TECHNICAL SUPPORT DOCUMENT:

ENERGY EFFICIENCY PROGRAM

FOR CONSUMER PRODUCTS AND 

COMMERCIAL AND INDUSTRIAL EQUIPMENT:

RESIDENTIAL CONVENTIONAL OVENS

MAY 2015

U.S. Department of Energy

Assistant Secretary

Office of Energy Efficiency and Renewable Energy

Building Technologies Program

Appliances and Commercial Equipment Standards

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

by staff members of

Navigant Consulting, Inc.

and

Ernest Orlando Lawrence Berkeley National Laboratory
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CHAPTER 1. INTRODUCTION

TABLE OF CONTENTS

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

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

1.3 OVERVIEW OF STANDARDS FOR RESIDENTIAL CONVENTIONAL 

COOKING PRODUCTS ................................................................................................. 1-7

1.4 PROCESS FOR SETTING ENERGY CONERVATION STANDARDS...................... 1-8

1.5 STRUCTURE OF THE DOCUMENT.......................................................................... 1-10

LIST OF TABLES

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

Conservation Standards for Residential Conventional Ovens*........................... 1-3

Table 1.2.2 Annualized Benefits and Costs of Proposed Energy Conservation 

Standards for Residential Conventional Ovens ................................................... 1-6

Table 1.4.1 Analyses Under the Process Rule*..................................................................... 1-10
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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 residential conventional cooking products. This NOPR TSD reports on the NOPR 

analyses conducted in support of the NOPR.

1.2 SUMMARY OF THE NATIONAL BENEFITS

DOE’s analyses indicate that the proposed standards would save a significant amount of 

energy. The lifetime energy savings from residential conventional oven products purchased in 

the 30-year period that begins in the assumed year of compliance with the proposed standards 

(2019–2048), relative to the base case without the proposed standards, amount to 0.71 

quadrillion Btu (quads).a This represents a savings of 11.2 percent relative to the energy use of 

these products in the base case.

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

the proposed standards for ovens in residential conventional cooking products ranges from $4.7 

billion (at a 7-percent discount rate) to $11.0 billion (at a 3-percent discount rate). This NPV 

expresses the estimated total value of future operating-cost savings minus the estimated 

increased product costs for products purchased in 2019–2048. 

In addition, the proposed standards would have significant environmental benefits. The 

energy savings described above are estimated to result in cumulative emission reductions of 41.1

million metric tons (Mt)b of carbon dioxide (CO2), 221.2 thousand tons of methane, 29.5

thousand tons of sulfur dioxide (SO2), 69 thousand tons of nitrogen oxides (NOX), 0.52 thousand 

tons of nitrous oxide (N2O), and 0.09 tons of mercury (Hg).c

 

a A quad is equal to 1015 British thermal units (Btu). The quantity refers to full-fuel-cycle (FFC) energy savings. 

FFC energy savings includes the energy consumed in extracting, processing, and transporting primary fuels (i.e., 

coal, natural gas, petroleum fuels), and thus presents a more complete picture of the impacts of energy efficiency 

standards. 

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 2014 (AEO 2014) Reference case, 

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

were available as of October 31, 2013.
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The cumulative reduction in CO2 emissions through 2030 amounts to 7.5 Mt, which is 

equivalent to the emissions resulting from the annual electricity use of 0.7 million homes.

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 section IV.L. Using 

discount rates appropriate for each set of SCC values (see Table I-4), DOE estimates the present 

monetary value of the CO2 emissions reduction is between $0.3 billion and $4.1 billion, with a 

value of $1.3 billion using the central SCC case represented by $41.2/t in 2015.

e DOE also 

estimates the present monetary value of the NOX emissions reduction, is $0.1 billion at a 7-

percent discount rate and $0.2 billion at a 3-percent discount rate.f

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

proposed standards for residential conventional ovens.

 

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, United States Government. May 2013; revised November 

2013. http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-social-cost-of-carbon-forregulator-impact-analysis.pdf.

e The values only include CO2 emissions, not CO2 equivalent emissions; other gases with global warming potential 

are not included.

f 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 Energy 

Conservation Standards for Residential Conventional Ovens*

Category

Present 

Value

Billion 2014$

Discount 

Rate

Benefits

Operating Cost Savings 5.0 7%

11.6 3%

CO2 Reduction Monetized Value ($12.2.0/t case)** 0.3 5%

CO2 Reduction Monetized Value ($41.2/t case)** 1.3 3%

CO2 Reduction Monetized Value ($63.4/t case)** 2.1 2.5%

CO2 Reduction Monetized Value ($121/t case)** 4.1 3%

NOX Reduction Monetized Value†

0.1 7%

0.2 3%

Total Benefits††

6.4 7%

13.2 3%

Costs

Incremental Installed Costs 0.3 7%

0.6 3%

Total Net Benefits

Including Emissions Reduction Monetized Value†† 6.1 7%

12.6 3%

* This table presents the costs and benefits associated with residential conventional ovens shipped in 2019−2048. 

These results include impacts to consumers which accrue after 2048 from the products purchased in 2019−2048. 

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

some of which may be incurred in preparation for the rule. 

** The CO2 values represent global monetized values of the SCC, in 2014$, 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 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 3-

percent discount rate ($40.5/t case).

The benefits and costs of these proposed standards, for products sold in 2019-2048, can 

also be expressed in terms of annualized values. The annualized monetary values are the sum of 
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(1) the annualized national economic value of the benefits from consumer operation of products 

that meet the new or amended standards (consisting primarily of operating cost savings from 

using less energy, minus increases in equipment purchase and installation costs, which is another 

way of representing consumer NPV), and (2) the annualized monetary value of the benefits of 

emission reductions, including CO2 emission reductions.g

Although DOE believes that the values of operating savings and CO2 emission reductions 

are both important, two issues are relevant. 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 time frames for 

analysis. The national operating cost savings is measured for the lifetime of residential 

conventional ovens shipped in 2019–2048. Because CO2 emissions have a very long residence 

time in the atmosphere,h the SCC values in future years reflect future climate-related impacts 

resulting from the emission of CO2 that continue well beyond 2100.

 

g To convert the time-series of costs and benefits into annualized values, DOE calculated a present value in 2014, the 

year used for discounting the NPV of total consumer costs and savings. For the benefits, DOE calculated a present 

value associated with each year’s shipments in the year in which the shipments occur (e.g., 2020 or 2030), and then 

discounted the present value from each year to 2014. The calculation uses discount rates of 3 and 7 percent for all 

costs and benefits except for the value of CO2 reductions, for which DOE used case-specific discount rates. Using

the present value, DOE then calculated the fixed annual payment over a 30-year period, starting in the compliance 

year that yields the same present value.

h The atmospheric lifetime of CO2 is estimated of the order of 30–95 years. Jacobson, MZ (2005). "Correction to 

"Control of fossil-fuel particulate black carbon and organic matter, possibly the most effective method of slowing 

global warming."" J. Geophys. Res. 110. pp. D14105.
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Estimates of annualized benefits and costs of the proposed 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 has a value of $41.2/t in 2015, the cost of the proposed 

standards is $33.5 million per year in increased equipment costs, while the benefits are $494

million per year in reduced equipment operating costs, $74 million in CO2 reductions, and $9

million in reduced NOX emissions. In this case, the net benefit amounts to $543 million per year. 

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

value of $41.2/t in 2015, the cost of the proposed standards is $33.1 million per year in increased 

equipment costs, while the benefits are $648 million per year in reduced operating costs, $74

million in CO2 reductions, and $13 million in reduced NOX emissions. In this case, the net 

benefit amounts to $701 million per year.
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Table 1.2.2 Annualized Benefits and Costs of Proposed Energy Conservation Standards 

for Residential Conventional Ovens

Discount Rate

Primary 

Estimate*

Low Net 

Benefits 

Estimate*

High Net 

Benefits 

Estimate*

million 2014$/year

Benefits

Operating Cost Savings

7% 494 457 542

3% 648 593 719

CO2 Reduction Monetized 

Value ($12.2/t case)* 5% 21 20 24

CO2 Reduction Monetized 

Value ($41.2/t case)* 3% 74 68 81

CO2 Reduction Monetized 

Value ($63.4/t case)* 2.5% 108 100 119

CO2 Reduction Monetized 

Value ($121/t case)* 3% 228 211 252

NOX Reduction Monetized 

Value† 

7% 9.24 8.66 10.11

3% 13.43 12.46 14.80

Total Benefits††

7% plus CO2

range

524 to 731 485 to 677 576 to 804

7% 577 534 634

3% plus CO2

range

682 to 889 625 to 817 758 to 986

3% 734 674 815

Costs

Consumer Incremental 

Product Costs

7% 34 34 33

3% 33 34 33

Net Benefits

Total††

7% plus CO2

range

491 to 697 451 to 642 543 to 771

7% 543 499 601

3% plus CO2

range

649 to 856 592 to 783 725 to 953

3% 701 640 783
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* This table presents the annualized costs and benefits associated with residential conventional ovens shipped in 

2019−2048. These results include benefits to consumers which accrue after 2048 from the products purchased in 

2014−2043. The results account for the incremental variable and fixed costs incurred by manufacturers due to any 

final 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 2015i

Reference case, Low Estimate, and High 

Estimate, respectively. In addition, incremental product costs reflect a medium decline rate in the Primary Estimate, 

a low decline rate in the Low Benefits Estimate, and a high decline rate f in the High Benefits Estimate. 

** The CO2 values represent global monetized values of the SCC, in 2014$, 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 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 the average SCC with 

3-percent discount rate ($41.2/t case). 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.

1.3 OVERVIEW OF STANDARDS FOR RESIDENTIAL CONVENTIONAL 

COOKING PRODUCTS 

The National Appliance Energy Conservation Act of 1987 (NAECA), Pub. L. No. 100-

12, amended EPCA to establish prescriptive standards for gas cooking products, requiring gas 

ranges and ovens with an electrical supply cord that are manufactured on or after January 1, 

1990, not to be equipped with a constant burning pilot light. NAECA also directed DOE to 

conduct two cycles of rulemakings to determine if more stringent or additional standards were 

justified for kitchen ranges and ovens. (42 U.S.C. 6295 (h)(1)-(2))

DOE undertook the first cycle of these rulemakings and published a final rule on 

September 8, 1998, which found that no standards were justified for conventional electric 

cooking products at that time. In addition, partially due to the difficulty of conclusively 

demonstrating that elimination of standing pilots for conventional gas cooking products without 

an electrical supply cord was economically justified, DOE did not include amended standards for 

conventional gas cooking products in the final rule. 63 FR 48038. For the second cycle of 

rulemakings, DOE published the April 2009 Final Rule amending the energy conservation 

standards for conventional cooking products to prohibit constant burning pilots for all gas 

cooking products (i.e., gas cooking products both with or without an electrical supply cord) 

manufactured on or after April 9, 2012. DOE decided to not adopt energy conservation standards 

pertaining to the cooking efficiency of conventional electric cooking products because it 

 

i

http://www.eia.gov/forecasts/AEO/
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determined that such standards would not be technologically feasible and economically justified 

at that time. 74 FR 16040, 16041–44.j

EPCA also requires that, not later than 6 years after the issuance of a final rule 

establishing or amending a standard, DOE publish a NOPR proposing new standards or a notice 

of determination that the existing standards do not need to be amended. (42 U.S.C. 6295(m)(1)) 

1.4 PROCESS FOR SETTING ENERGY CONERVATION STANDARDS

Under EPCA, when DOE evaluates 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 on the consumers of the 

products subject to such a standard; 

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

product in the type (or class) compared to any increases in the price of, or in the initial 

charges for, or maintenance expenses of, the covered products which are likely to result 

from the imposition of the standard; 

3) the total projected amount of energy, or as applicable, water, savings likely to result 

directly from the imposition of the standard; 

4) any lessening of the utility or the performance of the covered 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 and water 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), 

(3)–(4).

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

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

 

j

As part of the April 2009 Final Rule, DOE decided not to adopt energy conservation standards pertaining to the

cooking efficiency of microwave ovens. DOE also published a final rule on June 17, 2013 adopting energy 

conservation standards for microwave oven standby mode and off mode. 78 FR 36316. DOE is not considering 

energy conservation standards for microwave ovens as part of this rulemaking. 
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notices), DOE actively encourages the participation and interaction of all stakeholders during the 

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

during subsequent comment periods, interactions among stakeholders provide a balanced 

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

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(m)(2)(B)) Any new or 

amended standard must be designed to achieve significant additional conservation of energy 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 involves two formal public 

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

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

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

energy conservation standards; and the proposed energy conservation standards for the 

equipment. The second notice is the final rule, which presents a discussion of the comments 

received in response to the NOPR; the revised analyses; DOE’s weighting of these impacts; the 

amended energy conservation standards DOE is adopting for each product; and the effective 

dates of the amended energy conservation standards.

On February 12, 2014, DOE published a request for information (RFI) notice to initiate 

the mandatory review process imposed by EPCA (the February 2014 RFI). As part of the RFI, 

DOE sought input from the public to assist with its determination on whether new or amended 

standards pertaining to conventional cooking products are warranted. 79 FR 8337. In making this 

determination, DOE must evaluate whether new or amended standards would (1) yield a 

significant savings in energy use and (2) be both technologically feasible and economically 

justified. (42 U.S.C. 6295(o)(3)(B)) The February 2014 RFI document is available at: 

http://www.regulations.gov/#!documentDetail;D=EERE-2014-BT-STD-0005-0001.
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Table 1.4.1 Analyses Under the Process Rule*

Preliminary Analyses NOPR Final Rule

Market and technology assessment Revised preliminary analyses Revised NOPR analyses

Screening analysis Consumer sub-group analysis

Engineering analysis Manufacturer impact analysis

Energy and water use determination Emissions impacts analysis

Markups for equipment price 

determination

Monetization of emissions 

analysis

Life-cycle cost and payback period 

analysis

Utility impact analysis

Shipments analysis Employment impact analysis

National impact analysis Regulatory impact analysis

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

NOPR analysis.

DOE developed spreadsheets for the engineering, Life-Cycle Cost (LCC) Payback Period 

(PBP), and national impact analyses for each product. The LCC spreadsheet calculates the LCC 

and PBP at various energy efficiency levels. The national impact analysis spreadsheet calculates 

the national energy savings and national net present values at various energy efficiency levels. 

This spreadsheet includes a model that forecasts the impacts of amended energy conservation 

standards at various levels on product shipments. All of these spreadsheets are available on the 

DOE website for residential conventional cooking products at:

http://www1.eere.energy.gov/buildings/appliance_standards/product.aspx/productid/57. 

DOE can also provide quantitative outputs from its analyses in machine-readable format 

upon request. For example, outputs from trial runs of the LCC Monte Carlo simulations can be 

provided in such a format. DOE can also provide quantitative outputs from its analyses in 

machine-readable format upon request. For example, outputs from trial runs of the LCC Monte 

Carlo simulations can be provided in such a format.

1.5 STRUCTURE OF THE DOCUMENT

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

of the following chapters and appendices.

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

and how it applies to this rulemaking, and outlines the structure of the 

document.

Chapter 2 Analytical Framework: describes the rulemaking process.
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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 increased manufacturer cost and increased 

efficiency.

Chapter 6 Markups for Equipment Price Determination: 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 energy 

use estimates for the considered products as a function of standard 

levels.

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

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 

different subgroups of consumers.

Chapter 12 Manufacturer Impact Analysis: discusses the effects of standards on the 

finances and profitability of product manufacturers.

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

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

as well as carbon dioxide emissions.

Chapter 14 Monetization of Emission Reductions Benefits.
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Chapter 15 Utility Impact Analysis: discusses certain effects of the considered on 

electric and gas utilities.

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

national employment.

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

alternatives to efficiency standards.

Appendix 6A Detailed Data for Equipment Price Markups 

Appendix 7A Conventional Ovens: Determination of Energy Using Components

Appendix 8A User Instructions for Life-Cycle Cost and Payback Period Spreadsheet

Appendix 8B Uncertainty and Variability in LCC Analysis 

Appendix 8C Lifetime Distributions

Appendix 8D Distributions for Discount Rates

Appendix 10A User Instructions for Shipments and National Impact Analysis 

Spreadsheets

Appendix 10B Full-Fuel Cycle Multipliers

Appendix 10C National Net Present Value of Consumer Benefits Using Alternative 

Product Price Forecasts 

Appendix 10D National Energy Savings and Net Present Value Using Alternative 

Economic Growth Scenarios

Appendix 12A Manufacturer Impact Analysis Interview Guide

Appendix 12B Government Regulatory Impact Model Overview

Appendix 13A Emissions Analysis Methodology

Appendix 14A Social Cost of Carbon for Regulatory Impact Analsys Under Executive 

Order 12866
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Appendix 14B Technical Update of Social Cost of Carbon for Regulatory Impact 

Analysis Under Executive Order 12866

Appendix 15A Utility Impact Analysis Methodology

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

TABLE OF CONTENTS

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

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

2.2.1 Market Assessment .......................................................................................................... 2-4

2.2.2 Technology Assessment................................................................................................... 2-4

2.3 SCREENING ANALYSIS .............................................................................................. 2-5

2.4 ENGINEERING ANALYSIS.......................................................................................... 2-5

2.5 MARKUPS ANALYSIS ................................................................................................. 2-6

2.6 ENERGY USE ANALYSIS............................................................................................ 2-6

2.7 LIFE-CYCLE COST AND PAYBACK PERIOD ANALSYIS ..................................... 2-7

2.8 SHIPMENTS ANALYSIS .............................................................................................. 2-7

2.9 NATIONAL IMPACT ANALYSIS................................................................................ 2-8

2.9.1 National Energy Savings.................................................................................................. 2-8

2.9.2 Net Present Value of Consumer Benefit.......................................................................... 2-9

2.10 CONSUMER SUBGROUP ANALYSIS ........................................................................ 2-9

2.11 MANUFACTURER IMPACT ANALYSIS.................................................................. 2-10

2.12 EMISSIONS IMPACT ANALYSIS ............................................................................. 2-10

2.13 MONETIZING REDUCED CO2 AND OTHER EMISSIONS .................................... 2-12

2.14 UTILITY IMPACT ANALYSIS................................................................................... 2-14

2.15 EMPLOYMENT IMPACT ANALYSIS....................................................................... 2-14

2.16 REGULATORY IMPACT ANALYSIS ....................................................................... 2-14

LIST OF FIGURES

Figure 2.1 Flow Diagram of Analyses for the Rulemaking Process........................................ 2-2

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CHAPTER 2. ANALYTICAL FRAMEWORK

2.1 INTRODUCTION

Section 6295(o)(2)(A) of the Energy Policy and Conservation Act (EPCA), Pub. L. 94-

163, 42 U.S.C. 6291 et seq. requires the U.S. Department of Energy (DOE) to set forth energy 

conservation standards that achieve the maximum improvement in energy efficiency that is 

technologically feasible and economically justified. This chapter describes the general analytical 

framework that DOE uses in developing such standards, and in particular, new or amended 

energy conservation standards for residential conventional cooking products. The analytical 

framework is a description of the methodology, the analytical tools, and the 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 other knowledgeable experts within the field. 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 Flow Diagram of Analyses for the Rulemaking Processa

a Note: This rulemaking published a request for information notice in place of the framework and bypassed the 

preliminary analysis stage and went straight to the NOPR analysis stage.

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The analyses performed for this notice of proposed rulemaking (NOPR) and reported in 

this technical support document (TSD) are listed below.

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

existing technology options, including prototype designs.

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

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

affect product utility or product availability; or would have adverse effects on health and 

safety.

• An engineering analysis to develop cost-efficiency relationships, which indicate the 

manufacturer’s cost of achieving increased efficiency.

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

production cost (MPC) 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.

• A life-cycle cost (LCC) and payback period (PBP) analysis 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 customer characteristics that might 

cause a standard to disproportionately affect particular customer subpopulations.

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

manufacturers and to calculate impacts on costs, shipments, competition, employment, 

and manufacturing capacity.

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

the environment.

• An emissions monetization to assess the benefits associated with emissions reductions.

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• 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.

• 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 MARKET AND TECHNOLOGY ASSESSMENT

The market and technology assessment characterizes the relevant product markets and 

existing technology options, including prototype designs, for the considered products.

2.2.1 Market Assessment

When DOE begins an energy conservation standards rulemaking, it develops information 

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

the products, the industry structure, and market characteristics for the products. This activity 

assesses the industry and products both quantitatively and qualitatively based on publicly 

available information and encompasses the following: (1) manufacturer market share and 

characteristics, (2) existing regulatory and non-regulatory efficiency improvement initiatives, 

and (3) trends in product characteristics and retail markets. This information serves as resource 

material throughout the rulemaking. 

The subjects addressed in the market assessment for residential conventional cooking 

products included manufacturers, trade associations, and the quantities and types of products sold 

and offered for sale. DOE examined both large and small and foreign and domestic residential 

conventional cooking product manufacturers. DOE also examined publicly available data from 

the key trade association for this product category, the Association of Home Appliance 

Manufacturers (AHAM). DOE reviewed shipment data collected by AHAM and Appliance

magazine to evaluate annual shipment trends. Finally, DOE reviewed other energy efficiency 

programs from utilities, individual States, and other organizations. Chapter 3 of the NOPR TSD 

provides additional details on the market and technology assessment.

2.2.2 Technology Assessment

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

prototype designs as inputs to determine what technologies manufacturers may 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 it believes are 

technologically feasible.

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DOE developed its list of technologically feasible design options for residential 

conventional cooking products from trade publications and technical papers, and a review of the 

TSD published in support of the final rule published on April 8, 2009 (April 2009 Final Rule). 74 

FR 16040. Because some options for improving product efficiency are available in existing units, 

product literature and direct examination provided additional information.

Chapter 3 of the NOPR TSD includes the detailed list of all technology options identified 

for residential conventional cooking products.

2.3 SCREENING ANALYSIS

The screening analysis examines various technologies as to whether they: (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. DOE developed an initial list of efficiency-enhancement options from the technologies 

identified as technologically feasible in the technology assessment. Then DOE reviewed the list 

to determine if these options are practicable to manufacture, install, and service, would adversely 

affect product utility or availability, or would have adverse impacts on health and safety. In the 

engineering analysis, DOE further considered efficiency enhancement options that it did not 

screen out in the screening analysis. Chapter 4 of the NOPR TSD contains details on the 

screening analysis for residential conventional cooking products.

2.4 ENGINEERING ANALYSIS

The engineering analysis (chapter 5 of the NOPR TSD) establishes the relationship 

between the MPC and the efficiency for each class of residential conventional cooking products. 

This relationship serves as the basis for cost/benefit calculations in terms of individual 

consumers, manufacturers, and the nation. The engineering analysis discusses the product classes 

DOE analyzed, the representative baseline units, the incremental efficiency levels, the 

methodology DOE used to develop the MPCs, the cost-efficiency curves, and the impact of 

efficiency improvements on the considered products. The engineering analysis considered

technologies not eliminated in the screening analysis, designated as design options, in developing 

the cost-efficiency curves.

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 tear-downs of the product being analyzed.

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To create the cost-efficiency relationship, DOE structured its engineering analysis using a 

design-option approach, supplemented by reverse engineering (physical teardowns and testing of 

existing products in the market) to identify the incremental cost and efficiency improvement 

associated with each design option or design option combination. DOE considered the costefficiency data presented in the TSD published in support of the April 2009 Final Rule (the 2009 

TSD). DOE also conducted interviews with manufacturers of conventional cooking products to 

develop a deeper understanding of the various combinations of design options used to increase 

product efficiency, and their associated manufacturing costs. Chapter 5 of the NOPR TSD 

describes the methodology and results of the analysis used to derive the cost-efficiency 

relationships.

2.5 MARKUPS ANALYSIS

DOE performed a markups analysis to convert the manufacturer costs estimated in the 

engineering analysis to consumer prices, which then were used in the LCC and PBP and 

manufacturer impact analyses. DOE calculated markups for baseline products (baseline 

markups) and for more efficient products (incremental markups). The incremental markup relates 

the change in the MPC of higher efficiency models (the incremental cost increase) to the change 

in the retailer or distributor sales price.

To develop markups, DOE identified how the products are distributed from the 

manufacturer to the consumer. After establishing appropriate distribution channels, DOE relied

on economic data from the U.S. Census Bureau and other sources to determine how prices are 

marked up as the products pass from the manufacturer to the consumer. Chapter 6 of the NOPR

TSD provides details on DOE’s development of markups for residential conventional cooking 

products.

2.6 ENERGY USE ANALYSIS

DOE performed an energy use analysis to assess the energy savings potential from higher 

efficiency levels, providing the basis for the energy savings values used in the LCC and 

subsequent analyses. The goal of the energy use characterization is to generate a range of energy

use values that reflects actual product use in American homes. Chapter 7 of the NOPR TSD 

provides more detail about DOE’s approach for characterizing energy use of residential 

conventional cooking products.

DOE relied on California RASS and FSEC studiesb to establish the annual energy 

consumption of a cooking product. DOE determined a range of annual energy consumption of 

cooking products by utilizing the frequency of product usage data provided for each household in 

b Refer to Chapter 7 of the NOPR TSD for details about the studies.

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the representative sample of U.S. households based on RECS 2009. DOE utilized the range in

frequency of use to define the variability of the annual energy consumption.

2.7 LIFE-CYCLE COST AND PAYBACK PERIOD ANALSYIS

In determining whether an energy efficiency 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:

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

the life of the appliance or product. The LCC calculation includes total installed cost 

(equipment manufacturer selling price, distribution chain markups, sales tax, and 

installation costs), operating costs (energy, repair, and maintenance costs), equipment 

lifetime, and discount rate. Future operating costs are discounted to the time of purchase 

and summed over the lifetime of the appliance or product.

• PBP (payback period) 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. Inputs to the payback period calculation include the installed cost to the 

consumer and first-year operating costs.

DOE analyzed the net effect of potential amended dishwasher standards on consumers by 

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

the markups. 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 which allows DOE to identify the fraction of 

customers achieving LCC savings or incurring net cost at the considered efficiency levels.

Chapter 8 of the NOPR TSD describes the results from the LCC and PBP analyses.

2.8 SHIPMENTS ANALYSIS

DOE projected future shipments of residential conventional cooking products based on 

an analysis of key market drivers. Projections of shipments are needed to calculate the potential 

effects of standards on national energy use, NPV, and future manufacturer cash flows. DOE 

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generated shipments projections for each product class. The projections estimate the total 

number of conventional cooking products shipped each year during the 30-year analysis period 

(2019–2048). To create the projections, DOE combined current-year shipments with results of a 

shipments model that incorporates key market drivers for residential conventional cooking 

products. Chapter 9 of this TSD provides additional details on the shipments analysis.

2.9 NATIONAL IMPACT ANALYSIS

The national impact analysis (NIA) assesses the net present value (NPV), to the nation, of 

total consumer life-cycle cost (LCC) and net energy savings (NES). DOE determined both the 

NPV and NES for the efficiency levels considered for the product classes analyzed. To make the 

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

Excel spreadsheet model to forecast NES and the national consumer economic costs and savings 

resulting from new standards. The spreadsheet model uses as inputs typical values (as opposed to 

probability distributions). To assess the effect of input uncertainty on NES and NPV results, 

DOE may conduct sensitivity analyses by running scenarios on specific input variables. Chapter 

10 of the NOPR TSD provides additional details regarding the national impact analysis.

Several of the inputs for determining NES and NPV depend on the forecast trends in 

product energy efficiency. For the base case, DOE uses the efficiency distributions developed for 

the LCC analysis, and assumes some rate of change over the forecast period. In this analysis, 

DOE has used a roll-up scenario in developing its forecasts of efficiency trends after standards 

take effect. Under a roll-up scenario, all products that perform at levels below a prospective 

standard are moved, or rolled-up, to the minimum performance level allowed under the standard. 

Product efficiencies above the standard level under consideration would remain the same as 

before the revised standard takes effect. Because DOE has no reason to believe that 

implementation of standards would increase the demand for product that is more efficient than 

the minimum required, it did not incorporate an efficiency trend in the standards-case scenarios 

either.

2.9.1 National Energy Savings 

The inputs for determining the national energy savings for each product class are: (1) 

annual energy consumption per unit, (2) shipments, (3) product stock, (4) national energy 

consumption, and (5) site-to-primary energy and full-fuel cycle conversion factors for energy. 

DOE calculated national energy consumption by multiplying the number of units, or stock, of 

each product class (by vintage, or age) by the unit energy consumption (also by vintage). DOE 

calculated annual NES based on the difference in national energy consumption for the base case 

(without new efficiency standards) and for each efficiency standard being considered. 

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

the recommendations of a committee on “Point-of-Use and Full-Fuel-Cycle Measurement 

Approaches to Energy Efficiency Standards” appointed by the National Academy of Science, 

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DOE announced its intention to use full-fuel-cycle (FFC) measures of energy use and 

greenhouse gas and other emissions in the national impact analyses and emissions analyses 

included in future energy conservation standards rulemakings. 76 FR 51281 (August 18, 2011). 

After evaluating the approaches discussed in the August 18, 2011 notice, DOE published a 

statement of amended policy in the Federal Register in which DOE explained its determination 

that NEMS is the most appropriate tool for its FFC analysis and its intention to use NEMS for 

that purpose. 77 FR 49701 (August 17, 2012). The approach used for the NOPR is described in 

appendix 10-A of the TSD.

2.9.2 Net Present Value of Consumer Benefit

The inputs for determining NPV of the total costs and benefits experienced by consumers 

are: (1) total annual installed cost, (2) total annual savings in operating costs, and (3) a discount 

factor. DOE calculated the difference in total installed cost between the base case and each TSL. 

Because the more efficient equipment bought in a standards case usually costs more than 

equipment bought in the base case, cost increases appear as negative values in the NPV.

DOE calculated net savings each year as the difference in total savings in operating costs 

and total increases in installed costs between the base case and each standards case. DOE 

expressed savings in operating costs as decreases associated with the lower energy consumption 

of equipment bought in the standards case compared to the base case. DOE calculated savings 

throughout the life of each equipment class, accounting for differences in yearly energy rates. 

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

the present value of total installed costs. DOE used real discount rates of 3 percent and 7 percent 

to discount future costs and savings to present values.

Chapter 10 of the NOPR TSD provides additional details regarding the national impact 

analysis.

2.10 CONSUMER SUBGROUP ANALYSIS

The consumer subgroup analysis evaluates economic impacts on selected groups of 

consumers who might be adversely affected by a change in the national energy conservation 

standards for the considered products. DOE evaluates impacts on particular subgroups of 

consumers primarily by analyzing the LCC impacts and PBP for those particular consumers 

using the LCC spreadsheet model.

For this rulemaking, DOE analyzed as subgroups: (1) low-income households; and (2)

households solely occupied by senior citizens. Chapter 11 of NOPR TSD describes the consumer 

subgroup analysis.

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2.11 MANUFACTURER IMPACT ANALYSIS 

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

conservation standards on manufacturers of the considered products. Potential impacts include 

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

manufacturing practices for these products. 

DOE conducts the MIA in three phases, and will tailor the analytical framework based on 

interested parties’ comments. In Phase I, DOE created a cooking products manufacturing

industry profile and analyzed publicly available financial information to derive preliminary 

inputs for the GRIM. In Phase II, DOE prepared an industry cash flow model. In Phase III, 

industry and subgroup cash flow and NPV were assessed through the use of the Government 

Regulatory Impact Model (GRIM). Then, DOE assessed impacts on competition, manufacturing 

capacity, employment, and cumulative regulatory burden. DOE discusses its findings from the 

MIA in chapter 12 of the NOPR TSD.

2.12 EMISSIONS IMPACT ANALYSIS

In the emissions analysis, DOE estimates 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 will 

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

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

emissions. Together, these emissions account for the full-fuel-cycle (FFC). 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 conducts the emissions analysis using emissions factors for CO2 and most 

of the other gases derived from data in the latest version of EIA’s Annual Energy Outlook 

(AEO). Combustion emissions of CH4 and N2O are estimated using emissions intensity factors 

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

c

EIA prepares the Annual Energy Outlook using NEMS. Each annual version of NEMS 

incorporates the projected impacts of existing air quality regulations on emissions. The text 

below refers to AEO 2014, which generally represents current legislation and environmental 

regulations, including recent government actions, for which implementing regulations were 

available as of October 31, 2013.

Because the on-site operation of conventional cooking products requires use of fossil 

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

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

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DOE also accounts 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 also limited under the Clean Air 

Interstate Rule (CAIR), which created an allowance-based 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.d 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.e The court ordered EPA to continue 

administering CAIR. AEO 2014 assumes that CAIR remains a binding regulation through 2040.f

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 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 HCl 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 

d 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).

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

f 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.

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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, DOE believes that energy efficiency standards will 

reduce SO2 emissions in 2016 and beyond.

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 estimates 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 

will estimate 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 estimates for 

reductions in 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 NOPR TSD.

2.13 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 Social Cost of Carbon (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 greenhouse gas (GHG) emissions, including, but not limited to, net 

2-12
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. 

The Interagency Working Group on Social Cost of Carbon released an update of its 

previous report in 2013.g 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 $4,893 per ton in 2013$.

h 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 the NOPR TSD.

g 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.

www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-social-cost-of-carbon-for-regulatorimpact-analysis.pdf h 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.

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2.14 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). The utility impact analysis is based on 

output of the DOE/Energy Information Administration (EIA)’s National Energy Modeling 

System (NEMS). 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 alternative cases that implement a variety of 

efficiency-related policies.

Further detail is provided in chapter 15 of the NOPR TSD.

2.15 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.i

The ImSET model was developed for DOE’s Office of Planning, Budget, and Analysis 

to estimate the employment and income effects of energy-saving 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 the NOPR TSD.

2.16 REGULATORY IMPACT ANALYSIS

In the NOPR stage, DOE prepared a regulatory impact analysis (RIA) pursuant to 

Executive Order 12866, Regulatory Planning and Review, 58 FR 51735, October 4, 1993. The 

i

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).

2-14

 
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 the NOPR TSD.

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3-i

CHAPTER 3. MARKET AND TECHNOLOGY ASSESSMENT

TABLE OF CONTENTS

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

3.2 PRODUCT DEFINITIONS............................................................................................. 3-1

3.3 PRODUCT CLASSES..................................................................................................... 3-2

3.4 PRODUCT TEST PROCEDURES ................................................................................. 3-8

3.5 MANUFACTURER TRADE GROUPS ....................................................................... 3-10

3.5.1 Association of Home Appliance Manufacturers............................................................ 3-10

3.6 MANUFACTURER INFORMATION ......................................................................... 3-11

3.6.1 Manufacturers and Market Shares................................................................................. 3-11

3.6.2 Mergers and Acquisitions.............................................................................................. 3-14

3.6.3 Small Business Impacts ................................................................................................. 3-15

3.6.4 Distribution Channels.................................................................................................... 3-15

3.7 REGULATORY PROGRAMS ..................................................................................... 3-16

3.7.1 Federal Energy Conservation Standards........................................................................ 3-16

3.7.2 Energy Independence and Security Act of 2007............................................................ 3-17

3.7.3 Canadian Energy Conservation Standards..................................................................... 3-17

3.7.4 European Union Energy Conservation Standards.......................................................... 3-18

3.7.5 Foreign Standby Power Regulatory Programs............................................................... 3-19

3.8 HISTORICAL SHIPMENTS......................................................................................... 3-21

3.8.1 New Home Starts........................................................................................................... 3-21

3.8.2 Unit Shipments............................................................................................................... 3-22

3.8.3 Value of Shipments........................................................................................................ 3-22

3.8.4 Imports and Exports....................................................................................................... 3-23

3.9 MARKET SATURATION............................................................................................ 3-24

3.10 INDUSTRY COST STRUCTURE................................................................................ 3-25

3.11 INVENTORY LEVELS AND CAPACITY UTILIZATION RATES.......................... 3-27

3.12 TECHNOLOGY ASSESSMENT.................................................................................. 3-28

3.12.1 Product Operations and Components............................................................................. 3-28

3.12.2 Technology Options....................................................................................................... 3-30

3.12.3 Energy Efficiency .......................................................................................................... 3-37

LIST OF TABLES

Table 3.3.1 Performance Characteristics of Gas Oven Test Sample ........................................... 3-4

Table 3.3.2 Gas Oven Test Times................................................................................................ 3-6

Table 3.3.3 Gas Oven Measured Fan-Only Mode ....................................................................... 3-7

Table 3.3.4 Electric Oven Measured Fan-Only Mode................................................................. 3-7

Table 3.3.5 Proposed Product Classes for Conventional Ovens.................................................. 3-8

Table 3.6.1 Major and Other Range, Oven and Cooking Top Manufacturers.......................... 3-14

Table 3.6.2 Core Appliance Manufacturers.............................................................................. 3-14
3-ii

Table 3.7.1 Canadian Energy Conservation Standards for Electric Cooking Products............ 3-18

Table 3.7.2 European Union Energy Conservation Standards: Energy Efficiency Index 

Limits for Cavities of Ovens.............................................................................. 3-18

Table 3.8.1 New Privately Owned Single-Family and Multi-Family Housing Unit Starts 

in the United States from 1998–2013 (Thousands) ........................................... 3-21

Table 3.8.2 Industry Shipments of Cooking Products (Domestic and Import in 

Thousands of Units), , , ....................................................................................... 3-22

Table 3.8.3 Household Cooking Appliance Manufacturing Statistics by Year......................... 3-23

Table 3.8.4 Disposition of Previous Appliance (Percentage)................................................... 3-23

Table 3.8.5 2008–2009 Imports of Appliances Covered by this Rulemaking.......................... 3-24

Table 3.8.6 2008–2009 Exports of Appliances Covered by this Rulemaking.......................... 3-24

Table 3.9.1 Percentage of U.S. Households with Residential Conventional Cooking 

Products, ............................................................................................................ 3-25

Table 3.10.1 Household Cooking Appliance Industry Employment and Earnings................... 3-25

Table 3.10.2 Household Cooking Appliance Industry Census Data ......................................... 3-26

Table 3.10.3 Industry Cost Structure Using SEC Data............................................................. 3-26

Table 3.11.1 Household Cooking Appliance Industry Census Data ......................................... 3-27

Table 3.11.2 Full Production Capacity Utilization Rates.......................................................... 3-28

Table 3.12.1 Technology Options for Gas and Electric Ovens ................................................ 3-30

Table 3.12.2 Relative Percentage Increase in Cooking Efficiency due to Forced 

Convection ......................................................................................................... 3-32

LIST OF FIGURES

Figure 3.3.1 Test Load Temperature With and Without Insulation Lining the Interior 

Cavity Walls......................................................................................................... 3-5

Figure 3.6.1 2008 Market Shares for the Domestic Electric Range Market............................. 3-12

Figure 3.6.2 2008 Market Shares for the Domestic Gas Range Market................................... 3-13

Figure 3.6.3 2008 Core Appliance Market Shares.................................................................... 3-15

Figure 3.12.1 Electric Ovens in the NRCan Database – Number of Models as a Function 

of Annual Energy Consumption ........................................................................ 3-38

Figure 3.12.2 Electric Ovens in the NRCan Database – Annual Energy Consumption 

versus Cavity Volume........................................................................................ 3-39
3-1

CHAPTER 3. MARKET AND TECHNOLOGY ASSESSMENT

3.1 INTRODUCTION

This chapter provides a profile of the residential conventional cooking product, and 

specifically residential conventional oven, industries in the United States. The U.S. Department 

of Energy (DOE) developed the market and technology assessment presented in this chapter 

primarily from publicly available information. This assessment is helpful in identifying the major 

manufacturers and their product characteristics, which form the basis for the engineering and the 

life-cycle cost (LCC) analyses. Present and past industry structure and industry financial 

information help DOE in the process of conducting the manufacturer impact analysis.

3.2 PRODUCT DEFINITIONS

Title III, Part B of the Energy Policy and Conservation Act of 1975 (EPCA or the Act), 

Pub. L. 94-163 (42 U.S.C. 6291-6309, as codified) established the Energy Conservation Program 

for Consumer Products Other Than Automobiles, a program covering most major household 

appliances (collectively referred to as “covered products”), which includes kitchen ranges and 

ovens. DOE’s regulations define kitchen ranges and ovens, or “cooking products”, as consumer 

products that are used as the major household cooking appliances. They are designed to cook or 

heat different types of food by one or more of the following sources of heat: gas, electricity, or 

microwave energy. Each product may consist of a horizontal cooking top containing one or more 

surface units and/or one or more heating compartments. They must be one of the following 

classes: conventional ranges, conventional cooking tops, conventional ovens, microwave ovens, 

microwave/conventional ranges and other cooking products. (10 CFR 430.2) Each cooking 

product may consist of a horizontal cooking top containing one or more surface unitsa and/or one 

or more heating compartments. Based on these definitions, DOE has interpreted kitchen ranges 

and ovens to refer more generally to all types of cooking products including, for example, 

microwave ovens.

DOE notes that conventional ranges are defined in 10 CFR 430.2 as a class of kitchen 

ranges and ovens which is a household cooking appliance, consisting of a conventional cooking 

top and one or more conventional ovens. In this rulemaking, DOE is not considering gas and 

electric conventional ranges as a distinct product category and is not basing its product classes on 

that category. Instead, DOE plans to consider energy conservation standards for conventional 

cooking tops and conventional ovens separately. Because ranges consist of both a cooking top 

and oven, any potential cooking top or oven standards would apply to the individual components 

of the range.

 

a The term surface unit refers to burners for gas cooking tops, electric resistance heating elements for electric 

cooking tops, and inductive heating elements for induction cooking tops.
3-2

As part of this rulemaking, DOE intends only to address energy conservation standards 

for conventional ovens, including conventional ovens that are a part of conventional ranges. 

DOE has decided to defer its decision regarding whether to adopt amended energy conservation 

standards for conventional cooking tops, pending further rulemaking. In both the cooking 

product test procedure NOPR published on January 30, 2013 (78 FR 6232, the January 2013 TP 

NOPR) and the cooking product test procedure supplemental NOPR (SNOPR) published on 

December 3, 2014 (79 FR 71894, the December 2014 TP SNOPR), DOE proposed amendments 

to the cooking products test procedure in Appendix I to subpart B of Title 10 of the CFR part 430 

that would allow for the testing of active mode energy consumption of induction cooking tops. 

After reviewing public comments on the December 2014 TP SNOPR, conducting interviews 

with manufacturers, and performing additional analyses, DOE believes further study is required 

before a cooking top test procedure can be established that produces test results which measure 

energy use during a representative average use cycle, is repeatable, and reproducible, and is not 

unduly burdensome to conduct. For these reasons, this NOPR is limited to addressing energy 

conservation standards for conventional ovens. DOE intends to complete the rulemaking process 

for conventional cooking tops once additional key data and information become available.

3.3 PRODUCT CLASSES

DOE separated residential conventional cooking products into product classes. When 

evaluating and establishing energy conservation standards, DOE divides covered products into 

different product classes using the following criteria: (1) type of energy used, 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))

For conventional ovens, the product classes defined by DOE are based on energy source 

(i.e., gas or electric). DOE initially considered product classes based on the list of classes defined 

by DOE in its 2009 Final Rule Technical Support Document: Residential Dishwashers, 

Dehumidifiers, and Cooking Products and Commercial Clothes Washers (2009 TSD), which was 

released as part of the most recent standards rulemaking.b DOE also considered whether 

additional product classes were warranted based on the amended and proposed test procedure for 

conventional cooking products discussed in section 3.4.

For electric ovens, the 2009 TSD determined that the type of oven-cleaning system is a 

utility feature that affects performance. DOE found that standard ovens and ovens using a 

catalytic continuous-cleaning process use roughly the same amount of energy. On the other hand, 

Self-clean ovens use a pyrolytic process that provides enhanced consumer utility with lower 

 

b Available online at http://www.regulations.gov/#!documentDetail;D=EERE-2006-STD-0127-0097
3-3

overall energy consumption as compared to either standard or catalytically lined ovens. Thus, 

DOE defined the following product classes for electric ovens:

• Standard oven with or without a catalytic line; and

• Self-clean oven.

Based on DOE’s review of conventional ovens and ranges available on the U.S. market, 

and based on manufacturer interviews and testing conducted as part of the engineering analysis 

described in Chapter 5 of the TSD, DOE notes that the self-cleaning function of the self-clean 

oven may employ methods other than a high temperature pyrolytic cycle to perform the cleaning 

action. Specifically, DOE is aware of a type of self-cleaning oven that uses a proprietary oven 

coating and water to perform a self-clean cycle with a shorter duration and at a significantly 

lower temperature setting. The self-cleaning cycle for these ovens, unlike catalytically- lined 

standard ovens that provide continuous cleaning during normal baking, still have a separate selfcleaning mode that is user-selectable and must be tested separately. Thus, DOE is clarifying that

a self-cleaning electric or gas conventional oven is an oven that has a user-selectable mode 

separate from the normal baking mode, not intended to heat or cook food, which is dedicated to 

cleaning and removing cooking deposits from the oven cavity walls.

For gas ovens, for the same reasons as for electric ovens, DOE defined the following 

product classes:

• Standard oven with or without a catalytic line; and

• Self-clean oven.

As part of the most recent standards rulemaking for conventional cooking products, DOE 

decided to exclude residential conventional gas ovens with higher burner input rates, including 

products marketed as “commercial-style” or “professional-style,” from consideration of energy 

conservation standards due to a lack of available data for determining efficiency characteristics 

of those products. DOE considers these products to be gas ovens with burner input rates greater 

than 22,500 Btu/h. 74 FR 16040, 16054 (Apr. 8, 2009); 72 FR 64432, 64444-45 (Nov. 15, 2007). 

DOE also stated that the current DOE cooking products test procedures may not adequately 

measure performance of gas cooking tops and ovens with higher burner input rates. 72 FR 

64432, 64444-45 (Nov. 15, 2007).

For conventional gas ovens, based on DOE’s review of the residential conventional gas 

ovens available on the market, residential-style gas ovens typically have an input rate of 16,000 

to 18,000 Btu/h whereas residential gas ovens marketed as commercial-style typically have 

burner input rates ranging from 22,500 to 30,000 Btu/h.c Additional review of both the 

residential-style and commercial-style gas oven cavities indicated that there is significant overlap 

in oven cavity volume between the two oven types. Standard residential-style gas oven cavities 

 

c However, DOE noted that many gas ranges, while marketed as commercial- or professional-style and having 

multiple surface units with high input rates, did not have a gas oven with a burner input rate above 22,500 Btu/h.
3-4

ranged from 2.5 to 5.6 cubic feet (ft3) in volume and gas oven marketed as commercial-style 

have cavity volumes ranging from 3.0 to 6.0 ft3. Sixty percent of the commercial-style models 

surveyed had cavity volumes between 4.0 and 5.0 ft3 while fifty percent of the standard models 

had cavity volumes between 4.0 and 5.0 ft3. The primary differentiating factor between the two 

oven types was burner input rate, which is greater than 22,500 Btu/h for commercial-style gas 

ovens. 

As discussed in section 3.4, DOE determined as part of the concurrent test procedure 

rulemaking that the test load for ovens as specified in the existing DOE test procedure, was 

appropriate for gas ovens with burner input rates greater than 22,500 Btu/h. 79 FR 71915–16 

(Dec. 3, 2014). As a result, DOE conducted testing for this NOPR to determine whether 

conventional gas ovens with higher burner input rates warrant establishing a separate product 

class. DOE evaluated the cooking efficiency of the eight conventional gas ovens listed in Table 

3.3.1. Five of these ovens had burners rated at 18,000 Btu/h or less and the remaining three had 

burner input rates ranging from 27,000 Btu/h to 30,000 Btu/h. 

Table 3.3.1 Performance Characteristics of Gas Oven Test Sample

Test 

Unit 

# Type

Installation 

Configuration

Burner 

Input Rate 

(Btu/h)

Cavity 

Volume 

(cubic feet 

(ft3))

Measured 

Cooking 

Efficiency

Normalized 

Cooking 

Efficiency**

1 Standard Freestanding 18,000 4.8 6.6% 7.0%

2 Standard Freestanding 18,000 4.8 6.0% 6.3%

3 Self-Clean Freestanding 18,000 5.0 7.6% 8.1%

4 Standard Freestanding 16,500 4.4 6.2% 6.2%

5 Self-Clean Built-in 13,000 2.8 9.4% 8.3%

6 Standard * Freestanding 28,000 5.3 4.3% 5.1%

7 Standard * Slide-in 27,000 4.4 5.2% 5.2%

8 Standard * Freestanding 30,000 5.4 3.9% 4.7%

* These products are marketed as commercial-style gas ovens.

** Measured cooking efficiency normalized to a fixed cavity volume of 4.3ft3

.

The measured cooking efficiencies for ovens with burner input rates above 22,500 Btu/h 

were lower than for ovens with ratings below 22,500 Btu/h, even after normalizing cooking 

efficiency to a fixed cavity volume. However, DOE also noted that the conventional gas ovens 

with higher burner input rates in DOE’s test sample were marketed as commercial-style and had 

greater total thermal mass, including heavier racks and thicker cavity walls, even after 

normalizing for cavity volume. To determine whether the lower measured efficiency of these 

ovens was due to the higher input rate burners, DOE isolated the heating element from the 

thermal mass of the oven by placing 1-inch thick insulation on all surfaces inside the oven 

cavity, except for the bottom of the cavity where the burner was located, and ran tests according 

to the DOE test procedure. By adding insulation, heat transfer to the cavity walls was minimized 

and retained in the cavity to heat the test block. DOE selected test unit 3 and test unit 8 in Table 

3.3.1 for test because of the similarity in cavity volume, their difference in efficiency, and their 
3-5

differing input rate (18,000 Btu/h and 30,000 Btu/h, respectively). Figure 3.3.1 displays the 

resulting test block temperature increase as a function of test time, measured with and without 

insulation lining the interior oven cavity walls. 

Figure 3.3.1 Test Load Temperature With and Without Insulation Lining the Interior 

Cavity Walls

Without the added insulation inside the oven cavity, the temperature rise in the test block 

was similar for each oven, despite the large difference in burner input rate. In contrast, by adding 

insulation inside the cavity, the test block temperature in the 30,000 Btu/h oven increased at a 

faster rate than in the 18,000 Btu/h oven. This suggests that much of the energy input to the 

30,000 Btu/h oven goes to heating the added mass of the cavity, rather than the test load. 

DOE also investigated the time it took each oven in the test sample to heat the test load to 

a final test temperature of 234 degrees Fahrenheit (°F) above its initial temperature, as specified 

in the DOE test procedure. As shown in Table 3.3.2, gas ovens with burner input rates greater 

than 22,500 Btu/h do not heat the test load significantly faster than the ovens with lower burner 

input rates, and two out of the three units with the higher burner input rates took longer than the 

average time to heat the test load. Therefore, DOE preliminarily concludes that there is no 

unique utility associated with faster cook times that is provided by gas ovens with burner input 

rates greater than 22,500 Btu/h. 

0

50

100

150

200

250

300

350

400

450

500

0 1000 2000 3000 4000

Temperature (F°)

Time (s)

Test Unit 3 (18,000 Btu/h) Test Unit 3 (18,000 Btu/h) - Insulation

Test Unit 8 (30,000 Btu/h) Test Unit 8 (30,000 Btu/h) - Insulation
3-6

Table 3.3.2 Gas Oven Test Times

Unit Type

Burner Input 

Rate (Btu/h)

Bake Time to Reach 234°F 

Above Initial Temp 

(min)

Difference in 

Time from Avg

(min)

1 Standard 18,000 43.6 -3.8

2 Standard 18,000 43.6 -3.8

3 Self-Clean 18,000 47.2 -0.2

4 Standard 16,500 44.9 -2.5

5 Self-Clean 13,000 48.9 1.5

6 Standard * 28,000 48.9 1.5

7 Standard * 27,000 45.4 -2.0

8 Standard * 30,000 57.2 9.8

Average 47.4 -

* Test units 6, 7, and 8 are marketed as commercial-style ovens.

Based on DOE’s testing, reverse engineering analyses, and discussions with 

manufacturers, DOE determined that the major differentiation between conventional gas ovens 

with lower burner input rates and those with higher input rates, including those marketed as 

commercial-style, was design and construction related to aesthetics rather than improved cooking 

performance. Further, DOE did not identify any utility conferred by commercial-style gas ovens.

For the reasons discussed above, DOE is not proposing to establish a separate product class for 

conventional gas ovens with higher burner input rates. 

As discussed in section 3.4, DOE amended its test procedure for conventional cooking 

products in a final rule on October 31, 2012 to include methods for measuring fan-only mode. 77 

FR 65942. Fan-only mode is an active mode that is not user-selectable in which a fan circulates 

air internally or externally to the cooking product for a finite period of time after the end of the 

heating function. Table 3.3.3 and Table 3.3.4 list the fan-only mode duration and energy 

consumption measured for the gas and electric ovens in the DOE test sample described in 

chapter 5. The tables also specify the installation configuration of the oven and provide an 

estimate of the percentage of annual energy consumption due to fan-only mode operation alone.
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Table 3.3.3 Gas Oven Measured Fan-Only Mode

Unit Source Type Installation

Fan-Only 

Mode 

Duration 

(min)

Fan-Only Mode 

Energy 

Consumption 

Per Cycle (kWh)

% of Annual 

Energy 

Consumption

1 Gas Standard Freestanding 0.0 0.000 0

2 Gas Standard Freestanding 0.0 0.000 0

3 Gas Self-Clean Freestanding 0.0 0.000 0

4 Gas Standard Freestanding 0.0 0.000 0

5 Gas Self-Clean Built-in 4.5 0.001 0.1%

6 Gas Standard Freestanding 0.0 0.000 0

7 Gas Standard Slide-in 30.8 0.016 0.5%

8 Gas Standard Freestanding 0.0 0.000 0

Table 3.3.4 Electric Oven Measured Fan-Only Mode

Unit Source Type Installation

Fan-Only 

Mode 

Duration 

(min)

Fan-Only Mode 

Energy 

Consumption 

Per Cycle (kWh)

% of Annual 

Energy 

Consumption

1 Electric Self-Clean Freestanding 0 0.000 0

2 Electric Standard Freestanding 0 0.000 0

3 Electric Self-Clean Built-in 6.71 0.002 0.2%

4 Electric Standard Built-in 69 0.032 2.4%

5 Electric Self-Clean Built-in 69 0.032 2.1%

6 Electric Self-Clean Built-in 66.84 0.031 1.8%

7 Electric Self-Clean Built-in 41.32 0.030 1.6%

Based on DOE’s testing of freestanding, built-in, and slide-in installation configurations 

for conventional gas and electric ovens, DOE noted that all of the built-in and slide-in ovens 

consumed energy in fan-only mode, whereas freestanding ovens did not. The energy 

consumption in fan-only mode for built-in and slide-in ovens ranged from approximately 1.3 to 

37.6 watt-hours (Wh) per cycle (0.25 to 7.6 kWh/yr) and had fan-only mode durations ranging

from 4.5 to 69 minutes. The percentage of annual energy consumption represented by fan-only 

mode was less than 1.0 percent for gas ovens and less than 3.0 percent for electric ovens.

Based on DOE’s reverse engineering analyses discussed in chapter 5 of this TSD, DOE 

noted that built-in and slide-in products had an additional exhaust fan and vent assembly that was 

not present in freestanding products. The additional energy required to exhaust air from the oven 

cavity is necessary for slide-in and built-in installation configurations to meet safety-related 

temperature requirements since the oven is enclosed in cabinetry. For these reasons, DOE 

proposes to include separate product classes for built-in/slide-in ovens.
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The proposed product classes for this NOPR are listed in Table 3.3.5.

Table 3.3.5 Proposed Product Classes for Conventional Ovens

Product Class Product Type Sub-Category Installation Type

1

Electric oven

Standard with or without 

a catalytic line

Freestanding

2 Built-in/Slide-in

3 Self-clean Freestanding

4 Built-in/Slide-in

5

Gas oven

Standard with or without 

a catalytic line

Freestanding

6 Built-in/Slide-in

7 Self-clean Freestanding

8 Built-in/Slide-in

3.4 PRODUCT TEST PROCEDURES

DOE’s test procedures for conventional ranges, conventional cooking tops, and 

conventional ovens are codified at 10 CFR part 430, subpart B, appendix I (Appendix I). 

DOE established the test procedures in a final rule published in the Federal Register on 

May 10, 1978. 43 FR 20108, 20120–28. DOE revised its test procedures for cooking products to 

more accurately measure their efficiency and energy use, and published the revisions as a final 

rule in 1997. 62 FR 51976 (Oct. 3, 1997). These test procedure amendments included: (1) a 

reduction in the annual useful cooking energy; (2) a reduction in the number of self-clean oven 

cycles per year; and (3) incorporation of portions of International Electrotechnical Commission 

(IEC) Standard 705-1988, “Methods for measuring the performance of microwave ovens for 

household and similar purposes,” and Amendment 2-1993 for the testing of microwave ovens. 

Id. The test procedures for conventional cooking products establish provisions for determining 

estimated annual operating cost, cooking efficiency (defined as the ratio of cooking energy 

output to cooking energy input), and energy factor (EF) (defined as the ratio of annual useful 

cooking energy output to total annual energy input). 10 CFR 430.23(i); Appendix I.

Section 310 of the Energy Independence and Security Act of 2007 (EISA 2007) amended 

Section 325 of the EPCA to require that that the test procedures for cooking productsd be 

amended to include measurement of standby mode and off mode power, taking into 

consideration the most current version of IEC Standard 62301 Household electrical appliances –

Measurement of standby power (IEC Standard 62301) and IEC Standard 62087 Methods of 

 

d The term “cooking products” as used in this TSD refers to residential electric and gas kitchen ranges and ovens, 

including microwave ovens. See section 3.2 for additional information.
3-9

measurement for the power consumption of audio, video and related equipment.

e

 (42 U.S.C. 

6295(gg)) DOE conducted a rulemaking to address standby and off mode energy consumption, 

as well as certain active mode (i.e., fan-only mode) testing provisions, for residential 

conventional cooking products. DOE published a final rule on October 31, 2012 (77 FR 65942, 

the October 2012 TP Final Rule), adopting standby and off mode provisions that satisfy the 

EPCA requirement that DOE include measures of standby mode and off mode energy 

consumption in its test procedures for residential products, if technically feasible. (42 U.S.C. 

6295(gg)(2)(A)) In addition, DOE amended the test procedures to include methodology for the 

measurement of fan-only mode energy use. 77 FR 65942. The inclusion of methods to measure 

these additional modes allows for the calculation of integrated annual energy consumption 

(IAEC).

On January 30, 2013, DOE published a NOPR (78 FR 6232, the January 2013 TP NOPR) 

proposing amendments to Appendix I that would allow for testing the active mode energy 

consumption of induction cooking products; i.e., conventional cooking tops equipped with 

induction heating technology for one or more surface units on the cooking top. DOE proposed to 

incorporate induction cooking tops by amending the definition of “conventional cooking top” to 

include induction heating technology. Furthermore, DOE proposed to require for all cooking tops 

the use of test equipment compatible with induction technology. Specifically, DOE proposed to 

replace the solid aluminum test blocks currently specified in the test procedure for cooking tops 

with hybrid test blocks comprising two separate pieces: an aluminum body and a stainless steel 

base. 78 FR 6232, 6234 (Jan. 30, 2013).

On December 3, 2014, DOE published a supplemental NOPR (SNOPR) (the December

2014 TP SNOPR), in which DOE modified its proposal from the January 2013 TP NOPR to 

specify different test equipment that would allow for measuring the energy efficiency of 

induction cooking tops, and would include an additional test block size for electric surface units 

with large diameters (both induction and electric resistance). 79 FR 71894. In addition, DOE 

proposed methods to test non-circular electric surface units, electric surface units with flexible 

concentric cooking zones, and full-surface induction cooking tops. DOE further proposed

amendments to add a larger test block size to test gas cooking top burners with higher input rates. 

Id.

In February and March of 2015, DOE conducted a series of interviews with 

manufacturers regarding the proposed cooking top test procedure. Manufacturers agreed that the 

hybrid test block method, as proposed, presented many issues which had not yet been addressed, 

and which left the repeatability and reproducibility of the test procedure in question. These 

concerns were similar to those expressed in written comments but came from a larger group of 

contributing manufacturers and included: 

• Difficulty obtaining the hybrid test block materials;

 

e

 IEC Standard 62087 does not cover any products for this rulemaking, and therefore was not considered.
3-10

• Difficulty obtaining and applying the thermal grease without more detailed 

specifications (i.e., thermal conductivity alone was not sufficient to identify a 

grease that performed according to DOE’s descriptions in the SNOPR);

• Difficulty testing induction cooking tops that use different programming 

techniques to prevent overheating (some manufacturers still observed that power 

to the heating elements cut off prematurely during testing with the hybrid test 

block, despite adding thermal grease); and

• The need for larger test block sizes to test electric surface units having 12-inch 

and 13-inch diameters and gas surface units with high input rates.

For these reasons, DOE has decided to continue the energy conservation standards 

rulemaking for conventional ovens but to defer its decision regarding adoption of energy 

conservation standards for conventional cooking tops until a representative, repeatable and 

reproducible test method for cooking tops is finalized. 

In the December 2014 TP SNOPR, DOE also proposed to incorporate methods for 

measuring conventional oven volume, to clarify that the existing oven test block must be used to 

test all ovens regardless of input rate, and to measure the energy consumption and efficiency of 

conventional ovens equipped with an oven separator. Id. DOE is proposing energy conservation 

standards for conventional ovens in this NOPR based on the proposed oven related test methods

in the December 2014 TP SNOPR. DOE intends to update the standards rulemaking analyses 

based on any final amendments developed as part of the concurrent conventional oven test 

procedure rulemaking.

3.5 MANUFACTURER TRADE GROUPS

DOE recognizes the importance of trade groups in disseminating information and 

promoting the interests of the industry that they support. To gain insight into the residential 

cooking products industry, DOE researched various associations available to manufacturers, 

suppliers, and users of such equipment. DOE also used the member lists of these groups in the 

construction of an exhaustive database containing domestic manufacturers.

DOE identified one trade group that supports the residential cooking product industry, the 

Association of Home Appliance Manufacturers (AHAM).

3.5.1 Association of Home Appliance Manufacturers

AHAMf

, formed in 1967, aims to enhance the value of the home appliance industry 

through leadership, public education and advocacy. AHAM provides services to its members 

 

f For more information visit www.aham.org.
3-11

including government relations; certification programs for room air conditioners, dehumidifiers 

and room air cleaners; an active communications program; and technical services and research. 

In addition, AHAM conducts other market and consumer research studies and publishes a Fact 

Book. AHAM also develops and maintains technical standards for various appliances to provide 

uniform, repeatable procedures for measuring specific product characteristics and performance 

features.

3.6 MANUFACTURER INFORMATION

The following section details information regarding domestic manufacturers of 

residential cooking products, including estimated market shares (section 3.6.1), industry mergers 

and acquisitions (section 3.6.2), potential small business impacts (section 3.6.3), and product 

distribution channels (section 3.6.4). 

3.6.1 Manufacturers and Market Shares

Using publicly available data (e.g. Appliance Magazine and market assessments done by 

third parties), DOE estimates the market shares for domestic manufacturers of each of the 

products contained in this standards rulemaking. Manufacturers may offer multiple brand names. 

Some of the brand names come from independent appliance manufacturers which have been 

acquired over time, and domestic manufacturers may put their brand on a product manufactured 

overseas. Companies included in this analysis may also be off-shore manufacturers that maintain 

a significant domestic presence via a U.S. entity.

DOE estimates that there are approximately 20 manufacturers of residential conventional 

cooking products supplying the domestic market. As discussed in section 3.6.2, Maytag 

Corporation (Maytag) and Whirlpool Corporation (Whirlpool) merged in 2006 but have 

continued to maintain both product lines to this date. In addition, AB Electrolux (Frigidaire) 

reached an agreement in September 2014 to purchase the appliance division of GE Consumer & 

Industrial (GE). Electrolux/GE and Whirlpool/Maytag represent roughly 85 percent of the 

electric and gas range products market. Figure 3.6.1 and Figure 3.6.2 illustrate the 2008 market 

shares for the domestic residential electric and gas range markets, respectively.
3-12

Figure 3.6.1 2008 Market Shares for the Domestic Electric Range Market1
3-13

Figure 3.6.2 2008 Market Shares for the Domestic Gas Range Market2

In addition to the manufacturers presented above, manufacturers of ovens, cooking tops, 

and ranges also include BSH Home Appliances Corporation (Bosch-Siemens) (which acquired 

Thermador Corporation), Danby Products Inc. (Danby) Fagor America Inc. (Fagor), Haier 

America Trading, LLC (Haier) (which acquired Fisher & Paykel), LG Electronics, Inc. (LG), 

Miele, Inc. (Miele), Samsung Electronics America, Inc. (Samsung), Sub-Zero Freezer Company, 

Inc. (Sub-Zero) (which acquired the residential division of the Wolf Appliance Company 

(Wolf)), , and Viking Range Corporation (Viking). DOE also identified 9 small business 

manufacturers, including Acme Kitchenettes Corp. (Acme), American Range, Brown Stove 

Works, Inc. (Brown Stove), Capital Cooking Equipment, Inc. (Capital), Dacor, Inc. (Dacor), 

Evo, Inc. (Evo), Kenyon International, Inc. (Kenyon), Peerless-Premier Appliance Co. (PeerlessPremier), and Felix Storch, Inc. (Summit). Table 3.6.1 lists these manufacturers.
3-14

Table 3.6.1 Major and Other Range, Oven and Cooking Top Manufacturers

Major Manufacturers Other Manufacturers Small Manufacturers

GE/Electrolux Bosch-Siemens Acme Kitchenettes

Whirlpool/Maytag Danby American Range

Fagor Brown Stove 

Haier (Fisher & Paykel) Capital 

LG Dacor

Miele Evo

Samsung Kenyon 

Sub-Zero Peerless-Premier

Viking Summit

3.6.2 Mergers and Acquisitions

DOE described the merger between Whirlpool and Maytag, which was completed on 

March 31, 2006, in chapter 3 of the 2009 TSD. In September of 2014, Electrolux reached an 

agreement to purchase GE’s appliances business for $3.3 billion. This move will double 

Electrolux’s annual appliance sales in North America to over $10 billion.3

Due to mergers and acquisitions, the home appliance industry continues to consolidate. 

While this phenomenon varies from product to product within the industry, the large market 

shares of a few companies provide evidence in support of this characterization.

According to the September 2009 issue of Appliance Magazine, two manufacturers 

comprise 85 percent of the core appliance market share. “Core appliances” include dishwashers,

clothes dryers, freezers, ranges, refrigerators, and clothes washers. Table 3.6.2 lists these core 

appliance manufacturers, and Figure 3.6.3 illustrates the breakdown of 2008 market shares in the 

core appliance category.

Table 3.6.2 Core Appliance Manufacturers

Core Appliance Manufacturers

Whirlpool (Maytag)

GE/Electrolux (Frigidaire)
3-15

Figure 3.6.3 2008 Core Appliance Market Shares4

3.6.3 Small Business Impacts

DOE considers the possibility of small businesses being impacted by the promulgation of 

energy conservation standards for residential cooking products. At this time, DOE is aware of 

nine small cooking products manufacturers, defined by the Small Business Association (SBA) as 

having 750 employees or fewer, who produce products that fall under this rulemaking and who,

therefore, would be impacted by a minimum efficiency standard. These small business 

manufacturers are listed in Table 3.6.1. DOE evaluated the potential impacts on these small 

businesses as part of the manufacturer impact analysis (MIA), which it conducted as a part of the 

NOPR analysis. For further information on the cooking products small businesses, see chapter 12

of the TSD.

3.6.4 Distribution Channels

Understanding the distribution channels of products covered by this rulemaking is an 

important facet of the market assessment. DOE gathered information regarding the distribution 

channels for residential cooking products from publicly available sources. 
3-16

For residential appliances, including cooking products, the majority of consumers purchase 

their appliances directly from retailers. These retailers include: (1) home improvement, 

appliance, and department stores; (2) Internet retailers; (3) membership warehouse clubs; and (4) 

kitchen remodelers. The AHAM Fact Book 2005 reports that home improvement stores claim 

nearly one out of every four dollars spent on appliances.5

Home appliance retailers generally obtain products directly from manufacturers. The 

AHAM Fact Book 2003 shows that over 93 percent of residential appliances are distributed from 

the manufacturer directly to a retailer.6

3.7 REGULATORY PROGRAMS

The following section details current regulatory programs mandating energy conservation 

standards for residential conventional cooking products. Section 3.7.1 discusses Federal energy 

conservation standards, section 3.7.2 reviews standards under EISA 2007. In addition, section

3.7.3 reviews standards in Canada that may impact the companies servicing the North American 

market, section 3.7.4 reviews conventional oven regulations in the European Union, and section 

3.7.5 reviews foreign standby power regulatory programs.

3.7.1 Federal Energy Conservation Standards

The National Appliance Energy Conservation Act of 1987 (NAECA) (42 U.S.C. 6291-

6309) amended EPCA to establish prescriptive standards for gas cooking products, requiring gas 

cooking tops, ranges, and ovens with an electrical supply cord not to be equipped with constant 

burning pilots and directed DOE to conduct two cycles of rulemakings to determine if more 

stringent standards are justified. (42 U.S.C. 6295 (h)(1)-(2)) 

DOE undertook the first cycle of these rulemakings and published a final rule on 

September 8, 1998, which found that no standards were justified for conventional electric 

cooking products at that time. In addition, partially due to the difficulty of conclusively 

demonstrating that elimination of standing pilots for conventional gas cooking products without 

an electrical supply cord was economically justified, DOE did not include amended standards for 

conventional gas cooking products in the final rule. 63 FR 48038. For the second cycle of 

rulemakings, DOE published the April 2009 Final Rule amending the energy conservation 

standards for cooking products to prohibit constant burning pilots for all gas cooking products 

(i.e., gas cooking products both with or without an electrical supply cord) manufactured on or 

after April 9, 2012. As noted in the April 2009 Final Rule, DOE considered standards for 

conventional cooking tops and conventional ovens separately, and noted that any cooking top or 

oven standard would apply to the individual components of the conventional range. 74 FR 

16040, 16053. DOE decided to not adopt energy conservation standards pertaining to the 

cooking efficiency of conventional electric cooking products because it determined that such 
3-17

standards would not be technologically feasible and economically justified at that time. 74 FR 

16040, 16041–44.g

3.7.2 Energy Independence and Security Act of 2007

On December 19, 2007, the President signed into law EISA 2007, which contains 

numerous amendments to EPCA. Section 325 of EPCA is amended by section 310 of EISA 2007

to require DOE to regulate standby mode and off mode energy consumption as part of an energy 

conservation standard for all covered products, including residential ranges and ovens, for which 

a final rule is adopted after July 10, 2010. (42 U.S.C. 6295(gg)(1)(A)) Off mode is defined by 

EISA 2007 as “the condition in which an energy-using product – (I) is connected to a main 

power source; and (II) is not providing any standby or active mode function.” (42 U.S.C. 

6295(gg)(1)(A)(ii)) Active mode refers to the main (cooking) function, while standby is defined 

by EISA 2007 as “the condition in which an energy-using product (I) is connected to a main 

power source; and (II) offers 1 or more of the following user-oriented or protective functions: 

(aa) To facilitate the activation or deactivation of other functions (including active mode) by 

remote switch (including remote control), internal sensor, or timer. (bb) Continuous functions, 

including information or status displays (including clocks) or sensor-based functions.” (Id.; 42 

U.S.C. 6295(gg)(1)(A)(iii)) 

For the April 2009 Final Rule, DOE stated that it did not have any data on standby power 

consumption in conventional cooking products (i.e., electric and gas cooking tops and ovens) 

that indicate the potential for significant energy savings. For this reason, DOE did not consider 

regulating standby and off mode power for conventional cooking products as part of the April 

2009 Final Rule. 

As discussed in section 3.4, DOE published the October 2012 Final Rule to amend the 

test procedures for conventional cooking products in Appendix I to adopt standby and off mode 

provisions that satisfy the EISA 2007 amendments to EPCA. (42 U.S.C.6295(gg)(2)(A)) For this 

rulemaking, DOE is considering energy conservation standards for conventional cooking 

products that would include standby and off mode energy consumption.

3.7.3 Canadian Energy Conservation Standards

Canada’s Energy Efficiency Regulations (hereafter Regulations) mandate minimum 

energy conservation standards for certain residential conventional cooking products, including 

electric and gas ranges, cooking tops, and ovens. Like U.S. DOE standards, Canadian 

Regulations require that gas cooking products, including ranges, ovens, and cooking tops, with 

 

g As part of the April 2009 Final Rule, DOE decided not to adopt energy conservation standards pertaining to the 

cooking efficiency of microwave ovens. DOE also published a final rule on June 17, 2013 adopting energy 

conservation standards for microwave oven standby mode and off mode. 78 FR 36316. DOE is not considering 

energy conservation standards for microwave ovens as part of this rulemaking. 
3-18

an electrical supply cord not be equipped with constant burning pilots. Table 3.7.1 presents the 

Regulations for electric cooking products.

Table 3.7.1 Canadian Energy Conservation Standards for Electric Cooking Products

Cooking Product Classification Maximum Allowable Energy 

Consumption (kWh/year)*

Free-standing or built-in ranges with one or more surface elements and 

one or more ovens

2.0V + 458

Built-in or wall-mounted ranges without surface elements and with one or 

more ovens

2.0V + 200

Counter-mounted ranges without ovens and with one or more surface 

elements on a conventional (i.e., not modular) cooking top 258

* Where V = volume of oven in liters

3.7.4 European Union Energy Conservation Standards

The European Union (EU) recently enacted the Commission Regulation (EC) No. 

66/2014 of January 14, 2014, implementing Directive 2009/125/EC of the European Parliament 

and of the Council with regards to design requirements for residential conventional ovens. Annex 

I of the regulation specifies the following requirements:

Residential conventional ovens shall comply with maximum Energy Efficiency Indexh limits 

indicated in Table 3.7.2.

Table 3.7.2 European Union Energy Conservation Standards: Energy Efficiency Index 

Limits for Cavities of Ovens

Residential Gas & 

Electric Ovens

From 1 year after the entry into force EEIcavity < 146

From 2 years after the entry into force EEIcavity < 121

From 5 years after the entry into force EEIcavity < 96

From 5 years after entry into force, for multi-cavity ovens, at least one cavity shall comply with 

the maximum Energy Efficiency Index as indicated in Table 3.7.2 as applicable from 5 years 

after entry into force whereas the other cavities shall comply with the maximum Energy 

Efficiency Index as indicated in Table 1 as applicable from 2 years after entry into force.

 

h The Energy Efficiency Index compares the energy consumption of an oven with the standard energy consumption 

of an average 2012 oven with the same cavity volume
3-19

3.7.5 Foreign Standby Power Regulatory Programs

The International Energy Agency (IEA) has raised awareness of standby power through 

publications, international conferences, and policy advice to governments. In 1999, the IEA 

developed the “1-Watt Plan,” which proposed reducing standby power internationally in 

electronic devices and which advocates that all countries harmonize energy policies and adopt 

the same definition and test procedure. The IEA has advocated a 1 W requirement for all 

consumer electrical products (unless specifically excluded) in standby mode. The IEA also stated 

that IEC Standard 62301 provides an internationally sanctioned definition and test procedure for 

standby power, which is now widely specified and used.i

 

The EU recently enacted the EC No. 1275/2008 of December 17, 2008, implementing 

design requirements for standby and off mode power for electrical and electronic household and 

office equipment, including conventional cooking products. Annex II of the regulation specifies

the following maximum power requirements:

1. One year after this Regulation has come into force:

(a) Power consumption in ‘off mode’:

Power consumption of equipment in any off-mode condition shall not exceed [1.00]

W.

(b) Power consumption in ‘standby mode(s)’:

The power consumption of equipment in any condition providing only a reactivation 

function, or providing only a reactivation function and a mere indication of enabled 

reactivation function, shall not exceed [1.00] W.

The power consumption of equipment in any condition providing only information or 

status display, or providing only a combination of reactivation function and 

information or status display, shall not exceed [2.00] W.

(c) Availability of off mode and/or standby mode

Equipment shall, except where this is inappropriate for the intended use, provide off 

mode and/or standby mode and/or another condition which does not exceed the 

applicable power consumption requirements for off mode and/or standby mode when 

the equipment is connected to the mains power source.

2. Four years after this Regulation has come into force:

 

i

 For more information, visit www.iea.org/.
3-20

(a) Power consumption in ‘off mode’:

Power consumption of equipment in any off-mode condition shall not exceed [0.50] 

W.

(b) Power consumption in ‘standby mode(s)’:

The power consumption of equipment in any condition providing only a reactivation

function, or providing only a reactivation function and a mere indication of enabled 

reactivation function, shall not exceed [0.50] W.

The power consumption of equipment in any condition providing only information or 

status display, or providing only a combination of reactivation function and 

information or status display, shall not exceed [1.00] W.

(c) Availability of off mode and/or standby mode

Equipment shall, except where this is inappropriate for the intended use, provide off 

mode and/or standby mode and/or another condition which does not exceed the 

applicable power consumption requirements for off mode and/or standby mode when 

the equipment is connected to the mains power source.

(d) Power management

When equipment is not providing the main function, or when other energy-using 

product(s) are not dependent on its functions, equipment shall, unless inappropriate 

for the intended use, offer a power management function, or a similar function, that 

switches equipment after the shortest possible period of time appropriate for the 

intended use of the equipment, automatically into:

— standby mode, or 

— off mode, or 

— another condition which does not exceed the applicable power consumption 

requirements for off mode and/or standby mode when the equipment is connected 

to the mains power source. The power management function shall be activated 

before delivery.
3-21

3.8 HISTORICAL SHIPMENTS

Awareness of annual product shipment trends is an important aspect of the market 

assessment and in the development of the standards rulemaking. DOE reviewed data collected by 

the U.S. Census Bureau, EPA, and AHAM to evaluate residential appliance product shipment 

trends and the value of these shipments, which were used during the shipments analysis (chapter 

9 of this TSD.) 

3.8.1 New Home Starts

Trends in new home starts may directly affect shipments of certain home appliances. 

While there is certainly both a replacement and remodeling market for some appliances 

including cooking products, these products are also fixtures in virtually all new homes. 

Table 3.8.1 presents the number of new single-family and multi-family housing units 

started in the United States from 1998–2013. Over the period from 2000–2005, single-family 

home starts increased nearly 40 percent, to 1,716,000 units annually. However, between 2005 

and 2010, single-family home starts decreased 73 percent, to 471,000 units annually. Multifamily unit starts remained relatively stable during the period 1998–2005 at around 340,000 units 

annually. Between 2005 and 2010, multi-family units decreased 67.1 percent to 116,000 units 

annually. Over the period from 2010–2013, multi-family units have rebounded to near their pre2005 levels, while single-family units remain significantly lower.

Table 3.8.1 New Privately Owned Single-Family and Multi-Family Housing Unit Starts in 

the United States from 1998–2013 (Thousands)7

Year

Single 

Unit

MultiUnit

2013 618 307

2012 535 245

2011 431 178

2010 471 116

2009 445 109

2008 622 284

2007 1046 309

2006 1465 336

2005 1716 353

2004 1611 345

2003 1499 349

2002 1359 346

2001 1273 329

2000 1231 338

1999 1302 339

1998 1271 346
3-22

3.8.2 Unit Shipments

AHAM’s Fact Book provides annual unit shipments for conventional cooking products

from 1995 to 2005. Shipments for 2006 through 2010 were obtained from the January 2011 

Appliance Market Research Report’s “U.S. Appliance Shipment Statistics January 2011.” The 

two sources contain consistent shipment values for the overlapping years 2000 through 2005. 

Shipments for 2011 and 2012 were taken from Appliance Magazine’s “Full-Year Appliance 

Industry Shipment Statistics” reports for the respective years. Table 3.8.2 presents the annual 

shipments of conventional cooking products for the period from 1995 to 2012.

 

Table 3.8.2 Industry Shipments of Cooking Products (Domestic and Import in Thousands 

of Units)8, 9, 10, 11

Year

Cooking Products

Electric Ranges Gas Ranges

FreeStanding Built-In

Surface 

Cooking 

Units Total

FreeStanding Built-In

Surface 

Cooking 

Units Total

2012* 4,319 2,598

2011* 4,318 2,625

2010* 4,449 2,790

2009* 4,333 2,598

2008* 5,106 2,843

2007* 5,991 3,334

2006* 6,228 3,726

2005 4,685 973 542 6,201 3,139 64 560 3,762

2004 4,612 963 570 6,145 3,124 67 528 3,719

2003 4,238 841 543 5,622 2,897 67 455 3,419

2002 4,030 780 528 5,338 2,781 71 416 3,268

2001 3,842 726 498 5,066 2,580 72 384 3,036

2000 3,826 706 494 5,026 2,729 70 377 3,176

1999 3,785 705 493 4,983 2,698 72 367 3,137

1998 3,481 652 506 4,639 2,543 71 336 2,950

1997 3,177 617 446 4,240 2,391 73 280 2,744

1996 3,123 614 418 4,155 2,366 72 272 2,710

1995 2,931 598 389 3,917 2,391 84 240 2,715

* Disaggregated shipments data for electric and gas ranges was unavailable for 2006–2012.

3.8.3 Value of Shipments

Table 3.8.3 provides the value of shipments for the household cooking appliance industry 

from 2002–2011 based upon data from the U.S. Census Bureau’s Annual Survey of 
3-23

Manufacturers (ASM).

j

The ASM expresses all dollar values in nominal dollars; i.e., 2011 data 

are expressed in 2011 dollars, and 2010 data are expressed in 2010 dollars. The value of 

shipments has declined by nearly 12 percent over the 10-year period.

Table 3.8.3 Household Cooking Appliance Manufacturing Statistics by Year12

Year Value of Shipments in Nominal 

Dollars ($1,000)

2011 3,809,552

2010 3,740,373

2009 3,798,353

2008 3,884,230

2007 4,786,768

2006 4,864,268

2005 5,114,677

2004 4,798,227

2003 4,691,713

2002 4,327,308

The overall increase in shipment volumes combined with an overall decrease in the

shipment values indicates that the U.S. cooking appliance industry is very competitive.

According to data presented in the AHAM Fact Book 2003, many old appliances are still 

being used after consumers purchase new units of same product. Table 3.8.4 presents the various 

methods by which consumers dispose of their older appliances.

Table 3.8.4 Disposition of Previous Appliance (Percentage)13

Product Kept It

Left with 

Previous 

Home

Sold / Gave 

Away

Recycling 

Facility

Left at 

Curb for 

Disposal

Retailer 

Took Away

Ranges 6 37 21 13 8 15

Built-In Ranges 4 46 11 15 12 13

3.8.4 Imports and Exports

There is a large market for the import and export of home appliances. Each month 

AHAM publishes import and export data for certain home appliances. These data are released by 

the U.S. Census Bureau and aggregated by a third party. On the whole, major appliance unit 

imports decreased 9.7 percent in 2009 as compared to 2008. Major appliance unit exports 

decreased 17.5 percent over the same period.

 

j

Available online at http://www.census.gov/manufacturing/asm/.
3-24

Table 3.8.5 shows selected import data from AHAM’s Import/Export Trade Report –

December 2009.

14 For non-portable cooking products, both the number and value of units 

imported decreased significantly from 2008–2009. For and electric stoves, ranges, and ovens, the 

number of units imported increased over the 1-year period, while the value of those units 

decreased. Overall, the value of major appliance imports decreased 9.2 percent from 2008–2009.

Table 3.8.5 2008–2009 Imports of Appliances Covered by this Rulemaking15

Appliance Description Jan. – Dec. 

2009

Jan. – Dec. 

2008

% Change

Jan. – Dec. 

2009

Jan. – Dec. 

2008

Units $ Mil (Nominal) % Change

Non-portable cooking 

products 948,264 1,021,795 -7.2 244.736 277.779 -11.9

Electric stoves, ranges, 

and ovens 2,496,531 2,396,020 4.2 504.189 533.056 -5.4

Table 3.8.6 shows selected export data from AHAM’s Import/Export Trade Report –

December 2009.

16 For the 1-year period from 2008–2009, both the number and value of unit 

exports of non-portable cooking products and electric stoves, ranges, and ovens decreased. For 

the same time period, the number and value of coin-operated washing machines and microwave 

oven exports decreased. Overall, the total value of exports decreased 18.9 percent.

Table 3.8.6 2008–2009 Exports of Appliances Covered by this Rulemaking17

Appliance Description Jan. – Dec. 

2009

Jan. – Dec. 

2008

% Change

Jan. – Dec. 

2009

Jan. – Dec. 

2008

Units $ Mil (Nominal) % Change

Non-portable cooking 

products 95,237 126,022 -24.4 53.372 69.763 -23.5

Electric stoves, ranges, 

and ovens 477,448 524,362 -8.9 274.984 303.672 -9.4

3.9 MARKET SATURATION

AHAM’s Fact Book 2005 and the January 2010 Appliance Market Research Report

present the market saturation for conventional cooking products. The percentage of U.S. 

households with electric ranges and/or cooking tops and gas ranges and/or cooking tops has 

remained relatively steady since 2001. Table 3.9.1 presents the percentage of U.S. households 

with each product. 
3-25

Table 3.9.1 Percentage of U.S. Households with Residential Conventional Cooking 

Products18, 19

Year Electric Ranges / 

Cooking Tops

Gas Ranges / 

Cooking Tops

2008 61.0 40.0

2007 60.0 40.0

2006 60.0 39.0

2005 60.0 39.0

2004 61.0 39.0

2003 61.0 39.0

2002 62.0 38.0

2001 61.0 40.0

1990 62.6 38.7

1982* 58.0 42.7

1970* 40.6 57.7

*Cooking tops not included in 1970 or 1982 data

3.10 INDUSTRY COST STRUCTURE

DOE developed the household appliance industry cost structure from publicly available 

information from the ASM, (Table 3.10.1 and Table 3.10.2) and the U.S. Securities and 

Exchange Commission (SEC) 10-K reports filed by publicly owned manufacturers (summarized 

in Table 3.10.3). Table 3.10.1 presents the home cooking appliance industry employment levels 

and earnings from 2002-2011. The statistics illustrate a steady decline in the number of 

production and non-production workers in the industry. Consequently, the annual payroll for all 

employees also declines, although not as significantly as the number of employees, for this time 

period.

Table 3.10.1 Household Cooking Appliance Industry Employment and Earnings 20

Year Production Workers (‘000) All Employees (‘000) Payroll for All 

Employees ($1,000)

2011 8.8 9.9 366,199

2010 8.7 9.8 346,539

2009 8.7 10.0 352,709

2008 10.4 11.8 407,454

2007 13.1 15.5 465,854

2006 12.7 14.4 439,673

2005 14.3 16.4 518,033

2004 14.4 17.0 515,637

2003 14.5 17.3 491,283

2002 14.7 18.0 498,003
3-26

Table 3.10.2 presents the costs of materials and industry payroll as a percentage of value

of shipments from 2002–2011. The cost of materials as a percentage of value of shipments has 

fluctuated slightly over the 10-year period. Ranging from as high as 77.4 percent in 2008 to as 

low as 59.3 percent in 2011. DOE notes that fluctuations in raw material costs are common from 

year to year. The cost of payroll for production workers as a percentage of value of shipments 

has declined since 2002. Similarly, the cost of total payroll as a percentage of value of shipments 

has also declined since 2002.

Table 3.10.2 Household Cooking Appliance Industry Census Data 21

Year

Cost of Materials as a 

Percentage of Value of 

Shipments (%)

Cost of Payroll for 

Production Workers as a 

Percentage of Value of 

Shipments (%)

Cost of Total Payroll 

(Production + Admin.) as a 

Percentage of Value of 

Shipments (%)

2011 59.3 7.1 9.6

2010 62.0 6.9 9.3

2009 60.2 6.8 9.3

2008 77.4 8.1 10.5

2007 64.3 7.4 9.7

2006 66.8 7.0 9.0

2005 67.9 8.0 10.1

2004 67.3 8.1 10.7

2003 63.2 7.5 10.5

2002 64.6 8.0 11.5

Table 3.10.3 presents the industry cost structure derived from SEC 10-K reports of

publicly owned home appliance manufacturers. DOE averaged the financial data from 2002–

2007 of several companies to obtain an industry average. Each financial statement entry is 

presented as a percentage of total revenues.

Table 3.10.3 Industry Cost Structure Using SEC Data

Financial Statement Entry Percent of Revenues

Tax Rate 19.5%

Selling, general and administrative 11.2%

Capital expenditure 3.3%

Research and development 2.4%

Depreciation and amortization 3.0%

Net plant, property and equipment 16.2%

Working capital 4.5%

A detailed financial analysis of each of the products covered by this rulemaking is 

presented in the MIA. (See chapter 12 of this TSD.) This analysis identifies key financial inputs 

including cost of capital, working capital, depreciation, and capital expenditures.
3-27

3.11 INVENTORY LEVELS AND CAPACITY UTILIZATION RATES

Table 3.11.1 shows the year-end inventory for the household cooking appliance industry, 

according to the ASM. Both in dollars and as a percentage of value of shipments, the end-of-year 

inventory for the industry has declined since 2002. These data illustrate a general trend of 

domestic manufacturers retaining less of their inventories over time since 2002.

Table 3.11.1 Household Cooking Appliance Industry Census Data 22

Year End-of-Year Inventory 

($1,000)

End-of-Year Inventory as a 

Percentage of Value of 

Shipments (%)

2011 324,175 8.5%

2010 303,768 8.1%

2009 295,928 7.8%

2008 388,156 10.0%

2007 557,181 11.6%

2006 385,467 7.9%

2005 432,427 8.5%

2004 410,325 8.6%

2003 390,220 8.3%

2002 431,456 10.0%

DOE obtained full production capacity utilization rates from the U.S. Census Bureau, 

Survey of Plant Capacity from 2002–2006. Table 3.11.2 presents utilization rates for the 

household cooking appliance industry. Full production capacity is defined as the maximum level 

of production an establishment could attain under normal operating conditions. In the Survey of 

Plant Capacity report, the full production utilization rate is a ratio of the actual level of 

operations to the full production level. The full production capacity utilization rate for household 

cooking appliances show a fluxuation in utilization from 2002–2006, although in 2006, the last 

year the utilization rates are available, the utilization rate is higher than in previous years.
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Table 3.11.2 Full Production Capacity Utilization Rates23

Year

Household Cooking 

Appliance Industry 

Utilization Rates (%)

2006 65

2005 57

2004 61

2003 57

2002 56

3.12 TECHNOLOGY ASSESSMENT

This section provides a technology assessment for residential conventional ovens. 

Contained in this technology assessment are details about product characteristics and operation 

(section 3.12.1), an examination of possible technological improvements for each product 

(section 3.12.2), and a characterization of the product efficiency levels currently commercially

available (section 3.12.3).

3.12.1 Product Operations and Components

In preparation for the screening and engineering analyses, DOE prepared a brief 

description of the characteristics and operation of each product covered by this rulemaking. 

These descriptions provide a basis for understanding the technologies used to improve product 

efficiency.

Residential cooking products are appliances that enable the homeowner to heat and cook 

foods by means of transfer of input energy to the food load. Input energy may be electricity, gas, 

or a combination of the two. In conventional and ovens, the cooking vessel is placed inside a 

cavity within which the energy transfer to the food load takes place. Ranges incorporate both an 

oven and a cooking top in a single unit.

Gas ovens are appliances designed to bake, roast, or broil foods within an insulated 

cavity by means of the combustion of natural gas or propane. The major components of the oven 

include the cavity, the gas burners, an ignition system, and a control system. If the oven 

incorporates a convection cooking mode, one or more fans are situated within the cavity to 

provide a means for forced-air distribution.

The oven cavity is a formed sheet metal enclosure with provision for holding cooking 

racks at varying positions. The interior surface of the cavity may be bare metal (stainless steel), 

or it may have a porcelain coating for durability and cleanability. Additives in the porcelain 

coating can provide catalytic conversion of food spilled on the surface under normal cooking 

temperatures, thus enabling a continuous cleaning process. Alternatively, the oven may have 

features that allow it to be operated under a special self-clean mode, which heats the cavity to 
3-29

higher temperatures than those used for cooking. In the process, food spills are pyrolized, leaving 

an ash residue that is easily wiped off when the cavity cools down.

Accessories such as lights and sensors for control of cooking processes are located within 

the cavity, while an insulated glass window in the oven door allows observation of the cooking 

processes without requiring the door to be opened (which would incur substantial heat loss). The 

outside of the cavity is wrapped with insulation to minimize heat loss to ambient surroundings. 

The space between the cavity and the outer sheet metal enclosure which is filled by the insulation 

typically is made as small as practically possible in order to maximize the cavity volume.

Gas burners are situated at the bottom of the cavity for the bake function and the top for 

broiling. They are typically shielded by baffles or covers to protect the burners from spills and to 

help distribute heat evenly. Broil elements may also be of a radiant type in which the combustion 

of the fuel-air mixture heats a perforated ceramic matrix or a metal mesh. As the ceramic or 

metal heats, it emits infrared radiation that can produce heating and surface browning of the 

cooking load. Combustion products from each burner and gases released during the cooking 

process are vented from the top of the cavity.

As with gas cooking tops, gas ovens cannot have a constant burning pilot ignition system 

(10 CFR 430.32(j)(1)–(2)). Ignition may be achieved through the use of a hot surface igniter or 

an intermittently actuated spark igniter used to light the pilot when the oven controls are turned 

on. With hot surface ignition, a ceramic heating element is placed in a location where the 

incoming gas-air mixture will impinge on it. As the element is heated electrically, its resistance 

goes down and current draw goes up. A bi-metallic gas valve in electrical series with the igniter 

deforms as its corresponding current increases, allowing gas flow as long as the hot surface 

igniter is energized by the burner controller. For spark ignition, the pilot serves to heat a 

thermally-actuated switch that keeps the main gas valve open. 

Like gas ovens, electric ovens are designed to bake, roast, or broil food. The cavity is 

similar to those of gas ovens as well, in that the surface finishes may be bare or porcelainized, 

with or without the catalytic properties. In addition, electric ovens may incorporate a self-clean 

mode for pyrolysis of food matter on in the interior surfaces. Accessories and insulation tend to 

be similar between gas and electric ovens, and electric ovens also incorporate venting, although 

the demands of such venting are lower than those for gas ovens since there are no combustion 

products.

The heat source for the cooking process is typically provided by radiant elements. Bake 

elements are located at the bottom of the cavity, and may be either exposed or covered to provide 

spill protection and improve cleanability. Broil elements are situated at the top of the cavity. Far 

less common than radiant elements, halogen elements are also used to promote faster cooking.

An additional cooking feature on many electric ovens and certain gas ovens is 

convection mode, in which hot air within the cavity is circulated by means of one or more fans to 

speed the cooking process, promote surface crisping, and increase cooking uniformity. 
3-30

Supplemental heating of this recirculated air may be accomplished by means of a radiant heating 

element located near the fan.

Additional electrically-powered components in electric ovens may include cavity lights, 

electronic controls incorporating various types of displays, and cooking sensors.

 

3.12.2 Technology Options

In order to gain a deeper understanding of the technological improvements used to 

increase the efficiency of residential conventional ovens, DOE identified several possible 

technologies and examined the most common improvements used in today’s market.

DOE considered technologies identified in the following sources: (1) 2009 TSD from the 

most recent energy conservation standards rulemaking for residential conventional cooking 

products; (2) the 1996 Technical Support Document for Residential Cooking Products (1996 

TSD), which was released as part of the previous standards rulemaking.k (3) information 

provided by trade publications; and (4) design data identified in manufacturer product offerings.

For gas and electric ovens, DOE considered the technologies listed in Table 3.12.1.

Table 3.12.1 Technology Options for Gas and Electric Ovens

1. Bi-radiant oven (electric only)

2. Forced convection 

3. Halogen lamp oven (electric only) 

4. Improved and added insulation 

5. Improved door seals 

6. Low-standby-loss electronic controls 

7. No oven-door window 

8. Oven separator

9. Electronic spark ignition (gas only)

10. Reduced conduction losses

11. Optimized burner and cavity design

12. Reduced vent rate

13. Reflective surfaces

Bi-radiant oven (electric only)

A bi-radiant electric oven system was developed by Purdue University for Oak Ridge 

National Laboratory in the late 1970s.

24 The objective of the project was to develop an electric 

oven that offered significant energy savings without compromising food quality. The bi-radiant 

oven has three important features which provide improved performance: (1) the cavity walls are 

highly reflective rather than absorptive, thereby allowing these surfaces to operate at cooler 

 

k Available online at http://www.regulations.gov/#!documentDetail;D=EERE-2006-STD-0070-0053
3-31

temperatures; (2) the heating elements, similar in construction to those in conventional ovens but 

operating at much lower temperatures, provide a prescribed, balanced radiant flux to the top and 

bottom surfaces of the food product; and (3) the baking and roasting utensils have a highly 

absorptive finish. 

The bi-radiant oven was tested under a variety of cooking conditions (including the DOE 

test procedure) and also modeled (using computer thermal analysis programs) to determine its 

performance. It demonstrated a greater than 50-percent increase in efficiency over that of a 

conventional oven. In addition, the separate upper and lower heating elements required by the 

oven provided more flexibility in baking and roasting. 

As noted in the 2009 TSD, several important practical concerns have to be addressed by 

manufacturers in order to realize the demonstrated energy savings: (1) the oven lining material

must be durable enough to maintain the low-emissivity (less than 0.1) cavity surface; (2)

microprocessor controls must be used; and (3) as mentioned earlier, the baking and roasting 

utensils must have a highly absorptive exterior. However, given the assumption that all of these 

criteria are met, the previous rulemakings analyses assumed a 50-percent efficiency increase.

Forced convection

A forced convection oven uses a fan to distribute warm air evenly throughout the oven 

cavity. The use of forced circulation can reduce fuel consumption by cooking food more quickly, 

at lower temperatures, and in larger quantities than a natural convection oven of the same size 

and rating. The fan is placed within the rear cabinet wall and a protective screen is placed around 

it. The screen prevents any items being placed in the oven from “knocking” into the fan and 

causing damage. The screen may also assist in distributing the heated air evenly throughout the 

cavity. Cooking times can be reduced by using forced convection cooking.25 As a result, forced 

convection is widely used in electric ovens.

Additionally, ovens can use convection heating elements in addition to resistance and 

other types of elements to speed up the cooking process. By utilizing different cooking elements 

where they are most effective, such combination ovens can reduce the time and energy 

consumption required to cook food. 

In the previous rulemaking, DOE used estimates from manufacturers, researchers, 

published reports26,27 and interested parties28 to determine a relative cooking efficiency increase 

due to forced convection of 23 percent for gas self-clean ovens, 4.8 percent for gas standard 

ovens, and 2.4 percent for both standard and self-clean electric ovens. Additionally, DOE 

estimated that an increase in electrical energy consumption of approximately 15 watt-hours (Wh) 

would result from operation of the convection fan motor. 

As described further in chapter 5, DOE performed testing on conventional ovens in 

support of this NOPR to determine the improvement in EF associated with forced convection.

Included in the DOE test sample were four gas ovens and two electric ovens equipped with 

forced convection. DOE compared the measured energy consumption of each oven in bake mode 
3-32

to the average energy consumption of bake mode and convection mode (including energy 

consumption due to the fan motor) as specified in the test procedure. The absolute increase in EF

resulting from the use of forced convection in conventional ovens is 0.003 for gas ovens and 

0.005 for electric ovens. This translates to the relative increase in cooking efficiencies as shown 

in Table 3.12.2 below.

Table 3.12.2 Relative Percentage Increase in Cooking Efficiency due to Forced Convection

Forced Convection 

Oven Type

Relative Percentage 

Increase in Cooking 

Efficiency* (%)

Gas Standard Oven 3.5

Gas Self-Clean Oven 4.7

Electric Standard Oven 4.5

Electric Self-Clean Oven 5.8 * Measured cooking efficiency normalized to a fixed cavity volume of 3.9 ft3

.

Halogen lamp oven (electric only)

Halogen elements, similar to those used in electric cooking tops, can also be used in 

electric ovens. This oven type was first introduced in Europe, but according to U.S. 

manufacturers, its acceptance has been slow in the United States. Manufacturers stated in 

previous rulemakings that the cooking performance of the halogen lamp oven is relatively poor 

compared to that of a conventional oven, though it might be advantageous for certain broiling 

applications.

Alternatively, a conventional oven can use halogen elements in addition to resistance 

and/or convection elements to speed up the cooking process. By utilizing different cooking 

elements when they are most effective, combination ovens can reduce the time and energy 

consumption required to cook food. However, no data were found or submitted to demonstrate 

how efficiently halogen elements alone perform relative to conventional ovens. 

Improved and added insulation

The efficiency of an oven can be increased by either improving the insulation or adding 

more insulation to the cabinet walls and oven door. Most standard models have 2 inches of lowdensity (~1.09 pounds (lb)/ft3

) fiberglass insulation in the cabinet walls and door, while most 

self-clean ovens use 2 inches of high-density (~1.90 lb/ft3) insulation. Insulation is added 

primarily to pass UL surface temperature tests, which explains why self-clean ovens, which 

require high temperatures for pyrolysis, tend to have a more effective insulation package.

Since the DOE test procedure does not require maintaining heat in the oven over an 

extended period of time, manufacturers stated in the previous rulemakings that increasing the 

thickness or density of the oven’s insulation will demonstrate no energy savings. But data 

provided by several sources indicate that small energy savings can be realized under the 

conditions of the DOE test procedure.

 
3-33

The following sources were used in the 1996 TSD to establish the efficiency increase 

from using a denser insulation (1.09 to 1.90 lb/ft3): (1) manufacturers’ data provided by AHAM;

(2) the costing analysis of design options for residential appliances prepared by ADM Associates 

for LBNL;29 (3) the energy efficient electrical product knowledge base prepared by ORTECH 

International for the Canadian Electrical Association;

30 and (4) the 1980 DOE engineering 

analysis for residential appliances.

31 Averaging the data from these sources results in an 

efficiency increase of 4.9 percent for standard gas ovens and 5.2 percent for standard electric 

ovens.

As noted in the 2009 TSD, two sources of data were available which showed an increase 

in efficiency due to adding more insulation (2 to 4 inches): (1) manufacturers’ data provided by 

AHAM for the 1996 TSD and (2) the 1980 DOE engineering analysis for residential 

appliances.

32 Averaging these data points results in an efficiency increase of approximately 1.4 

percentage points. However, GRI reported no change in energy consumption by adding 

insulation.33 

Improved door seals

Door seals for standard ovens generally consist of a strip of silicone rubber, while selfclean ovens usually incorporate fiberglass seals. These seals are attached to the oven front frame 

and act as a seal for the door, which serves to reduce the loss of hot oven air through the door. 

Because some venting is required for proper cooking performance, a complete seal on the oven is 

undesirable. But the oven door seals can be improved further without sealing the oven 

completely.

As noted in the 2009 TSD, data from the energy efficient electrical product knowledge 

base prepared by ORTECH International for the Canadian Electrical Association34 were used to 

estimate the efficiency increase from improving the door seals. The data indicated that an 

approximately 7-percent increase in efficiency was possible for standard electric ovens and both 

standard and self-clean gas ovens. However, more recent data by GRI35 show efficiency 

increases much less than the 7-percent value previously reported. A value of 1 percent, therefore, 

was used for the standard and self-clean gas oven analysis. The GRI report also pointed out the 

need for sufficient air flow though the oven cavity for proper heating and moisture conditions 

while cooking.

Low-standby-loss electronic controls

Electronic controls may consume power even when the conventional oven is not 

performing its intended function. Depending on the implementation of the controller, standby 

power is required to enable the electronic controls to detect user input without the user first 

having to turn on a mechanical power switch or to enable displays, illuminate switches, etc. 

Reducing the standby power consumption of electronic controls would reduce the annual energy 

consumption of the conventional oven, but would not impact the energy consumption of the 

conventional oven during active mode operation. Since clocks are incorporated into the majority 
3-34

of both gas and electric ovens, DOE considered options for reducing the standby power 

consumption of electronic controls while maintaining the clock feature. 

A potential area for standby power improvements is the power supplies on the control 

board. Typically, conventional ovens incorporate unregulated plus regulated control board power 

supplies (also referred to as a linear power supply). The unregulated portion consists of a small 

transformer, a bridge rectifier, and an electrolytic capacitor. Voltage regulators then step down 

the voltage(s) to the level(s) required by the control logic, display, and cooking sensor. This 

approach results in a rugged power supply which is reliable, but typically has an efficiency of 

about 40 percent.

Switching power supplies offer the highest conversion efficiencies of up to 75 percent for 

switch mode power supply designs in appliance applications for power supply sizes similar to 

those of conventional ovensl

. They also offer the lowest no-load standby losses (0.2 W or less), 

although a higher part count and greater complexity may also result in lower overall reliability 

and take greater care to implement. For example, among other issues, a switching power supply 

can be prone to causing electromagnetic interference. Based on DOE’s reverse engineering 

analyses, discussed in detail in chapter 5 of this TSD, DOE observed that just less than 40 

percent of the conventional cooking products in DOE’s test sample incorporated switching 

power supplies. DOE research suggests that the component prices for switch mode power 

supplies and traditional linear power supplies are currently nearly equivalent. 

No oven-door window

Most ovens and ranges come equipped with windows in the door. Using the window, the 

contents of the oven can be viewed without opening the oven door. But oven-door windows 

allow more energy to be lost through the door and, thus, reduce the efficiency of the oven. It 

could be argued, however, that having no window in the door necessitates frequent door 

openings to check the contents of the oven. The lost energy caused by these door openings could 

offset any energy savings that would result from eliminating the door window. 

As noted in the 2009 TSD, GRI issued a topical report36 which discussed this technology 

option. GRI’s experimental tests showed a small savings in annual energy usage for both 

standard and self-clean ovens. However, they reported there could actually be a net energy loss 

due to consumer practices, which would be a function of the number of times a consumer would 

open the door to inspect the food while cooking. With four door openings per test according to 

the DOE test procedure, a standard oven would realize a net energy savings of 34 kBtu/yr. For a 

self-clean oven, however, GRI calculated a net energy loss of 3 kBtu/yr. 

Oven separator (electric only)

 

l

Information on design and efficiencies of switch mode power supplies is available from Power Integrations: 

http://ac-dc.power.com/applications/major-appliances/.
3-35

For loads that do not require the entire oven volume, an oven separator can be used to 

reduce the cavity volume that is used for cooking. With less oven volume to heat, the energy 

used to cook an item would be reduced. The oven separator considered here is the type that can 

be easily and quickly installed by the user. The side walls of the oven cavity would be fitted with 

“slots” that guide and hold the separator into position, and a switch to indicate when the 

separator has been installed. The oven would also require at least two separate heating elements 

to heat the two cavities. Different pairs of “slots” would be spaced throughout the oven cavity so 

that the user could select different positions to place the separator. 

Based on DOE’s review of products available on the market, DOE noted that at least one 

manufacturer offers a conventional electric oven that incorporates an oven separator. Based on 

DOE’s testing of this unit, DOE observed a 13 to 18 percent relative increase in cooking 

efficiency associated with an oven separator.

Electronic spark ignition (gas only)

Based on DOE’s review of products on the market and its reverse engineering analyses, 

DOE notes that gas ovens generally incorporate an electric “glo-bar” ignition system. The globar ignition system uses a ceramic “glo”-type igniter. When the thermostat is set to a specific 

temperature, line voltage is applied to the igniter. Once energized, the igniter draws typically 

slightly over three amps and heats to a high temperature. In series with the igniter is a safety 

valve that is electrically activated. Once the igniter current drops to a pre-determined amperage, 

the safety valve opens, allowing gas to flow to the oven burner. The hot glo-bar igniter then 

ignites the oven burner. Because the safety valve remains open only when the glo-bar igniter is 

drawing the correct current, the igniter must continually draw power to keep the burner ignited.37 

Based on DOE’s testing, the glo-bar ignition system consumed between 300 W and 450 W. Thus 

the electrical energy consumption of a glo-bar ignition system is significant (0.136 kWh and 

0.217 kWh per cycle, with an average of 0.184 kWh per cycle). 

DOE notes that the energy consumption could be reduced by replacing glo-bar ignition 

systems with an electronic spark ignition system. These igniters are controlled by switches that 

may be rotary-actuated so that when the burner valve is turned to the light position, a “starter” 

signal is sent to the control module. Alternatively, the signal can be generated by electronic 

controls on the user interface of the oven. Once the signal is received, the control module 

activates the spark igniters. The control module may be unsupervised, using a sensor located at 

the burner (often the igniter itself) to sense when the flame has been accidentally extinguished. 

The burner switches do not need to be re-activated, as the sensor sends a signal back to the 

control module to reactivate the igniters. Though they cost more, unsupervised ignition systems 

are preferred over systems that use supervised control modules since they prevent the need to 

check accidentally extinguished flames. DOE also identified battery-powered ignition systems as 

an alternative that does not require line power. Battery-powered ignition systems have been 

incorporated successfully in conventional ovens available on the U.S. market. Such intermittent 

spark ignition systems discussed above consume negligible amounts of electricity. Since the 

control module is powered directly from line voltage, there are no 24-volt transformer losses 
3-36

associated with it. The spark igniter is activated for an extremely short time period so that its 

cumulative on-time during the course of a year, and thus its electricity consumption, is 

negligible.

Reduced conduction losses

Conduction losses from the oven can be reduced by upgrading the oven door. This 

upgrade includes an additional thermal break and a modified inner panel. In the 1996 

rulemaking, manufacturers stated that with existing instrumentation, the DOE test procedure 

cannot measure the small energy gains that can be obtained by attempting to reduce conduction 

losses.

 

However, manufacturers’ data provided by AHAM for the 1996 TSD indicated that a 

very small efficiency increase is possible. The data indicate that only an absolute percentage 

point increase of 0.05 is expected from reducing conduction losses. No other data were obtained 

to demonstrate whether the efficiency increase should be any higher or lower.

Optimized burner and cavity design

As discussed in chapter 5 of this TSD, DOE testing and reverse engineering analyses 

revealed that gas cooking top efficiency was correlated to burner design (e.g., grate weight, 

flame angle, distance from burner ports to the cooking surface). For example, DOE’s testing 

indicated that reducing the thermal mass of the oven cavity can increase cooking efficiency. 

Energy is absorbed by the oven components as the oven warms to its operating temperature. By 

reducing the amount of material used in constructing the oven, the amount of energy that is 

absorbed is reduced and hence the efficiency increases. One method of achieving this thermal 

mass reduction is to reduce the gauge of sheet metal used in constructing the oven. Because oven 

cavity and burner design are interdependent, DOE is considering optimized burner and cavity 

design as a technology option for increasing efficiency for gas ovens consistent with products 

available on the market rather than the reduced thermal mass technology option considered for 

the previous rulemaking. Based on its testing, DOE believes that a 30.1 percent relative increase 

in cooking efficiency can be achieved through optimized burner and cavity design. 

Reduced vent rate

Oven vents function primarily to remove the moisture present during the baking process. 

Self-clean ovens have reduced vent diameters to limit the air flow in accordance with 

combustion safety regulations during the high-temperature cleaning cycle. For safety reasons for 

the combustion process, the vent rate found in self-clean ovens cannot be reduced any further. 

