 9.33 10.68 11.82 12.25 11.30 9.41 10.19 9.81

Wisconsin 9.37 9.16 9.33 7.66 7.67 8.78 9.11 9.26 8.48 7.02 9.64 9.39

Iowa, Minnesota, North 

Dakota, South Dakota 8.04 7.89 8.03 6.03 6.83 8.01 8.65 8.92 8.30 6.59 8.39 8.02

Kansas, Nebraska 8.81 8.86 8.87 7.21 8.12 9.51 10.18 10.69 10.39 8.92 9.70 9.08

Missouri 9.98 9.98 10.11 8.20 9.50 11.59 13.25 14.03 13.07 11.08 12.00 10.67

Virginia 11.23 10.82 10.69 8.65 10.23 11.95 12.83 12.63 12.59 10.21 11.53 11.20

Delaware, District of 

Columbia, Maryland 11.03 10.94 11.20 9.32 10.59 11.89 12.65 12.64 12.38 10.28 11.83 11.25

Georgia 11.58 12.31 12.86 9.19 11.42 12.74 13.40 13.30 12.88 11.08 13.22 12.39

North Carolina, South 

Carolina 11.26 11.22 11.53 9.20 10.56 12.46 13.22 13.70 13.15 10.93 12.31 11.94

Florida 12.65 12.90 13.78 11.41 12.42 13.21 13.62 13.91 13.71 13.48 15.83 14.05

Alabama, Kentucky, 

Mississippi 10.15 10.13 10.46 9.95 11.33 12.59 12.92 13.19 12.89 11.69 11.61 10.76

Tennessee 9.31 9.44 9.44 8.04 8.84 10.11 10.78 11.12 10.61 9.63 10.39 9.80

Arkansas, Louisiana, 

Oklahoma 8.88 8.84 9.07 7.70 9.16 10.09 10.74 11.08 10.78 10.05 10.96 9.43

Texas 8.01 8.06 8.28 6.68 7.66 8.54 8.80 9.02 8.97 8.10 9.45 8.35

Colorado 7.09 7.18 7.43 5.78 6.41 8.12 8.17 8.67 8.02 6.42 7.66 7.28

Idaho, Montana, Utah, 

Wyoming 7.84 7.87 8.01 6.82 7.13 7.71 8.38 8.76 8.18 7.22 8.13 7.97

Arizona 11.51 11.87 12.29 10.04 11.27 12.47 13.57 14.08 13.62 12.72 14.33 12.39

Nevada, New Mexico 7.69 7.88 8.08 7.10 8.13 9.46 9.20 9.56 9.24 8.15 8.50 7.77

California 9.94 9.84 9.48 7.45 7.77 8.17 8.22 8.10 7.89 7.95 9.75 9.77

Oregon, Washington 10.35 10.47 10.52 9.67 10.05 10.56 11.64 12.11 11.77 10.65 11.01 10.72

Alaska 7.82 7.89 7.95 7.20 7.55 7.81 8.41 8.25 7.63 7.25 7.87 8.12

Hawaii 45.38 46.34 46.42 39.47 40.05 40.49 41.44 42.49 42.37 42.15 49.08 48.09

West Virginia 10.26 10.30 10.42 9.02 9.99 12.08 13.44 13.28 12.20 9.95 10.91 10.59

United States 9.80 9.85 9.97 8.31 9.14 10.24 10.86 11.09 10.61 9.14 10.47 10.03

8-16
Table 8.2.14 Residential Average Electricity Prices for 2012 Using Monthly Price 

Factors (2013$/kWh)

Geographical Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Connecticut, Maine, 

New Hampshire, Rhode 

Island, Vermont

0.16 0.16 0.16 0.16 0.17 0.17 0.17 0.17 0.17 0.17 0.16 0.16

Massachusetts 0.15 0.15 0.15 0.15 0.15 0.16 0.15 0.15 0.15 0.15 0.15 0.15

New York 0.17 0.17 0.17 0.17 0.18 0.18 0.19 0.19 0.19 0.18 0.18 0.17

New Jersey 0.15 0.15 0.15 0.15 0.16 0.17 0.17 0.17 0.17 0.16 0.16 0.16

Pennsylvania 0.12 0.12 0.12 0.13 0.13 0.14 0.14 0.14 0.13 0.13 0.13 0.12

Illinois 0.10 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.11 0.11

Indiana, Ohio 0.10 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.11

Michigan 0.14 0.14 0.14 0.14 0.14 0.15 0.15 0.15 0.15 0.14 0.14 0.14

Wisconsin 0.13 0.13 0.13 0.13 0.14 0.14 0.13 0.13 0.14 0.14 0.13 0.13

Iowa, Minnesota, North 

Dakota, South Dakota 0.10 0.10 0.10 0.11 0.11 0.12 0.12 0.12 0.12 0.11 0.11 0.10

Kansas, Nebraska 0.09 0.10 0.10 0.11 0.11 0.12 0.12 0.12 0.12 0.11 0.10 0.10

Missouri 0.09 0.09 0.09 0.10 0.11 0.12 0.12 0.12 0.11 0.10 0.10 0.09

Virginia 0.10 0.10 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.11 0.11 0.10

Delaware, District of 

Columbia, Maryland 0.12 0.12 0.12 0.12 0.14 0.15 0.15 0.15 0.14 0.13 0.12 0.12

Georgia 0.10 0.10 0.11 0.11 0.11 0.12 0.12 0.13 0.12 0.11 0.11 0.10

North Carolina, South 

Carolina 0.11 0.11 0.11 0.11 0.12 0.11 0.12 0.12 0.12 0.12 0.11 0.11

Florida 0.11 0.11 0.12 0.12 0.12 0.11 0.12 0.12 0.12 0.12 0.12 0.12

Alabama, Kentucky, 

Mississippi 0.10 0.10 0.10 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.10

Tennessee 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.11 0.11 0.10

Arkansas, Louisiana, 

Oklahoma 0.08 0.09 0.09 0.09 0.09 0.10 0.10 0.10 0.10 0.10 0.09 0.09

Texas 0.10 0.10 0.11 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.11 0.11

Colorado 0.11 0.11 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.11

Idaho, Montana, Utah, 

Wyoming 0.09 0.09 0.09 0.09 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10

Arizona 0.10 0.10 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.11 0.11

Nevada, New Mexico 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12

California 0.15 0.15 0.15 0.15 0.16 0.16 0.16 0.16 0.16 0.15 0.15 0.16

Oregon, Washington 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09

Alaska 0.17 0.17 0.18 0.18 0.19 0.19 0.19 0.19 0.18 0.18 0.18 0.18

Hawaii 0.36 0.37 0.37 0.37 0.38 0.38 0.38 0.39 0.39 0.39 0.39 0.39

West Virginia 0.09 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.11 0.10 0.10

United States 0.11 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.11

8-17
Table 8.2.15 Residential Marginal Electricity Prices for 2012 Using Monthly Price 

Factors (2013$/kWh)

Geographical Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Connecticut, Maine, New Hampshire, 

Rhode Island, Vermont 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16

Massachusetts 0.15 0.16 0.16 0.14 0.15 0.15 0.14 0.15 0.15 0.15 0.16 0.16

New York 0.15 0.15 0.15 0.20 0.20 0.21 0.21 0.21 0.21 0.20 0.15 0.15

New Jersey 0.15 0.15 0.15 0.19 0.19 0.20 0.21 0.21 0.21 0.19 0.15 0.15

Pennsylvania 0.10 0.10 0.10 0.14 0.14 0.15 0.15 0.15 0.15 0.14 0.11 0.10

Illinois 0.07 0.08 0.08 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.08 0.08

Indiana, Ohio 0.08 0.08 0.08 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.09 0.08

Michigan 0.13 0.14 0.13 0.16 0.16 0.17 0.17 0.17 0.17 0.16 0.14 0.14

Wisconsin 0.11 0.12 0.12 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.12 0.12

Iowa, Minnesota, North Dakota, 

South Dakota 0.09 0.09 0.09 0.12 0.12 0.13 0.13 0.13 0.12 0.12 0.09 0.09

Kansas, Nebraska 0.07 0.07 0.07 0.12 0.13 0.14 0.14 0.14 0.14 0.13 0.08 0.07

Missouri 0.07 0.07 0.07 0.12 0.13 0.15 0.14 0.14 0.13 0.12 0.07 0.07

Virginia 0.09 0.09 0.09 0.12 0.13 0.13 0.13 0.13 0.13 0.12 0.09 0.09

Delaware, District of Columbia, 

Maryland 0.11 0.11 0.11 0.14 0.16 0.17 0.17 0.17 0.16 0.15 0.11 0.11

Georgia 0.09 0.09 0.09 0.13 0.13 0.14 0.14 0.15 0.14 0.13 0.09 0.09

North Carolina, South Carolina 0.09 0.09 0.09 0.11 0.11 0.11 0.11 0.11 0.11 0.12 0.09 0.09

Florida 0.11 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.11 0.11

Alabama, Kentucky, Mississippi 0.08 0.08 0.08 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.09 0.08

Tennessee 0.08 0.08 0.08 0.10 0.10 0.10 0.10 0.09 0.10 0.10 0.09 0.09

Arkansas, Louisiana, Oklahoma 0.06 0.06 0.06 0.09 0.10 0.10 0.10 0.10 0.10 0.10 0.07 0.06

Texas 0.09 0.09 0.10 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.10 0.10

Colorado 0.09 0.09 0.09 0.12 0.13 0.13 0.13 0.13 0.13 0.13 0.09 0.09

Idaho, Montana, Utah, Wyoming 0.09 0.09 0.09 0.10 0.11 0.11 0.11 0.11 0.11 0.11 0.09 0.09

Arizona 0.08 0.09 0.09 0.12 0.13 0.13 0.13 0.13 0.13 0.12 0.09 0.09

Nevada, New Mexico 0.10 0.10 0.10 0.12 0.12 0.12 0.12 0.12 0.12 0.13 0.11 0.10

California 0.17 0.17 0.17 0.18 0.19 0.19 0.20 0.20 0.19 0.18 0.18 0.18

Oregon, Washington 0.08 0.09 0.09 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.09 0.09

Alaska 0.16 0.16 0.16 0.15 0.16 0.16 0.16 0.16 0.16 0.16 0.17 0.16

Hawaii 0.33 0.33 0.33 0.54 0.55 0.55 0.56 0.56 0.56 0.57 0.35 0.35

West Virginia 0.08 0.08 0.08 0.09 0.10 0.09 0.09 0.09 0.09 0.10 0.09 0.08

United States 0.09 0.09 0.09 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.10 0.09

Household Energy Price Adjustment Factor

RECS 2009 reports the total annual consumption and expenditure of each energy use 

type. To take into account that household energy prices may vary inside a geographical area, 

DOE developed an adjustment factor based on the reported average RECS 2009 energy price for 

each household divided by the average energy price in the RECS 2009 geographical region. This 

factor was then multiplied by the monthly price developed above to determine the household 

energy price. Appendix 8C includes more details.

8-18
Energy Price Trends by Census Division

To arrive at prices in future years, DOE multiplied the prices described in the preceding 

section by the forecasts of annual average price changes in AEO 2014.

1 Figure 8.2.2 shows the 

national residential energy price factors with a 2012 base year (2012=1). To estimate the trend 

after 2040, DOE used the average rate of change during 2030–2040. 

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. Appendix 8C includes more details.

Figure 8.2.3 Projected National Commercial Energy Price Factors, AEO 2014 

(Reference Case)

8.2.2.1 Maintenance Cost

The maintenance cost is the routine cost to the consumer of maintaining equipment 

operation. DOE assumed that hearth product ignition systems do not require any maintenance.

8.2.2.2 Repair Cost

The repair cost is the cost of materials and labor to the consumer for replacing an ignition 

device that has failed. Repair occurs if the ignition system fails before the end of the hearth 

0.9

1

1.1

1.2

1.3

1.4

1.5

1.6

2012 2017 2022 2027 2032 2037

Price Factor

Year

Natural Gas

LPG

Electricity

8-19
product life. The repair costs are assumed to be equal to the retail price for replacement 

applications. DOE accounts for regional differences in labor and material costs.

The failure year distribution of the hearth product ignition device is assumed to be a 

Weibull function (see section 8.2.3.3). DOE estimated that the ignition system in 14 percent of 

all hearth products would fail and be repaired over the course of the product lifetime. DOE 

observed that typical warranties for hearth products cover the ignition system parts for 5 years. If 

the ignition system failed before the warranty period expired, it was assumed that the consumer 

only incurred the cost of labor for the repair; otherwise, the consumer incurred the cost of both 

parts and repair for the repair. The ignition system lifetime is determined as described in section 

8.2.3.3.

Table 8.2.16 presents the repair cost components that DOE derived from RS Means and 

the repair cost of hearth product ignition devices.

Table 8.2.16 Hearth Product Average Repair Cost Components (2013$)

Repair Description Bare Material Cost (2013$)* Total Labor Hours

Repair Standing Pilot Ignition $43.72 1.50

Repair Intermittent Pilot Ignition $142.89 1.50

*Does not include sales tax or RS Means markups by trade.

RS Means provides average national labor costs for different trade groups as shown in 

Table 8.2.17. Labor costs including overhead and profit (O&P) are the bare costs provided by RS 

Means multiplied by the RS Means markups by trade shown in Table 8.2.18.

Table 8.2.17 RS Means 2013 National Average Residential Labor Costs by Crew

Crew Type Crew Description Laborers 

per Crew

Cost per Labor-Hour

Bare Costs Incl. O&P*

Q1 1 Plumber, 1 Plumber Apprentice 2 33.18 54.61

* O&P includes markups in Table 8.2.8.

Table 8.2.18 RS Means Labor Costs Markups by Trade (Residential)

Trade Workers 

Comp.

Avg. Fixed 

Overhead Overhead Profit Total

Plumber 6.7% 17.9% 30.0% 10.0% 63.6%

DOE’s analysis of repair costs accounts for regional differences in labor and material 

costs. RS Means provides material and labor cost factors for 295 cities and towns in the U.S. To 

derive average repair labor cost values by state, DOE weighted these price factors by housing 

projections in 2021. DOE used the average material and labor cost factors for costs associated 

with plumbing and heating, ventilation, and air conditioning (HVAC). Table 8.2.9 shows the 

final regional material and labor price factors used in the analysis by geographical area. 

8-20
Table 8.2.19 shows the annualized repair cost estimates for hearth product ignition 

devices.

Table 8.2.19 Annualized Repair Cost for Hearth Product Ignition Devices (2013$)

Product Class Efficiency Level Annualized Repair Cost Incremental Cost

2013$ 2013$

Hearth Products Standing Pilot $1.83 -

Intermittent Pilot $2.40 $0.57

8.2.2.3 Lifetime

DOE defines lifetime as the age when a product is retired from service. DOE used 

warranty information and the lifetimes of similar appliances to estimate the distribution of both

hearth product and hearth product ignition system lifetimes.

Hearth product warranties typically cover the hearth product for 5 years and the ignition 

system for 1 year for both labor and parts. Therefore, because any repairs performed during the 

warranty period would be free to the consumer, DOE assumed that the minimum lifetime of both 

the hearth product and the ignition system corresponded to the end of the respective warranty 

period. In the analysis, a unit was only repaired if the ignition system failed before the overall 

hearth product did.

A Weibull distribution is a probability distribution function commonly used to measure 

failure rates.7 Its form is similar to an exponential distribution, which would model a fixed 

failure rate, except that it allows for a failure rate that changes over time in a particular fashion. 

The cumulative distribution takes the form:

for x > θ and P(x) = 1 for x ≤ θ,

Eq. 8-1

Where:

P(x) = probability that the appliance is still in use at age x,

x = appliance age,

α = the scale parameter, which is the decay length in an exponential distribution,

β = the shape parameter, which determines the way in which the failure rate changes in time, and

θ = the delay parameter, which allows for a delay before any failures occur.

When β = 1, the failure rate is constant over time, and this distribution takes the form of a 

cumulative exponential distribution. For the case of appliances, β is commonly greater than 1, 

which results from a rising failure rate as the appliance ages. A plot of a Weibull distribution 

(DOE’s calculated hearth product survival function) is shown as Figure 8.2.1.

e x

P x

β

α

θ 



 



 − −

( ) =

8-21
Figure 8.2.1 Lifetime Distribution of Hearth Products (whole unit)

Table 8.2.20 shows the average and minimum lifetime of both the hearth product and 

hearth product ignition system.

Table 8.2.20 Lifetime Parameters for Hearth Products

Product Class Lifetime

Minimum Average

Hearth Product (Whole Unit) 5 15.0

Hearth Product (Ignition System Only) 1 7.3

8.2.2.4 Discount Rates

The discount rate is the rate at which future savings and expenditures are discounted to 

establish their present value. DOE uses publicly available data (the Federal Reserve Board’s 

Survey of Consumer Finances (SCF)

8

) to estimate a consumer’s opportunity cost of funds related 

to appliance energy cost savings and maintenance costs. The discount rate value is applied in the 

LCC to future year energy cost savings and non-energy operations and maintenance costs in 

order to present the estimated net life-cycle cost and life-cycle cost savings. DOE notes that the 

discount rate used in the LCC analysis is distinct from an implicit discount rate, as it is not used 

to model consumer purchase decisions. The opportunity cost of funds in this case may include 

interest payments on debt and interest returns on assets.

DOE estimated separate discount rate distributions for six income groups, divided based 

on income percentile as reported in the Federal Reserve Board’s SCF. This disaggregation 

8-22
reflects the fact that low and high income consumers tend to have substantially different shares 

of debt and asset types, as well as facing different rates on debts and assets. Summaries of shares 

and rates presented in this chapter are averages across the entire population.

Table 8.2.21 Definitions of Income Groups 

Income Group Percentile of Income

1 1st to 20th

2 21st to 40th

3 41st to 60th

4 61st to 80th

5 81st to 90th

6 91th to 99th

Sources: Federal Reserve Board. Survey of Consumer Finances (SCF) for 1995, 1998, 2001, 2004, 2007, and 2010.

Shares of Debt and Asset Classes 

DOE’s approach involved identifying all relevant household debt or asset classes in order 

to approximate a consumer’s opportunity cost of funds related to appliance energy cost savings 

and maintenance costs. The approach assumes that in the long term, consumers are likely to draw 

from or add to their collection of debt and asset holdings approximately in proportion to their 

current holdings when future expenditures are required or future savings accumulate. DOE has 

included several previously excluded debt types (i.e., vehicle and education loans, mortgages, all 

forms of home equity loan) in order to better account for all of the options available to 

consumers.

The average share of total debt plus equity and the associated rate of each asset and debt 

type are used to calculate a weighted average discount rate for each SCF household (Table 

8.2.22). The household-level discount rates are then aggregated to form discount rate 

distributions for each of the six income groups. Note that previously DOE performed aggregation 

of asset and debt types over households by summing the dollar value across all households and 

then calculating shares. Weighting by dollar value gave disproportionate influence to the asset 

and debt shares and rates of higher income consumers. DOE has shifted to a household-level 

weighting to more accurately reflect the average consumer in each income group.

DOE estimated the average percentage shares of the various types of debt and equity 

using data from the Federal Reserve Board’s SCF for 1995, 1998, 2001, 2004, 2007, and 2010.c

DOE derived the household-weighted mean percentages of each source of financing throughout 

the 5 years surveyed. DOE posits that these long-term averages are most appropriate to use in its 

analysis. 

c Note that although two older versions of the SCF are also available (1989 and 1992); these surveys are not used in 

this analysis because they do not provide all of the necessary types of data (e.g., credit card interest rates, etc.). DOE 

feels that the 15-year span covered by the six surveys included is sufficiently representative of recent debt and 

equity shares and interest rates.

8-23

 
Table 8.2.22 Types of Household Debt and Equity by Percentage Shares (%)

Type of Debt or Equity Income Group

1 2 3 4 5 6

Debt:

Mortgage 18.9% 24.1% 33.1% 38.1% 39.3% 25.0%

Home equity loan 3.1% 3.3% 2.6% 3.6% 4.5% 7.2%

Credit card 15.3% 13.0% 11.8% 8.7% 6.0% 2.7%

Other installment loan 25.1% 20.6% 17.3% 13.2% 9.6% 4.7%

Other residential loan 0.7% 0.6% 0.6% 0.7% 1.0% 1.2%

Other line of credit 1.6% 1.5% 1.3% 1.5% 2.1% 1.8%

Equity:

Savings account 18.5% 16.0% 12.7% 10.6% 10.4% 7.9%

Money market account 3.6% 4.5% 4.0% 4.5% 5.0% 8.6%

Certificate of deposit 7.0% 7.8% 5.5% 5.0% 4.4% 4.2%

Savings bond 1.8% 1.7% 1.9% 2.2% 1.7% 1.1%

Bonds 0.2% 0.4% 0.5% 0.7% 0.8% 3.8%

Stocks 2.3% 3.1% 4.4% 5.7% 7.6% 15.8%

Mutual funds 2.1% 3.5% 4.3% 5.7% 7.6% 15.9%

Total 100.0 100.0 100.0 100.0 100.0 100.0

Sources: Federal Reserve Board. Survey of Consumer Finances (SCF) for 1995, 1998, 2001, 2004, 2007, and 2010.

Rates for Types of Debt 

DOE estimated interest rates associated with each type of debt. The source for interest 

rates for mortgages, loans, credit cards, and lines of credit was the Federal Reserve Board’s SCF

for 1995, 1998, 2001, 2004, 2007, and 2010, which associates an interest rate with each type of 

debt for each household in the survey. 

In calculating effective interest rates for home equity loans and mortgages, DOE 

accounted for the fact that interest on both such loans is tax deductible (Table 8.2.23). This rate 

corresponds to the interest rate after deduction of mortgage interest for income tax purposes and 

after adjusting for inflation (using the Fisher formula).d For example, a 6 percent nominal 

mortgage rate has an effective nominal rate of 5.5 percent for a household at the 25 percent 

marginal tax rate. When adjusted for an inflation rate of 2 percent, the effective real rate becomes 

2.45 percent.

d Fisher formula is given by: Real Interest Rate = [(1 + Nominal Interest Rate) / (1 + Inflation Rate)] – 1.

8-24

 
Table 8.2.23 Data Used to Calculate Real Effective Mortgage Rates

Year Mortgage Interest Rates in Selected Years (%)

Average Nominal 

Interest Rate

Inflation Rate9 Applicable Marginal 

Tax Rate10

Average Real Effective 

Interest Rate

1995 8.2 2.83 24.2 3.3

1998 7.9 1.56 25.0 4.3

2001 7.6 2.85 24.2 2.8

2004 6.2 2.66 20.9 2.2

2007 6.3 2.85 20.6 2.1

2010 5.7 1.64 20.0 2.9

Table 8.2.24 shows the household-weighted average effective real rates in each year and 

the mean rate across years. Because the interest rates for each type of household debt reflect 

economic conditions throughout numerous years and various phases of economic growth and 

recession, they are expected to be representative of rates in effect in 2021.

Table 8.2.24 Average Real Effective Interest Rates for Household Debt

Type of Debt Income Group

1 2 3 4 5 6

Mortgage 6.6% 6.2% 6.1% 5.2% 5.0% 4.0%

Home equity loan 7.0% 6.9% 6.7% 5.9% 5.7% 4.3%

Credit card 15.2% 15.0% 14.5% 14.2% 14.0% 14.5%

Other installment loan 10.8% 10.3% 9.9% 9.4% 8.7% 8.6%

Other residential loan 9.8% 10.2% 8.9% 8.2% 7.7% 7.4%

Other line of credit 9.1% 10.9% 9.6% 8.8% 7.4% 6.1%

Sources: Federal Reserve Board. Survey of Consumer Finances (SCF) for 1995, 1998, 2001, 2004, 2007, and 2010.

Rates for Types of Assets

No similar rate data are available from the SCF for classes of assets, so DOE derived 

asset interest rates from various sources of national historical data (1983-2013). The interest rates 

associated with certificates of deposit,11 savings bonds,12 and bonds (AAA corporate bonds)13

were collected from Federal Reserve Board time-series data. Rates on money market accounts 

came from Cost of Savings Index data.14Rates on savings accounts were estimated as one half of

the rate for money market accounts, based on recent differentials between the return to each of 

these assets. The rates for stocks are the annual returns on the Standard and Poor’s.15 Rates for 

mutual funds are a weighted average of the stock rates (two-thirds weight) and the bond rates 

(one-third weight) in each year. DOE assumed rates on checking accounts to be zero.

8-25
DOE adjusted the nominal rates to real rates using the annual inflation rate for each year. 

Average nominal and real interest rates for the classes of household assets are listed in Table 

8.2.25. Because the interest and return rates for each type of asset reflect economic conditions 

throughout numerous years, they are expected to be representative of rates that may be in effect 

in 2021. For each type, DOE developed a distribution of rates, as shown in appendix 8D.

Table 8.2.25 Average Nominal and Real Interest Rates for Household Equity 

Type of Equity Average Real 

Rate 

%

Savings accounts 1.0

Money market accounts 1.9

Certificates of deposit 1.9

Savings bonds 3.4

Bonds 4.2

Stocks 9.4

Mutual funds 7.4

Discount Rate Calculation and Summary

Using the asset and debt data discussed above, DOE calculated discount rate distributions 

for each income group as follows. First, DOE calculated the discount rate for each consumer in 

each of the six versions of the SCF, using the following formula:

DDDDii = �SShaaaaaaii,jj

jj

× RR ii,jj

Eq. 8.4

Where:

DDDDii = discount rate for consumer i,

SShaaaaaaii,jj = share of asset or debt type j for consumer i, and

RR ii,jj = real interest rate or rate of return of asset or debt type j for consumer i.

The rate for each debt type is drawn from the SCF data for each household. The rate for 

each asset type is drawn from the distributions described above. 

Once the real discount rate was estimated for each consumer, DOE compiled the 

distribution of discount rates in each survey by income group by calculating the proportion of 

consumers with discount rates in bins of 1 percent increments, ranging from 0-1 percent to 

greater than 30 percent. Giving equal weight to each survey, DOE compiled the six-survey 

distribution of discount rates. 

8-26
Table 8.2.26 presents the average real effective discount rate and its standard deviation 

for each of the six income groups. To account for variation among households, DOE sampled a 

rate for each RECS household from the distributions for the appropriate income group. (RECS 

provides household income data.) Appendix 8D presents the full probability distributions for 

each income group that DOE used in the LCC and PBP analysis.

Table 8.2.26 Average Real Effective Discount 

Income Group Discount Rate (%)

1 4.85

2 5.12

3 4.75

4 4.04

5 3.80

6 3.57

Overall Average 4.49

8.2.2.5 Compliance Date of Standard

Pursuant to 42 U.S.C. 6295(m), the compliance date of any new energy efficiency 

standard for hearth products is 5 years after the final rule is published. Consistent with its 

published regulatory agenda, DOE assumed that the final rule would be issued by the end of

2015 and that, therefore, the new standards would require compliance beginning in 2021. DOE 

calculated the LCC and PBP for all consumers considered as if they each would purchase a new 

hearth product with an intermittent pilot light in 2021.

8.2.2.6 Base Case Distribution of Efficiency Levels

DOE estimated the market share of standing pilots and intermittent pilots in each hearth 

product group using information from the tear-down analysis (see chapter 5) and consultant 

input. There is currently not enough publically available data to establish a trend in the adoption 

of intermittent pilots in hearth products, so the base case market share of each ignition system is 

projected to be the same through 2021. Table 8.2.27 lists the estimated percentage of intermittent 

pilot ignition systems by product group. In addition, DOE recognizes that match-lit hearth 

products comprise a third potential ignition system. These units were excluded from DOE’s 

analysis as they are not covered in this rulemaking, and provide different functionality than the 

other systems. However, because they are included in HPBA shipment information, these units 

were separated by product group to isolate covered products. Table 8.2.28 presents the market 

shares of match-lit units. The match-lit percentage shown in Table 8.2.28 and the initial 

disaggregation shown in Table 8.2.27 were used to determine the final base case pilot system 

distributions, as shown in Table 8.2.29.

8-27
Table 8.2.27 Market Shares of Intermittent Ignition in 2021 by Product Group 

Product Group Fraction of Models

Vented Heater 55%

Vented Decorative 55%

Unvented 12%

Vented Gas Log Sets 6%

Unvented Gas Log Sets 6%

Outdoor 24%

Table 8.2.28 Market Shares of Match-Lit Units in 2021 by Product Group

Product Group Fraction of Models

Vented Heater 5%

Vented Decorative 5%

Unvented -

Vented Gas Log Sets 50%

Unvented Gas Log Sets -

Outdoor 50%

Table 8.2.29 Market Shares of Hearth Products Ignition in 2021 by Product Group

Product Group Intermittent Pilot Standing Pilot

Vented Heater 58% 42%

Vented Decorative 58% 42%

Unvented 12% 88%

Vented Gas Log Sets 12% 88%

Unvented Gas Log Sets 6% 94%

Outdoor 48% 52%

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. 8.5

Where:

PBP = payback period in years,

∆ IC = difference in the total installed cost between the more efficient standard-level equipment 

and the baseline efficiency equipment, and

8-28
∆OC = difference in first year annual operating costs.

Payback periods are expressed in years. Payback periods can be greater than the life of 

the equipment if the increased total installed cost of the more efficient equipment is not 

recovered fast enough in reduced operating costs.

The data inputs to PBP are the total installed cost of the equipment to the 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, the annualized repair cost, and the annualized

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 are not required. Because

the PBP is a “simple” payback, the required electricity cost is only for the year in which a new 

efficient standard is to take effect—in this case, 2021.

8.4 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS

As discussed previously, DOE’s approach for conducting the LCC and PBP analysis 

relied on developing samples of households that use each of the considered products. DOE also 

used probability distributions to characterize the uncertainty in many of the inputs to the 

analysis. DOE used a Monte Carlo simulation technique to perform the LCC and PBP 

calculations on the households in the sample.

LCC and PBP calculations were performed 10,000 times on the sample of households 

established for each residential product. Each LCC and PBP calculation was performed on a 

single household that was selected from the sample of the residential users. The selection of a 

household was based on its sample weight (i.e., how representative a particular household is of 

other households in the distribution—either regionally or nationally), as described in chapter 7. 

Each LCC and PBP calculation also sampled from the probability distributions that DOE 

developed to characterize many of the inputs to the analysis.

DOE calculated LCC savings relative to the base case product it assigned to the 

households. DOE accounted for households that already have intermittent ignition pilots in the

base case. For this reason, the average LCC impacts are not equal to the difference between the 

LCC of the new standard level and the LCC of the baseline product. The calculation of average 

LCC savings includes households with zero LCC savings (no impact from a standard). DOE 

considered a household to receive no impact at a given efficiency level if DOE assigned it a 

base-case product having an efficiency equal to or greater than the efficiency level in question.

Table 8.4.1 and Table 8.4.2 show the LCC and PBP results for all efficiency levels 

considered for hearth product ignition devices. In Table 8.4.1, the simple payback is measured 

relative to the baseline product. In Table 8.4.2, the LCC savings are measured relative to the base 

case efficiency distribution in the compliance year. No impacts occur when the base case 

efficiency for a specific consumer equals or exceeds the efficiency at a given EL; a standard 

8-29
would have no effect on the individual consumer because the product installed would already 

have intermittent pilot ignition without amended standards.

Table 8.4.1 Average LCC and PBP Results by Efficiency Level for Hearth Product

Ignition Devices

TSL Efficiency 

Level

Average Costs 2013$ Simple 

Payback 

years

Average 

Lifetime 

years

Installed 

Cost

First Year’s 

Operating Cost

Lifetime 

Operating Cost LCC

-- 0 $166 $50 $602 $769 -- 15.0

1 1 $268 $15 $174 $442 2.9 15.0

Note: The results for each TSL are calculated assuming that all consumers use products with that efficiency level. 

The PBP is measured relative to the baseline product. 

Table 8.4.2 Average LCC Savings Relative to the Base Case Efficiency Distribution 

for Hearth Product Ignition Devices

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that Experience Net Cost Average Savings* 2013$

1 1 23% $165

* The calculation includes households with zero LCC savings (no impact).

Figure 8.4.2 shows the range of LCC savings for the efficiency level considered for 

hearth product ignition devices. 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 households have lifecycle cost savings above this value. The 

“whiskers” at the bottom and the top of the box indicate the 5th and 95th percentiles. The small 

box shows the average LCC savings for the new standard level. 

8-30
Figure 8.4.2 Distribution of LCC Savings for Hearth Product Ignition Devices

8.5 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 product 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 household samples and probability distributions, and it is based not on 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.

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 from the DOE test procedure.

Because no DOE test procedure exists for hearth products, the test procedure direct heating 

equipment (10 CFR 430, subpart B, appendix O) was used to calculate energy use because direct 

heating equipment are similar secondary heating appliances to hearth products, and in the past 

the two products shared the same test procedure. The calculation excludes repair costs and 

maintenance costs.

8-31
8.5.1 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 effective date of the standard is assumed to be 2021. 

8.5.2 Results

DOE calculated rebuttable PBPs for each standard level relative to the distribution of 

product energy efficiencies estimated for the base case. Table 8.5.1 presents the rebuttable PBPs 

for hearth product ignition devices.

Table 8.5.1 Rebuttable Payback Period for Hearth Product Ignition Devices

EL Technology Option Rebuttable Payback Period years

1 Intermittent Pilot 2.31

8-32
REFERENCES

1. U.S. Department of Energy-Energy Information Administration, Annual Energy Outlook 

2014 with Projections to 2040. 2014. Washington, DC. (Last accessed May 2014.) 

<www.eia.gov/forecasts/aeo/>

2. RS Means Company Inc., RS Means Residential Cost Data. 32nd Annual Edition ed. 

2013: Kingston, MA

3. U.S. Department of Energy: Energy Information Administration, Residential Energy 

Consumption Survey (RECS): 2009 RECS Survey Data. 2009. (Last accessed January 7, 

2015.) <www.eia.gov/consumption/residential/data/2009/>

4. U.S. Department of Energy - Energy Information Administration, Natural Gas 

Navigator. 2013. www.eia.gov/dnav/ng/ng_pri_sum_dcu_nus_m.htm

5. U.S. Department of Energy - Energy Information Administration, Form EIA-826 

Database Monthly Electric Utility Sales and Revenue Data. 2013.

<www.eia.gov/electricity/data/eia826/>

6. Energy Information Administration, 2012 State Energy Consumption, Price, and 

Expenditure Estimates (SEDS). 2013. Washington, DC. <www.eia.gov/state/seds/>

7. National Institute of Standards and Technology, NIST/SEMATECH Engineering Statistics 

Handbook. www.itl.nist.gov/div898/handbook/

8. The Federal Reserve Board, Survey of Consumer Finances 1989, 1992, 1995, 1998, 

2001, 2004, 2007, 2010. <www.federalreserve.gov/pubs/oss/oss2/scfindex.html>

9. U.S. Department of Labor - Bureau of Labor Statistics, Bureau of Labor Statistics Data, 

Prices & Living Conditions. 2012. <http://data.bls.gov>

10. National Bureau of Economic Research, U.S. Federal and State Average Marginal 

Income Tax Rates. 2012. <http://users.nber.org/~taxsim/allyup/fixed-ally.html>

11. The Federal Reserve Board, Federal Reserve Statistical Release, Selected Interest Rates, 

Historical Data, Instrument: CDs (secondary market), Maturity: 6-month, Frequency: 

Annual, Description: Average rate on 6-month negotiable certificates of deposit 

(secondary market), quoted on an investment basis. 2013.

<www.federalreserve.gov/releases/H15/data.htm>

12. The Federal Reserve Board, Federal Reserve Statistical Release, Selected Interest Rates, 

Historical Data, Instrument: State and local bonds, Maturity: 20-year, Frequency: 

8-33
Monthly, Description: Bond buyer go 20-bond municipal bond index. 2013.

<www.federalreserve.gov/releases/H15/data.htm>

13. The Federal Reserve Board, Federal Reserve Statistical Release, Selected Interest Rates, 

Historical Data, Instrument: Corporate bonds/Moody’s Seasoned AAA, Frequency: 

Annual, Description: Moody’s yield on seasoned corporate bonds—all industries, AAA. 

2013. <www.federalreserve.gov/releases/H15/data.htm>

14. Mortgage-X - Mortgage Information Service, Cost of Savings Index (COSI) Index 

History. 2013. <http://mortgage-x.com/general/indexes/default.asp>

15. 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/>

8-34
CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION ........................................................................................................... 9-1

9.2 FUNDAMENTAL MODEL EQUATIONS.................................................................... 9-1

9.3 DATA INPUTS ............................................................................................................... 9-2

9.3.1 Historical Shipments........................................................................................................ 9-2

9.3.2 Historical and Projected Housing Starts.......................................................................... 9-4

9.3.3 Calculation of the Fraction of New Construction Homes with Hearth Products............. 9-4

9.4 IMPACT OF STANDARDS ON SHIPMENTS ............................................................. 9-5

9.4.1 Purchase Price Elasticity.................................................................................................. 9-5

9.4.2 Impact from Increase in Relative Price............................................................................ 9-7

9.5 MODELING APPROACH.............................................................................................. 9-7

9.6 RESULTS ...................................................................................................................... 9-10

LIST OF TABLES

Table 9.3.1 HPBA Shipments Data ........................................................................................ 9-2

Table 9.3.2 Hearth Products Shipments Disaggregated by Product Group............................ 9-3

Table 9.3.3 Historical Hearth Product Shipments................................................................... 9-4

Table 9.4.1 Change in Relative Price Elasticity Following a Purchase Price Change ........... 9-7

Table 9.5.2 Calculation of Fraction of Hearth Product Shipments to Housing Starts............ 9-9

Table 9.6.1 Trial Standard Levels for Hearth Product Ignition Devices .............................. 9-10

LIST OF FIGURES

Figure 9.3.1 Historical Shipments of Hearth Products, 2005-2013 ......................................... 9-3

Figure 9.3.2 Historical and Projected Housing Starts.............................................................. 9-4

Figure 9.5.2 Normalized Historical Data of Hearth Product Shipments and Housing 

Starts .................................................................................................................... 9-8

Figure 9.6.1 Historical and Projected Base Case Shipments of Hearth Products.................. 9-10

Figure 9.6.2 Projected Hearth Products Groups Market Shares, 2021-2050 ......................... 9-11

Figure 9.6.3 Hearth Products Shipments by product type in Base Case and Standards 

Case, 2021-2050 ................................................................................................ 9-11

9-i
CHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION

Estimates of future product shipments are a necessary input to calculations of the national 

energy savings (NES) and net present value (NPV), as well as to the manufacturer impact 

analysis (MIA). This chapter describes the data and methods the U.S. Department of Energy 

(DOE) used to project annual product shipments and presents results for hearth product types 

considered in this analysis.

The shipments model divides the shipments of hearth products into specific market 

segments. The model starts from a historical base year and calculates retirements and shipments 

by market segment for each year of the analysis period. This approach produces an estimate of 

the total product stock, broken down by age or vintage, in each year of the analysis period. In 

addition, the product stock efficiency distribution is calculated for the base case and for each 

standards case for each product class. The stock distribution is used in the national impact 

analysis (NIA) to estimate the total costs and benefits associated with each efficiency level.

The shipments model was developed as a Microsoft Excel spreadsheet that is accessible 

on DOE’s Appliance and Commercial Equipment Standards website 

(www1.eere.energy.gov/buildings/appliance_standards/product.aspx?productid=83). Appendix 

10A discusses how to access and utilize the shipments model spreadsheet, which is integrated 

into the spreadsheet for the NIA. This chapter explains how the shipments model is constructed 

and provides some summary output. Sections 9.2 through 9.5 describe the methodological 

approach. 

9.2 FUNDAMENTAL MODEL EQUATIONS

The fundamental dependent variable in the shipments model is the product stock, which 

is represented as a function of analysis year (indexed by j), and product vintage or age (the 

product age is noted as a, and is equal to the analysis year minus the vintage). The stock function 

is adjusted in each year of the analysis period by new shipments coming in and broken or 

demolished products being taken out.

For existing stock:

Stock( j, a) = Stock( j −1, a −1) − Rem( j, a) + Ship( j −1, a −1)

Eq. 9.1

and for new shipments:

Stock( j, a =1) = Ship( j −1)

9-1
Eq. 9.2

Where:

Stock (j, a) = number of units of age a in analysis year j,

Rem (j, a) = number of units of age a removed in analysis year j, and

Ship(j) = number of units of shipped in year j.

Removals due to product failure contain a survival function fp(a) that is used to represent 

the probability that a unit of age a will survive in a given year; equivalently, the probability that 

this unit will fail is 1- fp(a).

Total removals in the base case are then:

Rem( j a) [ f (a)] Stock( j a) p , = 1− × ,

Eq. 9.3

9.3 DATA INPUTS

The primary data inputs to the hearth products shipments model are the historical hearth 

products shipments data, and historical and projected housing starts. 

9.3.1 Historical Shipments

DOE used historical shipments data to populate its shipments model for hearth products. 

DOE used historical shipment data provided by the Hearth, Patio and Barbecue Association 

(HPBA)

1 for all hearth products between 2005 and 2013. Table 9.3.1 shows the initial shipments 

data provided by HPBA, which includes all hearth products. 

Table 9.3.1 HPBA Shipments Data

2005 2006 2007 2008 2009 2010 2011 2012 2013

Shipments(millions) 1.54 1.184 1.027 0.715 0.421 0.443 0.385 0.397 0.534

DOE also estimated additional shipments from non-HPBA members. The magnitude of 

these non-HPBA shipments was determined to be equal to the HPBA shipments for outdoor 

units. However, the HPBA shipments do not include patio heaters, which are a type of outdoor 

hearth product. To account for all patio heaters shipments, DOE determined the number of patio 

heater models based on the HPBA-member hearth product model data and comments received 

from manufacturers during manufacturer interviews. DOE then applied a three-to-one ratio 

between non-HPBA and HPBA shipments to develop the total patio heater shipments. In total,

patio heater shipments account for an additional 9 percent of hearth product shipments relative to 

the HPBA member hearth products shipments. DOE added these shipments to the data HPBA

provided to determine final shipment values. Finally, through manufacturer interviews and 

analysis of the HPBA model database, DOE determined the fraction of match-lit units for each 

9-2
hearth product group, which was subtracted from the hearth product shipments. HPBA also 

provided market shares of each hearth product group relative to all hearth products. Because 

HPBA provided a range for the market shares, DOE used the midpoint of this range. Table 9.3.2

lists the disaggregated shipments data provided by HPBA, the value DOE determined, the 

match-lit units by product group, and the final base case market share relative to all hearth 

products.

Table 9.3.2 Hearth Products Shipments Disaggregated by Product Group

Product Group

HPBA-only 

Shipment Data

(%)

All Hearth 

Products 

Shipments* (%)

Match-Lit 

Shipments 

(%)

Final Base Case 

Disaggregation

(%)

Vented Fireplace 47 to 73 56.2 5 62

Unvented Fireplace 2 to 7 4.4 0 5

Vented Gas Logs 3 to 10 6.1 50 4

Unvented Gas Logs 16 to 23 18.3 0 21

Outdoor 4 to 9 15.0 50 9

* Includes match-lit units and non-HPBA hearth product shipments

The base case market shares are used to determine the yearly shipments for each hearth 

product group. In the analysis period (2021-2050), these market shares by product group are 

assumed to remain constant in the base case, as DOE does not have evidence of a change in the

trend of market shares. Figure 9.3.1 and Table 9.3.3 show the historical hearth products 

shipments used in the analysis, including non-HPBA shipments and excluding match-lit 

shipments.

Figure 9.3.1 Historical Shipments of Hearth Products, 2005-2013

0

0.5

1

1.5

2

Hearth Product Shipments (Millions)

9-3
Table 9.3.3 Historical Hearth Product Shipments

2005 2006 2007 2008 2009 2010 2011 2012 2013

Shipments

(millions) 1.69 1.30 1.13 0.785 0.462 0.487 0.423 0.436 0.586

9.3.2 Historical and Projected Housing Starts

DOE used projected housing starts to estimate the shipments of hearth products. DOE 

determined new residential housing starts by using Census data through 20132 and projections 

from the Energy Information Administration’s (EIA) Annual Energy Outlook 2014 (AEO 2014).3

Figure 9.3.2 shows AEO 2014 historical data as well as projections of housing starts through 

2040.

Figure 9.3.2 Historical and Projected Housing Starts

9.3.3 Calculation of the Fraction of New Construction Homes with Hearth Products

DOE estimated the new construction fraction by using historical hearth products 

shipments data and the number of gas fireplaces in newer homes from EIA’s 2009 Residential 

Energy Consumption Survey (RECS 2009) data.4 RECS 2009 data shows that there are 1.068 

million homes built from 2005-2009 with vented and ventless gas fireplaces, which are the 

hearth products most likely to be installed in a new construction home. DOE assumed that these 

households would on average have 1.2 gas fireplaces per home5 and that only half of the homes 

built in 2009 were surveyed in RECS 2009. This resulted in 1.335 million gas fireplaces in 

homes built between 2005 and 2009. During this same period there were 5.367 million hearth 

0

0.5

1

1.5

2

2.5

Housing Starts (Millions)

Projected

Historic

9-4
products shipped (see Table 9.3.3). This resulted in 25 percent market share for hearth products 

in new construction.

9.4 IMPACT OF STANDARDS ON SHIPMENTS

Consumer purchase decisions are influenced by the purchase price and operating cost of 

the product, and therefore may be different in the base case and under the standard case. These 

decisions are modeled by estimating the purchase price elasticity for hearth products. The 

purchase price elasticity is defined as the change in the percentage of consumers acquiring a 

hearth product divided by a change in the relative price (defined below) for that product. This 

elasticity, along with information obtained from the life-cycle cost (LCC) and payback period 

(PBP) analysis on the change in purchase price and operating costs at different ELs, are used in 

the shipments model to estimate the change in shipments under standards at different ELs.

9.4.1 Purchase Price Elasticity

DOE conducted a literature review and an analysis of appliance price and efficiency data 

to estimate the combined effects on product shipments from increases in product purchase price, 

decreases in product operating costs, and changes to household income. Appendix 9A provides a 

detailed explanation of the methodology DOE used to quantify the impacts from these variables.

Existing studies of appliance markets suggest that the demand for appliances is priceinelastic. Other information in the literature suggests that appliances are a normal good, so that 

rising incomes increase the demand for appliances, and that consumer behavior reflects relatively 

high implicit discount ratesa when comparing appliance prices and appliance operating costs. 

DOE used the available data for the period 1980-2002 on large appliance purchases to 

evaluate broad market trends and conduct simple regression analyses. These data indicate that 

there has been a rise in appliance shipments and a decline in appliance purchase price and

operating costs over the time period. Household income has also risen during this time. Because 

purchase decisions are sensitive to income, as well as to potential savings in the operating cost of 

the appliance, DOE combined the available economic information into one variable, termed the 

relative price. This variable was used in a regression analysis to parameterize historical market 

trends. The relative price is defined with the following expression:

Income

PP PVOC

Income

TP RP + = =

Eq. 9.4

Where:

RP = relative price,

TP = total price,

9-5

 
Income = household income,

PP = appliance purchase price, and

PVOC = present value of operating cost.

In Eq. 9.4, DOE used real prices, as opposed to nominal, and an implicit discount rate of 

37 percent to estimate the present value of operating costs. The rate of 37 percent is based on a 

survey of several studies of different appliances that suggests that the consumer implicit discount 

rate has a broad range and averages about 37 percent.

6

DOE’s regression analysis suggests that the relative price elasticity of demand is -0.34.

This implies that a relative price increase of 10 percent results in a 3.4 percent decrease in 

shipments. Note that the relative price elasticity incorporates the impacts from purchase price, 

operating cost, and household income, so the impact from any single effect can be mitigated by 

changes in the other two effects.

The relative price elasticity of -0.34 is consistent with estimates in the literature.

Nevertheless, DOE stresses that the measure is based on a small data set, using simple statistical 

analysis. More importantly, the measure is based on an assumption that economic variables, 

including purchase price, operating costs, and household income, explain most of the trend in 

appliances per household in the United States since 1980. Changes in appliance quality and 

consumer preferences may have occurred during this period, but DOE did not account for them 

in this analysis. Despite these uncertainties, DOE believes that its estimate of the relative price 

elasticity of demand provides a reasonable assessment of the impact that purchase price, 

operating cost, and household income have on product shipments.

Because projections of shipments and national impacts attributable to standards are

calculated for a lengthy time period, DOE needed to consider how the relative price elasticity is 

affected after a new standard takes effect. DOE considered the relative price elasticity, described 

above, to be a short-term value. It was unable to identify sources specific to household durable 

goods, such as appliances, to indicate how short-run and long-run price elasticities differ. 

Therefore, to estimate how the relative price elasticity changes over time, DOE relied on a study 

pertaining to automobiles.7 This study shows that the automobile price elasticity of demand 

changes in the years following a purchase price change, becoming smaller (more inelastic) until 

it reaches a terminal value around the tenth year after the price change. Table 9.4.1 shows the 

relative change in the price elasticity of demand for automobiles over time. DOE developed a 

time series of relative price elasticities based on the relative change in the automobile price 

elasticity of demand. For years not shown in Table 9.4.1, DOE performed a linear interpolation 

to obtain the relative price elasticity.

9-6
Table 9.4.1 Change in Relative Price Elasticity Following a Purchase Price Change

Years Following Price Change

1 2 3 5 10 20

Relative Change in 

Elasticity to 1st year

1.00 0.78 0.63 0.46 0.35 0.33

Relative Price Elasticity -0.34 -0.26 -0.21 -0.16 -0.12 -0.11

9.4.2 Impact from Increase in Relative Price 

Using the relative price elasticity, DOE was able to estimate the impact of the increase in 

relative price from a particular standard level. The impact, as shown in the equation below, is 

expressed as a percentage drop in market share for each year, dMSp

j.

( )

( ) e ( j) RP base j

RP std j dMS RP

p

p p

j ×































 = − _

_ 1

Eq. 9.5

Where:

dMSp

j = percentage market share drop for class p, year j, 

RP_stdp(j)= relative price in the standards case for product group p, year j, 

RPp(j)= relative price in the base case for product group p, year j, and

eRP(j) = relative price elasticity in year j.

The model calculates the relative percentage market drop, dMSp

j, due to the product price 

increase from a particular standard level. 

9.5 MODELING APPROACH

For the years where hearth product shipments data are available (2005-2013), DOE found 

that historical shipments and new housing starts were highly correlated.

b DOE believes this 

correlation reflects the relationship between hearth product installations in new homes, and is 

also because housing starts and hearth products shipments are both sensitive to the broader 

housing market and general economic activity. DOE applied this correlation to all hearth 

products shipments. The correlation coefficient (Correl) is determined as in the equation below. 

Figure 9.5.2 shows the historical correlation of hearth product shipments and housing starts, 

normalized for the average magnitude differences.c The correlation factor is calculated as:

 

b Historical shipments and new housing starts had a correlation factor of 0.98 (where 1 indicates perfect correlation).

c Because the graph in Figure 9.5.2 is a normalized representation of the relationship between hearth product 

shipments and housing starts, it contains unit-less quantities.

9-7

 
CCCCCCCCCCCC(XX, YY) = ∑(xx − xx�)(yy − yy�)

∑(xx − xx�)22(yy − yy�)22

Eq. 9.6

Where:

Correl(X,Y) = the correlation factor between X and Y,

X = a set of data being correlated,

Y = a set of data being correlated,

x = an element of set X,

y = an element of set Y,

xx̅= the global average of set X, and

yy� = the global average of set Y.

Figure 9.5.2 Normalized Historical Data of Hearth Product Shipments and 

Housing Starts

For the years between 2040 and 2050, for which AEO 2014 does not provide projected 

housing start data, DOE used a trailing 10-year average of housing starts to estimate housing 

starts during this period. This approach emphasizes more recent values and preserves the cyclical 

nature observed in historical trends. 

The average ratio of hearth product shipments to housing starts from 2005-2013 is 0.754

(see Table 9.5.1). This ratio is multiplied by the yearly projected housing starts in the years 

2014-2050, as shown in Figure 9.3.2, and the overall fraction of non-match-lit shipments to 

obtain the total projected hearth products shipments for these years (SHPProj,total (j)):

9-8
SSSSSSPPPPPPPP,tt tttttt(jj) = 0.754 ∗ PPPPPPPP(jj) ∗ MMMM

Eq. 9.7

Where:

0.754 = historical ratio of housing starts to hearth products shipments,

HSProj = projected housing starts in year (j), and

ML = historical Fraction of non-match-lit hearth products shipments, determined to be 86.64

percent.

Table 9.5.2 Calculation of Fraction of Hearth Product Shipments to Housing Starts

Year Housing Starts* Hearth Product 

Shipments**

Calculated 

Fraction

2005 2.068 1.691 0.818

2006 1.801 1.300 0.722

2007 1.355 1.128 0.832

2008 0.906 0.785 0.867

2009 0.554 0.462 0.835

2010 0.587 0.487 0.829

2011 0.609 0.423 0.695

2012 0.781 0.436 0.559

2013 0.925 0.586 0.634

Average 0.754

* Based on Census data through 2013.2 See section 9.3.2.

** Based on HPBA shipments data.1 See section 9.3.1.

To calculate the yearly projected shipments for each hearth product group, SHPProj,total is 

multiplied by the market share of that group and the yearly price elasticity value in the standards 

case, as below:

SSSSSSPPPPPPPP,gg gggg(ii,jj) = SSSSSSPPPPPPPP,tt tttttt(jj) ∗ MM (ii,jj) ∗ PP(ii,jj)

Eq. 9.1

Where:

SSSSSSPPPPPPPP,gg gggg(ii,jj) = projected hearth products shipments for product group i in year j,

MS(j,i) = market share as a percent of product group i of the total hearth product shipments in 

year j, and

PE(j,i) = price elasticity of product group i in year j. PE = 1 in the base case, and is equal to (1- p

j dMS ) in the standard case as calculated in Eq. 9.5.

9-9
9.6 RESULTS

As detailed in chapter 10, DOE created a trial standard level (TSL) that adopts 

intermittent pilot ignition for all product groups. Table 9.6.1 shows the analyzed TSL.

Table 9.6.1 Trial Standard Levels for Hearth Product Ignition Devices

Product Class TSL Description

Hearth Products 1 Adoption of Intermittent Pilot Ignition

Figure 9.6.1 shows the historical and projected shipments for hearth products. The 

shipments of hearth products decline after 2005 due to the economic recession. Shipments 

bottom in 2011- 2012, and recovery begins in 2013, when shipments increased by 36 percent 

compared to 2012. The increase in shipments correlates directly with an increase in housing 

starts and is expected to continue the trend as the economy continues to recover. Hearth product 

shipments are projected to recover to near pre-recession levels by 2021, which is the compliance 

year of the standard, and are expected to continue to closely correlate with the relatively stable 

housing start projections through 2050. The shipments data and the market shares of each 

product group are primary inputs into the NIA analysis, which is described in chapter 10. Figure 

9.6.2 shows each hearth product group’s projected market share under the proposed standard.

Figure 9.6.3 shows each hearth product group’s total shipments in the base case and in the 

standards case.

Figure 9.6.1 Historical and Projected Base Case Shipments of Hearth Products

0

0.5

1

1.5

2

Shipments (Millions)

Historical Shipments

Projected Shipments

9-10
Figure 9.6.2 Projected Hearth Products Groups Market Shares, 2021-2050

Figure 9.6.3 Hearth Products Shipments in Base Case and Standards Case, 2021-2050

0.9

0.95

1

1.05

1.1

Shipments (Millions)

All Hearth Products (Base Case)

All Hearth Products (Standards Case)

9-11
REFERENCES

1. Hearth, Patio and Barbecue Association, HPBA US Hearth Statistics, August 2014. (Last 

accessed August 28, 2014.) <www.hpba.org/statistics/hpba-us-hearth-statistics>

2. U.S. Department of Commerce-Bureau of the Census, New Privately Owned Housing 

Units Started: Annual Data 1959-2012, 2013. (Last accessed March 15, 2013.) 

<www.census.gov/construction/nrc/historical_data/>

3. U.S. Department of Energy-Energy Information Administration, Annual Energy Outlook 

2014 with Projections to 2040, 2014. Washington, DC. <www.eia.gov/forecasts/aeo/>

4. U.S. Department of Energy: Energy Information Administration, Residential Energy 

Consumption Survey (RECS): 2009 RECS Survey Data, 2009. (Last accessed January 7, 

2015.) <www.eia.gov/consumption/residential/data/2009/>

5. Houck, J. E., Residential Decorative Gas Fireplaces Usage Characteristics. 2010. 

Prepared for Hearth, Patio, & Barbecue Association. 

6. Train, K., Discount Rates in Consumers' Energy-Related Decisions. Energy: The 

International Journal, 1985. 10(12): pp. 1243-1253 

7. Hymans, S., Consumer Durable Spending: Explanation and Prediction. Brookings Papers 

on Economic Activity, 1971. 1971(1): pp. 234-239 

9-12
10-i

CHAPTER 10. NATIONAL IMPACT ANALYSES

TABLE OF CONTENTS

10.1 INTRODUCTION ......................................................................................................... 10-1

10.2 TRIAL STANDARD LEVELS ..................................................................................... 10-1

10.3 OVERVIEW OF THE NATIONAL IMPACTS ANALYSIS....................................... 10-1

10.3.1 National Energy Savings................................................................................................ 10-1

10.3.2 Net Present Value of Consumer Benefit........................................................................ 10-2

10.4 PROJECTED EFFICIENCY TRENDS......................................................................... 10-4

10.5 NATIONAL ENERGY SAVINGS ............................................................................... 10-4

10.5.1 Annual Energy Consumption per Unit .......................................................................... 10-5

10.5.2 Shipments....................................................................................................................... 10-6

10.5.3 Product Stock................................................................................................................. 10-6

10.5.4 Annual Energy Consumption......................................................................................... 10-7

10.5.5 Site-to-Power Plant Energy Use Factor......................................................................... 10-7

10.5.6 Full-Fuel-Cycle Energy Factors..................................................................................... 10-8

10.6 NET PRESENT VALUE OF CONSUMER BENEFITS.............................................. 10-8

10.6.1 Total Installed Cost per Unit.......................................................................................... 10-9

10.6.2 Annual Operating Cost per Unit .................................................................................... 10-9

10.6.3 Product Stock............................................................................................................... 10-10

10.6.4 Increases in Total Annual Installed Cost ..................................................................... 10-10

10.6.5 Savings in Total Annual Operating Cost ..................................................................... 10-10

10.6.6 Discount Factor............................................................................................................ 10-11

10.6.7 Present Value of Increased Installed Cost and Savings............................................... 10-11

10.7 RESULTS .................................................................................................................... 10-12

10.7.1 National Energy Savings.............................................................................................. 10-12

10.7.2 Net Present Value of Consumer Benefit...................................................................... 10-12

LIST OF TABLES

Table 10.2.1 Trial Standard Levels for Hearth Product Ignition Systems.............................. 10-1

Table 10.4.1 Hearth Products: Ignition System Distributions for the Base and Standards 

Cases in 2021 ..................................................................................................... 10-4

Table 10.5.1 Average Annual Hearth Product Ignition System Energy Use for the Base 

and Standard Cases in 2021 ............................................................................... 10-5

Table 10.5.2 Upstream Energy Multipliers (Based on AEO 2014)........................................ 10-8

Table 10.6.1 Average Installed Cost of Hearth Product Ignition Systems in 2021 for the 

Base and Standard Cases (2013$)...................................................................... 10-9

Table 10.6.2 Average Annual Operating Cost of Hearth Product Ignition Systems in 

2021 for the Base and Standard Cases (2013$) ............................................... 10-10

Table 10.7.1 Primary and Full-Fuel-Cycle National Energy Savings for Hearth Product 

Ignition Systems (quads).................................................................................. 10-12
10-ii

Table 10.7.2 Net Present Value of Consumer Benefit for Hearth Product Ignition 

Devices (billion 2013$).................................................................................... 10-12

LIST OF FIGURES

Figure 10.5.1 Combined Adjustment Factor for Hearth Products Energy Use........................ 10-6

Figure 10.5.2 Primary to Site Energy Use Factor for Hearth Product Electricity Use ............ 10-7
10-1

CHAPTER 10. NATIONAL IMPACT ANALYSES

10.1 INTRODUCTION

This chapter examines selected national impacts attributable to each trial standard level

(TSL) considered for hearth product ignition systems. The results presented here include: (1) 

national energy savings (NES); (2) operating cost savings; (3) increased total installed costs; and 

(4) the net present value (NPV) of the difference between the value of operating cost savings and 

increased total installed costs.

The calculations were performed using a Microsoft Excel spreadsheet model, which is 

accessible on the Internet 

(www1.eere.energy.gov/buildings/appliance_standards/product.aspx?productid=83). The 

spreadsheet model, termed the National Impact Analysis (NIA) model, calculates energy savings 

and NPV for the nation. Details regarding and instructions for using the NIA model are provided 

in appendix 10A.

The NIA model incorporates the shipments model that DOE used to project future 

purchases of hearth products.

10.2 TRIAL STANDARD LEVELS

For hearth products, there are currently two dominant ignition systems on the market, 

standing pilots and intermittent pilot ignitions. Standing pilot ignitions are the baseline 

technology for hearth product ignition devices. Trial standard level (TSL) 1 consists of the 

technology option of intermittent pilot ignitions for all hearth product groups (Table 10.2.1).

Table 10.2.1 Trial Standard Levels for Hearth Product Ignition Systems

Product Class TSL 1

Hearth Product Adoption of Intermittent Pilot

10.3 OVERVIEW OF THE NATIONAL IMPACTS ANALYSIS

10.3.1 National Energy Savings

DOE calculates annual national energy savings (NES) as the difference between two 

projections: the base case (without new standards) and a standard case (with a new standard). 

The calculation of annual nation energy savings (NESy) are represented by the following 

expression:
10-2

Eq. 10.1

Cumulative energy savings are the sum of each annual NES over the lifetime of products 

shipped in the period that extends from a standard’s assumed compliance date for 30 years. This 

calculation is represented by the following equation:

Eq. 10.2

DOE calculated AEC by multiplying the number or stock of a given product (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:

Eq. 10.3

Where:

AEC = annual energy consumption each year for the Nation in quadrillion British thermal units 

(Btus), or quads, summed over vintages of the product stock, STOCKV,

NESy = national annual energy savings (quads),

NEScum = national cumulative energy savings (quads),

STOCKV = stock of product (millions of units) of vintage V that survive in the year for which 

DOE calculated annual energy consumption,

UECV = annual energy consumption per product in kilowatt-hours (kWh); electricity 

consumption is converted from site energy to power plant energy (quads) by applying a timedependent 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 standard case,

y = year in the projection,

cum = cumulative over the projection period, and

V = year in which the product was purchased as a new unit.

The stock of products depends on annual shipments and the lifetime of the given product. 

As described in chapter 9, DOE projected shipments for the base case and the standard case. 

10.3.2 Net Present Value of Consumer Benefit

The NPV is the value in the present of a time-series of costs and savings. The NPV is 

described by the equation: 

NES y = AECnatl−base AECnatl−std

_

NEScum = ∑NES y

= ∑ V ×UECV AEC STOCK

NPV PVS PVC _ =
10-3

Eq. 10.4

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 product and 

installation). 

DOE determined the PVS and PVC according to the following expressions:

Eq. 10.5

Eq. 10.6

DOE calculated the total annual savings in operating cost by multiplying the number or 

stock of a given product (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 product (by vintage) by its per-unit total installed cost increase (also by vintage). Total 

annual savings in operating cost and increases in installed cost are calculated using the following 

equations.

Eq. 10.7

Eq. 10.8

Where:

OCS = total annual savings in operating cost each year summed over vintages of the product 

stock, STOCKV,

TIC = total annual increase in installed cost each year summed over vintages of the product 

stock, STOCKV,

DF =discount factor in each year,

STOCKV = stock of product (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,

V = year in which the product was purchased as a new unit, and 

y = year in the projection.

PVS =∑OCSy × DFy

PVC = ∑TICy × DFy

OCS y = ∑STOCKV ×UOCSV

TICy = ∑STOCKV ×UTICV
10-4

DOE determined the PVC for each year from the compliance date of the standard through

2050. DOE determined the PVS for each year from the compliance date of the standard until the 

year when units purchased in 2021−2050 retire. DOE calculated costs and savings as the 

difference between the standard 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 PROJECTED EFFICIENCY TRENDS

A key component of the NIA is the distribution of hearth product ignition systems

projected over time for the base case (without the new standard) and for the standard case (with

the potential new standard).

DOE developed a distribution of ignition system technologies in the base case for 2021

(the assumed compliance date for the new standard), as described in chapter 8. DOE did not have

sufficient data to establish a trend of adoption of intermittent pilot units. Therefore, DOE 

assumed constant distributions for the ignition systems in the base case.

In the standard case, DOE assumed a “roll-up” scenario to establish the distribution for 

2021. Products in the base case that did not meet the standard under consideration would “roll 

up” to meet the new standard level (Table 10.4.1). After the compliance year, the distribution

remains constant.

Table 10.4.1 Hearth Products: Ignition System Distributions for the Base and 

Standards Cases in 2021

Product Group

Market Share (percent)

Base Case TSL 1

Standing Pilot Intermittent 

Ignition Standing Pilot Intermittent Ignition

Vented Fireplaces 42 58 0 100

Unvented Fireplaces 88 12 0 100

Vented Gas Logs 87 13 0 100

Unvented Gas Logs 94 6 0 100

Outdoor 52 48 0 100

All Hearth Products 58 42 0 100

10.5 NATIONAL ENERGY SAVINGS

The inputs for calculating national energy savings are:

• average annual energy consumption per unit (UEC),
10-5

• shipments,

• product stock (STOCKV),

• annual energy consumption for the Nation (AEC), and

• power plant primary energy use factor (src_conv).

10.5.1 Annual Energy Consumption per Unit 

For hearth product ignition devices, DOE presented the per-unit annual energy 

consumption as a function of product efficiency in chapter 7, Energy Use Analysis. DOE used 

the shipments-weighted energy consumption of the base and standard cases presented in section 

10.4, along with the annual energy use data presented in chapter 7, to estimate the shipmentweighted average annual per-unit energy consumption (UEC) under the base and standard cases. 

