TECHNICAL SUPPORT DOCUMENT:

ENERGY EFFICIENCY PROGRAM

FOR CONSUMER PRODUCTS AND 

COMMERCIAL AND INDUSTRIAL EQUIPMENT:

RESIDENTIAL MICROWAVE OVENS – STAND-BY POWER

May 2013

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

and 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 NATIONAL BENEFITS ..................................................................... 1-1

1.3 OVERVIEW OF STANDARDS FOR MICROWAVE OVEN STANDBY 

POWER CONSUMPTION.............................................................................................. 1-5

1.4 PROCESS FOR ESTABLISHING ENERGY CONERVATION STANDARDS.......... 1-5

1.4.1 Process Rule..................................................................................................................... 1-6

1.4.2 Procedures, Analyses, and Stakeholder Involvement...................................................... 1-7

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

TABLE

Table 1.2.1 Annualized Benefits and Costs of New Standards for Microwave Oven 

Standby Mode and Off Mode for Products Sold 2016-2045 ............................... 1-4

Table 1.4.1 Analyses Performed Under the Process Rule ...................................................... 1-7
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CHAPTER 1. INTRODUCTION

1.1 PURPOSE OF THE DOCUMENT

The U.S. Department of Energy (DOE) has issued a final rulemaking regarding new 

energy efficiency standards for the electrical consumption of residential microwave ovens when 

in standby (nonoperating) mode. This document, termed a technical support document (TSD), is 

a stand-alone report that describes the technical analyses performed and results obtained when 

examining the standby power consumed by microwave ovens and evaluating the effects of 

efficiency standards on consumers, manufacturers, and the nation as a whole. This TSD was 

prepared in support of DOE’s final rule for the standby power consumption of residential 

microwave ovens.

1.2 SUMMARY OF NATIONAL BENEFITS

DOE’s analyses indicate that the new standards for microwave oven standby power will

save a significant amount of energy—an estimated 0.48 quadrillion (1015) British thermal units 

(quads) during 30 years (2016–2045). According to the Energy Information Administration’s 

Annual Energy Outlook 2012 (AEO2012), total residential energy consumption is projected to be 

20.8 quads in 2016. The amount of energy the standards will save throughout 30 years is 

equivalent to 2 percent of projected household energy use.

The cumulative national net present value (NPV) of the standards in 2011$, in terms of 

total consumer costs and savings for products shipped in 2016–2045, ranges from $1.53 billion 

(at a 7-percent discount rate) to $3.38 billion (at a 3-percent discount rate).a The NPV is the 

estimated total value of future operating cost savings during the analysis period, minus the 

estimated increased product costs, discounted to 2013. The industry net present value (INPV) is 

the sum of the discounted cash flows to the industry from the base year through the end of the 

analysis period (2013–2045). Using a real discount rate of 8.0 percent, DOE estimates that the 

INPV for manufacturers of all microwave ovens in the base case is $1.4 billion in 2011$. DOE 

expects that standards will cause manufacturers to lose up to 7 percent of their INPV, or 

approximately $96.6 million during the analysis period. Using a 7-percent discount rate, the NPV 

of consumer costs and savings from today’s standards will amount to 16 to 38 times the total 

estimated industry losses. Using a 3-percent discount rate, the NPV will amount to 35 to 84 times 

the total estimated industry losses.

The projected economic impacts of the new standards on individual consumers are 

positive. For microwave-only ovens and countertop convection microwave ovens (Product Class 

1), for example, the estimated average life-cycle cost (LCC) savings in 2011$ are $11, and 88

 a

 The discount rates DOE uses (7 percent and 3 percent) follow guidance from the White House Office of 

Management and Budget (OMB; Circular A-4, section E, September 17, 2003). 
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percent of consumers of Product Class 1 units will experience positive economic effects. For 

built-in and over-the-range convection microwave ovens (Product Class 2), the estimated 

average LCC savings in 2011$ are $12. All consumers of Product Class 2 units will experience

positive economic effects.

In addition, the new standards will have significant positive environmental benefits. The 

energy savings projected from the standards will result in cumulative reductions in greenhouse 

gas emissions of 38.11 million metric tonsb of carbon dioxide (CO2) during the analysis period 

(2016–2045). During that period, the standards will result in an emissions reduction of 32.67

thousand tons of nitrogen oxides (NOX) and 0.095 tons of mercury (Hg).c DOE estimates that the 

present monetary value of the reduction in CO2 emissions is between $255 million and $3,615

million, expressed in 2011$. DOE also estimates that the present monetary value of the reduction 

in NOX emissions, expressed in 2011$, is $21.8 million at a 7-percent discount rate and $44.5

million at a 3-percent discount rate.d

The benefits and costs of today’s standards also can be expressed using values that are 

annualized throughout a 30-year period. The annualized monetary values are the sum of (1) the 

annualized national economic value of the benefits from utilizing products that meet the 

standards (consisting primarily of operating cost savings from using less energy minus increases 

in product purchase costs, which is another way of representing consumer NPV), and (2) the 

monetary value of the benefits of emission reductions, including reductions in CO2 emissions.

e

The value of the CO2 reductions per metric ton of CO2, otherwise known as the social cost of 

carbon (SCC), is calculated using a range of values developed by a recent Federal interagency 

process. The monetary costs and benefits of cumulative emissions reductions are reported in 

2011$ to permit straightforward comparisons with the other costs and benefits. The derivation of 

the values for the SCC is discussed in appendix 16A.

Although combining the values of operating cost savings and CO2 reductions provides a 

useful perspective, two issues should be considered. First, the national operating savings are 

monetary savings that U.S. consumers experience as a result of market transactions, whereas 

CO2 reductions reflect a global valuation. Second, the operating cost savings and CO2 savings 

 b Results for NOX and Hg are presented in short tons. A metric ton is equivalent to 1.1 short tons. c DOE calculates emissions reductions relative to the AEO 2012 Reference case, which generally represents current 

legislation and environmental regulations, including recent government actions, for which implementing 

regulations were available as of December 31, 2011.

d

 DOE has decided to await further guidance regarding consistent valuation and reporting of Hg emissions before it 

monetizes Hg in its rulemakings. e DOE used a two-step calculation process to convert the time-series of costs and benefits into annualized values. 

First, DOE calculated a present value in 2013, the year used for discounting the NPV of total consumer costs and 

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

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

1.2.1. From the present value, DOE then calculated the fixed annual payment over a 30-year period (2016 through 

2045) that yields the same present value. The fixed annual payment is the annualized value. Although DOE 

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

annualized values were determined is a steady stream of payments.
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are assessed using different methods that incorporate different time frames for analysis. The 

national operating cost savings are measured for the lifetime of microwave ovens shipped during

2016–2045. The SCC values, on the other hand, reflect the present value of all future climaterelated impacts resulting from the emission of one ton of CO2 in each year. Those impacts 

continue well beyond 2100.

Table 1.2.1 shows the annualized values for the new standards, expressed in 2011$. The 

results under the primary estimate are as follows. Using a 7-percent discount rate for benefits and 

costs other than CO2 reductions, the cost of the standards promulgated in today’s final rule is 

$58.4 million per year in increased product costs. For CO2 reductions, DOE used a 3-percent 

discount rate and a value of $41.1/ton in 2011$ for the SCC in 2016. Total annualized benefits 

are $174 million in reduced product operating costs, $58.4 million in CO2 reductions, and $1.64 

million in reduced NOX emissions. In this case, the net benefit amounts to $175 million per year. 

Using a 3-percent discount rate for all benefits and costs and a value of $41.1/ton for the SCC in 

2016, the standards established in today’s final rule result in $66.4 million per year in increased 

product costs. The annualized benefits are $234 million in reduced operating costs, $58.4 million 

in CO2 reductions, and $2.20 million in reduced NOX emissions. The net benefit amounts to $228

million per year.
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Table 1.2.1 Annualized Benefits and Costs of New Standards for Microwave Oven 

Standby Mode and Off Mode for Products Sold 2016-2045

Discount Rate

Primary 

Estimate* Low Estimate* High Estimate*

Benefits

(Millions 2011$/year)

Operating Cost Savings

7% 174 162 191

3% 234 215 261

CO2 Reduction at $12.6/t** 5% 15.8 14.7 17.4

CO2 Reduction at $41.1/t** 3% 58.4 54.1 64.5

CO2 Reduction at $63.2/t** 2.5% 87.4 80.9 96.7

CO2 Reduction at $119.1/t** 3% 179 166 198

NOX Reduction at 

$2,567/ton**

7% 1.64 1.54 1.79

3% 2.20 2.05 2.42

Total Benefits†

7% plus CO2

range

191 to 354 178 to 329 210 to 391

7% 234 218 258

3% 294 271 328

3% plus CO2

range

252 to 415 232 to 383 281 to 462

Costs

Incremental Product Costs

7% 58.4 59.6 57.5

3% 66.4 67.8 64.3

Net Benefits/Costs

Total†

7% plus CO2

range

133 to 296 119 to 270 153 to 334

7% 175 158 200

3% 228 203 264

3% plus CO2

range

185 to 349 164 to 315 217 to 398

* This table presents the annualized costs and benefits associated with microwave ovens shipped in 2016–2045. 

These results include benefits to consumers which accrue after 2016 from the microwave ovens purchased from 

2016–2045. Costs incurred by manufacturers, some of which may be incurred prior to 2016 in preparation for the 

rule, are not directly included, but are indirectly included as part of incremental equipment costs. The Primary, 

Low Benefits, and High Benefits Estimates utilize projections of energy prices and housing starts from the AEO 

2012 Reference case, Low Estimate, and High Estimate, respectively. In addition, incremental product costs 

reflect a medium decline rate for projected product price trends in the Primary Estimate, constant product prices in 

the Low Benefits Estimate, and a high decline rate for projected product price trends in the High Benefits 

Estimate. The methods used to derive projected price trends are explained in chapter 8 and appendix 8C.
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† The CO2 values represent global monetized values of the SCC, in 2011$, in 2016 under several scenarios. The 

values of $12.6, $41.1, and $63.2 per metric ton are the averages of SCC distributions calculated using 5%, 3%, 

and 2.5% discount rates, respectively. The value of $119.1/t represents the 95th percentile of the SCC distribution 

calculated using a 3% discount rate. The SCC time series used by DOE incorporate an escalation factor. The value 

for NOX (in 2011$) is the average of the low and high values used in DOE’s analysis. ‡ Total Benefits for both the 3-percent and 7-percent cases are derived using the series corresponding to SCC value 

of $2,567/t. 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 MICROWAVE OVEN STANDBY 

POWER CONSUMPTION

The Energy Policy and Conservation Act of 1975 (42 U.S.C. 6291–6309; EPCA) 

established an energy conservation program for major household appliances. An amendment to 

EPCA gave DOE the authority to regulate the energy efficiency of several products, including 

residential microwave ovens. The amendments to EPCA in the National Appliance Energy 

Conservation Act of 1987 (42 U.S.C. 6291–6309; NAECA) prescribed energy conservation 

standards for cooking products, as well as stipulating requirements for determining whether 

those standards should be amended. 

Following NAECA directives, DOE issued a notice of proposed rulemaking (NOPR) on 

March 4, 1994, proposing performance standards for eight gas and electric residential cooking 

products, including microwave ovens operating in active mode. 59 FR 10464. Based on the July 

15, 1996, Procedures for Consideration of New or Revised Energy Conservation Standards for 

Consumer Products (the so-called Process Rule), DOE refined its analysis of cooking products. 

61 FR 36974. DOE issued a final rule on September 8, 1998, that covered the active mode 

performance of electric cooking products, including microwave ovens. 63 FR 48038.f

1.4 PROCESS FOR ESTABLISHING ENERGY CONERVATION STANDARDS

In the 

November 15, 2007, advance notice of proposed rulemaking (ANOPR) regarding energy 

conservation standards for kitchen ranges and ovens and commercial clothes washers, DOE 

concluded that energy consumption by microwave ovens in the standby (nonoperating) mode 

represents a significant fraction of microwave ovens’ energy use, and that a standard regulating 

such energy consumption likely would provide significant energy savings. 72 FR 64432, 64441–

42. The Energy Independence and Security Act of 2007 (EISA 2007; Pub. L. 110–140) directs

DOE to incorporate standby and off mode energy use into any final rule establishing or revising 

a standard for a covered product adopted after July 1, 2010. (42 U.S.C. 6295(gg)(3)) DOE 

therefore has amended standards that prescribe the maximum electricity consumed when the 

microwave oven is in standby mode. This TSD supports DOE’s final rule pertaining to the 

energy consumption associated with microwave oven standby mode and off mode power.

When DOE evaluates a new or amended energy conservation standard under EPCA, it 

must consider seven factors to the greatest extent practicable: (42 U.S.C. 6295(o)(2)(B)(i))

 

f The 1998 final rule maintained a “no standard” standard for electric cooking products, including microwave ovens.
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1. the economic impact of the standard on the manufacturers and consumers of the 

affected product; 

2. the savings in operating costs throughout the estimated average lifetime of the 

product compared to any increases in purchase price or maintenance costs; 

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

imposition of the standard; 

4. any lessening of the utility or performance of the product likely to result from the 

imposition of the standard; 

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

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

6. the need for national energy conservation; and 

7. other factors the Secretary of Energy considers relevant. 

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

(iii) and (3)–(4). Before DOE determines whether to adopt an energy conservation standard, it 

must 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 

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

determine whether a standard is economically justified, DOE must review comments on the 

proposed standard and conclude that the benefits of the standard exceed its burdens to the 

greatest extent practicable, weighing the seven factors listed above. (42 U.S.C. 6295(o)(2)(B)(i))

1.4.1 Process Rule

In conducting rulemakings for both test procedures and energy conservation standards for 

appliances, DOE employs the procedures set forth in section 1.2 of the 1996 Process Rule. DOE 

applied those procedures to the extent they were appropriate to developing energy conservation 

standards for microwave oven standby power. The Process Rule enhances opportunities for 

receiving public input and improving analytic approaches. The Process Rule sets forth policies 

and guidelines for providing early public review and consultation with interested parties 

regarding design options, proposed standards, and the final standard. Design options are 

identified and screened based on (1) technological feasibility; (2) practicability to manufacture, 

install, and service; (3) adverse impacts on product utility or availability; and (4) adverse impacts 

on health or safety (Process Rule at section 4(a)(4)). 61 FR 36982. The Process Rule also 

requires DOE to evaluate uncertainty and variability in analytical results through the use of 

scenario or probability analysis.

The Process Rule calls for experts and interested parties to review DOE's analyses, 

including (1) the qualitative and quantitative analytical methods used; (2) the economic, 

engineering, and life-cycle cost analyses; and (3) impacts on manufacturers and consumers, 

national energy savings, the economy, and the environment. See appendix A to subpart C of title 

10 of the Code of Federal Regulations part 430 (10 CFR part 430). 
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1.4.2 Procedures, Analyses, and Stakeholder Involvement 

DOE considers stakeholder participation vital to a balanced discussion of the information 

required for a standards rulemaking and to the adoption of energy conservation standards that 

are, to the extent practicable, consensus-based. DOE encourages the participation and interaction 

of all stakeholders by providing formal public notifications via the Federal Register and by 

designating comment periods at each major step in a standards rulemaking. DOE publishes a 

formal public notice of the initial framework document, then an ANOPR, which is designed to 

publicly vet the models and tools used in the rulemaking and to facilitate public participation 

before the proposed standards are evaluated in detail. Next DOE publishes in the Federal 

Register a NOPR, which presents a discussion of comments received in response to the ANOPR; 

analyses of the impacts of standards on consumers, manufacturers, and the nation; DOE’s 

weighting of those impacts; and the proposed standards. Lastly, DOE publishes in the Federal 

Register the final rule, which includes a discussion of comments received in response to the 

NOPR; the revised analysis of the impacts of standards; DOE’s weighting of those impacts; the 

standard(s) DOE is adopting; and the effective date(s) of the standard(s).

Table 1.4.1 lists the analyses that are performed at each step of a standards rulemaking.

Following guidance in the Process Rule, analyses are reported, examined, revised, and expanded

throughout the process.

Table 1.4.1 Analyses Performed Under the Process Rule

ANOPR NOPR Final Rule

Market and technology assessment

Revised ANOPR analyses

plus:

Revised NOPR 

analyses

Screening analysis 

Life-cycle cost analysis for 

identified sub-groups Engineering analysis

Markups for determining product price Manufacturer impact analysis

Energy (and water) use determination Employment impact analysis 

Life-cycle cost and payback period 

analysis Utility impact analysis 

Shipments analysis Environmental assessment

National impact analysis Regulatory impact analysis

Preliminary manufacturer impact 

analysis

On March 27, 2006, DOE published in the Federal Register the Rulemaking Framework 

for Commercial Clothes Washers and Residential Dishwashers, Dehumidifiers, and Cooking 

Products (the framework document), which described the procedural and analytical approaches 

DOE anticipated using to evaluate the development of energy conservation standards for all the 

named products. This document is available at:
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www.regulations.gov/#!documentDetail;D=EERE-2006-STD-0127-0007. At that time, DOE also 

announced in the Federal Register the date, time, and place of a public meeting at which the 

framework document would be discussed.

DOE held the public meeting on April 27, 2006, to discuss procedural and analytical 

approaches to the rulemaking and to inform and facilitate stakeholder involvement in the 

rulemaking process. The analytical framework presented at the public meeting described the 

various analyses to be performed, such as the engineering analysis and the LCC and payback 

period (PBP) analysis; the methods proposed for conducting the analyses; and the relationships 

among the analyses.

During the April 2006 meeting, stakeholders commented about various issues related to 

the rulemaking for all considered products. Comments relevant to the rulemaking for microwave 

oven standby power concerned:

1. current DOE test procedure;

2. definition of baseline units; 

3. number of product classes;

4. consideration of limiting standby power as a design option for all considered 

products; 

5. technology options for improving efficiency of all considered products; 

6. type of approach to the engineering analysis; 

7. efficiency levels to consider for all considered products; 

8. proposed approaches for specifying typical annual energy (and water) consumption 

for all considered products; 

9. potential data sources for characterizing variability in annual energy (and water) 

consumption; 

10. typical distribution channels and markups; 

11. data sources for retail prices; 

12. type of approach to employ for the LCC and PBP analysis; 

13. variability of forecasted energy prices; 

14. relationship of repair, maintenance, and installation costs to product efficiency; 

15. product lifetimes; 

16. development of consumer discount rates; 

17. effects of purchase price on product shipments; and

18. identification of consumer sub-groups.

Written stakeholder comments, submitted during the comment period for the framework 

document, elaborated on issues raised at the public meeting. DOE worked with its contractors to 

provide that the analyses would address the identified issues.

In preparation for developing the ANOPR, DOE organized and held interviews with 

manufacturers of microwave ovens. The companies DOE selected ranged from small to large 

manufacturers, including both members of the Association of Home Appliance Manufacturers 
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(AHAM) and non-AHAM member companies. The interviews were intended to achieve four 

objectives:

1. Solicit feedback on the draft engineering analysis (including methodology, production 

costs, manufacturing processes, and findings).

2. Solicit feedback on the preliminary manufacturer impact analysis.

3. Provide an opportunity, early in the rulemaking process, for manufacturers to express 

specific concerns to DOE.

4. Foster cooperation between manufacturers and DOE.

Six general topics related to the preliminary engineering analysis and manufacturer 

impact analysis were discussed during the interviews, including:

1. key issues identified by DOE and/or the manufacturer;

2. projected shipments;

3. capital conversion costs;

4. product mix and profitability;

5. market shares and industry consolidation; and

6. cumulative regulatory burden.

To meet the objectives of the Process Rule, DOE developed spreadsheet models for the 

LCC, PBP, and national impact analyses for microwave oven standby power. DOE developed an 

LCC spreadsheet that calculates the LCC and PBP at various energy efficiency levels. DOE also 

developed a national impact analysis spreadsheet that calculates the national energy savings 

(NES) and national net present values at various energy efficiency levels. This spreadsheet 

includes a model that forecasts the effects of energy efficiency standards at various levels on 

product shipments. All the spreadsheets are available on the DOE website. Spreadsheets specific 

to cooking products, including microwave ovens, are found at:

www.regulations.gov/#!documentDetail;D=EERE-2006-STD-0127-0062.

On November 15, 2007, DOE published in the Federal Register an Advance Notice of 

Proposed Rulemaking for Residential Dishwashers, Dehumidifiers, and Cooking Products, and 

Commercial Clothes Washers, which described potential new energy conservation standards for 

the named product types and announced a public meeting to receive comments. This document is 

available at: www.regulations.gov/#!documentDetail;D=EERE-2006-STD-0127-0002.

DOE held a public meeting on December 13, 2007, to provide stakeholders the 

opportunity to comment on the ANOPR and the analytical framework, models, and tools (e.g., 

LCC and NES spreadsheets) that DOE used to evaluate the effects of energy conservation 

standards; the results of preliminary analyses; and the candidate energy efficiency levels. 

After stakeholders commented on the ANOPR at the public meeting and in writing 

during the subsequent comment period, DOE again interviewed manufacturers of microwave 

ovens to assist with developing the manufacturer impact analysis for the NOPR. This second 
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round of interviews addressed 13 topics, some of which visited again and in greater detail the six 

topics discussed during initial interviews. The 13 topics were:

1. key issues identified by DOE and/or the manufacturer;

2. overview of company and organizational characteristics; 

3. company financial parameters; 

4. breakdown of production costs; 

5. projected shipments and market shares; 

6. product mixes; 

7. conversion costs; 

8. markups and profitability; 

9. cumulative regulatory burden; 

10. exports, foreign competition, and outsourcing; 

11. assessment of direct impacts on employment; 

12. market consolidation; and 

13. baseline products and alternative design options. 

DOE issued a NOPR for microwave oven standby and off mode on October 17, 2008,

and held a public meeting on November 14, 2008. This document is available at: 

www.regulations.gov/#!documentDetail;D=EERE-2006-STD-0127-0003. DOE incorporated the 

information gathered at the interviews, as well as during the public meeting and comment period,

into its engineering analysis (chapter 5 of the NOPR TSD) and preliminary manufacturer impact 

analysis (chapter 13 of the NOPR TSD). In addition, the Department conducted all the analyses 

listed in Table 1.4.1 that are required for the NOPR phase of a rulemaking. Taking into 

consideration the seven factors stipulated in 42 U.S.C. 6295(o)(2)(B)(i), which are listed in

section 1.4, and comments received from interested parties, the Department analyzed four trial 

standard levels for microwave oven standby power. DOE subsequently issued a supplemental 

notice of proposed rulemaking (SNOPR) for microwave oven standby mode and off mode 

standards on February 14, 2012. This document is available at:

www.regulations.gov/#!documentDetail;D=EERE-2011-BT-STD-0048-0001. The SNOPR TSD 

was issued October 2011, and the public meeting was held March 14, 2012.

This TSD for the Department’s final rule for microwave oven standby power outlines the 

analytical methods used in this rulemaking and the results of the analyses performed.

1.5 STRUCTURE OF THE DOCUMENT

This TSD comprises 17 chapters and 17 appendices.

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

and how it applies to this rulemaking; 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 

microwave ovens and the technologies available for decreasing 

microwave oven standby power consumption.

Chapter 4 Screening Analysis: determines which design options are viable for 

consideration in the engineering analysis.

Chapter 5 Engineering Analysis: discusses the methods used to develop the 

relationship between increased manufacturer price and increased 

efficiency.

Chapter 6 Markups for Product Price Determination: describes the methods used 

to establish markups for converting manufacturer costs to consumer

retail prices.

Chapter 7 Energy Use Analysis: discusses the process used for generating estimates 

of energy use for microwave ovens as a function of standard levels.

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

standards on individual consumers and users of microwave ovens; 

compares the LCC and PBP of products with and without higher 

efficiency standards.

Chapter 9 Shipments Analysis: describes the methods used for forecasting product 

shipments with and without higher efficiency standards, including how 

product purchase decisions are economically influenced and how DOE 

uses econometric equations to model this relationship.

Chapter 10 National Impact Analysis: describes the methods used for forecasting 

national energy consumption and national economic impacts based on 

annual product shipments and estimates of future product energy 

efficiency distributions in the absence and presence of amended energy 

conservation standards.

Chapter 11 Consumer Sub-Group Analysis: discusses the effects of standards on 

different sub-groups of consumers and compares the LCC and PBP for 

those consumers of products in the absence and presence of higher 

efficiency standards.

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

finances and profitability of product manufacturers.

Chapter 13 Employment Impact Analysis: discusses the effects of standards on 

national employment.
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Chapter 14 Utility Impact Analysis: discusses the effects of standards on electric 

utilities.

Chapter 15 Emissions Analysis: describes the effects of standards on emissions of 

carbon dioxide (CO2), nitrogen oxides (NOX), and mercury.

Chapter 16 Monetization of Benefits of Emissions Reductions: discusses the basis for 

estimating the monetary values assigned to the reduced emissions of 

CO2 and other pollutants that are expected to result from each trial 

standard level.

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

alternatives to efficiency standards.

Appendix 5A Data Submittal from the Association of Home Appliance Manufacturers

Appendix 5B Regression Methodology for Testing Microwave Oven Standby Power 

Appendix 5C Microwave Oven Display Configurations

Appendix 5D Microwave Oven Design Pathways

Appendix 7A Variables and Values from Residential Energy Consumption Survey

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

Appendix 8B Uncertainty and Variability

Appendix 8C Estimation of Product Price Trends for Microwave Ovens

Appendix 8D Distributions Used for Discount Rates

Appendix 9A Relative Price Elasticity of Demand For Appliances

Appendix 10A User Instructions for the National Impacts Analysis Spreadsheet

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

Economic Growth Scenarios

Appendix 10C National Net Present Value and Emissions Reductions using Alternative 

Product Price Forecasts

Appendix 12A Interview Guides for Manufacturer Impact Analysis 
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Appendix 12B Overview of Government Regulatory Impact Model

Appendix 16A Social Cost of Carbon for Regulatory Impact Analysis Under Executive 

Order 12866

Appendix 16B Technical Update Of The Social Cost Of Carbon For Regulatory Impact 

Analysis Under Executive Order 12866

Appendix 17A Supporting Material for Regulatory Impact Analysis 
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CHAPTER 2. ANALYTICAL FRAMEWORK

TABLE OF CONTENTS

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

2.2 MARKET AND TECHNOLOGY ASSESSMENT........................................................ 2-5

2.2.1 Market Assessment .......................................................................................................... 2-5

2.2.2 Technology Assessment................................................................................................... 2-5

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

2.4 ENGINEERING ANALYSIS.......................................................................................... 2-6

2.4.1 Manufacturing Cost Assessment...................................................................................... 2-6

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

2.6 ENERGY USE ANALYSIS............................................................................................ 2-7

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

2.8 SHIPMENTS ANALYSIS .............................................................................................. 2-8

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

2.9.1 National Energy Savings.................................................................................................. 2-9

2.9.2 Net Present Value ............................................................................................................ 2-9

2.9.3 Forecasted Efficiencies.................................................................................................. 2-10

2.10 CONSUMER SUB-GROUP ANALYSIS..................................................................... 2-10

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

2.12 EMPLOYMENT IMPACT ANALYSIS....................................................................... 2-11

2.13 UTILITY IMPACT ANALYSIS................................................................................... 2-11

2.14 EMISSIONS ANALYSIS.............................................................................................. 2-12

2.15 MONETIZING CARBON DIOXIDE AND OTHER EMISSIONS

REDUCTIONS .............................................................................................................. 2-14

2.16 REGULATORY IMPACT ANALYSIS ....................................................................... 2-15

FIGURE

Figure 2.1.1 Flow Diagram of Analyses for the Rulemaking Process..................................... 2-2
2-1

CHAPTER 2. ANALYTICAL FRAMEWORK

2.1 INTRODUCTION

Section 6295(o)(2)(A) of 42 U.S.C. requires the U.S. Department of Energy (DOE) to 

establish energy conservation standards that are technologically feasible and economically 

justified and will result in significant energy conservation. This chapter describes the general 

analytical framework that DOE uses in developing such standards, as well as aspects specific to 

the analysis of microwave oven standby power. The analytical framework summarizes the 

methodologies, analytical tools, and relationships among the various analyses that are part of a 

standards rulemaking. 