But the vent rate of standard ovens can be reduced to the vent rate of self-clean ovens. This can 

be accomplished by either reducing the vent-tube size or adding a baffle. A reduction in vent rate 

causes a corresponding increase in efficiency.

As noted in the 2009 TSD, Manufacturers stated as part of the previous rulemakings that 

reduced vent rates should only be considered for standard electric ovens. The vent diameters of 
3-37

standard and self-clean gas ovens are not significantly different, since both oven types need to 

maintain a satisfactory combustion environment. With regard to standard electric ovens, 

manufacturers asserted that vent sizes are unique to the design of the oven. The vent size is 

critical in maintaining the oven’s proper cooking and safety performance. According to the 

manufacturers, mandating a specific vent rate would require most oven models to be redesigned 

in order to maintain their proper performance.

But manufacturers’ data provided by AHAM for the 1996 TSD indicated that the vent 

size of both standard electric and standard gas ovens could be reduced. Since all self-clean ovens 

are already designed with this technology, no new improvements are required by the industry to 

incorporate this technology option. Averaging the manufacturers’ data with data obtained from 

the costing analysis of design options for residential appliances prepared by ADM Associates for 

LBNL38 results in an increase of approximately 0.62 absolute percentage points for standard 

electric ovens and 0.5 absolute percentage points for standard gas ovens.

Reflective surfaces

Oven efficiency can be improved by incorporating reflective surfaces onto the walls of 

the oven cavity. Reflective surfaces improve the oven’s performance by reflecting and retaining 

infrared radiation within the oven cavity, thus increasing the percentage of heat available to be 

transferred to the food load. 

 

GRI performed tests on this technology option which resulted in a decrease in energy 

efficiency.39 The reflective surface interfered with the convective currents and the thermostat, 

thus fooling the thermostat into cycling. GRI reported that increased reflectance from the 

chrome-plated inner surface of the oven caused repeated thermostat cycling that “might have 

contributed to the higher energy consumption,” which resulted in a 12.61-percent decrease in 

energy efficiency. ADL also commented that the reflected radiation is different from the normal 

radiation emitted by the oven cavities currently in use.

40

Based on these studies, it is uncertain whether, or how much, energy savings are 

realizable with this technology option. A smarter controller for the oven seems to be a reasonable 

fix for the thermostat cycling problem. However, there is a general lack of sophistication in the 

technology to maintain clean, reflective surfaces over the lifetime of the product. Manufacturers 

stated in the previous rulemaking that reflective surfaces degrade throughout the life of the oven, 

particularly for self-clean ovens.

3.12.3 Energy Efficiency

In preparation for the screening and engineering analyses, DOE gathered data on the 

energy efficiency of residential conventional ovens currently available in the marketplace. While 

this section is not intended to provide a complete characterization of the energy efficiency of all 

appliances currently available and in use, it does provide an overview of the energy efficiency of 

each product covered by this rulemaking.
3-38

Although not completely representative of the current U.S. cooking products market, 

Natural Resources Canada (NRCan) publishes a database of electric cooking appliance 

performance as measured by the applicable Canadian Standards Association (CSA) test 

procedures. The CSA test procedures for cooking appliances are equivalent to the existing DOE 

test procedures in Appendix I, except that they do not include the active mode fan-only mode, 

standby mode, and off mode testing provisions adopted in the October 2012 TP Final Rule 

discussed in section 3.4. The NRCan database covers products available in the Canadian market, 

which overlaps with the U.S. market. Data from the NRCan database are presented as the 

distribution of listed models as a function of annual energy consumption.

Figure 3.12.1 displays the annual energy consumption of electric ovens listed in the 

NRCan database. Because annual energy consumption is a function of cavity volume, DOE also 

presented the data from the NRCan database to show the distribution of annual energy 

consumption versus cavity volume in Figure 3.12.2.

Figure 3.12.1 Electric Ovens in the NRCan Database – Number of Models as a Function of 

Annual Energy Consumption 41

0 1

4

18

6 4

0 0 0 0 0 0 0

4

28

95

19

6

0 0

0

10

20

30

40

50

60

70

80

90

100

Number of Models

Annual Energy Consumption (kWh/yr)

Standard

Self-Clean
3-39

Figure 3.12.2 Electric Ovens in the NRCan Database – Annual Energy Consumption versus 

Cavity Volume42

0

50

100

150

200

250

300

350

400

450

500

0 1 2 3 4 5

Annual Energy Consumption (kWh/yr)

Cavity Volume (ft3)

NRCan Standard

Standard

Self-Clean
3-40

REFERENCES

 

1 “The Share-of-Market Picture for 2008.” 32nd Annual Portrait of the U.S. Appliance 

Industry, Appliance Magazine, September 2009.

2 Ibid.

3 “Electrolux takes on Whirlpool in U.S. with $3.3 billion GE Appliances buy,” Reuters, 

September 8, 2014, http://www.reuters.com/article/2014/09/08/us-general-electricdivestiture-idUSKBN0H30AG20140908. (Accessed October 13, 2014).

4 “The Share-of-Market Picture for 2008.” 2009. Op. Cit.

5 Association of Home Appliance Manufacturers (AHAM) Fact Book 2005.

6 AHAM Fact Book 2003.

7 U.S. Census Bureau. “New Residential Construction.” Available online at 

www.census.gov/construction/nrc/ (Accessed July 15, 2014). 

8 AHAM Fact Book 2005.

9 “U.S. Appliance Shipment Statistics Monthly: January 2011”. Appliance Magazine 

Market Research Report, January 2011.

10 “2011 Full-Year Appliance Industry Shipment Statistics”. Appliance Magazine Market 

Research Report, March 2012.

11 “2012 Full-Year Appliance Industry Shipment Statistics & Year-In-Review”. Appliance 

Magazine Market Research Report, March 2013.

12 U.S. Census Bureau. 2011. Statistics

13 AHAM Fact Book 2003.

14 AHAM. Import/Export Trade Report – December 2009. 2010.

15 Ibid.

16 Ibid.

17 Ibid.

18 AHAM Fact Book. 2005.
3-41

 

19 “U.S. Appliance Industry: Market Share, Life Expectancy & Replacement Market, and 

Saturation Levels”. Appliance Magazine Market Research Report, January 2010.

20 U.S. Census Bureau. 2006. “Statistics for Industry Groups and Industries: 2006 and 

Earlier Years.” 2006 Annual Survey of Manufacturers (ASM), December 2006.

21 Ibid.

22 Ibid.

23 U.S. Census Bureau, Survey of Plant Capacity: 2006 to 1994.

24 D. DeWitt and M. Peart. 1982. Bi-Radiant Oven: A Low-Energy Oven System. Purdue 

University, West Lafayette, IN. Prepared for Oak Ridge National Laboratory, Oak 

Ridge, TN, ORNL/Sub-80/0082/1, ORNL/Sub-80/0082/2, ORNL/Sub-80/0082/3, March 

1982.

25 Whirlpool Corporation Product Literature. Available online at 

http://www.whirlpool.com/kitchen-1/cooking-2/wall-ovens-3/-[WOD93EC0AS]-

1020949/WOD93EC0AS/ . (Accessed January 13, 2015.)

26 ORTECH. 1989. Op. cit.

27 Gas Research Institute (GRI). 1994. Topical Report: Technical Input to NAECA 

Rulemaking for Gas-Fired Ranges. Prepared by Battelle, Columbus, OH, American Gas 

Association Laboratories, Cleveland, OH, and ADL, Cambridge, MA for Gas Appliance 

Technology Center, Chicago, IL. Comment No. 001 to the NOPR, GRI-94/0195, July 

1994.

28 ADL. 1994. Op. cit.

29 ADM. 1987. Op. cit.

30 ORTECH. 1989. Op. cit.

31 U.S. DOE. 1980. Op. cit.

32 Ibid.

33 GRI. 1994. Op. cit.

34 ORTECH. 1989. Op. cit.
3-42

 

35 GRI. 1994. Op. cit.

36 Ibid.

37 P. Seibold. 1995. Personal Communications. Norton Company. Worcester, MA, March 

1995.

38 ADM. 1987. Op. cit.

39 GRI. 1994. Op. cit.

40 ADL. 1994. Op. cit.

41 Ibid.

42 Ibid.
CHAPTER 4. SCREENING ANALYSIS

TABLE OF CONTENTS

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

4.2 DISCUSSION OF TECHNOLOGY OPTIONS.............................................................. 4-1

4.2.1 Screened-Out Technology Options.................................................................................. 4-1

4.2.1.1 Electric and Gas Ovens.................................................................................. 4-2

4.2.2 Remaining Design Options.............................................................................................. 4-4

LIST OF TABLES

Table 4.2.1 Retained Design Options for Electric and Gas Ovens......................................... 4-4

4-i
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 

residential conventional ovensa (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 reduce energy consumption of the products covered in this rulemaking. The goal of 

the screening analysis is to identify any technologies that will be eliminated from further 

consideration in the rulemaking analyses. 

For both electric and gas ovens, the corresponding candidate technology options are 

assessed based on DOE analysis as well as inputs from interested parties, including 

manufacturers, trade organizations, and energy efficiency advocates. Technology options that

are judged to be viable approaches for improving energy efficiency are retained as inputs to the 

subsequent engineering analysis, and are designated as design options. Technology options that

are not incorporated in commercial products or in working prototypes, or that fail to meet certain 

criteria as to practicability to manufacture, install and service, as to impacts on product utility or 

availability, or as to health or safety will be eliminated from consideration in accordance with 

Energy Conservation Program for Consumer Products: Procedures for Consideration of New 

or Revised Energy Conservation Standards for Consumer Products. 61 FR at 36974 (July 15, 

1996). The rationale for either screening out or retaining each technology option is detailed in 

the following sections.

4.2 DISCUSSION OF TECHNOLOGY OPTIONS

For residential conventional ovens, the screening criteria specified in section 4.1 were 

applied to the technology options to either retain or eliminate each technology from the 

engineering analysis.

4.2.1 Screened-Out Technology Options

The technologies identified in the market and technology assessment were evaluated 

pursuant to the criteria set out in The Energy Policy and Conservation Act, as amended (EPCA 

or the Act). (42 U.S.C. 6291–6309) EPCA provides criteria for prescribing new or amended 

standards, which will achieve the maximum improvement in energy efficiency the Secretary of 

Energy determines is technologically feasible. (42 U.S.C. 6295(o)(2)(A)) It also establishes 

a The term “conventional ovens” refers to residential electric and gas ovens or the oven component of a range, but 

not microwave ovens.

4-1

 
guidelines for determining whether a standard is economically justified. (42 U.S.C. 

6295(o)(2)(B)) In view of the EPCA requirements for determining whether a standard is 

technologically feasible and economically justified, appendix A to subpart C of Title 10 Code of 

Federal Regulations part 430 (10 CFR part 430), Procedures, Interpretations and Policies for 

Consideration of New or Revised Energy Conservation Standards for Consumer Products (the 

“Process Rule”), sets forth procedures to guide DOE in the consideration and promulgation of 

new or revised product efficiency standards under EPCA. These procedures elaborate on the 

statutory criteria provided in 42 U.S.C. 6295 and in part eliminate problematic technologies early 

in the process of revising an energy efficiency standard. Under the guidelines, DOE eliminates 

from consideration technologies that present unacceptable problems with respect to the following 

four factors: 

(1) Technological feasibility. If it is determined that a technology has not been 

incorporated in commercial products or in working prototypes, then that technology will not be 

considered further.

(2) Practicability to manufacture, install, and service. If it is determined that mass 

production of a technology in commercial products and reliable installation and servicing of the 

technology could not be achieved on the scale necessary to serve the relevant market at the time 

of the effective date of the standard, then that technology will not be considered further.

(3) Impacts on product utility to consumers. If a technology is determined to have 

significant adverse impact on the utility of the product to significant subgroups of consumers, 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 be considered further. 

(4) Safety of technologies. If it is determined that a technology will have significant 

adverse impacts on health or safety, it will not be considered further.

The following sections detail the technology options that were screened out for each 

product class covered by this rulemaking and the reasons why each were eliminated.

4.2.1.1 Electric and Gas Ovens

For electric and gas ovens, DOE screened out added insulation, bi-radiant oven, halogen 

lamp oven, no oven door window, and reflective surfaces, for the reasons that follow.

Added insulation

Although some analyses indicated energy consumption could be reduced by increasing 

the thickness of the insulation in the cabinet walls and doors from 2 inches to 4 inches, consumer 

utility would be negatively impacted, since the oven cavity volume would have to be reduced to 

maintain standardized exterior dimensions. The reduced oven cavity volume would limit the size 

of large items that could be cooked in the oven. For this reason, this technology option was not 

4-2
analyzed. However, it should be noted that improved insulation, consisting of higher-density 

insulation with the baseline 2-inch thickness, was still analyzed for standard gas and electric 

ovens. This higher-density insulation is already used for self-clean gas and electric ovens.

Bi-radiant oven (electric only)

The 1996 TSD assumed that three major conditions would have to be met in order to 

consider the bi-radiant oven as a viable technology option. These included the use of (1) lowemissivity cavity lining materials; (2) electronic controls; and (3) highly-absorptive baking and 

roasting utensils. While electronic controls are currently in widespread use in electric ovens, 

cavity maintenance issues and the requirement for specialized cookware negatively impact 

consumer utility. In addition, there is currently no such product on the market and the last 

working prototype known to DOE was tested in the 1970s. 

Halogen lamp oven (electric only)

DOE is not aware of any ovens that utilize halogen lamps alone as the heating element, 

and no data were found or submitted to demonstrate how efficiently halogen elements alone 

perform relative to conventional ovens. DOE believes that it would not be practicable to 

manufacture, install and service halogen lamps for use in consumer ovens on the scale necessary 

to serve the relevant market at the time of the standard’s effective date. 

No oven door window

GRI issued a topical report1 that discussed this technology option in the previous

rulemaking. GRI’s experimental tests showed a small savings in annual energy usage (increase in 

efficiency) for both the standard and self-clean ovens by eliminating the door window. However, 

GRI reported there could actually be a net energy loss due to consumer practices, which would 

be a function of the number of times a consumer would open the door to inspect the food while 

cooking. With four door openings per test, a standard oven would realize a net energy savings of 

34 thousand British thermal units per year (kBtu/yr). For a self-clean oven there is a net energy 

loss of 3 kBtu/yr. The report also stated there would be reduced consumer utility and the 

possibility of failure of delicate food items (e.g., soufflés), as well as decreased safety without 

the window due to increased risk of burns from additional door openings while the oven is in 

use.

Reflective surfaces

As noted in the 1996 TSD, manufacturers stated that it has been very difficult to obtain 

satisfactory cooking performance with reflective surfaces. The reflective materials degrade after 

the first baking function and continue to degrade through the life of the product. This is 

especially true of self-clean ovens, as the self-clean process damages the reflective walls and 

negates any possible energy savings.2

4-3
GRI3 performed tests on this technology option that measured a decrease in energy 

efficiency. The reflective surface interfered with the convective currents and the thermostat, thus 

fooling the thermostat into cycling. GRI reported that increased reflectance from the chromeplated inner surface of the oven caused repeated thermostat cycling that “might have contributed 

to the higher energy consumption” which resulted in a 12.61 percent decrease in energy 

efficiency. Arthur D. Little Inc. (ADL)4 also commented that the reflected radiation was different 

from the normal radiation emitted by the oven cavities in use at the time.

Based on these studies, it is uncertain whether, or how much, energy savings is realizable 

with this technology option. A smarter controller for the oven could potentially compensate for 

the thermostat problems. However, there is a general lack of sophistication in the technology in 

terms of maintaining clean, reflective surfaces over the lifetime of the product. For these reasons, 

this technology option was not analyzed.

4.2.2 Remaining Design Options

The following sections list the technology options for both electric and gas conventional 

ovens that were retained by DOE and subsequently designated as design options. Each of these 

technologies were evaluated further in the subsequent engineering analysis.

Table 4.2.1 Retained Design Options for Electric and Gas Ovens

1. Electronic spark ignition (gas only)

2. Forced convection 

3. Improved insulation 

4. Improved door seals (standard ovens only)

5. Oven separator (electric only)

6. Reduced conduction losses

7. Reduced vent rate

8. Low-standby-loss electronic controls

9. Optimized burner and cavity design (gas only)

4-4
REFERENCES

1 Ibid.

2 Ibid.

3 GRI. 1994. Op. cit.

4 Arthur D. Little Inc. (ADL). 1994. Electric Oven and Cooktop Data Analysis. Prepared 

by ADL, Cambridge, MA, for Association of Home Appliance Manufacturers, Reference 

47066, submitted as comment No. 001 to the NOPR, July 15, 1994.

4-5

 
5-i

CHAPTER 5. ENGINEERING ANALYSIS

TABLE OF CONTENTS

5.1 INTRODUCTION ........................................................................................................... 5-1

5.2 PRODUCT CLASSES ANALYZED.............................................................................. 5-2

5.3 EFFICIENCY LEVELS................................................................................................... 5-3

5.3.1 Baseline Efficiency Levels .............................................................................................. 5-3

5.3.2 Incremental Efficiency Levels......................................................................................... 5-5

5.4 METHODOLOGY OVERVIEW.................................................................................... 5-9

5.4.1 Review of Previous Technical Support Documents and Models..................................... 5-9

5.4.2 Manufacturer Interviews.................................................................................................. 5-9

5.4.3 Product Testing .............................................................................................................. 5-10

5.4.4 Product Teardowns ........................................................................................................ 5-10

5.4.4.1 Selection of Units......................................................................................... 5-10

5.4.4.2 Generation of Bill of Materials.................................................................... 5-11

5.4.4.3 Cost Structure of the Spreadsheet Models................................................... 5-11

5.4.4.4 Cost Model and Definitions......................................................................... 5-13

5.4.4.5 Cost Model Assumptions............................................................................. 5-14

5.5 ANALYSIS AND RESULTS........................................................................................ 5-14

5.5.1 Manufacturer Interviews................................................................................................ 5-14

5.5.1.1 Product Classes............................................................................................ 5-14

5.5.1.2 Design Features of Current Baseline Products ............................................ 5-15

5.5.1.3 Proposed Incremental Efficiency Levels and Design Options .................... 5-15

5.5.1.4 Oven Energy Consumption as a Function of Cavity Volume...................... 5-15

5.5.1.5 Impact on Consumer Utility......................................................................... 5-15

5.5.2 Product Testing .............................................................................................................. 5-15

5.5.2.1 Product Selection ......................................................................................... 5-17

5.5.2.2 Test Results and Derivation of Incremental Efficiency Levels................... 5-18

5.5.3 Product Teardowns ........................................................................................................ 5-23

5.5.3.1 Baseline Construction .................................................................................. 5-23

5.5.4 Conventional Oven Energy Use versus Volume ........................................................... 5-25

5.5.5 Cost Estimates................................................................................................................ 5-29

5.5.5.1 Baseline Cost Estimates............................................................................... 5-29

5.5.5.2 Incremental Cost Estimates.......................................................................... 5-29

LIST OF TABLES

Table 5.2.1 Proposed Product Classes for Conventional Cooking Products............................... 5-3

Table 5.3.1 February 2014 RFI Conventional Oven Baseline Efficiency Levels ....................... 5-4

Table 5.3.2 Conventional Oven Baseline Efficiency Levels....................................................... 5-5

Table 5.3.3 February 2014 RFI Gas Standard Oven Efficiency Levels ...................................... 5-6

Table 5.3.4 February 2014 RFI Gas Self-Clean Oven Efficiency Levels ................................... 5-6
5-ii

Table 5.3.5 February 2014 RFI Electric Standard Oven Efficiency Levels................................ 5-6

Table 5.3.6 February 2014 RFI Electric Self-Clean Oven Efficiency Levels............................. 5-6

Table 5.3.7 Electric Standard Oven Efficiency Levels................................................................ 5-8

Table 5.3.8 Electric Self-Clean Oven Efficiency Levels............................................................. 5-8

Table 5.3.9 Gas Standard Oven Efficiency Levels...................................................................... 5-9

Table 5.3.10 Gas Self-Clean Oven Efficiency Levels................................................................. 5-9

Table 5.4.1 Major Manufacturing Processes ............................................................................. 5-12

Table 5.5.1 DOE Conventional Gas Oven Test Units............................................................... 5-17

Table 5.5.2 DOE Conventional Electric Oven Test Units......................................................... 5-18

Table 5.5.3 DOE Conventional Gas Oven Test Results............................................................ 5-19

Table 5.5.4 Glo-bar Energy Consumption for Gas Ovens......................................................... 5-21

Table 5.5.5 DOE Conventional Electric Oven Test Results...................................................... 5-22

Table 5.5.6 Standby Power of Ovens in the DOE Test Sample ................................................ 5-22

Table 5.5.7 Standby Power of Ranges in the DOE Test Sample............................................... 5-23

Table 5.5.8 Slopes and Intercepts of Electric Oven IAEC versus Cavity Volume 

Relationship ....................................................................................................... 5-28

Table 5.5.9 Slopes and Intercepts of Gas Oven IAEC versus Cavity Volume Relationship..... 5-29

Table 5.5.10 Baseline Manufacture Product Costs for all Product Classes............................... 5-29

Table 5.5.11 Electric Standard Oven Incremental Manufacturing Product Cost (2014$)......... 5-30

Table 5.5.12 Electric Self-Clean Incremental Manufacturing Product Cost (2014$)................ 5-30

Table 5.5.13 Gas Standard Oven Incremental Manufacturing Product Cost (2014$)............... 5-30

Table 5.5.14 Gas Self-Clean Oven Incremental Manufacturing Product Cost (2014$)............ 5-31

LIST OF FIGURES

Figure 5.4.1 Manufacturing Cost Assessment Stages................................................................ 5-12

Figure 5.5.1 Conventional Gas Oven Energy Factor versus Thermal Mass.............................. 5-20

Figure 5.5.2 Electric Standard Oven Slope................................................................................ 5-25

Figure 5.5.3 Electric Self-Clean Slope ...................................................................................... 5-26

Figure 5.5.4 Gas Standard Oven Slope...................................................................................... 5-26

Figure 5.5.5 Gas Self-Clean Oven Slope ................................................................................... 5-27

Figure 5.5.6 NRCan Electric Oven Data and New DOE Slope for Electric Ovens .................. 5-28
5-1

CHAPTER 5. ENGINEERING ANALYSIS

5.1 INTRODUCTION

After conducting the screening analysis, the U.S. Department of Energy (DOE) 

performed an engineering analysis based on the remaining design options. The engineering 

analysis consists of estimating the energy consumption and costs of conventional ovens at 

various levels of increased efficiency. This section provides an overview of the engineering 

analysis (section 5.1), discusses product classes (section 5.2), establishes baseline and 

incremental efficiency levels (section 5.3), explains the methodology used during data gathering 

(section 5.4) and discusses the analysis and results (section 5.5).

The primary inputs to the engineering analysis are baseline information from the market 

and technology assessment (chapter 3 of this notice of proposed rulemaking (NOPR) technical 

support document (TSD) and technology options from the screening analysis (chapter 4). 

Additional inputs were determined through teardown analysis and manufacturer interviews. The 

primary output of the engineering analysis is a set of cost-efficiency curves. In the subsequent 

markups analysis (chapter 6), DOE determined customer (i.e., product purchaser) prices by 

applying distribution markups, sales tax and contractor markups. After applying these markups, 

the cost-efficiency curves serve as the input to the building energy-use and end-use load 

characterization (chapter 7), and the life-cycle cost (LCC) and payback period (PBP) analyses 

(chapter 8). 

DOE typically structures its engineering analysis around one of three methodologies. 

These are: (1) the design-option approach, which calculates the incremental costs of adding 

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

the relative costs of achieving increases in energy efficiency levels, without regard to the 

particular design options used to achieve such increases; and/or (3) the reverse-engineering or 

cost-assessment approach, which involves a “bottom-up” manufacturing cost assessment based 

on a detailed bill of materials (BOM) derived from teardowns of the product or equipment being 

analyzed. Deciding which methodology to use for the engineering analysis depends on the 

covered product, the design options under study, and any historical data that DOE can draw on.
5-2

DOE stated in a request for information (RFI) notice published on February 12, 2014 (the 

February 2014 RFI) that in order to create the cost-efficiency relationship, it anticipated having 

to structure its engineering analysis using a design-option approach, supplemented by reverse 

engineering (physical teardowns and testing of existing products in the market) to identify the 

incremental cost and efficiency improvement associated with each design option or design option 

combination. In addition, DOE stated that it intended to consider cost-efficiency data from the 

2009 Final Rule Technical Support Document: Residential Dishwashers, Dehumidifiers, and 

Cooking Products and Commercial Clothes Washers (2009 TSD), which was released as part of 

the most recent standards rulemaking.a 79 FR 8337, 8347. DOE maintained this approach for this 

NOPR. In addition, DOE conducted interviews with manufacturers of conventional ovens to 

develop a deeper understanding of the various combinations of design options used to increase 

product efficiency, and their associated manufacturing costs.

5.2 PRODUCT CLASSES ANALYZED

DOE separated residential cooking products into several product classes based on the 

energy source (i.e., gas or electric) and installation configuration. These distinctions yielded 

eight conventional oven product classes.

For electric ovens, as discussed in previous rulemakings, DOE determined that the type 

of oven-cleaning system is a utility feature that affects performance. DOE also determined for 

this rulemaking that built-in and slide-in ovens are equipped with an additional exhaust fan and 

vent assembly that is not present in freestanding products, and which consumes additional energy

in fan-only mode every cooking cycle. A more detailed discussion of installation configurations 

is provided in chapter 3. DOE analyzed the following product classes for electric ovens: 

 Freestanding standard oven with or without a catalytic line;

 Built-in/slide-in standard oven with or without a catalytic line

 Freestanding self-cleaning oven; and 

 Built-in/slide-in self-cleaning oven. 

For gas ovens, DOE analyzed the following product classes based upon the same 

reasoning as electric ovens:

 Freestanding standard oven with or without a catalytic line;

 Built-in/slide-in standard oven with or without a catalytic line

 Freestanding self-cleaning oven; and 

 Built-in/slide-in self-cleaning oven. 

In summary, DOE analyzed the product classes listed in Table 5.2.1 for the NOPR.

 

a Available online at http://www.regulations.gov/#!documentDetail;D=EERE-2006-STD-0127-0097
5-3

Table 5.2.1 Proposed Product Classes for Conventional Cooking Products 

Product

Class Product Type Sub-Category Installation Type

1 Electric oven Standard with or without a 

catalytic line Freestanding

2 Built-in/Slide-in

3 Self-clean Freestanding

4 Built-in/Slide-in

5

Gas oven

Standard with or without a 

catalytic line

Freestanding

6 Built-in/Slide-in

7 Self-clean Freestanding

8 Built-in/Slide-in

5.3 EFFICIENCY LEVELS

5.3.1 Baseline Efficiency Levels

A baseline unit is a product that just meets current Federal energy conservation standards. 

DOE analyzed the baseline units for each product class in the engineering analysis, and the 

subsequent LCC and PBP analyses. To determine energy savings and changes in price, DOE 

compared more energy-efficient units to the baseline unit.

As part of the February 2014 RFI, DOE initially developed baseline efficiency levels by 

considering the current standards for conventional gas ovens and the baseline efficiency levels 

for conventional electric ovens from the previous standards rulemaking analysis. DOE developed 

tentative baseline efficiency levels for the February 2014 RFI considering the current test 

procedure in Appendix I based on the integrated annual energy consumption (IAEC) metric 

combining active mode (including fan-only mode), standby mode, and off mode energy use. The 

baseline efficiency levels proposed in the February 2014 RFI are presented in Table 5.3.1. DOE 

developed baseline efficiency levels for standby mode and off mode based on test data presented 

in the microwave oven test procedure SNOPR that published on May 16, 2012 (77 FR 28805, 

28811)b

. For fan-only mode, DOE developed baseline efficiency levels considering the 

additional annual energy consumption in fan-only mode based on test data presented in an 

SNOPR for the conventional cooking products test procedure published on May 25, 2012. The 

efficiency levels are based on an oven with a cavity volume of 3.9 cubic feet (ft3

). 77 FR 31444, 

31448. 

 

b In the May 2012 microwave oven test procedure SNOPR, DOE considered test procedure amendments for 

measuring the standby mode and off mode energy consumption of combined cooking products and, as a result, 

presented standby power data for microwave ovens, conventional cooking tops, and conventional ovens.
5-4

Table 5.3.1 February 2014 RFI Conventional Oven Baseline Efficiency Levels

Product Class

2009 Standards Rulemaking Proposed Integrated 

Annual Energy 

Consumption

(IAEC)

Energy 

Factor 

(EF)

Annual Energy 

Consumptionc

Electric Oven – Standard Oven with or 

without a Catalytic Line 0.1066 274.9 kWh 370.0 kWh

Electric Oven – Self-Clean Oven 0.1099 266.6 kWh 360.0 kWh

Gas Oven – Standard Oven with or 

without a Catalytic Line 0.0536 1656.7 kBtu 2076.5 kBtu

Gas Oven – Self-Clean Oven 0.0540 1644.4 kBtu 1965.0 kBtu

DOE developed baseline efficiency levels for this NOPR considering both data from the 

previous standards rulemaking and the measured energy use of units in the DOE test sample 

based on the test procedure in Appendix I discussed in section 5.5.2. DOE also requested energy 

use data as part of the manufacturer interviews. However, because manufacturers are not 

currently required to conduct testing according to the DOE test procedure, very little energy use 

information was available. As a result, the baseline efficiency levels for this NOPR differ from 

those presented in the February 2014 RFI. DOE compared the minimum cooking efficiency 

measured in its test sample to the minimum cooking efficiency levels assumed for the previous 

standards rulemaking analysis. Often, the lowest measured efficiency in DOE’s test sample for 

this NOPR was lower than the values for the previous rulemaking.

To update the baseline efficiency levels for conventional ovens, first DOE relied on the 

EF versus cavity volume relationship derived in the 2009 TSD and derived a new relationship 

between IAEC and cavity volume. Using the slope from the previous rulemaking, DOE selected 

new intercepts corresponding to the ovens in its test sample with the lowest efficiency, so that no 

ovens were cut off by the baseline curve. DOE then set baseline standby energy consumption for 

conventional ovens equal to that of the oven or range with the highest standby energy 

consumption in DOE’s test sample to maintain the full functionality of controls for consumer 

utility. While only DOE test data was available to validate the baseline equation for gas ovens, 

DOE compared the new baseline equation for electric ovens with data available in the Natural 

Resources Canada (NRCan) databases, which showed that DOE’s assumptions for slopes and 

intercepts reasonably represented the market. A detailed discussion of DOE’s derivation of the 

cavity volume relationship is provided in 5.1.2. 

In addition to the product classes proposed in the February 2014 RFI, DOE is also

proposing separate product classes for freestanding and built-in/slide-in ovens as discussed in 

 

c DOE notes that the previous conventional cooking products test procedure in Appendix I included the clock energy 

consumption. As a result, DOE subtracted the clock energy consumption before adding the standby and off mode 

energy consumption when considering integrated efficiency levels for this standards rulemaking.
5-5

section 5.2. DOE developed separate baseline efficiency levels for these product classes by

adding the maximum fan-only mode annual energy consumption measured in the test sample, as 

presented in section 5.5.2, to the baseline efficiency intercepts discussed above. 

The proposed baseline efficiency levels for this NOPR are presented in Table 5.3.2. After 

receiving manufacturer feedback and reviewing products currently on the market, DOE 

determined that a cavity volume of 3.9 ft3 no longer represents the market average. Thus, 

efficiency levels are based on an oven with a cavity volume of 4.3 ft3

.

Table 5.3.2 Conventional Oven Baseline Efficiency Levels

Product Class Sub Type Proposed IAEC*

Electric Oven – Standard Oven with or 

without a Catalytic Line

Freestanding 294.5 kWh

Built-in/Slide-in 301.5 kWh

Electric Oven – Self-Clean Oven Freestanding 355.0 kWh

Built-in/Slide-in 361.1 kWh

Gas Oven – Standard Oven with or 

without a Catalytic Line

Freestanding 2118.2 kBtu

Built-in/Slide-in 2128.1 kBtu

Gas Oven – Self-Clean Oven Freestanding 1883.8 kBtu

Built-in/Slide-in 1893.7 kBtu

* Proposed IAEC baseline efficiency levels are normalized based on a 4.3 ft3 volume oven. 

5.3.2 Incremental Efficiency Levels

For each product class, DOE analyzed several efficiency levels and determined the 

incremental cost at each of these levels. For the February 2014 RFI, DOE tentatively proposed 

the incremental efficiency levels presented in Table 5.3.3 through Table 5.3.6. DOE developed 

these levels based primarily on the efficiency levels presented in the 2009 TSD, adjusted to 

account for the proposed and amended test procedures. DOE also considered efficiency levels for 

standby mode and off mode associated with changing conventional linear power supplies to 

switch-mode power supplies (SMPS) and the Commission of the European Communities 

Regulation 1275/2008 (hereinafter “Ecodesign regulation”), which requires products to have a 

maximum standby power of 1 W. 79 FR 8337, 8345-6 (Feb. 12, 2014). The efficiency levels 

proposed in the February 2014 RFI are based on an oven with a cavity volume of 3.9 ft3

. 
5-6

Table 5.3.3 February 2014 RFI Gas Standard Oven Efficiency Levels

Level Efficiency Level Source

Proposed 

IAEC (kBtu)

Baseline 2009 TSD (Electric Glo-bar Ignition) 2076.5

1 2009 TSD (Electric Glo-bar Ignition) + SMPS 1932.0

2 2009 TSD (Improved Insulation) + SMPS 1844.2

3 2009 TSD (2 + Electronic Spark Ignition) + SMPS 1717.7

4 2009 TSD (3 + Improved Door Seals) + SMPS 1702.6

5 2009 TSD (4 + Reduced Vent Rate) + SMPS 1695.4

6 2009 TSD (5 + Reduced Conduction Losses) + SMPS 1685.9

7 2009 TSD (6 + Forced Convection) + SMPS 1636.0

8 2009 TSD (7) + 1W Standby 1499.1

Table 5.3.4 February 2014 RFI Gas Self-Clean Oven Efficiency Levels

Level Efficiency Level Source

Proposed 

IAEC (kBtu)

Baseline 2009 TSD (Baseline) 1965.0

1 2009 TSD (Baseline) + SMPS 1820.5

2 2009 TSD (Forced Convection) + SMPS 1596.9

3 2009 TSD (2) + Electronic Spark Ignition + SMPS 1482.3

4 2009 TSD (3 + Improved Door Seals) + SMPS 1472.0

5 2009 TSD (4 + Reduced Conduction Losses) + SMPS 1467.8

6 2009 TSD (5) + 1 W Standby 1330.9

Table 5.3.5 February 2014 RFI Electric Standard Oven Efficiency Levels

Level Efficiency Level Source

Proposed 

IAEC (kWh)

Baseline 2009 TSD (Baseline) 370.0

1 2009 TSD (Baseline) + SMPS 327.7

2 2009 TSD (Reduced Vent Rate) + SMPS 316.1

3 2009 TSD (2 + Improved Insulation) + SMPS 304.8

4 2009 TSD (3 + Improved Door Seals) + SMPS 300.9

5 2009 TSD (4 + Reduced Conduction Losses) + SMPS 300.3

6 2009 TSD (5 + Forced Convection) + SMPS 295.2

7 2009 TSD (6) + 1 W Standby 255.0

Table 5.3.6 February 2014 RFI Electric Self-Clean Oven Efficiency Levels

Level Efficiency Level Source

Proposed 

IAEC (kWh)

Baseline 2009 TSD (Baseline) 360.0

1 2009 TSD (Baseline) + SMPS 317.7

2 2009 TSD (Reduced Conduction Losses) + SMPS 317.0

3 2009 TSD (2 + Forced Convection) + SMPS 312.0

4 2009 TSD (3) + 1 W Standby 271.9

The baseline efficiency levels for this NOPR differ from those presented in the February 

2014 RFI. For the NOPR, DOE developed incremental efficiency levels for each product class 

by first considering information from the 2009 TSD. DOE retained the relative percent increase 
5-7

in efficiency determined in the previous rulemaking for reduced vent rate, improved insulation 

and door seals, and reduced conduction losses for all oven product classes. 

DOE updated the incremental efficiency levels in cases where DOE identified design options 

during testing and reverse engineering performed in support of this NOPR. DOE’s testing of 

conventional gas ovens showed that energy use was correlated to oven burner and cavity design 

(e.g., thermal mass of the cavity and racks) and can be significantly reduced when optimized.

Section 5.5.2 discusses how thermal mass, and in particular oven cavity thickness, relates to oven 

energy consumption and cooking efficiency. DOE determined the incremental increase in 

efficiency associated with optimized burner and cavity design by comparing the lowest measured 

efficiency for a gas oven having thin cavity walls and a low thermal mass to the lowest measured 

efficiency for a gas oven with cavity walls greater than 0.039 inches (1 mm) and a high thermal 

mass. 

DOE also added an efficiency level for electric ovens based on test data for a unit in its 

test sample equipped with an oven separator. The oven separator allows the user to reduce the 

cavity volume that is used for cooking so that the individual cavities are more appropriately sized 

to the load and so that different temperature settings can be used simultaneously. DOE first 

determined the efficiency of the oven when measured without the separator and then measured 

with the separator according to the proposed test method in the December 2014 TP SNOPR. 

Noting that the efficiency benefit provided by the oven separator is dependent on cavity volume, 

DOE then used the slope discussed in section 5.1.2 to derive a new intercept for the IAEC versus 

cavity volume relationship and to determine the relative percent increase in efficiency due to the 

oven separator.

To develop the efficiency levels for the electronic spark ignition design option, DOE 

compared two gas ovens of similar design but different ignition systems (i.e., glo-bar versus 

electronic spark ignition). Based on DOE’s testing, electronic spark ignition systems resulted in a 

relative increase in cooking efficiency ranging from 8 to 11 percent, depending on the product 

class. DOE notes that these testing based estimates account for any contribution of the glo-bar 

ignition system to heating of the test load. DOE performed a similar analysis to update the 

incremental efficiency increase resulting from the use of forced convection, comparing the 

convection and non-convection modes for ovens of similar construction. The resulting relative 

increase in cooking efficiency ranged from 3.5 to 6 percent, depending on the product class. 

DOE reevaluated the efficiency levels associated with standby power improvements 

based on product testing and reverse engineering. To determine standby power levels, DOE 

measured the standby power of the ovens and ranges in its test sample. The results are presented 

in section 5.5.2. As discussed in section 5.3.1, DOE selected the baseline standby power levels 

for conventional ovens based on the highest measured standby mode power consumption in 

DOE’s test sample. Based on DOE’s reverse engineering analyses, the baseline products were 

equipped with linear power supplies. DOE determined the reduction in standby power associated 

with changing from a linear power supply to a SMPS using the highest measured standby power 

for each power supply design to maintain the full functionality of controls for consumer utility.

DOE reevaluated the efficiency levels associated with standby power improvements based on 
5-8

design options identified during product testing and reverse engineering rather than considering 

an efficiency level specifically associated with the 1-W Ecodesign regulation standby 

requirement.

DOE’s testing of freestanding, built-in, and slide-in installation configurations for 

conventional gas and electric ovens revealed that built-in and slide-in ovens consume energy in 

fan-only mode, whereas freestanding ovens do not. The energy consumption in fan-only mode 

for built-in and slide-in ovens ranges from 1 watt-hour (Wh) to 32 Wh per cycle and can extend 

from 4.5 to 69 minutes after the cooking cycle ends. The percentage of annual energy 

consumption represented by fan-only mode ranged from 0.1 to 0.5 percent for gas ovens and 0.2 

to 2.4 percent for electric ovens. The variation in fan-only mode energy consumption depends on 

the controls and oven cavity design. DOE developed separate baseline IAEC values for each 

installation configuration. DOE notes that the relative decrease in IAEC for each incremental 

efficiency level remained constant across installation configuration since fan-only mode energy 

consumption is independent of the design options retained for this NOPR.

Table 5.3.7 through Table 5.3.10 show the incremental efficiency levels for each product 

class, including the source for the derivation of the efficiency level, whether it be the analysis in 

the 2009 TSD or the updates described above based on testing for the NOPR. The efficiency 

levels are normalized based on an oven with a cavity volume of 4.3 ft3

. 

Table 5.3.7 Electric Standard Oven Efficiency Levels

Level

Efficiency Level 

Source Design Option

Proposed IAEC (kWh)

Freestanding

Built-in / 

Slide-in

Baseline NOPR Testing Baseline 294.5 301.5

1 NOPR Testing Baseline + SMPS 284.6 291.4

2 2009 TSD 1 + Reduced Vent Rate 271.7 278.2

3 2009 TSD 2 + Improved Insulation 259.2 265.4

4 2009 TSD 3 + Improved Door Seals 254.9 261.0

5 NOPR Testing 4 + Forced Convection 244.6 250.5

6 NOPR Testing 5 + Oven Separator 207.8 212.8

7 2009 TSD 6 + Reduced Conduction Losses 207.3 212.2

Table 5.3.8 Electric Self-Clean Oven Efficiency Levels

Level

Efficiency Level 

Source Design Option

Proposed IAEC (kWh)

Freestanding

Built-in / 

Slide-in

Baseline NOPR Testing Baseline 355.0 361.1

1 NOPR Testing Baseline + SMPS 345.1 351.0

2 NOPR Testing 1 + Forced Convection 327.2 332.7

3 NOPR Testing 2 + Oven Separator 278.9 283.7

4 2009 TSD 3 + Reduced Conduction Losses 278.1 282.9
5-9

Table 5.3.9 Gas Standard Oven Efficiency Levels

Level

Efficiency Level 

Source Design Option

Proposed IAEC (kBtu)

Freestanding

Built-in / 

Slide-in

Baseline 2009 TSD Baseline 2118.2 2128.1

1 NOPR Testing Baseline + Optimized Burner/Cavity 1649.3 1657.0

2 NOPR Testing 1 + SMPS 1614.7 1622.2

3 NOPR Testing 2 + Electronic Spark Ignition 1490.7 1497.7

4 2009 TSD 3 + Improved Insulation 1414.8 1421.5

5 2009 TSD 4 + Improved Door Seals 1400.6 1407.2

6 NOPR Testing 5 + Forced Convection 1355.6 1362.0

7 2009 TSD 6 + Reduced Conduction Losses 1347.0 1353.3

Table 5.3.10 Gas Self-Clean Oven Efficiency Levels

Level

Efficiency Level 

Source Design Option

Proposed IAEC (kBtu)

Freestanding

Built-in / 

Slide-in

Baseline 2009 TSD Baseline 1883.8 1893.7

1 NOPR Testing Baseline + SMPS 1848.2 1858.0

2 NOPR Testing 1 + Electronic Spark Ignition 1668.7 1677.5

3 NOPR Testing 2 + Forced Convection 1596.3 1604.7

4 2009 TSD 3 + Reduced Conduction Losses 1591.0 1599.4

5.4 METHODOLOGY OVERVIEW

DOE relied on multiple sources of information for this engineering analysis. These 

sources include a review of TSDs from previous rulemakings, manufacturer interviews, internal 

product testing, and product teardowns.

5.4.1 Review of Previous Technical Support Documents and Models

DOE reviewed previous rulemaking TSDs to assess their applicability to the current 

standard setting process for residential conventional ovens. These previous rulemaking TSDs 

served as a source for design options and energy consumption analysis, in addition to other 

sources. For conventional ovens, the previous rulemaking TSD was developed in support of a 

final rule for establishing energy conservation standards for residential dishwashers, 

dehumidifiers, cooking products, and commercial clothes washers published in 2009. 74 FR 

16040 (April 8, 2009).

5.4.2 Manufacturer Interviews

DOE understands that there is variability among manufacturers in baseline units, design 

strategies, and cost structures. To better understand and explain these variances, DOE conducted

manufacturer interviews. These confidential interviews provided a deeper understanding of the 
5-10

various combinations of technologies used to increase residential conventional oven efficiency, 

and their associated manufacturing costs. DOE conducted interviews in advance of this NOPR 

analysis. Sample questions from the NOPR phase interviews are contained in appendix 12-A of 

this NOPR TSD.

During the interviews, DOE also gathered information about the capital expenditures 

required to increase the efficiency of the baseline units to various efficiency levels (i.e., 

conversion capital expenditures by efficiency or energy-use level). The interviews provided 

information about the size and the nature of the capital investments. DOE also requested 

information about the depreciation method used to expense the conversion capital. The 

manufacturer impact analysis in chapter 12 of the NOPR TSD includes a discussion of this 

information obtained during manufacturer interviews.

5.4.3 Product Testing

Because most manufacturers do not currently perform product testing according to the 

existing DOE test procedure, DOE conducted its own investigative testing using methods 

proposed in the December 2014 TP SNOPR to develop a better understanding of the design 

options and product features currently available on the market. The investigative testing also 

allowed DOE to characterize the distribution of product energy consumption in the marketplace.

5.4.4 Product Teardowns

Other than obtaining detailed manufacturing costs directly from a manufacturer, the most 

accurate method for determining the production cost of a product is to disassemble representative

units piece-by-piece and estimate the material, labor, and overhead costs associated with each 

component using a process commonly called a physical teardown. A supplementary method, 

called a catalog teardown, uses published manufacturer catalogs and supplementary component 

data to estimate the major physical differences between a product that has been physically 

disassembled and another similar product. DOE only performed a physical teardown analysis on 

conventional ovens and conventional ranges. The teardown methodology is explained in the 

following sections.

5.4.4.1 Selection of Units

DOE generally adopts the following criteria for selecting units for teardown analysis:

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

under consideration. Because manufacturers are not currently required to report product 

efficiency or energy use, DOE selected test units based on a review of design options listed 

in product literature;

• Within each product class, the selected products should, if possible, come from the same 

manufacturer and belong to the same product platform;
5-11

• The selected products should, if possible, come from manufacturers with large market shares

in that product class, although the highest efficiency products are chosen irrespective of 

manufacturer; and

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

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

5.4.4.2 Generation of Bill of Materials

The end result of each teardown is a structured BOM, which describes each product part

and its relationship to the other parts, in the estimated order of assembly. The BOMs describe 

each fabrication and assembly operation in detail, including the type of value—added equipment 

needed (e.g., stamping presses, injection molding machines, spot-welders, etc.) and the estimated

cycle times associated with each conversion step. The result is a thorough and explicit model of 

the production process.

Materials in the BOM are divided between raw materials that require conversion steps to 

be made ready for assembly, while purchased parts are typically delivered ready for installation.

The classification into raw materials or purchased parts is based on DOE’s previous industry 

experience, recent information in trade publications, and discussions with original equipment 

manufacturers (OEMs). For purchased parts, the purchase price is based on volume-variable 

price quotations and detailed discussions with suppliers.

For parts fabricated in-house, the prices of the underlying “raw” metals (e.g., tube, sheet 

metal) are estimated on the basis of 5-year averages to smooth out spikes in demand. Other 

“raw” materials such as plastic resins, insulation materials, etc. are estimated on a current-market 

basis. The costs of raw materials are based on manufacturer interviews, quotes from suppliers, 

secondary research, and by subscriptions to publications including the American Metals Marketd

(AMM). Past price quotes are indexed using applicable Bureau of Labor Statistics producer price 

index tables as well as AMM monthly data. 

5.4.4.3 Cost Structure of the Spreadsheet Models

The manufacturing cost assessment methodology used is a detailed, component-focused 

technique for rigorously calculating the manufacturing cost of a product (direct materials, direct 

labor and some overhead costs). Figure 5.4.1 shows the three major steps in generating the 

manufacturing cost.

 

d For information on American Metals Market, please visit: www.amm.com.
5-12

Figure 5.4.1 Manufacturing Cost Assessment Stages

The first step in the manufacturing cost assessment was the creation of a complete and 

structured BOM from the disassembly of the units selected for teardown. The units were 

dismantled, and each part was characterized according to weight, manufacturing processes used, 

dimensions, material, and quantity. The BOM incorporates all materials, components, and 

fasteners with estimates of raw material costs and purchased part costs. Assumptions on the 

sourcing of parts and in-house fabrication were based on industry experience, information in 

trade publications, and discussions with manufacturers. Interviews and plant visits were 

conducted with manufacturers to ensure accuracy on methodology and pricing.

Following the development of a detailed BOM, the major manufacturing processes were 

identified and developed for the spreadsheet model. Some of these processes are listed in Table 

5.4.1.

Table 5.4.1 Major Manufacturing Processes

Fabrication Finishing Assembly/Joining Quality Control

Fixturing

Stamping/Pressing

Brake Forming

Cutting and Shearing

Turret Punch

Tube Forming

Washing

Powder Coating

Enameling

De-burring

Polishing

Adhesive Bonding

Spot Welding

Seam Welding

Packaging

Inspecting & Testing

Fabrication process cycle times for each part made in-house were estimated and entered 

into the BOM. Based on estimated assembly and fabrication time requirements, the labor content 

of each appliance could be estimated. For this analysis, DOE estimated labor costs based on 

typical annual wages and benefits of industry employees.

Cycle requirements for fabrication steps were similarly aggregated by fabrication 

machine type while accounting for dedicated vs. non-dedicated machinery and/or change-over 

times (die swaps in a press, for example). 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: material, labor, and overhead. 

Design options used in units subject to teardown are noted in the summary sheet of each 

cost model and are cost-estimated individually. Thus, various implementations of design options 

can be accommodated, ranging from assemblies that are entirely purchased to units that are made 
5-13

entirely from raw materials. Hybrid assemblies, consisting of purchased parts and parts made on 

site are thus also accommodated.

5.4.4.4 Cost Model and Definitions

The cost model is based on production activities and divides factory costs into the 

following categories:

• Materials: Purchased parts (i.e., motors, valves, etc.), raw materials, (i.e., cold rolled 

steel, copper tube, etc.), 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.

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 (out-sourced) 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.

• Supervisory labor: Labor associated with fabrication and assembly basis. Assigned on a 

span basis (x number of employees per supervisor) that depends on the industry.

• Indirect labor: Labor costs that scale with fabrication and assembly labor. These included 

the cost of technicians, manufacturing engineering support, stocking, etc. that are 

proportional to all other labor. 

• Equipment depreciation: Money allocated to pay for initial equipment installation and 

replacement as the production equipment is amortized. All depreciation is assigned in a 

linear fashion and affected equipment life depends on the type of equipment.

• Tooling depreciation: Cost for initial tooling (including non-recurring engineering and 

debugging of the tools) and tooling replacement as it wears out or is rendered obsolete.

• Building depreciation: Money allocated to pay for the building space and the conveyors 

that feed and/or make up the assembly line.

• 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 of unit cost.
5-14

5.4.4.5 Cost Model Assumptions

As discussed in the previous section, assumptions about manufacturer practices and cost 

structure played an important role in estimating the final product cost. 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.

Site visits allowed DOE to confirm its cost model assumptions through direct observation of the 

manufacturing plant, as well as through manufacturer interviews, reviews of current Bureau of 

Labor Statistics data, etc.

5.5 ANALYSIS AND RESULTS

5.5.1 Manufacturer Interviews

DOE conducted interviews with residential conventional oven manufacturers to develop a 

better understanding of current product features and the technologies used to improve energy 

efficiency. The interviewed represent a wide range of U.S. market share and included both 

domestic and international companies that sell cooking products in the United States. During 

these interviews, DOE asked manufacturers questions about the following topics related to the 

engineering analysis:

• Product classes

• Design features of current baseline products

• Proposed incremental efficiency levels and design options

• Impacts on consumer utility

• Installation and repair costs as a function of efficiency

The discussion helped DOE understand what proposed design options have already been 

implemented and what additional design options DOE should consider. 

The discussion below represents a consolidation of the manufacturer responses.

5.5.1.1 Product Classes

DOE asked manufacturers whether separate product classes were warranted for gas ovens

with higher burner input rates, including products marketed as commercial-style. Manufacturers 

indicated that without an established test procedure, it was not possible to comment on whether 

commercial-style products warrant a separate product class. However, manufacturers generally 

agreed that the major difference between standard residential and commercial-style ovens and 

ranges was consumer-driven aesthetics and not performance. 
5-15

5.5.1.2 Design Features of Current Baseline Products

DOE discussed the features of baseline products identified during the previous energy 

conservation standards rulemaking with manufacturers. Manufacturers generally stated that the 

baseline identified in the previous rulemaking may not be representative of products currently 

being sold on the market. Most manufacturers indicated that they do not currently test their 

conventional ovens according to the existing DOE test procedure and thus have limited or no 

data to help support a baseline estimate.

5.5.1.3 Proposed Incremental Efficiency Levels and Design Options

DOE asked manufacturers to comment on the incremental efficiency levels presented in 

the February 2014 RFI. In general, manufacturers were not able to provide feedback on the 

proposed incremental efficiency levels due to the lack of available data. 

DOE also asked manufacturers to describe the changes associated with each active mode 

efficiency level relative to the baseline units in each product class. Manufacturers generally 

commented that there is little improvement available for insulation in most ovens. Given the 

consumer-based drive for ovens with larger cavity volumes, manufacturers claim to have already 

optimized insulation thickness and density to achieve the largest cavity size possible while still 

meeting exterior surface temperature safety requirements. Manufacturers stated there is little 

room for improvement in oven door seals beyond those already rated for use in self-clean ovens. 

5.5.1.4 Oven Energy Consumption as a Function of Cavity Volume

DOE asked manufacturers how oven energy consumption may scale with cavity volume. 

Manufacturers stated that ovens with smaller cavities are generally more efficient but did not 

supply data to support this statement. 

5.5.1.5 Impact on Consumer Utility

DOE asked manufacturers how the design option changes identified in the February 2014 

RFI may impact consumer utility. Manufacturers indicated that if an energy conservation 

standard resulted in reduced burner input rates for ovens, pre-heat and overall cooking times may

be affected. Manufacturers stated that pre-heat and faster cooking times are important consumer 

features.

5.5.2 Product Testing

DOE’s oven test procedure in Appendix I involves setting the oven controls to achieve an 

average internal cavity temperature that is 325° ± 5 °F higher than the room ambient air 

temperature and measuring the amount of energy required to raise the temperature of an 

aluminum block test load at room temperature by 234 °F above its initial temperature. The 

measured energy consumption includes the energy input during the time the load is being heated 
5-16

plus the energy consumed during fan-only mode. In the December 2014 TP SNOPR, DOE did 

not modify the active mode test method but proposed to incorporate methods for measuring 

conventional oven volume according to an AHAM proceduree

, to clarify that the existing oven 

test block must be used to test all ovens regardless of input rate, and to measure the energy 

consumption and efficiency of conventional ovens equipped with an oven separator. 79 FR 

71894.

The annual primary energy consumption for cooking, ECO, for electric ovens and for gas

ovens, is defined as:

EECCCC = EEOO×KKee× OO

WW1×CCpp×TTSS

for electric ovens, where,

EO = test energy consumption, as measured,

Ke = 3.412 Btu/Wh (3.6 kJ/Wh,) conversion factor of watt-hours to Btus,

OO = 29.3 kWh (105,480 kJ) per year, annual useful cooking energy output of conventional 

electric oven,

W1 = measured weight of test block in pounds (kg),

Cp = 0.23 Btu/lb-°F (0.96 kJ/kg ÷ °C), specific heat of test block,

TS = 234 °F (130 °C), temperature rise of test block.

EECCCC = EEOO× OO

WW1×CCpp×TTSS

for gas ovens, where,

EO = test energy consumption, as measured

OO = 88.8 kBtu (93,684 kJ) per year, annual useful cooking energy output of conventional 

gas oven,

and W1, Cp and TS are the same as defined above.

The DOE test procedure also includes a method for measuring the annual primary energy 

consumption for conventional oven self-cleaning operations, and the secondary energy 

consumption of a gas oven that uses electrical energy consumption for the ignition system and 

the display. The total integrated annual electrical energy consumption, IAEC, is defined as the 

sum of the annual energy consumption in each of these modes:

IIII II = EECCCC + EESS + EESS + EESSSS + EE OOOO + (EE OO × NN ) where,

ESO = annual secondary (electrical) cooking energy consumption for gas ovens only,

ESC = annual self-cleaning energy consumption,

ESS = annual secondary (electrical) self-cleaning energy consumption,

 

e The test standard published by the Association of Home Appliance Manufacturers titled, “Procedures for the 

Determination and Expression of the Volume of Household Microwave and Conventional Ovens,” Standard OV-1-

2011
5-17

EOTLP = annual standby mode energy consumption,

EOF = fan-only mode energy consumption as measured in section 3.2.1.2 of this appendix,

NO = representative number of annual conventional electric or gas oven cooking cycles per 

year, depending on the fuel type.

In support of this NOPR analysis, DOE selected a test sample which included units 

representing each product class. DOE then performed testing according to the proposed 

clarifications in the December 2014 TP SNOPR. DOE used this data to help determine 

appropriate product classes (as discussed in chapter 3 of this TSD) and efficiency levels, and to 

determine whether certain design changes resulted in reduced product energy consumption.

5.5.2.1 Product Selection

DOE conducted a market survey of conventional oven and range models and their 

associated features to identify the primary differentiators among commercially-available units. 

Because there are no performance-based energy conservation standards or energy reporting 

requirements for conventional ovens, DOE selected test units based on performance-related 

features and technologies advertised in product literature. These features included, among other 

things: 1) whether or not the product was marketed as commercial-style or professional-style; 2) 

oven fuel type; 3) oven cavity volume in ft3; 4) the presence of a forced convection cooking 

function; and 5) oven installation configuration (i.e., built-in/slide-in versus freestanding).

DOE’s test sample included 1 gas wall oven, 7 gas ranges, 5 electric wall ovens, and 2 electric 

ranges for a total of 15 conventional ovens covering all of the product classes considered in this 

NOPR. The key parameters for each of the test units are presented in Table 5.5.1 through Table 

5.5.2. 

Table 5.5.1 DOE Conventional Gas Oven Test Units

Test 

Unit 

# Type

Installation 

Configuration

Burner 

Input Rate 

(Btu/h)

Cavity 

Volume 

(ft3)

Ignition 

Type

Convection 

(Y/N)

1 Standard Freestanding 18,000 4.8 Spark N

2 Standard Freestanding 18,000 4.8 Glo-bar N

3 Self-Clean Freestanding 18,000 5.0 Glo-bar Y

4 Standard Freestanding 16,500 4.4 Glo-bar N

5 Self-Clean Built-in 13,000 2.8 Glo-bar N

6 Standard Freestanding 28,000 5.3 Glo-bar Y

7 Standard Slide-in 27,000 4.4 Glo-bar Y

8 Standard Freestanding 30,000 5.4 Glo-bar Y
5-18

Table 5.5.2 DOE Conventional Electric Oven Test Units

Test 

Unit 

# Type

Installation 

Configuration

Heating Element 

Wattage (W)

Cavity 

Volume 

(ft3)

Convection 

(Y/N)

1 Self-Clean Freestanding 3,000 5.9* Y

2 Standard Freestanding 2,000 2.4 N

3 Self-Clean Built-in 3,400 2.7 N

4 Standard Built-in 2,600 4.3 N

5 Self-Clean Built-in 2,600 4.3 N

6 Self-Clean Built-in 2,600 4.3 Y

7 Self-Clean Built-in 2,800 4.3** N

* Test Unit 1 was equipped with an oven separator that allowed for splitting the single cavity into two separate 

smaller cavities with volumes of 2.7 ft3 and 3.0 ft3

.

** Test Unit 7 was a double oven having two separate cavities with equal volumes. According to the DOE test 

procedure in Appendix I, the measured energy consumption for these cavities are averaged together.

Several units were selected from a single manufacturer that appeared to have similar 

construction, rated power, and volume, but differed in ancillary features such as whether or not 

the product was equipped with self-clean or forced convection. The range of input rates and 

cavity volumes were determined on the basis of manufacturer specifications. Products marketed 

as commercial-style or professional-style typically had oven burner input rates above 18,000 

Btu/h.

5.5.2.2 Test Results and Derivation of Incremental Efficiency Levels

As discussed above, each test unit was evaluated according to the oven test procedure 

proposed in the December 2014 TP SNOPR. Results are presented below as cooking efficiency 

and/or IAEC where appropriate. IAEC includes active mode (including fan-only mode for 

conventional ovens), standby mode, and off mode energy use. 

Table 5.5.3 presents the testing results for conventional gas ovens in DOE’s test sample. 

Because oven cooking efficiency and energy consumption depend on cavity volume, DOE 

normalized IAEC using the relationship between energy consumption and cavity volume 

discussed in section 5.1.2 for comparison.
5-19

Table 5.5.3 DOE Conventional Gas Oven Test Results

Test 

Unit

#** Oven Product Class

Cavity 

Volume 

(ft3)

Oven 

Cooking 

Efficiency

Fan-Only 

Mode 

Energy Use 

Per Cycle 

(kWh)

IAEC 

(kBtu/yr)

Normalized 

IAEC*

(kBtu/yr)

1 Gas Standard – Freestanding 4.8 6.6% 0.000 1341.4 1234.2

2 Gas Standard – Freestanding 4.8 6.0% 0.000 1503.7 1396.5

3 Gas Self-Clean - Freestanding 5.0 7.6% 0.000 1419.0 1269.0

4 Gas Standard – Freestanding 4.4 6.2% 0.000 1516.6 1495.2

5 Gas Self-Clean – Built-in/Slide-in 2.8 9.4% 0.001 1171.3 1492.9

6 Gas Standard – Freestanding 5.3 4.3% 0.000 2078.9 1864.5

7 Gas Standard – Built-in/Slide-in 4.4 5.2% 0.016 1938.0 1916.5

8 Gas Standard – Freestanding 5.4 3.9% 0.000 2315.1 2079.3

* Measured IAEC normalized to a fixed cavity volume of 4.3 ft3.

** Units 6, 7, and 8 have oven burner input rates greater than 18,000 Btu/h and were marketed as commercial-style.

The normalized IAEC for conventional gas ovens ranged from 1148 to 1994 kBtu/year, 

with lower IAEC corresponding to less energy consumption. DOE separated freestanding ovens 

and built-in/slide-in ovens into different product classes, as noted in section 5.2, because these 

products consume additional energy required by exhausting air from the oven cavity to meet 

safety-related temperature requirements since the oven is enclosed in cabinetry. 

As discussed in chapter 3, through testing, reverse engineering analyses, and discussions 

with manufacturers, DOE determined that the major differentiation between conventional gas 

ovens with lower burner input rates and those with higher input rates, including those marketed 

as commercial-style, was design and construction related to aesthetics rather than improved 

cooking performance. DOE also believes that the high thermal mass of products marketed as 

commercial-style could lead to a low oven cooking efficiency and possibly require higher oven 

input rates to compensate for the heat lost to heating the cavity. In order to quantify the impact 

on cooking efficiency and energy consumption, due to decreasing the mass of the oven cavity, 

DOE compared the cooking efficiency of the gas oven to the normalized cavity volume as shown 

in Figure 5.5.1. DOE normalized both the measured cooking efficiency and the mass of the 

cavity to a single volume of 4.3 ft3. The mass of the cavity walls were scaled by cavity 

dimensions and the oven rack by length. 
5-20

Figure 5.5.1 Conventional Gas Oven Energy Factor versus Thermal Massf

Figure 5.5.1 shows that cooking efficiency decreases with increasing thermal mass for 

conventional gas ovens. DOE’s reverse engineering also confirmed that thicker cavity walls and 

heavier racks were the primary differences contributing to the increased thermal mass of the 

ovens marketed as commercial-style. In review of the preparatory studies for the European 

Commission Ecodesign Requirements for ovens

g

, DOE noted that “modern ovens” in the 

European Union (EU) comprise steel having a thickness of approximately 0.039 inches (1 mm) 

while older and less efficient ovens had cavity thicknesses greater than 0.039 inches. Results 

from DOE’s reverse engineering analysis showed that the gas ovens included in DOE’s test 

sample with the lowest efficiencies had cavity wall thicknesses greater than 0.039 inches and the 

heaviest racks. In contrast, the gas ovens with the highest efficiencies had cavity wall thicknesses 

of less than 0.039 inches, suggesting that thicker cavities, and thus larger mass, leads to lower 

cooking efficiency for gas ovens.

DOE updated its estimates from the previous rulemaking for the measured energy use of 

ovens with glo-bar ignition by measuring the disaggregated energy use for the ignition system 

during the cooking cycle. Table 5.5.4 contains the glo-bar power and per-cycle energy 

consumption measured in DOE’s test sample.

 

f Measured cooking efficiency normalized to a fixed cavity volume of 4.3 ft3

.

g Lot 22 – Domestic and commercial ovens (electric, gas, microwave), including when incorporated in cookers –

Task 4: Technical analysis of existing products. August 2011. Available at: 

http://www.eceee.org/ecodesign/products/Lot22_23_kitchen/Lot22_Task4_Final.pdf

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

9.00%

0 20 40 60 80

Normalized Cooking Efficiency*

Total Thermal Mass of Oven (lbs)

Gas Standard

Gas Self-Clean
5-21

Table 5.5.4 Glo-bar Energy Consumption for Gas Ovens

Test 

Unit 

# Source Oven Product Class

Average 

Glo-bar 

Power 

(W)

Electrical 

Energy 

Consumption 

Per Cycle 

(Wh)

1* Gas Gas Standard – Freestanding 0 0

2 Gas Gas Standard – Freestanding 403.6 173.9

3 Gas Gas Self-Clean - Freestanding 403.7 186.5

4 Gas Gas Standard – Freestanding 408.1 182.9

5 Gas Gas Self-Clean – Built-in/Slide-in 312.8 135.9

6 Gas Gas Standard – Freestanding 389.1 210.1

7 Gas Gas Standard – Built-in/Slide-in 437.0 179.6

8 Gas Gas Standard – Freestanding 413.3 217.3

* Unit 1 had a battery powered electronic spark ignition system.

Based on DOE’s testing of units in its test sample, the average, measured glo-bar power 

ranged from 313 W to 437 W. Electric glo-bar ignition systems for units in DOE’s test sample

consumed between 0.136 kWh to 0.217 kWh per cycle, with an average of 0.184 kWh per cycle. 

DOE notes that the glo-bar energy consumption may vary depending on burner and cavity design 

(e.g., burner input rating, cavity volume). DOE also notes that the glo-bar ignition system was 

not powered on throughout the entire cooking cycle and only consumed power when gas flow to 

the burner was on, turning off when the burner cycled off. Any contribution of the glo-bar 

ignition system to heating the load would be accounted for in testing according to the DOE test 

procedure in Appendix I. Conversely, the gas flow in the oven using a battery powered electronic 

spark ignition system remained on continuously. As discussed in section 5.3.2, DOE updated its 

efficiency level analysis by first comparing energy consumption of a gas oven with glo-bar 

ignition to electronic spark ignition. 

Table 5.5.5 presents the testing results for conventional electric ovens in the test sample. 

As with gas ovens, DOE normalized IAEC using the energy consumption versus cavity volume 

relationship discussed in section 5.1.2 for comparison between units of differing cavity volume.
5-22

Table 5.5.5 DOE Conventional Electric Oven Test Results

Test 

Unit 

# Oven Product Class

Cavity 

Volume 

(ft3)

Oven 

Cooking 

Efficiency

Fan-Only 

Mode Energy 

Use Per Cycle 

(kWh)

IAEC 

(kWh/yr)

Normalized 

IAEC**

(kWh/yr)

1 Electric Self-Clean – Freestanding 5.9* 13.1% 0.000 266.2 198.6

2 Electric Standard – Freestanding 2.4 14.4% 0.000 213.7 274.1

3 Electric Self-Clean – Built-in/Slide-in 2.7 22.5% 0.002 158.7 226.4

4 Electric Standard – Built-in/Slide-in 4.3 10.6% 0.032 287.8 287.8

5 Electric Self-Clean – Built-in/Slide-in 4.3 10.9% 0.032 308.8 308.8

6 Electric Self-Clean – Built-in/Slide-in 4.3 9.9% 0.031 341.8 341.8

7 Electric Self-Clean – Built-in/Slide-in 4.3 10.0% 0.030 370.0 370.0

* Test Unit 1 was equipped with an oven separator that allowed for splitting the single cavity into two separate 

smaller cavities with volumes of 2.7 ft3 and 3.0 ft3

.

** Measured cooking efficiency normalized to a fixed cavity volume of 4.3 ft3

.

The normalized IAEC for conventional electric ovens ranged from 274 to 288 kWh/year

for standard ovens and 199 to 370 kWh/year for self-clean ovens. For the same reasons as 

conventional gas ovens, DOE separated freestanding and built-in/slide-in ovens into different 

product classes.

As IAEC takes into account any standby power consumed by electronic controls in 

addition to any active mode energy consumption, DOE measured standby power for the ovens 

and ranges in DOE’s test sample according to the test procedure specified in Appendix I. Table 

5.5.6 and Table 5.5.7 list measured standby power values for both ovens and ranges. Those units 

in the test sample that did not consume energy in a standby or off mode are not listed. 

Table 5.5.6 Standby Power of Ovens in the DOE Test Sample

Product Class

Standby 

Power 

(W) Power Supply Type

Electric Self-Clean Built-in/Slide-in 1.72 Linear

Electric Standard Built-in/Slide-in 0.53 SMPS

Electric Self-Clean Built-in/Slide-in 0.66 SMPS

Electric Self-Clean Built-in/Slide-in 0.84 SMPS

Electric Self-Clean Built-in/Slide-in 1.61 SMPS

Gas Self-Clean Built-in/Slide-in 1.67 Linear
5-23

Table 5.5.7 Standby Power of Ranges in the DOE Test Sample

Product Class

Standby 

Power 

(W) Power Supply Type

Electric Self-Clean Oven Built-in/Slide-in 1.19 SMPS

Electric Standard Oven Freestanding 1.23 Linear

Gas Self-Clean Oven Freestanding 1.61 Linear

Gas Standard Oven Freestanding 2.15 Linear

Gas Standard Oven Freestanding 0.79 Linear

As noted in section 5.3.1, DOE set baseline standby energy consumption for conventional 

ovens equal to that of the oven/range with the highest standby energy consumption in the test 

sample in order to maintain the full functionality of controls for consumer utility. Comparing 

results in Table 5.5.6 to Table 5.5.7, standby energy consumption was consistently higher for 

conventional ranges than for ovens that were not part of a range. Thus, DOE used standby 

measured for conventional ranges to establish the baseline.

5.5.3 Product Teardowns

After conducting the investigative testing described in the previous section, DOE 

conducted teardowns on all 15 of its test units. The test units spanned the range of product 

efficiencies and features available on the market from multiple manufacturers. DOE relied on the 

cooking product teardowns to supplement the information gained through manufacturer 

interviews and to investigate performance observed during testing. Specifically, the teardowns 

allowed DOE to identify design features for improving efficiency and to develop corresponding 

manufacturing costs for products at different efficiency levels.

5.5.3.1 Baseline Construction

Baseline Gas Ovens

The interior surface of the oven cavity for the gas ovens in DOE’s teardown sample had a 

porcelain enamel coating for durability and cleanability. Accessories such as an incandescent 

light to illuminate the food load without having to open the oven door and a temperature sensor

for control of cooking processes were also located within the cavity. The metal pieces 

comprising the cavity walls were formed by stamping and had grooves to support oven racks.

The back of the cavity was typically its own metal piece mechanically sealed to the top, bottom, 

and sides, which were composed of a single wrapped piece of sheet metal. Cavity construction 

did vary slightly by manufacturer. Baseline ovens were typically equipped with two to three 

oven racks made of enamel-coated steel rods.
5-24

The outside of the oven cavity was wrapped with insulation and DOE observed that the 

space between the cavity and the outer sheet metal enclosure was made as small as practically 

possible in order to maximize the cavity volume. Combustion products from the burner and 

gases released in the interior cavity during the cooking process are vented from the top of the 

cavity through a sheet metal air channel using natural convection. As discussed in previous 

sections, built-in/slide-in ovens had an added fan, motor, and vent assembly to provide cooling 

and venting for combustion byproducts. DOE also observed that some gas ovens in its test 

sample incorporated additional air channels between the exterior oven shell and the layer of 

insulation around the interior cavity to provide an added layer of insulation, keeping the outer 

sheet metal enclosure within a safe temperature range

The gas burners performing the bake function were situated at the bottom of the oven 

cavity. The bake element was shielded by a baffle to help distribute heat evenly but was also 

shielded by the cavity base which partially conceals the element to prevent damage from food 

spills. Broil burners were sometimes located at the top of the oven cavity but for many baseline 

products, a drawer was added below the main cavity so that the same bake burner could be 

employed for broiling. In baseline products, DOE observed that the bake burner was ignited with 

a glo-bar, or hot-surface igniter. A bi-metallic gas valve in electrical series with the igniter 

deformed as current in the circuit increases, allowing gas to flow as long as the hot surface 

igniter was energized by the burner controller.

All of the gas ovens examined had a door attached by two hinges at the bottom of the 

oven cavity opening. The oven door had an interior enamel-coated panel, a dual-pane glass 

window surrounded by insulation, and an exterior panel consisting typically consisting of 

ceramic glass or sheet metal. For standard ovens, baseline products had a silicone rubber gasket 

lining the perimeter of the cavity opening, but for self-clean ovens, even baseline products had a 

fiberglass door seal lined with a metallic mesh.

DOE observed that baseline gas ovens primarily had either electromechanical controls or 

electronic controls although the self-cleaning function required electronic control and a door 

locking mechanism for gas self-clean ovens. For gas ovens with electronic controls, the user 

interface and clock display were a push-button control panel with a Liquid Crystal Display 

(LCD) or Light-Emitting Diode (LED) display. 

Baseline Electric Ovens

The baseline electric oven cavities examined by DOE were similar in construction to gas 

ovens. Accessories, insulation, and door seals were also made of the same materials. The primary 

difference in construction between gas and electric ovens observed by DOE was that the bake 

and broil heating elements were radiant elements made up of a composite metal rod. The bake 

elements, while still located at the bottom of the cavity were unshielded in the baseline products. 

Additionally, baseline electric ovens featured electronic controls incorporating a push-button 

control panel and either an LCD or LED display.
5-25

5.5.4 Conventional Oven Energy Use versus Volume

The conventional oven efficiency levels detailed in the previous sections are predicated 

upon baseline ovens with a cavity volume of 4.3 ft3. Based on DOE’s testing of conventional gas 

and electric ovens and discussions with manufacturers, IAEC scales with oven cavity volume 

due to the fact that larger ovens have higher thermal masses and larger volumes of air (including 

larger vent rates) than smaller ovens. Because the DOE test procedure for measuring IAEC uses 

a fixed test load size, larger ovens with higher thermal mass will have a higher measured IAEC. 

As a result, DOE considered available data to characterize the relationship between IAEC and 

oven cavity volume. 

DOE determined the slope of the baseline curves by first reviewing data from the 2009 

TSD, which presented a relationship between measured energy factor (EF) and cavity volume for 

each product class. DOE believes these slopes continue to be relevant based on DOE’s testing 

described in the previous sections. Because DOE is proposing to use IAEC to establish 

incremental efficiency levels in this NOPR, DOE translated the EF determined using the 2009 

TSD relationship to IAEC by assuming a baseline standby mode energy consumption. DOE 

plotted baseline IAEC versus cavity volume for each product class and compared it to the 

measured test data discussed in section 5.5.2 as shown in Figure 5.5.2 through Figure 5.5.5.

Figure 5.5.2 Electric Standard Oven Slope

150

170

190

210

230

250

270

290

310

330

350

0 1 2 3 4 5 6

IEAC (kWh)

Cavity Volume (ft3)

2009 TSD Slope Converted from EF

DOE 2014 Data (Standard, Free)

DOE 2014 Data (Standard, Built)

New Slope - Free

New Slope - Built
5-26

Figure 5.5.3 Electric Self-Clean Slope

Figure 5.5.4 Gas Standard Oven Slope

0

50

100

150

200

250

300

350

400

450

0 1 2 3 4 5 6 7

IEAC (kWh)

Cavity Volume (ft3)

2009 TSD Slope Converted from EF

DOE 2014 Data (Self-Clean, Free)

DOE 2014 Data (Self-Clean, Built)

New Slope - Free

New Slope - Built

0

500

1000

1500

2000

2500

0 1 2 3 4 5 6

IEAC (kWh)

Cavity Volume (ft3)

2009 TSD Slope Converted from EF

DOE 2014 Data (Standard, Free)

DOE 2014 Data (Standard, Built)

New Slope - Free

New Slope - Built
5-27

Figure 5.5.5 Gas Self-Clean Oven Slope

Although the relationship between IAEC and cavity volume derived using the 2009 slope 

was not linear, DOE notes that the Canadian and European Union energy conservation standards 

(as discussed in section 3.7 of chapter 3 of this NOPR TSD) also use a linear relationship 

between energy consumption and cavity volume. DOE performed a linear curve fit on the IAEC 

evaluated for discrete cavity volumes that were considered to represent the range of cavity 

volumes available on the market. The resulting IAEC versus cavity volume equations were used 

to establish the baseline slope for each product class. If necessary, the baseline intercepts were 

adjusted so that none of the ovens in the DOE test sample were cut off by the baseline curve. 

DOE also noted that baseline built-in/slide-in conventional ovens would consume more energy

than freestanding ovens, so DOE offset the baseline intercepts for these product classes by 

adding an assumed value for fan-only energy mode energy consumption.

For electric ovens, DOE validated this approach using the data available in the NRCan

product databases.h DOE notes that this data is based annual energy consumption measured 

using the same test procedure considered for the previous DOE standards rulemaking. DOE used 

these annual energy consumption values from the NRCan product database to estimate IAEC by 

subtracting clock energy and adding baseline standby energy consumption and fan-only mode 

energy consumption. Figure 5.5.6 compares the NRCan built-in oven data against the proposed 

DOE baseline equation discussed above. 

 

h Available at: http://oee.nrcan.gc.ca/pml-lmp/index.cfm?action=app.search-recherche&appliance=OVENS_E. The 

NRCan product databases do not include information for conventional gas ovens.

0

500

1000

1500

2000

2500

0 1 2 3 4 5 6

IEAC (kWh)

Cavity Volume (ft3)

2009 TSD Slope Converted from EF

DOE 2014 Data (Self-Clean, Free)

DOE 2014 Data (Self-Clean, Built)

New Slope - Free

New Slope - Built
5-28

Figure 5.5.6 NRCan Electric Oven Data and New DOE Slope for Electric Ovens

Although some ovens in the NRCan database have IAEC values exceeding the DOE’s 

proposed baseline, the majority of the products in both product classes meet the DOE’s proposed 

baseline criteria. Values for the slopes and finalized intercepts for each conventional oven 

product class are presented in Table 5.5.8 and Table 5.5.9. The intercepts for each incremental

efficiency level were then chosen so that the equations pass through the desired IAEC 

corresponding to a particular volume. 

Table 5.5.8 Slopes and Intercepts of Electric Oven IAEC versus Cavity Volume 

Relationship

Level

Standard Electric Ovens Self-Clean Electric Ovens

Slope = 31.8 Slope = 42.3

Freestanding 

Intercepts

Built-in / Slide-in 

Intercepts

Freestanding 

Intercepts

Built-in / Slide-in 

Intercepts

Baseline 157.74 164.78 173.12 179.18

1 147.82 154.62 163.24 169.13

2 134.98 141.47 145.28 150.86

3 122.45 128.64 97.05 101.81

4 118.20 124.29 96.24 100.98

5 107.91 113.75 - -

6 71.10 76.07 - -

7 70.54 75.49 - -

0

50

100

150

200

250

300

350

400

450

0 1 2 3 4 5 6

IAEC (kWh/yr)

Cavity Volume (ft3)

NRCan Standard Electric Ovens

NRCan Self-Clean Electric Ovens

New DOE Slope - Standad Built

New DOE Slope - Self-Clean Built
5-29

Table 5.5.9 Slopes and Intercepts of Gas Oven IAEC versus Cavity Volume Relationship

Level

Standard Gas Ovens Self-Clean Gas Ovens

Slope = 214.4 Slope = 214.4

Freestanding 

Intercepts

Built-in / Slide-in 

Intercepts

Freestanding 

Intercepts

Built-in / Slide-in 

Intercepts

Baseline 1196.3 1206.2 961.8 971.8

1 727.4 735.1 926.3 936.0

2 692.7 700.3 746.7 755.5

3 568.8 575.8 674.4 682.8

4 492.9 499.5 669.1 677.5

5 478.7 485.2 - -

6 433.7 440.1 - -

7 425.1 431.4 - -

5.5.5 Cost Estimates

For the models in the NOPR analysis teardown sample, DOE developed manufacturer 

cost estimates based on the method outlined in section 5.4.4.

5.5.5.1 Baseline Cost Estimates

From the product teardowns discussed above, DOE developed the following baseline 

manufacturer product costs (MPCs) for each of the conventional cooking product, product 

classes. All costs presented are in 2013 dollars.

Table 5.5.10 Baseline Manufacture Product Costs for all Product Classes

Product

Class Product Type Sub-Category Installation Type

Baseline 

Manufacturer 

Product Cost 

(2014$)

1

Electric oven

Standard with or without a 

catalytic line

Freestanding $265.22

2 Built-in/Slide-in $280.76

3 Self-clean Freestanding $291.26

4 Built-in/Slide-in $306.80

5

Gas oven

Standard with or without a 

catalytic line

Freestanding $294.34

6 Built-in/Slide-in $309.88

7 Self-clean Freestanding $362.98

8 Built-in/Slide-in $378.52

5.5.5.2 Incremental Cost Estimates

Based on the analyses discussed above, DOE developed the cost-efficiency results for 

each product class shown in Table 5.5.11 through Table 5.5.14. Where available, DOE 

developed incremental MPCs based on manufacturing cost modeling of test units in its sample 
5-30

featuring the proposed design options. For design options that were not observed in DOE’s 

sample of test units for this NOPR, DOE used the incremental MPCs developed as part of the 

2009 TSD, then adjusted the values to reflect changes in the Bureau of Labor Statistics’ Producer 

Price Index (PPI) for household cooking appliance manufacturing.iDOE notes that the estimated 

incremental MPCs would be equivalent for the freestanding and built-in/slide-in oven product 

classes.

Table 5.5.11 Electric Standard Oven Incremental Manufacturing Product Cost (2014$)

Level Design Option Cost Source Cost

Baseline - - -

1 Baseline + SMPS Teardown Analysis $0.82

2 1 + Reduced Vent Rate 2009 TSD $2.76

3 2 + Improved Insulation Teardown Analysis $7.89

4 3 + Improved Door Seals Teardown Analysis $10.22

5 4 + Forced Convection Teardown Analysis $34.40

6 5 + Oven Separator Teardown Analysis $66.14

7 6 + Reduced Conduction Losses 2009 TSD $70.36

Table 5.5.12 Electric Self-Clean Incremental Manufacturing Product Cost (2014$)

Level Design Option Cost Source Cost

Baseline - - -

1 Baseline + SMPS Teardown Analysis $0.82

2 1 + Forced Convection Teardown Analysis $25.00

3 2 + Oven Separator Teardown Analysis $56.74

4 3 + Reduced Conduction Losses 2009 TSD $61.93

Table 5.5.13 Gas Standard Oven Incremental Manufacturing Product Cost (2014$)

Level Design Option Cost Source Cost

Baseline - - -

1 Baseline + Optimized Burner/Cavity Teardown Analysis $0.00

2 1 + SMPS Teardown Analysis $0.82

3 2 + Electronic Spark Ignition Teardown Analysis $7.31

4 3 + Improved Insulation Teardown Analysis $12.44

5 4 + Improved Door Seals Teardown Analysis $14.77

6 5 + Forced Convection Teardown Analysis $35.43

7 6 + Reduced Conduction Losses 2009 TSD $39.74

 

i

Available at: http://www.bls.gov/ppi/. 
5-31

Table 5.5.14 Gas Self-Clean Oven Incremental Manufacturing Product Cost (2014$)

Level Design Option Cost Source Cost

Baseline - - -

1 Baseline + SMPS Teardown Analysis $0.82

2 1 + Electronic Spark Ignition Teardown Analysis $7.31

3 2 + Forced Convection Teardown Analysis $27.96

4 3 + Reduced Conduction Losses 2009 TSD $33.15
CHAPTER 6. MARKUPS FOR EQUIPMENT PRICE DETERMINATION

TABLE OF CONTENTS

6.1 INTRODUCTION ........................................................................................................... 6-1

6.1.1 Distribution Channels...................................................................................................... 6-1

6.1.2 Markup Calculation Procedure ........................................................................................ 6-2

6.1.2.1 Approach for Manufacturer Markups............................................................ 6-2

6.1.2.2 Approach for Retailer Markups..................................................................... 6-2

6.1.2.3 Overall Markup.............................................................................................. 6-5

6.2 MANUFACTURER MARKUPS.................................................................................... 6-6

6.3 RETAILER MARKUP FOR CONVENTIONAL COOKING PRODUCTS ................. 6-7

6.4 SALES TAXES ............................................................................................................... 6-8

6.5 SUMMARY OF MARKUPS ........................................................................................ 6-10

LIST OF TABLES

Table 6.1.1 Four-Firm Concentration Ratio for Major Appliance Sales in Three Retail 

Channels............................................................................................................... 6-5

Table 6.2.1 Major Appliance Manufacturer Gross Margins and Markups............................. 6-7

Table 6.3.1 Data for Calculating Baseline Markup: Electronics and Appliance Stores......... 6-7

Table 6.3.2 Data for Calculating Incremental Markup: Electronics and Appliance 

Stores.................................................................................................................... 6-8

Table 6.4.1 Average Sales Tax Rates by Census Division and Large State ........................... 6-9

Table 6.5.1 Summary of Markups ........................................................................................ 6-10

6-i
CHAPTER 6. MARKUPS FOR EQUIPMENT PRICE DETERMINATION

6.1 INTRODUCTION

To carry out its economic analyses of potential new energy conservation standards for 

conventional cooking products, the U.S. Department of Energy (DOE) must determine the cost 

to the consumer of both baseline products (i.e., products not subject to new energy conservation 

standards) and more efficient products. DOE applies two types of markups, depending on the 

type of product: (1) baseline markups on the direct business costs of products having baseline 

efficiency (baseline products) and (2) incremental markups on incremental product costs of 

higher-efficiency products. DOE estimated consumer prices for baseline products by applying a 

baseline markup to the manufacturer selling prices (MSP) estimated in the engineering analysis. 

For products having higher-than-baseline efficiency, DOE estimated consumer prices by 

applying appropriate markups to the incremental MSP estimated in the engineering analysis. 

DOE developed one set of markups for all conventional cooking products. 

In the rulemaking for conventional cooking products, DOE is considering one product 

type: conventional ovens. DOE has identified eight product classes for conventional ovens.

6.1.1 Distribution Channels

The consumer equipment price depends on the distribution channel through which 

products move from manufacturers to purchasers. At each point in the distribution channel, 

companies mark up the price of the equipment to cover their business costs and profit margin. 

DOE based the distribution channel on data from the Association of Home Appliance 

Manufacturers (AHAM).1

 AHAM estimates that 93 percent of conventional cooking products

are sold through retail outlets. Because an overwhelming majority of products are sold through 

retail outlets, DOE assumed that all of the products are sold to retail outlets by manufacturers, 

and then purchased by consumers from retail outlets, as shown in Figure 6.1.1 .

 

Figure 6.1.1 Distribution Channel for Cooking Products

 

Manufacturer Retailer Consumer

6-1
6.1.2 Markup Calculation Procedure

At each point in the distribution channel, companies mark up the price of the equipment 

to cover their business costs and profit margin. In financial statements, gross margin is the 

difference between the company revenue and the company cost of sales or cost of goods sold 

(CGS). Inputs for calculating the gross margin include all corporate costs — overhead costs 

(sales, general, and administration); research and development (R&D) and interest expenses; 

depreciation, and taxes—and profits. In order for sales of a product to contribute positively to 

company cash flow, the price of products must include a markup greater than the corporate gross 

margin. Individual products may command a lower or higher markup, depending on their 

perceived added value and the competition they face from similar products in the market. In 

developing markups for manufacturers and retailers, DOE obtained data about the revenue, CGS, 

and expenses of firms that produce and sell cooking products. DOE’s approach categorizes the 

expenses into two categories: invariant costs, which are fixed labor and occupancy expenses that 

increase in proportion to the amount of labor required to produce or sell the product, and variant 

costs, which are variable operating costs that do not scale with labor and vary in proportion to 

CGS. 

6.1.2.1 Approach for Manufacturer Markups

DOE applies manufacturer markups to transform a manufacturer’s equipment cost into a 

manufacturer sales price (MSP). Using the CGS and gross margin, DOE calculated the 

manufacturer markup (MUMFG) with the following equation:

MFG

MFG MFG

MFG CGS

CGS GM

MU

+ =

where:

MUMFG = Manufacturer markup,

CGSMFG = Manufacturer’s cost of goods sold, and

GMMFG = Manufacturer’s gross margin.

6.1.2.2 Approach for Retailer Markups

DOE based the retailer markups for cooking products on financial data for electronics 

and appliance stores from the 2012 U.S. Census Annual Retail Trade Survey (ARTS), which is 

the most recent survey that includes industry-wide detailed operating expenses for that economic 

sector.2

 DOE organized the financial data into statements that break down cost components 

incurred by firms in the sector. DOE assumes that the income statements faithfully represent the 

various average costs incurred by firms selling home appliances. Although electronics and 

appliance stores handle multiple commodity lines, the data provide the best available indication 

of expenses incurred during the sale of cooking products. 

6-2
The baseline markup transforms the manufacturer sales price of baseline products to the 

retailer sales price. DOE considers baseline models to be products sold under current market 

conditions (i.e., without new energy conservation standards). DOE used the following equation 

to calculate an average baseline markup (MUBASE) for retailers.

RTL

RTL RTL

BASE CGS

CGS GM MU+ =

Where:

MUBASE = retailer’s baseline markup,

CGSRTL = retailer’s cost of goods sold (CGS), and

GMRTL = retailer’s gross margin (GM). 

Incremental markups are coefficients that relate the change in the MSP of higherefficiency models to the change in retailer sales price. DOE considers higher-efficiency models 

to be products sold under market conditions after implementation of new efficiency standards. 

The incremental markup reflects the retailer’s increase in a product’s CGS due to new or 

amended efficiency standards.

There is, unfortunately, a lack of empirical data regarding appliance retailer markup 

practices in response to a product’s cost increase (due to increased efficiency or other factors). 

DOE understands that real-world markup practices vary depending on the market conditions that 

retailers face and on the magnitude of the change in CGS. Pricing in retail stores also may 

involve rules of thumb that are difficult to quantify and to incorporate into DOE’s analysis.

Given the uncertainty about actual markup practices in appliance retailing, DOE’s 

approach reflects the following key concepts:

 

1. Changes in the efficiency of products sold are not expected to increase economic profits. 

Thus, DOE calculates markups/gross margins to allow cost recovery for retail companies 

in the distribution channel (including changes in the cost of capital) without changes in 

company profits. 

2. Efficiency improvements affect some distribution costs but not others. DOE estimates 

retail prices using markups that reflect the distribution costs expected to change with 

efficiency, but not the distribution costs that are not expected to change with efficiency. 

The approach to incremental markups is described in more detail in Dale et al (2004).3 To 

estimate incremental retailer markups, DOE divides retailers’ operating expenses into two 

categories: (1) those that do not change when CGS increases because of amended efficiency 

standards (“invariant”), and (2) those that increase proportionately with CGS (“variant”). DOE 

defines invariant costs as including labor and occupancy expenses, because those costs likely 

will not increase as a result of a rise in CGS. All other expenses, as well as net profit, are 

assumed to vary in proportion to CGS. Although it is possible that some other expenses may not 

6-3
scale with CGS, DOE takes a conservative position that includes other expenses as variant costs. 

Note that under DOE’s approach, a high fixed cost component yields a low incremental markup. 

DOE used the following equation to calculate the incremental markup (MUINCR) for 

retailers.

RTL

RTL RTL

INCR CGS

CGS VC MU+ =

Where:

MUINCR = retailer’s incremental markup,

CGSRTL = retailer’s cost of goods sold, and

VCRTL = retailer’s variant costs.

In developing incremental markups, DOE treats profits as constant over time. Although 

retailers may be able to reap higher profits for a limited time, DOE’s approach assumes that 

competition in the appliance retail market, combined with relatively inelastic demand (i.e., the 

demand is not expected to decrease significantly in response to a relatively small increase in 

price), will exert downward pressure on retail margins. 

To measure the degree of competition in appliance retailing, DOE estimated the four-firm 

concentration ratio (FFCR) of major appliance sales in three retail channels: electronics and 

appliance stores, building materials and supplies dealers, and general merchandise stores.

The FFCR represents the market share of the four largest firms in a given sector. Generally, an 

FFCR of less than 40 percent indicates that the sector is not concentrated; an FFCR of more than 

70 percent indicates that a sector is highly concentrated.4, 5

The FFCR of appliance sales within each retail channel is estimated as the sector FFCR 

times the percent of total sales within each channel accounted for by major appliances. As shown 

in Table 6.1.1, appliance sales in electronics and appliance stores, household appliance stores, 

building materials and supplies dealers, and general merchandise stores have a FFCR less than 

the 40-percent threshold.6 The electronics and appliance stores sector includes a subsector titled 

“household appliance stores.” Because that subsector includes numerous stores, it has a FFCR of 

only 21.3 percent.

6-4
Table 6.1.1 Four-Firm Concentration Ratio for Major Appliance Sales in Three 

Retail Channels 

Sector FFCR (% of 

Sector Sales)

Percent of Sales 

Accounted for by 

Major Appliances (%)

FFCR

(% of Major 

Appliance Sales)

Electronics and appliance 

stores 46.3 42.1 19.5

Subsector: household 

appliance stores 21.3 37.1 7.9

Building materials and 

supplies dealers 45.9 17.0 7.8

General merchandise stores 73.2 31.6 23.1

Source: U.S. Economic Census. Establishment and Firm Size (Including Legal Form of Organization). 2007.

*Note: It is assumed that major appliance sales are uniformly distributed within all firms in each sector.

6.1.2.3 Overall Markup

The overall markup is the product of the manufacturer and retailer markups, as well as 

sales taxes. 

DOE used the overall baseline markup to estimate the consumer equipment price of 

baseline models, given the manufacturer cost of the baseline models. As stated above, DOE 

considers baseline models to be equipment sold under existing market conditions (i.e., without 

new energy efficiency standards). The following equation shows how DOE applied the overall 

baseline markup to determine the equipment price for baseline models.

( ) EQPBASE = COSTMFG × MU MFG × MUBASE ×TaxSALES = COSTMFG × MUOVERALL _ BASE

where:

EQPBASE = Consumer equipment price for baseline models,

COSTMFG = Manufacturer cost for baseline models,

MUMFG = Manufacturer markup,

MUBASE = Baseline retailer markup,

TaxSALES = Sales tax, and

MUOVERALL_BASE = Baseline overall markup (product of manufacturer markup, baseline 

retailer markup, and sales tax).

Similarly, DOE used the overall incremental markup to estimate changes in the consumer 

equipment price, given changes in the manufacturer cost above the baseline model cost resulting 

from a standard to raise equipment efficiency. The total consumer equipment price for higherefficiency models is composed of two components: the consumer equipment price of the baseline 

model and the change in consumer equipment price associated with the increase in manufacturer 

cost to meet the new efficiency standard. The following equation shows how DOE used the 

6-5
overall incremental markup to determine the consumer equipment price for higher-efficiency 

models (i.e., models meeting new efficiency standards). 

( )

BASE MFG OVERALL INCR

STD MFG OVERALL BASE MFG MFG INCR SALES

EQP COST MU

EQP COST MU COST MU MU Tax

_

_

= + D ×

= × + D × × ×

where:

EQPSTD = Consumer equipment price for models meeting new efficiency 

standards,

EQPBASE = Consumer equipment price for baseline models, 

COSTMFG = Manufacturer cost for baseline models,

ΔCOSTMFG = Change in manufacturer cost for higher-efficiency models,

MUMFG = Manufacturer markup,

MUINCR = Incremental retailer markup,

TaxSALES = Sales tax,

MUOVERALL_BASE = Baseline overall markup (product of manufacturer markup, baseline 

retailer markup, and sales tax), and

MUOVERALL_INCR = Incremental overall markup (product of manufacturer markup, 

incremental retailer markup, and sales tax).

6.2 MANUFACTURER MARKUPS

DOE developed an average manufacturer markup by examining the annual Securities and 

Exchange Commission (SEC) 10-K reports filed by two publicly-traded manufacturers primarily 

engaged in appliance manufacturing and whose combined product range includes conventional 

cooking products.

7 The two manufacturers represent over 40 percent of the market share for

covered conventional cooking products. Because these companies are diversified, producing a 

range of different appliances, an industry average markup was assumed by DOE to be 

representative for the manufacture of each type of covered product. DOE evaluated markups for 

the years between 2007 and 2013, inclusive. 

Table 6.2.1 lists the average corporate gross margin during the years from 2007 to 2013, 

and corresponding markups, for both of the manufacturers. 

6-6
Table 6.2.1 Major Appliance Manufacturer Gross Margins and Markups

Mfr A Mfr B

Average Net Revenues (Million) $18,494 $15,606

Corporate Gross Margin 15.0% 20.9%

Markup 1.18 1.26

Source: SEC 10-K reports (2007-20013)

The weighted average markup value based on the market share of these two major 

publicly traded conventional cooking product manufacturers is 1.20.

6.3 RETAILER MARKUP FOR CONVENTIONAL COOKING PRODUCTS

The 2012 ARTS data for electronics and appliance stores provide total sales data and 

detailed operating expenses.8 To construct a complete data set for estimating markups, DOE 

needed to estimate CGS and GM. The most recent 2012 ARTS publishes a separate document 

containing historical sales and gross margin from 1993 to 2012 for household appliance stores. 

DOE combined the GM as a percent of sales reported for 2012 with the detailed operating 

expenses data from 2012 ARTS to construct a complete income statement for electronics and 

appliance stores. DOE used these data to estimate both baseline and incremental markups. 

Table 6.3.1 shows the calculation of the baseline retailer markup, which is estimated to 

be 1.39.

Table 6.3.1 Data for Calculating Baseline Markup: Electronics and Appliance Stores

Business Item Amount ($1,000,000)

Sales 102,998

Cost of goods sold (CGS) 73,946

Gross margin (GM) 29,052

Baseline markup = (CGS+GM)/CGS 1.39 Source: U.S. Census, 2012 Annual Retail Trade Survey.

Table 6.3.2 shows the breakdown of operating expenses for electronics and appliance 

stores as reported in the 2012 ARTS. The incremental markup is estimated to be 1.13.

6-7
Table 6.3.2 Data for Calculating Incremental Markup: Electronics and Appliance 

Stores

Business Item Amount 

($1,000,000)

Sales 102,998

Cost of goods sold (CGS) 73,946

Gross margin (GM) 29,052

Labor & Occupancy Expenses (invariant)

Annual payroll 11,371

Employer costs for fringe benefit 2,023

Contract labor costs, including temporary help 209

Purchased utilities, total 529

Cost of purchased repair and maintenance services 386

Cost of purchased professional and technical services 1,117

Purchased communication services 362

Lease and rental payments 3,166

Taxes and license fees (mostly income taxes) 451

Subtotal: 19,617

Other Operating Expenses & Profit (variant)

Expensed equipment 75

Cost of purchased packaging and containers 47

Other materials and supplies not for resale 463

Cost of purchased transportation, shipping, and warehousing services 567

Cost of purchased advertising and promotional services 1,961

Cost of purchased software 122

Cost of data processing and other purchased computer services, except 

communications + commissions paid 280

Depreciation and amortization charges 1,564

Other operating expenses 2,113

Net profit before tax (operating profit) 2,243

Subtotal: 9,435

Incremental markup = (CGS + Total Other Operating Expenses and 

Profit)/CGS 1.13

 Source: U.S. Census. 2012 Annual Retail Trade Survey.

6.4 SALES TAXES

The sales tax comprises state and local taxes applied to the price a consumer pays for a 

product. The sales tax is a multiplicative factor that increases the consumer product price.

6-8
DOE derived state and local taxes from data provided by the Sales Tax Clearinghouse.9

The data represent weighted averages that include county and city rates. DOE then derived 

population-weighted average tax values for each RECS region, as shown in Table 6.4.1.

10

Table 6.4.1 Average Sales Tax Rates by Census Division and Large State

RECS 

Region

State(s) U.S. Population in 

2019 (projected)

2014 Tax 

Rate (%)

1 Connecticut, Maine, New Hampshire, Rhode Island, 

Vermont

8,453,982 5.13

2 Massachusetts 6,855,546 6.25

3 New York 19,576,920 8.40

4 New Jersey 9,461,635 6.95

5 Pennsylvania 12,787,354 6.40

6 Illinois 13,236,720 8.05

7 Indiana, Ohio 18,271,066 6.87

8 Michigan 10,695,993 6.00

9 Wisconsin 6,004,954 5.45

10 Iowa, Minnesota, North Dakota, South Dakota 10,353,316 6.86

11 Kansas, Nebraska 4,693,244 7.13

12 Missouri 6,199,882 7.20

13 Virginia 8,917,395 5.60

14 Delaware, District of Columbia, Maryland, West 9,742,487 5.59

15 Georgia 10,843,753 7.10

16 North Carolina, South Carolina 15,531,866 7.00

17 Florida 23,406,525 6.65

18 Alabama, Kentucky, Mississippi 12,198,158 7.25

19 Tennessee 6,780,670 9.45

20 Arkansas, Louisiana, Oklahoma 11,515,069 8.67

21 Texas 28,634,896 7.95

22 Colorado 5,278,867 6.10

23 Idaho, Montana, Utah, Wyoming 6,285,110 5.29

24 Arizona 8,456,448 7.20

25 Nevada, New Mexico 5,536,624 7.31

26 California 42,206,743 8.45

27 Oregon, Washington, Alaska, Hawaii 13,879,323 5.30

Population-weighted average 7.144

6-9
6.5 SUMMARY OF MARKUPS

Table 6.5.1 summarizes the markups at each stage in the distribution channel and the 

overall baseline and incremental markups, as well as sales taxes.

Table 6.5.1 Summary of Markups

Markup Baseline Incremental

Manufacturer 1.20

Retailer 1.39 1.13

Sales Tax 1.071

Overall 1.79 1.45

6-10
REFERENCES

1 Association of Home Appliance Manufacturers. AHAM 2003 Fact Book, 2003. Washington, 

DC. p. 25.

2 U.S. Census Bureau, 2012 Annual Retail Trade Survey, Electronics and Appliance Stores.

2012: Washington, DC. http://www.census.gov/retail/index.html

3 Dale, L. et al, An Analysis of Price Determination and Markups in the Air-Conditioning and 

Heating Equipment Industry, January 2004. 

4 University of Maryland University College: 

http://info.umuc.edu/mba/public/AMBA607/IndustryStructure.html.

5 Quick MBA: http://www.quickmba.com/econ/micro/indcon.shtml.

6 U.S. Economic Census. Establishment and Firm Size (Including Legal Form of 

Organization). 2007.

7 Security Exchange Commission, SEC 10-K Reports, Various dates, 2007-2013, Security 

Exchange Commission. http://www.sec.gov/

8 U.S. Census Bureau, 2012 Annual Retail Trade Survey, Electronics and Appliance Stores.

2012: Washington, DC. http://www.census.gov/retail/index.html

9 Sales Tax Clearinghouse Inc., State Sales Tax Rates Along with Combined Average City and 

County Rates, 2014. (Last accessed July 18, 2014.) http://thestc.com/STrates.stm

10 U.S. Census Bureau, Population Division, Table A1: Interim Projections of the Total 

Population for the United States and States: April 1, 2000 to July 1, 2030. 2005.

6-11

 
CHAPTER 7. ENERGY USE ANALYSIS

TABLE OF CONTENTS

7.1 INTRODUCTION ........................................................................................................... 7-1

7.2 AVERAGE ANNUAL ENERGY CONSUMPTION ..................................................... 7-1

7.2.1 Annual Energy Consumption of Energy-Using Components.......................................... 7-4

7.2.2 Annual Energy Consumption by Efficiency Level.......................................................... 7-5

7.2.3 Variability of Annual Energy Consumption.................................................................. 7-10

LIST OF TABLES

Table 7.2.1 Annual Energy Consumption of Baseline Electric and Gas Ovens Based 

on DOE Test Procedure Energy Use Calculations .............................................. 7-4

Table 7.2.2 Component Annual Energy Use of Baseline Ovens............................................ 7-5

Table 7.2.3 Electric Standard Ovens - Freestanding: Annual Energy Consumption by 

Efficiency Level................................................................................................... 7-6

Table 7.2.4 Electric Standard Ovens – Built-in/Slide-in: Annual Energy Consumption 

by Efficiency Level.............................................................................................. 7-6

Table 7.2.5 Electric Self-Clean Ovens - Freestanding: Annual Energy Consumption 

by Efficiency Level.............................................................................................. 7-7

Table 7.2.6 Electric Self-Clean Ovens – Built-in/Slide-in: Annual Energy 

Consumption by Efficiency Level ....................................................................... 7-7

Table 7.2.7 Gas Standard Ovens - Freestanding: Annual Energy Consumption by 

Efficiency Level................................................................................................... 7-8

Table 7.2.8 Gas Standard Ovens – Built-in/Slide-in: Annual Energy Consumption by 

Efficiency Level................................................................................................... 7-8

Table 7.2.9 Gas Self-Clean Ovens - Freestanding: Annual Energy Consumption by 

Efficiency Level................................................................................................... 7-9

Table 7.2.10 Gas Self-Clean Ovens – Built-in/Slide-in: Annual Energy Consumption 

by Efficiency Level.............................................................................................. 7-9

Table 7.2.11 Annual Energy Use of Baseline Ovens with corresponding RECS Cooking 

Frequency........................................................................................................... 7-11

LIST OF FIGURES

Figure 7.2.1 Historical Estimates of Annual Electric Range Energy Use................................ 7-3

Figure 7.2.2 Historical Estimates of Annual Gas Range Energy Use...................................... 7-3

Figure 7.2.3 Distribution of Baseline Electric Standard Oven Annual Energy Use 

Based on 2009 RECS Cooking Frequency ........................................................ 7-12

7-i
Figure 7.2.4 Distribution of Baseline Electric Self-Cleaning Oven Annual Energy Use 

Based on 2009 RECS Cooking Frequency ........................................................ 7-12

Figure 7.2.5 Distribution of Baseline Gas Standard Oven Annual Energy Use Based on 

2009 RECS Cooking Frequency........................................................................ 7-13

Figure 7.2.6 Distribution of Baseline Gas Self-Cleaning Oven Annual Energy Use 

Based on 2009 RECS Cooking Frequency ........................................................ 7-14

7-ii
CHAPTER 7. ENERGY USE ANALYSIS

7.1 INTRODUCTION

To carry out the life-cycle cost (LCC) and payback period (PBP) calculations described 

in Chapter 8, DOE needed to determine the operating cost savings to consumers from moreefficient equipment. The LCC and PBP analysis requires data on annual energy use because, 

along with energy prices, DOE uses these data to establish the most significant component of 

consumer operating costs. (Maintenance and repair costs are the other contributors to operating 

cost.) This chapter describes how DOE determined the annual energy consumption of residential 

electric and gas ranges and how more-efficient equipment impacts annual energy consumption.

The annual energy consumption of electric and gas ranges has been in continual decline 

since the late 1970s. DOE’s 2009 technical support document (TSD) identified several studies 

that estimated the annual energy consumption of electric and gas ranges.1

 The studies that 

covered the time period of 1977–2004 showed a steady decline in the annual energy 

consumption. More recent studies from the 2010 California Residential Appliance Saturation 

Study (CA RASS)2 and the Florida Solar Energy Center (FSEC)3 show that the decline has 

somewhat levelled off in the annual energy consumption. 

7.2 AVERAGE ANNUAL ENERGY CONSUMPTION

Based on the research conducted for the 1996 TSD, DOE published revisions to its test 

procedure as a final rule in 1997 that included a reduction in the annual useful cooking energy 

output and a reduction in the number of self-cleaning oven cycles per year.

4

 The annual useful 

cooking energy output relates the efficiency of the cooking appliance to the annual energy 

consumption. 

The DOE test procedure implicitly assumes that any electrical energy consumption in a 

gas range is allocated to the oven rather than the cooking top.

For electric self-cleaning and non-self-cleaning ovens, the following DOE test procedure 

equation is used to determine the total annual energy consumption (EAO):

O

O

AO R

O

E =

where:

OO = 29.3 kWh per year, annual useful cooking energy output, and

RO = Oven energy factor. 

7-1
For gas self-cleaning and non-self-cleaning ovens, the annual energy consumption is 

composed of gas energy (EAOG) and electrical energy (EAOE). The following DOE test procedure 

equation is used to determine the total annual energy consumption:

O

O

AOG AOE e R

O

E + E × K =

where:

OO = 88.8 kBtu per year, annual useful cooking energy output,

RO = Oven energy factor, and 

Ke = 3,412 Btu/kWh, conversion factor for kWh to Btus. 

Based on the baseline cooking energy efficiency established in the engineering analysis

for conventional cooking products, the annual energy consumption of electric and gas ovens can 

be determined using the above DOE test procedure equations. 

DOE identified two additional studies that confirmed the continued downward trend in 

electric and gas range energy use: (1) the 2010 CA RASS and (2) a 2010 study conducted by 

FSEC. The CA RASS reported an average electric range annual energy consumption of 265

kWh per year while the FSEC study reported an average electric range annual energy 

consumption of 310 kWh per year, both of which are lower than the consumption values derived 

from the DOE test procedure for a non-self-cleaning range (266 + 253 = 519 kWh) and a selfcleaning range (266 + 298 = 563 kWh). The CA RASS also reported an average gas range 

annual energy consumption of 34.1 therms (3410 kBtu) per year.

Using the data from the studies in the 2009 TSD, the CA RASS, and FSEC, Figure 7.2.1 

and Figure 7.2.2 show how the annual energy consumption of electric ranges and gas ranges, 

respectively, have varied over time. The figures indicate whether the estimates came from 

metered studies or conditional demand analyses. The figures below demonstrate that the annual 

energy use of cooking products has continued to decline over time. As a result, DOE believes 

that an electric range annual energy consumption of 287.5 kWh per year (the average of the CA 

RASS and FSEC studies) is more representative of baseline annual energy use than that derived 

from the DOE test procedure. 

7-2
Figure 7.2.1 Historical Estimates of Annual Electric Range Energy Use

Figure 7.2.2 Historical Estimates of Annual Gas Range Energy Use

Table 7.2.1 shows the integrated annual energy consumption (IAEC), the new metric for 

efficiency of conventional cooking products, which are also based on DOE’s test procedure and 

0

200

400

600

800

1000

1200

1400

1600

1800

2000

2200

1975 1980 1985 1990 1995 2000 2005 2010

Annual Electric Cooking Use (kWh/yr)

Year

Metered

CDA

0

20

40

60

80

100

120

140

Annual Gas Cooking Energy (therms/yr)

1975 1980 1985 1990 1995 2000 2005 2010

Year

CDA

Metered

7-3
associated annual energy consumption based on the CA RASS and FSEC studies. DOE 

considers the annual energy consumption values to be representative of electric and gas cooking 

energy usage circa the mid-1990s. Note that, because the annual useful cooking energy output 

values based on the updated annual energy use data are lower than those in the current DOE test 

procedure, the annual energy consumption for each oven are lower than the IAEC shown in 

Table 7.2.1.