Table 10.5.1 presents the base case and standard case shipment-weighted annual UECs 

for hearth product ignition devices in 2021. Due to the constant product group market shares and 

ignition system distribution in each case, the annual energy use remains the same throughout the 

analysis period. 

Table 10.5.1 Average Annual Hearth Product Ignition System Energy Use for the Base 

and Standard Cases in 2021

Product Group

Standing Pilot Intermittent Pilot

Fuel Use

(MMBtu/yr)

Elec Use

(kWh/yr)

Fuel Use

(MMBtu/yr)

Elec Use

(kWh/yr)

Vented Fireplaces 3.99 0.000 0.499 13.60

Unvented Fireplaces 3.52 0.000 1.30 99.38

Vented Gas Logs 3.13 0.000 0.289 5.79

Unvented Gas Logs 2.29 0.000 0.924 70.61

Outdoor 3.91 0.000 0.000 0.175

All Hearth Products 3.58 0.000 0.579 28.54

DOE also considered the effects of changes in climate and building shell efficiency on 

the building heating load (BHL) of the hearth product, which determines the operating hours of 

the hearth product and ignition system and consequent energy use of the hearth product ignition 

system. The climate adjustment factor, which is based on the forecast of heating degree days 

(HDD) by region from Annual Energy Outlook 2014 (AEO 2014),

1 shows a declining trend due 

to warmer weather. Regional building shell efficiency factors are also from AEO 2014. For both 

factors, DOE applied regional weights to make the factors specific to users of hearth products.

Figure 10.5.1 shows the adjustment factor for hearth product ignition system energy use.
10-6

Figure 10.5.1 Combined Adjustment Factor for Hearth Products Energy Use

10.5.2 Shipments

DOE projected shipments for each product group under the base case and standard case

(see chapter 9). These shipments are used in conjunction with the LCC results to calculate 

national impacts from the standard, such as the NES and the NPV. Several factors impact 

projected shipments, including total installed costs, operating cost, household income, and 

equipment lifetime. As noted earlier, the increased total installed cost of more-efficient products 

is expected to cause some consumers to forgo product purchases. Consequently, shipments 

projected under the standard case are lower than under the base case. DOE believes it would be 

inappropriate to count energy savings that result from reduced shipments due to a standard. 

Therefore, DOE did not calculate annual energy consumption for the base case using the base 

case shipments projection. Instead, for each comparison of a standard case with the base case, 

DOE used shipments associated with that particular standard case. As a result, all of the 

calculated energy savings are due to lower energy consumption in the standard case.

10.5.3 Product Stock

The stock of product in any given year depends on annual shipments and the lifetime of a 

given product class. The NIA model keeps track of the number of units shipped each year. The 

lifetime of a unit determines how many units shipped in previous years survive in the given year. 

0.0

0.2

0.4

0.6

0.8

1.0

1.2

2010 2015 2020 2025 2030 2035 2040 2045

Factor = 1 at 2020
10-7

DOE assumes that products have an increasing probability of retiring as they age. The 

probability of survival as a function of years since purchase is termed the survival function. 

Refer to chapter 8 for further details on the survival functions that DOE used in its analysis.

10.5.4 Annual Energy Consumption

For each product group, DOE calculated the total national site (i.e., the energy consumed 

at the household or establishment) annual energy consumption (AEC). Total 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. This method accounts for differences in UEC 

from year to year.

10.5.5 Site-to-Power Plant Energy Use Factor

For hearth products, the considered TSL increases electricity use. DOE calculated

primary energy use (power plant consumption) from site electricity use 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 AEO 2014. The factors change over time 

in response to projected changes in the types of power plants projected to provide electricity to 

the country. Figure 10.5.2 shows the site-to-power plant factors from 2021 to 2040. For years 

after 2040 (the last year in the AEO), DOE maintained the 2040 value.

Figure 10.5.2 Primary to Site Energy Use Factor for Hearth Product Electricity Use

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

2015 2020 2025 2030 2035 2040 2045

Primary Btu/Site kWh
10-8

10.5.6 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 are referred to as “upstream” activities, DOE developed 

multipliers using the data and projections generated by the National Energy Modeling System 

(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. The method used to calculate FFC energy multipliers is described 

in appendix 10B.

Table 10.5.2 shows the upstream energy multipliers used for hearth products for selected 

years. The multipliers are applied to site energy. For years after 2040 (the last year in the AEO), 

DOE maintained the 2040 value.

Table 10.5.2 Upstream Energy Multipliers (Based on AEO 2014)

2021 2025 2030 2035 2040 2045 2050

Electricity 1.044 1.045 1.046 1.047 1.047 1.047 1.047

Natural Gas 1.110 1.111 1.113 1.114 1.114 1.114 1.114

10.6 NET PRESENT VALUE OF CONSUMER BENEFITS

Listed below are the inputs to DOE’s calculation of the NPV of costs and savings.

• Total installed cost per unit,

• annual per-unit savings in operation cost,

• shipments,

• product stock (STOCKV),

• total annual increases in installed cost (TIC),

• total annual operating cost (OCS),

• discount factor (DF),

• present value of costs (PVC), and

• 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 the base case and standard case) multiplied by the 

shipments projected for each TSL. As with calculating energy savings, DOE did not use basecase shipments to calculate total annual installed costs for all of the product groups. DOE used 

the projected shipments and stock for each TSL to calculate costs.
10-9

The annual operating cost includes energy, repair, and maintenance costs. The total 

annual savings in operating cost are equal to the change in the annual operating costs (difference 

between the base case and standard case) per unit multiplied by the shipments projected for each 

candidate standard level. As with calculating total annual installed costs, DOE used standardscase shipments to calculate savings in operating cost. 

10.6.1 Total Installed Cost per Unit 

DOE described the total per-unit installed cost for each product group as a function of 

product efficiency in chapter 8, Life-Cycle Cost and Payback Period Analysis. Because the total 

per-unit annual installed cost depends directly on efficiency, DOE used the shipment-weighted 

efficiencies for the base and standard cases, combined with the total installed cost presented in 

chapter 8, to estimate the shipment-weighted total per-unit average annual installed cost under 

the base and standard cases. Table 10.6.1 shows the average installed cost of hearth product

ignition devices in 2021 for the base and standard cases.

For reasons discussed in chapter 8, DOE used a constant product price assumption for the 

default projection in the NIA. To investigate the impact of different product price projections on 

the consumer net present value (NPV) for different efficiency levels, DOE also considered two 

alternative price trends. Details on how these alternative price trends were developed are in 

appendix 10C, which also presents the results of the sensitivity analysis.

Table 10.6.1 Average Installed Cost of Hearth Product Ignition Systems in 2021 for 

the Base and Standard Cases (2013$)

Product Group Base Case TSL 1

Fireplace (vented) $218.37 $253.55 

Fireplace (ventless) $244.32 $323.17 

Logs (vented) $128.67 $278.70 

Logs (ventless) $104.02 $225.71 

Outdoor $358.71 $432.34 

All Hearth Products $208.43 $268.82 

10.6.2 Annual Operating Cost per Unit 

The per-unit annual operating cost includes costs for energy, repair, and maintenance. 

DOE determined the per-unit annual savings in energy costs by multiplying the per-unit annual 

savings in energy consumption developed for hearth products by the appropriate energy price. 

Estimates of the per-unit annual energy consumption for the base case and the standard 

case were presented in section 10.5.1. DOE projected the per-unit annual energy consumption for 

the base case for all product groups by applying a growth trend in efficiency. 
10-10

Energy prices and trends in energy prices are described in chapter 8. DOE projected

energy prices based on annual changes in average residential energy prices in EIA’s AEO 2014

reference case scenario.

DOE described the total per-unit repair and maintenance costs for each product group as 

a function of product efficiency in chapter 8. Because the per-unit repair costs depend directly on 

efficiency, DOE used the efficiencies for the base and standard cases presented in section 10.4, 

combined with the repair costs presented in chapter 8, to estimate the per-unit average repair and 

maintenance costs under the base and standard cases. 

Table 10.6.2 shows the average operating cost of hearth product ignition devices in 2021

for the base and standard cases. The operating costs change over time, depending on change in 

annual energy use and energy prices.

Table 10.6.2 Average Annual Operating Cost of Hearth Product Ignition Systems in 

2021 for the Base and Standard Cases (2013$)

Product Group Base Case TSL 1

Fireplace (vented) $227.04 $72.46 

Fireplace (ventless) $506.44 $305.43 

Logs (vented) $299.19 $39.95 

Logs (ventless) $338.33 $214.81 

Outdoor $246.11 $2.99 

All Hearth Products $267.80 $105.67 

10.6.3 Product Stock

The product stock in any given year depends on annual shipments and the lifetime of a 

given product group. The NIA model keeps track of the number of units shipped each year. The 

lifetime of a unit determines how many units shipped in previous years survive in the given year. 

DOE assumes that products have an increasing probability of retiring as they age. The 

probability of survival as a function of years since purchase is termed the survival function. 

Refer to chapter 8 for further details on the survival functions that DOE used in its analysis.

10.6.4 Increases in Total Annual Installed Cost

The increase in total annual installed cost for a product under the standard case is the 

product of the increase in total installed cost per unit attributable to the standard and the number 

of units of each vintage. This method accounts for differences in total installed cost from year to 

year. 

10.6.5 Savings in Total Annual Operating Cost

The savings in total annual operating cost for any given trial standard level is the product 

of the annual per-unit savings in operating cost attributable to the standard and the number of 
10-11

units of each vintage. This method accounts for the year-to-year differences in annual operating 

cost savings. 

10.6.6 Discount Factor

DOE multiplied monetary values in future years by a discount factor to determine the 

present value. The discount factor (DF) is described by the equation:

Eq. 10.9

Where:

r = discount rate, 

y = year of the monetary value, and 

yP = year in which the present value is being determined.

DOE estimated NPV using both a 3-percent and a 7-percent real discount rate, in 

accordance with the Office of Management and Budget’s guidance to Federal agencies on the 

development of regulatory analysis, particularly section E therein: Identifying and Measuring 

Benefits and Costs.

2 DOE defined the present year as 2014.

10.6.7 Present Value of Increased Installed Cost and Savings

The present value of increased installed cost is the difference between installation cost in 

the standard case and the base case discounted to the present and summed throughout the period 

over which DOE is considering the installation of units (from the compliance date of standards, 

2021, through 2050). DOE calculated annual increases in installed cost as the difference in total 

installed cost for new product purchased each year, multiplied by the shipments in the standard 

case.

The present value of annual savings in operating cost is the difference between the base 

case and the standard case discounted to the present and summed throughout the period from the 

compliance date, 2021, to the time when the last unit installed in 2021−2050 is retired from 

service.

Savings represent decreases in operating cost (including electricity, repair, and 

maintenance) associated with the more energy-efficient product purchased in each standard case 

compared to the base case. Total annual savings in operating cost are the savings per unit 

multiplied by the number of units of each vintage that survive in a particular year. 

( y yp ) _

( r )

DF

1+

1 =
10-12

10.7 RESULTS

10.7.1 National Energy Savings 

This section provides the national energy savings that DOE calculated for each of the 

TSLs analyzed for hearth product ignition systems. See Table 10.7.1 for both primary energy 

savings and FFC energy savings. DOE based the inputs to the NIA model on weighted-average 

values, producing results that are discrete point values, rather than a distribution of values as is 

generated by the life-cycle cost and payback period analysis. 

Table 10.7.1 Primary and Full-Fuel-Cycle National Energy Savings for Hearth 

Product Ignition Systems (quads)

Product Class Energy Savings

Trial Standard Level

1

quads

Hearth Products Primary 0.62

Full-Fuel-Cycle 0.69

10.7.2 Net Present Value of Consumer Benefit

This section provides results of calculating the NPV for each trial standard level 

considered for hearth product ignition devices. Results, which are cumulative, are shown as the 

discounted dollar value of the net savings. See Table 10.7.2 for NPV results with both 3-percent 

and 7-percent discount rates. 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 is 

produced by the life-cycle cost and payback period analyses.

Table 10.7.2 Net Present Value of Consumer Benefit for Hearth Product Ignition 

Devices (billion 2013$)

Product Class Discount Rate 

%

Trial Standard Level

1

billion 2013$

Hearth Products 3% 3.124

7% 1.031
10-13

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. 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>
11-i

CHAPTER 11. CONSUMER SUBGROUP ANALYSIS

TABLE OF CONTENTS

11.1 INTRODUCTION ......................................................................................................... 11-1

11.2 APPROACH .................................................................................................................. 11-1

11.2.1 Subgroup Definition....................................................................................................... 11-1

11.2.2 Distribution of Subgroup Households with Hearth Products ........................................ 11-2

11.2.3 Estimation of Impacts.................................................................................................... 11-2

11.3 RESULTS ...................................................................................................................... 11-2

LIST OF TABLES

Table 11.2.1 RECS 2009 Definitions of Low-Income Households by Yearly Income.......... 11-1

Table 11.2.2 Household Population Data for Hearth Products............................................... 11-2

Table 11.3.1 Average LCC and PBP Results by Efficiency Level for Low-Income 

Households......................................................................................................... 11-3

Table 11.3.2 LCC Savings Relative to the Base Case Efficiency Distribution for LowIncome Households............................................................................................ 11-3

Table 11.3.3 Average LCC and PBP Results by Efficiency Level for Senior-Only 

Households......................................................................................................... 11-3

Table 11.3.4 LCC Savings Relative to the Base Case Efficiency Distribution for SeniorOnly Households................................................................................................ 11-3

Table 11.3.5 Summary of Average LCC Savings and Simple Payback Period Results 

for Consumer Subgroups and All Households................................................... 11-3
11-1

CHAPTER 11. CONSUMER SUBGROUP ANALYSIS

11.1 INTRODUCTION

The consumer subgroup analysis evaluates impacts on groups or consumers who may be 

disproportionately affected by any national energy conservation standard. The U.S. Department 

of Energy (DOE) evaluates impacts on particular subgroups of consumers by analyzing the lifecycle cost (LCC) impacts and payback period (PBP) for those consumers from the considered 

energy efficiency levels. DOE determined the impact on consumer subgroups using the LCC 

spreadsheet models for hearth products. Chapter 8 explains in detail the inputs to the models 

used in determining LCC impacts and PBPs.

DOE evaluated impacts on low-income households and households occupied solely by 

senior citizens (senior-only households).

11.2 APPROACH

11.2.1 Subgroup Definition

As defined in the RECS 2009 survey, low-income households are those at or below the 

“poverty line.” The poverty line varies with household size, head of household age, and family 

income and in RECS encompasses a group of households with incomes below the poverty level 

in 2009 as defined by the U.S. Bureau of the Census1 (see Table 11.2.1). The RECS 2009 survey 

classifies approximately 15 percent of U.S. households as low-income.

Table 11.2.1 RECS 2009 Definitions of Low-Income Households by Yearly Income

Household Size Weighted Average Threshold 

2009$

1 10,956

2 13,991

3 17,098

4 21,954

5 25,991

6 29,405

7 33,372

8 37,252

9+ 44,366

Senior-only households have occupants who are all at least 65 years of age. Based on the 

Energy Information Administration’s 2009 Residential Energy Consumption Survey (RECS 

2009),2 senior-only households comprise 17 percent of the country’s households.
11-2

11.2.2 Distribution of Subgroup Households with Hearth Products 

Of the 12,083 households in the 2009 RECS database, 4.7 million have vented hearth 

heaters and 1.8 million have ventless hearth heaters. Table 11.2.2 shows the household sample 

sizes for hearth product subgroups. The low-income sample included only 21 households 

representing approximately 235,000 households, and the senior-only household included 99 

households representing approximately 900,000 households.

Table 11.2.2 Household Population Data for Hearth Products

Product Type

General Population Low-Income Households Senior-Only Households

No. of 

Records

Number of 

Houses

No. of 

Records

Number of 

Houses

No. of 

Records

Number of 

Houses

Vented Hearth 

Heaters 541 4,666,601 13 124,196 77 662,864

Ventless Hearth 

Heaters 171 1,825,134 8 96,271 22 237,811

All Hearth 

Heaters 712 6,491,734 21 220,468 99 900,676

11.2.3 Estimation of Impacts

To calculate the subgroup results, DOE extracted the results of low-income and senioronly households from the national LCC results. Then DOE calculated the LCC and PBP statistics 

for the subgroups from the individual households.

In the LCC analysis in chapter 8, the national sample is separated into replacement and 

new construction samples. For the subgroup analysis, because the number of households in each 

subgroup is small, DOE chose to only use a single sample rather than disaggregating

replacement and new construction markets.

11.3 RESULTS

Table 11.3.1 through Table 11.3.4 summarize the LCC and PBP results for low-income

and senior-only households. Table 11.3.5 compares average LCC savings and simple PBP for the 

consumer subgroups with those for all households. For hearth products, the low-income have 

lower LCC savings but shorter PBP for the intermittent pilot ignition, while senior-only 

households have higher LCC savings but longer PBP than average. This may be due to both the 

higher discount rates in these demographics and the smaller sample size, which may include 

more outliers by percentage than the overall sample.
11-3

Table 11.3.1 Average LCC and PBP Results by Efficiency Level for Low-Income

Households

EL Efficiency Level

Average Costs Simple 

Payback

years

2013$

Installed 

Cost

First Year’s 

Operating Cost

Lifetime 

Operating Cost LCC

0 Standing Pilot 159 110 1,234 1,393 --

1 Intermittent Pilot 255 31 356 611 1.2

Note: The results for each EL are calculated assuming that all consumers use products with that efficiency level. The 

PBP is measured relative to the baseline product.

Table 11.3.2 LCC Savings Relative to the Base Case Efficiency Distribution for LowIncome Households

EL Efficiency Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Net Cost

Average Savings* 

2013$

1 Intermittent Pilot 21% 557

* The calculation includes buildings with zero LCC savings (no impact).

Table 11.3.3 Average LCC and PBP Results by Efficiency Level for Senior-Only

Households

EL Efficiency Level

Average Costs Simple 

Payback

years

2013$

Installed 

Cost

First Year’s 

Operating Cost

Lifetime 

Operating Cost LCC

0 Standing Pilot 168 43 505 673 --

1 Intermittent Pilot 270 13 155 425 3.5

Note: The results for each EL are calculated assuming that all consumers use products with that efficiency level. The 

PBP is measured relative to the baseline product.

Table 11.3.4 LCC Savings Relative to the Base Case Efficiency Distribution for SeniorOnly Households

EL Efficiency Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Net Cost

Average Savings* 

2013$

1 Intermittent Pilot 26% 121

* The calculation includes buildings with zero LCC savings (no impact).

Table 11.3.5 Summary of Average LCC Savings and Simple Payback Period Results 

for Consumer Subgroups and All Households

Technology Option Efficiency 

Level

Average LCC Savings Simple Payback Period

2013$ Years

LowIncomeSeniorOnly All LowIncomeSeniorOnly All

Intermittent Pilot 1 $557 $121 $165 1.2 3.5 2.9
11-4

REFERENCES

1. U. S. Department of Commerce - Bureau of the Census, Poverty Thresholds 2009. 2009.

U.S. Census. (Posted June 2, 2010) (Last accessed March 15, 2013.) 

<www.census.gov/hhes/www/poverty/data/threshld/thresh09.html>

2. U.S. Department of Energy: Energy Information Administration, Residential Energy 

Consumption Survey (RECS): 2009 RECS Survey Data. 2009. (Last accessed January 7, 

2015.) <www.eia.gov/consumption/residential/data/2009/>
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 ...................................................... 12-3

12.2.3 Phase III: Manufacturer Subgroup Analysis............................................... 12-3

12.2.3.1 Manufacturers of Gas Log Sets Subgroup.................................... 12-3

12.2.3.2 Small Business Manufacturer Subgroup ...................................... 12-4

12.2.4 Manufacturing Capacity Impact ................................................................. 12-5

12.2.5 Employment Impact.................................................................................... 12-5

12.2.6 Cumulative Regulatory Burden .................................................................. 12-5

12.3 MANUFACTURER IMPACT ANALYSIS KEY ISSUES.............................. 12-5

12.3.1 Impacts on Profitability .............................................................................. 12-6

12.3.2 Impacts on Industry Competition ............................................................... 12-6

12.3.3 Impacts on Product Performance................................................................ 12-6

12.4 GRIM INPUTS AND ASSUMPTIONS............................................................ 12-7

12.4.1 Overview of the Government Regulatory Impact Model........................... 12-7

12.4.2 Sources for GRIM Inputs............................................................................ 12-8

12.4.2.1 Final Rule for Residential Water Heaters, Direct Heating Equipment, 

and Pool Heaters............................................................................. 12-8

12.4.2.2 Shipment Model............................................................................ 12-9

12.4.2.3 Engineering Analysis.................................................................... 12-9

12.4.2.4 Manufacturer Interviews............................................................... 12-9

12.4.3 Trial Standard Levels.................................................................................. 12-9

12.4.4 NIA Shipments ......................................................................................... 12-10

12.4.5 Production Costs....................................................................................... 12-10

12.4.6 Conversion Costs...................................................................................... 12-12

12.4.6.1 Capital Conversion Costs ........................................................... 12-12

12.4.6.2 Product Conversion Costs........................................................... 12-12

12.4.7 Markup Scenarios..................................................................................... 12-13

12.4.7.1 Preservation of Gross Margin Percentage Scenario ................... 12-14

12.4.7.2 Preservation of Operating Profit Scenario.................................. 12-14

12.5 INDUSTRY FINANCIAL IMPACTS ............................................................ 12-14

12.5.1 Introduction............................................................................................... 12-15

12.5.2 Hearth Industry Financial Impacts............................................................ 12-15

12.6 IMPACTS ON SUBGROUPS OF MANUFACTURERS .............................. 12-17

12.6.1 Impacts on Manufacturers of Gas Log Sets.............................................. 12-17

12.6.2 Impacts on Small Business Manufacturers............................................... 12-19

12.7 OTHER IMPACTS.......................................................................................... 12-21

12.7.1 Direct Employment................................................................................... 12-21

12.7.1.1 Methodology............................................................................... 12-21

12-i
12.7.1.2 Direct Employment Impacts....................................................... 12-22

12.7.2 Production Capacity.................................................................................. 12-23

12.7.3 Cumulative Regulatory Burden ................................................................ 12-23

12.8 CONCLUSION................................................................................................ 12-24

LIST OF TABLES

Table 12.2.1 Classification of Small Business Manufacturers Potentially Affected by This 

Rulemaking.................................................................................................................... 12-4

Table 12.4.1 Financial Parameters for Gas Hearth Manufacturers................................ 12-8

Table 12.4.2 Trial Standard Levels for Analysis of Gas Hearth Products................... 12-10

Table 12.4.3 Manufacturer Production Cost Breakdown (2013$) for Vented Fireplaces, 

Inserts, and Stoves ....................................................................................................... 12-11

Table 12.4.4 Manufacturer Production Cost Breakdown (2013$) for Unvented Fireplaces, 

Inserts, and Stoves ....................................................................................................... 12-11

Table 12.4.5 Manufacturer Production Cost Breakdown (2013$) for Vented Gas Log Sets

...................................................................................................................................... 12-11

Table 12.4.6 Manufacturer Production Cost Breakdown (2013$) for Unvented Gas Log 

Sets............................................................................................................................... 12-11

Table 12.4.7 Manufacturer Production Cost Breakdown (2013$) for Outdoor Hearth 

Products........................................................................................................................ 12-11

Table 12.4.8 Industry Cumulative Capital Conversion Costs (2013$ Millions) ......... 12-12

Table 12.4.9 Industry Cumulative Product Conversion Costs (2013$ Millions) ........ 12-13

Table 12.5.1 Preservation of Gross Margin Percentage Scenario Changes in INPV for 

Hearth Products............................................................................................................ 12-16

Table 12.5.2 Preservation of Operating Profit Scenario Changes in INPV for Hearth 

Products........................................................................................................................ 12-16

Table 12.6.1. Added Cost of Electronic Ignition Systems vs. Standing Pilot Lights.. 12-18

Table 12.6.2 Added Cost of Electronic Ignition Systems at Representative Production 

Volumes....................................................................................................................... 12-21

Table 12.7.1 Potential Changes in the Total Number of Production Workers in the Gas 

Hearth Industry in 2021 ............................................................................................... 12-22

Table 12.8.1 Manufacturer Impact Analysis Results................................................... 12-24

LIST OF FIGURES

Figure 12.4.1 Using the GRIM to Calculate Cash Flow................................................ 12-7

Figure 12.5.1 Annual Industry Net Cash Flows under Preservation of Gross Margin 

Percentage Markup Scenario ....................................................................................... 12-16

Figure 12.5.2 Annual Industry Net Cash Flows under Preservation of Operating Profit 

Markup Scenario.......................................................................................................... 12-17

12-ii
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 consumers 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 an

energy conservation standard on manufacturers of hearth products and to assess the 

impact such standards would have 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 product 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 product by 

comparing changes in INPV between a base case (without new standards) and the various 

trial standard levels (TSLs) in the standards case. The qualitative part of the MIA 

addresses product characteristics, manufacturer characteristics, market and product 

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 gas hearth 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 hearth manufacturers to gather information on 

the industry as well as the potential impacts of an energy conservation standard. In Phase 

II, Industry Cash Flow Analysis, DOE used the GRIM to assess the potential impacts of 

an energy conservation standard 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 gas hearth 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 market 

research tools (e.g., Hoovers2

), corporate annual reports, the U.S. Census Bureau’s 2011

Annual Survey of Manufacturers (ASM)3

, and the 2010 Energy Conservation Standard

Final Rule for Residential Water Heaters, Direct Heating Equipment, and Pool Heaters 

12-1
(75 FR 20112 (April 16, 2010)); information obtained through DOE’s engineering 

analysis, life-cycle cost analysis, and market and technology assessment prepared for this 

rulemaking; and information obtained directly from manufacturers through interviews.

The industry profile includes an analysis of overall market and product 

characteristics (e.g., market structure, sales trends, competition) as well as estimation of 

financial parameters for the industry (e.g., typical product markups, costs of goods sold

(COGS), selling, general and administrative expenses (SG&A), net plant, property, and 

equipment (PPE), expenditures on research and development (R&D)). The financial 

parameters developed as part of the industry profile were subsequently used to develop 

the industry cash flow analysis conducted during Phase II.

12.2.1.1 Manufacturer Interviews

As part of Phase I, DOE conducted structured, detailed interviews with a 

representative cross-section of manufacturers. During these interviews, DOE discussed 

engineering, manufacturing, procurement, and financial topics. The interviews were 

designed to identify manufacturers’ key concerns regarding a potential energy 

conservation standard and to gather information on the potential effects of a standard on 

manufacturer finances, direct employment, capital assets, and industry competitiveness.

The interviews also presented an opportunity to discuss industry structure and market 

segmentation and to identify subgroups of manufacturers that could be disproportionately 

affected by an energy conservation standard. 

DOE scheduled interviews and distributed interview guides to manufacturers well 

in advance of conducting interviews. In doing so, DOE sought to provide every 

opportunity for key individuals to be available for comment and to afford manufacturers 

sufficient time to prepare. Although a written response to the interview guide was 

acceptable, DOE requested interactive interviews, which help to clarify responses and 

identify additional issues. Topics addressed during interviews included: (1) key issues to 

this rulemaking; (2) engineering analysis; (3) company overview; (4) manufacturer 

markups and profitability; (5) financial parameters; (6) conversion costs; (7) industry 

projections; (8) direct employment impact assessment; (9) exports, foreign competition, 

and outsourcing; (10) consolidation; (11) cumulative regulatory burden; and (12) impacts 

on small businesses.

Information obtained during interviews is protected by non-disclosure agreements

(NDAs) and resides with DOE’s contractors. This allows manufacturers to express their 

views on important issues privately and to share confidential or sensitive information for 

consideration as part of the rulemaking process. The opportunity to exchange confidential 

information enables DOE to refine its engineering and manufacturer impact analyses in a 

manner that would not be possible if relying solely on publicly available information. For 

instance, confidential financial data obtained under NDA allows DOE to better tailor the 

GRIM to reflect financial characteristics specific to the gas hearth industry.

12-2
12.2.2 Phase II: Industry Cash-Flow Analysis 

Phase II of the MIA focused on the financial impacts of potential energy

conservation standards on manufacturers of gas hearth products. 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 

hearth industry. In performing these analyses, DOE used financial values derived during

Phase I and shipment projections 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 energy conservation standards until 30 years after the 

standards’ compliance date. These factors include annual expected revenues, COGS, 

SG&A, taxes, and capital expenditures related to the standards. Inputs to the GRIM 

include 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 10 of the TSD, provides the basis for the 

shipment projections used 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 for the standards case 

are compared to results for the base-case scenario for the industry. The financial impact 

of an energy conservation standard is then evaluated as the difference between the 

discounted annual cash flows in the base case and the discounted annual cash flows in the

standards case.

The results of the industry cash-flow analysis are presented in section 12.5.

12.2.3 Phase III: Manufacturer Subgroup Analysis

For its GRIM analysis, DOE presented impacts on the hearth industry as a whole. 

However, using average cost assumptions to develop an industry cash-flow estimate may 

not adequately assess differential impacts of an energy conservation standard 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. DOE identified two subgroups of hearth manufacturers that could 

be disproportionately affected by an energy conservation standard and therefore 

warranted a separate impact analysis: (1) manufacturers of gas log sets; and (2) small 

business manufacturers.

12.2.3.1 Manufacturers of Gas Log Sets Subgroup

12-3
During interviews, multiple manufacturers commented that gas log sets represent 

a distinct market segment within the gas hearth industry. These manufacturers indicated 

that gas log sets serve a different market niche and face different space constraints than 

other gas hearth products. Additionally, manufacturers of gas log sets indicated that an 

energy conservation standard disallowing the use of standing pilot lights could have a 

proportionally greater impact on the manufacturing costs and, by extension, the retail 

prices, of gas log sets relative to other gas hearth products. Given the nature of the gas 

log set market, manufacturers indicated that the proposed energy conservation standard 

could trigger a decline in demand from price-sensitive consumers and, in turn, negatively 

affect manufacturer profitability. DOE reports further on the potential impact of this 

rulemaking on the subgroup of gas log set manufacturers in section 12.6.1.

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) codes, presented in Table 12.2.1, to 

determine whether any small entities would be affected by the rulemaking.4 For the 

products 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.

Manufacturing of hearth products is classified under NAICS code 333414, “Heating 

Equipment (Except Warm Air Furnaces) Manufacturing,” and under NAICS code 

335228, “Other Major Household Appliance Manufacturing.” For both NAICS codes, the 

SBA sets a threshold of 500 employees or fewer for an entity to be considered a small 

business.

Table 12.2.1 Classification of Small Business Manufacturers Potentially Affected by 

This Rulemaking

Industry Description Revenue Limit Employee Limit NAICS Code

Heating Equipment (Except Warm Air 

Furnaces) Manufacturing N/A 500 333414

Other Major Household Appliance 

Manufacturing N/A 500 335228

DOE used publicly available and proprietary information to identify potential 

small business manufactures of products covered by this rulemaking. DOE’s research 

involved industry trade association membership directories (e.g., Hearth, Patio & 

Barbecue Association (HPBA)), individual company websites, and market research tools 

(e.g., Hoovers.com) to create a list of small companies that manufacture products that 

would be covered by this rulemaking. In interviews, DOE presented its initial list of 

identified small business manufacturers and asked interviewees if they were aware of any 

small manufacturers not included on the list. DOE also reached out to other interested 

parties and industry representatives for information on small business manufacturers. 

DOE screened out companies that do not manufacture products potentially covered by 

this rulemaking, do not meet the definition of a small business, or are foreign owned and 

operated. Based on this analysis, DOE identified 66 domestic small businesses that 

12-4
manufacture gas hearth products affected by this rulemaking. DOE reports the potential 

impact of this rulemaking on small businesses in section 12.6.2.

12.2.4 Manufacturing Capacity Impact

One significant outcome of energy conservation standards could be the 

obsolescence of existing manufacturing assets, including tooling and investment. The 

manufacturer interview guides include a series of questions to help identify impacts of 

standards on manufacturing capacity. Specifically, these are: capacity utilization and 

plant location decisions in the United States, with and without 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 of any one-time 

changes to existing plant, property, and equipment (PPE). DOE’s estimates of the onetime capital changes and stranded assets affect the cash flow estimates in the GRIM. 

These estimates can be found in section 12.4.6. DOE’s discussion of the capacity impact 

can be found in section 12.7.2.

12.2.5 Employment Impact 

The impact of energy conservation standards on employment is an important 

consideration in the rulemaking process. To assess how domestic direct employment 

patterns might be affected, the interviewers explored with interested parties the current 

employment trends in the hearth industry. The interviewers also solicited manufacturer 

views on changes in employment patterns that may result from an energy conservation 

standard. The employment impacts section of the interview guide focuses on current 

employment levels associated with manufacturers at each production facility, expected 

future employment levels with and without energy conservation standards, and 

differences in workforce skills and issues related to the retraining of employees. The 

employment impacts are reported in section 12.7.1.

12.2.6 Cumulative Regulatory Burden

DOE seeks to mitigate the overlapping effects on manufacturers due to energy 

conservation standards and other regulatory actions affecting the same products. DOE 

analyzed the impact on manufacturers of multiple, product-specific regulatory actions. 

Discussion of the cumulative regulatory burden can be found in section 12.7.3. 

12.3 MANUFACTURER IMPACT ANALYSIS KEY ISSUES

Each MIA interview starts by asking: “What are the key issues for your company 

regarding the energy conservation standard rulemaking?” This question prompts 

manufacturers to identify the issues they consider important for DOE to explore and 

discuss further during the interviews. The following sections describe the most significant 

issues identified by manufacturers. These summaries are provided in aggregate to protect 

manufacturer confidentiality. 

12-5
12.3.1 Impacts on Profitability

According to manufacturers, units with electronic ignition systems are more 

expensive to manufacture than units with standing pilot lights. Manufacturers indicated 

that purchasing components for electronic ignition systems increases per-unit production 

costs and, by extension, raises the retail price of products. Manufacturers stated that by 

driving up their cost of goods sold as well as the end-user purchase price, a standard 

eliminating standing pilot lights could lead to a drop in consumer demand. Because gas 

hearth products are not typically purchased exclusively for heating purposes but rather 

are valued by customers for their aesthetic appeal, manufacturers indicated that higher 

prices could depress demand if customers decide the decorative benefit of gas hearth 

products does not merit the higher costs. A fall in sales could, in turn, affect industry 

profitability. 

Additionally, manufacturers stated that shipments of gas hearth products declined 

significantly over the last decade, in part due to the economic recession and a related 

decline in new-home construction. Several manufacturers forecast steady or declining 

shipments in future years absent an energy conservation standard. Those interviewed 

generally argued that if an energy conservation standard raises the price of gas hearth 

products, depresses demand, and reduces profitability, it could drive manufacturers to 

exit the market.

12.3.2 Impacts on Industry Competition

Small manufacturers expressed concern that an energy conservation standard for 

gas hearth products could alter the competitive dynamics of the market, favoring a subset 

of large manufacturers over their small-business competitors. Based on economies of 

scale, manufacturers that produce gas hearth products at high volumes are typically able 

to source components at lower per-unit prices than manufacturers that produce at lower 

volumes. In general, manufacturers of gas hearth products do not manufacture the 

components used for electronic ignition systems in house. Rather, they source them from 

component suppliers. In interviews, manufacturers indicated that large manufacturers 

with high production volumes are able to source these components at relatively low cost.

Small manufacturers with lower production volumes, in contrast, noted that the 

comparatively high cost they would incur to purchase electronic ignition system 

components would exacerbate the pricing advantage of large manufacturers and could 

lead to loss of price competitiveness for smaller players in the market. 

12.3.3 Impacts on Product Performance

Multiple manufacturers stated that electronic ignition systems represent a more 

complicated and less reliable technology than standing pilot lights. These manufacturers 

indicated that units with electronic ignition systems often require more effort to repair 

and maintain. One manufacturer stated that electronic ignition systems account for a 

small fraction of their sales but the vast majority of their service calls, and several 

manufacturers suggested higher costs of maintaining units with electronic ignition 

systems compared to standing pilot lights. Additionally, several manufacturers suggested 

12-6
that electronic ignition systems are not as well suited to cold climates, where standing 

pilot lights may help to maintain buoyancy through the flue and to prevent condensation 

from building up on glass.

12.4 GRIM INPUTS AND ASSUMPTIONS

The GRIM serves as the main tool for assessing the impacts on industry resulting 

from energy conservation standards. DOE relies on several sources (as detailed in section 

12.4.2) to obtain inputs for the GRIM. DOE then feeds the data and assumptions from 

these sources into an accounting model that calculates industry cash flows both with and 

without energy conservation standards.

12.4.1 Overview of the Government Regulatory Impact Model 

The GRIM is an annual cash flow model that uses manufacturer production costs, 

manufacturer selling prices, shipment projections, and industry financial information as 

inputs to arrive at a series of annual cash flows, beginning with the base year of the 

analysis, 2014, and continuing to 2050. As illustrated in Figure 12.4.1, the model 

calculates INPV by summing the stream of annual discounted cash flows during this 

period and adding a discounted terminal value.5 Imposing different conditions, such as 

changes in manufacturing costs, investment requirements, and anticipated markups,

enables DOE to analyze the potential effects of an energy conservation standard on 

industry finances.

Figure 12.4.1 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 scenarios induced by 

energy conservation standards. The difference in INPV between the base case and the 

standard case represents the estimated financial impact of the energy conservation 

12-7
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 assumes no energy 

conservation standard for gas hearth products. Accordingly, when comparing the INPV 

impacts of the GRIM model, the baseline assumes continued production and sale of 

hearth products with standing pilot lights, while the standard case assumes the 

elimination of all standing pilot lights and their replacement with electronic ignition 

systems.

12.4.2 Sources for GRIM Inputs

The GRIM uses several different sources for data inputs in determining industry 

cash flow. These sources include corporate annual reports, company profiles, census data, 

credit ratings, the shipments model, the engineering analysis, and manufacturer 

interviews.

12.4.2.1 Final Rule for Residential Water Heaters, Direct Heating 

Equipment, and Pool Heaters

The 2010 Energy Conservation Standard for Residential Water Heaters, Direct 

Heating Equipment, and Pool Heaters (75 FR 20112 (April 16, 2010)) provided many of 

the initial financial inputs to the GRIM. As part of that rule, DOE derived a series of

financial parameters for vented gas hearths based on a review of SEC public filings, 

corporate annual reports, company profiles, credit ratings, and manufacturer interviews. 

DOE used these parameters as a starting point for analysis, presenting them to 

manufacturers for review and comment during interviews conducted under Phase I of this 

rulemaking. Based on manufacturer feedback, DOE then revised its estimated financial 

parameters to better reflect the current gas hearth industry. Table 12.4.1 presents both the

initial estimates and the revised financial parameters used as inputs to the GRIM.

Table 12.4.1 Financial Parameters for Gas Hearth Manufacturers

Parameter Initial Estimate

%

Revised 

Estimate

%

Tax Rate (% of taxable income) 35 36

Discount Rate 8.5 8.7

Working Capital (% of revenue) 11.4 2.2

Net Property, Plant, and Equipment (% of 

Revenue) 13 10.8

SG&A (% of revenue) 20.9 25

R&D (% of revenue) 1.5 2.3

Depreciation (% of revenue) 2.2 2.1

Capital Expenditures (% of revenue) 2.4 2.1

12-8
12.4.2.2 Shipment Model

The GRIM used shipment projections derived from DOE’s shipments model in 

the national impact analysis (NIA). Chapter 10 of this TSD describes the methodology 

and analytical model DOE used to forecast shipments.

12.4.2.3 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 gas 

hearth products. DOE estimated a manufacturer markup and applied this to the MPC to 

arrive at the manufacturer selling price (MSP). 

12.4.2.4 Manufacturer Interviews

During the course of the MIA, DOE conducted interviews with a representative 

cross-section 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);

• Product cost structure;

• Industry financial parameters; and

• Possible profitability impacts.

12.4.3 Trial Standard Levels 

DOE typically considers multiple trial standard levels (TSLs) for a standards 

rulemaking. However, the hearth products rulemaking is proposing a prescriptive 

standard that would disallow the use of continuously burning pilots. There is currently 

only one other established alternative to a standing pilot, which is an intermittent pilot.

Other options that are present in other combustion appliances, such as hot surface 

ignition, are virtually non-existent in the hearth product market primarily due to the 

increased cost and additional engineering challenges. Therefore, hearth products have 

only one TSL, which reflects a standard that would disallow the use of a standing pilot. 

For the purposes of this analysis, TSL1 assumes that all covered hearth products would 

use an intermittent pilot.

12-9
Table 12.4.2 Trial Standard Levels for Analysis of Gas Hearth Products

Ignition Type TSL 1

%

Standing Pilot 0

Intermittent Pilot 100

12.4.4 NIA Shipments

The GRIM estimates manufacturer revenues based on total-unit-shipment 

forecasts and the distribution of these values by product group and ignition type. For this 

analysis, the GRIM applied the NIA shipments forecasts for the period 2014 (the base 

year of the MIA analysis) to 2050 (the end year of the analysis). As part of the shipments 

forecasts, DOE estimated the base-case shipment distribution by ignition type. 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 (i.e., use standing pilots) would instead ship at 

the new standard level (i.e., with electronic ignition systems). The shipments forecasts 

also assume price elasticity of demand, whereby shipment volumes in the standards case 

decline relative to the base case as MPCs rise and, in doing so, drive up end-user 

purchase prices. The key assumptions and methodology used to forecast shipments can 

be found in chapter 10 of this TSD.

12.4.5 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 gas hearth units in 

five product groups: (1) vented fireplaces, inserts, and stoves; (2) unvented fireplaces, 

inserts, and stoves; (3) vented gas log sets; (4) unvented gas log sets; and (5) outdoor 

hearth products. The engineering analysis also determined labor, materials, overhead, and 

depreciation percentages that constitute the full MPC for each product group. DOE uses 

these MPCs in combination with shipment projections derived as part of the national 

impact analysis to evaluate industry financials in both the base case and the standards 

case. DOE also applies a manufacturer markup to the MPCs in order to calculate MSPs 

for each product group. DOE adjusts this markup under multiple markup scenarios in 

order to analyze a range of potential financial impacts of an energy conservation standard 

on manufacturers.

Table 12.4.3 through Table 12.4.5show the manufacture production cost and 

manufacturer selling price estimates used in the GRIM for each analyzed product group.

A baseline markup of 1.45 was applied to all product groups. As explained in section 

12.4.7, this markup varies under alternative markup scenarios.

12-10
Table 12.4.3 Manufacturer Production Cost Breakdown (2013$) for Vented 

Fireplaces, Inserts, and Stoves

Materials

$

Labor

$

Depreciation

$

Overhead

$

MPC

$

Markup

%

MSP

$

Standing 

Pilot 140 72 71 39 322 1.45 468

Intermittent 

Pilot 167 72 71 40 350 1.45 508

Table 12.4.4 Manufacturer Production Cost Breakdown (2013$) for Unvented 

Fireplaces, Inserts, and Stoves

Materials

$

Labor

$

Depreciation

$

Overhead

$

MPC

$

Markup

%

MSP

$

Standing 

Pilot 107 70 70 34 281 1.45 407

Intermittent 

Pilot 134 75 69 35 313 1.45 454

Table 12.4.5 Manufacturer Production Cost Breakdown (2013$) for Vented Gas Log 

Sets

Materials

$

Labor

$

Depreciation

$

Overhead

$

MPC

$

Markup

%

MSP

$

Standing 

Pilot 56 45 62 27 190 1.45 275

Intermittent 

Pilot 121 47 62 29 260 1.45 376

Table 12.4.6 Manufacturer Production Cost Breakdown (2013$) for Unvented Gas 

Log Sets

Materials

$

Labor

$

Depreciation

$

Overhead

$

MPC

$

Markup

% MSP

$

Standing 

Pilot 81 60 44 23 208 1.45 301

Intermittent 

Pilot 126 59 52 28 264 1.45 383

Table 12.4.7 Manufacturer Production Cost Breakdown (2013$) for Outdoor 

Hearth Products

Materials

$

Labor

$

Depreciation

$

Overhead

$

MPC

$

Markup

%

MSP

$

Standing 

Pilot 117 33 39 21 210 1.45 304

Intermittent 

Pilot 168 37 37 22 265 1.45 384

12-11
12.4.6 Conversion Costs

Energy conservation standards typically cause manufacturers to incur one-time 

conversion costs to bring their production facilities and product 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 one-time investments in plant, property, and equipment to adapt or 

change existing production facilities in order to fabricate and assemble new product 

designs that comply with energy conservation standards. Product conversion costs are 

one-time investments in research, development, testing, marketing, and other costs to 

make product designs comply with 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.

12.4.6.1 Capital Conversion Costs

To estimate the level of capital conversion costs manufacturers would likely incur 

to comply with an energy conservation standard, DOE relied on information obtained 

through manufacturer interviews as well as the engineering analysis.

Based on both the engineering analysis and conversations with manufacturers, 

DOE has determined that the proposed standard eliminating standing pilot lights would 

primarily entail a component swap, in which manufacturers would assemble hearth 

products using a different set of purchased parts for the ignition system. Accordingly, 

capital investment by manufacturers to re-tool or reconfigure production facilities likely 

would be limited. Consistent with this expectation, interviewed manufacturers stated that 

they did not anticipate incurring significant capital conversion costs in order to comply 

with the proposed standard. As a result, DOE assigned a nominal capital conversion cost 

per manufacturer, equivalent to $10,000, to account for any one-time capital investments 

or reorganization of production lines that a standard eliminating standing pilot lights 

could potentially entail. DOE assigned this capital conversion cost estimate to each of the 

90 hearth product manufacturers identified. Table 12.4.8 presents estimated capital 

conversion costs for the industry resulting from an energy conservation standard.

Table 12.4.8 Industry Cumulative Capital Conversion Costs (2013$ Millions)

TSL Capital Conversion 

Costs (2013$ Millions)

1 0.9

12.4.6.2 Product Conversion Costs

As with capital conversion costs, DOE relied on manufacturer interviews and the 

engineering analysis to evaluate product conversion costs. Because most hearth product 

manufacturers already offer models with electronic ignition systems, and because the 

12-12
proposed standard would primarily entail a change in purchased parts rather than an 

entire product redesign, many manufacturers indicated in interviews that they did not 

expect to incur significant product conversion costs under the proposed standard. DOE 

based its product conversion cost estimates on the assumption that manufacturers would 

incur limited costs related to research and development, testing and certification, and 

development of marketing materials in order to bring into compliance models not 

currently offered with an electronic ignition system option.

During interviews, some manufacturers expressed concern that an energy 

conservation standard could entail significant product conversion costs related to labeling 

requirements. Under Canadian law, manufacturers must test and label gas fireplaces, 

stoves and inserts with a fireplace efficiency rating. If a federal energy conservation 

standard mandated an alternative efficiency metric for hearth products (e.g., annual fuel 

utilization efficiency), manufacturers indicated they could be required to hold separate 

stock-keeping units (SKUs) for the Canadian and U.S. markets to comply with each 

jurisdiction’s requirements. Because the proposed standard is a prescriptive design 

requirement and does not establish a minimum efficiency rating or require products to be 

labeled with a particular efficiency metric, DOE did not factor the cost of holding 

duplicate SKUs into its estimated product conversion costs. 

To analyze potential product conversion costs, DOE reviewed publicly available 

product literature to estimate an average number of models offered per manufacturer, as 

well as the average percentage of models offered only with standing pilot lights and 

without the option of electronic ignition. DOE scaled up these assumptions to arrive at an 

estimated total of 781 models for the industry that could require product conversion costs 

in the form design engineering, testing, certification, etc. DOE then assigned a fixed cost 

per model, equivalent to $10,000, to arrive at total estimated product conversion costs.

This methodology assumes all non-compliant models (i.e., models currently offered only 

with standing pilot lights) would be redesigned to accommodate electronic ignition 

systems. This represents a conservative assumption, as manufacturers may choose to 

discontinue some models with standing pilot lights. Models already available with the 

option of electronic ignition would not require any one-time conversion costs by the 

manufacturer in order to achieve compliance.

Table 12.4.9 presents estimated product conversion costs for the industry 

resulting from a design standard eliminating standing pilot lights.

Table 12.4.9 Industry Cumulative Product Conversion Costs (2013$ Millions)

TSL Product Conversion 

Costs (2013$ Millions)

1 7.8

12.4.7 Markup Scenarios

DOE modeled multiple standards-case markup scenarios to represent uncertainty 

surrounding the potential impacts of energy conservation standards on prices and 

12-13
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 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 that, when 

applied to the inputted MPCs, resulted in varying revenue and cash flow impacts.

12.4.7.1 Preservation of Gross Margin Percentage Scenario

Under the preservation of gross margin percentage scenario, DOE applied a single 

uniform gross margin percentage markup, which assumes that manufacturers would be 

able to maintain the same amount of profit as a percentage of revenues under an energy 

conservation standard. As production costs increase with implementation of energy 

conservation standards, this scenario implies that the absolute dollar markup will increase 

as well. Based on publicly available financial information for manufacturers of hearth 

products 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 

1.45 for all hearth products. 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 energy conservation standard, it represents a high bound to 

industry profitability.

12.4.7.2 Preservation of Operating Profit Scenario

Under the preservation of per unit operating profit scenario, manufacturer markups 

are set so that operating profit one year after the compliance date of the energy 

conservation standard is the same as in the base case on a per-unit basis. In this scenario, 

as production costs increase with implementation of energy conservation standards, 

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. Therefore, operating margin in percentage terms is 

reduced between the base case and standards case. To analyze this markup scenario, DOE 

adjusted the manufacturer markups in the GRIM 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 the proposed energy 

conservation standard.

12.5 INDUSTRY FINANCIAL IMPACTS

Using the inputs and scenarios described in the previous sections, the GRIM 

estimated indicators of financial impacts on the hearth 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: INPV and annual cash flows.

12-14
12.5.1 Introduction

The INPV measures the industry value and is used in the MIA to compare the 

economic impacts of an energy conservation standard (the standards case) to a base case 

that assumes no energy conservation standard. 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 analysis period discounted at the industry’s cost of capital, or discount rate. The 

GRIM for this rulemaking estimates cash flows from 2014 to 2050. This timeframe 

models both the short-term impacts on the industry from the announcement of the 

standard until the compliance date (2015 to 2021), and a long-term assessment over the 

30-year analysis period used in the NIA (2021 to 2050).

In the MIA, DOE compares the INPV of the base case to that of the standards 

case. The difference between the base case and the standards case INPV is an estimate of 

the economic impacts that implementing the standard would have on the industry. While 

INPV is useful for evaluating the long-term effects of 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. Figure 12.5.1 and

Figure 12.5.2 present the expected behavior of annual net cash flows over the analysis 

period. As the figures illustrate, industry cash flows begin to decline after the publication 

date of the final rule as companies use their financial resources to prepare for the energy 

conservation standard. Cash flows between the publication date and the compliance date 

are driven by the level of conversion costs and by the proportion of these investments 

made each year. All cash flows are discounted to the base year, 2014.

Free cash flow in the year the energy conservation standards take effect is driven 

by two competing factors. In addition to capital and product conversion costs, energy 

conservation standards could create (1) 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 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 (2) an increase in 

working capital, which 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. 

12.5.2 Hearth Industry Financial Impacts

Table 12.5.1 and Table 12.5.2 provide INPV estimates for the hearth industry

under the two markup scenarios analyzed. Figure 12.5.1 and Figure 12.5.2 present annual 

industry net cash flows under the two markup scenarios. As described in section 12.4.7, 

12-15
the preservation of gross margin percentage scenario presents an upper bound to industry 

profitability under an energy conservation standard while the preservation of operating 

profit scenario presents a lower bound to industry profitability.

Table 12.5.1 Preservation of Gross Margin Percentage Scenario Changes in INPV 

for Hearth Products

Units Base Case Standards 

Case

INPV 2013$M 125.3 125.8

Change in INPV 2013$M - 0.5

% Change - 0.4

Table 12.5.2 Preservation of Operating Profit Scenario Changes in INPV for Hearth 

Products

Units Base Case Standards 

Case*

INPV 2013$M 125.3 122.0

Change in INPV 2013$M - (3.3)

% Change - (2.6)

*Parentheses indicate negative values

Under an energy conservation standard, DOE estimates impacts on INPV to range 

from -$3.3 million to $0.5 million, or a change of -2.6 percent to 0.4 percent. See section 

12.8 for further discussion of results.

Figure 12.5.1 Annual Industry Net Cash Flows under Preservation of Gross Margin 

Percentage Markup Scenario

$0.0

$2.0

$4.0

$6.0

$8.0

$10.0

$12.0

2013 2018 2023 2028 2033 2038 2043 2048

2013$M

Base Case

TSL 1

12-16
Figure 12.5.2 Annual Industry Net Cash Flows under Preservation of Operating 

Profit Markup Scenario

12.6 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 of gas log sets, and (2) small business manufacturers.

12.6.1 Impacts on Manufacturers of Gas Log Sets

During interviews, multiple manufacturers commented that gas log sets represent 

a distinct market segment within the gas hearth industry. These manufacturers indicated 

that gas log sets serve a different market niche and face different space constraints than 

other gas hearth products. Additionally, gas log sets often sell at lower prices than other 

gas hearth products. As a result, an increase in manufacturing costs and, by extension, 

retail price resulting from an energy conservation standard, could have a proportionally 

greater impact on gas log sets relative to other gas hearth products.

Gas log sets are typically designed for use in existing wood-burning fireplaces. 

During interviews, manufacturers of gas log sets stated that unlike other gas hearth 

$0.0

$2.0

$4.0

$6.0

$8.0

$10.0

$12.0

2013 2018 2023 2028 2033 2038 2043 2048

2013$M

Base Case

TSL 1

12-17
products, gas log sets compete with wood, coal, and wood/wax logs. These alternatives 

are typically inexpensive to purchase, such that consumers could feasibly substitute away 

from gas log sets and toward wood and/or wood/wax logs if an energy conservation 

standard leads to higher prices. According to these manufacturers, if design constraints 

specific to gas log sets cause an energy conservation standard to alter product aesthetics, 

it could further drive consumers to switch products.

Because gas log sets are designed to fit within existing wood-burning fireplaces, 

manufacturers indicated that design options for gas log sets are constrained by the 

geometric configurations of pre-existing fireplaces. Manufacturers stated that electronic 

ignition systems take up more space than standing pilot lights, and that accommodating 

electronic ignition systems inside existing fireplaces could, in turn, reduce the size of the 

gas log sets consumers could purchase for their fireplaces. Manufacturers also indicated 

that electronic ignition system components can be difficult to conceal within a gas log 

set’s design. Unlike other gas hearth products, gas log sets are not sold as part of a 

packaged unit, leaving manufacturers with limited options for obscuring the gas valve, 

pilot assembly, control module, wiring, and other components that make up an electronic 

ignition system. As a result, these components may be more exposed when used with gas 

log sets compared to other gas hearth products. Manufacturers also stated that electric 

outlets may not be situated in close enough proximity to wood-burning fireplaces to 

enable ready installation of units with electronic ignition systems. In such cases, the need 

for extension cords could impact the aesthetic appeal of products. Alternatively, hiring an 

electrician could raise installation costs and potentially deter price-sensitive consumers.

Alongside aesthetic impacts, manufacturers expressed concern regarding the cost 

implications of a potential standard eliminating standing pilot lights. As discussed 

previously, purchasing components for electronic ignition systems typically costs 

manufacturers more than purchasing components for standing pilot lights. Higher 

manufacturing costs, in turn, lead to higher retail prices. To estimate the potential 

difference in cost resulting from a standard eliminating standing pilot lights, DOE 

modeled the MPCs for both vented and unvented gas log sets using both standing pilot 

lights and electronic ignition systems. DOE similarly modeled MPCs for other categories 

of gas hearth products. Table 12.6.1 presents the relative increase in MPC for products 

manufactured with electronic ignition systems as opposed to standing pilot lights. See 

chapter 5 of the TSD for a more detailed discussion of how MPCs were calculated.

Table 12.6.1. Added Cost of Electronic Ignition Systems vs. Standing Pilot Lights

Product Group

Estimated Increase in 

MPC of Switching from 

Standing Pilot to 

Electronic Ignition*

$

Increase in Cost 

of Ignition 

System

%

Increase in 

Overall MPC

%

Vented 

Fireplace/Insert/Stove 28 56 9

Unvented 

Fireplace/Insert/Stove 32 47 11

12-18
Vented Gas Logs 70 227 37

Unvented Gas Logs 56 194 27

Outdoor 55 65 26

* Standing pilot ignitions largely use two styles of gas valves: manual and millivolt. The incremental costs 

of switching from standing pilot lights to electronic ignition systems presented here assume gas fireplaces, 

inserts, and stoves use standing pilot lights with millivolt gas valves while gas log sets and outdoor hearth 

products use standing pilot lights with manual gas valves. The millivolt gas valve uses a thermopile place 

in the pilot light to generate a voltage difference, so that a remote control can be used to turn the burner on 

and off. These valves are larger and more expensive than manual gas valves, which are operated by 

hand. Based on public comments on previous rulemakings and manufacturer interviews, DOE recognizes 

the importance of space constraints and cost burden associated with control systems for gas log sets. For 

the purposes of analysis, DOE chose to represent gas log sets with standing pilots using manual gas valves. 

Fireplaces, inserts, and stoves provide more opportunity to package and conceal larger, more complex 

ignition systems. Accordingly, DOE chose to represent the standing pilot variation of this product category 

with models using millivolt gas valves.

As the results above indicate, DOE estimates that the cost of switching from a 

standing pilot light to an electronic ignition system could disproportionately affect gas 

log set manufacturers. These results are driven by two primary factors. First, they are

based on the assumption that gas log sets use standing pilot lights with manual gas 

valves, which are smaller and less expensive than standing pilot lights with millivolt gas 

valves. Under this assumption, the higher cost of purchasing electronic ignition system 

components would represent a more significant expenditure both in absolute dollars and 

in percentage terms for manufacturers of gas log sets using manual standing pilot lights 

relative to manufacturers of products using more expensive millivolt standing pilot lights. 

Second, the overall cost of manufacturing gas log sets is often lower than the overall cost 

of manufacturing other types of gas hearth products. This means the same increase in 

MPC in absolute dollars would result in a higher proportional increase in MPC for gas 

log sets. Assuming, as described, that manufacturers of gas log sets are likely to see a 

greater increase in MPC in absolute dollars compared to manufacturers of other products, 

this would imply an even greater proportional increase in overall MPC of gas log sets. If 

retail prices scale with MPCs, manufacturers indicated that demand for gas log sets from 

price-sensitive consumers could decline and, in turn, negatively affect manufacturer 

profitability. 

12.6.2 Impacts on Small Business Manufacturers

To better assess the potential impacts of this rulemaking on small entities, DOE 

conducted a more focused inquiry of companies that could be small business 

manufacturers of products covered by this rulemaking. For hearth products, the Small 

Business Administration (SBA) has set a size threshold of 500 employees or fewer for an 

entity to be considered a small business. This 500-employee threshold includes all 

employees in a business’s parent company and any other subsidiaries and applies to both 

heating and decorative hearth products, categorized respectively under North American 

Industry Classification System (NAICS) code 333414, “Heating Equipment (Except 

Warm Air Furnaces) Manufacturing” and NAICS code 335228, “Other Major Household 

Appliance Manufacturing.” Based on this threshold, DOE used publicly available 

12-19
information to identify potential small manufacturers. DOE’s research involved industry 

trade association membership directories (e.g., HPBA), information from previous 

rulemakings, individual company websites, and market research tools (e.g., Hoover’s

reports) to create a list of companies that manufacture hearth products covered by this 

rulemaking. DOE also asked stakeholders and industry representatives if they were aware 

of any additional small manufacturers during manufacturer interviews. DOE reviewed 

publicly available data and contacted various companies on its complete list of 

manufacturers to determine whether they met the SBA’s definition of a small business 

manufacturer. DOE screened out companies that do not manufacture products impacted

by this rulemaking, do not meet the definition of a “small business,” or are foreign owned 

and operated. 

DOE identified 90 manufacturers of gas hearth products that would be affected by 

today’s proposal. Of these, DOE identified 66 as domestic small business manufacturers.

DOE contacted a subset of identified small businesses to invite them to take part in a 

manufacturer impact analysis interview. Of 25 small businesses contacted, DOE was able 

to reach and discuss potential standards with five. DOE also obtained information about 

small businesses and potential impacts on small businesses while interviewing large 

manufacturers.

In interviews, small manufacturers expressed concern regarding the impact of a 

standard eliminating standing pilot lights on their ability to compete with larger 

manufacturers. Manufacturers stated that gas hearth products with electronic ignition 

systems cost more to produce than gas hearth products with standing pilot lights, as the 

components purchased for electronic ignition systems tend to be more expensive. Since 

large manufacturers often produce at higher volumes, they may be able to source 

components at lower per-unit prices than small manufacturers that produce at lower 

volumes. Because small manufacturers may not benefit from the same economies of scale 

as large manufacturers, an energy conservation standard eliminating standing pilot lights 

could disproportionately affect their production costs and, in turn, the prices at which 

they sell their products. This anticipated change in MPCs drove small manufacturer 

concerns surrounding the impact of an energy conservation standard on their ability to 

remain competitive in the gas hearth market.

To evaluate small manufacturers’ concerns regarding the competitive implications 

of a standard eliminating standing pilot lights, DOE modeled the difference in cost small 

manufactures might face when sourcing components at lower volumes. Due to limited 

available information on the relative sales volumes of small and large manufacturers, 

DOE selected volumes of 1,000 units (used to represent small manufacturers) and 10,000 

units (used to represent large manufacturers) for each product group analyzed. DOE 

developed its analysis based on the engineering teardown analysis and cost model as well 

as manufacturer feedback on the costs of ignition systems. 

Table 12.6.2 presents the estimated added per-unit cost of an electronic ignition 

system compared to a standing pilot system at the two representative production volumes 

modeled. As the results indicate, manufacturers likely would pay less per unit when 

12-20
producing 10,000 units versus 1,000 units. Estimated costs would decline further as 

production volumes climb higher.

Table 12.6.2 Added Cost of Electronic Ignition Systems at Representative 

Production Volumes

Product Group

Baseline 

MPC

$

Added Cost of 

EIS at 1,000 

units

$

Added Cost of 

EIS at 10,000 

units

$

Vented Fireplace/Insert/Stove 322 31 26

Unvented 

Fireplace/Insert/Stove 281 33 24

Vented Log Sets 190 70 58

Unvented Log Sets 208 69 51

Outdoor 210 65 42

DOE’s analysis suggests that a standard eliminating standing pilot lights would 

increase the per-unit MPCs of gas hearth products by a greater amount for small-volume 

producers than for large-volume producers. Higher MPCs, in turn, typically lead to 

higher end-user purchase prices. If products manufactured by small businesses cannot 

compete with products manufactured by large businesses at lower cost, small businesses 

could potentially experience a decline in profits and/or choose to exit the market. 

DOE provides additional analysis in section VI.B of the NOPR, Review Under 

the Regulatory Flexibility Act.

12.7 OTHER IMPACTS 

12.7.1 Direct Employment

12.7.1.1 Methodology

To quantitatively assess the impacts of an energy conservation standard on 

employment, DOE used the GRIM to estimate the domestic labor expenditures and 

number of employees in the base case and the standards case from 2014 through 2050. 

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 product 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 

12-21
the ASM). The production worker estimates in this section cover workers up to the linesupervisor level who are directly involved in fabricating and assembling a product 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.

12.7.1.2 Direct Employment Impacts

In the absence of energy conservation standards, DOE estimates that the hearth

industry would employ 1,565 domestic production workers in 2021. This estimate 

assumes U.S. production labor accounts for 86 percent of the industry total, a figure 

based on the percentage of domestic manufacturers identified as a share of total 

manufacturers identified for the industry. 

Table 12.7.1 shows the range of impacts of potential energy conservation 

standards on U.S. production workers of gas hearth products. The potential changes to 

direct employment presented suggest that the industry could experience anything from a 

slight loss in domestic direct employment to a loss of more than 900 domestic jobs.

Table 12.7.1 Potential Changes in the Total Number of Production Workers in the

Gas Hearth Industry in 2021

Base Case Standards Case

Domestic Production 

Workers in 2021 1,565 657 to 1,514

Potential Changes in 

Domestic Production 

Workers in 2021*

- (908) to (51)

*Parentheses indicate negative values

The less severe end of the range of potential employment impacts estimates a loss 

of 51 domestic production jobs in 2021 in the standards case. This assumes 

manufacturers would continue to produce the same scope of covered products within the 

United States. However, because the shipment model predicts a decline in shipment 

volumes under an energy conservation standard, DOE estimates a related reduction in 

labor inputs and employment. 

The more severe end of the range represents the maximum decrease in total 

number of U.S. production workers that could be expected to result from an energy 

12-22
conservation standard. For the hearths industry, DOE assumed a worst-case scenario in 

which all products sold with standing pilot lights in the base case would be eliminated in 

the standards case and would not be replaced by any additional sales of compliant 

products. DOE then assumed industry labor requirements would shrink in proportion to 

lost sales volumes. The NIA shipments analysis forecasts that 58 percent of base-case 

shipments would consist of units with standing pilot lights in 2021. Based on the worstcase scenario assumptions above, DOE modeled a 58-percent decline in direct production 

employment. As a result, DOE estimates a loss of up to 908 domestic production jobs in 

2021 resulting from a design standard that eliminates standing pilot lights.

The direct employment impacts discussed here do not include indirect

employment impacts on the broader U.S. economy, which are documented in the 

Employment Impact Analysis, chapter 16 of the TSD.

12.7.2 Production Capacity

According to gas hearth manufacturers interviewed, a standard eliminating

standing pilot lights would not likely constrain manufacturing production capacity. 

Converting a gas hearth product’s ignition system from a standing pilot light to electronic 

ignition is primarily a matter of purchasing and assembling different ignition system 

components. While this may entail higher costs for purchased parts and changes in 

assembly, it is not likely to impede manufacturers’ capacity to continue producing gas 

hearth products in line with demand. Moreover, several manufacturers stated that the 

higher costs of producing products with electronic ignition systems could lead to a 

decline in demand, potentially leaving them with excess production capacity. In that 

light, the proposed standard is not likely to trigger capacity constraints.