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

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

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

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

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

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

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

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

connecting analyses show the types of information that feed from one analysis to another.
2-2

Figure 2.1.1 Flow Diagram of Analyses for the Rulemaking Process

Utility Impact

Analysis 

Notice of Proposed Rulemaking (NOPR)

Final Rule

Approaches Key Inputs Analyses Key Outputs

Market and Technology

Assessment

Screening Analysis

Engineering Analysis

Markups for

Product Price

Determination Life-Cycle Cost and 

Payback Period 

Analysis

National Impact

Analysis

Energy Use

Revise ANOPR

Analyses

Life-Cycle Cost 

Sub-Group Analysis

Manufacturer Impact 

Analysis

Shipments

Analysis

Employment Impact

Analysis 

Revise Analyses

• Product Classes

• Technology Options

• Industry Cash Flow

• Sub-Group Cash-Flow

• Direct Employment Impacts

• Competitive Impacts

• Cumulative Regulatory Burden

• Cost-Efficiency Relationship

• Life-Cycle Costs

• Payback Periods

• National Energy Savings

• Net Present Values

• National Employment Impacts

• Revised Results

• Characterize Industry

• Market Segmentation

• Non-Regulatory Programs

Framework Document

• Efficiency-Level Approach

• Design Option Approach

• Energy Prices

• Installation Costs

• Maintenance & Repair Costs

• Accounting Approach

• Backcast and Forecast

• Market Saturation

• Stakeholder Comments

• Demographics

• Manufacturer Interviews

• GRIM Analysis

• Emission Rates

• National Energy Savings

• Stakeholder Comments

• Identify Firms/Products 

• Historical Shipments

• Define Distribution Channels

• Economic Census Data

Analysis

• Retail Price Collection and

Analysis

• Manufacturer Prices

• Average Costs

• Manufacturer Financial Data

Design Options

Product Classes

Retail Prices

Design Energy/ Use

Advance Notice of Proposed Rulemaking (ANOPR)

• Efficiency/Performance

• Manufacturing Cost

Candidate

Standard 

Levels

• Analysis of Energy Use 

Data

Environmental

Assessment

Regulatory Impact

Analysis

Technology Options

• Emission Estimates

• Utility Load Factors

• National Energy Savings

• Non-Regulatory

Alternatives

• NEMS-BT

• ImSET

• NEMS-BT

• Design Options

• Energy Price Forecasts

• Site-to-Source Factors

• Energy-Efficiency Levels

Direct Employment 

Impacts

• National Energy Savings

• National Product Costs

• National Operating Costs

• Utility Impacts

• Analysis of Market Data

• Analysis of Product Data

• Life-Cycle Costs

• Payback Periods

• Installation

Costs

• Maint Costs

• Repair Costs

• UEC

• Energy Prices

• Annual Energy Use (UEC)

Preliminary 

Manufacturer Impact 

Analysis

• Manufacturer Prices

• Average Costs

• Conversion Capital Expenditures

• Direct Employment Impacts

• National Energy Savings

• Net Present Values

• Trial Standard Levels (TSLs)

TSLs

• Product Prototypes

Energy-Efficiency 

Levels

• Department of Justice Review

Utility Impact

Analysis 

Notice of Proposed Rulemaking (NOPR)

Final Rule

Approaches Key Inputs Analyses Key Outputs

Market and Technology

Assessment

Screening Analysis

Engineering Analysis

Markups for

Product Price

Determination Life-Cycle Cost and 

Payback Period 

Analysis

National Impact

Analysis

Energy Use

Revise ANOPR

Analyses

Life-Cycle Cost 

Sub-Group Analysis

Manufacturer Impact 

Analysis

Shipments

Analysis

Employment Impact

Analysis 

Revise Analyses

• Product Classes

• Technology Options

• Industry Cash Flow

• Sub-Group Cash-Flow

• Direct Employment Impacts

• Competitive Impacts

• Cumulative Regulatory Burden

• Cost-Efficiency Relationship

• Life-Cycle Costs

• Payback Periods

• National Energy Savings

• Net Present Values

• National Employment Impacts

• Revised Results

• Characterize Industry

• Market Segmentation

• Non-Regulatory Programs

Framework Document

• Efficiency-Level Approach

• Design Option Approach

• Energy Prices

• Installation Costs

• Maintenance & Repair Costs

• Accounting Approach

• Backcast and Forecast

• Market Saturation

• Stakeholder Comments

• Demographics

• Manufacturer Interviews

• GRIM Analysis

• Emission Rates

• National Energy Savings

• Stakeholder Comments

• Identify Firms/Products 

• Historical Shipments

• Define Distribution Channels

• Economic Census Data

Analysis

• Retail Price Collection and

Analysis

• Manufacturer Prices

• Average Costs

• Manufacturer Financial Data

Design Options

Product Classes

Retail Prices

Design Energy/ Use

Advance Notice of Proposed Rulemaking (ANOPR)

• Efficiency/Performance

• Manufacturing Cost

Candidate

Standard 

Levels

• Analysis of Energy Use 

Data

Environmental

Assessment

Regulatory Impact

Analysis

Technology Options

• Emission Estimates

• Utility Load Factors

• National Energy Savings

• Non-Regulatory

Alternatives

• NEMS-BT

• ImSET

• NEMS-BT

• Design Options

• Energy Price Forecasts

• Site-to-Source Factors

• Energy-Efficiency Levels

Direct Employment 

Impacts

• National Energy Savings

• National Product Costs

• National Operating Costs

• Utility Impacts

• Analysis of Market Data

• Analysis of Product Data

• Life-Cycle Costs

• Payback Periods

• Installation

Costs

• Maint Costs

• Repair Costs

• UEC

• Energy Prices

• Annual Energy Use (UEC)

Preliminary 

Manufacturer Impact 

Analysis

• Manufacturer Prices

• Average Costs

• Conversion Capital Expenditures

• Direct Employment Impacts

• National Energy Savings

• Net Present Values

• Trial Standard Levels (TSLs)

TSLs

• Product Prototypes

Energy-Efficiency 

Levels

• Department of Justice Review
2-3

The following analyses are performed for the advance notice of proposed rulemaking 

(ANOPR) stage of a standards rulemaking (now called the Preliminary Analysis phase) and 

updated for the notice of proposed rulemaking (NOPR), and final rule. Periodically, and for this 

rulemaking, a supplemental notice of proposed rulemaking (SNOPR) is required,

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

technology options, including prototype designs.

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

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

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

health and safety.

• An engineering analysis to develop cost-efficiency relationships for product designs 

that passed the screening analysis; the manufacturer’s production costs (material, 

labor, and factory overhead) for achieving increased efficiency are evaluated.

• An energy (and water) use analysis to determine the annual energy use of the 

considered product(s) at specific efficiency levels.

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

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

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

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

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

throughout the estimated average life of the covered product(s), compared to any 

increase in the installed cost of the product(s) likely to result directly from the 

imposition of the standard.

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

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

(NPV), and future manufacturer cash flows.

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

total consumer LCC and national energy savings (NES).

• A preliminary manufacturer impact analysis to begin examining the financial effects

of potential standards on manufacturers.

The Energy Policy and Conservation Act of 1975 (42 U.S.C. 6291–6309; EPCA) 

established an energy conservation program for major household appliances. The amendments to 

EPCA in the National Appliance Energy Conservation Act of 1987 (42 U.S.C. 6291–6309; 

NAECA) prescribed energy conservation standards for cooking products, as well as stipulating 

requirements for determining whether those standards should be amended. Following NAECA 

directives, DOE issued a NOPR on March 4, 1994, proposing active-mode performance 

standards for eight gas and electric residential cooking products, including microwave ovens. 59 

FR 10464. DOE issued a final rule for electric cooking products (including microwave ovens) in 

1998.a

 

a The 1998 final rule maintained a “no standard” standard for electric cooking products, including microwave ovens.

63 FR 48038. DOE has continued to assess standards and the need for any amendments. 

In the November 15, 2007, ANOPR regarding energy conservation standards for kitchen ranges 
2-4

and ovens and commercial clothes washers, DOE concluded that energy consumption by 

microwave ovens in the standby (nonoperating) mode represents a significant fraction of 

microwave ovens’ energy use, and that a standard regulating such energy consumption likely 

would provide significant energy savings. 72 FR 64432, 64441–42. The Energy Independence 

and Security Act of 2007 (EISA 2007; Pub. L. 110–140) also directs DOE to incorporate standby 

and off mode energy use into any final rule establishing or revising a standard for a covered 

product adopted after July 1, 2010. (42 U.S.C. 6295(gg)(3)) DOE therefore amended standards to

prescribe the maximum electricity consumed when the microwave oven is in standby mode. The 

analyses DOE performed in the NOPR stage for microwave oven standby power, which were

updated for the SNOPR and final rule, are listed below.

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

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

differently than for the overall population.

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

manufacturers and to calculate effects on competition, employment, and 

manufacturing capacity.

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

national employment.

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

generation capacity and electricity generation of electric utilities.

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

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

(NOX), and mercury (Hg).

• A monetization of emission reduction benefits resulting from reduced emissions 

associated with potential amended standards.

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

conservation standards that could achieve substantially the same regulatory goal

The analyses conducted at the NOPR stage were discussed in the NOPR technical 

support document (TSD) for this rulemaking. After publication of the NOPR, DOE received 

comments from interested parties that indicated that further analyses were warranted for 

microwave ovens as to standby power. As a result, DOE continued the rulemaking for 

microwave ovens. A final rule for conventional cooking products (cooktops and ovens) and for 

microwave oven energy factor (operating power) was published on April 8, 2009. 74 FR 16040. 

Updated analyses for the final rule for conventional cooking products and microwave oven 

energy factor are presented in the final rule TSD for those products.

DOE issued a supplemental notice of proposed rulemaking (SNOPR) for microwave 

oven standby mode and off mode standards on February 14, 2012. 77 FR 8526. The SNOPR 

TSD was issued in October 2011, and the public meeting held March 14, 2012. This final rule 

TSD contains details of the revised NOPR and SNOPR analyses, outlined above, conducted for 

microwave oven standby power consumption.
2-5

2.2 MARKET AND TECHNOLOGY ASSESSMENT

The market and technology assessment characterizes the relevant product markets and

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

describes in detail the market and technology assessment for microwave oven standby power.

2.2.1 Market Assessment

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

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

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

available information. For microwave ovens, DOE addressed (1) manufacturer market share and 

characteristics; (2) current regulatory and nonregulatory initiatives for improving product 

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

resource material throughout the rulemaking.

DOE reviewed product literature and interviewed manufacturers to develop an overall 

picture of the market for microwave ovens in the United States. Industry publications and trade 

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

including information regarding (1) manufacturers and their market shares, (2) shipments by 

capacity, and (3) market saturation. The appropriate sections of this TSD, particularly chapter 3,

describe the resulting information as DOE used it in the analysis.

DOE used the most reliable and accurate data available at the time of each analysis 

performed for this rulemaking. All data are made available for public review. 

2.2.2 Technology Assessment

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

prototype designs to determine what technologies manufacturers use to attain higher 

performance levels. In consultation with interested parties, DOE develops a list of technologies 

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

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

microwave oven standby power through consultation with manufacturers of components and

systems and from trade publications and technical papers. 

2.3 SCREENING ANALYSIS

As described in section 2.2.2, DOE develops an initial list of efficiency enhancement 

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

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

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

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

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

well as any for which the energy consumption cannot be measured adequately by DOE test 
2-6

procedures. Chapter 4 of this TSD describes the screening analysis for microwave oven standby 

power. In the engineering analysis, DOE further considers those efficiency enhancement options 

that were not eliminated in the screening analysis

2.4 ENGINEERING ANALYSIS

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

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

between the cost and the efficiency of microwave ovens. This relationship serves as the basis for 

cost/benefit calculations related to individual consumers, manufacturers, and the nation. Chapter 

5 discusses the product classes DOE analyzed, the representative baseline units, the incremental 

efficiency levels, the methodology DOE used to develop manufacturing costs, the methodology 

it used to develop the energy consumption model, the cost-efficiency curves, and the effect of 

efficiency improvements on microwave ovens.

The purpose of the engineering analysis is to estimate the incremental production costs 

associated with increasing product efficiency above the level of the baseline model in each 

product class. The engineering analysis generally considers technologies not eliminated in the 

screening analysis, although certain technologies were not analyzed because they offered

negligible incremental improvements to efficiency. DOE considers the remaining technologies, 

designated design options, in developing cost-efficiency curves, which subsequently are used for 

the LCC and PBP analysis. For each product class, DOE selected efficiency levels and obtained 

incremental cost data at each level.

2.4.1 Manufacturing Cost Assessment 

The manufacturing cost assessment, which is part of the engineering analysis, estimated

the manufacturing costs associated with reducing microwave oven standby power. This approach 

involved the disassembly of units, analysis of the materials and manufacturing processes, and 

development of a spreadsheet cost model based on a clear and consistent manufacturing cost 

assessment methodology. DOE built a detailed cost assessment model that accurately predicts 

manufacturing costs. The model reports those costs in aggregated form to maintain 

confidentiality of the data. DOE obtained input from interested parties on the manufacturing cost 

estimates and assumptions to confirm their accuracy.

2.5 MARKUPS ANALYSIS

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

estimates from the engineering analysis to customer prices, which are then used in the LCC and 

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

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

TSD). To develop markups, DOE identified distribution channels (i.e., how the products are

distributed from the manufacturer to the consumer). After identifying appropriate distribution 
2-7

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

industry to define how products are marked up from the manufacturer to the consumer.

2.6 ENERGY USE ANALYSIS

The energy use analysis (chapter 7 of this TSD) assesses the energy-saving potential of 

various levels of microwave oven standby power. As part of the energy use analysis, DOE makes

certain engineering assumptions regarding product application, including how the product is 

operated and under what conditions. DOE also developed a test procedure to measure the 

efficiencies of microwave oven standby power. On March 9, 2011, DOE published in the 

Federal Register an interim final rule establishing a microwave oven standby and off mode test 

procedure. 76 FR 12825. SNOPRs for the test procedure were published on November 23, 2011 

(76 FR 72332) and May 16, 2012 (77 FR 28805). DOE subsequently published a final rule 

amending the microwave oven standby and off mode test procedure on January 18, 2013. 78 FR 

4015.

2.7 LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

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

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

DOE analyzed the net effect of new standards on consumers (chapter 8 of this TSD) by 

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

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

include the installed cost to the consumer (consumer price plus installation cost); operating 

expenses (energy expenses and maintenance costs); the lifetime of the appliance; and a discount 

rate.

Given that installed cost typically increases while operating cost typically decreases

under standards, there comes a time in the life of a product that operates at a higher-than-baseline 

efficiency when the net operating cost benefit (in dollars) from the time of purchase is equal to 

the incremental first cost of purchasing the higher-efficiency product. The length of time 

required for a product to reach this cost-equivalence point is known as the payback period.

To estimate the LCC and PBP for microwave oven standby power, DOE had to 

determine several input values, including retail prices; electricity prices; discount rates; 

maintenance, service, and installation costs; and product lifetimes. DOE used typical values to 

reflect conditions in the field for product life, product retail price, and energy consumption. DOE 

reviewed the Energy Information Administration (EIA)’s energy price data to establish 

electricity prices for residential consumers. DOE used EIA’s Annual Energy Outlook 2012 (AEO

2012) as the default source of projections of future energy prices for its LCC and PBP analysis.

To forecast product costs, DOE developed price factors based on learning rates calculated 

for manufacturing electric cooking equipment, including microwave ovens. The learning rates 
2-8

were calculated using the historical Producer Price Index for electric cooking products, along 

with historical shipments data that indicate cumulative production of the products.

DOE developed discount rates for residential consumers based on estimates of the 

interest or finance cost to purchase residential appliances. Following financial theory, the finance 

cost of raising funds can be interpreted as (1) the financial cost of any debt incurred to purchase 

products, primarily interest charges on debt; or (2) the opportunity cost of any equity used to 

purchase products, primarily interest earnings on household equity. 

DOE considered expected changes to maintenance, repair, and installation costs for the 

microwave ovens covered in this rulemaking. Typically, small incremental changes in product

efficiency incur no, or only small, changes in repair and maintenance costs compared to products 

having baseline efficiency. Products having efficiencies that are significantly greater than the 

baseline are more likely to incur increased repair and maintenance costs because such products 

are more likely to incorporate technologies that are new to the industry and not widely available. 

DOE used information from various literature sources (e.g., Appliance Magazine) and 

input from manufacturers and other stakeholders to establish average product lifetimes for use in 

the LCC and subsequent analyses.

2.8 SHIPMENTS ANALYSIS

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

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

basis for projecting future shipments of microwave ovens (chapter 9 of this TSD). In projecting 

shipments, DOE accounted for two market segments: (1) new construction, and (2) existing 

residential buildings (i.e., replacing failed products). To estimate the impacts of standards on 

product shipments, DOE accounted for the combined effects of changes in purchase price, 

annual operating cost, and household income on the consumer purchase decision.

2.9 NATIONAL IMPACT ANALYSIS

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

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

savings (NES) for the efficiency levels established for both product classes of microwave ovens. 

To make the analysis more accessible and transparent to all stakeholders, DOE prepared an NES 

spreadsheet model in Microsoft Excel to forecast energy savings and the national economic costs 

and savings resulting from new standards. The NES spreadsheet model does not use probability 

distributions for inputs or outputs. To assess the effect of input uncertainty on the NES and NPV 

results, DOE can conduct sensitivity analyses by running scenarios on input variables that are of 

interest to stakeholders. 
2-9

2.9.1 National Energy Savings 

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

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

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

stock, of products (by vintage) by the unit energy consumption (also by vintage).b DOE 

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

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

The analysis included estimated energy savings by fuel type used for generating electricity. DOE 

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

consumption and savings into source energy. Cumulative energy savings are the sum of the 

annual NES throughout the forecast period.

The stock of products depends on annual shipments and the lifetime of the product. DOE

projected shipments under the base-case efficiency and for various standards-case efficiency 

scenarios and product efficiency trends. DOE determined that projected shipments were lower 

under the higher efficiency cases than under base-case efficiency. The projections indicated that 

higher installed costs would cause some consumers to forego product purchases. As a result, 

DOE used the projection of shipments under each higher efficiency standards case and, in turn, 

the resulting stock of product under that case, to determine the NES. Calculating the NES in this 

way avoids including savings that result from displaced shipments.

2.9.2 Net Present Value 

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

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

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

savings and the present value of increased total installed costs. It calculated net savings each year 

as the difference between total operating cost savings (including savings in electricity, repair, 

and maintenance costs) and increases in total installed costs (product price). DOE calculated 

savings over the life of the product, accounting for differences in yearly energy rates, and used a 

discount factor to discount future costs and savings to the present.

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

total installed cost between the base case and standards case and the annual shipments in the 

standards case. Because costs of the more efficient products purchased in the standards case are 

higher than those of products bought in the base case, price increases appear as negative values 

in the NPV analysis.

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

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

efficiency case. Total operating cost savings are the product of savings per unit and the number 

of units of each vintage surviving in a particular year.

 b Vintage represents the age of the product.
2-10

2.9.3 Forecasted Efficiencies

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

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

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

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

efficiencies for future years.

DOE developed product efficiencies for the base case based on product efficiencies in 

2016. In other words, DOE determined the distribution of product efficiencies currently in the 

marketplace to develop a shipment-weighted efficiency for 2016. As described in chapter 10,

DOE assumed that forecasted efficiencies remain frozen at the 2016 efficiency level until the end 

of the forecast period (2045). 

DOE developed standards-case forecasts (forecasts of efficiency trends after standards 

take effect) based on a roll-up scenario. Under a roll-up scenario, the efficiencies of products that 

operate at performance levels below a prospective standard are assumed to move, or roll up, to

meet the minimum performance level allowed under the new standard. The distribution of 

products at efficiency levels above the new minimum standard levels is unaffected (that is, those

products remain at their pre-standard performance levels). The roll-up scenario dictates how 

DOE determines efficiency distributions in the first year a new standard takes effect. As it did for

the base case, DOE assumed that forecasted efficiencies under each standards case remain frozen 

at the efficiency level in 2016 until the end of the forecast period.

2.10 CONSUMER SUB-GROUP ANALYSIS

The consumer sub-group analysis evaluates economic impacts of standards on 

identifiable groups of consumers who might be disproportionately affected by new or amended 

energy efficiency standards. DOE evaluates the effects on particular sub-groups of consumers in 

part by using the LCC spreadsheet model to analyze the LCC and PBP for those particular 

consumers.

In the case of microwave ovens, DOE analyzed two sub-groups: (1) low-income 

households, and (2) households occupied by senior citizens. Chapter 11 of this TSD describes the 

sub-group analysis for consumers of microwave ovens.

2.11 MANUFACTURER IMPACT ANALYSIS

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

standards on manufacturers of microwave ovens. Potential impacts include financial effects, both 

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

products. DOE identifies those potential impacts through interviews with manufacturers and 

other interested parties.
2-11

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

power consumption in three phases, and further tailored the analytical framework based on 

stakeholder comments. In Phase I, DOE created a profile of the industry and conducted a 

preliminary MIA to identify important issues that required consideration. In Phase II, an industry 

cash flow model and an interview questionnaire were prepared to guide subsequent discussions. 

In Phase III, manufacturers were interviewed, and the impacts of standards were assessed both 

quantitatively and qualitatively. DOE assessed industry and sub-group cash flow and NPV 

through use of the Government Regulatory Impact Model. Effects on competition, 

manufacturing capacity, employment, and cumulative regulatory burden were assessed based on 

feedback and discussions during manufacturer interviews.

2.12 EMPLOYMENT IMPACT ANALYSIS

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

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

of employees at the plants that produce covered products and at affiliated distribution and service 

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

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

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

effect). 

As discussed in chapter 13 of this TSD, DOE investigated indirect employment impacts 

for microwave oven standby power standards using the Impact of Sector Energy Technologies 

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

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

employment and income effects of energy-saving technologies in buildings, industry, and 

transportation. In comparison with simple economic multiplier approaches, ImSET allows for 

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

investments.

2.13 UTILITY IMPACT ANALYSIS

The utility impact analysis (chapter 14 of this TSD) assesses the effects of higher 

efficiency standards on electric utility industries. To carry out this analysis, DOE used a variant 

of the EIA’s National Energy Modeling System (NEMS). NEMS is a large, multisectoral, 

partial-equilibrium model of the U.S. energy sector that EIA has developed throughout the past 

decade, primarily to help in preparing the AEO. NEMS, which is available in the public domain,

produces a widely recognized baseline energy forecast for the United States, currently through 

2040. Typical NEMS outputs include forecasts of electricity sales, electricity price, and avoided 

electric generating capacity. 
2-12

DOE uses a variant of NEMS known as NEMS-BT to provide key inputs to its analysis.c

2.14 EMISSIONS ANALYSIS

DOE conducted the utility impact analysis as a scenario departing from the latest AEO reference 

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

standards using NEMS-BT to generate forecasts that deviate from the AEO reference case.

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 conducted the emissions analysis using emissions factors derived from data in 

EIA’s Annual Energy Outlook 2012, supplemented by data from other sources. EIA prepares the 

Annual Energy Outlook using NEMS. Each annual version of NEMS incorporates the projected 

impacts of existing air quality regulations on emissions.

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 remains in effect. 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). On July 6, 2011 EPA issued a 

replacement for CAIR, the Cross-State Air Pollution Rule (CSAPR; also known as the Transport 

 a For more information on NEMS, refer to the U.S. Department of Energy, Energy Information Administration 

documentation. A useful summary is National Energy Modeling System: An Overview 2000, DOE/EIA0581(2000), March 2000. EIA approves use of the name NEMS to describe only an official version of the model 

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

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

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

NEMS-BT previously was called NEMS-BRS.
2-13

Rule). 76 FR 48208 (August 8, 2011). The AEO 2012 NEMS assumes the implementation of 

the CSAPR.d

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 2015, however, SO2 emissions will fall as a result of the Mercury and Air 

Toxics Standards (MATS) for power plants, which were announced by EPA on December 21, 

2011. 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 2012 assumes that, in order to continue 

operating, coal plants must have either flue gas desulfurization or dry sorbent injection systems 

installed by 2015. Both technologies, which are used to reduce acid gas emissions, also reduce 

SO2 emissions. Under the MATS, NEMS shows a reduction in SO2 emissions when electricity 

demand decreases (e.g., as a result of energy efficiency standards). Emissions will be far below 

the cap that would be established by CSAPR, 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 2015 and beyond.

CSAPR 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 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 CSAPR. Therefore, DOE estimates NOX emissions reductions from potential 

standards in the States where emissions are not capped.

 

d

 On December 30, 2011, the D.C. Circuit stayed the new rules while a panel of judges reviews them, and told EPA 

to continue administering CAIR (see EME Homer City Generation v. EPA, No. 11-1302, Slip Op. at *2 (D.C. Cir. 

Dec. 30, 2011)). On August 21, 2012, the D.C. Circuit issued a decision to vacate CSAPR. See EME Homer City 

Generation, LP v. EPA, No. 11-1302, 2012 WL 3570721 at *24 (D.C. Cir. Aug. 21, 2012). The court again ordered 

EPA to continue administering CAIR. AEO 2012 had been finalized prior to this decision, however. DOE 

understands that CAIR and CSAPR are similar with respect to their effect on emissions impacts of energy efficiency 

standards.
2-14

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. For 

this rulemaking, DOE estimated mercury emissions reductions using emissions factors based on 

AEO 2012, which incorporates the MATS.

With regard to the impact of standards on particulate matter (PM), the great majority of 

ambient PM associated with power plants is in the form of secondary sulfates, 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 SO2 and NOx emissions resulting from standards 

are in fact primarily related to the health benefits of reduced indirect PM. Power plants may also 

emit particulates from the smoke stack. These direct, or primary, PM emissions can be difficult 

to quantify, and DOE is not able to quantify them at this time.

2.15 MONETIZING CARBON DIOXIDE AND OTHER EMISSIONS

REDUCTIONS

DOE estimates the 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. 

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

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

and/or agreed to by an interagency work group. The SCC is intended to serve as a monetary 

measure of the incremental damage resulting from greenhouse gas (GHG) emissions, including, 

but not limited to, net loss of agricultural productivity, human health effects, property damage 

from sea level rise, and changes in ecosystem services. With full regard for the limits of both 

quantification and monetization, the SCC can be used to provide estimates of the social benefits 

of reductions in GHG emissions.

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

benefits resulting from reduced CO2 emissions in 2016, expressed in 2011$, were $12.6, $41.1, 

$63.2, and $119.1 per metric ton avoided.

e Those values increase in real terms over time. 

Additionally, the interagency group determined that a range of values from 7 percent to 23 

percent should be used to adjust the global SCC to calculate domestic effects, although DOE 

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

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

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

 

e Technical Update for Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866. 

Interagency Working Group on Social Cost of Carbon, United States Government, April 2013.
2-15

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 considered for microwave oven standby power. Available estimates 

suggest a wide range of monetary values for NOX emissions, from $455 to $4,679 per ton (in 

2011$).f In accordance with guidance from the U.S. Office of Management and Budget (OMB), 

DOE conducted two calculations of the monetary benefits derived based on each of the economic 

values for NOX, one using a real discount rate of 3 percent and another using a real discount rate 

of 7 percent.

g

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

monetized estimates of SO2 and Hg reduction in this rulemaking.

2.16 REGULATORY IMPACT ANALYSIS

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

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

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

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

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

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

standards.