Table 7.2.1 Annual Energy Consumption of Baseline Electric and Gas Ovens Based on 

DOE Test Procedure Energy Use Calculations

Product Type

Baseline Efficiency Level 

(Proposed IAEC)**

Annual Energy 

Consumption

Electric Standard Ovens, FreeStanding 294.5 kWh 135.6 kWh

Electric Standard Ovens, BuiltIn/Slide-In 301.5 kWh 135.6.0kWh

Electric Self-Clean Ovens, FreeStanding 355.0 kWh 174.6 kWh

Electric Self-Clean Ovens, BuiltIn/Slide-In 361.1 kWh 175.3 kWh

Gas Standard Ovens, Free-Standing 2,118.2 kBtu 1,040.1 kBtu

Gas Standard Ovens, Built-In/Slide-In 2,128.1 kBtu 1,040.1 kBtu

Gas Self-Clean Ovens, Free-Standing 1,883.8 kBtu 1,126.3 kBtu

Gas Self-Clean Ovens, Built-In/SlideIn 1,893.7 kBtu 1,127.6 kBtu

** Proposed IAEC baseline efficiency levels are normalized based on a 4.3 ft3 volume for ovens

7.2.1 Annual Energy Consumption of Energy-Using Components

DOE performed several calculation steps to disaggregate the representative baseline 

average annual energy consumption value for an electric range (287.5 kWh per year) into 

appropriate energy use values for the various energy-using components of electric and gas ovens. 

The calculations are presented in Appendix 7A. Table 7.2.2 shows the results of these 

calculations for ovens. In the tables, DOE presents the energy use values for standard ovens and 

self-cleaning ovens with their disaggregated energy use components (i.e., cooking, ignition, selfcleaning, and standby) that correspond to the baseline efficiency levels (see Table 7.2.1). 

7-4
Table 7.2.2 Component Annual Energy Use of Baseline Ovens

Energy Use 

Components

Electric 

Standard Oven

Electric Self-Clean 

Oven

Gas Standard 

Oven

Gas Self-Clean 

Oven

Freestanding

BuiltIn/

SlideIn

FreestandingBuilt-In/

Slide-In

Freestanding

BuiltIn/

Slide-In

FreestandingBuilt-In/

Slide-In

Cooking

Efficiency 10.9% 10.6% 9.9% 9.7% 4.4% 4.3% 5.7% 5.7%

Electric 

(kWh/yr) 114.1 114.1 123.9 123.9

Gas (kBtu/yr) 883.1 883.1 685.5 685.5

Self-Cleaning

Electric 

(kWh/yr) 32.8 32.8 5.8 5.8

Gas (kBtu/yr) 217.8 217.8

Ignition

Electric 

(kWh/yr) 43.5 43.5 43.5 43.5

Gas (kBtu/yr)

Standby 

(kWh/yr) 19.2 19.2 19.2 19.2 19.2 19.2 19.2 19.2

Total 135.6 

kWh

135.6

kWh

174.6 

kWh

175.3 

kWh

1,040.1 

kBtu

1,040.1 

kBtu

1,126.3 

kBtu

1,127.6 

kBtu

Annual Useful 

Cooking 

Energy Output

12.4

kWh

12.4

kWh 12.4kWh 12.4kWh

37.7

kBtu

37.7

kBtu

37.7

kBtu

37.7

kBtu

IAEC 294.5 

kWh

301.5 

kWh

355.0 

kWh

361.1 

kWh

2,118.2 

kBtu

2,128.1 

kBtu

1,883.8 

kBtu

1,893.7 

kBtu

7.2.2 Annual Energy Consumption by Efficiency Level

As discussed in Chapter 5, for the purposes of developing the cost-efficiency 

relationships of electric and gas range cooking products, DOE analyzed four efficiency levels for 

electric and gas self-clean ovens, and seven efficiency levels for electric and gas standard ovens, 

in addition to the baseline level. The following tables present the annual energy consumption of 

electric and gas range cooking products by efficiency level. DOE based the baseline annual 

energy consumption for each cooking product on the ‘total’ values shown in Table 7.2.2. 

Table 7.2.3 and Table 7.2.4 show the electric standard oven IAEC as well as their 

corresponding annual energy consumption. The baseline annual energy consumption of 135.6

kWh is taken from Table 7.2.2 and consists of two components: cooking energy and standby

energy. For efficiency levels 5, 6, and 7, it includes energy for forced convection through a 

convection fan. The convection fan has a test energy consumption of 1.07 kWh and 1.10 kWh

7-5
per year for freestanding and built-in units, respectively. The standby is based on a power 

consumption of 2.2 Watts in the baseline, and 1.1 Watts for efficiency levels 1 through 7. DOE 

determined the annual cooking energy consumption for a more efficient level by taking the ratio 

of the cooking efficiencies of the more efficient and baseline levels and multiplying it by the 

baseline annual cooking energy consumption. 

Table 7.2.3 Electric Standard Ovens - Freestanding: Annual Energy Consumption by 

Efficiency Level

Level

IAEC

(kWh)

Cooking Energy Non-Cooking* 

Energy Total

kWh/year kWh/year kWh/year

Baseline 294 116.3 19.3 135.6 

1 285 116.2 9.6 125.8 

2 272 110.7 9.6 120.4 

3 259 105.4 9.6 115.1 

4 255 103.7 9.6 113.3 

5 245 98.8 10.8 109.6 

6 208 83.3 10.8 94.1 

7 207 83.0 10.8 93.9 

*Includes Standby Energy

Table 7.2.4 Electric Standard Ovens – Built-in/Slide-in: Annual Energy Consumption 

by Efficiency Level

Level

IAEC

(kWh)

Cooking Energy Non-Cooking* 

Energy Total

kWh/year kWh/year kWh/year

Baseline 302 116.3 19.3 135.6 

1 291 116.1 9.6 125.7 

2 278 110.7 9.6 120.3 

3 265 105.4 9.6 115.0 

4 261 103.6 9.6 113.2 

5 250 98.7 10.9 109.6 

6 213 83.2 10.9 94.1 

7 212 83.0 10.9 93.8 

*Includes Standby and Forced Convection Energy

Table 7.2.5 and Table 7.2.6 show the electric self-clean oven IAEC and cooking 

efficiencies as well as their corresponding annual energy consumption. The baseline annual 

energy consumption values of 174.6 kWh and 175.3 are taken from Table 7.2.2 for freestanding 

7-6
and built-in models, respectively, and consist of three components: cooking energy, self-cleaning 

energy, and standby. For efficiency levels 2, 3, and 4, it includes energy for forced convection 

through a convection fan. The convection fan has a test energy consumption of 1.18 kWh and 

1.21 kWh per year for freestanding and built-in units, respectively. The self-cleaning energy is 

based on data from DOE’s test procedure, i.e., a consumption of 5.8 kWh per self-cleaning cycle

for freestanding and built-in units. There are four self-cleaning cycles per year. The standby is 

based on a power consumption of 2.2 Watts in the baseline and 1.1 Watts for efficiency levels 1 

through 4. DOE determined the annual cooking energy consumption for a more efficient level 

by taking the ratio of the cooking efficiencies of the more efficient and baseline levels and 

multiplying it by the baseline annual cooking energy consumption. DOE assumed that selfcleaning remains constant with increased efficiency. 

Table 7.2.5 Electric Self-Clean Ovens - Freestanding: Annual Energy Consumption by 

Efficiency Level

Level

IAEC

(kWh)

Cooking Energy Non-Cooking* 

Energy Total

kWh/year kWh/year kWh/year

Baseline 355 118.4 56.2 174.6 

1 345 118.3 46.6 164.9 

2 327 110.7 47.9 158.6 

3 279 91.5 47.9 139.5 

4 278 91.2 47.9 139.1 

*Includes Standby, Forced Convection and Self-Clean Energy

Table 7.2.6 Electric Self-Clean Ovens – Built-in/Slide-in: Annual Energy Consumption 

by Efficiency Level

Level

IAEC

(kWh)

Cooking Energy Non-Cooking* 

Energy Total

kWh/year kWh/year kWh/year

Baseline 361 118.4 56.8 175.3 

1 351 118.3 47.2 165.5 

2 333 110.6 48.6 159.2 

3 284 91.5 48.6 140.1 

4 283 91.2 48.6 139.8 

*Includes Standby, Forced Convection and Self-Clean Energy

Table 7.2.7 and Table 7.2.8 show the gas standard oven IAEC along with their 

corresponding annual energy consumption. The baseline annual energy consumption of 1,040.1

kBtu is taken from Table 7.2.2 and consists of three components: cooking energy, ignition 

energy and standby. For efficiency levels 6 and 7, it includes energy for forced convection 

through a convection fan. The convection fan has a test energy consumption of 2.48 kWh and 

7-7
2.49 kWh per year for freestanding and built-in units, respectively. The standby is based on a 

power consumption of 2.2 Watts in the baseline and 1.1 Watts for efficiency levels 1 through 7. 

DOE determined the annual cooking energy consumption for a more efficient level by taking the 

ratio of the IAEC of the more efficient and baseline levels and multiplying it by the baseline 

annual cooking energy consumption. 

Table 7.2.7 Gas Standard Ovens - Freestanding: Annual Energy Consumption by 

Efficiency Level

Level

IAEC

kBtu/yr

Cooking Energy Non-Cooking* 

Energy Total

kBtu/yr kBtu/yr kBtu/yr

Baseline 2,118 831.3 208.8 1040.1

1 1,649 626.6 208.8 835.4

2 1,615 625.8 175.9 801.7

3 1,491 571.7 175.9 747.6

4 1,415 603.4 32.9 636.3

5 1,401 597.2 32.9 630.1

6 1,356 573.9 41.0 614.9

7 1,347 570.1 41.0 611.1

*Includes Standby, Forced Convection and Ignition Energy

Table 7.2.8 Gas Standard Ovens – Built-in/Slide-in: Annual Energy Consumption by 

Efficiency Level

Level

IAEC

kBtu/yr

Cooking Energy Non-Cooking*

Energy Total

kBtu/yr kBtu/yr kBtu/yr

Baseline 2,128 831.3 208.8 1040.1

1 1,657 626.7 208.8 835.5

2 1,622 625.8 175.9 801.8

3 1,498 571.8 175.9 747.7

4 1,421 603.1 32.9 636.0

5 1,407 596.9 32.9 629.8

6 1,362 573.6 41.1 614.7

7 1,353 569.8 41.1 610.9

*Includes Standby, Forced Convection and Ignition Energy

Table 7.2.9 and Table 7.2.10 show the gas self-clean oven IAEC along with their 

corresponding annual energy consumption. The baseline annual energy consumption of 1,126.3

kBtu and 1,127.6 kBtu for free-standing and built-in ovens is taken from Table 7.2.2 and consists 

7-8
of four components: cooking energy, self-clean energy, ignition energy and standby. The 

baseline annual energy consumption assumes that the oven uses a globar or hot surface ignition 

device which, has a test energy consumption of 43.52 kWh per year. For efficiency levels 2, 3, 

and 4, it includes energy for forced convection through a convection fan. The convection fan has 

a test energy consumption of 2.21 kWh and 2.22 kWh per year for freestanding and built-in 

units, respectively. The self-cleaning energy is based on test data namely, gas consumption of 

57.45 kBtu and electrical consumption of 1.53 kWh per self-cleaning cycle. There are four selfcleaning cycles per year. The standby is based on a power consumption of 2.2 Watts in the 

baseline and 1.1 Watts for efficiency levels 1 through 4. DOE determined the annual cooking 

energy consumption for a more efficient level by taking the ratio of the IAEC of the more 

efficient and baseline levels and multiplying it by the baseline annual cooking energy 

consumption. DOE assumed that the self-cleaning energy remain constant with increased 

efficiency. 

Table 7.2.9 Gas Self-Clean Ovens - Freestanding: Annual Energy Consumption by 

Efficiency Level

Level

IAEC

kBtu/yr

Cooking Energy Non-Cooking*

Energy Total

kBtu/yr kBtu/yr kBtu/yr

Baseline 1,884 660.8 465.5 1126.3

1 1,848 659.6 432.6 1092.2

2 1,669 645.1 283.4 928.5

3 1,596 607.9 291.0 898.9

4 1,591 605.4 291.0 896.4

*Includes Standby, Self-Clean, Forced Convection, and Ignition Energy

Table 7.2.10 Gas Self-Clean Ovens – Built-in/Slide-in: Annual Energy Consumption by 

Efficiency Level

Level

IAEC

kBtu/yr

Cooking Energy Non-Cooking* 

Energy Total

kBtu/yr kBtu/yr kBtu/yr

Baseline 1,894 660.8 466.8 1127.6

1 1,858 659.5 433.9 1093.4

2 1,677 644.7 284.7 929.4

3 1,605 607.5 292.3 899.9

4 1,599 605.0 292.3 897.4

*Includes Standby, Self-Clean, Forced Convection, and Ignition Energy

7-9
7.2.3 Variability of Annual Energy Consumption

DOE’s Energy Information Administration (EIA) conducts a Residential Energy 

Consumption Survey (RECS) that collects energy-related data for occupied primary housing 

units in the U.S. The 2009 RECS collected data from 12,083 housing units representing almost 

113.6 million households.5 The RECS indicates which households in the survey use electric and 

gas ranges and ovens. With regard to electric cooking products, 7347 household records have 

standard ovens, and 5166 household records have self-cleaning ovens. With regard to gas 

cooking products, 3626 household records have standard ovens, and 1995 household records 

have self-cleaning ovens. The above totals represent ovens in households as either a stand-alone 

unit or as part of a range.

Although RECS does not provide the annual energy consumption of the cooking product 

for each household record, it does provide the frequency of cooking use. For each household 

using a conventional cooking product, RECS provides data on the frequency of use and number 

of meals cooked in the following bins: (1) less than once per week, (2) once per week, (3) a few 

times per week, (4) once per day, (5) two times per day, and (6) three or more times per day. 

Thus, DOE can utilize the frequency of use to define the variability of the annual energy 

consumption. Conducting the analysis in this manner captures the observed variability in annual 

energy consumption while maintaining the average annual energy consumption shown above in 

Table 7.2.1. To determine the variability of cooking product energy consumption, DOE first 

equated the weighted-average cooking frequency from RECS with the average energy use values 

reported in Table 7.2.1. Table 7.2.11presents the weighted-average cooking frequency values 

along with the corresponding annual energy use values from Table 7.2.1. 

7-10
Table 7.2.11 Annual Energy Use of Baseline Ovens with corresponding RECS Cooking 

Frequency

Ovens

Electric Gas

Standard

Freestanding/BuiltIn

Self-Clean

Freestanding/Built

-In

Standard

Freestanding/Built

-In

Self-Clean

Freestanding/BuiltIn

Annual Energy 

Consumption

135.6/135.6

kWh

174.6/175.3

kWh

1040.1/1040.1

kBtu

1126.3/1127.6

kBtu

RECS average

cooking frequency

(meals per day) 0.72 0.69 0.68 0.68

DOE then varied the annual energy consumption for each RECS household based on its 

reported cooking frequency. DOE determined the annual cooking energy consumption for each 

RECS household with an oven based on the following equation:

O _ AVG

AO _ AVG

AO _ HH O _ HH Freq

E

E = Freq ×

where:

EAO_HH = Oven annual energy consumption for specific RECS household,

FreqO_HH = Oven frequency for specific RECS household,

EAO_AVG = Average oven annual energy consumption (from Table 7.2.15); 135.6 kWh 

for electric standard ovens; 174.5 kWh and 175.3 kWh for electric self-clean

freestanding and built-in ovens respectively; 1040.1 kBtu for gas standard 

ovens; and 1126.3 kBtu and 1127.6 kBtu for gas self-clean freestanding and 

built-in ovens respectively, and 

FreqO_AVG = Average oven frequency (from Table 7.2.10); 0.72 meals/day for electric 

standard ovens; 0.69 meals/day for electric self-clean ovens; 0.68 meals/day 

for gas standard ovens; and 0.68 meals per day for gas self-clean ovens.

For all RECS households oven cooking frequency varies between zero and four meals per 

day. Figures 7.2.3 through 7.2.8 show the probability distributions of annual cooking energy 

consumption based on correlating the average cooking energy use to the cooking frequency data 

from RECS. 

Figures 7.2.3 and 7.2.4 show the distribution of annual energy use for electric standard 

and self-cleaning ovens, respectively.

7-11
Figure 7.2.3 Distribution of Baseline Electric Standard Oven 

Annual Energy Use Based on 2009 RECS Cooking 

Frequency

Figure 7.2.4 Distribution of Baseline Electric Self-Cleaning Oven

Annual Energy Use Based on 2009 RECS Cooking 

Frequency

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0 50 100 150 200 250 300 350 400 450 500 550

Annual Energy Use (kWh / year)

Probability

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0 50 100 150 200 250 300 350 400 450 500 550

Annual Energy Use (kWh/ year)

Probability

7-12
Figures 7.2.5 and 7.2.6 show the distribution of annual energy use for gas standard and 

self-cleaning ovens, respectively.

Figure 7.2.5 Distribution of Baseline Gas Standard Oven Annual 

Energy Use Based on 2009 RECS Cooking Frequency

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.10

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0

Annual Energy Use (MMBtu/year)

Probability

7-13
Figure 7.2.6 Distribution of Baseline Gas Self-Cleaning Oven 

Annual Energy Use Based on 2009 RECS Cooking 

Frequency

As will be described later in Chapter 8 on the LCC and PBP analysis, DOE used the 

RECS household samples with their associated baseline annual cooking energy consumption to 

conduct the LCC and PBP analysis.

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.10

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

Annual Energy Use (MMBtu/year)

Probability

7-14
REFERENCES

1 U.S. Department of Energy-Office of Codes and Standards. Technical Support Document for 

Consumer Products and Commercial and Industrial Equipment: Residential Dishwashers, 

Dehumidifiers, and Cooking Products, and Commercial Clothes Washers, March 2009. 

<http://www1.eere.energy.gov/buildings/appliance_standards/product.aspx/

productid/57#23standards> 

2 California Energy Commission. 2009 California Residential Appliance Saturation Study, 

October 2010. Prepared for the California Energy Commission by KEMA, Inc. Contract 

No. 200-2010-004. < http://www.energy.ca.gov/2010publications/CEC-200-2010-004/CEC200-2010-004-V2.PDF> 

3 FSEC 2010. Updated Miscellaneous Electricity Loads and Appliance Energy Usage Profiles 

for Use in Home Energy Ratings, the Building America Benchmark and Related 

Calculations. Published as FSEC-CR-1837-10, Florida Solar Energy Center, Cocoa, FL. 

<http://www.fsec.ucf.edu/en/publications/pdf/FSEC-CR-1837-10-R01.pdf> 

4 U.S. Federal Register. Energy Conservation Program for Consumer Products: Test 

Procedure for Kitchen Ranges and Ovens, Cooking tops, Ovens, and Microwave Ovens, 

Final Rule, October 3, 1997. 62 FR 51976. 

5 U.S. Department of Energy-Energy Information Administration. Residential Energy 

Consumption Survey, 2009 Public Use Data Files, 2009. Washington, DC. 

<http://www.eia.gov/consumption/residential/data/2009/index.cfm?view=microdata> 

7-15
CHAPTER 8. LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

TABLE OF CONTENTS

8.1 INTRODUCTION ........................................................................................................... 8-1

8.1.1 General Approach to Analysis......................................................................................... 8-1

8.1.2 Overview of Inputs to Analysis ....................................................................................... 8-3

8.2 INPUTS TO LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS ................ 8-5

8.2.1 Inputs to Total Installed Cost........................................................................................... 8-5

8.2.1.1 Forecasting Future Product Prices................................................................. 8-6

8.2.1.2 Baseline Manufacturer Costs....................................................................... 8-13

8.2.1.3 Incremental Manufacturer Cost by Efficiency Level................................... 8-14

8.2.1.4 Overall Markup............................................................................................ 8-17

8.2.1.5 Installation Cost ........................................................................................... 8-17

8.2.1.6 Total Installed Cost...................................................................................... 8-18

8.2.2 Inputs to Operating Cost ................................................................................................ 8-21

8.2.2.1 Annual Energy Consumption....................................................................... 8-23

8.2.2.2 Energy Prices............................................................................................... 8-26

8.2.2.3 Energy Price Trends..................................................................................... 8-32

8.2.2.4 Repair and Maintenance Costs..................................................................... 8-34

8.2.2.5 Product Lifetime .......................................................................................... 8-36

8.2.3 Discount Rate................................................................................................................. 8-38

8.2.4 Compliance Date of Standard ........................................................................................ 8-43

8.2.5 Product Energy Efficiency in the Base Case ................................................................. 8-43

8.3 RESULTS OF LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSES.......... 8-46

8.3.1 Summary of Results....................................................................................................... 8-47

8.3.1.2 Distribution of Impacts................................................................................ 8-55

8.3.1.3 Range of Impacts ......................................................................................... 8-62

8.4 REBUTTABLE PAYBACK PERIOD.......................................................................... 8-67

8.4.1 Inputs to the Rebuttable Payback Period Analysis........................................................ 8-68

8.4.2 Results of Rebuttable Payback Period Analysis............................................................ 8-68

LIST OF TABLES

Table 8.2.1 Inputs to Total Installed Cost............................................................................... 8-5

Table 8.2.2 Baseline Manufacturer Costs............................................................................. 8-14

Table 8.2.3 Electric Standard Ovens, Freestanding: Incremental Manufacturer Cost by 

Efficiency Level................................................................................................. 8-15

Table 8.2.4 Electric Standard Ovens, Built-In/Slide-In: Incremental Manufacturer Cost 

by Efficiency Level............................................................................................ 8-15

8-i
Table 8.2.5 Electric Self-Clean Ovens, Freestanding: Incremental Manufacturer Cost 

by Efficiency Level............................................................................................ 8-15

Table 8.2.6 Electric Self-Clean Ovens, Built-In/Slide-In: Incremental Manufacturer 

Cost by Efficiency Level ................................................................................... 8-16

Table 8.2.7 Gas Standard Ovens, Freestanding: Incremental Manufacturer Cost by 

Efficiency Level................................................................................................. 8-16

Table 8.2.8 Gas Standard Oven, Built-In/Slide-In: Incremental Manufacturer Cost by 

Efficiency Level................................................................................................. 8-16

Table 8.2.9 Gas Self-Clean Ovens, Freestanding: Incremental Manufacturer Cost by 

Efficiency Level................................................................................................. 8-17

Table 8.2.10 Gas Self-Clean Ovens, Built-In/Slide-In: Incremental Manufacturer Cost 

Increases by Efficiency Level............................................................................ 8-17

Table 8.2.11 Cooking Products: Overall Markup................................................................... 8-17

Table 8.2.12 Cooking Range (1 Oven): Baseline Installation Costs ...................................... 8-18

Table 8.2.13 Electric Standard Ovens, Freestanding: Consumer Product Prices, 

Installation Costs, and Total Installed Costs...................................................... 8-18

Table 8.2.14 Electric Standard Ovens, Built-In/Slide-In: Consumer Product Prices, 

Installation Costs, and Total Installed Costs...................................................... 8-19

Table 8.2.15 Electric Self-Clean Ovens, Freestanding: Consumer Product Prices, 

Installation Costs, and Total Installed Costs...................................................... 8-19

Table 8.2.16 Electric Self-Clean Ovens, Built-In/Slide-In: Consumer Product Prices, 

Installation Costs, and Total Installed Costs...................................................... 8-19

Table 8.2.17 Gas Standard Ovens, Freestanding: Consumer Product Prices, Installation 

Costs, and Total Installed Costs......................................................................... 8-20

Table 8.2.18 Gas Standard Ovens, Built-In/Slide-In: Consumer Product Prices, 

Installation Costs, and Total Installed Costs...................................................... 8-20

Table 8.2.19 Gas Self-Clean Ovens, Freestanding: Consumer Product Prices, 

Installation Costs, and Total Installed Costs...................................................... 8-20

Table 8.2.20 Gas Self-Clean Ovens, Built-In/Slide-In: Consumer Product Prices, 

Installation Costs, and Total Installed Costs...................................................... 8-21

Table 8.2.21 Inputs for Operating Cost................................................................................... 8-21

Table 8.2.22 Electric Standard Ovens, Freestanding: Annual Energy Consumption by 

Efficiency Level................................................................................................. 8-23

Table 8.2.23 Electric Standard Ovens, Built-In/Slide-In: Annual Energy Consumption 

by Efficiency Level............................................................................................ 8-24

Table 8.2.24 Electric Self-Clean Ovens, Freestanding: Annual Energy Consumption by 

Efficiency Level................................................................................................. 8-24

Table 8.2.25 Electric Self-Clean Ovens, Built-In/Slide-In: Annual Energy Consumption 

by Consumption by Efficiency Level ................................................................ 8-24

Table 8.2.26 Gas Standard Ovens, Freestanding: Annual Energy Consumption by 

Efficiency Level................................................................................................. 8-25

Table 8.2.27 Gas Standard Ovens, Built-In/Slide-In: Annual Energy Consumption by 

Efficiency Level................................................................................................. 8-25

8-ii
Table 8.2.28 Gas Self-Clean Ovens, Freestanding: Annual Energy Consumption by 

Efficiency Level................................................................................................. 8-25

Table 8.2.29 Gas Self-Clean Ovens, Built-In/Slide-In: Annual Energy Consumption by 

Efficiency Level................................................................................................. 8-26

Table 8.2.30 Average Residential Electricity Prices in 2013 ................................................. 8-27

Table 8.2.31 Average Residential Natural Gas Prices in 2013............................................... 8-29

Table 8.2.32 Residential Marginal Electricity Price Factors using EIA 2003-2013 Data ...... 8-31

Table 8.2.33 Residential Marginal Natural Gas Price Factors using EIA 2003-2013 

Data .................................................................................................................... 8-32

Table 8.2.34 Repair/Maintenance Findings for Gas Cooking Product Ignition Systems....... 8-35

Table 8.2.35 Repair/Maintenance Costs for Gas Cooking Product Ignition Systems............ 8-36

Table 8.2.36 Conventional Cooking Products Lifetime Reference Values............................ 8-36

Table 8.2.38 Definitions of Income Groups ........................................................................... 8-39

Table 8.2.39 Types of Household Debt and Equity by Percentage Shares (%)...................... 8-40

Table 8.2.40 Data Used to Calculate Real Effective Mortgage Rates.................................... 8-41

Table 8.2.41 Average Real Effective Interest Rates for Household Debt .............................. 8-41

Table 8.2.42 Average Nominal and Real Interest Rates for Household Equity ..................... 8-42

Table 8.2.43 Average Real Effective Discount....................................................................... 8-43

Table 8.2.44 Oven User Ownership by Fuel Type ................................................................. 8-45

Table 8.2.45 Best-fit Logit Parameters for Conventional Cooking Products......................... 8-45

Table 8.2.46 Base Case Market Share for Electric Ovens by Efficiency Level in 2019 ....... 8-46

Table 8.2.47 Base Case Market Share for Gas Ovens by Efficiency Level in 2019 ............. 8-46

Table 8.3.1 Average LCC and PBP Results by Efficiency Level for Electric Standard 

Ovens, Freestanding........................................................................................... 8-47

Table 8.3.2 Average LCC Savings Relative to the Base-Case Efficiency Distribution 

for Electric Standard Ovens, Freestanding ........................................................ 8-48

Table 8.3.3 Average LCC and PBP Results by Efficiency Level for Electric Standard 

Ovens, Built-In/Slide-In..................................................................................... 8-48

Table 8.3.4 Average LCC Savings Relative to the Base-Case Efficiency Distribution 

for Electric Standard Ovens, Built-In/Slide-In .................................................. 8-49

Table 8.3.5 Average LCC and PBP Results by Efficiency Level for Electric Self-Clean 

Ovens, Free-Standing......................................................................................... 8-49

Table 8.3.6 Average LCC Savings Relative to the Base-Case Efficiency Distribution 

for Electric Self-Clean Ovens, Freestanding ..................................................... 8-50

Table 8.3.7 Average LCC and PBP Results by Efficiency Level for Electric Self-Clean 

Ovens, Built-In/Slide-In..................................................................................... 8-50

Table 8.3.8 Average LCC Savings Relative to the Base-Case Efficiency Distribution 

for Electric Self-Clean Ovens, Built-In/Slide-In ............................................... 8-51

Table 8.3.9 Average LCC and PBP Results by Efficiency Level for Gas Standard 

Ovens, Freestanding........................................................................................... 8-51

Table 8.3.10 Average LCC Savings Relative to the Base-Case Efficiency Distribution 

for Gas Standard Ovens, Freestanding............................................................... 8-52

Table 8.3.11 Average LCC and PBP Results by Efficiency Level for Gas Standard 

Ovens, Built-In/Slide-In..................................................................................... 8-52

8-iii
Table 8.3.12 Average LCC Savings Relative to the Base-Case Efficiency Distribution 

for Gas Standard Ovens, Built-In/Slide-In......................................................... 8-53

Table 8.3.13 Average LCC and PBP Results by Efficiency Level for Gas Self-Clean 

Ovens, Freestanding........................................................................................... 8-53

Table 8.3.14 Average LCC Savings Relative to the Base-Case Efficiency Distribution 

for Gas Self-Clean Ovens, Freestanding............................................................ 8-54

Table 8.3.15 Average LCC and PBP Results by Efficiency Level for Gas Self-Clean 

Ovens, Built-In/Slide-In..................................................................................... 8-54

Table 8.3.16 Average LCC Savings Relative to the Base-Case Efficiency Distribution 

for Gas Self-Clean Ovens, Built-In/Slide-In...................................................... 8-55

Table 8.4.1 Electric Ovens: Rebuttable Payback Periods..................................................... 8-69

Table 8.4.2 Gas Ovens: Rebuttable Payback Periods........................................................... 8-69

LIST OF FIGURES

Figure 8.1.1 Flow Diagram of Inputs for the Determination of LCC and PBP ....................... 8-4

Figure 8.2.1 Nominal and Deflated Gas Oven PPI from 1982 to 2014 ................................... 8-8

Figure 8.2.2 Nominal and Deflated Electric Oven PPI from 1970 to 2014 ............................. 8-8

Figure 8.2.3 Historical and Projected Shipments of Gas Ovens.............................................. 8-9

Figure 8.2.4 Historical and Projected Shipments of Electric Ovens...................................... 8-10

Figure 8.2.5 Relative Price versus Cumulative Shipments of Gas Ovens from 1982 to 

2014, with Power Law Fit.................................................................................. 8-11

Figure 8.2.6 Relative Price versus Cumulative Shipments of Electric Ovens from 1970 

to 2014, with Power Law Fit.............................................................................. 8-12

Figure 8.2.7 Price Forecast Indices for Gas Electric Cooktop and Ovens............................. 8-13

Figure 8.2.8 Electricity Price Trends...................................................................................... 8-33

Figure 8.2.9 Natural Gas Price Trends................................................................................... 8-33

Figure 8.3.1 Electric Standard Ovens, Freestanding: Base-Case LCC Distribution.............. 8-56

Figure 8.3.2 Electric Standard Ovens, Built-In/Slide-In: Base-Case LCC Distribution........ 8-57

Figure 8.3.3 Electric Self-Clean Ovens, Freestanding: Base-Case LCC Distribution........... 8-58

Figure 8.3.4 Electric Self-Clean Ovens, Built-In/Slide-In: Base-Case LCC Distribution..... 8-59

Figure 8.3.5 Gas Standard Ovens, Freestanding: Base-Case LCC Distribution .................... 8-59

Figure 8.3.6 Gas Standard Ovens, Built-In/Slide-In: Base-Case LCC Distribution .............. 8-60

Figure 8.3.7 Gas Self-Clean Ovens, Free-standing: Base-Case LCC Distribution................ 8-61

Figure 8.3.8 Gas Self-Clean Ovens, Built-In/Slide-In: Base-Case LCC Distribution ........... 8-62

Figure 8.3.9 Electric Self-Clean Ovens, Built-In/Slide-In: Distribution of LCC Impacts 

at EL1................................................................................................................. 8-62

Figure 8.3.10 Electric Standard Ovens, Freestanding: Range of LCC Savings....................... 8-63

Figure 8.3.11 Electric Standard Ovens, Built-In/Slide-In: Range of LCC Savings................. 8-64

Figure 8.3.12 Electric Self-Clean Ovens, Freestanding: Range of LCC Savings.................... 8-64

Figure 8.3.13 Electric Self-Clean Ovens, Built-In/Slide-In: Range of LCC Savings.............. 8-65

8-iv
Figure 8.3.14 Gas Standard Ovens, Freestanding: Range of LCC Savings............................. 8-65

Figure 8.3.15 Gas Standard Ovens, Built-In/Slide-In: Range of LCC Savings....................... 8-66

Figure 8.3.16 Gas Self-Clean Ovens, Freestanding: Range of LCC Savings.......................... 8-66

Figure 8.3.17 Gas Self-Clean Ovens, Built-In/Slide-In: Range of LCC Savings.................... 8-67

8-v
CHAPTER 8. LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

8.1 INTRODUCTION

This chapter of the notice of public rulemaking (NOPR) technical support document 

(TSD) describes the Department of Energy (DOE)’s method for analyzing the economic impacts 

of new energy conservation standards on individual consumers. The effects of standards on 

individual consumers include a change in operating expense (usually decreased) and a change in 

purchase price (usually increased). This chapter describes three metrics DOE used in the 

consumer analysis to determine the effect of standards on individual consumers of conventional 

cooking products:

• Life-cycle cost (LCC) is the total consumer expense over the life of an appliance, 

including purchase price and operating costs (including energy expenditures). DOE 

discounts future operating costs to the time of purchase, and sums them over the lifetime 

of the product.

• Payback period (PBP) measures the amount of time it takes a consumer to recover the 

assumed higher purchase price of more energy-efficient equipment through lower 

operating costs.

• Rebuttable payback period is a special case of the PBP. Whereas LCC and PBP are 

estimated over a range of inputs that reflect field conditions, rebuttable payback period is 

based on laboratory conditions, specifically inputs to DOE’s test procedure.

Inputs to the LCC and PBP are discussed in section 8.2 of this chapter. Results for the 

LCC and PBP are presented in section 8.3. The rebuttable PBP is discussed in section 8.4. Key 

variables and calculations are presented for each metric. DOE performed the calculations 

discussed herein using a series of Microsoft Excel® spreadsheets which are accessible on the 

Internet (http://www.eere.energy.gov/buildings/appliance_standards/). Details and instructions 

for using the spreadsheets are discussed in appendix 8A. 

8.1.1 General Approach to Analysis

DOE uses the following equation to calculate life-cycle cost (LCC), the total consumer 

expense throughout the life of an appliance. 

( ) ∑= + = +

N

t

t

t

r

OC LCC IC

1 1

Eq. 8.1

Where:

LCC = life-cycle cost in dollars,

IC = total installed cost in dollars,

8-1
∑ = sum over the appliance lifetime, from year 1 to year N,

N = lifetime of the appliance in years,

OC = operating cost in dollars, 

r = discount rate, and

t = year for which operating cost is being determined.

Numerically, the payback period (PBP), defined above, is the ratio of the increase in 

purchase cost (i.e., from a less energy efficient design to a more efficient design) to the decrease 

in annual operating expenditures. This type of calculation results in what is termed a simple 

payback period, because it does not take into account changes in operating expenses over time or 

the time value of money. That is, the calculation is done at an effective discount rate of zero 

percent. The equation for PBP is:

OC

IC PBP

∆

∆ =

Eq. 8.2

Where:

ΔIC = difference in total installed cost between the more energy efficient design and the 

baseline design, and 

ΔOC = difference in annual operating expenses. 

Payback periods are expressed in years. Payback periods greater than the life of the 

product indicate that the increased total installed cost is not recovered through reduced operating 

expenses.

Recognizing that several inputs to the determination of consumer LCC and PBP are 

either variable or uncertain, DOE conducted the LCC and PBP analysis by modeling both the 

uncertainty and variability of the inputs using Monte Carlo simulation and probability 

distributions. Appendix 8B provides a detailed explanation of Monte Carlo simulation and the 

use of probability distributions. DOE used Microsoft Excel spreadsheets combined with Crystal 

Ball (a commercially available add-in program) to develop LCC and PBP spreadsheet models 

that incorporate both Monte Carlo simulation and probability distributions. 

In addition to using probability distributions to characterize several of the inputs to the 

analysis, DOE developed a sample of individual households that use electric and gas ovens. By 

developing household samples, DOE was able to calculate the LCC and PBP for each household 

to account for the variability in energy consumption and/or energy price associated with a range 

of households.

As described in chapter 7 (section 7.2.3) of this NOPR TSD, DOE used the Energy 

Information Administration’s (EIA’s) 2009 Residential Energy Consumption Survey (RECS 

2009) to develop household samples for electric and gas ovens1

. The EIA designed RECS 2009, 

which consists of 12,083 housing units, to be a national representation of 113.6 million 

8-2
households in the United States. Although RECS does not provide the annual energy 

consumption of the cooking product for each household record, it does provide the frequency of 

cooking use. Refer to chapter 7 of this NOPR TSD for details. DOE used RECS to establish the 

variability of annual cooking energy use and of energy prices. DOE assigned unique number of 

meals cooked to each household in the sample. The variability among households in annual oven

use and/or energy pricing contributes to the range of LCCs and PBPs calculated for the baseline 

efficiency level and each increased efficiency level.

DOE displays the LCC results as distributions of impacts compared to baseline 

conditions. Results, which are presented in section 8.3, are based on 10,000 samples per Monte 

Carlo simulation run. To illustrate the implications of the analysis, DOE generated a frequency 

chart that depicts the variation in LCC for each efficiency level being considered.

8.1.2 Overview of Inputs to Analysis

DOE categorizes inputs to the LCC and PBP analysis as (1) inputs for establishing the 

purchase expense, otherwise known as the total installed cost, and (2) inputs for calculating 

operating costs. The primary inputs for establishing the total installed cost are listed below.

• Baseline manufacturer cost: The costs incurred by the manufacturer to produce 

products that meet current minimum efficiency standards. 

• Standard-level manufacturer cost increases: The change in manufacturer costs 

associated with producing products that meet a given standard level.

• Markups and sales tax: The increases associated with converting the manufacturer 

cost to a consumer product cost. 

• Installation cost: The cost to the consumer of installing the product. The installation 

cost represents all costs required to install the product other than the marked-up 

consumer product cost. The installation cost includes labor, overhead, and any 

miscellaneous materials and parts. Thus, the total installed cost equals the consumer 

product cost plus the installation cost. 

• Learning rate: The cost reduction factor associated with economies of scale and 

technology learning. 

The primary inputs for calculating operating costs are listed below.

• Product energy consumption: The on-site energy use associated with operating a 

product. 

• Product efficiency: The product energy consumption associated with standard-level 

products (i.e., products having efficiencies greater than those of baseline products). 

• Energy prices: The prices consumers pay for energy (e.g., electricity or natural gas). 

• Energy price trends: DOE used the EIA’s Annual Energy Outlook 2014 (AEO 2014) 

to project energy prices2

.

8-3
• Repair and maintenance costs: Repair costs are associated with repairing or replacing 

components that have failed. Maintenance costs are associated with maintaining the 

operation of the product.

• Lifetime: The age at which the product is retired from service. 

• Discount rate: The rate at which DOE discounts future expenditures to establish their 

present value. 

The data inputs for calculating the PBP for each TSL are the total installed cost of the 

product to the consumer for each energy efficiency level and the annual (first-year) operating 

expenditures. The inputs to total installed cost are the product cost plus the installation cost. The 

inputs to operating costs are the first year energy cost, the annual repair cost, and the annual 

maintenance cost. The PBP uses the same inputs as the LCC analysis, except the PBP does not 

require energy price trends or discount rates. Because the PBP is what is termed a simple 

payback, the required energy price is only for the year in which a new energy efficiency standard 

takes effect. The energy price DOE uses in the PBP calculation is the price projected for that 

year. Discount rates are also not required for calculating the simple PBP.

Figure 8.1.1 depicts the relationships among inputs to the calculation of the LCC and 

PBP. In the figure, the yellow boxes indicate inputs, the green boxes indicate intermediate 

outputs, and the blue boxes indicate final outputs (the LCC and PBP).

Figure 8.1.1 Flow Diagram of Inputs for the Determination of LCC and PBP

8-4
8.2 INPUTS TO LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

DOE gathered most of the data for performing the LCC and PBP analysis in 2014. DOE 

expresses dollar values in 2014$.

8.2.1 Inputs to Total Installed Cost

DOE uses the following equation to define the total installed cost.

IC = CPC + INST

Eq. 8.3

Where:

IC = total installed cost, 

CPC = consumer product cost (i.e., consumer cost for the product only), and 

INST = consumer cost to install the product.

The product cost depends on how the consumer purchases the product. As discussed in 

chapter 6 of this NOPR TSD, DOE defined markups and sales taxes for converting 

manufacturing costs into consumer product costs. Table 8.2.1summarizes the inputs for 

determining total installed cost.

Table 8.2.1 Inputs to Total Installed Cost

Baseline manufacturer cost

Standard-level manufacturer Cost

Markups throughout distribution chain

Sales tax (replacement applications)

Installation cost

The baseline manufacturer cost is the cost incurred by the manufacturer to produce 

products that meet current minimum efficiency standards. Standard-level manufacturer cost

increases are the change in manufacturer cost associated with producing products that meet a 

new standard level. Markups and sales tax convert the manufacturer cost to a consumer product 

cost. The installation cost represents all costs required for the consumer to install the product, 

other than the marked-up consumer product cost. The installation cost includes labor, overhead, 

and any miscellaneous materials and parts. 

DOE calculated the total installed cost for baseline products based on the following 

equation.

8-5
MFG OVERALL BASE BASE

BASE BASE BASE

COST MU INST

IC CPC INST

= × +

= +

_

Eq. 8.4

Where:

ICBASE = total installed cost for baseline model,

CPCBASE = consumer product cost for baseline model, 

INSTBASE = installation cost for baseline model,

COSTMFG = manufacturer cost for baseline model, and

MUOVERALL_BASE = overall baseline markup (product of manufacturer markup, baseline 

retailer or distributor markup, and sales tax).

DOE used the following equation to calculate the total installed cost for standard-level 

products.

( ) ( )

( ) ( )

( ) BASE MFG OVERALL INCR STD

BASE BASE STD STD

BASE STD BASE STD

STD STD STD

IC COST MU INST

CPC INST CPC INST

CPC CPC INST INST

IC CPC INST

= + D × + D

= + + D + D

= + D + + D

= +

_

Eq. 8.5

Where:

ICSTD = total installed cost for standard-level model,

CPCSTD = consumer product cost for standard-level model, 

INSTSTD = installation cost for standard-level model,

CPCBASE = consumer product cost for baseline model, 

ΔCPCSTD = change in product cost for standard-level model,

INSTBASE = baseline installation cost,

ΔINSTSTD = change in installation cost for standard-level model,

ICBASE = baseline total installed cost,

ΔCOSTMFG = change in manufacturer cost for standard-level model, and

MUOVERALL_INCR = overall incremental markup (product of manufacturer markup, incremental 

retailer or distributor markup, and sales tax).

The rest of this section provides information about each of the above input variables, 

which DOE used to calculate the total installed cost for conventional cooking products.

8.2.1.1 Forecasting Future Product Prices

Examination of historical price data for certain appliances and equipment that 

have been subject to energy conservation standards indicates that the assumption of constant real 

prices and costs may, in many cases, overestimate long-term trends in appliance and equipment 

prices. Economic literature and historical data suggest that the real costs of these products may in 

8-6
fact trend downward over time according to “learning” or “experience” curves. Desroches et al.

(2013) summarizes the data and literature currently available that is relevant to price projections

for selected appliances and equipment3

. The extensive literature on the “learning” or 

“experience” curve phenomenon is typically based on observations in the manufacturing sectora. 

In the experience curve method, the real cost of production is related to the cumulative 

production or “experience” with a manufactured product. This experience is usually measured in 

terms of cumulative production. A common functional relationship used to model the evolution 

of production costs in this case is:

Y = a X -b

Eq. 8.6

Where:

a = an initial price (or cost), 

b = a positive constant known as the learning rate parameter, 

X = cumulative production, and 

Y = the price as a function of cumulative production.

Thus, as experience (production) accumulates, the cost of producing the next unit 

decreases. The percentage reduction in cost that occurs with each doubling of cumulative

production is known as the learning rate (LR), given by:

LR = 1 – 2-b

Eq. 8.7

In typical learning curve formulations, the learning rate parameter is derived using two 

historical data series: cumulative production and price (or cost).

To derive the learning rate parameter for gas and electric ovens, DOE obtained historical 

Producer Price Index (PPI) data for “gas household ranges, ovens surface cooking units, and 

equipment” and “electric household ranges, ovens surface cooking units, and equipment” from 

the Bureau of Labor Statistics’ (BLS) spanning the time period 1982-2014 and 1970-2014, 

respectivelyb

. These are the most representative price indices for these two product categories. 

Inflation-adjusted price indices were calculated by dividing the PPI series by the gross domestic 

product-chained price index for the same years. These inflation-adjusted price indices (shown in 

Figure 8.2.1and Figure 8.2.2) ware used in subsequent analysis steps.

a In addition to Desroches (2013), see Weiss, M., Junginger, H.M., Patel, M.K., Blok, K., (2010a). A Review of 

Experience Curve Analyses for Energy Demand Technologies. Technological Forecasting & Social Change. 77:411-

428. 

b Product series ID: PCU3352213Y for gas household ranges, ovens, surface cooking units and equipment and 

PCU3352211Y for electric household ranges, ovens, surface cooking units and equipment. Available at: 

http://www.bls.gov/ppi/.

8-7

 
Figure 8.2.1 Nominal and Deflated Gas Oven PPI from 1982 to 2014

Figure 8.2.2 Nominal and Deflated Electric Oven PPI from 1970 to 2014

DOE assembled a time-series of annual shipments for 1970 to 2012 for both gas and 

electric ovens from Association of Household Appliance Manufacturers (AHAM) and Market 

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

0

20

40

60

80

100

120

140

160

Deflated PPI (2014=1)

Nominal PPI (1981=100)

Nominal Gas Oven PPI Deflated Gas Oven PPI

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0

20

40

60

80

100

120

19701972197419761978198019821984198619881990199219941996199820002002200420062008201020122014

Deflated PPI (2014=1)

Nominal PPI (1981=100)

Nominal Electric Oven PPI Deflated Electric Oven PPI

8-8
Research Magazine.

4 The annual shipments data were used to estimate cumulative shipments 

(production). Projected shipments after 2012 were obtained from the base case projections made 

for the NIA (see chapter 9 of this TSD). Figure 8.2.3 and Figure 8.2.4 show the shipments time 

series used in the analysis.

Figure 8.2.3 Historical and Projected Shipments of Gas Ovens

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

1960 1980 2000 2020 2040 2060

Shipments (millions)

Year

Historical Gas Oven

Shipments

Projected Gas Oven

Shipments

8-9
Figure 8.2.4 Historical and Projected Shipments of Electric Ovens

To estimate learning rate parameter, a least-squares power-law fit was performed on the 

deflated price index versus cumulative shipments. See Figure 8.2.5 and Figure 8.2.6.

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

1960 1980 2000 2020 2040 2060

Shipments (millions)

Year

Historical Electric Oven

Shipments

Projected Electric Oven

Shipments

8-10
Figure 8.2.5 Relative Price versus Cumulative Shipments of Gas Ovens

from 1982 to 2014, with Power Law Fit

y = 8.7221x-0.482

R2 = 0.959

0.5

0.7

0.9

1.1

1.3

1.5

1.7

1.9

0 50 100 150

Price Index (2014=1)

Cumulative Shipments

Gas Oven Learning (1982-

2014)

Power (Gas Oven Learning

(1982-2014))

8-11
Figure 8.2.6 Relative Price versus Cumulative Shipments of Electric Ovens

from 1970 to 2014, with Power Law Fit

The form of the fitting equation is:

P(X) = Po X -b

,

Eq. 8.8

where, the two parameters, b (the learning rate parameter) and Po (the price or cost of the first 

unit of production), are obtained by fitting the model to the data. DOE notes that the cumulative 

shipments on the right hand side of the equation can have a dependence on price, so there is an 

issue with simultaneity where the independent variable is not truly independent. DOE’s use of a 

simple least squares fit is equivalent to an assumption of no significant first price elasticity 

effects in the cumulative shipments variable.

For gas ovens, the parameter values obtained are:

Po = 8.722−1.211

+1.406(95% confidence), and 

b = 0.482±0.036 (95% confidence).

The estimated learning rate (defined as the fractional reduction in price expected from 

each doubling of cumulative production) is 28.4%±1.8% (95% confidence). 

For electric ovens, the parameter values obtained are:

y = 5.2572x-0.308

R2 = 0.822

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0 50 100 150 200

Price Index (2014=1)

Cumulative Shipments (million)

Electric Oven Learning (1970-

2014)

Power (Electric Oven

Learning (1970-2014))

8-12
Po = 5.257−0.865

+1.035 (95% confidence), and 

b = 0.308±0.044 (95% confidence).

The estimated learning rate (defined as the fractional reduction in price expected from 

each doubling of cumulative production) is 19.2%±2.5% (95% confidence). 

DOE derived two price factor indices, with 2014 equal to 1, to project prices for gas and 

electric ovens in each future year in the analysis period. The index value in a given year is a 

function of the LR and the cumulative production forecast through that year. DOE applied the 

same value to project prices for both product categories at each considered efficiency level. The 

estimated price forecast index is shown in Figure 8.2.7.

Figure 8.2.7 Price Forecast Indices for Gas Electric Cooktop and Ovens

8.2.1.2 Baseline Manufacturer Costs

DOE developed the baseline manufacturer costs for all eight product classes of 

conventional cooking products (described in chapter 5 of this NOPR TSD, Engineering 

Analysis). Also included in the table are the associated baseline integrated annual energy 

consumption values (IAEC). 

0.0

0.2

0.4

0.6

0.8

1.0

1.2

201420162018202020222024202620282030203220342036203820402042204420462048

Price Index Factor (2014=1)

Year

Gas Oven

Electric Oven

8-13
Table 8.2.2 Baseline Manufacturer Costs

Product Class

Integrated Annual Energy 

Consumption (IAEC)

Baseline 

Manufacturer 

Cost

(2014$)

Gas 

(kBtu/year)

Electricity

(kWh/year)

Electric Standard 

Ovens, 

Freestanding

-- 294.5 $265.22

Electric Standard 

Ovens, BuiltIn/Slide-In-- 301.5 $280.76

Electric Self-Clean 

Ovens, 

Freestanding

-- 355.0 $291.26

Electric Self-Clean 

Ovens, BuiltIn/Slide-in-- 361.1 $306.80

Gas Standard 

Ovens, 

Freestanding

2,118.2 -- $294.34

Gas Standard 

Ovens, BuiltIn/Slide-In2,128.1 -- $309.88

Gas Self-Clean

Ovens, 

Freestanding

1,883.8 -- $362.98

Gas Self-Clean

Ovens, BuiltIn/Slide-In1,893.7 -- $378.52

8.2.1.3 Incremental Manufacturer Cost by Efficiency Level

DOE used a reverse-engineering analysis to develop manufacturer cost increases 

associated with increases in the efficiency of conventional cooking products. Refer to Chapter 5, 

Engineering Analysis, of this NOPR TSD for details. Table 8.2.3 through Table 8.2.10 present 

the incremental manufacturer costs at each efficiency level for all eight product classes of 

conventional cooking products. Also included in each of the tables are the associated integrated 

annual energy consumption (IAEC) values.

8-14
Table 8.2.3 Electric Standard Ovens, Freestanding: Incremental Manufacturer Cost by 

Efficiency Level

EL

IAEC Manufacturer Cost Increase 

kWh/year (2014$)

Baseline 294.5 --

1 284.6 $0.82

2 271.7 $2.76

3 259.2 $7.89

4 254.9 $10.22

5 244.6 $34.40

6 207.8 $66.14

7 207.3 $70.36

Table 8.2.4 Electric Standard Ovens, Built-In/Slide-In: Incremental Manufacturer Cost 

by Efficiency Level

EL

IAEC Manufacturer Cost Increase 

kWh/year (2014$)

Baseline 301.5 --

1 291.4 $0.82

2 278.2 $2.76

3 265.4 $7.89

4 261.0 $10.22

5 250.5 $34.40

6 212.8 $66.14

7 212.2 $70.36

Table 8.2.5 Electric Self-Clean Ovens, Freestanding: Incremental Manufacturer Cost by 

Efficiency Level

EL

IAEC Manufacturer Cost Increase 

kWh/year (2014$)

Baseline 355.0 --

1 345.1 $0.82

2 327.2 $25.00

3 278.9 $56.74

4 278.1 $61.93

8-15
Table 8.2.6 Electric Self-Clean Ovens, Built-In/Slide-In: Incremental Manufacturer Cost 

by Efficiency Level

EL

IAEC Manufacturer Cost Increase 

kWh/year (2014$)

Baseline 361.1 --

1 351.0 $0.82

2 332.7 $25.00

3 283.7 $56.74

4 282.9 $61.93

Table 8.2.7 Gas Standard Ovens, Freestanding: Incremental Manufacturer Cost by 

Efficiency Level

EL

IAEC Manufacturer Cost 

kBtu/year Increase (2014$)

Baseline 2118.2 --

1 1649.3 $0.00

2 1614.7 $0.82

3 1490.7 $7.31

4 1414.8 $12.44

5 1400.6 $14.77

6 1355.6 $35.43

7 1347.0 $39.74

Table 8.2.8 Gas Standard Oven, Built-In/Slide-In: Incremental Manufacturer Cost by 

Efficiency Level

EL

IAEC Manufacturer Cost 

kBtu/year Increase (2014$)

Baseline 2128.1 --

1 1657.0 $0.00

2 1622.2 $0.82

3 1497.7 $7.31

4 1421.5 $12.44

5 1407.2 $14.77

6 1362.0 $35.43

7 1353.3 $39.74

8-16
Table 8.2.9 Gas Self-Clean Ovens, Freestanding: Incremental Manufacturer Cost by 

Efficiency Level

EL

IAEC Manufacturer Cost 

kBtu/year Increase (2014$)

Baseline 1883.8 --

1 1848.2 $0.82

2 1668.7 $7.31

3 1596.3 $27.96

4 1591.0 $33.15

Table 8.2.10 Gas Self-Clean Ovens, Built-In/Slide-In: Incremental Manufacturer Cost 

Increases by Efficiency Level

EL

IAEC Manufacturer Cost 

kBty/year Increase (2014$)

Baseline 1893.7 --

1 1858.0 $0.82

2 1677.5 $7.31

3 1604.7 $27.96

4 1599.4 $33.15

8.2.1.4 Overall Markup

The overall markup is the value determined by multiplying the manufacturer and retailer 

markups and the sales tax together to arrive at a single markup value. Table 8.2.14 shows the 

overall baseline and incremental markups for conventional cooking products. Refer to chapter 6 

of this NOPR TSD for details. 

Table 8.2.11 Cooking Products: Overall Markup

Markup Baseline Incremental

Manufacturer 1.20

Retailer 1.39 1.13

Sales Tax 1.071

Overall 1.79 1.45

8.2.1.5 Installation Cost

DOE derived baseline installation costs for ovens from data in the RS Means Mechanical

Cost Data, 2013.

5

 The book estimates the labor required to install residential cooking range 

equipment. Table 8.2.12 summarizes the nationally representative costs associated with the 

installation of a 30-inch, free-standing cooking range as presented in RS Means Mechanical Cost 

8-17
Data. DOE decided that the costs of installing a range are representative of the costs of 

installing an oven. Table 8.2.12 provides both bare costs (i.e., costs before overhead and profit 

(O&P)) and installation costs including O&P. RS Means provides minimum and maximum 

costs. DOE used the average of the minimum and maximum labor costs as its estimate of 

installation costs for ovens. 

DOE used the cooking range installation cost data to estimate its installation costs for 

ovens. DOE determined that only gas ovens with electric or electronic ignition devices would 

incur added installation costs. 

Table 8.2.12 Cooking Range (1 Oven): Baseline Installation Costs 

Bare Costs (2014$) Including Overhead & Profit (2014$) 

Installation Type Material Labor Total Total Material* Labor**

Minimum $435 $37 $472 $540 $479 $61

Maximum $1,700 $92 $1,792 $2,025 $1,870 $155

Average (2014$) $108.00

* Material costs including O&P equal bare costs plus 10% profit.

** DOE derived labor costs including O&P by subtracting material with O&P from total with O&P.

Source: RS Means, Mechanical Cost Data, 2013.

8.2.1.6 Total Installed Cost

The total installed cost is the sum of the consumer product cost and installation cost. 8-

18Table 8.2.13 through Table 8.2.20 present the total installed costs for each conventional 

cooking product class at each efficiency level examined.

Table 8.2.13 Electric Standard Ovens, Freestanding: Consumer Product Prices, 

Installation Costs, and Total Installed Costs

EL Equipment Price 

(2014$)

Installation Cost 

(2014$)

Total Installed 

Cost

(2014$)

Baseline $448.71 $108.00 $556.71 

1 $449.83 $108.00 $557.83 

2 $452.50 $108.00 $560.50 

3 $459.55 $108.00 $567.55 

4 $462.76 $108.00 $570.76 

5 $496.02 $108.00 $604.02 

6 $539.67 $108.00 $647.67 

7 $545.47 $108.00 $653.47 

8-18
Table 8.2.14 Electric Standard Ovens, Built-In/Slide-In: Consumer Product Prices, 

Installation Costs, and Total Installed Costs

EL Equipment Price 

(2014$)

Installation Cost 

(2014$)

Total Installed 

Cost

(2014$)

Baseline $475.00 $108.00 $583.00 

1 $476.12 $108.00 $584.12 

2 $478.79 $108.00 $586.79 

3 $485.84 $108.00 $593.84 

4 $489.05 $108.00 $597.05 

5 $522.31 $108.00 $630.31 

6 $565.96 $108.00 $673.96 

7 $571.76 $108.00 $679.76 

Table 8.2.15 Electric Self-Clean Ovens, Freestanding: Consumer Product Prices, 

Installation Costs, and Total Installed Costs

EL Equipment Price 

(2014$)

Installation Cost 

(2014$)

Total Installed 

Cost

(2014$)

Baseline $492.48 $108.00 $600.48 

1 $493.60 $108.00 $601.60 

2 $526.85 $108.00 $634.85 

3 $570.47 $108.00 $678.47 

4 $577.61 $108.00 $685.61 

Table 8.2.16 Electric Self-Clean Ovens, Built-In/Slide-In: Consumer Product Prices, 

Installation Costs, and Total Installed Costs

EL Equipment Price 

(2014$)

Installation Cost 

(2014$)

Total Installed 

Cost

(2014$)

Baseline $518.75 $108.00 $626.75 

1 $519.88 $108.00 $627.88 

2 $553.12 $108.00 $661.12 

3 $596.75 $108.00 $704.75 

4 $603.88 $108.00 $711.88 

8-19
Table 8.2.17 Gas Standard Ovens, Freestanding: Consumer Product Prices, Installation 

Costs, and Total Installed Costs

EL Equipment Price 

(2014$)

Installation Cost 

(2014$)

Total Installed 

Cost

(2014$)

Baseline $493.77 $108.00 $601.77 

1 $493.77 $108.00 $601.77 

2 $494.89 $108.00 $602.89 

3 $503.73 $108.00 $611.73 

4 $510.73 $108.00 $618.73 

5 $513.91 $108.00 $621.91 

6 $542.08 $108.00 $650.08 

7 $547.96 $108.00 $655.96 

Table 8.2.18 Gas Standard Ovens, Built-In/Slide-In: Consumer Product Prices, 

Installation Costs, and Total Installed Costs

EL Equipment Price 

(2014$)

Installation Cost 

(2014$)

Total Installed 

Cost

(2014$)

Baseline $519.84 $108.00 $627.84 

1 $519.84 $108.00 $627.84 

2 $520.95 $108.00 $628.95 

3 $529.80 $108.00 $637.80 

4 $536.80 $108.00 $644.80 

5 $539.98 $108.00 $647.98 

6 $568.15 $108.00 $676.15 

7 $574.03 $108.00 $682.03 

Table 8.2.19 Gas Self-Clean Ovens, Freestanding: Consumer Product Prices, Installation 

Costs, and Total Installed Costs

EL Equipment Price 

(2014$)

Installation Cost 

(2014$)

Total Installed 

Cost

(2014$)

Baseline $608.40 $108.00 $716.40 

1 $609.52 $108.00 $717.52 

2 $618.36 $108.00 $726.36 

3 $646.51 $108.00 $754.51 

4 $653.58 $108.00 $761.58 

8-20
Table 8.2.20 Gas Self-Clean Ovens, Built-In/Slide-In: Consumer Product Prices, 

Installation Costs, and Total Installed Costs

EL Equipment Price 

(2014$)

Installation Cost 

(2014$)

Total Installed 

Cost

(2014$)

Baseline $634.45 $108.00 $742.45 

1 $635.57 $108.00 $743.57 

2 $644.41 $108.00 $752.41 

3 $672.55 $108.00 $780.55 

4 $679.62 $108.00 $787.62 

8.2.2 Inputs to Operating Cost

DOE defines operating cost (OC) by the following equation:

OC = EC + RC + MC

Eq. 8.9

where:

EC = Energy expenditure associated with operating the equipment, 

RC = Repair cost associated with component failure, and 

MC = Service cost for maintaining equipment operation.

Table 8.2.21 shows the inputs for determining annual operating costs and their discounted 

values throughout the product lifetime.

Table 8.2.21 Inputs for Operating Cost

Annual energy consumption

Energy prices and price trends

Repair and maintenance Costs

Energy Price Trends

Product Lifetime

Discount Rate

Effective Date of Standard

The annual energy consumption is the site energy use associated with operating the 

product. Annual energy consumption varies with product efficiency. Energy prices are the prices 

8-21
paid by consumers for energy (e.g., electricity or natural gas). Multiplying the annual energy 

consumption by the energy price yields the annual energy cost. Repair costs are associated with 

repairing or replacing components that have failed. Maintenance costs are associated with 

maintaining the operation of the product. DOE used energy price trends to forecast energy prices 

into the future and, along with the product lifetime and discount rate, to establish the present 

value of lifetime energy costs. 

DOE used the following equation to calculate the annual operating cost for baseline 

products.

OCBASE = ECBASE + RCBASE + MCBASE = AECBASE × PRICEENERGY + RCBASE + MCBASE

Eq. 8.10

where:

OCBASE = Baseline operating cost,

ECBASE = Energy expenditure associated with operating the baseline equipment, 

RCBASE = Repair cost associated with component failure for the baseline 

equipment,

MCBASE = Service cost for maintaining baseline equipment operation,

AECBASE = Annual energy consumption for baseline equipment, and

PRICEENERGY = Energy price.

DOE calculated the operating cost for standard-level products based on the following equation:

( ) ( ) ( ) BASE STD ENERGY BASE STD BASE STD

STD STD STD STD STD ENERGY STD STD

AEC AEC PRICE RC RC MC MC

OC EC RC MC AEC PRICE RC MC

= D × + + D + + D

= + + = × + +

__

Eq. 8.11

where:

OCSTD = Standard-level operating cost,

ECSTD = Energy expenditure associated with operating standard-level equipment, 

RCSTD = Repair cost associated with component failure for standard-level 

equipment,

MCSTD = Service cost for maintaining standard-level equipment operation,

AECSTD = Annual energy consumption for standard-level equipment, 

PRICEENERGY = Energy price,

ΔAECSTD = Change in annual energy consumption caused by standard-level

equipment, 

ΔRCSTD = Change in repair cost caused by standard-level equipment, and

ΔMCSTD = Change in maintenance cost caused by standard-level equipment.

The remainder of this section provides information about each of the above input 

variables that DOE used to calculate the operating costs for all product classes for conventional

cooking products. 

8-22
8.2.2.1 Annual Energy Consumption

Chapter 7, Energy Use Analysis, details how DOE determined the annual energy 

consumption for baseline and standard-level products. 

As described in section 7.2.3 of chapter 7 of this NOPR TSD, DOE developed a sample 

of individual households that use one of the product classes of conventional cooking products. 

By developing household samples, DOE was able to perform the LCC and PBP calculations for 

each household to account for the variability in both energy use and energy price associated with 

each household. DOE used EIA’s 2009 RECS to develop the household samples and, in turn, to 

establish the variability in both annual energy consumption and energy pricing. Refer to chapter 

7 to review the variability of annual energy consumption for conventional cooking products. 

The tables presented in this section are based on the energy use analysis described in 

chapter 7. Keep in mind that the annual energy consumption values in the tables below are 

averages. DOE captured the variability in energy consumption when it conducted its LCC and 

PBP analysis.

Table 8.2.22 through Table 8.2.29 provide the annual energy consumption by efficiency 

level for all eight product classes of conventional cooking products. 

Table 8.2.22 Electric Standard Ovens, Freestanding: Annual Energy Consumption by 

Efficiency Level

Efficiency Level IAEC* Cooking Energy Non-Cooking 

Energy** Total

kWh/year kWh/year kWh/year kWh/year

Baseline 294 116.3 19.3 135.6 

1 285 116.2 9.6 125.8 

2 272 110.7 9.6 120.4 

3 259 105.4 9.6 115.1 

4 255 103.7 9.6 113.3 

5 245 98.8 10.8 109.6 

6 208 83.3 10.8 94.1 

7 207 83.0 10.8 93.9 

*IAEC results based on DOE’s engineering analysis

**Includes Standby, Self-Clean, Forced Convection, and Ignition Energy

8-23
Table 8.2.23 Electric Standard Ovens, Built-In/Slide-In: Annual Energy Consumption by 

Efficiency Level

Efficiency Level IAEC* Cooking Energy Non-Cooking 

Energy** Total

kWh/year kWh/year kWh/year kWh/year

Baseline 302 116.3 19.3 135.6 

1 291 116.1 9.6 125.7 

2 278 110.7 9.6 120.3 

3 265 105.4 9.6 115.0 

4 261 103.6 9.6 113.2 

5 250 98.7 10.9 109.6 

6 213 83.2 10.9 94.1 

7 212 83.0 10.9 93.8 

*IAEC results based on DOE’s engineering analysis

**Includes Standby, Self-Clean, Forced Convection, and Ignition Energy

Table 8.2.24 Electric Self-Clean Ovens, Freestanding: Annual Energy Consumption by 

Efficiency Level

Efficiency Level IAEC* Cooking Energy Non-Cooking 

Energy** Total

kWh/year kWh/year kWh/year kWh/year

Baseline 355 118.4 56.2 174.6 

1 345 118.3 46.6 164.9 

2 327 110.7 47.9 158.6 

3 279 91.5 47.9 139.5 

4 278 91.2 47.9 139.1 

*IAEC results based on DOE’s engineering analysis

**Includes Standby, Self-Clean, Forced Convection, and Ignition Energy

Table 8.2.25 Electric Self-Clean Ovens, Built-In/Slide-In: Annual Energy Consumption by 

Consumption by Efficiency Level

Efficiency Level IAEC* Cooking Energy Non-Cooking 

Energy** Total

kWh/year kWh/year kWh/year kWh/year

Baseline 361 118.4 56.8 175.3 

1 351 118.3 47.2 165.5 

2 333 110.6 48.6 159.2 

3 284 91.5 48.6 140.1 

4 283 91.2 48.6 139.8 

*IAEC results based on DOE’s engineering analysis

**Includes Standby, Self-Clean, Forced Convection, and Ignition Energy

8-24
Table 8.2.26 Gas Standard Ovens, Freestanding: Annual Energy Consumption by 

Efficiency Level

Efficiency Level IAEC* Cooking Energy Non-Cooking 

Energy** Total

kBtu/year kBtu/year kBtu/year kBtu/year

Baseline 2,118 831.3 208.8 1040.1

1 1,649 626.6 208.8 835.4

2 1,615 625.8 175.9 801.7

3 1,491 571.7 175.9 747.6

4 1,415 603.4 32.9 636.3

5 1,401 597.2 32.9 630.1

6 1,356 573.9 41.0 614.9

7 1,347 570.1 41.0 611.1

*IAEC results based on DOE’s engineering analysis

**Includes Standby, Self-Clean, Forced Convection, and Ignition Energy

Table 8.2.27 Gas Standard Ovens, Built-In/Slide-In: Annual Energy Consumption by 

Efficiency Level

Efficiency Level IAEC* Cooking Energy Non-Cooking 

Energy** Total

kBtu/year kBtu/year kBtu/year kBtu/year

Baseline 2,128 831.3 208.8 1040.1

1 1,657 626.7 208.8 835.5

2 1,622 625.8 175.9 801.8

3 1,498 571.8 175.9 747.7

4 1,421 603.1 32.9 636.0

5 1,407 596.9 32.9 629.8

6 1,362 573.6 41.1 614.7

7 1,353 569.8 41.1 610.9

*IAEC results based on DOE’s engineering analysis

**Includes Standby, Self-Clean, Forced Convection, and Ignition Energy

Table 8.2.28 Gas Self-Clean Ovens, Freestanding: Annual Energy Consumption by 

Efficiency Level 

Efficiency 

Level

IAEC* Cooking Energy Non-Cooking Energy** Total

kBtu/year kBtu/year kBtu/year kBtu/year

Baseline 1,884 660.8 465.5 1126.3

1 1,848 659.6 432.6 1092.2

2 1,669 645.1 283.4 928.5

3 1,596 607.9 291.0 898.9

4 1,591 605.4 291.0 896.4

*IAEC results based on DOE’s engineering analysis

**Includes Standby, Self-Clean, Forced Convection, and Ignition Energy

8-25
Table 8.2.29 Gas Self-Clean Ovens, Built-In/Slide-In: Annual Energy Consumption by 

Efficiency Level

Efficiency 

Level

IAEC* Cooking Energy Non-Cooking Energy** Total

kBtu/year kBtu/year kBtu/year kBtu/year

Baseline 1,894 660.8 466.8 1127.6

1 1,858 659.5 433.9 1093.4

2 1,677 644.7 284.7 929.4

3 1,605 607.5 292.3 899.9

4 1,599 605.0 292.3 897.4

*IAEC results based on DOE’s engineering analysis

**Includes Standby, Self-Clean, Forced Convection, and Ignition Energy

8.2.2.2 Energy Prices

DOE used probability distributions to characterize the regional variability in energy and 

water prices. DOE developed the probability associated with each regional energy and water 

price based on the population weight of each region. DOE’s method for deriving energy and 

water prices is described here. 

Residential Electricity Prices

DOE used data from EIA Form 8616 to estimate electricity prices for residential 

consumers in each of the above geographic areas. Those data include, for every utility that serves 

final consumers, annual electricity sales in kilowatt-hours; revenues from electricity sales; and 

number of customers in the residential, commercial, and industrial sectors. DOE calculated 

prices for each of 27 geographic areas in accordance with RECS 2009 geographic areas.

 The calculation of average residential electricity price proceeded in two steps.

1. For each utility, DOE estimated an average residential price by dividing the residential 

revenues by residential sales.

2. DOE calculated a regional average price, weighting each utility that serves residences in 

a region by the number of residential consumers served in that region.

Table 8.2.30 shows the average prices for each geographic region.

.

8-26
Table 8.2.30 Average Residential Electricity Prices in 2013

Geographic Area 2014$/kWh

1 Connecticut, Maine, New Hampshire, Rhode Island, Vermont $0.168 

2 Massachusetts $0.161 

3 New York $0.191 

4 New Jersey $0.159 

5 Pennsylvania $0.131 

6 Illinois $0.109 

7 Indiana, Ohio $0.119 

8 Michigan $0.149 

9 Wisconsin $0.138 

10 Iowa, Minnesota, North Dakota, South Dakota $0.115 

11 Kansas, Nebraska $0.113 

12 Missouri $0.108 

13 Virginia $0.111 

14 Delaware, District of Columbia, Maryland, West Virginia $0.127 

15 Georgia $0.116 

16 North Carolina, South Carolina $0.116 

17 Florida $0.115 

18 Alabama, Kentucky, Mississippi $0.108 

19 Tennessee $0.102 

20 Arkansas, Louisiana, Oklahoma $0.097 

21 Texas $0.116 

22 Colorado $0.121 

23 Idaho, Montana, Utah, Wyoming $0.103 

24 Arizona $0.118 

25 Nevada, New Mexico $0.121 

26 California $0.164 

27 Alaska, Hawaii, Oregon, Washington $0.129 

Source: EIA From 861 for 2012.

Residential Natural Gas Prices

 DOE obtained data for calculating regional prices of natural gas from the EIA 

publication, Natural Gas Navigator.

7 This publication presents monthly volumes of natural gas 

deliveries and average prices by state for residential, commercial, and industrial customers. The 

Department used the complete annual data for 2012 to calculate an average annual price for each 

geographic area. 

8-27
 The calculation of average prices proceeded in two steps.

1. For each state, DOE calculated the annual residential price of natural gas using a simple 

average of data.

2. DOE then calculated a regional price, weighting each state in a region by its number of 

households. 8

The method used to calculate natural gas prices differs from that used to calculate 

electricity prices, because the EIA does not provide consumer- or utility-level data on gas 

consumption and prices. The prices in Table 8.2.31 are in dollars per million Btu ($/MMBtu).

8-28
Table 8.2.31 Average Residential Natural Gas Prices in 2013

Geographic Area 2014$/MMBtu

1 Connecticut, Maine, New Hampshire, Rhode Island, Vermont $15.54 

2 Massachusetts $14.19 

3 New York $14.57 

4 New Jersey $11.68 

5 Pennsylvania $14.05 

6 Illinois $10.54 

7 Indiana, Ohio $12.62 

8 Michigan $10.41 

9 Wisconsin $9.74 

10 Iowa, Minnesota, North Dakota, South Dakota $10.04 

11 Kansas, Nebraska $12.37 

12 Missouri $15.43 

13 Virginia $14.39 

14 Delaware, District of Columbia, Maryland, West Virginia $13.98 

15 Georgia $17.53 

16 North Carolina, South Carolina $16.15 

17 Florida $19.50 

18 Alabama, Kentucky, Mississippi $14.36 

19 Tennessee $12.13 

20 Arkansas, Louisiana, Oklahoma $13.30 

21 Texas $13.69 

22 Colorado $9.31 

23 Idaho, Montana, Utah, Wyoming $9.02 

24 Arizona $16.64 

25 Nevada, New Mexico $10.83 

26 California $10.18 

27 Alaska, Hawaii, Oregon, Washington $15.91 

Source: EIA Natural Gas Navigator for 2012.

Marginal Electricity and Gas Prices 

 Residential electricity and natural gas prices were adjusted by applying seasonal marginal 

price factors to reflect a change in a consumer’s bill associated with a change in energy 

consumed. They are appropriate for determining energy cost savings associated with possible 

changes to efficiency standards.

8-29
 EIA provides historical monthly electricity and natural gas consumption and expenditures 

by state. This data was used to determine 10-year average marginal prices factors for the RECS 

2009 geographical areas, which are then used to convert average monthly energy prices into 

marginal monthly energy prices. DOE interpreted the slope of the regression line (consumption 

vs. expenditures) for each state as the marginal energy price factor for that state. Because a 

cooking product operates all year around, DOE determined summer and winter marginal price 

factors. 

EIA also provides RECS 2009 billing data that was 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.

 Table 8.2.32 and Table 8.2.33 show the resulting electricity and natural gas marginal 

price factors for both residential and commercial sectors.

8-30
Table 8.2.32 Residential Marginal Electricity Price Factors using EIA 2003-2013 Data

Geographical Area Summer Winter

Connecticut, Maine, New Hampshire, Rhode Island, Vermont 0.936 0.999

Massachusetts 0.948 1.040

New York 1.141 0.913

New Jersey 1.198 0.985

Pennsylvania 1.073 0.832

Illinois 0.956 0.710

Indiana, Ohio 1.015 0.748

Michigan 1.126 0.959

Wisconsin 1.017 0.889

Iowa, Minnesota, North Dakota, South Dakota 1.073 0.844

Kansas, Nebraska 1.165 0.744

Missouri 1.229 0.764

Virginia 1.085 0.835

Delaware, District of Columbia, Maryland, West Virginia 1.103 0.903

Georgia 1.176 0.845

North Carolina, South Carolina 0.975 0.832

Florida 1.016 0.945

Alabama, Kentucky, Mississippi 0.998 0.827

Tennessee 0.945 0.857

Arkansas, Louisiana, Oklahoma 1.031 0.750

Texas 1.036 0.906

Colorado 1.112 0.799

Idaho, Montana, Utah, Wyoming 1.115 0.942

Arizona 1.053 0.841

Nevada, New Mexico 1.048 0.878

California 1.204 1.119

Alaska, Hawaii, Oregon, Washington 0.930 0.940

8-31
Table 8.2.33 Residential Marginal Natural Gas Price Factors using EIA 2003-2013 Data

Geographical Area Summer Winter

Connecticut, Maine, New Hampshire, Rhode Island, 

Vermont 0.811 0.928

Massachusetts 0.878 1.041

New York 0.733 0.885

New Jersey 0.832 0.983

Pennsylvania 0.708 0.929

Illinois 0.658 0.956

Indiana, Ohio 0.685 0.913

Michigan 0.776 0.971

Wisconsin 0.786 0.982

Iowa, Minnesota, North Dakota, South Dakota 0.707 0.967

Kansas, Nebraska 0.670 0.939

Missouri 0.570 0.807

Virginia 0.667 0.954

Delaware, District of Columbia, Maryland, West Virginia 0.700 0.934

Georgia 0.548 0.873

North Carolina, South Carolina 0.639 0.920

Florida 0.637 0.810

Alabama, Kentucky, Mississippi 0.722 0.873

Tennessee 0.726 0.929

Arkansas, Louisiana, Oklahoma 0.628 0.834

Texas 0.588 0.840

Colorado 0.679 0.895

Idaho, Montana, Utah, Wyoming 0.851 0.951

Arizona 0.637 0.848

Nevada, New Mexico 0.702 0.891

California 0.832 1.083

Alaska, Hawaii, Oregon, Washington 0.836 0.947

8.2.2.3 Energy Price Trends

DOE used EIA’s price forecasts to estimate future trends in electricity and natural gas 

prices. To arrive at prices in future years, DOE multiplied the average prices listed in 8-27Table 

8.2.30 and Table 8.2.31 by the forecast of annual average price changes based on the reference 

case in EIA’s AEO 2015.

2 To estimate the trend after 2040, DOE followed the guidance EIA 

previously provided to the Federal Energy Management Program (FEMP), to use the average 

rate of change during 2030–2040. 

DOE calculated LCC and PBP based on three separate projections from AEO 2015: 

reference, low economic growth, and high economic growth. These three cases reflect the 

8-32
uncertainty of economic growth in the forecast period. The high and low growth cases show the 

projected effects of alternative growth assumptions on energy markets. Figure 8.2.8 and Figure 

8.2.9 show the residential electricity and natural gas price trends, respectively, based on the three 

AEO 2015 projections. 

Figure 8.2.8 Electricity Price Trends

Figure 8.2.9 Natural Gas Price Trends

0.9

0.95

1

1.05

1.1

1.15

1.2

1.25

1.3

1.35

Electricity Price Trend (2012=100)

Residential Electricity Price Projection

Reference High Low

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

Gas Price Trend (2012=100)

Residential Electricity Price Projection

Reference High Low

8-33
8.2.2.4 Repair and Maintenance Costs

For all electric cooking products, DOE did not include any changes in repair and 

maintenance costs for products more efficient than baseline products.

For gas ovens, DOE determined the repair and maintenance costs associated with 

different types of ignition systems. Following the approach adopted in the April 2009 Final 

Rule9 for electric glo-bar/hot surface ignition systems, DOE estimated an average repair cost of 

$170 occurring every fifth year during the product’s lifetime. For electronic spark ignition 

systems, DOE estimated an average repair cost of $206 occurring in the tenth year of the 

product’s life. DOE determined the repair cost for the 2009 final rule by contacting six 

contractors, one each in Arizona, California, Colorado, Massachusetts, Minnesota, and Texas. 

Table 8.2.34 summarizes the findings from the discussions with the six contractors.

8-34
Table 8.2.34 Repair/Maintenance Findings for Gas Cooking Product Ignition Systems

Ignition System Findings

Glo-bar/hot surface

• Fragile, typically last only 5 years.

• Reasons for failure include burn out, cleaning 

sprays, jostling, and oxidation.

• Average repair cost = $170

Electronic spark

• Definitely longer-lasting than Glo-bar igniters, but 

harder to gauge performance statistically. 

Modules can break after 5 years, while some last 

the lifetime of the appliance. Average 10 years. 

Electrodes rarely have problems.

• Reasons for failure of spark module include power 

outage, pinched wire, and humidity issues.

• Reasons for failure of spark electrode include 

physical abuse from pots and pans

• Average repair cost = $206

Based on the contractors’ input, DOE determined for electric glo-bar/hot surface ignition 

systems, the glo-bar requires replacement approximately every five years. In the case of 

electronic ignition systems, control modules tend to last 10 years. The electrodes/igniters can 

fail due to hard contact from pots or pans, although failures are rare. Based on the above 

findings, DOE included repair and maintenance costs for gas cooking product ignition systems 

(see Table 8.2.35).

8-35
Table 8.2.35 Repair/Maintenance Costs for Gas Cooking Product Ignition Systems

Ignition System Repair/Maintenance Cost* Occurrence

Glo-bar/hot surface** $170 Every 5th year of Cooking Product’s Life

Electronic spark*** $206 Every 10th year of Cooking Product’s Life

* The costs indicate current day prices and are representative of 2014$.

** Applicable to Baseline through efficiency level 3 for gas standard ovens and the baseline and efficiency level 1 

for gas self-clean ovens.

*** Applicable to efficiency levels 4 through 7 for gas standard ovens and efficiency levels 2 through 4 for gas 

self-clean ovens.

8.2.2.5 Product Lifetime

For ovens, DOE considered the source from Appliance Magazine to estimate product 

lifetimes10. Table 8.2.25 shows the minimum, average, and maximum lifetime estimated for 

electric and gas cooking products, respectively. 

Table 8.2.36 Conventional Cooking Products Lifetime Reference Values

Product Minimum

years

Average

years

Maximum

years

Electric 10.0 15.0 19.0

Gas 12.0 17.0 22.0

Source: Appliance Magazine, 2012.

To perform the LCC and PBP analysis, DOE had to develop survival functions for

conventional cooking products. DOE estimated the percentage of appliances of a given age that 

would still be in operation in a given year. This survival function, which DOE assumes has the 

form of a cumulative Weibull distribution, provides an average and a median appliance lifetime.

The Weibull distribution is a probability distribution commonly used to measure failure 

rates.c Its form is similar to that of an exponential distribution, which models a fixed failure rate, 

except that a Weibull distribution allows for a failure rate that changes through time. The 

cumulative Weibull distribution takes the form:

e x

P x

β

α

θ 



 



 − −

( ) = for x > θ and

P(x) = 1 for x ≤ θ.

Where:

P(x) = probability that the appliance is still in use at age x;

x = age of appliance;

8-36

 
α = scale parameter, which would be the decay length in an exponential distribution;

β = shape parameter, which determines the way in which the failure rate changes through 

time; and

θ = delay parameter, which allows for a delay before any failures occur.

When β = 1, the failure rate is constant over time, giving the distribution the form of a 

cumulative exponential distribution. In the case of appliances, β commonly is greater than 1, 

reflecting an increasing failure rate as appliances age. Figure 8.2.10 and Figure 8.2.11 show the 

Weibull retirement and survival functions for electric and gas cooking products, respectively. 

The results of DOE’s analysis are shown in Table 8.2.26.

Figure 8.2.10 Weibull Function for Lifetime of Electric Cooking Products

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Lifetime Distribution - Electric

% Retiring - Elec % Surviving - Elec

8-37
Figure 8.2.11 Weibull Function for Lifetime of Gas Cooking Products

Table 8.2.37 Lifetime Parameters

Product Fuel Type Average (Years)

Weibull Parameters

Alpha (Scale) Beta (Shape)

Electric 15.0 14.87 7.99

Gas 17.0 17.06 7.35

8.2.3 Discount Rate

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)) 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 LCC and LCC 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.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Lifetime Distribution - Gas

% Retiring - Gas % Surviving - Gas

8-38
DOE estimates separate discount rate distributions for six income groups, divided based 

on income percentile as reported in the Federal Reserve Board’s SCF.

11 This disaggregation 

reflects the fact that low and high income consumers tend to have substantially different shares 

of debt and asset types and tend to face different rates on debts and assets. Summaries of shares 

and rates presented in this chapter are averages across the entire population.

Table 8.2.38 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 

8.2.48). 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.d

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. 

d Note that 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.

8-39

 
Table 8.2.39 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 8.2.29). 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).e For example, a 6-percent nominal 

mortgage rate has an effective nominal rate of 4.5 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.

e Fisher formula is given by: Real Interest Rate = [(1 + Nominal Interest Rate) / (1 + Inflation Rate)] – 1.

8-40

 
Table 8.2.40 Data Used to Calculate Real Effective Mortgage Rates

Year Mortgage Interest Rates in Selected Years (%)

Average 

Nominal 

Interest Rate

Inflation Rate12

Applicable 

Marginal Tax 

Rate13

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 8.2.30 shows the household-weighted average effective real rates for different 

types of household debt. 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 2019.

Table 8.2.41 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,14 savings bonds,15 and bonds (AAA corporate bonds)16

were collected from Federal Reserve Board time-series data. Rates on money market accounts 

came from Cost of Savings Index data.17 Rates 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.18 Rates for 

8-41
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.

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 

8.2.31. 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 2019. For each type, DOE developed a distribution of rates, as shown in appendix 8E.

Table 8.2.42 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 previously, 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. 8.12

Where:

DRi = discount rate for consumer i,

Sharei,j = share of asset or debt type j for consumer i, and

Ratei,j = 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 previously. 

8-42
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. 

Table 8.2.32 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 8F presents the full probability distributions for 

each income group that DOE used in the LCC and PBP analysis. 

Table 8.2.43 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

8.2.4 Compliance Date of Standard

The compliance date is the future date when manufacturers must comply with a new or 

amended standard. The compliance date of the potential energy conservation standards for 

conventional cooking products manufactured in, or imported into, the United States is April 1, 

2019. DOE calculated the LCC for all consumers as if each would purchase a new product in 

2019.

8.2.5 Product Energy Efficiency in the Base Case

To estimate the percentage of consumers who would be affected by a standard at any of 

the trial standard levels, DOE considered the projected distribution of efficiencies for products

that consumers purchase under the base case (the case without new or amended energy 

conservation standards). DOE refers to this distribution of product efficiencies as the base-case 

efficiency distribution. Using the projected distribution of efficiencies for each product class, 

DOE randomly assigned a product efficiency to each sampled household. The energy efficiency 

distributions that DOE used in the LCC analysis are described below. For its determination of 

base case projected efficiencies, DOE implemented a consumer-choice model that assumes 

consumers are sensitive to first cost, i.e., equipment price, and calculates the market share for 

available efficiency options based on the first cost for conventional cooking products users. 

8-43
The consumer-choice model uses a logit model to calculate the probability that a 

consumer will purchase product jj based on the logistic curve probability function of the form:

PPjj(zz) =

zzjj

∑ zzii nn

ii=1

Eq. 8.13

Where:

PPjj(zz) = the probability a consumer will purchase product jj among nn possible options, 

and

zz = the ‘logit’, which is defined as follows

zzjj = ββFFFF ∗ FFCCjj

Eq. 8.14

Where:

ββFFFF = consumer sensitivity to first cost,

FFCCjj = first cost of product option jj,

 In Eq.10.2 , ββFFFF can be found by fitting an exponential function to the first cost 

distribution in the engineering analysis:

MM (FF ) = NN ββFFFF∗FFFF

Eq. 8.15

Where:

NN = Normalization factor,

FF = First cost of a cooking product in 2014$, 

MM (FF ) = Market share for a cooking product that costs FF , and

ββFFFF = Consumer sensitivity to first cost.

The regression coefficients (ββ values) represent the consumer’s sensitivity to first cost 

(ββFFFF). The coefficients are determined using historical shipments and equipment price data. 

 For the conventional cooking products, consumer sensitivities are user-specific based on 

users’ housing type in the Residential Energy Consumption Survey (RECS) 2009.19 Table 

8.2.33 summarizes the market share between renters and home-owners by fuel type of cooking 

products that they use in RECS 2009. DOE assumed that landlords would have no economic 

incentive and renters would have no decision making power to purchase or replace an energy 

efficient cooking product, therefore, DOE assigned the percentage of renters found in the RECS 

2009 to the baseline efficiency level. DOE then assumed that home-owners would have 

incentive to purchase or replace an energy efficient cooking product based on their sensitivity to 

the initial purchase costs. DOE used shipments data collected by the Market Research 

Magazine20 and Producer Price Index (PPI) of household cooking appliance manufacturers21 

between the years 2002 – 2012 along with the manufactures costs data from the engineering 

analysis to analyze factors that influence consumer-purchasing decisions of cooking products. 

8-44
By using the logit statistical model described by Eq. 8.6, DOE found the historical shipments 

data has a strong dependence on the first costs by product type. DOE then developed the best-fit 

to capture the relationship between historical shipments and price data. Table 8.2.33 shows the 

best-fit logit parameters calculated for conventional cooking products. DOE then used the 

parameters in Table 8.2.34 to derive efficiency distribution in a given year for home-owners. 

DOE combined the market share of renters with the efficiency distribution derived from the 

consumer-choice model for home-owners to project its base case efficiency distribution for the 

period between 2019 and 2048.

Table 8.2.44 Oven User Ownership by Fuel Type

Owners/Renters Electric Ovens Gas Ovens

Home owners 69.3% 66.1%

Renters 30.7% 33.9%

Source: RECS 2009.

Table 8.2.45 Best-fit Logit Parameters for Conventional Cooking Products

Product Class N ββFFFF

Electric Standard Ovens, Freestanding 36.71287 -0.00471

Electric Standard Ovens, Built-In/Slide-In 36.71287 -0.00471

Electric Self-Clean Ovens, Freestanding 39.981985 -0.004472

Electric Self-Clean Ovens, Built-In/Slide-In 39.981985 -0.004472

Gas Standard Ovens, Freestanding 8.4805448 -0.002612

Gas Standard Ovens, Built-In/Slide-In 8.4805448 -0.002612

Gas Self-Clean Ovens, Freestanding 8.4810489 -0.002141

Gas Self-Clean Ovens, Built-In/Slide-In 8.4810489 -0.002141

 These efficiencies are then used to develop a trend for the average annual per-unit energy 

consumption. Table 8.2.35 and Table 8.2.36 show the base case efficiency distribution in 2019 

for each product class. 

8-45
Table 8.2.46 Base Case Market Share for Electric Ovens by Efficiency Level in 2019

EL

Electric Standard 

Ovens, 

Freestanding

Electric Standard 

Ovens, BuiltIn/Slide-In

Electric Self-Clean 

Ovens, 

Freestanding

Electric SelfClean Ovens, 

Built-In/Slide-In

0 40.4% 40.4% 46.5% 46.5%

1 9.7% 9.7% 15.8% 15.8%

2 9.6% 9.6% 14.0% 14.0%

3 9.3% 9.3% 12.0% 12.0%

4 9.2% 9.2% 11.7% 11.7%

5 8.1% 8.1% -- --

6 6.9% 6.9% -- --

7 6.8% 6.8% -- --

Table 8.2.47 Base Case Market Share for Gas Ovens by Efficiency Level in 2019

EL

Gas Standard 

Ovens, 

Freestanding

Gas Standard 

Ovens, BuiltIn/Slide-In

Gas Self-Clean 

Ovens, 

Freestanding

Gas Self-Clean 

Ovens, BuiltIn/Slide-In

0 42.5% 42.5% 47.5% 47.5%

1 8.6% 8.6% 13.6% 13.6%

2 8.6% 8.6% 13.4% 13.4%

3 8.4% 8.4% 12.8% 12.8%

4 8.3% 8.3% 12.6% 12.6%

5 8.2% 8.2% -- -

6 7.8% 7.8% -- -

7 7.7% 7.7% -- -

8.3 RESULTS OF LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSES

This section presents the results of the LCC and PBP for conventional cooking products. 

As discussed in section 8.1.1, DOE’s approach to the LCC analysis relied on developing samples 

of households that use each of the product classes. DOE also used probability distributions to 

characterize the uncertainty in many of the inputs to the analysis. DOE used a Monte Carlo 

simulation to perform the LCC calculations on the households in the sample. For each set of 

sample households that use the product in each product class, DOE calculated the average LCC 

and LCC savings and the median and average PBP for each of the efficiency levels. These 

standard levels are also referred to as trial standard levels (TSLs). 

DOE calculated LCC savings and PBPs relative to the base-case products that it assigned 

to sample households. For some consumers DOE assigned a base-case product that is more 

efficient than some of the TSLs. For that reason, the average LCC impacts are not equal to the 

difference between the LCC of a specific TSL and the LCC of the baseline product. DOE 

8-46
calculated the average LCC savings and the median PBP values by excluding the households that 

are not impacted by a standard at a given efficiency level.

 LCC and PBP calculations were performed 10,000 times on the sample of consumers 

established for each product class. Each LCC and PBP calculation was performed on a single 

household selected from the sample. A household was selected based on its weight (i.e., how 

representative it was of other households in the distribution). Each LCC and PBP calculation also 

sampled from the probability distributions that DOE developed to characterize many of the 

inputs to the analysis. 

Using the Monte Carlo simulations for each TSL, DOE calculated the percent of consumers who 

experience a net LCC benefit, a net LCC cost, and no effect. DOE considered a consumer to 

receive no effect at a given standard level if DOE assigned it a baseline product having the same 

or higher efficiency than the standard level. The following sections present figures that illustrate 

the range of LCC and PBP effects among sample consumers.

8.3.1 Summary of Results 

Table 8.3.1 through Table 8.3.16 show the LCC and simple PBP results by efficiency 

level for each oven product class. The average operating cost is the discounted sum. 

Table 8.3.1 Average LCC and PBP Results by Efficiency Level for Electric Standard 

Ovens, Freestanding

TSL Efficiency 

Level

Average Costs 

2014$ Simple 

Payback

years Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating 

Cost

LCC

-- 0 $557 $18 $206 $762 --

1 1 $558 $16 $191 $748 0.9

-- 2 $560 $16 $182 $743 1.9

2 3 $568 $15 $174 $742 4.0

-- 4 $571 $15 $171 $742 4.8

-- 5 $604 $14 $166 $770 13.8

-- 6 $648 $12 $142 $790 16.6

3 7 $653 $12 $142 $795 17.5

Note: The results for each TSL are calculated assuming that all consumers use products at that efficiency level. The 

PBP is measured relative to the baseline product.

8-47
Table 8.3.2 Average LCC Savings Relative to the Base-Case Efficiency Distribution for 

Electric Standard Ovens, Freestanding

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Average Savings*

Net Cost 2014$

-- 0 0% ---

1 1 0% $13.96

-- 2 3% $16.82

2 3 12% $15.18

-- 4 22% $12.66

-- 5 65% -$16.51

-- 6 73% -$34.87

3 7 82% -$37.60

*The calculation does not include households with zero LCC savings (no impact)

Table 8.3.3 Average LCC and PBP Results by Efficiency Level for Electric Standard 

Ovens, Built-In/Slide-In

TSL Efficiency 

Level

Average Costs 

2014$ Simple 

Payback

years Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating 

Cost

LCC

-- 0 $583 $18 $206 $789 --

1 1 $584 $16 $190 $775 0.9

-- 2 $587 $16 $182 $769 1.9

2 3 $594 $15 $174 $768 4.0

-- 4 $597 $15 $171 $768 4.7

-- 5 $630 $14 $166 $796 13.8

-- 6 $674 $12 $142 $816 16.6

3 7 $680 $12 $142 $821 17.5

Note: The results for each TSL are calculated assuming that all consumers use products at that efficiency level. The 

PBP is measured relative to the baseline product.

8-48
Table 8.3.4 Average LCC Savings Relative to the Base-Case Efficiency Distribution for 

Electric Standard Ovens, Built-In/Slide-In

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Average Savings*

Net Cost 2014$

-- 0 0% ---

1 1 0% $14.11

-- 2 3% $16.92

2 3 12% $15.25

-- 4 22% $12.72

-- 5 65% -$16.52

-- 6 73% -$34.92

3 7 82% -$37.64

*The calculation does not include households with zero LCC savings (no impact)

Table 8.3.5 Average LCC and PBP Results by Efficiency Level for Electric Self-Clean 

Ovens, Free-Standing

TSL Efficiency 

Level

Average Costs 

2014$ Simple 

Payback

years Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating 

Cost

LCC

-- 0 $600 $23 $266 $867 --

1,2 1 $602 $22 $251 $853 0.9

-- 2 $635 $21 $241 $876 16.1

-- 3 $678 $18 $212 $890 16.7

3 4 $686 $18 $211 $897 18.1

Note: The results for each TSL are calculated assuming that all consumers use products at that efficiency level. The 

PBP is measured relative to the baseline product.

8-49
Table 8.3.6 Average LCC Savings Relative to the Base-Case Efficiency Distribution for 

Electric Self-Clean Ovens, Freestanding

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Average Savings*

Net Cost 2014$

-- 0 0% ---

1,2 1 0% $14.10

-- 2 53% -$12.85

-- 3 62% -$24.59

3 4 76% -$27.79

*The calculation does not include households with zero LCC savings (no impact)

Table 8.3.7 Average LCC and PBP Results by Efficiency Level for Electric Self-Clean 

Ovens, Built-In/Slide-In

TSL Efficiency 

Level

Average Costs 

2014$ Simple 

Payback

years Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating 

Cost

LCC

-- Baseline $627 $23 $267 $894 --

1,2 1 $628 $22 $252 $880 0.9

-- 2 $661 $21 $242 $903 16.0

-- 3 $705 $18 $213 $918 16.7

3 4 $712 $18 $212 $924 18.1

Note: The results for each TSL are calculated assuming that all consumers use products at that efficiency level. The 

PBP is measured relative to the baseline product.

8-50
Table 8.3.8 Average LCC Savings Relative to the Base-Case Efficiency Distribution for 

Electric Self-Clean Ovens, Built-In/Slide-In

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Average Savings*

Net Cost 2014$

-- Baseline 0% ---

1,2 1 0% $14.20

-- 2 53% -$12.82

-- 3 62% -$24.60

3 4 76% -$27.80

*The calculation does not include households with zero LCC savings (no impact)

Table 8.3.9 Average LCC and PBP Results by Efficiency Level for Gas Standard Ovens, 

Freestanding

TSL Efficiency 

Level

Average Costs 

2014$ Simple 

Payback

years Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating 

Cost

LCC

1 Baseline $602 $20 $600 $1,202 --

-- 1 $602 $18 $572 $1,174 0.0

-- 2 $603 $16 $553 $1,156 0.3

-- 3 $612 $15 $545 $1,157 2.4

2 4 $619 $9 $277 $896 1.7

-- 5 $622 $9 $276 $898 2.0

-- 6 $650 $9 $278 $928 4.7

3 7 $656 $9 $277 $933 5.3

Note: The results for each TSL are calculated assuming that all consumers use products at that efficiency level. The 

PBP is measured relative to the baseline product.

8-51
Table 8.3.10 Average LCC Savings Relative to the Base-Case Efficiency Distribution for 

Gas Standard Ovens, Freestanding

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Average Savings*

Net Cost 2014$

1 Baseline 0% ---

-- 1 0% $27.91

-- 2 0% $41.37

-- 3 7% $33.83

2 4 0% $289.73

-- 5 8% $254.98

-- 6 17% $201.23

3 7 24% $178.91

*The calculation does not include households with zero LCC savings (no impact)

Table 8.3.11 Average LCC and PBP Results by Efficiency Level for Gas Standard Ovens, 

Built-In/Slide-In

TSL Efficiency 

Level

Average Costs 

2014$ Simple 

Payback

years Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating 

Cost

LCC

1 Baseline $628 $20 $600 $1,228 --

-- 1 $628 $18 $572 $1,200 0.0

-- 2 $629 $16 $553 $1,182 0.3

-- 3 $638 $15 $545 $1,183 2.4

2 4 $645 $9 $277 $922 1.7

-- 5 $648 $9 $276 $924 2.0

-- 6 $676 $9 $278 $954 4.7

3 7 $682 $9 $277 $959 5.3

Note: The results for each TSL are calculated assuming that all consumers use products at that efficiency level. The 

PBP is measured relative to the baseline product.

8-52
Table 8.3.12 Average LCC Savings Relative to the Base-Case Efficiency Distribution for 

Gas Standard Ovens, Built-In/Slide-In

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Average Savings*

Net Cost 2014$

1 Baseline 0% ---

-- 1 0% $27.90

-- 2 0% $41.37

-- 3 7% $33.83

2 4 0% $289.77

-- 5 8% $255.01

-- 6 17% $201.24

3 7 24% $178.92

*The calculation does not include households with zero LCC savings (no impact)

Table 8.3.13 Average LCC and PBP Results by Efficiency Level for Gas Self-Clean Ovens, 

Freestanding

TSL Efficiency 

Level

Average Costs 

2014$ Simple 

Payback

years Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating 

Cost

LCC

-- Baseline $716 $22 $631 $1,347 --

1 1 $718 $20 $612 $1,329 0.8

2 2 $726 $13 $334 $1,060 1.2

-- 3 $755 $13 $333 $1,087 4.6

3 4 $762 $13 $333 $1,094 5.4

Note: The results for each TSL are calculated assuming that all consumers use products at that efficiency level. The 

PBP is measured relative to the baseline product.

8-53
Table 8.3.14 Average LCC Savings Relative to the Base-Case Efficiency Distribution for 

Gas Self-Clean Ovens, Freestanding

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Average Savings*

Net Cost 2014$

-- Baseline 0% ---

1 1 0% $18.02

2 2 0% $282.80

-- 3 14% $203.50

3 4 27% $165.73

*The calculation does not include households with zero LCC savings (no impact)

Table 8.3.15 Average LCC and PBP Results by Efficiency Level for Gas Self-Clean Ovens, 

Built-In/Slide-In

TSL Efficiency 

Level

Average Costs 

2014$ Simple 

Payback

years Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating 

Cost

LCC

-- Baseline $742 $22 $631 $1,374 --

1 1 $744 $20 $612 $1,355 0.8

2 2 $752 $13 $334 $1,086 1.2

-- 3 $781 $13 $333 $1,114 4.6

3 4 $788 $13 $333 $1,120 5.4

Note: The results for each TSL are calculated assuming that all consumers use products at that efficiency level. The 

PBP is measured relative to the baseline product.

8-54
Table 8.3.16 Average LCC Savings Relative to the Base-Case Efficiency Distribution for 

Gas Self-Clean Ovens, Built-In/Slide-In

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Average Savings*

Net Cost 2014$

-- Baseline 0% ---

1 1 0% $18.03

2 2 0% $282.85

-- 3 14% $203.51

3 4 27% $165.75

*The calculation does not include households with zero LCC savings (no impact)

8.3.1.2 Distribution of Impacts

The figures in this section show the distribution of LCCs in the base case for each 

product class. The figures are presented as frequency charts that show the distribution of LCCs, 

and LCC impacts with their corresponding probability of occurrence. DOE generated the figures 

for the distributions from a Monte Carlo simulation run based on 10,000 samples. 

Base-Case LCC Distributions. Figure 8.3.1 through Figure 8.3.8 show the base-case 

LCC distributions for each product class of conventional cooking products.

8-55
Figure 8.3.1 Electric Standard Ovens, Freestanding: Base-Case LCC 

Distribution

8-56
Figure 8.3.2 Electric Standard Ovens, Built-In/Slide-In: Base-Case LCC 

Distribution

8-57
Figure 8.3.3 Electric Self-Clean Ovens, Freestanding: Base-Case LCC 

Distribution

8-58
Figure 8.3.4 Electric Self-Clean Ovens, Built-In/Slide-In: Base-Case LCC 

Distribution

Figure 8.3.5 Gas Standard Ovens, Freestanding: Base-Case LCC 

Distribution

8-59
Figure 8.3.6 Gas Standard Ovens, Built-In/Slide-In: Base-Case LCC 

Distribution

8-60
Figure 8.3.7 Gas Self-Clean Ovens, Free-standing: Base-Case LCC 

Distribution

8-61
Figure 8.3.8 Gas Self-Clean Ovens, Built-In/Slide-In: Base-Case LCC 

Distribution

Standard-Level Distributions of LCC Impacts

Figure 8.3.12 is an example of a frequency chart that shows the distribution of LCC 

differences for the case of Efficiency Level 1 for product class 4, Electric Self-Clean Ovens, 

Built-In/Slide-In . In the figure, a text box next to a vertical line at a given value on the x-axis 

shows the mean value of LCC (a savings of $879.7 in the example here). Refer to section 8.2.5

on the distribution of product efficiencies under the base case. DOE can generate a frequency 

chart like the one shown in Figure 8.3.9 for each efficiency level and product class. 

Figure 8.3.9 Electric Self-Clean Ovens, Built-In/Slide-In: Distribution of LCC 

Impacts at EL1

8.3.1.3 Range of Impacts

Figure 8.3.10 through Figure 8.3.17 show the range of LCC savings for all efficiency levels 

considered for each conventional cooking product classes. For each efficiency 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 households have LCC savings in excess of that value. 

8-62
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 each standard level.

Figure 8.3.10 Electric Standard Ovens, Freestanding: 

Range of LCC Savings

0

50

100

150

200

250

Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7

Life Cycle Cost Savings ($)

Efficiency Level

Electric Standard Oven, Free-Standing median

average

8-63
Figure 8.3.11 Electric Standard Ovens, Built-In/SlideIn: Range of LCC Savings

Figure 8.3.12 Electric Self-Clean Ovens, Freestanding: Range of LCC 

Savings 

0

50

100

150

200

250

Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7

Life Cycle Cost Savings ($)

Efficiency Level

Electric Standard Oven Built-in/Slide-in

median

average

0

50

100

150

200

250

Level 1 Level 2 Level 3 Level 4

Life Cycle Cost Savings ($)

Efficiency Level

Electric Self-Cleaning Oven, Free-Standing median

average

8-64
Figure 8.3.13 Electric Self-Clean Ovens, Built-In/Slide-In: Range of LCC 

Savings

Figure 8.3.14 Gas Standard Ovens, Freestanding: Range of LCC Savings

0

50

100

150

200

250

Level 1 Level 2 Level 3 Level 4

Life Cycle Cost Savings ($)

Efficiency Level

Electric Self Cleaning Oven, Built-in/Slide-in median

average

0

50

100

150

200

250

Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7

Life Cycle Cost Savings ($)

Efficiency Level

Gas Standard Oven, Free-Standing median

average

8-65
Figure 8.3.15 Gas Standard Ovens, Built-In/Slide-In: Range of LCC 

Savings

Figure 8.3.16 Gas Self-Clean Ovens, Freestanding: Range of LCC 

Savings

0

50

100

150

200

250

Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7

Life Cycle Cost Savings ($)

Efficiency Level

Gas Standard Oven, Built-in/Slide-in median

average

0

50

100

150

200

250

Level 1 Level 2 Level 3 Level 4

Life Cycle Cost Savings ($)

Efficiency Level

Gas Self-Cleaning Oven, Free-Standing median

average

8-66
Figure 8.3.17 Gas Self-Clean Ovens, Built-In/Slide-In: Range of LCC 

Savings

8.4 REBUTTABLE PAYBACK PERIOD

DOE develops rebuttable PBPs to provide the legally established rebuttable presumption 

that an energy conservation 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 

8.1.1. Unlike the analyses described in section 8.2.2, however, the rebuttable PBP is not based on 

household samples and probability distributions. The rebuttable PBP is based instead on discrete, 

single-point values. For example, whereas DOE uses a probability distribution of regional energy 

prices in the distributional PBP analysis, it uses only the national average energy price to 

determine the rebuttable PBP.

Other than the use of single-point values, the most notable difference between the 

distributional PBP and the rebuttable PBP is the latter’s reliance on the DOE test procedure to 

determine a product’s annual energy consumption. DOE based the annual energy consumption 

for the rebuttable PBP on the number of operating hours per year specified in DOE’s proposed 

test procedure for conventional cooking products. The following sections identify the 

0

50

100

150

200

250

Level 1 Level 2 Level 3 Level 4

Life Cycle Cost Savings ($)

Efficiency Level

Gas Self-Cleaning Oven, Built-in/Slide-in median

average

8-67
differences, if any, between the annual energy consumptions determined by the distributional 

PBP and the rebuttable PBP for all product classes of conventional cooking products.

8.4.1 Inputs to the Rebuttable Payback Period Analysis

Because inputs for determining total installed cost for calculating the distributional PBP were 

based on single-point values, only the variability and/or uncertainty in the inputs for determining 

operating cost contributed to variability in the distributional 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 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 

would take effect.

• An average discount rate or lifetime is not required in calculating the rebuttable PBP.

• The effective date of any new standard is assumed to be 2019. 

8.4.2 Results of Rebuttable Payback Period Analysis

DOE calculated rebuttable PBPs for each efficiency level relative to the distribution of 

product efficiencies estimated for the baseline. In other words, DOE did not determine the 

rebuttable PBP relative to the base case energy efficiency, but relative to the distribution of 

product energy efficiencies for the baseline (i.e., the case without new energy conservation 

standards). Table 8.4.1 and Table 8.4.2 present the rebuttable PBPs for each product class of 

conventional cooking products.

8-68
Table 8.4.1 Electric Ovens: Rebuttable Payback Periods

EL

Electric 

Standard, 

Freestanding

Electric Standard, 

Built-In/Slide-In

Electric Self-Clean, 

Freestanding

Electric Self-Clean, 

Built-In/Slide-In

PBP

years

PBP

years

PBP

years

PBP

years

Baseline -- -- -- --

1 0.87 0.85 0.87 0.86 

2 1.27 1.24 9.41 9.25 

3 2.34 2.29 7.82 7.69 

4 2.71 2.65 8.44 8.30 

5 7.24 7.07 

6 8.00 7.81 -- --

7 8.46 8.26 -- --

Table 8.4.2 Gas Ovens: Rebuttable Payback Periods

EL

Gas Standard, 

Freestanding

Gas Standard, 

Built-In/Slide-In Gas Self-Clean Gas Self-Clean

PBP

years

PBP

years

PBP

years

PBP

years

Baseline -- -- -- --

1 0.00 0.00 3.12 3.11 

2 0.22 0.22 4.59 4.57 

3 1.57 1.57 13.15 13.08 

4 2.39 2.38 15.31 15.23 

5 2.78 2.77 -- --

6 6.28 6.25 -- --

7 6.97 6.93 -- --

8-69
REFERENCES

1 U.S. Energy Information Administration, Residential Energy Consumption Survey 2009, 

Washington, DC. 