12.7.3 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 product 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 appliance 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 product offerings, potentially reducing competition. Smaller companies in particular 

can be affected by regulatory costs, as these companies have lower sales volumes over 

which they can amortize the costs of meeting new regulations. A proposed standard is not 

12-23
economically justified if it contributes to an unacceptable level of cumulative regulatory 

burden. 

For the cumulative regulatory burden analysis, DOE looks at other productspecific federal regulations that could affect gas hearth products and that will take effect 

approximately three years before or after the 2021 compliance date of the proposed

energy conservation standard. In interviews, manufacturers cited a Consumer Product 

Safety Commission regulation requiring barrier screens on gas hearth products. However, 

this requirement is set to take effect in January 2015 and therefore is not considered in 

this analysis. DOE did not identify any other federally mandated product-specific 

regulations that will take effect in the three years before or after the 2021 compliance 

date for this rulemaking, and therefore has not presented any other regulations in this 

analysis of cumulative regulatory burden.

12.8 CONCLUSION

The following section summarizes the range of financial impacts gas hearth

manufacturers are likely to experience as a result of energy conservation standards. 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. 

Table 12.8.1 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 12.4.7, the preservation of operating profit scenario 

accounts for the more severe impacts presented. Estimated conversion costs and free cash 

flow in the year prior to the effective date of standards do not vary with the markup 

scenario.

Table 12.8.1 Manufacturer Impact Analysis Results 

Units Base Case

$

Standards Case*

$

INPV 2013$M 125.3 122.0 to 125.8

Change in INPV 2013$M - (3.3) to 0.5

% Change - (2.6) to 0.4

Product Conversion 

Costs 2013$M - 7.8

Capital Conversion 

Costs 2013$M - 0.9

Total Conversion Costs 2013$M - 8.7

Free Cash Flow (2020) 2013$M 10.9 8.2

% Change - (24.0)

*Parentheses indicate negative values

12-24
Under a standards case that eliminates standing pilot lights, DOE estimates the 

impacts on INPV to range from -$3.3 million to $0.5 million, or a change of -2.6 percent 

to 0.4 percent. Industry free cash flow is estimated to decrease by $2.6 million, or a 

change of 24.0 percent compared to the base-case value of $10.9 million in the year 

before the compliance date (2020).

DOE estimates that in the year of compliance (2021), 42 percent of all gas hearth 

shipments will already be sold with electronic ignition systems. Because most 

manufacturers already offer units with electronic ignition systems, and because the 

conversion from standing pilot to electronic ignition primarily entails a change in 

purchased parts, DOE estimates limited capital conversion costs to achieve compliance 

with a new standard. Product conversion costs in the form of testing and certification 

costs and potential redesign requirements account for the bulk of estimated conversion 

costs. DOE estimates total industry conversion costs of $8.7 million under the proposed 

energy conservation standard. 

12-25
REFERENCES

1. U.S. Securities and Exchange Commission. Annual 10-K Reports. Various Years. 

<www.sec.gov>

2. Hoovers Inc. Company Profiles. Various Companies. <www.hoovers.com/>

3. U.S. Census Bureau, Annual Survey of Manufacturers: General Statistics: Statistics 

for Industry Groups and Industries (2010) (Available at: 

<http://www.census.gov/manufacturing/asm/index.html>) 

4. U.S. Small Business Association. Table of Small Business Size Standards. 2012. 

<http://www.sba.gov/content/table-small-business-size-standards>.

5. McKinsey & Company, Inc. Valuation: Measuring and Managing the Value of

 Companies, 3rd Edition, Copeland, Koller, Murrin. New York: John Wiley & Sons, 

2000.

12-26
13-i

CHAPTER 13. EMISSIONS IMPACT ANALYSIS

TABLE OF CONTENTS

13.1 INTRODUCTION ......................................................................................................... 13-1

13.2 AIR QUALITY REGULATIONS AND EMISSIONS IMPACTS............................... 13-2

13.3 POWER SECTOR AND SITE EMISSIONS FACTORS............................................. 13-3

13.4 UPSTREAM FACTORS ............................................................................................... 13-4

13.5 EMISSIONS IMPACT RESULTS................................................................................ 13-5

LIST OF TABLES

Table 13.3.1 Power Plant Emissions Factors.......................................................................... 13-3

Table 13.3.2 Natural Gas Site Combustion Emissions Factors .............................................. 13-4

Table 13.4.1 Electricity Upstream Emissions Factors............................................................ 13-5

Table 13.4.2 Natural Gas Upstream Emissions Factors.......................................................... 13-5

Table 13.5.1 Cumulative Emissions Reduction for Potential Standard for Hearth 

Product Ignition Devices.................................................................................... 13-6

LIST OF FIGURES

Figure 13.5.1 Hearth Product Ignition Devices: CO2 Total Emissions Reduction .................. 13-7

Figure 13.5.2 Hearth Product Ignition Devices: SO2 Total Emissions Reduction................... 13-7

Figure 13.5.3 Hearth Product Ignition Devices: NOx Total Emissions Reduction .................. 13-8

Figure 13.5.4 Hearth Product Ignition Devices: Hg Total Emissions Reduction .................... 13-8

Figure 13.5.5 Hearth Product Ignition Devices: N2O Total Emissions Reduction .................. 13-9

Figure 13.5.6 Hearth Product Ignition Devices: CH4 Total Emissions Reduction .................. 13-9
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).

For CH4 and N2O, DOE also presents results in terms of units of carbon dioxide 

equivalent (CO2e). Gases are converted to CO2e by multiplying the physical units by the gas

global warming potential (GWP) over a 100 year time horizon. Based on the Fifth Assessment 

Report of the Intergovernmental Panel on Climate Change,5 DOE used GWP values of 28 for 

CH4 and 265 for N2O.b

 a www.epa.gov/climateleadership/inventory/ghg-emissions.html

b The values are without inclusion of climate-carbon feedbacks in response to emissions of the indicated non-CO2 

gases.
13-2

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 

emissions cap on SO2 for affected EGUs in the 48 contiguous states and the District of Columbia 

(D.C.). SO2 emissions from 28 eastern states and D.C. were also limited under the Clean Air 

Interstate Rule (CAIR), which created an allowance-based trading program that that operates 

along with the Title IV program in those States and D.C. 70 FR 25162 (May 12, 2005). CAIR 

was remanded to EPA by the U.S. Court of Appeals for the District of Columbia Circuit (D.C. 

Circuit) but parts of it remained in effect. On July 6, 2011 EPA issued a replacement for CAIR, 

the Cross-State Air Pollution Rule (CSAPR). 76 FR 48208 (August 8, 2011). On August 21, 

2012, the D.C. Circuit issued a decision to vacate CSAPR. See EME Homer City Generation, LP 

v. EPA, 696 F.3d 7, 38 (D.C. Cir. 2012). The court ordered EPA to continue administering 

CAIR. The AEO 2014 emissions factors used for the present analysis assume that CAIR remains 

a binding regulation through 2040. c

The attainment of emissions caps is typically flexible among affected Electric Generating 

Units (EGUs) and is enforced through the use of emissions allowances and tradable permits. 

Under existing EPA regulations, any excess SO2 emissions allowances resulting from the lower 

electricity demand caused by the imposition of an efficiency standard could be used to permit 

offsetting increases in SO2 emissions by any regulated EGU. In past rulemakings, DOE 

recognized that there was uncertainty about the effects of efficiency standards on SO2 emissions 

covered by the existing cap-and-trade system, but it concluded that no reductions in power sector 

emissions would occur for SO2 as a result of standards.

Beginning in 2016, however, SO2 emissions will fall as a result of the Mercury and Air 

Toxics Standards (MATS) for power plants. 77 FR 9304 (Feb. 16, 2012). In the final MATS 

rule, EPA established a standard for hydrogen chloride as a surrogate for acid gas hazardous air 

pollutants (HAP), and also established a standard for SO2 (a non-HAP acid gas) as an alternative 

equivalent surrogate standard for acid gas HAP. The same controls are used to reduce HAP and 

non-HAP acid gas; thus, SO2 emissions will be reduced as a result of the control technologies 

installed on coal-fired power plants to comply with the MATS requirements for acid gas. AEO 

 c On April 29, 2014, the U.S. Supreme Court reversed the judgment of the D.C. Circuit and remanded the case for 

further proceedings consistent with the Supreme Court's opinion. The Supreme Court held in part that EPA's 

methodology for quantifying emissions that must be eliminated in certain states due to their impacts in other 

downwind states was based on a permissible, workable, and equitable interpretation of the Clean Air Act provision 

that provides statutory authority for CSAPR. See EPA v. EME Homer City Generation, No 12-1182, slip op. at 32 

(U.S. April 29, 2014). ). On October 23, 2014, the D.C. Circuit lifted the stay of CSAPR and CSAPR went into 

effect (and the CAIR sunset) in January 1, 2015. Because DOE is using emissions factors based on AEO 2014, the 

analysis assumes that CAIR, not CSAPR, is the regulation in force. The difference between CAIR and CSAPR is not 

relevant for the purpose of DOE's analysis of SO2 emissions.
13-3

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 

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 the NEMS-BT 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 heating in homes. The average factors for each year take into account 

the projected shares of each of the sources in total electricity generation. 

Table 13.3.2 presents the natural gas site combustion emissions factors for selected years.

Table 13.3.1 Power Plant Emissions Factors

Unit* 2021 2025 2030 2035 2040

CO2 kg/MWh 738 656 592 540 491

SO2 g/MWh 731 590 496 417 373

NOx g/MWh 585 497 434 382 345

Hg g/MWh 0.00225 0.00182 0.00153 0.00129 0.00115

N2O g/MWh 7.2 7.1 6.9 6.6 6.4

CH4 g/MWh 50.1 49.4 47.9 46.4 44.8
13-4

* Refers to site electricity savings.

Table 13.3.2 Natural Gas Site Combustion Emissions Factors

Unit* 2021 2025 2030 2035 2040

CO2 kg/mcf 54.2 54.2 54.2 54.2 54.2

SO2 g/ mcf 0.271 0.271 0.271 0.271 0.271

NOx g/ mcf 69.9 69.9 69.9 69.9 69.9

N2O g/ mcf 0.102 0.102 0.102 0.102 0.102

CH4 g/ mcf 1.022 1.022 1.022 1.022 1.022

* Refers to site gas savings.

13.4 UPSTREAM FACTORS 

The upstream emissions accounting uses the same approach as the upstream energy 

accounting described in appendix 10B. 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. The caps that apply to power sector NOX

emissions do not apply to upstream combustion sources.
13-5

Table 13.4.1 Electricity Upstream Emissions Factors

Unit 2021 2025 2030 2035 2040

CO2 kg/MWh 29.2 29.4 29.7 29.9 29.8

SO2 g/MWh 5.0 5.1 4.9 4.7 4.6

NOx g/MWh 370 375 382 387 387

Hg g/MWh 0.00001 0.00001 0.00001 0.00001 0.00001

N2O g/MWh 0.25 0.25 0.24 0.23 0.23

CH4 g/MWh 2,157 2,195 2,216 2,248 2,255

Table 13.4.2 illustrates the natural gas upstream emissions factors for selected years. 

These were used to estimate the emissions associated with the increased gas use at some of the 

considered efficiency levels.

Table 13.4.2 Natural Gas Upstream Emissions Factors

Unit 2021 2025 2030 2035 2040

CO2 kg/ mcf 7.2 7.2 7.3 7.4 7.4

SO2 g/ mcf 0.031 0.031 0.031 0.032 0.032

NOx g/ mcf 102 103 105 105 105

N2O g/ mcf 0.011 0.011 0.012 0.012 0.012

CH4 g/ mcf 661 665 666 670 670

13.5 EMISSIONS IMPACT RESULTS

Table 13.5.1 presents the estimated cumulative emissions reductions for the lifetime of 

products sold in 2021-2050 for each TSL. Negative values indicate that emissions increase.
13-6

Table 13.5.1 Cumulative Emissions Reduction for Potential Standard for Hearth 

Product Ignition Devices

TSL

1

Power Sector and Site Emissions

CO2 (million metric tons) 32.3

SO2 (thousand tons) -4.23

NOX (thousand tons) 49.2

Hg (tons) -0.0137

N2O (thousand tons) 0.279

CH4 (thousand tons) 0.00634

Upstream Emissions

CO2 (million metric tons) 4.78

SO2 (thousand tons) -0.0275

NOX (thousand tons) 75.8

Hg (tons) -0.00011

N2O (thousand tons) 485

CH4 (thousand tons) 0.00629

Total Emissions

CO2 (million metric tons) 37.0

SO2 (thousand tons) -4.26

NOX (thousand tons) 125

Hg (tons) -0.0138

N2O (thousand tons) 486

CH4 (thousand tons) 0.0126

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 in 2021-2050.
13-7

Figure 13.5.1 Hearth Product Ignition Devices: CO2 Total Emissions 

Reduction

Figure 13.5.2 Hearth Product Ignition Devices: SO2 Total Emissions 

Reduction

0

200

400

600

800

1,000

1,200

1,400

2021202620312036204120462051205620612066207120762081208620912096

kt

CO2 Emissions Reduction

TSL 1

-140

-120

-100

-80

-60

-40

-20

0

2021202620312036204120462051205620612066207120762081208620912096

ton

SO2 Emissions Reduction

TSL 1
13-8

Figure 13.5.3 Hearth Product Ignition Devices: NOx Total Emissions 

Reduction

Figure 13.5.4 Hearth Product Ignition Devices: Hg Total Emissions 

Reduction

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

2021202620312036204120462051205620612066207120762081208620912096

ton

NOx Emissions Reduction

TSL 1

-0.0005

-0.0004

-0.0004

-0.0003

-0.0003

-0.0002

-0.0002

-0.0001

-0.0001

0.0000

2021202620312036204120462051205620612066207120762081208620912096

ton

Hg Emissions Reduction

TSL 1
13-9

Figure 13.5.5 Hearth Product Ignition Devices: N2O Total Emissions 

Reduction

Figure 13.5.6 Hearth Product Ignition Devices: CH4 Total Emissions 

Reduction

0.0

0.1

0.1

0.2

0.2

0.3

0.3

0.4

0.4

0.5

0.5

2021202620312036204120462051205620612066207120762081208620912096

ton

N2OEmissions Reduction

TSL 1

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

2021202620312036204120462051205620612066207120762081208620912096

ton

CH4 Emissions Reduction

TSL 1
13-10

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. 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. 
14-i

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

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 Hearth Product Ignition Devices ........................................................ 14-5

Table 14.5.2 Estimates of Domestic Present Value of CO2 Emissions Reduction under 

TSLs for Hearth Product Ignition Devices ........................................................ 14-6

Table 14.5.3 Estimates of Present Value of NOX Emissions Reduction under TSLs for 

Hearth Product Ignition Devices........................................................................ 14-6
14-1

CHAPTER 14. MONETIZATION OF EMISSION REDUCTIONS BENEFITS

14.1 INTRODUCTION

As part of its assessment of the effects of potential energy conservation standards for 

hearth product ignition devices, 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 hearth products. 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 
14-2

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.

14.2.2 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 
14-3

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.

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 
14-4

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 

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 2013$. For the four SCC values, the values of emissions in 2015 

were $12.0, $40.5, $62.4, and $119 per metric ton avoided (values expressed in 2013$). 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 
14-5

values, DOE discounted the values in each of the four cases using the same discount rate that had 

been used to obtain the SCC values in each case.

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 $476 to $4,893 per ton (in 2013$).5

DOE calculated monetary benefits using a median value for NOX emissions of $2,684 per short 

ton (in 2013$), 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. 

Table 14.5.1 Estimates of Global Present Value of CO2 Emissions Reduction under 

TSLs for Hearth Product Ignition Devices

TSL

SCC Case*

5% discount

rate, average*

3% discount

rate, average*

2.5% discount

rate, average*

3% discount rate,

95th percentile*

Million 2013$

Primary Energy Emissions

1 196 956 1535 2966

Upstream Emissions

1 29 142 228 440

Full-Fuel-Cycle Emissions

1 226 1098 1763 3405

* For each of the four cases, the corresponding global SCC value for emissions in 2015 is $12.0, 

$40.5, $62.4, and $119 per metric ton (2013$).

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 Hearth Product Ignition Devices

TSL

SCC Case*

5% discount

rate, average*

3% discount

rate, average*

2.5% discount

rate, average*

3% discount rate,

95th percentile*

Million 2013$

Primary Energy Emissions

1 13.7 to 45.2 66.9 to 220.0 107.5 to 353.1 207.6 to 682.1

Upstream Emissions

1 2.0 to 6.7 9.9 to 32.6 15.9 to 52.3 30.8 to 101.1

Full-Fuel-Cycle Emissions

1 15.8 to 51.9 76.9 to 252.6 123.4 to 405.4 238.4 to 783.2

* For each of the four cases, the corresponding SCC value for emissions in 2015 is $12.0, $40.5, 

$62.4, and $119 per metric ton (2013$).

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.

Table 14.5.3 Estimates of Present Value of NOX Emissions Reduction under TSLs for 

Hearth Product Ignition Devices

TSL

3% discount rate 7% discount rate

Million 2013$

Primary Energy Emissions

1 58 23

Upstream Emissions

1 90 35

Full-Fuel-Cycle Emissions

1 148 58
14-7

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 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>
15-i

CHAPTER 15. UTILITY IMPACT ANALYSIS

TABLE OF CONTENTS

15.1 INTRODUCTION ......................................................................................................... 15-1

15.2 METHODOLOGY ........................................................................................................ 15-2

15.3 UTILITY IMPACT RESULTS ..................................................................................... 15-4

15.3.1 Installed Capacity........................................................................................................... 15-4

15.3.2 Electricity Generation .................................................................................................... 15-7

15.3.3 Results Summary ......................................................................................................... 15-11

LIST OF TABLES

Table 15.3.1 Hearth Product Ignition Devices: Summary of Utility Impact Results ........... 15-11

LIST OF FIGURES

Figure 15.3.1 Hearth Product Ignition Devices: Total Electric Capacity Reduction............... 15-4

Figure 15.3.2 Hearth Product Ignition Devices: Coal Capacity Reduction ............................. 15-5

Figure 15.3.3 Hearth Product Ignition Devices: Nuclear Capacity Reduction ........................ 15-5

Figure 15.3.4 Hearth Product Ignition Devices: Gas Combined Cycle Capacity 

Reduction ........................................................................................................... 15-6

Figure 15.3.5 Hearth Product Ignition Devices: Peaking Capacity Reduction........................ 15-6

Figure 15.3.6 Hearth Product Ignition Devices: Renewables Capacity Reduction.................. 15-7

Figure 15.3.7 Hearth Product Ignition Devices: Total Generation Reduction......................... 15-8

Figure 15.3.8 Hearth Product Ignition Devices: Coal Generation Reduction.......................... 15-8

Figure 15.3.9 Hearth Product Ignition Devices: Nuclear Generation Reduction..................... 15-9

Figure 15.3.10 Hearth Product Ignition Devices: Gas Combined Cycle Generation 

Reduction ........................................................................................................... 15-9

Figure 15.3.11 Hearth Product Ignition Devices: Oil Generation Reduction .......................... 15-10

Figure 15.3.12 Hearth Product Ignition Devices: Renewables Generation Reduction............ 15-10
15-1

CHAPTER 15. UTILITY IMPACT ANALYSIS

15.1 INTRODUCTION

In the utility impact analysis for hearth product ignition devices, the U.S. Department of 

Energy (DOE) analyzed the changes in electric installed capacity and power generation that 

result for each trial standard level (TSL). 

The utility impact analysis is based on output of the DOE/Energy Information 

Administration (EIA)’s National Energy Modeling System (NEMS).a NEMS is a public domain, 

multi-sectored, partial equilibrium model of the U.S. energy sector. Each year, DOE/EIA uses 

NEMS to produce an energy forecast for the United States, the Annual Energy Outlook (AEO). 

The EIA publishes a reference case, which incorporates all existing energy-related policies at the 

time of publication, and a variety of side cases which analyze the impact of different policies, 

energy price and market trends. As of 2014, DOE is using a new methodology based on results 

published for the Annual Energy Outlook 2014 (AEO 2014) Reference case and a set of side 

cases that implement a variety of efficiency-related policies.

2

The new approach retains key aspects of DOE’s previous methodology, and provides 

some improvements:

• The assumptions used in the AEO reference case and side cases are fully 

documented and receive detailed public scrutiny.

• NEMS is updated each year, with each edition of the AEO, to reflect changes in 

energy prices, supply trends, regulations, etc.

• The comprehensiveness of NEMS permits the modeling of interactions among 

the various energy supply and demand sectors. 

• Using EIA published side cases to estimate the utility impacts enhances the 

transparency of DOE’s analysis.

• The variability in impacts estimates from one edition of AEO to the next will be 

reduced under the new approach.

On average, however, over the full analysis period, the results from the new approach are 

comparable to results from the old approach.

 a For more information on NEMS, refer to the U.S. Department of Energy, Energy Information Administration 

documentation. A useful summary is National Energy Modeling System: An Overview.

1
15-2

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. 

NEMS uses predicted growth in demand for each end use to build up a projection of the 

total electric system load growth. The system load shapes are converted internally to load 

duration curves, which are then used to estimate the most cost-effective additions to capacity. 

When electricity demand deviates from the AEO reference case, in general there are three interrelated effects: the annual generation (TWh) from the stock of electric generating capacity 

changes, the total generation capacity itself (GW) may change, and the mix of capacity by fuel 

type may change. Each of these effects can vary for different types of end use. The change in 

total generating capacity is sensitive to the degree to which the end-use is peak coincident, while

the capacity mix is sensitive to the hourly load shape associated with the end use. Changes in 

generation by fuel type lead in turn to changes in total power sector emissions of SO2, NOx, Hg 

and CO2.

DOE’s new approach examines a series of AEO side cases to estimate the relationship 

between demand reductions and the marginal energy, emissions and capacity changes. The 

assumptions for each side case are documented in Appendix E of the AEO. The side cases, or 

scenarios, that incorporate significant changes to equipment efficiencies relative to the Reference 

case are: 

• 2013 Technology (leaves all technologies at 2013 efficiencies);

• Best Available Technology (highest efficiency irrespective of cost);

• High Technology (higher penetration rates for efficiency and demand management);

• Extended Policies (includes efficiency standards that are not in the reference).

Scenarios that incorporate policies that directly affect the power sector without changes 

in energy demand (for example, subsidies for renewables, or high fuel price assumptions) are not 

appropriate for this analysis. The methodology proceeds in seven steps:

1. Supply-side data on generation, capacity and emissions, and demand-side data on electricity 

use by sector and end-use, are extracted from each side case. The data are converted to 

differences relative to the AEO Reference case.

2. The changes in electricity use on the demand-side data are allocated to one of three 

categories: on-peak, shoulder, and off-peak. These categories are used in the utility sector to 

correlate end-use consumption with supply types. For each of the end-uses that are modeled 

explicitly in NEMS, load shape information is used to identify the fraction of annual 

electricity use assigned to each category. On-peak hours are defined as noon-5pm, June 

through September. Off-peak hours are nights and Sundays. All other hours are assigned to 

the shoulder period.
15-3

3. For each year and each side case, the demand-side reductions to on-peak, off-peak and 

shoulder-period electricity use are matched on the supply-side to reductions in generation by 

fuel type. The fuel types are petroleum fuels, natural gas, renewables, nuclear and coal. The 

allocation is based on the following rules:

3.1. All petroleum-based generation is allocated to peak periods;

3.2. Natural gas generation is allocated to any remaining peak reduction; this is consistent 

with the fact that oil and gas steam units are used in NEMS to meet peak demand;

3.3. Base-load generation (nuclear and coal) is allocated proportionally to all periods;

3.4. The remaining generation of all types is allocated to the remaining off-peak and shoulder 

reductions proportionally.

4. The output of step 3 defines fuel-share weights giving the fraction of energy demand in each 

load category that is met by each fuel type as a function of time. These are combined with the 

weights that define the load category shares by end-use to produce coefficients that allocate a 

marginal reduction in end-use electricity demand to each of the five fuel types.

5. A regression model is used to relate reductions in generation by fuel type to reductions in 

emissions of power sector pollutants. The model produces coefficients that define the change 

in total annual emissions of a given pollutant resulting from a unit change in total annual 

generation for each fuel type, as a function of time. These coefficients are combined with the 

weights calculated in step 4 to produce coefficients that relate emissions changes to changes 

in end-use demand.

6. A regression model is used to relate reductions in generation by fuel type to reductions in 

installed capacity. The categories used for installed capacity are the same as for generation 

except for peak: NEMS uses two peak capacity types (combustion turbine/diesel and oil and 

gas steam) which are combined here into a single “peak” category. The model produces 

coefficients that define the change in total installed capacity of a given type resulting from a

unit change in total annual generation for the corresponding fuel type. These coefficients are 

combined with the weights calculated in step 4 to produce coefficients that relate installed 

capacity changes to changes in end-use demand, as a function of time.

7. The coefficient time-series for fuel share, pollutant emissions and capacity for the 

appropriate end use are multiplied by the stream of energy savings calculated in the NIA to 

produce estimates of the utility impacts.

This analysis ignores pumped storage, fuel cells and distributed generation, as these 

generation types are not affected by the policy changes modeled in the EIA side cases. The 

methodology is described in more detail in K. Coughlin, “Utility Sector Impacts of Electricity 

Demand Reductions”.

3
15-4

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. Note that a negative number means an 

increase in capacity under a TSL.

Figure 15.3.1 Hearth Product Ignition Devices: Total Electric Capacity 

Reduction

-90

-80

-70

-60

-50

-40

-30

-20

-10

0

2021202620312036204120462051205620612066207120762081208620912096

MW

Total

TSL 1
15-5

Figure 15.3.2 Hearth Product Ignition Devices: Coal Capacity Reduction

Figure 15.3.3 Hearth Product Ignition Devices: Nuclear Capacity Reduction

-20.0

-18.0

-16.0

-14.0

-12.0

-10.0

-8.0

-6.0

-4.0

-2.0

0.0

2021202620312036204120462051205620612066207120762081208620912096

MW

Coal

TSL 1

-2.50

-2.00

-1.50

-1.00

-0.50

0.00

2021202620312036204120462051205620612066207120762081208620912096

MW

Nuclear

TSL 1
15-6

Figure 15.3.4 Hearth Product Ignition Devices: Gas Combined Cycle 

Capacity Reduction

Figure 15.3.5 Hearth Product Ignition Devices: Peaking Capacity Reduction

-40

-35

-30

-25

-20

-15

-10

-5

0

2021202620312036204120462051205620612066207120762081208620912096

MW

NGCC

TSL 1

-1.20

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

2021202620312036204120462051205620612066207120762081208620912096

MW

Peaking

TSL 1
15-7

Figure 15.3.6 Hearth Product Ignition Devices: 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.

-20.0

-18.0

-16.0

-14.0

-12.0

-10.0

-8.0

-6.0

-4.0

-2.0

0.0

2021202620312036204120462051205620612066207120762081208620912096

MW

Renewable

TSL 1
15-8

Figure 15.3.7 Hearth Product Ignition Devices: Total Generation Reduction

Figure 15.3.8 Hearth Product Ignition Devices: Coal Generation Reduction

-400

-350

-300

-250

-200

-150

-100

-50

0

2021202620312036204120462051205620612066207120762081208620912096

GWh

Total

TSL 1

-120

-100

-80

-60

-40

-20

0

2021202620312036204120462051205620612066207120762081208620912096

GWh

Coal

TSL 1
15-9

Figure 15.3.9 Hearth Product Ignition Devices: Nuclear Generation 

Reduction

Figure 15.3.10 Hearth Product Ignition Devices: Gas Combined Cycle

Generation Reduction

-16.0

-14.0

-12.0

-10.0

-8.0

-6.0

-4.0

-2.0

0.0

2021202620312036204120462051205620612066207120762081208620912096

GWh

Nuclear

TSL 1

-200

-180

-160

-140

-120

-100

-80

-60

-40

-20

0

2021202620312036204120462051205620612066207120762081208620912096

GWh

NGCC

TSL 1
15-10

Figure 15.3.11 Hearth Product Ignition Devices: Oil Generation Reduction

Figure 15.3.12 Hearth Product Ignition Devices: Renewables Generation 

Reduction

-0.025

-0.020

-0.015

-0.010

-0.005

0.000

2021202620312036204120462051205620612066207120762081208620912096

GWh

Oil

TSL 1

-70

-60

-50

-40

-30

-20

-10

0

2021202620312036204120462051205620612066207120762081208620912096

GWh

Renewable

TSL 1
15-11

15.3.3 Results Summary 

Table 15.3.1 presents a summary of the utility impact results for hearth product ignition 

devices.

Table 15.3.1 Hearth Product Ignition Devices: Summary of Utility Impact Results

Year TSL

1

Installed Capacity Reduction (MW)

2021 -3.58

2025 -17.8

2030 -40.3

2035 -61.0

2040 -74.6

Electricity Generation Reduction (GWh)

2021 -21.6

2025 -111

2030 -226

2035 -312

2040 -349
15-12

REFERENCES

1. U.S. Department of Energy-Energy Information Administration, National Energy 

Modeling System: An Overview, 2009. Report No. DOE/EIA-0581(2009). 

<www.eia.gov/oiaf/aeo/overview/>

2. U.S. Department of Energy-Energy Information Administration, Annual Energy Outlook 

2014 with Projections to 2040, 2014. Washington, DC. <www.eia.gov/forecasts/aeo/>

3. Coughlin, K., Utility Sector Impacts of Reduced Electricity Demand, 2014. Lawrence 

Berkeley National Laboratory. Report No. LBNL-6864E. 
16-i

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 RESULTS.............................................................................................. 16-3

LIST OF TABLES

Table 16.4.1 Net National Short-term Change in Employment (Number of Jobs) ................ 16-3
16-1

CHAPTER 16. EMPLOYMENT IMPACT ANALYSIS

16.1 INTRODUCTION

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 hearth products. Job increases or decreases reported in 

this chapter are separate from the direct hearth product production sector employment impacts 

reported in the manufacturer impact analysis (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 products, 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 for 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. Because input/output models do not allow prices to bring markets into 

equilibrium, they are best used for short-run analysis. DOE therefore include 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 ImBuild,

3 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 more complete and automated analysis of the 

economic impacts of energy-efficiency investments in buildings.
16-2

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 so 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 affects the overall level of employment.

ImSET uses a 187-sector model of the national economy to predict the economic effects 

of residential and commercial buildings 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 products. The increased cost of products leads to higher employment in 

the product 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 for energy-efficient products. Third, investment funds are released from the 

utility and energy production sectors for use in other sectors of the economy. When consumers 

use less energy, utilities and energy producers experience relative reductions in demand which 

leads to reductions in employment in those sectors.

DOE also notes that the employment impacts estimated with ImSET for the entire 

economy differ from the employment impacts in the hearth product 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 a hearth product ignition device standard 

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. This section presents the summary impact.

Conceptually, one can consider the impact of the rule in its first year on three aggregate 

sectors, the hearth product production sector, the energy generation sector, and the general 

consumer goods sector (as mentioned above, ImSET’s calculations are made at a much more 

disaggregate level). By raising energy efficiency, the rule generally increases the purchase price 

of hearth products; 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 
16-3

energy, freeing up this money to be spent in other sectors. The reduction in energy demand 

causes a reduction in employment in that sector. Finally, based on the net impact of increased 

expenditures on hearth products and reduced expenditures on energy, 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, which generates more employment, the converse is true for workers laid off).

Table 16.4.1 present the modeled net employment impact from the rule in 2021, rounded 

to the nearest ten jobs. The proposed standard is projected to slightly increase employment from 

2021 to 2026. Virtually all hearth products are domestically produced, so DOE does not consider 

imports in this analysis.

Table 16.4.1 Net National Short-term Change in Employment (Number of Jobs)

Trial Standard Level 2021 2026

TSL 1 80 150

For context, the Office of Management of Budget currently assumes that the 

unemployment rate may decline to 6.0 percent during 2014 and drop further to 5.4 percent by 

2017.5 The unemployment rate in 2021 is projected to be 5.4 percent, which is 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 RESULTS

Over the long term DOE expects the energy savings to consumers to increasingly 

dominate the increase in product costs, resulting in increased aggregate savings to consumers. As 

a result, DOE expects demand for energy to decline over time and demand for other goods to 

increase. Because the utility and energy production sectors are 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 

utilities and energy producers towards consumer goods. Note that in 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 in general be negligible over time due to the small magnitude of 

the short-term effects presented in Table 16.4.1 for most product classes and TSLs. The ImSET 

model projections, assuming no price or wage effects until 2026, 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. <www.pnl.gov/main/publications/external/technical_reports/PNNL15273.pdf>

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. Prepared for the U.S. Department of Energy under Contract 

DE-AC06-76RLO 1830. 

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 2014 Mid-Session Review: Budget of the 

U.S. Government, 

<www.whitehouse.gov/sites/default/files/omb/budget/fy2014/assets/14msr.pdf>
17-i

CHAPTER 17. REGULATORY IMPACT ANALYSIS

TABLE OF CONTENTS

17.1 INTRODUCTION ................................................................................................... 17-1

17.2 NON-REGULATORY POLICIES.......................................................................... 17-1

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 ................................................ 17-5

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........................................................................................... 17-12

17.3.4 Manufacturer Tax Credits...................................................................................... 17-14

17.3.5 Voluntary Energy Efficiency Targets.................................................................... 17-15

17.3.6 Bulk Government Purchases.................................................................................. 17-17

17.4 IMPACTS OF NON-REGULATORY ALTERNATIVES................................... 17-18

LIST OF TABLES

Table 17.1.1 Non-Regulatory Alternatives to National Standards...................................... 17-1

Table 17.2.1 Relative Energy Savings for Trial Standard Level 1 for Hearth Product 

Ignition Devices.............................................................................................. 17-4

Table 17.3.1 Benefit/Cost Ratios Without and With Rebates for Hearth Product 

Ignition Devices.............................................................................................. 17-7

Table 17.3.2 Market Penetrations in 2021 Attributable to Consumer Rebates for 

Hearth Product Ignition Devices................................................................... 17-11

Table 17.3.3 Market Penetrations in 2021 Attributable to Consumer Tax Credits for 

Hearth Product Ignition Devices................................................................... 17-13

Table 17.3.4 Market Penetrations in 2021 Attributable to Manufacturer Tax Credits 

for Hearth Product Ignition Devices............................................................. 17-15

Table 17.3.5 Market Barriers Changes Attributable to Voluntary Energy Efficiency 

Targets for Hearth Product Ignition Devices (TSL1)................................... 17-16

Table 17.3.6 Market Penetrations Attributable to Voluntary Energy Efficiency 

Targets for Hearth Product Ignition Devices (TSL1)................................... 17-17

Table 17.4.1 Impacts of Non-Regulatory Alternatives for Hearth Product Ignition 

Devices (TSL 1)............................................................................................ 17-21

LIST OF FIGURES

Figure 17.3.1 Market Penetration Curves for Hearth Product Ignition Devices (TSL 1)... 17-11

Figure 17.4.1 Market Penetration of Efficient Hearth Product Ignition Devices: 

Fireplace (vented) (TSL 1) ........................................................................... 17-18

Figure 17.4.2 Market Penetration of Efficient Hearth Product Ignition Devices: 

Fireplace (ventless) (TSL 1) ......................................................................... 17-19
17-ii

Figure 17.4.3 Market Penetration of Efficient Hearth Product Ignition Devices: Logs 

(vented) (TSL 1) ........................................................................................... 17-19

Figure 17.4.4 Market Penetration of Efficient Hearth Product Ignition Devices: Logs 

(ventless) (TSL 1)......................................................................................... 17-20

Figure 17.4.5 Market Penetration of Efficient Hearth Product Ignition Devices: 

Outdoor (TSL 1) ........................................................................................... 17-20
17-1

CHAPTER 17. REGULATORY IMPACT ANALYSIS

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. DOE identified five non-regulatory 

policy alternatives that possibly could provide incentives for the same energy efficiency level as 

the one in the proposed trial standard level for the hearth product ignition devices that 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 proposed standard for each of five types of 

hearth product ignition devices covered by this rulemaking. 

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 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 hearth product ignition devices. This 

section also describes the assumptions underlying the analysis. 
17-2

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 17A discusses the NIA-RIA integrated model approach.

DOE quantified the effect of each alternative on the purchase of equipment that meet the 

efficiency level corresponding to the proposed 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 level set for the proposed TSL. The shipments of equipment for any given 

year reflect a distribution of efficiency levels. DOE assumed, for the proposed TSL, that new 

energy efficiency standards would affect 100 percent of the shipments of products 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 hearth product ignition devices

attributable to each policy alternative.

Increasing the efficiency of a product 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 proposed 

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 energy saved over the lifetime of equipment purchased during the 

30-year analysis period starting in the effective date of the policy (2021-2050).

• Net Present Value (NPV), represents the value of net monetary savings in 2014, 

expressed in 2013$, from equipment purchased during the 30-year analysis period 

starting in the effective date of the policy (2021-2050). 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 the 

product.

 

a The base case for the NIA is a market-weighted average energy efficiency calculated from units at several 

efficiency levels.
17-3

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 hearth product ignition devices relative to their base case efficiency scenario (which 

involves no new regulatory action). The analysis considered that each alternative policy would 

induce consumers to purchase units having the same technology as required by the proposed 

standards (the target level) set for the proposed 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 relative energy savings from the technology stipulated in the 

proposed standard for hearth product ignition devices.
17-4

Table 17.2.1 Relative Energy Savings for Trial Standard Level 1 for Hearth Product 

Ignition Devices

Technology Annual Energy Consumption

Natural Gas

mcf

Electricity

kWh

Fireplace (vented)

Standing Pilot 3.9 0.0

Intermittent Pilot 0.5 13.6

Relative Energy Savings 87.5% n/a

Fireplace (ventless)

Standing Pilot 3.4 0.0

Intermittent Pilot 1.3 99.4

Relative Energy Savings 63.0% n/a

Logs (vented)

Standing Pilot 3.1 0.0

Intermittent Pilot 0.3 5.8

Relative Energy Savings 90.8% n/a

Logs (ventless)

Standing Pilot 2.2 0.0

Intermittent Pilot 0.9 70.6

Relative Energy Savings 59.6% n/a

Outdoor

Standing Pilot 3.8 0.0

Intermittent Pilot 0.0 0.2

Relative Energy Savings 100.0% n/a

DOE assumed that the effects of non-regulatory policies would last from the effective 

date of standards—2021—through the end of the analysis period, which is 2050.

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 hearth product ignition devices.
17-5

17.3 NON-REGULATORY POLICY ASSUMPTIONS

The following subsections describe DOE’s analysis of the impacts of the five nonregulatory policy alternatives to proposed standards for hearth product ignition devices. (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 products both 

with and without each of the non-regulatory policy alternatives.

17.3.1 No New Regulatory Action

The case in which no new regulatory action is taken with regard to the energy efficiency 

of hearth product ignition devices 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 hearth product ignition devices that operate at 

the same efficiency as stipulated in the proposed TSL. 

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.5

DOE 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 

 

b XENERGY is now owned by KEMA, Inc. (www.kema.com)
17-6

through word-of-mouth from early adopters. External sources affect consumer purchase 

decisions through marketing efforts and information from outside the consumer group. Appendix 

17A 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 

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 hearth product ignition devices

by determining, for the proposed 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 hearth product ignition devices. It gathered data on utility 

or agency rebates throughout the nation for this equipment, and used this data to calibrate the 

customized penetration curve it developed for hearth product ignition devices so they can best 

reflect the market barrier level faced by hearth product ignition devices. Section 17.3.2.2 shows 

the interpolated curve used in the analysis.

17.3.2.2 Analysis 

DOE estimated the effect of increasing the B/C ratio of hearth product ignition devices

via a rebate that would pay 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 hearth product ignition devices. It gathered data from a sample of utility and 

agency rebate programs that includes 8 rebates for hearth product ignition devices initiated by 6

utilities or agencies in various States. (Appendix 17A identifies the rebate programs.) DOE then 

estimated a market average rebate value as the simple average calculated over the rebate amounts 

offered by the existing rebate programs. Since the existing rebate programs target hearth product

ignition device units as a whole, DOE further scaled down the market average rebate value it 

calculated using the ratio of the price of an ignition module and the (full) price of a hearth 

 

c The baseline technology for hearth products 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

product ignition device. DOE assumed that rebates would remain in effect at the same level 

throughout the forecast period (2021-2050).

DOE first calculated the B/C ratio of a hearth product ignition device without a rebate 

using the difference in total installed costs (C) and lifetime operating cost savingsd (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 for the proposed TSL on the B/C ratio of hearth product ignition devices

shipped in the first year of the analysis period. Note that because ventless hearth product ignition 

devices present negative benefits and benefit-cost ratios, DOE did not assess the impacts from 

consumer rebates for those types of hearth product ignition devices. 

Table 17.3.1 Benefit/Cost Ratios Without and With Rebates for Hearth Product 

Ignition Devices

TSL 1

Fireplace (vented)

B/C Ratio Without Rebate 4.2

Rebate Amount (2013$) 37.03

B/C Ratio With Rebate 7.3

Estimated Market Barriers No-Low

Fireplace (ventless)

B/C Ratio Without Rebate 4.0

Rebate Amount (2013$) 37.03

B/C Ratio With Rebate 6.5

Estimated Market Barriers Mod

Logs (vented)

B/C Ratio Without Rebate 1.6

Rebate Amount (2013$) 37.03

B/C Ratio With Rebate 2.1

Estimated Market Barriers Low-Mod

Logs (ventless)

B/C Ratio Without Rebate 1.6

Rebate Amount (2013$) 37.03

B/C Ratio With Rebate 2.2

Estimated Market Barriers Low-Mod

Outdoor

B/C Ratio Without Rebate 3.2

Rebate Amount (2013$) 37.03

B/C Ratio With Rebate 4.2

Estimated Market Barriers No-Low

 d The cash flow of the operating cost savings is discounted to the purchase year using a 7 percent discount rate.
17-8

DOE used the B/C ratio along with the penetration curves shown in Table 17.3.1 to 

estimate the percentage of consumers who would purchase hearth product ignition devices that 

meet the target level both with and without a rebate incentive. The estimated level of market 

barriers corresponding to the penetration curves DOE calculated to represent the market behavior 

for hearth product ignition devices at the proposed TSL are indicated in Table 17.3.1. DOE 

assumed the estimated market barriers would remain the same over the whole analysis period.
17

-9

TSL 1

Fireplace (vented) Fireplace (ventless)
17

-10

Logs (vented) Logs (ventless) Outdoor
17-11

Figure 17.3.1 Market Penetration Curves for Hearth Product Ignition 

Devices (TSL 1)

DOE next estimated the percent increase represented by the change in penetration rate 

shown on the corresponding penetration curve. It then added this 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. 

Table 17.3.2 summarizes DOE’s assumptions for hearth product ignition devices

regarding the market penetration of products in 2021 that meet the target level at the proposed 

TSL given a consumer rebate. 

Table 17.3.2 Market Penetrations in 2021 Attributable to Consumer Rebates for 

Hearth Product Ignition Devices

TSL 1

Fireplace (vented)

Base-Case Market Share 57.8%

Policy Case Market Share 66.6%

Increased Market Share 8.8%

Fireplace (ventless)

Base-Case Market Share 12.3%

Policy Case Market Share 23.5%

Increased Market Share 11.2%

Logs (vented)

Base-Case Market Share 12.7%

Policy Case Market Share 17.3%

Increased Market Share 4.6%

Logs (ventless)

Base-Case Market Share 5.7%

Policy Case Market Share 10.0%

Increased Market Share 4.3%

Outdoor

Base-Case Market Share 47.8%

Policy Case Market Share 53.6%

Increased Market Share 5.8%

DOE used the resulting annual increases in market shares as inputs to represent the rebate 

policy case scenario in its NIA-RIA model. Appendix 17A 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 hearth product ignition devices. Because energy prices 

increase over the analysis period and equipment price is assumed constant, the B/C ratio 

increases over time. With increasing B/C ratio and constant market barriers, the increase in 

market penetration of a more efficient technology also increases over the analysis period.
17-12

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 hearth product ignition devices, 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, 15The 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 

hearth product ignition devices to warrant adjusting its analysis method for the Consumer Tax 

Credits policy case. Appendix 17A 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 

shares of ultra-high efficiency (UHE) residential clothes washers in Oregon, which offered both 
17-13

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 17A.

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 hearth product ignition devices.

Table 17.3.3 summarizes DOE’s assumptions for hearth product ignition devices

regarding the market penetration of units in 2021 that meet the efficiency level at the proposed

TSL given a consumer tax credit. 

Table 17.3.3 Market Penetrations in 2021 Attributable to Consumer Tax Credits for 

Hearth Product Ignition Devices

TSL 1

Fireplace (vented)

Base-Case Market Share 57.8%

Policy Case Market Share 63.1%

Increased Market Share 5.3%

Fireplace (ventless)

Base-Case Market Share 12.3%

Policy Case Market Share 19.0%

Increased Market Share 6.7%

Logs (vented)

Base-Case Market Share 12.7%

Policy Case Market Share 15.5%

Increased Market Share 2.8%

Logs (ventless)

Base-Case Market Share 5.7%

Policy Case Market Share 8.3%

Increased Market Share 2.6%

Outdoor

Base-Case Market Share 47.8%

Policy Case Market Share 51.3%

Increased Market Share 3.5%

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 17A 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 hearth product ignition devices that meet the efficiency 
17-14

level for the proposed TSL. Because the increase in market penetration for consumer tax credits 

is proportional to the increase in market penetration DOE calculated for consumer rebates, the 

former follows a similar increasing trend over the analysis period as the latter.

17.3.4 Manufacturer Tax Credits

To analyze the potential effects of a policy that offers tax credits to manufacturers that 

produce hearth product ignition devices that meet the target efficiency level at the proposed 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 be visible to consumers.e 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 products. 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 17A 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 hearth product ignition devices.

Table 17.3.4 summarize DOE’s assumptions for hearth product ignition devices

regarding the market penetration of units in 2021 meeting the efficiency level the proposed TSL 

given a manufacturer tax credit.

 

e 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.
17-15

Table 17.3.4 Market Penetrations in 2021 Attributable to Manufacturer Tax Credits

for Hearth Product Ignition Devices

TSL 1

Fireplace (vented)

Base-Case Market Share 57.8%

Policy Case Market Share 60.5%

Increased Market Share 2.6%

Fireplace (ventless)

Base-Case Market Share 12.3%

Policy Case Market Share 15.7%

Increased Market Share 3.3%

Logs (vented)

Base-Case Market Share 12.7%

Policy Case Market Share 14.1%

Increased Market Share 1.4%

Logs (ventless)

Base-Case Market Share 5.7%

Policy Case Market Share 7.0%

Increased Market Share 1.3%

Outdoor

Base-Case Market Share 47.8%

Policy Case Market Share 49.6%

Increased Market Share 1.7%

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 17A 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 hearth product ignition devices. Because the increase 

in market penetration for manufacturer tax credits is proportional to the increase in market 

penetration DOE calculated for consumer rebates, the former follows a similar increasing trend 

over the analysis period as the latter.

17.3.5 Voluntary Energy Efficiency Targets 

DOE assumed that voluntary energy efficiency targets would be achieved as 

manufacturers of hearth product ignition devices gradually stopped producing units that operated 

below the efficiency level set for the proposed 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 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 
17-16

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 products over time. During the rebate analysis, when assessing the B/C 

ratio and market penetration in the base case for hearth product ignition devices, DOE observed 

that the level of market barriers for more efficient hearth product ignition devices are in the range 

of no-to-low barriers to a moderate level of market barriers. DOE estimates that voluntary energy 

efficiency targets could reduce these barriers to lower levels over 10 years. Table 17.3.5 presents 

the levels of market barriers DOE estimated for hearth product ignition devices in the base case 

and in the policy case of voluntary energy efficiency targets. DOE followed the methodology 

presented by Blum et al (2011)22 to evaluate the effects that such a reduction in market barriers 

would have on the market penetration of efficient hearth product ignition devices.

f 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 Market Barriers Changes Attributable to Voluntary Energy Efficiency 

Targets for Hearth Product Ignition Devices (TSL1)

Base Case Voluntary Energy Efficiency Targets

Fireplace (vented) No-Low No

Fireplace (ventless) Moderate Moderate

Logs (vented) Low-Moderate Low

Logs (ventless) Low-Moderate Low

Outdoor No-Low No

Table 17.3.6 summarizes DOE’s assumptions for hearth product ignition devices

regarding the market penetration of units in selected years with the same efficient technology as 

the one in the proposed TSL given voluntary energy efficiency targets. Because of the decrease 

in the market barriers level over the first 10 years of the analysis period, the market increase of 

more efficient hearth product ignition devices significantly increases over that period. For the 

remaining 20 years of the analysis period, the increase in market penetration keeps growing as a 

result of increasing energy prices, which – with constant, same market barriers level as in 2030 –

 

f For the calculation of B/C ratios DOE discounted the cash flow of the operating cost savings to the purchase year 

using a 7 percent discount rate.
17-17

eventually lead to increasing B/C ratios and higher market penetration. Notice, however, that 

since ventless hearth product ignition devices present negative benefits and benefit-cost ratios, 

DOE did not assess the impacts from voluntary energy efficiency targets for those types of 

hearth product ignition devices and rather assumed zero increased market share.

Table 17.3.6 Market Penetrations Attributable to Voluntary Energy Efficiency 

Targets for Hearth Product Ignition Devices (TSL1)

2021 2030 2050

Fireplace (vented)

Base-Case Market Share 57.8% 57.8% 57.8%

Policy Case Market Share 60.3% 81.2% 85.0%

Increased Market Share 2.5% 23.4% 27.2%

Fireplace (ventless)

Base-Case Market Share 12.3% 12.3% 12.3%

Policy Case Market Share 13.2% 20.2% 20.2%

Increased Market Share 0.9% 7.9% 7.9%

Logs (vented)

Base-Case Market Share 12.7% 12.7% 12.7%

Policy Case Market Share 15.3% 35.2% 41.4%

Increased Market Share 2.6% 22.5% 28.7%

Logs (ventless)

Base-Case Market Share 5.7% 5.7% 5.7%

Policy Case Market Share 9.4% 31.6% 31.6%

Increased Market Share 3.7% 25.9% 25.9%

Outdoor

Base-Case Market Share 47.8% 47.8% 47.8%

Policy Case Market Share 51.2% 76.3% 80.8%

Increased Market Share 3.4% 28.5% 33.0%

The increased market shares attributable to voluntary energy efficiency targets shown in 

Table 17.3.6 were used as inputs in the NIA-RIA model. Appendix 17A 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 hearth product ignition devices that 

meet the efficiency level for the proposed TSL.

17.3.6 Bulk Government Purchases 

Bulk government purchases can lead to Federal, State, and local governments purchasing

large quantities of products 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 products 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 products.
17-18

DOE assumed that government agencies would administer bulk purchasing programs for

hearth product ignition devices, and that bulk government purchases would affect a subset of 

housing units for which government agencies purchased or influenced the purchase of hearth 

product ignition devices. This subset would consist primarily of public housing and housing on 

military bases. According to the 2009 Residential Energy Consumption Survey (RECS 2009), no 

housing units in public housing authority use a gas fueled fireplace either as a primary or a 

secondary source of heating.

23 DOE therefore estimated that there is no market for this 

alternative policy and, consequently, the increase in market share of more efficient hearth 

product ignition devices due to this alternative policy is zero.

17.4 IMPACTS OF NON-REGULATORY ALTERNATIVES

Figure 17.4.1 through Figure 17.4.5 show the effects of each non-regulatory policy on the 

market penetration of more efficient hearth product ignition devices. Relative to the base case, 

the alternative policy cases – excluding bulk government purchases – increase the market shares 

that meet the target level. Recall the proposed standards (not shown in the figures) would result 

in a 100-percent market penetration of products that meet the more efficient technology.

Figure 17.4.1 Market Penetration of Efficient Hearth Product Ignition 

Devices: Fireplace (vented) (TSL 1)
17-19

Figure 17.4.2 Market Penetration of Efficient Hearth Product Ignition 

Devices: Fireplace (ventless) (TSL 1)

Figure 17.4.3 Market Penetration of Efficient Hearth Product Ignition 

Devices: Logs (vented) (TSL 1)
17-20

Figure 17.4.4 Market Penetration of Efficient Hearth Product Ignition 

Devices: Logs (ventless) (TSL 1)

Figure 17.4.5 Market Penetration of Efficient Hearth Product Ignition 

Devices: Outdoor (TSL 1)

Table 17.4.1 shows the national energy savings and net present value for five nonregulatory policies analyzed in detail for hearth product ignition devices. The target level for 

each policy corresponds to the same efficient technology proposed for standards in TSL 1. The 

case in which no regulatory action is taken with regard to hearth product ignition devices
17-21

constitutes the base case (or "No New Regulatory Action" scenario), in which NES and NPV are 

zero by definition. For comparison, the tables include the impacts of the proposed standards. 

Energy savings are given in quadrillion British thermal units (quads).g The NPVs shown in Table 

17.4.1 are based on two discount rates, 7 percent and 3 percent.

The policy with the highest projected cumulative energy savings is voluntary energy 

efficiency targets. Savings from rebates and tax credits range from one third to less than on tenth 

of the savings from voluntary energy efficiency targets. Bulk government purchases, due to the 

lack of market for gas fueled hearth products in housing units in the public housing authority, 

lead to zero benefits. Overall, the energy saving benefits from the alternative policies, range from 

zero percent to 49.2 percent of the benefits from the proposed standards.

Table 17.4.1 Impacts of Non-Regulatory Alternatives for Hearth Product Ignition 

Devices (TSL 1)

Policy Alternative

Energy Savings*

quads

Net Present Value*

million 2013$

7% Discount 

Rate

3% Discount 

Rate

Consumer Rebates 0.107 (15.9%)** 182.3 483.0

Consumer Tax Credits 0.059 (8.7%) 109.4 289.8

Manufacturer Tax Credits 0.029 (4.4%) 54.7 144.9

Voluntary Energy Efficiency Targets 0.331 (49.2%) 418.0 1553.1

Bulk Government Purchases 0.000 (0.0%) 0.0 0.0

Proposed Standards*** 0.674 (100.0%) 1031.1 3123.8

* For products shipped 2021 – 2050

**The percentages show how the energy savings from each alternative policy compare to the (site) energy savings 

from the proposed standards (represented in the table as 100%).

*** Refers to site energy savings.

 

g For the alternative policies whose market penetration depends on B/C ratio, the energy savings in Table 17.4.1

correspond to the case where the cash flow of the operating cost savings was discounted to the purchase year using a 

7 percent discount rate. 
17-22

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_efficiency>

2. ICF International, Arizona Public Service Energy Efficient Market Potential Study, 2007. 

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communication. Optimal Energy. January, 2008.

4. Lee, A., Telephone Conversation with Barbara Atkinson of LBNL, personal 

communication. Quantec, LLC. January, 2008.

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6. Itron Inc. and KEMA Inc., California Energy Efficiency Potential Study, 2008. (Last 

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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. 
17-23

12. U.S. Department of Energy: Office of Codes and Standards, Technical Support 

Document: Energy Efficiency Standards for Consumer Products: Refrigerators, 

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(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. 

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2014.) <http://energytaxincentives.org/builders/appliances.php>

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Delivers?, May 30-June 4, 2005. ECEEE Summer Study Proceedings. Mandelieu La 

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Energy Efficiency Policy Measures, May, 2011. 
17-24

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Consumption Survey (RECS), 2009. (Last accessed December, 2013.) 

<http://www.eia.gov/consumption/residential/data/2009/index.cfm?view=microdata>
APPENDIX 6A. DETAILED DATA FOR PRODUCT PRICE MARKUPS

TABLE OF CONTENTS

6A.1 DETAILED WHOLESALER COST DATA ......................................................... 6A-1

6A.2 DETAILED MECHANICAL CONTRACTOR DATA......................................... 6A-3

6A.3 DETAILED GENERAL CONTRACTOR COST DATA...................................... 6A-5

6A.4 ESTIMATION OF CONTRACTOR MARK-UP BY STATE .............................. 6A-6

6A.5 STATE SALES TAX RATES.............................................................................. 6A-11

LIST OF TABLES

Table 6A.1.1 Disaggregated Costs and Expenses for Wholesalers.................................. 6A-2

Table 6A.2.1 Mechanical Contractor Expenses and Markups Used To Scale the 

Incremental Markups.................................................................................. 6A-4

Table 6A.3.1 Residential General Contractor Expenses and Markups............................. 6A-6

Table 6A.4.1 Mechanical Contractor Markup Estimation by State, 2007........................ 6A-6

Table 6A.4.2 Residential Building General Contractor Baseline Markups by State........ 6A-9

Table 6A.5.1 State Sales Tax Rates................................................................................ 6A-11

6A-i
APPENDIX 6A. DETAILED DATA FOR PRODUCT PRICE MARKUPS

6A.1 DETAILED WHOLESALER COST DATA

Based on data provided by the Heating Air-conditioning & Refrigeration Distributors 

International (HARDI), Table 6.5.1 of chapter 6 shows wholesaler revenues and costs in 

aggregated form. Table 6A.1.1 in this appendix provides the complete breakdown of costs and 

expenses. The column labeled “Scaling” in Table 6A.1.1 indicates which expenses the U.S. 

Department of Energy (DOE) assumed to scale with only the baseline markup and which with 

both the baseline and incremental markups. As described in chapter 6, section 6.4.1, only those 

expenses that scale with both baseline and incremental costs are marked up when there is an 

incremental change in equipment costs.

6A-1
Table 6A.1.1 Disaggregated Costs and Expenses for Wholesalers

Item

Percent of Revenue

% Scaling

Cost of Goods Sold 73.9

Gross Margin 26.1

Payroll Expenses 15.1 Baseline

Executive Salaries & Bonuses 1.6

Branch Manager Salaries and Commissions 1.3

Sales Executive Salaries & Commissions 0.5

Outside Sales Salaries & Commissions 2.3

Inside/Counter Sales/Wages 2.6

Purchasing Salaries/Wages 0.5

Credit Salaries/Wages 0.2

IT Salaries/Wages 0.2

Warehouse Salaries/Wages 1.4

Accounting 0.5

Delivery Salaries/Wages 0.8

All Other Salaries/Wages & Bonuses 0.8

Payroll Taxes 1.0

Group Insurance 1.0

Benefit Plans 0.4

Occupancy Expenses 3.5 Baseline

Utilities: Heat, Light, Power, Water 0.4

Telephone 0.3

Building Repairs & Maintenance 0.3

Rent or Ownership in Real Estate 2.5

Other Operating Expenses 5.2 Baseline & Incremental

Sales Expenses (incl. advertising & promotion) 0.9

Insurance (business liability & casualty) 0.2

Depreciation 0.4

Vehicle Expenses 1.2

Personal Property Taxes/Licenses 0.1

Collection Expenses 0.3

Bad Debt Losses 0.2

Data Processing 0.3

All Other Operating Expenses 1.6

Total Operating Expenses 23.8

Operating Profit 2.3 Baseline & Incremental

Other Income 0.4

Interest Expense 0.4

Other Non-operating Expenses 0.0

6A-2
Item

Percent of Revenue

% Scaling

Profit Before Taxes 2.3

Source: Heating, Air-conditioning & Refrigeration Distributors International. 2013. 2013 Profit Report (2012 Data).

6A.2 DETAILED MECHANICAL CONTRACTOR DATA

Table 6.5.3 and Table 6.5.4 of chapter 6 provide mechanical contractor revenues and 

costs in aggregated form by ‘Cost of Goods Sold’ and ‘Gross Margin.’ The tables are based on 

data in the 2005 edition of Financial Analysis for the HVACR Contracting Industry, published by 

the Air Conditioning Contractors of America (ACCA). The ACCA report did not provide a 

more disaggregated tabulation of these costs and expenses. As in section 6A.1, the gross margin 

category was assumed to scale only with the baseline markup.

A further disaggregated breakdown of costs used to scale the incremental markup as 

presented in Table 6.5.2 of chapter 6 are shown in Table 6A.2.1 by both dollar value and 

percentage terms from the 2007 Census of Business. As the ACCA data were used to calculate 

the baseline markup, in Table 6A.2.1 only the categories in the ‘Scaling’ column that are scaled 

with both the baseline and incremental markups are marked when there is an incremental change 

in equipment costs.

6A-3
Table 6A.2.1 Mechanical Contractor Expenses and Markups Used To Scale the 

Incremental Markups

Item

Dollar Value 

$1,000

Percentage

% Scaling

Total Cost of Equipment Sales 107,144,428 67.80

Total payroll, construction workers wages 31,373,558 19.85

Cost of materials, components, and supplies 59,023,964 37.35

Cost of construction work subcontracted out to others 13,646,192 8.63

Total cost of selected power, fuels, and lubricants 3,100,714 1.96

Gross Margin 50,895,129 32.20

Payroll Expenses 28,065,632 17.76

Baseline

Total payroll, other employee wages 14,041,336 8.88

Total fringe benefits 13,585,040 8.60

Temporary staff and leased employee expenses 439,256 0.28

Occupancy Expenses 3,436,208 2.17

Baseline

Rental costs of machinery and equipment 1,047,026 0.66

Rental costs of buildings 1,231,263 0.78

Communication services 640,851 0.41

Cost of repair to machinery and equipment 517,068 0.33

Other Operating Expenses 12,671,194 8.02

Baseline & 

Incremental

Purchased professional and technical services 843,641 0.53

Data processing and other purchased computer services 98,016 0.06

Expensed computer hardware and other equipment 255,474 0.16

Expensed purchases of software 64,195 0.04

Advertising and promotion services 1,018,265 0.64

All other expenses 6,944,674 4.39

Refuse removal (including hazardous waste) services 153,241 0.10

Taxes and license fees 996,138 0.63

Total depreciation ($1,000) 2,297,550 1.45

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. Construction: Geographic 

Area Series. Detailed Statistics for Establishments: 2007.

Note: Mechanical contractor costs and expenses are first presented as total dollar values and then converted to percentage values. This is in 

contrast to the cost per dollar of sales revenue values shown in Table 6.5.2.

6A-4
6A.3 DETAILED GENERAL CONTRACTOR COST DATA

Based on U.S. Department of Census data, Table 6.5.6 of chapter 6 show residential 

building general contractor revenues and costs in aggregated form. Table 6A.3.1 shows the 

complete breakdown of costs and expenses of residential building contractor provided by the 

U.S. Department of Census. The column labeled “Scaling” indicates which expenses DOE 

assumed to scale with only the baseline markup and which are 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.

6A-5
Table 6A.3.1 Residential General Contractor Expenses and Markups

Item

Dollar Value 

$1,000

Percentage 

% Scaling

Total Cost of Equipment Sales 238,431,389 67.55

Total payroll, construction workers wages 16,629,321 4.71

Cost of materials, components, and supplies 126,764,975 35.91

Cost of construction work subcontracted out to others 90,956,668 25.77

Total cost of selected power, fuels, and lubricants 4,080,425 1.16

Gross Margin 114,558,247 32.45

Payroll Expenses 28,806,792 8.16

Baseline Total payroll, other employee wages 20,843,029 5.90

Total fringe benefits 7,464,670 2.11

Temporary staff and leased employee expenses 499,093 0.14

Occupancy Expenses 3,558,796 1.01

Baseline

Rental costs of machinery and equipment 572,783 0.16

Rental costs of buildings 1,532,841 0.43

Communication services 810,436 0.23

Cost of repair to machinery and equipment 642,736 0.18

Other Operating Expenses 21,341,175 6.05

Baseline & 

Incremental

Purchased professional and technical services 1,834,816 0.52

Data processing and other purchased computer services 141,344 0.04

Expensed computer hardware and other equipment 261,701 0.07

Expensed purchases of software 105,338 0.03

Advertising and promotion services 2,544,687 0.72

All other expenses 10,840,757 3.07

Refuse removal (including hazardous waste) services 520,907 0.15

Taxes and license fees 1,791,539 0.51

Total depreciation ($1,000) 3,300,086 0.93

Net Profit Before Income Taxes 60,851,484 17.24 Baseline & 

Incremental

Source: U.S. Census Bureau. 2007. Residential Building Construction. Sector 23, EC0723I1: 236115 through 236118. 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. This is in contrast 

to the cost per dollar of sales revenue values shown in Table 6.5.6.

6A.4 ESTIMATION OF CONTRACTOR MARK-UP BY STATE

Table 6A.4.1 Mechanical Contractor Markup Estimation by State, 2007

State

Value of 

Const.

$1,000

Cost of 

Goods 

Sold 

$1,000

Baseline 

MU

Incremental 

MU

Replacem

ent 

Baseline

MU

Replacement 

Incremental

MU

New Const. 

Baseline

MU

New Const.

Incremental

MU

Alabama 2,010,305 1,401,223 1.435 1.148 1.498 1.198 1.393 1.114

6A-6
State

Value of 

Const.

$1,000

Cost of 

Goods 

Sold 

$1,000

Baseline 

MU

Incremental 

MU

Replacem

ent 

Baseline

MU

Replacement 

Incremental

MU

New Const. 

Baseline

MU

New Const.