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

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

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

initiatives on product energy use, consumer utility, and LCC. Although DOE based its 

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

regarding the effects current initiatives might have in the future.

 f

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

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

Mandates on State, Local, and Tribal Entities. Washington, D.C. g OMB. Circular A-4: Regulatory Analysis. September 17, 2003. Please see: 

www.whitehouse.gov/omb/circulars_a004_a-4. (Accessed May 2013.)
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..................................................................................................... 3-1

3.4 PRODUCT TEST PROCEDURES ................................................................................. 3-4

3.5 MANUFACTURER TRADE GROUPS ......................................................................... 3-8

3.6 MANUFACTURER INFORMATION ........................................................................... 3-9

3.6.1 Manufacturers and Market Shares................................................................................... 3-9

3.6.2 Mergers and Acquisitions.............................................................................................. 3-10

3.6.3 Small Business Impacts ................................................................................................. 3-12

3.6.4 Distribution Channels.................................................................................................... 3-12

3.7 REGULATORY PROGRAMS ..................................................................................... 3-13

3.7.1 Federal Energy Conservation Standards........................................................................ 3-13

3.7.2 Energy Independence and Security Act of 2007............................................................ 3-14

3.7.3 Foreign Standby Power Regulatory Programs............................................................... 3-15

3.8 VOLUNTARY PROGRAMS........................................................................................ 3-16

3.9 HISTORICAL SHIPMENTS......................................................................................... 3-17

3.9.1 New Home Starts........................................................................................................... 3-17

3.9.2 Unit Shipments............................................................................................................... 3-18

3.9.3 Value of Shipments........................................................................................................ 3-19

3.9.4 Imports and Exports....................................................................................................... 3-21

3.10 MARKET SATURATION............................................................................................ 3-22

3.11 PRODUCT RETAIL PRICES ....................................................................................... 3-23

3.12 INDUSTRY COST STRUCTURE................................................................................ 3-23

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

3.14 TECHNOLOGY ASSESSMENT.................................................................................. 3-30

3.14.1 Product Operations and Components............................................................................. 3-30

3.14.2 Technology Options....................................................................................................... 3-31

3.14.3 Energy Efficiency .......................................................................................................... 3-35

LIST OF TABLES

Table 3.3.1 Microwave Oven Product Classes............................................................................ 3-4

Table 3.6.1 Major and Other Microwave Oven Manufacturers.................................................. 3-9

Table 3.6.2 Core Appliance Manufacturers.............................................................................. 3-10

Table 3.7.1 EU Ecodesign Requirements for Standby and Off Mode...................................... 3-16

Table 3.8.1 FEMP Recommendations for Microwave Ovens.................................................. 3-17

Table 3.9.1 Industry Shipments of Microwave Ovens (Domestic and Import in 

Thousands of Units)........................................................................................... 3-19

Table 3.9.2 Household Appliance Manufacturing Statistics by Year....................................... 3-20
3-ii

Table 3.9.3 Disposition of Previous Appliance (Percentage) .................................................. 3-20

Table 3.9.4 Household Cooking Appliance Manufacturing Statistics by Year........................ 3-21

Table 3.9.5 2008–2009 Imports of Microwave Ovens............................................................. 3-22

Table 3.9.6 2008–2009 Exports of Microwave Ovens ............................................................ 3-22

Table 3.10.1 Appliance Saturation (Number in Millions) and Percentage of U.S. 

Households with Product ................................................................................... 3-23

Table 3.11.1 Residential Appliance Retail Prices..................................................................... 3-23

Table 3.12.1 Household Appliance Industry Employment and Earnings................................. 3-24

Table 3.12.2 Household Cooking Appliance Industry Employment and Earnings.................. 3-25

Table 3.12.3 Household Appliance Industry Census Data ....................................................... 3-26

Table 3.12.4 Household Cooking Appliance Industry Census Data ........................................ 3-27

Table 3.13.1 Household Appliance Industry Census Data ....................................................... 3-28

Table 3.13.2 Household Cooking Appliance Industry Census Data ........................................ 3-29

Table 3.13.3 Full Production Capacity Utilization Rates......................................................... 3-30

Table 3.14.1 Technology Options for Microwave Oven – Standby Power.............................. 3-31

LIST OF FIGURES

Figure 3.6.1 2008 Market Shares for the Domestic Microwave Oven Market......................... 3-10

Figure 3.6.2 2008 Core Appliance Market Shares.................................................................... 3-11

Figure 3.9.1 New Privately Owned Single-Family and Multi-Family Housing Unit Starts 

in the United States from 1998–2009 ............................................................... 3-18

Figure 3.14.1 AHAM and CBEEDAC Microwave Oven Standby Power versus Rated 

Output Power,..................................................................................................... 3-36

Figure 3.14.2 Microwave Oven Standby Power Consumption from the FEMP Database....... 3-37
3-1

CHAPTER 3. MARKET AND TECHNOLOGY ASSESSMENT

3.1 INTRODUCTION

This chapter provides a profile of the residential microwave ovens 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

DOE defines “microwave oven” under the Energy Policy and Conservation Act (EPCA) 

of 1975, Pub. L. 94-163, (42 United States Code (U.S.C.) 6291–6309) as “a class of kitchen 

ranges and ovens comprised of household cooking appliances consisting of a compartment 

designed to cook or heat food by means of microwave energy, including microwave ovens with 

or without thermal elements designed for surface browning of food and convection microwave

ovens.” (10 Code of Federal Regulations (CFR) 430.2)

3.3 PRODUCT CLASSES

DOE separated microwave ovens 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 performancerelated 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))

In the notice of proposed rulemaking (NOPR) published on October 17, 2008 (hereafter 

the “October 2008 NOPR”), DOE proposed a single product class for microwave ovens. DOE 

stated this product class encompasses microwave ovens with and without browning elements, but 

does not include microwave ovens that incorporate convection systems. 73 FR 62034, 62048.

DOE reassessed what products would be considered microwave ovens under the 

regulatory definition, and whether multiple product classes would be appropriate. As discussed 

in the microwave oven test procedure final rule which was published in the Federal Register on 

March 9, 2011 (hereafter, the “March 2011 TP Interim Final Rule”), DOE stated that microwave 

ovens with or without thermal elements would be considered covered products under the 
3-2

definition provided in 10 CFR 430.2. In addition, DOE stated that combination microwave ovens 

(i.e., microwave ovens that incorporate convection features and any other means of cooking in a 

single compartment, which DOE later designated as convection microwave ovens for clarity) 

would also be considered covered products under the regulatory definition in 10 CFR 430.2 

because they are capable of cooking or heating food by means of microwave energy. DOE 

stated, based on its preliminary analysis, it believes that the general standby mode and off mode 

operation for convection microwave ovens does not differ from that of microwave-only units and 

microwave ovens with thermal elements only. The standby mode operation for convection

microwave ovens, as with other types of microwave ovens, consists of an energized display with 

a clock. As a result, DOE amended the microwave oven test procedure to require that the same 

standby mode and off mode testing methods be used for all microwave ovens. 76 FR 12825, 

12828–30 (Mar. 9, 2011).

In order to determine whether specific types of microwave ovens should be separated into 

different product classes, DOE investigated whether there are any performance related features 

that would justify the establishment of a separate energy conservation standard. As discussed in 

the October 2008 NOPR, DOE tested a sample of 32 countertop microwave-only units and 

measured standby power ranging from 1.2 watts (W) to 5.8 W. 73 FR 62034, 62042 (Oct. 17, 

2008). None of these units was capable of operation in off mode, nor is DOE aware of any other 

current microwave ovens capable of such operation. DOE noted that standby power consumption 

for microwave-only units largely depended on the presence of a cooking sensor, the display 

technology, the power supply and control board, and implementation of a power-down feature. 

With regards to display technologies, DOE noted that microwave-only units incorporated Light 

Emitting Diode (LED) displays, Liquid Crystal Displays (LCDs), and Vacuum Fluorescent 

Displays (VFDs).

For the supplemental NOPR (SNOPR) published in the Federal Register on February 14, 

2012 (77 FR 8526) (hereafter the “February 2012 SNOPR”), DOE conducted a survey of overthe-range microwave-only units available on the U.S. market. DOE determined that the display 

technologies used are similar to those used in countertop microwave-only units (i.e., LED 

displays, LCDs, and VFDs). DOE’s market survey revealed that multiple over-the-range 

microwave-only units incorporate both LED displays and LCDs. DOE research suggests that 

LED displays and LCDs in both countertop and over-the-range microwave-only products offer 

acceptable consumer utility features, including brightness, viewing angle, and ability to display 

complex characters. DOE also conducted in-store standby mode testing on a limited sample of 

over-the-range microwave-only units which showed similar standby power consumption as 

countertop microwave-only units. For these reasons, DOE tentatively concluded that over-therange microwave-only units would not warrant a separate product class according to the criteria 

put forth by EPCA. (42 U.S.C. 6295(q)) DOE understands that over-the-range microwave-only 

units may have additional components that are energized during active mode operation (i.e., 

exhaust fan motors). However, DOE’s testing showed that the presence of such features did not 

increase the standby power consumption to warrant establishing a separate product class. 77 FR 

8526, 8536 (Feb. 14, 2012).
3-3

For the February 2012 SNOPR, DOE also conducted standby power testing on a sample 

of 13 representative convection microwave ovens, including 5 countertop convection microwave 

ovens, 6 over-the-range convection microwave ovens, and 2 built-in convection microwave 

ovens. DOE’s testing showed that the countertop convection microwave ovens use similar 

display technologies as countertop microwave-only units (i.e., LED displays, LCDs, and VFDs), 

and had standby power consumption ranging from 1.2 W to 4.7 W, which is similar to the 

standby power consumption for countertop microwave-only units. As a result, DOE tentatively 

concluded in the February 2012 SNOPR that countertop convection microwave ovens would not 

warrant a product class separate from microwave-only ovens. Id.

DOE’s testing of built-in and over-the-range convection microwave ovens showed that 

the standby power consumption for these products ranged from 4.1 W to 8.8 W, which is higher 

than the standby power consumption for other microwave oven product types (i.e., countertop 

microwave-only, over-the-range microwave-only, and countertop convection microwave ovens). 

DOE’s reverse-engineering analysis suggested that the additional features in built-in and overthe-range convection microwave ovens required to handle the thermal loads associated with their 

installation and to provide consumer utility, such as additional exhaust fan motors, convection 

fan motors and heaters, and additional lights, require a significant number of additional relays on 

the control board, and thus require a larger power supply for the control of such relays. While the

relays themselves do not consume power in standby mode, they increase the total power supply 

requirements of the control board and thus increase the standby losses of the power supply. As a 

result, DOE tentatively determined in the February 2012 SNOPR that a separate product class 

should be established for built-in and over-the-range convection microwave ovens. DOE 

recognized that built-in and over-the-range microwave-only units may similarly require some 

additional relays for exhaust fans and lights, and that countertop convection microwave ovens 

would require some additional relays for convection fans and heaters. However, DOE’s product 

testing and reverse-engineering analyses indicated that these product types use similar-sized 

power supplies as those found in countertop microwave-only units, and as a result would not 

warrant a separate product class from countertop microwave-only units. Id.

DOE notes that over-the-range microwave ovens must operate under conditions that are 

harsher than countertop microwave ovens are typically exposed to, in terms of elevated 

temperatures and humidity levels. For the components that are associated with standby mode and 

off mode energy use, these conditions have the most effect on the displays. Under long-term 

exposure, displays may degrade in illuminance over time, resulting in a consumer’s perception of 

reduced brightness. As discussed further in chapter 5 of this technical support document (TSD), 

DOE conducted accelerated lifetime testing of different microwave oven display types by 

subjecting a limited sample of microwave ovens to high temperatures and humidity levels for an 

extended period of operation in standby mode. The results of this testing demonstrated that the 

illuminance of each display tended to decrease over time to varying degrees, but did not reveal 

any correlation between display type and rate of illuminance reduction. In addition, DOE 

observed in its test sample a unit with an LED display that exhibited illuminance, a significant 

element of consumer utility, that was comparable to that of the VFD on another unit. Based on 

this lifetime testing, and the existence of multiple over-the-range microwave oven models on the 

market with each type of display technology, DOE does not have information that would suggest 
3-4

over-the-range microwave ovens would require certain display technologies (i.e., VFD) that have 

inherently higher power consumption than other display types that provide similar consumer 

utility. In addition, DOE is not aware of, nor did commenters provide information on, different 

standby power consumption that would be associated with controls that have the same 

functionality but different material selection.

As discussed in chapter 5 of this TSD, DOE also examined more closely in its final rule 

analysis whether combinations of design options are available that would allow over-the-range 

microwave-only ovens to meet the same standby power levels as countertop microwave ovens. 

From its analysis, DOE concluded that design pathways exist for all over-the-range microwaveonly ovens with LED displays and LCDs to meet a 1.0 W standard, so that none of these would 

warrant classification separate from countertop microwave-only ovens on the basis of energy use 

characteristics in standby mode. DOE further concludes that the range of these display 

technologies allows manufacturers to design over-the-range products with comparable consumer 

utility and durability of the display as for over-the-range microwave ovens with VFDs.

Therefore, DOE based its analysis on the following two product classes:

Table 3.3.1 Microwave Oven Product Classes

Product Class

1. Microwave-Only Ovens and Countertop Convection Microwave Ovens

2. Built-in and Over-the-Range Convection Microwave Ovens

DOE determined that separate product classes for the purposes of setting energy 

conservation standards addressing standby mode and off mode energy use are warranted on the 

basis of different standby power performance. DOE did not evaluate whether the same product 

class distinction would also be appropriate for any active mode energy use standards because 

DOE eliminated the regulatory provisions establishing the cooking efficiency test procedure for 

microwave ovens in a final rule published in the Federal Register on July 22, 2010 (hereafter, 

the “July 2010 TP Final Rule”). 75 FR 42579. If DOE adopts amendments to the microwave 

oven test procedure to include provisions for measuring active mode cooking efficiency, DOE 

may reevaluate these product classes as part of a future microwave oven energy conservation 

standards rulemaking. At that time, DOE may consider dividing countertop convection

microwave ovens and over-the-range/built-in microwave-only units into separate product classes 

to account for the energy performance of heating components other than the microwave portion.

3.4 PRODUCT TEST PROCEDURES

DOE originally established test procedures for microwave ovens to determine energy 

efficiency and annual energy use as the basis for representation and determination of compliance 

with energy conservation standards. DOE established test procedures for microwave ovens 

through the rulemaking process.
3-5

DOE’s test procedures for microwave ovens initially appeared at appendix I to subpart B 

of Title 10 of the CFR. Those test procedures were part of a May 10, 1978, final rule that first 

established test procedures for conventional ranges, cooktops, and ovens (including microwave 

ovens). 43 FR 20108. DOE later revised its test procedures for cooking products to measure their 

efficiency and energy use more accurately, publishing a final rule on October 3, 1997. 62 FR 

51976. The revised microwave oven test procedure incorporated portions of the International 

Electrotechnical Commission (IEC) Standard 705-1998 and Amendment 2-1993 (IEC Standard 

705), Methods for Measuring the Performance of Microwave Ovens for Households and Similar 

Purposes. It measured microwave oven cooking efficiency, but did not address energy use in the 

standby mode or off modes. Id.

Section 310 of the Energy Independence and Security Act of 2007 (EISA 2007) (Pub. L. 

No. 110-140) amends Section 325 of EPCA to require that that the test procedures for 

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

a

 If 

technically infeasible, DOE must prescribe a separate standby mode and off mode energy use 

test procedure. EPCA, as amended by the EISA 2007, also states that the final rule for this test 

procedure shall be published no later than March 31, 2011. (42 U.S.C. 6295(gg)) 

DOE published a test procedure NOPR on October 17, 2008 (hereafter referred to as the 

“October 2008 TP NOPR”), in which it proposed to incorporate provisions from IEC Standard 

62301 First Edition into the DOE microwave oven test procedure, as well as language to clarify 

application of these provisions for measuring standby mode and off mode power in microwave 

ovens. The October 2008 TP NOPR also proposed correcting a technical error in the calculation 

of microwave test cooking energy output. 73 FR 62134 (Oct. 17, 2008). DOE held a public 

meeting on the proposed rulemaking on November 14, 2008. DOE received comments from 

interested parties both in written responses to the October 2008 NOPR and at the November 

2008 Public Meeting.

After considering comments from interested parties and additional information, DOE 

issued a supplemental NOPR (SNOPR) on July 22, 2010 (hereafter referred to as the July 2010 

TP SNOPR) for the test procedure for measuring microwave oven standby mode and off mode 

power consumption. 75 FR 42612 (Jul. 22, 2010). In that SNOPR, DOE proposed adopting 

definitions of modes based on relevant provisions from IEC Standard 62301 Second Edition, 

Committee Draft for Vote (IEC Standard 62301 CDV), as well as language to clarify application 

of those provisions for measuring microwave oven standby mode and off mode power 

consumption. Id. 

In response to the October 2008 TP NOPR, DOE received comments from interested 

 

a

 IEC 62087 does not cover any products for this rulemaking, and therefore was not considered.
3-6

parties regarding the accuracy and repeatability of the existing DOE microwave oven test 

procedure for measuring cooking efficiency. Because of issues DOE identified with using its 

existing microwave oven test procedure, including the large test-to-test variation in cooking 

efficiency measurements, and because DOE is unaware of any test procedures that have been 

developed that address the concerns with the DOE microwave oven cooking efficiency test 

procedure raised by these interested parties, DOE published the July 2010 TP Final Rule 

repealing the provisions in the existing microwave oven test procedure relating to the 

measurement of cooking efficiency and energy factor (EF). 75 FR 42579 (Jul. 22, 2010). 

Therefore, the requirement to integrate energy consumption in standby mode and off mode into 

an overall energy descriptor does not apply. DOE also published a notice in the Federal Register

on July 22, 2010, announcing a public meeting to consider developing a new test procedure for 

active mode energy consumption of microwave ovens, and DOE will consider the statutory 

requirement to integrate the test procedures for standby and off mode as any active mode test 

procedures are developed. 75 FR 42611.

DOE held a public meeting on September 16, 2010, to receive oral comments on the July 

2010 TP SNOPR, and also sought and received written comments. DOE also invited inputs on 

microwave active mode test procedures for a potential new test procedure rulemaking. After 

consideration of these comments, DOE published the March 2011 TP Interim Final Rule 

addressing microwave oven standby mode and off mode power. 76 FR 12825 (Mar. 9, 2011). In 

that final rule, DOE adopted the provisions for the measurement of standby mode and off mode 

power use for both products that were proposed in the July 2010 TP SNOPR. In addition, DOE 

also adopted provisions to define the test duration for cases in which the measured power is not 

stable (i.e., varies over a cycle), recognizing that the power consumption of microwave oven 

displays can vary based on the displayed clock time. 76 FR 12825, 12837–40 (Mar. 9, 2011).

DOE provided a 180-day comment period on the March 2011 TP Interim Final Rule, during 

which it received several comments on potential improvements to the microwave oven test 

procedure recently adopted.

Based upon the public comment, however, DOE decided to further analyze the updated 

version of IEC Standard 62301, Edition 2.0 2011-01 (IEC Standard 62301 (Second Edition)), for 

the microwave oven test procedure. DOE reviewed this latest version of the IEC standard, which 

was issued on January 27, 2011, and believes that it improves some measurements of standby 

mode and off mode energy use. Accordingly, DOE published a second test procedure SNOPR on 

November 23, 2011 (hereafter referred to as the November 2011 TP SNOPR), proposing to 

incorporate certain provisions of IEC Standard 62301 (Second Edition), along with clarifying 

language, into the DOE test procedures for microwave ovens adopted in the March 2011 TP 

Interim Final Rule. 76 FR 72331 (Nov. 23, 2011).

In the course of reviewing comments on the November 2011 TP SNOPR, DOE 

determined that an additional test procedure SNOPR would be necessary before moving to a 

final rule. DOE subsequently published the additional test procedure SNOPR on May 16, 2012 

(hereafter the “May 2012 TP SNOPR”), to address comments received on the November 2011 

TP SNOPR regarding coverage of additional microwave oven product types in the DOE test 

procedure, and in particular products combining a microwave oven with other appliance 
3-7

functionality. 77 FR 28805. Comments on this topic and other topics received in response to both 

the November 2011 TP SNOPR and the May 2012 TP SNOPR were addressed in a final rule 

published January 18, 2013 (hereafter referred to as the January 2013 TP Final Rule. 78 FR 

4015.

The January 2013 TP Final Rule amended the microwave oven test procedure to 

incorporate by reference certain provisions of IEC Standard 62301 (Second Edition) for the 

measurement of standby mode and off mode energy use. DOE also determined that microwave 

ovens combined with other appliance functionality are covered under the definition of 

“microwave oven” at 10 CFR 430.2, but due to a lack of data and information, did not adopt 

provisions in the microwave oven test procedure to measure the standby mode and off mode 

energy use of the microwave component. 78 FR 4015, 4017–18 (Jan. 18, 2013).

In the November 2011 TP SNOPR, DOE examined IEC Standard 62301 (Second 

Edition), and determined that it improves the power measurement of the First Edition by adding 

specifications for the power supply and power-measuring instruments. DOE also examined the 

new testing methodology provided in the Second Edition under different scenarios of power 

consumption stability, and determined that, although in some cases the testing burden and 

complexity would be increased as compared to IEC Standard 62301 (First Edition), the added 

burden was outweighed in most cases by the improved accuracy and representativeness of the 

resulting power consumption measurement. In the narrow case of microwave ovens with power 

consumption that varies as a function of the time displayed, DOE concluded that the application 

of the test methodology from IEC Standard 62301 (Second Edition) would cause manufacturers 

to incur significant burden that would not be warranted by any potential improved accuracy of 

the test method. 76 FR 72332, 72339–42 (Nov. 23, 2011). For those products only, DOE 

maintained the use of IEC Standard 62301 (First Edition) in the January 2013 TP Final Rule for 

measuring standby mode power, including methodology in which the clock display is set to 3:23 

prior to a 10-minute stabilization period and a 10-minute measurement period. Standby mode 

and off mode power consumption for all other microwave ovens is measured according to the 

methodology of IEC Standard 62301 (Second Edition). 78 FR 4015, 4019–21 (Jan. 18, 2013).

Interested parties commented that they support incorporation by reference of IEC 

Standard 62301 (Second Edition) in the DOE microwave oven test procedure, but stated that 

DOE cannot determine appropriate standard levels in this rulemaking without testing based on 

the final test procedure to be used to determine compliance. DOE reviewed its testing that it had 

conducted in support of various stages of the microwave oven test procedure rulemaking, and 

determined that there were six microwave oven models that had been tested at different times 

according to both the First and Second Editions of IEC Standard 62301. In order to supplement 

this sample, DOE additionally tested eight more microwave ovens as part of its final rule 

analysis so that a comparison could be made between the standby power consumption 

measurements obtained with the First Edition and Second Edition for various installation 

configurations, display types, and manufacturers/brands. Table 3.4.1 presents the results of the 

comparison between testing to the First Edition and the Second Edition, which showed results 

for the two methodologies varying by no more than 5.5 percent, which DOE concludes 

demonstrates close enough agreement that manufacturers could apply the same design option 
3-8

pathways to achieve the varying standby power levels when measuring according to IEC 

Standard 62301 (Second Edition) as DOE’s analysis identified based on testing to IEC Standard 

62301 (First Edition).

Table 3.4.1 Comparison of Standby Power Measurements According to IEC Standard 

62301 (First Edition) and IEC Standard 62301 (Second Edition)

Configuration Display*

Standby 

Power (W), 

First Edition

Standby Power 

(W), Second 

Edition

% 

Difference

Countertop Microwave-Only Backlit LCD 3.84 3.66 -4.7

Countertop Microwave-Only Backlit LCD 2.18 2.18 -0.3

Countertop Microwave-Only Backlit LCD 3.81 3.78 -1.0

Countertop Microwave-Only LED 1.06 1.07 0.3

Countertop Microwave-Only LED 1.76 1.77 0.8

Countertop Microwave-Only LED 1.27 1.27 -0.4

Countertop Microwave-Only VFD 3.44 3.42 -0.6

Countertop Microwave-Only VFD 3.14 3.12 -0.7

Countertop Convection LED 1.20 1.24 3.2

Countertop Convection VFD 4.14 4.13 -0.1

Countertop Convection VFD 3.23 3.05 -5.5

Over-the-Range Microwave-Only VFD 1.66 1.67 0.4

Over-the-Range Microwave-Only LED 0.78 0.78 0.0

Over-the-Range Convection VFD 4.50 4.48 -0.4

* LCD = Liquid Crystal Display, LED = Light Emitting Diode, VFD = Vacuum Fluorescent Display

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 

microwave oven 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 the Association of Home Appliance Manufacturers (AHAM) as a trade 

group that supports the microwave oven industry, AHAM,

b

 

b For more information, visit:

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 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 biennial 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.

www.aham.org.
3-9

3.6 MANUFACTURER INFORMATION

The following section details information regarding domestic manufacturers of 

residential microwave ovens, 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.

The microwave oven market differs from the rest of the domestic cooking product market 

in that many of the manufacturers are foreign-owned companies with manufacturing facilities 

outside of the United States, and many of the domestic appliance manufacturers rebrand foreignmanufactured products. Table 3.6.1 illustrates the 2008 market shares for the U.S. microwave 

oven market. Microwave oven manufacturers with facilities inside the United States include 

Whirlpool, the Sharp Electronics Corporation (Sharp), Sub-Zero, Summit, and Viking. Figure 

3.6.1 lists microwave manufacturers active in the domestic market.

Table 3.6.1 Major and Other Microwave Oven Manufacturers

Major Manufacturers Other Manufacturers

LG Electronics, Inc. (LG) Daewoo Electronics Company, Ltd. 

(Daewoo)

Sharp Panasonic Corporation of America

(Panasonic)

Samsung Electronics America, Inc. 

(Samsung)

Whirlpool

Guangdong Galanz Enterprise Co., Ltd. 

(Galanz)

GD Midea Microwave Oven Mfg. Co., Ltd.

(Midea)

Sanyo

Sub-Zero

Summit 

Viking
3-10

Figure 3.6.1 2008 Market Shares for the Domestic Microwave Oven Market1

3.6.2 Mergers and Acquisitions

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 January 2010 issue of Appliance Magazine, three 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 3.6.2 illustrates the breakdown of 2008 market shares in the 

core appliance category.

Table 3.6.2 Core Appliance Manufacturers

Core Appliance Manufacturers

Whirlpool

GE

Frigidaire
3-11

Figure 3.6.2 2008 Core Appliance Market Shares2

On August 22, 2005, Whirlpool, headquartered in Benton Harbor, Michigan, and Maytag, 

based in Newton, Iowa, announced plans to merge in a deal worth $2.7 billion.3 Maytag 

shareholders approved the merger on December 22, 2005. Shortly after announcing the merger, 

Whirlpool submitted a pre-merger notification to the U.S. Department of Justice (DOJ). The DOJ 

Antitrust Division initiated an investigation, scheduled to end February 27, 2006, into the effects 

of the merger, including potential lessening of competition or the creation of a monopoly. 

Following this initial review, the DOJ asked for additional materials from each company and 

extended the review to March 30, 2006. 

Opponents of the merger asserted that the combined companies would control as much as 

70 percent of the residential laundry market and as much as 50 percent of the residential 

dishwasher market.4

 Whirlpool claimed that their large potential residential laundry market 

share was skewed because the company produces washing machines for Sears, which sells them 

under their Kenmore in-house brand. Whirlpool went on to say that they must periodically bid 

with other manufacturers to keep the Kenmore contract and that Sears controls the pricing of the 

Kenmore units.5

In early January 2006, U.S. Senator Tom Harkin and U.S. Representative Leonard 

Boswell, both of Iowa, called upon the DOJ to block the merger, claiming it would give 

Whirlpool an unfair advantage in the home appliance industry. The Congressmen wrote, that if 
3-12

the DOJ does not block the deal, the agency should at least “require that Whirlpool divest the 

washer and dryer portions of Maytag to a viable purchaser who will have the financial capability 

and desire to continue to operate that business.”6

On March 29, 2006, DOJ closed its investigation and approved the merger. DOJ claims 

“that the proposed transaction is not likely to reduce competition substantially. The combination 

of strong rival suppliers with the ability to expand sales significantly and large cost savings and 

other efficiencies that Whirlpool appears likely to achieve indicates that this transaction is not 

likely to harm consumer welfare.”7

The DOJ Antitrust Division focused its investigation on residential laundry, although it 

considered impacts across all products offered by the two companies. DOJ determined that the 

merger would not give Whirlpool excessive market power in the sale of its products and that any 

attempt to raise prices would likely be unsuccessful. In support of this claim, DOJ noted that (1) 

other U.S. brands, including Kenmore, GE, and Frigidaire, are well established; (2) foreign 

manufacturers, including LG and Samsung, are gaining market share; (3) existing U.S. 

manufacturers are below production capacity; (4) the large home appliance retailers have 

alternatives available to resist price increase attempts; and (5) Whirlpool and Maytag 

substantiated large cost savings and other efficiencies that would benefit consumers.8

Whirlpool and Maytag completed the merger on March 31, 2006. This large merger 

follows several other mergers and acquisitions in the home appliance industry. For example, 

Maytag acquired Jenn-Air Corporation (Jenn-Air) in 1982, Magic Chef, Inc. (Magic Chef) in 

1986, and Amana Appliances (Amana) in 2001. Whirlpool acquired the KitchenAid division of 

Hobart Corporation (KitchenAid) in 1986. White Consolidated Industries (WCI) acquired the 

Frigidaire division of General Motors Corporation in 1979, and AB Electrolux acquired WCI 

(and therefore Frigidaire) in 1986.