 <http://www.eia.gov/consumption/residential/data/2009/>

2 DOE–EIA. Annual Energy Outlook 2014 with Projections to 2040. 2014. Washington, 

 D.C., DOE/EIA-0383(2014).

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

3 Desroches, Louis-Benoit, et al., “Incorporating Experience Curves in Appliance Standards 

 Analysis”, Energy Policy 52 (2013): 402-416.

4 UBM Canon, Market Research Magazine: Appliance Historical Statistical Review, 

 2014.

5 RS Means. Mechanical Cost Data, 31st Annual Edition, 2013. Kingston, MA.

6 U.S. Department of Energy–Energy Information Administration, Form EIA-861 for 2012,

 2013. <http://www.eia.gov/electricity/data/eia861>

7 U.S. Department of Energy-Energy Information Administration, Natural Gas Prices, 2013. 

 Washington, DC. 

 <http://www.eia.gov/dnav/ng/ng_pri_sum_dcu_nus_m.htm>

8 U.S. Department of Commerce–Census Bureau, Population Estimates, Vintage 2012.

 <http://www.census.gov/popest/data/national/totals/2012/index.html> 

9 U.S. Department of Energy 2009. Energy Conservation Program: Energy Conservation 

Standards for Certain Consumer Products (Dishwashers, Dehumidifiers, Microwave Ovens, 

and Electric and Gas Kitchen Ranges and Ovens) and for Certain Commercial and 

Industrial Equipment (Commercial Clothes Washers); Final Rule. 2013. 

<http://www1.eere.energy.gov/buildings/appliance_standards/product.aspx/productid/57#sta

ndards >

10 UBM Canon, Appliance Magazine, 2012. 

11 The Federal Reserve Board, Survey of Consumer Finances 1989, 1992, 1995, 1998, 2001, 

2004, 2007, 2010. <http://www.federalreserve.gov/pubs/oss/oss2/scfindex.html>

12 U.S. Department of Labor - Bureau of Labor Statistics, Bureau of Labor Statistics Data, 

Prices & Living Conditions, 2012. <http://data.bls.gov>

13 National Bureau of Economic Research, U.S. Federal and State Average Marginal Income 

Tax Rates, 2010. <http://users.nber.org/~taxsim/marginal-tax-rates/>

8-70

 
14 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>

15 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>

16 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>

17 Mortgage-X - Mortgage Information Service, Cost of Savings Index (COSI) Index History, 

2013. <http://mortgage-x.com/general/indexes/default.asp>

18 Damodaran Online Data Page, The Data Page: Historical Returns on Stocks, Bonds and 

Bills - United States, 2013. Damodaran. 

<http://pages.stern.nyu.edu/~adamodar/New_Home_Page/datafile/histretSP.html>

8-71

 
CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION ........................................................................................................... 9-1

9.2 SHIPMENTS MODEL METHODOLOGY.................................................................... 9-1

9.3 DATA INPUTS AND MODEL CALIBRATION .......................................................... 9-3

9.3.1 Historical Shipments...................................................................................... 9-4

9.3.2 Markets and Model Calibration ................................................................... 9-11

9.3.3 Base Case Shipments................................................................................... 9-16

9.4 EFFECT OF INCREASED PURCHASE PRICE ON SHIPMENTS ........................... 9-17

9.5 AFFECTED STOCK ..................................................................................................... 9-18

9.6 RESULTS ...................................................................................................................... 9-18

LIST OF TABLES

Table 9.3.1 Cooking Products: Product Categories, Product Types, and Product 

Classes.................................................................................................................. 9-5

Table 9.3.2 Historical Shipments: Electric and Gas Cooking Products (Unit: million)......... 9-5

Table 9.3.3 Market Share of Freestanding and Built-in/Slide-In Ranges by Fuel Type......... 9-6

Table 9.3.4 Electric Cooking Products: Historical Shipment Market Shares by 

Product Type........................................................................................................ 9-7

Table 9.3.5 Gas Cooking Products: Historical Shipment Market Shares by Product 

Type ..................................................................................................................... 9-7

Table 9.3.6 Electric Cooking Products: Projected Shipment Market Shares by Product 

Type ..................................................................................................................... 9-8

Table 9.3.7 Gas Cooking Products: Projected Shipment Market Shares by Product 

Type ................................................................................................................... 9-10

Table 9.3.8 Saturation Rates of Cooking Products in New Housing Units in 2009 ............. 9-14

LIST OF FIGURES

Figure 9.3.1 Historical Housing Starts by Housing Type (1970 – 2013)............................... 9-12

Figure 9.3.2 Projected Single-Family Starts (2011 – 2048)................................................... 9-13

Figure 9.3.3 Projected Multi-Family Starts (2011 – 2048).................................................... 9-13

Figure 9.3.4 Projected Manufactured Home Placements (2011 – 2048) ............................... 9-14

Figure 9.3.5 Electric and Gas Cooking Products: Surviving Functions................................ 9-15

Figure 9.3.6 Electric Oven Products: Historical and Base Case Shipments Projection........ 9-16

Figure 9.3.7 Gas Oven Products: Historical and Base Case Shipments Projection .............. 9-16

Figure 9.3.8 Electric Oven Products: Disaggregated Base Case Shipments......................... 9-17

Projection ........................................................................................................... 9-17

9-i
CHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION

Product shipments estimates are a necessary input to the national energy savings (NES) 

and net present value (NPV) calculations. Shipments are also a necessary input to the 

manufacturer impact analysis (MIA), which DOE conducts for its notices of proposed 

rulemaking (NOPRs). This chapter describes DOE’s methodology for projecting annual 

shipments and presents results for cooking products.

DOE estimated shipments for oven products with a shipments model. DOE calibrated 

shipments model against historical shipments. For purposes of estimating the impacts of 

prospective trial standard levels (TSL) on product shipments, the shipments model accounts for 

the combined effects of changes in purchase price, annual operating cost on the consumer 

purchase decision.

The shipments model first considers specific market segments to estimate shipments by 

fuel category of oven products against historical shipments data. The results for which are then 

disaggregated to estimate shipments for each product class. DOE accounted for two market 

segments: (1) shipments due to new construction; (2) replacements of retired units from existing 

buildings. 

The shipments models are Microsoft Excel spreadsheets that are accessible on the 

Internet (http://www.eere.energy.gov/buildings/appliance_standards/). Appendix 10A discusses

how to access the shipments model spreadsheet contained in the NIA spreadsheets, and provides 

basic instructions for using them. The rest of this chapter explains the shipments models in more 

detail. Section 9.2 presents the shipments model methodology; section 9.3 describes the data 

inputs and the model calibration; section 9.4 discusses impacts on shipments from changes in 

equipment purchase price and operating cost; section 9.5 discusses the affected stock; and 

section 9.6 presents the results for different TSL scenarios.

9.2 SHIPMENTS MODEL METHODOLOGY

DOE first developed a national stock model for estimating annual shipments for the 

cooking products (i.e., cooking ranges and ovens) by its fuel category (i.e., electric and gas) 

considered for this standards rulemaking. The model considers market segmentation as a distinct 

input to the shipments forecast. As represented by the following equation, the two primary 

market segments are new installations and replacements. 

Ship ( j) Rpl ( j) NI ( j) p = p + p

9-1
where:

Shipp(j) = total shipments of product p in year j, 

Rplp(j) = units of product p retired and replaced in year j, and 

NIp(j) = number of new installations of product p in year j.

As the product-specific sections below discuss, DOE also considered a third market 

segment for the products to calibrate its shipments models to historical shipments data.

In principle, each market segment and each product class responds differently to both the 

base case demographic and economic trends and to the implementation of standards. 

Furthermore, retirements, early replacements, and efficiency trendsa are dynamic and can vary 

among product classes. Rather than simply extrapolating a current shipments trend, the base case 

shipments analysis (i.e., the case without new standards) uses driver input variables, such as 

construction projections and product lifetime distributions, to project sales in each market 

segment. Thus, DOE’s shipments models assume that construction, i.e., new housing units, 

drives new installations. In each year, the product shipments from the new construction market 

segment are equal to the number of new housing units built times the purchase rate, which is 

determined by the product class market share and the market saturation of the product under 

consideration. 

DOE’s shipments models take an accounting approach, tracking market shares of each 

product class, the vintage of units in the existing stock, and expected construction trends. The 

models estimate shipments due to replacements using sales in previous years and assumptions 

about the lifetime of the equipment. Therefore, estimated sales due to replacements in a given 

year are equal to the total stock of the appliance minus the sum of the appliances sold in previous 

years that still remain in the stock. DOE determined the useful service life of each appliance to 

estimate how long the appliance is likely to remain in stock. The following equation represents 

how DOE estimated replacement shipments.

( ) ( 1) ∑ ∑ ( )

0

1

_ _

_

Rpl j Stock j Ship prob age Rtr

ageMax

age

j

j N

p = p j ×

= =

where:

Stockp (j-1) = total stock of in-service appliances in year j-1,

probRtr (age) = probability that an appliance of a particular age will be retired, and

N = start year for when the model begins its stock accounting (start year is 

specific to each product based on available historical shipments data).

a Efficiency trends affect shipments only in the standards case. A change in the efficiency distribution of the stock 

results in a change in the purchase price and operating cost and, therefore, produces a purchase price and operating 

cost impact on the shipments. This is discussed later in the chapter in section 9.4.

9-2

 
Stock accounting takes product shipments, a retirement function, and initial in-service 

product stock as inputs and provides an estimate of the age distribution of in-service product 

stocks for all years. The age distribution of in-service product stocks is a key input to both the 

NES and NPV calculations—the operating costs for any year depend on the age distribution of 

the stock. The dependence of operating cost on the equipment age distribution occurs under a 

TSL that produces increasing efficiency over time, where older, less efficient units may have 

higher operating costs, while younger, more-efficient units will have lower operating costs. 

DOE calculated total in-service stock of equipment by integrating historical shipments 

data starting from a specific year. The start year depended on the historical data available for the 

product. As units are added to the in-service stock, some of the older ones retire and exit the 

stock. To estimate future shipments, DOE developed a series of equations that define the 

dynamics and accounting of in-service stocks. For new units, the equation is:

( , 1) ( 1) _ Stock j age = = Ship j

where: 

Stock(j, age) =the population of in-service units of a particular age,

j = year for which the in-service stock is being estimated, and

Ship (j) = number of units purchased in year j.

The above equation states that the number of one-year-old units is simply equal to the 

number of new units purchased the previous year. The slightly more complicated equations (e.g., 

the following equation) are those that describe the accounting of the existing in-service stock of 

units: 

( 1, 1) ( , ) [1 ( )] _ Stock j age Stock j age prob age + + = × Rtr

In the above equation, as the year is incremented from j to j+1, the age is also 

incremented from age to age+1. With time, a fraction of the in-service stock is removed, and 

that fraction is determined by a retirement probability function, probRtr(age), which is described 

in section 9.3. Because the products considered in this rulemaking are common appliances that 

have been used by U.S. consumers for a long time, replacements typically constitute the majority 

of shipments. Most replacements are made when equipment wears out and fails. 

9.3 DATA INPUTS AND MODEL CALIBRATION 

As discussed above, shipments are driven primarily by two market segments: new 

construction and replacements. 

DOE estimated new construction shipments using two inputs: new housing projections 

and market saturation data. New housing includes newly constructed single- and multi-family 

9-3
units, referred to as “new housing completions,” and mobile home placements. For new housing 

completions and mobile home placements, DOE used actual data through 2013 and adopted the 

projections from the DOE Energy Information Administration (EIA)’s Annual Energy Outlook 

2015 for the period of 2014–2040.

1 To determine new construction shipments for each fuel 

category product (i.e., electric and gas), DOE used estimations of its historical market 

saturations, combined with projections of housing starts.

DOE estimated replacements using product retirement functions that it developed from 

product lifetimes. DOE based the retirement function on a Weibull probability distribution for 

the product lifetime. The shipments models assume that no units are retired below a minimum 

product lifetime and all units are retired before exceeding a maximum product lifetime. The 

models determine the probability of retirement at a certain age for all products using the 

following equations:

prob (age) = 0 Rtr for age < AgeMin

AgeMax AgeMin prob age Rtr _

1 ( ) = for AgeMin ≤ age ≤ AgeMax

prob (age) =1 Rtr for age > AgeMax

where:

probRtr(age) = probability of retirement at the age of the product,

AgeMin = minimum retirement age, and 

AgeMax = maximum retirement age.

DOE used historical shipments of electric and gas cooking products as the basis for 

calibrating its shipments models. For both products, because new construction shipments and 

replacements were not accurately account for all product shipments, DOE developed another 

market segment to calibrate its shipments models. This additional market segment represented a 

small share of total shipments.

The sections below explain in detail each of the data inputs, including the third market 

segment that DOE developed to calibrate its shipments model for each fuel category cooking 

product.

9.3.1 Historical Shipments 

DOE designed its shipments model for cooking tops and ovens by dividing these 

products into two general fuel categories: electric and gas. Both the electric and gas categories 

comprised the following product configurations: freestanding, built-in cooking tops, and builtin/slide-in ovens. DOE developed two shipments models: one model estimated the electric 

cooking product shipments while the other model estimated gas cooking product shipments. 

9-4
After DOE estimated shipments for each fuel type, it then disaggregated the shipments into 

product types—eight product types for electric cooking products and five product types for gas 

cooking products. Since each product class consists of two or more product types, DOE then 

aggregated shipments for each product type into their appropriate product classes. 

Table 9.3.1 shows the product types and product classes under each general fuel category 

(i.e., electric and gas cooking products). For electric cooking products there are eight product 

types and six product classes; for gas cooking products there are five product types and five 

product classes. Because ranges are comprised of cooking top and oven product classes, DOE 

needed to disaggregate range shipments into the appropriate cooking top and oven product

classes to obtain the total shipments for each product class. 

Table 9.3.1 Cooking Products: Product Categories, Product Types, and Product Classes

Product 

Categories Electric Gas

Product 

Types

(PT): 

8 electric, 

5 gas

Freestanding Built-In 

Cooking Tops

BuiltIn/Slide-In

Oven

Freestanding

Built-In 

Cooking 

Tops

Built-In/SlideIn Oven

PT 1. 

CoilStd

PT 2. 

CoilSC

PT 3. 

SmthStd

PT 4. 

Smth

-SC

PT 5.

Coil

PT 6. 

Smooth

PT 7.

Std

PT 8.

SC

PT 9.

GasStd

PT 10.

Gas-SC

PT 11.

Gas

PT 12.

Std

PT 13.

SC

Product 

Classes

(PC): 

4 electric, 

4 gas

PC1: Electric Standard Oven with or without a Catalytic Line – Freestanding = Product Type 1, 3

PC2: Electric Standard Oven with or without a Catalytic Line – Built-In/Slide-In = Product Type 7

PC3: Electric Self-clean Oven – Freestanding = Product Type 2, 4

PC4: Electric Self-clean Oven – Built-In/Slide-In = Product Type 8

PC5: Gas Standard Oven with or without a Catalytic Line – Freestanding = Product Type 9

PC6: Gas Standard Oven with or without a Catalytic Line – Built-In/Slide-In = Product Type 12

PC7: Gas Self-clean Oven – Freestanding = Product Type 10

PC8: Gas Self-clean Oven – Built-In/Slide-In = Product Type 13

Std = standard; SC = self-clean; Smth = smooth

Table 9.3.2 shows the historical shipments data of electric and gas cooking products. 

DOE relied on two data sources to establish historical shipments data: (1) data from Market 

Research Magazine provided for the period 2006 – 20122, and (2) data from DOE’s 2006 

technical support document (TSD) on cooking products covering the period 1970–20053

.

Table 9.3.2 Historical Shipments: Electric and Gas Cooking Products (Unit: million)

Year Electric Gas Year Electric Gas Year Electric Gas

1970 3.00 2.33 1985 3.41 2.33 2000 5.03 3.18

1971 3.00 2.33 1986 3.41 2.33 2001 5.07 3.04

1972 3.23 2.33 1987 3.41 2.33 2002 5.34 3.27

1973 3.64 2.33 1988 3.41 2.33 2003 5.62 3.42

1974 3.22 2.33 1989 3.41 2.33 2004 6.14 3.72

1975 2.36 2.33 1990 3.35 2.35 2005 6.20 3.76

1976 2.73 2.33 1991 3.21 2.30 2006 6.23 3.59

9-5
Year Electric Gas Year Electric Gas Year Electric Gas

1977 3.28 2.33 1992 3.45 2.49 2007 5.99 3.33

1978 3.41 2.33 1993 3.73 2.63 2008 5.11 2.84

1979 3.41 2.33 1994 4.05 2.84 2009 4.33 2.60

1980 3.41 2.33 1995 3.92 2.72 2010 4.45 2.79

1981 3.41 2.33 1996 4.16 2.71 2011 4.32 2.62

1982 3.41 2.33 1997 4.24 2.74 2012 4.32 2.60

1983 3.41 2.33 1998 4.64 2.95

1984 3.41 2.33 1999 4.98 3.14

Source: 2006 – 2012: Market Research Magazine; 1970 – 2005: DOE’s 2006 rulemaking for cooking products. 

 DOE used the three sources to establish historical market shares for each product type 

under each fuel type: (1) data collected from the web-sites of AJ Madison, Home Depot, Lowes 

in 2013, and (2) data from Market Research Magazine provided for the period 2006 – 20122

, and 

(3) data from DOE’s 2006 TSD on cooking products covering the period 1970–20053

. Because 

the historical shipments data is reported by range, oven, and surface cooking top, which did not 

provide detailed shipments information of range types, i.e. freestanding and built-in/slide-in 

ranges proposed by this rulemaking, therefore, DOE used web collected product model 

information to estimate the market share between the freestanding and built-in/slide-in ranges.

Table 9.3.3 shows the market share between freestanding and built-in/slide-in ranges by fuel 

type. This information enables DOE to reallocate historical shipments data based on the newly 

proposed product classes. Table 9.3.4 presents the re-grouped market shares of the eight product 

types that comprise total electric cooking product shipments. Table 9.3.5 shows the re-grouped 

historical market shares of the five product types that comprise total gas cooking product 

shipments. For any given year, the sum of the product type market shares equals 100 percent 

under each fuel type. 

Table 9.3.3 Market Share of Freestanding and Built-in/Slide-In Ranges by Fuel Type

Range Type

Electric Ranges Gas Ranges

Number of 

Models % of Total Number of Models % of Total

Freestanding 533 94.2% 110 91.7%

Slide-In 33 5.8% 10 8.3%

Total 566 100.0% 120 100.0%

Source: AJ Madison, Home Depot, and Lowes.

9-6
Table 9.3.4 Electric Cooking Products: Historical Shipment Market Shares by Product 

Type

Year

Percent of Total Shipments

Freestanding Ranges Built-In Cooking Tops Built-In/Slide-In Ovens

Standard Self-Clean Coil Smooth Standard Self-Clean

1970–1989 32.80% 31.80% 4.10% 9.00% 7.10% 15.20%

1990 28.70% 36.20% 4.00% 8.80% 8.20% 14.10%

1991 32.10% 34.60% 3.80% 8.30% 4.50% 16.80%

1992 32.70% 33.80% 3.70% 8.30% 4.70% 16.80%

1993 32.80% 34.60% 3.60% 7.90% 4.20% 17.00%

1994 28.70% 40.20% 3.10% 6.90% 6.80% 14.20%

1995 30.90% 39.50% 3.10% 6.80% 3.90% 15.70%

1996 28.40% 42.50% 3.10% 6.90% 3.90% 15.30%

1997 26.50% 44.10% 3.30% 7.20% 3.60% 15.30%

1998 25.40% 45.30% 3.40% 7.50% 3.30% 15.20%

1999 24.70% 46.90% 3.10% 6.80% 3.00% 15.60%

2000 23.70% 48.00% 3.10% 6.80% 2.70% 15.70%

2001 22.60% 48.80% 3.10% 6.80% 2.50% 16.20%

2002 21.60% 49.60% 3.10% 6.80% 2.30% 16.70%

2003 17.80% 53.30% 3.30% 6.40% 2.30% 17.00%

2004 17.90% 52.70% 2.80% 6.50% 2.00% 18.10%

2005 19.60% 51.60% 2.60% 6.20% 1.90% 18.20%

2006 19.50% 51.20% 2.60% 6.20% 1.90% 18.60%

2007 20.00% 52.60% 2.50% 6.00% 1.80% 17.20%

2008 20.20% 53.10% 2.50% 6.00% 1.70% 16.50%

2009 20.60% 54.30% 2.30% 5.50% 1.60% 15.70%

2010 20.40% 53.90% 2.20% 5.30% 1.70% 16.50%

2012 20.60% 54.20% 2.20% 5.20% 1.70% 16.20%

Table 9.3.5 Gas Cooking Products: Historical Shipment Market Shares by Product 

Type

Year

Percent of Total Shipments

Freestanding Ovens

Built-In 

Cooking Tops

Built-In/Slide-In Ovens

Standard Self-Clean Standard Self-Clean

1970–1989 57.9% 20.1% 10.1% 10.3% 1.5%

1990 57.9% 19.7% 10.9% 9.5% 2.0%

1991 56.5% 21.1% 11.4% 9.0% 2.0%

1992 56.0% 21.7% 11.7% 8.9% 1.7%

1993 53.4% 24.4% 11.8% 7.7% 2.6%

9-7
Year

Percent of Total Shipments

Freestanding Ovens

Built-In 

Cooking Tops

Built-In/Slide-In Ovens

Standard Self-Clean Standard Self-Clean

1994 55.5% 23.1% 11.2% 8.5% 1.7%

1995 53.3% 27.4% 8.8% 7.8% 2.6%

1996 51.2% 28.8% 10.0% 8.0% 2.0%

1997 49.0% 30.9% 10.2% 7.4% 2.5%

1998 46.3% 32.7% 11.4% 7.1% 2.5%

1999 44.0% 34.8% 11.7% 7.0% 2.5%

2000 41.9% 36.9% 11.9% 6.9% 2.5%

2001 39.5% 38.4% 12.6% 6.9% 2.5%

2002 37.9% 40.2% 12.7% 6.7% 2.5%

2003 37.0% 40.6% 13.3% 6.0% 3.0%

2004 34.1% 42.9% 14.2% 6.6% 2.2%

2005 33.8% 42.7% 14.9% 6.4% 2.3%

2006 33.5% 42.2% 15.7% 6.3% 2.2%

2007 33.8% 42.6% 14.9% 6.4% 2.3%

2008 34.3% 43.3% 13.6% 6.4% 2.3%

2009 35.3% 44.6% 11.2% 6.6% 2.3%

2010 35.3% 44.6% 11.2% 6.5% 2.3%

2012 35.3% 44.5% 11.4% 6.5% 2.3%

Source: 2006 – 2012: Normalized based on 2005 market share. 

 DOE then calibrated historical market share data as shown in Table 9.3.4 and Table 9.3.5

to estimate market share projections of each product type for the period 2013 – 2048. Table 9.3.6

and Table 9.3.7 present the projected market share for electric and gas cooking products, 

respectively. 

Table 9.3.6 Electric Cooking Products: Projected Shipment Market Shares by Product 

Type

Year

Percent of Total Shipments

Freestanding Ranges Built-In Cooking Tops Built-In/Slide-In Ovens

Standard Self-Clean Coil Smooth Standard Self-Clean

2005 19.60% 51.60% 2.60% 6.20% 1.90% 18.20%

2006 19.50% 51.20% 2.60% 6.20% 1.90% 18.60%

2007 20.00% 52.60% 2.50% 6.00% 1.80% 17.20%

2008 20.20% 53.10% 2.50% 6.00% 1.70% 16.50%

2009 20.60% 54.30% 2.30% 5.50% 1.60% 15.70%

2010 20.40% 53.90% 2.20% 5.30% 1.70% 16.50%

2012 20.60% 54.20% 2.20% 5.20% 1.70% 16.20%

9-8
Year

Percent of Total Shipments

Freestanding Ranges Built-In Cooking Tops Built-In/Slide-In Ovens

Standard Self-Clean Coil Smooth Standard Self-Clean

2013 20.60% 54.20% 2.10% 4.90% 1.70% 16.50%

2014 20.60% 54.20% 2.10% 5.10% 1.30% 16.70%

2015 20.60% 54.10% 2.10% 5.00% 1.20% 16.90%

2016 20.60% 54.10% 2.10% 4.90% 1.20% 17.20%

2017 20.50% 54.10% 2.00% 4.80% 1.10% 17.40%

2018 20.50% 54.20% 2.00% 4.70% 1.00% 17.70%

2019 20.50% 54.10% 1.90% 4.60% 1.00% 17.90%

2020 20.50% 54.10% 1.90% 4.50% 0.90% 18.10%

2021 20.50% 54.30% 1.80% 4.40% 0.90% 18.10%

2022 20.60% 54.60% 1.80% 4.30% 0.80% 18.00%

2023 20.50% 54.80% 1.70% 4.20% 0.80% 18.00%

2024 20.60% 55.00% 1.70% 4.10% 0.70% 17.90%

2025 20.60% 55.20% 1.70% 4.00% 0.70% 17.80%

2026 20.60% 55.40% 1.60% 3.90% 0.60% 17.80%

2027 20.70% 55.70% 1.60% 3.80% 0.60% 17.70%

2028 20.70% 55.80% 1.60% 3.70% 0.60% 17.60%

2029 20.80% 56.10% 1.50% 3.60% 0.50% 17.50%

2030 20.70% 56.30% 1.50% 3.60% 0.50% 17.40%

2031 20.70% 56.60% 1.50% 3.50% 0.50% 17.30%

2032 20.80% 56.70% 1.40% 3.40% 0.40% 17.20%

2033 20.80% 56.90% 1.40% 3.30% 0.40% 17.10%

2034 20.90% 57.20% 1.40% 3.20% 0.40% 17.00%

2035 20.90% 57.40% 1.30% 3.20% 0.40% 16.80%

2036 20.90% 57.70% 1.30% 3.10% 0.40% 16.70%

2037 20.90% 57.80% 1.30% 3.00% 0.30% 16.60%

2038 20.90% 58.10% 1.20% 3.00% 0.30% 16.50%

2039 21.00% 58.30% 1.20% 2.90% 0.30% 16.30%

2040 21.00% 58.50% 1.20% 2.80% 0.30% 16.20%

2041 21.10% 58.70% 1.20% 2.80% 0.30% 16.10%

2042 21.10% 59.20% 1.10% 2.60% 0.20% 15.80%

2043 21.10% 59.40% 1.10% 2.60% 0.20% 15.60%

2044 21.10% 59.70% 1.10% 2.50% 0.20% 15.50%

2045 21.20% 59.80% 1.00% 2.50% 0.20% 15.30%

2046 21.20% 60.00% 1.00% 2.40% 0.20% 15.20%

2047 21.20% 60.30% 1.00% 2.40% 0.20% 15.00%

2048 21.20% 60.50% 1.00% 2.30% 0.20% 14.80%

9-9
Table 9.3.7 Gas Cooking Products: Projected Shipment Market Shares by Product Type

Year

Percent of Total Shipments

Freestanding Ovens

Built-In 

Cooking Tops

Built-In/Slide-In Ovens

Standard Self-Clean Standard Self-Clean

2005 33.8% 42.7% 14.9% 6.4% 2.3%

2006 33.5% 42.2% 15.7% 6.3% 2.2%

2007 33.8% 42.6% 14.9% 6.4% 2.3%

2008 34.3% 43.3% 13.6% 6.4% 2.3%

2009 35.3% 44.6% 11.2% 6.6% 2.3%

2010 35.3% 44.6% 11.2% 6.5% 2.3%

2012 35.3% 44.5% 11.4% 6.5% 2.3%

2013 35.3% 44.6% 11.6% 6.3% 2.2%

2014 34.5% 45.3% 11.7% 6.1% 2.5%

2015 34.5% 45.2% 11.8% 6.0% 2.5%

2016 34.5% 45.2% 11.9% 6.0% 2.5%

2017 34.5% 45.2% 11.9% 5.9% 2.5%

2018 34.5% 45.1% 12.0% 5.9% 2.5%

2019 32.2% 47.4% 12.1% 5.8% 2.5%

2020 31.9% 47.7% 12.2% 5.8% 2.5%

2021 31.5% 48.0% 12.2% 5.7% 2.5%

2022 31.2% 48.2% 12.3% 5.7% 2.5%

2023 30.9% 48.5% 12.4% 5.6% 2.5%

2024 30.6% 48.8% 12.5% 5.6% 2.5%

2025 30.3% 49.0% 12.5% 5.6% 2.5%

2026 30.1% 49.2% 12.6% 5.5% 2.6%

2027 29.8% 49.5% 12.7% 5.5% 2.6%

2028 29.5% 49.7% 12.8% 5.5% 2.6%

2029 29.3% 49.9% 12.9% 5.4% 2.6%

2030 29.0% 50.1% 12.9% 5.4% 2.6%

2031 28.8% 50.3% 13.0% 5.4% 2.6%

2032 28.5% 50.5% 13.1% 5.3% 2.6%

2033 28.3% 50.6% 13.2% 5.3% 2.6%

2034 27.8% 51.0% 13.3% 5.2% 2.6%

2035 27.6% 51.1% 13.4% 5.2% 2.6%

2036 27.4% 51.3% 13.5% 5.2% 2.6%

2037 27.2% 51.5% 13.6% 5.2% 2.6%

2038 27.0% 51.6% 13.7% 5.1% 2.6%

2039 26.8% 51.7% 13.8% 5.1% 2.6%

2040 26.6% 51.9% 13.9% 5.1% 2.6%

2041 26.4% 52.0% 13.9% 5.0% 2.6%

2042 26.2% 52.1% 14.0% 5.0% 2.6%

2043 26.0% 52.3% 14.1% 5.0% 2.6%

9-10
Year

Percent of Total Shipments

Freestanding Ovens

Built-In 

Cooking Tops

Built-In/Slide-In Ovens

Standard Self-Clean Standard Self-Clean

2044 25.8% 52.4% 14.2% 5.0% 2.6%

2045 25.6% 52.5% 14.3% 4.9% 2.6%

2046 25.5% 52.6% 14.4% 4.9% 2.7%

2047 25.3% 52.7% 14.5% 4.9% 2.7%

2048 25.1% 52.8% 14.6% 4.9% 2.7%

9.3.2 Markets and Model Calibration

For each general fuel category of cooking products, i.e., electric and gas, the market is 

primarily comprised of the following: replacement units for equipment that has been retired from 

service and units for new housing. In addition to normal replacements, DOE’s shipments model 

for each general category also assumed that a certain fraction of the stock would be not be 

replaced due to demolition of old housing units. Total electric cooking product shipments are 

represented by the following equation:

Ship ( j) Rpl ( j) NI ( j) NR ( j) ELEC = ELEC + ELEC + ELEC

where:

ShipELEC (j) = total shipments of electric cooking products in year j, 

RplELEC (j) = replacement shipments in year j, 

NIELEC (j) = shipments to new households in year j, and 

NRELEC (j)= non replaced shipments in year j due to building demolition. 

 Total gas cooking product shipments are represented by the same basic equation:

Ship ( j) Rpl ( j) NI ( j) NR ( j) GAS = GAS + GAS + GAS

where:

ShipGAS (j) = total shipments of gas cooking products in year j, 

RplGAS (j) = replacement shipments in year j, 

NIGAS (j) = shipments to new households in year j, and 

NRGAS (j)= non replaced shipments in year j due to building demolition. 

The sections below discuss in further detail all three of these markets for each general 

cooking product category (i.e., cooking tops and ovens). 

9-11
New Construction. To estimate shipments to new construction, DOE used projections of 

housing starts coupled with cooking product saturation data. In other words, to project the 

shipments for new construction for any given year, DOE multiplied the housing projections by 

the estimated saturation of cooking products for new housing units. 

Figure 9.3.1 presents historical new housing starts based on the U.S. Census data for the 

period 1970 – 20134. New housing is comprised of single- and multi-family units and mobile 

home placements. Figure 9.3.2 presents the projected new housing starts based on EIA’s

AEO2015 for the period 2014–20401

. The AEO typically provides three scenarios of housing 

starts: the Reference case, the High Economic Growth case, and the Low Economic Growth 

case. To estimate housing starts for the period 2041−2048, DOE froze housing starts at the level 

in the year 2040 for all three economic projections. All three housing starts projections are 

presented in Figure 9.3.2 through Figure 9.3.4. DOE used the projections from the Reference 

case as its default to estimate its shipments to new construction. 

0.0

0.5

1.0

1.5

2.0

2.5

Housing Starts (million)

Single-Family Starts Multi-Family Starts Manufactured Home Placements

Figure 9.3.1 Historical Housing Starts by Housing Type (1970 – 2013)

9-12
Figure 9.3.2 Projected Single-Family Starts (2011 – 2048)

Figure 9.3.3 Projected Multi-Family Starts (2011 – 2048)

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

Housing Starts (million)

Reference Low High

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Housing Starts (million)

Reference Low High

9-13
Figure 9.3.4 Projected Manufactured Home Placements (2011 – 2048)

To project saturation of cooking products in new housing starts, DOE reviewed data that 

provided by the Residential Energy Consumption Survey (RECS) 1997, 2001, 2005 and 20095

. 

DOE decided to only use the saturations for the year 2009 to estimate the shipments to new 

construction for future years, because the survey contains the largest households sample and its 

questions regarding saturation of cooking products are more accurately designed than previous 

versions. Because DOE conducted its shipments analysis by first projecting overall cooking 

product shipments by fuel category and then disaggregating the total shipments into product type 

using the historical market share data in Table 9.3.4 and Table 9.3.5, it used the overall 

saturation of electric and gas cooking products to estimate shipments to new construction, 

respectively. Table 9.3.8 summaries the saturation rates in new housing units in RECS 2009. To 

estimate saturation rates for the period 2010−2048, DOE froze saturation rates at the level in the 

year 2009.

Table 9.3.8 Saturation Rates of Cooking Products in New Housing Units in 2009

Electric Cooking Products Gas Cooking Products

SingleFamilyMultiFamilyMobileHomeSingleFamilyMultiFamilyMobileHome

81.6% 74.6% 88.7% 35.9% 27.6% 8.8%

Source: RECS2009.

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Housing Starts (million)

Reference Low High

9-14
Replacements. DOE determined shipments to the replacement market using an 

accounting method that tracks the total stock of units by vintage. DOE estimated stocks of 

electric and gas cooking products by vintage and by integrating historical shipments starting 

from the year 1970. Over time, some of the units will be retired and removed from the stock, 

thus triggering the shipment of a new unit. Because of the relationship between retirements and 

total stock, there is a strong correlation between past and future shipments, independent of 

efficiency standards.

Depending on the vintage, a certain percentage of each type of unit will fail and need to 

be replaced. To determine when an electric cooking product fails, DOE used a product survival 

function based on a Weibull lifetime distribution with an average value of 15 years. For gas 

cooking products, DOE used a product survival function based on the same Weibull lifetime 

distribution with an average value of 17 years. For a more complete discussion of cooking 

product lifetimes, refer back to section 8.2.3 of chapter 8. Figure 9.3.5 shows the survival 

functions that DOE used to estimate replacement shipments. 

Figure 9.3.5 Electric and Gas Cooking Products: Surviving Functions

Model Calibration—Non-Replacement. To calibrate estimated shipments with the 

historical data, DOE introduced into the model a non-replacement market function. DOE 

assumed that some of the retiring cooking products would not be replaced in this category due to 

building demolition occur at the rate of 2.8 percent for electric cooking products and 4.1 percent

for gas cooking products. DOE multiplied the not-replaced rates with the annual retiring electric 

and gas cooking products, respectively. DOE then excluded not-replaced units from the annual 

retiring units to estimate actual replacement of cooking products per annum for the period 2013–

2048.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

% Surviving - Gas % Surviving - Electric

9-15
9.3.3 Base Case Shipments

Figure 9.3.6 and Figure 9.3.7 show the projected shipments of electric and gas oven 

products, respectively, in the base case (i.e., the case without new energy efficiency standards) 

and the historical shipments DOE used to calibrate the projection. 

Figure 9.3.6 Electric Oven Products: Historical and Base Case Shipments Projection

Figure 9.3.7 Gas Oven Products: Historical and Base Case Shipments Projection

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

Shipments (million)

Historical Shipments Projected Shipments

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Shipments (million)

Historical Projected

9-16
Figure 9.3.8 presents total projected base case electric and gas oven product shipments 

over the analysis period (2019-2048). Note that electric oven shipments comprised 67 – 72 

percent of total oven shipments during the analysis period. 

Figure 9.3.8 Electric Oven Products: Disaggregated Base Case Shipments 

Projection

9.4 EFFECT OF INCREASED PURCHASE PRICE ON SHIPMENTS

Economic theory suggests that, all else being equal, an increase in the price of a good 

leads to a decrease in demand for it. Because DOE projects that appliance standards often result 

in an increase in the price of the product, DOE conducts a literature review and an analysis of 

appliance price and efficiency data to estimate the effects on product shipments from increases in 

product price. DOE also considers the decreases in operating costs from higher energy efficiency 

and changes over time in household income. 

In the case of oven products, the combined market of electric and gas oven products is 

completely saturated as indicated by the historical RECS data. Because of the nature of the enduse, every household is likely to be fitted with some type of oven product. A potential increase in 

purchase price could impact replacements in two ways – a unit due for replacement would either 

get replaced by the consumer immediately or the consumer would delay replacement by opting 

for repairing, before the eventual replacement. DOE did not get sufficient data to accurately 

characterize the replace versus repair decision. DOE therefore assumed that consumer price 

elasticity for oven products is zero. In other words, overall shipments for oven products would 

not be affected by any standards. However, since DOE uses a consumer choice model for 

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

Shipments (million)

Electric Oven Gas Oven

9-17
estimating the efficiency distribution of the shipments, the impact of a price increase is captured 

in the shipments model through the consumers’ choice of efficiency level while making a new 

purchase of the appliance.

9.5 AFFECTED STOCK

The affected stock is the in-service stock of the appliance or product that is affected by a 

TSL. In addition to the projection of product shipments under both the base case and the 

standards case, the affected stock (which represents the difference in the appliance stock for the 

base case and the standards case) is a key output of DOE’s Shipments Models. The affected 

stock quantifies the impact that new product shipments have on the appliance stock due to a 

TSL. Therefore, the affected stock consists of those in-service units that are purchased in or after 

the year the standard has taken effect, as described by the following equation:

∑

_

1

_

( ) ( ) ( )

j Std yr

age

p p p Aff Stock j Ship j Stock age

=

= +

where:

Aff Stockp(j) = affected stock of units of product p of all vintages that are operational in 

year j,

Shipp(j) = shipments of product p in year j, 

Stockp(j) = stock of units of product p of all vintages that are operational in year j,

age = age of the units (years), and

Std_yr = effective date of the standard.

As noted in the above equation, to calculate the affected stock, DOE must define the 

effective date of the standard. For the NES and NPV results presented in chapter 10, DOE 

assumed that new energy efficiency standards will become effective in the year 2019. Thus, all 

appliances purchased starting on the first day of the year 2019 are affected by the standard level. 

9.6 RESULTS

The following section discusses the shipments projections for the various TSLs that DOE 

considered for each of the products. The TSLs are identified and described in chapter 10.

Because the combined market of electric and gas cooking products is completely 

saturated, DOE assumed that all electric and gas oven product TSLs would neither impact 

standards case shipments nor cause shifts in electric and gas oven product market shares. Thus, 

DOE’s shipments model for electric and gas cooking products does not incorporate the use of 

relative price elasticities. As a result, projected shipments for all cooking product TSLs are equal

9-18
to the base case shipments projection. Refer back to Figure 9.3.6 and Figure 9.3.7 to review the 

base case shipments projections for electric and gas oven products, respectively.

9-19
REFERENCES

1 U.S. Department of Energy-Energy Information Administration. Annual Energy Outlook 

2015, April, 2015. Washington, DC. 

 <http://www.eia.gov/forecasts/aeo/>.

2 UBM Canon, Market Research Magazine: Appliance Historical Statistical Review, 2014.

3 U.S. Department of Energy-Energy Efficiency and Renewable Energy Office. Energy 

Conservation Standards for Electric and Gas Ranges and Ovens and Microwave Ovens, 

Dehumidifiers, and Commercial Clothes Washers, 2006, EERE-2006-STD-0127,

Washington, DC. 

4 U.S. Census Bureau, Historical Data: New Privately Owned Housing Units Started, 1959 – 

 2013.

 <http://www.census.gov/construction/nrc/historical_data/>.

5 U.S. Department of Energy-Energy Information Administration. Residential Energy 

Consumption Survey, 1997, 2001, 2005, and 2009. Washington, DC.

 <http://www.eia.gov/consumption/residential/data/2009/>.

9-20

 
10-i

CHAPTER 10. NATIONAL IMPACT ANALYSIS

TABLE OF CONTENTS

10.1 INTRODUCTION ......................................................................................................... 10-1

10.2 TRIAL STANDARD LEVELS ..................................................................................... 10-2

10.3 PROJECTED EFFICIENCY TRENDS......................................................................... 10-3

10.3.1 Base Case ....................................................................................................................... 10-3

10.3.2 Standards Case ............................................................................................................... 10-5

10.4 NATIONAL ENERGY SAVINGS ............................................................................... 10-5

10.4.1 Definition ....................................................................................................................... 10-5

10.4.2 Shipments and Product Stock ........................................................................................ 10-6

10.4.3 Annual Energy Consumption per Unit .......................................................................... 10-6

10.4.4 National Annual Energy Consumption.......................................................................... 10-7

10.4.5 Primary Energy Conversion Factors.............................................................................. 10-7

10.4.6 Full-Fuel-Cycle Energy Factors..................................................................................... 10-8

10.5 NET PRESENT VALUE............................................................................................... 10-9

10.5.1 Definition ....................................................................................................................... 10-9

10.5.2 Total Installed Cost...................................................................................................... 10-10

10.5.3 Annual Operating Cost ................................................................................................ 10-11

10.5.4 Discount Factor............................................................................................................ 10-12

10.6 RESULTS .................................................................................................................... 10-12

10.6.1 National Energy Savings.............................................................................................. 10-12

10.6.2 Net Present Value of Consumer Benefit...................................................................... 10-13

LIST OF TABLES

Table 10.2.1 Trial Standard Levels for Electric Ovens........................................................... 10-3

Table 10.2.2 Trial Standard Levels for Gas Ovens................................................................. 10-3

Table 10.3.1 Oven User Ownership by Fuel Type ................................................................. 10-4

Table 10.3.2 Best-fit Logit Parameters for Oven Products..................................................... 10-5

Table 10.6.1 Estimates of Cumulative Full-Fuel Cycle NES (quads) .................................. 10-12

Table 10.6.2 Cumulative NPV Results based on Three-Percent Discount Rates (billion 

2014$) .............................................................................................................. 10-13

Table 10.6.3 Cumulative NPV Results based on Seven-Percent Discount Rates (billion 

2014$) .............................................................................................................. 10-13

LIST OF FIGURES

Figure 10.4.1 Site-to-Power Plant Energy Use Factor for Oven Products............................... 10-8
10-1

CHAPTER 10. NATIONAL IMPACT ANALYSIS

10.1 INTRODUCTION

This chapter describes the method the U.S. Department of Energy (DOE) used to 

estimate the national impacts of each trial standard level (TSL) considered for oven products and 

presents the results of its calculations. For each TSL, DOE evaluated the following impacts: (1) 

national energy savings (NES) attributable to each potential standard level; (2) monetary value of 

the lifetime energy savings to consumers of oven products; (3) increased total installed costs; and 

(4) the net present value (NPV) of the difference between the value of the operating cost savings 

and the increased total installed costs. 

The calculations and results are presented in a Microsoft Excel spreadsheet model, which 

is accessible through the Department’s website1

. The spreadsheet model, termed the national 

impact analysis (NIA) model, calculates energy savings and NPV for the nation. Details and 

instructions for using the NIA model are provided in appendix 10A.

The NIA calculation started with the shipments model, described in chapter 9, that DOE 

used to project future purchases of oven products. Chapter 9 includes an analysis of consumers’ 

sensitivities to total installed cost, operating expense, and other factors that may lead to a change 

in total shipments relative to the base case under a proposed standard. DOE used the annual

shipments projection to produce an accounting of annual NES, annual national energy cost 

savings, and annual national incremental non-energy costs resulting from purchasing, installing 

and operating the covered equipment. The NIA analysis accounts for costs and energy use over 

the lifetime of each unit shipped during the analysis period 2019 - 2048. The national-level 

results presented for each year of the analysis period, and as cumulative totals. 

To calculate the annual NES, DOE estimated the lifetime site, primary and full-fuel-cycle 

(FFC) energy consumption at the unit level for each year in the analysis period. DOE defined 

these quantities as follows:

• Site energy consumption is the physical quantity of fossil fuels or electricity consumed at

 the site where the end-use service is provided. The site energy consumption is used to 

 calculate the energy cost input to the NPV calculation.

• Primary energy consumption is defined by converting the site fuel use from physical 

 units, for example cubic feet for natural gas, or kWh for electricity, to common energy 

 units (million Btu or MMBtu). This step used the conversion factors listed in appendix 

 10B. For electricity the conversion factor is a marginal heat rate that incorporate losses 

 in generation, transmission and distribution, and depends on the sector, end use and year. 

 For this rule DOE used the values for residential end-use.

• The full-fuel-cycle (FFC) energy use is equal to the primary energy use plus the energy 

 consumed “upstream” of the site in the extraction, processing and distribution of fuels. 

 The FFC energy use was calculated by applying a fuel-specific FFC energy multiplier to 

 the primary energy use. These multipliers are presented in appendix 10B. 

 

1 U.S. Department of Energy, http://www.eere.energy.gov/buildings/appliance_standards/.
10-2

 If a product uses multiple fuels2

, the energy use calculation estimated consumption for 

each fuel type separately. The unit’s lifetime primary and FFC energy consumptions were then 

scaled up to the national level based on the annual shipments projection and according to two 

scenarios: the base case scenario, with no changes in the existing energy efficiency standards; 

and (b) the standards case scenario, where energy efficiency standards are set at the energy 

efficiency level corresponding to one of the trial standard levels (TSLs). 

DOE followed a similar procedure to calculate the annual national energy cost savings 

and the annual national incremental installation, maintenance and other non-energy costs. For 

each unit shipped during the analysis period, and for each year of its lifetime, DOE estimated 

both the energy and the non-energy costs based on the unit’s efficiency and any appropriate price 

trends. The unit-level estimates were then scaled up to the national level based on the annual 

shipments projection for the base case and the trial standard levels. DOE calculated the 

difference between the aggregated national energy cost savings and national incremental nonenergy costs to obtain the NPV of each equipment class. DOE applied a weight to each 

equipment class based on its market share to sum these values to define the total NPV.

The two models used in the NIA—the NES model and the NPV model—are described 

more fully in subsequent sections. The descriptions include overviews of how DOE performed 

each model’s calculations and summaries of the major inputs. After the technical model 

descriptions, this chapter presents the results of the NIA calculations.

10.2 TRIAL STANDARD LEVELS

DOE analyzed the benefits and burdens of three trial standard levels (TSLs) for oven

products. The proposed criteria for grouping efficiency levels into TSLs to apply to each 

equipment class are outlined below, and the resulting efficiency level groupings by TSL are 

shown in Table 10.2.1 and Table 10.2.2.

1. TSL 3 was chosen to correspond to the max-tech efficiency level for each product class.

2. TSL 2 comprises efficiency levels that offer the maximum NPV. 

3. TSL 1 was configured with standby levels with maximum NES. 

 

2 For example, a furnace may use both natural gas for heating and auxiliary electricity.
10-3

Table 10.2.1 Trial Standard Levels for Electric Ovens

TSL

Electric Standard 

Ovens, Freestanding

Electric Standard 

Ovens, BuiltIn/Slide-In

Electric Self-Clean

Ovens, 

Freestanding

Electric Self-Clean

Ovens, BuiltIn/Slide-In

EL IAEC

(kWh/yr) EL IAEC(kWh/yr) EL IAEC(kWh/yr) EL IAEC(kWh/yr)

1 1 284.6 1 291.4 1 345.1 1 351.0

2 3 259.2 3 265.4 1 345.1 1 351.0

3 7 207.3 7 212.2 4 278.1 4 282.9

Table 10.2.2 Trial Standard Levels for Gas Ovens

TS

L

Gas Standard Ovens, 

Freestanding

Gas Standard 

Ovens, BuiltIn/Slide-In

Gas Self-Clean 

Ovens, 

Freestanding

Gas Self-Clean 

Ovens, BuiltIn/Slide-In

EL IAEC

(kBtu/yr) EL IAEC(kBtu/yr) EL IAEC(kBtu/yr) EL IAEC(kBtu/yr)

1 Baseline 2,118.2 Baseline 2,128.1 1 1,848.2 1 1,858.0

2 4 1,414.8 4 1,421.5 2 1,668.7 2 1,677.5

3 7 1,347.0 7 1,353.3 4 1,591.0 4 1,599.4

10.3 PROJECTED EFFICIENCY TRENDS

10.3.1 Base Case 

A key component of the NIA is the energy efficiency projected over time for the base 

case (without new standards) and for each of the standards cases. 

 For its determination of base case projected efficiencies, DOE implemented a consumerchoice model that assumes consumers are sensitive to first cost, i.e., equipment price, and 

calculates the market share for available efficiency options based on the first cost for oven

products users. 

The consumer-choice model uses a logit model to calculate the probability that a 

consumer will purchase product jj based on the logistic curve probability function of the form:

PPjj(zz) = eezzjj

∑ eezzii nn

ii=1

Eq. 10.1

Where:

PPjj(zz) = the probability a consumer will purchase product jj among nn possible options, 

and

zz = the ‘logit’, which is defined as follows

zzjj = ββFFFF ∗ FFCCjj

Eq. 10.2
10-4

Where:

ββFFFF = consumer sensitivity to first cost,

FFCCjj = first cost of product option jj,

 In Eq.10.2 , ββFFFF can be determined by fitting an exponential function to the first cost 

distribution in the engineering analysis:

MMMM(FF ) = NNeeββFFFF∗FFFF

Eq. 10.3

Where:

NN = Normalization factor,

FF = First cost of a oven product in 2014$, 

MMMM(FF ) = Market share for a oven product that costs FF , and

ββFFFF = Consumer sensitivity to first cost.

The regression coefficients (ββ values) represent the consumer’s sensitivity to first cost 

(ββFFFF). The coefficients are determined using historical shipments and equipment price data. 

 For oven products, consumer sensitivities are user-specific based on users’ housing type 

in the Residential Energy Consumption Survey (RECS) 20091. Table 10.3.1 summarizes the 

market share between renters and home-owners by fuel type of oven products that they use in 

RECS 2009. DOE assumed that landlords would have no economic incentive and renters would 

have no decision making to purchase or replace an energy efficient oven product, therefore, DOE 

assigned the percentage of renters found in the RECS 2009 to the baseline efficiency level. DOE 

then assumed home-owners would have incentive to purchase or replace an energy efficient oven

product based on their sensitivity to the initial purchase costs. DOE used shipments data 

collected by the Market Research Magazine2 and Producer Price Index (PPI) of household 

cooking appliance manufacturers3 between the years 2002 – 2012 along with the manufactures 

costs data from the engineering analysis to analyze factors that influence consumer-purchasing 

decisions of oven products. By using the logit statistical model described by Eq. 10.3, DOE 

found that historical shipments data has a strong dependence on the first costs by product type. 

DOE then developed the best-fit to capture the relationship between historical shipments and 

price data. Table 10.3.2 shows the best-fit logit parameters calculated for oven products. DOE 

then used the parameters in Table 10.3.2 to derive efficiency distribution in a given year for 

home-owners. DOE combined the market share of renters with the efficiency distribution derived 

from the consumer-choice model for home-owners to project its base case efficiency distribution 

for the period between 2019 and 2048.

Table 10.3.1 Oven User Ownership by Fuel Type

Owners/Renters Electric Ovens Gas Ovens

Home owners 69.3% 66.1%

Renters 30.7% 33.9%

Source: RECS 2009.
10-5

Table 10.3.2 Best-fit Logit Parameters for Oven Products

Product Class N ββFFFF

Electric Standard Ovens, Freestanding 36.71287 -0.00471

Electric Standard Ovens, Built-In/Slide-In 36.71287 -0.00471

Electric Self-Clean Ovens, Freestanding 39.981985 -0.004472

Electric Self-Clean Ovens, Built-In/Slide-In 39.981985 -0.004472

Gas Standard Ovens, Freestanding 8.4805448 -0.002612

Gas Standard Ovens, Built-In/Slide-In 8.4805448 -0.002612

Gas Self-Clean Ovens, Freestanding 8.4810489 -0.002141

Gas Self-Clean Ovens, Built-In/Slide-In 8.4810489 -0.002141

10.3.2 Standards Case

For its determination of standards case projected efficiencies, DOE assumed a “roll-up” 

scenario to establish the efficiency distribution under different TSLs. Product efficiencies in the 

base case that do not meet the standard under consideration would “roll up” to meet the new 

standard level. All efficiency shares in the base case that were above the standard under 

consideration would not be affected.

These assumptions are used to determine the average per-unit energy consumption 

UEC(L,F,v)= UEC(L,F,v)*eff(v,y0)

Where:

UEC = average annual per-unit site energy consumption

L = trial standard level

F = fuel type

v = vintage (year of purchase)

y0 = compliance year 2019

eff = population-average efficiency trend relative to 2019

10.4 NATIONAL ENERGY SAVINGS

DOE calculates annual national energy savings (NES) and cumulative NES throughout 

the projected period, which extends from 2019 to 2048. Positive values of NES represent energy 

savings, meaning national energy consumption under the proposed standards is lower than in the 

base case.

10.4.1 Definition

The NES calculation begins with the calculation of the projected annual site energy 

consumption (ASEC) over the analysis period. DOE calculated the ASEC in the base case 

(without new standards) and for each trial standard level (TSL). The trial standard level is 

labelled L, with L=0 corresponding to the base case.
10-6

DOE calculated the ASEC by multiplying the number or stock of a given product by its 

unit energy consumption (UEC). For each equipment class, both the stock and the UEC are 

calculated as a function of the TSL, the analysis year and the vintage (year of purchase of the 

equipment). The derivation of the stock model is described in chapter 9. For each equipment 

class, the calculation of the national AEC is represented by the following equation:

ASEC(L,F,y) = ∑v S(L,F,y,v)*UEC(L,F,v)

Where:

ASEC = annual site energy consumption

L = trial standard level

F = fuel type

y = analysis year

v = vintage (year of purchase)

S = stock of product (millions of units)

 UEC = annual energy consumption per unit

10.4.2 Shipments and Product Stock

DOE projected shipments of each product class under the base case and each standards 

case. Several factors affect projected shipments, including purchase cost, operating cost, and 

household income. As noted previously, the increased cost of more-efficient products causes 

some consumers to forego buying the products. Consequently, shipments projected under the 

standards cases are lower than under the base case. However, in the case of oven products, DOE 

determined that there would not be decrease in overall shipments due to an increase in cost of 

more-efficient products. The method DOE used to calculate and generate the shipments 

projections for each considered product class is described in detail in chapter 9, Shipments 

Analysis.

The product stock in a given year is the number of products shipped from earlier years 

that survive in that year. DOE assumes that products have an increasing probability of retiring as 

they age. The probability of survival as a function of years since the date of purchase constitutes

the survival function. Chapter 9 provides additional details on the survival function that DOE 

used.

10.4.3 Annual Energy Consumption per Unit

DOE developed annual per-unit energy consumption as a function of product energy 

efficiency for each product class (see chapter 7, Energy Use Analysis, and chapter 8, Life-Cycle 

Cost and Payback Period Analysis). For the NES calculation DOE used a national average value 

for each equipment class exported from the LCC (chapter 8) to define the UEC in the starting 

year of the analysis period, y0 = 2019. For subsequent years, DOE applied the efficiency trend 

discussed in section 10.3.
10-7

10.4.4 National Annual Energy Consumption

 DOE used two steps to convert the annual site energy consumption numbers to an NES 

value. First, the site energy numbers are converted to common units, using the conversion factors 

presented in appendix 10B, and the energy consumption is summed over fuel type. This converts 

the site energy ASEC to primary energy APEC:

APEC(L,y) = ∑F ASEC(L,F,y)* h(F,y).

 In this equation h(F,y) is the conversion factor for fuel type F in year y. For electricity the 

conversion factor is a marginal heat rate that incorporates losses in generation, transmission and 

distribution, and depends on the sector, end use and year. For this rule, DOE used the values for 

residential end-use.

DOE then defined the NES as the difference between the APEC in the base case (L=0) 

and in the standards case:

NES(L,y) = APEC(L=0,y) – APEC(L,y).

DOE presented results of the NES calculation as a cumulative sum over the analysis 

period. This period is defined as 30 years from the start date of the standard (2019-2048). DOE 

included in its NES estimate the lifetime energy savings for units shipped in the final year of the 

analysis period; hence the stock model is continued to 2072 in order to account for these savings. 

This calculation is represented by the equation

NEScum(L) = ∑y NES(L,y)

10.4.5 Primary Energy Conversion Factors

DOE calculates primary energy savings as the total site consumption across all fuel types 

converted to common units (MMBtu). For fossil fuels such as natural gas, fuel oil or propane, the 

conversion factor is a constant equal to the low-heating value for the fuel (listed in appendix 

10B). Because the fossil fuel conversion factors are constant over time, DOE may perform this 

conversion inside the LCC, reporting fossil fuel consumption in energy units rather than physical 

units. For electricity use, the conversion from site kWh to power plant primary MMBtu uses a 

marginal heat rate factor that accounts for losses associated with the generation, transmission, 

and distribution of electricity. DOE derived these marginal factors using data published with the 

Energy Information Administration (EIA’s) Annual Energy Outlook 2014 (AEO2014), following 

the methodology outlined in appendix 15A.

4 The factors depend on the sector and end-use, and 

also vary with time due to changes in the mix of fuels used for electric power generation. Figure 

10.4.1 shows the site-to-power plant factors from 2019 to the end of the AEO analysis period 

(2040). For years after 2040 DOE held the factors constant and equal to their 2040 values.
10-8

Figure 10.4.1 Site-to-Power Plant Energy Use Factor for Oven Products

10.4.6 Full-Fuel-Cycle Energy Factors

The full-fuel-cycle (FFC) energy use is equal to the primary energy use plus the energy 

consumed “upstream” of the site in the extraction, processing and distribution of fuels. The FFC 

energy use was calculated by applying a fuel-specific FFC energy multiplier to the primary 

energy use. DOE developed FFC multipliers using the data and projections generated by the 

National Energy Modeling System (NEMS) used for AEO2014. 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 multiplier for electricity 

represents the energy needed to produce and deliver the fuels that are consumed in electricity 

generation. The multipliers are dimensionless numbers that express the upstream energy use as a 

percentage of the primary energy use.

Because the FFC energy multipliers depend on the fuel type, the FFC energy is calculated 

starting with the annual site energy numbers ASEC. The equation is:

FFC(L,y) = ∑F ASEC(L,F,y)* h(F,y)*μ(F,y).

Where:

ASEC = annual site energy consumption

L = trial standard level

F = fuel type

7.00

7.50

8.00

8.50

9.00

9.50

10.00

10.50

11.00

MMBtu/MWh

Year
10-9

y = analysis year

h = energy unit conversion factor

μ= full fuel cycle multiplier

FFC = annual full fuel cycle energy consumption

 If a product uses only one fuel, then the FFC energy is equal to the primary energy APEC

multiplied by the FFC multiplier μ. For products that use multiple fuels, the relationship between 

the primary energy use and the FFC energy is less straight-forward.

 As with the NES, DOE calculated cumulative, national level energy savings in the fullfuel-cycle metric by calculating the difference relative to the base case and summing over the 

analysis period:

NES-FFC(L,y) = FFC(L=0,y) – FFC(L,y),

NES-FFCcum(L) = ∑y NES-FFC(L,y)

10.5 NET PRESENT VALUE

DOE defined the net present value (NPV) as the net consumer benefit associated with 

each trial standard level. The net consumer benefit is defined as the sum of the change in 

operating cost relative to the base case and the change in the total installed cost relative to the 

base case. Typically the change in operating cost is positive (a savings to consumers), while the 

change in total installed cost is negative (a cost to consumers). The costs and savings are 

calculated in each year of the analysis period for all the equipment shipped in that year, 

discounted, and summed to provide a net present value. 

10.5.1 Definition

The NPV is equal to the sum of two present-value estimates:

NPV = PVOCS + PVTIC

Where:

PVOCS = present value of the reduction in operating cost relative to the base case

PVTIC = present value of the increase in total installed cost relative to the base case

DOE determined the PV-OCS and PVC according to the following expressions:

PVOCS(L) = ∑y ( OC(L=0,y) – OC(L,y) )* DF(y)

PVTIC(L) = ∑y ( TIC(L=0,y) – TIC(L,y) )* DF(y)
10-10

 Where:

OC = operating cost of the stock in year y

L = trial standard level, with L=0 corresponding to the base case

y = analysis year

DF = discount factor

TIC = total installed cost of the shipments in year y

DOE calculated the energy-related component of the operating cost based on the site 

energy use (described in section 10.4.4), the energy price and the energy price trend over the 

analysis period. The operating cost also includes routine repair and maintenance costs. The 

operating costs are incurred over the full lifetime of the unit, so the operating cost calculation 

uses the equipment stock. DOE calculated the total installed cost by multiplying the number of 

shipments times in each year by the sum of the equipment price and installation cost. These costs 

are incurred only in the year of purchase, so the TIC calculation uses the shipments only. If the 

maintenance, repair or installation costs do not depend on the trial standard level, they can be left 

out of the calculation. Each of these calculation steps are discussed in more detail in the 

following sections. As with the NES, the analysis period starts in the compliance year of the 

standard 2019 and concludes thirty years later in 2048. Operating costs are calculated until the 

units shipped in 2048 retire (2072). 

10.5.2 Total Installed Cost

DOE described the total per-unit installed cost for each product class as a function of 

product efficiency or TSL in chapter 8. For the NPV calculation, DOE used the population 

average total installed cost exported from the LCC in the start year 2019 for the base and 

standards cases for each product class included in the model. In calculating the TIC, DOE used 

the shipments exported from the shipments model, which depend on the trial standard level.

DOE investigated the possibility that equipment prices, measured in constant dollars, 

might change over the analysis period. Incorporating the equipment price trend β(y), the equation 

for TIC is

TIC(L,y) = Ship(L,y)*UIC(L,y0)* b(y, y0),

Where

Ship = total shipments in year y as calculated in the shipments model

y0 = compliance year 2019

L = trial standard level, with L=0 corresponding to the base case

UIC = average per-unit total installed cost 2019 exported from the LCC

b = equipment price trend relative to year 2019

DOE determined that the equipment price trend followed the equation below. 

Y = a X -b
10-11

Where:

a = an initial price (or cost), 

b = a positive constant known as the learning rate parameter, 

X = cumulative production, and 

Y = the price as a function of cumulative production.

Thus, as experience (production) accumulates, the cost of producing the next unit 

decreases. The percentage reduction in cost that occurs with each doubling of cumulative 

production is known as the learning rate (LR), given by:

LR = 1 – 2-b

In typical learning curve formulations, the learning rate parameter is derived using two 

historical data series: cumulative production and price (or cost).

10.5.3 Annual Operating Cost

The per-unit annual operating cost includes costs for energy, repair, and maintenance. 

DOE determined the per-unit annual energy cost based on the annual site energy consumption 

(ASEC) discussed in section 10.4.3. The ASEC incorporates both changes in shipments and 

changes in equipment efficiency at each TSL. For each fuel type, DOE used the energy price in 

the start year, and energy price trend, that were used in the life-cycle cost analysis (chapter 8).

The price trends are taken from the EIA’s AEO2015 reference case scenario.

DOE described the total per-unit repair and maintenance costs for each product class as a 

function of product efficiency in the LCC analysis in chapter 8. The NPV calculation is based on 

the population average repair and maintenance costs exported from the LCC, for each TSL. 

These costs are assumed to remain constant in real terms over the analysis period.

The equation for the operating cost in year y and TSL L is:

OC(L,y) = ∑F ASEC(L,F,y)*e(F,y0)*a(F,y, y0)

Where

ASEC = annual site energy consumption

L = trial standard level, with L=0 corresponding to the base case

F = fuel type

y0 = compliance year 2019

e = energy price in 2013 exported from the LCC

L = trial standard level, with L=0 corresponding to the base case

a = energy price trend relative to year 2013
10-12

10.5.4 Discount Factor

DOE multiplied monetary values in future years by a discount factor to determine the 

present value. The discount factor (DF) is described by the equation:

DF(y) = (1+r) -(y-y

P

)

Where:

r = discount rate, 

y = analysis year

yP = year relative to which the present value is being determined.

Although DOE used consumer discount rates to determine the life-cycle cost of oven

products (chapter 8), it used national discount rates to calculate national NPV. DOE estimated 

NPV using both a 3-percent and a 7-percent real discount rate, in accordance with the Office of 

Management and Budget’s guidance to Federal agencies on the development of regulatory 

analysis, particularly section E therein: Identifying and Measuring Benefits and Costs.

5

 DOE 

defined the present year as 2014.

10.6 RESULTS 

10.6.1 National Energy Savings 

This section provides the national energy savings that DOE calculated for each of the 

TSLs analyzed for oven products. 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 such 

as is generated by the life-cycle cost and payback period analysis. Table 10.6.1 shows FFC 

energy savings for oven products by product class. 

Table 10.6.1 Estimates of Cumulative Full-Fuel Cycle NES (quads) 

Product Class TSL1 TSL2 TSL3

Electric Standard Oven, Freestanding 0.024 0.060 0.168

Electric Standard Oven, Built-In/Slide-In 0.000 0.001 0.003

Electric Self-Clean Oven, Freestanding 0.074 0.074 0.389

Electric Self-Clean Oven, Built-In/Slide-In 0.022 0.022 0.113

Gas Standard Oven, Freestanding 0.000 0.216 0.223

Gas Standard Oven, Built-In/Slide-In 0.000 0.041 0.042

Gas Self-Clean Oven, Freestanding 0.040 0.281 0.297

Gas Self-Clean Oven, Built-In/Slide-In 0.002 0.014 0.015

All 0.163 0.709 1.251
10-13

10.6.2 Net Present Value of Consumer Benefit

 This section provides results of calculating the NPV for each trial standard level 

considered for oven products. Results were calculated for the nation as a whole. Results, which 

are cumulative, are shown as the discounted dollar value of the net savings. DOE based the 

inputs to the NIA model on weighted-average values, yielding results that are discrete point 

values, rather than a distribution of values such as produced by the life-cycle cost and payback 

period analyses.

 Table 10.6.2 and Table 10.6.3 list the results for cumulative NPV for oven products for 3-

percent and seven-percent discount rates, respectively. A negative NPV indicates that the costs 

of a standard at a given efficiency level exceed the savings.

Table 10.6.2 Cumulative NPV Results based on Three-Percent Discount Rates (billion 

2014$)

Product Class TSL1 TSL2 TSL3

Electric Standard Oven, Freestanding 0.170 0.306 -0.581

Electric Standard Oven, Built-In/Slide-In 0.004 0.007 -0.016

Electric Self-Clean Oven, Freestanding 0.516 0.516 -1.052

Electric Self-Clean Oven, Built-In/Slide-In 0.153 0.153 -0.324

Gas Standard Oven, Freestanding 0.000 3.588 3.056

Gas Standard Oven, Built-In/Slide-In 0.000 0.670 0.571

Gas Self-Clean Oven, Freestanding 0.280 5.459 4.700

Gas Self-Clean Oven, Built-In/Slide-In 0.014 0.281 0.241

All 1.137 10.980 6.597

Table 10.6.3 Cumulative NPV Results based on Seven-Percent Discount Rates (billion 

2014$)

Product Class TSL1 TSL2 TSL3

Electric Standard Oven, Freestanding 0.072 0.113 -0.494

Electric Standard Oven, Built-In/Slide-In 0.002 0.003 -0.014

Electric Self-Clean Oven, Freestanding 0.211 0.211 -0.974

Electric Self-Clean Oven, Built-In/Slide-In 0.065 0.065 -0.303

Gas Standard Oven, Freestanding 0.000 1.547 1.233

Gas Standard Oven, Built-In/Slide-In 0.000 0.287 0.229

Gas Self-Clean Oven, Freestanding 0.115 2.302 1.861

Gas Self-Clean Oven, Built-In/Slide-In 0.006 0.119 0.096

All 0.470 4.646 1.634
10-14

REFERENCES

 

1 U.S. Energy Information Administration, Residential Energy Consumption Survey 2009, 

Washington, DC. 

 <http://www.eia.gov/consumption/residential/data/2009/>

2 UBM Canon, Market Research Magazine: Appliance Historical Statistical Review, 2014.

3 U.S. Bureau of Labor Statistics, Producer Price Index Industry Data: Household cooking 

 appliance manufacturing, 2014.

4 Energy Information Administration, Annual Energy Outlook 2014 with Projections to 

 2040, 2014. Washington, DC.

 < http://www.eia.gov/forecasts/aeo/>

5 U.S. Office of Management and Budget (OMB), Circular A-4: Regulatory Analysis, 2003.

 (Posted September 17, 2003) (Last accessed April, 2013.) 

 <http://www.whitehouse.gov/sites/default/files/omb/assets/regulatory_matters_pdf/a-4.pdf>
CHAPTER 11. CONSUMER SUBGROUP ANALYSIS

TABLE OF CONTENTS

11.1 INTRODUCTION ............................................................................................. 11-1

11.2 IDENTIFIED SUBGROUPS............................................................................. 11-1

11.2.1 Senior-Only Households.......................................................................... 11-1

11.2.2 Low-Income Households ......................................................................... 11-1

11.3 INPUTS TO THE CONSUMER SUBGROUP ANALYSIS ............................ 11-2

11.4 RESULTS .......................................................................................................... 11-5

LIST OF TABLES

Table 11.3.1 Electric Standard Ovens, Freestanding: Weighted-Average Annual 

Energy Use................................................................................................... 11-2

Table 11.3.2 Electric Standard Ovens, Built-In/Slide-In: Weighted-Average Annual 

Energy Use................................................................................................... 11-2

Table 11.3.3 Electric Self-Clean Ovens, Freestanding: Weighted-Average Annual 

Energy Use................................................................................................... 11-3

Table 11.3. Electric Self-Clean Ovens, Built-In/Slide-In: Weighted-Average 

Annual Energy Use ...................................................................................... 11-3

Table 11.3.5 Gas Standard Ovens, Freestanding: Weighted-Average Annual Energy 

Use ............................................................................................................... 11-3

Table 11.3.6 Gas Standard Ovens, Built-In/Slide-In: Weighted-Average Annual 

Energy Use................................................................................................... 11-4

Table 11.3.7 Gas Self-Clean Ovens, Freestanding: Weighted-Average Annual 

Electricity Use.............................................................................................. 11-4

Table 11.3.8 Gas Self-Clean Ovens, Built-In/Slide-In: Weighted-Average Annual 

Electricity Use.............................................................................................. 11-5

Table 11.4.1 Senior Only Households: Summary of LCC and PBP Results by 

Efficiency Level for Electric Standard Ovens, Freestanding....................... 11-5

Table 11.4.2 Senior Only Households: Summary of Life-Cycle Costs Savings 

Relative to the Base Case Efficiency Distribution for Electric Standard 

Ovens, Freestanding..................................................................................... 11-6

Table 11.4.3 Senior Only Households: Summary of LCC and PBP Results by 

Efficiency Level for Electric Standard Ovens, Built-In/Slide-In................. 11-6

Table 11.4.4 Senior Only Households: Summary of Life-Cycle Costs Savings 

Relative to the Base Case Efficiency Distribution for Electric Standard 

Ovens, Built-In/Slide-In............................................................................... 11-7

Table 11.4.5 Senior Only Households: Summary of LCC and PBP Results by 

Efficiency Level for Electric Self-Clean Ovens, Freestanding.................... 11-7

11-i
Table 11.4.6 Senior Only Households: Summary of Life-Cycle Costs Savings 

Relative to the Base Case Efficiency Distribution for Electric SelfClean Ovens, Freestanding .......................................................................... 11-8

Table 11.4.7 Senior Only Households: Summary of LCC and PBP Results by 

Efficiency Level for Electric Self-Clean Ovens, Built-In/Slide-In.............. 11-8

Table 11.4.8 Senior Only Households: Summary of Life-Cycle Costs Savings 

Relative to the Base Case Efficiency Distribution for Electric SelfClean Ovens, Built-In/Slide-In .................................................................... 11-9

Table 11.4.9 Senior Only Households: Summary of LCC and PBP Results by 

Efficiency Level for Gas Standard Ovens, Freestanding............................. 11-9

Table 11.4.10 Senior Only Households: Summary of Life-Cycle Costs Savings 

Relative to the Base Case Efficiency Distribution for Gas Standard 

Ovens, Freestanding................................................................................... 11-10

Table 11.4.11 Senior Only Households: Summary of LCC and PBP Results by 

Efficiency Level for Gas Standard Ovens, Built-In/Slide-In..................... 11-10

Table 11.4.12 Senior Only Households: Summary of Life-Cycle Costs Savings 

Relative to the Base Case Efficiency Distribution for Gas Standard 

Ovens, Built-In/Slide-In............................................................................. 11-11

Table 11.4.13 Senior Only Households: Summary of LCC and PBP Results by 

Efficiency Level for Gas Self-Clean Ovens, Freestanding........................ 11-11

Table 11.4.14 Senior Only Households: Summary of Life-Cycle Costs Savings 

Relative to the Base Case Efficiency Distribution for Gas Self-Clean 

Ovens, Freestanding................................................................................... 11-12

Table 11.4.15 Senior Only Households: Summary of LCC and PBP Results by 

Efficiency Level for Gas Self-Clean Ovens, Built-In/Slide-In.................. 11-12

Table 11.4.16 Senior Only Households: Summary of Life-Cycle Costs Savings 

Relative to the Base Case Efficiency Distribution for Gas Self-Clean 

Ovens, Built-In/Slide-In............................................................................. 11-13

Table 11.4.17 Low Income Households: Summary of LCC and PBP Results by 

Efficiency Level for Electric Standard Ovens, Freestanding..................... 11-13

Table 11.4.18 Low Income Households: Summary of Life-Cycle Costs Savings 

Relative to the Base Case Efficiency Distribution for Electric Standard 

Ovens, Freestanding................................................................................... 11-14

Table 11.4.19 Low Income Households: Summary of LCC and PBP Results by 

Efficiency Level for Electric Standard Ovens, Built-In/Slide-In............... 11-14

Table 11.4.20 Low Income Households: Summary of Life-Cycle Costs Savings 

Relative to the Base Case Efficiency Distribution for Electric Standard 

Ovens, Built-In/Slide-In............................................................................. 11-15

Table 11.4.21 Low Income Households: Summary of LCC and PBP Results by 

Efficiency Level for Electric Self-Clean Ovens, Freestanding.................. 11-15

Table 11.4.22 Low Income Households: Summary of Life-Cycle Costs Savings 

Relative to the Base Case Efficiency Distribution for Electric SelfClean Ovens, Freestanding ........................................................................ 11-16

Table 11.4.23 Low Income Households: Summary of LCC and PBP Results by 

Efficiency Level for Electric Self-Clean Ovens, Built-In/Slide-In............ 11-16

11-ii
Table 11.4.24 Low Income Households: Summary of Life-Cycle Costs Savings 

Relative to the Base Case Efficiency Distribution for Electric SelfClean Ovens, Built-In/Slide-In .................................................................. 11-17

Table 11.4.25 Low Income Households: Summary of LCC and PBP Results by 

Efficiency Level for Gas Standard Ovens, Freestanding........................... 11-17

Table 11.4.26 Low Income Households: Summary of Life-Cycle Costs Savings 

Relative to the Base Case Efficiency Distribution for Gas Standard 

Ovens, Freestanding................................................................................... 11-18

Table 11.4.27 Low Income Households: Summary of LCC and PBP Results by 

Efficiency Level for Gas Standard Ovens, Built-In/Slide-In..................... 11-18

Table 11.4.28 Low Income Households: Summary of Life-Cycle Costs Savings 

Relative to the Base Case Efficiency Distribution for Gas Standard 

Ovens, Built-In/Slide-In............................................................................. 11-19

Table 11.4.29 Low Income Households: Summary of LCC and PBP Results by 

Efficiency Level for Gas Self-Clean Ovens, Freestanding........................ 11-19

Table 11.4.30 Low Income Households: Summary of Life-Cycle Costs Savings 

Relative to the Base Case Efficiency Distribution for Gas Self-Clean 

Ovens, Freestanding................................................................................... 11-20

Table 11.4.31 Low Income Households: Summary of LCC and PBP Results by 

Efficiency Level for Gas Self-Clean Ovens, Built-In/Slide-In.................. 11-20

Table 11.4.32 Low Income Households: Summary of Life-Cycle Costs Savings 

Relative to the Base Case Efficiency Distribution for Gas Self-Clean 

Ovens, Built-In/Slide-In............................................................................. 11-21

11-iii
CHAPTER 11. CONSUMER SUBGROUP ANALYSIS

11.1 INTRODUCTION

Chapter 8 of this TSD describes the life-cycle cost (LCC) and payback period (PBP) 

analysis that examines energy savings and cost impacts of energy conservation standards on the 

U.S. population. In analyzing the potential impact of new or amended standards on consumers, 

the U.S. Department of Energy (DOE) further evaluates the impacts on identifiable groups of 

consumers (subgroups) that may be disproportionately affected by a national standard level. The 

consumer subgroup analysis evaluates effects by analyzing the LCC and PBPs for subgroups of

residential consumers. For cooking products, DOE identified two consumer subgroups that 

warranted further study: (1) senior-only households and (2) low income households.

DOE determined the impact on consumer subgroups for conventional cooking products 

using the LCC spreadsheet model, which enables DOE to analyze the LCC for any subgroup by 

sampling only the data that apply to that subgroup. (Chapter 8 explains in detail the inputs to the 

model used in determining LCC and PBPs.) As described in section 11.3, the energy use and 

energy price characteristics of the two subgroups (senior-only and low-income) differ from those 

for the general population. 

This chapter describes the identification of the two subgroups and gives the results of the 

LCC and PBP analyses for those subgroups.

11.2 IDENTIFIED SUBGROUPS

The following two sections describe how DOE defined the two consumer subgroups 

identified for further examination.

11.2.1 Senior-Only Households

Senior-only households comprise occupants who are all at least 65 years of age. Based on 

DOE’s Energy Information Administration’s Residential Energy Consumption Survey of 2009

(RECS), senior-only households represent 17 percent of the U.S. households.

1

11.2.2 Low-Income Households

As defined in the RECS survey, low-income household residents are living at or below 

the poverty line. The poverty line varies with household size, age of head of household, and 

family income. The RECS survey classifies 15 percent of the country’s households as lowincome.

11-1
11.3 INPUTS TO THE CONSUMER SUBGROUP ANALYSIS 

Table 11.3.1 through Table 11.3.8 summarize the weighted-average annual energy use 

for the households analyzed in the consumer subgroup analysis. These values are compared 

against the weighted-average values for the national sample. 

Table 11.3.1 Electric Standard Ovens, Freestanding: Weighted-Average Annual Energy 

Use

Efficiency Level

All

Households Senior-Only LowIncome

(kWh/year)

Baseline 135.56 113.36 164.37 

1 125.81 103.63 154.59 

2 120.38 99.24 147.82 

3 115.09 94.95 141.21 

4 113.29 93.50 138.97 

5 109.64 90.55 134.42 

6 94.09 77.97 115.02 

7 93.85 77.77 114.72 

Table 11.3.2 Electric Standard Ovens, Built-In/Slide-In: Weighted-Average Annual 

Energy Use

Efficiency Level

All

Households Senior-Only LowIncome

(kWh/year)

Baseline 135.56 113.36 164.37 

1 125.71 103.55 154.47 

2 120.29 99.17 147.71 

3 115.01 94.89 141.12 

4 113.22 93.44 138.88 

5 109.61 90.52 134.37 

6 94.08 77.96 115.00 

7 93.84 77.76 114.70 

11-2
Table 11.3.3 Electric Self-Clean Ovens, Freestanding: Weighted-Average Annual Energy 

Use

Efficiency Level

All

Households Senior-Only LowIncome

(kWh/year)

Baseline 174.63 157.71 208.03 

1 164.89 147.99 198.27 

2 158.57 142.57 190.16 

3 139.45 126.19 165.65 

4 139.13 125.91 165.24 

Table 11.3.4 Electric Self-Clean Ovens, Built-In/Slide-In: Weighted-Average Annual 

Energy Use

Efficiency Level

All

Households Senior-Only LowIncome

(kWh/year)

Baseline 175.26 158.34 208.66 

1 165.46 148.57 198.81 

2 159.17 143.17 190.75 

3 140.07 126.81 166.27 

4 139.75 126.53 165.86 

Table 11.3.5 Gas Standard Ovens, Freestanding: Weighted-Average Annual Energy Use 

Efficiency Level

All

Households Senior-Only LowIncome

(kBtu/year)

Baseline 1040.1 934.3 1288.9

1 835.4 751.8 1031.9

2 801.7 718.2 998.1

3 747.6 670.0 930.1

4 636.3 570.7 790.4

5 630.1 565.2 782.6

6 614.9 551.7 763.5

7 611.1 548.3 758.8

11-3
Table 11.3.6 Gas Standard Ovens, Built-In/Slide-In: Weighted-Average Annual Energy

Use 

Efficiency Level

All

Households Senior-Only LowIncome

(kBtu/year)

Baseline 1040.1 934.3 1288.9

1 835.5 751.9 1032.1

2 801.8 718.2 998.1

3 747.7 670.0 930.2

4 636.0 570.5 790.1

5 629.8 565.0 782.3

6 614.7 551.5 763.3

7 610.9 548.1 758.6

Table 11.3.7 Gas Self-Clean Ovens, Freestanding: Weighted-Average Annual Electricity 

Use 

Efficiency Level

All

Households Senior-Only LowIncome

(kBtu/year)

Baseline 1126.3 1069.8 1307.2

1 1092.2 1035.8 1272.8

2 928.5 883.6 1072.6

3 898.9 856.0 1036.4

4 896.4 853.7 1033.3

11-4
Table 11.3.8 Gas Self-Clean Ovens, Built-In/Slide-In: Weighted-Average Annual 

Electricity Use 

Efficiency Level

All

Households Senior-Only LowIncome

(kBtu/year)

Baseline 1127.6 1071.2 1308.6

1 1093.4 1037.0 1274.1

2 929.4 884.5 1073.4

3 899.9 857.0 1037.2

4 897.4 854.7 1034.2

11.4 RESULTS 

Table 11.4.1 through Table 11.4.32 summarize the LCC and PBP results from DOE’s 

subgroup analysis. The results describe the financial effects of potential standards on senior-only 

and low-income households. The tables present the average installed price; average lifetime 

operating cost (discounted); average life-cycle cost; average life-cycle cost savings; percentage 

of each subgroup who are burdened with net costs, realize net savings, or are not affected; and 

the simple payback period.

Table 11.4.1 Senior Only Households: Summary of LCC and PBP Results by Efficiency 

Level for Electric Standard Ovens, Freestanding

TSL Efficiency 

Level

Average Costs 

2014$ Simple 

Payback

years Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating 

Cost

LCC

-- 0 $557 $15 $172 $729 --

1 1 $558 $14 $157 $715 0.9

-- 2 $561 $13 $150 $711 2.0

2 3 $568 $12 $144 $711 4.4

-- 4 $571 $12 $142 $712 5.3

-- 5 $604 $12 $137 $741 15.7

-- 6 $648 $10 $118 $766 19.5

3 7 $654 $10 $118 $771 20.6

11-5
Note: The results for each TSL are calculated assuming that all consumers use products at that efficiency level. The 

PBP is measured relative to the baseline product.

Table 11.4.2 Senior Only Households: Summary of Life-Cycle Costs Savings Relative to 

the Base Case Efficiency Distribution for Electric Standard Ovens, 

Freestanding

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Average Savings*

Net Cost 2014$

-- 0 0% ---

1 1 0% $14.00

-- 2 4% $15.21

2 3 15% $12.28

-- 4 25% $9.63

-- 5 69% -$20.30

-- 6 76% -$42.92

3 7 84% -$45.09

*The calculation does not include households with zero LCC savings (no impact).

Table 11.4.3 Senior Only Households: Summary of LCC and PBP Results by Efficiency 

Level for Electric Standard Ovens, Built-In/Slide-In

TSL Efficiency 

Level

Average Costs 

2014$ Simple 

Payback

years Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating 

Cost

LCC

-- 0 $583 $15 $172 $755 --

1 1 $584 $14 $157 $741 0.9

-- 2 $587 $13 $150 $737 2.0

2 3 $594 $12 $144 $738 4.4

-- 4 $597 $12 $141 $739 5.3

-- 5 $630 $12 $137 $767 15.6

-- 6 $674 $10 $118 $792 19.5

3 7 $680 $10 $118 $798 20.6

11-6
Note: The results for each TSL are calculated assuming that all consumers use products at that efficiency level. The 

PBP is measured relative to the baseline product.

Table 11.4.4 Senior Only Households: Summary of Life-Cycle Costs Savings Relative to 

the Base Case Efficiency Distribution for Electric Standard Ovens, BuiltIn/Slide-In

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Average Savings*

Net Cost 2014$

-- 0 0% ---

1 1 0% $14.11

-- 2 4% $15.30

2 3 15% $12.34

-- 4 25% $9.68

-- 5 69% -$20.30

-- 6 76% -$42.96

3 7 84% -$45.13

*The calculation does not include households with zero LCC savings (no impact).

Table 11.4.5 Senior Only Households: Summary of LCC and PBP Results by Efficiency 

Level for Electric Self-Clean Ovens, Freestanding

TSL Efficiency 

Level

Average Costs 

2014$ Simple 

Payback

years Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating 

Cost

LCC

-- 0 $601 $21 $240 $840 --

1,2 1 $602 $19 $225 $826 0.9

-- 2 $635 $19 $216 $851 17.0

-- 3 $679 $16 $191 $870 18.8

3 4 $686 $16 $191 $877 20.3

Note: The results for each TSL are calculated assuming that all consumers use products at that efficiency level. The 

PBP is measured relative to the baseline product.

11-7
Table 11.4.6 Senior Only Households: Summary of Life-Cycle Costs Savings Relative to 

the Base Case Efficiency Distribution for Electric Self-Clean Ovens, 

Freestanding

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Average Savings*

Net Cost 2013$

-- 0 0% ---

1,2 1 0% $14.19

-- 2 54% -$14.29

-- 3 65% -$30.37

3 4 79% -$32.84

*The calculation does not include households with zero LCC savings (no impact).

Table 11.4.7 Senior Only Households: Summary of LCC and PBP Results by Efficiency 

Level for Electric Self-Clean Ovens, Built-In/Slide-In

TSL Efficiency 

Level

Average Costs 

2014$ Simple 

Payback

years Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating 

Cost

LCC

-- Baseline $627 $21 $241 $868 --

1,2 1 $628 $19 $225 $854 0.9

-- 2 $661 $19 $217 $879 17.0

-- 3 $705 $17 $192 $897 18.7

3 4 $712 $17 $192 $904 20.3

Note: The results for each TSL are calculated assuming that all consumers use products at that efficiency level. The 

PBP is measured relative to the baseline product.

11-8
Table 11.4.8 Senior Only Households: Summary of Life-Cycle Costs Savings Relative to 

the Base Case Efficiency Distribution for Electric Self-Clean Ovens, BuiltIn/Slide-In

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Average Savings*

Net Cost 2014$

-- Baseline 0% ---

1,2 1 0% $14.27

-- 2 54% -$14.27

-- 3 65% -$30.38

3 4 79% -$32.84

*The calculation does not include households with zero LCC savings (no impact).

Table 11.4.9 Senior Only Households: Summary of LCC and PBP Results by Efficiency 

Level for Gas Standard Ovens, Freestanding

TSL Efficiency 

Level

Average Costs 

2014$ Simple 

Payback

years Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating 

Cost

LCC

1 Baseline $602 $18 $580 $1,182 --

-- 1 $602 $16 $555 $1,157 0.0

-- 2 $603 $15 $535 $1,138 0.3

-- 3 $612 $14 $528 $1,140 2.6

2 4 $619 $8 $266 $885 1.8

-- 5 $622 $8 $265 $888 2.1

-- 6 $650 $9 $267 $917 5.1

3 7 $656 $9 $266 $923 5.7

Note: The results for each TSL are calculated assuming that all consumers use products at that efficiency level. The 

PBP is measured relative to the baseline product.

11-9
Table 11.4.10 Senior Only Households: Summary of Life-Cycle Costs Savings Relative to 

the Base Case Efficiency Distribution for Gas Standard Ovens, Freestanding

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Average Savings*

Net Cost 2014$

1 Baseline 0% ---

-- 1 0% $25.06

-- 2 0% $39.30

-- 3 7% $31.38

2 4 0% $282.03

-- 5 8% $248.06

-- 6 17% $194.95

3 7 24% $173.10

*The calculation does not include households with zero LCC savings (no impact).

Table 11.4.11 Senior Only Households: Summary of LCC and PBP Results by Efficiency 

Level for Gas Standard Ovens, Built-In/Slide-In

TSL Efficiency 

Level

Average Costs 

2014$ Simple 

Payback

years Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating 

Cost

LCC

1 Baseline $628 $18 $580 $1,208 --

-- 1 $628 $16 $555 $1,183 0.0

-- 2 $629 $15 $535 $1,164 0.3

-- 3 $638 $14 $528 $1,167 2.6

2 4 $645 $8 $266 $911 1.8

-- 5 $648 $8 $265 $914 2.1

-- 6 $677 $9 $267 $943 5.1

3 7 $682 $9 $266 $949 5.7

Note: The results for each TSL are calculated assuming that all consumers use products at that efficiency level. The 

PBP is measured relative to the baseline product.

11-10
Table 11.4.12 Senior Only Households: Summary of Life-Cycle Costs Savings Relative to 

the Base Case Efficiency Distribution for Gas Standard Ovens, BuiltIn/Slide-In

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Average Savings*

Net Cost 2014$

1 Baseline 0% ---

-- 1 0% $25.04

-- 2 0% $39.30

-- 3 7% $31.37

2 4 0% $282.07

-- 5 8% $248.09

-- 6 17% $194.95

3 7 24% $173.11

*The calculation does not include households with zero LCC savings (no impact).

Table 11.4.13 Senior Only Households: Summary of LCC and PBP Results by Efficiency 

Level for Gas Self-Clean Ovens, Freestanding

TSL Efficiency 

Level

Average Costs 

2014$ Simple 

Payback

years Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating 

Cost

LCC

-- Baseline $716 $21 $616 $1,333 --

1 1 $717 $19 $597 $1,314 0.7

2 2 $726 $13 $324 $1,050 1.3

-- 3 $754 $13 $324 $1,078 4.8

3 4 $762 $13 $323 $1,085 5.7

Note: The results for each TSL are calculated assuming that all consumers use products at that efficiency level. The 

PBP is measured relative to the baseline product.

11-11
Table 11.4.14 Senior Only Households: Summary of Life-Cycle Costs Savings Relative to 

the Base Case Efficiency Distribution for Gas Self-Clean Ovens, 

Freestanding

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Average Savings*

Net Cost 2014$

-- Baseline 0% ---

1 1 0% $18.39

2 2 0% $278.34

-- 3 14% $199.67

3 4 27% $162.47

*The calculation does not include households with zero LCC savings (no impact).

Table 11.4.15 Senior Only Households: Summary of LCC and PBP Results by Efficiency 

Level for Gas Self-Clean Ovens, Built-In/Slide-In

TSL Efficiency 

Level

Average Costs 

2014$ Simple 

Payback

years Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating 

Cost

LCC

-- Baseline $742 $21 $617 $1,359 --

1 1 $744 $19 $597 $1,341 0.7

2 2 $752 $13 $324 $1,077 1.3

-- 3 $780 $13 $324 $1,104 4.8

3 4 $788 $13 $324 $1,111 5.7

Note: The results for each TSL are calculated assuming that all consumers use products at that efficiency level. The 

PBP is measured relative to the baseline product.

11-12
Table 11.4.16 Senior Only Households: Summary of Life-Cycle Costs Savings Relative to 

the Base Case Efficiency Distribution for Gas Self-Clean Ovens, BuiltIn/Slide-In

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Average Savings

Net Cost 2014$

-- Baseline 0% ---

1 1 0% $18.40

2 2 0% $278.39

-- 3 14% $199.69

3 4 27% $162.48

*The calculation does not include households with zero LCC savings (no impact).

Table 11.4.17 Low Income Households: Summary of LCC and PBP Results by Efficiency 

Level for Electric Standard Ovens, Freestanding

TSL Efficiency 

Level

Average Costs 

2014$ Simple 

Payback

years Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating 

Cost

LCC

-- 0 $557 $21 $246 $803 --

1 1 $558 $20 $231 $790 0.9

-- 2 $561 $19 $221 $782 1.8

2 3 $568 $18 $211 $779 3.6

-- 4 $571 $18 $208 $779 4.2

-- 5 $604 $17 $201 $805 12.1

-- 6 $648 $15 $172 $820 14.1

3 7 $654 $15 $171 $825 14.9

Note: The results for each TSL are calculated assuming that all consumers use products at that efficiency level. The 

PBP is measured relative to the baseline product.

11-13
Table 11.4.18 Low Income Households: Summary of Life-Cycle Costs Savings Relative to 

the Base Case Efficiency Distribution for Electric Standard Ovens, 

Freestanding

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Average Savings*

Net Cost 2014$

-- 0 0% ---

1 1 0% $13.88

-- 2 3% $18.71

2 3 11% $18.70

-- 4 20% $16.36

-- 5 61% -$12.01

-- 6 68% -$25.37

3 7 76% -$28.75

*The calculation does not include households with zero LCC savings (no impact).

Table 11.4.19 Low Income Households: Summary of LCC and PBP Results by Efficiency 

Level for Electric Standard Ovens, Built-In/Slide-In

TSL Efficiency 

Level

Average Costs 

2014$ Simple 

Payback

years Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating 

Cost

LCC

-- 0 $583 $21 $246 $830 --

1 1 $585 $20 $231 $816 0.9

-- 2 $587 $19 $221 $808 1.7

2 3 $594 $18 $211 $805 3.6

-- 4 $597 $18 $208 $805 4.2

-- 5 $631 $17 $201 $832 12.1

-- 6 $674 $15 $172 $846 14.1

3 7 $680 $15 $171 $852 14.9

Note: The results for each TSL are calculated assuming that all consumers use products at that efficiency level. The 

PBP is measured relative to the baseline product.

11-14
Table 11.4.20 Low Income Households: Summary of Life-Cycle Costs Savings Relative to 

the Base Case Efficiency Distribution for Electric Standard Ovens, BuiltIn/Slide-In

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Average Savings*

Net Cost 2014$

-- 0 0% ---

1 1 0% $14.06

-- 2 3% $18.83

2 3 11% $18.79

-- 4 19% $16.43

-- 5 61% -$12.01

-- 6 68% -$25.42

3 7 76% -$28.80

*The calculation does not include households with zero LCC savings (no impact).

Table 11.4.21 Low Income Households: Summary of LCC and PBP Results by Efficiency 

Level for Electric Self-Clean Ovens, Freestanding

TSL Efficiency 

Level

Average Costs 

2014$ Simple 

Payback

years Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating 

Cost

LCC

-- 0 $601 $27 $313 $914 --

1,2 1 $602 $26 $298 $900 0.9

-- 2 $635 $25 $286 $921 14.6

-- 3 $679 $21 $249 $928 14.1

3 4 $686 $21 $248 $934 15.2

Note: The results for each TSL are calculated assuming that all consumers use products at that efficiency level. The 

PBP is measured relative to the baseline product.

11-15
Table 11.4.22 Low Income Households: Summary of Life-Cycle Costs Savings Relative to 

the Base Case Efficiency Distribution for Electric Self-Clean Ovens, 

Freestanding

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Average Savings*

Net Cost 2013$

-- 0 0% ---

1,2 1 0% $13.98

-- 2 50% -$10.46

-- 3 56% -$14.51

3 4 71% -$18.98

*The calculation does not include households with zero LCC savings (no impact).

Table 11.4.23 Low Income Households: Summary of LCC and PBP Results by Efficiency 

Level for Electric Self-Clean Ovens, Built-In/Slide-In

TSL Efficiency 

Level

Average Costs 

2014$ Simple 

Payback

years Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating 

Cost

LCC

-- Baseline $627 $27 $314 $941 --

1,2 1 $628 $26 $299 $927 0.9

-- 2 $661 $25 $287 $948 14.6

-- 3 $705 $21 $250 $955 14.1

3 4 $712 $21 $249 $961 15.2

Note: The results for each TSL are calculated assuming that all consumers use products at that efficiency level. The 

PBP is measured relative to the baseline product.

11-16
Table 11.4.24 Low Income Households: Summary of Life-Cycle Costs Savings Relative to 

the Base Case Efficiency Distribution for Electric Self-Clean Ovens, BuiltIn/Slide-In

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Average Savings*

Net Cost 2014$

-- Baseline 0% ---

1,2 1 0% $14.11

-- 2 50% -$10.43

-- 3 56% -$14.52

3 4 71% -$18.99

*The calculation does not include households with zero LCC savings (no impact).

Table 11.4.25 Low Income Households: Summary of LCC and PBP Results by Efficiency 

Level for Gas Standard Ovens, Freestanding

TSL Efficiency 

Level

Average Costs 

2014$ Simple 

Payback

years Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating 

Cost

LCC

1 Baseline $602 $24 $658 $1,260 --

-- 1 $602 $21 $623 $1,225 0.0

-- 2 $603 $20 $604 $1,207 0.3

-- 3 $612 $19 $594 $1,207 2.1

2 4 $619 $12 $307 $927 1.4

-- 5 $622 $11 $306 $929 1.6

-- 6 $651 $12 $308 $959 3.9

3 7 $657 $12 $307 $964 4.4

Note: The results for each TSL are calculated assuming that all consumers use products at that efficiency level. The 

PBP is measured relative to the baseline product.

11-17
Table 11.4.26 Low Income Households: Summary of Life-Cycle Costs Savings Relative to 

the Base Case Efficiency Distribution for Gas Standard Ovens, Freestanding

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Average Savings*

Net Cost 2014$

1 Baseline 0% ---

-- 1 0% $35.21

-- 2 0% $47.28

-- 3 6% $40.92

2 4 0% $314.79

-- 5 8% $277.45

-- 6 17% $221.22

3 7 24% $197.33

*The calculation does not include households with zero LCC savings (no impact).

Table 11.4.27 Low Income Households: Summary of LCC and PBP Results by Efficiency 

Level for Gas Standard Ovens, Built-In/Slide-In

TSL Efficiency 

Level

Average Costs 

2014$ Simple 

Payback

years Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating 

Cost

LCC

1 Baseline $628 $24 $658 $1,286 --

-- 1 $628 $21 $623 $1,251 0.0

-- 2 $630 $20 $604 $1,233 0.3

-- 3 $638 $19 $594 $1,233 2.1

2 4 $645 $12 $307 $953 1.4

-- 5 $649 $11 $306 $955 1.6

-- 6 $677 $12 $308 $985 3.9

3 7 $683 $12 $307 $990 4.4

Note: The results for each TSL are calculated assuming that all consumers use products at that efficiency level. The 

PBP is measured relative to the baseline product.

11-18
Table 11.4.28 Low Income Households: Summary of Life-Cycle Costs Savings Relative to 

the Base Case Efficiency Distribution for Gas Standard Ovens, BuiltIn/Slide-In

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Average Savings*

Net Cost 2014$

1 Baseline 0% ---

-- 1 0% $35.19

-- 2 0% $47.27

-- 3 6% $40.91

2 4 0% $314.84

-- 5 8% $277.49

-- 6 17% $221.22

3 7 24% $197.34

*The calculation does not include households with zero LCC savings (no impact).

Table 11.4.29 Low Income Households: Summary of LCC and PBP Results by Efficiency 

Level for Gas Self-Clean Ovens, Freestanding

TSL Efficiency 

Level

Average Costs 

2014$ Simple 

Payback

years Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating 

Cost

LCC

-- Baseline $718 $25 $674 $1,392 --

1 1 $719 $24 $656 $1,375 0.8

2 2 $728 $15 $361 $1,088 1.0

-- 3 $756 $15 $360 $1,116 4.0

3 4 $763 $15 $360 $1,122 4.7

Note: The results for each TSL are calculated assuming that all consumers use products at that efficiency level. The 

PBP is measured relative to the baseline product.

11-19
Table 11.4.30 Low Income Households: Summary of Life-Cycle Costs Savings Relative to 

the Base Case Efficiency Distribution for Gas Self-Clean Ovens, 

Freestanding

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Average Savings*

Net Cost 2014$

-- Baseline 0% ---

1 1 0% $17.28

2 2 0% $298.61

-- 3 14% $216.56

3 4 27% $176.87

*The calculation does not include households with zero LCC savings (no impact).

Table 11.4.31 Low Income Households: Summary of LCC and PBP Results by Efficiency 

Level for Gas Self-Clean Ovens, Built-In/Slide-In

TSL Efficiency 

Level

Average Costs 

2014$ Simple 

Payback

years Installed Cost First Year’s 

Operating Cost

Lifetime 

Operating 

Cost

LCC

-- Baseline $744 $25 $675 $1,418 --

1 1 $745 $24 $656 $1,401 0.8

2 2 $754 $15 $361 $1,115 1.0

-- 3 $782 $15 $360 $1,142 4.0

3 4 $789 $15 $360 $1,149 4.7

Note: The results for each TSL are calculated assuming that all consumers use products at that efficiency level. The 

PBP is measured relative to the baseline product.

11-20
Table 11.4.32 Low Income Households: Summary of Life-Cycle Costs Savings Relative to 

the Base Case Efficiency Distribution for Gas Self-Clean Ovens, BuiltIn/Slide-In

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers that 

Experience Average Savings*

Net Cost 2014$

-- Baseline 0% ---

1 1 0% $17.30

2 2 0% $298.68

-- 3 14% $216.59

3 4 27% $176.89

*The calculation does not include households with zero LCC savings (no impact).

The low-income and senior-only consumer subgroups show the same trend in average 

LCC differences and consumer impacts (i.e., percentage of consumers significantly or 

insignificantly impacted) as the overall sample. For all cooking products, the average LCC costs, 

savings and payback periods for low-income and senior-only households mirror the savings for 

the general population. 

11-21
REFERENCES 

1 U.S. Department of Energy–Energy Information Administration. Residential Energy 

Consumption Survey, 2009 Public Use Data Files. 2013. Washington, D.C. (Last accessed 

March 25, 2013.)

<http://www.eia.gov/consumption/residential/data/2009/index.cfm?view=characteristics>

11-22

 
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..................................................................................... 12-2

12.2.2 Phase II: Industry Cash-Flow Analysis and Interview Guide............................ 12-2

12.2.2.1 Industry Cash-Flow Analysis....................................................................... 12-2

12.2.2.2 Interview Guides.......................................................................................... 12-3

12.2.3 Phase III: Manufacturer Interviews ................................................................... 12-3

12.2.3.1 Manufacturing Interviews............................................................................ 12-3

12.2.3.2 Revised Industry Cash-Flow Analysis......................................................... 12-4

12.2.3.3 Small Business Subgroup ............................................................................ 12-4

12.2.3.4 Manufacturing Capacity Impact .................................................................. 12-5

12.2.3.5 Employment Impact..................................................................................... 12-5

12.2.3.6 Cumulative Regulatory Burden ................................................................... 12-5

12.3 MANUFACTURER IMPACT ANALYSIS KEY ISSUES.......................................... 12-5

12.3.1 Premium Products Tend to be Less Efficient .................................................... 12-6

12.3.2 Product Utility.................................................................................................... 12-6

12.3.3 Testing and Certification Burdens..................................................................... 12-6

12.4 GRIM INPUTS AND ASSUMPTIONS........................................................................ 12-6

12.4.1 Overview of the GRIM ...................................................................................... 12-6

12.4.2 Sources for GRIM Inputs................................................................................... 12-7

12.4.2.1 Corporate Annual Reports ........................................................................... 12-7

12.4.2.2 Standard and Poor Credit Ratings................................................................ 12-8

12.4.2.3 Shipment Model........................................................................................... 12-8

12.4.2.4 Engineering Analysis................................................................................... 12-8

12.4.2.5 Manufacturer Interviews.............................................................................. 12-9

12.4.3 Financial Parameters.......................................................................................... 12-9

12.4.4 Corporate Discount Rate.................................................................................. 12-10

12.4.5 Trial Standard Levels....................................................................................... 12-12

12.4.6 NIA Shipment Forecast.................................................................................... 12-12

12.4.7 Production Costs.............................................................................................. 12-13

12.4.8 Capital and Product Conversion Costs ............................................................ 12-15

12.4.9 Markup Scenarios ............................................................................................ 12-16

12.4.9.1 Preservation of Gross Margin Markup Scenario ....................................... 12-16

12.4.9.2 Preservation of Operating Profit Markup Scenario ................................... 12-17

12.5 INDUSTRY FINANCIAL IMPACTS ........................................................................ 12-20

12.5.1 Impacts on Industry Net Present Value ........................................................... 12-20

12.5.2 Impacts on Annual Cash Flow......................................................................... 12-21

12.6 IMPACTS ON MANUFACTURER SUBGROUPS................................................... 12-23

12.6.1 Impacts on Small Business Manufacturers...................................................... 12-23

12.6.1.1 Description and Estimated Number of Small Entities Regulated.............. 12-23

12.6.1.2 Comparison Between Large and Small Entities ........................................ 12-24

12.7 OTHER IMPACTS...................................................................................................... 12-25

12-i
12.7.1 Employment..................................................................................................... 12-25

12.7.2 Production Capacity......................................................................................... 12-27

12.7.3 Cumulative Regulatory Burden ....................................................................... 12-27

12.7.3.1 DOE Regulations for Other Products Produced by Residential Conventional 

Oven Manufacturers..................................................................................................... 12-28

12.8 CONCLUSION............................................................................................................ 12-29

REFERENCES ........................................................................................................................ 12-33

LIST OF TABLES

Table 12.2.1 SBA and NAICS Classifications of Small Businesses Potentially Affected 

by This Rulemaking....................................................................................................... 12-4

Table 12.4.1 GRIM Financial Parameters Based on 2007–2013 Weighted Company 

Financial Data ................................................................................................................ 12-9

Table 12.4.2 GRIM Revised Residential Conventional Oven Industry Financial 

Parameters.................................................................................................................... 12-10

Table 12.4.3 Cost of Equity Calculation.................................................................................. 12-11

Table 12.4.4 Cost of Debt Calculation..................................................................................... 12-11

Table 12.4.5 Trial Standard Levels for Residential Conventional Ovens ............................... 12-12

Table 12.4.6 Manufacturer Production Cost Breakdown (2014$) for Electric Standard 

Ovens, Free-Standing................................................................................................... 12-13

Table 12.4.7 Manufacturer Production Cost Breakdown (2014$) for Electric Standard 

Ovens, Built-in/Slide-in ............................................................................................... 12-14

Table 12.4.8 Manufacturer Production Cost Breakdown (2014$) for Electric Self-Clean 

Ovens, Free-Standing................................................................................................... 12-14

Table 12.4.9 Manufacturer Production Cost Breakdown (2014$) for Electric Self-Clean 

Ovens, Built-in/Slide-in ............................................................................................... 12-14

Table 12.4.10 Manufacturer Production Cost Breakdown (2014$) for Gas Standard 

Ovens, Free-Standing................................................................................................... 12-14

Table 12.4.11 Manufacturer Production Cost Breakdown (2014$) for Gas Standard 

Ovens, Built-in/Slide-in ............................................................................................... 12-15

Table 12.4.12 Manufacturer Production Cost Breakdown (2014$) for Gas Self-Clean 

Ovens, Free-Standing................................................................................................... 12-15

Table 12.4.13 Manufacturer Production Cost Breakdown (2014$) for Gas Self-Clean 

Ovens, Built-in/Slide-in ............................................................................................... 12-15

Table 12.4.14 Capital Conversion Costs for all Residential Conventional Ovens by TSL..... 12-16

Table 12.4.15 Product Conversion Costs for all Residential Conventional Ovens by TSL.... 12-16

Table 12.4.16 Preservation of Operating Profit Markups for Electric Standard Ovens, 

Free-Standing............................................................................................................... 12-17

Table 12.4.17 Preservation of Operating Profit Markups for Electric Standard Ovens, 

Built-in/Slide-in ........................................................................................................... 12-18

Table 12.4.18 Preservation of Operating Profit Markups for Electric Self-Clean Ovens, 

Free-Standing............................................................................................................... 12-18

Table 12.4.19 Preservation of Operating Profit Markups for Electric Self-Clean Ovens, 

Built-in/Slide-in ........................................................................................................... 12-18

12-ii
Table 12.4.20 Preservation of Operating Profit Markups for Gas Standard Ovens, FreeStanding ....................................................................................................................... 12-19

Table 12.4.21 Preservation of Operating Profit Markups for Gas Standard Ovens, Builtin/Slide-in..................................................................................................................... 12-19

Table 12.4.22 Preservation of Operating Profit Markups for Gas Self-Clean Ovens, FreeStanding ....................................................................................................................... 12-19

Table 12.4.23 Preservation of Operating Profit Markups for Gas Self-Clean Ovens, Builtin/Slide-in..................................................................................................................... 12-20

Table 12.5.1 Changes in Industry Net Present Value for Residential Conventional Ovens 

– Preservation of Gross Margin Markup Scenario ...................................................... 12-21

Table 12.5.2 Changes in Industry Net Present Value for Residential Conventional Ovens 

– Preservation of Operating Profit Markup Scenario .................................................. 12-21

Table 12.6.1 Conversion Costs Facing an Average Small Manufacturer versus an 

Average Large Manufacturer of Residential Conventional Ovens.............................. 12-25

Table 12.7.1 Potential Changes in the Total Number of All Domestic Residential 

Conventional Ovens Production Workers in 2019 ...................................................... 12-26

Table 12.7.2 Other DOE Regulations Potentially Affecting Residential Conventional 

Oven Manufacturers..................................................................................................... 12-28

LIST OF FIGURES

Figure 12.4.1 Using the GRIM to Calculate Cash Flow............................................................ 12-7

Figure 12.5.1 Annual Industry Free Cash Flows for Residential Conventional Ovens –

Preservation of Gross Margin Markup Scenario ......................................................... 12-22

Figure 12.5.2 Annual Industry Free Cash Flows for Residential Conventional Ovens –

Preservation of Operating Profit Markup Scenario ..................................................... 12-23

12-iii
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. 6312(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 new and amended energy conservation standards on 

manufacturers of residential conventional ovens, and assessed the impact of such standards on 

direct employment and manufacturing capacity.