Incremental

MU

Alaska 583,171 344,729 1.692 1.353 1.766 1.413 1.642 1.314

Arizona 3,522,116 2,326,475 1.514 1.211 1.580 1.264 1.470 1.176

Arkansas 1,065,754 743,395 1.434 1.147 1.496 1.197 1.392 1.113

California 16,726,969 10,865,201 1.539 1.232 1.607 1.286 1.495 1.196

Colorado 3,056,988 2,084,454 1.467 1.173 1.531 1.225 1.424 1.139

Connecticut 1,704,668 1,135,871 1.501 1.201 1.566 1.253 1.457 1.166

Delaware 481,900 D 1.421 1.137 1.483 1.186 1.379 1.104

District of 

Columbia 34,600 D 1.458 1.167 1.522 1.218 1.416 1.133

Florida 9,061,426 6,254,391 1.449 1.159 1.512 1.210 1.407 1.125

Georgia 4,700,799 3,329,842 1.412 1.129 1.474 1.179 1.371 1.096

Hawaii 800,221 455,122 1.758 1.407 1.835 1.468 1.707 1.366

Idaho 900,698 617,165 1.459 1.168 1.523 1.219 1.417 1.133

Illinois 7,641,642 5,058,047 1.511 1.209 1.577 1.262 1.467 1.173

Indiana 4,002,323 2,605,238 1.536 1.229 1.604 1.283 1.491 1.193

Iowa 1,868,483 1,305,883 1.431 1.145 1.493 1.195 1.389 1.111

Kansas 1,395,359 966,707 1.443 1.155 1.507 1.205 1.401 1.121

Kentucky 1,747,925 1,157,360 1.510 1.208 1.576 1.261 1.466 1.173

Louisiana 1,997,044 1,317,429 1.516 1.213 1.582 1.266 1.472 1.177

Maine 580,816 394,847 1.471 1.177 1.535 1.228 1.428 1.142

Maryland 5,329,135 3,739,560 1.425 1.140 1.487 1.190 1.383 1.107

Massachusetts 4,099,301 2,781,377 1.474 1.179 1.538 1.231 1.431 1.145

Michigan 4,420,638 3,015,948 1.466 1.173 1.530 1.224 1.423 1.138

Minnesota 3,402,921 2,315,330 1.470 1.176 1.534 1.227 1.427 1.141

Mississippi 1,025,452 715,571 1.433 1.146 1.496 1.197 1.391 1.113

Missouri 3,335,124 2,353,598 1.417 1.134 1.479 1.183 1.376 1.101

Montana 483,578 345,458 1.400 1.120 1.461 1.169 1.359 1.087

Nebraska 1,004,296 755,338 1.330 1.064 1.388 1.110 1.291 1.033

Nevada 2,327,842 1,600,555 1.454 1.164 1.518 1.214 1.412 1.130

New 

Hampshire 620,761 D 1.472 1.178 1.537 1.230 1.429 1.144

New Jersey 5,062,336 3,337,013 1.517 1.214 1.583 1.267 1.473 1.178

New Mexico 891,914 595,659 1.497 1.198 1.563 1.250 1.454 1.163

New York 10,364,779 6,760,337 1.533 1.227 1.600 1.280 1.488 1.191

North 

Carolina 5,111,396 3,631,802 1.407 1.126 1.469 1.175 1.366 1.093

6A-7
State

Value of 

Const.

$1,000

Cost of 

Goods 

Sold 

$1,000

Baseline 

MU

Incremental 

MU

Replacem

ent 

Baseline

MU

Replacement 

Incremental

MU

New Const. 

Baseline

MU

New Const.

Incremental

MU

North Dakota 360,683 255,057 1.414 1.131 1.476 1.181 1.373 1.098

Ohio 5,618,591 3,809,806 1.475 1.180 1.539 1.231 1.432 1.145

Oklahoma 1,352,943 924,264 1.464 1.171 1.528 1.222 1.421 1.137

Oregon 1,893,678 1,237,956 1.530 1.224 1.597 1.277 1.485 1.188

Pennsylvania 6,487,476 4,579,367 1.417 1.133 1.479 1.183 1.375 1.100

Rhode Island 631,202 410,653 1.537 1.230 1.604 1.284 1.492 1.194

South 

Carolina 1,991,303 1,326,690 1.501 1.201 1.567 1.253 1.457 1.166

South Dakota 386,186 239,017 1.616 1.293 1.686 1.349 1.569 1.255

Tennessee 2,595,613 1,834,242 1.415 1.132 1.477 1.182 1.374 1.099

Texas 10,810,308 7,532,064 1.435 1.148 1.498 1.198 1.393 1.115

Utah 1,746,398 1,235,004 1.414 1.131 1.476 1.181 1.373 1.098

Vermont 294,806 D 1.472 1.178 1.537 1.230 1.429 1.144

Virginia 4,623,151 3,099,329 1.492 1.193 1.557 1.246 1.448 1.158

Washington 4,111,543 2,734,093 1.504 1.203 1.570 1.256 1.460 1.168

West Virginia 655,100 D 1.464 1.171 1.528 1.222 1.421 1.137

Wisconsin 2,926,545 2,023,634 1.446 1.157 1.510 1.208 1.404 1.123

Wyoming 289,391 198,105 1.461 1.169 1.525 1.220 1.418 1.135

Sources: U.S. Bureau of the Census. American Factfinder: 2007. Sector 23: Plumbing, Heating, and Air-Conditioning Contractors (NAICS 

238220), Detailed Statistics for Establishments: 2007 

http://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_2007_US_23I1&prodType=table and

Geographic Area Series: Detailed Statistics for Establishments: 2007.

Notes: The Census Bureau withheld data for some states.

Markups may vary across states for several reasons, including differences in firm size.

Due to sample size and/or magnitude of reporting error relative to the mean, disaggregated information not provided for all of the Subcontract, 

Materials, and Fuels fields. In these cases, the state markup ratio is calculated as an average of neighboring states (ex. Delaware, District of 

Columbia, New Hampshire, Vermont, and West Virginia)

6A-8
Table 6A.4.2 Residential Building General Contractor Baseline Markups by State

State

Value of Residential 

Construction 

$1,000

Cost of 

Goods Sold 

$1,000

Baseline

Markup

Incremental 

Markup

Alabama 4,232,349 3,106,308 1.363 1.234

Alaska 598,572 322,897 1.854 1.678

Arizona 14,743,264 8,636,727 1.707 1.546

Arkansas 821,493 638,546 1.287 1.165

California 49,325,592 28,727,843 1.717 1.555

Colorado 9,711,667 6,478,218 1.499 1.357

Connecticut 2,835,015 1,914,706 1.481 1.341

Delaware 912,121 714,609 1.276 1.156

District of Columbia 177,004 115,545 1.532 1.387

Florida 33,290,091 21,780,175 1.528 1.384

Georgia 12,492,752 8,745,668 1.428 1.293

Hawaii 2,739,122 1,933,143 1.417 1.283

Idaho 2,565,176 2,014,522 1.273 1.153

Illinois 13,035,923 8,206,105 1.589 1.438

Indiana 4,637,976 3,418,576 1.357 1.228

Iowa 1,846,602 1,449,114 1.274 1.154

Kansas 1,940,745 1,443,265 1.345 1.217

Kentucky 3,074,656 2,244,283 1.370 1.240

Louisiana 2,429,529 1,650,884 1.472 1.332

Maine 821,980 630,393 1.304 1.181

Maryland 6,616,960 4,635,717 1.427 1.292

Massachusetts 7,693,991 5,728,767 1.343 1.216

Michigan 5,383,752 3,501,797 1.537 1.392

Minnesota 5,558,816 3,847,679 1.445 1.308

Mississippi 1,241,083 939,692 1.321 1.196

Missouri 4,754,552 3,588,694 1.325 1.200

Montana 1,148,453 919,206 1.249 1.131

Nebraska 577,746 424,822 1.360 1.231

Nevada 6,697,489 4,026,111 1.664 1.506

New Hampshire 292,227 228,854 1.277 1.156

New Jersey 8,492,015 5,649,618 1.503 1.361

New Mexico 2,236,262 1,395,073 1.603 1.451

New York 16,958,113 12,176,837 1.393 1.261

North Carolina 16,254,736 11,579,895 1.404 1.271

6A-9
State

Value of Residential 

Construction 

$1,000

Cost of 

Goods Sold 

$1,000

Baseline

Markup

Incremental 

Markup

North Dakota D D 1.331 1.205

Ohio 6,788,825 4,883,462 1.390 1.259

Oklahoma 1,419,859 1,075,586 1.320 1.195

Oregon 5,519,819 4,019,693 1.373 1.243

Pennsylvania 9,971,624 7,323,399 1.362 1.233

Rhode Island 309,403 205,383 1.506 1.364

South Carolina 5,921,453 4,350,205 1.361 1.232

South Dakota 297,424 228,839 1.300 1.177

Tennessee 5,243,037 3,874,974 1.353 1.225

Texas 32,123,700 21,429,103 1.499 1.357

Utah 4,201,276 3,095,214 1.357 1.229

Vermont 527,837 387,905 1.361 1.232

Virginia 12,761,751 8,799,880 1.450 1.313

Washington 11,158,559 7,361,497 1.516 1.372

West Virginia 348,291 225,500 1.545 1.398

Wisconsin 3,820,533 2,850,921 1.340 1.213

Wyoming 524,809 418,215 1.255 1.136

Sources: U.S. Bureau of the Census, American Factfinder. 2007 Economic Census. Sector 23: Subsectors 236115 (residential single-family), 

236116 (residential multifamily), 236117 (operative builders), and 236118 (residential remodelers). Sector 23: EC0723A1: Construction: 

Geographic Area Series: Detailed Statistics for Establishments: 2007.

Notes: The Census Bureau withheld data for some states.

Markups may vary across states for several reasons, including differences in firm size.

Due to sample size and/or magnitude of reporting error relative to the mean, disaggregated information not provided for all of the Subcontract, 

Materials, and Fuels fields. In these cases, the state markup ratio is calculated as an average of neighboring states (ex. North Dakota).

6A-10
6A.5 STATE SALES TAX RATES

Table 6A.5.1 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 Kentucky 6.00 North Dakota 5.90

Alaska 1.30 Louisiana 8.75 Ohio 7.10

Arizona 7.15 Maine 5.50 Oklahoma 8.35

Arkansas 8.90 Maryland 6.00 Oregon --

California 8.45 Massachusetts 6.25 Pennsylvania 6.40

Colorado 6.05 Michigan 6.00 Rhode Island 7.00

Connecticut 6.35 Minnesota 7.20 South Carolina 7.20

Delaware -- Mississippi 7.00 South Dakota 5.40

Dist. of Columbia 5.75 Missouri 7.45 Tennessee 9.45

Florida 6.65 Montana -- Texas 7.90

Georgia 7.05 Nebraska 6.00 Utah 6.70

Hawaii 4.40 Nevada 7.85 Vermont 6.05

Idaho 6.05 New Hampshire -- Virginia 5.60

Illinois 8.05 New Jersey 6.95 Washington 8.90

Indiana 7.00 New Mexico 6.60 West Virginia 6.10

Iowa 6.85 New York 8.40 Wisconsin 5.45

Kansas 7.85 North Carolina 6.90 Wyoming 5.50

Source: The Sales Tax Clearinghouse at https://thestc.com/STRates.stm (Accessed on January 22, 2014).

6A-11
APPENDIX 7A. BUILDING VARIABLES

TABLE OF CONTENTS

7A.1 INTRODUCTION .................................................................................................. 7A-1

7A.2 RECS SAMPLE DETERMINATION.................................................................... 7A-1

7A.3 RECS 2009 DATABASE VARIABLE RESPONSE CODES............................... 7A-3

LIST OF TABLES

Table 7A.2.1 List of RECS 2009 Variables Used for Hearth Products...................... 7A-2

Table 7A.3.1 Definitions of RECS 2009 Variables Used in Life-Cycle Cost 

Analysis................................................................................................. 7A-3

7A-i
APPENDIX 7A. BUILDING VARIABLES

7A.1 INTRODUCTION

DOE created a database containing a subset of the records and variables from DOE’s 

Energy Information Administration (EIA)’s 2009 Residential Energy Consumption Survey 

(RECS 2009) using Microsoft ACCESS.

1 DOE used this RECS 2009 subset in the life-cycle cost 

(LCC) analysis of the Hearth Products Rulemaking. This appendix explains the variable name 

abbreviations and provides definitions of the variable values. 

For the entire RECS 2009 dataset, refer to

www.eia.gov/consumption/residential/data/2009/index.cfm?view=microdata. 

7A.2 RECS SAMPLE DETERMINATION

The RECS consists of three parts:

• Personal interviews with households for information about energy used, how it is used, 

energy-using appliances, structural features, energy efficiency measures, and 

demographic characteristics of the household.

• Telephone interviews with rental agents for households that have any of their energy use 

included in their rent. This information augments information collected from those 

households that may not be knowledgeable about the fuels used for space heating or 

water heating.

• Mail questionnaires sent to energy suppliers (after obtaining permission from households) 

to collect the actual billing data on energy consumption and expenditures.

For vented hearth heaters, the subset of RECS 2009 records used in the analysis met all 

of the following criteria:

• used a fireplace for secondary or primary space heating,

• used a heating fuel that is natural gas or liquefied petroleum gas (LPG), and

• had a flue on the gas fireplace to the outside.

For ventless hearth heaters, the subset of RECS 2009 records used in the analysis met all 

of the following criteria:

• used a fireplace for secondary space heating,

• used a fuel for a fireplace that is used for secondary space heating that is natural gas or

LPG, and

• did not have a flue on the gas fireplace to the outside.

The RECS 2009 weighting indicates how commonly each household configuration 

occurs in the general population. 

7A-1
Table 7A.2.1 lists the variables use in the analysis. 

Table 7A.2.1 List of RECS 2009 Variables Used for Hearth Products

Variable Description

Location Variables

REGIONC Census Region

DIVISION Census Division

REPORTABLE_DOMAIN Reportable states and groups of states

HDD65 Heating degree days in 2009, base temperature 65F

CDD65 Heating degree days in 2009, base temperature 65F

Household Characteristics Variables

NWEIGHT Final sample weight

DOEID Unique identifier for each respondent

TYPEHUQ Type of housing unit

YEARMADE Year housing unit was built

BTUNGSPH Natural Gas usage for space heating, in thousand BTU, 2009

BTULPSPH LPG/Propane usage for space heating, in thousand BTU, 2009

CHIMNEY Fireplace used for secondary space heating

NGFPFLUE Flue on gas fireplace

USENGFP Frequency gas fireplace used

ROOMHEAT Built-in room heaters used for secondary space heating

EQUIPM Type of main space heating equipment used

FUELHEAT Main space heating fuel

HEATOTH Main space heating equipment heats other homes, business, or 

farm

MAINTHT Routine service or maintenance performed on main space heating 

equipment

EQUIPAGE Age of main space heating equipment

MONEYPY 2009 gross household income

RMHTFUEL Fuel used by built-in electric units for secondary space heating

FPFUEL Fuel used by fireplace for secondary space heating

EQMAMT Portion of space heating provided by main space heating 

equipment (for homes with main and secondary heating only)

NHSLDMEM Number of household members

Seniors* Number of household members age 65 or older

POVERTY100 Household income at or below 100% of poverty line

StationID* ID number of weather station identified with household (See 

Appendix 7B)

TOTSQFT Total square footage (includes all attached garages, all basements, 

and finished/heated/cooled attics)

TOTSQFT_EN

Total square footage (includes heated/cooled garages, all 

basements, and finished/heated/cooled attics). Used for EIA data 

tables.

TOTHSQFT Total heated square footage

CENACHP Central air conditioner is a heat pump

COOLTYPE Type of air conditioning equipment used

7A-2
* Not part of RECS 2009 variables.

7A.3 RECS 2009 DATABASE VARIABLE RESPONSE CODES

Table 7A.3.1 provides the response codes for all RECS 2009 variables used in the hearth 

products samples.

Table 7A.3.1 Definitions of RECS 2009 Variables Used in Life-Cycle Cost Analysis 

Variable Response Codes

BTULPSPH Thousand BTU

BTUNGSPH Thousand BTU

CDD65 Cooling degree days in 2009, base temperature 65F

CENACHP

0

1

-2

No

Yes

Not Applicable

CHIMNEY

0

1

-2

No

Yes

Not Applicable

COOLTYPE

1

2

3

-2

Central system

Window/wall units

Both a central system and window/wall units

Not Applicable

ROOMHEAT

0

1

-2

No

Yes

Not Applicable

DIVISION

1

2

3

4

5

6

7

8

9

10

New England Census Division (CT, MA, ME, NH, RI, VT)

Middle Atlantic Census Division (NJ, NY, PA)

East North Central Census Division (IL, IN, MI, OH, WI)

West North Central Census Division (IA, KS, MN, MO, ND, 

NE, SD)

South Atlantic Census Division (DC, DE, FL, GA, MD, NC, 

SC, VA, WV)

East South Central Census Division (AL, KY, MS, TN)

West South Central Census Division (AR, LA, OK, TX)

Mountain North Sub-Division (CO, ID, MT, UT, WY)

Mountain South Sub-Division (AZ, NM, NV)

Pacific Census Division (AK, CA, HI, OR, WA)

DOEID

00001 -

12083 Unique identifier for each respondent

EQMAMT

1

2

3

-2

Almost all

About three-fourths

Closer to half

Not Applicable

EQUIPAGE

1

2

3

41

42

Less than 2 years old

2 to 4 years old

5 to 9 years old

10 to 14 years old

15 to 19 years old

7A-3
5

-2

20 years or older

Not Applicable

EQUIPM

2

3

4

5

6

7

8

9

10

11

12

21

-2

Steam or Hot Water System

Central Warm-Air Furnace

Heat Pump

Built-In Electric Units

Floor or Wall Pipeless Furnace

Built-In Room Heater

Heating Stove

Fireplace

Portable Electric Heaters

Portable Kerosene Heaters

Cooking Stove

Other Equipment

Not Applicable

FPFUEL

1

2

7

21

-2

Natural Gas

Propane/LPG

Wood

Other Fuel

Not Applicable

FUELHEAT

1

2

3

4

5

7

8

9

21

-2

Natural Gas

Propane/LPG

Fuel Oil

Kerosene

Electricity

Wood

Solar

District Steam

Other Fuel

Not Applicable

HDD65 Heating degree days in 2009, base temperature 65F

HEATOTH

0

1

-2

No

Yes

Not Applicable

MAINTHT

0

1

-2

No

Yes

Not Applicable

MONEYPY

1

2

3

4

5

6

7

8

9

10

11

12

Less than $2,500

$2,500 to $4,999

$5,000 to $7,499

$7,500 to $9,999

$10,000 to $14,999

$15,000 to $19,999

$20,000 to $24,999

$25,000 to $29,999

$30,000 to $34,999

$35,000 to $39,999

$40,000 to $44,999

$45,000 to $49,999

7A-4
13

14

15

16

17

18

19

20

21

22

23

24

$50,000 to $54,999

$55,000 to $59,999

$60,000 to $64,999

$65,000 to $69,999

$70,000 to $74,999

$75,000 to $79,999

$80,000 to $84,999

$85,000 to $89,999

$90,000 to $94,999

$95,000 to $99,999

$100,000 to $119,999

$120,000 or More

NGFPFLUE

1

2

-2

Flue to the outside

Flueless

Not Applicable

NHSLDMEM 0 - 15 Number of household members

NWEIGHT Final sample weight

POVERTY100

0

1

No

Yes

REGIONC

1

2

3

4

Northeast Census Region

Midwest Census Region

South Census Region

West Census Region

REPORTABLE_DOMAIN

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

Connecticut, Maine, New Hampshire, Rhode Island, Vermont

Massachusetts

New York

New Jersey

Pennsylvania

Illinois

Indiana, Ohio

Michigan

Wisconsin

Iowa, Minnesota, North Dakota, South Dakota

Kansas, Nebraska

Missouri

Virginia

Delaware, District of Columbia, Maryland, West Virginia

Georgia

North Carolina, South Carolina

Florida

Alabama, Kentucky, Mississippi

Tennessee

Arkansas, Louisiana, Oklahoma

Texas

Colorado

Idaho, Montana, Utah, Wyoming

Arizona

Nevada, New Mexico

California

7A-5
27 Alaska, Hawaii, Oregon, Washington

RMHTFUEL

1

2

3

4

-2

Natural Gas

Propane/LPG

Fuel Oil

Kerosene

Not Applicable

Seniors

0

1

No

Yes

StationID Three character identifier for weather station

TOTHSQFT Square Feet

TOTSQFT Square Feet

TOTSQFT_EN Square Feet

TYPEHUQ

1

2

3

4

5

Mobile Home

Single-Family Detached

Single-Family Attached

Apartment in Building with 2 - 4 Units

Apartment in Building with 5+ Units

USENGFP

1

2

3

-2

Most days

About once a week

Fewer than 4 times each month

Not Applicable

YEARMADE

1600 -

2009 Year housing unit was built

* Not part of RECS 2009 variables.

7A-6
REFERENCES

1. U.S. Department of Energy: Energy Information Administration, Residential Energy 

Consumption Survey (RECS): 2009 RECS Survey Data, 2009. (Last accessed January 7, 

2015.) <www.eia.gov/consumption/residential/data/2009/>

7A-7
APPENDIX 7B. MAPPING OF WEATHER STATION DATA TO RECS 

HOUSEHOLDS

TABLE OF CONTENTS

7B.1 INTRODUCTION ...................................................................................................7B-1

7B.2 MAPPING METHODOLOGY ...............................................................................7B-1

7B.3 MAPPING RESULTS .............................................................................................7B-2

7B.3.2 Developing Monthly Heating Degree Day Fractions ..............................................7B-9

LIST OF TABLES

Table 7B.3.1 Weather Station Mapping Statistics, Heating and Cooling ODT, 

and Average Outdoor Temperature .......................................................7B-2

Table 7B.3.2 Weather Station Monthly Heating Degree Day Data (10-Year 

Average).................................................................................................7B-9

Table 7B.3.3 Weather Station Monthly Heating Degree Day Data Fractions (10-

Year Average)......................................................................................7B-16

7B-i
APPENDIX 7B. MAPPING OF WEATHER STATION DATA TO RECS 

HOUSEHOLDS

7B.1 INTRODUCTION

Energy Information Administration’s (EIA) 2009 Residential Energy Consumption 

Survey (RECS 2009)1 provides annual data on heating and cooling degree-days but not on other 

weather parameters needed for the analysis such as length of the heating and cooling season, 

monthly heating degree days (HDD) and monthly cooling degree days (CDD). Energy price data 

used in this analysis are available on a monthly basis. Monthly HDD are used to disaggregate the 

annual energy use provided by RECS by month. Monthly energy use is combined with monthly 

energy prices to find the monthly operating cost.

7B.2 MAPPING METHODOLOGY

To derive the additional weather data that is needed for the analysis (e.g., HDD, CDD), 

for each building in the sample, DOE developed an approach to assign a physical location to 

each RECS household. a The methodology consists of the following steps:

1. DOE assembled monthly weather data from 360 weather stations from the National 

Oceanic and Atmospheric Administration (NOAA) that provide the heating and cooling 

degree-days at base temperature 65oF for year 2009 for these weather stations.2 The 2009

heating and cooling degree days match the period used to determine the degree-days in 

RECS 2009.

2. RECS reports both HDD and CDD to base temperature 65oF for each household record.

DOE assigned each household to one of the 339 weather stations by calculating which 

weather station (within the appropriate region) was the closest using the best linear least 

squares fit of the RECS data to the weather data. The following equation calculates the 

U.S. weather station closest (or with minimum “distance”) to the RECS household:

" Distance" = ( HDD − HDD ) + (CDD − CDD ) 2 1

2

2 1

2

Eq. 7B.2.1

Where:

HDD1 = heating degree days from U.S. weather data,

HDD2 = heating degree days from RECS data,

CDD1 = cooling degree days from U.S. weather data, and

CDD2 = cooling degree days from RECS data.

a For confidentiality, heating and cooling degree day values were altered slightly by EIA to mask the exact 

geographic location of the housing unit.

7B-1

 
7B.3 MAPPING RESULTS

Table 7B.3.1 shows the imputation results for all RECS 2009 locations. Note that some 

U.S. weather station data match with several of the RECS weather data. The number of RECS

households that were matched to the specified weather station is indicated in the column 

“Count”. Table 7B.3.1 shows the data matches (321 weather stations) including the heating and 

cooling ODT as well as annual average outdoor temperature for the weather stations..

Table 7B.3.1 Weather Station Mapping Statistics, Heating and Cooling ODT, and 

Average Outdoor Temperature

Station Location Code RECS 2009 Heating 

ODT

Cooling 

ODT

Average 

Outdoor

State City HDD CDD Count Temp

AK Anchorage ANC 10335 2 8 -18 68 36

AK Bethel BET 12530 0 1 -24 68 30

AK Cold Bay CDB 9668 0 2 10 57 38

AK Cordova CDV 9511 0 2 1 67 38

AK Homer HOM 9817 0 10 4 62 38

AK Juneau JNU 8536 6 2 1 70 42

AK Kenai ENA 10423 0 1 -14 65 36

AK Ketchikan KTN 7359 68 2 20 68 38

AK King Salmon AKN 11088 0 1 -19 67 35

AK Kodiak ADQ 8903 0 1 13 65 41

AK Sitka SIT - - - 21 64 42

AK St Paul Island SNP 11420 0 4 3 52 35

AK Talkeetna TKA - - - -21 73 34

AK Valdez VWS 7074 23 2 7 66 38

AK Yakutat YAK 9295 1 1 2 63 40

AL Birmingham BHM 2605 1958 25 21 93 62

AL Huntsville HSV 2982 1863 26 16 92 61

AL Mobile MOB 1594 2681 59 29 92 67

AL Montgomery MGM 2137 2367 3 25 94 65

AL Muscle Shoals MSL 2948 1773 12 21 93 61

AL Tuscaloosa TCL 2349 2136 10 23 93 64

AR Fayetteville FYV 3957 1185 48 12 93 58

AR Fort Smith FSM 3174 1906 3 17 96 61

AR Little Rock LIT 2946 1943 27 20 95 62

AR Texarkana TXK 2573 2006 10 23 96 61

AZ Douglas DUG 2160 2204 27 31 98 61

AZ Flagstaff FLG 6741 176 2 4 83 46

AZ Phoenix PHX 807 4942 26 34 108 74

AZ Tucson TUS 1268 3626 85 32 104 69

AZ Winslow INW 4233 1395 4 10 93 55

AZ Yuma NYL 671 4757 82 39 109 75

CA Bakersfield BFL 1873 2644 177 32 101 65

CA Blythe BLH 968 4580 8 33 112 71

CA Eureka EKA 5137 2 2 33 65 53

CA Fresno FAT 2239 2390 50 30 101 63

CA Los Angeles LAX 1294 569 117 43 80 63

7B-2
Station Location Code RECS 2009 Heating 

ODT

Cooling 

ODT

Average 

Outdoor

State City HDD CDD Count Temp

CA Mt Shasta MHS 5474 433 5 21 88 49

CA Paso Robles PRB 2676 1095 144 29 98 58

CA Red Bluff RBL 2452 2122 70 32 102 62

CA Redding RDD 2750 2086 63 31 102 62

CA Sacramento SAC 2531 1357 30 32 98 61

CA San Diego SAN 1050 813 540 44 81 64

CA San Francisco SFO 2614 220 278 38 78 57

CA Stockton SCK 2451 1468 122 30 98 62

CO Alamosa ALS 8229 49 27 -16 82 41

CO Colorado Spring COS 6301 356 90 2 88 48

CO Denver DEN 5988 541 69 1 92 50

CO Eagle EGE 7593 124 15 -7 86 41

CO Pueblo PUB 5427 818 77 0 96 52

CO Trinidad TAD 5323 719 17 3 90 51

CT Bridgeport BDR 5484 669 57 9 85 52

CT Hartford BDL 6072 610 94 7 88 50

DC Washington DCA 4124 1427 39 17 93 58

DE Wilmington ILG 4789 1031 14 14 89 54

FL Daytona Beach DAB 753 3321 99 35 91 71

FL Fort Myers FMY 294 4151 63 44 93 75

FL Ft Lauderdale FLL 118 4839 30 46 91 75

FL Gainesville GNV 1181 2789 118 31 92 69

FL Jacksonville JAX 1339 2772 60 32 93 68

FL Key West EYW 108 5017 11 57 89 78

FL Melbourne MLB 526 3718 80 43 91 73

FL Miami MIA 109 4914 2 47 91 77

FL Orlando MCO 588 3620 103 38 93 73

FL Pensacola PNS 1443 2729 44 29 92 68

FL Tallahassee TLH 1574 2802 31 30 94 68

FL Tampa TPA 496 3876 112 40 91 73

FL Vero Beach VRB 477 3604 26 43 91 73

FL West Palm Beach PBI 239 4314 169 45 90 75

GA Albany ABY 1767 2686 5 29 95 66

GA Athens AHN 2882 1903 253 22 93 62

GA Atlanta ATL 2813 1838 87 22 92 62

GA Augusta AGS 2475 2068 55 23 95 63

GA Brunswick SSI - - - 32 91 69

GA Columbus CSG 2183 2194 2 24 94 65

GA Macon MCN 2288 2133 17 25 94 64

GA Savannah SAV 1739 2497 21 27 93 66

GA Waycross AYS 29 94 67

HI Hilo-Hawaii ITO 0 3050 14 62 85 74

HI Honolulu-Oahu HNL 0 4816 14 63 89 78

HI Kahului-Maui OGG 1 3746 21 61 88 76

HI Lihue-Kauai LIH 2 3611 5 62 85 76

IA Burlington BRL 5687 810 24 -3 91 50

IA Cedar Rapids CID 6977 419 15 -5 89 47

7B-3
Station Location Code RECS 2009 Heating 

ODT

Cooling 

ODT

Average 

Outdoor

State City HDD CDD Count Temp

IA Des Moines DSM 6124 898 33 -5 90 50

IA Dubuque DBQ 7204 345 1 -7 86 47

IA Mason City MCW 7856 338 15 -11 88 47

IA Ottumwa OTM 6317 588 43 -4 92 50

IA Sioux City SUX 6913 678 75 -7 90 48

IA Waterloo ALO 7253 448 58 -10 89 47

ID Boise BOI 5592 1199 9 10 95 52

ID Burley BYI 6697 397 1 2 90 47

ID Idaho Falls IDA - - - -6 89 47

ID Lewiston LWS 5386 1008 3 6 94 52

ID Pocatello PIH 7463 321 17 -1 91 47

IL Chicago ORD 6417 585 40 0 90 49

IL Moline MLI 6250 636 35 -4 91 50

IL Peoria PIA 5841 752 62 -4 90 51

IL Quincy UIN 5460 849 12 3 90 51

IL Rockford RFD 6738 433 58 -4 89 48

IL Springfield SPI 5234 933 41 2 91 53

IN Evansville EVV 4397 1283 13 9 92 56

IN Fort Wayne FWA 6077 601 41 1 88 50

IN Indianapolis IND 5203 953 22 2 89 53

IN South Bend SBN 6426 545 54 1 88 50

IN West Lafayette LAF 5436 826 32 3 90 50

KS Concordia CNK 5558 1094 18 3 96 54

KS Dodge City DDC 4975 1257 27 5 97 55

KS Garden City GCK 5014 1154 31 4 97 55

KS Goodland GLD 6016 722 11 0 94 51

KS Russell RSL 5298 1194 46 4 96 54

KS Salina SLN - - - 5 98 56

KS Topeka TOP 4968 1195 9 4 94 54

KS Wichita ICT 4552 1506 68 7 97 56

KY Bowling Green BWG 3808 1407 52 10 91 57

KY Jackson JKL 4237 984 15 14 87 56

KY Lexington LEX 4670 1020 40 8 89 55

KY Louisville SDF 4155 1316 29 10 91 57

KY Paducah PAH 4198 1239 39 12 93 57

LA Baton Rouge BTR 1404 2985 24 29 93 67

LA Lafayette LFT 1296 3086 3 30 93 68

LA Lake Charles LCH 1380 2980 10 31 93 68

LA Monroe MLU 2118 2547 11 25 95 66

LA New Orleans MSY 1156 3221 35 33 92 69

LA Shreveport SHV - - - 25 95 66

MA Boston BOS 5694 581 243 9 87 52

MA Worcester ORH 6699 370 258 4 83 47

MD Baltimore BWI 4745 1088 34 13 91 55

MD Salisbury SBY 4345 1149 19 16 90 57

ME Augusta AUG 7487 276 18 23 95 63

ME Bangor BGR 8098 246 19 -6 84 46

7B-4
Station Location Code RECS 2009 Heating 

ODT

Cooling 

ODT

Average 

Outdoor

State City HDD CDD Count Temp

ME Caribou CAR 9415 149 13 -13 82 39

ME Houlton HUL 9316 178 24 -13 85 40

ME Portland PWM 7107 294 108 -1 83 46

MI Alpena APN - - - -6 84 43

MI Detroit DTW 6224 588 81 6 87 50

MI Flint FNT 7068 328 40 1 86 47

MI Grand Rapids GRR 6580 444 35 5 86 48

MI Houghton Lake HTL - - - 1 85 43

MI Jackson JXN 6585 420 11 14 87 56

MI Lansing LAN 6830 372 36 1 86 47

MI Marquette MQT - - - -8 83 39

MI Muskegon MKG 6719 371 38 6 83 47

MI Saginaw MBS 6960 350 19 4 87 47

MI Sault St Marie SSM - - - -8 80 40

MI Traverse City TVC 7695 253 14 1 86 47

MN Alexandria AXN 8922 340 8 -16 86 42

MN Duluth DLH 9517 118 10 -16 81 39

MN Hibbing HIB 10159 64 4 -20 81 37

MN Int'l Falls INL 10648 72 8 -25 83 37

MN Minneapolis MSP 7613 646 48 -12 88 45

MN Rochester RST 7884 321 9 -12 85 43

MN Saint Cloud STC 8704 301 74 -11 88 42

MO Columbia COU 4999 958 125 4 92 54

MO Joplin JLN 4216 1382 98 10 94 56

MO Kansas City MCI 5084 1093 213 6 93 54

MO Saint Louis STL 4438 1457 70 6 93 56

MO Springfield SGF 4596 1114 180 2 91 53

MS Greenwood GWO 2376 2250 1 20 94 61

MS McComb MCB 1833 2472 34 26 92 64

MS Tupelo TUP 2842 1947 20 19 94 61

MT Billings BIL 6948 627 9 -10 90 47

MT Butte BTM - - - -17 84 40

MT Cut Bank CTB - - - -20 84 40

MT Great Falls GTF 7941 300 1 -15 89 44

MT Havre HVR - - - -11 90 44

MT Helena HLN 7704 444 1 -16 87 44

MT Kalispell FCA - - - -7 86 43

MT Lewistown LWT - - - -16 86 44

MT Miles City MLS 7700 716 1 -15 93 46

MT Missoula MSO 7588 355 2 -6 88 45

NC Asheville AVL 4194 768 23 14 86 55

NC Cape Hatteras HAT - - - 29 86 63

NC Charlotte CLT 3346 1611 71 22 91 61

NC Greensboro GSO 3605 1510 41 18 90 58

NC Hickory HKY 3593 1353 42 18 90 58

NC New Bern EWN 2769 1788 16 24 92 64

NC Raleigh Durham RDU 3164 1865 55 20 92 60

7B-5
Station Location Code RECS 2009 Heating 

ODT

Cooling 

ODT

Average 

Outdoor

State City HDD CDD Count Temp

NC Wilmington ILM 2521 1937 14 14 89 54

ND Bismarck BIS 9130 332 16 -19 90 42

ND Devil's Lake P11 10245 236 8 -21 87 40

ND Fargo FAR 9304 362 17 -18 88 42

ND Grand Forks GFK 9928 269 8 -22 89 40

ND Minot MOT 9559 314 9 -20 89 41

ND Williston ISN 9721 297 8 -21 92 41

NE Grand Island GRI 6431 788 26 -3 93 50

NE Lincoln LNK 6159 912 14 -2 94 51

NE Norfolk OFK 6789 643 4 -4 92 49

NE North Platte LBF 6946 534 14 -4 92 49

NE Omaha OMA 6288 851 32 -3 90 51

NE Scottsbluff BFF 6689 579 6 -3 92 48

NE Valentine VTN 7279 527 2 -8 94 47

NH Concord CON 7462 325 5 -3 87 46

NH Lebanon LEB 7312 371 18 -3 86 46

NJ Atlantic City ACY 4693 994 57 13 89 54

NJ Newark EWR 4790 1021 147 14 91 55

NM Albuquerque ABQ 3823 1435 17 16 93 57

NM Carlsbad CNM 2398 2376 2 19 98 63

NM Clayton CAO 4517 1143 31 9 91 53

NM Gallup GUP 6134 442 6 5 87 53

NM Roswell ROW 3098 1961 7 18 96 61

NV Elko EKO 6948 450 1 -2 92 46

NV Ely ELY 7925 125 4 -4 87 45

NV Las Vegas LAS 1882 3818 66 28 106 68

NV Lovelock LOL - - - 12 97 50

NV Reno RNO - - - 10 92 51

NV Tonopah TPH 5298 874 5 10 92 51

NV Winnemucca WMC 6236 611 2 3 94 49

NY Albany ALB 6644 433 149 29 95 66

NY Binghamton BGM 7067 261 59 1 82 46

NY Buffalo BUF 6651 361 54 6 84 48

NY Glens Falls GFL 7612 285 26 -5 85 46

NY Massena MSS 7980 298 2 -8 84 46

NY New York LGA 4647 1041 469 15 89 55

NY Rochester ROC 6765 315 46 -12 85 43

NY Syracuse SYR 6687 439 23 2 86 47

NY Utica UCA - - - -6 85 47

NY Watertown ART 7707 298 11 -6 83 46

OH Akron Canton CAK 6131 497 6 6 86 50

OH Cincinnati CVG 4950 874 13 6 90 54

OH Cleveland CLE 5833 664 44 5 87 50

OH Columbus CMH 5243 874 32 24 94 65

OH Dayton DAY 5602 732 45 4 88 52

OH Findlay FDY 5901 698 34 3 87 50

OH Mansfield MFD 6214 468 10 5 85 49

7B-6
Station Location Code RECS 2009 Heating 

ODT

Cooling 

ODT

Average 

Outdoor

State City HDD CDD Count Temp

OH Toledo TOL 6283 592 32 1 88 50

OH Youngstown YNG 6239 443 8 4 86 49

OK Hobart HBR 3392 2034 1 16 101 60

OK McAlester MLC 3136 1845 6 19 96 60

OK Oklahoma City OKC 3519 1849 37 13 96 60

OK Tulsa TUL 3608 1885 24 13 97 61

OR Astoria AST 4871 39 4 29 72 51

OR Baker BKE 7529 220 2 6 91 45

OR Eugene EUG 4999 331 89 22 88 52

OR Medford MFR - - - 23 95 54

OR Pendleton PDT 5713 720 6 5 93 52

OR Portland PDX 4357 635 32 -1 83 46

OR Redmond RDM 6737 313 17 9 90 44

OR Salem SLE 4660 457 50 23 88 53

PA Allentown ABE 5725 622 22 9 88 51

PA Altoona AOO 6109 433 17 5 86 50

PA Bradford BFD - - - -1 80 50

PA Du Bois DUJ 6753 254 5 5 84 50

PA Erie ERI 6183 423 9 9 84 50

PA Harrisburg CXY 5097 866 111 11 90 53

PA Philadelphia PHL 4557 1219 46 14 90 55

PA Pittsburgh PIT 5661 617 6 5 87 51

PA Williamsport IPT 5636 644 69 7 87 50

RI Providence PVD 5717 579 69 9 86 51

SC Charleston CHS 1941 2390 13 27 93 65

SC Columbia CAE 2561 2220 19 4 92 54

SC Florence FLO 2541 2061 13 25 94 64

SC Greenville GSP 3116 1735 42 22 91 60

SD Aberdeen ABR 8872 329 13 -15 91 44

SD Huron HON 8070 469 105 -14 91 45

SD Pierre PIR 7738 577 36 -10 95 48

SD Rapid City RAP 7738 362 12 -7 91 47

SD Sioux Falls FSD 7670 481 42 -11 90 45

TN Bristol TRI 4267 930 28 14 87 55

TN Chattanooga CHA 3168 1808 35 18 92 60

TN Crossville CSV 4100 940 33 15 87 54

TN Jackson MKL 3379 1597 22 14 87 56

TN Knoxville TYS 3643 1392 91 19 90 58

TN Memphis MEM 2906 2091 3 18 94 62

TN Nashville BNA 3615 1558 37 14 92 59

TX Abilene ABI 2359 2494 217 20 97 64

TX Alice ALI 738 4832 23 34 99 72

TX Amarillo AMA 4034 1340 33 11 95 57

TX Austin AUS 1722 3214 45 28 96 69

TX Brownsville BRO 525 4300 20 39 94 73

TX College Station CLL 1404 3476 29 29 96 69

TX Corpus Christi CRP 811 4058 8 35 94 72

7B-7
Station Location Code RECS 2009 Heating 

ODT

Cooling 

ODT

Average 

Outdoor

State City HDD CDD Count Temp

TX Dallas-Ft. Worth DFW 2097 2745 61 22 98 66

TX Del Rio DRT 1252 3807 29 31 98 70

TX El Paso ELP 2106 2783 43 24 98 65

TX Galveston GLS 907 3640 3 36 91 71

TX Houston IAH 1267 3410 170 32 96 69

TX Laredo LRD 602 5330 1 36 101 73

TX Lubbock LBB 3178 1965 10 15 96 60

TX Lufkin LFK 1803 2839 64 29 95 69

TX McAllen MFE 393 5387 3 39 99 73

TX Midland Odessa MAF 2495 2445 81 21 98 63

TX San Angelo SJT 2020 2814 56 22 97 65

TX San Antonio SAT 1270 3598 28 30 97 69

TX Victoria VCT 1123 3608 35 32 95 70

TX Waco ACT 1927 3086 18 26 99 67

TX Wichita Falls SPS 2838 2394 14 18 100 63

UT Cedar City CDC 6058 645 56 5 91 52

UT Salt Lake City SLC 5716 1147 29 8 95 52

VA Lynchburg LYH 4433 1003 159 16 90 55

VA Norfolk ORF 3330 1659 41 -4 92 49

VA Richmond RIC 3781 1564 47 17 92 58

VA Roanoke ROA 3931 1173 34 16 90 56

VT Burlington BTV - - - -3 91 50

VT Montpelier MPV 7998 237 12 -6 83 45

WA Bellingham BLI 5568 115 8 15 76 51

WA Olympia OLM 5614 178 24 22 83 50

WA Quillayute UIL 5869 44 7 27 74 49

WA Seattle Tacoma SEA 4879 319 94 26 81 52

WA Spokane GEG 6942 599 5 2 89 47

WA Walla Walla ALW 5062 1144 12 7 95 54

WA Yakima YKM 6204 699 25 5 92 49

WI Eau Claire EAU 8208 333 23 -11 87 44

WI Green Bay GRB 8005 275 55 -9 85 44

WI Lacrosse LSE 7334 536 16 -9 89 47

WI Madison MSN 7343 368 66 -7 87 46

WI Milwaukee MKE 6816 474 28 -4 87 48

WI Wausau AUW 8337 277 54 -12 85 44

WV Beckley BKW 5325 404 16 4 84 52

WV Charleston CRW 4443 960 3 27 93 65

WV Elkins EKN 5993 284 3 6 83 50

WV Huntington HTS 4557 922 3 10 89 55

WV Martinsburg MRB 5046 854 63 10 91 54

WV Morgantown MGW 4957 836 15 8 87 54

WV Parkersburg PKB 4910 850 19 11 88 54

WY Casper CPR - - - -5 91 45

WY Cheyenne CYS 7390 203 11 -1 86 45

WY Cody COD 7551 410 2 -13 87 45

WY Lander LND 7743 351 1 -11 87 45

7B-8
Station Location Code RECS 2009 Heating 

ODT

Cooling 

ODT

Average 

Outdoor

State City HDD CDD Count Temp

WY Rock Springs RKS 8204 230 3 -3 84 43

WY Sheridan SHR 7844 287 2 -8 90 45

WY Worland WRL 7757 467 2 -13 93 45

7B.3.2 Developing Monthly Heating Degree Day Fractions

Table 7B.3.2 shows the 10-year average monthly HDD data based on NOAA data for 

each weather station.2 This data was then used to determine the monthly fractions of HDD as 

shown in Table 7B.3.3. Monthly HDD are used to disaggregate the annual energy use provided 

by RECS 2009 by month. The monthly energy use is then combined with monthly energy prices 

to find the monthly operating cost (see appendix 8C for more details).

Table 7B.3.2 Weather Station Monthly Heating Degree Day Data (10-Year Average)

Station Location Code Monthly Heating Degree Days

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

AK Anchorage ANC 1558 1202 1238 824 510 292 184 234 464 855 1316 1421

AK Bethel BET 1914 1447 1684 1155 661 355 273 318 547 1004 1477 1690

AK Cold Bay CDB 1167 971 1177 939 755 559 440 388 497 744 944 1068

AK Cordova CDV 1225 1001 1074 797 594 413 321 330 490 762 1064 1127

AK Homer HOM 1314 1052 1163 829 622 436 320 337 487 786 1127 1201

AK Juneau JNU 1138 971 1006 720 492 310 248 280 444 707 980 1110

AK Kenai ENA 1608 1223 1285 838 560 362 248 292 478 828 1303 1439

AK Ketchikan KTN 930 823 861 666 479 298 212 203 349 600 805 924

AK King Salmon AKN 1632 1199 1427 932 620 406 299 307 505 886 1342 1474

AK Kodiak ADQ 1090 928 1063 806 621 442 313 294 454 724 969 1059

AK Sitka SIT 895 787 868 670 513 354 258 233 345 577 779 876

AK St Paul Island SNP 1277 1131 1314 1086 910 671 552 489 572 802 965 1127

AK Talkeetna TKA 1636 1267 1281 836 494 234 155 239 504 909 1394 1529

AK Valdez VWS 1212 912 952 622 371 194 136 154 319 594 996 1036

AK Yakutat YAK 1144 966 1045 796 602 404 297 316 462 719 987 1086

AL Birmingham BHM 635 502 261 105 21 0 0 0 4 114 312 563

AL Huntsville HSV 722 578 324 135 25 0 0 0 9 145 371 641

AL Mobile MOB 427 331 143 45 4 0 0 0 0 55 201 385

AL Montgomery MGM 543 417 198 70 9 0 0 0 2 80 265 481

AL Muscle Shoals MSL 716 576 317 135 25 0 0 0 7 149 366 640

AL Tuscaloosa TCL 606 477 234 95 16 0 0 0 2 110 299 539

AR Fayetteville FYV 859 724 446 230 76 2 0 1 32 233 458 803

AR Fort Smith FSM 745 599 313 120 26 0 0 0 4 122 343 685

AR Little Rock LIT 708 579 310 114 22 0 0 0 3 122 341 654

AR Texarkana TXK 606 488 240 84 15 0 0 0 1 96 274 559

AZ Douglas DUG 555 426 298 118 14 0 0 0 0 70 307 571

AZ Flagstaff FLG 1066 946 828 640 389 136 13 36 202 533 797 1085

AZ Phoenix PHX 234 172 63 11 0 0 0 0 0 4 68 283

AZ Tucson TUS 342 280 143 44 2 0 0 0 0 15 140 381

7B-9
Station Location Code Monthly Heating Degree Days

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

AZ Winslow INW 918 716 526 311 97 4 0 0 20 256 586 913

AZ Yuma NYL 148 128 46 13 0 0 0 0 0 2 54 227

CA Bakersfield BFL 484 327 217 133 21 2 0 0 1 51 274 491

CA Blythe BLH 271 191 73 13 0 0 0 0 0 5 105 337

CA Eureka EKA 557 513 527 496 414 314 276 258 308 365 467 579

CA Fresno FAT 527 367 250 146 25 1 0 0 2 52 289 518

CA Los Angeles LAX 215 220 200 152 68 13 2 2 4 33 124 251

CA Mt Shasta MHS 876 754 707 568 322 126 11 20 114 414 704 908

CA Paso Robles PRB 507 418 341 257 89 22 2 2 17 123 344 535

CA Red Bluff RBL 530 408 325 218 54 7 0 0 4 86 343 558

CA Redding RDD 551 423 351 241 61 9 0 0 5 100 374 583

CA Sacramento SAC 544 397 307 211 56 8 0 0 6 84 340 549

CA San Diego SAN 214 196 157 107 47 14 2 0 0 15 105 243

CA San Francisco SFO 438 344 308 262 187 105 61 52 53 104 266 427

CA Stockton SCK 549 393 299 190 45 5 0 0 3 72 327 541

CO Alamosa ALS 1432 1202 914 655 390 139 25 63 283 642 1012 1438

CO Colorado Spring COS 1025 942 712 503 259 57 7 19 133 443 736 1074

CO Denver DEN 1016 946 687 487 252 54 4 10 107 422 727 1075

CO Eagle EGE 1322 1086 854 621 367 112 8 26 223 567 918 1305

CO Pueblo PUB 1020 895 623 393 151 17 1 4 71 377 698 1062

CO Trinidad TAD 960 861 626 412 176 26 2 6 79 357 665 1005

CT Bridgeport BDR 1076 920 758 432 190 27 0 1 35 283 544 886

CT Hartford BDL 1204 1013 811 441 195 32 1 5 74 376 649 1007

DC Washington DCA 885 753 520 245 68 3 0 0 9 191 438 755

DE Wilmington ILG 1000 867 639 341 124 13 0 1 26 274 529 849

FL Daytona Beach DAB 240 173 75 21 1 0 0 0 0 11 60 172

FL Fort Myers FMY 119 65 24 3 0 0 0 0 0 3 15 75

FL Ft Lauderdale FLL 61 34 11 1 0 0 0 0 0 0 4 37

FL Gainesville GNV 345 249 116 37 1 0 0 0 0 28 120 279

FL Jacksonville JAX 385 287 140 51 3 0 0 0 0 32 148 313

FL Key West EYW 42 19 5 0 0 0 3 0 0 0 1 16

FL Melbourne MLB 181 117 49 15 0 0 0 0 0 9 35 116

FL Miami MIA 54 29 8 1 0 0 0 0 0 0 3 31

FL Orlando MCO 196 124 48 11 0 0 0 0 0 9 40 134

FL Pensacola PNS 394 306 128 36 1 0 0 0 0 36 159 344

FL Tallahassee TLH 424 328 149 53 2 0 0 0 0 42 189 368

FL Tampa TPA 183 117 46 10 0 0 0 0 0 6 32 129

FL Vero Beach VRB 165 107 47 13 0 0 0 0 0 6 26 102

FL West Palm Beach PBI 92 53 20 4 0 0 0 0 0 3 11 55

GA Albany ABY 469 370 173 56 2 0 0 0 0 51 217 418

GA Athens AHN 662 522 304 131 23 0 0 0 4 126 352 602

GA Atlanta ATL 650 519 292 118 21 0 0 0 3 110 325 581

GA Augusta AGS 598 475 265 106 11 0 0 0 2 100 314 532

GA Brunswick SSI 411 328 151 58 3 0 0 0 0 36 154 333

GA Columbus CSG 543 421 204 67 5 0 0 0 1 68 240 472

GA Macon MCN 563 445 239 91 10 0 0 0 2 89 295 510

GA Savannah SAV 478 373 191 69 6 0 0 0 0 50 215 399

GA Waycross AYS 412 317 143 50 2 12 0 0 0 40 180 369

7B-10
Station Location Code Monthly Heating Degree Days

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

HI Hilo-Hawaii ITO 0 0 0 0 0 0 0 0 0 0 0 0

HI Honolulu-Oahu HNL 0 0 0 0 0 0 0 0 0 0 0 0

HI Kahului-Maui OGG 0 0 0 0 0 0 0 0 0 0 0 0

HI Lihue-Kauai LIH 0 0 0 0 0 0 0 0 0 0 0 0

IA Burlington BRL 1233 1043 667 337 125 8 1 5 71 339 617 1078

IA Cedar Rapids CID 1404 1197 807 441 187 21 5 14 128 450 763 1259

IA Des Moines DSM 1310 1109 716 360 128 6 0 4 72 359 697 1159

IA Dubuque DBQ 1421 1215 846 475 219 31 6 16 141 466 774 1275

IA Mason City MCW 1514 1288 924 514 230 33 6 23 153 505 865 1370

IA Ottumwa OTM 1310 1110 724 395 159 15 2 9 103 396 699 1152

IA Sioux City SUX 1393 1177 792 424 166 17 2 8 112 435 807 1282

IA Waterloo ALO 1449 1225 841 468 192 19 3 14 128 458 802 1304

ID Boise BOI 985 785 611 430 220 54 0 6 62 356 722 993

ID Burley BYI 1119 945 747 559 339 124 5 24 170 496 817 1106

ID Idaho Falls IDA 1379 1179 885 610 385 159 11 36 220 585 934 1270

ID Lewiston LWS 867 718 593 416 211 57 1 4 58 376 699 937

ID Pocatello PIH 1229 1046 806 589 366 128 5 22 182 535 887 1196

IL Chicago ORD 1256 1078 773 451 208 30 2 6 79 371 678 1109

IL Moline MLI 1280 1095 716 375 147 13 1 5 82 369 675 1145

IL Peoria PIA 1221 1034 666 346 134 10 0 5 64 348 646 1086

IL Quincy UIN 1178 996 628 328 120 8 0 5 66 332 611 1041

IL Rockford RFD 1330 1137 794 437 192 22 4 9 96 403 724 1193

IL Springfield SPI 1155 972 606 308 111 9 0 5 61 323 586 1006

IN Evansville EVV 981 828 505 242 73 4 0 1 27 247 516 863

IN Fort Wayne FWA 1247 1086 751 414 174 18 1 7 86 388 669 1074

IN Indianapolis IND 1131 968 620 316 120 10 1 3 46 314 592 987

IN South Bend SBN 1253 1090 783 442 210 29 2 11 96 404 685 1079

IN West Lafayette LAF 1178 1006 653 350 145 14 0 5 63 345 612 1022

KS Concordia CNK 1117 938 595 329 121 7 0 2 57 306 641 1067

KS Dodge City DDC 979 847 555 317 110 5 0 2 45 276 609 978

KS Garden City GCK 1010 868 572 336 112 6 1 2 43 294 624 1007

KS Goodland GLD 1059 946 668 436 196 24 3 5 89 394 720 1078

KS Russell RSL 1074 911 582 328 116 7 0 2 50 297 638 1050

KS Salina SLN 1043 872 532 279 87 3 0 1 36 256 570 997

KS Topeka TOP 1079 892 545 271 86 3 0 2 46 274 574 972

KS Wichita ICT 985 810 487 240 69 2 0 0 24 227 528 924

KY Bowling Green BWG 877 727 438 220 59 3 0 1 21 215 467 779

KY Jackson JKL 924 774 479 230 88 7 1 2 37 242 464 802

KY Lexington LEX 993 842 547 278 99 6 0 2 42 278 540 874

KY Louisville SDF 932 780 473 210 58 3 0 0 19 213 462 809

KY Paducah PAH 933 779 484 240 71 5 0 2 35 253 515 842

LA Baton Rouge BTR 398 310 128 40 3 0 0 0 0 48 182 376

LA Lafayette LFT 380 289 116 29 1 0 0 0 0 40 149 349

LA Lake Charles LCH 383 299 121 32 1 0 0 0 0 33 153 348

LA Monroe MLU 548 439 208 63 7 0 0 0 1 80 257 501

LA New Orleans MSY 347 263 99 23 0 0 0 0 0 24 124 309

LA Shreveport SHV 521 418 193 58 7 0 0 0 0 69 236 490

MA Boston BOS 1111 947 792 467 230 56 3 5 55 315 574 905

7B-11
Station Location Code Monthly Heating Degree Days

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

MA Worcester ORH 1273 1083 892 514 268 79 9 16 107 435 682 1057

MD Baltimore BWI 969 837 602 309 109 8 0 0 26 265 511 836

MD Salisbury SBY 929 806 599 328 141 14 1 1 29 254 481 775

ME Augusta AUG 1410 1181 992 594 317 101 12 23 148 484 769 1168

ME Bangor BGR 1475 1245 1039 633 333 106 14 30 177 510 808 1212

ME Caribou CAR 1680 1420 1228 766 423 153 36 79 268 628 944 1401

ME Houlton HUL 1630 1393 1184 756 438 172 48 84 272 620 907 1359

ME Portland PWM 1318 1116 950 598 332 103 11 19 138 463 727 1089

MI Alpena APN 1419 1264 1076 685 387 118 31 54 209 545 826 1219

MI Detroit DTW 1226 1077 812 445 192 22 2 5 77 378 665 1049

MI Flint FNT 1306 1148 884 513 252 47 11 19 130 452 733 1116

MI Grand Rapids GRR 1264 1118 853 488 229 34 5 11 108 434 713 1077

MI Houghton Lake HTL 1433 1280 1044 629 335 103 37 65 223 562 836 1225

MI Jackson JXN 1286 1126 842 483 238 45 9 17 131 451 713 1100

MI Lansing LAN 1303 1148 873 510 250 46 10 19 130 455 729 1110

MI Marquette MQT 1515 1339 1152 765 441 162 64 82 264 616 945 1356

MI Muskegon MKG 1215 1097 875 528 268 54 8 17 119 432 701 1043

MI Saginaw MBS 1323 1172 917 540 261 48 10 20 129 450 738 1129

MI Sault St Marie SSM 1526 1324 1144 715 404 152 51 61 217 571 869 1299

MI Traverse City TVC 1312 1186 980 630 342 90 21 26 155 486 763 1132

MN Alexandria AXN 1675 1443 1085 589 298 66 10 29 183 561 967 1497

MN Duluth DLH 1676 1419 1140 713 430 156 45 69 256 630 1004 1506

MN Hibbing HIB 1780 1506 1170 737 454 181 75 126 334 695 1073 1599

MN Int'l Falls INL 1853 1586 1229 734 443 167 69 125 328 695 1099 1660

MN Minneapolis MSP 1512 1267 921 466 203 25 2 8 116 460 842 1353

MN Rochester RST 1531 1308 944 512 236 38 8 25 147 489 851 1377

MN Saint Cloud STC 1625 1385 1036 565 288 56 10 29 184 553 949 1463

MO Columbia COU 1077 896 547 274 96 5 0 3 48 287 557 955

MO Joplin JLN 914 751 439 218 70 3 0 1 28 217 445 821

MO Kansas City MCI 1104 918 564 280 92 5 0 1 46 281 575 993

MO Saint Louis STL 1002 835 498 230 67 3 0 1 26 227 498 883

MO Springfield SGF 970 800 498 258 89 4 0 2 39 261 511 881

MS Greenwood GWO 628 506 255 98 17 0 0 0 5 123 282 565

MS McComb MCB 490 384 174 61 9 0 0 0 1 71 230 446

MS Tupelo TUP 692 555 294 124 23 0 0 0 6 134 343 621

MT Billings BIL 1148 1004 776 536 332 93 3 13 146 499 829 1170

MT Butte BTM 1349 1201 996 783 564 301 67 117 366 724 1096 1424

MT Cut Bank CTB 1288 1157 981 709 487 238 50 88 300 671 984 1319

MT Great Falls GTF 1189 1070 889 641 431 188 22 50 231 575 900 1211

MT Havre HVR 1435 1273 990 608 380 142 13 42 234 612 1003 1418

MT Helena HLN 1252 1035 837 594 370 140 7 30 188 566 935 1285

MT Kalispell FCA 1145 946 764 513 281 132 20 45 216 517 882 1158

MT Lewistown LWT 1254 1139 939 702 490 235 41 72 275 631 956 1279

MT Miles City MLS 1368 1191 866 519 312 82 2 12 155 535 918 1331

MT Missoula MSO 1201 979 807 598 384 168 15 33 205 600 960 1246

NC Asheville AVL 857 712 500 267 89 6 0 0 33 272 521 786

NC Cape Hatteras HAT 440 383 263 90 21 0 0 0 1 24 102 292

NC Charlotte CLT 734 598 373 172 40 0 0 0 9 169 412 658

7B-12
Station Location Code Monthly Heating Degree Days

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

NC Greensboro GSO 780 648 415 192 55 2 0 0 14 187 429 700

NC Hickory HKY 780 647 412 190 53 3 0 0 15 189 427 706

NC New Bern EWN 659 548 352 148 29 0 0 0 2 117 315 552

NC Raleigh Durham RDU 729 599 384 170 43 1 0 0 9 163 385 642

NC Wilmington ILM 617 510 324 128 24 0 0 0 1 103 297 517

ND Bismarck BIS 1595 1394 1044 589 328 80 8 28 198 594 1015 1472

ND Devil's Lake P11 1771 1580 1237 670 373 117 21 45 227 630 1091 1612

ND Fargo FAR 1718 1498 1119 575 288 56 12 31 185 557 992 1522

ND Grand Forks GFK 1803 1593 1226 639 355 83 22 46 223 618 1078 1609

ND Minot MOT 1606 1438 1119 609 360 101 17 37 204 624 1051 1512

ND Williston ISN 1635 1447 1089 620 381 119 13 36 226 654 1090 1553

NE Grand Island GRI 1231 1042 698 398 160 17 2 5 86 389 739 1172

NE Lincoln LNK 1261 1055 680 377 137 10 1 5 83 371 717 1169

NE Norfolk OFK 1313 1106 752 426 176 22 3 8 109 417 777 1223

NE North Platte LBF 1210 1048 752 487 234 37 3 10 135 477 821 1219

NE Omaha OMA 1292 1088 705 363 133 10 1 5 75 360 714 1179

NE Scottsbluff BFF 1138 1000 742 510 263 53 5 12 133 482 811 1190

NE Valentine VTN 1262 1117 816 510 260 51 4 12 143 496 848 1247

NH Concord CON 1380 1159 965 571 282 81 9 26 151 487 770 1150

NH Lebanon LEB 1439 1217 1000 598 285 81 9 28 152 511 794 1199

NJ Atlantic City ACY 975 847 663 366 151 20 0 1 31 263 512 817

NJ Newark EWR 1016 856 651 338 113 11 0 1 20 253 510 844

NM Albuquerque ABQ 821 668 469 236 58 1 0 0 15 199 536 859

NM Carlsbad CNM 617 470 255 86 21 0 0 0 4 94 349 638

NM Clayton CAO 853 788 564 343 126 11 2 5 52 278 558 888

NM Gallup GUP 1078 901 765 535 279 52 1 4 124 464 801 1090

NM Roswell ROW 701 545 314 123 27 0 0 0 7 127 418 740

NV Elko EKO 1221 990 795 593 349 109 2 16 156 516 863 1180

NV Ely ELY 1215 1032 864 663 434 151 6 33 218 569 884 1214

NV Las Vegas LAS 470 350 165 58 6 0 0 0 0 28 228 522

NV Lovelock LOL 987 771 617 431 183 35 0 4 71 373 741 1039

NV Reno RNO 886 727 585 431 190 43 0 3 47 303 637 912

NV Tonopah TPH 955 807 675 482 211 42 0 3 62 347 697 1011

NV Winnemucca WMC 1053 859 727 564 317 96 2 15 151 498 826 1097

NY Albany ALB 1303 1106 885 489 215 37 2 11 99 438 713 1092

NY Binghamton BGM 1328 1145 942 561 274 77 12 26 150 487 743 1133

NY Buffalo BUF 1242 1122 911 551 247 49 4 10 94 424 674 1041

NY Glens Falls GFL 1437 1236 996 581 282 74 12 34 177 518 789 1193

NY Massena MSS 1531 1318 1083 622 302 81 10 36 169 537 835 1287

NY New York LGA 976 834 657 338 118 12 0 1 11 206 456 786

NY Rochester ROC 1241 1106 888 537 254 54 6 15 107 427 692 1038

NY Syracuse SYR 1271 1125 908 523 232 46 3 12 99 417 690 1069

NY Utica UCA 1198 1011 784 386 142 32 2 11 57 307 599 991

NY Watertown ART 1434 1269 1007 607 308 89 14 35 157 486 757 1159

OH Akron Canton CAK 1208 1066 800 439 192 39 3 8 91 406 664 1036

OH Cincinnati CVG 1063 908 603 304 110 9 1 2 44 309 577 930

OH Cleveland CLE 1166 1030 795 451 197 32 2 5 71 365 619 994

OH Columbus CMH 1109 963 665 326 124 11 0 2 47 329 586 950

7B-13
Station Location Code Monthly Heating Degree Days

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

OH Dayton DAY 1171 1010 697 368 151 15 1 4 67 358 628 1013

OH Findlay FDY 1212 1055 755 412 166 19 1 5 81 371 639 1041

OH Mansfield MFD 1229 1079 799 447 203 42 6 11 100 415 664 1053

OH Toledo TOL 1231 1079 797 446 189 22 1 6 86 389 678 1067

OH Youngstown YNG 1215 1068 820 464 219 55 7 16 108 425 666 1037

OK Hobart HBR 769 642 367 174 41 0 0 0 6 149 401 753

OK McAlester MLC 731 588 308 140 33 0 0 0 9 135 335 673

OK Oklahoma City OKC 785 640 347 148 35 1 0 0 9 144 390 744

OK Tulsa TUL 809 663 363 155 37 1 0 0 11 151 388 751

OR Astoria AST 646 584 578 493 365 222 127 112 184 362 547 682

OR Baker BKE 1176 964 817 649 426 214 38 62 256 611 923 1191

OR Eugene EUG 723 629 558 455 305 147 22 23 100 358 595 763

OR Medford MFR 760 595 527 391 188 52 1 3 39 276 606 790

OR Pendleton PDT 910 745 606 454 248 85 5 8 88 399 715 972

OR Portland PDX 718 594 523 388 217 89 8 8 59 290 554 755

OR Redmond RDM 946 835 760 640 414 214 42 60 214 540 801 1029

OR Salem SLE 712 616 554 443 269 121 14 14 86 336 583 755

PA Allentown ABE 1134 974 739 413 168 23 1 4 61 362 623 966

PA Altoona AOO 1176 1037 788 440 203 42 5 12 108 413 659 1028

PA Bradford BFD 1367 1213 991 621 350 138 58 75 229 574 797 1176

PA Du Bois DUJ 1276 1125 867 509 255 78 16 27 141 473 714 1103

PA Erie ERI 1167 1068 869 529 252 47 5 8 78 372 619 976

PA Harrisburg CXY 1049 918 670 355 133 15 0 2 44 318 573 908

PA Philadelphia PHL 986 848 613 308 94 7 0 1 15 238 490 822

PA Pittsburgh PIT 1141 997 731 386 165 27 2 5 76 381 631 981

PA Williamsport IPT 1165 1011 766 414 175 26 1 6 71 380 649 1001

RI Providence PVD 1104 946 781 453 218 44 2 3 57 331 585 916

SC Charleston CHS 504 393 205 72 7 0 0 0 1 62 226 412

SC Columbia CAE 606 478 269 103 12 0 0 0 3 101 321 543

SC Florence FLO 624 498 285 113 18 0 0 0 3 106 301 524

SC Greenville GSP 688 567 344 145 32 1 0 0 6 147 375 632

SD Aberdeen ABR 1624 1399 1024 572 285 57 7 28 192 573 993 1471

SD Huron HON 1494 1288 919 516 245 45 5 15 147 505 909 1378

SD Pierre PIR 1384 1215 887 513 267 51 3 9 129 495 893 1316

SD Rapid City RAP 1228 1116 840 559 332 88 6 16 154 509 863 1228

SD Sioux Falls FSD 1479 1265 900 502 239 35 4 15 141 488 872 1356

TN Bristol TRI 903 748 497 258 82 4 0 1 28 266 531 820

TN Chattanooga CHA 737 589 338 141 30 0 0 0 6 144 393 668

TN Crossville CSV 898 742 482 252 89 6 1 2 37 256 489 793

TN Jackson MKL 797 660 373 173 41 1 0 1 17 186 418 712

TN Knoxville TYS 808 654 397 182 46 1 0 0 11 191 444 738

TN Memphis MEM 700 566 296 111 18 0 0 0 3 115 329 623

TN Nashville BNA 813 665 392 175 45 2 0 0 12 175 422 718

TX Abilene ABI 584 469 230 79 20 0 0 0 3 87 279 570

TX Alice ALI 242 176 58 9 0 0 0 0 0 9 64 224

TX Amarillo AMA 820 717 457 242 81 2 0 1 25 218 501 821

TX Austin AUS 470 356 165 46 3 0 0 0 0 44 192 436

TX Brownsville BRO 144 109 28 7 0 0 0 0 0 1 28 145

7B-14
Station Location Code Monthly Heating Degree Days

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

TX College Station CLL 417 316 127 33 2 0 0 0 0 32 145 375

TX Corpus Christi CRP 252 177 58 11 0 0 0 0 0 9 66 226

TX Dallas-Ft. Worth DFW 550 438 195 53 9 0 0 0 0 53 213 506

TX Del Rio DRT 384 253 80 16 1 0 0 0 0 18 135 380

TX El Paso ELP 560 416 216 62 7 0 0 0 1 52 303 584

TX Galveston GLS 291 224 73 12 0 0 0 0 0 7 62 233

TX Houston IAH 362 271 106 23 0 0 0 0 0 23 129 332

TX Laredo LRD 216 146 31 6 0 0 0 0 0 5 47 199

TX Lubbock LBB 710 584 337 150 45 0 0 0 10 143 405 718

TX Lufkin LFK 474 368 165 49 4 0 0 0 0 53 202 433

TX McAllen MFE 160 117 22 10 0 0 0 0 0 3 31 149

TX Midland Odessa MAF 622 480 254 83 19 0 0 0 4 87 329 614

TX San Angelo SJT 548 422 196 67 10 0 0 0 1 72 259 524

TX San Antonio SAT 374 275 105 24 2 0 0 0 1 28 135 354

TX Victoria VCT 337 243 90 21 1 0 0 0 0 22 116 306

TX Waco ACT 521 404 184 55 6 0 0 0 0 54 211 485

TX Wichita Falls SPS 665 540 267 95 23 0 0 0 2 94 306 641

UT Cedar City CDC 1100 910 731 535 268 58 0 3 103 436 771 1110

UT Salt Lake City SLC 1092 857 641 432 218 45 0 3 58 350 718 1055

VA Lynchburg LYH 904 773 526 269 101 7 0 2 34 271 523 822

VA Norfolk ORF 737 632 440 201 55 1 0 0 3 129 349 605

VA Richmond RIC 821 694 463 213 60 2 0 0 10 181 409 704

VA Roanoke ROA 846 719 478 229 78 4 0 0 27 224 474 765

VT Burlington BTV 1427 1226 1002 581 263 61 5 23 133 495 768 1188

VT Montpelier MPV 1501 1280 1065 652 335 114 24 52 200 568 842 1269

WA Bellingham BLI 767 672 628 492 341 187 78 78 209 442 647 792

WA Olympia OLM 772 686 636 510 342 186 67 68 180 433 658 817

WA Quillayute UIL 702 639 651 549 430 282 179 160 237 441 619 742

WA Seattle Tacoma SEA 706 611 586 451 285 142 34 29 122 368 597 745

WA Spokane GEG 1094 904 769 556 320 141 17 23 147 511 879 1144

WA Walla Walla ALW 869 701 532 383 195 51 1 3 51 335 662 930

WA Yakima YKM 1029 785 662 476 242 84 8 15 124 456 817 1102

WI Eau Claire EAU 1546 1309 977 533 258 49 7 26 169 524 878 1386

WI Green Bay GRB 1452 1253 979 576 289 60 12 23 165 494 813 1274

WI Lacrosse LSE 1452 1226 870 457 197 27 2 11 117 442 790 1303

WI Madison MSN 1404 1202 887 510 240 36 6 17 140 462 784 1247

WI Milwaukee MKE 1288 1111 858 550 289 64 7 9 100 397 713 1135

WI Wausau AUW 1547 1312 1019 585 294 67 13 31 192 549 892 1369

WV Beckley BKW 1050 897 647 355 171 36 9 9 87 368 607 938

WV Charleston CRW 942 810 537 257 100 7 0 1 36 273 519 838

WV Elkins EKN 1130 996 750 455 225 62 17 11 122 436 693 1005

WV Huntington HTS 956 811 533 253 95 6 0 1 39 280 528 846

WV Martinsburg MRB 1020 884 642 341 138 15 0 1 48 323 569 906

WV Morgantown MGW 1040 904 633 337 138 19 3 2 52 333 558 890

WV Parkersburg PKB 1040 891 605 302 124 14 0 2 50 329 567 904

WY Casper CPR 1191 1088 864 644 408 129 12 28 208 574 880 1244

WY Cheyenne CYS 1084 1037 826 630 390 120 14 30 201 556 837 1163

WY Cody COD 1171 1048 793 600 382 139 14 36 196 551 869 1209

7B-15
Station Location Code Monthly Heating Degree Days

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

WY Lander LND 1301 1128 842 605 380 121 7 22 171 563 923 1328

WY Rock Springs RKS 1320 1173 941 691 444 159 8 35 219 598 980 1336

WY Sheridan SHR 1220 1081 843 612 404 138 11 30 197 555 908 1272

WY Worland WRL 1416 1161 795 529 321 85 5 16 167 553 934 1415

Table 7B.3.3 Weather Station Monthly Heating Degree Day Data Fractions (10-Year 

Average)