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 not aware 

of any small microwave oven 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. DOE estimates that there is 

one small business which manufactures a product which combines a microwave oven with other 

appliance functionality. However, because DOE is not amending energy conservation standards 

at this time for the microwave oven portion of such combined products, this manufacturer would 

not be impacted. For further information, 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 
3-13

channels for residential cooking products from publicly available sources. This section contains 

distribution channel information for residential appliances.

The distribution chain for residential appliances, including microwave ovens, differs from 

commercial products, as 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.9

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.10

3.7 REGULATORY PROGRAMS

The following section details current regulatory programs mandating energy conservation 

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

standards and section 3.7.2 reviews upcoming standards under EISA 2007. In addition, section

3.7.3 reviews foreign standby power regulatory programs that may impact the companies 

servicing the North American market. 

3.7.1 Federal Energy Conservation Standards

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

6309), which amended EPCA, established prescriptive standards for kitchen ranges and ovens, 

but no standards were established for microwave ovens. (42 U.S.C. 6295(h)) The NAECA 

amendments also required DOE to conduct two cycles of rulemakings to determine whether to 

revise the standard. (42 U.S.C. 6295 (h)(1)–(2)) DOE undertook the first cycle of these 

rulemakings and issued a final rule on September 8, 1998 in which DOE found that no amended 

standards were justified for electric cooking products, including microwave ovens. 63 FR 48038.

DOE initiated the second cycle of energy conservation standards rulemakings for cooking 

products by publishing a framework document covering, in part, microwave ovens, and giving 

notice of a public meeting and the availability of the document. 71 FR 15059 (Mar. 27, 2006). In 

its subsequent advance notice of proposed rulemaking (ANOPR) concerning energy conservation 

standards for commercial clothes washers and residential dishwashers, dehumidifiers, and 

cooking products, including microwave ovens, DOE determined that energy consumption by 

microwave ovens in the standby mode represents a significant portion of microwave oven energy 

use, and that a standard regulating such energy consumption would likely have significant energy 

savings. 72 FR 64432, 64441–42 (Nov. 15, 2007). DOE published a NOPR for the appliance 

standards rulemaking on October 17, 2008, in which it tentatively concluded that a standard for 

microwave oven standby mode and off mode energy consumption would be technologically 
3-14

feasible and economically justified. 73 FR 62034. DOE received responses to the NOPR from 

interested parties regarding the harmonization of standards and test procedures with those of 

other countries and international agencies. As a result of these comments, DOE decided to 

consider the revised version of IEC Standard 62301 (i.e., IEC Standard 62301 Second Edition) in 

the development of energy conservation standards for the standby mode and off mode power 

consumption of microwave ovens. In a final rule published on April 8, 2009 (74 FR 16040), 

DOE established amended standards for gas cooking products, but again found that no active 

mode cooking efficiency standards were justified for electric cooking products, including 

microwave ovens. This rulemaking completed the second cycle of rulemakings required by the 

NAECA amendments to EPCA. (42 U.S.C. 6295(h)(2))

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 310 of EISA 2007 amends section 325 of EPCA to 

require any final rule establishing or revising a standard for a covered product, adopted after July 

10, 2010, shall incorporate standby mode and off mode energy use into a single amended or new 

standard. (42 U.S.C. 6295(gg)(3)(A)) If not feasible, the Secretary shall prescribe within the final 

rule a separate standard for standby mode and off mode energy consumption, if justified. (42 

U.S.C. 6295(gg)(3)(B))

Off mode is defined by EPCA 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 EPCA 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)) 

On November 15, 2007 DOE published an advanced notice of proposed rulemaking 

(ANOPR) (hereafter the “November 2007 ANOPR”) in which it stated that it was considering 

including standby power in the energy conservation standards and intended to initiate 

amendment of its test procedure to measure microwave oven standby power because: 1) energy 

conservation in standby mode represents a significant proportion of microwave oven annual 

energy consumption; and 2) the range of standby power among microwave ovens currently on 

the market suggests that the likely impact of a standard would be significant. 72 FR 64432. Such 

a test procedure change is a prerequisite to incorporate a standby power requirement as part of 

the energy conservation standard for microwave ovens.c

 

 Data obtained by DOE during testing of 

a representative sample of microwave ovens and from AHAM show standby power ranging from 
3-15

1.1 to 5.8 W. At the Federal Energy Management Program (FEMP)-defined limit of 2 W, the 

resulting 17.2 kWh of annual standby energy use represents 13 percent of the estimated annual 

cooking energy consumption for a baseline microwave oven. DOE was unaware of any 

microwave ovens currently on the market in the United States that consume energy in off-mode, 

differentiable from standby mode. Therefore, DOE proposed for the purposes of the October 

2008 NOPR to put forth standards for microwave oven standby power only, but stated it was 

open to consideration of off-mode standards. More recently, DOE determined, based on a survey 

of products available on the U.S. market, that four countertop microwave-only models produced 

by two manufacturers have electromechanical rotary dial controls and no displays, and which, 

therefore, are capable of operation in off mode. Because these units represent less than 1 percent 

of the models in Product Class 1 and because their power consumption is already low due to the 

lack of a display, any energy savings associated with off mode energy conservation standards for 

microwave ovens would be trivial. For these reasons, DOE did not consider standards for 

microwave oven off mode in this rulemaking.

As discussed above in section 3.4, the test procedure rulemaking to incorporate methods 

for measuring standby mode and off mode energy consumption for microwave ovens was 

initiated in parallel with the current rulemaking. Because DOE repealed the provisions in the 

existing microwave oven test procedure relating to the measurement of cooking efficiency and 

EF, the requirement to integrate energy consumption in standby mode and off mode into an 

overall energy descriptor does not apply. The absence of active mode provisions results in a de 

facto separate energy use descriptor for microwave oven standby mode and off mode energy use. 

Accordingly, DOE is amending energy conservation standards for microwave ovens based on

these separate energy use descriptors.

3.7.3 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.d

 

A number of countries have implemented regulatory approaches to standby power in 

microwave ovens. Australia has announced plans to implement a mandatory 1-W requirement 

for all consumer electrical products by 2013, including microwave ovens.11 The Korea Energy 

Management Corporation developed their “e-standby” program, which established a mandatory 

standby power limit of 1 W for microwave ovens that became effective in 2010.

12

 

d

 For more information, visit:

In accordance 

www.iea.org/.
3-16

with Japan’s Top Runner Program,e Japanese appliance manufacturers made a voluntary 

declaration to reduce standby power consumption of microwave ovens without a timer as close 

to 0 W as possible and that of microwave ovens with a timer to 1 W or lower.13 

The European Union (EU) passed the Ecodesign Directive in 2005 which calls for an 

implementing measure to reduce standby power for a specified list of energy using products, 

which include microwave ovens. On December 17, 2008, the Commission of the European 

Communities published Commission Regulation 1275/2008 implementing Ecodesign 

requirements for standby and off mode electric power consumption. Table 3.7.1 presents the 

criteria for standby and off mode power for microwave ovens, which are well in line with the 

IEA 1-W plan.14 

Table 3.7.1 EU Ecodesign Requirements for Standby and Off Mode

Mode Maximum Power (W)

Tier 1 (December 17, 2009)

Off (no function) 1.0 W

Standby (only reactivation function) 1.0 W

Standby (information or status display plus reactivation function 2.0 W

Tier 2 (December 17, 2012)

Off (no function) 0.5 W

Standby (only reactivation function) 0.5 W

Standby (information or status display plus reactivation function 1.0 W

3.8 VOLUNTARY PROGRAMS

DOE reviewed voluntary programs promoting energy efficient residential cooking 

products in the United States. DOE noted that FEMP establishes voluntary energy conservation 

standards for microwave ovens.

DOE’s FEMPf works to reduce the cost and environmental impact of the Federal 

government by advancing energy efficiency and water conservation, promoting the use of 

distributed and renewable energy, and improving utility management decisions at Federal sites. 

FEMP helps Federal buyers identify and purchase energy efficient equipment, including 

microwave ovens.

Executive Order 13221 Energy Efficient Standby Power Devices, signed July 31, 2001, 

requires that Federal agencies purchase commercially available products with low standby 

 

e Japan’s Energy Conservation Act uses a “top runner” method to set energy efficiency targets for residential, 

commercial, and transportation sector equipment. Target values for future products are set based on the level of the 

most energy efficient products on the market at the time of the value setting process. For more information, visit:

http://www.eccj.or.jp/index_e.html (Accessed May 2013.)

f For more information, visit: www.eere.energy.gov/femp.
3-17

power. 66 FR 40571. FEMP, therefore, issues standby power recommendations for microwave 

ovens. Table 3.8.1 presents the microwave oven recommendations.

Table 3.8.1 FEMP Recommendations for Microwave Ovens

Microwave Oven Classification Standby Power (W)*

Standard Microwave Oven ≤ 2 

* Based on test procedures specified in IEC Standard 62301

3.9 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 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.) 

3.9.1 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 microwave ovens, these products are also fixtures in virtually all new homes. 

Figure 3.9.1 presents the number of new single-family and multi-family housing units 

started in the United States from 1998–2011. Over the 5-year period from 2000–2005, singlefamily home starts increased 39.4 percent, to 1,716,000 units annually. However, between 2005 

and 2011, single-family home starts decreased 74.9 percent, to 430,600 units annually. Multifamily unit starts have remained relatively flat between 1998 and 2005, hovering around 350,000 

units annually. However, between 2005 and 2011, multi-family unit starts decreased by 49.4

percent, to around 178,200 units annually.15
3-18

Figure 3.9.1 New Privately Owned Single-Family and Multi-Family Housing Unit Starts in

the United States from 1998–2011 16

3.9.2 Unit Shipments

Appliance Magazine provides annual unit shipments for residential appliances, including 

microwave ovens. Table 3.9.1 presents annual shipments of microwave ovens. The shipments of 

microwave ovens increased 90.2 percent from 1995 to 2004, to about 15.5 million units in 2004. 

However, between 2004 and 2011, shipments of microwave ovens decreased by 38.7 percent, to 

9.5 million units in 2011.

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

1998 2000 2002 2004 2006 2008 2010

Housing Units Started (thousands of units)

Single-Family Units Multi-Family Units
3-19

Table 3.9.1 Industry Shipments of Microwave Ovens (Domestic and Import in Thousands of 

Units) 17

Year Microwave Ovens

2011 9,522

2010 9,334

2009 9,626

2008 11,340

2007 11,851

2006 13,687

2005 13,860

2004 15,526

2003 14,274

2002 13,311

2001 13,446

2000 12,644

1999 11,422

1998 10,365

1997 8,883

1996 8,771

1995 8,162

3.9.3 Value of Shipments

Table 3.9.2 provides the value of shipments for the household appliance industry from 

1994–2011 based upon data from the U.S. Census Bureau’s Annual Survey of Manufacturers

(ASM).

g

 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. Using the Gross Domestic Product 

Implicit Price Deflator, DOE converted each year’s value of shipments to 2011 dollars. In 

constant 2011 dollars, the value of shipments has declined by approximately 45 percent over the 

18-year period.

 

g Available online at: www.census.gov/manufacturing/asm/index.html.
3-20

Table 3.9.2 Household Appliance Manufacturing Statistics by Year18

Year Value of Shipments in 

Nominal Dollars ($1000) 

Value of Shipments in 2011

Dollars ($1000)

2011 17,969,800 17,969,800

2010 18,019,808 18,373,128

2009 18,707,942 19,454,109

2008 21,504,780 22,567,556

2007 23,639,716 25,350,157

2006 24,162,513 26,673,372

2005 24,620,117 28,063,733

2004 23,989,141 28,257,210

2003 23,026,196 27,892,529

2002 22,269,541 27,556,375

2001 22,298,897 28,039,541

2000 23,414,965 30,108,200

1999 22,372,219 29,390,353

1998 22,471,878 29,955,233

1997 21,394,445 28,841,448

1996 22,156,900 30,396,671

1995 22,391,300 31,302,947

1994 22,793,900 32,529,651

The increase in shipment volumes combined with an overall decrease in constant-dollar 

shipment values indicates that the U.S. 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 3.9.3 presents the various 

methods by which consumers dispose of their older appliances. 

Table 3.9.3 Disposition of Previous Appliance (Percentage)

19

Product Kept It

Left with 

Previous 

Home

Sold / 

Gave 

Away

Recycling 

Facility

Left at 

Curb for 

Disposal

Retailer 

Took 

Away

Microwave Ovens 17 11 32 12 23 4

The value of shipments of residential cooking products, a subset of the household 

appliance industry as defined in the ASM, has fluctuated over the last 18 years when viewed in 

2011 real dollars. Overall, the real value of shipments in 2011 was roughly 30 percent lower than 

the value in 1994. Table 3.9.4 provides the industry value of shipments for household cooking 

appliance manufacturing.
3-21

Table 3.9.4 Household Cooking Appliance Manufacturing Statistics by Year20

Year Value of Shipments in 

Nominal Dollars ($1000) 

Value of Shipments in 2011

Dollars ($1000)

2011 3,817,189 3,817,189

2010 3,719,380 3,792,307

2009 3,835,185 3,988,152

2008 4,261,482 4,472,086

2007 4,786,768 5,133,112

2006 4,864,268 5,369,741

2005 5,114,677 5,830,067

2004 4,798,227 5,651,912

2003 4,691,713 5,683,255

2002 4,327,308 5,354,620

2001 3,773,442 4,744,880

2000 4,178,958 5,373,525

1999 3,915,499 5,143,786

1998 3,849,167 5,130,977

1997 3,520,635 4,746,102

1996 3,564,700 4,890,351

1995 3,788,200 5,295,888

1994 3,813,900 5,442,896

3.9.4 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.

Table 3.9.5 shows selected import data from AHAM’s Import/Export Trade Report –

December 2009.21 For microwave ovens less than or equal to 22.5 inches in width, both the 

number and value of units imported decreased by more than 17 percent from 2008 to 2009. For 

microwave ovens 22.5 inches to 31.0 inches in width, both the number and value of units 

imported increased by more than 14 percent from 2008 to 2009. For microwave ovens greater 

than 31.0 inches in width, both the number and value of units imported decreased by more than 

10 percent. Overall, the value of major appliance imports decreased 9.2 percent from 2008 to 

2009.
3-22

Table 3.9.5 2008–2009 Imports of Microwave Ovens22

Appliance 

Description

Jan. –

Dec. 2009

Jan. –

Dec. 2008 % 

Change

Jan. –

Dec. 2009

Jan. –

Dec. 2008 % 

Units $ Mil (Nominal) Change

Microwave ovens ≤ 

22.5 inches 3,776,772 4,567,709 -17.3 130.042 165.033 -21.2

Microwave ovens > 

22.5-31.0 inches 3,263,577 2,392,880 36.4 135.885 118.232 14.9

Microwave ovens > 

31.0 inches 7,442,569 8,378,,434 -11.2 610.732 679.688 -10.1

Table 3.9.6 shows selected export data from AHAM’s Import/Export Trade Report –

December 2009.

23 For the 1-year period from 2008 to 2009, both the number and value of unit 

exports of microwave oven exports decreased by more than 16 percent. Overall, the total value 

of exports decreased 18.9 percent.

Table 3.9.6 2008–2009 Exports of Microwave Ovens 24

Appliance 

Description

Jan. –

Dec. 2009

Jan. –

Dec. 2008 %

Change

Jan. –

Dec. 2009

Jan. –

Dec. 2008 % 

Units $ Mil (Nominal) Change

Microwave ovens 235,883 294,660 -19.9 33.479 40.083 -16.5

3.10 MARKET SATURATION

AHAM’s Fact Book 2005 and Appliance Magazine’s annual Portrait of the Appliance 

Industry presents the market saturation for microwave ovens. As shown in Table 3.10.1, the 

percentage of households with microwave ovens has decreased slightly since 2001.
3-23

Table 3.10.1 Microwave Oven Saturation (Percentage of U.S. Households with Product) 25

Year Saturation (%)

2008 91.0

2007 90.0

2006 90.0

2005 95.0

2004 95.5

2003 95.0

2002 95.0

2001 95.7

2000 95.6

1999 93.1

1990 82.7

1982 25.6

1970 Negligible

3.11 PRODUCT RETAIL PRICES

Table 3.11.1 presents the average retail prices (in nominal dollars) of microwave ovens and 

the Consumer Price Index (CPI) of each year (1982–84 = 100). Prices of microwave ovens have 

decreased since 1980. 

Table 3.11.1 Residential Appliance Retail Prices 26

Product Average Retail Prices (Nominal $) Percent Change

1980 1994 2002 2005 1980–2005

Microwave Ovens 450 239 145 99 -78.0%

Consumer Price Index* 82.4 148.2 179.9 195.3 137.0% * U.S. Department of Labor, Bureau of Labor Statistics, Consumer Price Index: U.S. city average. 1982–84 = 100.

3.12 INDUSTRY COST STRUCTURE

DOE developed the household appliance industry cost structure from publicly available 

information from the ASM, (Table 3.12.1 and Table 3.12.2) and the U.S. Securities and 

Exchange Commission (SEC) 10-K reports filed by publicly owned manufacturers. Table 3.12.1

presents the home appliance industry employment levels and earnings from 1994–2011. The 

statistics illustrate a steady decline in the number of production and non-production workers in 

the industry. 
3-24

Because the ASM expresses all dollar values in constant 2011 dollars, the following table 

shows that as industry employment levels decline, the industry payroll is also decreasing. The 

percent decrease in total industry employees tracks closely with the percent decrease in payroll 

for all employees. 

Table 3.12.1 Household Appliance Industry Employment and Earnings 27

Year Production Workers 

(‘000)

All Employees 

(‘000)

Payroll for All Employees 

(2011 $ Mil)

2011 36.0 42.4 1,775.6

2010 38.3 45.0 1,924.4

2009 41.1 48.6 2,016.0

2008 48.0 56.4 2,359.9

2007 56.2 65.5 2,664.6

2006 60.0 69.7 2,918.8

2005 64.3 76.9 3,076.7

2004 69.4 83.7 3,408.7

2003 71.0 85.3 3,494.9

2002 73.5 88.8 3,704.1

2001 76.6 92.6 3,880.8

2000 82.7 99.5 4,227.6

1999 82.3 97.9 4,089.6

1998 82.4 99.5 4,118.4

1997 79.8 97.2 3,932.1

1996 87.1 108.1 4,214.1

1995 88.9 110.8 4,283.9

1994 90.4 110.4 4,269.2

Table 3.12.2 presents the employments levels and earnings for just the household laundry 

portion of the appliance industry from 1994–2011. Statistics for both employment levels and 

payroll show a steady fluctuation from 1994–2002, but both have declined since that time. 

Payroll and employment levels again show a positive correlation.
3-25

Table 3.12.2 Household Cooking Appliance Industry Employment and Earnings 28

Year Production Workers 

(‘000)

All Employees 

(‘000)

Payroll for All Employees 

(2011 $ Mil)

2011 8.8 9.9 366.2

2010 8.7 9.8 353.3

2009 8.7 10.0 367.1

2008 10.4 11.8 427.6

2007 13.1 15.5 499.6

2006 12.7 14.4 485.4

2005 14.3 16.4 590.5

2004 14.4 17.0 607.4

2003 14.5 17.3 595.1

2002 14.7 18.0 616.2

2001 14.8 17.5 620.7

2000 15.3 18.0 658.6

1999 16.4 19.1 729.5

1998 15.3 18.2 697.3

1997 14.4 17.5 643.8

1996 14.7 18.1 709.8

1995 15.2 19.2 705.0

1994 15.0 18.6 701.0

Table 3.12.3 presents the costs of materials and industry payroll as a percentage of value

of shipments from 1994–2011. The cost of materials as a percentage of value of shipments has 

fluctuated slightly over the 18-year period. 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 2000. Similarly, the cost of total payroll as a percentage of value 

of shipments has declined since 2000.
3-26

Table 3.12.3 Household Appliance Industry Census Data 29

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 55.7 7.3 9.9

2010 56.1 7.7 10.5

2009 54.0 7.5 10.4

2008 59.3 7.7 10.5

2007 57.8 8.0 10.5

2006 57.9 8.4 10.9

2005 56.9 8.3 11.0

2004 57.2 9.0 12.1

2003 55.9 9.2 12.5

2002 54.9 9.7 13.4

2001 56.8 10.1 13.8

2000 55.8 10.2 14.0

1999 54.7 10.2 13.9

1998 56.0 10.0 13.7

1997 52.8 9.9 13.6

1996 56.8 9.9 13.9

1995 57.7 9.4 13.7

1994 55.5 9.2 13.1

Table 3.12.4 shows the cost of materials and industry payroll as a percentage of value of 

shipments for the household cooking appliance industry from 1994–2011. DOE observed a 

decline in the cost of payroll as a percentage of value of shipments from 1999–2011. During that 

same time period, the cost of materials as a percentage of value of shipments has increased.
3-27

Table 3.12.4 Household Cooking Appliance Industry Census Data 30

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 59.2 7.1 9.6

2010 62.4 6.9 9.3

2009 60.7 6.8 9.2

2008 70.5 7.4 9.6

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

2001 63.3 9.3 13.1

2000 57.1 8.5 12.3

1999 58.4 10.1 14.2

1998 57.8 9.3 13.6

1997 51.0 9.4 13.6

1996 61.5 9.3 14.5

1995 53.6 8.4 13.3

1994 52.2 8.5 12.9

A detailed financial analysis of each of the products covered by this rulemaking is 

presented in the manufacturer impact analysis. (See chapter 12 of this TSD.) This analysis 

identifies key financial inputs including cost of capital, working capital, depreciation, and capital 

expenditures.

3.13 INVENTORY LEVELS AND CAPACITY UTILIZATION RATES

Table 3.13.1 and Table 3.13.2 show the year-end inventory for the household appliance 

and household cooking appliance industries, respectively, 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 1995. These data illustrate a general trend of domestic manufacturers retaining 

less of their inventories over time since 1994.
3-28

Table 3.13.1 Household Appliance Industry Census Data 31

Year End-of-Year Inventory 

(2011 $ Mil)

End-of-Year Inventory as a 

Percentage of Value

of Shipments (%)

2011 1,666.6 9.3

2010 1,768.8 9.6

2009 1,650.9 8.5

2008 1,942.6 8.6

2007 2,141.8 8.4

2006 1,864.0 7.0

2005 1,861.9 6.6

2004 2,058.7 7.3

2003 2,063.6 7.4

2002 2,218.9 8.1

2001 2,466.7 8.8

2000 2,783.4 9.2

1999 2,773.5 9.4

1998 2,990.3 10.0

1997 2,874.1 10.0

1996 3,154.1 10.4

1995 3,483.7 11.1

1994 3,610.8 11.1
3-29

Table 3.13.2 Household Cooking Appliance Industry Census Data 32

Year End-of-Year Inventory 

(2011 $ Mil)

End-of-Year Inventory as a 

Percentage of Value

of Shipments (%)

2011 324.2 8.5

2010 309.7 8.2

2009 307.9 7.7

2008 407.3 9.1

2007 597.5 11.6

2006 425.5 7.9

2005 492.9 8.5

2004 483.3 8.6

2003 472.7 8.3

2002 533.9 10.0

2001 420.3 8.9

2000 474.6 8.8

1999 533.6 10.4

1998 635.0 12.4

1997 549.2 11.6

1996 630.5 12.9

1995 749.2 14.1

1994 764.2 14.0

DOE obtained full production capacity utilization rates from the U.S. Census Bureau, 

Survey of Plant Capacity from 1994–2006. Table 3.13.2 presents utilization rates for various 

sectors of the household 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 

appliances in aggregate, along with the rates for cooking appliances and household laundry 

appliances, show a decrease in utilization from 1994–2006, although trends in subsets of that 

time period have fluctuated.
3-30

Table 3.13.3 Full Production Capacity Utilization Rates33

Year Rates (%)

Household Appliances Cooking Appliances

2006 77 65

2005 74 57

2004 76 61

2003 78 57

2002 72 56

2001 70 50

2000 70 55

1999 75 59

1998 73 66

1997 73 61

1996 76 66

1995 79 66

1994 82 72

3.14 TECHNOLOGY ASSESSMENT

This section provides a technology assessment for residential microwave ovens. 

Contained in this technology assessment are details about product characteristics and operation 

(section 3.14.1), an examination of possible technological improvements (section 3.14.2), and a 

characterization of the product efficiency levels currently commercially available (section 

3.14.3).

3.14.1 Product Operations and Components

In preparation for the screening and engineering analyses, DOE prepared a brief 

description of the characteristics and operation of microwave ovens. This description provides a 

basis for understanding the technologies used to improve product efficiency.

A microwave oven is an appliance which heats foods and beverages through the 

conversion of electric energy to electromagnetic (microwave) energy. The food load is placed 

inside the oven where it is subjected to the microwave energy. In the portion of the 

electromagnetic spectrum where microwave ovens operate, typically 2.45 gigahertz, water, fats, 

and sugars have high absorptivity and thus food heats readily. Glass, ceramic, and paper have 

low absorptivity at these wavelengths, while metals reflect the microwaves. Therefore 

microwave ovens efficiently heat foods while the cavity and utensils remain cool.

The microwave energy is generated by an electrically powered magnetron. Line voltage 

is converted to the high voltage required by the magnetron through the use of a high-voltage 

transformer. The microwaves are directed from the magnetron into the cavity via a metal duct, or 

waveguide. To produce a more uniform distribution of energy in the cavity, one or more sets of 
3-31

rotating metal blades may be inserted in the waveguide to alter the microwave patterns 

temporally. Additionally, a turntable or translating tray may be provided in the cavity to move 

the load and thus promote even input energy distribution to the food. Some microwave ovens are 

equipped with electrically powered resistive or halogen heating elements for surface browning,

and in the case of convection microwave ovens, an additional fan for forced air distribution to 

enhance cooking performance.

To achieve reduced power levels for cooking processes such as defrosting, the magnetron 

is typically cycled at an appropriate duty cycle which results in a time-averaged power at the 

desired level. Electronic controls may include a clock/timer function as well as a humidity or 

infrared sensor for detecting when the desired level of cooking is achieved. Standby power 

consumption associated with these electronics, as well as the power supply, may be several 

watts. In addition, a light is typically provided that is turned on when the door is opened or when 

the microwave oven is in operation.

Installation configurations include countertop, built-in, and over-the-range. Over-therange microwave ovens may include a ventilation fan that turns on automatically when the 

temperature from the cooktop below it rises; however, such an option is not required for 

certification under the Underwriters Laboratories (UL) Standard UL 923 - Edition 5, Standard 

for Microwave Cooking Appliances.

3.14.2 Technology Options

In order to gain a deeper understanding of the technological improvements used to 

increase the efficiency of microwave ovens, DOE identified several possible technologies and 

examined the most common improvements used in today’s market. Because DOE only addressed

energy conservation standards for standby mode and off mode in this rulemaking, the following 

discusses only technology options associated with reducing standby mode and off mode energy 

consumption.

For microwave oven standby power, DOE investigated technology options for decreasing 

standby power. DOE identified lower-power display technologies, cooking sensors with no 

standby power requirement, improved power supplies, improved control boards, and automatic 

power-down from research, literature review, and reverse-engineering conducted as part of the 

microwave oven analysis discussed in chapter 5 of this TSD.

Table 3.14.1 Technology Options for Microwave Oven – Standby Power

1. Lower-power display technologies

2. Cooking sensors with no standby power requirement

3. Improved power supply and control board options

4. Automatic power-down

Lower-power display technologies 
3-32

Microwave ovens are generally constructed using one of the following display types to 

show clock time and other parameters: (1) VFD; (2) backlit and non-backlit LCD; and (3) LED 

display. Within the 32-unit sample that DOE examined as part of the supplemental microwave 

oven analysis, microwave ovens equipped with VFDs consumed the most power on average, 

followed by units featuring backlit LCDs, LED displays, and non-backlit LCDs. Data gathered 

by DOE show that standby power levels between 1.1 W and 1.4 W have been achieved in 

microwave ovens using either LED displays or LCDs, and standby power as low as 1.7 W has 

been achieved using a backlit LCD. The lowest standby power observed for a microwave oven

with a VFD was 2.5 W. One display manufacturer stated to DOE that backlit LCDs can consume 

up to 50 percent less energy than VFDs in sizes applicable to microwave ovens, depending on 

the number of LED backlights used to light the display. The manufacturer also stated that power 

requirements for LED displays are generally lower than for VFDs, but the difference depends 

considerably on the number of diode segments being lit. 