The MIA has both quantitative and qualitative aspects. The quantitative part of the MIA 

primarily relies on the Government Regulatory Impact Model (GRIM), an industry cash-flow 

model adapted for the products 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 new and amended energy 

conservation standards for each product by comparing changes in INPV between a base case and 

the various trial standard levels (TSLs) in the standards case. The qualitative part of the MIA 

addresses 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 an industry characterization for the residential conventional oven industry, including 

data on market share, sales volumes and trends, pricing, employment, and financial structure. In 

Phase II, “Industry Cash Flow,” DOE created a GRIM for residential conventional ovens, as well 

as an interview guide, to gather information on the potential impacts new and amended energy 

conservation standards would have on residential conventional oven manufacturers. DOE 

presented the MIA results for residential conventional ovens based on a set of considered TSLs. 

These TSLs are described in section 12.4.5.

In Phase III, “Manufacturer Interviews,” DOE interviewed manufacturers that account for 

more than 85 percent of residential conventional oven sales. Interviewees included large and 

small manufacturers with various market shares and market focuses, providing a representative 

cross-section of the industry. During interviews, DOE discussed financial topics specific to each 

manufacturer and obtained each manufacturer’s view of the residential conventional oven

industry. The interviews provided DOE with valuable information for evaluating the impacts of 

new and amended energy conservation standards on manufacturer cash flows, investment 

requirements, and production employment.

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12.2.1 Phase I: Industry Profile

In Phase I of the MIA, DOE prepared a profile of the residential conventional oven

industry that built upon the market and technology assessment prepared for this rulemaking, see 

chapter 3 of this notice of proposed rulemaking (NOPR) technical support document (TSD).

Before initiating the detailed impact studies, DOE collected information on the present and past 

structure and market characteristics of the industry. This information included market share data,

unit shipments, manufacturer markups, and cost structures for various manufacturers. The 

industry profile includes: (1) further detail on the overall market and product characteristics; (2) 

estimated manufacturer market shares; (3) financial parameters such as net plant, property, and 

equipment (PPE); selling, general and administrative (SG&A) expenses; cost of goods sold, etc.; 

and (4) trends in the number of firms, specific residential appliance markets, and general product

characteristics. The industry profile included a top-down cost analysis of residential conventional 

oven manufacturers that DOE used to derive preliminary financial inputs for the GRIM (e.g., 

revenues, depreciation, SG&A, and research and development [R&D] expenses).

DOE also used public information to further calibrate its initial characterization of the 

residential conventional oven industry, including Securities and Exchange Commission (SEC) 

10–K reports,1 Standard & Poor’s (S&P) stock reports,2 and corporate annual reports. DOE 

supplemented this public information with data released by privately held companies.

12.2.2 Phase II: Industry Cash-Flow Analysis and Interview Guide

Phase II focused on the financial impacts of new and amended energy conservation 

standards on residential conventional oven manufacturers. New or more stringent energy 

conservation standards can affect manufacturers’ cash flows 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 residential conventional ovens. In performing 

these analyses, DOE used the financial values derived during Phase I and the shipment scenarios 

used in the national impact analysis (NIA). In Phase II, DOE performed this preliminary industry 

cash-flow analysis and prepared written guides for manufacturer interviews.

12.2.2.1 Industry Cash-Flow Analysis

The GRIM uses several factors to determine a series of annual cash flows from the 

announcement year of new and amended energy conservation standards until several years after 

the standards’ compliance date. These factors include annual expected revenues, costs of goods 

sold, SG&A, taxes, and capital expenditures related to the new and amended standards. Inputs 

for the GRIM include manufacturer production costs (MPCs) and shipment forecasts developed 

in other analyses. DOE derived the MPCs from the engineering analysis through purchasing and 

tearing down products. DOE then estimated typical manufacturer markups for residential 

conventional ovens from public financial reports and interviews with manufacturers to derive 

MSPs for all covered residential conventional ovens. In addition to the base case scenario, DOE 

developed alternative markup scenarios for the standards case scenarios for the GRIM based on 

discussions with manufacturers. DOE’s shipments analysis, presented in chapter 9 of this NOPR 

TSD, provided the basis for the shipment projections in the GRIM. The financial parameters 

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were developed using publicly available manufacturer data and were revised with information 

submitted confidentially during manufacturer interviews. The GRIM results are compared to 

base case projections for the industry. The financial impact of new and amended energy 

conservation standards is the difference between the discounted annual cash flows in the base 

case and in the standards case at each TSL.

12.2.2.2 Interview Guides

During Phase III of the MIA, DOE interviewed manufacturers to gather information on 

the effects of new and amended energy conservation standards on revenues and finances, direct 

employment, capital assets, and industry competitiveness. Before the interviews, DOE 

distributed interview guides for the residential conventional oven industry. The interview guide 

provided a starting point to identify relevant issues and help identify the impacts of new and 

amended energy conservation standards on individual manufacturers or subgroups of 

manufacturers in their industry. Most of the information DOE received from these meetings is 

protected by non-disclosure agreements and resides with DOE’s contractors. Before each 

telephone interview or site visit, DOE provided company representatives with an interview guide 

that included the topics for which DOE sought input. The MIA interview topics included (1) 

engineering, (2) key issues, (3) company overview and organizational characteristics, (4) 

markups and profitability, (5) shipment projections, (6) industry average financial parameters,

(7) conversion costs, (8) cumulative regulatory burden, (9) direct employment impacts, (10) 

manufacturing capacity / exports / foreign competition / outsourcing, (11) industry consolidation,

and (12) impacts on small businesses. This interview guide is presented in appendix 12A.

12.2.3 Phase III: Manufacturer Interviews

Using average cost and financial assumptions to develop an industry cash-flow model is 

not adequate for assessing differential impacts among a potential subgroup of manufacturers. 

Small manufacturers, niche players, or manufacturers exhibiting a cost structure that differs 

largely from the industry average could be more negatively impacted. During interviews, DOE 

identified one potential manufacturer subgroup (small manufacturers) that could be 

disproportionately impacted by new and amended energy conservation standards. As a result, 

DOE will analyze small business manufacturers as a subgroup.

12.2.3.1 Manufacturing Interviews

The information gathered in Phase I and the cash-flow analysis performed in Phase II are 

supplemented with information gathered from manufacturer interviews in Phase III. The 

interview process provides an opportunity for interested parties to express their views on 

important issues privately, allowing confidential or sensitive information to be considered in the 

rulemaking process.

DOE used these interviews to tailor the GRIM to reflect unique financial characteristics 

of residential conventional oven manufacturers. DOE contacted companies from its database of 

manufacturers and interviewed small and large companies, subsidiaries and independent firms, 

and public and private corporations to provide an accurate representation of the industry. 

Interviews were scheduled well in advance to provide every opportunity for key individuals to be 

12-3
available for comment. Although a written response to the questionnaire was acceptable, DOE 

sought interactive interviews, which helped clarify responses and identify additional issues. The 

resulting information provides valuable inputs to the GRIM developed for the residential 

conventional oven industry.

12.2.3.2 Revised Industry Cash-Flow Analysis

In Phase II of the MIA, DOE provided manufacturers with preliminary GRIM input 

financial figures for review and evaluation. During the interviews, DOE requested for comment

on the values it selected for the parameters. DOE revised its industry cash-flow models based on 

this feedback. Section 12.4.3 provides more information on how DOE calculated the parameters.

12.2.3.3 Small Business Subgroup

As part of Phase III, DOE investigated whether small businesses should be analyzed as a 

subgroup. DOE used the Small Business Administration (SBA) small business size standards 

published on July 14, 2014, as amended, and the North American Industry Classification System 

(NAICS) code, presented in Table 12.2.1, to determine whether any small entities would be 

affected by this rulemaking.a For the industry under review, the SBA bases its small business 

definition on the total number of employees for a business, its subsidiaries, and its parent 

companies. An aggregated business entity with fewer employees than the listed limit is 

considered a small business.

Table 12.2.1 SBA and NAICS Classifications of Small Businesses Potentially Affected by 

This Rulemaking

Industry Description Revenue Limit Employee Limit NAICS

Household Cooking Appliance Manufacturing N/A 750 335221

DOE used the Association of Home Appliance Manufacturers (AHAM)

3 member 

directory, SBA’s database, information from the previous rulemaking adopting standards for 

residential conventional ovens, individual company websites, and market research tools (e.g., 

Hoover’s reports) to create a list of companies that potentially sell residential conventional ovens

covered by this rulemaking. Additionally, DOE asked interested parties and industry 

representatives if they were aware of other small businesses in the residential conventional oven

industry. DOE contacted select companies on its list, as necessary, to determine whether they 

met the SBA’s definition of a residential conventional oven small business. DOE screened out 

companies that did not offer products covered by this rulemaking, did not meet the definition of 

a “small business,” or are foreign owned and operated.

During its research, DOE identified seven companies that sell residential conventional 

ovens covered by this rulemaking and qualify as a small business per the SBA employment 

threshold for this industry. DOE contacted the residential conventional oven small businesses to 

solicit feedback on the potential impacts of new and amended energy conservation standards.

One of the residential conventional oven small businesses consented to be interviewed during the 

a The size standards are available on the SBA’s website at http://www.sba.gov/content/table-small-business-sizestandards

12-4

 
MIA interviews. In addition to posing the standard MIA interview questions, DOE solicited data 

from manufacturers on differential impacts that these small businesses might experience from 

new and amended energy conservation standards. Because DOE was not able to certify that the 

proposed rulemaking for residential conventional ovens would not have a significant economic 

impact on a substantial number of small entities, DOE has analyzed small businesses as a 

subgroup as part of this rulemaking. The results of this subgroup analysis are presented in section 

12.6.

12.2.3.4 Manufacturing Capacity Impact

One significant outcome of new and amended energy conservation standards could be the 

obsolescence of existing manufacturing assets, including tooling and investment. The 

manufacturer interview guide has a series of questions to help identify impacts of new and 

amended standards on manufacturing capacity. These include questions regarding capacity 

utilization and plant location decisions in the United States and North America (with and without 

new and amended standards); the ability of manufacturers to upgrade or remodel existing 

facilities to accommodate the new requirements; the nature and value of any stranded assets; and 

estimates for any one-time changes to existing PPE. DOE’s estimates of the one-time capital 

changes and stranded assets affect the cash-flow estimates in the GRIM. These estimates can be 

found in section 12.4.8; DOE’s discussion of the capacity impacts can be found in section 12.7.2.

12.2.3.5 Employment Impact

The impact of new and amended energy conservation standards on employment is an 

important consideration in the rulemaking process. To assess how domestic direct employment 

patterns might be affected, the interviews explored current employment trends in the residential 

conventional oven industry. The interviews also solicited manufacturers’ views on changes in 

employment patterns that may result from new and amended standards. The employment impacts 

section of the interview guide focused on current employment levels associated with 

manufacturers at each production facility, expected future employment levels with and without 

new and amended energy conservation standards, and differences in workforce skills and issues 

related to retraining employees. The employment impacts are reported in section 12.7.1.

12.2.3.6 Cumulative Regulatory Burden

DOE seeks to mitigate the overlapping effects on manufacturers due to new and amended 

energy conservation standards and other regulatory actions affecting the same products. DOE 

analyzed the impact on manufacturers of multiple, product-specific regulatory actions. Based on 

its own research and discussions with manufacturers, DOE identified regulations relevant to 

residential conventional oven manufacturers, such as state regulations and other Federal 

regulations that impact other products made by the same manufacturers. Discussion of the 

cumulative regulatory burden can be found in section 12.7.3.

12.3 MANUFACTURER IMPACT ANALYSIS KEY ISSUES

Each MIA interview begins 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 believe DOE should explore and discuss further during the interview. 

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The following sections describe the most significant issues identified by manufacturers. These 

summaries are provided in aggregate to protect manufacturer confidentiality.

12.3.1 Premium Products Tend to be Less Efficient

Manufacturers stated that their premium products are usually less efficient than their 

baseline products. For example, ovens typically have bigger cavities with hidden heat sources 

under the floor of the cavity. This makes the heat source less direct, therefore decreasing the 

efficiency. On the other hand, baseline ovens tend to use direct heating sources which are more 

efficient. Manufacturers warned DOE that focusing only on the efficiency of residential 

conventional ovens could cause some manufacturers to redesign their products in a way that 

reduces consumer satisfaction as consumers tend to value premium features.

12.3.2 Product Utility

Manufacturers stated that energy efficiency is not one of the most important aspects that 

consumers value when purchasing residential conventional ovens. Manufacturers state that there 

are several other factors, such as performance and durability, which consumers value more when 

purchasing residential conventional ovens. Forcing manufacturers to improve the efficiency of 

their products could lead to some manufacturers removing premium features that consumers 

desire from their products, reducing overall consumer utility.

12.3.3 Testing and Certification Burdens

Several manufacturers expressed concern about the testing and recertification costs 

associated with new and amended energy conservation standards for residential conventional 

ovens. Because testing and certification costs are incurred on a per model basis, if a large number 

of models are required to be redesigned to meet new and amended standards, manufacturers 

would be forced to spend a significant amount of money testing and certifying products that were 

redesigned due to new and amended standards. Manufacturers stated that these testing and 

certification costs associated with residential conventional ovens could significantly strain their 

limited resources if these costs were all incurred in the three year time frame from the 

publication of a final rule to the implementation of the standards.

12.4 GRIM INPUTS AND ASSUMPTIONS

The GRIM serves as the main tool for assessing the impacts on industry due to new and 

amended energy conservation standards. DOE relies on several sources to obtain inputs for the 

GRIM. Data and assumptions from these sources are then fed into the accounting model that 

calculates the industry cash flow both with and without new and amended energy conservation 

standards.

12.4.1 Overview of the GRIM

The basic structure of the GRIM, illustrated in Figure 12.4.1, is an annual cash-flow 

analysis that uses manufacturer prices, manufacturing costs, shipments, and industry financial 

information as inputs, and accepts a set of regulatory conditions such as changes in costs, 

investments, and associated margins. The GRIM spreadsheet uses a number of inputs to arrive at 

12-6
a series of annual cash flows, beginning with the base year of the analysis, 2015, and continuing 

to 2048. The model calculates the INPV by summing the stream of annual discounted cash flows 

during this period and adding a discounted terminal value.4

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 scenario induced by new and 

amended energy conservation standards. The difference in INPV between the base case and the 

standard case(s) represents the estimated financial impact of the new and amended energy 

conservation standards on manufacturers. Appendix 12B provides more technical details and 

user information for the GRIM.

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 the manufacturer interviews.

12.4.2.1 Corporate Annual Reports

Corporate annual reports to the SEC (SEC 10-Ks) provided many of the initial financial 

inputs to the GRIM. These reports exist for publicly held companies and are freely available to 

the general public. DOE developed initial financial inputs to the GRIM by examining the annual 

SEC 10-K reports filed by publicly traded manufacturers that produce residential conventional 

ovens, among other products. Since these companies do not provide detailed information about 

their individual product lines, DOE used financial information at the parent company level as its 

initial estimates of the financial parameters in the GRIM. These figures were later revised using 

feedback from interviews to be representative of residential conventional oven manufacturing. 

DOE used corporate annual reports to derive the following initial inputs to the GRIM:

• Tax rate

12-7
• Working capital

• SG&A

• R&D

• Depreciation

• Capital expenditures

• Net PPE

12.4.2.2 Standard and Poor Credit Ratings

S&P provides independent credit ratings, research, and financial information. DOE relied 

on S&P reports to determine the industry’s average cost of debt when calculating the cost of 

capital.

12.4.2.3 Shipment Model

The GRIM used shipment projections derived from DOE’s shipments model in the NIA. 

In the base case shipment analysis, DOE developed shipment projections based on historical data 

and an analysis of key market drivers for each product class. In the standards case, DOE modeled 

a roll-up scenario. The roll-up scenario represents the case in which all shipments in the base 

case that do not meet the new and amended standards shift up in efficiency to meet the new or 

amended standard level but do not exceed the new or amended standards. Also, no shipments 

that meet or exceed the new and amended standards have an increase in efficiency due to the 

new and amended standards. Chapter 9 of this NOPR TSD describes the methodology and 

analytical model DOE used to forecast shipments.

12.4.2.4 Engineering Analysis

The engineering analysis establishes the relationship between end-user price and the 

efficiency level for all residential conventional ovens covered in this rulemaking. DOE based its 

engineering analysis on commercially available residential conventional ovens that met the 

design options identified in the technology assessment and screening analysis (chapters 3 and 4 

of this NOPR TSD). DOE’s engineering approach consisted of the following steps: 1) identifying

representative product classes to analyze, 2) selecting baseline residential conventional ovens, 3)

identifying more efficient substitutes for the baseline residential conventional ovens, and 4)

developing efficiency levels for the product classes. DOE developed MPCs for each product 

class at each EL analyzed. DOE purchased a number of units for each product class, then tested 

and tore down those units to create a unique bill of materials for the purchased units. Using the 

bill of materials for each residential conventional oven, DOE was able to create an aggregated 

MPC based on the material costs from the bill of materials, the labor costs based on an average 

labor rate and the labor hours necessary to manufacture the residential conventional oven

analyzed, and the overhead costs, including depreciation, based on a markup applied to the 

material and labor costs based on the materials used. These MPCs are then used as inputs to the 

life-cycle cost (LCC) analysis and NIA after applying the appropriate manufacturer markup and 

distribution chain markup to each product. See chapter 5 of this NOPR TSD for a complete 

discussion of the engineering analysis.

12-8
12.4.2.5 Manufacturer Interviews

During the course of the MIA, DOE conducted interviews with a representative crosssection of manufacturers. DOE also interviewed manufacturers that account for a significant 

portion of sales in every product class. During these discussions, DOE obtained information to 

determine and verify GRIM input assumptions in the industry. 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, certification, and marketing);

• product cost structure, or the portion of the MPCs related to materials, labor, overhead, 

and depreciation costs;

• possible profitability impacts;

• impacts on small businesses; and

• cost-efficiency curves calculated in the engineering analysis.

12.4.3 Financial Parameters

Table 12.4.1 provides financial parameters for two public companies engaged in 

manufacturing and selling residential conventional ovens. The values listed are averages over a 

seven-year period (2007 to 2013).

Table 12.4.1 GRIM Financial Parameters Based on 2007–2013 Weighted Company 

Financial Data

Parameter Weighted Average Manufacturers

A B

Tax Rate % of taxable income 19.5 15.3 28.3

Working Capital % of revenues 4.5 3.2 7.1

SG&A % of revenues 11.2 9.2 15.5

R&D % of revenues 2.4 2.8 1.7

Depreciation % of revenues 3.0 3.0 3.0

Capital Expenditures % of revenues 3.3 3.0 3.8

Net PPE % of revenues 16.2 16.7 15.0

During interviews, residential conventional oven manufacturers were asked to comment 

on these financial parameters derived from SEC-10Ks and listed in Table 12.4.1. Where 

applicable, DOE adjusted the parameters in the GRIM using this manufacturer feedback to 

reflect the current residential conventional oven industry. Table 12.4.2 presents the revised 

parameters used for residential conventional oven manufacturers for this NOPR.

12-9
Table 12.4.2 GRIM Revised Residential Conventional Oven Industry Financial Parameters

Parameter Weighted Average

Tax Rate % of taxable income 30.0

Working Capital % of revenues 4.5

SG&A % of revenues 11.2

R&D % of revenues 2.4

Depreciation % of revenues 3.0

Capital Expenditures % of revenues 3.3

Net PPE % of revenues 16.2

12.4.4 Corporate Discount Rate

DOE used the weighted average cost of capital (WACC) as the discount rate to calculate 

the INPV. A company’s assets are financed by a combination of debt and equity. The WACC is 

the total cost of debt and equity weighted by their respective proportions in the capital structure 

of the industry. DOE estimated the WACC for the residential conventional oven industry based 

on representative companies, using the following formula:

WACC = After-Tax Cost of Debt x (Debt Ratio) + Cost of Equity x (Equity Ratio) 

Equation 12.1

The cost of equity is the rate of return that equity investors (including, potentially, the 

company) expect to earn on a company’s stock. These expectations are reflected in the market 

price of the company’s stock. The capital asset pricing model (CAPM) provides one widely used 

means to estimate the cost of equity. According to the CAPM, the cost of equity (expected 

return) is:

Cost of Equity = Riskless Rate of Return + β x Risk Premium 

Equation 12.2

Where:

Riskless rate of return = the rate of return on a “safe” benchmark investment, typically 

considered the short-term Treasury Bill (T-Bill) yield,

Risk premium = the difference between the expected return on stocks and the riskless rate, and

Beta (β) = the correlation between the movement in the price of the stock and that of the broader 

market. In this case, Beta equals one if the stock is perfectly correlated with the S&P 500 market 

index. A Beta lower than one means the stock is less volatile than the market index.

DOE determined that the industry average cost of equity for the residential conventional 

oven industry is 15.5 percent.

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Table 12.4.3 Cost of Equity Calculation

Parameter Industry-Weighted 

Average

Manufacturers

A B

(1) Average Beta 1.70 1.70 1.71

(2) Yield on 10-Year T-Bill (1928-2013) % 5.2 - -

(3) Market Risk Premium (1928-2013) % 6.1 - -

Cost of Equity (2)+[(1)*(3)] % 15.5 - -

Equity/Total Capital % 66.8 72.7 54.5

Bond ratings are a tool to measure default risk and arrive at a cost of debt. Each bond 

rating is associated with a particular spread. One way of estimating a company’s cost of debt is 

to treat it as a spread (usually expressed in basis points) over the risk-free rate. DOE used this 

method to calculate the cost of debt for both manufacturers by using S&P ratings and adding the 

relevant spread to the risk-free rate.

In practice, investors use a variety of different maturity Treasury bonds to estimate the 

risk-free rate. DOE used the 10-year Treasury bond return because it captures long-term inflation 

expectations and is less volatile than short-term rates. The risk-free rate is estimated to be 

approximately 5.2 percent, which is the average 10-year Treasury bond return between 1928 and 

2013.

For the cost of debt, S&P’s Credit Services provided the average spread of corporate 

bonds for both the public manufacturers. DOE added the industry-weighted average spread to the 

average T-Bill rate. Since proceeds from debt issuance are tax deductible, DOE adjusted the 

gross cost of debt by the industry average tax rate to determine the net cost of debt for the 

industry. Table 12.4.4 presents the derivation of the cost of debt and the capital structure of the 

industry (i.e., the debt ratio [debt/total capital]).

Table 12.4.4 Cost of Debt Calculation

Parameter Industry-Weighted 

Average

Manufacturer

A B

S&P Bond Rating - BBB BBB

(1) Yield on 10-Year T-Bill (1928-2013) % 5.2 - -

(2) Gross Cost of Debt % 6.8 6.8 6.8

(3) Tax Rate % 19.5 15.3 28.3

Net Cost of Debt (2) x (1-(3)) % 5.4 5.8 4.9

Debt/Total Capital % 33.2 27.3 45.5

Using public information for both these companies, the initial estimate for the residential 

conventional oven industry WACC was approximately 12.2 percent. Subtracting an inflation rate 

of 3.1 percent between 1928 and 2013, the inflation-adjusted WACC, which was the initial 

estimate of the discount rate used in the straw-man GRIM, was 9.1 percent. DOE asked for 

feedback on the 9.1 percent discount rate during manufacturer interviews. Most manufacturers 

agreed the 9.1 discount rate was appropriate to use for the residential conventional oven industry.

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12.4.5 Trial Standard Levels

DOE developed TSLs for residential conventional ovens consistent with the engineering 

analysis. DOE analyzed 11 product classes for residential conventional ovens. Table 12.4.5

shows the efficiency levels at each TSL for the residential conventional ovens analyzed by DOE.

Table 12.4.5 Trial Standard Levels for Residential Conventional Ovens

Product Class Product Class Description TSL 1 TSL 2 TSL 3

1 Electric Standard Ovens,

Free-Standing EL 1 EL 3 EL 7

2 Electric Standard Ovens,

Built-in/Slide-in

EL 1 EL 3 EL 7

3 Electric Self-Clean Ovens,

Free-Standing EL 1 EL 1 EL 4

4 Electric Self-Clean Ovens,

Built-in/Slide-in EL 1 EL 1 EL 4

5 Gas Standard Ovens,

Free-Standing Baseline EL 4 EL 7

6 Gas Standard Ovens,

Built-in/Slide-in Baseline EL 4 EL 7

7 Gas Self-Clean Ovens,

Free-Standing EL 1 EL 2 EL 4

8 Gas Self-Clean Ovens,

Built-in/Slide-in EL 1 EL 2 EL 4

TSL 1 sets the efficiency level at baseline for two product classes (gas standard ovens, 

free-standing; and gas standard ovens, built-in/slide-in), and EL 1 for six product classes (electric 

standard ovens, free-standing; electric standard ovens, built-in/slide-in; electric self-clean ovens, 

free-standing; electric self-clean ovens, built-in/slide-in; gas self-clean ovens, free-standing; and 

gas self-clean ovens, built-in/slide-in).

TSL 2 sets the efficiency level at EL 1 for two product classes (electric self-clean ovens, 

free-standing; and electric self-clean ovens, built-in/slide-in), EL 2 for two product classes (gas 

self-clean ovens, free-standing; and gas self-clean ovens, built-in/slide-in), EL 3 for two product 

classes (electric standard ovens, free-standing and electric standard ovens, built-in/slide-in); and 

EL 4 for two product classes (gas standard ovens, free-standing and gas standard ovens, builtin/slide-in).

TSL 3 sets the efficiency level at max tech for all product classes. This corresponds to EL 

4 for four product classes (electric self-clean ovens, free-standing; electric self-clean ovens, 

built-in/slide-in; gas self-clean ovens, free-standing; and gas self-clean ovens, built-in/slide-in); 

and EL 7 for four product classes (electric standard ovens, free-standing; electric standard ovens, 

built-in/slide-in; gas standard ovens, free-standing; and gas standard ovens, built-in/slide-in.

12.4.6 NIA Shipment Forecast

The GRIM estimate manufacturer revenues based on the total unit-shipment forecasts and 

the distribution of those shipments by efficiency level. Changes in the efficiency distribution at 

each standards level are a key driver of manufacturer finances. For this analysis, the GRIM used 

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the NIA’s annual shipment forecasts from 2015 to 2048, the end of the analysis period. In the 

base case shipment analysis, DOE develops shipment projections based on historical data and an 

analysis of key market drivers for each product class. In the standards case, DOE modeled a rollup shipment scenario. The roll-up scenario represents the case in which all shipments that in the 

base case do not meet the analyzed standard level, will increase in efficiency to now meet the 

analyzed standard level but do not exceed that standard level. Also, no shipments that meet or 

exceed the analyzed standard level increase in efficiency due to potential standards. The 

assumptions and methodology that drive the shipments analysis are described in chapter 9 of this

NOPR TSD.

12.4.7 Production Costs

During the engineering analysis, DOE developed the MPC for all product classes at each 

EL analyzed. DOE purchased a number of units for each product class, then tested and tore down 

those units to create a unique bill of materials for the purchased units. Using the bill of materials

for each residential conventional oven, DOE was able to create an aggregated MPC based on the 

material costs from the bill of materials, the labor costs based on an average labor rate and the 

labor hours necessary to manufacture the residential conventional oven analyzed, and the 

overhead costs, including depreciation, based on a markup applied to the material and labor costs 

based on the materials used.

Table 12.4.6 through Table 12.4.13 show the average production cost estimates for 

residential conventional ovens used in the GRIM for each product class at each efficiency level.

Table 12.4.6 Manufacturer Production Cost Breakdown (2014$) for Electric Standard 

Ovens, Free-Standing

EL Materials Labor Depreciation Overhead MPC Markup MSP

Baseline $192.29 $29.17 $9.55 $34.21 $265.22 1.20 $318.27

EL 1 $192.88 $29.26 $9.58 $34.32 $266.04 1.20 $319.25

EL 2 $194.29 $29.48 $9.65 $34.57 $267.98 1.20 $321.58

EL 3 $198.01 $30.04 $9.83 $35.23 $273.11 1.20 $327.73

EL 4 $199.70 $30.30 $9.92 $35.53 $275.44 1.20 $330.53

EL 5 $217.23 $32.96 $10.79 $38.65 $299.63 1.20 $359.55

EL 6 $240.24 $36.45 $11.93 $42.75 $331.37 1.20 $397.64

EL 7 $243.30 $36.91 $12.08 $43.29 $335.58 1.20 $402.70

12-13
Table 12.4.7 Manufacturer Production Cost Breakdown (2014$) for Electric Standard 

Ovens, Built-in/Slide-in

EL Materials Labor Depreciation Overhead MPC Markup MSP

Baseline $203.55 $30.88 $10.11 $36.22 $280.76 1.20 $336.92

EL 1 $204.15 $30.97 $10.14 $36.32 $281.58 1.20 $337.90

EL 2 $205.55 $31.19 $10.21 $36.57 $283.52 1.20 $340.22

EL 3 $209.27 $31.75 $10.39 $37.24 $288.65 1.20 $346.38

EL 4 $210.96 $32.01 $10.48 $37.54 $290.98 1.20 $349.18

EL 5 $228.50 $34.67 $11.35 $40.66 $315.17 1.20 $378.20

EL 6 $251.51 $38.16 $12.49 $44.75 $346.91 1.20 $416.29

EL 7 $254.56 $38.62 $12.64 $45.29 $351.12 1.20 $421.34

Table 12.4.8 Manufacturer Production Cost Breakdown (2014$) for Electric Self-Clean 

Ovens, Free-Standing

EL Materials Labor Depreciation Overhead MPC Markup MSP

Baseline $211.17 $32.04 $10.49 $37.57 $291.26 1.20 $349.52

EL 1 $211.76 $32.13 $10.51 $37.68 $292.08 1.20 $350.50

EL 2 $229.29 $34.79 $11.39 $40.80 $316.27 1.20 $379.52

EL 3 $252.31 $38.28 $12.53 $44.89 $348.01 1.20 $417.61

EL 4 $256.07 $38.85 $12.72 $45.56 $353.20 1.20 $423.83

Table 12.4.9 Manufacturer Production Cost Breakdown (2014$) for Electric Self-Clean 

Ovens, Built-in/Slide-in

EL Materials Labor Depreciation Overhead MPC Markup MSP

Baseline $222.43 $33.75 $11.04 $39.58 $306.80 1.20 $368.16

EL 1 $223.03 $33.84 $11.07 $39.68 $307.62 1.20 $369.15

EL 2 $240.56 $36.50 $11.95 $42.80 $331.81 1.20 $398.17

EL 3 $263.57 $39.99 $13.09 $46.90 $363.55 1.20 $436.26

EL 4 $267.33 $40.56 $13.27 $47.57 $368.74 1.20 $442.48

Table 12.4.10 Manufacturer Production Cost Breakdown (2014$) for Gas Standard Ovens, 

Free-Standing

EL Materials Labor Depreciation Overhead MPC Markup MSP

Baseline $213.40 $32.38 $10.60 $37.97 $294.34 1.20 $353.21

EL 1 $213.40 $32.38 $10.60 $37.97 $294.34 1.20 $353.21

EL 2 $213.99 $32.47 $10.63 $38.08 $295.16 1.20 $354.20

EL 3 $218.70 $33.18 $10.86 $38.91 $301.65 1.20 $361.98

EL 4 $222.42 $33.75 $11.04 $39.58 $306.78 1.20 $368.14

EL 5 $224.11 $34.00 $11.13 $39.88 $309.11 1.20 $370.94

EL 6 $239.08 $36.27 $11.87 $42.54 $329.77 1.20 $395.72

EL 7 $242.21 $36.75 $12.03 $43.10 $334.08 1.20 $400.90

12-14
Table 12.4.11 Manufacturer Production Cost Breakdown (2014$) for Gas Standard Ovens, 

Built-in/Slide-in

EL Materials Labor Depreciation Overhead MPC Markup MSP

Baseline $224.67 $34.09 $11.16 $39.98 $309.88 1.20 $371.86

EL 1 $224.67 $34.09 $11.16 $39.98 $309.88 1.20 $371.86

EL 2 $225.26 $34.18 $11.19 $40.08 $310.70 1.20 $372.85

EL 3 $229.96 $34.89 $11.42 $40.92 $317.19 1.20 $380.63

EL 4 $233.68 $35.46 $11.60 $41.58 $322.32 1.20 $386.79

EL 5 $235.37 $35.71 $11.69 $41.88 $324.65 1.20 $389.58

EL 6 $250.35 $37.98 $12.43 $44.55 $345.31 1.20 $414.37

EL 7 $253.47 $38.46 $12.59 $45.10 $349.62 1.20 $419.54

Table 12.4.12 Manufacturer Production Cost Breakdown (2014$) for Gas Self-Clean 

Ovens, Free-Standing

EL Materials Labor Depreciation Overhead MPC Markup MSP

Baseline $263.16 $39.93 $13.07 $46.82 $362.98 1.20 $435.58

EL 1 $263.76 $40.02 $13.10 $46.93 $363.80 1.20 $436.56

EL 2 $268.46 $40.73 $13.33 $47.77 $370.29 1.20 $444.35

EL 3 $283.44 $43.00 $14.07 $50.43 $390.95 1.20 $469.13

EL 4 $287.20 $43.57 $14.26 $51.10 $396.13 1.20 $475.36

Table 12.4.13 Manufacturer Production Cost Breakdown (2014$) for Gas Self-Clean 

Ovens, Built-in/Slide-in

EL Materials Labor Depreciation Overhead MPC Markup MSP

Baseline $274.43 $41.64 $13.63 $48.83 $378.52 1.20 $454.23

EL 1 $275.02 $41.73 $13.66 $48.94 $379.34 1.20 $455.21

EL 2 $279.73 $42.44 $13.89 $49.77 $385.83 1.20 $462.99

EL 3 $294.70 $44.71 $14.63 $52.44 $406.49 1.20 $487.78

EL 4 $298.46 $45.28 $14.82 $53.11 $411.67 1.20 $494.01

12.4.8 Capital and Product Conversion Costs

DOE expects new and amended energy conservation standards for residential 

conventional ovens to cause manufacturers to incur conversion costs to bring their production 

facilities and product designs into compliance with the new and amended standards. For the 

MIA, DOE classified these conversion costs into two major groups: (1) capital conversion costs,

and (2) product conversion costs. Capital conversion costs are investments in property, plant, and 

equipment necessary to adapt or change existing production facilities such that new product 

designs can be fabricated and assembled. Product conversion costs are investments in research, 

development, testing, marketing, certification, and other non-capitalized costs necessary to make 

product designs comply with new and amended standards.

Using feedback from manufacturer interviews, DOE conducted a top-down analysis to 

calculate the capital and product conversion costs for residential conventional oven

manufacturers. DOE asked manufacturers during interviews to estimate the total capital and 

12-15
product conversion costs they would need to incur to be able to produce each residential 

conventional oven at specific ELs. DOE then summed these values provided by manufacturers to 

arrive at total top-down industry conversion cost for residential conventional ovens.

DOE’s estimates of the capital and product conversion costs for all residential 

conventional ovens can be found in Table 12.4.14 and Table 12.4.15.

Table 12.4.14 Capital Conversion Costs for all Residential Conventional Ovens by TSL

Product 

Class Product Class Description TSL 1(2014$ millions)

TSL 2

(2014$ millions)

TSL 3

(2014$ millions)

1 & 2 Electric Standard Ovens $3.0 $15.0 $168.0

3 & 4 Electric Self-Clean Ovens $3.0 $3.0 $153.0

5 & 6 Gas Standard Ovens - $18.0 $111.0

7 & 8 Gas Self-Clean Ovens $3.0 $6.0 $96.0

Total $9.0 $42.0 $528.0

Table 12.4.15 Product Conversion Costs for all Residential Conventional Ovens by TSL

Product 

Class Product Class Description TSL 1(2014$ millions)

TSL 2

(2014$ millions)

TSL 3

(2014$ millions)

1 Electric Standard Ovens, Free-Standing $1.4 $12.2 $112.2

2 Electric Standard Ovens, Built-in/Slide-in $0.1 $0.6 $5.1

3 Electric Self-Clean Ovens, Free-Standing $1.5 $1.5 $83.2

4 Electric Self-Clean Ovens, Built-in/Slide-in $0.5 $0.5 $27.9

5 Gas Standard Ovens, Free-Standing - $34.9 $106.2

6 Gas Standard Ovens, Built-in/Slide-in - $6.3 $19.2

7 Gas Self-Clean Ovens, Free-Standing $0.8 $11.3 $45.4

8 Gas Self-Clean Ovens, Built-in/Slide-in $0.0 $0.6 $2.4

Total $4.3 $67.9 $401.5

12.4.9 Markup Scenarios

In the base case, DOE used the same baseline markup of 1.20 for all residential 

conventional ovens. In the standards case, DOE used two markup scenarios to represent the 

uncertainty about the impacts of new and amended energy conservation standards on prices and 

profitability following the implementation of new and amended energy conservation standards: 

(1) a preservation of gross margin markup scenario, and (2) a preservation of operating profit 

markup scenario. These scenarios lead to different markup values, which when applied to the 

inputted MPCs, result in varying revenue and cash-flow impacts.

12.4.9.1 Preservation of Gross Margin Markup Scenario

Under the preservation of gross margin markup scenario DOE applied a single uniform 

markup across all product classes and efficiency levels. As production costs increase with 

efficiency, this scenario implies that the absolute dollar markup will increase as well. Based on 

publicly available financial information for manufacturers of residential conventional ovens and 

comments from manufacturer interviews, DOE assumed the non-production cost markup—

which includes SG&A expenses; R&D expenses; interest; and profit—to be 1.20 for all 

residential conventional ovens. Because this markup scenario assumes that manufacturers would 

be able to maintain their gross margin percentage as production costs increase in response to new

12-16
and amended standards, it represents the upper bound to industry profitability under new and 

amended standards.

12.4.9.2 Preservation of Operating Profit Markup Scenario

DOE implemented the preservation of operating profit markup scenario because 

manufacturers stated that in the standards case, they do not expect to be able to mark up the full 

cost of production given the highly competitive market. The preservation of operating profit 

markup scenario assumes that manufacturers are able to maintain only the base case total 

operating profit in absolute dollars in the standards case, despite higher production costs and 

investment. The base case total operating profit is derived from marking up the cost of goods 

sold for each product by a flat percentage (the preservation of operating profit markup discussed 

in the previous section) to cover standard SG&A expenses, R&D expenses, interest, and profit. 

DOE adjusted the manufacturer markups in the GRIM at each TSL to yield approximately the 

same earnings before interest and taxes in the standards cases in the year after the compliance 

date of the new and amended standards as in the base case. DOE altered the markups only for the 

minimally compliant products in this scenario, with margin impacts not occurring for products

that already exceed the new and amended standards. The preservation of operating profit markup 

scenario represents the lower bound of industry profitability following new and amended 

standards. Under this scenario, manufacturers are not able to earn additional operating profit on 

higher production costs and the investments required to comply with new and amended 

standards, like they are in the preservation of gross margin markup scenario. However, 

manufacturers are able to maintain the same operating profit in absolute dollars in the standards 

cases as they would have earned in the base case.

For residential conventional ovens, Table 12.4.16 through Table 12.4.23 lists the product 

classes DOE analyzed with the corresponding preservation of operating profit markups at each 

analyzed EL.

Table 12.4.16 Preservation of Operating Profit Markups for Electric Standard Ovens, 

Free-Standing

EL Markups by Selected EL

Baseline EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7

Baseline 1.200

EL 1 1.200 1.200

EL 2 1.200 1.200 1.200

EL 3 1.200 1.200 1.200 1.199

EL 4 1.200 1.200 1.200 1.200 1.200

EL 5 1.200 1.200 1.200 1.200 1.200 1.200

EL 6 1.200 1.200 1.200 1.200 1.200 1.200 1.200

EL 7 1.200 1.200 1.200 1.200 1.200 1.200 1.200 1.193

12-17
Table 12.4.17 Preservation of Operating Profit Markups for Electric Standard Ovens, 

Built-in/Slide-in

EL Markups by Selected EL

Baseline EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7

Baseline 1.200

EL 1 1.200 1.200

EL 2 1.200 1.200 1.200

EL 3 1.200 1.200 1.200 1.199

EL 4 1.200 1.200 1.200 1.200 1.200

EL 5 1.200 1.200 1.200 1.200 1.200 1.200

EL 6 1.200 1.200 1.200 1.200 1.200 1.200 1.200

EL 7 1.200 1.200 1.200 1.200 1.200 1.200 1.200 1.193

Table 12.4.18 Preservation of Operating Profit Markups for Electric Self-Clean Ovens, 

Free-Standing

EL Markups by Selected EL

Baseline EL 1 EL 2 EL 3 EL 4

Baseline 1.200

EL 1 1.200 1.200

EL 2 1.200 1.200 1.200

EL 3 1.200 1.200 1.200 1.200

EL 4 1.200 1.200 1.200 1.200 1.195

Table 12.4.19 Preservation of Operating Profit Markups for Electric Self-Clean Ovens, 

Built-in/Slide-in

EL Markups by Selected EL

Baseline EL 1 EL 2 EL 3 EL 4

Baseline 1.200

EL 1 1.200 1.200

EL 2 1.200 1.200 1.200

EL 3 1.200 1.200 1.200 1.200

EL 4 1.200 1.200 1.200 1.200 1.195

12-18
Table 12.4.20 Preservation of Operating Profit Markups for Gas Standard Ovens, FreeStanding

EL Markups by Selected EL

Baseline EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7

Baseline 1.200

EL 1 1.200 1.200

EL 2 1.200 1.200 1.200

EL 3 1.200 1.200 1.200 1.200

EL 4 1.200 1.200 1.200 1.200 1.199

EL 5 1.200 1.200 1.200 1.200 1.200 1.200

EL 6 1.200 1.200 1.200 1.200 1.200 1.200 1.200

EL 7 1.200 1.200 1.200 1.200 1.200 1.200 1.200 1.196

Table 12.4.21 Preservation of Operating Profit Markups for Gas Standard Ovens, Builtin/Slide-in

EL Markups by Selected EL

Baseline EL 1 EL 2 EL 3 EL 4 EL 5 EL 6 EL 7

Baseline 1.200

EL 1 1.200 1.200

EL 2 1.200 1.200 1.200

EL 3 1.200 1.200 1.200 1.200

EL 4 1.200 1.200 1.200 1.200 1.199

EL 5 1.200 1.200 1.200 1.200 1.200 1.200

EL 6 1.200 1.200 1.200 1.200 1.200 1.200 1.200

EL 7 1.200 1.200 1.200 1.200 1.200 1.200 1.200 1.196

Table 12.4.22 Preservation of Operating Profit Markups for Gas Self-Clean Ovens, FreeStanding

EL Markups by Selected EL

Baseline EL 1 EL 2 EL 3 EL 4

Baseline 1.200

EL 1 1.200 1.200

EL 2 1.200 1.200 1.199

EL 3 1.200 1.200 1.200 1.200

EL 4 1.200 1.200 1.200 1.200 1.197

12-19
Table 12.4.23 Preservation of Operating Profit Markups for Gas Self-Clean Ovens, Builtin/Slide-in

EL Markups by Selected EL

Baseline EL 1 EL 2 EL 3 EL 4

Baseline 1.200

EL 1 1.200 1.200

EL 2 1.200 1.200 1.199

EL 3 1.200 1.200 1.200 1.200

EL 4 1.200 1.200 1.200 1.200 1.197

12.5 INDUSTRY FINANCIAL IMPACTS

Using the inputs and scenarios described in the previous sections, the GRIM estimates the 

financial impact on the residential conventional oven industry. The following sections detail 

additional inputs and assumptions for residential conventional ovens. 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.

12.5.1 Impacts on Industry Net Present Value

The INPV measures the residential conventional oven industry value and is used in the 

MIA to compare the economic impacts of different TSLs in the standards cases. The INPV is 

different from DOE’s net present value, which is applied to the U.S. economy. The INPV is the 

sum of all net cash flows discounted at the industry’s cost of capital, or discount rate. The 

residential conventional ovens GRIM estimates cash flows from 2016 to 2048. This timeframe 

models both the short-term impacts on the industry from the announcement of the standard until 

the compliance date (2016 until an estimated compliance date of 2019) and a long-term 

assessment over the 30-year analysis period used in the NIA (2019 – 2048).

In the MIA, DOE compares the INPV of the base case (no new or amended energy 

conservation standards) to that of each TSL in the standards cases. The difference between the 

base case and a standards case INPV is an estimate of the economic impacts that implementing 

that particular TSL would have on the industry. For the residential conventional oven industry, 

DOE examined the two markup scenarios previously described, the preservation of gross margin

markup scenario and the preservation of operating profit markup scenario.

Table 12.5.1 and Table 12.5.2 provide the INPV estimates for the two markup scenarios

for the residential conventional oven industry.

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Table 12.5.1 Changes in Industry Net Present Value for Residential Conventional Ovens –

Preservation of Gross Margin Markup Scenario

Units Base Case

Trial Standard Level

1 2 3

INPV (2014$ millions) 783.5 762.8 702.6 140.6 

Change in INPV (2014$ millions) - (20.7) (80.9) (642.9)

(%) - (2.6) (10.3) (82.0)

Table 12.5.2 Changes in Industry Net Present Value for Residential Conventional Ovens –

Preservation of Operating Profit Markup Scenario

Units Base Case

Trial Standard Level

1 2 3

INPV (2014$ millions) 783.5 762.1 697.1 56.0 

Change in INPV (2014$ millions) - (21.4) (86.4) (727.5)

(%) - (2.7) (11.0) (92.9)

12.5.2 Impacts on Annual Cash Flow

While INPV is useful for evaluating the long-term effects of new and amended energy 

conservation standards, short-term changes in cash flow are also important indicators of the 

industry’s financial situation. For example, a large investment over one or two years could strain 

the industry’s access to capital. Consequently, the sharp drop in financial performance could 

cause investors to flee, even though recovery may be possible. Thus, a short-term disturbance 

can have long-term effects that the INPV cannot capture. To get an idea of the behavior of 

annual free cash flows, Figure 12.5.1 and Figure 12.5.2 present the annual free cash flows from 

2015 through 2028 for the base case and different TSLs in the standards case.

Annual cash flows are discounted to the base year, 2015. Between 2016 and the 2019

compliance date of the new and amended energy conservation standards, cash flows are driven 

by the level of conversion costs and the proportion of these investments spent every year. After 

the standard’s announcement date (i.e., the publication date of the final rule), industry cash flows 

begin to decline as companies use their financial resources to prepare for the new and amended 

energy conservation standards. The more stringent the new and amended energy conservation 

standards, the greater the impact on industry cash flows in the years leading up to the compliance 

date, as product conversion costs lower cash inflows from operations and capital conversion 

costs increase cash outflows for capital expenditures.

Free cash flow in the year the new and amended energy conservation standards take 

effect is driven by two competing factors. In addition to capital and product conversion costs, 

new and amended energy conservation standards could create stranded assets (i.e., tooling and 

equipment that would have enjoyed longer use if the energy conservation standards 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 new and amended energy conservation 

standards. This one-time write-down acts as a tax shield that alleviates decreases in cash flow 

from operations in the year of the write-down. In this year, there is also an increase in working 

capital that reduces cash flow from operations. A large increase in working capital is needed due 

12-21
to more costly production components and materials, higher inventories of 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 

standards takes effect.

In the years following the compliance date of the standards, the impact on cash flow 

depends on the operating revenue. In the preservation of gross margin markup scenario, the 

manufacture markup is held constant to yield the same gross margin percentage in the standards 

case at each TSL as in the base case in the year after the standards take effect. The implicit 

assumption is that manufacturers can freely pass on and mark up higher cost units. The result

under this scenario is that operating cash flow increases (in absolute terms) as revenue increases.

At the highest TSL where MPCs dramatically increase, this scenario drives large increases in

operating cash flow relative to the base case. The larger the production cost increase, the more 

likely it is that the increase in operating cash flow after the standards take effect will outweigh 

the initial conversion costs.

Under the preservation of operating profit scenario, cash flow decreases at each TSL in 

the standards case compared to the base case because the absolute dollar amount of the gross 

margin does not change despite an increase in sales and cost of goods sold. Therefore, the gross 

margin percentage is reduced.

Figure 12.5.1 and Figure 12.5.2 present the annual free cash flows for the residential 

conventional oven industry.

Figure 12.5.1 Annual Industry Free Cash Flows for Residential Conventional Ovens –

Preservation of Gross Margin Markup Scenario

($350.0)

($300.0)

($250.0)

($200.0)

($150.0)

($100.0)

($50.0)

$0.0

$50.0

$100.0

$150.0

Millions $

Net Free Cash Flow Totals

Base Case TSL 1 TSL 2 TSL 3

12-22
Figure 12.5.2 Annual Industry Free Cash Flows for Residential Conventional Ovens –

Preservation of Operating Profit Markup Scenario

12.6 IMPACTS ON MANUFACTURER SUBGROUPS

As described in Section 12.2.3, DOE identified one subgroup of residential conventional 

oven manufacturers: small businesses. The results of this subgroup analysis are described in the 

following section.

12.6.1 Impacts on Small Business Manufacturers

12.6.1.1 Description and Estimated Number of Small Entities Regulated

DOE conducted a more focused inquiry on the companies that could be small businesses

of residential conventional ovens covered by this rulemaking. To estimate the number of 

companies that could be small business manufacturers of residential conventional ovens covered 

by this rulemaking, DOE conducted a market survey using publicly available information. 

DOE’s research involved industry trade association membership directories (including AHAM), 

information from previous rulemakings, individual company websites, SBA’s database, and 

market research tools (e.g., Hoover’s reports). DOE also asked stakeholders and industry 

representatives if they were aware of any small manufacturers during manufacturer interviews 

and DOE public meetings. DOE used information from these sources to create a list of 

companies that potentially manufacture or sell residential conventional ovens and would be 

impacted by this rulemaking. As necessary, DOE contacted companies to determine whether 

they met the SBA’s definition of a small business manufacturer of residential conventional 

ovens. DOE screened out companies that do not offer products covered by this rulemaking, do 

not meet the definition of a “small business,” or are completely foreign owned and operated.

($350.0)

($300.0)

($250.0)

($200.0)

($150.0)

($100.0)

($50.0)

$0.0

$50.0

$100.0

$150.0

Millions $

Net Free Cash Flow Totals

Base Case TSL 1 TSL 2 TSL 3

12-23
DOE identified 19 companies that sell residential conventional ovens that would be 

affected by this proposal. Of these 19 companies, DOE identified seven as small businesses.

DOE contacted identified businesses to invite them to take part in a manufacturer impact analysis 

interview. Of the businesses contacted, DOE was able to reach and discuss potential standards 

with one small business. DOE also obtained information about small businesses and potential 

impacts on small businesses while interviewing large manufacturers.

Three major manufacturers supply approximately 85 percent of the market for residential 

conventional ovens. DOE estimates that the remaining 15 percent of the market is served by a 

combination of small businesses and large businesses. None of the three major manufacturers of 

residential conventional ovens affected by this rulemaking is a small business.

12.6.1.2 Comparison Between Large and Small Entities

In general, small manufacturers differ from large manufacturers in several ways that 

affect the extent to which a manufacturer may be impacted by proposed standards. 

Characteristics of small manufacturers typically include: lower production volumes, fewer 

engineering resources, and less access to capital. Lower production volumes in particular may 

place small manufacturers at a competitive disadvantage relative to large manufacturers as they 

convert products and facilities to comply with new and amended standards. When producing at 

lower volumes, a small manufacturer’s conversion costs must be spread over fewer units than a 

larger competitor’s. Therefore, unless a small manufacturer can differentiate its products in order 

to earn a price premium, the small manufacturer may experience a disproportionate cost penalty 

as it spreads one-time conversion costs over fewer unit sales. Additionally, when producing at 

lower volumes, small manufacturers may lack the purchasing power of their larger competitors 

and may therefore face higher costs when sourcing components for more efficient products. 

Disadvantages tied to lower production volumes may be further exacerbated by the fact that 

small manufacturers often have more limited engineering resources than their larger competitors, 

thereby complicating the redesign effort required to comply with new and amended standards. 

Finally, small manufacturers often have less access to capital, which may be needed to cover the 

conversion costs associated with new and amended standards. Combined, these factors may 

entail a disproportionate burden on small manufacturers.

At TSL 1 DOE estimates capital conversion costs of $0.3 million and product conversion 

costs of $0.6 million for an average small manufacturer. For an average large manufacturer, 

DOE estimates capital conversion costs of $1.1 million and product conversion costs of $0.5 

million.

At TSL 2, the level proposed here, DOE estimates capital conversion costs of $1.3 

million and product conversion costs of $3.1 million for an average small manufacturer. For an 

average large manufacturer, DOE estimates capital conversion costs of $2.7 million and product 

conversion costs of $3.3 million. Table 12.6.1 presents the estimated conversion costs as a 

percentage of annual revenue for an average small manufacturer relative to an average large 

manufacturer.

12-24
Table 12.6.1 Conversion Costs Facing an Average Small Manufacturer versus an Average 

Large Manufacturer of Residential Conventional Ovens

Capital Conversion 

Costs as a Percentage 

of Annual Revenue

Product Conversion 

Costs as a Percentage 

of Annual Revenue

Total Conversion 

Costs as a Percentage 

of Annual Revenue

Average Small 

Manufacturer 2% 5% 7%

Average Large 

Manufacturer 1% 1% 1%

As the results indicate, new and amended energy conservation standards could potentially 

impact small businesses disproportionately. Although estimated conversion costs at TSL 2 are 

higher for an average large manufacturer than an average small manufacturer, the relative 

impacts of conversion costs on large manufacturers will likely be offset by higher annual 

revenues. This is consistent with the dynamic previously described, whereby large manufacturers 

tend to have larger production and sales volumes over which to spread costs and may also enjoy 

a competitive advantage due to their size and ability to access capital that may not be available to 

small manufacturers. Since the proposed standards could cause competitive concerns for small 

manufacturers, DOE cannot certify that the proposed standards would not have a significant 

impact on a substantial number of small businesses.

12.7 OTHER IMPACTS

12.7.1 Employment

DOE quantitatively assessed the impacts of new and amended energy conservation 

standards on direct employment. DOE used the GRIM to estimate the domestic labor 

expenditures and number of domestic production workers in the base case and at each TSL from 

2019 to 2048. 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 involved with the manufacture of the residential 

conventional ovens are a function of the labor intensity of the products, the sales volume, and an 

assumption that wages remain fixed in real terms over time.

In the GRIM, DOE used the labor content of each product and the manufacturing 

production costs to estimate the annual labor expenditures in the industry. DOE used Census data 

and interviews with manufacturers to estimate the portion of the total labor expenditures that is 

attributable to domestic labor.

The production worker estimates in this section cover only workers up to the linesupervisor level directly involved in fabricating and assembling a product within a 

manufacturing facility. Workers performing services that are closely associated with production 

operations, such as material handing with a forklift, are also included as production labor. DOE’s 

estimates account for production workers who manufacture only the specific products covered in 

this rulemaking. For example, a worker on a microwave oven production line would not be 

included with the estimate of the number of residential conventional oven workers.

12-25
The employment impacts shown in Table 12.7.1 represent the potential production 

employment that could result following new and amended energy conservation standards. The 

upper bound of the results estimates the maximum change in the number of production workers 

that could occur after compliance with new and amended energy conservation standards, when 

assuming that manufacturers continue to produce the same scope of covered products in the same 

production facilities. It also assumes that domestic production does not shift to lower labor-cost 

countries. Because there is a real risk of manufacturers evaluating sourcing decisions in response 

to new and amended energy conservation standards, the lower bound of the employment results 

includes the estimated total number of U.S. production workers in the industry who could lose 

their jobs if some or all existing production were moved outside of the United States. While the 

results present a range of employment impacts following 2019, the following sections also 

include qualitative discussions of the likelihood of negative employment impacts at the various 

TSLs. Finally, the employment impacts shown are independent of the employment impacts from 

the broader U.S. economy, documented in chapter 16 of this NOPR TSD.

Using 2011 ASM data and interviews with manufacturers, DOE estimates that 

approximately 60 percent of the residential conventional ovens sold in the United States are 

manufactured domestically. With this assumption, DOE estimates that in the absence of new and 

amended energy conservation standards, there would be approximately 6,564 domestic 

production workers involved in manufacturing residential conventional ovens in 2019. Table 

12.7.1 shows the range of impacts of the analyzed new and amended energy conservation 

standards on U.S. production workers in the residential conventional oven industry.

Table 12.7.1 Potential Changes in the Total Number of All Domestic Residential 

Conventional Ovens Production Workers in 2019

Base Case Trial Standard Level

1 2 3

Total Number of Domestic Production 

Workers in 2019

(without changes in production locations)

6,564 6,571 6,622 7,397

Potential Changes in Domestic 

Production Workers in 2019* - 0 - 7 (1,641) - 58 (3,282) – 833

* DOE presents a range of potential employment impacts. Numbers in parentheses indicate negative numbers.

At the upper end of the range, all examined TSLs show a slight increase in the number of 

domestic employment for residential conventional ovens. DOE believes that manufacturers 

would increase production hiring due to the increase in the labor associated with adding the 

required components to make residential conventional ovens more efficient. However, as 

previously stated, this assumes that in addition to hiring more production employees, all existing 

domestic production would remain in the United States and not shift to lower labor-cost 

countries.

DOE does not expect any significant changes in domestic employment at TSL 1 because 

standards would only affect standby mode power consumption at this TSL. Most manufacturers 

stated that this TSL would not require significant design changes and therefore would not have a 

significant impact on domestic employment decisions.

12-26
At TSLs 2 and 3, all product classes would require higher efficiency standards and 

therefore most manufacturers would be required to make modifications to their existing 

production lines. However, manufacturers stated that due to the larger size of most residential 

conventional ovens, very few units are shipped from far distances such as Asia or Europe. The 

vast majority of residential conventional ovens are currently made in North America. Some 

manufacturers stated that even significant changes to production line would not cause them to 

shift their production to lower labor-cost countries, as several manufacturers either only produce 

residential conventional ovens domestically or have recently made significant investments to 

continue to produce residential conventional ovens domestically. DOE estimates that at most 25 

percent of the domestic labor for residential conventional ovens could move to other countries in 

response to the standards proposed at TSL 2. However, DOE believes this to be a high upper 

bound estimate as most manufacturers would not significantly alter their production locations at 

the efficiency levels prescribed at TSL 2.

At TSL 3, manufacturers could alter production locations in response to standards since 

all product classes would be required to meet max tech. DOE estimated that at most 50 percent 

of the domestic labor for residential conventional ovens could move to other countries in 

response to the standards prescribed at TSL 3.

12.7.2 Production Capacity

Residential conventional oven manufacturers stated that they did not anticipate any 

capacity constraints for the efficiency levels analyzed for either electric or gas residential 

conventional ovens.

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 industry as a whole. Assessing the impact of a 

single regulation may overlook the cumulative regulatory burden faced by manufacturers. For 

this cumulative regulatory burden analysis, DOE examines other significant product-specific 

regulations that could affect residential conventional oven manufacturers that will take effect 

three years before or three years after the effective date of the new and amended energy 

conservation standards for residential conventional ovens. DOE also describes additional state, 

Federal, and international regulations with which residential conventional oven manufacturers

must comply.

Companies that produce a wide range of regulated products may be faced with more 

capital and product conversion costs than their competitors. Additional regulations can prompt 

those companies to exit the market or reduce their product offerings, potentially reducing overall 

competition. Companies that could be affected by this rulemaking also typically manufacture 

microwave ovens, residential clothes washers, residential clothes dryers, residential refrigerators 

and freezers, miscellaneous residential refrigeration equipment, and dishwashers, which are also 

either subject or potentially subject to other DOE energy conservation standards. Small 

businesses in particular may experience greater regulatory impacts due to lower sales volumes 

over which they must amortize the costs of meeting new and amended standards. DOE considers 

12-27
a proposed standard not to be economically justified if it contributes to an unacceptable level of 

cumulative regulatory burden.

12.7.3.1 DOE Regulations for Other Products Produced by Residential Conventional 

Oven Manufacturers

In addition to the proposed new and amended energy conservation standards on 

residential conventional ovens, several other existing and pending DOE energy conservation 

standards may apply to other products produced by residential conventional oven manufacturers. 

DOE acknowledges that each regulation can impact a manufacturer’s financial operations. 

Multiple regulations affecting the same manufacturer can quickly strain manufacturers’ profit 

and possibly cause them to exit particular markets. Table 12.7.2 lists the other DOE energy 

conservation standards that could also affect residential conventional oven manufacturers in the 

three years leading up to and after the estimated compliance date of new and amended energy 

conservation standards for residential conventional ovens.

Table 12.7.2 Other DOE Regulations Potentially Affecting Residential Conventional Oven

Manufacturers

Regulation Approximate 

Compliance Date

Number of Impacted 

Companies from the 

Market and Technology 

Assessment (MTA) (See 

Chapter 3)

Estimated Industry 

Total Conversion 

Expenses

Residential Clothes Washers 2015 & 2018 9 $418.5 million (2010$)b

Commercial Distribution 

Transformers 2016 1 $61.0 million (2011$)c

Microwave Ovens 2016 5 $43.1 million (2011$)d

Electric Motors 2016 1 $84.6 million (2013$)e

Metal Halide Lamp Fixtures 2017 1 $3.0 million (2012$)f

b Estimated industry conversion expenses were published in the TSD for the May 2012 residential clothes washers 

direct final rule. 77 FR 32308 The TSD for the 2012 residential clothes washers direct final rule can be found at 

http://www.regulations.gov/#!documentDetail;D=EERE-2008-BT-STD-0019-0047 c Estimated industry conversion expenses were published in the TSD for the April 2013 distribution transformers 

final rule. 78 FR 23336 The TSD for the 2013 distribution transformers final rule can be found at 

http://www.regulations.gov/#!documentDetail;D=EERE-2010-BT-STD-0048-0760 d Estimated industry conversion expenses were published in the TSD for the June 2013 microwave ovens final rule. 

78 FR 36316 The TSD for the 2013 microwave ovens final rule can be found at 

http://www.regulations.gov/#!documentDetail;D=EERE-2011-BT-STD-0048-0021 e Estimated industry conversion expenses were published in the TSD for the May 2014 electric motors final rule. 79

FR 30933 The TSD for 2014 electric motors final rule can be found at 

http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/42

f Estimated industry conversion expenses were published in the TSD for the February 2014 metal halide lamp 

fixtures final rule. 79 FR 7745 The TSD for the 2014 metal halide lamp fixtures final rule can be found at 

http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/16

12-28

 
Regulation Approximate 

Compliance Date

Number of Impacted 

Companies from the 

Market and Technology 

Assessment (MTA) (See 

Chapter 3)

Estimated Industry 

Total Conversion 

Expenses

General Service Fluorescent 

Lamps and Incandescent 

Reflector Lamps

2018 1 $26.6 million (2012$)g

HID Lamps 2018* 1 N/A††

Commercial Clothes 

Washers Update 2018* 3 N/A††

Packaged Terminal Air 

Conditioners and Heat 

Pumps (ASHRAE)

2019* 1 N/A††

Commercial Compressors 2019* 1 N/A††

Miscellaneous Residential 

Refrigeration 2019* 1 N/A††

Single Packaged Vertical 

Units 2020* 1 N/A††

Candelabra Base 

Incandescent Lamps and 

Intermediate Base 

Incandescent Lamps

N/Aβ 1 N/A††

Other Incandescent Reflector 

Lamps N/Aβ 1 N/A††

*The dates listed are an approximation. The exact dates are pending final DOE action.

† For minimum performance requirements prescribed by the Energy Independence and Security Act of 2007 (EISA 

2007), DOE did not estimate total industry conversion costs because an MIA was not completed as part of a 

rulemaking. Pub. L. 110-140. EISA 2007 made numerous amendments to the Energy Policy and Conservation Act 

(EPCA) of 1975, Pub. L. 94-163, (42 U.S.C. 6291–6309), which established an energy conservation program for 

major household appliances and industrial and commercial equipment.

†† For energy conservation standards for rulemakings awaiting DOE final action, DOE does not have a finalized 

estimated total industry conversion cost.

β These rulemakings are placed on hold due to the Consolidated Appropriations Act, 2012 (Public Law 112-74).

12.8 CONCLUSION

The following section summarizes the impacts for the scenarios DOE believes are most 

likely to capture the range of impacts on residential conventional oven manufacturers as a result 

of new and amended energy conservation standards. DOE also notes that while these scenarios 

bound the range of most plausible impacts on manufacturers, there potentially could be 

circumstances that cause manufacturers to experience impacts outside of this range.

TSL 1 sets the efficiency level at baseline for two product classes (gas standard ovens, 

free-standing; and gas standard ovens, built-in/slide-in), and EL 1 for six product classes (electric 

standard ovens, free-standing; electric standard ovens, built-in/slide-in; electric self-clean ovens, 

g Estimated industry conversion expenses were published in the TSD for the January 2015 general service 

fluorescent lamp and incandescent reflector lamp final rule. XX FR XXXX the TSD for the 2015 general service 

fluorescent lamp and incandescent reflector lamp can be found at XX

12-29

 
free-standing; electric self-clean ovens, built-in/slide-in; gas self-clean ovens, free-standing; and 

gas self-clean ovens, built-in/slide-in). At TSL 1, DOE estimates impacts on INPV range from -

$21.4 million to -$20.7 million, or a change in INPV of -2.7 percent to -2.6 percent. At TSL 1, 

industry free cash flow (operating cash flow minus capital expenditures) is estimated to decrease 

to $52.1 million, or a drop of 14.3 percent, compared to the base-case value of $60.8 million in 

2018, the year leading up to new and amended energy conservation standards.

Percentage impacts on INPV are slightly negative at TSL 1. DOE does not anticipate that 

manufacturers would lose a significant portion of their INPV at this TSL. DOE projects that in 

the expected year of compliance (2019), 100 percent of gas standard oven, free-standing 

shipments; and gas standard oven, built-in/slide-in shipments would meet or exceed the 

efficiency levels required at TSL 1. Meanwhile in 2019, 60 percent of electric standard oven, 

free-standing shipments; 60 percent of electric standard oven, built-in/slide-in shipments; 53

percent of electric self-clean oven, free-standing shipments; 53 electric self-clean oven, builtin/slide-in shipments; 52 percent of gas self-clean oven, free-standing shipments; and 52 percent 

of gas self-clean oven, built-in/slide-in shipments would meet the efficiency levels at TSL 1.

DOE expects conversion costs to be small at TSL 1 because the design changes 

prescribed at this TSL only affect standby mode power consumption and do not apply to active 

mode power consumption. DOE expects residential conventional oven manufacturers to incur 

$4.3 million in product conversion costs for product redesigns that will convert residential 

conventional ovens from using linear power supply to switch mode power supply to reduce 

standby power consumption. DOE expects $9.0 million in capital conversion costs for 

manufacturers to upgrade production lines and retool equipment associated with achieving this 

reduction in standby power.

At TSL 1, under the preservation of gross margin markup scenario, the shipmentweighted average MPC increases very slightly by approximately 0.1 percent relative to the basecase MPC. This extremely slight price increase is outweighed by the $13.3 million in conversion 

costs estimated at TSL 1, resulting in slightly negative INPV impacts at TSL 1 under the 

preservation of gross margin markup scenario.

Under the preservation of operating profit markup scenario, manufacturers earn the same 

nominal operating profit as would be earned in the base case, but manufacturers do not earn 

additional profit from their investments. The very slight increase in the shipment weightedaverage MPC is again outweighed by a slightly lower average manufacturer markup (slightly 

smaller than the 1.20 manufacturer markup used in the base case) and $13.3 million in 

conversion costs, resulting in slightly negative impacts at TSL 1.

TSL 2 sets the efficiency level at EL 1 for two product classes (electric self-clean ovens, 

free-standing; and electric self-clean ovens, built-in/slide-in), EL 2 for two product classes (gas 

self-clean ovens, free-standing; and gas self-clean ovens, built-in/slide-in), EL 3 for two product 

classes (electric standard ovens, free-standing and electric standard ovens, built-in/slide-in); and 

EL 4 for two product classes (gas standard ovens, free-standing and gas standard ovens, builtin/slide-in). At TSL 2, DOE estimates impacts on INPV to range from -$86.4 million to -$80.9 

million, or a change in INPV of -11.0 percent to -10.3 percent. At this standard level, industry 

12-30
free cash flow is estimated to decrease to $17.6, or a drop of 71.0 percent, compared to the basecase value of $60.8 million in 2018.

Percentage impacts on INPV are moderately negative at TSL 2. While the $109.9 million 

in industry conversion costs represent a significant investment for manufacturers, DOE does not 

anticipate that manufacturers would lose a significant portion of their INPV at this TSL since the 

base case INPV for manufacturers is slightly less than $800 million. DOE projects that in 2019, 

40 percent of electric standard oven, free-standing shipments; 40 percent of electric standard 

oven, built-in/slide-in shipments; 53 percent of electric self-clean oven, free-standing shipments; 

53 percent of electric self-clean oven, built-in/slide-in shipments; 32 percent of gas standard 

oven, free-standing shipments; 32 percent of gas standard oven, built-in/slide-in shipments; 39 

percent of gas self-clean oven, free-standing shipments; and 39 percent of gas self-clean oven, 

built-in/slide-in shipments would meet or exceed the efficiency levels at TSL 2.

While DOE expects conversion costs to be a large investment at TSL 2, the much larger 

base case INPV reduces the overall INPV impact on a percentage basis at TSL 2. DOE expects 

that product conversion costs will significantly rise from $4.3 million at TSL 1 to $67.9 million 

at TSL 2 for extensive product redesigns and testing. Capital conversion costs will also 

significantly increase from $9.0 million at TSL 1 to $42.0 million at TSL 2 to upgrade 

production equipment to accommodate for added or redesigned features in each product class. 

The large conversion costs at TSL 2 are driven by reduce vent rate and improve insulation in the 

electric oven product classes, and conversion from glo-bar to electronic spark ignition systems in 

the gas oven product classes.

At TSL 2, under the preservation of gross margin markup scenario, the shipment 

weighted-average MPC only slightly increases by 0.9 percent, relative to the base-case MPC. In 

this scenario, INPV impacts are moderately negative because manufacturers incur sizable 

conversion costs ($109.9 million) and are not able to recover much of those conversion costs 

through the slight increase in the shipment weighted-average MPC at TSL 2.

Under the preservation of operating profit markup scenario, the 0.9 percent shipment 

weighted-average MPC increase is outweighed by a slightly lower average manufacturer markup 

(slightly smaller than the 1.20 manufacturer markup used in the base case) and $109.9 million in 

conversion costs, resulting in moderately negative INPV impacts at TSL 2.

TSL 3 sets the efficiency level at max tech for all product classes. At TSL 3, DOE 

estimates impacts on INPV to range from -$727.5 million to -$642.9 million, or a change in 

INPV of -92.9 percent to -82.0 percent. At this standard level, industry free cash flow is 

estimated to decrease by approximately 635.3 percent to -$325.5 million, compared to the basecase value of $60.8 million in 2018.

At TSL 3 conversion costs significantly increase causing free cash flow to become 

significantly negative in the year leading up to energy conservation standards and cause 

manufacturers to loss a substantial amount of INPV. Also, the percent change in INPV at TSL 3 

is significantly negative due to the extremely large conversion costs. Manufacturers at this TSL 

would have a very difficult time in the short term to make the necessary investments to comply 

12-31
with new and amended energy conservation standards prior to when standards went into effect. 

Also, the long-term profitability of residential conventional oven manufacturers could be 

seriously jeopardized as some manufacturers would struggle to comply with standards at this 

TSL.

A high percentage of total shipments will need to be redesigned to meet efficiency levels 

prescribed at TSL 3. DOE projects that in 2019, only 7 percent of electric standard oven, freestanding shipments; 7 percent of electric standard oven, built-in/slide-in shipments; 12 percent of 

electric self-clean oven, free-standing shipments; 12 percent of electric self-clean oven, builtin/slide-in shipments; 8 percent of gas standard oven, free-standing shipments; 8 percent of gas 

standard oven, built-in/slide-in shipments; 13 percent of gas self-clean oven, free-standing 

shipments; and 13 percent of gas self-clean oven, built-in/slide-in shipments would meet the 

efficiency levels prescribed at TSL 3.

DOE expects significant conversion costs at TSL 3, which represents max tech. DOE

expects product conversion costs to significantly increase from $67.9 million at TSL 2 to $401.5 

million at TSL 3. Large increases in product conversion are due to the vast majority of shipments 

needing extensive redesign as well as a significant increase in testing and recertification for 

redesigned products. DOE estimates that capital conversion costs will also significantly increase 

from $42.0 million at TSL 2 to $528.0 million at TSL 3. Capital conversion costs are driven by 

investments in production equipment to accommodate for forced convection and reduced 

conduction losses in the electric and gas oven product classes.

At TSL 3, under the preservation of gross margin markup scenario, the shipment 

weighted-average MPC increases by 12.7 percent relative to the base-case MPC. In this scenario, 

INPV impacts are significantly negative because the $929.5 million in conversion costs 

significantly outweighs the modest increase in shipment weighted-average MPC.

Under the preservation of operating profit markup scenario, the 12.7 percent MPC 

increase is again significantly outweighed by a lower average manufacturer markup of 1.19 

(compared to 1.20 used in the base case) and $929.5 million in conversion costs, resulting in 

significantly negative impacts at TSL 3.

12-32
REFERENCES

1 Securities and Exchange Commission, Annual 10-K Reports, Various Years,

Washington DC. <www.sec.gov>.

2 Standard and Poors Financial Services LLC, Company Credit Ratings, Various Companies, 

New York, NY. <www2.standardandpoors.com>.

3 AHAM – AHAM Members. Last Accessed December 2, 2014. 

<http://www.aham.org/ht/d/Organizations/pid/2344/divisions/Y>

4 McKinsey & Company, Inc. Valuation: Measuring and Managing the Value of

Companies, 3rd Edition, Copeland, Koller, Murrin. New York: John Wiley & Sons, 2000.

12-33
CHAPTER 13. EMISSIONS IMPACT ANALYSIS

TABLE OF CONTENTS

13.1 INTRODUCTION ......................................................................................................... 13-1

13.2 AIR QUALITY REGULATIONS AND EMISSIONS IMPACTS............................... 13-1

13.3 EMISSIONS IMPACT RESULTS................................................................................ 13-3

REFERENCES .......................................................................................................................... 13-7

LIST OF TABLES

Table 13.3.1 Cumulative Emissions Reduction for Potential Standards for 

Conventional Cooking Products.................................................................. 13-3

LIST OF FIGURES

Figure 13.3.1 Conventional Cooking Products: CO2 Total Emissions Reduction............. 13-4

Figure 13.3.2 Conventional Cooking Products: SO2 Total Emissions Reduction ............. 13-4

Figure 13.3.3 Conventional Cooking Products: NOx Total Emissions Reduction............. 13-5

Figure 13.3.4 Conventional Cooking Products: Hg Total Emissions Reduction............... 13-5

Figure 13.3.5 Conventional Cooking Products: N2O Total Emissions Reduction............. 13-6

Figure 13.3.6 Conventional Cooking Products: CH4 Total Emissions Reduction............. 13-6

13-i
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 potential standards on emissions of two additional 

greenhouse gases, methane (CH4) and nitrous oxide (N2O), as well as the impacts 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 

appendix 15A to this TSD, and in the report “Utility Sector Impacts of Reduced Electricity 

Demand” (Coughlin, 2014).4 For site combustion of natural gas or petroleum fuels, the 

combustion emissions of CO2 and NOX are estimated using emissions intensity factors from a 

publication of the Environmental Protection Agency (EPA).

2

The emissions intensity factors are expressed in terms of physical units per MWh or 

MMBtu of site energy savings. Total emissions reductions are estimated by multiplying the 

emissions intensity factor by the energy savings calculated in the national impact analysis 

(chapter 10). This chapter presents the results of the emissions analysis. The emissions factors 

used in the calculations are provided in Appendix 13A. For power sector emissions, the factors 

depend on the sector and end use. The results presented here use factors from the power plant 

types that supply electricity for cooking in homes.

13.2 AIR QUALITY REGULATIONS AND EMISSIONS IMPACTS

Each annual version of the Annual Energy Outlook (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. 

13-1
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. a

The attainment of emissions caps is typically flexible among affected Electric Generating 

Units (EGUs) and is enforced through the use of emissions allowances and tradable permits. 

Under existing EPA regulations, any excess SO2 emissions allowances resulting from the lower 

electricity demand caused by the imposition of an efficiency standard could be used to permit 

offsetting increases in SO2 emissions by any regulated EGU. In past rulemakings, DOE 

recognized that there was uncertainty about the effects of efficiency standards on SO2 emissions 

covered by the existing cap-and-trade system, but it concluded that no reductions in power sector 

emissions would occur for SO2 as a result of standards.

Beginning in 2016, however, SO2 emissions will fall as a result of the Mercury and Air 

Toxics Standards (MATS) for power plants. 77 FR 9304 (Feb. 16, 2012). In the final MATS 

rule, EPA established a standard for hydrogen chloride as a surrogate for acid gas hazardous air 

pollutants (HAP), and also established a standard for SO2 (a non-HAP acid gas) as an alternative 

equivalent surrogate standard for acid gas HAP. The same controls are used to reduce HAP and 

non-HAP acid gas; thus, SO2 emissions will be reduced as a result of the control technologies 

installed on coal-fired power plants to comply with the MATS requirements for acid gas. AEO

2014 assumes that, in order to continue operating, coal plants must have either flue gas 

desulfurization or dry sorbent injection systems installed by 2016. Both technologies, which are 

used to reduce acid gas emissions, also reduce SO2 emissions. Under the MATS, emissions will 

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.

a 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.

13-2

 
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 marginal mercury emissions reductions using the reference and side 

cases published with AEO 2014, which incorporate the MATS.

13.3 EMISSIONS IMPACT RESULTS

Table 13.3.1 presents the estimated cumulative emissions reductions for the lifetime of 

products sold in 2019-2048 for each TSL. Negative values indicate that emissions increase.

Table 13.3.1 Cumulative Emissions Reduction for Potential Standards for Conventional 

Cooking Products

TSL

1 2 3

Power Sector and Site Emissions

CO2 (million metric tons) 8.98 38.6 68.2

SO2 (thousand tons) 7.44 29.1 51.8

NOX (thousand tons) 6.95 32.2 56.7

Hg (tons) 0.023 0.090 0.160

N2O (thousand tons) 0.126 0.499 0.885

CH4 (thousand tons) 0.882 3.51 6.22

Upstream Emissions

CO2 (million metric tons) 0.524 2.52 4.42

SO2 (thousand tons) 0.091 0.356 0.632

NOX (thousand tons) 7.47 36.6 64.2

Hg (tons) 0.000 0.001 0.001

N2O (thousand tons) 0.004 0.018 0.032

CH4 (thousand tons) 43.6 218 381

Total Emissions

CO2 (million metric tons) 9.50 41.1 72.6

SO2 (thousand tons) 7.53 29.5 52.4

NOX (thousand tons) 14.4 68.8 121

Hg (tons) 0.023 0.091 0.161

N2O (thousand tons) 0.131 0.517 0.918

CH4 (thousand tons) 44.4 221 387

Figure 13.3.1 through Figure 13.3.6 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 2019-2048.

13-3
Figure 13.3.1 Conventional Cooking Products: CO2 Total Emissions Reduction

Figure 13.3.2 Conventional Cooking Products: SO2 Total Emissions Reduction

0.0

500.0

1000.0

1500.0

2000.0

2500.0

3000.0

20192024202920342039204420492054205920642069

kt

CO2 Emissions Reduction

TSL 1 TSL 2 TSL 3

0.0

200.0

400.0

600.0

800.0

1000.0

1200.0

1400.0

1600.0

1800.0

20192024202920342039204420492054205920642069

ton

SO2 Emissions Reduction

TSL 1 TSL 2 TSL 3

13-4
Figure 13.3.3 Conventional Cooking Products: NOx Total Emissions Reduction

Figure 13.3.4 Conventional Cooking Products: Hg Total Emissions Reduction

0.0

500.0

1000.0

1500.0

2000.0

2500.0

3000.0

3500.0

4000.0

4500.0

20192024202920342039204420492054205920642069

ton

NOx Emissions Reduction

TSL 1 TSL 2 TSL 3

0.0

0.0

0.0

0.0

0.0

0.0

0.0

20192024202920342039204420492054205920642069

ton

Hg Emissions Reduction

TSL 1 TSL 2 TSL 3

13-5
Figure 13.3.5 Conventional Cooking Products: N2O Total Emissions Reduction

Figure 13.3.6 Conventional Cooking Products: CH4 Total Emissions Reduction

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

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N2OEmissions Reduction

TSL 1 TSL 2 TSL 3

0.0

2000.0

4000.0

6000.0

8000.0

10000.0

12000.0

14000.0

16000.0

20192024202920342039204420492054205920642069

ton

CH4 Emissions Reduction

TSL 1 TSL 2 TSL 3

13-6
REFERENCES

1. Energy Information Administration, Annual Energy Outlook 2014 with Projections to 

2040, June 2013. Washington, DC. 

<http://www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf>

2. U.S. Environmental Protection Agency, Compilation of Air Pollutant Emission Factors, 

AP-42, Fifth Edition, Volume I: Stationary Point and Area Sources. 1998. 

http://www.epa.gov/ttn/chief/ap42/index.html

3. Coughlin, K., Projections of Full-Fuel-Cycle Energy and Emissions Metrics, 2013. 

Lawrence Berkeley National Laboratory. Report No. LBNL-6025E. 

4. Coughlin, K., Utility Sector Impacts of Reduced Electricity Demand, 2014. Lawrence 

Berkeley National Laboratory. Report No. LBNL-6864E. 

http://www.osti.gov/scitech/biblio/1165372/

13-7
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 14.4.1 Summary of the total dollar value (mortality and morbidity) per ton of 

directly emitted PM2.5 precursor reduced by Electricity Generating 

Units (2010$) ............................................................................................... 14-5

Table 14.5.1 Estimates of Global Present Value of CO2 Emissions Reduction under 

TSLs for Conventional Cooking Products................................................... 14-6

Table 14.5.2 Estimates of Domestic Present Value of CO2 Emissions Reduction 

under TSLs for Conventional Cooking Products......................................... 14-7

Table 14.5.3 Estimates of Present Value of NOX Emissions Reduction under TSLs 

for Conventional Cooking Products............................................................. 14-8

14-i
CHAPTER 14. MONETIZATION OF EMISSION REDUCTIONS BENEFITS 

14.1 INTRODUCTION

As part of its assessment of the effects of potential energy conservation standards for 

conventional cooking products, the U.S. Department of Energy (DOE) estimated the monetary 

benefits of the reduced emissions of carbon dioxide (CO2) and nitrogen oxides (NOX) that would 

be expected to result from each trial standard level (TSL) considered for this rulemaking. This 

chapter summarizes the basis for the monetary values assigned to emissions and presents the 

modeled benefits of estimated reductions. 

14.2 MONETIZING CARBON DIOXIDE EMISSIONS

One challenge for anyone attempting to calculate the monetary benefits of reduced 

emissions of CO2 is what value to assign to each unit eliminated. The value must encompass a 

broad range of physical, economic, social, and political effects. Analysts developed the concept 

of the social cost of carbon (SCC) to represent the broad cost or value associated with 

producing—or reducing—a quantifiable amount of CO2 emissions.

14.2.1 Social Cost of Carbon

The SCC is an estimate of the monetized damages associated with an incremental 

increase in carbon emissions in a given year. The SCC is intended to include (but is not limited 

to) changes in net agricultural productivity, human health, property damages from increased 

flood risk, and the value of ecosystem services. SCC estimates are provided in dollars per metric 

ton of carbon dioxide. A value for the domestic SCC is meant to represent the damages in the 

United States resulting from a unit change in carbon dioxide emissions, whereas a global SCC is 

meant to reflect the value of damages worldwide.

Under section 1(b)(6) of Executive Order 12866,

1 agencies must, to the extent permitted 

by law, “assess both the costs and the benefits of the intended regulation and, recognizing that 

some costs and benefits are difficult to quantify, propose or adopt a regulation only upon a 

reasoned determination that the benefits of the intended regulation justify its costs.” The purpose 

of the SCC estimates required by the Executive Order is to enable agencies to incorporate the 

monetized social benefits of reducing CO2 emissions into cost-benefit analyses of regulatory 

actions. The estimates are presented with an acknowledgement of the many uncertainties 

involved and with a clear understanding that they will need updating in response to increasing 

knowledge of the science and economics of climate impacts.

As part of the interagency process that developed the SCC estimates, technical experts 

from numerous agencies met regularly to explore the technical literature in relevant fields, 

discuss key model inputs and assumptions, and consider public comments. The primary objective 

of the process was to develop a range of SCC values using a defensible set of assumptions 

regarding model inputs that was grounded in the scientific and economic literature. In this way, 

key uncertainties and model differences transparently and consistently inform the range of SCC 

estimates developed for use in the rulemaking process.

14-1
14.2.2 Monetizing Carbon Dioxide Emissions

When attempting to assess the incremental economic impacts of carbon dioxide 

emissions, the analyst faces several serious challenges. A report from the National Research 

Council2 points out that any assessment will suffer from uncertainty, speculation, and lack of 

information about (1) future emissions of greenhouse gases, (2) the effects of past and future 

emissions on the climate system, (3) the effects of changes in climate on the physical and 

biological environment, and (4) the translation of those environmental impacts into economic 

damages. As a result, any effort to quantify and monetize the harms associated with climate 

change raises serious questions of science, economics, and ethics and should be viewed as 

provisional. 

Despite the limits of both quantification and monetization, SCC estimates can be useful 

in estimating the social benefits of reducing CO2 emissions. An agency can estimate the benefits 

from reduced (or costs from increased) emissions in any future year by multiplying the change in 

emissions in that year by the SCC values appropriate for that year. Then the net present value of 

the benefits can be calculated by multiplying each of the future benefits by an appropriate 

discount factor and summing across all affected years. This approach assumes that the marginal 

damages from increased emissions are constant for small departures from the baseline emissions 

path, an approximation that is reasonable for policies that have effects on emissions that are 

small relative to cumulative global carbon dioxide emissions.

14.3 DEVELOPMENT OF SOCIAL COST OF CARBON VALUES

In 2009, an interagency process was initiated to develop a preliminary assessment of how 

best to quantify the benefits from reducing carbon dioxide emissions. To provide consistency in 

how benefits are evaluated across Federal agencies, the Administration sought to develop a 

transparent and defensible method, specifically designed for the rulemaking process, to quantify 

avoided climate change damages from reduced CO2 emissions. The interagency group did not 

undertake any original analysis. Instead, it combined SCC estimates from the literature to use as 

interim values until a more comprehensive analysis could be conducted. The outcome of the 

preliminary assessment was a set of five interim values: global SCC estimates for 2007 (in 

2006$) of $55, $33, $19, $10, and $5 per ton of CO2.

3 Those interim values represented the first 

sustained interagency effort within the U.S. government to develop an SCC for use in regulatory 

analysis. The results of that preliminary effort were presented in several proposed and final rules.

14.3.1 Current Approach and Key Assumptions

After the release of the interim values, the interagency group reconvened regularly to 

improve the SCC estimates. Specifically, the group considered public comments and further 

explored the technical literature in relevant fields. The interagency group relied on three 

integrated assessment models commonly used to estimate the SCC. The models are known by 

their acronyms of FUND, DICE, and PAGE. Those three models frequently are cited in the peerreviewed literature and were used in the most recent assessment of the Intergovernmental Panel 

on Climate Change. Each model was given equal weight in developing SCC values.

14-2
Each model takes a slightly different approach to calculating how increases in emissions 

produce economic damages. A key objective of the interagency process was to enable a 

consistent exploration of the three models while respecting the different approaches taken by the 

key modelers in the field. An extensive review of the literature identified three sets of input 

parameters for the models: climate sensitivity; socioeconomic and emissions trajectories; and 

discount rates. A probability distribution for climate sensitivity was specified as an input to all 

three models. In addition, the interagency group used a range of scenarios for the socioeconomic 

parameters and a range of values for the discount rate. All other model features were left 

unchanged, relying on the model developers’ best estimates and judgments.

The interagency group selected four SCC values for use in regulatory analyses. Three 

values are based on the average SCC from the three integrated assessment models, at discount

rates of 2.5 percent, 3 percent, and 5 percent. The fourth value, which represents the 95th

percentile of the SCC estimate across all three models at a 3-percent discount rate, is included to 

represent larger-than-expected effects from temperature changes farther out in the tails of the 

SCC distribution. The values increase in real terms over time. Additionally, the interagency 

group determined that a range of values from 7 percent to 23 percent should be used to adjust the 

global SCC to calculate domestic effects, although preference is given to consideration of the 

global benefits of reducing CO2 emissions. Table 14.3.1 presents the values in the 2010 

interagency group report.

4

Table 14.3.1 Annual SCC Values for 2010-2050 from 2010 Interagency Report (in 2007$

per Metric Ton)

Year

Discount Rate (%)

5 3 2.5 3

Average Average Average 95th Percentile

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

The SCC values used for the analysis of the effects of potential standards for hearth 

products were generated using the most recent versions of the three integrated assessment 

models that have been published in the peer-reviewed literature, as described in the 2013 update 

from the interagency working group (revised November 2013). Table 14.3.2 shows the updated 

sets of SCC estimates in 5-year increments from 2010 to 2050. The full set of annual SCC 

estimates for 2010–2050 is presented in appendix 14B of this TSD. The central value that 

emerges is the average SCC across models at a 3-percent discount rate. To capture the 

14-3
uncertainties involved in regulatory impact analysis, however, the interagency group emphasizes 

the importance of including all four sets of SCC values.

Table 14.3.2 Annual SCC Values for 2010–2050 from 2013 Interagency Update (in 2007$

per Metric Ton of CO2)

Year

Discount Rate (%)

5 3 2.5 3

Average Average Average 95th Percentile

2010 11 32 51 89

2015 11 37 57 109

2020 12 43 64 128

2025 14 47 69 143

2030 16 52 75 159

2035 19 56 80 175

2040 21 61 86 191

2045 24 66 92 206

2050 26 71 97 220

14.3.2 Limitations of Current Estimates

The interagency group recognizes that current models are imperfect and incomplete. 

Because key uncertainties remain, current SCC estimates should be treated as provisional and 

revisable. Estimates doubtless will evolve in response to improved scientific and economic 

understanding. The 2009 National Research Council report points out the tension between 

producing quantified estimates of the economic damages from an incremental ton of carbon and 

the limits of current modeling efforts. Several analytic challenges are being addressed by the 

research community, some by research programs housed in many of the Federal agencies 

participating in the interagency process. The interagency group intends to review and reconsider 

SCC estimates periodically to incorporate expanding knowledge of the science and economics of 

climate impacts, as well as improvements in modeling.

In summary, in considering the potential global benefits resulting from reduced CO2

emissions, DOE used the values from the 2013 interagency report, applying the GDP price 

deflator to adjust the values to 2014$. For the four SCC values, the values of emissions in 2015 

were $12.2, $41.2, $63.4, and $121 per metric ton avoided (values expressed in 2014$). DOE 

derived values after 2050 using the relevant growth rates for 2040–2050 in the interagency 

update.

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

for that year under each discount rate. To calculate a present value of the stream of monetary 

values, DOE discounted the values in each of the four cases using the same discount rate that had 

been used to obtain the SCC values in each case.

14-4
14.4 VALUATION OF OTHER EMISSIONS REDUCTIONS

DOE considered the potential monetary benefits of reduced NOX emissions attributable to 

the TSLs considered for conventional cooking products. 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 the total dollar value (mortality and morbidity) per ton of 

directly emitted PM2.5 precursor reduced by electricity generating units. The estimates were 

developed by Krewski et al. (2009) and are reported in EPA’s Office of Air Quality Planning and 

Standards report “Technical Support Document: Estimating the Benefit per Ton of Reducing 

PM2.5 Precursors from 17 Sectors.”5 Table 14.4.1 summarizes the monetized values estimated in 

2010$ for NOX emission reductions in 2016, 2020, 2025 and 2030, at discount rates of 3 percent 

and 7 percent. DOE applied the GDP price deflator to adjust the values to 2014$. For the two 

NOX values, the values of emissions in 2016 were $5562 and $4920 per ton avoided (values 

expressed in 2014$). DOE further interpolated the values between the intervals, and extrapolated 

the values after 2030 using the relevant growth rates for 2016–2030. DOE then multiplied the 

NOX emissions reduction estimated for each year by the NOX value for that year under each 

discount rate. To calculate a present value of the stream of monetary values, DOE discounted the 

values calculated under each discount rate using the same discount rate that had been used to 

obtain the NOX values.

Table 14.4.1 Summary of the total dollar value (mortality and morbidity) per ton of 

directly emitted PM2.5 precursor reduced by Electricity Generating Units 

(2010$)

Year Discount Rate (%)

3 7

2016 5200 4600

2020 5400 4900

2025 5800 5200

2030 6200 5600

DOE continues to evaluate appropriate values for monetizing avoided SO2 and Hg 

emissions. DOE did not monetize those emissions for this analysis.

14.5 RESULTS

Table 14.5.1 presents the global values of CO2 emissions reductions for each considered 

TSL. 

14-5
Table 14.5.1 Estimates of Global Present Value of CO2 Emissions Reduction under TSLs 

for Conventional Cooking Products

TSL

SCC Case*

5% discount

rate, average*

3% discount

rate, average*

2.5% discount

rate, average*

3% discount rate,

95th percentile*

Million 2014$

Primary Energy Emissions

1 62 288 458 893

2 267 1239 1969 3837

3 473 2194 3485 6794

Upstream Emissions

1 3.54 16.6 26.5 51.5

2 17.1 80 127 248

3 30 141 224 436

Full-Fuel-Cycle Emissions

1 65.5 305 484 944

2 284 1319 2096 4085

3 503 2335 3709 7230

* For each of the four cases, the corresponding global SCC value for emissions in 2015 is $12.2, $41.2, $63.4, and 

$121 per metric ton (2014$).

After calculating global values of CO2 emissions reductions for each considered TSL, 

DOE calculated domestic values as a range of from 7 percent to 23 percent of the global values. 

Results for domestic values are presented in Table 14.5.2.

14-6
Table 14.5.2 Estimates of Domestic Present Value of CO2 Emissions Reduction under 

TSLs for Conventional Cooking Products

TSL

SCC Case*

5% discount

rate, average*

3% discount

rate, average*

2.5% discount

rate, average*

3% discount rate,

95th percentile*

Million 2014$

Primary Energy Emissions

1 4.3 to 14.3 20.2 to 66.3 32.1 to 105.3 62.5 to 205.3

2 18.7 to 61.3 86.7 to 285.0 137.8 to 452.8 268.6 to 882.4

3 33.1 to 108.8 153.6 to 504.6 244.0 to 801.7 475.6 to 1562.7

Upstream Emissions

1 0.2 to 0.8 1.2 to 3.8 1.9 to 6.1 3.6 to 11.9

2 1.2 to 3.9 5.6 to 18.4 8.9 to 29.3 17.4 to 57.0

3 2.1 to 6.9 9.8 to 32.3 15.7 to 51.5 30.5 to 100.2

Full-Fuel-Cycle Emissions

1 4.6 to 15.1 21.3 to 70.1 33.9 to 111.4 66.1 to 217.2

2 19.9 to 65.3 92.3 to 303.4 146.7 to 482.1 285.9 to 939.5

3 35.2 to 115.7 163.4 to 537.0 259.6 to 853.1 506.1 to 1662.9

* For each of the four cases, the corresponding global SCC value for emissions in 2015 is $12.2, $41.2, $63.4, and 

$121 per metric ton (2014$).

Table 14.5.3 presents the present value of cumulative NOX emissions reductions for each 

TSL. Monetary values are calculated using the average dollar-per-ton values assigned to NOX

emissions at 3-percent and 7-percent discount rates.

14-7
Table 14.5.3 Estimates of Present Value of NOX Emissions Reduction under TSLs for 

Conventional Cooking Products

TSL

3% discount rate 7% discount rate

Million 2014$

Primary Energy Emissions

1 24.6 9.71

2 114 45.2

3 201 80.1

Upstream Emissions

1 25.9 9.74

2 127 48.4

3 223 85.1

Full-Fuel-Cycle Emissions

1 50.4 19.4

2 241 93.5

3 424 165

* The corresponding NOX values for emissions in 2016 are $5562 and $4920 per ton (2014$).

14-8
REFERENCES

1. Presidential Documents--Executive Order 12866--Regulatory Planning and Review, 

October 4, 1993. <http://www.archives.gov/federal-register/executiveorders/pdf/12866.pdf>

2. National Research Council, Hidden Costs of Energy: Unpriced Consequences of Energy 

Production and Use, 2010. National Academies Press. Washington, DC. 

<http://www.nap.edu/catalog.php?record_id=12794>

3. Interagency Working Group on Social Cost of Carbon, Social Cost of Carbon for 

Regulatory Impact Analysis under Executive Order 12866, February, February 2010. 

United States Government. Washington, D.C. 

<http://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. 

<http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-updatesocial-cost-of-carbon-for-regulator-impact-analysis.pdf>

5. U.S. Environmental Protection Agency (EPA), Technical Support Document - Estimating 

the Benefit per Ton of Reducing PM2.5 Precursors from 17 Sectors, 2013. Office of Air 

and Radiation, Office of Air Quality Planning and Standards, Research Triangle Park, 

NC. < http://www2.epa.gov/sites/production/files/2014-

10/documents/sourceapportionmentbpttsd.pdf>

14-9
CHAPTER 15. UTILITY IMPACT ANALYSIS

TABLE OF CONTENTS

15.1 INTRODUCTION ......................................................................................................... 15-1

15.2 METHODOLOGY ........................................................................................................ 15-1

15.3 UTILITY IMPACT RESULTS ..................................................................................... 15-2

15.3.1 Installed Capacity............................................................................................... 15-2

15.3.2 Electricity Generation ........................................................................................ 15-6

15.3.3 Results Summary ............................................................................................... 15-8

REFERENCES ........................................................................................................................ 15-10

LIST OF TABLES

Table 15.3.1 Conventional Cooking Products: Summary of Utility Impact Results........ 15-9

LIST OF FIGURES

Figure 15.3.1 Conventional Cooking Products: Total Electric Capacity Reduction.......... 15-3

Figure 15.3.2 Conventional Cooking Products: Coal Capacity Reduction........................ 15-3

Figure 15.3.3 Conventional Cooking Products: Nuclear Capacity Reduction................... 15-4

Figure 15.3.4 Conventional Cooking Products: Gas Combined Cycle Capacity 

Reduction ..................................................................................................... 15-4

Figure 15.3.5 Conventional Cooking Products: Peaking Capacity Reduction................... 15-5

Figure 15.3.6 Conventional Cooking Products: Renewables Capacity Reduction ............ 15-5

Figure 15.3.7 Conventional Cooking Products: Total Generation Reduction ................... 15-6

Figure 15.3.8 Conventional Cooking Products: Coal Generation Reduction .................... 15-6

Figure 15.3.9 Conventional Cooking Products: Nuclear Generation Reduction ............... 15-7

Figure 15.3.10 Conventional Cooking Products: Gas Combined Cycle Generation 

Reduction ..................................................................................................... 15-7

Figure 15.3.11 Conventional Cooking Products: Oil Generation Reduction....................... 15-8

Figure 15.3.12 Conventional Cooking Products: Renewables Generation Reduction......... 15-8

15-i
CHAPTER 15. UTILITY IMPACT ANALYSIS

15.1 INTRODUCTION

In the utility impact analysis, the U.S. Department of Energy (DOE) analyzes the changes 

in electric installed capacity and power generation that result for each trial standard level (TSL). 

The utility impact analysis is based on output of the DOE/Energy Information 

Administration (EIA)’s National Energy Modeling System (NEMS).a NEMS is a public domain, 

multi-sectored, partial equilibrium model of the U.S. energy sector. Each year, DOE/EIA uses 

NEMS to produce an energy forecast for the United States, the Annual Energy Outlook (AEO). 

The EIA publishes a Reference case, which incorporates all existing energy-related policies at 

the time of publication, and a variety of side cases which analyze the impact of different policies, 

energy price and market trends. As of 2014, DOE is using a new methodology based on results 

published for the Annual Energy Outlook 2014 (AEO 2014) Reference case and a set of the side 

cases that implement a variety of efficiency-related policies.

2

The new approach retains key aspects of DOE’s previous methodology, and provides 

some improvements:

• The assumptions used in the AEO reference case and side cases are fully documented and 

receive detailed public scrutiny.

• NEMS is updated each year, with each edition of the AEO, to reflect changes in energy 

prices, supply trends, regulations, etc.

• The comprehensiveness of NEMS permits the modeling of interactions among the 

various energy supply and demand sectors. 

• Using EIA published side cases to estimate the utility impacts enhances the transparency 

of DOE’s analysis.

• The variability in impacts estimates from one edition of AEO to the next will be reduced 

under the new approach.

The methodology is presented in appendix 15A. The methodology is described in more 

detail in K. Coughlin, “Utility Sector Impacts of Reduced Electricity Demand.”

3

This chapter presents the results for conventional cooking products.

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 

a For more information on NEMS, refer to the U.S. Department of Energy, Energy Information Administration 

documentation. A useful summary is National Energy Modeling System: An Overview.

1

15-1

 
energy conservation standards. DOE represents these marginal impacts using time series of 

impact factors.

The impact factors are calculated based on output from NEMS for the AEO 2014. NEMS 

uses predicted growth in demand for each end use to build up a projection of the total electric 

system load growth. The system load shapes are converted internally to load duration curves, 

which are then used to estimate the most cost-effective additions to capacity. When electricity 

demand deviates from the AEO reference case, in general there are three inter-related effects: the 

annual generation (TWh) from the stock of electric generating capacity changes, the total 

generation capacity itself (GW) may change, and the mix of capacity types and technologies may 

change. Technology changes lead to a change in the proportion of fuel consumption to electricity 

generated (referred to as the heat rate). Each of these effects can vary for different types of end 

use. The change in total generating capacity is sensitive to the degree to which the end-use is 

peak coincident, while the capacity mix is sensitive to the hourly load shape associated with the 

end use. Changes in generation by fuel type lead in turn to changes in total power sector 

emissions of SO2, NOx, Hg and CO2.

DOE defined impact factors describing the change in emissions, installed capacity, and 

fuel consumption per unit reduction of site electricity demand. The impact factors vary by sector 

and end-use, as well as by year. DOE multiplied the impact factors by the stream of site energy 

savings calculated in the NIA (chapter 10) to produce estimates of the utility impacts. The 

emissions impact factors are presented in appendix 15A, and the marginal heat rates in appendix 

10B. For conventional cooking products DOE used the impact factors for cooking in homes.

15.3 UTILITY IMPACT RESULTS

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. The changes have been calculated 

based on the impact factors for capacity presented in appendix 15A. Units are megawatts of 

capacity per gigawatt-hour of site electricity use (MW/GWh).b Note that a negative number 

means an increase in capacity under a TSL.

b These units are identical to GW/TWh.

15-2

 
Figure 15.3.1 Conventional Cooking Products: Total Electric Capacity Reduction

Figure 15.3.2 Conventional Cooking Products: Coal Capacity Reduction

0

200

400

600

800

1000

1200

1400

1600

20192024202920342039204420492054205920642069

MW

Total

TSL 1 TSL 2 TSL 3

0

50

100

150

200

250

20192024202920342039204420492054205920642069

MW

Coal

TSL 1 TSL 2 TSL 3

15-3
Figure 15.3.3 Conventional Cooking Products: Nuclear Capacity Reduction

Figure 15.3.4 Conventional Cooking Products: Gas Combined Cycle Capacity Reduction

0

5

10

15

20

25

20192024202920342039204420492054205920642069

MW

Nuclear

TSL 1 TSL 2 TSL 3

0

100

200

300

400

500

600

20192024202920342039204420492054205920642069

MW

NGCC

TSL 1 TSL 2 TSL 3

15-4
Figure 15.3.5 Conventional Cooking Products: Peaking Capacity Reduction

Figure 15.3.6 Conventional Cooking Products: Renewables Capacity Reduction

0

50

100

150

200

250

300

350

400

450

20192024202920342039204420492054205920642069

MW

Peaking

TSL 1 TSL 2 TSL 3

0

50

100

150

200

250

20192024202920342039204420492054205920642069

MW

Renewable

TSL 1 TSL 2 TSL 3

15-5
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. The change by fuel type has been calculated based on factors calculated 

as described in appendix 15A.

Figure 15.3.7 Conventional Cooking Products: Total Generation Reduction

Figure 15.3.8 Conventional Cooking Products: Coal Generation Reduction

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

20192024202920342039204420492054205920642069

GWh

Total

TSL 1 TSL 2 TSL 3

0

200

400

600

800

1000

1200

1400

20192024202920342039204420492054205920642069

GWh

Coal

TSL 1 TSL 2 TSL 3

15-6
Figure 15.3.9 Conventional Cooking Products: Nuclear Generation Reduction

Figure 15.3.10 Conventional Cooking Products: Gas Combined Cycle Generation 

Reduction

0

20

40

60

80

100

120

140

160

180

200

20192024202920342039204420492054205920642069

GWh

Nuclear

TSL 1 TSL 2 TSL 3

0

500

1000

1500

2000

2500

3000

20192024202920342039204420492054205920642069

GWh

NGCC

TSL 1 TSL 2 TSL 3

15-7
Figure 15.3.11 Conventional Cooking Products: Oil Generation Reduction

Figure 15.3.12 Conventional Cooking Products: Renewables Generation Reduction

15.3.3 Results Summary 

Table 15.3.1 presents a summary of the utility impact results for conventional cooking 

products.

0

1

2

3

4

5

6

7

8

9

20192024202920342039204420492054205920642069

GWh

Oil

TSL 1 TSL 2 TSL 3

0

100

200

300

400

500

600

700

800

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GWh

Renewable

TSL 1 TSL 2 TSL 3

15-8
Table 15.3.1 Conventional Cooking Products: Summary of Utility Impact Results

TSL

1 2 3

Installed Capacity Reduction (MW)

2020 11.6 61.6 117

2025 60.7 248 445

2030 115 443 798

2035 163 631 1123

2040 188 724 1287

Electricity Generation Reduction (GWh)

2020 52.2 278 269

2025 253 1031 1001

2030 443 1710 1661

2035 584 2257 2191

2040 629 2418 2347

15-9
REFERENCES

1 U.S. Department of Energy-Energy Information Administration. National Energy 

Modeling System: An Overview, 2009. Report No. DOE/EIA-0581 (2009). (Last 

Accessed August 10, 2014.) http://www.eia.gov/oiaf/aeo/overview/

2 U.S. Department of Energy-Energy Information Administration. Annual Energy Outlook 

2014 with Projections to 2040. Report No. DOE/EIA-03832014. Washington, D.C. 

http://www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf

3 Coughlin, K. Utility Sector Impacts of Reduced Electricity Demand, 2014. Lawrence 

Berkeley National Laboratory. Report No. LBNL-6864E. 

http://www.osti.gov/scitech/biblio/1165372/

15-10
CHAPTER 16. EMPLOYMENT IMPACT ANALYSIS

TABLE OF CONTENTS

16.1 INTRODUCTION ......................................................................................................... 16-1

16.2 ASSUMPTIONS............................................................................................................ 16-1

16.3 METHODOLOGY ........................................................................................................ 16-1

16.4 SHORT-TERM RESULTS............................................................................................ 16-2

16.5 LONG-TERM RESULTS.............................................................................................. 16-3

LIST OF TABLES

Table 16.4.1 Net National Short-term Change in Employment (1000s of Jobs).................... 16-3

16-i
CHAPTER 16. EMPLOYMENT IMPACT ANALYSIS

16.1 INTRODUCTION

DOE’s employment impact analysis for cooking products 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 ovens. Job increases or decreases reported 

in this chapter are separate from the direct cooking product manufacturing 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 the ImSET input/output econometric model of the U.S. economy, this analysis 

estimated the short-term effect of these expenditure impacts on net economic output and 

employment. DOE intends this analysis to quantify the indirect employment impacts of these 

expenditure changes. It evaluated direct employment impacts at manufacturers’ facilities in the 

manufacturer impact analysis (see Chapter 12).

DOE notes that ImSET is not a general equilibrium forecasting model, and understands 

the uncertainties involved in projecting employment impacts, especially changes in the later 

years of the analysis.1 Because ImSET does not incorporate price changes, the employment 

effects predicted by ImSET would over-estimate the magnitude of actual job impacts over the 

long run for this rule. Since input/output models do not allow prices to bring markets into 

equilibrium, they are best used for short-run analysis. DOE therefore includes a qualitative 

discussion of how labor markets are likely to respond in the longer term. In future rulemakings, 

DOE may consider the use of other modeling approaches for examining long run employment 

impacts.

16.3 METHODOLOGY

The Department based its analysis on an input/output model of the U.S. economy that 

estimates the effects of standards on major sectors of the economy related to buildings and the

net impact of standards on jobs. The Pacific Northwest National Laboratory developed the 

model, ImSET 3.1.12 (Impact of Sector Energy Technologies) as a successor to ImBuild3

, a 

special-purpose version of the IMPLAN4 national input/output model. ImSET estimates the 

employment and income effects of building energy technologies. In comparison with simple 

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

economic impacts of energy-efficiency investments in buildings.

16-1
In an input/output model, the level of employment in an economy is determined by the 

relationships between different sectors of the economy and the spending flows among them. 

Different sectors have different levels of labor intensity, thus 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 national 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.

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. Third, utility sector investment funds are released for use in other sectors of 

the economy. When consumers use less energy, utilities experience relative reductions in 

demand which leads to reductions in utility sector investment and employment.

DOE also notes that the employment impacts estimated with ImSET for the entire 

economy differ from the employment impacts in the cooking 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 cooking product standards relative to the 

base case. DOE disaggregated the impact of standards on employment into three component 

effects: increased capital investment costs, decreased energy costs, and changes in operations and 

maintenance costs. DOE presents the summary impact.

Conceptually, one can consider the impact of the rule in its first year on three aggregate 

sectors, the cooking product manufacturing sector, the energy generation sector, and the general 

consumer good 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 cooking 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 energy, freeing up this money to be spent in other sectors. The reduction in energy demand 

16-2
causes a reduction in employment in that sector. Finally, based on the net impact of increased 

expenditures on cooking 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 (e.g. 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 2019, rounded 

to the nearest hundred jobs. Approximately 24% of electric cooking products and 28% of gas 

cooking products are domestically produced, with the remainder imported. The net employment 

impact estimate is sensitive to assumptions regarding the return to the U.S. economy of money 

spent on imported cooking products. The two scenarios bounding the ranges presented in Table 

16.4.1 represent situations in which none of the money spent on imported cooking products

returns to the U.S. economy and all of the money spent on imported cooking products returns to 

the U.S. economy. The U.S. trade deficit in recent years suggests that between 50% and 75% of 

the money spent on imported cooking products is likely to return, with employment impacts 

falling within the ranges presented below.