Station Location Code Monthly Heating Degree Day Fractions

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

AK Anchorage ANC 15% 12% 12% 8% 5% 3% 2% 2% 5% 8% 13% 14%

AK Bethel BET 15% 12% 13% 9% 5% 3% 2% 3% 4% 8% 12% 13%

AK Cold Bay CDB 12% 10% 12% 10% 8% 6% 5% 4% 5% 8% 10% 11%

AK Cordova CDV 13% 11% 12% 9% 6% 4% 3% 4% 5% 8% 12% 12%

AK Homer HOM 14% 11% 12% 9% 6% 5% 3% 3% 5% 8% 12% 12%

AK Juneau JNU 14% 12% 12% 9% 6% 4% 3% 3% 5% 8% 12% 13%

AK Kenai ENA 15% 12% 12% 8% 5% 3% 2% 3% 5% 8% 12% 14%

AK Ketchikan KTN 13% 12% 12% 9% 7% 4% 3% 3% 5% 8% 11% 13%

AK King Salmon AKN 15% 11% 13% 8% 6% 4% 3% 3% 5% 8% 12% 13%

AK Kodiak ADQ 12% 11% 12% 9% 7% 5% 4% 3% 5% 8% 11% 12%

AK Sitka SIT 13% 11% 12% 9% 7% 5% 4% 3% 5% 8% 11% 12%

AK St Paul Island SNP 12% 10% 12% 10% 8% 6% 5% 4% 5% 7% 9% 10%

AK Talkeetna TKA 16% 12% 12% 8% 5% 2% 1% 2% 5% 9% 13% 15%

AK Valdez VWS 16% 12% 13% 8% 5% 3% 2% 2% 4% 8% 13% 14%

AK Yakutat YAK 13% 11% 12% 9% 7% 5% 3% 4% 5% 8% 11% 12%

AL Birmingham BHM 25% 20% 10% 4% 1% 0% 0% 0% 0% 5% 12% 22%

AL Huntsville HSV 24% 20% 11% 5% 1% 0% 0% 0% 0% 5% 13% 22%

AL Mobile MOB 27% 21% 9% 3% 0% 0% 0% 0% 0% 3% 13% 24%

AL Montgomery MGM 26% 20% 10% 3% 0% 0% 0% 0% 0% 4% 13% 23%

AL Muscle Shoals MSL 24% 20% 11% 5% 1% 0% 0% 0% 0% 5% 12% 22%

AL Tuscaloosa TCL 25% 20% 10% 4% 1% 0% 0% 0% 0% 5% 13% 23%

AR Fayetteville FYV 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 21%

AR Fort Smith FSM 25% 20% 11% 4% 1% 0% 0% 0% 0% 4% 12% 23%

AR Little Rock LIT 25% 20% 11% 4% 1% 0% 0% 0% 0% 4% 12% 23%

AR Texarkana TXK 26% 21% 10% 4% 1% 0% 0% 0% 0% 4% 12% 24%

AZ Douglas DUG 24% 18% 13% 5% 1% 0% 0% 0% 0% 3% 13% 24%

AZ Flagstaff FLG 16% 14% 12% 10% 6% 2% 0% 1% 3% 8% 12% 16%

AZ Phoenix PHX 28% 21% 8% 1% 0% 0% 0% 0% 0% 1% 8% 34%

AZ Tucson TUS 25% 21% 11% 3% 0% 0% 0% 0% 0% 1% 10% 28%

AZ Winslow INW 21% 16% 12% 7% 2% 0% 0% 0% 0% 6% 13% 21%

AZ Yuma NYL 24% 21% 7% 2% 0% 0% 0% 0% 0% 0% 9% 37%

CA Bakersfield BFL 24% 16% 11% 7% 1% 0% 0% 0% 0% 3% 14% 25%

CA Blythe BLH 27% 19% 7% 1% 0% 0% 0% 0% 0% 1% 11% 34%

CA Eureka EKA 11% 10% 10% 10% 8% 6% 5% 5% 6% 7% 9% 11%

CA Fresno FAT 24% 17% 11% 7% 1% 0% 0% 0% 0% 2% 13% 24%

CA Los Angeles LAX 17% 17% 16% 12% 5% 1% 0% 0% 0% 3% 10% 20%

CA Mt Shasta MHS 16% 14% 13% 10% 6% 2% 0% 0% 2% 7% 13% 16%

CA Paso Robles PRB 19% 16% 13% 10% 3% 1% 0% 0% 1% 5% 13% 20%

7B-16
Station Location Code Monthly Heating Degree Day Fractions

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

CA Red Bluff RBL 21% 16% 13% 9% 2% 0% 0% 0% 0% 3% 14% 22%

CA Redding RDD 20% 16% 13% 9% 2% 0% 0% 0% 0% 4% 14% 22%

CA Sacramento SAC 22% 16% 12% 8% 2% 0% 0% 0% 0% 3% 14% 22%

CA San Diego SAN 19% 18% 14% 10% 4% 1% 0% 0% 0% 1% 10% 22%

CA San Francisco SFO 17% 13% 12% 10% 7% 4% 2% 2% 2% 4% 10% 16%

CA Stockton SCK 23% 16% 12% 8% 2% 0% 0% 0% 0% 3% 13% 22%

CO Alamosa ALS 17% 15% 11% 8% 5% 2% 0% 1% 3% 8% 12% 18%

CO Colorado Spring COS 17% 16% 12% 9% 4% 1% 0% 0% 2% 7% 12% 18%

CO Denver DEN 18% 16% 12% 8% 4% 1% 0% 0% 2% 7% 13% 19%

CO Eagle EGE 18% 15% 12% 8% 5% 2% 0% 0% 3% 8% 12% 18%

CO Pueblo PUB 19% 17% 12% 7% 3% 0% 0% 0% 1% 7% 13% 20%

CO Trinidad TAD 19% 17% 12% 8% 3% 1% 0% 0% 2% 7% 13% 19%

CT Bridgeport BDR 21% 18% 15% 8% 4% 1% 0% 0% 1% 5% 11% 17%

CT Hartford BDL 21% 17% 14% 8% 3% 1% 0% 0% 1% 6% 11% 17%

DC Washington DCA 23% 19% 13% 6% 2% 0% 0% 0% 0% 5% 11% 20%

DE Wilmington ILG 21% 19% 14% 7% 3% 0% 0% 0% 1% 6% 11% 18%

FL Daytona Beach DAB 32% 23% 10% 3% 0% 0% 0% 0% 0% 2% 8% 23%

FL Fort Myers FMY 39% 21% 8% 1% 0% 0% 0% 0% 0% 1% 5% 25%

FL Ft Lauderdale FLL 41% 23% 7% 1% 0% 0% 0% 0% 0% 0% 3% 25%

FL Gainesville GNV 29% 21% 10% 3% 0% 0% 0% 0% 0% 2% 10% 24%

FL Jacksonville JAX 28% 21% 10% 4% 0% 0% 0% 0% 0% 2% 11% 23%

FL Key West EYW 49% 22% 6% 0% 0% 0% 4% 0% 0% 0% 1% 18%

FL Melbourne MLB 35% 22% 9% 3% 0% 0% 0% 0% 0% 2% 7% 22%

FL Miami MIA 43% 23% 7% 0% 0% 0% 0% 0% 0% 0% 3% 25%

FL Orlando MCO 35% 22% 9% 2% 0% 0% 0% 0% 0% 2% 7% 24%

FL Pensacola PNS 28% 22% 9% 3% 0% 0% 0% 0% 0% 3% 11% 24%

FL Tallahassee TLH 27% 21% 10% 3% 0% 0% 0% 0% 0% 3% 12% 24%

FL Tampa TPA 35% 22% 9% 2% 0% 0% 0% 0% 0% 1% 6% 25%

FL Vero Beach VRB 35% 23% 10% 3% 0% 0% 0% 0% 0% 1% 6% 22%

FL West Palm Beach PBI 39% 22% 8% 2% 0% 0% 0% 0% 0% 1% 5% 23%

GA Albany ABY 27% 21% 10% 3% 0% 0% 0% 0% 0% 3% 12% 24%

GA Athens AHN 24% 19% 11% 5% 1% 0% 0% 0% 0% 5% 13% 22%

GA Atlanta ATL 25% 20% 11% 5% 1% 0% 0% 0% 0% 4% 12% 22%

GA Augusta AGS 25% 20% 11% 4% 0% 0% 0% 0% 0% 4% 13% 22%

GA Brunswick SSI 28% 22% 10% 4% 0% 0% 0% 0% 0% 2% 10% 23%

GA Columbus CSG 27% 21% 10% 3% 0% 0% 0% 0% 0% 3% 12% 23%

GA Macon MCN 25% 20% 11% 4% 0% 0% 0% 0% 0% 4% 13% 23%

GA Savannah SAV 27% 21% 11% 4% 0% 0% 0% 0% 0% 3% 12% 22%

GA Waycross AYS 27% 21% 9% 3% 0% 1% 0% 0% 0% 3% 12% 24%

HI Hilo-Hawaii ITO 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8%

HI Honolulu-Oahu HNL 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8% 8%

HI Kahului-Maui OGG 0% 50% 50% 0% 0% 0% 0% 0% 0% 0% 0% 0%

HI Lihue-Kauai LIH 0% 0% 100% 0% 0% 0% 0% 0% 0% 0% 0% 0%

IA Burlington BRL 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 11% 20%

IA Cedar Rapids CID 21% 18% 12% 7% 3% 0% 0% 0% 2% 7% 11% 19%

IA Des Moines DSM 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 20%

IA Dubuque DBQ 21% 18% 12% 7% 3% 0% 0% 0% 2% 7% 11% 19%

IA Mason City MCW 20% 17% 12% 7% 3% 0% 0% 0% 2% 7% 12% 18%

7B-17
Station Location Code Monthly Heating Degree Day Fractions

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

IA Ottumwa OTM 22% 18% 12% 6% 3% 0% 0% 0% 2% 7% 12% 19%

IA Sioux City SUX 21% 18% 12% 6% 3% 0% 0% 0% 2% 7% 12% 19%

IA Waterloo ALO 21% 18% 12% 7% 3% 0% 0% 0% 2% 7% 12% 19%

ID Boise BOI 19% 15% 12% 8% 4% 1% 0% 0% 1% 7% 14% 19%

ID Burley BYI 17% 15% 12% 9% 5% 2% 0% 0% 3% 8% 13% 17%

ID Idaho Falls IDA 18% 15% 12% 8% 5% 2% 0% 0% 3% 8% 12% 17%

ID Lewiston LWS 18% 15% 12% 8% 4% 1% 0% 0% 1% 8% 14% 19%

ID Pocatello PIH 18% 15% 12% 8% 5% 2% 0% 0% 3% 8% 13% 17%

IL Chicago ORD 21% 18% 13% 7% 3% 0% 0% 0% 1% 6% 11% 18%

IL Moline MLI 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 11% 19%

IL Peoria PIA 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 20%

IL Quincy UIN 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 20%

IL Rockford RFD 21% 18% 13% 7% 3% 0% 0% 0% 2% 6% 11% 19%

IL Springfield SPI 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 11% 20%

IN Evansville EVV 23% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 20%

IN Fort Wayne FWA 21% 18% 13% 7% 3% 0% 0% 0% 1% 7% 11% 18%

IN Indianapolis IND 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 19%

IN South Bend SBN 21% 18% 13% 7% 3% 0% 0% 0% 2% 7% 11% 18%

IN West Lafayette LAF 22% 19% 12% 6% 3% 0% 0% 0% 1% 6% 11% 19%

KS Concordia CNK 22% 18% 11% 6% 2% 0% 0% 0% 1% 6% 12% 21%

KS Dodge City DDC 21% 18% 12% 7% 2% 0% 0% 0% 1% 6% 13% 21%

KS Garden City GCK 21% 18% 12% 7% 2% 0% 0% 0% 1% 6% 13% 21%

KS Goodland GLD 19% 17% 12% 8% 3% 0% 0% 0% 2% 7% 13% 19%

KS Russell RSL 21% 18% 12% 6% 2% 0% 0% 0% 1% 6% 13% 21%

KS Salina SLN 22% 19% 11% 6% 2% 0% 0% 0% 1% 5% 12% 21%

KS Topeka TOP 23% 19% 11% 6% 2% 0% 0% 0% 1% 6% 12% 20%

KS Wichita ICT 23% 19% 11% 6% 2% 0% 0% 0% 1% 5% 12% 22%

KY Bowling Green BWG 23% 19% 11% 6% 2% 0% 0% 0% 1% 6% 12% 20%

KY Jackson JKL 23% 19% 12% 6% 2% 0% 0% 0% 1% 6% 11% 20%

KY Lexington LEX 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 19%

KY Louisville SDF 24% 20% 12% 5% 1% 0% 0% 0% 0% 5% 12% 20%

KY Paducah PAH 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 20%

LA Baton Rouge BTR 27% 21% 9% 3% 0% 0% 0% 0% 0% 3% 12% 25%

LA Lafayette LFT 28% 21% 9% 2% 0% 0% 0% 0% 0% 3% 11% 26%

LA Lake Charles LCH 28% 22% 9% 2% 0% 0% 0% 0% 0% 2% 11% 25%

LA Monroe MLU 26% 21% 10% 3% 0% 0% 0% 0% 0% 4% 12% 24%

LA New Orleans MSY 29% 22% 8% 2% 0% 0% 0% 0% 0% 2% 10% 26%

LA Shreveport SHV 26% 21% 10% 3% 0% 0% 0% 0% 0% 3% 12% 25%

MA Boston BOS 20% 17% 15% 9% 4% 1% 0% 0% 1% 6% 11% 17%

MA Worcester ORH 20% 17% 14% 8% 4% 1% 0% 0% 2% 7% 11% 16%

MD Baltimore BWI 22% 19% 13% 7% 2% 0% 0% 0% 1% 6% 11% 19%

MD Salisbury SBY 21% 19% 14% 8% 3% 0% 0% 0% 1% 6% 11% 18%

ME Augusta AUG 20% 16% 14% 8% 4% 1% 0% 0% 2% 7% 11% 16%

ME Bangor BGR 19% 16% 14% 8% 4% 1% 0% 0% 2% 7% 11% 16%

ME Caribou CAR 19% 16% 14% 8% 5% 2% 0% 1% 3% 7% 10% 16%

ME Houlton HUL 18% 16% 13% 9% 5% 2% 1% 1% 3% 7% 10% 15%

ME Portland PWM 19% 16% 14% 9% 5% 1% 0% 0% 2% 7% 11% 16%

MI Alpena APN 18% 16% 14% 9% 5% 2% 0% 1% 3% 7% 11% 16%

7B-18
Station Location Code Monthly Heating Degree Day Fractions

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

MI Detroit DTW 21% 18% 14% 7% 3% 0% 0% 0% 1% 6% 11% 18%

MI Flint FNT 20% 17% 13% 8% 4% 1% 0% 0% 2% 7% 11% 17%

MI Grand Rapids GRR 20% 18% 13% 8% 4% 1% 0% 0% 2% 7% 11% 17%

MI Houghton Lake HTL 18% 16% 13% 8% 4% 1% 0% 1% 3% 7% 11% 16%

MI Jackson JXN 20% 17% 13% 7% 4% 1% 0% 0% 2% 7% 11% 17%

MI Lansing LAN 20% 17% 13% 8% 4% 1% 0% 0% 2% 7% 11% 17%

MI Marquette MQT 17% 15% 13% 9% 5% 2% 1% 1% 3% 7% 11% 16%

MI Muskegon MKG 19% 17% 14% 8% 4% 1% 0% 0% 2% 7% 11% 16%

MI Saginaw MBS 20% 17% 14% 8% 4% 1% 0% 0% 2% 7% 11% 17%

MI Sault St Marie SSM 18% 16% 14% 9% 5% 2% 1% 1% 3% 7% 10% 16%

MI Traverse City TVC 18% 17% 14% 9% 5% 1% 0% 0% 2% 7% 11% 16%

MN Alexandria AXN 20% 17% 13% 7% 4% 1% 0% 0% 2% 7% 12% 18%

MN Duluth DLH 19% 16% 13% 8% 5% 2% 0% 1% 3% 7% 11% 17%

MN Hibbing HIB 18% 15% 12% 8% 5% 2% 1% 1% 3% 7% 11% 16%

MN Int'l Falls INL 19% 16% 12% 7% 4% 2% 1% 1% 3% 7% 11% 17%

MN Minneapolis MSP 21% 18% 13% 6% 3% 0% 0% 0% 2% 6% 12% 19%

MN Rochester RST 21% 18% 13% 7% 3% 1% 0% 0% 2% 7% 11% 18%

MN Saint Cloud STC 20% 17% 13% 7% 4% 1% 0% 0% 2% 7% 12% 18%

MO Columbia COU 23% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 20%

MO Joplin JLN 23% 19% 11% 6% 2% 0% 0% 0% 1% 6% 11% 21%

MO Kansas City MCI 23% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 20%

MO Saint Louis STL 23% 20% 12% 5% 2% 0% 0% 0% 1% 5% 12% 21%

MO Springfield SGF 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 20%

MS Greenwood GWO 25% 20% 10% 4% 1% 0% 0% 0% 0% 5% 11% 23%

MS McComb MCB 26% 21% 9% 3% 0% 0% 0% 0% 0% 4% 12% 24%

MS Tupelo TUP 25% 20% 11% 4% 1% 0% 0% 0% 0% 5% 12% 22%

MT Billings BIL 18% 15% 12% 8% 5% 1% 0% 0% 2% 8% 13% 18%

MT Butte BTM 15% 13% 11% 9% 6% 3% 1% 1% 4% 8% 12% 16%

MT Cut Bank CTB 16% 14% 12% 9% 6% 3% 1% 1% 4% 8% 12% 16%

MT Great Falls GTF 16% 14% 12% 9% 6% 3% 0% 1% 3% 8% 12% 16%

MT Havre HVR 18% 16% 12% 7% 5% 2% 0% 1% 3% 8% 12% 17%

MT Helena HLN 17% 14% 12% 8% 5% 2% 0% 0% 3% 8% 13% 18%

MT Kalispell FCA 17% 14% 12% 8% 4% 2% 0% 1% 3% 8% 13% 17%

MT Lewistown LWT 16% 14% 12% 9% 6% 3% 1% 1% 3% 8% 12% 16%

MT Miles City MLS 19% 16% 12% 7% 4% 1% 0% 0% 2% 7% 13% 18%

MT Missoula MSO 17% 14% 11% 8% 5% 2% 0% 0% 3% 8% 13% 17%

NC Asheville AVL 21% 18% 12% 7% 2% 0% 0% 0% 1% 7% 13% 19%

NC Cape Hatteras HAT 27% 24% 16% 6% 1% 0% 0% 0% 0% 1% 6% 18%

NC Charlotte CLT 23% 19% 12% 5% 1% 0% 0% 0% 0% 5% 13% 21%

NC Greensboro GSO 23% 19% 12% 6% 2% 0% 0% 0% 0% 5% 13% 20%

NC Hickory HKY 23% 19% 12% 6% 2% 0% 0% 0% 0% 6% 12% 21%

NC New Bern EWN 24% 20% 13% 5% 1% 0% 0% 0% 0% 4% 12% 20%

NC Raleigh Durham RDU 23% 19% 12% 5% 1% 0% 0% 0% 0% 5% 12% 21%

NC Wilmington ILM 24% 20% 13% 5% 1% 0% 0% 0% 0% 4% 12% 21%

ND Bismarck BIS 19% 17% 13% 7% 4% 1% 0% 0% 2% 7% 12% 18%

ND Devil's Lake P11 19% 17% 13% 7% 4% 1% 0% 0% 2% 7% 12% 17%

ND Fargo FAR 20% 18% 13% 7% 3% 1% 0% 0% 2% 7% 12% 18%

ND Grand Forks GFK 19% 17% 13% 7% 4% 1% 0% 0% 2% 7% 12% 17%

7B-19
Station Location Code Monthly Heating Degree Day Fractions

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

ND Minot MOT 19% 17% 13% 7% 4% 1% 0% 0% 2% 7% 12% 17%

ND Williston ISN 18% 16% 12% 7% 4% 1% 0% 0% 3% 7% 12% 18%

NE Grand Island GRI 21% 18% 12% 7% 3% 0% 0% 0% 1% 7% 12% 20%

NE Lincoln LNK 21% 18% 12% 6% 2% 0% 0% 0% 1% 6% 12% 20%

NE Norfolk OFK 21% 17% 12% 7% 3% 0% 0% 0% 2% 7% 12% 19%

NE North Platte LBF 19% 16% 12% 8% 4% 1% 0% 0% 2% 7% 13% 19%

NE Omaha OMA 22% 18% 12% 6% 2% 0% 0% 0% 1% 6% 12% 20%

NE Scottsbluff BFF 18% 16% 12% 8% 4% 1% 0% 0% 2% 8% 13% 19%

NE Valentine VTN 19% 17% 12% 8% 4% 1% 0% 0% 2% 7% 13% 18%

NH Concord CON 20% 16% 14% 8% 4% 1% 0% 0% 2% 7% 11% 16%

NH Lebanon LEB 20% 17% 14% 8% 4% 1% 0% 0% 2% 7% 11% 16%

NJ Atlantic City ACY 21% 18% 14% 8% 3% 0% 0% 0% 1% 6% 11% 18%

NJ Newark EWR 22% 19% 14% 7% 2% 0% 0% 0% 0% 5% 11% 18%

NM Albuquerque ABQ 21% 17% 12% 6% 2% 0% 0% 0% 0% 5% 14% 22%

NM Carlsbad CNM 24% 19% 10% 3% 1% 0% 0% 0% 0% 4% 14% 25%

NM Clayton CAO 19% 18% 13% 8% 3% 0% 0% 0% 1% 6% 13% 20%

NM Gallup GUP 18% 15% 13% 9% 5% 1% 0% 0% 2% 8% 13% 18%

NM Roswell ROW 23% 18% 10% 4% 1% 0% 0% 0% 0% 4% 14% 25%

NV Elko EKO 18% 15% 12% 9% 5% 2% 0% 0% 2% 8% 13% 17%

NV Ely ELY 17% 14% 12% 9% 6% 2% 0% 0% 3% 8% 12% 17%

NV Las Vegas LAS 26% 19% 9% 3% 0% 0% 0% 0% 0% 2% 12% 29%

NV Lovelock LOL 19% 15% 12% 8% 3% 1% 0% 0% 1% 7% 14% 20%

NV Reno RNO 19% 15% 12% 9% 4% 1% 0% 0% 1% 6% 13% 19%

NV Tonopah TPH 18% 15% 13% 9% 4% 1% 0% 0% 1% 7% 13% 19%

NV Winnemucca WMC 17% 14% 12% 9% 5% 2% 0% 0% 2% 8% 13% 18%

NY Albany ALB 20% 17% 14% 8% 3% 1% 0% 0% 2% 7% 11% 17%

NY Binghamton BGM 19% 17% 14% 8% 4% 1% 0% 0% 2% 7% 11% 16%

NY Buffalo BUF 19% 18% 14% 9% 4% 1% 0% 0% 1% 7% 11% 16%

NY Glens Falls GFL 20% 17% 14% 8% 4% 1% 0% 0% 2% 7% 11% 16%

NY Massena MSS 20% 17% 14% 8% 4% 1% 0% 0% 2% 7% 11% 16%

NY New York LGA 22% 19% 15% 8% 3% 0% 0% 0% 0% 5% 10% 18%

NY Rochester ROC 19% 17% 14% 8% 4% 1% 0% 0% 2% 7% 11% 16%

NY Syracuse SYR 20% 18% 14% 8% 4% 1% 0% 0% 2% 7% 11% 17%

NY Utica UCA 22% 18% 14% 7% 3% 1% 0% 0% 1% 6% 11% 18%

NY Watertown ART 20% 17% 14% 8% 4% 1% 0% 0% 2% 7% 10% 16%

OH Akron Canton CAK 20% 18% 13% 7% 3% 1% 0% 0% 2% 7% 11% 17%

OH Cincinnati CVG 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 19%

OH Cleveland CLE 20% 18% 14% 8% 3% 1% 0% 0% 1% 6% 11% 17%

OH Columbus CMH 22% 19% 13% 6% 2% 0% 0% 0% 1% 6% 11% 19%

OH Dayton DAY 21% 18% 13% 7% 3% 0% 0% 0% 1% 7% 11% 18%

OH Findlay FDY 21% 18% 13% 7% 3% 0% 0% 0% 1% 6% 11% 18%

OH Mansfield MFD 20% 18% 13% 7% 3% 1% 0% 0% 2% 7% 11% 17%

OH Toledo TOL 21% 18% 13% 7% 3% 0% 0% 0% 1% 6% 11% 18%

OH Youngstown YNG 20% 18% 13% 8% 4% 1% 0% 0% 2% 7% 11% 17%

OK Hobart HBR 23% 19% 11% 5% 1% 0% 0% 0% 0% 5% 12% 23%

OK McAlester MLC 25% 20% 10% 5% 1% 0% 0% 0% 0% 5% 11% 23%

OK Oklahoma City OKC 24% 20% 11% 5% 1% 0% 0% 0% 0% 4% 12% 23%

OK Tulsa TUL 24% 20% 11% 5% 1% 0% 0% 0% 0% 5% 12% 23%

7B-20
Station Location Code Monthly Heating Degree Day Fractions

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

OR Astoria AST 13% 12% 12% 10% 7% 5% 3% 2% 4% 7% 11% 14%

OR Baker BKE 16% 13% 11% 9% 6% 3% 1% 1% 3% 8% 13% 16%

OR Eugene EUG 15% 13% 12% 10% 7% 3% 0% 0% 2% 8% 13% 16%

OR Medford MFR 18% 14% 12% 9% 4% 1% 0% 0% 1% 7% 14% 19%

OR Pendleton PDT 17% 14% 12% 9% 5% 2% 0% 0% 2% 8% 14% 19%

OR Portland PDX 17% 14% 12% 9% 5% 2% 0% 0% 1% 7% 13% 18%

OR Redmond RDM 15% 13% 12% 10% 6% 3% 1% 1% 3% 8% 12% 16%

OR Salem SLE 16% 14% 12% 10% 6% 3% 0% 0% 2% 7% 13% 17%

PA Allentown ABE 21% 18% 14% 8% 3% 0% 0% 0% 1% 7% 11% 18%

PA Altoona AOO 20% 18% 13% 7% 3% 1% 0% 0% 2% 7% 11% 17%

PA Bradford BFD 18% 16% 13% 8% 5% 2% 1% 1% 3% 8% 11% 15%

PA Du Bois DUJ 19% 17% 13% 8% 4% 1% 0% 0% 2% 7% 11% 17%

PA Erie ERI 19% 18% 15% 9% 4% 1% 0% 0% 1% 6% 10% 16%

PA Harrisburg CXY 21% 18% 13% 7% 3% 0% 0% 0% 1% 6% 11% 18%

PA Philadelphia PHL 22% 19% 14% 7% 2% 0% 0% 0% 0% 5% 11% 19%

PA Pittsburgh PIT 21% 18% 13% 7% 3% 0% 0% 0% 1% 7% 11% 18%

PA Williamsport IPT 21% 18% 14% 7% 3% 0% 0% 0% 1% 7% 11% 18%

RI Providence PVD 20% 17% 14% 8% 4% 1% 0% 0% 1% 6% 11% 17%

SC Charleston CHS 27% 21% 11% 4% 0% 0% 0% 0% 0% 3% 12% 22%

SC Columbia CAE 25% 20% 11% 4% 1% 0% 0% 0% 0% 4% 13% 22%

SC Florence FLO 25% 20% 12% 5% 1% 0% 0% 0% 0% 4% 12% 21%

SC Greenville GSP 23% 19% 12% 5% 1% 0% 0% 0% 0% 5% 13% 22%

SD Aberdeen ABR 20% 17% 12% 7% 3% 1% 0% 0% 2% 7% 12% 18%

SD Huron HON 20% 17% 12% 7% 3% 1% 0% 0% 2% 7% 12% 18%

SD Pierre PIR 19% 17% 12% 7% 4% 1% 0% 0% 2% 7% 12% 18%

SD Rapid City RAP 18% 16% 12% 8% 5% 1% 0% 0% 2% 7% 12% 18%

SD Sioux Falls FSD 20% 17% 12% 7% 3% 0% 0% 0% 2% 7% 12% 19%

TN Bristol TRI 22% 18% 12% 6% 2% 0% 0% 0% 1% 6% 13% 20%

TN Chattanooga CHA 24% 19% 11% 5% 1% 0% 0% 0% 0% 5% 13% 22%

TN Crossville CSV 22% 18% 12% 6% 2% 0% 0% 0% 1% 6% 12% 20%

TN Jackson MKL 24% 20% 11% 5% 1% 0% 0% 0% 0% 6% 12% 21%

TN Knoxville TYS 23% 19% 11% 5% 1% 0% 0% 0% 0% 6% 13% 21%

TN Memphis MEM 25% 21% 11% 4% 1% 0% 0% 0% 0% 4% 12% 23%

TN Nashville BNA 24% 19% 11% 5% 1% 0% 0% 0% 0% 5% 12% 21%

TX Abilene ABI 25% 20% 10% 3% 1% 0% 0% 0% 0% 4% 12% 25%

TX Alice ALI 31% 23% 7% 1% 0% 0% 0% 0% 0% 1% 8% 29%

TX Amarillo AMA 21% 18% 12% 6% 2% 0% 0% 0% 1% 6% 13% 21%

TX Austin AUS 27% 21% 10% 3% 0% 0% 0% 0% 0% 3% 11% 25%

TX Brownsville BRO 31% 24% 6% 2% 0% 0% 0% 0% 0% 0% 6% 31%

TX College Station CLL 29% 22% 9% 2% 0% 0% 0% 0% 0% 2% 10% 26%

TX Corpus Christi CRP 32% 22% 7% 1% 0% 0% 0% 0% 0% 1% 8% 28%

TX Dallas-Ft. Worth DFW 27% 22% 10% 3% 0% 0% 0% 0% 0% 3% 11% 25%

TX Del Rio DRT 30% 20% 6% 1% 0% 0% 0% 0% 0% 1% 11% 30%

TX El Paso ELP 25% 19% 10% 3% 0% 0% 0% 0% 0% 2% 14% 27%

TX Galveston GLS 32% 25% 8% 1% 0% 0% 0% 0% 0% 1% 7% 26%

TX Houston IAH 29% 22% 8% 2% 0% 0% 0% 0% 0% 2% 10% 27%

TX Laredo LRD 33% 22% 5% 1% 0% 0% 0% 0% 0% 1% 7% 31%

TX Lubbock LBB 23% 19% 11% 5% 1% 0% 0% 0% 0% 5% 13% 23%

7B-21
Station Location Code Monthly Heating Degree Day Fractions

State City Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

TX Lufkin LFK 27% 21% 9% 3% 0% 0% 0% 0% 0% 3% 12% 25%

TX McAllen MFE 33% 24% 5% 2% 0% 0% 0% 0% 0% 1% 6% 30%

TX Midland Odessa MAF 25% 19% 10% 3% 1% 0% 0% 0% 0% 3% 13% 25%

TX San Angelo SJT 26% 20% 9% 3% 0% 0% 0% 0% 0% 3% 12% 25%

TX San Antonio SAT 29% 21% 8% 2% 0% 0% 0% 0% 0% 2% 10% 27%

TX Victoria VCT 30% 21% 8% 2% 0% 0% 0% 0% 0% 2% 10% 27%

TX Waco ACT 27% 21% 10% 3% 0% 0% 0% 0% 0% 3% 11% 25%

TX Wichita Falls SPS 25% 21% 10% 4% 1% 0% 0% 0% 0% 4% 12% 24%

UT Cedar City CDC 18% 15% 12% 9% 4% 1% 0% 0% 2% 7% 13% 18%

UT Salt Lake City SLC 20% 16% 12% 8% 4% 1% 0% 0% 1% 6% 13% 19%

VA Lynchburg LYH 21% 18% 12% 6% 2% 0% 0% 0% 1% 6% 12% 19%

VA Norfolk ORF 23% 20% 14% 6% 2% 0% 0% 0% 0% 4% 11% 19%

VA Richmond RIC 23% 20% 13% 6% 2% 0% 0% 0% 0% 5% 11% 20%

VA Roanoke ROA 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 20%

VT Burlington BTV 20% 17% 14% 8% 4% 1% 0% 0% 2% 7% 11% 17%

VT Montpelier MPV 19% 16% 13% 8% 4% 1% 0% 1% 3% 7% 11% 16%

WA Bellingham BLI 14% 13% 12% 9% 6% 4% 1% 1% 4% 8% 12% 15%

WA Olympia OLM 14% 13% 12% 10% 6% 3% 1% 1% 3% 8% 12% 15%

WA Quillayute UIL 12% 11% 12% 10% 8% 5% 3% 3% 4% 8% 11% 13%

WA Seattle Tacoma SEA 15% 13% 13% 10% 6% 3% 1% 1% 3% 8% 13% 16%

WA Spokane GEG 17% 14% 12% 9% 5% 2% 0% 0% 2% 8% 14% 18%

WA Walla Walla ALW 18% 15% 11% 8% 4% 1% 0% 0% 1% 7% 14% 20%

WA Yakima YKM 18% 14% 11% 8% 4% 1% 0% 0% 2% 8% 14% 19%

WI Eau Claire EAU 20% 17% 13% 7% 3% 1% 0% 0% 2% 7% 11% 18%

WI Green Bay GRB 20% 17% 13% 8% 4% 1% 0% 0% 2% 7% 11% 17%

WI Lacrosse LSE 21% 18% 13% 7% 3% 0% 0% 0% 2% 6% 11% 19%

WI Madison MSN 20% 17% 13% 7% 3% 1% 0% 0% 2% 7% 11% 18%

WI Milwaukee MKE 20% 17% 13% 8% 4% 1% 0% 0% 2% 6% 11% 17%

WI Wausau AUW 20% 17% 13% 7% 4% 1% 0% 0% 2% 7% 11% 17%

WV Beckley BKW 20% 17% 12% 7% 3% 1% 0% 0% 2% 7% 12% 18%

WV Charleston CRW 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 19%

WV Elkins EKN 19% 17% 13% 8% 4% 1% 0% 0% 2% 7% 12% 17%

WV Huntington HTS 22% 19% 12% 6% 2% 0% 0% 0% 1% 6% 12% 19%

WV Martinsburg MRB 21% 18% 13% 7% 3% 0% 0% 0% 1% 7% 12% 19%

WV Morgantown MGW 21% 18% 13% 7% 3% 0% 0% 0% 1% 7% 11% 18%

WV Parkersburg PKB 22% 18% 13% 6% 3% 0% 0% 0% 1% 7% 12% 19%

WY Casper CPR 16% 15% 12% 9% 6% 2% 0% 0% 3% 8% 12% 17%

WY Cheyenne CYS 16% 15% 12% 9% 6% 2% 0% 0% 3% 8% 12% 17%

WY Cody COD 17% 15% 11% 9% 5% 2% 0% 1% 3% 8% 12% 17%

WY Lander LND 18% 15% 11% 8% 5% 2% 0% 0% 2% 8% 12% 18%

WY Rock Springs RKS 17% 15% 12% 9% 6% 2% 0% 0% 3% 8% 12% 17%

WY Sheridan SHR 17% 15% 12% 8% 6% 2% 0% 0% 3% 8% 12% 17%

WY Worland WRL 19% 16% 11% 7% 4% 1% 0% 0% 2% 7% 13% 19%

7B-22
REFERENCES

1. U.S. Department of Energy: Energy Information Administration, Residential Energy 

Consumption Survey (RECS): 2009 RECS Survey Data, 2009. (Last accessed January 7, 

2015.) <www.eia.gov/consumption/residential/data/2009/>

2. National Oceanic And Atmospheric Administration, Degree Days Archives, 1997-

Present.

<ftp://ftp.cpc.ncep.noaa.gov/htdocs/products/analysis_monitoring/cdus/degree_days/archi

ves/>

7B-23
APPENDIX 8A. USER INSTRUCTIONS FOR THE LIFE-CYCLE COST ANALYSIS 

SPREADSHEET FOR HEARTH PRODUCTS

TABLE OF CONTENTS

8A.1 USER INSTRUCTIONS .............................................................................................. 8A-1

8A.2 STARTUP..................................................................................................................... 8A-1

8A.3 DESCRIPTION OF THE LIFE-CYCLE COST WORKSHEET................................. 8A-1

8A.4 BASIC INSTRUCTIONS FOR OPERATING THE LIFE-CYCLE COST 

SPREADSHEET........................................................................................................... 8A-4

LIST OF FIGURES

Figure 8A.3.1 LCC and Payback Calculation Process....................................................... 8A-3

8A-i
APPENDIX 8A. USER INSTRUCTIONS FOR THE LIFE-CYCLE COST ANALYSIS 

SPREADSHEET FOR HEARTH PRODUCTS

8A.1 USER INSTRUCTIONS

The results obtained in this analysis can be examined and reproduced using the Microsoft 

Excel spreadsheets available on the Department of Energy’s (DOE's) hearth product rulemaking 

website: www1.eere.energy.gov/buildings/appliance_standards/product.aspx?productid=83.

From that page, follow the links to the notice of data availability rulemaking phase and then to 

Analytical Tools. 

8A.2 STARTUP

DOE’s spreadsheets enables users to perform life-cycle cost (LCC) and payback period 

(PBP) analyses for each product class. A spreadsheet labeled LCC exists for the hearth which 

contains the data inputs and calculations for the LCC analysis.

To examine the spreadsheet, DOE assumes that the user has access to a personal 

computer with a hardware configuration capable of running Windows XP or later. The LCC 

spreadsheet requires Microsoft Excel 2003 or later installed under the Windows operating 

system. Because certain variables inside the spreadsheets are defined as distributions, a copy of 

Crystal Balla (a commercially available add-on program) is required to view them. 

8A.3 DESCRIPTION OF THE LIFE-CYCLE COST WORKSHEET

For the hearth products analysis, DOE created a single LCC spreadsheet containing a 

collection of worksheets. Each worksheet represents a conceptual component within the LCC 

calculation. To facilitate navigability and identify how worksheets are related, each worksheet 

contains an area on the extreme left showing variables imported to and exported from the current 

worksheet. The LCC spreadsheet contains the following worksheets: 

Introduction The Introduction worksheet contains an overview of each worksheet and a 

flow chart of the inputs and outputs of the spreadsheet,

Statistics The Statistics worksheet contains the statistics of key parameters from the 

outcome of the Monte Carlo simulations for the sample of households

Summary The Summary worksheet contains a user interface to manipulate energy price 

trends and start year inputs, and to run the Crystal Ball simulation. LCC and 

PBP simulation results for each efficiency level are also displayed here.

a

 See www.oracle.com/us/products/applications/crystalball/overview/index.html

8A-1

 
LCC&PB

Calcs*

The LCC&PB Calcs worksheet shows LCC calculation results for different 

efficiency levels for a single Residential Energy Consumption Survey (RECS) 

2009 household.

1 During a Crystal Ball simulation, the spreadsheet records 

the LCC and PBP values for every sampled household.

Rebuttable 

Payback

The manufacturer costs, retail prices, installation costs, repair and 

maintenance costs, energy use calculations, and the simple PBP calculations 

for each efficiency level. DOE’s direct heating equipment test procedure is 

used to calculate parameters Rebuttable Payback worksheet contains the total 

and incremental used in energy use calculations.

Equip Price* The Equip Price worksheet calculates retail price values used as inputs in the 

LCC calculations in the Summary worksheet. 

Markups* The Markups worksheet calculates markup values used as inputs in the Equip 

Price worksheet. DOE applied baseline and incremental markups to calculate 

final retail prices. DOE calculated the markups differently for replacement 

units and new units.

Installation 

Cost*

The Installation Cost worksheet provides the weighted average installation 

cost for each design option. These results are used to calculate the total 

installed prices of the design options.

Maintenance 

and Repair 

Cost*

The Maintenance and Repair Cost worksheet provides the maintenance and 

repair costs for each design option. These results are used to determine 

operating costs for the design options.

Labor Costs* The Labor Cost worksheet provides the labor cost by region as used to 

determine the installation and repair/maintenance costs.

Base Case EL The Base Case Efficiency Level worksheet provides the fraction of hearth 

product types that have an intermittent pilot ignition under the base case.

Building 

Sample*

The Building Sample worksheet contains the RECS 2009 household data for 

each product class. During a Crystal Ball simulation, DOE uses these 

household characteristics to determine the analysis parameters.

Energy Use* The Energy Use worksheet calculates annual energy use by fuel type. The 

annual energy use calculations for each design option are inputs to the 

LCC&PB Calcs worksheet to calculate the annual operating cost of the LCC.

Energy Use 

(Calcs)*

The Energy Use (Calcs) worksheet displays intermediate energy use 

calculations. The intermediate energy use calculations for each design option 

are inputs to the Energy Use worksheet to calculate the annual energy use by 

fuel type, depending on product class.

Energy Price* The Energy Price worksheet shows the estimated monthly natural gas, liquid 

petroleum gas (LPG), electricity, and oil prices.

8A-2
Energy Price 

Trends*

The Energy Price Trends worksheet shows the future price trends of the 

different fuels. DOE used energy price data and forecasts from the Energy 

Information Administration’s (EIA's) Annual Energy Outlook 2014 for the 

period until 2040 and extrapolated beyond 2040.2

Discount Rate* The Discount Rate worksheet contains the distributions of discount rates for 

replacement and new units.

Lifetime* The Lifetime worksheet contains the distribution of lifetimes for equipment of 

that product class.

Energy Use 

Adjustment 

Factors*

The Energy Use Adjustment Factors worksheet contains adjustment factors 

for normal heating degree days and cooling degree days, as well as building 

shell efficiency index.

Weather Data* The Weather Data worksheet contains heating degree days, cooling degree 

days, heating and cooling outdoor design temperature, and annual mean 

temperature by weather station.

* Results displayed in these worksheets are for only one household, not the entire population.

Figure 8A.3.1 depicts how these various inputs are used in order to generate the LCC and 

PBP outputs.

Figure 8A.3.1 LCC and Payback Calculation Process 

8A-3
8A.4 BASIC INSTRUCTIONS FOR OPERATING THE LIFE-CYCLE 

COST SPREADSHEET 

Basic instructions for operating the LCC spreadsheet are as follows:

1. Once the LCC spreadsheet has been downloaded, open the file using Excel. Click 

“Enable Macro” when prompted and then click on the tab for the Summary worksheet.

2. Use Excel's View/Zoom commands at the top menu bar to change the size of the display 

to fit your monitor.

3. The user can change the parameters listed under USER OPTIONS on the Summary

worksheet. There are three drop-down boxes and one command button. The default 

parameters are:

a. Energy Price Trend: Defaults to “AEO 2014 - Reference Case.” To change the 

input, use the drop-down menu and select the desired trend (Reference, Low, or 

High).

b. Start Year: Defaults to “2021.” To change the value, use the drop-down menu and 

select the desired year (2018, 2019, 2020, 2021, or 2022).

c. # of Trials: Defaults to “10,000.” To change the value, use the drop-down menu 

and select the desired number of trials (1,000, 2,000, 3,000, 5,000, 10,000, or 

20,000).

d. Equipment Price Trend: Defaults to “Constant”, which is the only option in this 

analysis.

4. To run the Crystal Ball simulation, click the “run” button (you must re-run after changing 

any parameters). The spreadsheet will then be minimized. You can monitor the progress 

of the simulation by watching the count of iterations at the left bottom corner. When the 

simulation is finished, the worksheet named Summary will reappear with the results.

8A-4
REFERENCES

1. U.S. Department of Energy: Energy Information Administration, Residential Energy 

Consumption Survey (RECS): 2009 RECS Survey Data, 2009. (Last accessed January 7, 

2015.) <www.eia.gov/consumption/residential/data/2009/>

2. U.S. Department of Energy: Energy Information Administration, Annual Energy Outlook 

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

<http://www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf>

8A-5
APPENDIX 8B. UNCERTAINTY AND VARIABILITY IN THE LCC ANALYSIS

TABLE OF CONTENTS

8B.1 INTRODUCTION .........................................................................................................8B-1

8B.2 UNCERTAINTY ...........................................................................................................8B-1

8B.3 VARIABILITY..............................................................................................................8B-1

8B.4 APPROACHES TO UNCERTAINTY AND VARIABILITY .....................................8B-1

8B.5 PROBABILITY ANALYSIS AND THE USE OF CRYSTAL BALL.........................8B-2

LIST OF FIGURES

Figure 8B.5.1 Normal, Triangular, Uniform, Weibull, and Custom Probability 

Distributions.................................................................................................8B-3

8B-i
APPENDIX 8B. UNCERTAINTY AND VARIABILITY IN THE LCC ANALYSIS

8B.1 INTRODUCTION

Analysis of energy conservation standards involves calculations of impacts, for example, 

the impact of a standard on consumer life-cycle cost (LCC). In order to perform the calculation, 

the analyst must: 1) specify the equation or model that will be used; 2) define the quantities in 

the equation; and 3) provide numerical values for each quantity. In the simplest case, the 

equation is unambiguous (contains all relevant quantities and no others), each quantity has a 

single numerical value, and the calculation results in a single value. However, unambiguity and 

precision are rarely the case. In almost all cases, the model and/or the numerical values for each 

quantity in the model are not completely known (i.e., there is uncertainty) or the model and/or 

the numerical values for each quantity in the model depend upon other conditions (i.e., there is 

variability).

Thorough analysis involves accounting for uncertainty and variability. While the simplest 

analysis involves a single numerical value for each quantity in a calculation, arguments can arise 

about what the appropriate value is for each quantity. Explicit analysis of uncertainty and 

variability is intended to provide more complete information to the decision-making process.

8B.2 UNCERTAINTY

When making observations of past events or speculating about the future, imperfect 

knowledge is the rule rather than the exception. For example, the energy actually consumed by a 

particular appliance type (such as the average U.S. water heater, direct heating equipment, or 

pool heater) is not directly recorded, but rather estimated based upon available information. Even 

direct laboratory measurements have some margin of error. When estimating numerical values 

expected for quantities at some future date, the exact outcome is rarely known in advance.

8B.3 VARIABILITY

Variability results when different applications or situations produce different numerical 

values when calculating a quantity. Specifying an exact value for a quantity may be difficult 

because the value depends on something else. For example, water heater energy consumption 

depends upon the specific circumstances and behaviors of the occupants (e.g., number of 

persons, length and temperature of showers, etc.). Variability makes specifying an appropriate 

population value more difficult inasmuch as any one value may not be representative of the 

entire population. Surveys can be helpful here, and analysis of surveys can relate the variable of 

interest (e.g., hours of use) to other variables that are better known or easier to forecast (e.g., 

persons per household).

8B.4 APPROACHES TO UNCERTAINTY AND VARIABILITY

This section describes two approaches to uncertainty and variability: 

8B-1
• scenario analysis, and 

• probability analysis.

Scenario analysis uses a single numerical value for each quantity in a calculation, then 

changes one (or more) of the numerical values and repeats the calculation. A number of 

calculations are done, which provide some indication of the extent to which the result depends 

upon the assumptions. For example, the life-cycle cost of an appliance could be calculated for 

energy rates of 2, 8, and 14¢ per kWh. 

The advantages of scenario analysis are that each calculation is simple; a range of 

estimates is used and crossover points can be identified. (An example of a crossover point is the 

energy rate above which the life-cycle cost is reduced, holding all other inputs constant. That is, 

the crossover point is the energy rate at which the consumer achieves savings in operating 

expense that more than compensate for the increased purchase expense.) The disadvantage of 

scenario analysis is that there is no information about the likelihood of each scenario.

Probability analysis considers the probabilities within a range of values. For quantities 

with variability (e.g., electricity rates in different households), surveys can be used to generate a 

frequency distribution of numerical values (e.g., the number of households with electricity rates 

at particular levels) to estimate the probability of each value. For quantities with uncertainty, 

statistical or subjective measures can be used to provide probabilities (e.g., manufacturing cost to 

improve energy efficiency to some level may be estimated to be $10 ± $3). 

The major disadvantage of the probability approach is that it requires more information, 

namely information about the shapes and magnitudes of the variability and uncertainty of each 

quantity. The advantage of the probability approach is that it provides greater information about 

the outcome of the calculations; that is, it provides the probability that the outcome will be in a 

particular range. 

Scenario and probability analysis provide some indication of the robustness of the policy 

given the uncertainties and variability. A policy is robust when the impacts are acceptable over a 

wide range of possible conditions.

8B.5 PROBABILITY ANALYSIS AND THE USE OF CRYSTAL BALL

To quantify the uncertainty and variability that exist in inputs to the engineering, LCC, 

and payback period (PBP) analyses, DOE used Microsoft Excel spreadsheets combined with 

Crystal Ball, a commercially available add-in, to conduct probability analyses. The probability 

analyses used Monte Carlo simulation and probability distributions.

Simulation refers to any analytical method meant to imitate a real-life system, especially 

when other analyses are too mathematically complex or too difficult to reproduce. Without the 

aid of simulation, a spreadsheet model will only reveal a single outcome, generally the most 

likely or average scenario. Spreadsheet risk analysis uses both a spreadsheet model and 

8B-2
simulation to automatically analyze the effect of varying inputs on outputs of the modeled 

system. One type of spreadsheet simulation is Monte Carlo simulation, which randomly 

generates values for uncertain variables again and again to simulate a model. Monte Carlo 

simulation was named for Monte Carlo, Monaco, where the primary attractions are casinos 

containing games of chance. Games of chance, such as roulette wheels, dice, and slot machines, 

exhibit random behavior. The random behavior in games of chance is similar to how Monte 

Carlo simulation selects variable values at random to simulate a model. When you roll a die, you 

know that a 1, 2, 3, 4, 5, or 6 will come up, but you do not know which for any particular roll. 

The same applies to the variables that have a known range of values but an uncertain value for 

any particular time or event (e.g., equipment lifetime, discount rate, and installation cost). 

For each uncertain variable (one that has a range of possible values), possible values are 

defined with a probability distribution. The type of distribution selected is based on the 

conditions surrounding that variable. Probability distribution types include:

Figure 8B.5.1 Normal, Triangular, Uniform, Weibull, and Custom Probability 

Distributions

During a simulation, multiple scenarios of a model are calculated by repeatedly sampling 

values from the probability distributions for the uncertain variables and using those values for the 

cell. Crystal Ball simulations can consist of as many trials (or scenarios) as desired—hundreds or 

even thousands. During a single trial, Crystal Ball randomly selects a value from the defined 

possibilities (the range and shape of the probability distribution) for each uncertain variable and 

then recalculates the spreadsheet. 

8B-3

NORMAL TRIANGULAR UNIFORM

WEIBULL CUSTOM
APPENDIX 8C. ENERGY PRICE CALCULATIONS FOR HEARTH PRODUCTS

TABLE OF CONTENTS

8C.1 INTRODUCTION .........................................................................................................8C-1

8C.2 RECS SAMPLE MAPPING PROCESS .......................................................................8C-1

8C.3 AVERAGE MARGINAL MONTHLY PRICES ..........................................................8C-1

8C.3.1 Average Annual Prices Determination ....................................................................8C-1

8C.3.1.1 Annual Electrical Prices........................................................................ 8C-1

8C.3.1.2 Annual Natural Gas Prices.................................................................... 8C-4

8C.3.1.3 Annual LPG Prices ............................................................................... 8C-7

8C.3.2 Monthly Energy Price Factors Determination .........................................................8C-9

8C.3.2.1 Monthly Residential Electricity Price Factor Calculations................. 8C-10

8C.3.2.2 Monthly Residential Natural Gas Price Factor Calculations.............. 8C-14

8C.3.2.3 Monthly Residential Liquid Petroleum Gas Price Factor 

Calculations......................................................................................... 8C-18

8C.3.3 Seasonal Marginal Price Factors Determination....................................................8C-20

8C.3.3.1 Marginal Price Factor Calculation for Electricity and Natural Gas.... 8C-20

8C.3.4 Results....................................................................................................................8C-22

8C.4 HOUSEHOLD ENERGY PRICE ADJUSTMENT FACTOR....................................8C-27

8C.5 ENERGY PRICE TRENDS ........................................................................................8C-28

8C.5.1 Residential Energy Price Trends............................................................................8C-28

LIST OF TABLES

Table 8C.3.1 2012 Monthly Residential Electricity Prices by State (2012¢/kWh)...........8C-2

Table 8C.3.2 Average Residential Electricity Prices by Region in 2012..........................8C-4

Table 8C.3.3 2012 Monthly Residential Natural Gas Prices by State (2012$/tcf)............8C-5

Table 8C.3.4 Average Residential Natural Gas Prices by Region in 2012 .......................8C-7

Table 8C.3.5 2012 Residential Average LPG Prices by State (2012$/MMBtu)...............8C-7

Table 8C.3.6 Average Residential LPG Prices by Region in 2012...................................8C-9

Table 8C.3.7 1990-2011 Average Residential Electricity Prices for New York 

(nominal cents/kWh)..................................................................................8C-11

Table 8C.3.8 Monthly Electricity Price Factors for 1990-2011 for New York...............8C-12

Table 8C.3.9 Monthly Residential Electricity Price Factors...........................................8C-13

Table 8C.3.10 1989-2010 Average Residential Natural Gas Prices for New York 

($/tcf)..........................................................................................................8C-14

Table 8C.3.11 1989-2011 Monthly Natural Gas Price Factors for New York..................8C-15

Table 8C.3.12 Monthly Residential Natural Gas Energy Price Factors............................8C-17

Table 8C.3.13 Average LPG Prices for the Northeast (nominal cents/gallon) .................8C-18

Table 8C.3.14 Monthly LPG Price Factors for 1995-2009 for the Northeast ...................8C-19

Table 8C.3.15 Monthly Residential LPG Energy Price Factors........................................8C-20

Table 8C.3.16 Residential Marginal Electricity Price Factors using EIA 2003-2012 

Data ............................................................................................................8C-21

8C-i
Table 8C.3.17 Residential Marginal Natural Gas Price Factors using EIA 2003-2012 

Data ............................................................................................................8C-22

Table 8C.3.18 Residential Average Monthly Electricity Prices for 2012 Using 

Monthly Price Factors (2013$/kWh).........................................................8C-23

Table 8C.3.19 Residential Average Monthly Natural Gas Prices for 2012 Using 

Monthly Price Factors (2013$/MMBtu)....................................................8C-24

Table 8C.3.20 Residential Marginal Monthly Electricity Prices for 2012 Using 

Marginal Price Factors (2013$/MMBtu) ...................................................8C-25

Table 8C.3.21 Residential Marginal Monthly Natural Gas Prices for 2012 Using

Marginal Price Factors (2012$/MMBtu) ...................................................8C-26

Table 8C.3.22 Residential Monthly LPG Prices for 2012 Using Average Price Factors 

(2013$/MMBtu).........................................................................................8C-27

LIST OF FIGURES

Figure 8C.1.1 Energy Price Calculation Process................................................................8C-1

Figure 8C.3.1 Monthly Electricity Price Factors for 1990-2011 for New York...............8C-12

Figure 8C.3.2 1989-2011 Monthly Natural Gas Price Factors for New York..................8C-15

Figure 8C.3.3 Monthly LPG Factors for 1995-2009 for the Northeast ............................8C-19

Figure 8C.5.1 Projected Residential National Electricity Price........................................8C-28

Figure 8C.5.2 Projected Residential Division Electricity Prices......................................8C-29

Figure 8C.5.3 Projected Residential National Natural Gas Price .....................................8C-29

Figure 8C.5.4 Projected Residential Division Natural Gas Prices....................................8C-30

Figure 8C.5.5 Projected Residential National LPG Prices ...............................................8C-30

Figure 8C.5.6 Projected Residential Division LPG Prices ...............................................8C-31

8C-ii
APPENDIX 8C. ENERGY PRICE CALCULATIONS FOR HEARTH PRODUCTS

8C.1 INTRODUCTION

Figure 8C.1.1 depicts the energy price calculation process, which also encompasses 

average energy price, seasonal marginal price factor, and monthly price factor calculations.

Figure 8C.1.1 Energy Price Calculation Process

8C.2 RECS SAMPLE MAPPING PROCESS

To match the state data from EIA to the RECS 2009 household samples, DOE used the 

housing projections in 2021 for each geographic area. See appendix 7A for more details. RECS

2009 utilizes 27 regions (also called reportable domains). DOE further subdivided the RECS 

2009 regions into 30 regions based on climate data to disaggregate northern and rest of country 

states. The 27th RECS region includes Oregon, Washington, Alaska, and Hawaii. DOE 

subdivided Alaska and Hawaii into separate regions (28 and 29, respectively), based on cooling 

and heating degree days. In addition, West Virginia, which is in RECS region 14, was 

disaggregated into region 30 based on cooling and heating degree days.

8C.3 AVERAGE MARGINAL MONTHLY PRICES

8C.3.1 Average Annual Prices Determination

8C.3.1.1 Annual Electrical Prices

DOE derived 2012 annual electricity prices from EIA Form 826 data.

1 The EIA Form 

EIA Historical 

Data

Seasonal 

Marginal Price 

Factors

Energy 

Prices

Average 

Marginal 

Monthly Prices

Energy Price 

Trends by 

Census Division

Location

Monthly Price 

Factors

RECS Sample

AEO Energy 

Price Trends

Average Energy 

Prices

8C-1
826 data include energy prices by state. DOE calculated residential annual electricity prices for 

each geographical area by averaging monthly energy prices by state to get state electricity prices. 

For areas with more than one state, DOE weighted each state’s average price by its 2021 housing 

projection. Table 8C.3.1 shows the monthly residential electricity prices for each state. Note that 

all energy prices were converted from 2012$ to 2013$ in the LCC spreadsheet using the 

consumer price index (CPI).a

Table 8C.3.1 2012 Monthly Residential Electricity Prices by State (2012¢/kWh)

Geographical Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Avg. 