The display format on each microwave oven can be characterized as either numeric, 

alphanumeric, or dot matrix. Whereas numeric displays can only show time, alphanumeric 

displays can also display letters, though usually in simplified format. Dot matrix displays can 

show various fonts, letters, and symbols via matrices of pixels, making them the most flexible 

display technology. In the reverse-engineering analysis DOE conducted as part of chapter 5 of 

this TSD, DOE observed that LED displays found in microwave ovens generally contain four

numeric characters and a row of small words. LCDs and VFDs allow for more flexibility in the 

numbers and words being displayed by using alphanumeric characters without increasing the 

power requirements. LED displays could conceivably achieve this, but would require more diode 

segments, increasing the cost of the display. However, this would not affect standby power,

assuming that in standby mode only the clock time is being displayed. 

Cooking sensors with no standby power requirement

Cooking sensors detect that a desired cooking state of the food load has been reached and 

then signal the controller to shut off the microwave oven, thereby preventing excess energy 

consumption in addition to optimizing food quality. During product teardowns (see chapter 5 of 

this TSD), DOE found that the most common identifiable cooking sensors are absolute humidity 

sensors, which infer the cooking state of the food load from the amount of water vapor being 

released into the cavity. This sensor technology requires standby power in the range of 1–2 W to 

maintain a resistive heating element, whose conductivity changes in response to the presence of 

water vapor or other gases, at operating temperature. If the power to the sensor is switched off

and back on, warm-up times in excess of two minutes may be required. Standby testing by DOE 

and AHAM revealed no microwave ovens with cooking sensors that consume less than 2 W in 

standby mode. One Japanese microwave oven manufacturer stated during discussions with DOE

that they are unaware of any humidity sensors that do not require standby power to stay warm.

DOE has conducted research of cooking sensor technologies and has identified cooking 

sensors with zero standby power requirements and little to no warm-up time. During the reverseengineering analysis for microwave ovens (discussed in more detail in chapter 5 of this TSD),

DOE observed that microwave ovens from one manufacturer use a piezoelectric steam sensor 
3-33

which requires zero power in standby mode. This sensor technology functions by detecting a 

burst of steam created from moisture in the food as it cooks. Low frequency (approximately 10 

hertz) components of the output signal of the device correlate with the amount of hot steam 

emitted from the load. In addition to steam sensors, DOE has identified infrared and weight 

sensors with little to no warm-up time that do not consume standby power and that have been 

applied in microwave ovens currently on the Japanese market. Infrared cooking sensors detect 

the temperature of the load in the field of view of the sensing element. These sensors may require 

multiple elements or a motor to rotate the sensor in order to expand the field of view to 

accurately measure the temperature of the food load depending on where it is placed in the oven 

cavity. Weight sensors determine the cooking state based upon the reduction in weight of the 

food load, which results from moisture evaporating from the load. DOE has also identified 

relative humidity sensors as a type of zero-standby power sensor that can be applied in a 

microwave oven, but is unaware of any microwave ovens on the market that use this type of 

sensor. 

Lastly, DOE is aware of an absolute humidity sensor capable of short warm-up times (5–

10 seconds) that requires no standby power. Based on DOE’s discussions with the manufacturer 

of the zero-standby power absolute humidity sensor, only the microwave oven controls would 

need to be modified to add transistors to de-energize the cooking sensor while in standby mode, 

and the added cost of components would be approximately $0.10. DOE also notes that the sensor 

manufacturer supplies absolute humidity sensors (all of which are capable of short warm-up 

times) to multiple microwave oven manufacturers that comprise a significant portion of the U.S. 

market, and one manufacturer indicated that it is currently using this zero-standby power cooking 

sensor in its microwave ovens. Manufacturers also noted that the reliability of these zero-standby 

power absolute humidity sensors has not been an issue. One manufacturer noted that the 

reliability is expected to be improved compared to previous sensor types because the zerostandby absolute humidity sensors are only energized during the cooking cycle, whereas the 

previous sensors are energized continuously for the lifetime of the product.

Improved power supply and control board options

A potential area for standby power improvements is the power supplies on the control 

board. Multiple improvement paths with varying risk to manufacturers are available, including 

the selective upgrading of power supply components to boost efficiency, the reduction of peak 

power demand through the use of lower-power components, and the transition to switching 

power supplies.

Typically, microwave 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.
3-34

According to power supply topology experts that DOE consulted, the quality of the 

transformer core material, diode type, capacitor quality, and voltage regulator selection could cut 

no-load standby power for the power supply in half and boost conversion efficiency from 40 to 

70 percent. For a typical 5 W-output microwave-only unit power supply, that no-load power 

consumption improvement is approximately 0.4 W. DOE notes that other sources indicate that 

optimized transformers may reduce no-load losses by approximately 0.6–0.7 W.34

DOE also notes that switching from low-cost electromechanical relays (used to drive 

components such as heaters, motors, lights, etc.) to low operating power electromechanical 

relays or solid state relays capable of driving the same loads as the low-cost electromechanical 

relays would decrease the total power requirements of the control board and thus decrease the 

standby losses of the power supply. As a result the overall standby power consumption of the 

microwave oven would also decrease.

Switching power supplies offer the highest conversion efficiencies of up to 75 percent for 

switch mode power supply designs for appliance applications for power supply sizes similar to 

those of microwave ovensh and lowest no-load standby losses (0.2 W or less), though at a higher 

cost, higher part count, and greater complexity. DOE is unaware of any switching power supply 

designs for application in microwave ovens that can achieve efficiencies above 75 percent. 

Switching power supplies 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. DOE notes that there already are some premium 

microwave ovens on the U.S. market that incorporate switching power supplies. DOE research 

suggests that the component prices for switch mode power supplies and traditional linear power 

supplies are currently nearly equivalent. However, DOE concludes that the industry will likely 

transition to switch mode power supplies as it gains more experience with them, causing 

switching component prices to fall further as volumes increase. Additionally, the adoption of 

switch mode power supplies would facilitate standardized control boards for world-wide use, 

thereby reducing testing and development costs.

Automatic power-down

Manufacturers could also meet very low (i.e., less than 1 W) standby power levels 

according to the definition of “standby mode” in the DOE test procedure by incorporating an 

automatic function that turns off most power-consuming components once a period of inactivity 

has elapsed. Such a low-consumption state could be user-selectable on demand, or could be the 

default condition in which the microwave oven is shipped such that the consumer would be 

required to opt in to maintaining the display, cooking sensor, or other utility feature during 

standby. DOE has determined that some microwave oven suppliers to the U.S. market have 

already taken such approaches to meet prescriptive standby power standards in other markets 

such as Japan. DOE understands that such a technology option would likely require a relay-

 

h Information on design and efficiencies of switch mode power supplies is available from Power Integrations at: 

www.powerint.com/en/applications/major-appliances. (Accessed May 2013.)
3-35

driven disconnect between line power and the power filtration board typically incorporated in 

microwave ovens. The automatic power-down module would feature a 1.5 W switch mode 

power supply that can respond to a simple switch signal to power up and enable microwave oven 

operation via a relay on the power filtration board. If the existing door switches do not suffice, an 

additional door switch could provide the necessary signal to enable the power supply, whose 

power consumption is otherwise nearly 0 W. Thus, the microwave oven would power up, 

enabling a light to be energized, with a delay short enough to be perceived as instantaneous when 

the consumer opens the door.

3.14.3 Energy Efficiency

In preparation for the screening and engineering analyses, DOE gathered data on the 

energy efficiency of microwave ovens currently available in the marketplace. These data were 

taken from databases maintained by a variety of regulatory agencies. 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 microwave 

ovens covered by this rulemaking.

Microwave ovens consume energy in standby mode while powering the clock and 

electronic controls. AHAM recently provided data on microwave oven standby power for a 

sample of microwave ovens available in the U.S. market.

35 Figure 3.14.1 displays AHAMmeasured microwave oven standby power data as a function of rated cooking output power. 

Figure 3.14.1 also contains standby power measurements made by the Canadian Building Energy 

End-Use Data and Analysis Center (CBEEDAC) at the University of Alberta. The data show a 

wide range of standby power, but there is no correlation between standby power and rated

cooking output power. The data did not indicate the product type (i.e., microwave only, 

convection) or the installation configuration (i.e., countertop, over-the-range, built-in). 
3-36

Figure 3.14.1 AHAM and CBEEDAC Microwave Oven Standby Power versus Rated 

Output Power36,37

Until recently, FEMP published a database of microwave ovens, sorted by standby 

power, although this database was also not completely representative of microwave ovens 

available in the U.S. market. Figure 3.14.2 displays the number of microwave oven models 

available with different standby power levels as listed in the FEMP standby power database as of 

May 2011.

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

600 800 1000 1200 1400 1600

Rated Output Power (W)

Standby Power Consumption (W)

AHAM Data CBEEDAC Data
3-37

Figure 3.14.2 Microwave Oven Standby Power Consumption from the FEMP Database38

FEMP did not specify microwave oven rated cooking output power. Therefore, it is not 

possible to make a direct comparison between the FEMP microwave oven standby power data 

and the AHAM microwave oven test data. However, the distributions of standby power in the 

FEMP database and the AHAM data are similar. No residential microwave ovens consume less 

than 1 W of standby power, some microwave ovens consume less than 2 W of standby power, 

but the large majority of microwave ovens consume between 2.1 and 3 W of standby power.

0

26

56

33

0

10

20

30

40

50

60

0 to 1 W 1.1 to 2 W 2.1 to 3 W 3.1 W or greater

Number of Models

Standby Power
3-38

REFERENCES

 1 “U.S. Appliance Industry: Market Share, Life Expectancy & Replacement Market, and 

Saturation Levels.” Appliance Market Research Report, Appliance Magazine, January 2010.

2 Ibid.

3 Maytag. 2005. “Whirlpool Corporation and Maytag Corporation Sign Definitive Merger 

Agreement.” Maytag Press Release, August 22, 2005.

4 P. Hussmann. 2006. “Justice to Extend Maytag-Whirlpool Merger Review.” Newton Daily 

News Online, February 14, 2006.

5 W. Ryberg. 2006. “Harkin, Boswell want Maytag sale blocked.” Des Moines Register, 

January 13, 2006.

6 Ibid.

7 U.S. Department of Justice (U.S. DOJ). 2006. Department of Justice Antitrust Division 

Statement on the Closing of its Investigation of Whirlpool’s Acquisition of Maytag, March 29, 

2006.

8 Ibid.

9 Association of Home Appliance Manufacturers (AHAM) Fact Book 2005.

10 AHAM Fact Book 2003.

11 Tiele, Simone. 2008. Australia’s Approach to Standby Power. Department of the 

Environment, Water, Heritage and the Arts, Australia, April 2, 2008. Available online at: 

www.energyrating.gov.au/wpcontent/uploads/Energy_Rating_Documents/Library/Standby_Power/Standby_Power/2008-

standby-holt.pdf. (Accessed May 2013.)

12 Korea’s Energy Standards & Labeling–Market Transformation. Ministry of Knowledge 

Economy and Korea Energy Management Corporation. Available online at:

www.kemco.or.kr/new_eng/pg02/pg02100101.asp. (Accessed May 2013.)

13 Japan’s Top Runner Program. 2005. Final Report by Microwave Oven Evaluation Standard 

Subcommittee. Prepared by Japan’s Energy Efficiency Standards Subcommittee of the 

Advisory Committee for Natural Resources and Energy. November 2005. Available online at 

www.eccj.or.jp/top_runner/pdf/tr_microwaveoven.pdf. (Accessed May 2013.)

14 Commission of the European Communities. 2008. Commission Regulation (EC) No 

1275/2008 implementing Directive 2005/32/EC of the European Parliament and of the Council 
3-39

 

with regard to ecodesign requirements for standby and off mode electric power consumption of 

electrical and electronic household and office equipment. December 17, 2008. Available 

online at: http://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2008:339:0045:0052:en:PDF. (Accessed 

May 2013)

15 U.S. Census Bureau. 2012. “Table 3. New Privately Owned Housing Units Started.” New 

Residential Construction, October 2012. Available online at:

www.census.gov/construction/nrc/pdf/newresconst.pdf. (Accessed May 2013.)

16 Ibid.

17 “2011 Full-Year Appliance Industry Shipment Statistics.” Market Research, Appliance 

Magazine, March 2012, and AHAM Fact Book 2005. Op. cit.

18 U.S. Census Bureau. Annual Survey of Manufacturers (ASM): Value of Product Shipments: 

Value of Shipments in Product Classes. 1994–2011, issued annually.

19 AHAM Fact Book 2003.

20 U.S. Census Bureau. 2009. Statistics.

21 AHAM. Import/Export Trade Report – December 2009. 2010.

22 Ibid.

23 Ibid.

24 Ibid.

25 AHAM Fact Book 2005 and “The Share-of-Market Picture” Appliance Magazine, September 

2007–2009.

26 AHAM Fact Book 2005.

27 U.S. Census Bureau. 2011. “Statistics for Industry Groups and Industries.” Annual Survey of 

Manufactures (ASM), November 2012. Available online at:

www.census.gov/manufacturing/asm/. (Accessed May 2013.)

28 Ibid.

29 Ibid.

30 Ibid.
3-40

 

31 Ibid.

32 Ibid.

33 U.S. Census Bureau, Survey of Plant Capacity: 2006 to 1996.

34 N. Nielsen. “Loss Optimizing Low Power 50 Hz Transformers Intended for AC/DC Standby 

Power Supplies.” Applied Power Electronics Conference and Exposition, 2004. IEEE, pp. 

420–425, September 9, 2004.

35 AHAM. 2006. AHAM Test Data on Microwave Ovens. October 23, 2006. Docket Number 

EE-2006-STD-0127, Comment Number 17, November 30, 2006.

36 Ibid.

37 N. Fish, D. White, and D. Ryan. 2006. Standby Power Energy Use of Common Household 

Appliances. Canadian Building Energy End-Use Data and Analysis Center, University of 

Alberta, Canada, July 2006.

38 FEMP Microwave Standby Power Database. Available online at

http://www1.eere.energy.gov/femp/technologies/standby_power.aspx. (Accessed May 2013.)
4-i

CHAPTER 4. SCREENING ANALYSIS

TABLE OF CONTENTS

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

4.2 SCREENED-OUT TECHNOLOGY OPTIONS ............................................................. 4-1

4.3 REMAINING DESIGN OPTIONS ................................................................................. 4-4

LIST OF TABLES

Table 4.2.1 Retained Design Options for Microwave Ovens–Standby Power....................... 4-4
4-1

CHAPTER 4. SCREENING ANALYSIS

4.1 INTRODUCTION

This chapter details 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 microwave ovens (chapter 3 of this technical support document (TSD)). In the market 

and technology assessment, DOE presented an initial list of technologies that can be used to 

reduce the standby power consumption of microwave ovens. The goal of the screening analysis 

is to identify any technology options that will be eliminated from further consideration in the 

rulemaking analyses.

The candidate technology options are assessed based on DOE’s 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 standby 

consumption 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 SCREENED-OUT TECHNOLOGY OPTIONS

The following section details the specific technology options that were screened out prior 

to the engineering analysis, along with the rationale for elimination.

The technologies identified in the market and technology assessment were evaluated 

pursuant to the criteria set out in the Energy Policy and Conservation Act (EPCA). (42 United 

States Code (U.S.C.) 6291-6309) EPCA establishes criteria for prescribing new or amended 

standards designed to achieve the maximum improvement in energy efficiency. Furthermore, 

EPCA directs the Secretary of Energy to determine whether a standard is technologically feasible 

and economically justified. (42 U.S.C. 6295(o)(2)(A)(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 energy conservation 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 conservation
4-2

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.

After reviewing the technology options to reduce standby power consumption of 

microwave against the screening criteria, DOE did not eliminate any technology options from 

consideration in its standby power rulemaking analysis for microwave ovens.

For lower-power display technologies, DOE understands that the consumer utility of a 

microwave oven display is associated with its brightness, viewing angle, and ability to display 

complex characters. Interviews DOE conducted with display manufacturers revealed that 

vacuum fluorescent displays (VFDs) can achieve higher brightness levels, wider viewing angles, 

and higher contrast than backlit liquid crystal displays (LCDs). Display manufacturers also stated 

that light-emitting diode (LED) displays have largely comparable performance to VFDs in terms 

of brightness and viewing angle. A VFD manufacturer mentioned that, while VFD technologies 

with efficiencies comparable to backlit LCDs do exist, such displays are substantially more 

expensive than the ones commonly found in microwave ovens today. 

Multiple manufacturers of cooking products stated in interviews the need to differentiate 

their cooking appliance lines from those of their competitors with (among other features) 

coordinated displays and user interfaces. Manufacturers noted that LCD displays (backlit or not) 

do not work well in appliances that get very hot, such as ovens, due to thermal limitations in the 

technology. Manufacturers also opposed switching entirely to LED-based displays since it could 

make it harder for them to differentiate their products, particularly in a market as commoditized 

as microwave ovens. Lastly, manufacturers noted that larger, more complex, and more colorful 

displays are usually associated with premium appliances, which will thus have a harder time 
4-3

achieving the same standby power consumption as units with smaller, dimmer, and simpler 

displays. 

DOE notes that the current rulemaking does not seek to regulate the standby power 

consumption of conventional cooking appliances. DOE also notes that not all high-end appliance 

manufacturers use the same display technology across all cooking appliances that they 

manufacture. For example, at least one manufacturer uses a backlit LCD in its microwave oven, 

with the backlighting LEDs color-coordinated with the VFDs found in its ovens.

DOE recognizes that certain microwave ovens, such as over-the-range microwave ovens,

may be exposed to higher temperature and humidity conditions than other installation conditions. 

As discussed in chapter 5 of this TSD, DOE conducted accelerated lifetime testing of different 

microwave oven display types by subjecting a limited sample of microwave ovens to high 

temperatures and humidity levels for an extended period of operation in standby mode. The 

results of this testing demonstrated that the illuminance of each display tended to decrease over 

time to varying degrees, but did not reveal any correlation between display type and rate of 

illuminance reduction. In addition, DOE observed in its test sample a unit with an LED display 

that exhibited illuminance, a significant element of consumer utility, that was comparable to that 

of the VFD on another unit. Based on this lifetime testing, and the existence of multiple over-therange microwave oven models on the market with each type of display technology, DOE does 

not have information that would suggest over-the-range microwave ovens would require certain 

display technologies (i.e., VFD) that have inherently higher power consumption than other 

display types that provide similar consumer utility.

For these reasons, DOE believes that lower-power display technologies which maintain 

consumer utility can be incorporated in microwave ovens, and meet all of the screening criteria.

Similarly, the number of different sensor technologies available on the market that do not 

require standby power suggests that the utility of a cooking sensor can be maintained with zero 

standby power. Since improved power supplies and control board options also have no consumer 

utility impacts and have been incorporated in products currently available on the market, both of 

these technology options meet the screening criteria.

For automatic power-down, DOE interviewed the Japanese Electrical Manufacturers’ 

Association (JEMA). These discussions revealed that there are a large number of microwave 

ovens in the Japanese market that implement this feature. For a power-down function, DOE 

believes that the entire microwave oven can be switched off, with a dedicated, low power 

controller waiting for a controls input, door opening, etc. before re-animating the rest of the 

microwave. This controller then subsequently detects a period of inactivity before switching the 

rest of the microwave oven off again. Power consumption could be reduced to well below 1 W, 

though at a reduction in consumer utility since displays, night lights, etc. would remain off until 

a consumer interacts with the microwave oven. DOE has determined, however, that control 

strategies are available to allow manufacturers to make design tradeoffs between incorporating 

standby-power-consuming features such as displays or cooking sensors and including a function 
4-4

to turn power off to these components during standby. Therefore, this technology option was 

retained for further analysis.

4.3 REMAINING DESIGN OPTIONS

The following sections list the technology options for each product covered by this 

rulemaking that were retained by DOE and subsequently designated as design options. Each of 

these technologies will be evaluated further in the subsequent engineering analysis.

Table 4.3.1 Retained Design Options for Microwave Ovens–Standby Power

1. Lower-power display technologies

2. Cooking sensors with no standby power requirement

3. Improved power supply and control board options

4. Automatic power-down
5-i

CHAPTER 5. ENGINEERING ANALYSIS

TABLE OF CONTENTS

5.1 INTRODUCTION ........................................................................................................... 5-1

5.2 PRODUCT CLASSES ANALYZED.............................................................................. 5-1

5.3 EFFICIENCY LEVELS................................................................................................... 5-2

5.3.1 Baseline Units.................................................................................................................. 5-2

5.3.2 Incremental Standby Power Levels ................................................................................. 5-3

5.4 METHODOLOGY OVERVIEW.................................................................................... 5-9

5.4.1 AHAM Data Submittal .................................................................................................. 5-10

5.4.2 Manufacturer Interviews................................................................................................ 5-10

5.4.3 Product Teardowns ........................................................................................................ 5-10

5.4.3.1 Selection of Units......................................................................................... 5-11

5.4.3.2 Generation of Bill of Materials.................................................................... 5-11

5.4.3.3 Cost Structure of the Spreadsheet Models................................................... 5-12

5.4.4 Product Testing .............................................................................................................. 5-13

5.5 ANALYSIS AND RESULTS........................................................................................ 5-13

5.5.1 AHAM Data................................................................................................................... 5-13

5.5.2 Product Testing and Teardowns..................................................................................... 5-15

5.5.2.1 General Microwave Oven Construction ...................................................... 5-16

5.5.2.2 Test Units..................................................................................................... 5-17

5.5.2.3 Microwave-Only Ovens: Product Testing and Teardowns.......................... 5-19

5.5.2.4 Countertop Convection Microwave Ovens: Product Testing and 

Teardowns.................................................................................................... 5-26

5.5.2.5 Built-In and Over-the-Range Convection Microwave Ovens: Product 

Testing and Teardowns................................................................................ 5-28

5.5.2.6 Over-the-Range Microwave Oven Display Accelerated Lifetime 

Testing5-30

5.5.3 Incremental Cost Data.................................................................................................... 5-35

5.5.3.1 Incremental Cost Data for Microwave-Only Ovens and Countertop 

Convection Microwave Ovens..................................................................... 5-35

5.5.3.2 Incremental Cost Data for Built-In and Over-the-Range Convection 

Microwave Ovens........................................................................................ 5-37

LIST OF TABLES

Table 5.3.1 Standby Power Levels for Microwave-Only Ovens and Countertop 

Convection Microwave Ovens............................................................................. 5-4

Table 5.3.2 Standby Power Levels for Built-In and Over-the-Range Convection 

Microwave Ovens................................................................................................ 5-4

Table 5.4.1 Engineering Analysis Methods.......................................................................... 5-10
5-ii

Table 5.5.1 Microwave Oven Standby Power Comparison.................................................. 5-15

Table 5.5.2 Microwave Oven Features in DOE Sample....................................................... 5-18

Table 5.5.3 DOE Microwave-Only Oven Standby Power (Table 1 of 4)............................. 5-20

Table 5.5.4 DOE Microwave-Only Oven Standby Power (Table 2 of 4)............................. 5-20

Table 5.5.5 DOE Microwave-Only Oven Standby Power (Table 3 of 4)............................. 5-20

Table 5.5.6 DOE Microwave-Only Oven Standby Power (Table 4 of 4)............................. 5-21

Table 5.5.7 DOE Countertop Convection Microwave Oven Standby Power....................... 5-27

Table 5.5.8 Control Power Supply Transformer Standby Power Test Results..................... 5-28

Table 5.5.9 DOE Built-In and Over-the-Range Convection Microwave Oven Standby 

Power ................................................................................................................. 5-29

Table 5.5.10 Incremental Manufacturing Costs for Product Class 1 Microwave Oven 

Standby Power ................................................................................................... 5-37

Table 5.5.11 Incremental Manufacturing Costs for Product Class 2 Microwave Oven 

Standby Power ................................................................................................... 5-38

LIST OF FIGURES

Figure 5.4.1 Manufacturing Cost Assessment Stages............................................................ 5-12

Figure 5.5.1 AHAM Microwave Oven Standby Power......................................................... 5-14

Figure 5.5.2 Countertop Microwave Oven Standby Power Consumption as a Function 

of Rated Output Power in DOE Reverse-Engineering Sample ......................... 5-22

Figure 5.5.3 Built-In Microwave Oven Standby Power Consumption as a Function of 

Rated Output Power in DOE Reverse-Engineering Sample.............................. 5-23

Figure 5.5.4 AHAM Microwave Oven Standby Power Consumption as a Function of 

Rated Output Power........................................................................................... 5-24

Figure 5.5.5 DOE Standby Power Consumption as a Function of Display Size for 

Countertop Convection Microwave Ovens........................................................ 5-27

Figure 5.5.6 DOE Standby Power Consumption as a Function of Display Size for 

Built-In and Over-the-Range Convection Microwave Ovens ........................... 5-30

Figure 5.5.7 67.5 °C, 90-percent RH, Illuminance at Start of 10-Minute Measurement 

Periods................................................................................................................ 5-32

Figure 5.5.8 67.5 °C, 90-percent RH, Illuminance at End of 10-Minute Measurement 

Periods................................................................................................................ 5-33

Figure 5.5.9 82.5 °C, 90-percent RH, Illuminance at Start of 10-Minute Measurement 

Periods................................................................................................................ 5-34

Figure 5.5.10 82.5 °C, 90-percent RH, Illuminance at End of 10-Minute Measurement 

Periods................................................................................................................ 5-35
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 standby energy consumption and costs of products at various 

levels of increased efficiency. This section provides an overview of the engineering analysis 

(section 5.1), discusses proposed product classes (section 5.2), establishes baseline unit 

specifications (section 5.3.1), discusses incremental efficiency levels (section 5.3.2), explains the 

methodology used during data gathering (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 technical support document (TSD)) and technology 

options from the screening analysis (chapter 4.) Additional inputs include laboratory testing and 

reverse-engineering of representative microwave ovens. 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, they serve as the input to the building 

energy-use and end-use load characterization (chapter 7) and the life-cycle cost (LCC) and 

payback period (PBP) analyses (chapter 8).

DOE typically structures its engineering analysis around one of three methodologies. 

These are: (1) the design-option approach, which calculates the incremental costs of adding 

specific design options to a baseline model that will improve its efficiency (i.e., lower its energy 

use in standby mode and off mode); (2) the efficiency-level approach, which calculates the 

relative costs of achieving increases in energy efficiency levels (in this case, levels of reduced 

standby power), 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 being analyzed. Deciding which methodology to use for the 

engineering analysis depends on the product, the design options under study, and any historical 

data that DOE can draw on.

5.2 PRODUCT CLASSES ANALYZED

DOE separated residential microwave ovens into product classes. Because DOE 

formulated a separate energy conservation standard for standby power consumption for each 

product class, the criteria for separation into different classes are (1) type of energy used (natural 

gas or electricity), 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)) 
5-2

For the reasons discussed in chapter 3 of this TSD, DOE has analyzed for the purposes of 

this final rule the following two product classes for microwave ovens:

• Product Class 1: Microwave-only ovens and countertop convection microwave ovens

• Product Class 2: Built-in and over-the-range convection microwave ovens

5.3 EFFICIENCY LEVELS

5.3.1 Baseline Units

DOE selected baseline units as reference points for each product class, against which 

DOE measured changes resulting from energy conservation standards. The baseline unit in each 

product class represents the basic characteristics of equipment in that class. Typically, a baseline 

unit is a unit that just meets current required energy conservation standards and provides basic 

consumer utility. 

DOE used the baseline units in the engineering analysis and the life-cycle-cost and 

payback-period analysis. To determine energy savings and changes in price, DOE compared 

each higher energy efficiency design option with the baseline unit. 