Table 16.4.1 Net National Short-term Change in Employment (1000s of Jobs)

Trial 

Standard 

Level

2019 2024

TSL 1 0.0 0.0 to0.1

TSL 2 -0.3 to 0.3 0.4 to 2.1

TSL 3 -5.8 to -1.4 -4.0 to -0.1

For context, the Office of Management of Budget currently assumes that the 

unemployment rate may decline to 5.3% in 2017.5 The unemployment rate in 2019 is projected 

to be close to “full employment.” When an economy is at full employment any effects on net 

employment are likely to be transitory as workers change jobs, rather than enter or exit longerterm 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 electricity to decline over time and demand for other goods to 

increase. Since the electricity generation sector is relatively capital intensive compared to the 

consumer goods sector, the net effect will be an increase in labor demand. In equilibrium, this 

should lead to upward pressure on wages and a shift in employment away from electricity 

generation towards consumer goods. Note that in long-run equilibrium there is no net effect on 

total employment since 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 

16-3
will in general be negligible over time due to the small magnitude of the short-term effects 

presented in Table 16.4.1. The ImSET model projections, assuming no price or wage effects 

until 2024, are included in the second column of Table 16.4.1.

16-4
REFERENCES

1 Scott, M., JM Roop, RW Schultz, DM Anderson, KA Cort, The Impact of DOE Building 

Technology Energy Efficiency Programs on U.S. Employment, Income, and Investment.

Energy Economics, 2008. 30(5): pp. 2283-2301

2 Roop, J. M., M. J. Scott, and R. W. Schultz, ImSET: Impact of Sector Energy 

Technologies, 2005. Pacific Northwest National Laboratory. Richland, WA. Report No. 

PNNL- 15273. 

3 Scott, M. J., D. J. Hostick, and D. B. Belzer, ImBuild: Impact of Building Energy 

Efficiency Programs, April, 1998. Pacific Northwest National Laboratory. Richland, WA. 

Report No. PNNL-11884. 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 2013 Mid-Session Review: Budget of the 

U.S. Government, Washington, DC. <

<http://www.whitehouse.gov/sites/default/files/omb/budget/fy2013/assets/13msr.pdf>

16-5
CHAPTER 17. REGULATORY IMPACT ANALYSIS

TABLE OF CONTENTS

17.1 INTRODUCTION ......................................................................................................... 17-1

17.2.1 Methodology.................................................................................................................. 17-2

17.2.2 Assumptions Regarding Non-Regulatory Policies ........................................................ 17-3

17.2.3 Policy Interactions ......................................................................................................... 17-3

17.3 NON-REGULATORY POLICY ASSUMPTIONS ...................................................... 17-4

17.3.1 No New Regulatory Action ........................................................................................... 17-4

17.3.2 Consumer Rebates ......................................................................................................... 17-4

17.3.2.1 Methodology ............................................................................................ 17-4

17.3.2.2 Analysis.................................................................................................... 17-5

17.3.3 Consumer Tax Credits................................................................................................. 17-11

17.3.4 Manufacturer Tax Credits............................................................................................ 17-13

17.3.5 Voluntary Energy Efficiency Targets.......................................................................... 17-15

17.3.6 Bulk Government Purchases........................................................................................ 17-17

17.4 IMPACTS OF NON-REGULATORY ALTERNATIVES......................................... 17-20

REFERENCES ........................................................................................................................ 17-24

LIST OF TABLES

Table 17.1.1 Non-Regulatory Alternatives to National Standards................................... 17-1

Table 17.1.2 Energy Efficiency by TSL (IAEC) .............................................................. 17-3

Table 17.3.1 Benefit/Cost Ratios Without and With Rebates........................................... 17-6

Table 17.3.2 Market Penetrations in 2019 Attributable to Consumer Rebates............... 17-11

Table 17.3.3 Market Penetrations in 2019 Attributable to Consumer Tax Credits ........ 17-13

Table 17.3.4 Market Penetrations in 2019 Attributable to Manufacturer Tax Credits... 17-14

Table 17.3.5 Market Barrier Changes Attributable to Voluntary Energy Efficiency 

Targets (TSL #2)........................................................................................ 17-16

Table 17.3.6 Market Penetrations in 2019 Attributable to Voluntary Energy 

Efficiency Targets...................................................................................... 17-16

Table 17.3.7 Market Penetrations in Selected Years Attributable to Voluntary 

Energy Efficiency Targets for TSL 2......................................................... 17-17

Table 17.3.8 Percentage of U.S. Households in Public Housing Authority ................... 17-18

Table 17.3.9 Market Penetrations in 2019 Attributable to Bulk Government 

Purchases.................................................................................................... 17-19

Table 17.4.1 Impacts of Non-Regulatory Policy Alternatives (TSL 2).......................... 17-23

17-i
LIST OF FIGURES

Figure 17.3.1 Market Penetration Curves for Conventional Cooking Products .............. 17-10

Figure 17.4.1 Market Penetration of Efficient Electric Standard Ovens –

Freestanding (TSL 2)................................................................................. 17-20

Figure 17.4.2 Market Penetration of Efficient Electric Self-clean Ovens –

Freestanding (TSL 2)................................................................................. 17-21

Figure 17.4.3 Market Penetration of Efficient Electric Self-Clean Ovens – BuiltIn/Slide-In (TSL 2) .................................................................................... 17-21

Figure 17.4.4 Market Penetration of Efficient Gas Standard Ovens – Freestanding 

(TSL 2)....................................................................................................... 17-22

Figure 17.4.5 Gas Self-Clean Ovens – Freestanding (TSL 2).......................................... 17-22

17-ii
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 levels as 

the ones in the proposed trial standard levels for the conventional cooking products 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 standards for five of the eight

product classes of conventional cooking products covered by this rulemaking. a

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 in Table 17.1.1

(excluding the alternative of “No New Regulatory Action”). Section 17.4 presents the results of 

the policy alternatives.

a For this RIA, DOE did not evaluate the following product classes, as their cumulative shipments represented 

together less than 2% of the total shipments of conventional cooking products: Electric Standard Ovens BuiltIn/Slide-In, Gas Standard Ovens – Built-In/Slide-In, Gas Self-Clean Ovens – Built-In/Slide-In. 

17-1

 
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 conventional cooking products. This 

section also describes the assumptions underlying the analysis. 

17.2.1 Methodology 

DOE used its integrated NIA-RIA spreadsheet model to calculate the national energy 

savings (NES) and net present value (NPV) associated with each non-regulatory policy 

alternative. Chapter 10 of the technical support document (TSD) describes the NIA spreadsheet 

model. Appendix 17A discusses the NIA-RIA integrated model approach.

DOE quantified the effect of each alternative on the purchase of equipment that meets the 

efficiency levels corresponding to each TSL. After establishing the quantitative assumptions 

underlying each alternative, DOE appropriately revised inputs to the NIA-RIA spreadsheet 

model. The primary model inputs revised were market shares of equipment meeting the target 

efficiency levels set for each TSL. The shipments of equipment for any given year reflect a 

distribution of efficiency levels. DOE assumed, for each TSL, that new energy efficiency 

standards would affect 100 percent of the shipments of products that did not meet the TSL target 

levels in the base case, b 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 conventional cooking products 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 (2019-2048).

• Net Present Value (NPV), represents the value of net monetary savings in 2015, 

expressed in 2014$, from equipment purchased during the 30-year analysis period 

starting in the effective date of the policy (2019-2048). DOE calculated the NPV as the 

difference between the present values of installed equipment cost and operating 

b The base case for the NIA is a market-weighted average energy efficiency calculated from units at several 

efficiency levels.

17-2

 
expenditures in the base case and the present values of those costs in each policy case. 

DOE calculated operating expenses (including energy costs) for the life of the product.

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’ response 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 conventional cooking products 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 standards (the 

target level), according to the minimum energy efficiency set for each TSL. As opposed to the 

standards case, however, the policy cases may not lead to 100 percent market penetration of units 

that meet the target level.

Table 17.1.2 shows the energy efficiencies from the technologies stipulated for

conventional cooking products for each TSL.

Table 17.1.2 Energy Efficiency by TSL (IAEC) 

TSL 1 TSL 2 TSL 3

Electric Standard Ovens – Freestanding 284.6 259.2 207.3

Electric Self-Clean Ovens – Freestanding 345.1 345.1 278.1

Electric Self-Clean Ovens – Built-In/Slide-In 351.0 351.0 282.9

Gas Standard Ovens – Freestanding 2,118.2 1,414.8 1,347.0

Gas Self-Clean Ovens – Freestanding 1,848.2 1,668.7 1,591.0

DOE assumed that the effects of non-regulatory policies would last from the effective 

date of standards—2019—through the end of the analysis period, which is 2048. 

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, 

17-3
DOE attempted to make conservative assumptions to avoid double-counting policy impacts. The 

resulting policy impacts are therefore not additive, and 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 conventional cooking products.

17.3 NON-REGULATORY POLICY ASSUMPTIONS

The following subsections describe DOE’s analysis of the impacts of the five nonregulatory policy alternatives to the standards proposed for conventional cooking products.

(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 more efficient 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 conventional cooking products 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 equipment. This 

policy provides a consumer rebate for purchasing conventional cooking products that operate at 

the same efficiency levels as stipulated in each 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., c summarized experiences with various utility rebate programs.1 XENERGY’s analytical 

method utilized graphs, or penetration curves, that estimate the market penetration of a 

technology based on its benefit/cost (B/C) ratio. DOE consulted with experts and reviewed other 

methods of estimating the effect of consumer rebate programs on the market penetration of 

efficient technologies. The other methods, developed after the referenced XENERGY report was 

published,2, 3, 4, 5, 6, 7 used different approaches: other economic parameters (e.g., payback 

period), expert surveys, or model calibration based on specific utility program data rather than 

multi-utility data. Some models in use by energy efficiency program evaluation experts were so 

client-specific that generic relationships between economic parameters and consumer response 

could not be established.5DOE decided that the most appropriate available method for this RIA 

c XENERGY is now owned by KEMA, Inc. (www.kema.com)

17-4

 
was the XENERGY approach of penetration curves based on B/C ratio, which incorporates 

lifetime operating cost savings.

XENERGY’s model estimates market impacts induced by financial incentives based on 

the premise that two types of information diffusion drive the adoption of new technologies. 

Internal sources of information encourage consumers to purchase new equipment primarily 

through word-of-mouth from early adopters. External sources affect consumer purchase 

decisions through marketing efforts and information from outside the consumer group. Appendix 

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 conventional cooking products

by determining, for each TSL, the increase in market penetration of equipment meeting the target 

level relative to their market penetration in the base case. It used the interpolation method 

presented in Blum et al (2011)9 to create customized penetration curves based on relationships 

between actual base case market penetrations and actual B/C ratios. To inform its estimate of 

B/C ratios provided by a rebate program DOE performed a thorough nationwide search for 

existing rebate programs for conventional cooking products. It gathered data on utility or agency 

rebates throughout the nation for this equipment, and used this data to calibrate the customized 

penetration curves it developed for each product class covered by this RIA so they can best 

reflect the market barrier levels that consumer rebates for conventional cooking products would 

face. Section 17.3.2.2 shows the resulting interpolated curves used in the analysis.

17.3.2.2 Analysis 

DOE estimated the effect of increasing the B/C ratio of conventional cooking products

via a rebate that would pay for all of the increased installed cost of units that meet the target 

efficiency levels compared to units meeting the baseline efficiency level.

d To inform its estimate 

of an appropriate rebate amount, DOE performed a thorough nationwide search for existing 

rebate programs for conventional cooking products. DOE could not find rebate programs for this 

product so it estimated a rebate value for each product class covered in this RIA by assuming 

that a rebate would cover all increased installed costs. DOE applied this in the calculation of the 

B/C ratio of conventional cooking products under the effect of consumer rebates. (Appendix 

d The baseline technology is defined in the engineering analysis, Chapter 5, as the technology that represents the 

basic characteristics of conventional cooking products. A baseline unit typically is one that just meets current 

Federal energy conservation standards and provides basic consumer utility. 

17-5

 
17A, identifies the rebate programs and details the methodology DOE used to estimate a market 

representative rebate amount.) DOE assumed that rebates would remain in effect at the same 

level throughout the forecast period (2019-2048). 

DOE first calculated the B/C ratio of a cooking product without a rebate using the 

difference in total installed costs (C) and lifetime operating cost savings e (B) between a unit 

meeting the target level and a 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 

each TSL on the B/C ratio of conventional cooking products shipped in the first year of the 

analysis period.

Table 17.3.1 Benefit/Cost Ratios Without and With Rebates 

TSL 1 TSL 2 TSL 3

Electric Standard Ovens – Freestanding

B/C Ratio Without Rebate 11.2 2.4 0.6

Rebate Amount (2014$) 1.13 10.85 96.76

B/C Ratio With Rebate infinite infinite infinite

Estimated Market Barriers High Mod Low

Electric Self-Clean Ovens – Freestanding

B/C Ratio Without Rebate 11.2 11.2 0.5

Rebate Amount (2014$) 1.13 1.13 85.13

B/C Ratio With Rebate infinite infinite infinite

Estimated Market Barriers High High No-Low

Electric Self-Clean Ovens – Built-In/Slide-In

B/C Ratio Without Rebate 11.2 11.2 0.5

Rebate Amount (2014$) 1.13 1.13 85.13

B/C Ratio With Rebate infinite infinite infinite

Estimated Market Barriers High High No-Low

Gas Standard Ovens – Freestanding

B/C Ratio Without Rebate 0.0 13.6 4.3

Rebate Amount (2014$) n/a 16.96 54.19

B/C Ratio With Rebate n/a infinite infinite

Estimated Market Barriers n/a High Mod-High

Gas Self-Clean Ovens – Freestanding

B/C Ratio Without Rebate 12.1 21.3 4.7

Rebate Amount (2014$) 1.12 9.96 45.17

B/C Ratio With Rebate infinite infinite infinite

Estimated Market Barriers High High Mod-High

* No-Low: No-to-Low market barriers; Mod: Moderate market barriers; Low-Mod: Low-to-Moderate market 

barriers; Mod-High: Moderate-to-High market barriers; xHigh: Extremely-High market barriers; Hg-xHg: Highto-Extremely-High market barriers.

e The cash flow of the operating cost savings is discounted to the purchase year using a 7 percent discount rate.

17-6

 
DOE used the B/C ratio along with the customized penetration curves shown in Figure 

17.3.1 to estimate the percentage of consumers who would purchase conventional cooking 

products that meet the target levels both with and without a rebate incentive. The estimated 

levels of market barriers corresponding to the penetration curves DOE calculated to represent the 

market behavior for conventional cooking products at the proposed TSL are indicated

(highlighted) in Table 17.3.1. DOE assumed the estimated market barriers would remain the 

same over the whole analysis period. In Figure 17.3.1, due to DOE’s assumption that rebates 

would offset incremental costs in full, which would eventually lead all product classes to present 

infinite benefit-cost ratio with rebates, none of the charts include the point corresponding to that 

benefit-cost ratio. As for Gas Standard Ovens – Freestanding and Gas Self-Clean Ovens –

Freestanding, the base case market penetration is below the expected penetration of a product 

with same benefit-cost ratio in a market with extremely-high level of market barriers.

17-7
TSL #2

Electric Standard Ovens – Freestanding Electric Self-Clean Ovens – Freestanding

17-8
Electric Self-Clean Ovens – Built-In/Slide-In Gas Standard Ovens – Freestanding

17-9
Gas Self-clean Ovens 

– Freestanding

Figure 17.3.1 Market Penetration Curves for Conventional Cooking Products

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 conventional cooking products

regarding the market penetration of products in 2019 that meet the target levels at each TSL

given a consumer rebate. 

17-10
Table 17.3.2 Market Penetrations in 2019 Attributable to Consumer Rebates

TSL 1 TSL 2 TSL 3

Electric Standard Ovens – Freestanding

Base-Case Market Share 9.7% 9.3% 6.8%

Policy Case Market Share 50.0% 68.9% 77.5%

Increased Market Share 40.4% 59.6% 70.7%

Electric Self-Clean Ovens – Freestanding

Base-Case Market Share 15.8% 15.8% 11.7%

Policy Case Market Share 54.8% 54.8% 81.5%

Increased Market Share 39.0% 39.0% 69.9%

Electric Self-Clean Ovens – Built-In/Slide-In

Base-Case Market Share 15.8% 15.8% 11.7%

Policy Case Market Share 54.8% 54.8% 81.5%

Increased Market Share 39.0% 39.0% 69.9%

Gas Standard Ovens – Freestanding

Base-Case Market Share 42.5% 8.3% 7.7%

Policy Case Market Share 42.5% 50.0% 61.0%

Increased Market Share 0.0% 41.7% 53.3%

Gas Self-Clean Ovens – Freestanding

Base-Case Market Share 13.6% 13.4% 12.6%

Policy Case Market Share 52.7% 50.0% 62.8%

Increased Market Share 39.1% 36.6% 50.1%

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 for the whole forecast period. Section 17.4 presents the resulting 

market penetration trends for the policy case of consumer rebates for conventional cooking 

products. Because energy prices increase and equipment prices decrease over time, the B/C 

ratios increase over the forecast period. Since the B/C ratios grow in higher proportions than the 

base case market shares do, the estimated market barriers increase over time and eventually 

reduce the market penetration of more efficient technologies over the forecast period.

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.

17-11
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 conventional cooking products, DOE also reviewed other tax credit programs that have been 

offered at both the Federal and State levels for energy-efficient appliances.

The Energy Policy Act of 2005 (EPACT 2005) included Federal tax credits for 

consumers who purchase energy-efficient products.

13 Those tax credits were in effect in 2006 

and 2007, expired in 2008, were reinstated for 2009–2010 by the American Recovery and 

Reinvestment Act of 2009 (ARRA), extended by Congress for 2011 with some modifications, 

and expired at the end of 2011.

14, 15 The American Taxpayer Relief Act of 2012 extended, with 

some modifications, residential tax credits for air conditioners, heat pumps, furnaces, and water 

heaters placed in service between January 1, 2012 and December 31, 2013.16 DOE reviewed 

Internal Revenue Service data on the numbers of taxpayers who claimed the tax credits during 

tax years 2006 and 2007. DOE also reviewed data from an earlier Federal energy conservation 

tax credit program in place in the 1980s. However, DOE did not find data specific enough to 

conventional cooking products 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 

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 increase in 

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 customized penetration curves it developed for conventional 

cooking products (See Figure 17.3.1).

Table 17.3.3 summarizes DOE’s assumptions for conventional cooking products

regarding the market penetration of products in 2019 that meet the target levels at each TSL

given a consumer tax credit. 

17-12
Table 17.3.3 Market Penetrations in 2019 Attributable to Consumer Tax Credits

TSL 1 TSL 2 TSL 3

Electric Standard Ovens – Freestanding

Base-Case Market Share 9.7% 9.3% 6.8%

Policy Case Market Share 33.9% 45.1% 49.2%

Increased Market Share 24.2% 35.8% 42.4%

Electric Self-Clean Ovens – Freestanding

Base-Case Market Share 15.8% 15.8% 11.7%

Policy Case Market Share 39.2% 39.2% 53.6%

Increased Market Share 23.4% 23.4% 41.9%

Electric Self-clean Ovens – Built-In/Slide-In

Base-Case Market Share 15.8% 15.8% 11.7%

Policy Case Market Share 39.2% 39.2% 53.6%

Increased Market Share 23.4% 23.4% 41.9%

Gas Standard Ovens – Freestanding

Base-Case Market Share 42.5% 8.3% 7.7%

Policy Case Market Share 42.5% 33.3% 39.7%

Increased Market Share 0.0% 25.0% 32.0%

Gas Self-Clean Ovens – Freestanding

Base-Case Market Share 13.6% 13.4% 12.6%

Policy Case Market Share 37.1% 35.4% 42.7%

Increased Market Share 23.5% 22.0% 30.1%

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 for the whole forecast period. Section 17.4 presents the resulting 

market penetration trends for the policy case of consumer tax credits for conventional cooking 

products that meet the efficiency levels for the proposed TSL. Because the market penetration 

for consumer tax credits is proportional to the market penetration DOE calculated for consumer 

rebates, the former follows a similar trend over the forecast 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 conventional cooking products that meet the target efficiency levels at each TSL, DOE 

assumed that a manufacturer tax credit would lower the consumer’s purchase cost by an amount 

equivalent to that provided by the consumer rebates or tax credits described above. DOE further 

assumed that manufacturers would pass on some of their reduced costs to consumers, causing a 

direct price effect. DOE assumed that no announcement effect would occur, because the program 

would not be visible to consumers.f Because the direct price effect is approximately equivalent to 

the announcement effect,10 DOE estimated that a manufacturer tax credit would induce half the 

f Note that this is a conservative assumption, since it is possible that manufacturers or utility/agency efficiency 

programs might promote the models for which manufacturers increase production due to the tax credits, which in 

turn might induce some announcement effect. However, DOE found no data on such programs on which to base an 

estimate of the magnitude of this possible announcement effect on consumer behavior.

17-13

 
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 increase in penetration 

rates predicted for the rebate policy to estimate the effects of a manufacturer tax credit policy. In 

doing so, DOE incorporated the assumptions for consumer response to financial incentives from 

the customized penetration curves it developed for conventional cooking products. (See Figure 

17.3.1).

Table 17.3.4 summarizes DOE’s assumptions for conventional cooking products

regarding the market penetration of products in 2019 that meet the target levels at each TSL 

given a manufacturer tax credit.

Table 17.3.4 Market Penetrations in 2019 Attributable to Manufacturer Tax Credits

TSL 1 TSL 2 TSL 3

Electric Standard Ovens – Freestanding

Base-Case Market Share 9.7% 9.3% 6.8%

Policy Case Market Share 21.8% 27.2% 28.0%

Increased Market Share 12.1% 17.9% 21.2%

Electric Self-Clean Ovens – Freestanding

Base-Case Market Share 15.8% 15.8% 11.7%

Policy Case Market Share 27.5% 27.5% 32.6%

Increased Market Share 11.7% 11.7% 21.0%

Electric Self-Clean Ovens – Built-In/Slide-In

Base-Case Market Share 15.8% 15.8% 11.7%

Policy Case Market Share 27.5% 27.5% 32.6%

Increased Market Share 11.7% 11.7% 21.0%

Gas Standard Ovens – Freestanding

Base-Case Market Share 42.5% 8.3% 7.7%

Policy Case Market Share 42.5% 20.8% 23.7%

Increased Market Share 0.0% 12.5% 16.0%

Gas Self-Clean Ovens – Freestanding

Base-Case Market Share 13.6% 13.4% 12.6%

Policy Case Market Share 25.3% 24.4% 27.7%

Increased Market Share 11.7% 11.0% 15.0%

17-14
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 for the whole forecast period. Section 17.4 presents the resulting 

market penetration trends for the policy case of manufacturer tax credits for conventional 

cooking products. Because the market penetration for manufacturer tax credits is proportional to 

the market penetration DOE calculated for consumer rebates, the former follows a similar trend 

over the forecast period as the latter.

17.3.5 Voluntary Energy Efficiency Targets 

DOE assumed that voluntary energy efficiency targets would lead manufacturers of 

conventional cooking products to gradually stop producing units that operate below the

efficiency levels set for each TSL. DOE assumed that the impetus for phasing out production of 

low-efficiency units would be a program with impacts similar to those of the ENERGY STAR 

labeling program conducted by the Environmental Protection Agency (EPA) and DOE in 

conjunction with industry partners. The ENERGY STAR program specifies the minimum energy 

efficiencies that various products must have to receive the ENERGY STAR label. ENERGY 

STAR encourages consumers to purchase efficient products via marketing that promotes 

consumer label recognition, various incentive programs that adopt the ENERGY STAR 

specifications, and manufacturers’ promotion of their qualifying appliances. ENERGY STAR 

projects market penetration of compliant appliances and estimates the percentage of sales of 

compliant appliances that are attributable to the ENERGY STAR program. 

Researchers have analyzed the ENERGY STAR program’s effects on sales of several 

consumer products. Program efforts generally involve a combination of information 

dissemination and utility or agency rebates. The analyses have been based on State-specific data 

on percentages of shipments of various appliances that meet ENERGY STAR specifications. The 

analyses generally have concluded that the market penetration of ENERGY STAR-qualifying 

appliances is higher in regions or States where ancillary promotional programs have been 

active.

19, 20, 21

DOE believes that informational incentive programs – like ENERGY STAR, or any other 

labeling program sponsored by industry or other organizations – are likely to reduce the market 

barriers to more efficient products over time. During the rebate analysis, when assessing the B/C 

ratio and market penetration in the base case for conventional cooking products, DOE observed 

that the level of market barriers for more efficient conventional cooking products are in the range

of low- to high levels 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 conventional cooking products 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 conventional cooking products.

g 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.

g 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.

17-15

 
Table 17.3.5 Market Barrier Changes Attributable to Voluntary Energy Efficiency 

Targets (TSL #2) 

Base Case Voluntary Energy

Efficiency Targets

Electric Standard Ovens – Freestanding Moderate Low

Electric Self-Clean Ovens – Freestanding High Moderate-High

Electric Self-Clean Ovens – Built-In/Slide-In High Moderate-High

Gas Standard Ovens – Freestanding High Moderate-High

Gas Self-clean Ovens – Freestanding High Moderate-High

Table 17.3.6 summarizes DOE’s assumptions for conventional cooking products

regarding the market penetration of products in 2019 that meet the target levels at each TSL 

given voluntary energy efficiency targets. Table 17.3.7 expands on Table 17.3.6 to include, for 

the proposed TSL, DOE’s assumptions regarding the market penetration of units in selected 

years.

Table 17.3.6 Market Penetrations in 2019 Attributable to Voluntary Energy Efficiency 

Targets

TSL 1 TSL 2 TSL 3

Electric Standard Ovens – Freestanding

Base-Case Market Share 9.7% 9.3% 6.8%

Policy Case Market Share 11.2% 12.9% 7.8%

Increased Market Share 1.5% 3.5% 1.0%

Electric Self-Clean Ovens – Freestanding

Base-Case Market Share 15.8% 15.8% 11.7%

Policy Case Market Share 16.6% 16.6% 14.3%

Increased Market Share 0.9% 0.9% 2.7%

Electric Self-Clean Ovens – Built-In/Slide-In

Base-Case Market Share 15.8% 15.8% 11.7%

Policy Case Market Share 16.7% 16.7% 14.4%

Increased Market Share 0.9% 0.9% 2.7%

Gas Standard Ovens – Freestanding

Base-Case Market Share 42.5% 8.3% 7.7%

Policy Case Market Share 42.5% 12.3% 9.2%

Increased Market Share 0.0% 4.0% 1.6%

Gas Self-Clean Ovens – Freestanding

Base-Case Market Share 13.6% 13.4% 12.6%

Policy Case Market Share 15.0% 21.2% 14.0%

Increased Market Share 1.4% 7.7% 1.4%

17-16
Table 17.3.7 Market Penetrations in Selected Years Attributable to Voluntary Energy 

Efficiency Targets for TSL 2 

2019 2028 2048

Electric Standard Ovens – Freestanding

Base-Case Market Share 9.3% 9.3% 9.2%

Policy Case Market Share 12.9% 43.9% 49.7%

Increased Market Share 3.5% 34.6% 40.4%

Electric Self-Clean Ovens – Freestanding

Base-Case Market Share 15.8% 15.6% 15.4%

Policy Case Market Share 16.6% 27.1% 33.7%

Increased Market Share 0.9% 11.5% 18.3%

Electric Self-Clean Ovens – Built-In/Slide-In

Base-Case Market Share 15.8% 15.6% 15.4%

Policy Case Market Share 16.7% 27.3% 33.9%

Increased Market Share 0.9% 11.6% 18.5%

Gas Standard Ovens – Freestanding

Base-Case Market Share 8.3% 8.3% 8.3%

Policy Case Market Share 12.3% 31.7% 37.1%

Increased Market Share 4.0% 23.4% 28.9%

Gas Self-Clean Ovens – Freestanding

Base-Case Market Share 13.4% 13.4% 13.4%

Policy Case Market Share 21.2% 41.5% 45.0%

Increased Market Share 7.7% 28.1% 31.6%

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 for the whole forecast period. Section 17.4 presents the 

resulting market penetration trends for the policy case of voluntary energy efficiency targets for

conventional cooking products that meet the efficiency levels for the proposed TSL. Because of 

the decrease in the market barriers level over the first 10 years of the analysis period, the market 

penetration of more efficient conventional cooking products significantly increases over that 

period. For the remaining 20 years of the forecast period the increase in market penetration keeps 

growing because, even though the market barriers level remains constant (at 2028 level), the

increase in energy prices and decrease in equipment price lead to increasing B/C ratios and 

eventually to higher market penetrations.

17.3.6 Bulk Government Purchases 

Bulk government purchases can lead to Federal, State, and local governments purchasing

large quantities of products that meet a certain, target efficiency level. Combining the market 

demands of multiple public sectors 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.

17-17
Most of the previous bulk government purchase (procurement) initiatives at the Federal, 

State, and municipal levels have not tracked data on numbers of purchases or degree of 

compliance with procurement specifications. In many cases, procurement programs are 

decentralized, being part of larger State or regional initiatives. DOE based its assumptions 

regarding the effects of this policy on studies the Federal Energy Management Program (FEMP) 

performed regarding the savings potential of its procurement specifications for appliances and 

other products. FEMP, however, does not track purchasing data, because of the complex range of 

purchasing systems, large number of vendors, and so on. States, counties, and municipalities

have demonstrated increasing interest and activity in “green purchasing." Although many of the 

programs target office equipment, the growing infrastructure for developing and applying 

efficient purchasing specifications indicates that bulk government purchase programs are 

feasible.

23, 24

DOE assumed that government agencies would administer bulk purchasing programs for

conventional cooking products. At the federal level, this type of program could lead to FEMP 

procurement guidelines for conventional cooking products, which would refer to the target levels 

of the proposed TSL as the minimum efficiency levels of conventional cooking products to be 

purchased by federal government agencies. DOE reviewed its own previous research on the 

potential for market transformation through bulk government purchases. Its major study analyzed 

several scenarios based on the assumption that 20 percent of Federal equipment purchases in 

2000 already incorporated energy efficiency requirements based on FEMP guidelines. One 

scenario in the DOE report showed energy efficient purchasing ramping up during 10 years from 

20 percent to 80 percent of all Federal purchases.25 Based on this study, DOE estimated that a

bulk government purchase program instituted within a 10-year period would result in at least 80 

percent of government-purchased conventional cooking products meeting the target efficiency 

level.

DOE assumed that bulk government purchases would affect a subset of housing units for 

which government agencies purchased or influenced the purchase of conventional cooking 

products. This subset would consist primarily of public housing and housing on military bases.

According to the 2009 Residential Energy Consumption Survey (RECS 2009), the percentage of 

all U.S. households that are housing units in public housing authority ranges – depending on the 

product class – from 4.2 percent to 4.8 percent (see Table 17.3.8).

26 DOE therefore estimated 

that those percentages of the U.S. housing units constitute, for each product class, the population 

to which this policy would apply.

Table 17.3.8 Percentage of U.S. Households in Public Housing Authority 

Electric Standard Ovens – Freestanding 4.2%

Electric Self-Clean Ovens – Freestanding 4.2%

Electric Self-Clean Ovens – Built-In/Slide-In 4.2%

Gas Standard Ovens – Freestanding 4.8%

Gas Self-Clean Ovens – Freestanding 4.8%

DOE estimated that starting in 2019, each year of a bulk government purchase policy 

would result in an increasing percent of shipments of government-purchased units beyond the 

base case that would meet the target efficiency level. DOE estimated that within 10 years (by 

17-18
2028 bulk government purchasing programs would result in 80 percent h of the market for 

conventional cooking products used in publicly owned housing meeting the target level. DOE 

modeled the bulk government purchase program assuming that the market share for conventional 

cooking products achieved in 2028 would be at least maintained throughout the rest of the 

forecast period. 

Table 17.3.9 summarizes DOE’s assumptions for conventional cooking products

regarding the market penetration of products in 2019 that meet the target levels at each TSL 

given bulk government purchases.

Table 17.3.9 Market Penetrations in 2019 Attributable to Bulk Government Purchases

TSL 1 TSL 2 TSL 3

Electric Standard Ovens – Freestanding

Base-Case Market Share 9.7% 9.3% 6.8%

Policy Case Market Share 9.8% 9.6% 7.1%

Increased Market Share 0.2% 0.3% 0.3%

Electric Self-clean Ovens – Freestanding

Base-Case Market Share 15.8% 15.8% 11.7%

Policy Case Market Share 16.0% 16.0% 12.0%

Increased Market Share 0.2% 0.2% 0.3%

Electric Self-clean Ovens – Built-In/Slide-In

Base-Case Market Share 15.8% 15.8% 11.7%

Policy Case Market Share 16.0% 16.0% 12.0%

Increased Market Share 0.2% 0.2% 0.3%

Gas Standard Ovens – Freestanding

Base-Case Market Share 42.5% 8.3% 7.7%

Policy Case Market Share 42.5% 8.6% 8.0%

Increased Market Share 0.0% 0.3% 0.3%

Gas Self-Clean Ovens – Freestanding

Base-Case Market Share 13.6% 13.4% 12.6%

Policy Case Market Share 13.8% 13.7% 13.0%

Increased Market Share 0.2% 0.3% 0.3%

The increased market shares attributable to bulk government purchases shown in Table 

17.3.9 were used as inputs in the NIA-RIA model. Appendix 17A shows the annual market share 

increases due to this policy for the whole forecast period. Section 17.4 presents the resulting 

market penetration trends for the policy case of bulk government purchases for conventional 

cooking products. Market penetrations increase over the first 10 years of the forecast period, and 

steady for the rest of the analysis period.

h The 80 percent target to be achieved within 10 years may not be reached, as it is constrained by the market share 

below the target level in the base case scenario.

17-19

 
17.4 IMPACTS OF NON-REGULATORY ALTERNATIVES

Figure 17.4.1 through Figure 17.4.5 show the effects of each non-regulatory policy 

alternative on the market penetration of more efficient conventional cooking products. Relative 

to the base case, the alternative policy cases 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 Electric Standard Ovens – Freestanding

(TSL 2)

17-20
Figure 17.4.2 Market Penetration of Efficient Electric Self-clean Ovens – Freestanding

(TSL 2)

Figure 17.4.3 Market Penetration of Efficient Electric Self-Clean Ovens – Built-In/SlideIn (TSL 2)

17-21
Figure 17.4.4 Market Penetration of Efficient Gas Standard Ovens – Freestanding (TSL 

2)

Figure 17.4.5 Gas Self-Clean Ovens – Freestanding (TSL 2)

17-22
Table 17.4.1shows the national energy savings and net present value for the five nonregulatory policy alternatives analyzed in detail for conventional cooking products. The target 

level for each policy corresponds to the same efficient technology proposed for standards in TSL 

2. The case in which no regulatory action is taken with regard to conventional cooking products

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).iThe 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 consumer rebates, 

followed by consumer and manufacturer tax credits, and voluntary energy efficiency targets. 

Savings for these four alternative policy measures range from 9.3 percent to 67.7 percent of the 

savings from standards at TSL 2. Bulk government purchases have the lowest cumulative energy 

savings. Overall, the energy saving benefits from the alternative policies, range from 0.6 percent 

to 67.7 percent of the benefits from the proposed standards.

Table 17.4.1 Impacts of Non-Regulatory Policy Alternatives (TSL 2) 

Policy Alternative

Energy Savings*

quads

Net Present Value*

million 2014$

7% Disc Rate 3% Disc Rate

Consumer Rebates 0.421 (67.7%)** 2519.9 6143.9

Consumer Tax Credits 0.253 (40.6%) 1511.9 3686.3

Manufacturer Tax Credits 0.126 (20.3%) 756.0 1843.2

Voluntary Energy Efficiency Targets 0.058 (9.3%) 426.9 1793.3

Bulk Government Purchases 0.004 (0.6%) 25.6 62.6

Proposed Standards 0.622 (100.0%) 3991.0 9790.4

* For products covered in this RIA, shipped 2019-2048.

**The percentages show how the energy savings from each policy alternative compare to the energy 

savings from the proposed standards (represented in the table as 100%).

i

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. 

17-23

 
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25C note,. 2005. www.gpo.gov/fdsys/pkg/BILLS-109hr6pcs/pdf/BILLS-109hr6pcs.pdf

14. American Recovery and Reinvestment Act of 2009, 2009. Government Printing Office. 

(Last accessed March, 2014.) <http://www.gpo.gov/fdsys/pkg/BILLS111hr1enr/pdf/BILLS-111hr1enr.pdf>

15. Tax Incentives Assistance Project, Consumer Tax Incentives: Home Heating & Cooling 

Equipment., 2011. (Last accessed March, 2014.) 

<www.energytaxincentives.org/consumers/heating-cooling.php>

16. Tax Incentives Assistance Project, The Tax Incentives Assistance Project (TIAP), 2013.

(Last accessed March, 2014.) <http://energytaxincentives.org/>

17. U.S. Department of Energy - Energy Efficiency & Renewable Energy, Technical Support 

Document: Energy Efficiency Program for Consumer Products and Commercial and 

Industrial Equipment, Residential Dishwashers, Dehumidifiers, and Cooking Products, 

and Commercial Clothes Washers, 2008. Washington, DC. 

18. Tax Incentives Assistance Project, Manufacturers Incentives, (Last accessed March, 

2014.) <http://energytaxincentives.org/builders/appliances.php>

19. Feldman, S., L. Hoegfen, L.Wilson-Wright, and A. Li, Modelling the Effects of U.S. 

ENERGY STAR® Appliance Programs. In Energy Savings: What Works and Who 

Delivers?, May 30-June 4, 2005. ECEEE Summer Study Proceedings. Mandelieu La 

Napoule, France. 

<www.eceee.org/conference_proceedings/eceee/2005c/Panel_4/4228feldman/paper>

20. Rosenberg, M., The Impact of Regional Programs on the Market Share of Energy Star® 

Appliances, August 20-22, 2003. Seattle, WA. Published in the 2003 International Energy 

Program Evaluation Conference Proceedings. 

21. Titus, E. and S. Feldman, Tracking the Effectiveness of Energy-Efficient Appliance 

Programs in the U.S., August 20-22, 2003. In 2003 International Energy Program 

Evaluation Conference Proceedings. Seattle, WA. 

22. Blum, H., Atkinson B., and Lekov, A., A Framework for Comparative Assessments of 

Energy Efficiency Policy Measures, May, 2011. 

17-25
23. Smith, N., Telephone conversation with Barbara Atkinson of LBNL, personal 

communication. National Association of State Procurement Officials. April, 2008.

24. Responsible Purchasing Network, (Last accessed March, 2014.) 

<www.responsiblepurchasing.org/>

25. Harris, J. and F. Johnson, Potential Energy, Cost, and CO2 Savings from EnergyEfficient Government Purchase. ACEEE: Summer Study on Energy Efficiency in 

Buildings, 2000: pp. 147-166 

<http://www.aceee.org/files/proceedings/2000/data/papers/SS00_Panel4_Paper13.pdf>

26. U.S. Department of Energy: Energy Information Administration, Residential Energy 

Consumption Survey (RECS), 2009. (Last accessed December, 2013.) 

<http://www.eia.gov/consumption/residential/data/2009/index.cfm?view=microdata>

17-26
APPENDIX 6A. DETAILED DATA FOR EQUIPMENT PRICE MARKUPS

TABLE OF CONTENTS

6A.1 STATE SALES TAX RATES................................................................................ 6A-1

LIST OF TABLES

Table 6A.4.1 State Sales Tax Rates................................................................................. 6A-1

6A-i
APPENDIX 6A. DETAILED DATA FOR EQUIPMENT PRICE MARKUPS

6A.1 STATE SALES TAX RATES

Table 6A.4.1 State Sales Tax Rates

State

Combined State 

and Local Tax 

Rate

% State

Combined State 

and Local Tax 

Rate

% State

Combined State 

and Local Tax 

Rate

%

Alabama 8.55 Kentucky 6.00 North Dakota 5.95

Alaska 1.30 Louisiana 8.80 Ohio 7.10

Arizona 7.15 Maine 5.50 Oklahoma 8.40

Arkansas 8.90 Maryland 6.00 Oregon --

California 8.40 Massachusetts 6.25 Pennsylvania 6.35

Colorado 6.10 Michigan 6.00 Rhode Island 7.00

Connecticut 6.35 Minnesota 7.20 South Carolina 7.00

Delaware -- Mississippi 7.05 South Dakota 5.45

Dist. of Columbia 5.75 Missouri 7.40 Tennessee 9.45

Florida 6.65 Montana -- Texas 7.95

Georgia 7.05 Nebraska 6.05 Utah 6.65

Hawaii 4.35 Nevada 7.95 Vermont 6.10

Idaho 6.05 New Hampshire -- Virginia 5.60

Illinois 8.00 New Jersey 6.95 Washington 8.90

Indiana 7.00 New Mexico 6.60 West Virginia 6.05

Iowa 6.80 New York 8.45 Wisconsin 5.45

Kansas 7.90 North Carolina 6.90 Wyoming 5.50

Source: The Sales Tax Clearinghouse at https://thestc.com/STRates.stm (Accessed on July 18, 2014).

6A-1
APPENDIX 7A. CONVENTIONAL OVENS: DETERMINATION OF ENERGY-USING 

COMPONENTS

TABLE OF CONTENTS

7A.1 INTRODUCTION .................................................................................................. 7A-1

7A.2 METHODOLOGY FOR DISAGGREGATING ELECTRIC RANGE 

ANNUAL ENERGY CONSUMPTION................................................................. 7A-1

7A.3 METHODOLOGY FOR ESTABLISHING GAS COOKING ENERGYUSING COMPONENTS ........................................................................................ 7A-4

LIST OF TABLES

Table 7A.2.1 Electric Cooking Products: Energy-Using Components............................ 7A-3

Table 7A.3.1 Gas Cooking Products: Energy-Using Components.................................. 7A-6

7A-i
APPENDIX 7A. 

CONVENTIONAL OVENS: DETERMINATION OF ENERGY-USING COMPONENTS

7A.1 INTRODUCTION

As presented in chapter 7, section 7.2, based on recent survey data, DOE determined that 

the representative annual energy consumption of an electric range is 287.5 kWh per year. DOE 

disaggregated the range energy consumption into two portions - one allocated to the oven and the 

other portion allocated to the cooking top. In addition, because oven energy use may consist of 

several energy-using components (i.e., cooking, ignition, self-clean, and clock) and potential 

increases in efficiency may affect only a subset of these components, DOE had to disaggregate 

oven energy consumption into its specific energy-using components. The following sections 

detail: (1) DOE’s method for disaggregating the representative electric range energy 

consumption into the various energy-using components of electric ovens; and (2) DOE’s method 

for establishing the representative energy use of gas cooking product energy-using components 

based on the values that were determined for the electric cooking product energy-using 

components.

7A.2 METHODOLOGY FOR DISAGGREGATING ELECTRIC RANGE 

ANNUAL ENERGY CONSUMPTION

As noted above, DOE determined that the representative annual energy consumption of 

an electric range is 287.5 kWh per year. Based on the following equation, DOE assumed the 

energy consumption was equal to the sales weighted-average of standard and self-clean oven 

energy consumption plus the cooking top energy consumption. Also included is the standby 

power associated with electric ovens.

( ) (1 ) ( 8760 0.001)

287.5 /

_ tan

_ = × _ + × + × + + × ×

=

SC CO SC e SC CO STD CA S dby

elec cooking

MSe E Ees S MSe E E P

UEC kWh yr

where:

UECelec cooking = Annual energy consumption of electric cooking (kWh/yr),

MSeSC = Market share of electric ovens that are self-clean, 68.4%,1

ECO_SC = Annual cooking energy consumption of self-clean electric ovens

(kWh/yr),

Ees = Typical self-clean energy consumption per cycle for electric self-clean

ovens, 32.8 kWh (as reported in Chapter 7, Table 7.2.3),

Se = Number of self-clean cycles per year for electric self-clean ovens 

according to the DOE test procedure, 4,2

7A-1
ECO_STD = Annual cooking energy consumption of standard electric ovens

(kWh/yr),

ECA = Annual energy consumption of electric cooking tops (kWh/yr), 

PStandby = Power input for standby (watts), 3,

a

8760 = Hours in a year, and

0.001 = Conversion to convert watts in kW.

DOE estimated the annual cooking energy consumption of electric self-clean ovens as a 

fraction of the cooking energy consumption of a standard electric oven. This fraction was taken 

from the ratio of energy consumption as established by the DOE test procedure. The following 

equation represents the calculation used by DOE:

ECO _ SC = ReSC _ STD × ECO _ STD

ECA = ReCT _ STD × ECO _ STD

where,

ReSC_STD = Ratio of annual self-clean electric oven cooking energy to annul 

standard electric cooking energy.

To calculate the above ratios, DOE took the annual useful cooking energy output values 

from the DOE test procedure and divided them by the baseline cooking efficiencies reported in 

chapter 7, Table 7.2.2. 

( )

( ) Re 1.1757 _ = =

O STD

O SC

SC STD O EFFeo

O EFFeo

where,

OO = Annual useful cooking energy output for ovens according to the DOE 

test procedure, 29.3 kWh,

2

EFFeoSC = Cooking efficiency of the baseline self-clean electric oven, 10.06%, 

EFFeoSTD = Cooking efficiency of the baseline standard electric oven, 11.89%, and

 

a DOE assumed a baseline standby consumption of 3 watts for purposes of disaggregating the electric range annual 

energy consumption into its energy-using components. As reported in Chapter 7, the baseline standby power 

consumption for electric standard and self-clean ovens are 1.7 watts.

7A-2

 
With the annual cooking energy consumption of self-clean electric ovens expressed as a 

function of standard electric oven annual cooking energy consumption, DOE solved for the 

standard electric oven annual cooking energy consumption by the using the following equation:

kWh yr

MSe MSe

MSe Ees S P

E

SC SC STD SC CT STD

SC e S dby

CO STD

114.1 /

68.4% 1.1757 1 68.4% 1.0494

287.5 68.4% 8.2 4 2 8760 0.001

Re 1 Re

287.5 8760 0.001

_

_ _

_

_

_

tan

_ _

_

= × + +

× × × × =

× + +

× × × × =

With the standard electric oven annual cooking energy consumption established

(EAO_STD), DOE solved for the self-clean electric oven annual cooking energy consumption

values by using the following equation:

kWh yr

ECO SC SC STD ECO STD

1.1757 114.1 123.9 /

Re _ _ _

= × =

= ×

Table 7A.2.1 summarizes the energy-using components of electric cooking products. 

Also provided are the annual useful cooking energy output values—one set based on the DOE 

test procedure and another set deduced from the lower annual energy consumption values. 

Table 7A.2.1 Electric Cooking Products: Energy-Using Components

Energy-Use Components

Standard Oven Self-Clean Oven

Free-Standing BuiltIn/Slide-In Free-Standing BuiltIn/Slide-In

Cooking Efficiency 10.9% 10.6% 9.9% 9.7%

Cooking Energy (kWh/yr) 114.1 114.1 123.9 123.9

Self-clean Energy (kWh/yr) 32.8 32.8

Standby (kWh/yr) 19.2 19.2 19.2 19.2

Total (kWh/yr) 133.3 133.3 175.9 175.9

Annual Useful Cooking Energy Output (OO for ovens)

Current DOE test procedure (kWh) 29.3 29.3 29.3 29.3

Based on electric range annual cooking 

energy of 287.5 kWh/yr (kWh) 12.4 12.4 12.4 12.4

7A-3
7A.3 METHODOLOGY FOR ESTABLISHING GAS COOKING ENERGYUSING COMPONENTS

DOE estimated the annual energy consumption of gas cooking products based on the 

lower (revised) annual useful cooking energy output values that DOE deduced from the electric 

range annual energy consumption of 287.5 kWh per year. As represented by the following

equation, DOE assumed that the ratio of the revised-to-current annual useful energy output 

values for electric cooking products applied to gas cooking products as well:

kBtu

kWh

kWh kBtu

O

O

O O

O ELEC DOE

O ELEC REV

O GAS REV O GAS DOE 37.7

29.3

12.4 88.8

_ _

_ _

_ _ = _ _ × = × =

where:

OO_GAS_REV = Revised annual useful cooking energy output for gas ovens,

OO_GAS_DOE = DOE test procedure aannual useful cooking energy output for gas ovens,

OO_ELEC_REV = Revised annual useful cooking energy output for electric ovens, and

OO_ELEC_DOE = DOE test procedure annual useful cooking energy output for electric 

ovens.

With the revised annual useful cooking output values known for gas cooking products, 

DOE used the following test procedure equations to calculate the annual cooking energy 

consumption of gas cooking products. In the equations below, DOE used the baseline cooking 

efficiencies reported in chapter 7, Table 7.2.2.

kBtu yr

kBtu

EFFgo

O

E

SC

O GAS REV

CO SC GAS 657.7 /

5.7%

_ _ 37.7 _ _ = = =

kBtu yr

kBtu

EFFgo

O

E

STD

O GAS REV

CO STD GAS 863.1 /

4.4%

_ _ 37.7 _ _ = = =

where:

ECO_SC_GAS = Annual cooking energy consumption of gas self-clean ovens,

EFFgoSC = Cooking efficiency of the baseline self-clean gas oven, 5.7%,

ECO_STD_GAS = Annual cooking energy consumption of gas standard ovens, and

EFFgoSTD = Cooking efficiency of the baseline standard gas oven, 4.4%.

7A-4
Table 7A.3.1 summarizes the energy-using components of gas cooking products. Also 

provided are the annual useful cooking energy output values—one set based on the DOE test 

procedure and the other based on the revised set of values. Ignition, self-clean, and clock 

standby energy consumption values are described and reported in chapter 7, Tables 7.2.2 for gas 

standard ovens, and self-clean ovens, respectively.

7A-5
Table 7A.3.1 Gas Cooking Products: Energy-Using Components

Energy-Using 

Components

Standard Oven Self-clean Oven

Freestanding Built-In/Slide-In Freestanding Built-In/Slide-In

Cooking 

Efficiency 4.6% 4.6% 6.0% 6.0%

Cooking 

Energy 

(kBtu/yr)

863.1 863.1 657.7 657.7

Self-clean Energy

 Gas (kBtu/yr) 217.8 217.8

 Electric 

(kWh/yr)

5.8 5.8

Ignition

 Gas (kBtu/yr)

 Electric 

(kWh/yr) 43.5 43.5 43.5 43.5

Standby

(kWh/yr)* 19.2

Total

 Gas (kBtu/yr) 1,011.6 1,011.6 1,043.7 1,043.7

Annual Useful Cooking Energy Output (OO for ovens)

 Current DOE 

test procedure 

(kBtu)

88.8 88.8 88.8 88.8

Revised values 

(kBtu) 37.7 37.7 37.7 37.7

7A-6
REFERENCES

1 U.S. Energy Information Administration, Residential Energy Consumption Survey 2009, 

Washington, DC. 

 <http://www.eia.gov/consumption/residential/data/2009/>.

2 U.S. Office of the Federal Register. Code of Federal Regulations, Title 10, Energy. Part 430, 

Subpart B, Appendix I: Uniform Test Method for Measuring the Energy Consumption of 

Conventional Ranges, Conventional Cooking Tops, Conventional Ovens, and Microwave 

Ovens, 2006. Washington, DC.

7A-7

 
APPENDIX 8A. USER INSTRUCTIONS FOR LIFE-CYCLE COST AND 

PAYBACK PERIOD SPREADSHEET

TABLE OF CONTENTS

8A.1 DEFINITIONS........................................................................................................ 8A-1

8A.2 BASIC INSTRUCTIONS....................................................................................... 8A-2

8A-i
APPENDIX 8A. USER INSTRUCTIONS FOR LIFE-CYCLE COST AND PAYBACK 

PERIOD SPREADSHEET

8A.1 DEFINITIONS

The interested reader can examine and reproduce detailed results of the U.S. Department 

of Energy’s (DOE’s) life-cycle cost (LCC) and payback period (PBP) analysis for conventional 

cooking products by using Microsoft Excel spreadsheets available on DOE’s website at

http://energy.gov/eere/buildings/appliance-and-equipment-standards-program. 

To fully execute the spreadsheets requires both Microsoft Excel and Crystal Ball software. Both 

applications are commercially available. Crystal Ball is available at www.decisioneering.com. 

The latest version of the workbook, which is posted on the DOE website, was tested 

using Microsoft Excel 2010. The LCC and PBP workbook for conventional cooking products

comprises the following worksheets.

Summary Presents the results of an analysis in terms of average LCC, LCC

savings, and simple PBP for all conventional cooking product 

classes. A table includes, for each efficiency level considered, 

installed price; lifetime operating cost; LCC average savings; and 

the percentage of customers that would incur a net cost from each 

standard level. The user can stipulate three parameters for a 

simulation run: whether the AEO energy price trend reflects an 

economic case that is reference, low-growth, or high-growth 

(reference is default); the number of simulation runs to be 

performed within a range of 1000–10,000 (10,000 is default); and 

equipment price trend, i.e., price based on PPI trend, or constant 

equipment price.

LCC & Payback The LCC&Payback worksheet shows LCC and PBP calculation 

results for different efficiency levels for a single Residential 

Energy Consumption Survey (RECS) 2009 household. During a 

Crystal Ball simulation, the spreadsheet records the LCC and PBP 

values for every sampled household.

Rebuttable Payback The Rebuttable Payback worksheet contains the installation costs, 

cooking efficiencies, energy use calculations, and the simple PBP 

calculations for each efficiency level.

RECS Sample The RECS Sample worksheet contains the RECS 2009 household 

data for each product type. During a Crystal Ball simulation, DOE 

8A-1
uses these household characteristics to determine the analysis 

parameters.

Repair and Maintenance Gives repair and maintenance costs by age for all product classes 

at every efficiency level. 

Energy Use Provides energy use components for all product classes at every 

efficiency level.

Base-Case Efficiency Gives the market shares for efficiency levels in the base case.

Distribution

Equipment Prices Develops total installed cost for conventional cooking products in 

2014$. This sheet provides baseline and incremental manufacturer 

costs, retail price, sales tax, and installation cost for all product 

classes and each efficiency level. Includes the assumptions used 

about markups and sales tax.

Energy Prices Contains the regional prices in 2014$ for electricity used in the 

LCC and PBP analysis.

Energy Price Trends Contains the electricity price trends for the reference, high, and 

low economic growth scenarios based on AEO 2015.

Discount Rate Contains data from which an average discount rate and a 

distribution of discount rates are determined.

Lifetime Presents the average lifetime, in years, for all product classes, the 

Weibull parameters used for the survival function, and a graph of 

the Weibull retirement function for ovens.

Forecast Cells Gives details regarding base-case efficiency distributions for all 

conventional cooking product classes. Median, minimum, 

maximum, and average values are given, along with 5th, 25th, 50th, 

75th, and 95th percentile values. Included are product prices and 

details of the LCC and PBP (LCC savings in terms of money, 

energy, and the percentages of customers that would experience a 

net cost, no impact, or net savings from each efficiency level). 

8A.2 BASIC INSTRUCTIONS

Basic instructions for operating the LCC spreadsheet are provided below.

8A-2
1. After downloading the LCC file from DOE’s website, use Microsoft Excel to open 

it. At the bottom of the workbook, click on the tab for the sheet labeled Summary. 

2. Use Excel’s “View/Zoom” command in the top menu bar to change the size of the

display so that it fits your monitor.

3. Use the graphical interface in the spreadsheet to choose parameters or enter data. 

You can change the default choices for the three inputs listed under "User Input" 

(energy price trend, start year, and number of simulation runs). To change a default 

input, select the desired value from the drop-down choices by the input box.

4. After selecting the desired parameters, click the “Run” button. The spreadsheet will 

minimize until the simulation is complete, and will then re-open with the updated 

results.

8A-3
8B-i

APPENDIX 8B. UNCERTAINTY AND VARIABILITY IN 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-2

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-1

APPENDIX 8B. UNCERTAINTY AND VARIABILITY IN 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 first: 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 means that 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 in as much 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-2

8B.4 APPROACHES TO UNCERTAINTY AND VARIABILITY

This section describes two approaches to uncertainty and variability: 

• 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.
8B-3

NORMAL TRIANGULAR UNIFORM

WEIBULL CUSTOM

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 

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 either a 1, 2, 3, 4, 5, or 6 will come up, but you do not know which for any particular 

roll. It's the same with 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. Types of probability distributions include those in Figure 

8B.5.1.

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 
8B-4

possibilities (the range and shape of the probability distribution) for each uncertain variable and 

then recalculates the spreadsheet. 
APPENDIX 8C. LIFETIME DISTRIBUTIONS

TABLE OF CONTENTS

8C.1 INTRODUCTION .................................................................................................... 8C-1

8C.2 DERIVATION OF WEIBULL DISTRIBTUION PARAMETERS ......................... 8C-1

LIST OF TABLES

Table 8C.2.1 Conventional Cooking Products ......................................................................8C-2

LIST OF FIGURES

Figure 8C.2.1 Surviving Probability of Electric Cooking Products .....................................8C-3

Figure 8C.2.2 Cumulative Lifetime Length of Electric Cooking Products..........................8C-3

Figure 8C.2.3 Surviving Probability of Gas Cooking Products............................................8C-4

Figure 8C.2.4 Cumulative Lifetime Length of Gas Cooking Products ................................8C-4

8C-i
APPENDIX 8C. LIFETIME DISTRIBUTIONS

8C.1 INTRODUCTION

The U.S. Department of Energy (DOE) characterized the lifetime of both fuel types of 

conventional cooking products being considered for new energy efficiency standards (electric 

and gas conventional cooking products). DOE characterized conventional cooking products

lifetimes using a Weibull probability distribution that ranged from the minimum to maximum 

lifetime estimates, as described in chapter 8, section 8.2.3. The Weibull distribution is 

recommended for evaluating lifetime data, because it can be shaped to match low, most likely (or 

average), and high values. The probability of exceeding the high value is contained in the long 

tail of the Weibull distribution.

1,2

8C.2 DERIVATION OF WEIBULL DISTRIBUTION PARAMETERS

Weibull distributions utilize available data to assign low, average, and high values to a 

random variable that has unknown distribution parameters. DOE applied Weibull distributions to 

product lifetime data to derive low, average, and high lifetime values, along with a percentile

containing a high value. A similar approach is described in a technical note to the software 

Crystal Ball, which uses a most likely value in place of an average value.3 The Weibull 

distribution can be defined as:

Where:

L = location,

α = scale, and

β = shape.

The cumulative distribution therefore is:

Based on available data, Weibull distribution parameters are specified as follows.

1. The output deviates must be greater than the expert opinion of low value.

2. The average, Xavg, must be equal to the average value from the available data.

3. The high value, xb, must correspond to some particular percentile point (e.g., 95

percent or 90 percent).

8C-1
The values for the parameters in the equations were determined using the approach 

outlined in Crystal Ball’s technical note.

3

Crystal Ball can be used to check a solution by specifying a Weibull distribution that has 

the calculated parameters (location, scale, and shape) in an assumption cell, then generating a 

forecast that equals that assumption. The forecast histogram and statistics will confirm whether

the Weibull distribution matches the desired shape.

 This solution can be checked using Crystal Ball by specifying a Weibull distribution with 

the calculated parameters (location, scale, and shape) in an assumption cell and generate a 

forecast that equals the assumption. Forecast histogram and statistics verify that the Weibull 

distribution matches the desired shape.

Table 8C.2.1shows the average, minimum, maximum lifetime, and maximum percentile 

values used to determine the Weibull distribution parameters alpha and beta. For conventional 

cooking products, DOE developed two lifetime estimates based on product fuel type—one for 

electricity and another for natural gas. DOE estimated that product lifetimes did not vary based 

on whether the product was a cooktop or an oven. DOE estimated that the maximum lifetime 

percentile for both fuel types was 99 percent.

Table 8C.2.1 Conventional Cooking Products

Product Fuel Type

Expert Opinion Values Weibull 

Parameters

Minimum

years

Average

years

Maximum

years

Maximum

percentile

%

Alpha 

(scale)

Beta 

(shape)

Electric 10.0 15.0 19.0 99 14.87 7.99

Gas 12.0 17.0 22.0 99 17.06 7.35

 Figure 8C.2.1 through Figure 8C.2.4 show the Weibull distribution as well as the 

cumulative Weibull distribution for each fuel type of conventional cooking products.

8C-2
 Figure 8C.2.1 Surviving Probability of Electric Cooking Products

 Figure 8C.2.2 Cumulative Lifetime Length of Electric Cooking Products

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

Probability of Failure (%)

Age of Equipment (Years)

Lifetime Distribution - Electric

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Probability (%)

Age of Equipment (Years)

Cumulative Lifetime - Electric

8C-3
 Figure 8C.2.3 Surviving Probability of Gas Cooking Products

 Figure 8C.2.4 Cumulative Lifetime Length of Gas Cooking Products

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

Probability of Failure (%)

Age of Equipment (Years)

Lifetime Distribution - Gas

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Probability (%)

Age of Equipment (Years)

Cumulative Lifetime - Gas

8C-4
REFERENCES

1 Barnes, P. R., J. W. Van Dyke, B. W. McConnell, S. M. Cohn, and S. L. Purucker, The 

Feasibility of Replacing or Upgrading Utility Distribution Transformers During Routine 

Maintenance, 1995. Oak Ridge National Laboratory. Oak Ridge, TN. Report No. Report 

No. ORNL-6804/R1. 

 <http://www.ornl.gov/~webworks/cpr/v823/rpt/78562.pdf>

2 Karr, T., Making the Most of Life Test Data. Appliance Magazine, 2003. 

<http://www.appliancemagazine.com/print.php?article=197&zone=1&first=1>

3 Crystal Ball, Technote: Derivation of Weibull Distribution Parameters When the 

Minimum, Most Likely Value, and a Percentile are Known, 2007. (Last accessed 

November, 2013.) 

 <http://www.crystalball.com/support/simulation/cbl_gen_021A.html>

8C-5
APPENDIX 8D. DISTRIBUTIONS FOR DISCOUNT RATES

TABLE OF CONTENTS

8D.1 INTRODUCTION ........................................................................................................ 8D-1

8D.2 DISTRIBUTIONS OF RATES FOR DEBT CLASSES .............................................. 8D-1

8D.3 DISTRIBUTIONS OF RATES FOR EQUITY CLASSES.......................................... 8D-5

8D.4 DISTRIBUTION OF REAL EFFECTIVE DISCOUNT RATES BY INCOME 

GROUP......................................................................................................................... 8D-9

LIST OF TABLES

Table 8D.4.1 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 Other Lines of Credit Loan Interest Rates.............................. 8D-4

Figure 8D.3.1 Distribution of Annual Rates of Return on CDs......................................... 8D-5

Figure 8D.3.2 Distribution of Annual Rates of Return on Savings Bonds........................ 8D-6

Figure 8D.3.3 Distribution of Annual Rates of Return on Corporate AAA Bonds........... 8D-6

Figure 8D.3.4 Distribution of Annual Rates of Return on Savings Accounts................... 8D-7

Figure 8D.3.5 Distribution of Annual Rates of Return on Money Market Accounts........ 8D-7

Figure 8D.3.6 Distribution of Annual Rates of Return on the S&P 500 ........................... 8D-8

Figure 8D.3.7 Distribution of Annual Rates of Return on Mutual Funds.......................... 8D-8

Figure 8D.4.1 Distribution of Real Discount Rates by Income Group.............................. 8D-9

8D-i
APPENDIX 8D. DISTRIBUTIONS FOR DISCOUNT RATES

8D.1 INTRODUCTION

The U.S. Department of Energy (DOE) derived 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 DISTRIBUTIONS 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 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.3 DISTRIBUTIONS OF RATES FOR EQUITY CLASSES

Figure 8D.3.1 through Figure 8D.3.7 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.3.1 Distribution of Annual Rates of Return on CDs

-0.04

-0.02

0

0.02

0.04

0.06

0.08

198419861988199019921994199619982000200220042006200820102012

Annual Rate of Return (%)

Year

CDs (Average of 12 and 30 month)

8D-5
Figure 8D.3.2 Distribution of Annual Rates of Return on Savings Bonds

Figure 8D.3.3 Distribution of Annual Rates 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%

19841986198819901992

1994

199619982000200220042006200820102012

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%

198419861988199019921994199619982000200220042006200820102012

Annual Rate of Return (%)

Year

AAA Corporate Bonds

8D-6
Figure 8D.3.4 Distribution of Annual Rates of Return on Savings Accounts

Figure 8D.3.5 Distribution of Annual Rates of Return on Money Market Accounts

-0.5%

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

19841986

1988199019921994

19961998200020022004200620082010

2012

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.3.6 Distribution of Annual Rates of Return on the S&P 500

Figure 8D.3.7 Distribution of Annual Rates of Return on Mutual Funds

-40.0%

-30.0%

-20.0%

-10.0%

0.0%

10.0%

20.0%

30.0%

40.0%

19841986198819901992

1994

199619982000200220042006200820102012

Annual Rate of Return (%)

Year

Stocks (S&P 500)

-30.0%

-20.0%

-10.0%

0.0%

10.0%

20.0%

30.0%

1984

198619881990

19921994

1996199820002002

20042006

200820102012

Annual Rate of Return (%)

Year

Mutual Funds

8D-8
8D.4 DISTRIBUTION OF REAL EFFECTIVE DISCOUNT RATES BY INCOME 

GROUP

Figure 8D.4.1 and Table 8D.4.1 presents the distributions of real discount rates for each 

income group.

Figure 8D.4.1 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.4.1 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-10
REFERENCES

1 The Federal Reserve Board, Survey of Consumer Finances 1995, 1998, 2001, 2004, 

2007, 2010. <http://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. <http://pages.stern.nyu.edu/~adamodar/>

8D-11
APPENDIX 10A. USER INSTRUCTIONS FOR SHIPMENTS AND NATIONAL 

IMPACT ANALYSIS SPREADSHEETS

TABLE OF CONTENTS

10A.1 INTRODUCTION ................................................................................................ 10A-1

10A.2 BASIC INSTRUCTIONS..................................................................................... 10A-2

10A-i
APPENDIX 10A. USER INSTRUCTIONS FOR SHIPMENTS AND NATIONAL

IMPACT ANALYSIS SPREADSHEETS

10A.1 INTRODUCTION

The interested reader can examine and reproduce detailed results of the U.S. Department 

of Energy’s (DOE’s) shipments and national impact analysis (NIA) for conventional cooking 

products using Microsoft Excel spreadsheets that are available on DOE’s website.

<http://www.eere.energy.gov/buildings/appliance_standards/> 

The latest version of the Microsoft Excel shipments and NIA workbook, which is posted 

on the DOE website, was tested using Microsoft Excel 2010. To execute the spreadsheet requires 

Microsoft Excel 2010 or a later version. The NIA spreadsheet performs calculations to forecast 

the change in national energy use and net present value due to an energy conservation standard. 

The energy use and associated costs and savings attributable to a given standard are determined 

first by calculating the shipments and then the energy use and costs for all products shipped 

under that standard. The differences between results under the standard case and the base case

then can be compared and the nationwide energy savings and net present values (NPVs) 

determined. 

The shipments and NIA workbook for oven products comprises the following 

worksheets.

Inputs and Summary

This sheet contains user input selections under “User Inputs” and 

summary tables calculating Cumulative Energy Savings and NPV 

for the selected standard level. The sheet contains the efficiency 

levels being considered for the selected product classes and the 

associated incremental prices. This sheet also contains efficiency 

weighted average energy use and equipment price for the base and 

standards cases for the selected product classes.

LCC Inputs This sheet contains the inputs from the Life-cycle cost analysis.

Efficiency Distribution_ 

Oven

This sheet contains base case and standards case efficiency trends 

for ovens.

Historical Shipment This sheet contains data for historical sales and market share of each 

cooking product class. 

Base Case 

Ship._Electric Cooking 

Products

This sheet calculates the estimation of base case shipments for 

electric cooking products. 

Base Case Ship._Gas

Cooking Products

This sheet calculates the estimation of base case shipments for gas

cooking products. 

Base Case Ship. 

Cooking Top & Oven

This sheet calculates the estimation of base case shipments for 

cooking tops and ovens.

Oven Base & Stds Case This sheet calculates the estimation of base case and standards case 

10A-1
shipments for ovens. It also calculates the energy savings and 

operating cost savings. The energy and operating cost savings in a 

single year are the difference between the base case energy use and 

operating costs for that year and the standard case energy use and 

operating costs in the same year.

Housing Projections This sheet provides projected new housing construction starts by 

housing type.

Price Trend The sheet contains the projections for default, low and high product 

price trends. 

Energy Prices This worksheet contains projected average electricity and gas prices 

for the three economic scenarios. 

Heat Rates

The sheet contains the site-to-power plants and full-fuel-cycle 

conversion factors that are used in the primary and full-fuel-cycle 

energy savings calculations.

Lifetime

This sheet contains the probability of survival of a cooking unit at a 

given age of the unit by its fuel type. The sheet also provides the 

average lifetime of a unit by its fuel type. 

10A.2 BASIC INSTRUCTIONS

Basic instructions for operating the NIA spreadsheets are as follows:

1. Once the NIA spreadsheets have been downloaded from the Web, open the file using

Excel. At the bottom, click on the tab for the worksheet Inputs and Summary.

2. Use 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 model parameters listed in the box labelled “User Inputs”. The 

parameters are:

a. Discount Rate: To the change value, type in the desired Discount Rate (3% or 

7%).

b. Economic Growth: To change the growth scenario, use the drop-down arrow and 

select the desired Growth level (Reference, Low, or High).

c. Trial Standards Level (TSL): To change level, use the drop-down menu and select 

the desired trial standards level (TSL 1, TSL 2 or TSL 3). 

d. Learning Sensitivity: To change the price trend, use the drop-down menu and 

select the desired price scenario (Default, High, or Low).

4. Once the parameters have been set, the results are automatically updated and are reported 

in the “National Impact Summary” table for each product category to the right of the 

“User Inputs” box.

10A-2
APPENDIX 10B. FULL-FUEL-CYCLE MULTIPLIERS

TABLE OF CONTENTS

10B.1 INTRODUCTION .......................................................................................................10B-1

10B.2 METHODOLOGY ......................................................................................................10B-1

10B.3 ENERGY MULTIPLIERS FOR THE FULL FUEL CYCLE.....................................10B-3

LIST OF TABLES

Table 10B.2.1 Dependence of FFC Parameters on AEO Inputs........................................10B-3

Table 10B.3.1 Energy Multipliers for the Full Fuel Cycle (Based on AEO2014).............10B-3

10B-i
APPENDIX 10B. FULL-FUEL-CYCLE MULTIPLIERS

10B.1 INTRODUCTION

This appendix summarizes the methods the U.S. Department of Energy (DOE) used to 

calculate the full-fuel-cycle (FFC) energy savings estimated from potential standards for 

conventional cooking products. The FFC measure includes point-of-use (site) energy; the energy 

losses associated with generation, transmission, and distribution of electricity; and the energy 

consumed in extracting, processing, and transporting or distributing primary fuels. DOE’s 

method of analysis previously encompassed only site energy and the energy lost through 

generation, transmission, and distribution of electricity. In 2011 DOE announced its intention, 

based on recommendations from the National Academy of Sciences, to use FFC measures of 

energy use and emissions when analyzing proposed energy conservation standards.1 This 

appendix summarizes the methods DOE used to incorporate impacts of the full fuel cycle into 

the analysis.

This analysis uses several terms to describe aspects of energy use. The physical sources 

of energy are primary fuels such as coal, natural gas, or liquid fuel. Primary energy is equal to 

the heat content (British thermal units [Btu]) of the primary fuel used to produce an end-use 

service. Site energy use is defined as the energy consumed at the point of use in a house or 

establishment. When natural gas or petroleum fuels are consumed at the site (for example in an 

on-site furnace), site energy is identical to primary energy, with both equal to the heat content of 

the primary fuel consumed.

For electricity generated by an off-site power plant, site energy is measured in kilowatthours (kWh). In such a case the primary energy is equal to the quads (quadrillion Btu) of primary 

energy required to generate and deliver electricity to the site. For the FFC analysis, upstream 

energy use is defined as the energy consumed in extracting, processing, and transporting or 

distributing primary fuels. FFC energy use is the sum of primary plus upstream energy use. 

Both primary fuels and electricity are used in upstream activities. The treatment of 

electricity in full-fuel-cycle analysis must distinguish between electricity generated by fossil 

fuels and uranium and electricity generated from renewable sources (wind, solar, and hydro). For 

the former, the upstream fuel cycle relates to the amount of fuel consumed at the power plant. 

There is no upstream component for the latter, because no fuel per se is used.

10B.2 METHODOLOGY

The mathematical approach to determining FCC is discussed in Coughlin (2012).2 Details 

on analyzing the fuel production chain are presented in Coughlin (2013).3 The methods used to 

calculate FFC energy use are summarized here. When all energy quantities are normalized to the 

same units, FFC energy use can be represented as the product of the primary energy use and an 

FFC multiplier. Mathematically the FFC multiplier is a function of a set of parameters that 

10B-1
represent the energy intensity and material losses at each stage of energy production. Those 

parameters depend only on physical data, so the calculations require no assumptions about prices 

or other economic factors. Although the parameter values often differ by geographic region, this 

analysis utilizes national averages. 

The fuel cycle parameters are defined as follows.

• ax is the quantity of fuel x burned per unit of electricity produced, on average, for grid 

electricity. The calculation of ax includes a factor to account for losses incurred 

through the transmission and distribution systems. 

• by is the amount of grid electricity used in producing fuel y, in MWh per physical unit 

of fuel y.

• cxy is the amount of fuel x consumed in producing one unit of fuel y.

• qx is the heat content of fuel x (MBtu/physical unit). 

• zx(s) is the emissions intensity for fuel x (mass of pollutant s per physical unit of x

produced).

All the parameters are calculated as functions of an annual time step; hence, when 

evaluating the effects of potential new standards, a time series of annual values is used to 

estimate the FFC energy and emissions savings in each year of the analysis period and 

cumulatively. Fossil fuel quantities are converted to energy units using the heat content factor qx. 

To convert electricity in kWh to primary energy units, on-site electricity consumption is 

multiplied by the site-to-power-plant energy use factor, described in chapter 10. The site-topower-plant energy use factor is defined as the ratio of the total primary energy consumption by 

the electric power sector (in quads) divided by the total electricity generated each year.

The FFC multiplier is denoted μ (mu). A separate multiplier is calculated for each fuel 

used on site. Also calculated is a multiplier for electricity that reflects the fuel mix used in its 

generation. The multipliers are dimensionless numbers applied to primary energy savings to 

obtain the FFC energy savings. The upstream component of the energy savings is proportional to 

(μ-1). The fuel type is denoted by a subscript on the multiplier μ.

When DOE estimates energy savings attributable to appliance standards, the method for 

performing the full-fuel-cycle analysis utilizes data and projections published in the Annual 

Energy Outlook (AEO); in the case of c, the AEO2014.

4 Table 10B.2.1 summarizes the AEO2014

data used as inputs to the calculation of various parameters. The column titled “AEO Table”

gives the name of the table that provided the reference data.

10B-2
Table 10B.2.1 Dependence of FFC Parameters on AEO Inputs

Parameter(s) Fuel(s) AEO Table Variables

qx All Conversion factors MMBtu per physical unit

ax All

Electricity supply, disposition, 

prices, and emissions Generation by fuel type

Energy consumption by sector 

and source

Electric energy consumption 

by the power sector

bc, cnc, cpc Coal

Coal production by region and 

type

Coal production by type and 

sulfur content

bp, cnp, cpp Petroleum

Refining industry energy 

consumption Refining-only energy use

Liquid fuels supply and 

disposition Crude supply by source

International liquids supply and 

disposition Crude oil imports

Oil and gas supply Domestic crude oil production

cnn Natural gas

Oil and gas supply U.S. dry gas production

Natural gas supply, disposition, 

and prices Pipeline, lease, and plant fuel

zx All

Electricity supply, disposition, 

prices, and emissions Power sector emissions

The AEO2014 does not provide all the information needed to estimate total energy use in 

the fuel production chain. Coughlin (2013) describes the additional data sources needed to 

complete the analysis. The time dependence in the FFC multipliers for conventional cooking 

products, however, arises exclusively from variables taken from the AEO2014.

10B.3 ENERGY MULTIPLIERS FOR THE FULL FUEL CYCLE

 FFC energy multipliers for selected years are presented in Table 10B.3.1. The 2040 value 

was held constant for the analysis period beyond 2040, which is the last year in the AEO2014 

projection. The multiplier for electricity reflects the shares of various primary fuels in total 

electricity generation throughout the forecast period. 

Table 10B.3.1 Energy Multipliers for the Full Fuel Cycle (Based on AEO2014)

2019 2020 2025 2030 2035 2040

Electricity 1.043 1.044 1.045 1.046 1.047 1.047

Natural gas 1.108 1.109 1.111 1.113 1.114 1.114

Petroleum fuels 1.176 1.176 1.176 1.174 1.172 1.170

10B-3
REFERENCES

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

Conservation Program for Consumer Products and Certain Commercial and Industrial 

Equipment. Statement of Policy for Adopting Full Fuel Cycle Analyses into Energy 

Conservation Standards Program. Federal Register. August 18, 2011. Vol. 76, no. 160: pp.

51281–51289.

2 Coughlin, K. A Mathematical Analysis of Full Fuel Cycle Energy Use. Energy. 2012. 37(1): 

pp. 698–708. (Last accessed June 22, 2014.) 

<http://www.sciencedirect.com/science/article/pii/S0360544211006803>

3 Coughlin, K. Projections of Full-Fuel-Cycle Energy and Emissions Metrics. 2013. Lawrence 

Berkeley National Laboratory. Report No. LBNL-6025E. 

4 U.S. Energy Information Administration. Annual Energy Outlook 2014 with Projections to 

2040. April 2014. DOE/EIA-0383(2014). Washington, D.C. (Last accessed June 22, 2014.) 

<http://www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf >

10B-4

 
APPENDIX 10C. NATIONAL NET PRESENT VALUE OF CONSUMER BENEFITS USING 

ALTERNATIVE PRODUCT PRICE FORECASTS

TABLE OF CONTENTS

10C.1 INTRODUCTION .................................................................................................10C-1

10C.2 PRICE SCENARIOS FOR GAS OVENS.............................................................10C-1

10C.3 PRICE SCENARIOS FOR ELECTRIC OVENS..................................................10C-3

10C.4 SUMMARY...........................................................................................................10C-5

10C.5 CONVENTIONAL COOKING PRODUCTS NPV RESULTS USING 

ALTERNATIVE LEARNING RATES.................................................................10C-7

LIST OF TABLES

Table 10C.4.1 Price Trend Scenarios................................................................................10C-5

Table 10C.5.1 Conventional Cooking Products: Net Present Value of Consumer 

Impacts Under Alternative Product Price Forecasts (3 Percent 

Discount Rate, billion 2014$)....................................................................10C-7

Table 10C.5.2 Conventional Cooking Products: Net Present Value of Consumer 

Impacts.......................................................................................................10C-8

LIST OF FIGURES

Figure 10C.2.1 Low Price Decline Scenario: Relative Price versus Cumulative 

Shipments of Gas Ovens............................................................................10C-2

Figure 10C.2.2 High Price Decline Scenario: Relative Price versus Cumulative 

Shipments of Gas Ovens............................................................................10C-2

Figure 10C.3.1 Low Price Decline Scenario: Relative Price versus Cumulative 

Shipments of Electric Ovens......................................................................10C-4

Figure 10C.3.2 High Price Decline Scenario: Relative Price versus Cumulative 

Shipments of Electric Ovens......................................................................10C-4

Figure 10C.4.1 Gas Oven Price Factor Indexes for the Default Case and Sensitivity 

Cases..........................................................................................................10C-6

Figure 10C.4.2 Oven Price Factor Indexes for the Default Case and Sensitivity Cases ....10C-7

10C-i
APPENDIX 10C. NATIONAL NET PRESENT VALUE OF CONSUMER BENEFITS

USING ALTERNATIVE PRODUCT PRICE FORECASTS

10C.1 INTRODUCTION

The NPV results presented in chapter 10 are based on future price projection derived

from historical PPI data from the Bureau of Labor Statistics (BLS). DOE collected PPI data of 

“gas household ranges, ovens, surface cooking units and equipment” from 1982 to 2014 to 

project future price for gas ovens, and PPI data of “electric household ranges, ovens, surface 

cooking units and equipment” from 1970 to 2014 to project future price for electric ovens. DOE 

also investigated the impact of different product price forecasts on the consumer net present 

value (NPV) for the trial standard levels of both types of ovens. The two price sensitivity 

scenarios DOE considered for both types of ovens are based on the same PPI series used in their

default case but covering different periods of time to estimate a low price decline scenario and a 

high price decline scenario respectively.

10C.2 PRICE SCENARIOS FOR GAS OVENS

For the price sensitivity analysis for gas ovens, DOE used the same experience curve 

approach as the default case to forecast their future price. The low price decline scenario is based 

on the “gas household ranges, ovens, surface cooking units and equipment” PPI series from 1998 

to 2014, and the high price decline scenario is based on the “gas household ranges, ovens, 

surface cooking units and equipment” PPI series from 1982 to 1997. In the experience curve 

method, the real cost of production is related to the cumulative production, or experience, with a 

manufactured product. DOE modeled the experience curve by fitting the inflation –adjusted PPI 

series to the corresponding cumulative shipments, a proxy of cumulative production, of gas 

ovens. The percentage reduction in cost that occurs with each doubling of cumulative production 

is known as the learning rate. 

To estimate an experience rate parameter, a least-squares power-law fit was performed on 

the unified price index versus cumulative shipments. The form of the fitting equation is:

P(X) = PoX-b

,

 where the two parameters, b (the learning rate parameter) and Po (the price or cost of the 

first unit of production), are obtained by fitting the model to the data. DOE notes that the 

cumulative shipments on the right hand side of the equation can have a dependence on price, so 

there is an issue with simultaneity where the independent variable is not truly independent. 

DOE’s use of a simple least squares fit is equivalent to an assumption of no significant first price 

elasticity effects in the cumulative shipments variable.

10C-1
Figure 10C.2.1 and Figure 10C.2.2 present the fit of experience curve for gas ovens under 

low price decline and high price decline scenarios. 

Figure 10C.2.1 Low Price Decline Scenario: Relative Price versus 

Cumulative Shipments of Gas Ovens

Figure 10C.2.2 High Price Decline Scenario: Relative Price versus 

Cumulative Shipments of Gas Ovens

10C-2
For the low price decline scenario, the regression performed as power-law fit results in an 

R-square of 0.61, which indicates an acceptable fit to the data. The parameter values obtained 

are:

Po = 4.63−2.258

+4.411 (95% confidence), and 

b = 0.3396±0.151 (95% confidence)

The estimated experience rate for the low price decline scenario (defined as the fractional 

reduction in price expected from each doubling of cumulative production) is 21.0−8.7

+7.8% (95% 

confidence).

For the high price decline scenario, the regression performed as power-law fit results in 

an R-square of 0.96, which indicates a great fit to the data. The parameter values obtained are:

Po = 9.16−1.81

+2.25 (95% confidence), and 

b = 0.49±0.059 (95% confidence)

The estimated experience rate for the high price decline scenario (defined as the 

fractional reduction in price expected from each doubling of cumulative production) is 

29.0−2.9

+2.8% (95% confidence).

DOE then derived two price factor indices for gas ovens, and the price index value in a 

given year is a function of the experience rate and the cumulative production projection through 

that year, which is based on the shipments forecast described in chapter 9.

10C.3 PRICE SCENARIOS FOR ELECTRIC OVENS

DOE used the same experience curve approach as the default case to forecast future 

prices of electric ovens. The low price decline scenario is based on the “electric household 

ranges, ovens, surface cooking units and equipment” PPI series from 1970 to 1992, and the high 

price decline scenario is based on the “electric household ranges, ovens, surface cooking units 

and equipment” PPI series from 1993 to 2014. Similar to the approach described above, DOE 

modeled the experience curve by fitting the inflation –adjusted PPI series to the corresponding 

cumulative shipments, a proxy of cumulative production, of electric ovens with power-law 

functional form. 

Figure 10C.3.1and Figure 10C.3.2 present the fit of experience curve for electric ovens 

under low price decline and high price decline scenarios. 

10C-3
Figure 10C.3.1 Low Price Decline Scenario: Relative Price versus 

Cumulative Shipments of Electric Ovens

Figure 10C.3.2 High Price Decline Scenario: Relative Price versus 

Cumulative Shipments of Electric Ovens

10C-4
For the low price decline scenario, the regression performed as power-law fit results in an 

R-square of 0.65, which indicates an acceptable fit to the data. The parameter values obtained 

are:

Po = 3.44−0.564

+0.675 (95% confidence), and 

b = 0.159±0.053 (95% confidence)

The estimated experience rate for the low price decline scenario (defined as the fractional 

reduction in price expected from each doubling of cumulative production) is 10.4−3.4

+3.2% (95% 

confidence).

For the high price decline scenario, the regression performed as power-law fit results in 

an R-square of 0.92, which indicates a good fit to the data. The parameter values obtained are:

Po = 12.14−3.38

+4.68 (95% confidence), and 

b = 0.50±0.069 (95% confidence)

The estimated experience rate for the high price decline scenario (defined as the 

fractional reduction in price expected from each doubling of cumulative production) is 

29.2−3.5

+3.3% (95% confidence).

DOE then derived two price factor indices for electric ovens, and the price index value in 

a given year is a function of the experience rate and the cumulative production projection 

through that year, which is based on the shipments forecast described in chapter 9.

10C.4 SUMMARY

 Table 10C.4.1 shows the summary of the learning rate and average annual price decline 

rate for the product price index in each scenario. Figure 10C.4.1 and Figure 10C.4.2 shows the 

resulting price trends for gas and electric ovens respectively.

Table 10C.4.1 Price Trend Scenarios

Product Scenario Price Trend Learning 

Rate %

Annual Price 

Decline Rate %

Gas Ovens

Default

Experience curve estimation 

using gas household ranges, 

ovens, surface cooking units and 

equipment PPI (1982 to 2014)

28.4 1.00

Low Price 

Decline

Experience curve estimation 

using gas household ranges, 

ovens, surface cooking units and 

equipment PPI (1998 to 2014)

21.0 0.70

10C-5
High Price 

Decline

Experience curve estimation 

using gas household ranges, 

ovens, surface cooking units and 

equipment PPI (1982 to 1997)

29.0 1.02

Electric 

Ovens Default

Experience curve estimation 

using gas household ranges, 

ovens, surface cooking units and 

equipment PPI (1970 to 2014)

19.2 0.80

Low Price 

Decline

Experience curve estimation 

using gas household ranges, 

ovens, surface cooking units and 

equipment PPI (1970 to 1992)

10.4 0.41

High Price 

Decline

Experience curve estimation 

using gas household ranges, 

ovens, surface cooking units and 

equipment PPI (1993 to 2014)

29.2 1.29

Figure 10C.4.1 Gas Oven Price Factor Indexes for the Default Case 

and Sensitivity Cases

10C-6
Figure 10C.4.2 Electric Oven Price Factor Indexes for the Default 

Case and Sensitivity Cases

10C.5 CONVENTIONAL COOKING PRODUCTS NPV RESULTS USING 

ALTERNATIVE LEARNING RATES

Table 10C.5.1 Conventional Cooking Products: Net Present Value of Consumer Impacts 

Under Alternative Product Price Forecasts (3 Percent Discount Rate, 

billion 2014$)

Price Trend Product Class TSL1 TSL2 TSL3

Default

Electric Standard Oven, Freestanding 0.172 0.311 -0.569

Electric Standard Oven, Built-In/Slide-In 0.004 0.007 -0.015

Electric Self-Clean Oven, Freestanding 0.539 0.539 -1.005

Electric Self-Clean Oven, Built-In/Slide-In 0.161 0.161 -0.310

Gas Standard Oven, Freestanding 0.000 3.595 3.061

Gas Standard Oven, Built-In/Slide-In 0.000 0.672 0.572

Gas Self-Clean Oven, Freestanding 0.283 5.478 4.716

Gas Self-Clean Oven, Built-In/Slide-In 0.015 0.282 0.242

Low Price 

Decline

Electric Standard Oven, Freestanding 0.171 0.300 -0.713

Electric Standard Oven, Built-In/Slide-In 0.004 0.007 -0.018

Electric Self-Clean Oven, Freestanding 0.536 0.536 -1.317

Electric Self-Clean Oven, Built-In/Slide-In 0.160 0.160 -0.400

Gas Standard Oven, Freestanding 0.000 3.583 3.012

Gas Standard Oven, Built-In/Slide-In 0.000 0.669 0.563

10C-7
Price Trend Product Class TSL1 TSL2 TSL3

Gas Self-Clean Oven, Freestanding 0.281 5.466 4.646

Gas Self-Clean Oven, Built-In/Slide-In 0.014 0.281 0.239

High Price 

Decline

Electric Standard Oven, Freestanding 0.173 0.324 -0.401

Electric Standard Oven, Built-In/Slide-In 0.004 0.007 -0.012

Electric Self-Clean Oven, Freestanding 0.542 0.542 -0.642

Electric Self-Clean Oven, Built-In/Slide-In 0.161 0.161 -0.205

Gas Standard Oven, Freestanding 0.000 3.605 3.103

Gas Standard Oven, Built-In/Slide-In 0.000 0.674 0.580

Gas Self-Clean Oven, Freestanding 0.284 5.489 4.777

Gas Self-Clean Oven, Built-In/Slide-In 0.015 0.282 0.245

Table 10C.5.2 Conventional Cooking Products: Net Present Value of Consumer Impacts

 Under Alternative Product Price Forecasts (7 Percent Discount Rate, 

billion 2014$) 

Price Trend Product Class TSL1 TSL2 TSL3

Default

Electric Standard Oven, Freestanding 0.072 0.115 -0.488

Electric Standard Oven, Built-In/Slide-In 0.002 0.003 -0.014

Electric Self-Clean Oven, Freestanding 0.213 0.213 -0.961

Electric Self-Clean Oven, Built-In/Slide-In 0.065 0.065 -0.299

Gas Standard Oven, Freestanding 0.000 1.550 1.235

Gas Standard Oven, Built-In/Slide-In 0.000 0.288 0.229

Gas Self-Clean Oven, Freestanding 0.117 2.310 1.868

Gas Self-Clean Oven, Built-In/Slide-In 0.006 0.119 0.096

Low Price 

Decline

Electric Standard Oven, Freestanding 0.072 0.110 -0.558

Electric Standard Oven, Built-In/Slide-In 0.002 0.003 -0.016

Electric Self-Clean Oven, Freestanding 0.212 0.212 -1.112

Electric Self-Clean Oven, Built-In/Slide-In 0.065 0.065 -0.344

Gas Standard Oven, Freestanding 0.000 1.544 1.211

Gas Standard Oven, Built-In/Slide-In 0.000 0.287 0.225

Gas Self-Clean Oven, Freestanding 0.116 2.304 1.834

Gas Self-Clean Oven, Built-In/Slide-In 0.006 0.119 0.095

High Price 

Decline

Electric Standard Oven, Freestanding 0.073 0.121 -0.406

Electric Standard Oven, Built-In/Slide-In 0.002 0.003 -0.012

Electric Self-Clean Oven, Freestanding 0.215 0.215 -0.784

Electric Self-Clean Oven, Built-In/Slide-In 0.066 0.066 -0.247

10C-8
Price Trend Product Class TSL1 TSL2 TSL3

Gas Standard Oven, Freestanding 0.000 1.555 1.255

Gas Standard Oven, Built-In/Slide-In 0.000 0.289 0.233

Gas Self-Clean Oven, Freestanding 0.117 2.315 1.896

Gas Self-Clean Oven, Built-In/Slide-In 0.006 0.120 0.098

10C-9
10D-i

APPENDIX 10D. NATIONAL ENERGY SAVINGS AND NET PRESENT VALUE 

USING ALTERNATIVE ECONOMIC GROWTH SCENARIOS 

TABLE OF CONTENTS

10D.1 INTRODUCTION.........................................................................10D-1

10D.2 NIA RESULTS FOR HIGH ECONOMIC GROWTH SCENARIO...............10D-3

10D.3 NIA RESULTS FOR LOW ECONOMIC GROWTH SCENARIO................10D-4

LIST OF TABLES

Table 10D.2.1 Cumulative Full-Fuel Cycle Energy Savings in Quads, High Economic 

Growth Scenario ....................................................................................... 10D-3

Table 10D.2.2 Cumulative Net Present Value of Consumer Benefits, High Economic 

Growth Scenario ....................................................................................... 10D-3

Table 10D.3.1 Cumulative Full-Fuel Cycle Energy Savings in Quads, Low Economic 

Growth Scenario ....................................................................................... 10D-4

Table 10D.3.2 Cumulative Net Present Value of Consumer Benefits, Low Economic ... 10D-4

LIST OF FIGURES

Figure 10D.1.1 New Housing Starts Projection under Alternative.................................... 10D-1

Figure 10D.1.2 Average Residential Electricity Price Projection under Alternative 

AEO2015 Economic Growth Scenarios ................................................... 10D-2

Figure 10D.1.3 Average Residential Natural Gas Price Forecasts under Alternative 

AEO2015 Economic Growth Scenarios ................................................... 10D-2
10D-1

APPENDIX 10D. NATIONAL ENERGY SAVINGS AND NET PRESENT VALUE 

USING ALTERNATIVE ECONOMIC GROWTH SCENARIOS

10D.1 INTRODUCTION

This appendix presents national energy savings (NES) and net present value (NPV) 

results using inputs from alternative economic growth scenarios. The scenarios use the energy 

price and housing starts forecasts in the High Economic Growth case and the Low Economic 

Growth case from EIA’s Annual Energy Outlook 2015 (AEO 2015).

1

Figure 10D.1.1 shows the projection for new housing starts. Figure 10D.1.2 and Figure 

10D.1.3 show residential electricity prices and natural gas prices under the different economic 

growth scenarios, respectively. AEO2015 provides a projection to 2040. To estimate the trend 

after 2040, DOE followed guidelines that the EIA had provided to the Federal Energy 

Management Program, which called for using the average rate of change for electricity and 

natural gas during 2030–2040, respectively. 

Figure 10D.1.1 New Housing Starts Projection under Alternative 

AEO2015 Economic Growth Scenarios

0.00

0.50

1.00

1.50

2.00

2.50

3.00

Housing Starts (million)

New Housing Starts Projection

Series1 Series2 Series3
10D-2

Figure 10D.1.2 Average Residential Electricity Price Projection under 

Alternative AEO2015 Economic Growth Scenarios

Figure 10D.1.3 Average Residential Natural Gas Price Forecasts under 

Alternative AEO2015 Economic Growth Scenarios

0.90

0.95

1.00

1.05

1.10

1.15

1.20

1.25

1.30

Electricity Price Trend (2013=100)

Residential Electricity Price Projection

Reference Low High

0.80

0.90

1.00

1.10

1.20

1.30

1.40

1.50

1.60

1.70

Gas Price Trend (2012=100)

Residential Natural Gas Price Projection

Reference Low High
10D-3

10D.2 NIA RESULTS FOR HIGH ECONOMIC GROWTH SCENARIO

Table 10D.2.1 Cumulative Full-Fuel Cycle Energy Savings in Quads, High Economic 

Growth Scenario

Product Class TSL1 TSL2 TSL3

Electric Standard Oven, Freestanding 0.028 0.069 0.193

Electric Standard Oven, Built-In/Slide-In 0.001 0.001 0.004

Electric Self-Clean Oven, Freestanding 0.087 0.087 0.449

Electric Self-Clean Oven, Built-In/Slide-In 0.025 0.025 0.130

Gas Standard Oven, Freestanding 0.000 0.236 0.243

Gas Standard Oven, Built-In/Slide-In 0.000 0.044 0.046

Gas Self-Clean Oven, Freestanding 0.044 0.307 0.325

Gas Self-Clean Oven, Built-In/Slide-In 0.002 0.016 0.017

All 0.187 0.786 1.407

Table 10D.2.2 Cumulative Net Present Value of Consumer Benefits, High Economic 

Growth Scenario

Discount 

Rates Product Class TSL 1 TSL 2 TSL 3

3% 

(billion 

2013$)

Electric Standard Oven, Freestanding 0.202 0.370 -0.594

Electric Standard Oven, Built-In/Slide-In 0.004 0.008 -0.016

Electric Self-Clean Oven, Freestanding 0.634 0.634 -1.022

Electric Self-Clean Oven, Built-In/Slide-In 0.188 0.188 -0.318

Gas Standard Oven, Freestanding 0.000 3.963 3.390

Gas Standard Oven, Built-In/Slide-In 0.000 0.741 0.634

Gas Self-Clean Oven, Freestanding 0.318 6.033 5.216

Gas Self-Clean Oven, Built-In/Slide-In 0.016 0.310 0.268

7% 

(billion 

2013$)

Electric Standard Oven, Freestanding 0.083 0.135 -0.530

Electric Standard Oven, Built-In/Slide-In 0.002 0.003 -0.015

Electric Self-Clean Oven, Freestanding 0.261 0.261 -1.025

Electric Self-Clean Oven, Built-In/Slide-In 0.080 0.080 -0.319

Gas Standard Oven, Freestanding 0.000 1.688 1.352

Gas Standard Oven, Built-In/Slide-In 0.000 0.314 0.251

Gas Self-Clean Oven, Freestanding 0.129 2.514 2.042

Gas Self-Clean Oven, Built-In/Slide-In 0.007 0.130 0.105
10D-4

10D.3 RESULTS FOR LOW ECONOMIC GROWTH SCENARIO

Table 10D.3.1 Cumulative Full-Fuel Cycle Energy Savings in Quads, Low Economic 

Growth Scenario

Product Class TSL1 TSL2 TSL3

Electric Standard Oven, Freestanding 0.022 0.054 0.150

Electric Standard Oven, Built-In/Slide-In 0.000 0.001 0.003

Electric Self-Clean Oven, Freestanding 0.068 0.068 0.350

Electric Self-Clean Oven, Built-In/Slide-In 0.020 0.020 0.102

Gas Standard Oven, Freestanding 0.000 0.201 0.206

Gas Standard Oven, Built-In/Slide-In 0.000 0.038 0.039

Gas Self-Clean Oven, Freestanding 0.037 0.259 0.275

Gas Self-Clean Oven, Built-In/Slide-In 0.002 0.013 0.014

All 0.149 0.653 1.139

Table 10D.3.2 Cumulative Net Present Value of Consumer Benefits, Low Economic 

 Growth Scenario

Discount 

Rates Product Class TSL 1 TSL 2 TSL 3

3% 

(billion 

2013$)

Electric Standard Oven, Freestanding 0.150 0.267 -0.553

Electric Standard Oven, Built-In/Slide-In 0.003 0.006 -0.015

Electric Self-Clean Oven, Freestanding 0.469 0.469 -0.998

Electric Self-Clean Oven, Built-In/Slide-In 0.140 0.140 -0.308

Gas Standard Oven, Freestanding 0.000 3.300 2.800

Gas Standard Oven, Built-In/Slide-In 0.000 0.616 0.523

Gas Self-Clean Oven, Freestanding 0.254 5.033 4.317

Gas Self-Clean Oven, Built-In/Slide-In 0.013 0.259 0.222

7% 

(billion 

2013$)

Electric Standard Oven, Freestanding 0.064 0.099 -0.463

Electric Standard Oven, Built-In/Slide-In 0.002 0.003 -0.013

Electric Self-Clean Oven, Freestanding 0.199 0.199 -0.905

Electric Self-Clean Oven, Built-In/Slide-In 0.061 0.061 -0.282

Gas Standard Oven, Freestanding 0.000 1.435 1.138

Gas Standard Oven, Built-In/Slide-In 0.000 0.266 0.211

Gas Self-Clean Oven, Freestanding 0.106 2.139 1.722

Gas Self-Clean Oven, Built-In/Slide-In 0.005 0.110 0.089
10D-5

REFERENCES

 

1 U.S. Department of Energy-Energy Information Administration. Annual Energy Outlook 

2015 with Projections to 2040, 2015. Washington, DC. Report Number: DOE/EIA0383(2015).

 < http://www.eia.gov/forecasts/aeo/>
12A-i

APPENDIX 12A. MANUFACTURER IMPACT ANALYSIS INTERVIEW GUIDE

TABLE OF CONTENTS

12A.1 MANUFACTURER IMPACT ANALYSIS INTERVIEW GUIDE.......................... 12A-2
12A-1

APPENDIX 12A. MANUFACTURER IMPACT ANALYSIS INTERVIEW GUIDE

2014
12A-2

12A.1 MANUFACTURER IMPACT ANALYSIS INTERVIEW GUIDE

Introduction

As part of the rulemaking process for amending energy conservation standards for residential 

conventional cooking products, the Department of Energy (DOE) is conducting a manufacturer impact 

analysis (MIA). In this analysis, DOE uses publicly available information and information provided by 

manufacturers during confidential interviews to assess possible impacts on manufacturers due to 

amended energy conservation standards.

This questionnaire is a part of the MIA process and is intended to inform the Department’s 

understanding of how changes in the energy conservation standard may affect manufacturers of 

residential conventional cooking products. All information provided in response to this questionnaire 

will be treated as confidential. The topics below range from general questions about the key issues facing 

the industry in light of amended energy conservation standards to specific requests to validate industry 

average financial parameters used to model industry cash flow.

Topics covered will include:

1) Engineering

2) Key Issues

3) Company Overview And Organizational Characteristics

4) Markups And Profitability

5) Shipment Projections

6) Industry Average Financial Parameters

7) Conversion Costs

8) Cumulative Regulatory Burden

9) Direct Employment Impacts

10) Manufacturing Capacity / Exports / Foreign Competition / Outsourcing

11) Industry Consolidation

12) Impacts On Small Businesses
12A-3

The questions in this interview guide refer to the product classes and potential efficiency levels described in Table A through Table G and the 

design options listed in Table H.

Table A Gas Cooking Top Product Classes and Potential Efficiency Levels

Level Efficiency Level Source

2009 Standards 

Rulemaking

Proposed Test 

Procedure 

Cooking 

Efficiency 

Proposed IAEC 

(kBtu)

Cooking 

Efficiency EF

Baseline 2009 TSD (Electronic 

Ignition) 0.399 0.399 0.365 1445.0

1 2009 TSD Max-Tech 

(Sealed Burners) 0.420 0.420 0.384 1372.7

Baseline: Cooktop cooking efficiency = 39.9%, four conventional 9,000 BTU/hr burners and electronic ignition. 

(1) Sealed Burners: Cooking efficiency increase = 4.8% (relative percent).

Table B Electric Open (Coil) Element Cooking Top Product Classes and Potential Efficiency Levels

Level Efficiency Level Source

2009 Standards 

Rulemaking

Proposed Test 

Procedure 

Cooking 

Efficiency 

Proposed IAEC 

(kWh)

Cooking 

Efficiency EF

Baseline 2009 TSD (Baseline) 0.737 0.737 0.674 256.7

1 2009 TSD (Improved 

Contact Conductance) 0.769 0.769 0.704 246.0

Baseline: Cooktop cooking efficiency = 73.7%, two 6-inch 1,250 W and two 8-inch 2,100 W elements.

(1) Improved Contact Conductance: Cooking efficiency increase = 4.3% (relative percent).
12A-4

Table C Electric Smooth Element Cooking Top Product Classes and Potential Efficiency Levels

Level Efficiency Level Source

2009 Standards 

Rulemaking

Proposed Test 

Procedure 

Cooking 

Efficiency 

Proposed IAEC 

(kWh)

Cooking 

Efficiency EF

Baseline 2009 TSD (Baseline) 0.742 0.742 0.679 280.6

1 Baseline + Switch-Mode 

Power Supply (SMPS) 0.742 0.742 0.679 268.6

2 Baseline + 1 W Standby 0.742 0.742 0.679 263.5

3

2009 TSD (Halogen 

Lamp Element) + 1 W 

Standby

0.753 0.753 0.689 259.8

4 Induction + SMPS - - 0.746 245.9

5 Induction + 1 W Standby - - 0.746 240.7

Baseline: Cooktop cooking efficiency = 74.2%, two 6-inch 1,500 W and two 8-inch 2,000 W solid disk elements, 3 W standby power. 

(1) Baseline + SMPS 1.6 W standby power.

(2) Baseline + 1 W standby power.

(3) 2 + Halogen Element: Cooking efficiency increase = 1.5% (relative percent), two small 1,200 W and two large 1,800 W circular lamps. 

(4) Induction: Proposed TP cooking efficiency increase = 9.8% (relative percent), SMPS 1.6 W standby power.

(5) 4 + 1 W standby power.
12A-5

Table D Gas Standard Oven (with or without a catalytic line) Product Classes and Potential Efficiency Levels

Level Efficiency Level Source

2009 Standards Rulemaking

Proposed IAEC 

EF (kBtu)

Annual Energy 

Consumption 

(kBtu)

Baseline 2009 TSD (Electric Glo-bar Ignition) 0.0536 1656.7 2076.5

1 2009 TSD (Electric Glo-bar Ignition) + SMPS 0.0536 1656.7 1932.0

2 2009 TSD (Improved Insulation) + SMPS 0.0566 1568.9 1844.2

3 2009 TSD (2 + Electronic Spark Ignition) + 

SMPS 0.0616 1442.4 1717.7

4 2009 TSD (3 + Improved Door Seals) + SMPS 0.0622 1427.3 1702.6

5 2009 TSD (4 + Reduced Vent Rate) + SMPS 0.0625 1420.1 1695.4

6 2009 TSD (5 + Reduced Conduction Losses) 

+ SMPS 0.0630 1410.6 1685.9

7 2009 TSD (6 + Forced Convection) + SMPS 0.0653 1360.7 1636.0

8 2009 TSD (7) + 1W Standby 0.0653 1360.7 1499.1

Baseline: Cooking efficiency = 5.8% (electric glo-bar ignition = 176 Wh, 2 inches of 1.09 lb/cubic foot insulation), 10.7 W standby power, 175 Wh 

fan-only mode per-cycle energy use.

(2) 1 + Improved Insulation: Cooking efficiency increase = 4.9% (relative percent)

(3) 2 + Electronic Spark Ignition: Decrease in electricity consumption = 176 Wh 

(4) 3 + Improved Door Seals: Cooking efficiency increase = 1.0% (relative percent)

(5) 4 + Reduced Vent Rate: Cooking efficiency increase = 0.5% (relative percent)

(6) 5 + Reduced Conduction Losses: Cooking efficiency increase = 0.05 (absolute percentage points) 

(7) 6 + Forced Convection: Cooking efficiency increase = 4.8% (relative percent), added electricity consumption = 15 Wh

(8) 7 + 1 W standby power.
12A-6

Table E Gas Self-Clean Oven Product Classes and Potential Efficiency Levels

Level Efficiency Level Source

2009 Standards Rulemaking

Proposed IAEC 

EF (kBtu)

Annual Energy 

Consumption 

(kBtu)

Baseline 2009 TSD (Baseline) 0.0540 1644.4 1965.0

1 2009 TSD (Baseline) + SMPS 0.0540 1644.4 1820.5

2 2009 TSD (Forced Convection) + SMPS 0.0625 1420.8 1596.9

3 2009 TSD (2) + Electronic Spark Ignition + 

SMPS 0.0680 1306.3 1482.3

4 2009 TSD (3 + Improved Door Seals) + SMPS 0.0685 1295.9 1472.0

5 2009 TSD (4 + Reduced Conduction Losses) + 

SMPS 0.0687 1291.8 1467.8

6 2009 TSD (5) + 1 W Standby 0.0687 1291.8 1330.9

Baseline: Cooking efficiency = 7.13%, clock power = 3.6 W, 2 inches of 1.90lb/cubic foot insulation, electronic ignition = 176 Wh, self-cleaning 

energy consumption = 43,158 Btu, 10.7 W standby power, 175 Wh fan-only mode per-cycle energy use.

(1) Baseline + SMPS 5.7 W standby power

(2) 1 + Forced Convection: Cooking efficiency increase = 23% (relative percent), added electricity consumption (during cooking and cleaning 

cycles) = 15 Wh

(3) 2 + Electronic Spark Ignition: Decrease in electricity consumption = 176 Wh

(4) 3 + Improved Door Seals: Cooking efficiency increase = 1.0% (relative percent) 

(5) 4 + Reduced Conduction Losses: Cooking efficiency increase = 0.05 (absolute percentage points)
12A-7

Table F Electric Standard Oven (with or without a catalytic line) Product Classes and Potential Efficiency Levels

Level Efficiency Level Source

2009 Standards Rulemaking

Proposed IAEC 

EF (kWh)

Annual Energy 

Consumption (kWh)

Baseline 2009 TSD (Baseline) 0.1066 274.9 370.0

1 2009 TSD (Baseline) + SMPS 0.1066 274.9 327.7

2 2009 TSD (Reduced Vent Rate) + SMPS 0.1113 263.3 316.1

3 2009 TSD (2 + Improved Insulation) + SMPS 0.1163 251.9 304.8

4 2009 TSD (3 + Improved Door Seals) + SMPS 0.1181 248.1 300.9

5 2009 TSD (4 + Reduced Conduction Losses) + 

SMPS 0.1184 247.5 300.3

6 2009 TSD (5 + Forced Convection) + SMPS 0.1209 242.3 295.2

7 2009 TSD (6) + 1 W Standby 0.1209 242.3 255.0

Baseline: Cooking efficiency = 12.15%, 2 inches of 1.09 lb/cubic foot insulation, 10.7 W standby power, 175 Wh fan-only mode per-cycle energy use.

(1) Baseline + SMPS 5.7 W standby power.

(2) 1 + Reduced Vent Rate: Cooking efficiency increase = 0.62 (absolute percentage points)

(3) 2 + Improved Insulation: Cooking efficiency increase = 0.52% (relative percent)

(4) 3 + Improved Door Seals: Cooking efficiency increase = 0.24 (absolute percentage points)

(5) 4 + Reduced Conduction Losses: Cooking efficiency increase = 0.05 (absolute percentage points)

(6) 5 + Forced Convection: Cooking efficiency increase = 0.33 (absolute percentage points), added electricity consumption = 15 Wh

(7) 6 + 1 W standby power.
12A-8

Table G Electric Self-Clean Oven Product Classes and Potential Efficiency Levels

Level Efficiency Level Source

2009 Standards Rulemaking

Proposed IAEC 

EF (kWh)

Annual Energy 

Consumption (kWh)

Baseline 2009 TSD (Baseline) 0.1099 266.6 360.0

1 2009 TSD (Baseline) + SMPS 0.1099 266.6 317.7

2 2009 TSD (Reduced Conduction Losses) + 

SMPS 0.1102 265.9 317.0

3 2009 TSD (2 + Forced Convection) + SMPS 0.1123 260.9 312.0

4 2009 TSD (3) + 1 W Standby 0.1123 260.9 271.9

Baseline: Cooking efficiency = 13.79%, 2 inches of 1.09 lb/cubic foot insulation, self-cleaning energy consumption = 5,286 Wh, 10.7 W standby 

power, 175 Wh fan-only mode per-cycle energy use.