2012

United States 11.41 11.51 11.70 11.92 11.90 12.09 12.00 12.17 12.30 12.03 11.75 11.62 11.87

Alabama 10.91 11.21 11.48 11.56 11.14 11.56 11.58 11.71 11.76 11.71 11.04 10.96 11.39

Alaska 18.04 17.44 17.97 17.64 18.43 18.10 19.44 19.07 17.34 17.82 17.10 17.06 17.95

Arizona 10.01 10.26 10.44 11.17 11.88 11.90 11.86 11.83 11.66 11.36 10.73 10.41 11.13

Arkansas 8.41 8.68 8.99 9.35 9.14 9.58 9.62 9.69 9.80 9.39 9.46 9.07 9.27

California 15.29 14.56 14.70 14.66 15.02 15.92 15.22 16.46 16.82 14.22 14.88 15.49 15.27

Colorado 10.61 10.76 10.84 11.04 11.27 12.01 12.32 12.15 12.11 11.33 11.38 11.07 11.41

Connecticut 17.32 17.09 17.16 17.64 17.71 17.30 17.08 17.12 17.24 18.08 17.82 17.06 17.39

Delaware 13.03 13.17 13.52 13.93 14.37 13.98 13.32 13.57 13.74 14.53 13.63 13.03 13.65

District of Columbia 11.78 12.21 12.20 12.49 12.39 13.18 12.26 12.23 12.22 12.35 12.21 11.98 12.29

Florida 11.55 11.33 11.09 11.42 11.00 11.49 11.31 11.49 11.60 11.53 11.82 11.38 11.42

Georgia 10.23 10.61 10.58 10.77 11.08 11.88 12.13 12.18 11.87 10.89 10.28 10.29 11.07

Hawaii 36.25 36.80 37.05 37.51 38.21 40.16 37.84 37.18 37.21 36.96 36.81 36.33 37.36

Idaho 8.15 8.09 8.18 8.16 8.39 9.04 9.75 9.77 8.31 9.11 8.67 8.54 8.68

Illinois 11.21 11.45 11.97 12.45 12.73 11.24 10.81 10.78 11.36 11.91 11.35 10.62 11.49

Indiana 10.00 10.18 10.85 11.56 10.85 10.54 9.94 10.44 10.92 11.09 10.45 10.49 10.61

Iowa 9.74 9.77 10.26 11.23 10.98 11.32 11.54 12.03 11.38 10.99 10.34 10.16 10.81

Kansas 10.23 10.71 10.81 11.15 11.28 11.69 11.74 11.72 11.52 11.29 10.96 10.90 11.17

Kentucky 8.98 9.07 9.47 9.63 9.72 9.48 9.27 9.45 9.83 9.72 9.41 9.46 9.46

Louisiana 8.15 8.40 8.40 8.38 8.48 8.01 8.38 8.29 8.56 8.68 8.39 8.47 8.38

Maine 15.02 15.07 14.28 14.46 14.41 14.21 14.63 14.66 14.81 14.76 14.83 14.70 14.65

Maryland 12.48 12.55 12.86 12.90 12.95 13.14 12.88 13.09 13.17 13.03 12.40 12.67 12.84

Massachusetts 15.10 15.41 15.72 14.80 15.39 15.48 14.58 14.16 15.20 14.38 13.77 15.19 14.93

Michigan 13.48 13.48 13.63 13.69 14.39 14.13 15.06 14.53 14.37 14.22 13.98 14.08 14.09

Minnesota 10.76 10.71 10.94 11.14 11.30 11.79 11.84 12.18 12.09 11.54 11.03 10.95 11.36

Mississippi 10.03 10.43 10.62 10.66 10.37 10.26 10.14 9.95 9.98 10.31 10.65 10.29 10.31

Missouri 8.66 8.90 9.33 10.06 10.90 11.53 11.31 11.49 10.46 9.89 9.20 9.03 10.06

Montana 9.57 9.59 9.70 9.86 10.13 10.43 10.60 10.52 10.74 10.54 10.04 9.89 10.13

Nebraska 8.61 8.86 9.16 9.95 9.68 10.71 11.38 11.24 11.50 10.43 9.43 9.13 10.01

Nevada 11.38 12.55 12.16 12.40 12.10 11.80 11.52 11.46 11.58 12.04 12.45 11.82 11.94

New Hampshire 16.17 16.12 16.33 16.45 16.53 16.51 15.81 15.59 15.88 16.03 15.90 15.83 16.10

New Jersey 16.07 16.22 15.86 15.91 15.93 15.67 16.12 15.81 15.57 15.17 15.24 15.49 15.76

New Mexico 10.79 10.62 10.71 10.77 11.04 12.08 12.19 12.49 11.98 11.51 10.69 10.71 11.30

New York 16.79 16.51 16.64 16.70 17.33 18.31 18.38 18.12 18.52 18.44 17.44 17.47 17.55

North Carolina 10.09 10.76 11.00 11.42 10.80 11.00 11.03 11.23 11.52 11.41 10.53 10.41 10.93

a http://www.bls.gov/cpi/

8C-2

 
North Dakota 7.65 8.12 8.43 9.22 9.60 10.57 10.00 10.27 10.47 9.42 8.61 8.61 9.25

Ohio 11.08 11.04 11.35 11.93 11.85 12.24 12.16 12.14 12.27 12.06 11.67 11.39 11.77

Oklahoma 8.70 9.48 9.87 10.31 9.53 9.50 9.12 9.70 10.13 10.09 9.57 8.71 9.56

Oregon 9.58 9.63 9.66 9.73 9.86 9.92 10.01 10.01 10.00 10.00 9.82 9.74 9.83

Pennsylvania 12.82 12.79 12.77 12.97 12.99 12.89 12.62 12.68 12.57 12.80 12.60 12.62 12.76

Rhode Island 14.75 14.94 14.82 13.15 14.41 15.32 13.90 14.00 15.09 13.66 13.06 15.70 14.40

South Carolina 11.15 11.64 12.04 11.94 11.40 12.32 11.48 12.14 11.85 11.80 11.67 11.87 11.78

South Dakota 9.22 9.26 9.49 10.22 10.61 10.47 10.59 10.60 10.85 10.66 10.01 9.59 10.13

Tennessee 9.80 9.64 9.83 10.11 10.24 10.24 10.14 10.00 10.18 10.41 10.33 10.48 10.12

Texas 10.77 11.05 11.01 11.02 10.84 10.97 10.87 10.96 11.11 11.16 11.11 10.99 10.99

Utah 9.23 9.33 9.36 9.47 9.87 10.35 10.68 10.66 10.23 9.59 9.70 9.73 9.85

Vermont 16.52 17.07 16.76 16.87 16.41 16.93 16.77 16.38 16.38 17.51 19.26 17.20 17.01

Virginia 10.45 10.89 11.09 11.38 11.50 11.84 11.42 11.25 11.10 11.03 10.71 10.51 11.10

Washington 8.37 8.28 8.35 8.46 8.52 8.70 8.70 8.72 8.79 8.68 8.61 8.53 8.56

West Virginia 9.49 9.68 9.85 9.84 10.48 9.92 9.88 9.85 10.04 10.18 9.85 9.63 9.89

Wisconsin 12.66 12.88 13.10 13.36 13.35 13.36 13.28 13.50 13.83 13.43 13.16 12.61 13.21

Wyoming 9.12 9.16 9.40 9.74 10.08 10.22 10.38 10.27 10.38 10.36 10.01 9.89 9.92

All prices in 2012$ were converted to 2013$ to be consistent with the rest of the prices 

used in the analysis. This conversion was performed using the Consumer Price Index. Table 

8C.3.2 shows the average residential electricity prices weighted by housing projections in 2021 

for each geographic area.

8C-3
Table 8C.3.2 Average Residential Electricity Prices by Region in 2012

Geographic Area 2013$/MMBtu

1 Connecticut, Maine, New Hampshire, Rhode Island, Vermont $0.16

2 Massachusetts $0.15

3 New York $0.18

4 New Jersey $0.16

5 Pennsylvania $0.13

6 Illinois $0.12

7 Indiana, Ohio $0.12

8 Michigan $0.14

9 Wisconsin $0.13

10 Iowa, Minnesota, North Dakota, South Dakota $0.11

11 Kansas, Nebraska $0.11

12 Missouri $0.10

13 Virginia $0.11

14 Delaware, District of Columbia, Maryland $0.13

15 Georgia $0.11

16 North Carolina, South Carolina $0.11

17 Florida $0.12

18 Alabama, Kentucky, Mississippi $0.11

19 Tennessee $0.10

20 Arkansas, Louisiana, Oklahoma $0.09

21 Texas $0.11

22 Colorado $0.12

23 Idaho, Montana, Utah, Wyoming $0.10

24 Arizona $0.11

25 Nevada, New Mexico $0.12

26 California $0.15

27 Oregon, Washington $0.09

28 Alaska $0.18

29 Hawaii $0.38

30 West Virginia $0.10

31 U.S. Average $0.12

8C.3.1.2 Annual Natural Gas Prices

DOE obtained the data for natural gas prices from EIA’s Natural Gas Navigator,

2 which

includes monthly natural gas prices by state for residential, commercial, and industrial 

customers. For areas with more than one state, DOE weighted each state’s average price by its

8C-4
2021 housing projection. Table 8C.3.3 shows the monthly residential natural gas prices for each 

state.

Table 8C.3.3 2012 Monthly Residential Natural Gas Prices by State (2012$/tcf)

Geographical 

Area

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Avg. 

2012

United States 9.67 9.52 10.45 11.01 12.66 14.25 15.2 15.89 14.81 11.78 10.06 9.75 12.09

Alabama 14.27 14.41 15.25 18.67 19.77 21.34 21.77 21.96 21.04 20.05 15.9 15.25 18.31

Alaska 8.26 8.48 8.17 8.47 8.93 9.57 9.7 10.07 8.74 8.21 7.86 8.57 8.75

Arizona 13.3 14.09 14.68 15.94 18.55 20.39 21.99 22.43 21.8 19.34 15.74 14.94 17.77

Arkansas 10.57 10.78 11.84 15.02 13.59 15.26 16.64 17.47 16.27 13.67 10.96 10.71 13.57

California 9.27 8.36 8.69 8.48 9.04 9.7 9.97 10.12 10.07 9.79 9.09 9.33 9.33

Colorado 7.68 7.79 8.65 8.23 10.36 12.9 12.92 12.93 11.44 8.62 7.64 7.06 9.69

Connecticut 12.11 12.16 12.54 14.53 16.24 20.41 20.85 21.47 20.45 17.85 14.74 13.08 16.37

Delaware 13.87 14.03 14.52 16.12 17.76 22.2 23.55 24.59 24.14 20.49 14.52 12.8 18.22

District of 

Columbia 11.92 11.11 12.64 11.93 17.28 17.62 17.96 14.31 13.35 12.38 11 11.58 13.59

Florida 16.19 15.93 16.65 18.02 19.39 21.14 21.43 22.42 21.81 21.43 17.98 16.29 19.06

Georgia 14 14.66 14.86 16.65 20.53 21.52 22.29 23.82 23.03 21.8 16.39 15.09 18.72

Hawaii 49.97 54.89 57.26 54.93 50.72 51.23 51.92 54.81 56.21 52.76 49.59 49.76 52.84

Idaho 8.43 8.27 8.45 8.37 8.6 9.07 8.8 9.28 9.15 8.01 7.71 7.81 8.50

Illinois 7.09 6.66 8.25 7.62 11.49 12.67 15.31 15.68 13.14 9.12 7.83 7.56 10.20

Indiana 8.06 8.31 11.24 11.18 12.18 12.89 14.84 14.7 12.23 8.04 7.04 7.63 10.70

Iowa 8.26 7.97 10.34 9.04 10.86 13.41 17.34 17.16 14.6 11.78 8.84 8.23 11.49

Kansas 8.16 8.66 8.92 12.31 14.22 16.09 17.69 19.11 18.15 13.67 9.87 9.07 12.99

Kentucky 8.62 9.2 11.41 11.97 15.02 16.95 19.34 18.81 17.68 11.16 8.36 8.44 13.08

Louisiana 10.24 10.91 11.63 12.3 12.24 13.48 14.12 14.92 13.86 13.51 10.84 10.06 12.34

Maine 15.87 16.65 16.44 17.12 16.33 16.62 16.47 17.93 16.55 13.28 13.41 15.81 16.04

Maryland 11.33 11.06 13.63 13.91 16.1 17.83 18.44 18.5 15.81 12.91 10.01 10.74 14.19

Massachusetts 13.69 12.59 12.83 12.75 12.54 12.18 14.1 14.84 14.9 12.74 13.77 13.28 13.35

Michigan 10.32 10.35 11.12 10.64 13.95 14.34 15.99 16.16 13.66 10.79 9.99 9.67 12.25

Minnesota 7.53 7.42 8.2 7.41 9.34 10.56 11.47 11.86 9.95 7.85 7.81 7.55 8.91

Mississippi 7.55 9.41 10.28 12.28 12.26 12.21 12.59 13.05 12 11.6 8.52 8.39 10.85

Missouri 9.41 9.6 11.07 17.24 18.56 22.53 24.82 26.15 23.73 16.25 11.86 9.9 16.76

Montana 8.05 7.78 7.82 7.99 8.03 8.48 10.61 10.81 11.02 8.06 7.61 7.56 8.65

Nebraska 7.57 7.37 7.47 10.34 10.64 12.06 12.4 15.01 14.43 11.49 8.61 8.08 10.46

Nevada 9.07 9.43 9.77 10.62 12.08 12.75 13.36 13.8 13.37 12.43 9.44 8.46 11.22

New Hampshire 13.02 13.25 13.56 15.41 13.94 16.03 18.18 19.98 18.85 14.63 12.74 12.19 15.15

New Jersey 10.29 9.82 10.46 11.11 12.01 13.21 13.77 14.34 13.83 12.51 11.61 11.27 12.02

New Mexico 7.41 7.41 7.48 8.65 10.23 11.82 13.71 14.31 14.3 12.59 9.32 8.17 10.45

New York 11.67 11.69 12.99 13.06 15.13 18 17.4 18.78 18.16 15.26 11.35 11.97 14.62

North Carolina 10.95 10.44 13.92 15.21 16.87 18.85 21.56 21.41 20.48 14.26 10.32 10.84 15.43

North Dakota 7.18 6.53 7.28 7.2 8.78 12.45 13.95 14.17 11.99 7.1 6.55 6.69 9.16

Ohio 8.56 8.05 8.53 10.74 13.9 22.17 28.1 29.98 25.43 14.67 9.93 9.27 15.78

Oklahoma 8.33 8.07 9.03 14.4 17.92 19.96 22.84 24.51 23.52 17.7 11.78 9.16 15.60

Oregon 11.47 11.53 10.7 10.56 12.33 12.25 12.53 13.18 14.12 11.79 10.49 10.27 11.77

Pennsylvania 11 11.12 11.34 12.04 13.42 18.09 18.65 19.7 18.56 13.9 11 10.68 14.13

Rhode Island 13.1 13.96 14.16 16.06 18.05 17.88 18.91 19.36 19.58 14.84 12.33 12.01 15.85

8C-5
South Carolina 11.28 11.18 13.94 17.96 19.81 23.52 25.17 22.38 24.25 15.79 10.63 11.78 17.31

South Dakota 7.84 7.64 9.14 7.78 8.55 10.89 13.37 13.71 12.44 8.82 7.81 7.66 9.64

Tennessee 8.97 8.73 9.48 12.53 13.79 14.54 15.55 17.35 15.22 12.3 9.13 8.87 12.21

Texas 8.56 8.2 9.7 12.05 13.18 14.18 14.51 16.21 15.67 14.54 11.35 10.11 12.36

Utah 7.97 8.5 8.66 9.13 8.87 9.09 9.92 10.24 10.13 9.88 8.76 8.47 9.14

Vermont 15.64 15.45 15.79 16.53 17.4 21.35 23.4 24.02 23.23 19.28 16.62 15.21 18.66

Virginia 11.86 11.14 13.19 13.43 15.77 19.29 19.93 20.13 19.31 12.97 9.94 11.04 14.83

Washington 11.25 11.38 11.45 11.9 12.7 13.53 14.85 15.59 15.63 13.1 11.41 10.56 12.78

West Virginia 10.32 10.29 10.81 11.1 14.33 18 18.38 17.85 14.51 12.02 9.65 9.35 13.05

Wisconsin 8.63 8.73 10.05 9.73 9.58 11.44 13.35 13.6 10.83 8.25 9.03 8.93 10.18

Wyoming 7.87 7.7 7.59 8.06 8.74 10.58 14.37 14.9 14.23 9.7 7.87 7.51 9.93

All prices in 2012$ were converted to 2013$ to be consistent with the rest of the prices 

used in the analysis. This conversion was performed using the Consumer Price Index. DOE also 

used a conversion factor (1.025) to convert from cubic feet of natural gas to MMBtu.b Table

8C.3.4 displays the annual average residential natural gas prices weighted by housing projections 

in 2021 by geographic region.

b www.eia.gov/tools/faqs/faq.cfm?id=45&t=7

8C-6

 
Table 8C.3.4 Average Residential Natural Gas Prices by Region in 2012

Geographic Area 2013$/MMBtu

1 Connecticut, Maine, New Hampshire, Rhode Island, Vermont $16.10

2 Massachusetts $13.21

3 New York $14.47

4 New Jersey $11.89

5 Pennsylvania $13.99

6 Illinois $10.09

7 Indiana, Ohio $13.85

8 Michigan $12.16

9 Wisconsin $10.07

10 Iowa, Minnesota, North Dakota, South Dakota $9.67

11 Kansas, Nebraska $11.88

12 Missouri $16.64

13 Virginia $14.69

14 Delaware, District of Columbia, Maryland $14.51

15 Georgia $18.56

16 North Carolina, South Carolina $15.88

17 Florida $18.88

18 Alabama, Kentucky, Mississippi $14.57

19 Tennessee $12.08

20 Arkansas, Louisiana, Oklahoma $13.63

21 Texas $12.24

22 Colorado $9.62

23 Idaho, Montana, Utah, Wyoming $8.88

24 Arizona $17.63

25 Nevada, New Mexico $10.77

26 California $9.23

27 Oregon, Washington $12.30

28 Alaska $8.67

29 Hawaii $52.52

30 West Virginia $12.94

31 U.S. Average $16.10

8C.3.1.3 Annual LPG Prices

DOE collected 2012 average LPG prices from EIA’s 2012 State Energy Consumption, 

Price, and Expenditures Estimates (SEDS).3 SEDS includes annual LPG prices for residential, 

commercial, industrial, and transportation consumers by state. For areas with more than one 

state, DOE weighted each state’s average price by its 2021 housing projection. Table 8C.3.5

shows the annual residential LPG prices for each state.

Table 8C.3.5 2012 Residential Average LPG Prices by State (2012$/MMBtu)

Geographical Area Avg. 2012

United States 28.18

Alabama 29.73

8C-7
Alaska 38.46

Arizona 35.22

Arkansas 30.25

California 33.84

Colorado 21.93

Connecticut 34.13

Delaware 33.37

District of Columbia 35.37

Florida 42.42

Georgia 28.42

Hawaii 64.01

Idaho 22.59

Illinois 23.02

Indiana 24.78

Iowa 22.97

Kansas 23.03

Kentucky 28.49

Louisiana 29.68

Maine 36

Maryland 38.13

Massachusetts 38.04

Michigan 22.97

Minnesota 23.16

Mississippi 30.74

Missouri 22.58

Montana 21.31

Nebraska 22.86

Nevada 35.45

New Hampshire 34.83

New Jersey 37.58

New Mexico 28.8

New York 35.44

North Carolina 30.28

North Dakota 22.75

Ohio 28.19

Oklahoma 22.64

Oregon 28.85

Pennsylvania 31.96

Rhode Island 43.86

South Carolina 31.84

South Dakota 22.53

Tennessee 29.9

Texas 30.76

Utah 22.58

Vermont 34.44

Virginia 26.67

Washington 29.24

West Virginia 35.78

8C-8
Wisconsin 20.8

Wyoming 22.16

All prices in 2012$ were converted to 2013$ to be consistent with the rest of the prices 

used in the analysis. This conversion was performed using the Consumer Price Index. Table 

8C.3.6 shows the housing-projection-weighted average residential LPG prices for each 

geographic area.

Table 8C.3.6 Average Residential LPG Prices by Region in 2012

Geographic Area 2013$/MMBtu

1 Connecticut, Maine, New Hampshire, Rhode Island, Vermont $32.55

2 Massachusetts $34.94

3 New York $33.08

4 New Jersey $34.31

5 Pennsylvania $31.19

6 Illinois $21.69

7 Indiana, Ohio $25.35

8 Michigan $23.30

9 Wisconsin $21.51

10 Iowa, Minnesota, North Dakota, South Dakota $22.34

11 Kansas, Nebraska $21.47

12 Missouri $22.09

13 Virginia $24.48

14 Delaware, District of Columbia, Maryland $33.32

15 Georgia $26.61

16 North Carolina, South Carolina $26.64

17 Florida $27.55

18 Alabama, Kentucky, Mississippi $23.62

19 Tennessee $26.99

20 Arkansas, Louisiana, Oklahoma $20.33

21 Texas $15.30

22 Colorado $24.35

23 Idaho, Montana, Utah, Wyoming $23.36

24 Arizona $29.91

25 Nevada, New Mexico $24.77

26 California $28.49

27 Oregon, Washington $26.18

28 Alaska $28.45

29 Hawaii $38.22

30 West Virginia $31.69

31 U.S. Average $18.28

8C.3.2 Monthly Energy Price Factors Determination

For hearth products, the Department of Energy (DOE) developed monthly energy price 

factors and used monthly energy consumption data for the life-cycle cost and payback period 

8C-9
calculation. DOE developed monthly energy price factors to capture robust seasonal trends in 

monthly energy prices. To convert annual energy prices into monthly energy prices, DOE 

determined monthly energy price factors. 

8C.3.2.1 Monthly Residential Electricity Price Factor Calculations

DOE collected historical electricity prices from 1993 to 2012 from EIA’s Form 826.1

These data are published annually and include annual electricity sales, revenues from electricity 

sales, and average price for the residential, commercial, industrial, and transportation sectors by 

state. DOE aggregated the data into 30 geographical areas described in Chapter 8 (section 8.2.3.2 

Energy Prices).

For each geographic region, DOE determined average electricity prices from 1993 to 

2012 by weighting the average residential electricity prices for each state by the housing

projections in 2021 in each state. 

As an example, to illustrate the methodology for producing monthly price factors, the 

following tables and charts show the calculation of monthly average electricity price factors, 

based on New York historic electricity price data. Table 8C.3.7 shows the average residential 

electricity prices for New York. 

8C-10
Table 8C.3.7 1990-2011 Average Residential Electricity Prices for New York (nominal 

cents/kWh)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Avg

1990 10.71 11.12 11.19 11.02 11.31 11.64 11.80 11.89 11.98 11.71 11.62 11.48 11.46

1991 11.48 11.44 11.63 11.58 11.82 12.41 12.38 12.63 12.57 12.02 11.84 11.77 11.96

1992 11.43 11.42 11.49 11.60 12.21 13.09 13.31 13.46 13.46 12.95 12.71 12.55 12.47

1993 12.17 12.14 12.35 12.71 13.35 13.93 13.94 13.92 13.90 13.28 13.35 13.10 13.18

1994 12.92 12.74 13.01 13.19 13.61 14.11 14.19 14.30 14.37 13.43 13.50 13.15 13.54

1995 13.16 13.11 13.34 13.54 14.06 14.63 14.69 14.58 14.51 13.76 13.81 13.50 13.89

1996 13.39 13.46 13.71 13.80 14.00 14.54 14.67 14.78 14.59 13.97 13.83 13.75 14.04

1997 13.75 13.67 13.83 13.69 13.84 14.70 14.80 14.68 14.56 14.01 13.93 13.84 14.11

1998 13.87 13.73 13.77 13.84 14.05 13.78 13.78 13.65 13.66 13.29 13.04 12.92 13.62

1999 12.85 12.75 12.95 13.34 12.85 13.44 13.44 13.54 13.74 13.64 13.44 13.24 13.27

2000 12.90 13.18 13.33 13.52 13.54 14.22 15.40 14.77 14.52 14.12 13.94 13.98 13.95

2001 13.89 13.93 13.58 13.44 14.01 14.41 14.99 14.61 14.23 14.22 13.53 13.25 14.01

2002 12.95 13.00 12.81 12.69 13.30 14.01 14.19 14.16 14.42 13.87 13.37 13.19 13.50

2003 12.77 13.30 13.91 14.55 14.77 14.98 15.14 14.94 14.92 14.75 14.23 13.63 14.32

2004 13.32 14.02 13.98 14.03 14.20 14.99 15.36 15.32 15.10 14.93 14.88 14.29 14.53

2005 14.05 14.53 14.40 14.64 15.36 15.58 15.63 16.16 16.69 17.36 17.57 16.53 15.71

2006 16.61 16.66 15.89 16.36 16.56 17.33 17.56 17.74 17.92 17.22 16.33 15.88 16.84

2007 16.09 15.89 16.83 17.14 17.50 18.17 17.27 17.96 17.15 17.48 16.94 16.66 17.09

2008 16.86 17.31 16.92 18.08 18.79 19.42 19.66 20.93 19.49 17.57 16.95 16.61 18.22

2009 16.83 16.72 16.40 16.57 16.86 18.22 18.79 18.21 18.75 18.12 16.72 17.47 17.47

2010 17.29 18.04 17.55 18.92 19.21 19.41 20.11 19.35 20.09 18.36 18.25 17.72 18.69

2011 17.25 17.45 17.58 17.63 18.30 19.07 19.22 19.25 18.84 18.78 17.93 17.26 18.21

DOE then calculated monthly energy price factors by dividing the monthly prices by the 

annual average for each year. Table 8C.3.8 and Figure 8C.3.1 show the calculated results for 

New York. 

8C-11
Table 8C.3.8 Monthly Electricity Price Factors for 1990-2011 for New York

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1990 0.94 0.97 0.98 0.96 0.99 1.02 1.03 1.04 1.05 1.02 1.01 1.00

1991 0.96 0.96 0.97 0.97 0.99 1.04 1.03 1.06 1.05 1.00 0.99 0.98

1992 0.92 0.92 0.92 0.93 0.98 1.05 1.07 1.08 1.08 1.04 1.02 1.01

1993 0.92 0.92 0.94 0.96 1.01 1.06 1.06 1.06 1.05 1.01 1.01 0.99

1994 0.95 0.94 0.96 0.97 1.00 1.04 1.05 1.06 1.06 0.99 1.00 0.97

1995 0.95 0.94 0.96 0.97 1.01 1.05 1.06 1.05 1.04 0.99 0.99 0.97

1996 0.95 0.96 0.98 0.98 1.00 1.04 1.04 1.05 1.04 0.99 0.98 0.98

1997 0.97 0.97 0.98 0.97 0.98 1.04 1.05 1.04 1.03 0.99 0.99 0.98

1998 1.02 1.01 1.01 1.02 1.03 1.01 1.01 1.00 1.00 0.98 0.96 0.95

1999 0.97 0.96 0.98 1.01 0.97 1.01 1.01 1.02 1.04 1.03 1.01 1.00

2000 0.92 0.94 0.96 0.97 0.97 1.02 1.10 1.06 1.04 1.01 1.00 1.00

2001 0.99 0.99 0.97 0.96 1.00 1.03 1.07 1.04 1.02 1.02 0.97 0.95

2002 0.96 0.96 0.95 0.94 0.99 1.04 1.05 1.05 1.07 1.03 0.99 0.98

2003 0.89 0.93 0.97 1.02 1.03 1.05 1.06 1.04 1.04 1.03 0.99 0.95

2004 0.92 0.96 0.96 0.97 0.98 1.03 1.06 1.05 1.04 1.03 1.02 0.98

2005 0.89 0.93 0.92 0.93 0.98 0.99 0.99 1.03 1.06 1.11 1.12 1.05

2006 0.99 0.99 0.94 0.97 0.98 1.03 1.04 1.05 1.06 1.02 0.97 0.94

2007 0.94 0.93 0.98 1.00 1.02 1.06 1.01 1.05 1.00 1.02 0.99 0.97

2008 0.93 0.95 0.93 0.99 1.03 1.07 1.08 1.15 1.07 0.96 0.93 0.91

2009 0.96 0.96 0.94 0.95 0.97 1.04 1.08 1.04 1.07 1.04 0.96 1.00

2010 0.93 0.97 0.94 1.01 1.03 1.04 1.08 1.04 1.07 0.98 0.98 0.95

2011 0.95 0.96 0.97 0.97 1.00 1.05 1.06 1.06 1.03 1.03 0.98 0.95

Figure 8C.3.1 Monthly Electricity Price Factors for 1990-2011 for New York

0.80

0.85

0.90

0.95

1.00

1.05

1.10

1.15

1.20

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Electricity Price Factors

1990 1991 1992

1993 1994 1995

1996 1997 1998

1999 2000 2001

2002 2003 2004

2005 2006 2007

2008 2009 2010

2011

8C-12
DOE then averaged the monthly energy price factors for 1993 to 2012 to develop an 

average energy price factor for each month. DOE performed the same calculations for each 

geographic region to develop the average monthly energy price factors weighted by 2021 

housing projections shown in Table 8C.3.9, which includes the results for New York.

Table 8C.3.9 Monthly Residential Electricity Price Factors

Geographical Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Connecticut, Maine, 

New Hampshire, Rhode 

Island, Vermont

0.98 0.98 0.99 1.00 1.01 1.01 1.00 1.01 1.01 1.01 1.00 1.00

Massachusetts 0.98 0.99 0.99 0.99 1.00 1.03 1.00 1.00 1.02 1.01 0.99 1.01

New York 0.95 0.96 0.96 0.98 1.00 1.04 1.05 1.05 1.05 1.02 0.99 0.97

New Jersey 0.95 0.95 0.96 0.96 0.98 1.06 1.08 1.08 1.06 0.97 0.97 0.97

Pennsylvania 0.93 0.94 0.95 0.98 1.02 1.06 1.07 1.06 1.04 1.02 0.98 0.95

Illinois 0.89 0.93 0.96 1.01 1.05 1.07 1.06 1.04 1.05 1.06 0.97 0.91

Indiana, Ohio 0.90 0.92 0.95 1.01 1.05 1.06 1.03 1.04 1.05 1.05 1.01 0.94

Michigan 0.97 0.97 0.97 0.98 0.99 1.03 1.05 1.05 1.02 0.99 0.98 0.99

Wisconsin 0.96 0.99 0.98 1.00 1.02 1.03 1.00 1.01 1.01 1.02 1.00 0.98

Iowa, Minnesota, North 

Dakota, South Dakota 0.91 0.93 0.95 0.98 1.03 1.07 1.08 1.07 1.05 1.02 0.97 0.94

Kansas, Nebraska 0.87 0.91 0.94 0.98 1.02 1.09 1.10 1.11 1.10 1.00 0.96 0.91

Missouri 0.84 0.86 0.90 0.96 1.08 1.18 1.17 1.16 1.06 0.98 0.94 0.87

Virginia 0.91 0.93 0.95 0.99 1.04 1.06 1.07 1.07 1.04 1.02 0.98 0.93

Delaware, District of 

Columbia, Maryland 0.90 0.90 0.92 0.94 1.03 1.12 1.11 1.11 1.08 1.00 0.95 0.93

Georgia 0.90 0.93 0.96 0.97 1.01 1.09 1.10 1.11 1.06 1.00 0.95 0.91

North Carolina, South 

Carolina 0.94 0.96 0.98 1.01 1.01 1.00 1.02 1.02 1.03 1.05 1.01 0.97

Florida 0.98 0.99 1.00 1.01 1.00 0.99 1.00 1.00 1.00 1.01 1.02 1.00

Alabama, Kentucky, 

Mississippi 0.92 0.94 0.97 1.02 1.03 1.03 1.02 1.03 1.02 1.03 1.01 0.96

Tennessee 0.96 0.96 0.98 1.01 1.02 1.01 0.99 0.99 1.00 1.04 1.03 1.01

Arkansas, Louisiana, 

Oklahoma 0.90 0.93 0.97 1.00 1.02 1.05 1.05 1.05 1.07 1.04 0.99 0.93

Texas 0.92 0.93 0.97 0.99 1.02 1.05 1.05 1.05 1.04 1.04 0.98 0.96

Colorado 0.95 0.96 0.97 0.99 1.02 1.03 1.02 1.02 1.03 1.02 1.00 0.97

Idaho, Montana, Utah, 

Wyoming 0.95 0.96 0.96 0.97 1.01 1.04 1.04 1.04 1.03 1.03 0.99 0.98

Arizona 0.88 0.91 0.93 0.98 1.09 1.08 1.07 1.06 1.06 1.06 0.94 0.95

Nevada, New Mexico 0.96 0.98 1.00 1.01 1.01 1.00 0.99 1.00 1.00 1.03 1.02 0.99

California 1.00 0.97 0.97 0.97 1.00 1.02 1.05 1.05 1.01 0.96 1.00 1.01

Oregon, Washington 0.98 0.99 0.99 0.98 0.98 1.00 1.00 1.01 1.01 1.02 1.02 1.02

Alaska 0.95 0.96 0.98 0.99 1.02 1.02 1.04 1.03 1.01 1.01 1.00 0.99

Hawaii 0.96 0.97 0.96 0.97 0.99 1.00 1.01 1.02 1.02 1.03 1.03 1.02

West Virginia 0.94 0.95 0.98 1.01 1.04 1.02 1.00 1.00 1.02 1.05 1.02 0.97

United States 0.93 0.94 0.96 0.99 1.02 1.04 1.04 1.05 1.04 1.02 0.99 0.96

8C-13
8C.3.2.2 Monthly Residential Natural Gas Price Factor Calculations

DOE collected historical natural gas prices from 1993 to 2012 from the Energy 

Information Administration’s (EIA’s) Natural Gas Navigator.2 The Natural Gas Navigator 

includes annual and monthly natural gas prices for residential, commercial, and industrial 

consumers by state. DOE aggregated the data into 30 geographical areas for residential buildings 

and 9 census division for commercial buildings as described in Chapter 8. 

For each geographic area, DOE determined average natural gas prices from 1993 to 2012

by weighting the average residential natural gas prices for each state by the housing projections

in 2021 in each state. 

Again, as an example for how DOE determined monthly natural gas price factors, the 

methodology used to determine monthly average price factors can be seen below. Table 8C.3.10

shows the historic average residential gas prices for New York.

Table 8C.3.10 1989-2010 Average Residential Natural Gas Prices for New York ($/tcf)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Avg

1990 6.60 6.66 7.46 6.87 7.11 8.31 9.01 9.36 9.18 7.98 7.58 6.77 7.74

1991 6.77 7.18 6.91 7.08 7.64 8.13 9.02 9.61 9.01 8.38 7.41 7.35 7.87

1992 6.96 6.97 6.82 6.91 7.53 8.91 9.53 9.79 8.90 8.03 7.44 7.14 7.91

1993 7.10 6.83 6.89 6.79 7.40 8.70 9.70 10.28 9.92 8.62 8.68 7.74 8.22

1994 7.57 7.20 7.14 7.71 9.17 9.83 10.95 11.43 10.88 9.33 8.38 8.43 9.00

1995 7.69 7.99 8.33 8.87 9.20 10.64 11.64 12.00 11.42 9.70 9.36 8.64 9.62

1996 8.10 7.66 7.66 7.88 8.70 10.20 11.64 12.00 11.81 11.09 8.70 7.77 9.43

1997 8.01 8.22 8.12 8.39 8.80 10.03 11.08 0.00 0.00 0.00 0.00 0.00 5.22

1998 9.88 9.55 8.86 8.51 9.01 10.83 12.70 11.62 12.59 11.38 9.93 9.34 10.35

1999 9.17 9.09 8.90 9.56 10.73 11.99 7.08 13.24 12.66 11.62 9.50 9.30 10.24

2000 8.21 8.49 8.05 8.74 10.10 11.79 12.65 12.01 11.93 10.29 9.66 9.01 10.08

2001 7.97 8.49 9.55 9.82 11.66 13.30 14.81 14.68 15.05 12.15 10.16 9.02 11.39

2002 12.47 11.24 10.53 11.43 13.50 13.84 14.25 14.41 13.14 11.57 11.16 10.89 12.37

2003 9.35 8.65 9.17 9.34 9.96 11.66 13.04 13.18 13.21 11.84 10.08 9.48 10.75

2004 9.63 9.88 11.69 12.22 12.93 14.71 16.01 16.17 15.58 13.01 12.02 11.36 12.93

2005 11.41 11.33 11.48 11.51 13.07 15.34 16.29 16.89 16.22 14.41 13.44 13.19 13.72

2006 12.80 12.65 12.42 13.45 14.49 16.16 17.62 18.48 20.78 22.24 20.21 17.44 16.56

2007 16.61 15.11 13.99 14.58 16.09 16.69 18.04 18.91 18.43 13.37 14.75 14.97 15.96

2008 15.24 14.43 15.08 15.47 17.33 19.59 19.95 18.94 18.53 18.64 16.04 14.83 17.01

2009 14.99 14.91 15.21 16.76 19.95 22.88 24.96 24.20 21.66 18.42 16.48 16.26 18.89

2010 15.46 14.84 14.63 14.19 15.13 16.82 18.24 17.81 17.74 14.71 14.97 14.02 15.71

2011 12.97 13.01 13.60 15.08 15.82 18.42 20.00 20.17 18.54 16.47 13.88 12.09 15.84

DOE then calculated monthly energy price factors for each year by dividing the 

residential natural gas prices for each month by the natural gas annual average price for each 

year. Table 8C.3.11 and Figure 8C.3.2 show the calculated results for New York.

8C-14
Table 8C.3.11 1989-2011 Monthly Natural Gas Price Factors for New York

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1990 0.85 0.86 0.96 0.89 0.92 1.07 1.16 1.21 1.19 1.03 0.98 0.87

1991 0.86 0.91 0.88 0.90 0.97 1.03 1.15 1.22 1.14 1.06 0.94 0.93

1992 0.88 0.88 0.86 0.87 0.95 1.13 1.20 1.24 1.13 1.02 0.94 0.90

1993 0.86 0.83 0.84 0.83 0.90 1.06 1.18 1.25 1.21 1.05 1.06 0.94

1994 0.84 0.80 0.79 0.86 1.02 1.09 1.22 1.27 1.21 1.04 0.93 0.94

1995 0.80 0.83 0.87 0.92 0.96 1.11 1.21 1.25 1.19 1.01 0.97 0.90

1996 0.86 0.81 0.81 0.84 0.92 1.08 1.23 1.27 1.25 1.18 0.92 0.82

1997 0.95 0.92 0.86 0.82 0.87 1.05 1.23 1.12 1.22 1.10 0.96 0.90

1998 0.90 0.89 0.87 0.93 1.05 1.17 0.69 1.29 1.24 1.14 0.93 0.91

1999 0.81 0.84 0.80 0.87 1.00 1.17 1.26 1.19 1.18 1.02 0.96 0.89

2000 0.70 0.75 0.84 0.86 1.02 1.17 1.30 1.29 1.32 1.07 0.89 0.79

2001 1.01 0.91 0.85 0.92 1.09 1.12 1.15 1.16 1.06 0.94 0.90 0.88

2002 0.87 0.80 0.85 0.87 0.93 1.08 1.21 1.23 1.23 1.10 0.94 0.88

2003 0.74 0.76 0.90 0.94 1.00 1.14 1.24 1.25 1.20 1.01 0.93 0.88

2004 0.83 0.83 0.84 0.84 0.95 1.12 1.19 1.23 1.18 1.05 0.98 0.96

2005 0.77 0.76 0.75 0.81 0.87 0.98 1.06 1.12 1.25 1.34 1.22 1.05

2006 1.04 0.95 0.88 0.91 1.01 1.05 1.13 1.18 1.15 0.84 0.92 0.94

2007 0.90 0.85 0.89 0.91 1.02 1.15 1.17 1.11 1.09 1.10 0.94 0.87

2008 0.79 0.79 0.81 0.89 1.06 1.21 1.32 1.28 1.15 0.98 0.87 0.86

2009 0.98 0.94 0.93 0.90 0.96 1.07 1.16 1.13 1.13 0.94 0.95 0.89

2010 0.82 0.82 0.86 0.95 1.00 1.16 1.26 1.27 1.17 1.04 0.88 0.76

2011 0.77 0.78 0.81 0.86 1.01 1.26 1.26 1.26 1.26 1.05 0.89 0.80

Figure 8C.3.2 1989-2011 Monthly Natural Gas Price Factors for New York

0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

1.40

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Natural Gas Price Factors

1989 1990 1991

1992 1993 1994

1995 1997 1998

1999 2000 2001

2002 2003 2004

2005 2006 2007

2008 2009 2010

2011

8C-15
DOE then averaged the monthly energy price factors for 1993 to 2012 to develop an 

average energy price factor for each month. DOE performed the same calculations for each 

geographic area to develop the average monthly energy price factors weighted by housing 

projections in 2021 shown in Table 8C.3.12, which also includes the monthly energy price factor 

results calculated for New York.

8C-16
Table 8C.3.12 Monthly Residential Natural Gas Energy Price Factors

Geographical Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Connecticut, Maine, 

New Hampshire, Rhode 

Island, Vermont

0.89 0.90 0.91 0.93 0.97 1.05 1.14 1.16 1.13 1.01 0.97 0.94

Massachusetts 0.98 0.98 0.97 0.99 0.93 0.96 1.05 1.11 1.07 0.93 1.00 1.00

New York 0.88 0.87 0.88 0.92 1.02 1.16 1.23 1.16 1.13 1.00 0.89 0.84

New Jersey 0.91 0.90 0.90 0.93 0.98 1.08 1.12 1.12 1.10 1.01 0.96 0.94

Pennsylvania 0.82 0.82 0.84 0.88 0.99 1.14 1.26 1.29 1.23 1.00 0.89 0.84

Illinois 0.80 0.81 0.81 0.86 1.04 1.20 1.29 1.31 1.21 0.96 0.85 0.80

Indiana, Ohio 0.82 0.82 0.85 0.91 1.02 1.17 1.28 1.29 1.20 0.96 0.85 0.83

Michigan 0.83 0.83 0.84 0.89 0.99 1.13 1.25 1.29 1.19 0.99 0.90 0.87

Wisconsin 0.95 0.93 0.94 0.96 0.96 1.10 1.14 1.15 1.06 0.88 0.97 0.95

Iowa, Minnesota, North 

Dakota, South Dakota 0.85 0.84 0.85 0.87 0.99 1.16 1.25 1.29 1.20 0.95 0.89 0.85

Kansas, Nebraska 0.80 0.80 0.80 0.88 0.99 1.15 1.24 1.30 1.26 1.08 0.88 0.82

Missouri 0.73 0.73 0.74 0.83 0.96 1.17 1.34 1.42 1.32 1.12 0.88 0.78

Virginia 0.82 0.79 0.78 0.87 1.03 1.20 1.29 1.27 1.26 1.02 0.84 0.82

Delaware, District of 

Columbia, Maryland 0.82 0.81 0.83 0.91 1.04 1.16 1.24 1.24 1.21 1.01 0.88 0.84

Georgia 0.72 0.77 0.80 0.89 1.10 1.23 1.29 1.28 1.24 1.07 0.82 0.77

North Carolina, South 

Carolina 0.79 0.79 0.81 0.87 1.00 1.18 1.25 1.30 1.25 1.04 0.87 0.84

Florida 0.81 0.83 0.88 0.94 1.02 1.09 1.12 1.14 1.13 1.11 1.02 0.90

Alabama, Kentucky, 

Mississippi 0.80 0.80 0.83 0.92 1.04 1.16 1.19 1.22 1.19 1.08 0.92 0.85

Tennessee 0.82 0.83 0.83 0.90 0.99 1.13 1.20 1.24 1.19 1.08 0.91 0.86

Arkansas, Louisiana, 

Oklahoma 0.77 0.77 0.79 0.87 1.04 1.14 1.21 1.25 1.22 1.13 0.96 0.82

Texas 0.77 0.78 0.80 0.92 1.05 1.17 1.21 1.24 1.23 1.11 0.91 0.80

Colorado 0.81 0.82 0.85 0.88 0.97 1.23 1.24 1.31 1.22 0.97 0.88 0.83

Idaho, Montana, Utah,

Wyoming 0.92 0.93 0.94 0.92 0.96 1.04 1.13 1.18 1.10 0.97 0.96 0.94

Arizona 0.77 0.79 0.82 0.90 1.01 1.11 1.21 1.26 1.22 1.14 0.96 0.83

Nevada, New Mexico 0.80 0.82 0.84 0.91 1.04 1.21 1.18 1.23 1.19 1.05 0.89 0.81

California 1.00 0.99 0.95 0.95 0.99 1.04 1.05 1.03 1.01 1.01 0.98 0.98

Oregon, Washington 0.90 0.91 0.91 0.93 0.97 1.02 1.12 1.17 1.14 1.03 0.95 0.93

Alaska 0.94 0.94 0.95 0.96 1.01 1.05 1.13 1.10 1.02 0.97 0.94 0.97

Hawaii 0.95 0.97 0.97 0.97 0.99 1.00 1.02 1.05 1.05 1.04 1.02 1.00

West Virginia 0.83 0.84 0.84 0.87 0.97 1.17 1.30 1.29 1.18 0.96 0.88 0.86

United States 0.85 0.86 0.87 0.91 1.00 1.12 1.19 1.22 1.16 1.00 0.91 0.87

8C-17
8C.3.2.3 Monthly Residential Liquid Petroleum Gas Price Factor Calculations

DOE collected historical liquid petroleum gas (LPG) prices from 1995 to 2009 from 

EIA’s Short-Term Energy Outlook.4 The Short-Term Energy Outlook includes monthly LPG 

prices by Census Region (Northeast, South, Midwest, and West).c

The same process as used for electricity and natural gas price factors was used for 

calculating the monthly LPG price factors. These monthly price factors were calculated below, 

using data from the Northeast region. Table 8C.3.13 shows the Northeast residential LPG prices 

from 1995 to 2009.

Table 8C.3.13 Average LPG Prices for the Northeast (nominal cents/gallon)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1995 119 118 120 121 124 126 126 125 122 121 118 117

1996 123 125 128 125 130 131 129 127 127 133 135 145

1997 143 137 131 131 130 130 130 127 126 127 123 122

1998 121 120 120 123 124 124 122 121 119 118 115 114

1999 112 113 114 118 122 124 126 129 127 129 128 128

2000 132 148 148 145 148 151 155 154 157 159 156 160

2001 176 170 162 160 162 160 156 152 150 150 144 139

2002 139 138 139 143 142 144 143 141 141 142 142 142

2003 150 166 182 164 161 161 159 156 155 155 155 158

2004 169 173 171 168 170 173 173 176 181 187 193 187

2005 186 186 190 197 199 200 202 205 217 224 220 217

2006 221 220 220 225 231 237 242 244 240 232 229 228

2007 227 229 235 239 247 252 253 252 254 260 274 275

2008 282 280 284 292 306 320 333 329 324 305 280 267

2009 268 267 267 263 258 255 255 251 249 250 252 255

DOE then calculated monthly energy price factors for each year by dividing the prices for 

each month by the average price for each year. Table 8C.3.14 and Figure 8C.3.3 show the 

calculated results for the Northeast.

c Refer to www.census.gov/geo/www/us_regdiv.pdf.

8C-18

 
Table 8C.3.14 Monthly LPG Price Factors for 1995-2009 for the Northeast

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1995 0.98 0.97 0.99 1.00 1.02 1.03 1.04 1.03 1.00 1.00 0.97 0.96

1996 0.94 0.97 0.99 0.96 1.00 1.01 0.99 0.98 0.98 1.02 1.04 1.12

1997 1.10 1.06 1.01 1.01 1.01 1.00 1.00 0.98 0.97 0.98 0.95 0.94

1998 1.01 1.00 1.00 1.02 1.03 1.03 1.02 1.01 0.99 0.98 0.96 0.95

1999 0.92 0.92 0.93 0.96 1.00 1.02 1.03 1.05 1.04 1.06 1.05 1.04

2000 0.87 0.98 0.98 0.96 0.98 1.00 1.03 1.02 1.04 1.05 1.03 1.06

2001 1.12 1.08 1.03 1.02 1.04 1.02 0.99 0.97 0.96 0.96 0.92 0.89

2002 0.98 0.97 0.98 1.01 1.01 1.02 1.01 1.00 1.00 1.01 1.00 1.00

2003 0.94 1.04 1.13 1.03 1.01 1.00 1.00 0.97 0.97 0.97 0.97 0.99

2004 0.95 0.98 0.96 0.95 0.96 0.98 0.98 1.00 1.02 1.06 1.09 1.06

2005 0.91 0.91 0.93 0.97 0.98 0.98 0.99 1.01 1.07 1.10 1.08 1.07

2006 0.96 0.95 0.95 0.98 1.00 1.03 1.05 1.06 1.04 1.01 0.99 0.99

2007 0.91 0.92 0.94 0.96 0.99 1.01 1.01 1.01 1.02 1.04 1.10 1.10

2008 0.94 0.93 0.95 0.97 1.02 1.06 1.11 1.10 1.08 1.02 0.93 0.89

2009 1.04 1.04 1.04 1.02 1.00 0.99 0.99 0.97 0.97 0.97 0.98 0.99

Avg 0.97 0.98 0.99 0.99 1.00 1.01 1.02 1.01 1.01 1.01 1.00 1.00

Figure 8C.3.3 Monthly LPG Factors for 1995-2009 for the Northeast

DOE then averaged the monthly energy price factors for 1995 to 2009 to develop an 

average energy price factor for each month. DOE performed the same calculations for each 

Census Region to develop the average monthly energy price factors weighted by housing 

projections in 2021 shown in Table 8C.3.15, which includes the calculated Northeast region 

monthly LPG energy price factors from 1995 to 2009.

0.85

0.90

0.95

1.00

1.05

1.10

1.15

1 2 3 4 5 6 7 8 9 10 11 12

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Avg

8C-19
Table 8C.3.15 Monthly Residential LPG Energy Price Factors

Census Regions Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Northeast 0.97 0.98 0.99 0.99 1.00 1.01 1.02 1.01 1.01 1.01 1.00 1.00

South 1.04 1.04 1.03 1.01 1.00 0.97 0.94 0.93 0.96 0.98 1.03 1.07

Midwest 1.04 1.04 1.03 1.01 0.99 0.97 0.95 0.93 0.96 1.00 1.03 1.06

West 1.05 1.05 1.03 1.01 0.99 0.96 0.92 0.91 0.95 1.01 1.04 1.08

U.S. 1.02 1.03 1.02 1.02 1.02 1.00 0.95 0.93 0.96 0.99 1.02 1.05

8C.3.3 Seasonal Marginal Price Factors Determination

Marginal energy prices are the prices consumers pay for the last unit of energy used. 

DOE used the marginal energy prices for each building for the cost of saved energy associated 

with the use of higher-efficiency equipment. Because marginal prices reflect a change in a 

consumer’s bill associated with a change in energy consumed, such prices are appropriate for 

determining energy cost savings associated with possible changes to efficiency standards.

EIA provides historical monthly consumption and expenditures by state. This data were

used to determine 10 year average marginal prices for the RECS 2009 geographical areas, which 

are then used to convert average monthly energy prices into marginal monthly energy prices. 

Because a hearth product operates during both the heating and cooling seasons, DOE determined 

summer and winter marginal price factors. EIA provided RECS 2009 billing data that had been 

gathered from a subset of RECS housing records. For each household with billing data, the 

following are provided for each billing cycle: the start and end date, the electricity consumption 

in kWh, the electricity cost in dollars, the natural gas bill in dollars, and the gas consumption in 

hundreds of cubic feet. This data was used to validate marginal energy price factors by RECS 

2009 geographical area.

For LPG-fired hearth products, DOE used the average LPG prices for each house for both 

base case products and higher-efficiency products, as the data necessary for estimating marginal 

prices were not available.

8C.3.3.1 Marginal Price Factor Calculation for Electricity and Natural Gas

EIA provides historical monthly consumption and expenditures by state. This data was 

used to determine 10 year average marginal prices for the RECS 2009 geographical areas DOE 

interpreted the slope of the regression line (consumption vs. expenditures) for each state as the 

marginal energy price for that state. 

Table 8C.3.16 and Table 8C.3.17 show the resulting marginal electricity and natural gas 

marginal price factors.

8C-20
Table 8C.3.16 Residential Marginal Electricity Price Factors using EIA 2003-2012 Data

Geographical Area Summer Winter

Connecticut, Maine, New Hampshire, Rhode Island, Vermont 0.95 0.99

Massachusetts 0.96 1.04

New York 1.13 0.87

New Jersey 1.21 0.98

Pennsylvania 1.08 0.83

Illinois 0.98 0.72

Indiana, Ohio 1.00 0.75

Michigan 1.14 0.97

Wisconsin 1.01 0.89

Iowa, Minnesota, North Dakota, South Dakota 1.06 0.84

Kansas, Nebraska 1.16 0.74

Missouri 1.21 0.76

Virginia 1.08 0.85

Delaware, District of Columbia, Maryland 1.16 0.91

Georgia 1.16 0.84

North Carolina, South Carolina 0.97 0.83

Florida 1.01 0.93

Alabama, Kentucky, Mississippi 1.00 0.82

Tennessee 0.93 0.84

Arkansas, Louisiana, Oklahoma 1.04 0.74

Texas 1.05 0.90

Colorado 1.08 0.79

Idaho, Montana, Utah, Wyoming 1.10 0.93

Arizona 1.05 0.84

Nevada, New Mexico 1.04 0.88

California 1.21 1.13

Oregon, Washington 0.89 0.95

Alaska 0.85 0.91

Hawaii 1.46 0.89

West Virginia 0.92 0.84

United States 1.07 0.81

8C-21
Table 8C.3.17 Residential Marginal Natural Gas Price Factors using EIA 2003-2012

Data

Geographical Area Summer Winter

Connecticut, Maine, New Hampshire, Rhode Island, Vermont 0.83 0.92

Massachusetts 0.89 1.03

New York 0.75 0.89

New Jersey 0.84 0.95

Pennsylvania 0.73 0.93

Illinois 0.68 0.97

Indiana, Ohio 0.73 0.92

Michigan 0.78 0.93

Wisconsin 0.79 0.98

Iowa, Minnesota, North Dakota, South Dakota 0.72 0.97

Kansas, Nebraska 0.69 0.93

Missouri 0.60 0.82

Virginia 0.68 0.93

Delaware, District of Columbia, Maryland 0.70 0.92

Georgia 0.56 0.87

North Carolina, South Carolina 0.66 0.89

Florida 0.64 0.82

Alabama, Kentucky, Mississippi 0.75 0.87

Tennessee 0.74 0.94

Arkansas, Louisiana, Oklahoma 0.65 0.84

Texas 0.59 0.85

Colorado 0.69 0.91

Idaho, Montana, Utah, Wyoming 0.84 0.96

Arizona 0.64 0.85

Nevada, New Mexico 0.72 0.89

California 0.85 1.08

Oregon, Washington 0.84 0.94

Alaska 0.86 0.96

Hawaii 0.77 0.91

West Virginia 0.80 0.95

United States 0.74 0.94

8C.3.4 Results

DOE applied the regional monthly energy price factors to develop residential and 

commercial monthly energy prices for 2012 for electricity and natural gas (Table 8C.3.18 and 

Table 8C.3.19). Each geographical area was matched with the appropriate Census Region.

8C-22
Table 8C.3.18 Residential Average Monthly Electricity Prices for 2012 Using Monthly 

Price Factors (2013$/kWh)

Geographical Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Connecticut, Maine, 

New Hampshire, Rhode 

Island, Vermont

0.16 0.16 0.16 0.16 0.17 0.17 0.17 0.17 0.17 0.17 0.16 0.16

Massachusetts 0.15 0.15 0.15 0.15 0.15 0.16 0.15 0.15 0.15 0.15 0.15 0.15

New York 0.17 0.17 0.17 0.17 0.18 0.18 0.19 0.19 0.19 0.18 0.18 0.17

New Jersey 0.15 0.15 0.15 0.15 0.16 0.17 0.17 0.17 0.17 0.16 0.16 0.16

Pennsylvania 0.12 0.12 0.12 0.13 0.13 0.14 0.14 0.14 0.13 0.13 0.13 0.12

Illinois 0.10 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.11 0.11

Indiana, Ohio 0.10 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.11

Michigan 0.14 0.14 0.14 0.14 0.14 0.15 0.15 0.15 0.15 0.14 0.14 0.14

Wisconsin 0.13 0.13 0.13 0.13 0.14 0.14 0.13 0.13 0.14 0.14 0.13 0.13

Iowa, Minnesota, North 

Dakota, South Dakota 0.10 0.10 0.10 0.11 0.11 0.12 0.12 0.12 0.12 0.11 0.11 0.10

Kansas, Nebraska 0.09 0.10 0.10 0.11 0.11 0.12 0.12 0.12 0.12 0.11 0.10 0.10

Missouri 0.09 0.09 0.09 0.10 0.11 0.12 0.12 0.12 0.11 0.10 0.10 0.09

Virginia 0.10 0.10 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.11 0.11 0.10

Delaware, District of 

Columbia, Maryland 0.12 0.12 0.12 0.12 0.14 0.15 0.15 0.15 0.14 0.13 0.12 0.12

Georgia 0.10 0.10 0.11 0.11 0.11 0.12 0.12 0.13 0.12 0.11 0.11 0.10

North Carolina, South 

Carolina 0.11 0.11 0.11 0.11 0.12 0.11 0.12 0.12 0.12 0.12 0.11 0.11

Florida 0.11 0.11 0.12 0.12 0.12 0.11 0.12 0.12 0.12 0.12 0.12 0.12

Alabama, Kentucky, 

Mississippi 0.10 0.10 0.10 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.10

Tennessee 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.11 0.11 0.10

Arkansas, Louisiana, 

Oklahoma 0.08 0.09 0.09 0.09 0.09 0.10 0.10 0.10 0.10 0.10 0.09 0.09

Texas 0.10 0.10 0.11 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.11 0.11

Colorado 0.11 0.11 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.11

Idaho, Montana, Utah, 

Wyoming 0.09 0.09 0.09 0.09 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10

Arizona 0.10 0.10 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.11 0.11

Nevada, New Mexico 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12

California 0.15 0.15 0.15 0.15 0.16 0.16 0.16 0.16 0.16 0.15 0.15 0.16

Oregon, Washington 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09

Alaska 0.17 0.17 0.18 0.18 0.19 0.19 0.19 0.19 0.18 0.18 0.18 0.18

Hawaii 0.36 0.37 0.37 0.37 0.38 0.38 0.38 0.39 0.39 0.39 0.39 0.39

West Virginia 0.09 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.11 0.10 0.10

United States 0.11 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.11

8C-23
Table 8C.3.19 Residential Average Monthly Natural Gas Prices for 2012 Using Monthly 

Price Factors (2013$/MMBtu)

Geographical Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Connecticut, Maine, 

New Hampshire, Rhode 

Island, Vermont

14.4 14.5 14.6 14.9 15.6 16.9 18.4 18.7 18.2 16.3 15.5 15.1

Massachusetts 12.9 12.9 12.8 13.1 12.3 12.6 13.9 14.7 14.2 12.3 13.3 13.2

New York 12.8 12.6 12.8 13.3 14.8 16.9 17.8 16.8 16.4 14.4 12.9 12.1

New Jersey 10.9 10.7 10.7 11.0 11.7 12.9 13.4 13.4 13.2 12.1 11.5 11.2

Pennsylvania 11.4 11.6 11.8 12.3 13.8 15.9 17.6 18.1 17.2 14.0 12.4 11.8

Illinois 8.1 8.2 8.2 8.7 10.6 12.2 13.1 13.3 12.3 9.7 8.6 8.1

Indiana, Ohio 11.3 11.4 11.8 12.6 14.1 16.2 17.7 17.9 16.6 13.3 11.8 11.5

Michigan 10.1 10.1 10.3 10.8 12.0 13.7 15.2 15.7 14.5 12.1 10.9 10.5

Wisconsin 9.6 9.3 9.5 9.6 9.7 11.1 11.5 11.7 10.7 8.8 9.8 9.6

Iowa, Minnesota, North 

Dakota, South Dakota 8.3 8.1 8.2 8.4 9.6 11.2 12.1 12.5 11.6 9.2 8.6 8.2

Kansas, Nebraska 9.5 9.5 9.5 10.4 11.7 13.7 14.7 15.4 15.0 12.9 10.4 9.8

Missouri 12.2 12.2 12.3 13.8 15.9 19.4 22.2 23.5 21.9 18.6 14.6 13.0

Virginia 12.1 11.6 11.5 12.8 15.1 17.6 18.9 18.6 18.6 15.1 12.4 12.0

Delaware, District of 

Columbia, Maryland 11.9 11.8 12.1 13.2 15.0 16.9 18.0 17.9 17.6 14.6 12.8 12.2

Georgia 13.4 14.2 14.9 16.5 20.5 22.8 24.0 23.8 23.1 19.9 15.3 14.3

North Carolina, South 

Carolina 12.6 12.5 12.9 13.9 15.9 18.8 19.9 20.7 19.8 16.5 13.8 13.3

Florida 15.4 15.7 16.7 17.7 19.3 20.5 21.2 21.6 21.3 20.9 19.2 17.0

Alabama, Kentucky, 

Mississippi 11.7 11.7 12.1 13.3 15.2 16.9 17.3 17.7 17.3 15.7 13.4 12.4

Tennessee 9.9 10.0 10.0 10.9 12.0 13.7 14.6 15.0 14.3 13.0 11.0 10.4

Arkansas, Louisiana, 

Oklahoma 10.6 10.5 10.8 11.9 14.1 15.6 16.6 17.1 16.6 15.5 13.1 11.2

Texas 9.5 9.5 9.8 11.2 12.9 14.4 14.8 15.2 15.1 13.6 11.2 9.9

Colorado 7.8 7.9 8.2 8.4 9.3 11.8 11.9 12.6 11.7 9.3 8.4 8.0

Idaho, Montana, Utah, 

Wyoming 8.2 8.2 8.4 8.2 8.5 9.2 10.0 10.5 9.8 8.6 8.5 8.3

Arizona 13.5 14.0 14.5 15.8 17.7 19.6 21.4 22.1 21.4 20.0 16.9 14.6

Nevada, New Mexico 8.7 8.9 9.1 9.8 11.3 13.1 12.8 13.3 12.8 11.3 9.6 8.7

California 9.2 9.1 8.8 8.8 9.2 9.6 9.7 9.6 9.3 9.4 9.0 9.0

Oregon, Washington 11.0 11.2 11.2 11.5 12.0 12.6 13.9 14.4 14.0 12.7 11.7 11.4

Alaska 8.1 8.2 8.3 8.4 8.8 9.1 9.8 9.6 8.9 8.4 8.2 8.4

Hawaii 49.6 50.7 50.8 51.0 51.8 52.3 53.6 54.9 54.8 54.5 53.7 52.6

West Virginia 10.8 10.8 10.9 11.3 12.5 15.1 16.9 16.6 15.3 12.5 11.5 11.1

United States 10.5 10.5 10.6 11.2 12.3 13.8 14.6 14.9 14.3 12.3 11.2 10.7

DOE then applied the marginal price factors to the monthly energy prices to develop 

marginal residential monthly energy prices for 2012 for electricity and natural gas (Table 

8C.3.20 and Table 8C.3.21).

8C-24
Table 8C.3.20 Residential Marginal Monthly Electricity Prices for 2012 Using Marginal 

Price Factors (2013$/MMBtu)

Geographical Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Connecticut, Maine, New Hampshire, 

Rhode Island, Vermont 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16

Massachusetts 0.15 0.16 0.16 0.14 0.15 0.15 0.14 0.15 0.15 0.15 0.16 0.16

New York 0.15 0.15 0.15 0.20 0.20 0.21 0.21 0.21 0.21 0.20 0.15 0.15

New Jersey 0.15 0.15 0.15 0.19 0.19 0.20 0.21 0.21 0.21 0.19 0.15 0.15

Pennsylvania 0.10 0.10 0.10 0.14 0.14 0.15 0.15 0.15 0.15 0.14 0.11 0.10

Illinois 0.07 0.08 0.08 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.08 0.08

Indiana, Ohio 0.08 0.08 0.08 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.09 0.08

Michigan 0.13 0.14 0.13 0.16 0.16 0.17 0.17 0.17 0.17 0.16 0.14 0.14

Wisconsin 0.11 0.12 0.12 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.12 0.12

Iowa, Minnesota, North Dakota, 

South Dakota 0.09 0.09 0.09 0.12 0.12 0.13 0.13 0.13 0.12 0.12 0.09 0.09

Kansas, Nebraska 0.07 0.07 0.07 0.12 0.13 0.14 0.14 0.14 0.14 0.13 0.08 0.07

Missouri 0.07 0.07 0.07 0.12 0.13 0.15 0.14 0.14 0.13 0.12 0.07 0.07

Virginia 0.09 0.09 0.09 0.12 0.13 0.13 0.13 0.13 0.13 0.12 0.09 0.09

Delaware, District of Columbia, 

Maryland 0.11 0.11 0.11 0.14 0.16 0.17 0.17 0.17 0.16 0.15 0.11 0.11

Georgia 0.09 0.09 0.09 0.13 0.13 0.14 0.14 0.15 0.14 0.13 0.09 0.09

North Carolina, South Carolina 0.09 0.09 0.09 0.11 0.11 0.11 0.11 0.11 0.11 0.12 0.09 0.09

Florida 0.11 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.11 0.11

Alabama, Kentucky, Mississippi 0.08 0.08 0.08 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.09 0.08

Tennessee 0.08 0.08 0.08 0.10 0.10 0.10 0.10 0.09 0.10 0.10 0.09 0.09

Arkansas, Louisiana, Oklahoma 0.06 0.06 0.06 0.09 0.10 0.10 0.10 0.10 0.10 0.10 0.07 0.06

Texas 0.09 0.09 0.10 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.10 0.10

Colorado 0.09 0.09 0.09 0.12 0.13 0.13 0.13 0.13 0.13 0.13 0.09 0.09

Idaho, Montana, Utah, Wyoming 0.09 0.09 0.09 0.10 0.11 0.11 0.11 0.11 0.11 0.11 0.09 0.09

Arizona 0.08 0.09 0.09 0.12 0.13 0.13 0.13 0.13 0.13 0.12 0.09 0.09

Nevada, New Mexico 0.10 0.10 0.10 0.12 0.12 0.12 0.12 0.12 0.12 0.13 0.11 0.10

California 0.17 0.17 0.17 0.18 0.19 0.19 0.20 0.20 0.19 0.18 0.18 0.18

Oregon, Washington 0.08 0.09 0.09 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.09 0.09

Alaska 0.16 0.16 0.16 0.15 0.16 0.16 0.16 0.16 0.16 0.16 0.17 0.16

Hawaii 0.33 0.33 0.33 0.54 0.55 0.55 0.56 0.56 0.56 0.57 0.35 0.35

West Virginia 0.08 0.08 0.08 0.09 0.10 0.09 0.09 0.09 0.09 0.10 0.09 0.08

United States 0.09 0.09 0.09 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.10 0.09

8C-25
Table 8C.3.21 Residential Marginal Monthly Natural Gas Prices for 2012 Using 

Marginal Price Factors (2012$/MMBtu)

Geographical Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Connecticut, Maine, New 

Hampshire, Rhode Island, 

Vermont

13.21 13.32 13.42 12.40 12.97 14.03 15.26 15.52 15.11 13.52 14.27 13.84

Massachusetts 13.30 13.29 13.19 11.75 10.98 11.30 12.45 13.13 12.71 11.00 13.66 13.60

New York 11.44 11.24 11.38 10.01 11.12 12.67 13.38 12.61 12.35 10.85 11.47 10.81

New Jersey 10.33 10.20 10.22 9.23 9.82 10.78 11.23 11.18 11.03 10.13 10.89 10.64

Pennsylvania 10.58 10.70 10.90 8.93 10.02 11.55 12.78 13.11 12.45 10.16 11.50 10.91

Illinois 7.93 7.96 8.01 5.91 7.17 8.26 8.88 8.99 8.33 6.58 8.40 7.91

Indiana, Ohio 10.43 10.53 10.88 9.17 10.20 11.78 12.82 12.99 12.08 9.65 10.91 10.65

Michigan 9.40 9.41 9.55 8.38 9.33 10.68 11.82 12.25 11.30 9.41 10.19 9.81

Wisconsin 9.37 9.16 9.33 7.66 7.67 8.78 9.11 9.26 8.48 7.02 9.64 9.39

Iowa, Minnesota, North 

Dakota, South Dakota 8.04 7.89 8.03 6.03 6.83 8.01 8.65 8.92 8.30 6.59 8.39 8.02

Kansas, Nebraska 8.81 8.86 8.87 7.21 8.12 9.51 10.18 10.69 10.39 8.92 9.70 9.08

Missouri 9.98 9.98 10.11 8.20 9.50 11.59 13.25 14.03 13.07 11.08 12.00 10.67

Virginia 11.23 10.82 10.69 8.65 10.23 11.95 12.83 12.63 12.59 10.21 11.53 11.20

Delaware, District of 

Columbia, Maryland 11.03 10.94 11.20 9.32 10.59 11.89 12.65 12.64 12.38 10.28 11.83 11.25

Georgia 11.58 12.31 12.86 9.19 11.42 12.74 13.40 13.30 12.88 11.08 13.22 12.39

North Carolina, South 

Carolina 11.26 11.22 11.53 9.20 10.56 12.46 13.22 13.70 13.15 10.93 12.31 11.94

Florida 12.65 12.90 13.78 11.41 12.42 13.21 13.62 13.91 13.71 13.48 15.83 14.05

Alabama, Kentucky, 

Mississippi 10.15 10.13 10.46 9.95 11.33 12.59 12.92 13.19 12.89 11.69 11.61 10.76

Tennessee 9.31 9.44 9.44 8.04 8.84 10.11 10.78 11.12 10.61 9.63 10.39 9.80

Arkansas, Louisiana, 

Oklahoma 8.88 8.84 9.07 7.70 9.16 10.09 10.74 11.08 10.78 10.05 10.96 9.43

Texas 8.01 8.06 8.28 6.68 7.66 8.54 8.80 9.02 8.97 8.10 9.45 8.35

Colorado 7.09 7.18 7.43 5.78 6.41 8.12 8.17 8.67 8.02 6.42 7.66 7.28

Idaho, Montana, Utah, 

Wyoming 7.84 7.87 8.01 6.82 7.13 7.71 8.38 8.76 8.18 7.22 8.13 7.97

Arizona 11.51 11.87 12.29 10.04 11.27 12.47 13.57 14.08 13.62 12.72 14.33 12.39

Nevada, New Mexico 7.69 7.88 8.08 7.10 8.13 9.46 9.20 9.56 9.24 8.15 8.50 7.77

California 9.94 9.84 9.48 7.45 7.77 8.17 8.22 8.10 7.89 7.95 9.75 9.77

Oregon, Washington 10.35 10.47 10.52 9.67 10.05 10.56 11.64 12.11 11.77 10.65 11.01 10.72

Alaska 7.82 7.89 7.95 7.20 7.55 7.81 8.41 8.25 7.63 7.25 7.87 8.12

Hawaii 45.38 46.34 46.42 39.47 40.05 40.49 41.44 42.49 42.37 42.15 49.08 48.09

West Virginia 10.26 10.30 10.42 9.02 9.99 12.08 13.44 13.28 12.20 9.95 10.91 10.59

United States 9.80 9.85 9.97 8.31 9.14 10.24 10.86 11.09 10.61 9.14 10.47 10.03

DOE applied the regional monthly energy price factors to the annual LPG data presented 

in chapter 8 to develop residential energy prices for 2012 (Table 8C.3.22). Each geographical 

area was matched with the appropriate Census Region.