The identification of baseline units required establishing the baseline standby power

level. For microwave oven standby power, energy conservation standard levels are expressed as 

a maximum standby power consumption, in watts (W). As discussed in chapter 3 of this TSD, 

DOE determined based on a survey of products available on the U.S. market that four countertop 

microwave-only models produced by two manufacturers have electromechanical rotary dial 

controls and no displays, and which, therefore, are capable of operation in off mode. Because 

these units represent less than 1 percent of the models in product class 1 and because their power 

consumption is already low due to the lack of a display, any energy savings associated with off 

mode energy conservation standards for microwave ovens would be trivial. For these reasons, 

DOE did not consider standards for microwave oven off mode. Based on product testing (see 

section 5.6.1.3), DOE selected a baseline standby power level which was the highest typical 

standby power for a microwave oven to provide full consumer utility for each product class. For 

the purpose of this rulemaking, DOE determined consumer utility to be the ability to display 

complex characters, brightness, and viewing angle of the display as well as the ability to 

automatically control the cooking operation (i.e., through the use of a cooking sensor). For the 

built-in and over-the-range convection microwave ovens product class, consumer utility also 

included a sufficiently sized power supply capable of operating additional features required to 

handle the thermal loads associated with their installation and to provide consumer utility, such 

as additional exhaust fan motors, convection fan motors and heaters, and additional lights. For 

both product classes, the baseline power supply used a conventional linear power supply with an 

efficiency of approximately 40 percent or less. DOE selected a baseline standby power level of 

4.0 W for the microwave-only ovens and countertop convection microwave ovens product class.
5-3

For the built-in and over-the-range convection microwave ovens product class, DOE selected a 

baseline standby power level of 4.5 W. As discussed in section 5.5.1.2, DOE noted that the builtin convection microwave ovens in its test sample consumed more standby power than the overthe-range convection microwave ovens. However, DOE notes that built-in and over-the-range 

convection microwave ovens have essentially the same features (i.e., exhaust fans and 

convection heaters). DOE also noted that the two built-in convection microwave ovens in its test 

sample were high-end units with large displays and a larger number of cooking settings and 

options. As a result, DOE believes that the standby power level of 4.5 W for the built-in and 

over-the-range convection microwave ovens product class provides full consumer utility and that 

the higher levels observed in DOE’s test sample for built-in convection microwave ovens are not 

appropriate for establishing the baseline standby power levels. 

5.3.2 Incremental Standby Power Levels

For the product classes presented in section 5.2, DOE analyzed several standby power

levels and obtained incremental cost data at each of these levels. Table 5.3.1 and Table 5.3.2

provide standby power levels and the reference source of each level. DOE conducted the

engineering analysis for microwave oven standby power for the two product classes DOE 

identified for microwave ovens. To analyze the cost-energy use relationship for microwave oven

standby power, DOE defined standby power levels expressed as a maximum average standby 

power, in W. For the reasons noted above in section 5.3.1, the standard levels do not include off 

mode power.

For the microwave-only ovens and countertop convection microwave ovens product 

class, the standby power levels were derived from review of the Federal Energy Management 

Program (FEMP) procurement efficiency recommendation, the International Energy Agency’s 

(IEA) One-Watt program to lower standby power consumption below 1 W for various electrical 

appliances, and the current maximum microwave oven standby technology (i.e. lowest standby 

power) that DOE believes is or could be commercially available at the time the energy 

conservation standards become effective. The max-tech microwave-only oven and countertop 

convection microwave oven standby power level of 0.02 W corresponds to a unit equipped with 

a default automatic power-down function that shuts off certain power-consuming components 

after a specified period of user inactivity. The standby power at max-tech was obtained from a 

microwave oven currently on the market in Korea which incorporates such a feature. DOE also 

added a standby power level as a gap-fill between the FEMP Procurement Efficiency 

Recommendation and IEA One-Watt Program levels. Table 5.3.1 provides the microwave-only 

ovens and countertop convection microwave ovens product class standby power levels and the 

reference source for each level that DOE has analyzed. 
5-4

Table 5.3.1 Standby Power Levels for Microwave-Only Ovens and Countertop 

Convection Microwave Ovens

Standby

Power Level Standby Power Level Source Standby Power (W)

Baseline Baseline 4.0

1 FEMP Procurement Efficiency Recommendation 2.0

2 Gap Fill 1.5

3 IEA 1-Watt Program 1.0

4 Max-Tech 0.02

For the built-in and over-the-range convection microwave oven product class, standby 

power consumption is higher than the levels from the reference sources used for microwave-only 

ovens and countertop convection microwave ovens. Therefore, the standby power levels were 

determined based on reverse-engineering analyses on a representative sample of built-in and 

over-the-range convection microwave ovens to analyze the various components that contribute to 

the standby power consumption of the unit. DOE also measured the standby power consumed by 

these components individually. Note that, depending on the specific microwave oven design, 

manufacturers may choose different design options to reach the standby power levels.

Table 5.3.2 presents the standby power levels that DOE analyzed for built-in and overthe-range convection microwave ovens.

Table 5.3.2 Standby Power Levels for Built-In and Over-the-Range Convection

Microwave Ovens

Standby

Level Standby Power Level Source Standby Power (W)

Baseline Baseline 4.5

1 Zero W Cooking Sensor 3.7

2 Switch Mode Power Supply 2.7

3 Improved Relay Power Supply Design 2.2

4 Max-Tech (Automatic Power-Down) 0.04

In its analysis, DOE observed that the absolute humidity cooking sensors used in these 

convection microwave ovens on average consume 0.9 W of line power. DOE research suggests 

that even microwave ovens without such sensors which use linear power supplies consume a 

minimum of about 1 W as long as they are connected to line power. Several factors influence the 

inherent efficiency of a linear power supply, which typically consist of a transformer, a rectifier, 

a linear regulator, and multiple smoothing capacitors.

The transformer in a typical microwave oven steps line voltage (120 volts AC (VAC)) 

down to lower voltages, such as 24, 18, or 12VAC. Even when the transformer has no loads 

attached to it, the device consumes power. Transformer design influences these no-load losses; 

low-loss transformers (LLTX) are available but are larger and more expensive to manufacture.
5-5

For the purpose of the final rule, DOE considered the use of an LLTX to reduce standby power 

requirements associated with linear power supplies.

Once a transformer has stepped down the line voltage to a lower AC voltage, the AC is 

typically rectified using a full-wave bridge rectifier consisting of four diodes. The diodes used in 

the rectification circuit have a typically minor effect on linear power supply efficiency as a 

function of the voltage drop that they impose. High efficiency rectifiers (using Schottky diodes, 

for example) feature lower voltage losses than standard ones, though at a higher cost. 

The rectified signal is direct current (DC) but exhibits significant ripple. To address the 

ripple, linear power supplies typically feature one or more capacitors, which smooth the DC 

voltage. However, as the voltage being delivered by the transformer is not actively regulated, the 

resultant DC power is considered unregulated power, meaning that its voltage will fluctuate 

significantly as a function of the load attached to the transformer. The type of capacitor used will 

also have a minor impact on power supply efficiency.

The next component in a linear power supply is the linear regulator, an integrated circuit 

which receives the unregulated voltage and produces a lower, regulated voltage. This regulated 

voltage output usually features at least one capacitor for further smoothing and to help with step 

transistions in power demand. The linear regulator works by monitoring the output voltage and 

adjusting its internal resistance to maintain it at a constant level. 

The greater the difference between the unregulated input voltage and the regulated output 

voltage, the more heat a linear regulator will produce and the less efficient its operation. For 

example, several microwave ovens with linear power supplies in the DOE reverse engineering 

sample incorporated a 24 VDC unregulated power supply and a 5 VDC regulated power supply. 

If the circuit board attached to the regulated power supply consumes 1/2 ampere (A) of current, 

then the linear regulator would dissipate about 9.5 W of heat while delivering 2.5 W of power to 

the control board. Even if the transformer, rectifier, and capacitors were 100-percent efficient, 

such a linear power supply would be about 20-percent efficient overall due to the difference 

between the unregulated and regulated voltage.

Most microwave ovens torn down by DOE contained at least one linear regulator to 

achieve the required number of regulated bus voltages. DOE research suggests that quantifying 

the impact of changing the unregulated voltage levels is difficult because other factors, such as 

the losses in the transformer core, may overshadow the efficiency gains in “downstream” linear 

regulator circuits. However, a lower voltage unregulated power supply may provide an 

advantage when considering the efficiency of the linear power supply as part of a comprehensive 

design strategy. 

DOE research also suggests that the no-load losses in a linear power supply are a function 

of the power supply capacity. Thus, to the extent that the power requirements of the microwave 

oven control circuitry can be minimized, the size of the power supply can be reduced and hence 

standby losses reduced. In addition to the display and the microcontroller, key microwave oven 

control components that consume significant power provided by the power supply include the 
5-6

relays that control the operation of components inside the microwave oven, such as the 

magnetron. All microwave ovens reverse-engineered by DOE contained electromechanical 

relays. These relays are typically activated with unregulated power with the use of Darlington 

transistor arrays. 

DOE research suggests that electromechanical relays are offered in a wide range of 

efficiencies, meaning that the coil power needed to activate a relay for a given power, voltage, 

and/or current capacity varies considerably. Solid state relays are another option, usually offering 

the lowest activation energy requirements though at higher cost. For the final rule cost analysis, 

DOE considered three relay types: a low efficiency, low-cost electromechanical relay; a high

efficiency, high-cost electromechanical relay; and a solid state relay. These relays were chosen 

on the basis of relays currently offered on the market that have capabilities equivalent to the 

relays observed in the DOE reverse-engineering sample.

Another option to improve power supply efficiency and reduce standby power 

consumption is to substitute a switch-mode power supply (SMPS) in place of a linear power 

supply. An SMPS converts power at efficiencies approaching 74-76 percent and typically offers 

very low standby losses. DOE reverse-engineering activities in multiple appliance categories 

have noted an increasing percentage of appliances equipped with an SMPS. DOE research 

suggests that the adoption of SMPSs is driven by factors including conversion efficiency, 

standby power considerations, increasing experience in implementation, and decreasing SMPS 

component costs.

Since SMPSs can be designed for a wide range of input voltages (i.e., 90–260 VAC), 

they allow the possibility of designing one control board for world-wide use versus designing 

one for each major market. This in turn could reduce the costs to the manufacturer by allowing 

them to produce one reference design that only has to be certified once for worldwide use.

One inherent advantage that SMPSs typically offer is much lower standby power 

requirements because they do not feature a large, always-on transformer with its inherent core 

losses like a linear power supply. Instead, a microprocessor is used to control a switching 

transistor that feeds just enough power into the ”downstream” circuits to maintain the desired 

voltage. As a result, the standby losses associated with the SMPS itself are minimized.

Additionally, some SMPS designs offer built-in “zero-standby” deep-sleep states that can be 

started and stopped by a microprocessor or a low-cost tactile switch.

The DOE sample of microwave ovens included two models with an identical SMPS. 

DOE supplemented the data it gathered through its reverse-engineering efforts by using reference 

designs published by SMPS component suppliers that have been certified by testing agencies.

The DOE reference SMPS designs were designed to be drop-in replacements for the current 

generation of linear power supplies, i.e. they feature two output voltages. The higher voltage (12

VDC) is not tightly regulated, reflecting current standard practice for unregulated power supplies 

for use with electromechanical relays, while the lower voltage (5 VDC) is tightly regulated, 

which also reflects current practices regarding the power supplied to microprocessors. 
5-7

DOE observes that these reference SMPSs can be adapted to produce other voltages, i.e.

24 VDC and 3.3 VDC, for example. The required changes in terms of circuitry and components 

are minor, and DOE research suggests that the cost of doing so would be essentially zero. 

Microwave oven manufacturers also have the option of adopting electromechanical relays which 

are compatible with the output voltages provided by certified SMPS designs. Such relays are 

usually offered with multiple control voltages in the same exterior case, allowing manufacturers 

to select drop-in replacements. 

For the final rule, DOE considered 18 separate design pathways that were a function of 

the microwave oven type and the standby power measurements obtained during testing of 44

microwave ovens. For example, microwave ovens with a cooking sensor could implement

different design pathways than units without a cooking sensor. Design pathways also differed by 

display type as the standby power measurements of microwaves with vacuum fluorescent 

displays (VFDs) differed significantly from those using light emitting diode (LED) displays, for 

example.

In order to properly account for all potential energy savings, DOE conducted a market 

survey of all microwave oven types on the market as of October 2012. DOE classified these 

microwave oven types on the basis of: 

• Display type (LED, liquid crystal displays (LCD), LCD with backlighting, VFD, 

or no display)

• Cooking sensor (present or not) 

• Inverter power supply for the magnetron (present or not)

• Convection or other alternative cooking system (present or not)

• Installation location (i.e., countertop or built-in)

In total, the DOE sample contained 540 stock-keeping units (SKUs), of which 459 were 

classified as belonging to Product Class 1 and 81 belonging to Product Class 2. For Product 

Class 1, the SKUs were distributed as follows by display type: 41-percent LED, 2-percent LCD, 

20-percent backlit LCD, 36-percent VFD, and 1-percent no display. For Product Class 2, SKUs 

were distributed as follows: 11-percent LED, 4-percent LCD, 16-percent backlit LCD, and 69-

percent VFD. 

The costs of achieving the various standby power levels were calculated on the weighted 

basis of the market share of each microwave oven type as a function of its percentage of the total 

number of SKUs in each product class. For example, 69 percent of the total cost of achieving the 

the standby power levels for Product Class 2 was derived from the individual design pathways 

associated with the various types of VFD-equipped, built-in microwave ovens currently on the 

market. Each design pathway considered the measured average standby power consumption for a 

given microwave type. 
5-8

Some design pathways were estimated, as not all microwave types were measured in 

representative quantities during the reverse engineering process. For example, the presence of an 

inverter drive for the magnetron adds an average of 0.9 W to the standby power consumption of 

a microwave oven. Similarly, convection microwave ovens consume about 1 W of standby 

power more than comparable microwave-only ovens. In order to ensure that a given power 

supply option would be adequate for existing product designs, DOE investigated the actual 

power requirements of the components inside the microwave ovens in its reverse-engineering 

sample.

Internal power draws under standby conditions were estimated on the basis of a 40-

percent efficient conventional linear power supply, which is representative of many units on the 

market featuring 12 VDC unregulated power and 5 VDC regulated power. The baseline standby 

power consumption is driven in part by the power supply and also the display, sensor, and other

features. Power draws for the various devices attached to the power supply such as cooking 

sensors, relays, etc. were also noted.

The design pathways, by which manufacturers could implement various combinations of 

design options to meet given power levels, were then modeled. For every design pathway, DOE 

verified that the power supply chosen could meet the power demands placed on it, i.e. baseline 

power, a cooking sensor (as applicable), and the simultaneous activation of up to four relays. 

Some microwave ovens may contain more relays, but a review of operating conditions suggest 

that four relays is the maximum number of relays that are activated simultaneously at any given 

time. 

For microwave-only ovens, these relays typically consist of one high-power relay (16+ 

A) for the magnetron power supply and three lower-capacity relays (1–3 A) to activate other 

features such the interior light, rotating turntable, etc. For the purpose of this analysis, DOE 

evaluated three types of relays of varying efficiency. These three efficiencies were reflected in 

the power required to activate each relay. Based on the 44 microwave ovens disassembled as part 

of DOE’s reverse-engineering, low efficiency, low-cost relays were considered the default relay 

option.

Each design path for a given microwave oven type consisted of a starting configuration at 

baseline efficiency, and then each layering of design options that a manufacturer could choose to 

reduce standby power. For every standby power level, up to 18 different design option

combinations were considered, such as a 5 W conventional linear power supply with low-cost 

relays, a 5 W SMPS with high-efficiency relays, and so on. Where multiple approaches could be 

taken to meet a given standby power level, the most cost-effective solution which could still 

meet all applicable power demands was then selected.

For example, the countertop LED-equipped microwave-only ovens in the DOE reverseengineering sample that featured no cooking sensor or inverter drew an average of 1.5 W standby 

power. As a result, such microwave ovens are expected to require no changes to their power 

supply until standby power is reduced below 1.5 W. Based on a review of available options, 

DOE identified a change to a 5 W SMPS to meet the 1.0 W standby power level. No additional 
5-9

changes were deemed necessary, as this SMPS has enough spare power to drive the four relays 

discussed above. To meet the max-tech standby power level, the deep-sleep option for automatic 

power-down was selected. 

The deep-sleep module consists of a dedicated 1.5 W power supply and microchip that 

monitor the door switch of a microwave oven. Once a consumer opens the door, one of the door

microswitches activates the power supply, which in turn connects the rest of the microwave to 

line power using a high-amperage-rated relay. Thus, whenever the microwave is in deep-sleep 

mode, the power filtration board and any downstream components are disconnected from line 

power and hence cannot consume any power. DOE is aware of other potential approaches to 

enabling automatic power-down, but the above approach has been tested and verified by the 

component manufacturers to meet the maximum power threshold set by standby level (SL) 4. 

Due to the added circuitry, wiring, and the extra door microswitch, the deep-sleep module 

adds significantly more cost compared to other standby power design options. For example, 

SMPSs in the 5 W power range have only marginally higher costs than conventional linear 

power supplies. As a result of current part prices, high efficiency linear power supplies 

employing an LLTX do not appear to be cost competitive with SMPS, and hence were not 

considered viable pathways in the final rule analysis.

Most microwave oven types could meet SL 1 with conventional power supplies as long 

as applicable models incorporated zero-standby power cooking sensors. The only exception were 

VFD-equipped units, which have inherently higher standby power requirements associated with 

their displays. Such units typically incorporated SMPS power supplies at the baseline. Further, 

none of the VFD-equipped microwaves could meet the standby power limits of either SL 3 or 

SL 4 without the use of automatic power-down, and a subset could not meet SL 2 except with 

that design option.

5.4 METHODOLOGY OVERVIEW

In order to develop the cost-efficiency relationships for microwave ovens, DOE 

performed detailed product teardowns and reverse-engineering analyses on a sample of product 

models spanning a range of standby power levels and product features. DOE also conducted 

detailed standby power testing to gain insights into energy performance in standby mode and to 

evaluate individual components contributing to standby power consumption. AHAM also 

submitted data to DOE on standby power testing that it conducted. DOE supplemented this 

information by conducting interviews with manufacturers of microwave ovens. Table 5.4.1

below shows which methods DOE used for each product class. 
5-10

Table 5.4.1 Engineering Analysis Methods

Method

Microwave-Only Ovens and 

Countertop Convection 

Microwave Ovens

Built-In and Over-theRange Convection

Microwave Ovens

AHAM Data √

Product Teardown √ √

Product Testing √ √

Manufacturer 

Interviews √

5.4.1 AHAM Data Submittal

In support of this rulemaking effort, AHAM submitted data regarding standby power in 

microwave ovens. The AHAM submittal contained general descriptions of components related to 

standby power as well as data from standby power testing. The data provided to DOE by AHAM

are contained in appendix 5A.

5.4.2 Manufacturer Interviews

DOE conducted confidential interviews with microwave ovens manufacturers to develop 

a deeper understanding of the various combinations of technologies used to reduce standby 

power consumption, and their associated manufacturing costs. Sample questions asked during 

the follow-up interviews, which were also conducted in support of the manufacturer impact 

analysis, are contained in appendix 12B. 

During the interviews, DOE also gathered information about the capital expenditures 

required to reduce the standby power consumption of the baseline units to various standby power 

levels (i.e., conversion capital expenditures by standby power 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.

5.4.3 Product Teardowns

Other than obtaining detailed manufacturing costs directly from a manufacturer, the most 

accurate method for determining the production cost of a piece of equipment is to disassemble 

the equipment piece-by-piece and estimate the material and labor cost of each component using a 

process commonly called a physical teardown. A supplementary method, called a catalog 

teardown, uses published manufacturer catalogs and supplementary component data to estimate 

the major physical differences between a piece of equipment that has been physically 

disassembled and another piece of similar equipment. DOE performed physical teardown 

analysis on microwave ovens for this rulemaking. The teardown methodology is described in 

detail in section 5.4.3.1 through section 5.4.3.3.
5-11

5.4.3.1 Selection of Units

During the process of selecting units for teardown, DOE considers three main questions:

 What standby power levels should be captured in the teardown analysis?

 Are there units on the market that capture all potential standby power levels and design 

options?

 Which of the available units are most representative?

In responding to the preceding questions, DOE adopts the following criteria for selecting 

units for the teardown analysis:

 The selected products should span the full range of standby power levels for each product 

class under consideration;

 Within each product class, if possible, the selected products should come from the same 

manufacturer and be within the same product series;

 The selected products should primarily come from manufacturers with large market share in 

that product class, although the highest efficiency products were chosen irrespective of 

manufacturer; and

 The selected products should have non-standby power-related features that are the same as, 

or similar to, features of other products in the same class and at the same standby power 

level.

5.4.3.2 Generation of Bill of Materials

The end result of each teardown is typically a structured bill of materials (BOM). The 

process is discussed here in the context of the general methodology used for teardown analysis, 

although it should be noted a more limited approach was used for microwave ovens due to the 

substantial uniformity in design and construction among the sample units. Structured BOMs 

describe each equipment part and its relationship to the other parts, in the estimated order of 

assembly. The BOMs describe each fabrication and assembly operation in detail, including the 

type of equipment needed (e.g., stamping presses, injection molding machines, spot-welders, 

etc.) and the process cycle times. The result is a thorough and explicit model of the production 

process. 

The BOMs incorporate all materials, components, and fasteners, classified as either raw 

materials or purchased parts and assemblies. The classification into raw materials or purchased 

parts is based on DOE’s previous industry experience, recent information in trade publications, 

and discussions with high- and low-volume original equipment manufacturers (OEMs). 

For purchased parts, the purchase price is an estimate based on volume-variable price 

quotations and detailed discussions with suppliers. For fabricated parts, the price of intermediate 

materials (e.g., tube, sheet metal) and the cost of transforming them into finished parts are an 

estimate based on current industry pricing. 
5-12

The cost of raw materials is determined using prices for copper, steel and aluminum from 

the American Metals Market.1

5.4.3.3 Cost Structure of the Spreadsheet Models

 The price of steel drastically increased in 2005, and the price of 

copper has increased steadily since 2004. Because DOE is using a 5-year average in material 

prices from 2007–2011, these price increases are normalized, which better represents long-term 

material prices.

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.

Figure 5.4.1 Manufacturing Cost Assessment Stages

The first step in the manufacturing cost assessment is the creation of a complete and 

structured BOM from the disassembly of the units selected for teardown. The units are

dismantled, and each part is characterized according to weight, manufacturing processes used, 

dimensions, material, and quantity. The BOM incorporates all materials, components, and 

fasteners with estimates of raw material costs and purchased part costs. Assumptions on the 

sourcing of parts and in-house fabrication are based on industry experience, information in trade 

publications, and discussions with manufacturers. Interviews and plant visits are also conducted 

with manufacturers to ensure accuracy on methodology and pricing.

Following the development of a detailed BOM, the major manufacturing processes are

identified and developed for the spreadsheet model. These processes are listed in Table 5.4.2.

Table 5.4.2 Major Manufacturing Processes

Fabrication Finishing Assembly/Joining Quality Control

Fixturing Washing Adhesive Bonding Inspecting & Testing

Stamping/Pressing Powder Coating Spot Welding

Brake Forming De-burring Seam Welding

Cutting and Shearing Polishing

Insulating

Fabrication process cycle times are estimated and entered into the BOM. For this 

analysis, $24.00 per hour was used as the average fully-burdened labor rate based on typical 
5-13

annual wages and benefits of industry employees. In the final step of the cost assessment, 

assembly times and associated direct labor costs are estimated. Once the cost estimate for each 

teardown unit is finalized, a detailed summary is prepared for relevant components, 

subassemblies and processes. The BOM thus details all aspects of unit costs. 

Design options used in units subject to teardown are noted in the summary sheet of each 

cost model and are cost-estimated individually. Thus, various implementations of design options 

can be accommodated, ranging from assemblies that are entirely purchased to units that are made 

entirely from raw materials. Hybrid assemblies, consisting of purchased parts and parts made on 

site are thus also accommodated. 

5.4.4 Product Testing

DOE conducted product testing on microwave ovens to develop a better understanding of 

the potential standby power improvements associated with various design options. As part of this 

testing, DOE measured the power consumption of components contributing to standby power 

individually to further evaluate the strategies manufacturers may take to reduce standby power.

5.5 ANALYSIS AND RESULTS

5.5.1 AHAM Data

AHAM provided data on microwave oven standby power for a sample of microwave 

ovens currently available in the U.S. market.2

 Standby power was tested in accordance with the 

International Electrotechnical Commission (IEC) Standard 62301-2005, Household electrical 

appliances – Measurement of standby power (IEC Standard 62301 (First Edition)).

Figure 5.5.1 displays AHAM-measured microwave oven standby power data as a 

function of rated cooking output power. Unlike efficiency, these data show a wide range of 

standby power, but there is no correlation between standby power and cooking output power. 

Standby power ranged from 1.5 to 5.8 W, with 9 of the 21 microwave ovens meeting the FEMP 

recommendation of 2.0 W or less. The average standby power measured was 2.9 W. AHAM did 

not provide any information with which DOE could correlate standby power with other features 

such as cooking sensors or displays. The data did not indicate the product type (i.e., microwave 

only, convection) or the installation configuration (i.e., countertop, over-the-range, built-in). 

DOE notes that microwave oven standby power consumption is impacted significantly by 

the digital clock display, with more complex graphical displays drawing more power. According 

to AHAM, the three major types of displays used in microwave ovens are (1) LED displays, (2) 

VFDs, and (3) LCDs. Typically, LCDs without back-lighting use the least amount of energy 

while VFDs use the most. Energy consumed by LCDs would depend on whether back-lighting is 

present or not, and if present, the number of LEDs used for back-lighting. For LED displays, the 

number of segments, color, and operating temperature affect energy consumption. The size of the 
5-14

display (number of digits and icons) also affects the energy consumed by the display. For VFDs, 

the size of the display is a major factor in energy consumption.3

 Standby power consumption 

may also be affected by the electronic controls required for features such as cooking sensors, 

along with associated power supplies.

Figure 5.5.1 AHAM Microwave Oven Standby Power4

Table 5.5.1 compares the AHAM-measured microwave oven standby power data with 

data from other studies that have examined standby power in the United States and Canada. It 

can be seen that average standby power has remained relatively constant since 1997 even though 

minimum and maximum levels have varied by a greater amount.

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

600 700 800 900 1000 1100 1200 1300 1400

Rated Output Power (W)

Standby Power (W)
5-15

Table 5.5.1 Microwave Oven Standby Power Comparison

Source

Microwave Oven Standby Power

Min (W)

Average 

(W) Max (W)

Std Dev 

(W) # Samples

AHAM 20065 1.5 2.9 5.8 0.8 21

CBEEDAC 20066 0.4 1.9 3.3 0.8 19

CREEDAC 2001 (New Stock)7 0.8 3.8 12.6 - 4

CREEDAC 2001 (Existing Stock)8 0.0 2.1 7.3 - 64

LBNL 20089 1.4 3.1 4.9 - 18

Florida Solar Energy Center 199810 - 3.0 - - 25

LBNL 199711 - 3.1 - - -

DOE recognizes that annual energy consumption can be significantly affected by standby 

power in real-world usage. For example, a 1-W increase in standby power corresponds to an 

increase of 8.76 kilowatt-hours (kWh) in annual energy consumption. Prior to it being repealed 

in a final rule published on July 22, 2010 (75 FR 42579), the microwave oven test procedure 

assumed an annual useful cooking energy output of 79.8 kWh. For a baseline microwave oven 

that operates at 55.7 percent efficiency, annual energy consumption not including standby power 

would be 143.3 kWh. Therefore each W of standby power included in the annual energy 

consumption metric would increase it by 6 percent for a baseline microwave oven. Since the 

highest measured standby power was 5.8 W, the increment of 3.8 W over the FEMPrecommended level of 2.0 W would represent a 23-percent increase in annual energy 

consumption for a baseline microwave oven. 

5.5.2 Product Testing and Teardowns

Section 310 of the Energy Independence and Security Act of 2007 (EISA 2007) (Pub. L. 

No. 110-140) amends Section 325 of the EPCA to require DOE to incorporate standby mode and 

off mode energy use into a single amended or new standard, if feasible, for covered products, 

including microwave ovens, for any final rule establishing or revising a standard adopted after 

July 1, 2010. If such a single standard is not feasible, DOE shall prescribe a separate standard for 

standby mode and off mode energy consumption, if justified. (42 U.S.C. 6295(gg)(3)) As 

discussed in chapter 3 of this TSD, DOE published a final rule on July 22, 2010 repealing the 

provisions in the existing microwave oven test procedure relating to the measurement of cooking 

efficiency and energy factor (EF). 75 FR 42579. DOE notes that the absence of active mode 

testing provisions in the microwave oven test procedure results in a de facto separate energy use 

descriptor for microwave oven standby mode and off mode energy use. Therefore, DOE 

conducted microwave oven standby power testing to supplement the AHAM-supplied data,

investigated design options, and developed a relationship between incremental manufacturing 

cost and standby power levels.