(1) Baseline + SMPS 5.7 W standby power

(2) 1 + Reduced Conduction Losses: Cooking efficiency increase = 0.05 (absolute percentage points)

(3) 2 + Forced Convection: Cooking efficiency increase = 0.33 (absolute percentage points), added electricity consumption = 15 Wh

(4) 3 + 1 W standby power
12A-9

Table H Potential Design Options

Design Options

Gas cooking tops:

• Reflective surfaces

• Sealed burners

• Thermostatically controlled burners

Open (coil) element electric cooking tops:

• Improved contact conductance

• Reflective surfaces

Smooth element electric cooking tops:

• Radiant elements

• Halogen elements

• Induction elements

• Low-standby-loss electronic controls

Gas and electric ovens:

• Bi-radiant oven (electric only)

• Electronic spark ignition (gas only)

• Forced convection

• Improved insulation

• Improved door seals

• Oven separator

• Reduced conduction losses

• Reduced thermal mass

• Reduced vent rate

• Steam cooking

• Low-standby-loss electronic controls
12A-10

Table I Manufacturer Model Energy Use

Brand Model

Product 

Class

Oven Annual Energy Consumption

Cooktop Annual Energy 

Consumption

Cooking, 

ECO (kWh or 

Btu)1

Self-Clean, ESC

(kWh or Btu)1

Standby/Off 

Mode, EOTLP

(kWh)

Fan-Only 

Mode, EOF × 

N (kWh)

Cooking 

ECA/ECC (kWh 

or Btu)

Standby/Off 

Mode, ECTSO

(kWh)

1 For gas cooking products, please include both the primary and secondary energy consumption in these modes.
12A-11

1. ENGINEERING

1.1 Because there are currently no energy conservation standards or energy labeling 

requirements for conventional cooking products, would you please provide the energy use 

information requested in Table I above with regards to models that you manufacture? For 

cooking tops, please provide energy use information according to the test procedure 

proposed in the January 30, 2013 NOPR (78 FR 6232) if possible. Otherwise, please indicate 

that the energy use information is based on the current DOE test procedure in appendix I.

1.2 Which design features impacting energy use are generally incorporated into a “baseline” unit 

in each product class listed in Table A through Table G? What are the typical costs associated 

with the major components of the baseline products in each of these product classes (e.g. 

heaters, gas valves/burners, ignition systems, insulation, controls, etc.)?

1.3 As part of the previous standards rulemaking, DOE established the product classes listed in 

Table A through Table G, differentiating classes by energy source (electric vs. gas), coil vs. 

smooth cooking tops, and standard vs. self-clean ovens. 1 For the current rulemaking, DOE is 

considering whether separate product classes should be established for commercial-style 

(professional-style) gas cooking products2 or residential-style units with higher input rates. 

Do you believe separate product classes are warranted for these products? If so, how should 

these product classes be defined (in particular with respect to burner input rates, number of 

high input rate burners, cooking top grate materials, oven cavity volume, or any other 

characteristics)? Are there other product classes that DOE should consider?

1.4 DOE is proposing to analyze the efficiency levels listed in Table A through Table G. Do you 

 1 EPCA requires that a rule prescribing standards for a class of covered products shall specify a level of energy use 

or efficiency higher or lower than that which applies for such class for any group of covered products which have 

the same function or intended use, if the Secretary determines that covered products within such group – (A) 

consume a different kind of energy; or (B) have a capacity or other performance-related feature that justifies a 

higher or lower standard, considering the utility of the feature to the consumer and other factors deemed appropriate 

by the Secretary. (42 U.S.C. 6295(q))

2 As part of the previous standards rulemaking, DOE considered commercial-style gas cooking tops to be those 

products that incorporate cooking tops with higher input rate burners (i.e., greater than 14,000 Btu/(h and heavyduty grates that provide faster cooking and the ability to cook larger quantities of food in larger cooking vessels.

DOE considered commercial-style gas ovens to have higher input rates (i.e., greater than 22,500 Btu/h) and 

dimensions to accommodate larger cooking utensils or greater quantity of food items, as well as features to optimize 

cooking performance.
12A-12

agree with the proposed efficiency levels? If not, can you suggest appropriate values? For 

ovens, the cost-efficiency relationships are predicated on ovens with a cavity volume of 3.9 

ft3. For cooking tops, the proposed efficiency levels are based on the test procedure proposed 

in the January 2013 NOPR. Can you please provide data showing the difference in measured 

efficiency for cooking tops using the current DOE test procedure in appendix I, which uses 

an aluminum test block, compared to the test procedure proposed in the January 2013 NOPR, 

which uses a hybrid test block (a stainless steel alloy 430 base and an aluminum body).

1.5 If you believe commercial-style gas cooking products or residential-style units with higher 

input rates or any other product types should be analyzed as separate product classes, can 

you suggest baseline, max-tech, and if necessary, gap fill efficiency levels for these product 

classes?

1.6 How does efficiency/energy use scale with oven cavity volume? Do you have data showing 

the effects of energy use versus oven cavity volume and, if so, can you provide this data? As 

part of the previous rulemaking, DOE presented the linear equations for both electric and gas 

ovens relating EF to oven cavity volume (no distinction was found to exist between standard 

and self-cleaning ovens). The values of the slopes were determined to be -0.0157 for electric 

ovens and -0.0073 for gas ovens3. Are these slopes representative of the relationship between 

EF and oven cavity volume?

1.7 Does oven efficiency/energy use have a strong relationship with electric heater input or gas 

burner firing rate (or a combination of heat input/firing rate and oven cavity volume)? If so, 

can you please provide data showing this relationship?

1.8 Which design features impacting energy use are incorporated into products to reach each 

incremental efficiency level for each product class? What are the costs of the individual 

design options selected for each efficiency level? Are the incremental manufacturing costs at 

each efficiency level specified in Table 12A.1 through Table 12A.3 representative of costs your 

company would incur? If not, please provide a quantitative indication of the differences in 

 3 For the slope, energy factor is expressed as a decimal and the volume in cubic feet.
12A-13

costs and design changes. For active mode design changes, the incremental costs are based on 

data from the previous standards rulemaking (April 2009 Final Rule), updated using the 

producer price index (PPI). Because standby power design changes were not considered as 

part of the previous standards rulemaking, DOE determined incremental cost data separately 

for standby power design changes based on product teardowns and reverse engineering.

Table 12A.1 Cost-Efficiency Relationships – Cooking Tops

Product Class Level

Proposed 

IAEC

Incremental 

Cost ($2013) Comments

Gas cooking tops

Baseline (Electronic 

Ignition) 1445.0 kBtu -

1 (Sealed Burners) 1372.7 kBtu $23.74

Open (coil) element 

electric cooking tops

Baseline 256.7 kWh -

1 (Improved Contact 

Conductance) 246.0 kWh $2.71

Smooth element 

electric cooking tops

Baseline 280.6 kWh -

1 (Baseline + SMPS) 268.6 kWh Not 

Available

2 (Baseline + 1 W 

Standby) 263.5 kWh Not Available

3 (Halogen Lamp 

Element + 1 W 

Standby)

259.8 kWh $105.77 + 

Standby

4 (Induction + SMPS) 245.9 kWh $335.27 + 

Standby

5 (Induction + 1 W 

Standby) 240.7 kWh $335.27 + Standby
12A-14

Table 12A.2 Cost-Efficiency Relationships – Gas Ovens

Product Class Level

Proposed 

IAEC (kBtu)

Incremental 

Cost ($2013) Comments

Gas ovens –

standard ovens with 

or without a 

catalytic line

Baseline (Electric Globar Ignition) 2076.5 -

1 (Electric Glo-bar 

Ignition + SMPS) 1932.0 Not Available

2 (Improved Insulation 

+ SMPS) 1844.2 $4.25 + Standby

3 (2 + Electronic Spark 

Ignition + SMPS) 1717.7 $22.06 + Standby

4 (3 + Improved Door 

Seals + SMPS) 1702.6 $23.34 + Standby

5 (4 + Reduced Vent 

Rate + SMPS) 1695.4 $25.26 + Standby

6 (5 + Reduced 

Conduction Losses + 

SMPS)

1685.9 $29.57 + 

Standby

7 (6 + Forced 

Convection + SMPS) 1636.0 $55.86 + Standby

8 (7 + 1W Standby) 1499.1 $55.86 + 

Standby

Gas ovens – selfclean ovens

Baseline 1965.0 -

1 (Baseline + SMPS) 1820.5 Not 

Available

2 (Forced Convection + 

SMPS) 1596.9 $13.07 + Standby

3 (2 + Electronic Spark 

Ignition + SMPS) 1482.3 $22.66 + Standby

4 (3 + Improved Door 

Seals + SMPS) 1472.0 $24.11 + Standby

5 (4 + Reduced 

Conduction Losses + 

SMPS)

1467.8 $29.30 + 

Standby

6 (5 + 1 W Standby) 1330.9 $29.30 + 

Standby
12A-15

Table 12A.3 Cost-Efficiency Relationships – Electric Ovens

Product Class Level

Proposed 

IAEC (kWh)

Incremental 

Cost ($2013) Comments

Electric ovens –

standard ovens with 

or without a 

catalytic line

Baseline 370.0 -

1 (Baseline + SMPS) 327.7 Not 

Available

2 (Reduced Vent Rate + 

SMPS) 316.1 $1.94 + Standby

3 (2 + Improved 

Insulation + SMPS) 304.8 $5.75 + Standby

4 (3 + Improved Door 

Seals + SMPS) 300.9 $10.13 + Standby

5 (4 + Reduced 

Conduction Losses + 

SMPS)

300.3 $14.34 + 

Standby

6 (5 + Forced 

Convection + SMPS) 295.2 $61.37 + Standby

7 (6 + 1 W Standby) 255.0 $61.37 + 

Standby

Electric ovens – selfclean ovens

Baseline 360.0 -

1 (Baseline + SMPS) 317.7 Not 

Available

2 (Reduced 

Conduction Losses + 

SMPS)

317.0 $5.19 + 

Standby

3 (2 + Forced 

Convection + SMPS) 312.0 $52.21 + Standby

4 (3 + 1 W Standby) 271.9 $52.21 + 

Standby

1.9 How does the selection of design options and associated costs and efficiency improvement 

compare for residential-style gas cooking products with lower input rates versus commercialstyle gas cooking products and residential-style units with higher input rates? How do 

commercial-style gas cooking products and residential-style units with higher input rates 

compare to residential-style gas cooking products with lower input rates in terms of 

achievable efficiency levels? What unique challenges do commercial-style gas cooking 

products and residential-style units with higher input rates encounter when trying to 

improve energy efficiency?
12A-16

1.10 Are any of the design options listed in Table H more effective in residential-style or 

commercial-style cooking products? Are there any other design options for improving 

efficiency that are not listed in the table? If so, what is the incremental cost associated with 

implementing those design options?

1.11 What percentage of commercial-style gas cooking products and residential-style units with 

higher input rates are equipped with a standing pilot light?

1.12 Does your company collect consumer usage information that can be provided in detail or 

summary (e.g., number of annual cooking cycles, cooking cycle length, cooking modes 

selected, cookware dimensions, etc.)?

1.13 Are installation costs a function of efficiency? Maintenance costs? Repair costs? If yes, please 

characterize this relationship by providing incremental installation, maintenance, and/or 

repair cost data. How do these installation/maintenance/repair costs compare for 

commercial-style gas cooking products?

1.14 Information gathered from analysis of common industry practices were used to formulate 

factory parameters for manufacturers. Please comment on the following factory parameter 

assumptions listed in Table 12A.4.
12A-17

Table 12A.4 Factory Parameters

Parameter Estimate Manufacturer Feedback

Actual Annual Production Volume (units/year) REDACTED

Work Days Per Year (days) REDACTED

Assembly Shifts Per Day (shifts) REDACTED

Fabrication Shifts Per Day (shifts) REDACTED

Fabrication Labor Wages ($/hr) REDACTED

Assembly Labor Wages ($/hr) REDACTED

Assembly Worker Hours Per Year REDACTED

Fabrication Worker Hours Per Year REDACTED

Length of Shift (hrs) REDACTED

Units Per Day REDACTED

Average Equipment Installation Cost (% of purchase price) REDACTED

Fringe Benefits Ratio REDACTED

Indirect to Direct Labor Ratio REDACTED

Average Scrap Recovery Value REDACTED

Worker Downtime REDACTED

Burdened Assembly Labor Wage ($/hr) REDACTED

Burdened Fabrication Labor Wage ($/hr) REDACTED

Supervisor Span (workers/supervisor) REDACTED

Supervisor Wage Premium (over fabrication and assembly 

wage) REDACTED

1.15 Do you believe that the current test procedures in Appendix I and the proposed test 

procedure amendments in the January 2013 TP NOPR (78 FR 6232), are appropriate for 

measuring performance? If not, can you suggest an alternative method (providing specific 

details on test conditions, such as test load size, temperature rises, etc.)? Can you also suggest 

appropriate test methods for commercial-style gas cooking products or residential-style units 

with higher input rates? Can you also provide data showing the effects of the test procedure 

change on the measured efficiency/energy-use as compared to the existing test procedure? 

Can you comment on the following issues related to the test procedure:

a. Test load size for gas cooktop burners having input rates > 14,000 Btu/hr

i. What range of cookware diameters are commercial-style gas cooktops and 

residential-style units with higher input rates designed for use with?

b. Test load size for gas ovens having input rates > 22,500 Btu/hr

c. Test load size for electric cooktops

i. What is the maximum surface unit diameter you produce for electric coil cooktops?

ii. What is the maximum surface unit diameter you produce for smooth electric 

cooktops?

iii. What percentage of your electric cooktop shipments have non-circular surface units 

(e.g., square, oval, rectangular bridge)? Are the non-circular markings on the glassceramic surface decorative or are the underlying radiant elements also non-circular? 

Is the same true of induction elements?
12A-18

2 KEY ISSUES

DOE is interested in understanding the impact of amended energy conservation standards on 

manufacturers of residential conventional cooking products. This section provides an opportunity for 

manufacturers to identify high-priority issues that DOE should take into consideration when conducting 

the Manufacturer Impact Analysis.

2.1 In general, what are the key concerns for your company regarding the amended energy 

conservation standards rulemaking for residential conventional cooking products?

2.2 For the issues you have identified, how significant are they for different product classes 

and/or efficiency levels?

2.3 How would amended energy conservation standards affect your ability to compete in the 

marketplace? Would you expect your market share to change?

3 COMPANY OVERVIEW AND ORGANIZATIONAL CHARACTERISTICS

3.1 Do you have a parent company and/or subsidiary? If so, please provide their name(s).

3.2 What is your company’s approximate market share of the residential conventional cooking 

products market? Does this vary significantly for any particular product class that you 

manufacture?

3.3 Do you manufacture any products other than residential conventional cooking products? If 

so, what other products do you manufacture? Do you manufacturer them in the same 

facilities as your residential conventional cooking products? What percentage of your overall 

revenue is derived from sales of residential conventional cooking products?
12A-19

3.4 What are your product line niches and relative strengths in the residential conventional 

cooking products market?

3.5 Where are your production facilities located, and what type of product is manufactured at 

each location? Please provide production figures for your company’s manufacturing at each 

location by product class.

Table 12A-5 Manufacturing locations

Location Product Class(es) Employees

(Production)

Employees

(Non-production)

Units/Yr 

Produced

e.g. -

Brunswick, 

ME

e.g. - Gas Cooking Top 

Products,

Gas Ovens – Self-Cleaning

e.g. - 350 e.g. - 75

e.g. - 8,500 Total 

(4000 and 4500 

respectively)

3.6 Are higher efficiency products built at different plants than lower efficiency products of the 

same equipment class?

4 MARKUPS AND PROFITABILITY

One of the primary objectives of the Manufacturer Impact Analysis is to assess the impact of amended 

energy conservation standards on industry profitability. In this section, DOE would like to understand 

the current markup structure of the industry and how an amended energy conservation standard may 

impact your company’s markup structure and profitability. The manufacturer markup is a multiplier 

applied to manufacturer production costs to cover research and development, selling, general, and 

administrative expenses, and profit. It is not a profit margin. The manufacturer production cost 

multiplied by the manufacturer markup covers all costs involved in manufacturing as well as profit for 

the product.
12A-20

4.1 DOE estimated a markup of 1.205 for all product classes. Please comment on the accuracy of 

this figure and whether or not it may vary by product class.

4.2 Within each product class, do the per-unit mark-ups vary by efficiency level? Is the markup 

for more efficient designs different than the markup for baseline models?

4.3 What factors besides efficiency affect markups in the same product class?

4.4 Would you expect amended energy conservation standards to affect your profitability? If so, 

please explain why.

5 SHIPMENT PROJECTIONS

An amended energy conservation standard can change overall shipments by altering product attributes, 

marketing approaches, product availability, and price. Having an accurate estimate of these changes 

allows DOE to better examine impacts on profitability due to amended standards. DOE’s shipments 

model includes forecasts for the base case shipments (i.e., total industry shipments absent amended 

energy conservation standards) and the standards case shipments (i.e., total industry shipments with 

amended energy conservation standards).

5.1 Can you provide your historical shipments of commercial-style gas residential conventional 

cooking products for the last 10 years?

5.2 Can you provide the historical market share (last 5 years) of your shipments by efficiency 

level for each product class?
12A-21

5.3 Can you provide the historical market share (last 5 years) of your shipments by standby level 

for each product class?

5.4 Are there any anticipated or observed differences in the repair and maintenance costs or 

lifetime of induction type cooking units, compared to their conventional smooth top 

counterpart?

5.5 In the residential conventional cooking products industry, which party in the supply chain 

typically pays for shipping of the product to the distributor warehouse?

5.6 If the manufacturer pays for shipping costs, is it industry practice to mark-up shipping costs?

5.7 Do any of the efficiency levels trigger substantial increases in shipping costs? If so, which 

ones and for which products?

5.8 Currently, what fraction of your sales are high efficiency products, i.e., products that exceed 

the baseline efficiency level?

5.9 How do you think amended energy conservation standards will impact the sales of more 

efficient products? For example, would customers continue to buy products that exceed the 

energy conservation standard level? Would your response change for higher mandated 

efficiency levels?

5.10 Are there any types of equipment that you expect will soon be phased out in the absence of 
12A-22

an amended standard?

6 FINANCIAL PARAMETERS

Using publicly available data, Navigant Consulting, Inc. (NCI) has developed a “straw man” model of 

financial performance for the residential conventional cooking products manufacturing industry. This 

section attempts to understand how your company’s financial situation differs from our industry 

aggregate picture.

6.1 Please compare the financial parameters for the business unit responsible for manufacturing 

residential conventional cooking products to those tabulated below.

Table 12A-6 Financial Parameters for Residential Conventional Cooking Products Manufacturing

GRIM Input Definition

Industry 

Estimated

Value

Your Actual

Income Tax 

Rate

Corporate effective income tax paid (percentage of 

earnings before taxes, EBT) 19.5%

Discount Rate

Weighted average cost of capital (inflationadjusted weighted average of corporate cost of 

debt and return on equity)

9.1%

Working 

Capital

Current assets less current liabilities (percentage of 

revenues) 4.5%

SG&A Selling, general, and administrative expenses 

(percentage of revenues) 11.2%

R&D Research and development expenses (percentage 

of revenues) 2.4%

Depreciation Amortization of fixed assets (percentage of 

revenues) 3.0%

Capital 

Expenditures

Outlay of cash to acquire or improve capital assets 

(percentage of revenues, not including acquisition 

or sale of business units)

3.3%

Cost of Goods 

Sold

Includes material, labor, overhead, and 

depreciation (percentage of revenues) 83.0%

6.2 Are the figures in Table 6-1 representative of the residential conventional cooking products 

industry as a whole? If not, why?

6.3 Do any of the financial parameters in Table 6-1 change for a particular subgroup of 

manufacturers? Please describe any differences.
12A-23
12A-24

7 CONVERSION COSTS

An increase in energy conservation standards may cause the industry to incur capital and product 

conversion costs to meet the amended energy conservation standard. The Manufacturer Impact Analysis 

considers three types of conversion expenditures:

• Capital conversion costs – One-time investments in plant, property, and equipment (PPE) 

required to manufacture products that comply with amended energy conservation standards. 

These may be incremental changes or upgrades to existing PPE or the replacement of existing 

PPE. Included are expenditures on buildings, equipment, and tooling.

• Product conversion costs – One-time investments in research, product development, testing, 

marketing and other costs for redesigning and bringing to market products that comply with 

amended energy conservation standards.

• Stranded assets – The residual undepreciated value of assets replaced before the end of their 

useful lives as a direct result of the change in energy conservation standard.

With a detailed understanding of the conversion costs necessitated by different standard levels, DOE can 

better model the impact on the residential conventional cooking products industry resulting from 

amended energy conservation standards.

7.1 At your manufacturing facilities, would amended energy conservation standards be difficult 

to implement? If so, would your company modify existing facilities or develop new facilities?

7.2 What level of conversion costs do you anticipate incurring at each efficiency level? Please 

provide dollar amounts as well as descriptions of the investment in the tables that follow. In 

the description column, DOE is interested in understanding the kind of changes that would 

need to be implemented in production lines and production facilities. Where applicable, 

please quantify the number and cost of new production equipment that would be required to 

meet the specified efficiency levels.
12A-25

Table 12A-7 Conversion Costs – Cooking Tops

Product 

Class

Efficiency 

Level (EL)

Proposed 

IAEC

Estimated Capital 

Conversion Costs

Estimated Product 

Conversion Costs

Estimated Stranded 

Assets

Gas 

cooking 

tops

Baseline 1445.0 

kBtu - - -

EL 1 1372.7 

kBtu

Open 

(coil) 

element 

electric 

cooking 

tops

Baseline 256.7 kWh - - -

EL 1 246.0 kWh

Smooth 

element 

electric 

cooking 

tops

Baseline 280.6 kWh - - -

EL 1 268.6 kWh

EL 2 263.5 kWh

EL 3 259.8 kWh

EL 4 245.9 kWh

EL 5 240.7 kWh
12A-26

Table 12A-8 Conversion Costs – Gas Ovens

Product 

Class

Efficiency 

Level (EL)

Proposed 

IAEC

Estimated Capital 

Conversion Costs

Estimated Product 

Conversion Costs

Estimated Stranded 

Assets

Gas 

ovens –

standard 

ovens 

with or 

without a 

catalytic 

line

Baseline 2076.5 

kBtu - - -

EL 1 1932.0 

kBtu

EL 2 1844.2 

kBtu

EL 3 1717.7 

kBtu

EL 4 1702.6 

kBtu

EL 5 1695.4 

kBtu

EL 6 1685.9 

kBtu

EL 7 1636.0 

kBtu

EL 8 1499.1 

kBtu

Gas 

ovens –

self-clean 

ovens

Baseline 1965.0 

kBtu - - -

EL 1 1820.5 

kBtu

EL 2 1596.9 

kBtu

EL 3 1482.3 

kBtu

EL 4 1472.0 

kBtu

EL 5 1467.8 

kBtu

EL 6 1330.9 

kBtu
12A-27

Table 12A-9 Conversion Costs – Electric Ovens

Product 

Class

Efficiency 

Level (EL)

Proposed 

IAEC

Estimated Capital 

Conversion Costs

Estimated Product 

Conversion Costs

Estimated Stranded 

Assets

Electric 

ovens –

standard 

ovens 

with or 

without a 

catalytic 

line

Baseline 370.0 kWh - - -

EL 1 327.7 kWh

EL 2 316.1 kWh

EL 3 304.8 kWh

EL 4 300.9 kWh

EL 5 300.3 kWh

EL 6 295.2 kWh

EL 7 255.0 kWh

Electric 

ovens –

self-clean 

ovens

Baseline 360.0 kWh - - -

EL 1 317.7 kWh

EL 2 317.0 kWh

EL 3 312.0 kWh

EL 4 271.9 kWh

7.3 Please comment on any potential stranded assets that may result from an amended energy 

conservation standard.

7.4 For any efficiency levels that would require new production equipment, please describe how 

much downtime would be required. What impact would downtime have on your business?

7.5 Please provide any additional qualitative information that might help DOE understand the 

type and nature of your conversion investments, including plant and tooling changes and 

product development efforts required for different efficiency levels and equipment classes.
12A-28

8 CUMULATIVE REGULATORY BURDEN

Cumulative regulatory burden refers to the burden that industry faces from overlapping effects of new or 

revised DOE standards, and/or other regulatory actions affecting the same product or industry.

8.1 Are there other recent or impending standards that manufacturers of residential conventional 

cooking products face from DOE, other US federal agencies, state regulators, foreign 

governmental agencies, or other standard setting bodies? If so, please identify the regulation 

and the corresponding possible effective dates for those regulations in the table below.

Table 12A-10 Other Regulations Identified by DOE

Regulation Effective Date(s) Expected Expenses / Comments

DOE’s Amended Energy 

Conservation Standards for 

Other Products

International Energy 

Efficiency Standards

State Emission Requirements 

and Other State Regulations

Industry Standards and Codes

8.2 Under what circumstances would you be able to coordinate expenditures related to these 

other regulations with an amended energy conservation standard, thereby lessening the 

cumulative burden?
12A-29

9 DIRECT EMPLOYMENT IMPACT ASSESSMENT

The impact of amended energy conservation standards on employment is an important consideration in 

the rulemaking process. This section of the interview guide seeks to explore current trends in residential 

conventional cooking product manufacturing employment and to solicit manufacturer views on how 

domestic employment patterns might be affected by amended energy conservation standards.

9.1 Would your domestic employment levels be expected to change significantly under amended 

energy conservation standards? If so, please identify particular standard levels which may 

trigger changes in employment.

9.2 Would the workforce skills necessary under amended energy conservation standards require 

extensive retraining or replacement of employees at your manufacturing facilities?

10 MANUFACTURING CAPACITY/EXPORTS/FOREIGN COMPETITION/OUTSOURCING

A disparity between domestic and foreign energy conservation standards could impact exports or 

imports. Labor content and material changes, resulting from amended energy conservation standards, 

may impact sourcing decisions.

How would amended energy conservation standards impact your company’s manufacturing 

capacity, in both the short term and the long term?

10.1 What percentage of your U.S. made residential conventional cooking products is exported?

10.2 Absent amended energy conservation standards, are production facilities being relocated to 

foreign countries?

10.3 Would amended energy conservation standards impact your domestic vs. foreign 

manufacturing decision?
12A-30

10.4 What percentage of the U.S. market for residential conventional cooking products is 

imported? Would amended energy conservation standards have an impact on foreign 

competition?

10.5 Do any foreign manufacturers of residential conventional cooking products also have North 

American production facilities?

11 INDUSTRY CONSOLIDATION

Amended energy conservation standards can alter the competitive dynamics of the market. This can 

include prompting companies to enter or exit the market, or to merge. DOE and the Department of Justice 

are both interested in any potential reduction in competition that would result from amended energy 

conservation standards.

11.1 Do you know of any occurrences of industry consolidation that have happened within the 

last 10 years? If so, please comment on them.

11.2 In the absence of amended energy conservation standards, do you expect any industry 

consolidation? Please describe your expectations.

11.3 How would industry competition change as a result of amended conservation standards?

11.4 To your knowledge, are there any niche manufacturers for which the adoption of amended 

energy conservation standards would have a particularly severe impact?

11.5 To your knowledge, are there any component manufacturers for which the adoption of 

amended energy conservation standards would have a particularly severe impact?
12A-31

12 IMPACTS ON SMALL BUSINESS

12.1 The Small Business Association (SBA) considers manufacturers of residential conventional 

cooking products which employ 750 or fewer employees4 to be small businesses. By this 

definition, is your company considered a small business?

12.2 Below is a list of small business manufacturers of residential conventional cooking products 

compiled by DOE. Are there any small manufacturers that should be added to (or removed 

from) this list? Are there specific manufacturers on this list that may be more severely 

impacted by an amended energy conservation standard than others?

• Acme Kitchenettes 

• American Range

• Brown Stove Works, Inc.

• Capital Cooking

• Dacor 

• Evo, Inc.

• Kenyon International Inc.

• Peerless Premier

• Summit Appliances

12.3 Are there any reasons that a small business might be at a disadvantage relative to a larger 

business under amended energy conservation standards? Please consider such factors as 

technical expertise, access to capital, bulk purchasing power for materials/components, 

engineering resources, and any other relevant issues.

12.4 To your knowledge, are there any small businesses manufacturers for which the adoption of 

amended energy conservation standards would have a particularly severe impact? If so, 

would small business manufacturers have different incremental impacts from amended 

energy conservation standards than the rest of the industry?

 4 DOE uses the SBA small business size standards effective January 22, 2014 to determine whether a company is a 

small business. To be categorized as a small business, a residential conventional cooking product manufacturer and 

its affiliates may employ a maximum of 750 employees. The 750 employee threshold includes all employees in a 

business’s parent company and any other subsidiaries.
APPENDIX 12B. GOVERNMENT REGULATORY IMPACT MODEL OVERVIEW

TABLE OF CONTENTS

12B.1 Introduction and Purpose .................................................................................................B-1

12B.2 Model Description ...........................................................................................................B-1

LIST OF TABLES

Table 12B.1 Detailed Cash Flow Example..................................................................................B-4

12B-i
APPENDIX 12B. GOVERNMENT REGULATORY IMPACT MODEL OVERVIEW

12B.1 INTRODUCTION AND PURPOSE

The purpose of the Government Regulatory Impact Model (GRIM) is to help quantify the 

impacts of energy conservation standards and other regulations on manufacturers. The basic 

mode of analysis is to estimate the change in the value of the industry or manufacturers(s) 

following a regulation or a series of regulations. The model structure also allows an analysis of 

multiple equipment types with regulations taking effect over a period of time, and of multiple 

regulations on the same equipment.

Industry net present value is defined, for the purpose of this analysis, as the discounted 

sum of industry free cash flows plus a discounted terminal value. The model calculates the actual 

cash flows by year and then determines the present value of those cash flows both without an 

energy conservation standard (i.e., the base case) and under different trial standard levels (i.e., 

the standards case).

Outputs from the model consist of summary financial metrics, graphs of major variables, 

and, when appropriate, access to the complete cash flow calculation.

12B.2 MODEL DESCRIPTION

The basic structure of the GRIM is a standard annual cash flow analysis that uses 

manufacturer selling prices, manufacturing costs, a shipments forecast, and financial parameters 

as inputs and accepts a set of regulatory conditions as changes in costs and investments. The cash 

flow analysis is separated into two major blocks: income and cash flow. The income calculation 

determines net operating profit after taxes. The cash flow calculation converts net operating 

profit after taxes into an annual cash flow by including investment and non-cash items. Below 

are definitions of listed items on the printout of the output sheet of the GRIM.

(1) Unit Sales: Total annual shipments for the industry were obtained from the National Impact 

Analysis Spreadsheet.

(2) Revenues: Annual revenues – computed by multiplying equipment unit prices at each 

efficiency level by the appropriate manufacturer markup.

(3) Material: The portion of COGS that includes materials.

(4) Labor: The portion of cost of goods sold (COGS) that includes direct labor, commissions, 

dismissal pay, bonuses, vacation, sick leave, social security contributions, fringe, and 

assembly labor up-time.

(5) Depreciation: The portion of overhead that includes an allowance for the total amount of 

fixed assets used to produce that one unit. Annual depreciation is computed as a percentage 

of COGS. While included in overhead, the depreciation is shown as a separate line item.

12B-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, the depreciation is shown as a separate line item.

(7) Standard SG&A: Selling, general, and administrative costs are computed as a percentage of 

Revenues (2).

(8) R&D: GRIM separately accounts for ordinary research and development (R&D) 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 equipment designs 

comply with the new energy conservation standard. The GRIM allocates these costs over 

the period between the standard’s announcement and compliance dates.

(10) Stranded Assets: In the year the standard becomes effective, a one-time write-off of 

stranded assets is accounted for.

(11) Earnings Before Interest and Taxes (EBIT): Includes profits before deductions for interest 

paid and taxes.

(12) EBIT as a Percentage of Sales (EBIT/Revenues): GRIM calculates EBIT as a percentage 

of sales to compare with the industry’s average reported in financial statements.

(13) Taxes: Taxes on EBIT (11) are calculated by multiplying the tax rate contained in Major 

Assumptions by EBIT (11).

(14) Net Operating Profits After Taxes (NOPAT): Computed by subtracting Cost of Goods 

Sold ((3) to (6)), SG&A (7), R&D (8), Product Conversion Costs (9), and Taxes (13) from 

Revenues (2).

(15) NOPAT repeated: NOPAT is repeated in the Statement of Cash Flows.

(16) Depreciation repeated: Depreciation and Stranded Assets are added back in the Statement 

of Cash Flows because they are non-cash expenses.

(17) Change in Working Capital: Change in cash tied up in accounts receivable, inventory, and 

other cash investments necessary to support operations is calculated by multiplying 

working capital (as a percentage of revenues) by the change in annual revenues.

(18) Cash Flow From Operations: Calculated by taking NOPAT (15), adding back non-cash 

items such as 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 

12B-2
equipment designs can be fabricated and assembled under the new regulation. The GRIM 

allocates these costs over the period between the standard’s announcement and compliance 

dates.

(21) Capital Investment: Total investments in property, plant, and equipment are computed by 

adding Ordinary Capital Expenditures (19) and Capital Conversion Costs (20).

(22) Free Cash Flow: Annual cash flow from operations and investments; computed by 

subtracting Capital Investment (21) from Cash Flow from Operations (18).

(23) Terminal Value: Estimate of the continuing value of the industry after the analysis period. 

Computed by growing the Free Cash Flow at a constant rate in perpetuity.

(24) Present Value Factor: Factor used to calculate an estimate of the present value of an 

amount to be received in the future.

(25) Discounted Cash Flow: Free Cash Flows (22) multiplied by the Present Value Factor 

(24). For the end of 2048, the discounted cash flow includes the discounted Terminal Value 

(23).

(26) Industry Value thru the end of 2048: The sum of Discounted Cash Flows (25).

12B-3
Table 12B.1 Detailed Cash Flow Example

12B-4
APPENDIX 13A. EMISSIONS ANALYSIS METHODOLOGY

TABLE OF CONTENTS

13A.1 INTRODUCTION ...................................................................................................... 13A-1

13A.2 POWER SECTOR AND SITE EMISSIONS FACTORS.......................................... 13A-1

13A.3 UPSTREAM FACTORS ............................................................................................ 13A-2

13A.4 DATA TABLES ......................................................................................................... 13A-2

REFERENCES ..................................................................................................................... 13A-11

LIST OF TABLES

Table 13A.4.1 Site Combustion Emissions Factors.......................................................... 13A-3

Table 13A.4.2 Power Sector Emissions Factors for CO2 in tons/KWh............................ 13A-4

Table 13A.4.3 Power Sector Emissions Factors for Hg in tons/TWh .............................. 13A-5

Table 13A.4.4 Power Sector Emissions Factors for NOx in tons/MWh........................... 13A-6

Table 13A.4.5 Power Sector Emissions Factors for SO2 in tons/MWh............................ 13A-7

Table 13A.4.6 Power Sector Emissions Factors for CH4 in tons/MWh ........................... 13A-8

Table 13A.4.7 Power Sector Emissions Factors for N2O in tons/MWh........................... 13A-9

Table 13A.4.8 Electricity Upstream Emissions Factors................................................... 13A-9

Table 13A.4.9 Natural Gas Upstream Emissions Factors............................................... 13A-10

Table 13A.4.10 Fuel Oil Upstream Emissions Factors..................................................... 13A-10

13A-i
APPENDIX 13A. EMISSIONS ANALYSIS METHODOLOGY

13A.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 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 appendix 15A of 

this TSD and in the report “Utility Sector Impacts of Reduced Electricity Demand” (Coughlin,

2014).2 This Appendix describes the development of the upstream emissions factors, and 

combustion factors for CH4 and N2O.

13A.2 POWER SECTOR AND SITE EMISSIONS FACTORS 

Marginal emissions factors are calculated by looking at the difference, over the full 

analysis period, between the AEO reference case and the policy side cases. The analysis 

produces a set of emissions intensity factors that quantify the reduction in emissions of a given 

pollutant per unit reduction of site consumption of electricity. Distinct factors are calculated for 

the residential and commercial sectors, and for each of the end uses that are modeled explicitly in 

NEMS as listed in the tables below. Total emissions reductions are estimated by multiplying the 

intensity factors times the energy savings calculated in the national impact analysis (chapter 10).

The AEO does not publish estimates of the CH4 and N2O emissions associated with 

combustion of fossil fuels. For these pollutants, the power sector emissions are estimated using 

emissions intensity factors published by the EPA3. This publication provides emissions intensity 

factors for different grades of coal, petroleum fuels and natural gas. DOE uses these fuel-specific 

emissions factors to develop time-dependent emissions factors as a function of the changing fuel 

mix in the power sector.

Site combustion of fossil fuels in buildings (for example in water-heating, space-heating 

or cooking applications) also produces emissions of CO2 and other pollutants. DOE used

emissions factors published by the Environmental Protection Agency3, which are constant in 

time. These factors are presented in Table 13A.4.1.

13A-1
13A.3 UPSTREAM FACTORS 

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 FFC accounting approach is described briefly in appendix 10B and in Coughlin 

(2013)4

. When demand for a particular fuel is reduced, there is a corresponding reduction in the 

upstream activities associated with production of that fuel (mining, refining etc.) These upstream 

activities also consume energy and therefore produce combustion emissions. The FFC 

accounting estimates the total consumption of electricity, natural gas and petroleum-based fuels 

in these upstream activities. The relevant combustion emissions factors are then applied to this 

fuel use to determine the total upstream emissions intensities from combustion, per unit of fuel 

delivered to the consumer. 

In addition to combustion emissions, extraction and processing of fossil fuels also 

produces fugitive emissions of CO2 and CH4. Fugitive emissions of CO2 are small relative to 

combustion emissions, comprising about 2-3 percent of total CO2 emissions for natural gas and 

1-2 percent for petroleum fuels. In contrast, the fugitive emissions of methane from fossil fuel 

production are relatively large compared to combustion emissions of CH4. 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. 

Fugitive emissions factors for CO2 and methane from coal mining and natural gas 

production were estimated based on a review of recent studies compiled by Burnham (2011).5

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.6,7 As more data are made available, DOE will continue to update these 

estimated emissions factors.

Upstream emissions factors account for both fugitive emissions and combustion 

emissions in extraction, processing, and transport of primary fuels. 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 13A.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, so some components of the upstream fuel cycle (particularly off-road mobile engines) 

can contribute significantly to the upstream NOx emissions factors. 

13A.4 DATA TABLES

Summary tables of all the emissions factor data used by DOE for rules using AEO 2014

are presented in the tables below. Table 13A.4.1 provides combustion emissions factors for fuels 

commonly used in buildings. Table 13A.4.2 to Table 13A.4.7 present the marginal power sector 

emissions factors as a function of sector and end use for a selected set of years. Table 13A.4.8 to 

Table 13A.4.10 provide the upstream emissions factors for all pollutants, for site electricity,

13A-2
natural gas and petroleum fuels. In all cases, the emissions factors are defined relative to site use 

of the fuel.

Table 13A.4.1 Site Combustion Emissions Factors

Species Natural Gas 

lb/mmcf

Distillate Oil

lb/1000 gal

Propane

lb/1000 gal

Kerosene

lb/1000 gal

CO2 1.20E+05 2.25E+04 1.25E+04 2. 24E+04

SO2 6.00E-01 142*(S) 0.1*(S) 142*(S)

NOx 9.60E+01 1.90E+01 1.40E+01 1.80E+01

N2O 2.20E+00 1.76E-01 1.10E-01 1.76E-01

CH4 2.30E+00 9.04E-01 5.95E-01 9.04E-01

13A-3
Table 13A.4.2 Power Sector Emissions Factors for CO2 (tons of CO2 per kWh of site 

electricity use)

2020 2025 2030 2035 2040

Commercial Sector

cooking 7.91E-04 7.03E-04 6.34E-04 5.80E-04 5.30E-04

lighting 7.95E-04 7.05E-04 6.37E-04 5.82E-04 5.32E-04

office equipment (non-pc) 7.78E-04 6.93E-04 6.26E-04 5.73E-04 5.25E-04

office equipment (pc) 7.78E-04 6.93E-04 6.26E-04 5.73E-04 5.25E-04

other uses 7.83E-04 6.97E-04 6.29E-04 5.76E-04 5.27E-04

refrigeration 8.10E-04 7.17E-04 6.46E-04 5.90E-04 5.39E-04

space cooling 7.67E-04 6.83E-04 6.17E-04 5.66E-04 5.21E-04

space heating 8.21E-04 7.26E-04 6.54E-04 5.97E-04 5.43E-04

ventilation 8.10E-04 7.17E-04 6.47E-04 5.91E-04 5.39E-04

water heating 7.97E-04 7.07E-04 6.38E-04 5.83E-04 5.33E-04

Industrial Sector

all uses 7.83E-04 6.97E-04 6.29E-04 5.76E-04 5.27E-04

Residential Sector

clothes dryers 7.97E-04 7.08E-04 6.39E-04 5.84E-04 5.33E-04

cooking 7.90E-04 7.02E-04 6.34E-04 5.80E-04 5.29E-04

freezers 8.09E-04 7.16E-04 6.46E-04 5.90E-04 5.38E-04

lighting 8.10E-04 7.17E-04 6.47E-04 5.91E-04 5.38E-04

other uses 7.97E-04 7.07E-04 6.38E-04 5.83E-04 5.32E-04

refrigeration 8.08E-04 7.16E-04 6.45E-04 5.89E-04 5.38E-04

space cooling 7.69E-04 6.85E-04 6.19E-04 5.68E-04 5.21E-04

space heating 8.18E-04 7.24E-04 6.52E-04 5.95E-04 5.42E-04

water heating 7.99E-04 7.09E-04 6.40E-04 5.85E-04 5.33E-04

13A-4
Table 13A.4.3 Power Sector Emissions Factors for Hg (tons/TWh)

2020 2025 2030 2035 2040

Commercial Sector

cooking 2.39E-03 1.87E-03 1.57E-03 1.32E-03 1.18E-03

lighting 2.41E-03 1.88E-03 1.58E-03 1.33E-03 1.19E-03

office equipment (non-pc) 2.31E-03 1.80E-03 1.52E-03 1.27E-03 1.14E-03

office equipment (pc) 2.31E-03 1.80E-03 1.52E-03 1.27E-03 1.14E-03

other uses 2.34E-03 1.83E-03 1.54E-03 1.29E-03 1.15E-03

refrigeration 2.50E-03 1.95E-03 1.64E-03 1.38E-03 1.23E-03

space cooling 2.21E-03 1.72E-03 1.45E-03 1.21E-03 1.08E-03

space heating 2.59E-03 2.02E-03 1.70E-03 1.43E-03 1.27E-03

ventilation 2.50E-03 1.95E-03 1.64E-03 1.38E-03 1.23E-03

water heating 2.42E-03 1.89E-03 1.59E-03 1.33E-03 1.19E-03

Industrial Sector

all uses 2.34E-03 1.83E-03 1.54E-03 1.29E-03 1.15E-03

Residential Sector

clothes dryers 2.44E-03 1.91E-03 1.60E-03 1.35E-03 1.20E-03

cooking 2.40E-03 1.88E-03 1.58E-03 1.32E-03 1.18E-03

freezers 2.49E-03 1.95E-03 1.64E-03 1.37E-03 1.23E-03

lighting 2.52E-03 1.97E-03 1.66E-03 1.39E-03 1.24E-03

other uses 2.44E-03 1.91E-03 1.60E-03 1.34E-03 1.20E-03

refrigeration 2.49E-03 1.94E-03 1.64E-03 1.37E-03 1.23E-03

space cooling 2.23E-03 1.74E-03 1.46E-03 1.22E-03 1.09E-03

space heating 2.57E-03 2.01E-03 1.69E-03 1.42E-03 1.27E-03

water heating 2.46E-03 1.92E-03 1.62E-03 1.36E-03 1.21E-03

13A-5
Table 13A.4.4 Power Sector Emissions Factors for NOx (tons/MWh)

2020 2025 2030 2035 2040

Commercial Sector

cooking 2.39E-03 1.87E-03 1.57E-03 1.32E-03 1.18E-03

lighting 2.41E-03 1.88E-03 1.58E-03 1.33E-03 1.19E-03

office equipment (non-pc) 2.31E-03 1.80E-03 1.52E-03 1.27E-03 1.14E-03

office equipment (pc) 2.31E-03 1.80E-03 1.52E-03 1.27E-03 1.14E-03

other uses 2.34E-03 1.83E-03 1.54E-03 1.29E-03 1.15E-03

refrigeration 2.50E-03 1.95E-03 1.64E-03 1.38E-03 1.23E-03

space cooling 2.21E-03 1.72E-03 1.45E-03 1.21E-03 1.08E-03

space heating 2.59E-03 2.02E-03 1.70E-03 1.43E-03 1.27E-03

ventilation 2.50E-03 1.95E-03 1.64E-03 1.38E-03 1.23E-03

water heating 2.42E-03 1.89E-03 1.59E-03 1.33E-03 1.19E-03

Industrial Sector

all uses 2.34E-03 1.83E-03 1.54E-03 1.29E-03 1.15E-03

Residential Sector

clothes dryers 2.44E-03 1.91E-03 1.60E-03 1.35E-03 1.20E-03

cooking 2.40E-03 1.88E-03 1.58E-03 1.32E-03 1.18E-03

freezers 2.49E-03 1.95E-03 1.64E-03 1.37E-03 1.23E-03

lighting 2.52E-03 1.97E-03 1.66E-03 1.39E-03 1.24E-03

other uses 2.44E-03 1.91E-03 1.60E-03 1.34E-03 1.20E-03

refrigeration 2.49E-03 1.94E-03 1.64E-03 1.37E-03 1.23E-03

space cooling 2.23E-03 1.74E-03 1.46E-03 1.22E-03 1.09E-03

space heating 2.57E-03 2.01E-03 1.69E-03 1.42E-03 1.27E-03

water heating 2.46E-03 1.92E-03 1.62E-03 1.36E-03 1.21E-03

13A-6
Table 13A.4.5 Power Sector Emissions Factors for SO2 (tons/MWh)

2020 2025 2030 2035 2040

Commercial Sector

cooking 7.75E-04 6.05E-04 5.09E-04 4.27E-04 3.82E-04

lighting 7.81E-04 6.10E-04 5.13E-04 4.30E-04 3.85E-04

office equipment (non-pc) 7.49E-04 5.85E-04 4.92E-04 4.12E-04 3.68E-04

office equipment (pc) 7.49E-04 5.85E-04 4.92E-04 4.12E-04 3.68E-04

other uses 7.59E-04 5.92E-04 4.98E-04 4.18E-04 3.73E-04

refrigeration 8.10E-04 6.32E-04 5.32E-04 4.46E-04 3.99E-04

space cooling 7.15E-04 5.59E-04 4.70E-04 3.94E-04 3.51E-04

space heating 8.38E-04 6.54E-04 5.50E-04 4.62E-04 4.13E-04

ventilation 8.11E-04 6.33E-04 5.33E-04 4.47E-04 4.00E-04

water heating 7.85E-04 6.13E-04 5.15E-04 4.32E-04 3.86E-04

Industrial Sector

all uses 7.59E-04 5.92E-04 4.98E-04 4.18E-04 3.73E-04

Residential Sector

clothes dryers 7.92E-04 6.18E-04 5.20E-04 4.36E-04 3.90E-04

cooking 7.79E-04 6.08E-04 5.11E-04 4.29E-04 3.83E-04

freezers 8.08E-04 6.31E-04 5.31E-04 4.45E-04 3.98E-04

lighting 8.17E-04 6.38E-04 5.37E-04 4.50E-04 4.03E-04

other uses 7.91E-04 6.18E-04 5.20E-04 4.36E-04 3.90E-04

refrigeration 8.07E-04 6.30E-04 5.30E-04 4.45E-04 3.98E-04

space cooling 7.21E-04 5.64E-04 4.74E-04 3.97E-04 3.54E-04

space heating 8.33E-04 6.50E-04 5.47E-04 4.59E-04 4.11E-04

water heating 7.98E-04 6.23E-04 5.24E-04 4.40E-04 3.93E-04

13A-7
Table 13A.4.6 Power Sector Emissions Factors for CH4 (tons/MWh)

2020 2025 2030 2035 2040

Commercial Sector

Cooking 7.75E-05 6.21E-05 5.30E-05 4.54E-05 4.07E-05

Lighting 7.80E-05 6.25E-05 5.34E-05 4.57E-05 4.10E-05

office equipment (non-pc) 7.49E-05 6.01E-05 5.13E-05 4.39E-05 3.94E-05

office equipment (pc) 7.49E-05 6.01E-05 5.13E-05 4.39E-05 3.94E-05

other uses 7.58E-05 6.08E-05 5.19E-05 4.44E-05 3.99E-05

Refrigeration 8.08E-05 6.46E-05 5.52E-05 4.72E-05 4.24E-05

space cooling 7.15E-05 5.74E-05 4.90E-05 4.20E-05 3.77E-05

space heating 8.35E-05 6.68E-05 5.70E-05 4.88E-05 4.38E-05

Ventilation 8.09E-05 6.48E-05 5.53E-05 4.73E-05 4.25E-05

water heating 7.83E-05 6.28E-05 5.36E-05 4.59E-05 4.12E-05

Industrial Sector

all uses 7.58E-05 6.08E-05 5.19E-05 4.44E-05 3.99E-05

Residential Sector

clothes dryers 7.91E-05 6.34E-05 5.41E-05 4.63E-05 4.16E-05

Cooking 7.79E-05 6.24E-05 5.33E-05 4.56E-05 4.10E-05

Freezers 8.06E-05 6.45E-05 5.51E-05 4.71E-05 4.23E-05

Lighting 8.15E-05 6.53E-05 5.57E-05 4.77E-05 4.28E-05

other uses 7.91E-05 6.33E-05 5.41E-05 4.63E-05 4.16E-05

Refrigeration 8.05E-05 6.44E-05 5.50E-05 4.71E-05 4.23E-05

space cooling 7.22E-05 5.79E-05 4.95E-05 4.23E-05 3.81E-05

space heating 8.31E-05 6.64E-05 5.67E-05 4.85E-05 4.36E-05

water heating 7.97E-05 6.38E-05 5.45E-05 4.67E-05 4.19E-05

13A-8
Table 13A.4.7 Power Sector Emissions Factors for N2O (tons/MWh)

2020 2025 2030 2035 2040

Commercial Sector

cooking 1.12E-05 8.86E-06 7.54E-06 6.41E-06 5.75E-06

lighting 1.12E-05 8.93E-06 7.59E-06 6.46E-06 5.79E-06

office equipment (non-pc) 1.08E-05 8.57E-06 7.28E-06 6.20E-06 5.56E-06

office equipment (pc) 1.08E-05 8.57E-06 7.28E-06 6.20E-06 5.56E-06

other uses 1.09E-05 8.68E-06 7.38E-06 6.28E-06 5.63E-06

refrigeration 1.16E-05 9.24E-06 7.86E-06 6.69E-06 6.00E-06

space cooling 1.03E-05 8.18E-06 6.96E-06 5.92E-06 5.31E-06

space heating 1.20E-05 9.56E-06 8.13E-06 6.92E-06 6.20E-06

ventilation 1.17E-05 9.26E-06 7.87E-06 6.70E-06 6.01E-06

water heating 1.13E-05 8.97E-06 7.62E-06 6.48E-06 5.82E-06

Industrial Sector

all uses 1.09E-05 8.68E-06 7.38E-06 6.28E-06 5.63E-06

Residential Sector

clothes dryers 1.14E-05 9.06E-06 7.70E-06 6.55E-06 5.87E-06

cooking 1.12E-05 8.91E-06 7.58E-06 6.45E-06 5.78E-06

freezers 1.16E-05 9.23E-06 7.84E-06 6.67E-06 5.99E-06

lighting 1.18E-05 9.34E-06 7.94E-06 6.75E-06 6.06E-06

other uses 1.14E-05 9.05E-06 7.69E-06 6.54E-06 5.87E-06

refrigeration 1.16E-05 9.22E-06 7.83E-06 6.66E-06 5.98E-06

space cooling 1.04E-05 8.26E-06 7.02E-06 5.97E-06 5.35E-06

space heating 1.20E-05 9.51E-06 8.08E-06 6.88E-06 6.17E-06

water heating 1.15E-05 9.13E-06 7.76E-06 6.60E-06 5.92E-06

Table 13A.4.8 Electricity Upstream Emissions Factors

Unit 2020 2025 2030 2035 2040

CO2 kg/MWh 29.1 29.4 29.7 29.9 29.8

SO2 g/MWh 5.0 5.1 4.9 4.7 4.6

NOx g/MWh 368 375 382 387 387

Hg g/MWh 0.00001 0.00001 0.00001 0.00001 0.00001

N2O g/MWh 0.25 0.25 0.24 0.23 0.23

CH4 g/MWh 2,149 2,195 2,216 2,248 2,255

13A-9
Table 13A.4.9 Natural Gas Upstream Emissions Factors

Unit 2020 2025 2030 2035 2040

CO2

kg/ 

mcf 7.1 7.2 7.3 7.4 7.4

SO2 g/ mcf 0.030 0.031 0.031 0.032 0.032

NOx g/ mcf 101 103 105 105 105

N2O g/ mcf 0.011 0.011 0.012 0.012 0.012

CH4 g/ mcf 659 665 666 670 670

Table 13A.4.10 Fuel Oil Upstream Emissions Factors

Unit 2020 2025 2030 2035 2040

CO2 kg/bbl 70.8 70.3 69.9 68.9 68.3

SO2 g/bbl 14.5 14.1 14.0 13.9 13.9

NOx g/bbl 765 742 737 733 732

Hg g/bbl 0.000007 0.000007 0.000007 0.000007 0.000007

N2O g/bbl 0.598 0.579 0.574 0.569 0.568

CH4 g/bbl 897 905 902 888 877

13A-10
REFERENCES

1. Energy Information Administration, Annual Energy Outlook 2012 with Projections to 

2035, 2012. Washington, DC. <http://www.eia.gov/forecasts/aeo/pdf/0383(2012).pdf>

2. Coughlin, K., Utility Sector Impacts of Reduced Electricity Demand, Forthcoming. 

Lawrence Berkeley National Laboratory. 

3. US EPA, Emissions Factors for Greenhouse Gas Inventories, 2014. (Last accessed 

December 1, 2014.) <http://www.epa.gov/climateleadership/documents/emissionfactors.pdf>

4. Coughlin, K., Projections of Full-Fuel-Cycle Energy and Emissions Metrics, 2013. 

Lawrence Berkeley National Laboratory. Report No. LBNL-6025E. 

5. 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 

6. U.S. Environmental Protection Agency, Fugitive Emissions Reporting from the 

Petroleum and Natural Gas Industry, 2009. U.S. Environmental Protection Agency. 

Washington, DC. 

7. 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. 

13A-11
APPENDIX 14A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT 

ANALYSIS UNDER EXECUTIVE ORDER 12866

TABLE OF CONTENTS

14A.1 EXECUTIVE SUMMARY .................................................................................. 14A-1

14A.2 MONETIZING CARBON DIOXIDE EMISSIONS............................................ 14A-2

14A.3 SOCIAL COST OF CARBON VALUES USED IN PAST REGULATORY 

ANALYSES.......................................................................................................... 14A-4

14A.4 APPROACH AND KEY ASSUMPTIONS ......................................................... 14A-5

14A.4.1 Integrated Assessment Models ............................................................................. 14A-5

14A.4.2 Global versus Domestic Measures of SCC......................................................... 14A-11

14A.4.3 Valuing Non-CO2 Emissions.............................................................................. 14A-13

14A.4.4 Equilibrium Climate Sensitivity ......................................................................... 14A-13

14A.4.5 Socioeconomic and Emissions Trajectories........................................................ 14A-16

14A.4.6 Discount Rate...................................................................................................... 14A-19

14A.5 REVISED SCC ESTIMATES ............................................................................ 14A-26

14A.6 LIMITATIONS OF THE ANALYSIS ............................................................... 14A-32

14A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPACTS AND 

DAMAGE FUNCTIONS ................................................................................... 14A-34

14A.8 CONCLUSION................................................................................................... 14A-37

14A.9 ANNEX............................................................................................................... 14A-44

14A.9.1 Other (non-CO2) gases........................................................................................ 14A-45

14A.9.2 Extrapolating Emissions Projections to 2300 ..................................................... 14A-47

LIST OF TABLES

Table 14A.1.1 Social Cost of CO2, 2010 – 2050 (in 2007 dollars) .................................. 14A-2

Table 14A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity 

Distributions............................................................................................ 14A-14

Table 14A.4.2 Socioeconomic and Emissions Projections from Select EMF-22 

Reference Scenarios................................................................................ 14A-18

Table 14A.5.1 Disaggregated Social Cost of CO2 Values by Model, Socioeconomic 

Trajectory, and Discount Rate for 2010 (in 2007 dollars)...................... 14A-29

Table 14A.5.2 Social Cost of CO2, 2010 – 2050 (in 2007 dollars) ................................ 14A-31

Table 14A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates 

between 2010 and 2050 .......................................................................... 14A-31

Table 14A.7.1 Probabilities of Various Tipping Points from Expert Elicitation............ 14A-35

Table 14A.9.1 Annual SCC Values: 2010–2050 (in 2007 dollars) ................................ 14A-44

Table 14A.9.2 2010 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton 

CO2) ........................................................................................................ 14A-55

Table 14A.9.3 2010 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton 

CO2) ........................................................................................................ 14A-56

14A-i
Table 14A.9.4 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton 

CO2) ........................................................................................................ 14A-57

Table 14A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates............. 14A-58

LIST OF FIGURES

Figure 14A.4.1 Annual Consumption Loss as a Fraction of Global GDP in 2100 Due 

to an Increase in Annual Global Temperature in the DICE, FUND, and 

PAGE models.......................................................................................... 14A-10

Figure 14A.4.2 Annual Consumption Loss for Lower Temperature Changes in DICE, 

FUND, and PAGE .................................................................................. 14A-11

Figure 14A.4.3 Estimates of the Probability Density Function for Equilibrium Climate 

Sensitivity (°C) ....................................................................................... 14A-16

Figure 14A.5.1 Level of Global GDP across EMF Scenarios ......................................... 14A-30

Figure 14A.9.2 Sulfur Dioxide Emission Scenarios........................................................ 14A-47

Figure 14A.9.3 Global Population, 2000-2300 (Post-2100 extrapolations assume the 

population growth rate changes linearly to reach a zero growth rate by 

2200.) ...................................................................................................... 14A-49

Figure 14A.9.4 World GDP, 2000-2300 (Post-2100 extrapolations assume GDP per 

capita growth declines linearly, reaching zero in the year 2300) ........... 14A-50

Figure 14A.9.5 Global Fossil and Industrial CO2 Emissions, 2000-2300 (Post-2100 

extrapolations assume growth rate of CO2 intensity (CO2/GDP) over 

2090-2100 is maintained through 2300)................................................. 14A-51

Figure 14A.9.6 Global Net Land Use CO2 Emissions, 2000-2300 (Post-2100 

extrapolations assume emissions decline linearly, reaching zero in the 

year 2200) ............................................................................................... 14A-52

Figure 14A.9.7 Global Non-CO2 Radiative Forcing, 2000-2300 (Post-2100 

extrapolations assume constant non-CO2 radiative forcing after 2100) . 14A-53

Figure 14A.9.8 Global CO2 Intensity (fossil & industrial CO2 emissions/GDP), 2000-

2300 (Post-2100 extrapolations assume decline in CO2/GDP growth 

rate over 2090-2100 is maintained through 2300).................................. 14A-54

Figure 14A.9.9 Histogram of Global SCC Estimates in 2010 (2007$/ton CO2), by 

discount rate ............................................................................................ 14A-57

14A-ii
APPENDIX 14A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT 

ANALYSIS UNDER EXECUTIVE ORDER 12866a

14A.1 EXECUTIVE SUMMARY

Under Executive Order 12866, agencies are required, to the extent permitted by law, “to 

assess both the costs and the benefits of the intended regulation and, recognizing that some costs 

and benefits are difficult to quantify, propose or adopt a regulation only upon a reasoned 

determination that the benefits of the intended regulation justify its costs.” The purpose of the 

“social cost of carbon” (SCC) estimates presented here is to allow agencies to incorporate the 

social benefits of reducing carbon dioxide (CO2) emissions into cost-benefit analyses of 

regulatory actions that have small, or “marginal,” impacts on cumulative global emissions. The 

estimates are presented with an acknowledgement of the many uncertainties involved and with a 

clear understanding that they should be updated over time to reflect increasing knowledge of the 

science and economics of climate impacts.

The SCC is an estimate of the monetized damages associated with an incremental 

increase in carbon emissions in a given year. It is intended to include (but is not limited to) 

changes in net agricultural productivity, human health, property damages from increased flood 

risk, and the value of ecosystem services due to climate change.

This document presents a summary of the interagency process that developed these SCC 

estimates. Technical experts from numerous agencies met on a regular basis to consider public 

comments, explore the technical literature in relevant fields, and discuss key model inputs and 

assumptions. The main objective of this process was to develop a range of SCC values using a 

defensible set of input assumptions grounded in the existing scientific and economic literatures. 

In this way, key uncertainties and model differences transparently and consistently inform the 

range of SCC estimates used in the rulemaking process.

a Prepared by Interagency Working Group on Social Cost of Carbon, United States Government.

With participation by:

Council of Economic Advisers 

Council on Environmental Quality 

Department of Agriculture

Department of Commerce

Department of Energy

Department of Transportation

Environmental Protection Agency

National Economic Council

Office of Energy and Climate Change

Office of Management and Budget

Office of Science and Technology Policy 

Department of the Treasury

14A-1

 
The interagency group selected four SCC values for use in regulatory analyses. Three 

values are based on the average SCC from three integrated assessment models, at discount rates 

of 2.5, 3, and 5 percent. The fourth value, which represents the 95th percentile SCC estimate 

across all three models at a 3 percent discount rate, is included to represent higher-than-expected 

impacts from temperature change further out in the tails of the SCC distribution.

Table 14A.1.1 Social Cost of CO2, 2010 – 2050 (in 2007 dollars)

Discount Rate

5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

14A.2 MONETIZING CARBON DIOXIDE EMISSIONS

The SCC is an estimate of the monetized damages associated with an incremental 

increase in carbon emissions in a given year. We report estimates of the SCC in dollars per 

metric ton of carbon dioxide throughout this document.b

When attempting to assess the incremental economic impacts of carbon dioxide 

emissions, the analyst faces a number of serious challenges. A recent report from the National 

Academies of Science (NRC 2009) points out that any assessment will suffer from uncertainty, 

speculation, and lack of information about (1) future emissions of greenhouse gases, (2) the 

effects of past and future emissions on the climate system, (3) the impact of changes in climate 

on the physical and biological environment, and (4) the translation of these environmental 

impacts into economic damages. As a result, any effort to quantify and monetize the harms 

associated with climate change will raise serious questions of science, economics, and ethics and 

should be viewed as provisional.

Despite the serious limits of both quantification and monetization, SCC estimates can be 

useful in estimating the social benefits of reducing carbon dioxide emissions. Under Executive 

b In this document, we present all values of the SCC as the cost per metric ton of CO2 emissions. Alternatively, one 

could report the SCC as the cost per metric ton of carbon emissions. The multiplier for translating between mass of 

CO2 and the mass of carbon is 3.67 (the molecular weight of CO2 divided by the molecular weight of carbon = 44/12 

= 3.67). 

14A-2

 
Order 12866, agencies are required, to the extent permitted by law, “to assess both the costs and 

the benefits of the intended regulation and, recognizing that some costs and benefits are difficult 

to quantify, propose or adopt a regulation only upon a reasoned determination that the benefits of 

the intended regulation justify its costs.” The purpose of the SCC estimates presented here is to 

make it possible for agencies to incorporate the social benefits from reducing carbon dioxide 

emissions into cost-benefit analyses of regulatory actions that have small, or “marginal,” impacts 

on cumulative global emissions. Most federal regulatory actions can be expected to have 

marginal impacts on global emissions.

For such policies, the benefits from reduced (or costs from increased) emissions in any 

future year can be estimated by multiplying the change in emissions in that year by the SCC 

value appropriate for that year. The net present value of the benefits can then be calculated by 

multiplying each of these future benefits by an appropriate discount factor and summing across 

all affected years. This approach assumes that the marginal damages from increased emissions 

are constant for small departures from the baseline emissions path, an approximation that is 

reasonable for policies that have effects on emissions that are small relative to cumulative global 

carbon dioxide emissions. For policies that have a large (non-marginal) impact on global 

cumulative emissions, there is a separate question of whether the SCC is an appropriate tool for 

calculating the benefits of reduced emissions; we do not attempt to answer that question here.

An interagency group convened on a regular basis to consider public comments, explore 

the technical literature in relevant fields, and discuss key inputs and assumptions in order to 

generate SCC estimates. Agencies that actively participated in the interagency process include 

the Environmental Protection Agency, and the Departments of Agriculture, Commerce, Energy, 

Transportation, and Treasury. This process was convened by the Council of Economic Advisers 

and the Office of Management and Budget, with active participation and regular input from the 

Council on Environmental Quality, National Economic Council, Office of Energy and Climate 

Change, and Office of Science and Technology Policy. The main objective of this process was to 

develop a range of SCC values using a defensible set of input assumptions that are grounded in 

the existing literature. In this way, key uncertainties and model differences can more 

transparently and consistently inform the range of SCC estimates used in the rulemaking process.

The interagency group selected four SCC estimates for use in regulatory analyses. For 

2010, these estimates are $5, $21, $35, and $65 (in 2007 dollars). The first three estimates are 

based on the average SCC across models and socioeconomic and emissions scenarios at the 5, 3, 

and 2.5 percent discount rates, respectively. The fourth value is included to represent the higherthan-expected impacts from temperature change further out in the tails of the SCC distribution. 

For this purpose, we use the SCC value for the 95th percentile at a 3 percent discount rate. The 

central value is the average SCC across models at the 3 percent discount rate. For purposes of 

capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance 

and value of considering the full range. These SCC estimates also grow over time. For instance, 

the central value increases to $24 per ton of CO2 in 2015 and $26 per ton of CO2 in 2020. See 

section 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 

14A-3
society improves over time. Specifically, we have set a preliminary goal of revisiting the SCC 

values within two years or at such time as substantially updated models become available, and to 

continue to support research in this area. In the meantime, we will continue to explore the issues 

raised in this document and consider public comments as part of the ongoing interagency 

process.

14A.3 SOCIAL COST OF CARBON VALUES USED IN PAST REGULATORY 

ANALYSES

To date, economic analyses for Federal regulations have used a wide range of values to 

estimate the benefits associated with reducing carbon dioxide emissions. In the final model year 

2011 CAFE rule, the Department of Transportation (DOT) used both a “domestic” SCC value of 

$2 per ton of CO2 and a “global” SCC value of $33 per ton of CO2 for 2007 emission reductions 

(in 2007 dollars), increasing both values at 2.4 percent per year. It also included a sensitivity 

analysis at $80 per ton of CO2. A domestic SCC value is meant to reflect the value of damages in 

the United States resulting from a unit change in carbon dioxide emissions, while a global SCC 

value is meant to reflect the value of damages worldwide.

A 2008 regulation proposed by DOT assumed a domestic SCC value of $7 per ton CO2

(in 2006 dollars) for 2011 emission reductions (with a range of $0-$14 for sensitivity analysis), 

also increasing at 2.4 percent per year. A regulation finalized by DOE in October of 2008 used a 

domestic SCC range of $0 to $20 per ton CO2 for 2007 emission reductions (in 2007 dollars). In 

addition, EPA’s 2008 Advance Notice of Proposed Rulemaking for Greenhouse Gases identified 

what it described as “very preliminary” SCC estimates subject to revision. EPA’s global mean 

values were $68 and $40 per ton CO2 for discount rates of approximately 2 percent and 3 

percent, respectively (in 2006 dollars for 2007 emissions).

In 2009, an interagency process was initiated to offer a preliminary assessment of how 

best to quantify the benefits from reducing carbon dioxide emissions. To ensure consistency in 

how benefits are evaluated across agencies, the Administration sought to develop a transparent 

and defensible method, specifically designed for the rulemaking process, to quantify avoided 

climate change damages from reduced CO2 emissions. The interagency group did not undertake 

any original analysis. Instead, it combined SCC estimates from the existing literature to use as 

interim values until a more comprehensive analysis could be conducted. 

The outcome of the preliminary assessment by the interagency group was a set of five 

interim values: global SCC estimates for 2007 (in 2006 dollars) of $55, $33, $19, $10, and $5 per 

ton of CO2. The $33 and $5 values represented model-weighted means of the published estimates 

produced from the most recently available versions of three integrated assessment models—

DICE, PAGE, and FUND—at approximately 3 and 5 percent discount rates. The $55 and $10 

values were derived by adjusting the published estimates for uncertainty in the discount rate 

(using factors developed by Newell and Pizer (2003)) at 3 and 5 percent discount rates, 

respectively. The $19 value was chosen as a central value between the $5 and $33 per ton 

estimates. All of these values were assumed to increase at 3 percent annually to represent growth 

in incremental damages over time as the magnitude of climate change increases.

14A-4
These interim values represent the first sustained interagency effort within the U.S. 

government to develop an SCC for use in regulatory analysis. The results of this preliminary 

effort were presented in several proposed and final rules and were offered for public comment in 

connection with proposed rules, including the joint EPA-DOT fuel economy and CO2 tailpipe 

emission proposed rules.

14A.4 APPROACH AND KEY ASSUMPTIONS

Since the release of the interim values, the interagency group has reconvened on a regular 

basis to generate improved SCC estimates. Specifically, the group has considered public 

comments and further explored the technical literature in relevant fields. This section details the 

several choices and assumptions that underlie the resulting estimates of the SCC. 

It is important to recognize that a number of key uncertainties remain, and that current 

SCC estimates should be treated as provisional and revisable, since they will evolve with 

improved scientific and economic understanding. The interagency group also recognizes that the 

existing models are imperfect and incomplete. The National Academy of Science (2009) points 

out that there is tension between the goal of producing quantified estimates of the economic 

damages from an incremental ton of carbon and the limits of existing efforts to model these 

effects. Throughout this document, we highlight a number of concerns and problems that should 

be addressed by the research community, including research programs housed in many of the 

agencies participating in the interagency process to estimate the SCC.

The U.S. Government will periodically review and reconsider estimates of the SCC used 

for cost-benefit analyses to reflect increasing knowledge of the science and economics of climate 

impacts, as well as improvements in modeling. In this context, statements recognizing the 

limitations of the analysis and calling for further research take on exceptional significance. The 

interagency group offers the new SCC values with all due humility about the uncertainties 

embedded in them and with a sincere promise to continue work to improve them.

14A.4.1 Integrated Assessment Models 

We rely on three integrated assessment models (IAMs) commonly used to estimate the 

SCC: the FUND, DICE, and PAGE models.c These models are frequently cited in the peerreviewed literature and used in the IPCC assessment. Each model is given equal weight in the 

SCC values developed through this process, bearing in mind their different limitations (discussed 

below).

c The DICE (Dynamic Integrated Climate and Economy) model by William Nordhaus evolved from a series of 

energy models and was first presented in 1990 (Nordhaus and Boyer 2000, Nordhaus 2008). The PAGE (Policy 

Analysis of the Greenhouse Effect) model was developed by Chris Hope in 1991 for use by European decisionmakers in assessing the marginal impact of carbon emissions (Hope 2006, Hope 2008). The FUND (Climate 

Framework for Uncertainty, Negotiation, and Distribution) model, developed by Richard Tol in the early 1990s, 

originally to study international capital transfers in climate policy. is now widely used to study climate impacts (e.g., 

Tol 2002a, Tol 2002b, Anthoff et al. 2009, Tol 2009).

14A-5

 
These models are useful because they combine climate processes, economic growth, and 

feedbacks between the climate and the global economy into a single modeling framework. At the 

same time, they gain this advantage at the expense of a more detailed representation of the 

underlying climatic and economic systems. DICE, PAGE, and FUND all take stylized, reducedform approaches (see NRC 2009 for a more detailed discussion; see Nordhaus 2008 on the 

possible advantages of this approach). Other IAMs may better reflect the complexity of the 

science in their modeling frameworks but do not link physical impacts to economic damages.

There is currently a limited amount of research linking climate impacts to economic damages, 

which makes this exercise even more difficult. Underlying the three IAMs selected for this 

exercise are a number of simplifying assumptions and judgments reflecting the various modelers’ 

best attempts to synthesize the available scientific and economic research characterizing these 

relationships.

The three IAMs translate emissions into changes in atmospheric greenhouse 

concentrations, atmospheric concentrations into changes in temperature, and changes in 

temperature into economic damages. The emissions projections used in the models are based on 

specified socioeconomic (GDP and population) pathways. These emissions are translated into 

concentrations using the carbon cycle built into each model, and concentrations are translated 

into warming based on each model’s simplified representation of the climate and a key 

parameter, climate sensitivity. Each model uses a different approach to translate warming into 

damages. Finally, transforming the stream of economic damages over time into a single value 

requires judgments about how to discount them.

Each model takes a slightly different approach to model how changes in emissions result 

in changes in economic damages. In PAGE, for example, the consumption-equivalent damages 

in each period are calculated as a fraction of GDP, depending on the temperature in that period 

relative to the pre-industrial average temperature in each region. In FUND, damages in each 

period also depend on the rate of temperature change from the prior period. In DICE, 

temperature affects both consumption and investment. We describe each model in greater detail 

here. In a later section, we discuss key gaps in how the models account for various scientific and 

economic processes (e.g. the probability of catastrophe, and the ability to adapt to climate change 

and the physical changes it causes).

The parameters and assumptions embedded in the three models vary widely. A key 

objective of the interagency process was to enable a consistent exploration of the three models 

while respecting the different approaches to quantifying damages taken by the key modelers in 

the field. An extensive review of the literature was conducted to select three sets of input 

parameters for these models: climate sensitivity, socioeconomic and emissions trajectories, and 

discount rates. A probability distribution for climate sensitivity was specified as an input into all 

three models. In addition, the interagency group used a range of scenarios for the socioeconomic

parameters and a range of values for the discount rate. All other model features were left 

unchanged, relying on the model developers’ best estimates and judgments. In DICE, these 

parameters are handled deterministically and represented by fixed constants; in PAGE, most 

parameters are represented by probability distributions. FUND was also run in a mode in which 

parameters were treated probabilistically.

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The sensitivity of the results to other aspects of the models (e.g. the carbon cycle or 

damage function) is also important to explore in the context of future revisions to the SCC but 

has not been incorporated into these estimates. Areas for future research are highlighted at the 

end of this document.

The DICE Model

The DICE model is an optimal growth model based on a global production function with 

an extra stock variable (atmospheric carbon dioxide concentrations). Emission reductions are 

treated as analogous to investment in “natural capital.” By investing in natural capital today 

through reductions in emissions—implying reduced consumption—harmful effects of climate 

change can be avoided and future consumption thereby increased.

For purposes of estimating the SCC, carbon dioxide emissions are a function of global 

GDP and the carbon intensity of economic output, with the latter declining over time due to 

technological progress. The DICE damage function links global average temperature to the 

overall impact on the world economy. It varies quadratically with temperature change to capture 

the more rapid increase in damages expected to occur under more extreme climate change, and is 

calibrated to include the effects of warming on the production of market and nonmarket goods 

and services. It incorporates impacts on agriculture, coastal areas (due to sea level rise), “other 

vulnerable market sectors” (based primarily on changes in energy use), human health (based on 

climate-related diseases, such as malaria and dengue fever, and pollution), non-market amenities 

(based on outdoor recreation), and human settlements and ecosystems. The DICE damage 

function also includes the expected value of damages associated with low probability, high 

impact “catastrophic” climate change. This last component is calibrated based on a survey of 

experts (Nordhaus 1994). The expected value of these impacts is then added to the other market 

and non-market impacts mentioned above.

No structural components of the DICE model represent adaptation explicitly, though it is 

included implicitly through the choice of studies used to calibrate the aggregate damage function.

For example, its agricultural impact estimates assume that farmers can adjust land use decisions 

in response to changing climate conditions, and its health impact estimates assume 

improvements in healthcare over time. In addition, the small impacts on forestry, water systems, 

construction, fisheries, and outdoor recreation imply optimistic and costless adaptation in these 

sectors (Nordhaus and Boyer, 2000; Warren et al., 2006). Costs of resettlement due to sea level 

rise are incorporated into damage estimates, but their magnitude is not clearly reported.

Mastrandrea’s (2009) review concludes that “in general, DICE assumes very effective 

adaptation, and largely ignores adaptation costs."

Note that the damage function in DICE has a somewhat different meaning from the 

damage functions in FUND and PAGE. Because GDP is endogenous in DICE and because 

damages in a given year reduce investment in that year, damages propagate forward in time and 

14A-7
reduce GDP in future years. In contrast, GDP is exogenous in FUND and PAGE, so damages in 

any given year do not propagate forward.d

The PAGE Model

PAGE2002 (version 1.4epm) treats GDP growth as exogenous. It divides impacts into 

economic, non-economic, and catastrophic categories and calculates these impacts separately for 

eight geographic regions. Damages in each region are expressed as a fraction of output, where 

the fraction lost depends on the temperature change in each region. Damages are expressed as 

power functions of temperature change. The exponents of the damage function are the same in 

all regions but are treated as uncertain, with values ranging from 1 to 3 (instead of being fixed at 

2 as in DICE).

PAGE2002 includes the consequences of catastrophic events in a separate damage subfunction. Unlike DICE, PAGE2002 models these events probabilistically. The probability of a 

“discontinuity” (i.e., a catastrophic event) is assumed to increase with temperature above a 

specified threshold. The threshold temperature, the rate at which the probability of experiencing 

a discontinuity increases above the threshold, and the magnitude of the resulting catastrophe are 

all modeled probabilistically.

Adaptation is explicitly included in PAGE. Impacts are assumed to occur for temperature 

increases above some tolerable level (2°C for developed countries and 0°C for developing 

countries for economic impacts, and 0°C for all regions for non-economic impacts), but 

adaptation is assumed to reduce these impacts. Default values in PAGE2002 assume that the 

developed countries can ultimately eliminate up to 90 percent of all economic impacts beyond 

the tolerable 2°C increase and that developing countries can eventually eliminate 50 percent of 

their economic impacts. All regions are assumed to be able to mitigate 25 percent of the noneconomic impacts through adaptation (Hope 2006).

The FUND Model

Like PAGE, the FUND model treats GDP growth as exogenous. It includes separately 

calibrated damage functions for eight market and nonmarket sectors: agriculture, forestry, water, 

energy (based on heating and cooling demand), sea level rise (based on the value of land lost and 

the cost of protection), ecosystems, human health (diarrhea, vector-borne diseases, and 

cardiovascular and respiratory mortality), and extreme weather. Each impact sector has a 

different functional form, and is calculated separately for sixteen geographic regions. In some 

impact sectors, the fraction of output lost or gained due to climate change depends not only on 

d Using the default assumptions in DICE 2007, this effect generates an approximately 25 percent increase in the 

SCC relative to damages calculated by fixing GDP. In DICE2007, the time path of GDP is endogenous. Specifically, 

the path of GDP depends on the rate of saving and level of abatement in each period chosen by the optimizing 

representative agent in the model. We made two modifications to DICE to make it consistent with EMF GDP 

trajectories (see next section): we assumed a fixed rate of savings of 20%, and we re-calibrated the exogenous path 

of total factor productivity so that DICE would produce GDP projections in the absence of warming that exactly 

matched the EMF scenarios.

14A-8

 
the absolute temperature change but also on the rate of temperature change and level of regional 

income.e In the forestry and agricultural sectors, economic damages also depend on CO2

concentrations.

Tol (2009) discusses impacts not included in FUND, noting that many are likely to have a 

relatively small effect on damage estimates (both positive and negative). However, he 

characterizes several omitted impacts as “big unknowns”: for instance, extreme climate 

scenarios, biodiversity loss, and effects on economic development and political violence. With 

regard to potentially catastrophic events, he notes, “Exactly what would cause these sorts of 

changes or what effects they would have are not well-understood, although the chance of any one 

of them happening seems low. But they do have the potential to happen relatively quickly, and if 

they did, the costs could be substantial. Only a few studies of climate change have examined 

these issues.”

Adaptation is included both implicitly and explicitly in FUND. Explicit adaptation is seen 

in the agriculture and sea level rise sectors. Implicit adaptation is included in sectors such as 

energy and human health, where wealthier populations are assumed to be less vulnerable to 

climate impacts. For example, the damages to agriculture are the sum of three effects: (1) those 

due to the rate of temperature change (damages are always positive); (2) those due to the level of 

temperature change (damages can be positive or negative depending on region and temperature); 

and (3) those from CO2 fertilization (damages are generally negative but diminishing to zero).

Adaptation is incorporated into FUND by allowing damages to be smaller if climate 

change happens more slowly. The combined effect of CO2 fertilization in the agricultural sector, 

positive impacts to some regions from higher temperatures, and sufficiently slow increases in 

temperature across these sectors can result in negative economic damages from climate change.

Damage Functions

To generate revised SCC values, we rely on the IAM modelers’ current best judgments of 

how to represent the effects of climate change (represented by the increase in global-average 

surface temperature) on the consumption-equivalent value of both market and non-market goods 

(represented as a fraction of global GDP). We recognize that these representations are 

incomplete and highly uncertain. But given the paucity of data linking the physical impacts to 

economic damages, we were not able to identify a better way to translate changes in climate into 

net economic damages, short of launching our own research program.

The damage functions for the three IAMs are presented in Figure 14A.4.1 and Figure 

14A.4.2, using the modeler’s default scenarios and mean input assumptions. There are significant 

differences between the three models both at lower (Figure 14A.4.2) and higher (Figure 14A.4.1) 

increases in global-average temperature.

e In the deterministic version of FUND, the majority of damages are attributable to increased air conditioning 

demand, while reduced cold stress in Europe, North America, and Central and East Asia results in health benefits in 

those regions at low to moderate levels of warming (Warren et al., 2006).

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Figure 14A.4.1 Annual Consumption Loss as a Fraction of Global GDP in 

2100 Due to an Increase in Annual Global Temperature in the 

DICE, FUND, and PAGE modelsf

The lack of agreement among the models at lower temperature increases is underscored 

by the fact that the damages from FUND are well below the 5th percentile estimated by PAGE, 

while the damages estimated by DICE are roughly equal to the 95th percentile estimated by 

PAGE. This is significant because at higher discount rates we expect that a greater proportion of 

the SCC value is due to damages in years with lower temperature increases. For example, when 

the discount rate is 2.5 percent, about 45 percent of the 2010 SCC value in DICE is due to 

damages that occur in years when the temperature is less than or equal to 3 °C. This increases to 

approximately 55 percent and 80 percent at discount rates of 3 and 5 percent, respectively.

These differences underscore the need for a thorough review of damage functions—in 

particular, how the models incorporate adaptation, technological change, and catastrophic 

damages. Gaps in the literature make modifying these aspects of the models challenging, which 

highlights the need for additional research. As knowledge improves, the Federal government is 

committed to exploring how these (and other) models can be modified to incorporate more 

accurate estimates of damages.

f The x-axis represents increases in annual, rather than equilibrium, temperature, while the y-axis represents the 

annual stream of benefits as a share of global GDP. Each specific combination of climate sensitivity, socioeconomic, 

and emissions parameters will produce a different realization of damages for each IAM. The damage functions 

represented in Figures 1A and 1B are the outcome of default assumptions. For instance, under alternate assumptions, 

the damages from FUND may cross from negative to positive at less than or greater than 3 °C.

14A-10

 
Figure 14A.4.2 Annual Consumption Loss for Lower Temperature Changes in 

DICE, FUND, and PAGE

14A.4.2 Global versus Domestic Measures of SCC

Because of the distinctive nature of the climate change problem, we center our current 

attention on a global measure of SCC. This approach is the same as that taken for the interim 

values, but it otherwise represents a departure from past practices, which tended to put greater 

emphasis on a domestic measure of SCC (limited to impacts of climate change experienced 

within U.S. borders). As a matter of law, consideration of both global and domestic values is 

generally permissible; the relevant statutory provisions are usually ambiguous and allow 

selection of either measure.g

Global SCC

Under current OMB guidance contained in Circular A-4, analysis of economically 

significant proposed and final regulations from the domestic perspective is required, while 

analysis from the international perspective is optional. However, the climate change problem is 

highly unusual in at least two respects. First, it involves a global externality: emissions of most 

greenhouse gases contribute to damages around the world even when they are emitted in the 

United States. Consequently, to address the global nature of the problem, the SCC must 

incorporate the full (global) damages caused by GHG emissions. Second, climate change 

g It is true that federal statutes are presumed not to have extraterritorial effect, in part to ensure that the laws of the 

United States respect the interests of foreign sovereigns. But use of a global measure for the SCC does not give 

extraterritorial effect to federal law and hence does not intrude on such interests.

14A-11

 
presents a problem that the United States alone cannot solve. Even if the United States were to 

reduce its greenhouse gas emissions to zero, that step would be far from enough to avoid 

substantial climate change. Other countries would also need to take action to reduce emissions if 

significant changes in the global climate are to be avoided. Emphasizing the need for a global 

solution to a global problem, the United States has been actively involved in seeking 

international agreements to reduce emissions and in encouraging other nations, including 

emerging major economies, to take significant steps to reduce emissions. When these 

considerations are taken as a whole, the interagency group concluded that a global measure of 

the benefits from reducing U.S. emissions is preferable. 

When quantifying the damages associated with a change in emissions, a number of 

analysts (e.g., Anthoff, et al. 2009a) employ “equity weighting” to aggregate changes in 

consumption across regions. This weighting takes into account the relative reductions in wealth 

in different regions of the world. A per-capita loss of $500 in GDP, for instance, is weighted 

more heavily in a country with a per-capita GDP of $2,000 than in one with a per-capita GDP of 

$40,000. The main argument for this approach is that a loss of $500 in a poor country causes a 

greater reduction in utility or welfare than does the same loss in a wealthy nation.

Notwithstanding the theoretical claims on behalf of equity weighting, the interagency group 

concluded that this approach would not be appropriate for estimating a SCC value used in 

domestic regulatory analysis.h For this reason, the group concluded that using the global (rather 

than domestic) value, without equity weighting, is the appropriate approach.

Domestic SCC

As an empirical matter, the development of a domestic SCC is greatly complicated by the 

relatively few region- or country-specific estimates of the SCC in the literature. One potential 

source of estimates comes from the FUND model. The resulting estimates suggest that the ratio 

of domestic to global benefits of emission reductions varies with key parameter assumptions. For 

example, with a 2.5 or 3 percent discount rate, the U.S. benefit is about 7-10 percent of the 

global benefit, on average, across the scenarios analyzed. Alternatively, if the fraction of GDP 

lost due to climate change is assumed to be similar across countries, the domestic benefit would 

be proportional to the U.S. share of global GDP, which is currently about 23 percent.i

On the basis of this evidence, the interagency workgroup determined that a range of 

values from 7 to 23 percent should be used to adjust the global SCC to calculate domestic 

effects. Reported domestic values should use this range. It is recognized that these values are 

approximate, provisional, and highly speculative. There is no a priori reason why domestic 

benefits should be a constant fraction of net global damages over time. Further, FUND does not 

h It is plausible that a loss of $X inflicts more serious harm on a poor nation than on a wealthy one, but development 

of the appropriate "equity weight" is challenging. Emissions reductions also impose costs, and hence a full account 

would have to consider that a given cost of emissions reductions imposes a greater utility or welfare loss on a poor 

nation than on a wealthy one. Even if equity weighting—for both the costs and benefits of emissions reductions—is 

appropriate when considering the utility or welfare effects of international action, the interagency group concluded 

that it should not be used in developing an SCC for use in regulatory policy at this time.

i

Based on 2008 GDP (in current US dollars) from the World Bank Development Indicators Report.

14A-12

 
account for how damages in other regions could affect the United States (e.g., global migration, 

economic and political destabilization). If more accurate methods for calculating the domestic 

SCC become available, the Federal government will examine these to determine whether to 

update its approach.

14A.4.3 Valuing Non-CO2 Emissions

While CO2 is the most prevalent greenhouse gas emitted into the atmosphere, the U.S. 

included five other greenhouse gases in its recent endangerment finding: methane, nitrous oxide, 

hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride. The climate impact of these 

gases is commonly discussed in terms of their 100-year global warming potential (GWP). GWP 

measures the ability of different gases to trap heat in the atmosphere (i.e., radiative forcing per 

unit of mass) over a particular timeframe relative to CO2. However, because these gases differ in 

both radiative forcing and atmospheric lifetimes, their relative damages are not constant over 

time. For example, because methane has a short lifetime, its impacts occur primarily in the near 

term and thus are not discounted as heavily as those caused by longer-lived gases. Impacts other 

than temperature change also vary across gases in ways that are not captured by GWP. For 

instance, CO2 emissions, unlike methane and other greenhouse gases, contribute to ocean 

acidification. Likewise, damages from methane emissions are not offset by the positive effect of 

CO2 fertilization. Thus, transforming gases into CO2-equivalents using GWP, and then 

multiplying the carbon-equivalents by the SCC, would not result in accurate estimates of the 

social costs of non-CO2 gases.

In light of these limitations, and the significant contributions of non-CO2 emissions to 

climate change, further research is required to link non-CO2 emissions to economic impacts.

Such work would feed into efforts to develop a monetized value of reductions in non-CO2

greenhouse gas emissions. As part of ongoing work to further improve the SCC estimates, the 

interagency group hopes to develop methods to value these other greenhouse gases. The goal is 

to develop these estimates by the time we issue revised SCC estimates for carbon dioxide 

emissions.

14A.4.4 Equilibrium Climate Sensitivity

Equilibrium climate sensitivity (ECS) is a key input parameter for the DICE, PAGE, and 

FUND models.jIt is defined as the long-term increase in the annual global-average surface 

temperature from a doubling of atmospheric CO2 concentration relative to pre-industrial levels 

(or stabilization at a concentration of approximately 550 parts per million (ppm)). Uncertainties 

in this important parameter have received substantial attention in the peer-reviewed literature.

The most authoritative statement about equilibrium climate sensitivity appears in the 

Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC):

j

The equilibrium climate sensitivity includes the response of the climate system to increased greenhouse gas 

concentrations over the short to medium term (up to 100-200 years), but it does not include long-term feedback 

effects due to possible large-scale changes in ice sheets or the biosphere, which occur on a time scale of many 

hundreds to thousands of years (e.g. Hansen et al. 2007).

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Basing our assessment on a combination of several independent lines of evidence...including 

observed climate change and the strength of known feedbacks simulated in [global climate 

models], we conclude that the global mean equilibrium warming for doubling CO2, or 

‘equilibrium climate sensitivity’, is likely to lie in the range 2 °C to 4.5 °C, with a most likely 

value of about 3 °C. Equilibrium climate sensitivity is very likely larger than 1.5 °C. k

For fundamental physical reasons as well as data limitations, values substantially higher 

than 4.5 °C still cannot be excluded, but agreement with observations and proxy data is 

generally worse for those high values than for values in the 2 °C to 4.5 °C range. (Meehl et 

al., 2007, p 799)

After consulting with several lead authors of this chapter of the IPCC report, the 

interagency workgroup selected four candidate probability distributions and calibrated them to 

be consistent with the above statement: Roe and Baker (2007), log-normal, gamma, and Weibull.

Table 14A.4.1 included below gives summary statistics for the four calibrated distributions.

Table 14A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity Distributions

Roe & Baker Log-normal Gamma Weibull

Pr(ECS < 1.5°C) 0.013 0.050 0.070 0.102

Pr(2°C < ECS < 4.5°C) 0.667 0.667 0.667 0.667

5th percentile 1.72 1.49 1.37 1.13

10th percentile 1.91 1.74 1.65 1.48

Mode 2.34 2.52 2.65 2.90

Median (50th percentile) 3.00 3.00 3.00 3.00

Mean 3.50 3.28 3.19 3.07

90th percentile 5.86 5.14 4.93 4.69

95th percentile 7.14 5.97 5.59 5.17

Each distribution was calibrated by applying three constraints from the IPCC:

(1) a median equal to 3°C, to reflect the judgment of “a most likely value of about 3 °C”;l

k This is in accord with the judgment that it “is likely to lie in the range 2 °C to 4.5 °C” and the IPCC definition of 

“likely” as greater than 66 percent probability (Le Treut et al.2007). “Very likely” indicates a greater than 90 percent 

probability.

l

Strictly speaking, “most likely” refers to the mode of a distribution rather than the median, but common usage 

would allow the mode, median, or mean to serve as candidates for the central or “most likely” value and the IPCC 

report is not specific on this point. For the distributions we considered, the median was between the mode and the 

mean. For the Roe and Baker distribution, setting the median equal to 3°C, rather than the mode or mean, gave a 95th

percentile that is more consistent with IPCC judgments and the literature. For example, setting the mean and mode 

equal to 3°C produced 95th percentiles of 5.6 and 8.6 °C, respectively, which are in the lower and upper end of the 

range in the literature. Finally, the median is closer to 3°C than is the mode for the truncated distributions selected 

by the IPCC (Hegerl, et al., 2006); the average median is 3.1 °C and the average mode is 2.3 °C, which is most 

consistent with a Roe and Baker distribution with the median set equal to 3 °C.

14A-14

 
(2) two-thirds probability that the equilibrium climate sensitivity lies between 2 and 4.5 °C; 

and

(3) zero probability that it is less than 0°C or greater than 10°C (see Hegerl et al. 2006, p. 

721).

We selected the calibrated Roe and Baker distribution from the four candidates for two 

reasons. First, the Roe and Baker distribution is the only one of the four that is based on a 

theoretical understanding of the response of the climate system to increased greenhouse gas 

concentrations (Roe and Baker 2007, Roe 2008). In contrast, the other three distributions are 

mathematical functions that are arbitrarily chosen based on simplicity, convenience, and general 

shape. The Roe and Baker distribution results from three assumptions about climate response: (1) 

absent feedback effects, the equilibrium climate sensitivity is equal to 1.2 °C; (2) feedback 

factors are proportional to the change in surface temperature; and (3) uncertainties in feedback 

factors are normally distributed. There is widespread agreement on the first point and the second 

and third points are common assumptions. 

Second, the calibrated Roe and Baker distribution better reflects the IPCC judgment that 

“values substantially higher than 4.5°C still cannot be excluded.” Although the IPCC made no 

quantitative judgment, the 95th percentile of the calibrated Roe & Baker distribution (7.1 °C) is 

much closer to the mean and the median (7.2 °C) of the 95th percentiles of 21 previous studies 

summarized by Newbold and Daigneault (2009). It is also closer to the mean (7.5 °C) and 

median (7.9 °C) of the nine truncated distributions examined by the IPCC (Hegerl, et al., 2006) 

than are the 95th percentiles of the three other calibrated distributions (5.2-6.0 °C).

Finally, we note the IPCC judgment that the equilibrium climate sensitivity “is very 

likely larger than 1.5°C.” Although the calibrated Roe & Baker distribution, for which the 

probability of equilibrium climate sensitivity being greater than 1.5 °C is almost 99 percent, is 

not inconsistent with the IPCC definition of “very likely” as “greater than 90 percent 

probability,” it reflects a greater degree of certainty about very low values of ECS than was 

expressed by the IPCC. 

14A-15
Figure 14A.4.3 Estimates of the Probability Density Function for Equilibrium 

Climate Sensitivity (°C)

To show how the calibrated Roe and Baker distribution compares to different estimates 

of the probability distribution function of equilibrium climate sensitivity in the empirical 

literature, Figure 14A.4.3 (above) overlays it on Figure 9.20 from the IPCC Fourth Assessment 

Report. These functions are scaled to integrate to unity between 0 °C and 10 °C. The horizontal 

bars show the respective 5 percent to 95 percent ranges; dots indicate the median estimate.m

14A.4.5 Socioeconomic and Emissions Trajectories

Another key issue considered by the interagency group is how to select the set of 

socioeconomic and emissions parameters for use in PAGE, DICE, and FUND. Socioeconomic

pathways are closely tied to climate damages because, all else equal, more and wealthier people 

tend to emit more greenhouse gases and also have a higher (absolute) willingness to pay to avoid 

climate disruptions. For this reason, we consider how to model several input parameters in 

tandem: GDP, population, CO2 emissions, and non-CO2 radiative forcing. A wide variety of 

scenarios have been developed and used for climate change policy simulations (e.g., SRES 2000, 

CCSP 2007, EMF 2009). In determining which scenarios are appropriate for inclusion, we aimed 

to select scenarios that span most of the plausible ranges of outcomes for these variables. 

m The estimates based on instrumental data are from Andronova and Schlesinger (2001), Forest et al. (2002; dashed 

line, anthropogenic forcings only), Forest et al. (2006; solid line, anthropogenic and natural forcings), Gregory et al. 

(2002a), Knutti et al. (2002), Frame et al. (2005), and Forster and Gregory (2006). Hegerl et al. (2006) are based on 

multiple palaeoclimatic reconstructions of north hemisphere mean temperatures over the last 700 years. Also shown 

are the 5-95 percent approximate ranges for two estimates from the last glacial maximum (dashed, Annan et al. 

2005; solid, Schneider von Deimling et al. 2006), which are based on models with different structural properties.

14A-16

 
To accomplish this task in a transparent way, we decided to rely on the recent Stanford 

Energy Modeling Forum exercise, EMF-22. EMF-22 uses ten well-recognized models to 

evaluate substantial, coordinated global action to meet specific stabilization targets. A key 

advantage of relying on these data is that GDP, population, and emission trajectories are 

internally consistent for each model and scenario evaluated. The EMF-22 modeling effort also is 

preferable to the IPCC SRES due to their age (SRES were developed in 1997) and the fact that 3 

of 4 of the SRES scenarios are now extreme outliers in one or more variables. Although the 

EMF-22 scenarios have not undergone the same level of scrutiny as the SRES scenarios, they are 

recent, peer-reviewed, published, and publicly available.

To estimate the SCC for use in evaluating domestic policies that will have a small effect 

on global cumulative emissions, we use socioeconomic and emission trajectories that span a 

range of plausible scenarios. Five trajectories were selected from EMF-22 (see Table 14A.4.2

below). Four of these represent potential business-as-usual (BAU) growth in population, wealth, 

and emissions and are associated with CO2 (only) concentrations ranging from 612 to 889 ppm 

in 2100. One represents an emissions pathway that achieves stabilization at 550 ppm CO2e (ii.e., 

CO2-only concentrations of 425 – 484 ppm or a radiative forcing of 3.7 W/m2) in 2100, a lowerthan-BAU trajectory.n Out of the 10 models included in the EMF-22 exercise, we selected the 

trajectories used by MiniCAM, MESSAGE, IMAGE, and the optimistic scenario from MERGE.

For the BAU pathways, we used the GDP, population, and emission trajectories from each of 

these four models. For the 550 ppm CO2e scenario, we averaged the GDP, population, and 

emission trajectories implied by these same four models.

n Such an emissions path would be consistent with widespread action by countries to mitigate GHG emissions, 

though it could also result from technological advances. It was chosen because it represents the most stringent case 

analyzed by the EMF-22 where all the models converge: a 550 ppm, not to exceed, full participation scenario.

14A-17

 
Table 14A.4.2 Socioeconomic and Emissions Projections from Select EMF-22 Reference 

Scenarios

Reference Fossil and Industrial CO2 Emissions (GtCO2/yr)

EMF – 22 Based Scenarios 2000 2010 2020 2030 2050 2100

IMAGE 26.6 31.9 36.9 40.0 45.3 60.1

MERGE Optimistic 24.6 31.5 37.6 45.1 66.5 117.9

MESSAGE 26.8 29.2 37.6 42.1 43.5 42.7

MiniCAM 26.5 31.8 38.0 45.1 57.8 80.5

550 ppm average 26.2 31.1 33.2 32.4 20.0 12.8

Reference GDP (using market exchange rates in trillion 2005$)o

EMF – 22 Based Scenarios 2000 2010 2020 2030 2050 2100

IMAGE 38.6 53.0 73.5 97.2 156.3 396.6

MERGE Optimistic 36.3 45.9 59.7 76.8 122.7 268.0

MESSAGE 38.1 52.3 69.4 91.4 153.7 334.9

MiniCAM 36.1 47.4 60.8 78.9 125.7 369.5

550 ppm average 37.1 49.6 65.6 85.5 137.4 337.9

Global Population (billions)

EMF – 22 Based Scenarios 2000 2010 2020 2030 2050 2100

IMAGE 6.1 6.9 7.6 8.2 9.0 9.1

MERGE Optimistic 6.0 6.8 7.5 8.2 9.0 9.7

MESSAGE 6.1 6.9 7.7 8.4 9.4 10.4

MiniCAM 6.0 6.8 7.5 8.1 8.8 8.7

550 ppm average 6.1 6.8 7.6 8.2 8.7 9.1

We explore how sensitive the SCC is to various assumptions about how the future will 

evolve without prejudging what is likely to occur. The interagency group considered formally 

assigning probability weights to different states of the world, but this proved challenging to do in 

an analytically rigorous way given the dearth of information on the likelihood of a full range of 

future socioeconomic pathways.

o While the EMF-22 models used market exchange rates (MER) to calculate global GDP, it is also possible to use 

purchasing power parity (PPP). PPP takes into account the different price levels across countries, so it more 

accurately describes relative standards of living across countries. MERs tend to make low-income countries appear 

poorer than they actually are. Because many models assume convergence in per capita income over time, use of 

MER-adjusted GDP gives rise to projections of higher economic growth in low income countries. There is an 

ongoing debate about how much this will affect estimated climate impacts. Critics of the use of MER argue that it 

leads to overstated economic growth and hence a significant upward bias in projections of greenhouse gas 

emissions, and unrealistically high future temperatures (e.g., Castles and Henderson 2003). Others argue that 

convergence of the emissions-intensity gap across countries at least partially offset the overstated income gap so that 

differences in exchange rates have less of an effect on emissions (Holtsmark and Alfsen, 2005; Tol, 2006). 

Nordhaus (2007b) argues that the ideal approach is to use superlative PPP accounts (i.e., using cross-sectional PPP 

measures for relative incomes and outputs and national accounts price and quantity indexes for time-series 

extrapolations). However, he notes that it important to keep this debate in perspective; it is by no means clear that 

exchange-rate-conversion issues are as important as uncertainties about population, technological change, or the 

many geophysical uncertainties.

14A-18

 
There are a number of caveats. First, EMF BAU scenarios represent the modelers’ 

judgment of the most likely pathway absent mitigation policies to reduce greenhouse gas 

emissions, rather than the wider range of possible outcomes. Nevertheless, these views of the 

most likely outcome span a wide range, from the more optimistic (e.g., abundant low-cost, lowcarbon energy) to more pessimistic (e.g., constraints on the availability of nuclear and 

renewables).p Second, the socioeconomic trajectories associated with a 550 ppm CO2e 

concentration scenario are not derived from an assessment of what policy is optimal from a 

benefit-cost standpoint. Rather, it is indicative of one possible future outcome. The emission 

trajectories underlying some BAU scenarios (e.g., MESSAGE’s 612 ppm) also are consistent 

with some modest policy action to address climate change.q We chose not to include 

socioeconomic trajectories that achieve even lower GHG concentrations at this time, given the 

difficulty many models had in converging to meet these targets.

For comparison purposes, the Energy Information Agency in its 2009 Annual Energy 

Outlook projected that global carbon dioxide emissions will grow to 30.8, 35.6, and 40.4 

gigatons in 2010, 2020, and 2030, respectively, while world GDP is projected to be $51.8, $71.0 

and $93.9 trillion (in 2005 dollars using market exchange rates) in 2010, 2020, and 2030, 

respectively. These projections are consistent with one or more EMF-22 scenarios. Likewise, the 

United Nations’ 2008 Population Prospect projects population will grow from 6.1 billion people 

in 2000 to 9.1 billion people in 2050, which is close to the population trajectories for the 

IMAGE, MiniCAM, and MERGE models.

In addition to fossil and industrial CO2 emissions, each EMF scenario provides 

projections of methane, nitrous oxide, fluorinated greenhouse gases, and net land use CO2

emissions out to 2100. These assumptions also are used in the three models while retaining the 

default radiative forcings due to other factors (e.g., aerosols and other gases). See the Annex for 

greater detail.

14A.4.6 Discount Rate

The choice of a discount rate, especially over long periods of time, raises highly 

contested and exceedingly difficult questions of science, economics, philosophy, and law.

Although it is well understood that the discount rate has a large influence on the current value of 

future damages, there is no consensus about what rates to use in this context. Because carbon 

dioxide emissions are long-lived, subsequent damages occur over many years. In calculating the 

SCC, we first estimate the future damages to agriculture, human health, and other market and 

non-market sectors from an additional unit of carbon dioxide emitted in a particular year in terms 

of reduced consumption (or consumption equivalents) due to the impacts of elevated 

temperatures, as represented in each of the three IAMs. Then we discount the stream of future 

damages to its present value in the year when the additional unit of emissions was released using 

p For instance, in the MESSAGE model’s reference case total primary energy production from nuclear, biomass, and 

non-biomass renewables is projected to increase from about 15 percent of total primary energy in 2000 to 54 percent 

in 2100. In comparison, the MiniCAM reference case shows 10 percent in 2000 and 21 percent in 2100. q For example, MiniCAM projects if all non-US OECD countries reduce CO2 emissions to 83 percent below 2005 

levels by 2050 (per the G-8 agreement) but all other countries continue along a BAU path CO2 concentrations in 

2100 would drop from 794 ppmv in its reference case to 762 ppmv.

14A-19

 
the selected discount rate, which is intended to reflect society's marginal rate of substitution 

between consumption in different time periods.

For rules with both intra- and intergenerational effects, agencies traditionally employ 

constant discount rates of both 3 percent and 7 percent in accordance with OMB Circular A-4.

As Circular A-4 acknowledges, however, the choice of discount rate for intergenerational 

problems raises distinctive problems and presents considerable challenges. After reviewing those 

challenges, Circular A-4 states, “If your rule will have important intergenerational benefits or 

costs you might consider a further sensitivity analysis using a lower but positive discount rate in 

addition to calculating net benefits using discount rates of 3 and 7 percent.” For the specific 

purpose of developing the SCC, we adapt and revise that approach here.

Arrow et al. (1996) outlined two main approaches to determine the discount rate for 

climate change analysis, which they labeled “descriptive” and “prescriptive.” The descriptive 

approach reflects a positive (non-normative) perspective based on observations of people’s 

actual choices—e.g., savings versus consumption decisions over time, and allocations of savings 

among more and less risky investments. Advocates of this approach generally call for inferring 

the discount rate from market rates of return “because of a lack of justification for choosing a 

social welfare function that is any different than what decision makers [individuals] actually use” 

(Arrow et al. 1996).

One theoretical foundation for the cost-benefit analyses in which the social cost of carbon 

will be used—the Kaldor-Hicks potential-compensation test—also suggests that market rates 

should be used to discount future benefits and costs, because it is the market interest rate that 

would govern the returns potentially set aside today to compensate future individuals for climate 

damages that they bear (e.g., Just et al. 2004). As some have noted, the word “potentially” is an 

important qualification; there is no assurance that such returns will actually be set aside to 

provide compensation, and the very idea of compensation is difficult to define in the 

intergenerational context. On the other hand, societies provide compensation to future 

generations through investments in human capital and the resulting increase in knowledge, as 

well as infrastructure and other physical capital.

The prescriptive approach specifies a social welfare function that formalizes the 

normative judgments that the decision-maker wants explicitly to incorporate into the policy 

evaluation—e.g., how inter-personal comparisons of utility should be made, and how the welfare 

of future generations should be weighed against that of the present generation. Ramsey (1928), 

for example, has argued that it is “ethically indefensible” to apply a positive pure rate of time 

preference to discount values across generations, and many agree with this view.

Other concerns also motivate making adjustments to descriptive discount rates. In 

particular, it has been noted that the preferences of future generations with regard to 

consumption versus environmental amenities may not be the same as those today, making the 

current market rate on consumption an inappropriate metric by which to discount future climaterelated damages. Others argue that the discount rate should be below market rates to correct for 

market distortions and uncertainties or inefficiencies in intergenerational transfers of wealth, 

14A-20
which in the Kaldor-Hicks logic are presumed to compensate future generations for damage (a 

potentially controversial assumption, as noted above) (Arrow et al. 1996, Weitzman 1999).

Further, a legitimate concern about both descriptive and prescriptive approaches is that 

they tend to obscure important heterogeneity in the population. The utility function that underlies 

the prescriptive approach assumes a representative agent with perfect foresight and no credit 

constraints. This is an artificial rendering of the real world that misses many of the frictions that 

characterize individuals’ lives and indeed the available descriptive evidence supports this. For 

instance, many individuals smooth consumption by borrowing with credit cards that have 

relatively high rates. Some are unable to access traditional credit markets and rely on payday 

lending operations or other high-cost forms of smoothing consumption. Whether one puts greater 

weight on the prescriptive or descriptive approach, the high interest rates that credit-constrained 

individuals accept suggest that some account should be given to the discount rates revealed by 

their behavior. 

We draw on both approaches but rely primarily on the descriptive approach to inform the 

choice of discount rate. With recognition of its limitations, we find this approach to be the most 

defensible and transparent given its consistency with the standard contemporary theoretical 

foundations of benefit-cost analysis and with the approach required by OMB’s existing guidance.

The logic of this framework also suggests that market rates should be used for discounting future 

consumption-equivalent damages. Regardless of the theoretical approach used to derive the 

appropriate discount rate(s), we note the inherent conceptual and practical difficulties of 

adequately capturing consumption trade-offs over many decades or even centuries. While relying 

primarily on the descriptive approach in selecting specific discount rates, the interagency group 

has been keenly aware of the deeply normative dimensions of both the debate over discounting 

in the intergenerational context and the consequences of selecting one discount rate over another.

Historically Observed Interest Rates

In a market with no distortions, the return to savings would equal the private return on 

investment, and the market rate of interest would be the appropriate choice for the social 

discount rate. In the real world risk, taxes, and other market imperfections drive a wedge 

between the risk-free rate of return on capital and the consumption rate of interest. Thus, the 

literature recognizes two conceptual discount concepts—the consumption rate of interest and the 

opportunity cost of capital.

According to OMB’s Circular A-4, it is appropriate to use the rate of return on capital 

when a regulation is expected to displace or alter the use of capital in the private sector. In this 

case, OMB recommends Agencies use a discount rate of 7 percent. When regulation is expected 

to primarily affect private consumption—for instance, via higher prices for goods and services—

a lower discount rate of 3 percent is appropriate to reflect how private individuals trade-off 

current and future consumption. 

The interagency group examined the economics literature and concluded that the 

consumption rate of interest is the correct concept to use in evaluating the benefits and costs of a 

marginal change in carbon emissions (see Lind 1990, Arrow et al 1996, and Arrow 2000). The 

14A-21
consumption rate of interest also is appropriate when the impacts of a regulation are measured in 

consumption (-equivalent) units, as is done in the three integrated assessment models used for 

estimating the SCC.

Individuals use a variety of savings instruments that vary with risk level, time horizon, 

and tax characteristics. The standard analytic framework used to develop intuition about the 

discount rate typically assumes a representative agent with perfect foresight and no credit 

constraints. The risk-free rate is appropriate for discounting certain future benefits or costs, but 

the benefits calculated by IAMs are uncertain. To use the risk-free rate to discount uncertain 

benefits, these benefits first must be transformed into "certainty equivalents," that is the 

maximum certain amount that we would exchange for the uncertain amount. However, the 

calculation of the certainty-equivalent requires first estimating the correlation between the 

benefits of the policy and baseline consumption.