8C-26
Table 8C.3.22 Residential Monthly LPG Prices for 2012 Using Average Price Factors 

(2013$/MMBtu)

Geographical Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Connecticut, Maine, New 

Hampshire, Rhode Island, 

Vermont

31.63 31.96 32.13 32.15 32.64 32.97 33.07 32.87 32.84 32.99 32.66 32.64

Massachusetts 33.96 34.31 34.49 34.51 35.04 35.39 35.51 35.29 35.26 35.42 35.06 35.04

New York 32.15 32.48 32.65 32.67 33.17 33.50 33.61 33.40 33.38 33.53 33.19 33.17

New Jersey 33.35 33.69 33.87 33.89 34.41 34.75 34.86 34.65 34.62 34.78 34.43 34.40

Pennsylvania 30.32 30.63 30.79 30.81 31.28 31.59 31.70 31.50 31.48 31.62 31.30 31.28

Illinois 22.60 22.59 22.32 21.97 21.70 20.94 20.31 20.25 20.72 21.34 22.31 23.29

Indiana, Ohio 26.41 26.40 26.08 25.67 25.36 24.47 23.74 23.67 24.22 24.93 26.07 27.22

Michigan 24.27 24.26 23.97 23.60 23.30 22.49 21.82 21.75 22.26 22.91 23.96 25.02

Wisconsin 22.40 22.40 22.13 21.78 21.51 20.76 20.14 20.08 20.54 21.15 22.12 23.09

Iowa, Minnesota, North 

Dakota, South Dakota 23.27 23.26 22.98 22.62 22.34 21.56 20.92 20.85 21.34 21.97 22.97 23.99

Kansas, Nebraska 22.37 22.36 22.09 21.75 21.48 20.72 20.10 20.05 20.51 21.12 22.08 23.06

Missouri 23.01 23.00 22.72 22.37 22.09 21.32 20.68 20.62 21.10 21.72 22.71 23.72

Virginia 25.45 25.53 25.14 24.66 24.27 23.81 23.27 22.83 23.40 24.37 25.12 25.94

Delaware, District of 

Columbia, Maryland 34.64 34.75 34.23 33.56 33.03 32.41 31.67 31.07 31.85 33.17 34.20 35.31

Georgia 27.67 27.75 27.33 26.80 26.38 25.89 25.29 24.82 25.43 26.49 27.31 28.20

North Carolina, South 

Carolina 27.70 27.78 27.36 26.83 26.41 25.91 25.32 24.84 25.46 26.52 27.34 28.23

Florida 28.63 28.72 28.29 27.74 27.31 26.79 26.18 25.68 26.33 27.42 28.27 29.19

Alabama, Kentucky, 

Mississippi 24.55 24.63 24.26 23.79 23.41 22.97 22.44 22.02 22.57 23.51 24.24 25.02

Tennessee 28.05 28.14 27.72 27.18 26.75 26.25 25.65 25.16 25.79 26.86 27.70 28.59

Arkansas, Louisiana, 

Oklahoma 21.13 21.20 20.88 20.48 20.15 19.77 19.32 18.96 19.43 20.24 20.86 21.54

Texas 15.90 15.95 15.71 15.40 15.16 14.88 14.54 14.26 14.62 15.22 15.70 16.21

Colorado 25.58 25.63 25.19 24.62 24.02 23.34 22.50 22.21 23.09 24.47 25.26 26.24

Idaho, Montana, Utah, 

Wyoming 24.54 24.59 24.17 23.62 23.05 22.39 21.58 21.31 22.15 23.48 24.23 25.18

Arizona 31.43 31.48 30.94 30.24 29.51 28.67 27.63 27.29 28.36 30.06 31.03 32.24

Nevada, New Mexico 26.03 26.07 25.63 25.04 24.44 23.74 22.89 22.60 23.49 24.90 25.70 26.70

California 29.93 29.99 29.48 28.81 28.11 27.31 26.32 25.99 27.02 28.63 29.56 30.71

Oregon, Washington 27.50 27.56 27.08 26.47 25.83 25.09 24.19 23.88 24.83 26.31 27.16 28.21

Alaska 29.89 29.95 29.43 28.76 28.07 27.27 26.28 25.95 26.98 28.59 29.51 30.66

Hawaii 40.16 40.24 39.55 38.65 37.72 36.64 35.32 34.87 36.25 38.42 39.65 41.20

West Virginia 32.94 33.04 32.55 31.92 31.41 30.82 30.11 29.55 30.28 31.54 32.52 33.57

United States 18.70 18.81 18.69 18.56 18.58 18.20 17.38 16.98 17.50 18.07 18.65 19.21

8C.4 HOUSEHOLD ENERGY PRICE ADJUSTMENT FACTOR

RECS 2009 reports the total annual consumption and expenditure of each energy use 

type. From this data DOE determined average energy prices per geographical area. To take into 

account that household energy prices vary inside a geographical area, DOE developed an 

8C-27
adjustment factor based on the reported average energy price in RECS 2009 divided by the 

average energy price of the geographical region. This factor was then multiplied times the 

monthly marginal energy prices (for natural gas and electricity) or the monthly price developed 

above to come up with the household energy price.

8C.5 ENERGY PRICE TRENDS

8C.5.1 Residential Energy Price Trends

DOE used AEO 2014 Reference Case scenarios for the nine census divisions. DOE

applied the projected energy price for each of the nine census divisions to each household in the 

sample based on the household’s location.

To arrive at prices in future years, DOE multiplied the prices described in the preceding 

section by the forecast of annual average price changes in EIA’s AEO 2014.

5 Figure 8C.5.1

shows the national residential electricity price trend. To estimate the trend after 2040, DOE 

followed past guidelines provided to the Federal Energy Management Program (FEMP) by EIA 

and used the average rate of change during 2025–2040 for electricity, natural gas, and LPG. 

Figure 8C.5.1 Projected Residential National Electricity Price

Figure 8C.5.2 shows the residential national electricity price trends, disaggregated by the nine 

census divisions. 

8C-28
Figure 8C.5.2 Projected Residential Division Electricity Prices

Figure 8C.5.3 shows the residential national natural gas price trends.

Figure 8C.5.3 Projected Residential National Natural Gas Price

Figure 8C.5.4 shows the residential national natural gas price trends, disaggregated by the nine 

census divisions. 

8C-29
Figure 8C.5.4 Projected Residential Division Natural Gas Prices

Figure 8C.5.5 shows the residential national LPG price trends.

Figure 8C.5.5 Projected Residential National LPG Prices

Figure 8C.5.6 shows the residential national LPG price trends, disaggregated by the nine census 

divisions. 

8C-30
Figure 8C.5.6 Projected Residential Division LPG Prices

8C-31
REFERENCES

1. U.S. Department of Energy - Energy Information Administration, Form EIA-826 

Database Monthly Electric Utility Sales and Revenue Data, 2013.

<www.eia.gov/electricity/data/eia826/>

2. U.S. Department of Energy - Energy Information Administration, Natural Gas 

Navigator. 2013. www.eia.gov/dnav/ng/ng_pri_sum_dcu_nus_m.htm

3. Energy Information Administration, 2012 State Energy Consumption, Price, and 

Expenditure Estimates (SEDS), 2013. Washington, DC. <www.eia.gov/state/seds/>

4. U.S. Department of Energy - Energy Information Administration, Short-Term Energy 

Outlook. 2009. http://eia.doe.gov/steo

5. U.S. Department of Energy-Energy Information Administration, Annual Energy Outlook 

2014 with Projections to 2040, 2014. Washington, DC. <www.eia.gov/forecasts/aeo/>

8C-32
APPENDIX 8D. DISTRIBUTIONS USED FOR DISCOUNT RATES

TABLE OF CONTENTS

8D.1 INTRODUCTION .................................................................................................. 8D-1

8D.2 DISTRIBUTIONS USED FOR RESIDENTIAL CONSUMER DISCOUNT 

RATES.................................................................................................................... 8D-1

8D.2.1 Distribution of Rates for Debt Classes.................................................................... 8D-1

8D.2.2 Distribution of Rates for Equity Classes................................................................. 8D-5

8D.3 DISTRIBUTION OF REAL EFFECTIVE DISCOUNT RATES BY INCOME 

GROUP................................................................................................................... 8D-9

8D.4 DISTRIBUTIONS USED FOR COMMERCIAL DISCOUNT RATES............. 8D-10

LIST OF TABLES 

Table 8D.3.1 Distribution of Real Discount Rates by Income Group............................ 8D-10

LIST OF FIGURES

Figure 8D.2.1 Distribution of Mortgage Interest Rates ..................................................... 8D-2

Figure 8D.2.2 Distribution of Home Equity Loan Interest Rates ...................................... 8D-2

Figure 8D.2.3 Distribution of Credit Card Interest Rates.................................................. 8D-3

Figure 8D.2.4 Distribution of Installment Loan Interest Rates.......................................... 8D-3

Figure 8D.2.5 Distribution of Other Residence Loan Interest Rates................................. 8D-4

Figure 8D.2.6 Distribution of Other Lines of Credit Loan Interest Rates ......................... 8D-4

Figure 8D.2.7 Distribution of Annual Rate of Return on CDs .......................................... 8D-5

Figure 8D.2.8 Distribution of Annual Rate of Return on Savings Bonds.......................... 8D-6

Figure 8D.2.9 Distribution of Annual Rate of Return on Corporate AAA Bonds............. 8D-6

Figure 8D.2.10 Distribution of Annual Rate of Savings Accounts...................................... 8D-7

Figure 8D.2.11 Distribution of Annual Rate of Money Market Accounts .......................... 8D-7

Figure 8D.2.12 Distribution of Annual Rate of Return on S&P 500................................... 8D-8

Figure 8D.2.13 Distribution of Annual Rate of Return on Mutual Funds........................... 8D-8

Figure 8D.3.1 Distribution of Real Discount Rates by Income Group.............................. 8D-9

Figure 8D.4.1 Distribution of Commercial Discount Rates: Retail ................................. 8D-11

Figure 8D.4.2 Distribution of Commercial Discount Rates: Property............................. 8D-12

Figure 8D.4.3 Distribution of Commercial Discount Rates: Medical.............................. 8D-12

Figure 8D.4.4 Distribution of Commercial Discount Rates: Industrial ........................... 8D-13

Figure 8D.4.5 Distribution of Commercial Discount Rates: Lodging ............................. 8D-13

Figure 8D.4.6 Distribution of Commercial Discount Rates: Food Service ..................... 8D-14

Figure 8D.4.7 Distribution of Commercial Discount Rates: Office ................................ 8D-14

Figure 8D.4.8 Distribution of Commercial Discount Rates: State and Local 

Government............................................................................................... 8D-15

Figure 8D.4.9 Distribution of Commercial Discount Rates: Federal Government.......... 8D-15

Figure 8D.4.10 Distribution of Commercial Discount Rates: Other.................................. 8D-16

8D-i
APPENDIX 8D. DISTRIBUTIONS USED FOR DISCOUNT RATES

8D.1 INTRODUCTION

The Department of Energy (DOE) estimated discount rate distributions by consumer 

type: residential and commercial consumers. This appendix describes the distributions used.

8D.2 DISTRIBUTIONS USED FOR RESIDENTIAL CONSUMER DISCOUNT 

RATES

The Department of Energy (DOE) derived consumer discount rates for the life-cycle cost 

(LCC) analysis using data on interest or return rates for various types of debt and equity to 

calculate a real effective discount rate for each household in the Federal Reserve Board’s Survey 

of Consumer Finances (SCF) in 1995, 1998, 2001, 2004, 2007, and 2010.

1 To account for 

variation among households in rates for each of the types, DOE sampled a rate for each 

household in its building sample from a distribution of discount rates for each of six income 

groups. This appendix describes the distributions used.

8D.2.1 Distribution of Rates for Debt Classes 

Figure 8D.2.1 through Figure 8D.2.6 show the distribution of real interest rates for 

different types of household debt. The data source for the interest rates for mortgages, home 

equity loans, credit cards, installment loans, other residence loans, and other lines of credit is the 

Federal Reserve Board’s SCF in 1995, 1998, 2001, 2004, 2007, and 2010.

1 DOE adjusted the 

nominal rates to real rates using the annual inflation rate in each year.

Using the appropriate SCF data for each year, DOE adjusted the nominal mortgage 

interest rate and the nominal home equity loan interest rate for each relevant household in the 

SCF for mortgage tax deduction and inflation. In cases where the effective interest rate is equal 

to or below the inflation rate (resulting in a negative real interest rate), DOE set the real effective 

interest rate to zero.

8D-1
Figure 8D.2.1 Distribution of Mortgage Interest Rates

Figure 8D.2.2 Distribution of Home Equity Loan Interest Rates

0%

5%

10%

15%

20%

25%

30%

35%

0.5%1.6%

2.5%3.5%

4.4%5.4%6.3%7.4%8.5%9.4%

10.4%11.6%12.5%

13.2%14.4%

16.6%22.8%

Probability

Real Interest Rate

0%

5%

10%

15%

20%

25%

30%

0.4%1.5%

2.5%3.5%

4.5%5.4%6.4%

7.5%8.4%9.4%

10.5%11.5%12.5%13.8%14.7%15.7%16.6%17.1%22.0%

Probability

Real Interest Rate

8D-2
Figure 8D.2.3 Distribution of Credit Card Interest Rates

Figure 8D.2.4 Distribution of Installment Loan Interest Rates

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

0.0%2.5%4.5%6.6%8.6%

10.5%12.5%14.7%16.4%18.4%20.4%22.7%24.7%26.8%28.5%

Probability

Real Interest Rate

0%

5%

10%

15%

20%

25%

30%

35%

40%

0.0%2.4%4.5%6.6%8.5%

10.5%12.5%14.7%16.4%18.4%20.5%22.5%24.6%26.8%28.5%

Probability

Real Interest Rate

8D-3
Figure 8D.2.5 Distribution of Other Residence Loan Interest Rates

Figure 8D.2.6 Distribution of Other Lines of Credit Loan Interest Rates

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

0.2%1.5%2.5%3.5%4.5%5.5%6.5%7.5%8.6%9.4%

10.4%11.5%12.5%13.1%14.7%15.7%16.6%

Probability

Real Interest Rate

0%

2%

4%

6%

8%

10%

12%

0.1%2.5%4.5%6.6%8.6%

10.5%12.5%14.7%16.4%18.4%20.2%22.6%25.4%

Probability

Real Interest Rate

8D-4
8D.2.2 Distribution of Rates for Equity Classes 

Figure 8D.2.7 through Figure 8D.2.13 show the distribution of real interest rates for 

different types of equity. Data for equity classes are not available from the Federal Reserve 

Board’s SCF, so DOE derived data for these classes from national-level historical data (1984-

2013). The interest rates associated with certificates of deposit (CDs),2 savings bonds,3 and AAA 

corporate bonds4 are from Federal Reserve Board time-series data. DOE assumed rates on 

checking accounts to be zero. Rates on savings and money market accounts are from Cost of 

Savings Index data.

5 The rates for stocks are the annual returns on the Standard and Poor’s 

(S&P) 500.

6 The mutual fund rates are a weighted average of the stock rates (two-thirds weight) 

and the bond rates (one-third weight) in each year. DOE adjusted the nominal rates to real rates 

using the annual inflation rate in each year.

Figure 8D.2.7 Distribution of Annual Rate of Return on CDs

-0.04

-0.02

0

0.02

0.04

0.06

0.08

19841986

1988

19901992199419961998

2000

20022004200620082010

2012

Annual Rate of Return (%)

Year

CDs (Average of 12 and 30 month)

8D-5
Figure 8D.2.8 Distribution of Annual Rate of Return on Savings Bonds

Figure 8D.2.9 Distribution of Annual Rate of Return on Corporate AAA Bonds

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

9.0%

1984198619881990199219941996

1998200020022004

2006200820102012

Annual Rate of Return (%)

Year

Savings Bonds

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

9.0%

19841986

1988

199019921994199619982000200220042006200820102012

Annual Rate of Return (%)

Year

AAA Corporate Bonds

8D-6
Figure 8D.2.10 Distribution of Annual Rate of Savings Accounts

Figure 8D.2.11 Distribution of Annual Rate of Money Market Accounts

-0.5%

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

1984

1986198819901992

1994199619982000200220042006

200820102012

Annual Rate of Return (%)

Year

Savings Accounts

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

198419861988199019921994199619982000200220042006200820102012

Annual Rate of Return (%)

Year

Money Market Accounts

8D-7
Figure 8D.2.12 Distribution of Annual Rate of Return on S&P 500

Figure 8D.2.13 Distribution of Annual Rate of Return on Mutual Funds

-40.0%

-30.0%

-20.0%

-10.0%

0.0%

10.0%

20.0%

30.0%

40.0%

198419861988199019921994199619982000200220042006200820102012

Annual Rate of Return (%)

Year

Stocks (S&P 500)

-30.0%

-20.0%

-10.0%

0.0%

10.0%

20.0%

30.0%

198419861988199019921994199619982000200220042006200820102012

Annual Rate of Return (%)

Year

Mutual Funds

8D-8
8D.3 DISTRIBUTION OF REAL EFFECTIVE DISCOUNT RATES BY 

INCOME GROUP

Figure 8D.3.1 and Table 8D.3.1 present the distributions of real discount rates for each 

income group.

Figure 8D.3.1 Distribution of Real Discount Rates by Income Group

0%

5%

10%

15%

20%

25%

0.0% 5.0% 10.0% 15.0% 20.0%

Share of Individuals in Group (%)

Real Effective Discount Rate (%)

Group 1

Group 2

Group 3

Group 4

Group 5

Group 6

8D-9
Table 8D.3.1 Distribution of Real Discount Rates by Income Group 

DR 

Bin

Income Group 1 Income Group 2 Income Group 3 Income Group 4 Income Group 5 Income Group 6

(1-20 percentile)(21-40 percentile)(41-60 percentile)(61-80 percentile)(81-90 percentile)(90-99 percentile)

rate weight rate weight rate weight rate weight rate weight rate weight

0-1 0.5% 0.238 0.6% 0.152 0.6% 0.104 0.6% 0.077 0.6% 0.056 0.6% 0.057

1-2 1.6% 0.110 1.6% 0.120 1.6% 0.105 1.6% 0.146 1.6% 0.142 1.6% 0.185

2-3 2.5% 0.087 2.5% 0.112 2.6% 0.131 2.5% 0.205 2.5% 0.219 2.5% 0.207

3-4 3.5% 0.117 3.5% 0.137 3.5% 0.164 3.5% 0.173 3.5% 0.200 3.5% 0.178

4-5 4.5% 0.097 4.5% 0.113 4.5% 0.136 4.5% 0.129 4.5% 0.153 4.5% 0.144

5-6 5.5% 0.083 5.5% 0.084 5.5% 0.100 5.5% 0.093 5.5% 0.098 5.5% 0.120

6-7 6.5% 0.058 6.5% 0.062 6.5% 0.075 6.5% 0.067 6.5% 0.063 6.4% 0.079

7-8 7.5% 0.036 7.5% 0.051 7.6% 0.054 7.4% 0.041 7.4% 0.029 7.3% 0.011

8-9 8.5% 0.036 8.4% 0.039 8.4% 0.034 8.5% 0.015 8.4% 0.012 8.5% 0.005

9-10 9.5% 0.017 9.5% 0.018 9.5% 0.017 9.5% 0.010 9.5% 0.008 9.6% 0.005

10-11 10.5% 0.014 10.5% 0.019 10.5% 0.013 10.5% 0.011 10.6% 0.004 10.7% 0.004

11-12 11.5% 0.010 11.5% 0.015 11.5% 0.014 11.5% 0.007 11.4% 0.004 11.7% 0.001

12-13 12.5% 0.011 12.5% 0.012 12.5% 0.009 12.4% 0.005 12.4% 0.002 12.4% 0.002

13-14 13.6% 0.012 13.5% 0.008 13.5% 0.009 13.5% 0.004 13.5% 0.002 13.3% 0.001

14-15 14.6% 0.016 14.6% 0.014 14.6% 0.009 14.5% 0.005 14.6% 0.003 14.2% 0.001

15-16 15.5% 0.011 15.5% 0.010 15.5% 0.006 15.6% 0.004 15.6% 0.002 15.3% 0.000

16-17 16.5% 0.013 16.5% 0.009 16.5% 0.004 16.5% 0.003 16.5% 0.001 0.0% 0.000

17-18 17.5% 0.009 17.6% 0.006 17.5% 0.005 17.5% 0.003 17.6% 0.001 17.7% 0.001

18-19 18.4% 0.005 18.5% 0.005 18.6% 0.003 18.4% 0.001 18.2% 0.000 0.0% 0.000

19-20 19.4% 0.006 19.4% 0.004 19.4% 0.002 19.7% 0.000 19.7% 0.000 19.4% 0.000

20-21 20.6% 0.004 20.4% 0.002 20.5% 0.001 20.3% 0.001 20.5% 0.000 20.3% 0.000

21-22 21.4% 0.003 21.4% 0.002 21.4% 0.001 21.5% 0.001 0.0% 0.000 21.4% 0.000

22-23 22.5% 0.002 22.4% 0.001 22.6% 0.001 22.9% 0.000 22.8% 0.000 22.3% 0.000

23-24 23.6% 0.001 23.4% 0.001 23.6% 0.001 0.0% 0.000 0.0% 0.000 24.0% 0.000

24-25 24.6% 0.001 24.5% 0.000 24.6% 0.000 24.1% 0.000 24.3% 0.000 0.0% 0.000

25-26 25.4% 0.001 25.4% 0.001 25.5% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000

26-27 26.5% 0.001 26.5% 0.000 26.4% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000

27-28 27.8% 0.000 27.6% 0.000 27.8% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000

28-29 28.2% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000

29-23 29.9% 0.000 29.3% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000 0.0% 0.000

>30 59.1% 0.001 142.7% 0.002 0.0% 0.000 53.3% 0.000 0.0% 0.000 0.0% 0.000

8D.4 DISTRIBUTIONS USED FOR COMMERCIAL DISCOUNT RATES

DOE derived commercial discount rates (i.e. weighted average cost of capital) for the 

life-cycle cost (LCC) analysis using the capital asset pricing model and firm-level data provided 

by Damodaran Online.7 State and local government discount rates were estimated using the rate 

8D-10
of return on 20-year municipal bonds, as provided by the Federal Reserve Board.3 Separate 

distributions were constructed for each major industry. Figure 8D.4.1 through Figure 8D.4.10

show the probability distributions of commercial discount rates by industry.

Figure 8D.4.1 Distribution of Commercial Discount Rates: Retail

8D-11
Figure 8D.4.2 Distribution of Commercial Discount Rates: Property

Figure 8D.4.3 Distribution of Commercial Discount Rates: Medical

8D-12
Figure 8D.4.4 Distribution of Commercial Discount Rates: Industrial

Figure 8D.4.5 Distribution of Commercial Discount Rates: Lodging

8D-13
Figure 8D.4.6 Distribution of Commercial Discount Rates: Food Service

Figure 8D.4.7 Distribution of Commercial Discount Rates: Office

8D-14
Figure 8D.4.8 Distribution of Commercial Discount Rates: State and Local 

Government

Figure 8D.4.9 Distribution of Commercial Discount Rates: Federal Government

8D-15
Figure 8D.4.10 Distribution of Commercial Discount Rates: Other 

8D-16
REFERENCES

1. The Federal Reserve Board, Survey of Consumer Finances 1989, 1992, 1995, 1998, 

2001, 2004, 2007, 2010. <www.federalreserve.gov/pubs/oss/oss2/scfindex.html>

2. The Federal Reserve Board, Federal Reserve Statistical Release, Selected Interest Rates, 

Historical Data, Instrument: CDs (secondary market), Maturity: 6-month, Frequency: 

Annual, Description: Average rate on 6-month negotiable certificates of deposit 

(secondary market), quoted on an investment basis, 2013.

<www.federalreserve.gov/releases/H15/data.htm>

3. The Federal Reserve Board, Federal Reserve Statistical Release, Selected Interest Rates, 

Historical Data, Instrument: State and local bonds, Maturity: 20-year, Frequency: 

Monthly, Description: Bond buyer go 20-bond municipal bond index, 2013.

<www.federalreserve.gov/releases/H15/data.htm>

4. The Federal Reserve Board, Federal Reserve Statistical Release, Selected Interest Rates, 

Historical Data, Instrument: Corporate bonds/Moody’s Seasoned AAA, Frequency: 

Annual, Description: Moody’s yield on seasoned corporate bonds—all industries, AAA, 

2013. <www.federalreserve.gov/releases/H15/data.htm>

5. Mortgage-X - Mortgage Information Service, Cost of Savings Index (COSI) Index 

History, 2013. <http://mortgage-x.com/general/indexes/default.asp>

6. 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/>

7. Damodaran Online, Data Page: Costs of Capital by Industry Sector, 2012. Damodaran. 

(Last accessed July, 2013.) <http://pages.stern.nyu.edu/~adamodar/>

8D-17
APPENDIX 8E. LIFE-CYCLE COST ANALYSIS USING ALTERNATIVE ECONOMIC 

GROWTH SCENARIOS FOR HEARTH PRODUCTS

TABLE OF CONTENTS

8E.1 INTRODUCTION ...................................................................................................8E-1

8E.2 DESCRIPTION OF HIGH AND LOW ECONOMIC SCENARIOS .....................8E-1

8E.3 RESULTS ................................................................................................................8E-3

LIST OF TABLES

Table 8E.3.1 Average LCC and PBP Results by Efficiency Level for Hearth Product 

Ignition Systems: High Economic Growth Scenario...................................8E-3

Table 8E.3.2 LCC Savings Relative to the Base Case Efficiency Distribution for 

Hearth Product Ignition Systems: High Economic Growth Scenario..........8E-4

Table 8E.3.3 Average LCC and PBP Results by Efficiency Level for Hearth Product 

Ignition Systems: Low Economic Growth Scenario....................................8E-4

Table 8E.3.4 LCC Savings Relative to the Base Case Efficiency Distribution for 

Hearth Product Ignition Systems: Low Economic Growth Scenario ..........8E-4

Table 8E.3.5 Comparison of Average LCC Savings and Simple Payback Period 

Results for Reference Case and High and Low Economic Growth 

Scenarios......................................................................................................8E-4

LIST OF FIGURES

Figure 8E.2.1 Electricity Price Forecasts for Reference Case and High and Low 

Economic Growth Scenarios (National)......................................................8E-2

Figure 8E.2.2 Natural Gas Price Forecasts for Reference Case and High and Low 

Economic Growth Scenarios (National)......................................................8E-2

Figure 8E.2.3 LPG Price Forecasts for Reference Case and High and Low Economic 

Growth Scenarios (National) .......................................................................8E-3

8E-i
APPENDIX 8E. LIFE-CYCLE COST ANALYSIS USING ALTERNATIVE ECONOMIC

GROWTH SCENARIOS FOR HEARTH PRODUCTS

8E.1 INTRODUCTION

This appendix presents life-cycle cost (LCC) results using energy price projections from 

alternative economic growth scenarios. The scenarios are based on the High Economic Growth 

case and the Low Economic Growth case from Energy Information Administration (EIA)’s

Annual Energy Outlook 2014 (AEO 2014).

1

This appendix describes the High and Low Economic Growth scenarios in further detail.

See appendix 8A for details about how to generate LCC results for High Economic Growth and 

Low Economic Growth scenarios using the LCC spreadsheet.

8E.2 DESCRIPTION OF HIGH AND LOW ECONOMIC SCENARIOS

To generate LCC results reported in chapter 8, DOE uses the Reference case energy price 

projections from AEO 2014. The reference case is a business-as-usual estimate, given known 

market, demographic, and technological trends. For AEO 2014, EIA explored the impacts of 

alternative assumptions in other scenarios with different macroeconomic growth rates, world oil 

prices, rates of technology progress, and policy changes. 

To reflect uncertainty in the projection of U.S. economic growth, EIA’s AEO 2014 uses 

High and Low Economic Growth scenarios to project the possible impacts of alternative 

economic growth assumptions on energy markets. The High Economic Growth scenario

incorporates population, labor force and productivity growth rates that are higher than the 

Reference scenario, while these values are lower for the Low Economic Growth scenario.

Economic output as measured by real GDP increases by 2.4 percent per year from 2012 through 

2040, in the Reference case, 1.9 percent per year in the Low Economic Growth case, and 2.8

percent per year in the High Economic Growth case.

2

In general, energy prices are higher in the High Economic Growth scenario and lower in 

the Low Economic Growth scenario than they are in the Reference Case. The energy price 

forecasts affect the operating cost savings at different efficiency levels. Figure 8E.2.1 through

Figure 8E.2.3 show the national residential energy price trends for the Reference, High 

Economic Growth, and Low Economic Growth scenarios. AEO 2014 projections stop in 2040. 

To estimate energy prices after 2040 in the high and low scenarios, DOE used the growth rate 

between 2030 and 2040, which are represented with a dashed line in the charts.

Because AEO 2014 provides the price trends by census division, each sampled household 

is matched to the appropriate census division price trend. See appendix 8C for details about how 

energy price trends by census division are applied in the LCC analysis.

8E-1
Figure 8E.2.1 Electricity Price Forecasts for Reference Case and High and Low 

Economic Growth Scenarios (National)

Figure 8E.2.2 Natural Gas Price Forecasts for Reference Case and High and Low

Economic Growth Scenarios (National)

10.00

12.00

14.00

16.00

18.00

20.00

22.00

2010 2015 2020 2025 2030 2035 2040 2045 2050

Reference Case

High Growth

Low Growth

32.00

34.00

36.00

38.00

40.00

42.00

2010 2015 2020 2025 2030 2035 2040 2045 2050

Reference Case

High Growth

Low Growth

8E-2
Figure 8E.2.3 LPG Price Forecasts for Reference Case and High and Low Economic 

Growth Scenarios (National)

8E.3 RESULTS

Table 8E.3.1 and Table 8E.3.2 summarize the LCC and PBP results for the HighEconomic Growth scenario by efficiency level (EL) for hearth products. Table 8E.3.3 and Table 

8E.3.4 summarize the LCC and PBP results for the Low Economic Growth scenario by EL for 

hearth products. Table 8E.3.5 compares average LCC savings and simple payback for these 

scenarios to the Reference case.

Table 8E.3.1 Average LCC and PBP Results by Efficiency Level for Hearth Product 

Ignition Systems: High Economic Growth Scenario

TSL Efficiency 

Level

Average Costs 2013$

Simple 

Payback 

years

Average 

Lifetime 

years

Installed 

Cost

First 

Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

-- 0 $166 $52 $625 $792 -- 15.0

1 1 $268 $15 $179 $447 2.8 15.0

Note: The results for each TSL are calculated assuming that all consumers use products with that efficiency level. 

The PBP is measured relative to the baseline product.

22.00

23.00

24.00

25.00

26.00

27.00

28.00

29.00

30.00

31.00

2010 2015 2020 2025 2030 2035 2040 2045 2050

Reference Case

High Growth

Low Growth

8E-3
Table 8E.3.2 LCC Savings Relative to the Base Case Efficiency Distribution for Hearth 

Product Ignition Systems: High Economic Growth Scenario 

TSL Efficiency Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Net Cost Average Savings* 2013$

1 1 22% $175

* The calculation includes buildings with zero LCC savings (no impact).

Table 8E.3.3 Average LCC and PBP Results by Efficiency Level for Hearth Product 

Ignition Systems: Low Economic Growth Scenario

TSL Efficiency 

Level

Average Costs 2013$

Simple 

Payback 

years

Average 

Lifetime 

years

Installed 

Cost

First 

Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

-- 0 $166 $49 $584 $750 -- 15.0

1 1 $268 $14 $170 $438 2.9 15.0

Note: The results for each TSL are calculated assuming that all consumers use products with that efficiency level. 

The PBP is measured relative to the baseline product.

Table 8E.3.4 LCC Savings Relative to the Base Case Efficiency Distribution for Hearth 

Product Ignition Systems: Low Economic Growth Scenario 

TSL Efficiency Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Net Cost Average Savings* 2013$

1 1 23% $157

* The calculation includes buildings with zero LCC savings (no impact).

Table 8E.3.5 Comparison of Average LCC Savings and Simple Payback Period Results 

for Reference Case and High and Low Economic Growth Scenarios

EL Efficiency Level

Average LCC Savings Simple Payback Period

2013$ Years

High 

Growth

Low 

Growth

Reference 

Case

High 

Growth

Low 

Growth

Reference 

Case

1 Intermittent Pilot $175 $157 $165 2.8 2.9 2.9

8E-4
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. Energy Information Administration, Macroeconomic Activity Module for Annual Energy 

Outlook 2014, 2014. Washington, DC. 

<www.eia.gov/forecasts/aeo/assumptions/pdf/macroeconomic.pdf>

8E-5
APPENDIX 9A. RELATIVE PRICE ELASTICITY OF DEMAND FOR APPLIANCES

TABLE OF CONTENTS

9A.1 INTRODUCTION .................................................................................................. 9A-1

9A.2 LITERATURE REVIEW ....................................................................................... 9A-1

9A.2.1 Price ........................................................................................................................ 9A-1

9A.2.2 Income..................................................................................................................... 9A-1

9A.2.3 Appliance Efficiency and Discount Rates .............................................................. 9A-2

9A.3 VARIABLES DESCRIBING THE MARKET FOR REFRIGERATORS, 

CLOTHES WASHERS, AND DISHWASHERS .................................................. 9A-2

9A.3.1 Physical Household/Appliance Variables............................................................... 9A-3

9A.3.2 Economic variables................................................................................................. 9A-3

9A.4 REGRESSION ANALYSIS OF VARIABLES AFFECTING APPLIANCE 

SHIPMENTS .......................................................................................................... 9A-4

9A.4.1 Broad Trends........................................................................................................... 9A-5

9A.4.2 Specification of Model............................................................................................ 9A-7

9A.4.3 Discussion of Model ............................................................................................... 9A-8

9A.4.4 Analysis Results...................................................................................................... 9A-8

9A.4.4.1 Individual Appliance Model ................................................................. 9A-8

9A.4.4.2 Combined Appliance Model................................................................. 9A-9

9A.4.5 Additional Regression Specifications and Results ............................................... 9A-10

9A.4.5.1 Lower Consumer Discount Rate......................................................... 9A-10

9A.4.5.2 Disaggregated Variables..................................................................... 9A-11

9A.5 LONG RUN IMPACTS........................................................................................ 9A-12

9A.6 SUMMARY.......................................................................................................... 9A-13

9A.7 DATA USED IN THE ANALYSIS ..................................................................... 9A-14

LIST OF TABLES

Table 9A.2.1 Estimates of the Impact of Price, Income and Efficiency on 

Automobile and Appliance Sales................................................................ 9A-2

Table 9A.3.1 Physical Household/Appliance Variables................................................... 9A-3

Table 9A.3.2 Economic Variables.................................................................................... 9A-4

Table 9A.4.1 Estimate of Total Price Elasticity of Demand............................................. 9A-6

Table 9A.4.2 Tabular Estimate of Relative Price Elasticity of Appliance Demand......... 9A-7

Table 9A.4.3 Individual Appliance Model Results .......................................................... 9A-9

Table 9A.4.4 Combined Appliance Model Result.......................................................... 9A-10

Table 9A.4.5 Combined and Individual Results, 20 percent discount rate..................... 9A-11

Table 9A.4.6 Disaggregated Regression Results, 37 percent discount rate.................... 9A-12

Table 9A.5.1 Change in Price Elasticity of Demand for Automobiles following a 

Purchase Price Change.............................................................................. 9A-12

Table 9A.5.2 Change in Relative Price Elasticity for Home Appliances following a 

Purchase Price Change.............................................................................. 9A-13

9A-i
LIST OF FIGURE

Figure 9A.3.1 Trends in Appliance Shipment, Housing Starts and Replacements............ 9A-4

Figure 9A.4.1 Residual Shipments and Appliance Price ................................................... 9A-6

9A-ii
APPENDIX 9A. RELATIVE PRICE ELASTICITY OF DEMAND FOR APPLIANCES

9A.1 INTRODUCTION

This appendix summarizes DOE’s study of the price elasticity of demand for home 

appliances, including refrigerators, clothes washers and dishwashers. DOE chose this particular 

set of appliances because of the availability of data to determine a price elasticity. Section 9A.2

reviews the existing economics literature describing the impact of economic variables on the sale 

of durable goods. Section 9A.3 describes the market for home appliances and the changes that 

have occurred over the past 20 years. In section 9A.4, DOE summarizes the results of its

regression analysis and presents estimates of the price elasticity of demand for the three 

appliances. In section 9A.5, DOE presents development of an ‘effective’ purchase price 

elasticity. DOE’s interpretation of its results is presented in section 9A.6. Finally, section 9A.7

describes the data used in DOE’s analysis.

9A.2 LITERATURE REVIEW

Relatively few studies measure the impact of price, income and efficiency on the sale of 

household appliances. This section briefly reviews the literature that describes the likely 

importance of these variables on the purchase of household appliances.

9A.2.1 Price

DOE reviewed many studies that sought to measure the impact of price on sales in a 

dynamic market. One study of the automobile market prior to 1970 finds the price elasticity of 

demand to decline over time. The author explains this as the result of buyers delaying purchases 

after a price increase but eventually making the purchase (Table 9A.2.1).1 A contrasting study of 

household white goods also prior to 1970, finds the elasticity of demand to increase over time as 

more price-conscious buyers enter the market.2 An analysis of refrigerator market survey data 

finds that consumer purchase probability decreases with survey asking price.3 Estimates of the 

price elasticity of demand for different brands of the same product tend to vary. A review of 41 

studies of the impact of price on market share found the average price elasticity to be -1.75.4 The 

average estimate of price elasticity of demand reported in these studies is -0.33 in the appliance 

market and -0.47 in the combined automobile and appliance markets.

9A.2.2 Income

Higher income households are more likely to own household appliance.

5 The impact of 

income on appliance shipments is explored in two econometric studies of the automobile and 

appliance markets.1, 2

 The average income elasticity of demand is 0.50 in the appliance study 

cited in the literature review, much larger in the automobile study (Table 9A.2.1).

9A-1
9A.2.3 Appliance Efficiency and Discount Rates

Many studies estimate the impact of appliance efficiency on consumers’ choice of

appliance. Typically, this impact is summarized by the implicit discount rate; that is, the rate 

consumers use to compare future savings in appliance operating costs against a higher initial 

purchase price of an appliance. One early and much cited study concludes that consumers use a 

20 percent implicit discount rate when purchasing room air conditioners (Table 9A.2.1).6 A 

survey of several studies of different appliances suggests that the consumer implicit discount rate

has a broad range and averages about 37 percent.

7

Table 9A.2.1 Estimates of the Impact of Price, Income and Efficiency on Automobile 

and Appliance Sales

Durable Good Price 

Elasticity

Income 

Elasticity

Brand 

Price 

Elasticity

Implicit 

Discount 

Rate

Model Data 

Years

Time 

Period

Automobiles1 -1.07 3.08 - - Linear Regression, stock 

adjustment - Short run

Automobiles1 -0.36 1.02 - - Linear Regression, stock 

adjustment - Long run

Clothes Dryers2 -0.14 0.26 - - Cobb-Douglas, diffusion 1947-1961 Mixed

Room Air 

Conditioners2 -0.378 0.45 - - Cobb-Douglas, diffusion 1946-1962 Mixed

Dishwashers2 -0.42 0.79 - - Cobb-Douglas, diffusion 1947-1968 Mixed

Refrigerators3 -0.37 - - 39% Logit probability, survey 

data 1997 Short run

Various4 - - -1.769 - Multiplicative regression - Mixed

Room Air 

Conditioners5 - - -1.72 - Non-linear diffusion 1949-1961 Short run

Clothes Dryers5 - - -1.32 - Non-linear diffusion 1963-1970 Short run

Room Air 

Conditioners6 - - - 20% Qualitative choice, survey data - -

Household 

Appliances7 - - - 37%10 Assorted - -

Sources: 1 S. Hymens. 1971; 2 P. Golder and G. Tellis, 1998; 3 D. Revelt and K. Train, 1997; 4 G. Tellis, 1988; 5 D. Jain and R. Rao; 6 J. Hausman; 7 K. Train, 1985.

Notes: 8 Logit probability results are not directly comparable to other elasticity estimates in this table.

9 Average brand price elasticity across 41 studies.

10 Averaged across several household appliance studies referenced in this work.

9A.3 VARIABLES DESCRIBING THE MARKET FOR REFRIGERATORS,

CLOTHES WASHERS, AND DISHWASHERS

In this section DOE evaluates variables that appear to account for refrigerator, clothes 

washer and dishwasher shipments, including physical household/appliance variables and 

economic variables.

9A-2
9A.3.1 Physical Household/Appliance Variables

Several variables influence the sale of refrigerators, clothes washers and dishwashers.

The most important for explaining appliance sales trends are the annual number of new 

households formed (housing starts) and the number of appliances reaching the end of their 

operating life (replacements). Housing starts influence sales because new homes are often 

provided with, or soon receive, new appliances, including dishwashers and refrigerators.

Replacements are correlated with sales because new appliances are typically purchased when old 

ones wear out. In principle, if households maintain a fixed number of appliances, shipments 

should equal housing starts plus appliance replacements. 

9A.3.2 Economic variables

Appliance price, appliance operating cost and household income are important economic 

variables affecting shipments. Low prices and costs encourage household appliance purchases 

and a rise in income increases householder ability to purchase appliances. In principle, changes 

in economic variables should explain changes in the number of appliances per household.

During a 1980–2002 study period, annual shipments grew 69 percent for clothes washers, 

81 percent for refrigerators and 105 percent for dishwashers (Table 9A.3.1). This rising 

shipments trend is explained in part by housing starts, which increased six percent and by 

appliance replacements, which rose between 49 percent and 90 percent, depending on the 

appliance, over the period (Table 9A.3.1).a For mature markets such as these, replacements 

exceed appliance sales associated with new housing construction.

Table 9A.3.1 Physical Household/Appliance Variables

Shipmentsi(millions) Housing Startsii (millions) Replacementsiii (millions)

Appliance 1980 2002 Change 1980 2002 Change 1980 2002 Change

Refrigerators 5.124 9.264 81% 1.723 1.822 6% 3.93 5.84 49%

Clothes Washers 4.426 7.492 69% 1.723 1.822 6% 3.66 5.50 50%

Dishwashers 2.738 5.605 105% 1.723 1.822 6% 1.99 3.79 90% i

Shipments: Number of units sold. Sources: AHAM Fact Book and Appliance Magazine. iiHousing Starts: Annual number of new homes constructed. Source: U.S. Census. iiiReplacements: Average of annual lagged shipments, with lag equal to expected appliance operating life, ± 5 years.

Shipments increased somewhat more rapidly than housing starts and replacements. This 

is shown by comparing the beginning and end points of lines that represent “starts plus 

replacements” (uppermost solid line in Figure 9A.3.1) and “shipments” (diamond linked line in 

Figure 9A.3.1). In 1980, the “shipment” line begins below the “starts plus replacements” line. In 

2002, the “shipments” line ends above the “starts plus replacements” line. This more rapid 

a Appliance replacements are determined from the expected operating life of refrigerators (19 years), clothes 

washers (14 years), and dishwashers (12 years) and from past shipments. Replacements are further discussed in 

section 9-A.3. The dishwasher lifetime used in this analysis does not match the dishwasher used in the primary 

analysis. 

9A-3

 
increase in shipments, compared to housing starts plus replacements, suggests that the appliance 

per household ratio increased over the study period. 

Figure 9A.3.1 Trends in Appliance Shipment, Housing Starts and 

Replacements

Economic variables, including price, cost and income, may explain this increase in 

appliances per household. Over the period, appliance prices decreased 40 percent to 50 percent, 

operating costs fell between 33 percent and 72 percent, and median household income rose 16 

percent (Table 9A.3.2).

Table 9A.3.2 Economic Variables

Pricei(1999$) Operating Costii (1999$) Household Incomeiii (1999$)

Appliance 1980 2002 Change 1980 2002 Change 1980 2002 Change

Refrigerators 1208 726 -40% 333 94 -72% 37,447 43,381 16%

Clothes Washers 779 392 -50% 262 175 -33% 37,447 43,381 16%

Dishwashers 713 369 -48% 183 95 -48% 37,447 43,381 16% i

Price: Shipment weighted retail sales price. Sources: AHAM Fact Book and Appliance Magazine. iiOperating Cost: Annual electricity price times electricity consumption. Source: AHAM Fact Book. iiiIncome: Mean Household income. Source: U.S. Census.

9A.4 REGRESSION ANALYSIS OF VARIABLES AFFECTING APPLIANCE

SHIPMENTS

Few data are available to estimate the impact of economic variables on the demand for 

appliances. Industry operating cost data is incomplete—appliance energy use data are available 

for only 12 years of the 1980-2002 study period. Industry price data are also incomplete—

available for only 8 years of the study period for each of the appliances.

The lack of data suggests that regression analysis can at best evaluate broad data trends, 

utilizing relatively few explanatory variables. This section begins by describing broad trends 

Refrigerator Shipments

0

1

2

3

4

5

6

7

8

9

10

1980 1984 1988 1992 1996 2000

Millions 

New Housing

Retirements

Shipments

Clothes Washer Shipments

0

1

2

3

4

5

6

7

8

1980 1984 1988 1992 1996 2000

Millions 

Dishwasher Shipments

0

1

2

3

4

5

6

1980 1984 1988 1992 1996 2000

Millions 

9A-4
apparent in the economic and physical household data sets and then specifies a simple regression 

model to measure these trends, making assumptions to minimize the number of explanatory 

variables. Finally, results of the regression analysis are presented along with an estimate of the 

price elasticity of demand for appliances. In section 9A.4.5, DOE presents the results of 

regression analysis performed with more complex models, which are used to test assumptions 

underlying the simple model. These results support the specification of the simple model and the 

price elasticity of appliance demand estimated with that model.

9A.4.1 Broad Trends 

In this section, DOE reviews trends in the physical household and economic data sets and 

posits a simple approach for estimating the price elasticity of appliance demand. As noted above, 

the physical household variables (housing starts and appliance replacements) explain most of the 

variability in appliance shipments during the study period (1980-2002).

b DOE assumes the rest 

of the variability in shipments (referred to as “residual shipments”) is explained by economic 

variables. Below, DOE presents a tabular method for measuring price elasticities.

To illustrate this tabular approach, DOE defines two new variables—residual shipments 

and total price. Residual shipments are defined as the difference between shipments and physical 

household demand (starts plus replacements). Total price, represented by the following equation,

is defined as appliance price plus the present value of lifetime appliance operating cost:

c

TP = PP + PVOC

Eq. 9A.1

Where:

TP = total price,

PP = appliance purchase price, and

PVOC = present value of operating cost.

Over the study period, residual shipments increased in proportion to total shipments by 

30 percent for refrigerators, 19 percent for clothes washers, and 23 percent for dishwashers. At 

the same time, total prices declined 47 percent, 45 percent and 48 percent for refrigerators, 

clothes washers, and dishwashers, respectively. Assuming that total price explains the entire 

change in per household appliance usage, a rough estimate is calculated of the total price 

elasticity of demand equal to -0.48 for refrigerators, -0.32 for clothes washers and -0.37 for 

dishwashers (Table 9A.4.1).

b A log regression of the form: Shipments = a + b • Housing Starts + c • Retirements, indicates that these two 

variables explain 89 percent of the variation in refrigerator shipments, 97 percent of the variation in clothes washer 

shipments, and 97 percent of the variation in dishwasher shipments.

c Present value operating cost is calculated assuming a 19-year operating life for refrigerators, 14-year operating life 

for clothes washers, and a 12-year operating life for dishwashers. A 37 percent discount rate is used to sum annual 

operating costs into a present value operating cost.

9A-5

 
Table 9A.4.1 Estimate of Total Price Elasticity of Demand

Residual Shipments (millions) Total Price (1999$)

Appliance 1980 2002 Difference Change 1980 2002 Change Elasticity

Refrigerators -0.5 1.6 2.1 30% 1541 820 -61% -0.48

Clothes Washers -1.0 0.2 1.1 19% 1042 567 -59% -0.32

Dishwashers -1.0 -0.01 1.0 23% 896 464 -64% -0.37

The negative correlation between total price and residual shipments suggested by these 

negative price elasticities is illustrated in a graph of residual shipments on the y-axis and total 

price on the x-axis (Figure 9A.4.1).

Yellow points are observed price data; red points are interpolated price data.

Figure 9A.4.1 Residual Shipments and Appliance Price

Household income rose during the study period, making it easier for households to 

purchase appliances. Assuming that a rise in income has a similar impact on shipments as a 

decline in price, the impact of income is incorporated by defining a third variable, termed 

relative price, which is calculated as total price divided by household income and represented by 

the following equation.d

Income

TP

RP =

Eq. 9A.2

Where:

RP = relative price,

TP = total price, and

Income = household income.

d Recall that the income elasticity of demand cited in the literature review is 0.50 and the price elasticity of demand 

cited in the review averages -0.35. This suggests that combining the effects of income and price will yield an 

elasticity less negative than price elasticity alone.

Refrigerators

400

800

1200

1600

2000

-2.0 -1.0 0.0 1.0 2.0

Total Price

Residual Shipments 

Clothes Washers

400

600

800

1000

1200

-1.5 -1.0 -0.5 0.0 0.5

Total Price

Residual Shipments 

Dishwashers

200

400

600

800

1000

-2.0 -1.5 -1.0 -0.5 0.0 0.5

Total Price

Residual Shipments 

9A-6

 
The percent decline in relative price for the three appliances divided by the percent 

decline in residual shipments suggests a rough estimate of relative price elasticity equal to -0.40 

for refrigerators, -0.26 for clothes washers and -0.30 for dishwashers (Table 9A.4.2).

Table 9A.4.2 Tabular Estimate of Relative Price Elasticity of Appliance Demand

Residual Shipments (millions) Relative Price (1999$)

Appliance 1980 2002 Change 1980 2002 Change Elasticity

Refrigerators -0.532 1.597 30% 0.041 0.019 -74% -0.40

Clothes Washers -0.953 0.174 19% 0.028 0.013 -72% -0.26

Dishwashers -0.974 -0.005 23% 0.024 0.011 -76% -0.30

9A.4.2 Specification of Model

The limited price data suggest it is appropriate to use a simple regression model to 

estimate the impact of economic variables on shipments, using few explanatory variables. The 

following equation, chosen for this analysis, includes one physical household variable (housing 

starts plus replacements) and one relative price variable (the sum of purchase price plus 

operating cost, divided by income). 

Ship = a + b× RP + c ×[Starts + Rplc]

Eq. 9A.3

Where:

Ship = quantity of appliance sold,

RP = relative price,

Starts = number of new homes, and

Rplc = number of appliances at the end of their operating life.

The natural logs are taken of all variables so that the estimated coefficients for each 

variable in the model may be interpreted as the percent change in shipments associated with the 

percent change in the variable. Thus, the coefficient b in this model is interpreted as the relative

price elasticity of demand for the three appliances.

DOE used the following combined regression equation to estimate an average price 

elasticity of demand across the three appliances, using pooled data in a single regression. A 

combined regression specification is justified, given the limited data available and the similarity 

in price and shipment behavior across appliances (see Figure 9A.4.1). Thus, the model 

represented by the combined regression equation is considered the basic model in DOE’s

analysis of appliance shipments.

Ship = a + b× RP + c ×[Starts + Rplc]+ d ×CW + e × DW

Eq. 9A.4

Where:

CW = quantity of clothes washers sold, and

9A-7
DW = quantity of dishwashers sold.

9A.4.3 Discussion of Model 

The most important assumption used to specify this model is that changes in economic 

variables over the study period—income, price, and operating cost—are responsible for all 

observed growth in residual appliance shipments. In other words, DOE assumes no impact from

other possible factors, such as changing consumer preferences or increases in the quality of 

appliances. This assumption seems unlikely, but without additional data, the impact of this 

assumption on the price elasticity of demand cannot be measured. DOE effectively assumes that 

changes in consumer preferences and appliance characteristics, while affecting which models are 

purchased, have relatively little impact on the total number of appliances purchased in a year.

Three additional assumptions used to specify this model deserve comment. The relative

price variable is specified in the model, assuming that (1) the correct implicit discount rate is 

used to combine appliance price and operating cost and that (2) rising income has the same 

impact on shipments as falling total price. The “starts + replacements” variable is specified, 

assuming (3) that starts and replacements have similar impacts on shipments.

To investigate the first assumption about discount rates, DOE calculated “present value 

operating cost” using a 20 percent implicit discount rate and performed a second regression 

analysis based on the models described in Eq. 9A.1 and Eq. 9A.2. The results of this analysis, 

presented in section 9A.4.5, indicate that the elasticity of relative price is fairly insensitive to 

changes in the discount rate.

To investigate the second and third assumptions, DOE specified a regression model 

separating income from total price and replacements from starts, thereby adding two additional 

explanatory variables to the basic model as shown in the following equation:

Ship = a + b ×TP + c × Incone + d × Start + e× Rplc + f ×CW + g × DW

Eq. 9A.5

The results of the regression analysis of this model are presented in section 9A.4.5. These 

results suggest that the elasticity of total price (coefficient b) is relatively insensitive to changes 

in the treatment of income and “starts + replacements” in the model.

9A.4.4 Analysis Results

The following sections describe results of analyses using both the individual and 

combined models for appliances and the effects of a lower consumer discount rate and 

disaggregated variables.

9A.4.4.1 Individual Appliance Model

The individual appliance regression equations are specified in Eq. 9A.6.

9A-8
Ship = a + b× RP + c ×[Starts + Rplc]

Eq. 9A.6

In regression analysis of this model, the elasticity of relative price (b) is estimated to be

-0.40 for refrigerators, -0.31 for clothes washers and -0.32 for dishwashers (Table 9A.4.3), 

averaging -0.35. These elasticities are similar to those reported in the literature survey for 

appliances (Table 9A.2.1). They are remarkably similar to the price elasticity calculated using a 

tabular approach (Table 9A.4.2).

The estimated coefficient associated with the “starts + replacements” variable is close to 

one. A coefficient equal to one for this variable would imply that, holding economic variables 

constant, shipments increase in direct proportion to an increase in “starts + replacements.” The 

high R-squared values (above 95) and t-statistics (above 5) in the results provide a measure of 

confidence in this analysis, despite the very small data set.

Table 9A.4.3 Individual Appliance Model Results

Refrigerator Clothes Washer Dishwasher

Variable Coefficient t-stat Coefficient t-stat Coefficient t-stat

Intercept -1.51 -7.26 -1.47 -8.23 -2.08 -16.78

Relative Price -0.40 -6.60 -0.31 -5.69 -0.32 -7.03

Starts + Replacements 1.05 5.90 1.08 6.41 1.35 11.46

R2 0.954 0.954 0.975

Observations 23 23 23

9A.4.4.2 Combined Appliance Model

The combined appliance regression equation is specified in Eq. 9A.7. 

Ship = a + b× RP + c ×[Starts + Rplc]+ d ×CW + e × DW

Eq. 9A.7

This regression analysis indicates that the model fits the existing shipments data well 

(high R-squared) and that the variables included in the model are statistically significant (Table 

9A.4.4). Estimated with this model, the elasticity of relative price is -0.34, close to the average 

value estimated in the individual appliance models (-0.35). It is also similar to elasticity 

estimates reported in the literature survey and calculated using the tabular approach in Table 

9A.4.2. 

9A-9
Table 9A.4.4 Combined Appliance Model Result

Variable Coefficient t-statistic

Intercept -1.60 -15.54

Relative Price -0.34 -10.74

Starts + Replacements 1.21 13.95

CW -0.20 -9.04

DW -0.32 -6.58

R2 0.983

Observations 69

9A.4.5 Additional Regression Specifications and Results

As described in section 9A.4.3, DOE used three assumptions to specify its appliance 

models. The first, made to aggregate appliance price and operating cost, is that the implicit price 

variable in the basic regression model is specified using a 37 percent implicit discount rate. The 

second states that the implicit price variable is defined assuming that rising income has the same 

impact on shipments as falling total price. The third states that the “starts + replacements”

variable is defined assuming that housing starts have a similar impact on shipments as appliance 

replacements. 

9A.4.5.1 Lower Consumer Discount Rate

To investigate the first assumption about discount rates, DOE calculated “present value

operating cost” using a 20 percent implicit discount rate and performed a second regression 

analysis based on the models described in Eq. 9A.3 and Eq. 9A.4. The estimated coefficient 

associated with the relative price variable in these regressions is almost identical to the 

coefficients estimated for the same variable based on a 37 percent implicit discount rate. The 

elasticity of relative price calculated using a 20 percent discount rate is -0.33 in the combined 

regression and averages -0.35 for the three appliances (Table 9A.4.5). The elasticity of price 

calculated using a 37 percent discount rate is -0.34 in the combined regression and averages -

0.35 for the three appliances. DOE concludes from this analysis that the elasticity of relative

price is fairly insensitive to changes in the discount rate.

9A-10
Table 9A.4.5 Combined and Individual Results, 20 percent discount rate

9A.4.5.2 Disaggregated Variables

To investigate the second and third assumptions, DOE constructed a regression model 

that separates income from total price and replacements from starts, thus adding two additional 

explanatory variables to the basic model (as shown earlier as Eq. 9A.5 and shown below).

Ship = a + b ×TP + c × Income + d × Start + e× Rplc + f ×CW + g × DW

Eq. 9A.8

The estimated coefficient associated with the total price variable in these regressions is 

almost identical to the coefficients estimated for the relative price variable reported above. The 

elasticity of total price in the above equation is -0.36 in the combined appliance regression and 

averages -0.35 for the three appliances (Table 9A.4.6). The elasticity of relative price based on 

the model described in Eq. 9A.4 is -0.34 in the combined regression (Table 9A.4.4) and averages 

-0.35 across the individual appliances (Table 9A.4.3). DOE concludes that the price elasticity 

calculated in this analysis is relatively insensitive to the specification of household income and 

“starts + replacements” variables in the model.

Three Appliances

Variable Coefficient t-Stat

Intercept -1.53 -14.61

Total Price / Income -0.33 -10.69

Starts + Retirements 1.20 13.65

CW -0.18 -8.69

DW -0.32 -6.57

R2 0.982

Observations 69

Refrigerator Clothes Washers Dishwasher

Variable Coefficient t-Stat Coefficient t-Stat Coefficient t-Stat

Intercept -1.36 -6.26 -1.41 -7.49 -2.04 -17.23

Total Price / Income -0.38 -6.50 -0.32 -5.29 -0.33 -7.30

Starts + Retirements 1.04 5.73 1.06 5.83 1.34 11.64

R2 0.953 0.950 0.977

Observations 23 23 23

9A-11
Table 9A.4.6 Disaggregated Regression Results, 37 percent discount rate

9A.5 LONG RUN IMPACTS

As noted above in Table 9A.2.1, the literature review provides price elasticities over short 

and long time periods, also referred to as short run and long run price elasticities. As noted in the 

first two rows of Table 9A.2.1, one source (i.e., Hymans) shows that the price elasticity of 

demand is significantly different over the short run and long run for automobiles.1 Because 

DOE’s forecasts of shipments and national impacts due to standards is over a 30-year time 

period, consideration must be given to how the relative price elasticity is affected once a new 

standard takes effect. 

DOE considers the relative price elasticities determined above in section 9A.4 to be short 

run elasticities. DOE was unable to identify sources specific to household durable goods, such as 

appliances, to indicate how short run and long run price elasticities differ. Therefore, to estimate 

how the relative price elasticity changes over time, DOE relied on the Hymans study pertaining 

to automobiles. Based on the Hymans study, Table 9A.5.1 shows how the automobile price 

elasticity of demand changes in the years following a purchase price change. With increasing 

years after the price change, the price elasticity becomes more inelastic until it reaches a terminal 

value around the tenth year after the price change.

Table 9A.5.1 Change in Price Elasticity of Demand for Automobiles following a 

Purchase Price Change

Years Following Price Change

1 2 3 5 10 20

Price Elasticity of Demand -1.20 -0.93 -0.75 -0.55 -0.42 -0.40

Relative Change in 

Elasticity to 1st year

1.00 0.78 0.63 0.46 0.35 0.33

Source: Hymans, 1971.

Three Appliances

Variable Coefficient t-Stat

Intercept -2.92 -1.26

Income 0.58 2.92

Total Price -0.36 -7.06

Housing Starts 0.44 10.02

Retirements 0.62 8.12

CW -0.24 -9.25

DW -0.46 -7.68

R2 0.985

Observations 69

Refrigerator Clothes Washers Dishwasher

Variable Coefficient t-Stat Coefficient t-Stat Coefficient t-Stat

Intercept -6.19 -2.24 -6.64 -1.63 1.00 0.23

Income 0.89 3.80 0.87 2.31 0.20 0.52

Total Price -0.35 -5.48 -0.27 -2.51 -0.43 -5.18

Housing Starts 0.41 7.38 0.25 3.29 0.62 8.24

Retirements 0.56 6.06 0.56 2.09 0.65 5.86

R2 0.984 0.958 0.979

Observations 23 23 23

9A-12
Based on the relative change in the automobile price elasticity of demand shown in Table 

9A.5.1, DOE developed a time series of relative price elasticities for home appliances. Table 

9A.5.2 presents the time series. 

Table 9A.5.2 Change in Relative Price Elasticity for Home Appliances following a 

Purchase Price Change

Years Following Price Change

1 2 3 5 10 20

Relative Change in 

Elasticity to 1st year

1.00 0.78 0.63 0.46 0.35 0.33

Relative Price Elasticity -0.34 -0.26 -0.21 -0.16 -0.12 -0.11

9A.6 SUMMARY

This appendix describes the results of a literature search, tabular analysis, and regression 

analyses of the impact of price and other variables on appliance shipments. In the literature, DOE

found only a few studies of appliance markets that are relevant to this analysis and no studies

after 1980 using time series price and shipments data. The information that can be summarized 

from the literature suggests that the demand for appliances is price inelastic. Other information in 

the literature suggests that appliances are a normal good, such that rising incomes increase the 

demand for appliances. Finally, the literature suggests that consumers use relatively high implicit 

discount rates, when comparing appliance prices and appliance operating costs. 

There are too few price and operating cost data available to perform complex analysis of 

dynamic changes in the appliance market. In this analysis, DOE used data available for 

refrigerators, clothes washers, and dishwashers to evaluate broad market trends and perform 

simple regression analysis.

These data indicate an increase in appliance shipments and a decline in appliance price 

and operating cost over the study period 1980-2002. Household income has also risen during this 

time. To simplify the analysis, DOE combined the available economic information into one 

variable, termed relative price, and used that variable in a tabular analysis of market trends and a 

regression analysis.

DOE’s tabular analysis of trends in the number of appliances per household suggests that 

the price elasticity of demand for the three appliances is inelastic. Our regression analysis of 

these same variables suggests that the relative price elasticity of demand is -0.34. The price 

elasticity is consistent with estimates in the literature. Nevertheless, DOE stresses that the 

measure is based on a small data set, using very simple statistical analysis. More important, the 

measure is based on an assumption that economic variables, including price, income and 

operating costs, explain most of the trend in appliances per household in the United States since 

1980. Changes in appliance quality and consumer preferences may have occurred during this 

period, but they are not accounted for in this analysis.

9A-13
9A.7 DATA USED IN THE ANALYSIS

• Appliance Shipments are defined as the annual number of units shipped in millions. These 

data were collected from the Association of Home Appliance Manufacturers (AHAM)

8, 9 and 

Appliance Magazine10 as annual values for each year, 1980–2002. AHAM was used for the 

period 1989–2002 while Appliance Magazine was used for the period 1980–1988.