As discussed in chapter 3 of this TSD, in the March 2011 TP Interim Final Rule, DOE 

adopted definitions of standby, off, and active mode based on the provisions in IEC Standard 
5-16

62301 Second Edition, Final Draft International Standard (IEC Standard 62301 FDIS). 76 FR 

12825, 12834–37 (Mar. 9, 2011). DOE defines “standby mode” as the condition in which an 

energy-using product is connected to a mains power source and offers one or more of the 

following user oriented or protective functions which may persist for an indefinite time –

(I) a remote switch (including a remote control), internal sensor, or timer to facilitate the 

activation of other modes (including activation or deactivation of active mode); and

(II) continuous functions, including information or status displays (including clocks) or 

sensor-based functions. 76 FR 12825, 12836 (Mar. 9, 2011).

DOE defines “off mode” as the condition in which an energy-using product is connected 

to a mains power source, is not providing any active or standby mode function, and may persist 

for an indefinite time. Off mode would also include an indicator that shows the user only that the 

product is in the off position. Id.

DOE defines “active mode,” as the condition in which an energy-using product is 

connected to a main power source, has been activated, and provides 1 or more main functions.

Delay start mode is a one off user initiated short duration function that is associated with an 

active mode. Id.

DOE considers “main functions” for a microwave oven to be those operations in which 

the magnetron and/or thermal element is energized for at least a portion of the time for purposes 

of heating, cooking, and/or defrosting the load. According to these definitions, standby mode 

would thus comprise the conditions in which the microwave oven is plugged in and consuming 

power for features including displays, cooking sensors, and sensors to reactivate the microwave 

oven from a low-power state after a period of user inactivity. For reasons discussed later in this 

section, DOE does not believe that any microwave ovens currently for sale in the United States 

are capable of operation in off mode.

5.5.2.1 General Microwave Oven Construction

All microwave ovens examined by DOE contain a metal cavity that is mechanically 

attached to the front and rear chassis frames. Each of these pieces had been formed by stamping,

joined, and then the assembly is painted. To this frame, a bottom panel is attached. This bottom 

panel covers the turntable motor and also acts as an electrical ground for the magnetron power 

supply and the magnetron. The inner cavity occupies, on average, about 70 percent of the width 

and height of the microwave oven. The remaining space is used for cooling channels, the 

magnetron, the magnetron power supply, the cooling fan, the microwave oven controller, and 

other miscellaneous components. 

All of the microwave ovens examined have a door that is hinged on the left side of the 

unit. Beneath the right edge of the door, a latch mechanism with three switches ensures that the 

microwave oven cannot operate its magnetron without the door being closed. To the right of the 

door, the user interface frame typically features a foil-based button array under a flexible plastic
5-17

cover, along with the display. The display may be either attached directly to the user interface 

frame or integrated into the main control board for the microwave oven. The main control board 

typically consists of a single-sided printed circuit board (PCB) which features a small alternating 

current to direct current (AC-DC) power supply, several relays, and control logic. All power 

coming into the microwave oven is filtered through a separate power filtration board to which 

the power cord is attached.

In addition to the aforementioned components, typical microwave ovens contain a 

magnetron power supply featuring a microwave oven transformer, a high voltage diode, and a 

high-voltage aluminum electrolytic capacitor. A single-speed fan keeps the magnetron and 

microwave oven transformer cool during operation, while a turntable may rotate the food load 

inside the cavity while the microwave oven is operating. A single incandescent light bulb (20 W 

and higher) typically provides cavity illumination during operation and when the door is open. 

More sophisticated microwave ovens may contain cooking sensors, larger and multi-colored 

displays with more complex graphics, and/or inverter-based magnetron power supplies. Such 

inverter-equipped microwave ovens also feature larger cooling fans and ducts to help dissipate 

the heat generated by the inverter power supply. Convection microwave ovens additionally 

incorporate thermal heating elements and a fan to circulate the heated air within the cavity.

5.5.2.2 Test Units

DOE conducted standby power testing and reverse engineering analyses on three sets of 

microwave ovens, 32 in the first set, an additional 12 in the second set. DOE noted key 

parameters for each test unit, presented in Table 5.5.2. Since manufacturers are not required to 

report standby power for microwave ovens, DOE could not select its sample test units on the 

basis of rated standby power consumption. Instead, DOE selected its sample microwave ovens to 

cover a wide range of cavity sizes and output power, as well as other features such as display 

type, magnetron power supply type, and presence of cooking sensors. DOE was unable to 

identify any microwave oven models with an automatic power-down feature. Within the test 

sample, DOE observed significant standby power differences which it believes are attributable to 

specific design options or design option combinations.
5-18

Table 5.5.2 Microwave Oven Features in DOE Sample

Installation 

Location

Rated 

Output 

Power

Display 

Type

Cooking 

Sensor?

Inverter 

Used?

Combination 

Oven?

Power 

Supply 

Type

Avg. 

Standby

Countertop 800 LCD - - - Linear 1.38

Countertop 800 LCD - - - Linear 1.40

Countertop 1100 LCD - - - Linear 1.31

Built-In 1200 LCD-BL Yes - Yes SMPS 4.19

Built-In 1200 LCD-BL Yes - Yes SMPS 4.37

Countertop 900 LCD-BL Yes - Yes Linear 2.36

Countertop 1200 LCD-BL Yes - - Linear 3.52

Countertop 1200 LCD-BL Yes - - Linear 3.41

Countertop 1200 LCD-BL Yes - - Linear 3.88

Countertop 1100 LCD-BL Yes - - Linear 3.10

Countertop 1100 LCD-BL Yes Yes Yes Linear 3.55

Countertop 1200 LCD-BL Yes Yes - Linear 2.59

Countertop 1300 LCD-BL Yes Yes - Linear 2.09

Countertop 1300 LCD-BL Yes Yes - Linear 2.82

Countertop 1250 LCD-BL Yes Yes - Linear 2.82

Countertop 1100 LCD-BL - - - Linear 1.65

Countertop 1100 LCD-BL - - - Linear 1.66

Countertop 1200 LCD-BL - - - Linear 1.70

Countertop 1100 LCD-BL - - - Linear 1.72

Countertop 1000 LED Yes - Yes Linear 1.20

Countertop 1000 LED - - Yes Linear 1.23

Countertop 700 LED - - - Linear 1.77

Countertop 700 LED - - - Linear 1.74

Countertop 800 LED - - - Linear 1.51

Countertop 1100 LED - - - Linear 1.55

Countertop 1100 LED - - - Linear 1.40

Countertop 1100 LED - - - Linear 1.65

Countertop 900 LED - - - Linear 1.49

Countertop 700 LED - - - Linear 1.07

Countertop 700 LED - - - Linear 1.28

Countertop 1000 LED - - - Linear 1.75

Countertop 1000 LED - - - Linear 1.26

Built-In 975 VFD Yes - Yes Linear 8.16

Built-In 975 VFD Yes - Yes Linear 8.76

Countertop 900 VFD Yes - Yes Linear 3.23

Countertop 1000 VFD Yes - Yes Linear 4.68

Built-In 1100 VFD Yes - Yes Linear 4.50

Built-In 1100 VFD Yes - Yes Linear 4.59

Built-In 1000 VFD Yes - Yes Linear 4.14

Built-In 900 VFD Yes - Yes Linear 6.65

Countertop 800 VFD Yes - - Linear 5.28

Countertop 1100 VFD Yes - - Linear 3.08

Countertop 1150 VFD Yes - - Linear 3.05

Countertop 1000 VFD - - Yes Linear 2.48
5-19

Three different display technologies were observed to be incorporated in the sample 

units: 1) LED; 2) LCD; and 3) VFD. Most LCDs with backlights (LCD-BL) used LEDs for 

illumination, though other technologies such as electroluminescent emitters can be used also. 

Backlit LCDs allow a microwave oven user to read the display both at night and in daylight. For 

the purpose of Table 5.5.2, all units that are fixed installations (i.e. built-in or over-the-range) are 

identified as “built-in,” while units that can be moved by hand are considered “countertop.”

DOE notes that many premium countertop units are available with optional “trim kits”

for fixed installation. However, as these trim kits are optional add-on purchases, these 

microwave ovens are considered to be countertop units for the final rule. The availability of such 

trim kits, however, underscores the sometimes subtle differences between units that are 

designated for built-in installation and the more common countertop units. Standby testing also 

suggested similar power profiles, with the average standby power difference between otherwise 

similar built-in and countertop microwave-only units being 0.3 W. However, the difference was 

significantly higher for similar built-in and countertop convection microwave ovens—about 3 W. 

5.5.2.3 Microwave-Only Ovens: Product Testing and Teardowns

For its first round of testing and teardowns, DOE examined 32 microwave-only ovens in 

support of its standby power investigation. DOE also analyzed IEC Standard 62301 (First 

Edition) to determine its suitability for measuring standby power in microwave ovens. DOE 

found the results produced by IEC Standard 62301 (First Edition) to be fairly consistent for 

many microwave ovens. However, for a few microwave ovens in the test sample, the standby 

power consumption varied by more than 5 percent over the course of 5 minutes, a threshold 

above which IEC Standard 62301 (First Edition) requires a longer 12-hour standby test duration. 

Investigating the source of these variations in power consumption, DOE determined that standby 

power can vary significantly with clock time, depending on the display type. 

DOE tested a sub-sample of microwave ovens over 12-hour periods and subsequently 

developed an alternate 10-minute standby power test that achieved results for average power 

consumption that were within 1 to 2 percent of the 12-hour test results (see appendix 5B). Using 

the most consistent data from: (1) the IEC Standard 62301 5-minute test, (2) the regression 

analysis as described in appendix 5B, and/or (3) 12-hour tests, standby power for each 

microwave oven in the sub-sample was characterized, the minimum, maximum, and average 

input powers were recorded, and from the average standby power, an annual energy consumption 

in kWh was calculated. Table 5.5.3 through Table 5.5.6 summarize these results.
5-20

Table 5.5.3 DOE Microwave-Only Oven Standby Power (Table 1 of 4)

Table 5.5.4 DOE Microwave-Only Oven Standby Power (Table 2 of 4)

Table 5.5.5 DOE Microwave-Only Oven Standby Power (Table 3 of 4)

DOE1 DOE2 DOE3 DOE4 DOE5 DOE6 DOE7 DOE8

700.0 700.0 700.0 700.0 800.0 800.0 800.0 800.0

0.7 0.7 0.7 0.7 1.0 1.0 0.8 0.8

- - - - Y - - -

Type LED LED LED LED VFD LED LCD LCD

Characters #, 1 x 4 #, 1 x 4 #, 1 x 4 #, 1 x 4 A, 1 x 7 #, 1 x 4 #, 1 x 4 #, 1 x 4

# of LEDs for backlighting N/A N/A N/A N/A N/A N/A None None

Size 1.50" x 

0.43"

1.50" x 

0.43"

1.45" x 

0.70"

1.90" x 

0.83"

2.25" x 

0.75"

1.50" x 

0.80"

1.87" x 

0.83"

1.87" x 

0.83"

Min (W) 1.47 1.49 1.14 0.96 5.28 1.33 1.37 1.38

Average (W) 1.74 1.77 1.28 1.07 5.28 1.51 1.38 1.40

Max (W) 1.85 1.90 1.35 1.14 5.53 1.60 1.41 1.41

Annual (kWh) 15.22 15.47 11.21 9.40 46.28 13.22 12.12 12.28

- - - - - DOE5 - -

DOE Unit Number

Stated Volume (ft3)

Unit is Similar to Display Standby

Cooking Sensor

Rated Output Power (W)

DOE9 DOE10 DOE11 DOE12 DOE13 DOE14 DOE15 DOE16

900.0 1,000.0 1,000.0 1,000.0 1,000.0 1,100.0 1,100.0 1,100.0

0.9 1.1 1.1 1.0 0.0 1.1 1.1 1.8

- - - - - - Y Y

Type LED LED LED LED VFD LED LCD VFD

Characters #, 1 x 4 #, 1 x 4 #, 1 x 4 #, 1 x 4 #, 1 x 4 #, 1 x 4 A, 1 x 6 A, 1 x 7

# of LEDs for backlighting N/A N/A N/A N/A N/A N/A 5 N/A

Size 2.00" x 

0.65"

1.85" x 

1.10"

1.75" x 

0.85"

1.75" x 

0.70"

2.20" x 

0.65"

2.00" x 

0.65"

2.53" x 

0.95"

2.25" x 

0.75"

Min (W) 1.26 1.09 1.11 1.42 2.46 1.16 3.08 3.08

Average (W) 1.49 1.23 1.26 1.75 2.48 1.40 3.10 3.08

Max (W) 1.58 1.30 1.33 1.88 2.49 1.49 3.12 3.31

Annual (kWh) 13.09 10.73 11.01 15.32 21.71 12.26 27.14 26.95

- - - - - - DOE14 -

DOE Unit Number

Rated Output Power (W)

Stated Volume (ft3)

Cooking Sensor Display Standby

Unit is Similar to

DOE17 DOE18 DOE19 DOE20 DOE21 DOE22 DOE23 DOE24

1,100.0 1,100.0 1,100.0 1,100.0 1,100.0 1,100.0 1,150.0 1,200.0

1.0 1.4 1.0 1.3 1.1 1.0 1.4 2.2

- - - - - - Y Y

Type LCD LCD LCD LED LED LCD VFD LCD

Characters #, 1 x 4 A, 1 x 5 #, 1 x 4 #, 1 x 4 #, 1 x 4 #, 1 x 4 A, 1 x 7 A, 1 x 6

# of LEDs for backlighting 3 4 None N/A N/A 3 N/A 10

Size 1.83" x 

0.80"

2.03" x 

0.81"

1.87" x 

0.83"

1.50" x 

0.80"

1.47" x 

0.80"

2.00" x 

0.83"

2.25" x 

0.75"

2.05" x 

1.10"

Min (W) 1.64 1.64 1.30 1.46 1.32 1.70 3.05 2.57

Average (W) 1.65 1.66 1.31 1.65 1.55 1.72 3.05 2.59

Max (W) 1.67 1.67 1.34 1.73 1.66 1.73 3.18 2.60

Annual (kWh) 14.48 14.51 11.48 14.44 13.57 15.02 26.68 22.66

- - DOE17 - - DOE17 DOE16 DOE30

DOE Unit Number

Rated Output Power (W)

Stated Volume (ft3)

Cooking Sensor

Unit is Similar to Display Standby
5-21

Table 5.5.6 DOE Microwave-Only Oven Standby Power (Table 4 of 4)

In the course of examining the 32 microwave-only ovens in this round of test samples, 

DOE noted that all logic boards used linear power supplies. In the second round of 12 samples,

reverse-engineered in 2012, all but two units featured linear power supplies for the logic boards. 

The remaining two units featured the same SMPS power supply. Even though the two units were 

largely identical, the measured standby powered varied slightly. DOE research suggests that this 

difference was likely due to the higher input voltage (230 V) of one unit versus the other (115

V). The unit with the higher input voltage also featured a slightly higher standby power 

consumption, reflecting the losses incurred in the power supply when converting from line power 

to board power.

None of the microwave ovens reverse-engineered by DOE featured an on/off switch or 

electromechanical controls. The October 2012 DOE survey of 540 microwave ovens suggests 

that a single countertop model is offered with electromechanical controls and no display, thus 

likely consuming only minimal power while not in use. Although DOE did not test this unit, the 

off-mode power consumption is believed to be minimal. Past DOE research suggests that the

power filtration board typically found in a countertop microwave oven will consume some power 

on its own. 

Such power filtration boards ensure that microwaves do no interfere with domestic 

power, and typically consist of multiple capacitors, a dual-mode choke, a fuse, and other 

components mounted on a printed circuit board. The board is typically connected permanently to 

the incoming line power, and all power used inside the microwave oven goes through it. Since 

the capacitors on these filtration boards are charged with potentially lethal voltages, safety 

regulations require that the capacitors are drained to safe voltages within about a second of the 

oven being disconnected from line power. The bleed resistors commonly used for this task 

typically consume more power than the max-tech SL 4 limits. 

DOE is aware of an integrated circuit (IC) offered for the express purpose of draining 

filtration capacitors upon line power disconnect, while consuming less than 5 milliwatts (mW) of

standby power. This approach consists of a single IC mounted to the underside of the printed 

circuit board that a standard filtration capacitor is mounted on. The IC monitors line voltage and 

DOE25 DOE26 DOE27 DOE28 DOE29 DOE30 DOE31 DOE32

1,200.0 1,200.0 1,200.0 1,200.0 1,200.0 1,250.0 1,300.0 1,300.0

1.2 2.2 1.2 2.0 2.0 2.2 1.2 1.2

- Y Y Y Y Y Y Y

Type LCD LCD LCD LCD LCD LCD LCD LCD

Characters #, 1 x 4 A, 1 x 6 A, 1 x 7 D, 2 x 8 A, 1 x 7 A, 1 x 6 #, 1 x 4 A, 1 x 6

# of LEDs for backlighting 5 10 4 6 4 4 10 10

Size 1.85" x 

0.83"

2.05" x 

1.10"

2.03" x 

0.78"

2.45" x 

1.17"

2.03" x 

0.78"

2.05" x 

1.10"

1.75" x 

0.80"

2.05" x 

1.10"

Min (W) 1.68 2.57 3.50 3.86 3.38 2.79 2.08 2.81

Average (W) 1.70 2.59 3.52 3.88 3.41 2.82 2.09 2.82

Max (W) 1.71 2.60 3.60 3.93 3.46 2.86 2.10 2.86

Annual (kWh) 14.86 22.66 30.84 34.02 29.90 24.69 18.28 24.72

DOE17 DOE30 DOE25 - - DOE31 - DOE31

Rated Output Power (W)

Unit is Similar to

Stated Volume (ft3)

Cooking Sensor Display Standby

DOE Unit Number
5-22

keeps the bleed resistors disconnected unless the line power is absent. However, as the solution 

appears to be proprietary, DOE did not consider it as a potential implementation path to reach SL

4. Instead, DOE researched a separate, deep-sleep module that could disconnect the microwave 

oven from line power upstream of the filtration board. 

All 539 other microwave ovens surveyed in October 2012 incorporated an electronic 

display. Thus, virtually all sampled microwave ovens currently available on the U.S. market 

consume standby power while waiting for user input. 

The average standby power, disaggregated by display type and the presence of a cooking 

sensor as shown in Figure 5.5.2, exhibits a relatively wide range of values. These measured data 

correlate well with the standby power measurements that AHAM submitted to DOE (see Figure 

5.5.4). In the following figures, the legend key includes designations for the presence of a

cooking sensor (CS), backlighting (BL), an inverter drive for the magnetron (I) and alternative 

cooking systems (Combo). 

Figure 5.5.2 Countertop Microwave Oven Standby Power Consumption as a Function of 

Rated Output Power in DOE Reverse-Engineering Sample

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

600 800 1000 1200 1400

Average Standby (W)

Rated Output Power (W)

LCD

LCD-BL-CSCombo

LCD-BL-CS

LCD-BL-CS-ICombo

LCD-BL-CS-I

LCD-BL

LED-CS-Combo

LED-Combo

LED
5-23

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

6.5

7

7.5

8

8.5

9

9.5

600 700 800 900 1000 1100 1200 1300

Average Standby (W)

Rated Output Power (W)

LCD-BL-CS-Combo

VFD-CS-Combo

Figure 5.5.3 Built-In Microwave Oven Standby Power Consumption as a Function of 

Rated Output Power in DOE Reverse-Engineering Sample

While AHAM did not identify specific models in its data submittal, and thus DOE could 

not derive information on display type, the data does note which microwave ovens contain 

cooking sensors. Like the microwave ovens in the DOE test sample, the microwave ovens with

cooking sensors in the AHAM sample, have on average, higher standby power consumption than 

microwave ovens without cooking sensors. 
5-24

Figure 5.5.4 AHAM Microwave Oven Standby Power Consumption as a Function of 

Rated Output Power12

Based on these standby power observations, DOE concludes that standby power is a 

function of features including the display, the presence of a cooking sensor, and controller power 

requirements. The following sections discuss the impact of each of these features on overall 

standby power consumption.

Impact of Display Type on Standby Power

DOE noted three different types of displays among the 44 microwave ovens in its 

reverse-engineering sample. The sampled display capabilities ranged from only being able to 

display numbers, to displaying alphanumeric characters, to incorporating dot-matrix-based 

displays which allow the display of multiple fonts, symbols, and, in some cases, graphics. 

Display power requirements depend on the fundamental technology as well as the size of the 

display, the brightness, and the number and range of characters it can display. See appendix 5C

for a further discussion of display technologies. 

1. Light Emitting Diode (LED)

LED displays operate by individually lighting up each element in the display to create a 

character or number or to illuminate a symbol. DOE confirmed that the power consumption of 

such displays varies depending on how many elements (and hence diodes) are active at any given 

time. Thus, LED-display-equipped microwave ovens demonstrate the greatest variation in 

standby power consumption over the course of a day as the clock cycles.

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

650 750 850 950 1050 1150 1250 1350

Rated Output Power (W)

Standby Power (W)

No Sensor

Sensor
5-25

The span between the maximum and minimum standby power consumption, based on the 

number of elements illuminated, ranges from 0.2 to 0.4 W in the DOE test sample. DOE found 

no apparent correlation between display size and standby power consumption. DOE recognizes 

that diode energy efficiency characteristics may be an important variable, but one for which 

DOE was not able to ascertain any information.

2. Vacuum-Fluorescent Display (VFD)

VFDs typically require more power than LCD- or LED-based displays and offer high 

brightness levels and wide viewing angles. Three of the VFD models with cooking sensors in the 

first set of DOE reverse-engineering samples had a feature that allows the display to be switched 

off, but selecting this option decreased standby power consumption by only 0.1 to 0.25 W. 

According to the IEC Standard 62301 test procedure, standby power would be measured with the 

displays powered off, resulting in a constant average value over the 12-hour period. DOE also 

measured the variations in standby power with the displays on during the 12-hour cycle for these 

units.

3. Liquid Crystal Display (LCD), with or without backlighting

An LCD without backlighting requires some ambient light to allow a user to read it 

because it relies on reflected light to create a visible contrast. LCDs without backlighting are 

frequently found on entry-level microwave ovens, whereas more expensive units tend to have 

backlit displays. LCDs with backlighting can be read regardless of ambient lighting conditions. 

The backlighting typically consists of multiple LEDs mounted behind the LCD display on the 

same circuit board, though other illumination options exist, such as electroluminescent layers 

sandwiched behind the LCD display. The standby power consumption for microwave ovens with 

LCDs in the DOE sample remains nearly constant over time (since the bars showing variation 

during 12-hour tests are virtually indistinguishable from the symbols denoting the average 

values.)

Based on its research, DOE concludes that certain display technologies such as VFDs 

inherently consume more power, on average, than comparably-sized displays using other 

illumination technologies. Standby power is also a function of the size and the complexity of the 

display; more complex and larger displays typically consume more power than smaller, simpler 

displays.

Impact of a Cooking Sensor on Standby Power

DOE noted from its testing that microwave ovens with cooking sensors typically 

consume more power than comparable models that do not feature a cooking sensor. From its 

reverse-engineering activities, DOE identified two different types of cooking sensors generally 

used in microwave ovens today: (1) absolute humidity sensors; and (2) piezo-electric steam 

sensors. However, DOE is also aware of other approaches to measuring humidity, such as 

capacitance-based sensors that are commonly used in other appliance types, such as 

dehumidifiers.
5-26

A literature review of identifiable absolute humidity cooking sensors in the reverseengineering sample indicates that such sensors typically supply power to a resistive heating 

element whose conductivity changes in response to the presence of water vapor or other gases 

associated with food cooking processes. Based on its standby power observations between 

otherwise identical microwave ovens and information provided by sensor manufacturers, DOE 

concludes that the resistive elements in such cooking sensors are typically maintained at 

operating temperature during standby mode because warmup times (which can be several 

minutes) are longer than typical microwave oven cooking times.

The specification sheet for one representative absolute humidity cooking sensor with 

standby power notes that the sensor requires approximately 0.9 W to remain energized and ready 

for use. Based on a typical linear power supply efficiency of about 40 percent, the required 

microwave oven input power associated with the absolute humidity cooking sensor is thus 

approximately 2.3 W. Other microwave-only ovens in the DOE sample use absolute humidity 

sensors whose power requirements appear comparable to those for the absolute humidity sensor 

referenced above.

DOE research suggests that suppliers of such absolute humidity cooking sensors are now

offering versions of their sensors that feature no standby power consumption due to an internal 

redesign. DOE research suggests that such zero-standby absolute humidity sensors are being 

offered at the same price as existing humidity sensors that do consume standby power. The 

implementation cost to the microwave oven manufacturer is likely zero, as the sensor can be 

switched on and off with the help of a single transistor which may already be available on the 

main printed circuit board.

Testing performed on the steam sensors used in a number of microwave-only ovens in the 

DOE sample revealed that these sensors do not consume power in standby mode. For further 

discussion of steam cooking sensors, refer to chapter 3 of this TSD. Past DOE reverseengineering efforts in other appliance categories also identified multiple manufacturers of 

capacitance-based humidity sensors which require no standby power, have response times of less 

than 5 seconds, and can measure relative humidity to within less than 2-percent resolution. Thus, 

DOE concludes that manufacturers have multiple zero-standby power cooking sensor choices 

available.

5.5.2.4 Countertop Convection Microwave Ovens: Product Testing and 

Teardowns

DOE also conducted standby power testing on 5 countertop convection microwave ovens

within the DOE sample, presented below in Table 5.5.7. Testing showed standby power levels 

ranging from 1.2 W to 4.7 W, with an average of 3.0 W. As discussed above in section 5.2, these 

standby power levels are similar to the standby power levels for microwave-only ovens. DOE 

also noted that the countertop convection microwave ovens used similar display types as 

microwave-only ovens (i.e., LED displays, LCDs, and VFDs). 
5-27

Table 5.5.7 DOE Countertop Convection Microwave Oven Standby Power

DOE Countertop 

Combo MWO 

Test Unit

Rated Microwave 

Cooking Output 

Power, W

Display 

Type

Display 

Size, in.2

Cooking 

Sensor

# of 

Heaters

Average 

Standby 

Power, W

DOE CTC1 1,000 LED 2.25 - 2 1.20

DOE CTC2 900

LCD w/ 

LED 

Backlight

3.75 - 2 2.36

DOE CTC3 900 VFD 3.00 Yes 1 3.23

DOE CTC4 1,100

LCD w/ 

LED 

Backlight

2.66 Yes 1 3.55

DOE CTC5 1,000 VFD 10.86 Yes 1 4.68

Figure 5.5.5 illustrates the measured standby power consumption as a function of display 

size for countertop convection microwave ovens. Similar to microwave-only ovens, DOE 

observed that standby power tends to increase as display size increases.

Figure 5.5.5 DOE Standby Power Consumption as a Function of Display Size for 

Countertop Convection Microwave Ovens

DOE also conducted product teardowns and standby power testing of individual 

components contributing to the total standby power consumption. DOE noted that the cooking 
5-28

sensors used in these models were similar to those used in microwave-only ovens. DOE also 

investigated the requirements for the size of the controls power supply for countertop convection

microwave ovens. DOE noted that the capacity of a conventional linear power supply is strongly 

correlated to the size of the transformer used to step down the line input voltage to the 

unregulated voltage levels. DOE isolated the power supply transformers in the controls these 

countertop convection microwave ovens and measured the standby power consumption. DOE 

similarly measured the power supply transformer standby power consumption for a limited 

number of the microwave-only ovens in its test sample. 

The results from this testing, presented below in Table 5.5.8, show that the size of the 

controls power supplies (i.e., the standby losses associated with the power supply transformers)

were similar for both countertop convection microwave ovens and microwave-only ovens. In 

addition, as presented above in Table 5.5.7, the total standby power consumption for countertop 

convection microwave ovens is similar to that for microwave-only ovens. For these reasons, 

DOE believes that the sizes of the power supplies for countertop microwave ovens are similar to 

those for microwave-only ovens. As a result, DOE did not establish a separate product class for 

countertop convection microwave ovens.