If the IAM projections of future impacts represent expected values (not certaintyequivalent values), then the appropriate discount rate generally does not equal the risk-free rate.

If the benefits of the policy tend to be high in those states of the world in which consumption is 

low, then the certainty-equivalent benefits will be higher than the expected benefits (and vice 

versa). Since many (though not necessarily all) of the important impacts of climate change will 

flow through market sectors such as agriculture and energy, and since willingness to pay for 

environmental protections typically increases with income, we might expect a positive (though 

not necessarily perfect) correlation between the net benefits from climate policies and market 

returns. This line of reasoning suggests that the proper discount rate would exceed the riskless 

rate. Alternatively, a negative correlation between the returns to climate policies and market 

returns would imply that a discount rate below the riskless rate is appropriate.

This discussion suggests that both the post-tax riskless and risky rates can be used to 

capture individuals’ consumption-equivalent interest rate. As a measure of the post-tax riskless

rate, we calculate the average real return from Treasury notes over the longest time period 

available (those from Newell and Pizer 2003) and adjust for Federal taxes (the average marginal 

rate from tax years 2003 through 2006 is around 27 percent).r This calculation produces a real 

interest rate of about 2.7 percent, which is roughly consistent with Circular A-4’s 

recommendation to use 3 percent to represent the consumption rate of interest.s A measure of the 

post-tax risky rate for investments whose returns are positively correlated with overall equity 

market returns can be obtained by adjusting pre-tax rates of household returns to risky 

investments (approximately 7 percent) for taxes, which yields a real rate of roughly 5 percent.t

r The literature argues for a risk-free rate on government bonds as an appropriate measure of the consumption rate of 

interest. Arrow (2000) suggests that it is roughly 3-4 percent. OMB cites evidence of a 3.1 percent pre-tax rate for 

10-year Treasury notes in the A-4 guidance. Newell and Pizer (2003) find real interest rates between 3.5 and 4 

percent for 30-year Treasury securities. s The positive approach reflects how individuals make allocation choices across time, but it is important to keep in 

mind that we wish to reflect preferences for society as a whole, which generally has a longer planning horizon.

t

Cambell et al (2001) estimates that the annual real return from stocks for 1900-1995 was about 7 percent. The 

annual real rate of return for the S&P 500 from 1950 – 2008 was about 6.8 percent. In the absence of a better way to 

population-weight the tax rates, we use the middle of the 20 – 40 percent range to derive a post-tax interest rate 

(Kotlikoff and Rapson 2006).

14A-22

 
The Ramsey Equation

Ramsey discounting also provides a useful framework to inform the choice of a discount 

rate. Under this approach, the analyst applies either positive or normative judgments in selecting 

values for the key parameters of the Ramsey equation: η (coefficient of relative risk aversion or 

elasticity of the marginal utility of consumption) and ρ (pure rate of time preference).u These are 

then combined with g (growth rate of per-capita consumption) to equal the interest rate at which 

future monetized damages are discounted: ρ + η∙g.v In the simplest version of the Ramsey model, 

with an optimizing representative agent with perfect foresight, what we are calling the “Ramsey 

discount rate,” ρ + η∙g, will be equal to the rate of return to capital, i.e., the market interest rate.

A review of the literature provides some guidance on reasonable parameter values for the 

Ramsey discounting equation, based on both prescriptive and descriptive approaches. 

• η. Most papers in the climate change literature adopt values for η in the range of 0.5 to 3 

(Weitzman cites plausible values as those ranging from 1 to 4), although not all authors 

articulate whether their choice is based on prescriptive or descriptive reasoning.w

Dasgupta (2008) argues that η should be greater than 1 and may be as high as 3, because

η equal to 1 suggests savings rates that do not conform to observed behavior. 

• ρ. With respect to the pure rate of time preference, most papers in the climate change 

literature adopt values for ρ in the range of 0 to 3 percent per year. The very low rates 

tend to follow from moral judgments involving intergenerational neutrality. Some have 

argued that to use any value other than ρ = 0 would unjustly discriminate against future 

generations (e.g., Arrow et al. 1996, Stern et al. 2006). However, even in an interu The parameter ρ measures the pure rate of time preference: people’s behavior reveals a preference for an 

increase in utility today versus the future. Consequently, it is standard to place a lower weight on utility in the future. 

The parameter η captures diminishing marginal utility: consumption in the future is likely to be higher than 

consumption today, so diminishing marginal utility of consumption implies that the same monetary damage will 

cause a smaller reduction of utility for wealthier individuals, either in the future or in current generations. If η = 0, 

then a one dollar increase in income is equally valuable regardless of level of income; if η = 1, then a one percent 

increase in income is equally valuable no matter the level of income; and if η > 1, then a one percent increase in 

income is less valuable to wealthier individuals.

v In this case, g could be taken from the selected EMF socioeconomic scenarios or alternative assumptions about the 

rate of consumption growth.

w Empirical estimates of η span a wide range of values. A benchmark value of 2 is near the middle of the range of 

values estimated or used by Szpiro (1986), Hall and Jones (2007), Arrow (2007), Dasgupta (2006, 2008), Weitzman 

(2007, 2009), and Nordhaus (2008). However, Chetty (2006) developed a method of estimating η using data on 

labor supply behavior. He shows that existing evidence of the effects of wage changes on labor supply imposes a 

tight upper bound on the curvature of utility over wealth (CRRA < 2) with the mean implied value of 0.71 and 

concludes that the standard expected utility model cannot generate high levels of risk aversion without contradicting 

established facts about labor supply. Recent work has jointly estimated the components of the Ramsey equation. 

Evans and Sezer (2005) estimate η = 1.49 for 22 OECD countries. They also estimate ρ = 1.08 percent per year 

using data on mortality rates. Anthoff, et al. (2009b) estimate η = 1.18, and ρ = 1.4 percent. When they multiply the 

bivariate probability distributions from their work and Evans and Sezer (2005) together, they find η = 1.47, and ρ = 

1.07. 

14A-23

 
generational setting, it may make sense to use a small positive pure rate of time 

preference because of the small probability of unforeseen cataclysmic events (Stern et al. 

2006).

• g. A commonly accepted approximation is around 2 percent per year. For the 

socioeconomic scenarios used for this exercise, the EMF models assume that g is about 

1.5-2 percent to 2100.

Some economists and non-economists have argued for constant discount rates below 2 

percent based on the prescriptive approach. When grounded in the Ramsey framework, 

proponents of this approach have argued that a ρ of zero avoids giving preferential treatment to 

one generation over another. The choice of η has also been posed as an ethical choice linked to 

the value of an additional dollar in poorer countries compared to wealthier ones. Stern et al. 

(2006) applies this perspective through his choice of ρ = 0.1 percent per year, η = 1 and g = 1.3 

percent per year, which yields an annual discount rate of 1.4 percent. In the context of permanent 

income savings behavior, however, Stern’s assumptions suggest that individuals would save 93 

percent of their income.x

Recently, Stern (2008) revisited the values used in Stern et al. (2006), stating that there is 

a case to be made for raising η due to the amount of weight lower values place on damages far in 

the future (over 90 percent of expected damages occur after 2200 with η = 1). Using Stern’s 

assumption that ρ = 0.1 percent, combined with a η of 1.5 to 2 and his original growth rate, 

yields a discount rate of greater than 2 percent.

We conclude that arguments made under the prescriptive approach can be used to justify 

discount rates between roughly 1.4 and 3.1 percent. In light of concerns about the most 

appropriate value for η, we find it difficult to justify rates at the lower end of this range under the 

Ramsey framework.

Accounting for Uncertainty in the Discount Rate

While the consumption rate of interest is an important driver of the benefits estimate, it is 

uncertain over time. Ideally, we would formally model this uncertainty, just as we do for climate 

sensitivity. Weitzman (1998, 2001) showed theoretically and Newell and Pizer (2003) and 

Groom et al. (2006) confirm empirically that discount rate uncertainty can have a large effect on 

net present values. A main result from these studies is that if there is a persistent element to the 

uncertainty in the discount rate (e.g., the rate follows a random walk), then it will result in an 

effective (or certainty-equivalent) discount rate that declines over time. Consequently, lower 

discount rates tend to dominate over the very long term (see Weitzman 1998, 1999, 2001; 

Newell and Pizer 2003; Groom et al. 2006; Gollier 2008; Summers and Zeckhauser 2008; and 

Gollier and Weitzman 2009).

x Stern (2008) argues that building in a positive rate of exogenous technical change over time reduces the implied 

savings rate and that η at or above 2 are inconsistent with observed behavior with regard to equity. (At the same 

time, adding exogenous technical change—all else equal—would increase g as well.)

14A-24

 
The proper way to model discount rate uncertainty remains an active area of research.

Newell and Pizer (2003) employ a model of how long-term interest rates change over time to 

forecast future discount rates. Their model incorporates some of the basic features of how 

interest rates move over time, and its parameters are estimated based on historical observations 

of long-term rates. Subsequent work on this topic, most notably Groom et al. (2006), uses more 

general models of interest rate dynamics to allow for better forecasts. Specifically, the volatility 

of interest rates depends on whether rates are currently low or high and the variation in the level 

of persistence over time.

While Newell and Pizer (2003) and Groom et al (2006) attempt formally to model 

uncertainty in the discount rate, others argue for a declining scale of discount rates applied over 

time (e.g., Weitzman 2001, and the UK’s “Green Book” for regulatory analysis). This approach 

uses a higher discount rate initially, but applies a graduated scale of lower discount rates further 

out in time.y A key question that has emerged with regard to both of these approaches is the 

trade-off between potential time inconsistency and giving greater weight to far future outcomes 

(see the EPA Science Advisory Board’s recent comments on this topic as part of its review of 

their Guidelines for Economic Analysis).z

The Discount Rates Selected for Estimating SCC

In light of disagreement in the literature on the appropriate market interest rate to use in 

this context and uncertainty about how interest rates may change over time, we use three 

discount rates to span a plausible range of certainty-equivalent constant discount rates: 2.5, 3, 

and 5 percent per year. Based on the review in the previous sections, the interagency workgroup 

determined that these three rates reflect reasonable judgments under both descriptive and 

prescriptive approaches.

The central value, 3 percent, is consistent with estimates provided in the economics 

literature and OMB’s Circular A-4 guidance for the consumption rate of interest. As previously 

mentioned, the consumption rate of interest is the correct discounting concept to use when future 

damages from elevated temperatures are estimated in consumption-equivalent units. Further, 3 

percent roughly corresponds to the after-tax riskless interest rate. The upper value of 5 percent is 

included to represent the possibility that climate damages are positively correlated with market 

returns. Additionally, this discount rate may be justified by the high interest rates that many 

consumers use to smooth consumption across periods.

y For instance, the UK applies a discount rate of 3.5 percent to the first 30 years; 3 percent for years 31 - 75; 2.5

percent for years 76 - 125; 2 percent for years 126 - 200; 1.5 percent for years 201 - 300; and 1 percent after 300 

years. As a sensitivity, it recommends a discount rate of 3 percent for the first 30 years, also decreasing over time. z Uncertainty in future damages is distinct from uncertainty in the discount rate. Weitzman (2008) argues that 

Stern’s choice of a low discount rate was “right for the wrong reasons.” He demonstrates how the damages from a 

low probability, catastrophic event far in the future dominate the effect of the discount rate in a present value 

calculation and result in an infinite willingness-to-pay for mitigation today. Newbold and Daigneault, (2009) and 

Nordhaus (2009) find that Weitzman’s result is sensitive to the functional forms chosen for climate sensitivity, 

utility, and consumption. Summers and Zeckhauser (2008) argue that uncertainty in future damages can also work in 

the other direction by increasing the benefits of waiting to learn the appropriate level of mitigation required. 

14A-25

 
The low value, 2.5 percent, is included to incorporate the concern that interest rates are 

highly uncertain over time. It represents the average certainty-equivalent rate using the meanreverting and random walk approaches from Newell and Pizer (2003) starting at a discount rate 

of 3 percent. Using this approach, the certainty equivalent is about 2.2 percent using the random 

walk model and 2.8 percent using the mean reverting approach.aa Without giving preference to a 

particular model, the average of the two rates is 2.5 percent. Further, a rate below the riskless 

rate would be justified if climate investments are negatively correlated with the overall market 

rate of return. Use of this lower value also responds to certain judgments using the prescriptive 

or normative approach and to ethical objections that have been raised about rates of 3 percent or 

higher.

14A.5 REVISED SCC ESTIMATES

Our general approach to estimating SCC values is to run the three integrated assessment 

models (FUND, DICE, and PAGE) using the following inputs agreed upon by the interagency 

group:

• A Roe and Baker distribution for the climate sensitivity parameter bounded between 0 

and 10 with a median of 3 °C and a cumulative probability between 2 and 4.5 °C of twothirds.

• Five sets of GDP, population, and carbon emissions trajectories based on EMF-22.

• Constant annual discount rates of 2.5, 3, and 5 percent.

Because the climate sensitivity parameter is modeled probabilistically, and because PAGE and 

FUND incorporate uncertainty in other model parameters, the final output from each model run 

is a distribution over the SCC in year t. 

For each of the IAMs, the basic computational steps for calculating the SCC in a particular year t

are:

1. Input the path of emissions, GDP, and population from the selected EMF-22 

scenarios, and the extrapolations based on these scenarios for post-2100 years.

2. Calculate the temperature effects and (consumption-equivalent) damages in each 

year resulting from the baseline path of emissions.

a. In PAGE, the consumption-equivalent damages in each period are 

calculated as a fraction of the EMF GDP forecast, depending on the 

temperature in that period relative to the pre-industrial average 

temperature in each region.

b. In FUND, damages in each period depend on both the level and the rate of 

temperature change in that period. 

aa Calculations done by Pizer et al. using the original simulation program from Newell and Pizer (2003).

14A-26

 
c. In DICE, temperature affects both consumption and investment, so we 

first adjust the EMF GDP paths as follows: Using the Cobb-Douglas 

production function with the DICE2007 parameters, we extract the path of 

exogenous technical change implied by the EMF GDP and population 

paths, then we recalculate the baseline GDP path taking into account 

climate damages resulting from the baseline emissions path.

3. Add an additional unit of carbon emissions in year t. (The exact unit varies by 

model.)

4. Recalculate the temperature effects and damages expected in all years beyond t

resulting from this adjusted path of emissions, as in step 2. 

5. Subtract the damages computed in step 2 from those in step 4 in each year. (DICE 

is run in 10-year time steps, FUND in annual time steps, while the time steps in 

PAGE vary.)

6. Discount the resulting path of marginal damages back to the year of emissions 

using the agreed upon fixed discount rates.

7. Calculate the SCC as the net present value of the discounted path of damages 

computed in step 6, divided by the unit of carbon emissions used to shock the 

models in step 3.

8. Multiply by 12/44 to convert from dollars per ton of carbon to dollars per ton of 

CO2 (2007 dollars) in DICE and FUND. (All calculations are done in tons of CO2 

in PAGE).

The steps above were repeated in each model for multiple future years to cover the time horizons 

anticipated for upcoming rulemaking analysis. To maintain consistency across the three IAMs, 

climate damages are calculated as lost consumption in each future year.

It is important to note that each of the three models has a different default end year. The 

default time horizon is 2200 for PAGE, 2595 for DICE, and 3000 for the latest version of FUND.

This is an issue for the multi-model approach because differences in SCC estimates may arise 

simply due to the model time horizon. Many consider 2200 too short a time horizon because it 

could miss a significant fraction of damages under certain assumptions about the growth of 

marginal damages and discounting, so each model is run here through 2300. This step required a 

small adjustment in the PAGE model only. This step also required assumptions about GDP, 

population, and greenhouse gas emission trajectories after 2100, the last year for which these 

data are available from the EMF-22 models. (A more detailed discussion of these assumptions is 

included in the Annex.)

This exercise produces 45 separate distributions of the SCC for a given year, the product 

of 3 models, 3 discount rates, and 5 socioeconomic scenarios. This is clearly too many separate 

distributions for consideration in a regulatory impact analysis. 

14A-27
To produce a range of plausible estimates that still reflects the uncertainty in the 

estimation exercise, the distributions from each of the models and scenarios are equally weighed 

and combined to produce three separate probability distributions for SCC in a given year, one for 

each assumed discount rate. These distributions are then used to define a range of point estimates 

for the global SCC. In this way, no IAM or socioeconomic scenario is given greater weight than 

another. Because the literature shows that the SCC is quite sensitive to assumptions about the 

discount rate, and because no consensus exists on the appropriate rate to use in an 

intergenerational context, we present SCCs based on the average values across models and 

socioeconomic scenarios for each discount rate.

The interagency group selected four SCC values for use in regulatory analyses. Three 

values are based on the average SCC across models and socioeconomic and emissions scenarios 

at the 2.5, 3, and 5 percent discount rates. The fourth value is included to represent the higherthan-expected economic impacts from climate change further out in the tails of the SCC 

distribution. For this purpose, we use the SCC value for the 95th percentile at a 3 percent discount 

rate. (The full set of distributions by model and scenario combination is included in the Annex.)

As noted above, the 3 percent discount rate is the central value, and so the central value that 

emerges is the average SCC across models at the 3 percent discount rate. For purposes of 

capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance 

and value of considering the full range.

As previously discussed, low probability, high impact events are incorporated into the 

SCC values through explicit consideration of their effects in two of the three models as well as 

the use of a probability density function for equilibrium climate sensitivity. Treating climate 

sensitivity probabilistically results in more high-temperature outcomes, which in turn lead to 

higher projections of damages. Although FUND does not include catastrophic damages (in 

contrast to the other two models), its probabilistic treatment of the equilibrium climate sensitivity 

parameter will directly affect the non-catastrophic damages that are a function of the rate of 

temperature change.

In Table 14A.5.1, we begin by presenting SCC estimates for 2010 by model, scenario, 

and discount rate to illustrate the variability in the SCC across each of these input parameters. As 

expected, higher discount rates consistently result in lower SCC values, while lower discount 

rates result in higher SCC values for each socioeconomic trajectory. It is also evident that there 

are differences in the SCC estimated across the three main models. For these estimates, FUND 

produces the lowest estimates, while PAGE generally produces the highest estimates. 

14A-28
Table 14A.5.1 Disaggregated Social Cost of CO2 Values by Model, Socioeconomic

Trajectory, and Discount Rate for 2010 (in 2007 dollars)

Discount rate: 5% 3% 2.5% 3%

Model Scenario Avg Avg Avg 95th DICE

IMAGE 10.8 35.8 54.2 70.8

MERGE 7.5 22.0 31.6 42.1

Message 9.8 29.8 43.5 58.6

MiniCAM 8.6 28.8 44.4 57.9

550 Average 8.2 24.9 37.4 50.8

PAGE

IMAGE 8.3 39.5 65.5 142.4

MERGE 5.2 22.3 34.6 82.4

Message 7.2 30.3 49.2 115.6

MiniCAM 6.4 31.8 54.7 115.4

550 Average 5.5 25.4 42.9 104.7

FUND

IMAGE -1.3 8.2 19.3 39.7

MERGE -0.3 8.0 14.8 41.3

Message -1.9 3.6 8.8 32.1

MiniCAM -0.6 10.2 22.2 42.6

550 Average -2.7 -0.2 3.0 19.4

These results are not surprising when compared to the estimates in the literature for the 

latest versions of each model. For example, adjusting the values from the literature that were 

used to develop interim SCC values to 2007 dollars for the year 2010 (assuming, as we did for

the interim process, that SCC grows at 3 percent per year), FUND yields SCC estimates at or 

near zero for a 5 percent discount rate and around $9 per ton for a 3 percent discount rate. There 

are far fewer estimates using the latest versions of DICE and PAGE in the literature: Using 

similar adjustments to generate 2010 estimates, we calculate a SCC from DICE (based on 

Nordhaus 2008) of around $9 per ton for a 5 percent discount rate, and a SCC from PAGE 

(based on Hope 2006, 2008) close to $8 per ton for a 4 percent discount rate. Note that these 

comparisons are only approximate since the literature generally relies on Ramsey discounting, 

while we have assumed constant discount rates.bb

bb Nordhaus (2008) runs DICE2007 with ρ = 1.5 and η = 2. The default approach in PAGE2002 (version 1.4epm) 

treats ρ and η as random parameters, specified using a triangular distribution such that the min, mode, and max = 

0.1, 1, and 2 for ρ, and 0.5, 1, and 2 for η, respectively. The FUND default value for η is 1, and Tol generates SCC 

estimates for values of ρ = 0, 1, and 3 in many recent papers (e.g. Anthoff et al. 2009). The path of per-capita 

consumption growth, g, varies over time but is treated deterministically in two of the three models. In DICE, g is 

14A-29

 
The SCC estimates from FUND are sensitive to differences in emissions paths but 

relatively insensitive to differences in GDP paths across scenarios, while the reverse is true for 

DICE and PAGE. This likely occurs because of several structural differences among the models.

Specifically in DICE and PAGE, the fraction of economic output lost due to climate damages 

increases with the level of temperature alone, whereas in FUND the fractional loss also increases 

with the rate of temperature change. Furthermore, in FUND increases in income over time 

decrease vulnerability to climate change (a form of adaptation), whereas this does not occur in 

DICE and PAGE. These structural differences among the models make FUND more sensitive to 

the path of emissions and less sensitive to GDP compared to DICE and PAGE.

Figure 14A.5.1 shows that IMAGE has the highest GDP in 2100 while MERGE 

Optimistic has the lowest. The ordering of global GDP levels in 2100 directly corresponds to the 

rank ordering of SCC for PAGE and DICE. For FUND, the correspondence is less clear, a result 

that is to be expected given its less direct relationship between its damage function and GDP.

Figure 14A.5.1 Level of Global GDP across EMF Scenarios

Table 14A.5.2 shows the four selected SCC values in five-year increments from 2010 to 

2050. Values for 2010, 2020, 2040, and 2050 are calculated by first combining all outputs 

(10,000 estimates per model run) from all scenarios and models for a given discount rate. Values 

for the years in between are calculated using a simple linear interpolation.

endogenous. Under Ramsey discounting, as economic growth slows in the future, the large damages from climate 

change that occur far out in the future are discounted at a lower rate than impacts that occur in the nearer term.

0

50

100

150

200

250

300

350

400

450

2000 2010 2020 2030 2050 2100

Global 

GDP

(trillions

of U.S. 

dollars)

IMAGE

MERGE Optimistic

MESSAGE

MiniCAM

3.7 W/m2 radiative forcing

14A-30

 
Table 14A.5.2 Social Cost of CO2, 2010 – 2050 (in 2007 dollars)

Discount 5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

The SCC increases over time because future emissions are expected to produce larger 

incremental damages as physical and economic systems become more stressed in response to 

greater climatic change. Note that this approach allows us to estimate the growth rate of the SCC 

directly using DICE, PAGE, and FUND rather than assuming a constant annual growth rate as 

was done for the interim estimates (using 3 percent). This helps to ensure that the estimates are 

internally consistent with other modeling assumptions. Table 14A.5.3 illustrates how the growth 

rate for these four SCC estimates varies over time. The full set of annual SCC estimates between 

2010 and 2050 is reported in the Annex.

Table 14A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates between 

2010 and 2050

Average Annual 

Growth Rate (%)

5% 3% 2.5% 3.0%

Avg Avg Avg 95th

2010-2020 3.6% 2.1% 1.7% 2.2%

2020-2030 3.7% 2.2% 1.8% 2.2%

2030-2040 2.7% 1.8% 1.6% 1.8%

2040-2050 2.1% 1.4% 1.1% 1.3%

While the SCC estimate grows over time, the future monetized value of emissions 

reductions in each year (the SCC in year t multiplied by the change in emissions in year t) must 

be discounted to the present to determine its total net present value for use in regulatory analysis.

Damages from future emissions should be discounted at the same rate as that used to calculate 

the SCC estimates themselves to ensure internal consistency—i.e., future damages from climate 

change, whether they result from emissions today or emissions in a later year, should be 

discounted using the same rate. For example, climate damages in the year 2020 that are 

14A-31
calculated using a SCC based on a 5 percent discount rate also should be discounted back to the 

analysis year using a 5 percent discount rate.cc

14A.6 LIMITATIONS OF THE ANALYSIS

As noted, any estimate of the SCC must be taken as provisional and subject to further 

refinement (and possibly significant change) in accordance with evolving scientific, economic, 

and ethical understandings. During the course of our modeling, it became apparent that there are 

several areas in particular need of additional exploration and research. These caveats, and 

additional observations in the following section, are necessary to consider when interpreting and 

applying the SCC estimates.

Incomplete treatment of non-catastrophic damages. The impacts of climate change are 

expected to be widespread, diverse, and heterogeneous. In addition, the exact magnitude of these 

impacts is uncertain because of the inherent complexity of climate processes, the economic 

behavior of current and future populations, and our inability to accurately forecast technological 

change and adaptation. Current IAMs do not assign value to all of the important physical, 

ecological, and economic impacts of climate change recognized in the climate change literature 

(some of which are discussed above) because of lack of precise information on the nature of 

damages and because the science incorporated into these models understandably lags behind the 

most recent research. Our ability to quantify and monetize impacts will undoubtedly improve 

with time. But it is also likely that even in future applications, a number of potentially significant 

damage categories will remain non-monetized. (Ocean acidification is one example of a 

potentially large damage from CO2 emissions not quantified by any of the three models. Species 

and wildlife loss is another example that is exceedingly difficult to monetize.) 

Incomplete treatment of potential catastrophic damages. There has been considerable 

recent discussion of the risk of catastrophic impacts and how best to account for extreme 

scenarios, such as the collapse of the Atlantic Meridional Overturning Circulation or the West 

Antarctic Ice Sheet, or large releases of methane from melting permafrost and warming oceans.

Weitzman (2009) suggests that catastrophic damages are extremely large—so large, in fact, that 

the damages from a low probability, catastrophic event far in the future dominate the effect of 

the discount rate in a present value calculation and result in an infinite willingness-to-pay for 

mitigation today. However, Nordhaus (2009) concluded that the conditions under which 

Weitzman's results hold “are limited and do not apply to a wide range of potential uncertain 

scenarios." 

Using a simplified IAM, Newbold and Daigneault (2009) confirmed the potential for 

large catastrophe risk premiums but also showed that the aggregate benefit estimates can be 

highly sensitive to the shapes of both the climate sensitivity distribution and the damage function 

at high temperature changes. Pindyck (2009) also used a simplified IAM to examine highcc However, it is possible that other benefits or costs of proposed regulations unrelated to CO2 emissions will be 

discounted at rates that differ from those used to develop the SCC estimates.

14A-32

 
impact, low-probability risks, using a right-skewed gamma distribution for climate sensitivity as 

well as an uncertain damage coefficient, but in most cases found only a modest risk premium.

Given this difference in opinion, further research in this area is needed before its practical 

significance can be fully understood and a reasonable approach developed to account for such 

risks in regulatory analysis. (The next section discusses the scientific evidence on catastrophic 

impacts in greater detail.)

Uncertainty in extrapolation of damages to high temperatures: The damage functions in 

these IAMs are typically calibrated by estimating damages at moderate temperature increases 

(e.g., DICE was calibrated at 2.5 °C) and extrapolated to far higher temperatures by assuming 

that damages increase as some power of the temperature change. Hence, estimated damages are 

far more uncertain under more extreme climate change scenarios.

Incomplete treatment of adaptation and technological change: Each of the three 

integrated assessment models used here assumes a certain degree of low- or no-cost adaptation.

For instance, Tol assumes a great deal of adaptation in FUND, including widespread reliance on 

air conditioning; so much so, that the largest single benefit category in FUND is the reduced 

electricity costs from not having to run air conditioning as intensively (NRC 2009).

Climate change also will increase returns on investment to develop technologies that 

allow individuals to cope with adverse climate conditions, and IAMs to do not adequately 

account for this directed technological change.dd For example, scientists may develop crops that 

are better able to withstand higher and more variable temperatures. Although DICE and FUND 

have both calibrated their agricultural sectors under the assumption that farmers will change land 

use practices in response to climate change (Mastrandrea, 2009), they do not take into account 

technological changes that lower the cost of this adaptation over time. On the other hand, the 

calibrations do not account for increases in climate variability, pests, or diseases, which could 

make adaptation more difficult than assumed by the IAMs for a given temperature change.

Hence, models do not adequately account for potential adaptation or technical change that might 

alter the emissions pathway and resulting damages. In this respect, it is difficult to determine 

whether the incomplete treatment of adaptation and technological change in these IAMs 

understate or overstate the likely damages.

Risk aversion: A key question unanswered during this interagency process is what to 

assume about relative risk aversion with regard to high-impact outcomes. These calculations do 

not take into account the possibility that individuals may have a higher willingness to pay to

reduce the likelihood of low-probability, high-impact damages than they do to reduce the 

likelihood of higher-probability, but lower-impact, damages with the same expected cost. (The 

inclusion of the 95th percentile estimate in the final set of SCC values was largely motivated by 

this concern.) If individuals do show such a higher willingness to pay, a further question is 

whether that fact should be taken into account for regulatory policy. Even if individuals are not 

risk-averse for such scenarios, it is possible that regulatory policy should include a degree of 

risk-aversion.

dd However these research dollars will be diverted from whatever their next best use would have been in the absence 

of climate change (so productivity/GDP would have been still higher).

14A-33

 
Assuming a risk-neutral representative agent is consistent with OMB’s Circular A-4, 

which advises that the estimates of benefits and costs used in regulatory analysis are usually 

based on the average or the expected value and that “emphasis on these expected values is 

appropriate as long as society is ‘risk neutral’ with respect to the regulatory alternatives. While 

this may not always be the case, [analysts] should in general assume ‘risk neutrality’ in [their] 

analysis.”

Nordhaus (2008) points to the need to explore the relationship between risk and income 

in the context of climate change across models and to explore the role of uncertainty regarding 

various parameters in the results. Using FUND, Anthoff et al (2009) explored the sensitivity of 

the SCC to Ramsey equation parameter assumptions based on observed behavior. They conclude

that “the assumed rate of risk aversion is at least as important as the assumed rate of time 

preference in determining the social cost of carbon.” Since Circular A-4 allows for a different 

assumption on risk preference in regulatory analysis if it is adequately justified, we plan to 

continue investigating this issue.

14A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPACTS AND 

DAMAGE FUNCTIONS

As noted above, the damage functions underlying the three IAMs used to estimate the 

SCC may not capture the economic effects of all possible adverse consequences of climate 

change and may therefore lead to underestimates of the SCC (Mastrandrea 2009). In particular, 

the models’ functional forms may not adequately capture: (1) potentially discontinuous “tipping 

point” behavior in Earth systems, (2) inter-sectoral and inter-regional interactions, including 

global security impacts of high-end warming, and (3) limited near-term substitutability between 

damage to natural systems and increased consumption.

It is the hope of the interagency group that over time researchers and modelers will work 

to fill these gaps and that the SCC estimates used for regulatory analysis by the Federal 

government will continue to evolve with improvements in modeling. In the meantime, we 

discuss some of the available evidence.

Extrapolation of climate damages to high levels of warming

The damage functions in the models are calibrated at moderate levels of warming and 

should therefore be viewed cautiously when extrapolated to the high temperatures found in the 

upper end of the distribution. Recent science suggests that there are a number of potential 

climatic “tipping points” at which the Earth system may exhibit discontinuous behavior with 

potentially severe social and economic consequences (e.g., Lenton et al, 2008, Kriegler et al., 

2009). These tipping points include the disruption of the Indian Summer Monsoon, dieback of 

the Amazon Rainforest and boreal forests, collapse of the Greenland Ice Sheet and the West 

Antarctic Ice Sheet, reorganization of the Atlantic Meridional Overturning Circulation, 

strengthening of El Niño-Southern Oscillation, and the release of methane from melting 

14A-34
permafrost. Many of these tipping points are estimated to have thresholds between about 3 °C 

and 5 °C (Lenton et al., 2008). Probabilities of several of these tipping points were assessed 

through expert elicitation in 2005–2006 by Kriegler et al. (2009); results from this study are 

highlighted in Table 14A.7.1. Ranges of probability are averaged across core experts on each 

topic.

As previously mentioned, FUND does not include potentially catastrophic effects. DICE 

assumes a small probability of catastrophic damages that increases with increased warming, but 

the damages from these risks are incorporated as expected values (i.e., ignoring potential risk 

aversion). PAGE models catastrophic impacts in a probabilistic framework (see Figure 14A.4.1), 

so the high-end output from PAGE potentially offers the best insight into the SCC if the world 

were to experience catastrophic climate change. For instance, at the 95th percentile and a 3 

percent discount rate, the SCC estimated by PAGE across the five socioeconomic and emission 

trajectories of $113 per ton of CO2 is almost double the value estimated by DICE, $58 per ton in 

2010. We cannot evaluate how well the three models account for catastrophic or noncatastrophic impacts, but this estimate highlights the sensitivity of SCC values in the tails of the 

distribution to the assumptions made about catastrophic impacts. 

Table 14A.7.1 Probabilities of Various Tipping Points from Expert Elicitation

Possible Tipping Points

Duration before

effect is fully

realized (in years)

Additional Warming by 2100

0.5-1.5 C 1.5-3.0 C 3-5 C

Reorganization of Atlantic Meridional

Overturning Circulation about 100 0-18% 6-39% 18-67%

Greenland Ice Sheet collapse at least 300 8-39% 33-73% 67-96%

West Antarctic Ice Sheet collapse at least 300 5-41% 10-63% 33-88%

Dieback of Amazon rainforest about 50 2-46% 14-84% 41-94%

Strengthening of El Niño-Southern Oscillation about 100 1-13% 6-32% 19-49%

Dieback of boreal forests about 50 13-43% 20-81% 34-91%

Shift in Indian Summer Monsoon about 1 Not formally assessed

Release of methane from melting permafrost Less than 100 Not formally assessed.

PAGE treats the possibility of a catastrophic event probabilistically, while DICE treats it 

deterministically (that is, by adding the expected value of the damage from a catastrophe to the 

aggregate damage function). In part, this results in different probabilities being assigned to a 

catastrophic event across the two models. For instance, PAGE places a probability near zero on a 

catastrophe at 2.5 °C warming, while DICE assumes a 4 percent probability of a catastrophe at 

2.5 °C. By comparison, Kriegler et al. (2009) estimate a probability of at least 16-36 percent of 

14A-35
crossing at least one of their primary climatic tipping points in a scenario with temperatures 

about 2-4 °C warmer than pre-Industrial levels in 2100. 

It is important to note that crossing a climatic tipping point will not necessarily lead to an 

economic catastrophe in the sense used in the IAMs. A tipping point is a critical threshold across 

which some aspect of the Earth system starts to shifts into a qualitatively different state (for 

instance, one with dramatically reduced ice sheet volumes and higher sea levels). In the IAMs, a 

catastrophe is a low-probability environmental change with high economic impact.

Failure to incorporate inter-sectoral and inter-regional interactions

The damage functions do not fully incorporate either inter-sectoral or inter-regional 

interactions. For instance, while damages to the agricultural sector are incorporated, the effects 

of changes in food supply on human health are not fully captured and depend on the modeler’s 

choice of studies used to calibrate the IAM. Likewise, the effects of climate damages in one 

region of the world on another region are not included in some of the models (FUND includes 

the effects of migration from sea level rise). These inter-regional interactions, though difficult to 

quantify, are the basis for climate-induced national and economic security concerns (e.g., 

Campbell et al., 2007; U.S. Department of Defense 2010) and are particularly worrisome at 

higher levels of warming. High-end warming scenarios, for instance, project water scarcity 

affecting 4.3-6.9 billion people by 2050, food scarcity affecting about 120 million additional 

people by 2080, and the creation of millions of climate refugees (Easterling et al., 2007; 

Campbell et al., 2007).

Imperfect substitutability of environmental amenities

Data from the geological record of past climate changes suggests that 6 °C of warming 

may have severe consequences for natural systems. For instance, during the Paleocene-Eocene 

Thermal Maximum about 55.5 million years ago, when the Earth experienced a geologically 

rapid release of carbon associated with an approximately 5 °C increase in global mean 

temperatures, the effects included shifts of about 400-900 miles in the range of plants (Wing et 

al., 2005), and dwarfing of both land mammals (Gingerich, 2006) and soil fauna (Smith et al., 

2009).

The three IAMs used here assume that it is possible to compensate for the economic 

consequences of damages to natural systems through increased consumption of non-climate 

goods, a common assumption in many economic models. In the context of climate change, 

however, it is possible that the damages to natural systems could become so great that no 

increase in consumption of non-climate goods would provide complete compensation (Levy et 

al., 2005). For instance, as water supplies become scarcer or ecosystems become more fragile 

and less bio-diverse, the services they provide may become increasingly more costly to replace.

Uncalibrated attempts to incorporate the imperfect substitutability of such amenities into IAMs 

(Sterner and Persson, 2008) indicate that the optimal degree of emissions abatement can be 

considerably greater than is commonly recognized. 

14A-36
14A.8 CONCLUSION

The interagency group selected four SCC estimates for use in regulatory analyses. For 

2010, these estimates are $5, $21, $35, and $65 (in 2007 dollars). The first three estimates are 

based on the average SCC across models and socioeconomic and emissions scenarios at the 5, 3, 

and 2.5 percent discount rates, respectively. The fourth value is included to represent the higherthan-expected impacts from temperature change further out in the tails of the SCC distribution. 

For this purpose, we use the SCC value for the 95th percentile at a 3 percent discount rate. The 

central value is the average SCC across models at the 3 percent discount rate. For purposes of 

capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance 

and value of considering the full range. These SCC estimates also grow over time. For instance, 

the central value increases to $24 per ton of CO2 in 2015 and $26 per ton of CO2 in 2020.

We noted a number of limitations to this analysis, including the incomplete way in which 

the integrated assessment models capture catastrophic and non-catastrophic impacts, their 

incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of 

damages to high temperatures, and assumptions regarding risk aversion. The limited amount of 

research linking climate impacts to economic damages makes this modeling exercise even more 

difficult. It is the hope of the interagency group that over time researchers and modelers will 

work to fill these gaps and that the SCC estimates used for regulatory analysis by the Federal 

government will continue to evolve with improvements in modeling. 

14A-37
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48 Nordhaus, W. 2007a. Accompanying notes and documentation on development of DICE2007 model: notes on DICE-2007.delta.v8 as of September 21, 2007.

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49 Nordhaus, W. 2007b. “Alternative measures of output in global economic-environmental 

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50 Nordhaus W. 2008. A Question of Balance: Weighing the Options on Global Warming 

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52 Nordhaus W., and Boyer J. 2000. Warming the World: Economic Models of Global 

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sensitivity estimated from ensemble simulations of glacial climate.” Clim. Dyn., 27, 149–

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58 Smith, J. et al. 2009. “Transient dwarfism of soil fauna during the Paleocene-Eocene 

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60 Stern N. 2008. “The economics of climate change.” American Economic Review 98(2):1-

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14A-42
61 Sterner, T., and U. Persson. 2008. An even Sterner review: Introducing relative prices 

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14A-43
14A.9 ANNEX

Table 14A.9.1 Annual SCC Values: 2010–2050 (in 2007 dollars)

Discount Rate 5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2011 4.9 21.9 35.7 66.5

2012 5.1 22.4 36.4 68.1

2013 5.3 22.8 37.0 69.6

2014 5.5 23.3 37.7 71.2

2015 5.7 23.8 38.4 72.8

2016 5.9 24.3 39.0 74.4

2017 6.1 24.8 39.7 76.0

2018 6.3 25.3 40.4 77.5

2019 6.5 25.8 41.0 79.1

2020 6.8 26.3 41.7 80.7

2021 7.1 27.0 42.5 82.6

2022 7.4 27.6 43.4 84.6

2023 7.7 28.3 44.2 86.5

2024 7.9 28.9 45.0 88.4

2025 8.2 29.6 45.9 90.4

2026 8.5 30.2 46.7 92.3

2027 8.8 30.9 47.5 94.2

2028 9.1 31.5 48.4 96.2

2029 9.4 32.1 49.2 98.1

2030 9.7 32.8 50.0 100.0

2031 10.0 33.4 50.9 102.0

2032 10.3 34.1 51.7 103.9

2033 10.6 34.7 52.5 105.8

2034 10.9 35.4 53.4 107.8

2035 11.2 36.0 54.2 109.7

2036 11.5 36.7 55.0 111.6

2037 11.8 37.3 55.9 113.6

2038 12.1 37.9 56.7 115.5

2039 12.4 38.6 57.5 117.4

2040 12.7 39.2 58.4 119.3

2041 13.0 39.8 59.0 121.0

2042 13.3 40.4 59.7 122.7

2043 13.6 40.9 60.4 124.4

2044 13.9 41.5 61.0 126.1

2045 14.2 42.1 61.7 127.8

2046 14.5 42.6 62.4 129.4

2047 14.8 43.2 63.0 131.1

2048 15.1 43.8 63.7 132.8

2049 15.4 44.4 64.4 134.5

2050 15.7 44.9 65.0 136.2

This Annex provides additional technical information about the non-CO2 emission 

projections used in the modeling and the method for extrapolating emissions forecasts through 

2300 and shows the full distribution of 2010 SCC estimates by model and scenario combination.

14A-44
14A.9.1 Other (non-CO2) gases

In addition to fossil and industrial CO2 emissions, each EMF scenario provides 

projections of methane (CH4), nitrous oxide (N2O), fluorinated gases, and net land use CO2

emissions to 2100. These assumptions are used in all three IAMs while retaining each model’s 

default radiative forcings (RF) due to other factors (e.g., aerosols and other gases). Specifically, 

to obtain the RF associated with the non-CO2 EMF emissions only, we calculated the RF 

associated with the EMF atmospheric CO2 concentrations and subtracted them from the EMF 

total RF.

ee This approach respects the EMF scenarios as much as possible and at the same time 

takes account of those components not included in the EMF projections. Since each model treats 

non-CO2 gases differently (e.g., DICE lumps all other gases into one composite exogenous 

input), this approach was applied slightly differently in each of the models. 

FUND: Rather than relying on RF for these gases, the actual emissions from each 

scenario were used in FUND. The model default trajectories for CH4, N20, SF6, and the CO2

emissions from land were replaced with the EMF values.

PAGE: PAGE models CO2, CH4, sulfur hexafluoride (SF6), and aerosols and contains an 

"excess forcing" vector that includes the RF for everything else. To include the EMF values, we 

removed the default CH4 and SF6 factorsff, decomposed the excess forcing vector, and 

constructed a new excess forcing vector that includes the EMF RF for CH4, N20, and fluorinated 

gases, as well as the model default values for aerosols and other factors. Net land use CO2

emissions were added to the fossil and industrial CO2 emissions pathway. 

DICE: DICE presents the greatest challenge because all forcing due to factors other than 

industrial CO2 emissions is embedded in an exogenous non-CO2 RF vector. To decompose this 

exogenous forcing path into EMF non-CO2 gases and other gases, we relied on the references in 

DICE2007 to the Intergovernmental Panel on Climate Change’s (IPCC) Fourth Assessment 

Report (AR4) and the discussion of aerosol forecasts in the IPCC’s Third Assessment Report 

(TAR) and in AR4, as explained below. In DICE2007, Nordhaus assumes that exogenous forcing 

from all non-CO2 sources is -0.06 W/m2 in 2005, as reported in AR4, and increases linearly to 

0.3 W/m2 in 2105, based on GISS projections, and then stays constant after that time.

According to AR4, the RF in 2005 from CH4, N20, and halocarbons (approximately 

similar to the F-gases in the EMF-22 scenarios) was 0.48 + 0.16 + 0.34 = 0.98 W/m2 and RF 

from total aerosols was -1.2 W/m2. Thus, the -.06 W/m2 non-CO2 forcing in DICE can be 

ee Note EMF did not provide CO2 concentrations for the IMAGE reference scenario. Thus, for this scenario, we fed 

the fossil, industrial, and land CO2 emissions into MAGICC (considered a "neutral arbiter" model, which is tuned to 

emulate the major global climate models) and the resulting CO2 concentrations were used. Note also that MERGE 

assumes a neutral biosphere so net land CO2 emissions are set to zero for all years for the MERGE Optimistic 

reference scenario, and for the MERGE component of the average 550 scenario (i.e., we add up the land use 

emissions from the other three models and divide by 4).

ff Both the model default CH4 emissions and the initial atmospheric CH4 is set to zero to avoid double counting the 

effect of past CH4 emissions.

14A-45

 
decomposed into: 0.98 W/m2 due to the EMF non-CO2 gases, -1.2 W/m2 due to aerosols, and the 

remainder, 0.16 W/m2, due to other residual forcing.

For subsequent years, we calculated the DICE default RF from aerosols and other nonCO2 gases based on the following two assumptions:

(1) RF from aerosols declines linearly from 2005 to 2100 at the rate projected by the TAR 

and then stays constant thereafter; and 

(2) With respect to RF from non-CO2 gases not included in the EMF-22 scenarios, the share 

of non-aerosol RF matches the share implicit in the AR4 summary statistics cited above and 

remains constant over time.

Assumption (1) means that the RF from aerosols in 2100 equals 66 percent of that in 2000, 

which is the fraction of the TAR projection of total RF from aerosols (including sulfates, black 

carbon, and organic carbon) in 2100 vs. 2000 under the A1B SRES emissions scenario. Since the 

SRES marker scenarios were not updated for the AR4, the TAR provides the most recent IPCC 

projection of aerosol forcing. We rely on the A1B projection from the TAR because it provides 

one of the lower aerosol forecasts among the SRES marker scenarios and is more consistent with 

the AR4 discussion of the post-SRES literature on aerosols: 

Aerosols have a net cooling effect and the representation of aerosol and aerosol precursor 

emissions, including sulfur dioxide, black carbon and organic carbon, has improved in the 

post-SRES scenarios. Generally, these emissions are projected to be lower than reported in 

SRES. {WGIII 3.2, TS.3, SPM}.gg

Assuming a simple linear decline in aerosols from 2000 to 2100 also is more consistent 

with the recent literature on these emissions. For example, the figure below shows that the sulfur 

dioxide emissions peak over the short term of some SRES scenarios above the upper bound 

estimates of the more recent scenarios.hh Recent scenarios project sulfur emissions to peak earlier 

and at lower levels compared to the SRES in part because of new information about present and 

planned sulfur legislation in some developing countries, such as India and China.ii The lowerbound projections of the recent literature have also shifted downward slightly compared to the 

SRES scenario (IPCC 2007). 

gg AR4 Synthesis Report, p. 44, http://www.ipcc.ch/pdf/assessment-report/ar4/syr/ar4_syr.pdf

hh See Smith, S.J., R. Andres, E. Conception, and J. Lurz, 2004: Historical sulfur dioxide emissions, 1850-2000: 

methods and results. Joint Global Research Institute, College Park, 14 pp.

ii See Carmichael, G., D. Streets, G. Calori, M. Amann, M. Jacobson, J. Hansen, and H. Ueda, 2002: Changing 

trends in sulphur emissions in Asia: implications for acid deposition, air pollution, and climate. Environmental 

Science and Technology, 36(22):4707- 4713; Streets, D., K. Jiang, X. Hu, J. Sinton, X.-Q. Zhang, D. Xu, M. 

Jacobson, and J. Hansen, 2001: Recent reductions in China’s greenhouse gas emissions. Science, 294(5548): 1835-

1837.

14A-46

 
With these assumptions, the DICE aerosol forcing changes from -1.2 in 2005 to -0.792 in 

2105 W/m2; forcing due to other non-CO2 gases not included in the EMF scenarios declines from 

0.160 to 0.153 W/m2

.

.

Figure 14A.9.2 Sulfur Dioxide Emission Scenarios

Notes: Thick colored lines depict the four SRES marker scenarios and black dashed lines 

show the median, 5th, and 95th percentile of the frequency distribution for the full 

ensemble of 40 SRES scenarios. The blue area (and the thin dashed lines in blue) 

illustrates individual scenarios and the range of Smith et al. (2004). Dotted lines indicate 

the minimum and maximum of SO2 emissions scenarios developed pre-SRES.

Source: IPCC (2007), AR4 WGIII 3.2, 

http://www.ipcc.ch/publications_and_data/ar4/wg3/en/ch3-ens3-2-2-4.html.

Although other approaches to decomposing the DICE exogenous forcing vector are 

possible, initial sensitivity analysis suggests that the differences among reasonable alternative 

approaches are likely to be minor. For example, adjusting the TAR aerosol projection above to 

assume that aerosols will be maintained at 2000 levels through 2100 reduces average SCC values 

(for 2010) by approximately 3 percent (or less than $2); assuming all aerosols are phased out by 

2100 increases average 2010 SCC values by 6-7 percent (or $0.50-$3)–depending on the 

discount rate. These differences increase slightly for SCC values in later years but are still well 

within 10 percent of each other as far out as 2050.

Finally, as in PAGE, the EMF net land use CO2 emissions are added to the fossil and 

industrial CO2 emissions pathway. 

14A.9.2 Extrapolating Emissions Projections to 2300

To run each model through 2300 requires assumptions about GDP, population, 

greenhouse gas emissions, and radiative forcing trajectories after 2100, the last year for which 

14A-47
these projections are available from the EMF-22 models. These inputs were extrapolated from 

2100 to 2300 as follows:

1. Population growth rate declines linearly, reaching zero in the year 2200.

2. GDP/per capita growth rate declines linearly, reaching zero in the year 2300.

3. The decline in the fossil and industrial carbon intensity (CO2/GDP) growth rate over 2090-

2100 is maintained from 2100 through 2300.

4. Net land use CO2 emissions decline linearly, reaching zero in the year 2200.

5. Non-CO2 radiative forcing remains constant after 2100.

Long run stabilization of GDP per capita was viewed as a more realistic simplifying 

assumption than a linear or exponential extrapolation of the pre-2100 economic growth rate of 

each EMF scenario. This is based on the idea that increasing scarcity of natural resources and the 

degradation of environmental sinks available for assimilating pollution from economic 

production activities may eventually overtake the rate of technological progress. Thus, the 

overall rate of economic growth may slow over the very long run. The interagency group also 

considered allowing an exponential decline in the growth rate of GDP per capita. However, since 

this would require an additional assumption about how close to zero the growth rate would get 

by 2300, the group opted for the simpler and more transparent linear extrapolation to zero by 

2300.

The population growth rate is also assumed to decline linearly, reaching zero by 2200.

This assumption is reasonably consistent with the United Nations long run population forecast, 

which estimates global population to be fairly stable after 2150 in the medium scenario (UN 

2004).jj The resulting range of EMF population trajectories (figure below) also encompass the 

UN medium scenario forecasts through 2300—global population of 8.5 billion by 2200, and 9 

billion by 2300.

Maintaining the decline in the 2090-2100 carbon intensity growth rate (i.e., CO2 per 

dollar of GDP) through 2300 assumes that technological improvements and innovations in the 

areas of energy efficiency and other carbon reducing technologies (possibly including currently 

unavailable methods) will continue to proceed at roughly the same pace that is projected to occur 

towards the end of the forecast period for each EMF scenario. This assumption implies that total 

cumulative emissions in 2300 will be between 5,000 and 12,000 GtC, which is within the range 

of the total potential global carbon stock estimated in the literature.

Net land use CO2 emissions are expected to stabilize in the long run, so in the absence of 

any post 2100 projections, the group assumed a linear decline to zero by 2200. Given no a priori 

reasons for assuming a long run increase or decline in non-CO2 radiative forcing, it is assumed to 

remain at the 2100 levels for each EMF scenario through 2300.

jj United Nations. 2004. World Population to 2300. 

http://www.un.org/esa/population/publications/longrange2/WorldPop2300final.pdf

14A-48

 
Figures below show the paths of global population, GDP, fossil and industrial CO2

emissions, net land CO2 emissions, non-CO2 radiative forcing, and CO2 intensity (fossil and 

industrial CO2 emissions/GDP) resulting from these assumptions. 

Figure 14A.9.3 Global Population, 2000-2300 (Post-2100 extrapolations 

assume the population growth rate changes linearly to reach a 

zero growth rate by 2200.)

Note: In the fifth scenario, 2000-2100 population is equal to the average of the population under 

the 550 ppm CO2e, full-participation, not-to-exceed scenarios considered by each of the four 

models.

5.5

6.5

7.5

8.5

9.5

10.5

11.5

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Global Population [billion]

Year

14A-49
Figure 14A.9.4 World GDP, 2000-2300 (Post-2100 extrapolations assume GDP 

per capita growth declines linearly, reaching zero in the year 

2300)

Note: In the fifth scenario, 2000-2100 GDP is equal to the average of the GDP under the 550 

ppm CO2e, full-participation, not-to-exceed scenarios considered by each of the four models.

0

500

1000

1500

2000

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Year

World GDP [$trillion]

14A-50
Figure 14A.9.5 Global Fossil and Industrial CO2 Emissions, 2000-2300 (Post2100 extrapolations assume growth rate of CO2 intensity 

(CO2/GDP) over 2090-2100 is maintained through 2300)

Note: In the fifth scenario, 2000-2100 emissions are equal to the average of the emissions under 

the 550 ppm CO2e, full-participation, not-to-exceed scenarios considered by each of the four 

models.

0

50

100

150

200

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Fossil&Industrial CO2 Emissions [GtCO2/decade]

Year

14A-51
Figure 14A.9.6 Global Net Land Use CO2 Emissions, 2000-2300 (Post-2100 

extrapolations assume emissions decline linearly, reaching zero 

in the year 2200)

kk

Note: In the fifth scenario, 2000-2100 emissions are equal to the average of the emissions under 

the 550 ppm CO2e, full-participation, not-to-exceed scenarios considered by each of the four 

models.

kk MERGE assumes a neutral biosphere so net land CO2 emissions are set to zero for all years for the MERGE 

Optimistic reference scenario, and for the MERGE component of the average 550 scenario (i.e., we add up the land 

use emissions from the other three models and divide by 4).

-3.00

-2.00

-1.00

0.00

1.00

2.00

3.00

4.00

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scneario

Extrapolations

Net land CO2 emissions [GtCO2/yr]

Year

z

14A-52

 
Figure 14A.9.7 Global Non-CO2 Radiative Forcing, 2000-2300 (Post-2100 

extrapolations assume constant non-CO2 radiative forcing 

after 2100)

Note: In the fifth scenario, 2000-2100 emissions are equal to the average of the emissions under 

the 550 ppm CO2e, full-participation, not-to-exceed scenarios considered by each of the four 

models.

0.00

0.75

1.50

2.25

3.00

3.75

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

nonCO2 RF [W/m^2]

Year

14A-53
Figure 14A.9.8 Global CO2 Intensity (fossil & industrial CO2 emissions/GDP), 

2000-2300 (Post-2100 extrapolations assume decline in 

CO2/GDP growth rate over 2090-2100 is maintained through 

2300)

Note: In the fifth scenario, 2000-2100 emissions are equal to the average of the emissions under 

the 550 ppm CO2e, full-participation, not-to-exceed scenarios considered by each of the four 

models.

0

0

0

0

0

1

1

1

1

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Fossil & Industrial CO2 intensity [GtCO2/trillion $]

Year

14A-54
Table 14A.9.2 2010 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 3.3 5.9 8.1 13.9 28.8 65.5 68.2 147.9 239.6 563.8

MERGE optimistic 1.9 3.2 4.3 7.2 14.6 34.6 36.2 79.8 124.8 288.3

Message 2.4 4.3 5.8 9.8 20.3 49.2 50.7 114.9 181.7 428.4

MiniCAM base 2.7 4.6 6.4 11.2 22.8 54.7 55.7 120.5 195.3 482.3

5th scenario 2.0 3.5 4.7 8.1 16.3 42.9 41.5 103.9 176.3 371.9

Scenario DICE

IMAGE 16.4 21.4 25 33.3 46.8 54.2 69.7 96.3 111.1 130.0

MERGE optimistic 9.7 12.6 14.9 19.7 27.9 31.6 40.7 54.5 63.5 73.3

Message 13.5 17.2 20.1 27 38.5 43.5 55.1 75.8 87.9 103.0

MiniCAM base 13.1 16.7 19.8 26.7 38.6 44.4 56.8 79.5 92.8 109.3

5th scenario 10.8 14 16.7 22.2 32 37.4 47.7 67.8 80.2 96.8

Scenario FUND

IMAGE -33.1 -18.9 -13.3 -5.5 4.1 19.3 18.7 43.5 67.1 150.7

MERGE optimistic -33.1 -14.8 -10 -3 5.9 14.8 20.4 43.9 65.4 132.9

Message -32.5 -19.8 -14.6 -7.2 1.5 8.8 13.8 33.7 52.3 119.2

MiniCAM base -31.0 -15.9 -10.7 -3.4 6 22.2 21 46.4 70.4 152.9

5th scenario -32.2 -21.6 -16.7 -9.7 -2.3 3 6.7 20.5 34.2 96.8

14A-55
Table 14A.9.3 2010 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 2.0 3.5 4.8 8.1 16.5 39.5 41.6 90.3 142.4 327.4

MERGE optimistic 1.2 2.1 2.8 4.6 9.3 22.3 22.8 51.3 82.4 190.0

Message 1.6 2.7 3.6 6.2 12.5 30.3 31 71.4 115.6 263.0

MiniCAM base 1.7 2.8 3.8 6.5 13.2 31.8 32.4 72.6 115.4 287.0

5th scenario 1.3 2.3 3.1 5 9.6 25.4 23.6 62.1 104.7 222.5

Scenario DICE

IMAGE 11.0 14.5 17.2 22.8 31.6 35.8 45.4 61.9 70.8 82.1

MERGE optimistic 7.1 9.2 10.8 14.3 19.9 22 27.9 36.9 42.1 48.8

Message 9.7 12.5 14.7 19 26.6 29.8 37.8 51.1 58.6 67.4

MiniCAM base 8.8 11.5 13.6 18 25.2 28.8 36.9 50.4 57.9 67.8

5th scenario 7.9 10.1 11.8 15.6 21.6 24.9 31.8 43.7 50.8 60.6

Scenario FUND

IMAGE -25.2 -15.3 -11.2 -5.6 0.9 8.2 10.4 25.4 39.7 90.3

MERGE optimistic -24.0 -12.4 -8.7 -3.6 2.6 8 12.2 27 41.3 85.3

Message -25.3 -16.2 -12.2 -6.8 -0.5 3.6 7.7 20.1 32.1 72.5

MiniCAM base -23.1 -12.9 -9.3 -4 2.4 10.2 12.2 27.7 42.6 93.0

5th scenario -24.1 -16.6 -13.2 -8.3 -3 -0.2 2.9 11.2 19.4 53.6

14A-56
Table 14A.9.4 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 0.5 0.8 1.1 1.8 3.5 8.3 8.5 19.5 31.4 67.2

MERGE optimistic 0.3 0.5 0.7 1.2 2.3 5.2 5.4 12.3 19.5 42.4

Message 0.4 0.7 0.9 1.6 3 7.2 7.2 17 28.2 60.8

MiniCAM base 0.3 0.6 0.8 1.4 2.7 6.4 6.6 15.9 24.9 52.6

5th scenario 0.3 0.6 0.8 1.3 2.3 5.5 5 12.9 22 48.7

Scenario DICE

IMAGE 4.2 5.4 6.2 7.6 10 10.8 13.4 16.8 18.7 21.1

MERGE optimistic 2.9 3.7 4.2 5.3 7 7.5 9.3 11.7 12.9 14.4

Message 3.9 4.9 5.5 7 9.2 9.8 12.2 15.4 17.1 18.8

MiniCAM base 3.4 4.2 4.7 6 7.9 8.6 10.7 13.5 15.1 16.9

5th scenario 3.2 4 4.6 5.7 7.6 8.2 10.2 12.8 14.3 16.0

Scenario FUND

IMAGE -11.7 -8.4 -6.9 -4.6 -2.2 -1.3 0.7 4.1 7.4 17.4

MERGE optimistic -10.6 -7.1 -5.6 -3.6 -1.3 -0.3 1.6 5.4 9.1 19.0

Message -12.2 -8.9 -7.3 -4.9 -2.5 -1.9 0.3 3.5 6.5 15.6

MiniCAM base -10.4 -7.2 -5.8 -3.8 -1.5 -0.6 1.3 4.8 8.2 18.0

5th scenario -10.9 -8.3 -7 -5 -2.9 -2.7 -0.8 1.4 3.2 9.2

Figure 14A.9.9 Histogram of Global SCC Estimates in 2010 (2007$/ton CO2), 

by discount rate 

* The distribution of SCC values ranges from -$5,192 to $66,116, but the X-axis has been 

truncated at approximately the 1st and 99th percentiles to better show the data.

0

2000

4000

6000

8000

10000

12000

14000

16000

-25-12

1

14274053667992

105118131144157170183196209222235248261274

All 2010, 5.0% All 2010, 3.0% All 2010, 2.5%

14A-57
Table 14A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates 

Discount 

Rate

Scenario

DICE PAGE FUND

5%

Mean 9 6.5 -1.3

Variance 13.1 136 70.1

Skewness 0.8 6.3 28.2

Kurtosis 0.2 72.4 1,479.00

3%

Mean 28.3 29.8 6

Variance 209.8 3,383.70 16,382.50

Skewness 1.1 8.6 128

Kurtosis 0.9 151 18,976.50

2.50%

Mean 42.2 49.3 13.6

Variance 534.9 9,546.00 #######

Skewness 1.2 8.7 149

Kurtosis 1.1 143.8 23,558.30

14A-58
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-3

14B.3.2 FUND...............................................................................................................14B-4

14B.3.2.1 Space Heating.........................................................................................14B-4

14B.3.2.2 Sea Level Rise and Land Loss ...............................................................14B-5

14B.3.2.3 Agriculture .............................................................................................14B-5

14B.3.2.4 Temperature Response Model................................................................14B-6

14B.3.2.5 Methane..................................................................................................14B-6

14B.3.3 PAGE...............................................................................................................14B-6

14B.3.3.1 Sea Level Rise........................................................................................14B-6

14B.3.3.2 Revised Damage Function to Account for Saturation............................14B-7

14B.3.3.3 Regional Scaling Factors........................................................................14B-7

14B.3.3.4 Probability of a Discontinuity ................................................................14B-7

14B.3.3.5 Adaptation ..............................................................................................14B-8

14B.3.3.6 Other Noteworthy Changes....................................................................14B-8

14B.4 REVISED SCC ESTIMATES .....................................................................................14B-8

14B.5 OTHER MODEL LIMITATIONS OR RESEARCH GAPS ....................................14B-11

14B.6 ANNEX......................................................................................................................14B-14

14B-i
LIST OF TABLES 

Table 14B.4.1 Revised Social Cost of CO2, 2010 – 2050 (in 2007 dollars per ton of 

CO2) ...........................................................................................................14B-9

Table 14B.4.2 Average Annual Growth Rates of SCC Estimates between 2010 and 

2050..........................................................................................................14B-10

Table 14B.6.1 Annual SCC Values: 2010-2050 (2007$/ton CO2)..................................14B-14

Table 14B.6.2 202 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton 

CO2) .........................................................................................................14B-15

Table 14B.6.3 SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2) .................14B-15

Table 14B.6.4 2020 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton 

CO2) .........................................................................................................14B-16

LIST OF FIGURES 

Figure 14B.4.1 Distribution of SCC Estimates for 2020 (in 2007$ per ton CO2) ............14B-10

14B-ii
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 http://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 

decade, 9.7 percent of the carbon in the shallow ocean is transferred to the atmosphere, 85.3 

percent remains in the shallow ocean, and 5 percent is transferred to the deep ocean.

d MAGICC is a simple climate model initially developed within the U.S. National Center for Atmospheric Research 

that has been used heavily by the Intergovernmental Panel on Climate Change (IPCC) to emulate projections from 

much more sophisticated state of the art earth system simulation models (Randall et al. 2007).

4

14B-2

 
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.

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 

e Documentation on the new sea level rise module of DICE is available on William Nordhaus’ website at: 

http://nordhaus.econ.yale.edu/documents/SLR_021910.pdf. f For a review of post-IPCC AR4 research on sea level rise, see Nicholls et al. (2011)

5 and NAS (2011).6

14B-3

 
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 o

C in DICE2007. However, in 

DICE2010 (as downloaded from the homepage of William Nordhaus), annual damages are lower 

in most of the early periods but higher in later periods of the time horizon than would be 

calculated using the DICE2007 damage function. Specifically, the percent difference between 

damages in the base run of DICE2010 and those that would be calculated using the DICE2007 

damage function starts at +7 percent in 2005, decreases to a low of -14 percent in 2065, then 

continuously increases to +20 percent by 2300 (the end of the interagency analysis time horizon), 

and to +160 percent by the end of the model time horizon in 2595. The large increases in the far 

future years of the time horizon are due to the permanence associated with damages from sea 

level rise, along with the assumption that the sea level is projected to continue to rise long after 

the global average temperature begins to decrease. The changes to the loss function generally 

decrease the interagency working group SCC estimates slightly, all else equal.

14B.3.2 FUND

FUND version 3.8 includes a number of changes over the previous version 3.5 used in 

the interagency report. Documentation supporting FUND and the model’s source code for all 

versions of the model is available from the model authors.g Notable changes, due to their impact 

on the estimates of expected SCC, are adjustments to the space heating, agriculture, and sea level 

rise damage functions in addition to changes to the temperature response function and the 

inclusion of indirect effects from methane emissions.h We discuss each of these in turn.

14B.3.2.1 Space Heating

In FUND, the damages associated with the change in energy needs for space heating are 

based on the estimated impact due to one degree of warming. These baseline damages are scaled 

based on the forecasted temperature anomaly’s deviation from the one degree benchmark and 

adjusted for changes in vulnerability due to economic and energy efficiency growth. In FUND 

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 

g http://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

 
has been modified to ensure that the function is everywhere concave, meaning that for every 

simulation there will exist an upper bound on the benefits a region may receive from reduced 

space heating needs. The new formulation approaches a value of two in the limit as the

temperature anomaly increases, or in other words, assuming no decrease in vulnerability, the 

reduced expenditures on space heating at any level of warming will not exceed two times the 

reductions experienced at one degree of warming. Since the reduced need for space heating 

represents a benefit of climate change in the model, or a negative damage, this change will 

increase the estimated SCC. This update accounts for a significant portion of the difference in the 

expected SCC estimates reported by the two versions of the model when run probabilistically.

14B.3.2.2 Sea Level Rise and Land Loss

The FUND model explicitly includes damages associated with the inundation of dry land 

due to sea level rise. The amount of land lost within a region is dependent upon the proportion of 

the coastline being protected by adequate sea walls and the amount of sea level rise. In FUND 

3.5 the function defining the potential land lost in a given year due to sea level rise is linear in 

the rate of sea level rise for that year. This assumption implicitly assumes that all regions are 

well represented by a homogeneous coastline in length and a constant uniform slope moving 

inland. In FUND 3.8 the function defining the potential land lost has been changed to be a nonlinear function of sea level rise, thereby assuming that the slope of the shore line is not constant 

moving inland, with a positive first derivative. The effect of this change is to typically reduce the 

vulnerability of some regions to sea level rise based land loss, therefore having an effect of 

lowering the expected SCC estimate. The model has also been updated to assume that the value 

of dry land at risk of inundation is not uniform across a region but will be a decreasing function 

of protection measure, thereby implicitly assuming that the most valuable land will be protected 

first.

14B.3.2.3 Agriculture

In FUND, the damages associated with the agricultural sector are measured as 

proportional to the sector’s value. The fraction is made up of three additively separable 

components that represent the effects from carbon fertilization, the rate of temperature change, 

and the level of the temperature anomaly. In both FUND 3.5 and FUND 3.8, the fraction of the 

sector’s value lost due to the level of the temperature anomaly is modeled as a quadratic function 

with an intercept of zero. In FUND 3.5, the linear and quadratic coefficients are modeled as the 

ratio of two normal distributions. Within this specification, as draws from the distribution in the 

denominator approached zero the share of the sector’s value “lost” approaches (+/-) infinity 

independent of the temperature anomaly itself. In FUND 3.8, the linear and quadratic 

coefficients are drawn directly from truncated normal distributions so that they remain in the 

range [0,∞) and (−∞,0], respectively, where the means for the new distributions are set equal to 

the ratio of the means from the normal distributions used in the previous version. In general the 

impact of this change has been to increase the likelihood that increases in the temperature level 

will have either larger positive or negative effects on the agricultural sector relative to the 

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-5
14B.3.2.4 Temperature Response Model

The temperature response model translates changes in global levels of radiative forcing 

into the current expected temperature anomaly. In FUND, a given year’s increase in the 

cumulative temperature anomaly is based on a mean reverting function where the mean equals 

the equilibrium temperature anomaly that would eventually be reached if that year’s level of 

radiative forcing were sustained. The rate of mean reversion defines the rate at which the 

transient temperature approaches the equilibrium. In FUND 3.5, the rate of temperature response 

is defined as a decreasing linear function of equilibrium climate sensitivity to capture the fact 

that the progressive heat uptake of the deep ocean causes the rate to slow at higher values of the 

equilibrium climate sensitivity. In FUND 3.8, the rate of temperature response has been updated 

to a quadratic function of the equilibrium climate sensitivity. This change reduces the sensitivity 

of the rate of temperature response to the level of the equilibrium climate sensitivity. Therefore 

in FUND 3.8, the temperature response will typically be faster than in the previous version. The 

overall effect of this change is likely to increase estimates of the SCC as higher temperatures are 

reached during the timeframe analyzed and as the same damages experienced in the previous 

version of the model are now experienced earlier and therefore discounted less.

14B.3.2.5 Methane

The IPCC notes a series of indirect effects of methane emissions, and has developed 

methods for proxying such effects when computing the global warming potential of methane 

(Forster et al. 2007).8 FUND 3.8 now includes the same methods for incorporating the indirect 

effects of methane emissions. Specifically, the average atmospheric lifetime of methane has been 

set to 12 years to account for the feedback of CH4 emissions on its own lifetime. The radiative 

forcing associated with atmospheric methane has also been increase by 40% to account for its net 

impact on ozone production and increase in stratospheric water vapor. The general effect of this 

increased radiative forcing will be to increase the estimated SCC values, where the degree to 

which this occurs will be dependent upon the relative curvature of the damage functions with 

respect to the temperature anomaly.

14B.3.3 PAGE

PAGE09 (Hope 2012)9 includes a number of changes from PAGE2002, the version used 

in the 2009 SCC interagency report. The changes that most directly affect the SCC estimates 

include: explicitly modeling the impacts from sea level rise, revisions to the damage function to 

ensure damages are constrained by GDP, a change in the regional scaling of damages, a revised 

treatment for the probability of a discontinuity within the damage function, and revised 

assumptions on adaptation. The model also includes revisions to the carbon cycle feedback and 

the calculation of regional temperatures. More details on PAGE2009 can be found in three 

working papers (Hope 2011a, 2011b, 2011c).10, 11, 12 A description of PAGE2002 can be found 

in Hope (2006).13

14B.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 

14B-6
categories. PAGE09 models damages from sea level rise as increasing less than linearly with sea 

level based on the assumption that low-lying shoreline areas will be associated with higher 

damages than current inland areas. Damages from the economic and non-economic sector were 

adjusted to account for the introduction of this new category. 

14B.3.3.2 Revised Damage Function to Account for Saturation

In PAGE09, small initial economic and non-economic benefits (negative damages) are 

modeled for small temperature increases, but all regions eventually experience positive economic 

damages from climate change, where damages are the sum of additively separable polynomial 

functions of temperature and sea level rise. Damages transition from this polynomial function to 

a logistic path once they exceed a certain proportion of remaining Gross Domestic Product 

(GDP) to ensure that damages do not exceed 100 percent of GDP. This differs from PAGE2002, 

which allowed Eastern Europe to potentially experience large benefits from temperature 

increases, and which also did not bound the possible damages that could be experienced.

14B.3.3.3 Regional Scaling Factors

As in the previous version of PAGE, the PAGE09 model calculates the damages for the 

European Union (EU) and then, assumes that damages for other regions are proportional based 

on a given scaling factor. The scaling factor in PAGE09 is based on the length of a region’s 

coastline relative to the EU (Hope 2011b).11 Because of the long coastline in the EU, other 

regions are, on average, less vulnerable than the EU for the same sea level and temperature 

increase, but all regions have a positive scaling factor. PAGE2002 based its scaling factors on 

four studies reported in the IPCC’s third assessment report, and allowed for benefits from 

temperature increase in Eastern Europe, smaller impacts in developing countries, and higher 

damages in developing countries. 

14B.3.3.4 Probability of a Discontinuity

In PAGE2002, the damages associated with a “discontinuity” were modeled as an 

expected value. That is, additional damages from an extreme event, such as extreme melting of 

the Greenland ice sheet, were multiplied by the probability of the event occurring and added to 

the damage estimate. In PAGE09, the probability of “discontinuity” is treated as a discrete event 

for each year in the model. The damages for each model run are estimated with or without a 

discontinuity occurring, rather than as an expected value. A large‐scale discontinuity becomes 

possible when the temperature rises beyond some threshold value between 2 and 4°C. The 

probability that a discontinuity will occur beyond this threshold then increases by between 10 

and 30 percent for every 1°C rise in temperature beyond the threshold. If a discontinuity occurs, 

the EU loses an additional 5 to 25 percent of its GDP (drawn from a triangular distribution with a 

mean of 15 percent) in addition to other damages, and other regions lose an amount determined 

by the regional scaling factor. The threshold value for a possible discontinuity is lower than in 

PAGE2002, while the rate at which the probability of a discontinuity increases with the 

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-7
14B.3.3.5 Adaptation

As in PAGE2002, adaptation is available to increase the tolerable level of temperature 

change and can help mitigate any climate change impacts that still occur. In PAGE this 

adaptation is the same regardless of the temperature change or sea level rise and is therefore akin 

to what is more commonly considered a reduction in vulnerability. It is modeled by modifying 

the temperature change and sea level rise used in the damage function or by reducing the 

damages by some percentage. PAGE09 assumes a smaller decrease in vulnerability than the 

previous version of the model and assumes that it will take longer for this change in vulnerability 

to be realized. In the aggregated economic sector, at the time of full implementation, this 

adaptation will mitigate all damages up to a temperature increase of 1°C, and for temperature 

anomalies between 1°C and 3°C, it will reduce damages by 15-30 percent (depending on the 

region). However, it takes 20 years to fully implement this adaptation. In PAGE2002, adaptation 

was assumed to reduce economic sector damages up to 3°C by 50-90 percent after 20 years. 

Beyond 3°C, no adaptation is assumed to be available to mitigate the impacts of climate change. 

For the non-economic sector, in PAGE09 adaptation is available to reduce 15 percent of the 

damages due to a temperature increase between 0°C and 2°C and is assumed to take 40 years to 

fully implement, instead of 25 percent of the damages over 20 years assumed in PAGE2002. 

Similarly, adaptation is assumed to alleviate 25-50 percent of the damages from the first 0.20 to 

0.25 meters of sea level rise but is assumed to be ineffective thereafter. Hope (2011c)12 estimates 

that the less optimistic assumptions regarding the ability to offset impacts of temperature and sea 

level rise via adaptation increase the SCC by approximately 30 percent.

14B.3.3.6 Other Noteworthy Changes

Two other changes in the model are worth noting. A revised carbon cycle feedback is 

introduced to simulate decreased CO2 absorption by the terrestrial biosphere and ocean as the 

temperature rises. This feedback is linear in the average global and annual temperature anomaly 

but is capped at a maximum value. In the previous version of PAGE, an additional amount was 

added to the CO2 emissions each period to account for a decrease in ocean absorption and a loss 

of soil carbon. Also updated is the method by which the average global and annual temperature 

anomaly is downscaled to determine annual average regional temperature anomalies to be used 

in the regional damage functions. In the previous version of PAGE, the scaling was determined 

solely based on regional difference in emissions of sulfate aerosols. In PAGE09, this regional 

temperature anomaly is further adjusted using an additive factor that is based on the average 

absolute latitude of a region relative to the area weighted average absolute latitude of the Earth’s 

landmass.

14B.4 REVISED SCC ESTIMATES

The updated versions of the three integrated assessment models were run using the same 

methodology detailed in the 2010 TSD.1 The approach along with the inputs for the 

socioeconomic emissions scenarios, equilibrium climate sensitivity distribution, and discount 

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.

14B-8
As was previously the case, the use of three models, three discount rates, and five 

scenarios produces 45 separate distributions for the SCC. The approach laid out in the TSD 

applied equal weight to each model and socioeconomic scenario in order to reduce the 

dimensionality down to three separate distributions representative of the three discount rates. The 

interagency group selected four values from these distributions for use in regulatory analysis. 

Three values are based on the average SCC across models and socio-economic-emissions 

scenarios at the 2.5, 3, and 5 percent discount rates, respectively. The fourth value was chosen to 

represent the higher-than-expected economic impacts from climate change further out in the tails 

of the SCC distribution. For this purpose, the 95th percentile of the SCC estimates at a 3 percent 

discount rate was chosen. (A detailed set of percentiles by model and scenario combination is 

available in the Annex.) As noted in the original TSD, “the 3 percent discount rate is the central 

value, and so the central value that emerges is the average SCC across models at the 3 percent 

discount rate” (TSD, p. 25). However, for purposes of capturing the uncertainties involved in 

regulatory impact analysis, the interagency group emphasizes the importance and value of 

including all four SCC values.

Table 14B.4.1 shows the four selected SCC estimates in five year increments from 2010 

to 2050. Values for 2010, 2020, 2030, 2040, and 2050 are calculated by first combining all 

outputs (10,000 estimates per model run) from all scenarios and models for a given discount rate. 

Values for the years in between are calculated using basic linear interpolation. The full set of 

annual SCC estimates between 2010 and 2050 is reported in the Annex.

Table 14B.4.1 Revised Social Cost of CO2, 2010 – 2050 (in 2007 dollars per ton of CO2) 

Discount Rate 5.0% 3.0% 2.5% 3.0%

Year Avg Avg Avg 95th

2010 11 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.4.1 illustrates where the four SCC values for 2020 fall within the full distribution for each 

discount rate based on the combined set of runs for each model and scenario (150,000 estimates 

in total for each discount rate). In general, the distributions are skewed to the right and have long 

tails. The Figure also shows that the lower the discount rate, the longer the right tail of the 

distribution.

14B-9
Figure 14B.4.1 Distribution of SCC Estimates for 2020 (in 2007$ per ton CO2)

As was the case in the original TSD, the SCC increases over time because future 

emissions are expected to produce larger incremental damages as physical and economic systems 

become more stressed in response to greater climatic change. The approach taken by the 

interagency group is to allow the growth rate to be determined endogenously by the models 

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-10
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. <http://www.whitehouse.gov/sites/default/files/omb/inforeg/foragencies/Social-Cost-of-Carbon-for-RIA.pdf>

2 Nordhaus, W., A Question of Balance. 2008. Yale University Press: New Haver.

3 Nordhaus, W., Economic aspects of global warming in a post-Copenhagen environment.

Proceedings of the National Academy of Sciences, 2010. 107(26): pp. 11721-11726 

4 Randall, D. A., R.A. Wood, S. Bony, R. Colman, T. Fichefet, J. Fyfe, V. Kattsov, A. 

Pitman, J. Shukla, J. Srinivasan, R.J. Stouffer, A. Sumi and K.E. Taylor, Climate Models 

and Their Evaluation. In Climate Change 2007: The Physical Science Basis. 

Contribution of Working Group I to the Fourth Assessment Report of the 

Intergovernmental Panel on Climate Change, S. Solomon, D. Qin, M. Manning, Z. Chen, 

M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller, Editor. 2007. Cambridge University 

Press: Cambridge, United Kingdom and New York, NY, USA

5 Nicholls, R. J., N. Marinova, J.A. Lowe, S. Brown, P. Vellinga, D. de Gusmão, J. Hinkel 

and R.S.J. Tol, Sea-level rise and its possible impacts given a ‘beyond 4°C world’ in the 

twenty-first century. Phil. Trans. R. Soc. A 2011. 369(1934): pp. 161-181 

6 National Academy of Sciences, Climate Stabilization Targets: Emissions, 

Concentrations, and Impacts over Decades to Millennia. 2011. National Academies 

Press, Inc: Washington, DC.

7 Anthoff, D. and R. S. J. Tol, The uncertainty about the social cost of carbon: a 

decomposition analysis using FUND. Climatic Change, 2013(Forthcoming)

8 Forster, P., V. Ramaswamy, P. Artaxo, T. Berntsen, R. Betts, D.W. Fahey, J. Haywood, 

J. Lean, D.C. Lowe, G. Myhre, J. Nganga, R. Prinn, G. Raga, M. Schulz and R. Van 

Dorland, Changes in Atmospheric Constituents and in Radiative Forcing. In Climate 

Change 2007: The Physical Science Basis. Contribution of Working Group I to the 

Fourth Assessment Report of the Intergovernmental Panel on Climate Change S. 

Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. 

Miller Editor. 2007. Cambridge University Press: Cambridge, United Kingdom and New 

York, NY, USA

9 Hope, C., Critical issues for the calculation of the social cost of CO2: why the estimates 

from PAGE09 are higher than those from PAGE2002. Climatic Change, 

2012(Forthcoming)

14B-12
10 Hope, C., The PAGE09 Integrated Assessment Model: A Technical Description. 2011, 

Cambridge Judge Business School

<http://www.jbs.cam.ac.uk/fileadmin/user_upload/research/workingpapers/wp1104.pdf>

11 Hope, C., The Social Cost of CO2 from the PAGE09 Model. 2011, Cambridge Judge 

Business School 

<http://www.jbs.cam.ac.uk/fileadmin/user_upload/research/workingpapers/wp1105.pdf>

12 Hope, C., New Insights from the PAGE09 Model: The Social Cost of CO2. 2011, 

Cambridge Judge Business School

<http://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
14B.6 ANNEX

Table 14B.6.1 Annual SCC Values: 2010-2050 (2007$/ton CO2) 

Discount Rate 5.0% 3.0% 2.5% 3.0%

Year Avg Avg Avg 95th

2010 11 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.6.2 202 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton CO2) 

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 6 11 15 27 58 129 139 327 515 991

MERGE 4 6 9 16 34 78 82 196 317 649

MESSAGE 4 8 11 20 42 108 107 278 483 918

MiniCAM Base 5 9 12 22 47 107 113 266 431 872

5th Scenario 2 4 6 11 25 85 68 200 387 955

Scenario DICE

IMAGE 25 31 37 47 64 72 92 123 139 161

MERGE 14 18 20 26 36 40 50 65 74 85

MESSAGE 20 24 28 37 51 58 71 95 109 221

MiniCAM Base 20 25 29 38 53 61 76 102 117 135

5th Scenario 17 22 25 33 45 52 65 91 106 126

Scenario FUND

IMAGE -17 -1 5 17 34 44 59 90 113 176

MERGE -7 2 7 16 30 35 49 72 91 146

MESSAGE -19 -4 2 12 27 32 46 70 87 135

MiniCAM Base -9 1 8 18 35 45 59 87 108 172

5th Scenario -30 -12 -5 6 19 24 35 57 72 108

Table 14B.6.3 SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2) 

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 4 7 10 18 38 91 95 238 385 727

MERGE 2 4 6 11 23 56 58 142 232 481

MESSAGE 3 5 7 13 29 75 74 197 330 641

MiniCAM Base 3 5 8 14 30 73 75 184 300 623

5th Scenario 1 3 4 7 17 58 48 136 264 660

Scenario DICE

IMAGE 16 21 24 32 43 48 60 79 90 102

MERGE 10 13 15 19 25 28 35 44 50 58

MESSAGE 14 18 20 26 35 40 49 64 73 83

MiniCAM Base 13 17 20 26 35 39 49 65 73 85

5th Scenario 12 15 17 22 30 34 43 58 67 79

Scenario FUND

IMAGE -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.6.4 2020 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton CO2) 

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 1 2 2 5 10 28 27 71 123 244

MERGE 1 1 2 3 7 17 17 45 75 153

MESSAGE 1 1 2 4 9 24 22 60 106 216

MiniCAM Base 1 1 2 3 8 21 21 54 94 190

5th Scenario 0 1 1 2 5 18 14 41 78 208

Scenario DICE

IMAGE 6 8 9 11 14 15 18 22 25 27

MERGE 4 5 6 7 9 10 12 15 16 18

MESSAGE 6 7 8 10 12 13 16 20 22 25

MiniCAM Base 5 6 7 8 11 12 14 18 20 22

5th Scenario 5 6 6 8 10 11 14 17 19 21

Scenario FUND

IMAGE -9 -5 -3 -1 2 3 6 11 15 25

MERGE -6 -3 -2 0 3 4 7 12 16 27

MESSAGE -10 -6 -4 -1 2 2 5 9 13 23

MiniCAM Base -7 -3 -2 0 3 4 7 11 15 26

5th Scenario -11 -7 -5 -2 0 0 3 6 8 14

14B-16
15A-i

APPENDIX 15A. UTILITY IMPACT ANALYSIS METHODOLOGY

TABLE OF CONTENTS

15A.1 INTRODUCTION ...................................................................................................... 15A-1

15A.2 METHODOLOGY ..................................................................................................... 15A-1

15A.3 MODEL RESULTS.................................................................................................... 15A-4

15A.3.1 Installed Capacity............................................................................................ 15A-4

15A.3.2 Electricity Generation ..................................................................................... 15A-5

LIST OF FIGURES

Figure 15A.3.1 Installed capacity impact factors for commercial end-uses...................... 15A-4

Figure 15A.3.2 Installed capacity impact factors for residential end-uses ........................ 15A-5

Figure 15A.3.3 Fuel-share weights for commercial end-uses............................................ 15A-6

Figure 15A.3.4 Fuel-share weights for residential end-uses.............................................. 15A-6
15A-1

APPENDIX 15A. UTILITY IMPACT ANALYSIS METHODOLOGY

15A.1 INTRODUCTION

In the utility impact analysis, the U.S. Department of Energy (DOE) analyzes the changes 

in electric installed capacity and power generation that result for each trial standard level (TSL). 

These changes are estimated by multiplying the site savings of electricity by a set of impact 

factors which measure the corresponding change in generation by fuel type, installed capacity, 

and power sector emissions. This Appendix describes the methods that DOE used to calculate 

these impact factors. The methodology is more fully described in Coughlin (2014).

1

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 is minimized.

15A.2 METHODOLOGY 

DOE’s national energy savings (NES) analysis calculates the expected reduction in 

electricity demand associated with a given trial standard level (TSL). These correspond to 

marginal reductions electricity demand, which in turn lead to marginal impacts on the electric 

power sector in the form of reduced generation, emissions, and installed capacity. DOE’s 

 
15A-2

approach calculates the relationship, at the margin, between changes in generation and changes 

in other power sector quantities. In principle marginal values provide a better estimate of the 

actual impact of energy conservation standards. 

NEMS uses predicted growth in demand by sector and 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 dispatch of generation 

and additions to capacity. When electricity demand deviates from the AEO reference case, in 

general there are three inter-related effects: the annual generation (TWh) from the stock of 

electric generating capacity changes, the total generation capacity itself (GW) may change, and 

the mix of capacity by fuel type may change. Each of these effects may 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 or fuel 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 of the green-house gases CO2, N2O and CH4.

DOE’s new approach examines a series of AEO side cases to estimate the relationship 

between marginal demand reductions and the resulting generation, 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 collected for 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.

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:
15A-3

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, per unit of electricity demand added or subtracted 

at the margin, as a function of time. DOE also calculates fuel-specific marginal heat rates, 

equal to the primary energy (heat content) consumed per unit of electricity generated at the 

margin for that fuel. These differ from the average heat rate, equal to the total primary energy 

use divided by total generation for that fuel, because the technology mix for marginal plants 

is different from the average for the grid. (The marginal heat rates are presented in Appendix 

10-B).The product of the fuel-share weight and the marginal heat rate defines coefficients 

that allocate a marginal reduction in end-use electricity demand to a reduction in quads of 

fuel use for each of the five fuel types.

5. A regression model is used to relate reductions in fuel consumption in quads by fuel type to 

reductions in emissions of power sector pollutants (CO2, Hg, NOX, SO2). The model 

produces coefficients that define the change in total annual emissions of a given pollutant 

resulting from a unit change in total fuel consumption 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. Power sector 

emissions of the green-house gases CH4 and N2O are not tabulated in AEO. For these 

species, DOE used Environmental Protection Agency estimates of the emissions intensities 

(mass of pollutant per unit of fuel energy), combined with the fuel-share weights, to estimate 

the impact factors (ref epa ghg factors).

3

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 fuel-share weights calculated in step 4 to produce the annual impact 

factors relating installed capacity changes to changes in end-use demand.

7. The impact factor 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 
15A-4

methodology is described in more detail in K. Coughlin, “Utility Sector Impacts of Reduced 

Electricity Demand.”

4

15A.3 MODEL RESULTS

This section summarizes the impact factors for fuel share and capacity. The marginal heat 

rates are presented in appendix 10-B and emissions factors in appendix 13-A. Detailed results for 

the product considered in this rule-making can be found in chapter 13.

15A.3.1 Installed Capacity

The figures in this section show the total change in installed capacity per unit of 

electricity demand distributed over the five capacity types: coal), natural gas, peaking, 

renewables, and nuclear. Figure 15A.3.1 shows the results for commercial sector end uses, and 

Figure 15A.3.2 for residential end uses. The units are GW of installed capacity per TWh of

reduced site electricity use. Each bar corresponds to one year, with factors calculated for the 

period 2019 through 2040. To extrapolate to years beyond 2040, DOE uses the 2040 values.

Figure 15A.3.1 Installed capacity impact factors for commercial end-uses
15A-5

Figure 15A.3.2 Installed capacity impact factors for residential end-uses

15A.3.2 Electricity Generation

The figures below presents the distribution across fuel types of a unit reduction in 

generation for the commercial and residential sectors, referred to as fuel-share weights. The fuel 

types are coal, natural gas, petroleum, renewables and nuclear. DOE calculated these weights for 

all end uses, but for clarity the figures show only three: cooling (representative of peaking loads), 

lighting (representative of intermediate loads) and refrigeration (representative of base loads). To 

extrapolate to years beyond 2040, DOE uses the 2040 values.
15A-6

Figure 15A.3.3 Fuel-share weights for commercial end-uses

Figure 15A.3.4 Fuel-share weights for residential end-uses
15A-7

REFERENCES

1 U.S. Department of Energy-Energy Information Administration, National Energy 

Modeling System: An Overview, 2009. Report No. DOE/EIA-0581(2009). 

<http://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, D.C.

3 US EPA, Emissions Factors for Greenhouse Gas Inventories, 2014. (Last accessed 

December 1, 2014.) <http://www.epa.gov/climateleadership/documents/emissionfactors.pdf>

4 Coughlin, K., Utility Sector Impacts of Reduced Electricity Demand.: Lawrence Berkeley 

National Laboratory.
17A-i

APPENDIX 17A. REGULATORY IMPACT ANALYSIS: SUPPORTING MATERIALS

TABLE OF CONTENTS

17A.1 INTRODUCTION ...................................................................................................... 17A-1

17A.2 MARKET SHARE ANNUAL INCREASES BY POLICY....................................... 17A-2

17A.3 NIA-RIA INTEGRATED MODEL............................................................................ 17A-7

17A.4 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-11

17A.6.1 Federal Tax Credits for Consumers..................................................................... 17A-11

17A.6.2 Federal Tax Credits for Manufacturers................................................................ 17A-13

17A.6.3 State Tax Credits.................................................................................................. 17A-13

REFERENCES ..................................................................................................................... 17A-15

LIST OF TABLES

Table 17A.1 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Electric Standard Ovens, Freestanding (TSL 2) ................. 17A-2

Table 17A.2 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Electric Self-Clean Ovens, Freestanding (TSL 2) .............. 17A-3

Table 17A.3 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Electric Self-Clean Ovens, Built-In/Slide-In (TSL 2) ........ 17A-4

Table 17A.4 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Gas Standard Ovens, Freestanding (TSL 2) ....................... 17A-5

Table 17A.5 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Gas Self-Clean Ovens, Freestanding (TSL 2)..................... 17A-6

Table 17A.6 Rebates Amounts by TSL* ..................................................................... 17A-11

LIST OF FIGURES

Figure 17A.4.1 S-Curves Showing Effects of External and Internal Sources on 

Adoption of New Technologies................................................................ 17A-9
17A-1

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;

• Market penetration curves used to analyze consumer rebates and voluntary energy 

efficiency targets, including:

o Background material on XENERGY’s approach,

o DOE’s adjustment of these curves for this analysis, and

o The method DOE used to derive interpolated, customized curves;

• Detailed table of rebates offered for the considered product, as well as DOE’s approach 

to estimate a market representative rebate value for this RIA; and

• Background material on Federal and State tax credits for appliances.
17A-2

17A.2 MARKET SHARE ANNUAL INCREASES BY POLICY

Table 17A.2.1 through Table 17A.5 show the annual increases in market shares of 

conventional cooking products meeting the target efficiency levels for the proposed TSL (TSL

2). DOE used these market share increases as inputs to the NIA-RIA spreadsheet model.

Table 17A.1 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Electric Standard Ovens, Freestanding (TSL 2)

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Vol Energy

Eff Targets

Bulk Govt 

Purchases

2019 56.3% 33.8% 16.9% 0.5% 0.3%

2020 56.3% 33.8% 16.9% 1.3% 0.5%

2021 56.1% 33.6% 16.8% 2.1% 0.8%

2022 55.9% 33.5% 16.8% 2.9% 1.0%

2023 55.7% 33.4% 16.7% 3.6% 1.3%

2024 55.5% 33.3% 16.7% 4.4% 1.5%

2025 55.4% 33.2% 16.6% 5.2% 1.8%

2026 55.2% 33.1% 16.6% 5.9% 2.0%

2027 55.1% 33.0% 16.5% 6.7% 2.3%

2028 54.9% 33.0% 16.5% 7.5% 2.5%

2029 54.8% 32.9% 16.4% 7.8% 2.5%

2030 54.7% 32.8% 16.4% 8.1% 2.5%

2031 54.5% 32.7% 16.3% 8.4% 2.5%

2032 54.4% 32.6% 16.3% 8.8% 2.5%

2033 54.2% 32.5% 16.2% 9.1% 2.5%

2034 54.0% 32.4% 16.2% 9.5% 2.5%

2035 53.9% 32.3% 16.2% 9.8% 2.5%

2036 53.7% 32.2% 16.1% 10.2% 2.5%

2037 53.6% 32.2% 16.1% 10.5% 2.5%

2038 53.4% 32.1% 16.0% 10.9% 2.5%

2039 53.3% 32.0% 16.0% 11.2% 2.5%

2040 53.1% 31.9% 15.9% 11.5% 2.5%

2041 53.1% 31.8% 15.9% 11.9% 2.5%

2042 52.9% 31.8% 15.9% 12.2% 2.5%

2043 52.8% 31.7% 15.9% 12.5% 2.5%

2044 52.7% 31.6% 15.8% 12.8% 2.5%

2045 52.6% 31.5% 15.8% 13.1% 2.5%

2046 52.5% 31.5% 15.7% 13.5% 2.5%

2047 52.4% 31.4% 15.7% 13.8% 2.5%

2048 52.3% 31.4% 15.7% 14.1% 2.5%

 
17A-3

Table 17A.2 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Electric Self-Clean Ovens, Freestanding (TSL 2)

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Vol Energy

Eff Targets

Bulk Govt 

Purchases

2019 36.6% 21.9% 11.0% 1.6% 0.2%

2020 36.6% 22.0% 11.0% 3.5% 0.4%

2021 36.4% 21.9% 10.9% 5.4% 0.6%

2022 36.3% 21.8% 10.9% 7.3% 0.8%

2023 36.2% 21.7% 10.9% 9.1% 1.0%

2024 36.1% 21.7% 10.8% 10.8% 1.2%

2025 36.0% 21.6% 10.8% 12.5% 1.4%

2026 35.9% 21.6% 10.8% 14.1% 1.6%

2027 35.8% 21.5% 10.7% 15.7% 1.8%

2028 35.7% 21.4% 10.7% 17.2% 2.0%

2029 35.6% 21.4% 10.7% 17.5% 2.0%

2030 35.6% 21.3% 10.7% 17.9% 2.0%

2031 35.4% 21.3% 10.6% 18.2% 2.0%

2032 35.3% 21.2% 10.6% 18.6% 2.0%

2033 35.2% 21.1% 10.6% 18.9% 2.0%

2034 35.1% 21.1% 10.5% 19.3% 2.0%

2035 35.0% 21.0% 10.5% 19.6% 2.0%

2036 34.9% 21.0% 10.5% 20.0% 2.0%

2037 34.8% 20.9% 10.4% 20.3% 2.0%

2038 34.7% 20.8% 10.4% 20.7% 2.0%

2039 34.6% 20.8% 10.4% 21.0% 2.0%

2040 34.6% 20.7% 10.4% 21.3% 2.0%

2041 34.5% 20.7% 10.4% 21.6% 2.0%

2042 34.5% 20.7% 10.4% 21.9% 2.0%

2043 34.6% 20.7% 10.4% 22.2% 2.0%

2044 34.6% 20.7% 10.4% 22.5% 2.0%

2045 34.6% 20.7% 10.4% 22.7% 2.0%

2046 34.6% 20.7% 10.4% 23.0% 2.0%

2047 34.6% 20.8% 10.4% 23.3% 2.0%

2048 34.6% 20.8% 10.4% 23.6% 2.0%
17A-4

Table 17A.3 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Electric Self-Clean Ovens, Built-In/Slide-In (TSL 2)

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Vol Energy

Eff Targets

Bulk Govt 

Purchases

2019 36.5% 21.9% 11.0% 1.6% 0.2%

2020 36.6% 21.9% 11.0% 3.6% 0.4%

2021 36.4% 21.8% 10.9% 5.5% 0.6%

2022 36.3% 21.8% 10.9% 7.4% 0.8%

2023 36.2% 21.7% 10.8% 9.2% 1.0%

2024 36.1% 21.6% 10.8% 10.9% 1.2%

2025 36.0% 21.6% 10.8% 12.6% 1.4%

2026 35.8% 21.5% 10.8% 14.2% 1.6%

2027 35.8% 21.5% 10.7% 15.8% 1.8%

2028 35.7% 21.4% 10.7% 17.4% 2.0%

2029 35.6% 21.4% 10.7% 17.7% 2.0%

2030 35.5% 21.3% 10.6% 18.0% 2.0%

2031 35.4% 21.2% 10.6% 18.4% 2.0%

2032 35.3% 21.2% 10.6% 18.7% 2.0%

2033 35.2% 21.1% 10.6% 19.1% 2.0%

2034 35.1% 21.0% 10.5% 19.4% 2.0%

2035 35.0% 21.0% 10.5% 19.8% 2.0%

2036 34.9% 20.9% 10.5% 20.1% 2.0%

2037 34.8% 20.9% 10.4% 20.5% 2.0%

2038 34.7% 20.8% 10.4% 20.8% 2.0%

2039 34.6% 20.8% 10.4% 21.1% 2.0%

2040 34.5% 20.7% 10.4% 21.4% 2.0%

2041 34.5% 20.7% 10.4% 21.7% 2.0%

2042 34.5% 20.7% 10.4% 22.0% 2.0%

2043 34.6% 20.7% 10.4% 22.3% 2.0%

2044 34.6% 20.7% 10.4% 22.6% 2.0%

2045 34.6% 20.7% 10.4% 22.9% 2.0%

2046 34.6% 20.7% 10.4% 23.2% 2.0%

2047 34.6% 20.8% 10.4% 23.4% 2.0%

2048 34.6% 20.8% 10.4% 23.7% 2.0%
17A-5

Table 17A.4 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Gas Standard Ovens, Freestanding (TSL 2)

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Vol Energy

Eff Targets

Bulk Govt 

Purchases

2019 41.7% 25.0% 12.5% 8.9% 0.3%

2020 41.7% 25.0% 12.5% 11.7% 0.6%

2021 41.7% 25.0% 12.5% 14.4% 1.0%

2022 41.7% 25.0% 12.5% 16.9% 1.3%

2023 41.7% 25.0% 12.5% 19.3% 1.6%

2024 41.7% 25.0% 12.5% 21.5% 1.9%

2025 41.7% 25.0% 12.5% 23.7% 2.3%

2026 41.7% 25.0% 12.5% 25.7% 2.6%

2027 41.7% 25.0% 12.5% 27.6% 2.9%

2028 41.7% 25.0% 12.5% 29.5% 3.2%

2029 41.7% 25.0% 12.5% 29.7% 3.2%

2030 41.7% 25.0% 12.5% 30.0% 3.2%

2031 41.7% 25.0% 12.5% 30.2% 3.2%

2032 41.7% 25.0% 12.5% 30.5% 3.2%

2033 41.7% 25.0% 12.5% 30.8% 3.2%

2034 41.7% 25.0% 12.5% 31.0% 3.2%

2035 41.7% 25.0% 12.5% 31.3% 3.2%

2036 41.7% 25.0% 12.5% 31.5% 3.2%

2037 41.7% 25.0% 12.5% 31.8% 3.2%

2038 41.7% 25.0% 12.5% 32.0% 3.2%

2039 41.7% 25.0% 12.5% 32.3% 3.2%

2040 41.7% 25.0% 12.5% 32.5% 3.2%

2041 41.7% 25.0% 12.5% 32.7% 3.2%

2042 41.7% 25.0% 12.5% 32.9% 3.2%

2043 41.7% 25.0% 12.5% 33.1% 3.2%

2044 41.7% 25.0% 12.5% 33.4% 3.2%

2045 41.7% 25.0% 12.5% 33.6% 3.2%

2046 41.7% 25.0% 12.5% 33.8% 3.2%

2047 41.7% 25.0% 12.5% 34.0% 3.2%

2048 41.7% 25.0% 12.5% 34.2% 3.2%
17A-6

Table 17A.5 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Gas Self-Clean Ovens, Freestanding (TSL 2)

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Vol Energy

Eff Targets

Bulk Govt 

Purchases

2019 36.6% 22.0% 11.0% 13.4% 0.3%

2020 36.6% 22.0% 11.0% 16.4% 0.6%

2021 36.6% 22.0% 11.0% 19.1% 0.9%

2022 36.6% 22.0% 11.0% 21.5% 1.2%

2023 36.6% 22.0% 11.0% 23.7% 1.5%

2024 36.6% 22.0% 11.0% 25.8% 1.7%

2025 36.6% 22.0% 11.0% 27.7% 2.0%

2026 36.6% 22.0% 11.0% 29.4% 2.3%

2027 36.6% 22.0% 11.0% 31.1% 2.6%

2028 36.6% 22.0% 11.0% 32.6% 2.9%

2029 36.6% 22.0% 11.0% 32.8% 2.9%

2030 36.6% 22.0% 11.0% 33.0% 2.9%

2031 36.6% 22.0% 11.0% 33.1% 2.9%

2032 36.6% 22.0% 11.0% 33.3% 2.9%

2033 36.6% 22.0% 11.0% 33.4% 2.9%

2034 36.6% 22.0% 11.0% 33.6% 2.9%

2035 36.6% 22.0% 11.0% 33.8% 2.9%

2036 36.6% 22.0% 11.0% 33.9% 2.9%

2037 36.6% 22.0% 11.0% 34.1% 2.9%

2038 36.6% 22.0% 11.0% 34.2% 2.9%

2039 36.6% 22.0% 11.0% 34.3% 2.9%

2040 36.6% 22.0% 11.0% 34.5% 2.9%

2041 36.6% 22.0% 11.0% 34.6% 2.9%

2042 36.6% 22.0% 11.0% 34.7% 2.9%

2043 36.6% 22.0% 11.0% 34.8% 2.9%

2044 36.6% 22.0% 11.0% 35.0% 2.9%

2045 36.6% 22.0% 11.0% 35.1% 2.9%

2046 36.6% 22.0% 11.0% 35.2% 2.9%

2047 36.6% 22.0% 11.0% 35.3% 2.9%

2048 36.6% 22.0% 11.0% 35.5% 2.9%
17A-7

17A.3 NIA-RIA INTEGRATED MODEL

For this analysis, DOE used its integrated NIA-RIAa model approach that the Department 

built on the NIA model discussed in Chapter 10 and documented in Appendix 10A.The resulting 

integrated NIA-RIA model features both the NIA and RIA inputs, analyses and results. It has the 

capability to generate results, by product class and TSL, for the mandatory standards and each of 

the RIA policies. Separate modules estimate increases in market penetration of more efficient 

equipment for consumer rebates, voluntary energy efficiency targets and bulk government 

purchases. b The consumer rebates module calculates benefit-cost (B/C) ratios and market 

barriers, and generates customized market penetration curves for each product class; the 

voluntary energy efficiency targets module relies on the market barriers calculated in the

consumer rebates module to project a reduction in those barriers over the first ten years of the 

forecast period and estimate the market effects of such a reduction; and the bulk government 

purchases module scales down the market for conventional cooking products to housing units in 

public housing authority. A separate module summarizes the market impacts from mandatory 

standards and all policy alternatives, and an additional module produces all tables and figures 

presented in chapter 17 as well as the tables of market share increases for each policy reported in 

Section 17A.2 of this Appendix.

17A.4 MARKET PENETRATION CURVES

This section first discusses the theoretical basis for the market penetration curves that 

DOE used to analyze the Consumer Rebates and Voluntary Energy Efficiency Targets policies. 

Next it discusses the adjustments it made to the maximum penetration rates. It then refers to the 

method it used to develop interpolated penetration curves for conventional cooking products that 

meet the target efficiency levels at each TSL. The resulting curves are presented in chapter 17.

17A.4.1 Introduction

XENERGY, Inc. c, 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 

 a NIA = National Impact Analysis; RIA = Regulatory Impact Analysis

b As mentioned in Chapter 17, the increase in market penetrations for consumer tax credits and manufacturer tax 

credits are estimated as a fraction of the increase in market penetration of consumer rebates. 

c XENERGY is now owned by KEMA, Inc. (www.kema.com)
17A-8

conclusively develop a universally applicable model. Some key findings, however, generally are 

accepted in academia and industry. 

One accepted finding is that, regardless of their economic benefits and technological 

merits, new technologies are unlikely to be adopted by all potential users. For many products, a 

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-9

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, 

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-10

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. d 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. e 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. 

DOE used the above referred method to interpolate market implementation curves, to 

generate customized curves that were used to estimate the effects of consumer rebates and 

voluntary energy efficiency targets for each product class covered by this RIA. For consumer 

rebates, DOE derived such curves based on an algorithm that finds the market implementation 

curve that best fits, for the first year of the analysis period, the B/C ratio of the target efficiency 

level and the market penetration of equipment with that level of energy efficiency in the base 

case. For the analysis of voluntary energy efficiency targets, DOE departs from the market 

barriers level corresponding to the market implementation curve it derived for consumer rebates,

to linearly decrease it over the ten initial years of the analysis period. For each year, as market 

barriers decline, the corresponding market implementation curve leads – for the same B/C ratio –

to higher market penetrations. 

 d The RIA chapter refers to these curves as penetration curves. This section, in references to the original source, uses 

the term implementation curve. e 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 are considered in this RIA proportional 

to the impacts from rebates. 
17A-11

17A.5 CONSUMER REBATE PROGRAMS 

DOE performed a nationwide search for rebate programs that offered incentives for

conventional cooking products. Since DOE could not find rebate programs for this equipment, 

DOE assumed that a rebate program would pay for all of the increased installed cost of each 

product class at each TSL. Table 17A.6 presents the rebate amounts DOE estimated for each 

product class, at each TSL.

Table 17A.6 Rebates Amounts by TSL* 

TSL 1 TSL 2 TSL 3

Electric Standard Ovens, Freestanding 1.13 10.85 96.76

Electric Self-Clean Ovens, Freestanding 1.13 1.13 85.13

Electric Self-Clean Ovens, Built-In/Slide-In 1.13 1.13 85.13

Gas Standard Ovens, Freestanding - 16..96 54.19

Gas Self-clean Ovens, Freestanding 1.12 9.96 45.17

* In 2014$.

17A.6 FEDERAL AND STATE TAX CREDITS

This section summarizes the Federal and State tax credits available to consumers who 

purchase energy efficient appliances. This section also describes tax credits available to 

manufacturers who produce certain energy efficient appliances.

17A.6.1 Federal Tax Credits for Consumers

EPACT 2005 included Federal tax credits for consumers who installed efficient air 

conditioners or heat pumps; gas, oil and propane furnaces and boilers; furnace fans; and/or gas, 

oil, or electric heat pump water heaters in new or existing homes.8, 9 These tax credits were in 

effect in 2006 and 2007, expired in 2008, and were reinstated for 2009–2010 by the American 

Recovery and Reinvestment Act (ARRA).10 There was a $1,500 cap on the credit per home, 

including the amount received for insulation, windows, and air and duct sealing. Congress 

extended this provision for 2011, with some modifications to eligibility requirements, and 

reductions in the cap to $500 per home. The American Taxpayer Relief Act of 2012 extended, 

with some modifications, residential tax credits for air conditioners, heat pumps, furnaces, and 

water heaters placed in service between January 1, 2012 and December 31, 2013.8, 11

The tax credit for furnace fans was $50 in 2011, after which it expired.

The importance of the Federal tax credits has been emphasized in research in the 

residential heating industry on the impacts of the relatively large credits that were available for 

HVAC (heating, ventilating, and air conditioning) equipment. In a survey of HVAC distributors 

conducted by Vermont Energy Investment Corporation, respondents indicated that the ample 
17A-12

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 distributors 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.

As discussed in chapter 17, DOE analyzed the percentage of participation in consumer 

tax credit programs using its estimates of consumer participation in rebate programs that was 

based on benefit/cost data specific to each product class of conventional cooking products

covered by this RIA. 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-13

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, Section 

17.3.3, on tax credit data for clothes washers.) Montana’s Department of Revenue does not 

disaggregate participation data by appliance, although DOE reviewed Montana's overall 

participation trends and found them congruent with its analysis of Oregon's clothes washer tax 

credits. 

Oregon’s Residential Energy Tax Credit (RETC) was created in 1977. The Oregon

legislature expanded the RETC program in 1997 to include residential refrigerators, clothes 

washers, and dishwashers, which significantly increased participation in the program. The 

program subsequently added credits for high-efficiency heat pump systems, air conditioners, and 

water heaters (2001); furnaces and boilers (2002); and duct/air sealing, fuel cells, heat recovery, 

and renewable energy equipment. Beginning in 2012 a Tax Credit Extension Bill (HB3672) 

eliminated refrigerators, clothes washers, dishwashers, air conditioners, and boilers from the 

RETC program, leaving credits for water heaters, furnaces, heat pumps, tankless water heaters, 

and heat pump water heaters.21, 22 Those technologies recognized by the Oregon Department of 

Energy as “premium efficiency” are eligible for tax credit of $0.60 per kWh saved in the first 

year (up to $1,500).21, 23

Montana has had an Energy Conservation Tax Credit for residential measures since 

1998.24 The tax credit covers various residential energy and water efficient products, including 

split system central air conditioning; package system central air conditioning; split system air 
17A-14

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

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