• Appliance Price is defined as the shipments weighted retail sales price of the unit in 1999 

dollars. Price values for 1980, 1985, 1986, 1991, 1993, 1994, 1998, and 2002 were collected 

from AHAM Fact Books.

11 Price values for other years were interpolated from these eight 

years of data.

• Housing Starts data were collected from the U.S. Census construction statistics (C25 

reports) as annual values for each year, 1980–2002.12

• Replacements, driven by equipment retirements, are estimated with the assumption that 

some fraction of sales arise from consumers replacing equipment at the end of its useful life.

Since each appliance has a different expected lifespan (19 years for refrigerators,

13 14 years 

for clothes washers,14 12 years for dishwashers15), replacements are calculated differently for 

each appliance type. Replacements are estimated as the average of shipments 14–24 years 

previous for refrigerators, 9–19 years previous for clothes washers, and 7–17 years previous 

for dishwashers. Historical shipments data were collected from AHAM and Appliance 

Magazine.

• Annual Electricity Consumption (UEC) is defined as the energy consumption of the unit in 

kilowatt-hours. Electricity consumption depends on appliance capacity and efficiency. These 

data were provided by AHAM for 1980, 1990–1997 and 1999–2002.9 Data were interpolated 

in the years for which data were not available.

• Operating Cost is the present value of the electricity consumption of an appliance over its 

expected lifespan. The lifespans of refrigerators, clothes washers and dishwashers are 

assumed to be 19, 14, and 12 years respectively. Discount rates of 20 percent6 and 37

percent16 were used, producing similar estimates of price elasticity. A study by Hausman 

recommended a discount rate of “about 20 percent” in its introduction and presented results 

ranging from 24.1 percent to 29 percent based on his calculations for room air conditioners.

A study by Train suggests a range of implicit discount rates averaging 35 percent for 

appliances.

• Income: Median annual household income in 2003 dollars. These data were collected for 

each year, 1980–2002, from Table H-6 of the U.S. Census.

17

9A-14
REFERENCES

1. Hymans, S., Consumer Durable Spending: Explanation and Prediction. Brookings Papers 

on Economic Activity, 1971. 1971(1): pp. 234-239 

2. Golder, P. N. and G. J. Tellis, Beyond Diffusion: An Affordability Model of the Growth 

of New Consumer Durables. Journal of Forecasting, 1998. 17

3. D. Revelt and K. Train, Mixed Logit with Repeated Choices: Household's Choice of 

Appliance Efficiency Level. Review of Economics and Statistics, 1997

4. Tellis, G., The Price Elasticity of Selective Demand: A Meta-Analysis of Econometric 

Models of Sales. Journal of Marketing Research, 1988

5. U.S. Department of Energy - Energy Information Agency, The Effect of Income on 

Appliances in U.S. Households, 2002. Washington, DC. 

<www.eia.doe.gov/emeu/recs/appliances/appliances.html>

6. Hausman, J., Individual discount rates and the purchase and utilization of energy using 

durables. The Bell Journal of Economics, 1979

7. Train, K., Discount Rates in Consumers' Energy-Related Decisions. Energy: The 

International Journal, 1985. 10(12)

8. Association of Home Appliance Manufacturers, AHAM 2000 Fact Book. 2000. 

Washington, DC.

9. Association of Home Appliance Manufacturers, AHAM 2003 Fact Book. 2003. 

Washington, DC.

10. Statistical Review. Appliance Magazine, April, 1990

11. Association of Home Appliance Manufacturers, AHAM Fact Books, 1987, 1988, 1993, 

1995, 2000, and 2003. Washington, DC. 

12. U.S. Census Bureau, Current Construction Reports, C25 – New One-Family Houses 

Sold. Washington, DC. <www.census.gov/construction/nrc/pdf/startsan.pdf>

13. Duemling, R., Energy and Resources Group, University of California. Berkeley, 1999. 

14. U.S. Department of Energy-Office of Building Research and Standards, Final Rule 

Technical Support Document (TSD): Energy Efficiency Standards for Consumer 

Products: Clothes Washers, 2000. U.S. Department of Energy. Washington, DC. Report 

No. LBNL-47462. 

<www.eren.doe.gov/buildings/codes_standards/reports/cwtsd/index.html>

9A-15
15. U.S. Department of Energy-Office of Codes and Standards, Technical Support 

Document: Energy Conservation Standards for Consumer Products: Dishwashers, 

Clothes Washers, and Clothes Dryers including: Environmental Impacts, Regulatory 

Impact Analysis, December, 1990. Washington, DC. Report No. DOE/CE-0299P. 

16. K.E. Train and T. Atherton, Rebates, loans, and customers’ choice of appliance efficiency 

level: combining stated- and revealed-preference data. The Energy Journal, 1995

17. U.S. Census Bureau, Income, Historical Income Tables – Households, Table H-6. 

Washington, DC. <www.census.gov/hhes/www/income/data/historical/household/>

9A-16
APPENDIX 10A. USER INSTRUCTIONS FOR NATIONAL IMPACT ANALYSIS 

SPREADSHEET MODEL

TABLE OF CONTENTS

10A.1 USER INSTRUCTIONS ...................................................................................... 10A-1

10A.2 STARTUP............................................................................................................. 10A-1

10A.3 DESCRIPTION OF NATIONAL IMPACT ANALYSIS WORKSHEETS........ 10A-1

10A.4 BASIC INSTRUCTIONS FOR OPERATING THE NATIONAL IMPACT 

ANALYSIS SPREADSHEETS............................................................................ 10A-2

LIST OF FIGURES

Figure 10A.4.1 The default user input parameters............................................................. 10A-3

Figure 10A.4.2 Set the spreadsheet to automatic calculation mode................................... 10A-3

10A-i
APPENDIX 10A. USER INSTRUCTIONS FOR NATIONAL IMPACT ANALYSIS 

SPREADSHEET MODEL

10A.1 USER INSTRUCTIONS

The results obtained in this analysis can be examined and reproduced using the Microsoft 

Excel® spreadsheets accessible on the Internet from the Department of Energy’s (DOE's) hearth 

products rulemaking page:

www1.eere.energy.gov/buildings/appliance_standards/product.aspx?productid=83. From that 

page, follow the links to the Preliminary Analysis phase of the rulemaking and then to the 

analytical tools.

10A.2 STARTUP

The NIA spreadsheets enable the user to perform a National Impact Analysis (NIA) for 

hearth product ignition devices. To utilize the spreadsheet, the Department assumes that the user 

has access to a PC with a hardware configuration capable of running Windows 2003 or later. To 

use the NIA spreadsheets, the user requires Microsoft Excel® 2003 or later installed under the 

Windows operating system.

10A.3 DESCRIPTION OF NATIONAL IMPACT ANALYSIS WORKSHEETS

The NIA spreadsheets perform calculations to project the change in national energy use 

and net present value of financial impacts due to revised energy efficiency standards. The energy 

use and associated costs for a given standard level are determined by calculating the shipments 

and then calculating the energy use and costs for all hearth products shipped under that standard. 

The differences between the standards and base case can then be compared and the overall 

energy savings and net present values determined. The NIA spreadsheets consist of the following 

worksheets:

10A-1
All Scenarios

Contains NPV, NES, and intermediate results for all product types,

AEO economic scenarios, and discount rates. Also contains a 

summary pivot table of NES and NPV results by TSL for userselected AEO economic scenario and discount rate parameters.

Summary Contains a summary of disaggregated NIA and site NES results for all 

product types

Fireplace (vented) Contains vented fireplace NIA calculations.

Fireplace (ventless) Contains ventless fireplace NIA calculations.

Logs (vented) Contains vented log set NIA calculations.

Logs (ventless) Contains ventless log set NIA calculations.

Outdoor Contains outdoor hearth product NIA calculations.

PC Inputs

Contains energy use, electricity use, retail price, installation cost, 

annual repair costs, and annual maintenance costs, for each product 

class.

Shipments Includes historical and projected shipments data for each product type.

Price_Elasticity

Includes the price elasticity to account for the change in the 

percentage of consumers acquiring a hearth product divided by a 

change in the relative price.

Base-Case Shipments Contains shipments projections by product type under the base case 

(no standard).

Lifetime Includes the lifetime and the retirement function for each product type.

Energy Price Contains energy prices for each product type by year.

10A.4 BASIC INSTRUCTIONS FOR OPERATING THE NATIONAL IMPACT 

ANALYSIS SPREADSHEETS

Basic instructions for operating the NIA spreadsheets are as follows:

1. Once the NIA spreadsheet file has been downloaded from the Department’s web site, 

open the file using MS Excel. Click “Enable Macro” when prompted and then click on 

the tab for the worksheet User Inputs.

2. Use MS Excel's View/Zoom commands at the top menu bar to change the size of the 

display to make it fit your monitor.

3. The user can change the parameters in the sheet “Summary”. The default parameters

(shown in Figure 10A.4.1) are:

10A-2
Figure 10A.4.1 The default user input parameters

a) Economic Growth Scenario: Set to “Reference” (AEO 2014 Reference Case). To 

change value, click on the pull down menu next to cell D3 “Economic Growth 

Scenario” and change to desired scenario (Reference, Low, or High).

b) Number of Years: Set to 30. To change value, click on the pull down menu next 

to cell D5 and change to desired analysis period (30 or 9 years). The year that 

analysis ends is automatically calculated based on the year standards in effect and 

analysis period. 

c) Discount Rates: Set to 7%. To change value, click on the pull down menu next to 

cell D4 and change to desired value (7% or 3%).

d) TSL: Set to 1.

4. The spreadsheet automatically updates the analysis results based on user inputs: National 

Energy Savings, Net Present Values and intermediate results by product type, in cells H4

to M9.

Note: Make sure that the spreadsheet is in automatic calculation mode. The calculation 

mode could be changed by (shown in Figure 10A.4.2): 

1. In Excel 2010 and later, go to the tab “Formulas” in the Office ribbon. 

2. Click on the button “Calculation Options” and select “Automatic”.

The results are automatically updated and are reported in the source energy savings 

matrix, net present value matrix, and summary table for each product class.

Figure 10A.4.2 Set the spreadsheet to automatic calculation mode

10A-3
APPENDIX 10B. FULL FUEL CYCLE MULTIPLIERS

TABLE OF CONTENTS

10B.1 INTRODUCTION .................................................................................................10B-1

10B.2 METHODOLOGY ................................................................................................10B-1

10B.3 FULL-FUEL-CYCLE ENERGY MULTIPLIERS ...............................................10B-3

LIST OF TABLES

Table 10B.2.1 Dependence of FFC Parameters on AEO Inputs .......................................10B-3

Table 10B.3.1 Upstream Energy Multipliers (Based on AEO 2014)................................10B-3

10B-i
APPENDIX 10B. FULL FUEL CYCLE MULTIPLIERS

10B.1 INTRODUCTION

This appendix summarizes the methods used to calculate full-fuel-cycle (FFC) energy 

savings expected to result from proposed 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. DOE’s traditional approach encompassed only site energy and the energy losses 

associated with generation, transmission, and distribution of electricity. 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.1 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 hearth product), 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 energy at the site or power plant 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 based on the amount of fuel consumed at the 

power plant. For the latter, no fuel per se is used, so there is no upstream component.

10B.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.

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 

10B-1
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 power sector primary energy use factor indicated in chapter 10. 

The power sector primary energy use factor is defined as the ratio of the total primary energy 

consumption by the electric power sector (in quadrillion BTUs) divided by the total electricity 

generation in each year.

The FFC multiplier is a dimensionless number denoted μ (mu). The upstream component 

of the energy savings is proportional to (μ-1). A separate multiplier is calculated for each fuel 

used on site. The fuel type is denoted by a subscript on the multiplier μ. A multiplier is also 

calculated for electricity reflecting the fuel mix used in its generation. 

For DOE’s appliance standards energy savings estimates, the FFC analysis methodology 

is designed to make use of data and projections published in the Annual Energy Outlook (AEO). 

Table 10B.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. The Projections of Full Fuel Cycle Energy and Emissions Metrics

paper

3 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 this rulemaking used data from AEO 2014.

4

10B-2
Table 10B.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

10B.3 FULL-FUEL-CYCLE ENERGY MULTIPLIERS

Upstream energy multipliers are presented in Table 10B.3.1 for selected years. For years 

after 2040 (the last year in the AEO), DOE maintained the 2040 value. The multipliers are 

applied to site energy. The multiplier for electricity reflects the shares of various primary fuels in 

total electricity generation over the forecast period.

Table 10B.3.1 Upstream Energy Multipliers (Based on AEO 2014)

2021 2025 2030 2035 2040 2045 2050

Electricity 1.044 1.045 1.046 1.047 1.047 1.047 1.047

Natural Gas 1.110 1.111 1.113 1.114 1.114 1.114 1.114

10B-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. U.S. Department of Energy-Energy Information Administration, Annual Energy Outlook 

2014 with Projections to 2040, 2014. Washington, DC. <www.eia.gov/forecasts/aeo/>

10B-4
APPENDIX 10C. NATIONAL IMPACT ANALYSIS USING ALTERNATIVE 

ECONOMIC GROWTH SCENARIOS FOR HEARTH PRODUCTS

TABLE OF CONTENTS

10C.1 INTRODUCTION .................................................................................................10C-1

10C.2 DESCRIPTION OF HIGH AND LOW ECONOMIC SCENARIOS ...................10C-1

10C.3 RESULTS ..............................................................................................................10C-2

10C.3.1 National Energy Savings........................................................................................10C-2

10C.3.2 Net Present Value of Consumer Impacts...............................................................10C-3

10C.3.3 Summary................................................................................................................10C-4

LIST OF TABLES

Table 10C.3.1 Full Fuel Cycle National Energy Savings (Quads) – Reference Case......10C-2

Table 10C.3.2 Full Fuel Cycle National Energy Savings (Quads) – High Economic 

Growth .......................................................................................................10C-2

Table 10C.3.3 Full Fuel Cycle National Energy Savings (Quads) – Low Economic 

Growth .......................................................................................................10C-3

Table 10C.3.4 Net Present Value, Discounted at 3 Percent (Billion 2013$) –

Reference Case...........................................................................................10C-3

Table 10C.3.5 Net Present Value, Discounted at 7 Percent (Billion 2013$) –

Reference Case...........................................................................................10C-3

Table 10C.3.6 Net Present Value, Discounted at 3 Percent (Billion 2013$) – High 

Economic Growth ......................................................................................10C-3

Table 10C.3.7 Net Present Value, Discounted at 7 Percent (Billion 2013$) – High 

Economic Growth ......................................................................................10C-3

Table 10C.3.8 Net Present Value, Discounted at 3 Percent (Billion 2013$) – Low 

Economic Growth ......................................................................................10C-4

Table 10C.3.9 Net Present Value, Discounted at 7 Percent (Billion 2013$) – Low 

Economic Growth ......................................................................................10C-4

Table 10C.3.10 Comparison of Energy Savings and Net Present Value Results for 

Reference Case and High and Low Economic Growth Scenarios.............10C-4

LIST OF FIGURES

Figure 10C.2.1 Shipment Projections for Reference Case and High and Low 

Economic Growth Scenarios (Base Case) .................................................10C-2

10C-i
APPENDIX 10C. NATIONAL IMPACT ANALYSIS USING ALTERNATIVE 

ECONOMIC GROWTH SCENARIOS FOR HEARTH PRODUCTS

10C.1 INTRODUCTION

This appendix presents National Impact Analysis (NIA) results using energy price 

forecasts from alternative economic growth scenarios. The scenarios are based on the High 

Economic Growth case and the Low Economic Growth case from Energy Information 

Administration’s (EIA’s) Annual Energy Outlook 2014 (AEO 2014).1 To estimate energy prices 

after 2040 in the high and low scenarios, DOE used the growth rate between 2021 and 2040. See 

appendix 8C for details about alternative economic growth scenarios.

This appendix also describes the High and Low Economic Growth scenarios in further 

detail. See appendix 10A for details about how to generate NIA results for High Economic 

Growth and Low Economic Growth scenarios using the NIA spreadsheet.

10C.2 DESCRIPTION OF HIGH AND LOW ECONOMIC SCENARIOS

To generate NIA results reported in chapter 10, DOE uses the Reference case energy 

price and housing projections from AEO 2014. The reference case is a business-as-usual 

estimate, given known market, demographic, and technological trends. For AEO 2014, EIA 

explored the impacts of alternative assumptions in other scenarios with different macroeconomic 

growth rates, world oil prices, rates of technology progress, and policy changes. 

To reflect uncertainty in the projection of U.S. economic growth, EIA’s AEO 2014 uses 

High and Low Economic Growth scenarios to project the possible impacts of alternative 

economic growth assumptions on energy markets.2

 

In general, energy prices are higher in the High Economic Growth scenario and lower in 

the Low Economic Growth scenario. See appendix 8E for details about the effect of these 

alternative economic scenarios on energy prices.

Because AEO 2014 provides the price trends by census division, each sampled household 

is then matched to the appropriate census division price trend. See chapter 10 for details about 

how energy price trends by census division are applied in the NIA analysis.

In addition, the High and Low Economic Growth scenarios provide different housing 

starts projections that affect the hearth product shipments projections. Figure 10C.2.1 shows the 

shipments projections based on the different AEO 2014 scenarios.

10C-1
Figure 10C.2.1 Shipment Projections for Reference Case and High and Low Economic

Growth Scenarios (Base Case)

10C.3 RESULTS

10C.3.1 National Energy Savings 

Table 10C.3.1 through Table 10C.3.3 show the national energy savings (NES) results for 

the Trial Standard Levels (TSL) analyzed for hearth product ignition systems using the

Reference case, High Economic Growth scenario, and Low Economic Growth scenario.

Table 10C.3.1 Full Fuel Cycle National Energy Savings (Quads) – Reference Case

Product Classes

Trial Standard Levels

1

Hearth Products 0.69

Table 10C.3.2 Full Fuel Cycle National Energy Savings (Quads) – High Economic 

Growth

Product Classes Trial Standard Levels

1

Hearth Products 1.01

10C-2
Table 10C.3.3 Full Fuel Cycle National Energy Savings (Quads) – Low Economic 

Growth

Product Classes

Trial Standard Levels

1

Hearth Products 0.44

10C.3.2 Net Present Value of Consumer Impacts

Table 10C.3.4 through Table 10C.3.9 show the national present value (NPV) results for 

the TSLs analyzed for hearth product ignition systems using the Reference case, High Economic 

Growth scenario, and Low Economic Growth scenario. 

Table 10C.3.4 Net Present Value, Discounted at 3 Percent (Billion 2013$) – Reference 

Case

Product Classes Trial Standard Levels

1

Hearth Products 3.1

Table 10C.3.5 Net Present Value, Discounted at 7 Percent (Billion 2013$) – Reference 

Case

Product Classes Trial Standard Levels

1

Hearth Products 1.0

Table 10C.3.6 Net Present Value, Discounted at 3 Percent (Billion 2013$) – High 

Economic Growth

Product Classes

Trial Standard Levels

1

Hearth Products 4.9

Table 10C.3.7 Net Present Value, Discounted at 7 Percent (Billion 2013$) – High 

Economic Growth

Product Classes

Trial Standard Levels

1

Hearth Products 1.6

10C-3
Table 10C.3.8 Net Present Value, Discounted at 3 Percent (Billion 2013$) – Low 

Economic Growth

Product Classes

Trial Standard Levels

1

Hearth Products 1.8

Table 10C.3.9 Net Present Value, Discounted at 7 Percent (Billion 2013$) – Low 

Economic Growth

Product Classes

Trial Standard Levels

1

Hearth Products 0.6

10C.3.3 Summary

Table 10C.3.10 shows the NES and NPV results for each of the TSL for the Reference 

case and the High Economic Growth and Low Economic Growth scenarios. NES and NPV 

results are larger for High Economic Growth scenario and smaller for Low Economic Growth 

scenario compared to Reference case.

Table 10C.3.10 Comparison of Energy Savings and Net Present Value Results for 

Reference Case and High and Low Economic Growth Scenarios 

Trial Standard Level

1

Reference

Full Fuel Cycle Energy Savings (quads) 0.69

NPV 3% (billion 2013$) 3.1

NPV 7% (billion 2013$) 1.0

High Economic Growth

Full Fuel Cycle Energy Savings (quads) 1.01

NPV 3% (billion 2013$) 4.9

NPV 7% (billion 2013$) 1.6

Low Economic Growth

Full Fuel Cycle Energy Savings (quads) 0.44

NPV 3% (billion 2013$) 1.8

NPV 7% (billion 2013$) 0.6

10C-4
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. Energy Information Administration, Model Documentation Report: Macroeconomic 

Activity Module (MAM) of the National Energy Modeling System (AEO 2012), 2013. 

Washington, DC. 

<www.eia.gov/forecasts/aeo/nems/documentation/macroeconomic/pdf/m065(2013).pdf>

10C-5
APPENDIX 12A. GOVERNMENT REGULATORY IMPACT MODEL OVERVIEW

TABLE OF CONTENTS

12A.1 INTRODUCTION AND PURPOSE ....................................................................................1

12A.2 MODEL DESCRIPTION .....................................................................................................1

LIST OF TABLES

Table 12A.2.1 Detailed Cash Flow Example.......................................................................... 12A-4

12A-i
APPENDIX 12A. GOVERNMENT REGULATORY IMPACT MODEL OVERVIEW

12A.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(s) 

following a regulation or a series of regulations. The model structure also allows an analysis of 

multiple products with regulations taking effect over a period of time, and of multiple regulations 

on the same products.

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.

12A.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) Unit sales: Total annual shipments for the industry were obtained from the national impact 

analysis spreadsheet.

(2) Revenues: Annual revenues are computed by multiplying products’ unit prices at each 

efficiency level by the appropriate manufacturer markup.

(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.

12A-1
(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, depreciation is shown as a separate line item.

(7) Standard SG&A: Selling, general, and administrative costs are computed as a percentage of 

revenues (2).

(8) R&D: GRIM separately accounts for ordinary research and development as a percentage of 

revenues (2).

(9) Product conversion costs: Product conversion costs are one-time investments in research, 

development, testing, marketing, and other costs focused on making products 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 COGS ((3) to (6)), 

SG&A (7), R&D (8), product conversion costs (9), and taxes (13) from revenues (2).

(15) NOPAT repeated: NOPAT is repeated in the statement of cash flows.

(16) Depreciation repeated: Depreciation and stranded assets are added back into 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 a 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 (2).

(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 product 

12A-2
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 2050, the discounted cash flow includes the discounted terminal value (23).

(26) Industry value through the end of 2050: The sum of discounted cash flows (25).

12A-3
Table 12A.2.1 Detailed Cash Flow Example

12A-4
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-6

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-14

14A.4.5 Socioeconomic and Emissions Trajectories........................................................ 14A-17

14A.4.6 Discount Rate...................................................................................................... 14A-19

14A.5 REVISED SCC ESTIMATES ............................................................................ 14A-26

14A.6 LIMITATIONS OF THE ANALYSIS ............................................................... 14A-33

14A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPACTS AND 

DAMAGE FUNCTIONS ................................................................................... 14A-35

14A.8 CONCLUSION................................................................................................... 14A-38

14A.9 ANNEX............................................................................................................... 14A-45

14A.9.1 Other (non-CO2) gases........................................................................................ 14A-46

14A.9.2 Extrapolating Emissions Projections to 2300 ..................................................... 14A-48

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-15

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-30

Table 14A.5.2 Social Cost of CO2, 2010 – 2050 (in 2007 dollars) ................................ 14A-32

Table 14A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates 

between 2010 and 2050 .......................................................................... 14A-32

Table 14A.7.1 Probabilities of Various Tipping Points from Expert Elicitation............ 14A-36

Table 14A.9.1 Annual SCC Values: 2010–2050 (in 2007 dollars) ................................ 14A-45

Table 14A.9.2 2010 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton 

CO2) ........................................................................................................ 14A-56

Table 14A.9.3 2010 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton 

CO2) ........................................................................................................ 14A-57

Table 14A.9.4 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton 

CO2) ........................................................................................................ 14A-58

14A-i
Table 14A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates............. 14A-59

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-31

Figure 14A.9.2 Sulfur Dioxide Emission Scenarios........................................................ 14A-48

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-50

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-51

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-52

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-53

Figure 14A.9.7 Global Non-CO2 Radiative Forcing, 2000-2300 (Post-2100 

extrapolations assume constant non-CO2 radiative forcing after 2100) . 14A-54

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-55

Figure 14A.9.9 Histogram of Global SCC Estimates in 2010 (2007$/ton CO2), by 

discount rate ............................................................................................ 14A-58

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.

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

 
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 

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, 

14A-3
the central value increases to $24 per ton of CO2 in 2015 and $26 per ton of CO2 in 2020. See 

section 16-A.5 for the full range of annual SCC estimates from 2010 to 2050.

It is important to emphasize that the interagency process is committed to updating these 

estimates as the science and economic understanding of climate change and its impacts on 

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—

14A-4
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.

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-5
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).

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 

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-6

 
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.

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.

14A-7
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 

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 

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 percent, 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

 
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 

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.

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

 
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.

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

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

 
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.

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 

14A-11
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 

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.

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.

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.

14A-12

 
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 

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 

i

Based on 2008 GDP (in current US dollars) from the World Bank Development Indicators Report.

14A-13

 
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):

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.

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). 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.

14A-14

 
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

(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 

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-15

 
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. 

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

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

 
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. 

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 

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

 
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 

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 

14A-20
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, 

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—

14A-21
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 

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 

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. 

14A-22

 
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

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 

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).

u The parameter ρ measures the pure rate of time preference: people’s behavior reveals a preference for an increase 

in utility today versus the future. Consequently, it is standard to place a lower weight on utility in the future. The 

parameter η captures diminishing marginal utility: consumption in the future is likely to be higher than consumption 

today, so diminishing marginal utility of consumption implies that the same monetary damage will cause a smaller 

reduction of utility for wealthier individuals, either in the future or in current generations. If η = 0, then a one dollar 

increase in income is equally valuable regardless of level of income; if η = 1, then a one percent increase in income 

is equally valuable no matter the level of income; and if η > 1, then a one percent increase in income is less valuable 

to wealthier individuals.

v In this case, g could be taken from the selected EMF socioeconomic scenarios or alternative assumptions about the 

rate of consumption growth.

14A-23

 
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 intergenerational setting, it may make sense to use a small positive pure rate of time 

preference because of the small probability of unforeseen cataclysmic events (Stern et al. 

2006).

• g. A commonly accepted approximation is around 2 percent per year. For the 

socioeconomic scenarios used for this exercise, the EMF models assume that g is about 

1.5-2 percent to 2100.

Some economists and non-economists have argued for constant discount rates below 2 

percent based on the prescriptive approach. When grounded in the Ramsey framework, 

proponents of this approach have argued that a ρ of zero avoids giving preferential treatment to 

one generation over another. The choice of η has also been posed as an ethical choice linked to 

the value of an additional dollar in poorer countries compared to wealthier ones. Stern et al. 

(2006) applies this perspective through his choice of ρ = 0.1 percent per year, η = 1 and g = 1.3 

percent per year, which yields an annual discount rate of 1.4 percent. In the context of permanent 

income savings behavior, however, Stern’s assumptions suggest that individuals would save 93 

percent of their income.x

Recently, Stern (2008) revisited the values used in Stern et al. (2006), stating that there is 

a case to be made for raising η due to the amount of weight lower values place on damages far in 

the future (over 90 percent of expected damages occur after 2200 with η = 1). Using Stern’s 

assumption that ρ = 0.1 percent, combined with a η of 1.5 to 2 and his original growth rate, 

yields a discount rate of greater than 2 percent.

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. 

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

 
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).

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

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, 

14A-25

 
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.

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.

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. 

aa Calculations done by Pizer et al. using the original simulation program from Newell and Pizer (2003).

14A-26

 
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. 

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.

14A-27
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. 

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.

14A-28
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-29
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 

14A-30

 
The SCC estimates from FUND are sensitive to differences in emissions paths but 

relatively insensitive to differences in GDP paths across scenarios, while the reverse is true for 

DICE and PAGE. This likely occurs because of several structural differences among the models.

Specifically in DICE and PAGE, the fraction of economic output lost due to climate damages 

increases with the level of temperature alone, whereas in FUND the fractional loss also increases 

with the rate of temperature change. Furthermore, in FUND increases in income over time 

decrease vulnerability to climate change (a form of adaptation), whereas this does not occur in 

DICE and PAGE. These structural differences among the models make FUND more sensitive to 

the path of emissions and less sensitive to GDP compared to DICE and PAGE.

Figure 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.

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 

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-31

 
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-32
calculated using a SCC based on a 5 percent discount rate also should be discounted back to the 

analysis year using a 5 percent discount rate.cc

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-33

 
impact, low-probability risks, using a right-skewed gamma distribution for climate sensitivity as 

well as an uncertain damage coefficient, but in most cases found only a modest risk premium.

Given this difference in opinion, further research in this area is needed before its practical 

significance can be fully understood and a reasonable approach developed to account for such 

risks in regulatory analysis. (The next section discusses the scientific evidence on catastrophic 

impacts in greater detail.)

Uncertainty in extrapolation of damages to high temperatures: The damage functions in 

these IAMs are typically calibrated by estimating damages at moderate temperature increases 

(e.g., DICE was calibrated at 2.5 °C) and extrapolated to far higher temperatures by assuming 

that damages increase as some power of the temperature change. Hence, estimated damages are 

far more uncertain under more extreme climate change scenarios.

Incomplete treatment of adaptation and technological change: Each of the three 

integrated assessment models used here assumes a certain degree of low- or no-cost adaptation.

For instance, Tol assumes a great deal of adaptation in FUND, including widespread reliance on 

air conditioning; so much so, that the largest single benefit category in FUND is the reduced 

electricity costs from not having to run air conditioning as intensively (NRC 2009).

Climate change also will increase returns on investment to develop technologies that 

allow individuals to cope with adverse climate conditions, and IAMs to do not adequately 

account for this directed technological change.dd For example, scientists may develop crops that 

are better able to withstand higher and more variable temperatures. Although DICE and FUND 

have both calibrated their agricultural sectors under the assumption that farmers will change land 

use practices in response to climate change (Mastrandrea, 2009), they do not take into account 

technological changes that lower the cost of this adaptation over time. On the other hand, the 

calibrations do not account for increases in climate variability, pests, or diseases, which could 

make adaptation more difficult than assumed by the IAMs for a given temperature change.

Hence, models do not adequately account for potential adaptation or technical change that might 

alter the emissions pathway and resulting damages. In this respect, it is difficult to determine 

whether the incomplete treatment of adaptation and technological change in these IAMs 

understate or overstate the likely damages.

Risk aversion: A key question unanswered during this interagency process is what to 

assume about relative risk aversion with regard to high-impact outcomes. These calculations do 

not take into account the possibility that individuals may have a higher willingness to pay to

reduce the likelihood of low-probability, high-impact damages than they do to reduce the 

likelihood of higher-probability, but lower-impact, damages with the same expected cost. (The 

inclusion of the 95th percentile estimate in the final set of SCC values was largely motivated by 

this concern.) If individuals do show such a higher willingness to pay, a further question is 

whether that fact should be taken into account for regulatory policy. Even if individuals are not 

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-34

 
risk-averse for such scenarios, it is possible that regulatory policy should include a degree of 

risk-aversion.

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 

14A-35
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 

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 

14A-36
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 

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.

14A-37
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.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-38
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14A-44
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-45
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-46

 
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.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). 

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.

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-47

 
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 

these projections are available from the EMF-22 models. These inputs were extrapolated from 

2100 to 2300 as follows:

14A-48
1. Population growth rate declines linearly, reaching zero in the year 2200.

2. GDP/per capita growth rate declines linearly, reaching zero in the year 2300.

3. The decline in the fossil and industrial carbon intensity (CO2/GDP) growth rate over 2090-

2100 is maintained from 2100 through 2300.

4. Net land use CO2 emissions decline linearly, reaching zero in the year 2200.

5. Non-CO2 radiative forcing remains constant after 2100.

Long run stabilization of GDP per capita was viewed as a more realistic simplifying 

assumption than a linear or exponential extrapolation of the pre-2100 economic growth rate of 

each EMF scenario. This is based on the idea that increasing scarcity of natural resources and the 

degradation of environmental sinks available for assimilating pollution from economic 

production activities may eventually overtake the rate of technological progress. Thus, the 

overall rate of economic growth may slow over the very long run. The interagency group also 

considered allowing an exponential decline in the growth rate of GDP per capita. However, since 

this would require an additional assumption about how close to zero the growth rate would get 

by 2300, the group opted for the simpler and more transparent linear extrapolation to zero by 

2300.

The population growth rate is also assumed to decline linearly, reaching zero by 2200.

This assumption is reasonably consistent with the United Nations long run population forecast, 

which estimates global population to be fairly stable after 2150 in the medium scenario (UN 

2004).jj The resulting range of EMF population trajectories (figure below) also encompass the 

UN medium scenario forecasts through 2300—global population of 8.5 billion by 2200, and 9 

billion by 2300.

Maintaining the decline in the 2090-2100 carbon intensity growth rate (i.e., CO2 per 

dollar of GDP) through 2300 assumes that technological improvements and innovations in the 

areas of energy efficiency and other carbon reducing technologies (possibly including currently 

unavailable methods) will continue to proceed at roughly the same pace that is projected to occur 

towards the end of the forecast period for each EMF scenario. This assumption implies that total 

cumulative emissions in 2300 will be between 5,000 and 12,000 GtC, which is within the range 

of the total potential global carbon stock estimated in the literature.

Net land use CO2 emissions are expected to stabilize in the long run, so in the absence of 

any post 2100 projections, the group assumed a linear decline to zero by 2200. Given no a priori 

reasons for assuming a long run increase or decline in non-CO2 radiative forcing, it is assumed to 

remain at the 2100 levels for each EMF scenario through 2300.

Figures below show the paths of global population, GDP, fossil and industrial CO2

emissions, net land CO2 emissions, non-CO2 radiative forcing, and CO2 intensity (fossil and 

industrial CO2 emissions/GDP) resulting from these assumptions. 

jj United Nations. 2004. World Population to 2300. 

www.un.org/esa/population/publications/longrange2/WorldPop2300final.pdf

14A-49

 
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-50
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-51
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)

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-52
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-53

 
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-54
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-55
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-56
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-57
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-58
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-59
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 Carbon Cycle Parameters............................................................... 14B-2

14B.3.1.2 Sea Level Dynamics....................................................................... 14B-3

14B.3.1.3 Re-calibrated Damage Function..................................................... 14B-4

14B.3.2 FUND.....................................................................................................................14B-4

14B.3.2.1 Space Heating................................................................................. 14B-5

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-9

14B.5 OTHER MODEL LIMITATIONS OR RESEARCH GAPS ..............................14B-11

LIST OF TABLES

Table 14B.4.1 Revised Social Cost of CO2, 2010 – 2050 (in 2007 dollars per ton of 

CO2) .........................................................................................................14B-10

Table 14B.4.2 Average Annual Growth Rates of SCC Estimates between 2010 and 

2050..........................................................................................................14B-11

Table 14B.5.1 Annual SCC Values: 2010-2050 (2007$/ton CO2)..................................14B-14

Table 14B.5.2 202 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton 

CO2) .........................................................................................................14B-15

Table 14B.5.3 SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2) .................14B-15

Table 14B.5.4 2020 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton 

CO2) .........................................................................................................14B-16

LIST OF FIGURES

Figure 14B.4.2 Distribution of SCC Estimates for 2020 (in 2007$ per ton CO2) ............14B-10

14B-i
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-1

 
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.

14B.3.1.1 Carbon Cycle Parameters

DICE uses a three-box model of carbon stocks and flows to represent the accumulation 

and transfer of carbon among the atmosphere, the shallow ocean and terrestrial biosphere, and 

the deep ocean. These parameters are “calibrated to match the carbon cycle in the Model for the 

Assessment of Greenhouse Gas Induced Climate Change (MAGICC)” (Nordhaus 2008 p 44).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 

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-2

 
decade, 9.7 percent of the carbon in the shallow ocean is transferred to the atmosphere, 85.3 

percent remains in the shallow ocean, and 5 percent is transferred to the deep ocean.

The implication of these changes for DICE2010 is in general a weakening of the ocean as 

a carbon sink and therefore a higher concentration of carbon in the atmosphere than in 

DICE2007, for a given path of emissions. All else equal, these changes will generally increase 

the level of warming and therefore the SCC estimates in DICE2010 relative to those from 

DICE2007.

14B.3.1.2 Sea Level Dynamics

A new feature of DICE2010 is an explicit representation of the dynamics of the global 

average sea level anomaly to be used in the updated damage function (discussed below). This 

section contains a brief description of the sea level rise (SLR) module; a more detailed 

description can be found on the model developer’s website.e The average global sea level 

anomaly is modeled as the sum of four terms that represent contributions from: 1) thermal 

expansion of the oceans, 2) melting of glaciers and small ice caps, 3) melting of the Greenland 

ice sheet, and 4) melting of the Antarctic ice sheet. 

The parameters of the four components of the SLR module are calibrated to match 

consensus results from the IPCC’s Fourth Assessment Report.4,f The rise in sea level from 

thermal expansion in each time period (decade) is 2 percent of the difference between the sea 

level in the previous period and the long run equilibrium sea level, which is 0.5 meters per 

degree Celsius (°C) above the average global temperature in 1900. The rise in sea level from the 

melting of glaciers and small ice caps occurs at a rate of 0.008 meters per decade per °C above 

the average global temperature in 1900.

The contribution to sea level rise from melting of the Greenland ice sheet is more 

complex. The equilibrium contribution to SLR is 0 meters for temperature anomalies less than 1 o

C and increases linearly from 0 meters to a maximum of 7.3 meters. The contribution to SLR in 

each period is proportional to the difference between the previous period’s sea level anomaly and 

the equilibrium sea level anomaly, where the constant of proportionality increases with the 

temperature anomaly in the current period.

The contribution to SLR from the melting of the Antarctic ice sheet is -0.001 meters per 

decade when the temperature anomaly is below 3 °C and increases linearly to a maximum rate of 

0.025 meters per decade at a temperature anomaly of 6 °C.

e Documentation on the new sea level rise module of DICE is available on William Nordhaus’ website at: 

www.econ.yale.edu/. f For a review of post-IPCC AR4 research on sea level rise, see Nicholls et al. (2011)

5 and NAS (2011).6

14B-3

 
14B.3.1.3 Re-calibrated Damage Function

Economic damages from climate change in the DICE model are represented by a 

fractional loss of gross economic output in each period. A portion of the remaining economic 

output in each period (net of climate change damages) is consumed and the remainder is invested 

in the physical capital stock to support future production, so each period’s climate damages will 

reduce consumption in that period and in all future periods due to the lost investment. The 

fraction of output in each period that is lost due to climate change impacts is represented as one 

minus a fraction, which is one divided by a quadratic function of the temperature anomaly, 

producing a sigmoid (“S”-shaped) function. The loss function in DICE2010 has been expanded 

by adding a quadratic function of SLR to the quadratic function of temperature. In DICE2010 the 

temperature anomaly coefficients have been recalibrated to avoid double-counting damages from 

sea level rise that were implicitly included in these parameters in DICE2007. 

The aggregate damages in DICE2010 are illustrated by Nordhaus (2010 p 3),3 who notes 

that “...damages in the uncontrolled (baseline) (i.e., reference) case ... in 2095 are $12 trillion, 

or 2.8 percent of global output, for a global temperature increase of 3.4 o

C above 1900 levels.” 

This compares to a loss of 3.2 percent of global output at 3.4 oC in DICE2007. However, in 

DICE2010 (as downloaded from the homepage of William Nordhaus), annual damages are lower 

in most of the early periods but higher in later periods of the time horizon than would be 

calculated using the DICE2007 damage function. Specifically, the percent difference between 

damages in the base run of DICE2010 and those that would be calculated using the DICE2007 

damage function starts at +7 percent in 2005, decreases to a low of -14 percent in 2065, then 

continuously increases to +20 percent by 2300 (the end of the interagency analysis time horizon), 

and to +160 percent by the end of the model time horizon in 2595. The large increases in the far 

future years of the time horizon are due to the permanence associated with damages from sea 

level rise, along with the assumption that the sea level is projected to continue to rise long after 

the global average temperature begins to decrease. The changes to the loss function generally 

decrease the interagency working group SCC estimates slightly, all else equal.

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.

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-4

 
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 

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 

14B-5
denominator approached zero the share of the sector’s value “lost” approaches (+/-) infinity 

independent of the temperature anomaly itself. In FUND 3.8, the linear and quadratic 

coefficients are drawn directly from truncated normal distributions so that they remain in the 

range [0,∞) and (−∞,0], respectively, where the means for the new distributions are set equal to 

the ratio of the means from the normal distributions used in the previous version. In general the 

impact of this change has been to increase the likelihood that increases in the temperature level 

will have either larger positive or negative effects on the agricultural sector relative to the 

previous version (through eliminating simulations in which the “lost” value approached (+/-) 

infinity). The net effect of this change on the SCC estimates is difficult to predict. 

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 

14B-6
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.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 

14B-7
the Greenland ice sheet, were multiplied by the probability of the event occurring and added to 

the damage estimate. In PAGE09, the probability of “discontinuity” is treated as a discrete event 

for each year in the model. The damages for each model run are estimated with or without a 

discontinuity occurring, rather than as an expected value. A large‐scale discontinuity becomes 

possible when the temperature rises beyond some threshold value between 2 and 4°C. The 

probability that a discontinuity will occur beyond this threshold then increases by between 10 

and 30 percent for every 1°C rise in temperature beyond the threshold. If a discontinuity occurs, 

the EU loses an additional 5 to 25 percent of its GDP (drawn from a triangular distribution with a 

mean of 15 percent) in addition to other damages, and other regions lose an amount determined 

by the regional scaling factor. The threshold value for a possible discontinuity is lower than in 

PAGE2002, while the rate at which the probability of a discontinuity increases with the 

temperature anomaly and the damages that result from a discontinuity are both higher than in 

PAGE2002. The model assumes that only one discontinuity can occur and that the impact is 

phased in over a period of time, but once it occurs, its effect is permanent.

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 

14B-8
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 

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.

14B-9
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 33 52 90

2015 12 38 58 109

2020 12 43 65 129

2025 14 48 70 144

2030 16 52 76 159

2035 19 57 81 176

2040 21 62 87 192

2045 24 66 92 206

2050 27 71 98 221

The SCC estimates using the updated versions of the models are higher than those 

reported in the TSD due to the changes to the models outlined in the previous section. Figure 

14B.4.2 illustrates where the four SCC values for 2020 fall within the full distribution for each 

discount rate based on the combined set of runs for each model and scenario (150,000 estimates 

in total for each discount rate). In general, the distributions are skewed to the right and have long 

tails. The Figure also shows that the lower the discount rate, the longer the right tail of the 

distribution.

Figure 14B.4.2 Distribution of SCC Estimates for 2020 (in 2007$ per ton CO2)

As was the case in the original TSD, the SCC increases over time because future 

emissions are expected to produce larger incremental damages as physical and economic systems 

become more stressed in response to greater climatic change. The approach taken by the 

interagency group is to allow the growth rate to be determined endogenously by the models 

14B-10
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% 3.2% 2.4% 4.3%

2020-2030 3.4% 2.1% 1.7% 2.4%

2030-2040 3.0% 1.8% 1.5% 2.0%

2040-2050 2.6% 1.6% 1.3% 1.5%

The future monetized value of emission reductions in each year (the SCC in year t 

multiplied by the change in emissions in year t) must be discounted to the present to determine 

its total net present value for use in regulatory analysis. As previously discussed in the original 

TSD, damages from future emissions should be discounted at the same rate as that used to 

calculate the SCC estimates themselves to ensure internal consistency – i.e., future damages from 

climate change, whether they result from emissions today or emissions in a later year, should be 

discounted using the same rate.

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-11
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. Washington, D.C. 

<www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/Social-Cost-ofCarbon-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-12
10. Hope, C., The PAGE09 Integrated Assessment Model: A Technical Description, April, 

2011. Cambridge Judge Business School 

<www.jbs.cam.ac.uk/fileadmin/user_upload/research/workingpapers/wp1104.pdf>

11. Hope, C., The Social Cost of CO2 from the PAGE09 Model, June, 2011. Cambridge 

Judge Business School 

<www.jbs.cam.ac.uk/fileadmin/user_upload/research/workingpapers/wp1105.pdf>

12. Hope, C., New Insights from the PAGE09 Model: The Social Cost of CO2, July, 2011. 

Cambridge Judge Business School 

<www.jbs.cam.ac.uk/fileadmin/user_upload/research/workingpapers/wp1108.pdf>

13. Hope, C., The Marginal Impact of CO2 from PAGE2002: An Integrated Assessment 

Model Incorporating the IPCC’s Five Reasons for Concern. The Integrated Assessment 

Journal, 2006. 6(1): pp. 19-56 

14B-13
ANNEX

Table 14B.5.1 Annual SCC Values: 2010-2050 (2007$/ton CO2)

Discount Rate 5.0% 3.0% 2.5% 3.0%

Year Avg Avg Avg 95th

2010 11 33 52 90

2011 11 34 54 94

2012 11 35 55 98

2013 11 36 56 102

2014 11 37 57 106

2015 12 38 58 109

2016 12 39 60 113

2017 12 40 61 117

2018 12 41 62 121

2019 12 42 63 125

2020 12 43 65 129

2021 13 44 66 132

2022 13 45 67 135

2023 13 46 68 138

2024 14 47 69 141

2025 14 48 70 144

2026 15 49 71 147

2027 15 49 72 150

2028 15 50 73 153

2029 16 51 74 156

2030 16 52 76 159

2031 17 53 77 163

2032 17 54 78 166

2033 18 55 79 169

2034 18 56 80 172

2035 19 57 81 176

2036 19 58 82 179

2037 20 59 84 182

2038 20 60 85 185

2039 21 61 86 188

2040 21 62 87 192

2041 22 63 88 195

2042 22 64 89 198

2043 23 65 90 200

2044 23 65 91 203

2045 24 66 92 206

2046 24 67 94 209

2047 25 68 95 212

2048 25 69 96 215

2049 26 70 97 218

2050 27 71 98 221

14B-14
Table 14B.5.2 202 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 6 11 15 27 58 129 139 327 515 991

MERGE 4 6 9 16 34 78 82 196 317 649

MESSAGE 4 8 11 20 42 108 107 278 483 918

MiniCAM Base 5 9 12 22 47 107 113 266 431 872

5th Scenario 2 4 6 11 25 85 68 200 387 955

Scenario DICE

IMAGE 25 31 37 47 64 72 92 123 139 161

MERGE 14 18 20 26 36 40 50 65 74 85

MESSAGE 20 24 28 37 51 58 71 95 109 221

MiniCAM Base 20 25 29 38 53 61 76 102 117 135

5th Scenario 17 22 25 33 45 52 65 91 106 126

Scenario FUND

IMAGE -17 -1 5 17 34 44 59 90 113 176

MERGE -7 2 7 16 30 35 49 72 91 146

MESSAGE -19 -4 2 12 27 32 46 70 87 135

MiniCAM Base -9 1 8 18 35 45 59 87 108 172

5th Scenario -30 -12 -5 6 19 24 35 57 72 108

Table 14B.5.3 SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 4 7 10 18 38 91 95 238 385 727

MERGE 2 4 6 11 23 56 58 142 232 481

MESSAGE 3 5 7 13 29 75 74 197 330 641

MiniCAM Base 3 5 8 14 30 73 75 184 300 623

5th Scenario 1 3 4 7 17 58 48 136 264 660

Scenario DICE

IMAGE 16 21 24 32 43 48 60 79 90 102

MERGE 10 13 15 19 25 28 35 44 50 58

MESSAGE 14 18 20 26 35 40 49 64 73 83

MiniCAM Base 13 17 20 26 35 39 49 65 73 85

5th Scenario 12 15 17 22 30 34 43 58 67 79

Scenario FUND

IMAGE -14 -3 1 9 20 25 35 54 69 111

MERGE -8 -1 3 9 18 22 31 47 60 97

MESSAGE -16 -5 -1 6 16 18 28 43 55 88

MiniCAM Base -9 -1 3 10 21 27 35 53 67 107

5th Scenario -22 -10 -5 2 10 13 20 33 42 63

14B-15
Table 14B.5.4 2020 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 1 2 2 5 10 28 27 71 123 244

MERGE 1 1 2 3 7 17 17 45 75 153

MESSAGE 1 1 2 4 9 24 22 60 106 216

MiniCAM Base 1 1 2 3 8 21 21 54 94 190

5th Scenario 0 1 1 2 5 18 14 41 78 208

Scenario DICE

IMAGE 6 8 9 11 14 15 18 22 25 27

MERGE 4 5 6 7 9 10 12 15 16 18

MESSAGE 6 7 8 10 12 13 16 20 22 25

MiniCAM Base 5 6 7 8 11 12 14 18 20 22

5th Scenario 5 6 6 8 10 11 14 17 19 21

Scenario FUND

IMAGE -9 -5 -3 -1 2 3 6 11 15 25

MERGE -6 -3 -2 0 3 4 7 12 16 27

MESSAGE -10 -6 -4 -1 2 2 5 9 13 23

MiniCAM Base -7 -3 -2 0 3 4 7 11 15 26

5th Scenario -11 -7 -5 -2 0 0 3 6 8 14

14B-16
APPENDIX 17A. REGULATORY IMPACT ANALYSIS: SUPPORTING MATERIALS

TABLE OF CONTENTS

17A.1 INTRODUCTION ................................................................................................ 17A-1

17A.2 MARKET SHARE ANNUAL INCREASES BY POLICY................................. 17A-1

17A.3 NIA-RIA INTEGRATED MODEL...................................................................... 17A-7

17A.4 CONSUMER REBATE POLICY MARKET PENETRATION CURVES ......... 17A-7

17A.4.1 Introduction........................................................................................................... 17A-7

17A.4.2 Adjustment of XENERGY Penetration Curves.................................................... 17A-9

17A.4.3 Interpolation of Penetration Curves.................................................................... 17A-10

17A.5 CONSUMER REBATE PROGRAMS............................................................... 17A-11

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-14

17A.6.3 State Tax Credits................................................................................................. 17A-14

LIST OF TABLES

Table 17A.2.1 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Hearth Products: Fireplace (vented) (TSL 1)...................... 17A-2

Table 17A.2.2 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Hearth Products: Fireplace (ventless) (TSL 1) ................... 17A-3

Table 17A.2.3 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Hearth Products: Logs (vented) (TSL 1) ............................ 17A-4

Table 17A.2.4 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Hearth Products: Logs (ventless) (TSL 1) .......................... 17A-5

Table 17A.2.5 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Hearth Products: Outdoor (TSL 1) ..................................... 17A-6

Table 17A.5.1 Calculation Steps of the Representative Rebate Value........................... 17A-11

Table 17A.5.2 Rebates for Hearth Products ................................................................... 17A-12

LIST OF FIGURES

Figure 17A.4.1 S-Curves Showing Effects of External and Internal Sources on 

Adoption of New Technologies................................................................ 17A-9

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.2 MARKET SHARE ANNUAL INCREASES BY POLICY

Table 17A.2.1 through Table 17A.2.5 show the annual increases in market shares of 

hearth products meeting the target efficiency levels for the proposed TSL (TSL 1). DOE used 

these market share increases as inputs to the NIA-RIA spreadsheet model.

17A-1
Table 17A.2.1 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Hearth Products: Fireplace (vented) (TSL 1)

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Voluntary 

Energy 

Efficiency 

Targets

Bulk 

Government 

Purchases

2021 7.9% 4.8% 2.4% 3.2% 0.0%

2022 7.9% 4.8% 2.4% 6.3% 0.0%

2023 7.9% 4.8% 2.4% 9.2% 0.0%

2024 7.9% 4.8% 2.4% 12.1% 0.0%

2025 7.9% 4.8% 2.4% 14.8% 0.0%

2026 7.9% 4.8% 2.4% 17.4% 0.0%

2027 7.9% 4.8% 2.4% 19.9% 0.0%

2028 7.9% 4.8% 2.4% 22.4% 0.0%

2029 7.9% 4.8% 2.4% 24.7% 0.0%

2030 7.9% 4.8% 2.4% 27.0% 0.0%

2031 7.9% 4.8% 2.4% 27.2% 0.0%

2032 7.9% 4.8% 2.4% 27.3% 0.0%

2033 7.9% 4.8% 2.4% 27.6% 0.0%

2034 7.9% 4.8% 2.4% 27.7% 0.0%

2035 7.9% 4.8% 2.4% 27.9% 0.0%

2036 7.9% 4.8% 2.4% 28.1% 0.0%

2037 7.9% 4.8% 2.4% 28.3% 0.0%

2038 7.9% 4.8% 2.4% 28.4% 0.0%

2039 7.9% 4.8% 2.4% 28.6% 0.0%

2040 7.9% 4.8% 2.4% 28.7% 0.0%

2041 7.9% 4.8% 2.4% 28.9% 0.0%

2042 7.9% 4.8% 2.4% 29.0% 0.0%

2043 7.9% 4.8% 2.4% 29.2% 0.0%

2044 7.9% 4.8% 2.4% 29.3% 0.0%

2045 7.9% 4.8% 2.4% 29.4% 0.0%

2046 7.9% 4.8% 2.4% 29.6% 0.0%

2047 7.9% 4.8% 2.4% 29.7% 0.0%

2048 7.9% 4.8% 2.4% 29.8% 0.0%

2049 7.9% 4.8% 2.4% 30.0% 0.0%

2050 7.9% 4.8% 2.4% 30.1% 0.0%

17A-2
Table 17A.2.2 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Hearth Products: Fireplace (ventless) (TSL 1)

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Voluntary 

Energy 

Efficiency 

Targets

Bulk 

Government 

Purchases

2021 10.8% 6.5% 3.2% 1.7% 0.0%

2022 10.8% 6.5% 3.2% 3.4% 0.0%

2023 10.8% 6.5% 3.2% 5.0% 0.0%

2024 10.8% 6.5% 3.2% 6.5% 0.0%

2025 10.8% 6.5% 3.2% 7.9% 0.0%

2026 10.8% 6.5% 3.2% 9.3% 0.0%

2027 10.8% 6.5% 3.2% 10.6% 0.0%

2028 10.8% 6.5% 3.2% 11.8% 0.0%

2029 10.8% 6.5% 3.2% 13.0% 0.0%

2030 10.8% 6.5% 3.2% 14.1% 0.0%

2031 10.8% 6.5% 3.2% 14.1% 0.0%

2032 10.8% 6.5% 3.2% 14.1% 0.0%

2033 10.8% 6.5% 3.2% 14.1% 0.0%

2034 10.8% 6.5% 3.2% 14.1% 0.0%

2035 10.8% 6.5% 3.2% 14.1% 0.0%

2036 10.8% 6.5% 3.2% 14.1% 0.0%

2037 10.8% 6.5% 3.2% 14.1% 0.0%

2038 10.8% 6.5% 3.2% 14.1% 0.0%

2039 10.8% 6.5% 3.2% 14.1% 0.0%

2040 10.8% 6.5% 3.2% 14.1% 0.0%

2041 10.8% 6.5% 3.2% 14.1% 0.0%

2042 10.8% 6.5% 3.2% 14.1% 0.0%

2043 10.8% 6.5% 3.2% 14.1% 0.0%

2044 10.8% 6.5% 3.2% 14.1% 0.0%

2045 10.8% 6.5% 3.2% 14.1% 0.0%

2046 10.8% 6.5% 3.2% 14.1% 0.0%

2047 10.8% 6.5% 3.2% 14.1% 0.0%

2048 10.8% 6.5% 3.2% 14.1% 0.0%

2049 10.8% 6.5% 3.2% 14.1% 0.0%

2050 10.8% 6.5% 3.2% 14.1% 0.0%

17A-3
Table 17A.2.3 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Hearth Products: Logs (vented) (TSL 1)

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Voluntary 

Energy 

Efficiency 

Targets

Bulk 

Government 

Purchases

2021 4.7% 2.8% 1.4% 4.1% 0.0%

2022 4.7% 2.8% 1.4% 7.9% 0.0%

2023 4.7% 2.8% 1.4% 11.3% 0.0%

2024 4.7% 2.8% 1.4% 14.3% 0.0%

2025 4.7% 2.8% 1.4% 17.0% 0.0%

2026 4.7% 2.8% 1.4% 19.5% 0.0%

2027 4.7% 2.8% 1.4% 21.9% 0.0%

2028 4.7% 2.8% 1.4% 24.1% 0.0%

2029 4.7% 2.8% 1.4% 26.3% 0.0%

2030 4.7% 2.8% 1.4% 28.3% 0.0%

2031 4.7% 2.8% 1.4% 28.7% 0.0%

2032 4.7% 2.8% 1.4% 29.0% 0.0%

2033 4.7% 2.8% 1.4% 29.4% 0.0%

2034 4.7% 2.8% 1.4% 29.7% 0.0%

2035 4.7% 2.8% 1.4% 30.0% 0.0%

2036 4.7% 2.8% 1.4% 30.3% 0.0%

2037 4.7% 2.8% 1.4% 30.6% 0.0%

2038 4.7% 2.8% 1.4% 30.9% 0.0%

2039 4.7% 2.8% 1.4% 31.2% 0.0%

2040 4.7% 2.8% 1.4% 31.5% 0.0%

2041 4.7% 2.8% 1.4% 31.8% 0.0%

2042 4.7% 2.8% 1.4% 32.0% 0.0%

2043 4.7% 2.8% 1.4% 32.3% 0.0%

2044 4.7% 2.8% 1.4% 32.6% 0.0%

2045 4.7% 2.8% 1.4% 32.8% 0.0%

2046 4.7% 2.8% 1.4% 33.1% 0.0%

2047 4.7% 2.8% 1.4% 33.4% 0.0%

2048 4.7% 2.8% 1.4% 33.6% 0.0%

2049 4.7% 2.8% 1.4% 33.9% 0.0%

2050 4.7% 2.8% 1.4% 34.1% 0.0%

17A-4
Table 17A.2.4 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Hearth Products: Logs (ventless) (TSL 1)

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Voluntary 

Energy 

Efficiency 

Targets

Bulk 

Government 

Purchases

2021 4.2% 2.5% 1.3% 0.8% 0.0%

2022 4.2% 2.5% 1.3% 1.8% 0.0%

2023 4.2% 2.5% 1.3% 8.2% 0.0%

2024 4.2% 2.5% 1.3% 13.4% 0.0%

2025 4.2% 2.5% 1.3% 17.5% 0.0%

2026 4.2% 2.5% 1.3% 21.0% 0.0%

2027 4.2% 2.5% 1.3% 24.1% 0.0%

2028 4.2% 2.5% 1.3% 26.8% 0.0%

2029 4.2% 2.5% 1.3% 29.4% 0.0%

2030 4.2% 2.5% 1.3% 31.7% 0.0%

2031 4.2% 2.5% 1.3% 31.7% 0.0%

2032 4.2% 2.5% 1.3% 31.7% 0.0%

2033 4.2% 2.5% 1.3% 31.7% 0.0%

2034 4.2% 2.5% 1.3% 31.7% 0.0%

2035 4.2% 2.5% 1.3% 31.7% 0.0%

2036 4.2% 2.5% 1.3% 31.7% 0.0%

2037 4.2% 2.5% 1.3% 31.7% 0.0%

2038 4.2% 2.5% 1.3% 31.7% 0.0%

2039 4.2% 2.5% 1.3% 31.7% 0.0%

2040 4.2% 2.5% 1.3% 31.7% 0.0%

2041 4.2% 2.5% 1.3% 31.7% 0.0%

2042 4.2% 2.5% 1.3% 31.7% 0.0%

2043 4.2% 2.5% 1.3% 31.7% 0.0%

2044 4.2% 2.5% 1.3% 31.7% 0.0%

2045 4.2% 2.5% 1.3% 31.7% 0.0%

2046 4.2% 2.5% 1.3% 31.7% 0.0%

2047 4.2% 2.5% 1.3% 31.7% 0.0%

2048 4.2% 2.5% 1.3% 31.7% 0.0%

2049 4.2% 2.5% 1.3% 31.7% 0.0%

2050 4.2% 2.5% 1.3% 31.7% 0.0%

17A-5
Table 17A.2.5 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Hearth Products: Outdoor (TSL 1)

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Voluntary 

Energy 

Efficiency 

Targets

Bulk 

Government 

Purchases

2021 5.5% 3.3% 1.6% 1.7% 0.0%

2022 5.5% 3.3% 1.6% 3.6% 0.0%

2023 5.5% 3.3% 1.6% 6.7% 0.0%

2024 5.5% 3.3% 1.6% 11.3% 0.0%

2025 5.5% 3.3% 1.6% 15.4% 0.0%

2026 5.5% 3.3% 1.6% 19.3% 0.0%

2027 5.5% 3.3% 1.6% 23.0% 0.0%

2028 5.5% 3.3% 1.6% 26.5% 0.0%

2029 5.5% 3.3% 1.6% 29.8% 0.0%

2030 5.5% 3.3% 1.6% 32.8% 0.0%

2031 5.5% 3.3% 1.6% 33.1% 0.0%

2032 5.5% 3.3% 1.6% 33.3% 0.0%

2033 5.5% 3.3% 1.6% 33.5% 0.0%

2034 5.5% 3.3% 1.6% 33.8% 0.0%

2035 5.5% 3.3% 1.6% 34.0% 0.0%

2036 5.5% 3.3% 1.6% 34.2% 0.0%

2037 5.5% 3.3% 1.6% 34.4% 0.0%

2038 5.5% 3.3% 1.6% 34.6% 0.0%

2039 5.5% 3.3% 1.6% 34.8% 0.0%

2040 5.5% 3.3% 1.6% 34.9% 0.0%

2041 5.5% 3.3% 1.6% 35.1% 0.0%

2042 5.5% 3.3% 1.6% 35.3% 0.0%

2043 5.5% 3.3% 1.6% 35.5% 0.0%

2044 5.5% 3.3% 1.6% 35.6% 0.0%

2045 5.5% 3.3% 1.6% 35.8% 0.0%

2046 5.5% 3.3% 1.6% 36.0% 0.0%

2047 5.5% 3.3% 1.6% 36.1% 0.0%

2048 5.5% 3.3% 1.6% 36.3% 0.0%

2049 5.5% 3.3% 1.6% 36.4% 0.0%

2050 5.5% 3.3% 1.6% 36.6% 0.0%

17A-6
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 and documented in Appendix 10A.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

17A.4.3 below), and reported market share impacts for the rebate and tax credit policies for 

hearth products. 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 17A.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 an interpolated 

penetration curve for hearth products that meet the target efficiency level. The resulting curve for 

hearth products is 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. 

a NIA = national impact analysis; RIA = regulatory impact analysis

b XENERGY is now owned by KEMA, Inc. (www.kema.com)

17A-7

 
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 

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-8
Figure 17A.4.1 S-Curves Showing Effects of External and Internal Sources on 

Adoption of New Technologies

17A.4.2 Adjustment of XENERGY 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-9
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 great 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 Chapter 17 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-10

 
17A.5 CONSUMER REBATE PROGRAMS 

DOE performed a search for rebate programs that offered incentives for hearth products.

Some organizations nationwide, comprising electric utilities and regional agencies, offer rebate 

programs for this equipment. Table 17A.5.2 provides the organizations’ names, states, rebate 

amounts, and program websites. If there is more than one entry for an organization, it offers 

different rebates in different states. When an organization offers rebates through several utilities, 

it is represented only once in each table. 

DOE relied on the data it gathered from 8 rebates programs offered by 6 organizations

(see Table 17A.5.2) to calculate a market representative rebate amount for hearth product 

ignition modules. First DOE calculated a market average rebate value for hearth products as the 

simple average of the rebate amounts offered by the programs. Then – because the programs 

target consumers shopping for a whole hearth product unit – DOE scaled down the market 

average rebate value it calculated from the available programs, to estimate a rebate amount that 

would be offered to consumers willing to purchase a hearth product unit with intermittent pilot. 

DOE estimated that amount by multiplying the market average rebate value by the ratio of the 

price of the efficient ignition module divided by the price of the (entire) hearth product. Table 

17A.5.1 presents the values DOE used for this calculation.

Table 17A.5.1 Calculation Steps of the Representative Rebate Value

2013$

Market average rebate value 140.62

Hearth product unit price 308.00

Ignition module price 81.10

Rebate amount 37.03

17A-11
Table 17A.5.2 Rebates for Hearth Products

Organization State Rebate Website

Columbia Gas of 

Kentucky KY $100 

www.columbiagasky.com/en/ways-tosave/warmwise-energy-saving-solutions/naturalgas-appliance-rebate-program

Columbia Gas of 

Kentucky KY $100 

www.columbiagasky.com/en/ways-tosave/warmwise-energy-saving-solutions/naturalgas-appliance-rebate-program

Austin Utilities MN $75 www.austinutilities.com/pages/residentialconserve-incentives/

CenterPoint 

Energy MN $75 

www.centerpointenergy.com/services/naturalgas

/buildersandtradeallies/residentialdealersanddistr

ibutors/rebateprograms/7ea971be83d63410Vgn

VCM10000026a10d0aRCRD/HO/

Owatonna Public 

Utilities MN $75 www.owatonnautilities.com/residentialcustomers/residential-rebates

Cascade Natural 

Gas WA $200 www.cngconserve.com/savings-for-your-home

Cascade Natural 

Gas WA $300 www.cngconserve.com/savings-for-your-home

Puget Sound 

Energy WA $200 https://pse.com/savingsandenergycenter/ForHomes/Pages/Fireplace-Rebate.aspx

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.

17A-12
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 

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.

17A-13
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 hearth products. 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.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 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 

17A-14
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-15
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. Hall, B. H. and B. Khan, Adoption of New Technology. Working Paper No. E03-330, 

2003. Department of Economics, University of California, Berkeley, CA. 

3. Lekvall, P. and C. Wahlbin, A Study of Some Assumptions Underlying Innovation 

Diffusion Functions. Swedish Journal of Economics, 1973

4. Geroski, P. A., Models of Technology Diffusion. Research Policy, 2000. 29: pp. 603-625 

5. Van den Bulte, C., "Want to Know How Diffusion Speed Varies Across Countries and 

Products? Try a Bass Model". Product Development & Management Association, 2002. 

XXVI(4)

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