Table 5.5.8 Control Power Supply Transformer Standby Power Test Results

DOE Countertop Combo 

MWO Test Unit

Transformer 

Standby Power, W

DOE MicrowaveOnly Test UnitTransformer 

Standby Power, W

DOE CTC1 0.57 DOE28 1.44

DOE CTC2 0.95 DOE29 0.89

DOE CTC3 0.93 DOE30 1.11

DOE CTC4 1.81

DOE CTC5 1.08

5.5.2.5 Built-In and Over-the-Range Convection Microwave Ovens: Product

Testing and Teardowns

As discussed above in section 5.2, DOE proposed to analyze a separate product class for 

built-in and over-the-range convection microwave ovens. DOE conducted standby power testing 

on a sample of six over-the-range convection microwave ovens and two built-in convection

microwave ovens. The results from this testing are shown below in Table 5.5.9. Testing showed 

standby power levels ranging from 4.1 W to 8.8 W. DOE noted that all of the units in its test 

sample had a cooking sensor and either a LCD touch screen with backlighting or a VFD. DOE 

noted that the built-in convection microwave ovens in its test sample consumed more standby 

power than the over-the-range convection microwave ovens. However, DOE also noted that 

these two units were higher-end units with larger displays and a larger number of cooking 

settings and options.
5-29

Table 5.5.9 DOE Built-In and Over-the-Range Convection Microwave Oven Standby 

Power

DOE BuiltIn/Over-theRange Combo 

MWO Test Unit Installation

Rated 

Microwave 

Cooking Output 

Power, W

Display 

Type

Display 

Size, 

in.2

Cooking 

Sensor

# of 

Heaters

Average 

Standby 

Power, 

W

DOE OTRC1 Over-theRange 1,200 LCD Touch 

w/ Backlight 13.66 Y 4 4.19

DOE OTRC2 Over-theRange 1,200 LCD Touch 

w/ Backlight 13.66 Y 4 4.37

DOE OTRC3 Over-theRange 1,100 VFD 3.91 Y 4 4.50

DOE OTRC4 Over-theRange 1,100 VFD 3.91 Y 1 4.59

DOE OTRC5 Over-theRange 1,000 VFD 7.75 Y 1 4.14

DOE OTRC6 Over-theRange 900 VFD 4.00 Y 4 6.65

DOE BC1 Built-In 975 VFD 7.39 Y 4 8.16

DOE BC2 Built-In 950 VFD 7.39 Y 4 8.76

As discussed above for microwave-only ovens, display power requirements depend on 

the fundamental technology as well as the size of the display, the brightness, and the number and 

range of characters it can display. Figure 5.5.6 illustrates the measured standby power 

consumption as a function of display size for built-in and over-the-range convection microwave 

ovens. 
5-30

Figure 5.5.6 DOE Standby Power Consumption as a Function of Display Size for Built-In 

and Over-the-Range Convection Microwave Ovens

5.5.2.6 Over-the-Range Microwave Oven Display Accelerated Lifetime 

Testing

DOE evaluated the reliability and consumer utility of LED displays, LCDs, and VFDs in 

over-the-range environments. Display manufacturers indicated that most LED displays and 

VFDs have maximum operating temperatures of 85 degrees Celsius (°C), while most LCDs have 

maximum operating temperatures of 70 °C. DOE also noted that display reliability testing is 

generally conducted at 90 percent relative humidity (RH). According to display manufacturers, 

the rated lifetime (i.e., the time at which the display brightness will have decreased by 50 

percent) for most LED displays is approximately 50,000 hours, whereas the lifetime for VFDs is 

between 35,000 and 50,000 hours. Display manufacturers also noted that LED displays and 

VFDs can achieve similar levels of brightness. For LCDs with LED backlighting, display 

manufacturers stated that the lifetime of approximately 50,000 hours is based on the LED 

backlights, because the LED backlighting will fail before the LCD itself as long as the display is 

operated within the rated temperature and humidity conditions. According to display 

manufacturers, if LED displays, LCDs, and VFDs are operated below their maximum rated 

operating temperature and humidity, the lifetime would not be affected.
5-31

To further investigate reliability under the conditions experienced in over-the-range 

installations, DOE conducted testing on a sample of over-the-range microwave ovens with 

different display types. DOE selected 2 LED display, 2 LCD, and 3 VFD over-the-range 

microwave oven models from different manufacturers for testing. For each model, DOE 

purchased two identical units to evaluate the reliability under two separate temperature and 

humidity conditions. Prior to the start of testing, the illuminance for each display was measured 

from a fixed distance under dark room conditions. In order to obtain consistent and comparable 

measurements, each clock display was set to 12:00 prior to the illuminance measurements. 

Because some displays may dim after a period of user inactivity, the illuminance for each unit 

was measured again after a period of 10 minutes of inactivity.

The microwave ovens were then operated in standby mode in an environmental chamber 

for twelve 20-hour periods at 82.5 ± 2.5 °C and 90 ± 5 percent RH for one set of the 7

microwave oven models, and for twelve 20-hour periods at 67.5 ± 2.5 °C and 90 ± 5 percent RH 

for the other set of 7 microwave ovens. The temperature conditions were selected based on the 

maximum rated operating conditions for the different display types, as discussed above. After 

each 20 hour period at elevated temperature and humidity, the environmental chamber and 

microwave ovens were cooled to ambient room temperature (23 ± 5°C), at which point the 

illuminance of each display was measured before and after a 10-minute period of inactivity using 

the same method described above. Each set of microwave ovens was exposed to the elevated 

temperature and humidity conditions for a total of 12 cycles of 20 hours (240 total hours), with 

illuminance measured twice at ambient room conditions after each cycle. In addition, the power 

and current draw were measured throughout the 20-hour cycle and the 10-minute illuminance 

measurement periods.

Figure 5.5.7 and Figure 5.5.8 show the illuminance measurements for the 67.5 °C and 90 

percent RH testing. Figure 5.5.9 and Figure 5.5.10 show the results for the 82.5 °C and 90 

percent RH testing. The test results showed that display illuminance tended to degrade over time 

at these elevated conditions for most of the units tested, but the data did not reveal a correlation 

between the rate of degradation and display type. In particular, different VFDs in DOE’s test 

sample degraded both more and less rapidly than the LED displays under both set of 

temperature/humidity conditions, including an LED display with illuminance comparable to the 

VFDs. DOE notes that one of the models with a backlit LCD failed after 20 hours at 82.5 °C and 

60 hours at 67.5 °C.a However, DOE also notes that the other backlit LCD model had similar 

illuminance levels as two of the VFD models and showed little to no degradation. The power and 

current measurements similarly did not show any trends indicating a correlation between the rate 

of degradation and display type. Based on these test data, DOE concludes that all display types 

can be used in over-the-range microwave oven applications without a loss in consumer utility.

 

a Failed LCD unit not presented in graphs.
5-32

Figure 5.5.7 67.5 °C, 90-percent RH, Illuminance at Start of 10-Minute Measurement 

Periodsb

 

b “LCD” unit presented in graph was only available for 40 hours of exposure to the 67.5 °C, 90-percent RH

environmental conditions.

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0 50 100 150 200 250 300

Display Illuminance (lux)

Time at Elevated Conditions (hours)

LED 1

LED 2

LCD

VFD 1

VFD 2

VFD 3
5-33

Figure 5.5.8 67.5 °C, 90-percent RH, Illuminance at End of 10-Minute Measurement 

Periodsc

 

c “LCD” unit presented in graph was only available for 40 hours of exposure to the 67.5 °C, 90-percent RH

environmental conditions.

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0 50 100 150 200 250 300

Display Illuminance (lux)

Time at Elevated Conditions (hours)

LED 1

LED 2

LCD

VFD 1

VFD 2

VFD 3
5-34

Figure 5.5.9 82.5 °C, 90-percent RH, Illuminance at Start of 10-Minute Measurement 

Periods

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0 50 100 150 200 250

Display Illuminance (lux)

Time at Elevated Conditions (hours)

LED 1

LED 2

LCD

VFD 1

VFD 2

VFD 3
5-35

Figure 5.5.10 82.5 °C, 90-percent RH, Illuminance at End of 10-Minute Measurement 

Periods

5.5.3 Incremental Cost Data

5.5.3.1 Incremental Cost Data for Microwave-Only Ovens and Countertop 

Convection Microwave Ovens

From the standby power testing and reverse-engineering, DOE observed correlations 

between (1) specific components and technologies, or combinations thereof, and (2) measured 

standby power. Costs for each of the components and technologies were estimated by DOE using 

quotes obtained from suppliers, interviews with manufacturers, interviews with subject matter 

experts, research and literature review, and numerical modeling. Preliminary incremental 

manufacturing costs associated with the standby power levels were then obtained by considering 

combinations of these components as well as other technology options identified to reduce 

standby power. Manufacturer interviews were also conducted to obtain greater insight into the 

design strategies to improve efficiency and the associated costs. As discussed above in section 

5.2, DOE believes the standby power levels and characteristics of power supply designs (i.e., 

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0 50 100 150 200 250

Display Illuminance (lux)

Time at Elevated Conditions (hours)

LED 1

LED 2

LCD

VFD 1

VFD 2

VFD 3
5-36

size of the power supply) associated with microwave-only countertop and over-the-range units as 

well as countertop convection microwave ovens are similar. As a result, DOE proposed to 

analyze microwave-only ovens and countertop convection microwave ovens as a single product 

class. Therefore, DOE developed a single cost-energy use relationship for this product class.

The design options DOE considered in the engineering analysis include various display 

technologies, cooking sensors with no standby power requirement, higher-efficiency controller 

power supplies and relays, and improved control strategies. For details on the microwave oven 

standby design options, refer to chapter 3 of this TSD. DOE selected the design option(s) it 

believed manufactures would most likely implement to achieve a given standby power level for a 

given display technology. 

The incremental cost to improve the standby power efficiency for each display 

technology was weighted by the estimated percentage of shipments based on a market survey of 

SKUs that DOE conducted in October 2012. For example, DOE research suggests that 21 

percent of all Product Class 1 SKUs are represented by countertop microwave-only ovens that 

incorporate an LED display, no cooking sensor, and a standard magnetron power supply. Thus, 

21 percent of the incremental cost to reach each standby power level for Product Class 1 is based 

on the design option pathway estimate for that particular type of microwave oven. The design 

pathways for other microwave oven types were calculated and included in the weighted average 

costs for both product classes. In total, 25 different types of microwave ovens were identified in 

the market survey, and up to 18 design pathways were investigated for each. 

Every design pathway is unique, with test data and DOE estimates used to determine the 

power consumption and incremental manufacturing cost at each standby power level. Appendix 

5D presents the details of the design pathways, including the design options and product-based 

share of each microwave oven type. Although these likely pathways vary, the product type 

weighting suggests generalized trends of power supply and microwave oven design changes as a 

function of standby power level for Product Class 1.

For SL 1, DOE estimates that manufacturers would transition to cooking sensors with no 

standby power requirements for all units that include such a feature. In general, industry could 

continue using linear power supplies, though minor internal changes (such as lowering the 

unregulated voltage levels) may be implemented by manufacturers to further reduce standby 

power. Some pathways require switch-mode power supplies even at efficiency level 1, 

particularly microwave ovens with VFD and backlit LCD displays. 

To achieve SL 2, most product types would require the transition to SMPSs, as linear 

power supplies would likely no longer meet the standby power requirements. However, LED and 

LCD-equipped countertop microwave-only ovens with or without cooking sensors, which 

represent approximately 30 percent of available models, could continue to use linear power 

supplies.

To transition from SL 2 to SL 3, all products would need incorporate SMPSs, with the 

exception of VFD- and most backlit LCD-equipped microwave ovens, for which the power
5-37

consumption is too high to meet SL 3 even with am SMPS. Such products would either have to 

transition to an automatic power-down function or switch to lower-power display technologies. 

For the final rule, DOE did not consider a switch to a different display type. However, DOE 

research suggests that manufacturers could convert current microwave ovens featuring VFD 

displays to lower-power technologies such as LEDs without the loss in consumer utility that 

would be associated with automatic power-down. At SL 3, inverter-based units would also 

require higher-efficiency relays.

DOE research suggests that no microwave oven on the market in the United States can 

attain the max-tech SL 4 without the use of automatic power-down. Therefore, the max-tech 

incremental cost reflects the cost of transitioning 100 percent of microwave ovens in the 

marketplace to a deep-sleep module. Since the deep-sleep module disconnects all power inside 

the microwave oven, there is no need for a highly efficient power supply. Thus, the automatic 

power-down design option includes the use of a conventional linear power supply. This approach 

minimizes the cost of the power supply and is a strategy employed by manufacturers of other 

products that DOE has reverse engineered.

Table 5.5.10 Incremental Manufacturing Costs for Product Class 1 Microwave Oven 

Standby Power

Standby Power 

Level

Standby 

Power, W Typical Design Options

Incremental 

Cost, 2011$

Baseline 4.0 – $ 0

1 2.0 Zero-standby sensor $0.26

2

1.5 SL 1 + Switch-mode power supply (except countertop 

microwave-only LED and LCD)

$0.38

3

1.0 SL 2 + Switch-mode power supply (non-VFD) or 

Auto power-down (VFD)

$3.28

4 0.02 Auto power-down (all) $6.23

5.5.3.2 Incremental Cost Data for Built-In and Over-the-Range Convection 

Microwave Ovens

DOE conducted standby power testing and reverse engineering analyses on the 8 built-in 

and over-the-range convection microwave ovens in its test sample in order to develop the costefficiency relationships. DOE used a design-option approach to determine the incremental 

manufacturing costs of convection microwave ovens for each standby power level. As discussed 

above for the microwave-only oven and countertop convection microwave oven product class, 

DOE observed correlations between specific components and technologies, or combinations 

thereof, and measured standby power. Costs for each of the components and technologies used at 

each standby power level were estimated by DOE using quotes obtained from various 

component suppliers as well as interviews with manufacturers.

For SL 1, DOE believes that, similar to the microwave-only oven and countertop 

convection microwave oven product class, manufacturers would likely have to utilize a cooking 
5-38

sensor with no standby power requirements. DOE estimated that the manufacturing cost of a 

zero-standby cooking sensor would be the same as that of the cooking sensor with high standby 

power. In addition, all units other than non-backlit LCD-equipped models would require 

changing the conventional linear power supply to an SMPS. DOE used reverse engineering to 

determine the cost of the components used in a design of a switch mode power supply capable of 

delivering the same output power as the baseline conventional linear power supply.

DOE believes that no additional changes other than implementing an SMPS for nonbacklit LCD-equipped units would be required to achieve SL 2. Therefore, there is little 

difference in the incremental costs for SL 1 and SL 2.

To move from SL 2 to SL 3, DOE determined that backlit LCD- and VFD-equipped 

microwave ovens would require an automatic power-down function. DOE estimated the 

manufacturing cost for this design option based on the cost of the components needed to design 

an automatic power-down function that uses a transformerless power supply for the 

microprocessor chip along with a conventional linear power supply that is activated when the 

unit goes into active mode. DOE also estimated the costs associated with a door limit switch and 

Triac to control power through the transformer. DOE further estimated that LED- and LCDequipped models would need no additional design changes.

At SL 4, all models would be required to implement the automatic power-down feature.

The results of this analysis for built-in and over-the-range convection microwave ovens 

are summarized in Table 5.5.11.

Table 5.5.11 Incremental Manufacturing Costs for Product Class 2 Microwave Oven 

Standby Power

Standby Power 

Level

Standby 

Power, W Typical Design Options

Incremental 

Cost, 2011$

Baseline 4.5 – $ 0

1

3.7 Zero-standby sensor + Switch-mode power supply 

(except LCD)

$0.06

2 2.7 SL 1 + Switch-mode power supply (LCD) $0.08

3

2.2 SL 2 (LED and LCD) or Auto power-down (backlit 

LCD, VFD)

$5.01

4 0.04 Auto power-down (all) $5.86
5-39

REFERENCES

 1 American Metals Market. Available online at www.amm.com/. (Accessed May 2013.)

2 Association of Home Appliance Manufacturers (AHAM). 2006. AHAM Test Data on 

Microwave Ovens. October 23, 2006. Docket No. EE-2006-STD-0127, Comment Number 

17.

3 Ibid.

4 Ibid.

5 Ibid.

6 Fish, N. et al. 2006. Standby Power Energy Use of Common Household Appliances. 

Canadian Building Energy End-Use Data and Analysis Centre, CBEEDAC 2006-RP-04-

DRAFT, July 2006.

7 Aulenback, A. et al. 2001. Stand-By Power Requirements for Household Appliances. 

Canadian Residential Energy End-Use Data and Analysis Centre, CREEDAC-2001-05-04, 

May 2001.

8 Ibid.

9 Lawrence Berkeley National Laboratory (LBNL). 2008. Low-Power Mode Energy 

Consumption in California Homes. Report CEC-500-2008-035 prepared for California 

Energy Commission, September 2008. Available online at 

www.energy.ca.gov/2008publications/CEC-500-2008-035/CEC-500-2008-035.PDF. 

(Accessed May 2013.)

10 Floyd, D. and Webber, C. 1998. “Leaking Electricity: Individual Field Measurement of 

Consumer Electronics.” Proceedings of American Council for an Energy-Efficient Economy, 

Florida Solar Energy Center, August 1998.

11 Meier, A. and Huber, W. 1997. Results from the Investigations on Leaking Electricity in the 

USA. Lawrence Berkeley National Laboratory, Berkeley, CA, LBNL-40909, 1997.
6-i

CHAPTER 6. MARKUPS FOR PRODUCT PRICE DETERMINATION

TABLE OF CONTENTS

6.1 INTRODUCTION ........................................................................................................... 6-1

6.1.1 Distribution Channels...................................................................................................... 6-1

6.1.2 Procedure for Calculating Markups................................................................................. 6-1

6.2 MANUFACTURER MARKUPS.................................................................................... 6-1

6.3 RETAILER MARKUPS.................................................................................................. 6-2

6.3.1 Approach to Calculating Retailer Markups ..................................................................... 6-2

6.3.1.1 Baseline Retailer Markup .............................................................................. 6-3

6.3.1.2 Incremental Retailer Markups........................................................................ 6-4

6.4 SALES TAXES ............................................................................................................... 6-8

6.5 SUMMARY OF MARKUPS .......................................................................................... 6-9

LIST OF TABLES

Table 6.2.1 Major Appliance Manufacturers: Gross Margins and Markups.......................... 6-2

Table 6.3.1 Data for Calculating Baseline Markup: Electronics and Appliance Stores 

(2002)................................................................................................................... 6-3

Table 6.3.2 Data for Calculating Incremental Markup: Electronics and Appliance 

Stores (2002)........................................................................................................ 6-6

Table 6.3.3 Electronics and Appliance Stores: Concentration by Four Large Firms ............. 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 .......................................................................................... 6-9
6-1

CHAPTER 6. MARKUPS FOR DETERMINING PRODUCT PRICES

6.1 INTRODUCTION

To analyze the effects of standards on consumer costs, DOE must determine the cost to 

the consumer of both baseline products and more efficient units. For products having higherthan-baseline efficiency, DOE estimated consumer prices by applying appropriate markups to the 

incremental manufacturing costs estimated in the engineering analysis. 

6.1.1 Distribution Channels

The appropriate markups for determining consumer product prices depend on the type of 

distribution channels through which products move from manufacturers to purchasers. At each 

point in the distribution channel, companies mark up the price of products to cover their business 

costs and profit margins. 

Data from the Association of Home Appliance Manufacturers (AHAM)1

6.1.2 Procedure for Calculating Markups

indicate that an 

overwhelming majority of residential appliances are sold through retail outlets. Because DOE is 

not aware of any other distribution channel that plays a significant role in the distribution of 

residential microwave ovens, DOE assumed that all microwaves are purchased by consumers 

from retail outlets. 

At each point in the distribution channel, companies mark up the price of a product to 

cover their business costs and profit margins. In company income statements, gross margin is the 

difference between the company’s revenue and the company’s cost of goods sold (CGS). The 

gross margin includes the expenses of companies in the distribution channel, including overhead 

costs (sales, general, and administration); research and development and interest expenses; 

depreciation and taxes; and company profit. To cover costs and to contribute positively to 

company cash flow, the price of a product must include a markup. Products command lower or 

higher markups depending on company expenses associated with the product and the degree of 

market competition. In developing markups for manufacturers and retailers, DOE obtained data 

about the revenue, CGS, and expenses of firms that sell residential microwave ovens. 

6.2 MANUFACTURER MARKUPS

DOE uses manufacturer markups to transform a manufacturer’s production costs into a 

manufacturer sales price. Using the CGS and gross margin, DOE calculated the manufacturer 

markup (MUMFG) using the following equation.

MFG

MFG MFG

MFG CGS

CGS GM

MU

+ =
6-2

Where:

MUMFG = manufacturer markup,

CGSMFG = manufacturer’s CGS or manufacturer production cost (MPC), and

GMMFG = manufacturer’s gross margin.

The manufacturer’s CGS (or MPC) plus its GM equals the manufacturer selling price 

(MSP).

DOE developed an average manufacturer markup by examining the annual Securities and 

Exchange Commission (SEC) 10-K reports filed by four publicly traded manufacturers engaged 

primarily in manufacturing appliances and having a combined product range that includes

residential cooking products.2 The four manufacturers represent a nearly 50-percent market share 

for major appliances. Because the companies typically are diversified, producing a wide range of 

appliances, DOE assumed that an industry average markup was representative for the 

manufacture of cooking products. DOE evaluated markups for 2002–2005. 

Table 6.2.1 lists the average corporate gross margin for 2002–2005, and corresponding 

markups, for each of the four manufacturers. The average markup value based on the four 

companies is 1.26, which is the value that DOE used. 

Table 6.2.1 Major Appliance Manufacturers: Gross Margins and Markups

Mfr. A Mfr. B Mfr. C Mfr. D

Average net revenues (million) 372 280 4,770 12,682

Corporate gross margin (%) 15 28 16 22

Markup (%) 1.18 1.39 1.19 1.28

Source: SEC 10-K reports (2002–2005).

6.3 RETAILER MARKUPS

The following sections describe DOE’s approach to calculating retailer markups and the 

development of baseline and incremental markups.

6.3.1 Approach to Calculating Retailer Markups

DOE based the retailer markups for residential microwave ovens on financial data for 

electronics and appliance store category from the 2002 U.S. Census Business Expenditure 

Survey (BES).

3 DOE organized the financial data into statements that break down cost 

components incurred by firms in that category. DOE assumed that the income statements 

faithfully represent the various average costs incurred by firms that sell home appliances. 

Although electronics and appliance stores handle many commodity lines, the data for the sector 

provide the most accurate available indication of expenses for selling home appliances.
6-3

The BES data provided for electronics and appliance stores contain only total sales and 

detailed operating expenses. In order to construct a complete data set to estimate markups, DOE 

needed to estimate CGS and GM. The 1997 Business Expenses Survey provides total sales, gross 

margin, and detailed operating expenses for the category of household appliance stores. The 

CGS and GM account for approximately 70 percent and 30 percent of total sales, respectively.

DOE found that GM as percent of sales was roughly constant in this sector from 1993 to 2007.a

Therefore, DOE assumed that the fractions of CGS and GM as percent of sales in 2002 are the 

same as in 1997. Based on this assumption, DOE calculated the CGS, gross margin, and net 

profit for electronics and appliance stores in the 2002 BES.

b

6.3.1.1 Baseline Retailer Markup

The baseline markup relates 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 efficiency standards). DOE used the following equation to 

calculate the baseline markup (MUBASE) for retailers as an average markup.

RTL

RTL RTL

BASE CGS

CGS GM MU + =

Where:

MUBASE = baseline retailer markup,

CGSRTL = retailer’s cost of goods sold,

GMRTL = retailer’s gross margin, 

Table 6.3.1 shows DOE’s calculation of the baseline retailer markup for residential 

microwave ovens.

Table 6.3.1 Data for Calculating Baseline Markup: Electronics and Appliance Stores 

(2002)

Type of Business Item Amount $1,000

Sales 83,896,811

Cost of goods sold (CGS) 57,888,800

Gross margin (GM) 26,008,011

Baseline markup = (CGS + GM)/CGS 1.45

 Sources: U.S. Census. 2002 Business Expenses Survey (for sales) and 1997 Business Expenses Survey (for 

percentages of CGS and GM).

 a U.S. Census. Annual Retail Trade Report: Electronics and Appliance Stores Sales and Gross Margin. 2007.

Gross margin as a percent of sales ranges between 25 percent and 33 percent during this period, with an average 

of 30 percent and no discernible upward or downward trend. 

b The full version of 2002 Business Expenses Survey is available at: www.census.gov/prod/ec02/be0200i1.pdf . 

(Accessed May 2013)
6-4

6.3.1.2 Incremental Retailer Markups

Incremental markups are coefficients that relate the change in the manufacturer sales 

price of higher-efficiency models to the change in the retailer sales price. DOE considers higherefficiency models to be products sold under market conditions affected by new or amended 

efficiency standards. Under new or amended standards, the retailer faces an increase in CGS for 

a particular product.

Unfortunately, there is limited empirical evidence regarding appliance retailer markup 

practices when a product increases in cost (due to increased efficiency or other factors). DOE 

understands that real-world markup practices will vary depending on the market conditions 

retailers face, on the magnitude of the change in CGS, and on any associated changes in retail 

costs. Pricing in retail stores may also involve rules of thumb that are difficult to identify 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 goods sold are not expected to increase economic profits. 

Thus, DOE calculates markups/gross margins to allow cost recovery for retail 

companies in the distribution chain (including changes in the cost of capital) without 

changes in company profit margins. 

2. Efficiency improvements affect only some distribution costs. DOE sets markups and 

retail prices to cover the distribution costs that are expected to change with efficiency 

but not those that are not expected to change with efficiency. 

The incremental markup approach is described in more detail in Dale et al. (2004).4

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 (fixed), and (2) those that increase proportionately with CGS (variable). DOE defines 

fixed costs to include labor and occupancy expenses, because those costs are not likely to 

increase as a result of a rise in CGS. All other expenses, as well as net profit, are assumed to 

change in proportion to CGS. Although some of those other expenses may not scale with CGS, 

DOE takes a more conservative position and includes them as variable costs.c

 

DOE used the following equation to calculate the incremental markup (MUINCR) for 

retailers.

RTL

RTL RTL

INCR CGS

CGS VC MU + =

 c Note that under DOE’s approach, a component that has a high fixed cost yields a low incremental markup.
6-5

Where:

MUINCR = incremental retailer markup,

CGSRTL = retailer’s cost of goods sold, and

VCRTL = retailer’s variable costs.

Table 6.3.2 shows the breakdown of operating expenses based on the 2002 BES data. 

DOE estimates that the incremental markup for retailers of microwave ovens is 1.17.
6-6

Table 6.3.2 Data for Calculating Incremental Markup: Electronics and Appliance Stores 

(2002)

Amount 

$1,000

Sales 83,896,811

Cost of Goods Sold (CGS) 57,888,800

Gross Margin (GM) 26,008,011

Labor & Occupancy Expenses (Fixed)

Annual payroll 10,267,605

Employer costs for fringe benefit1 1,407,970

Contract labor costs including temporary help 160,094

Purchased utilities, total 427,809

Cost of purchased repair and maintenance services 308,789

Cost of purchased management consulting, administrative, and other 

professional services 300,548

Purchased communication services 400,598

Lease and rental payments 2,655,286

Taxes and license fees (predominantly income taxes) 385,538

Subtotal: 16,314,237

Other Operating Expenses & Profit (Variable)

Expensed computer-related supplies 86,751

Cost of purchased packaging and containers 41,866

Other materials and supplies not for resale 611,361

Cost of purchased transportation, shipping, and warehousing services 500,233

Cost of purchased printing services 285,012

Cost of purchased advertising and promotional services 1,840,898

Cost of purchased legal services 90,020

Cost of purchased accounting, auditing, and bookkeeping services 86,292

Cost of purchased custom-coded original software (expensed),

including adaptation of off-the-shelf software 18,944

Cost of system support design and services, including web design 35,748

Cost of insurance 393,201

Cost of data processing and other purchased computer services, except 

communications 41,056

Depreciation and amortization charges 1,229,110

Commissions paid 106,061

Other operating expenses 2,929,906

Cost of contract work 21,955

Net profit before taxes 1,375,360

Subtotal: 9,693,774

Incremental Markup = (CGS + total other operating expenses and 

profit)/CGS 1.17

Source: U.S. Census. 2002 Business Expenses Survey.
6-7

By dividing expenses into fixed and variable components, DOE’s calculation of 

incremental markups assumes that retailers cover cost changes without changing profit 

margins. Although retailers may be able to obtain higher profits for a 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 with a relatively small 

increase in price), will exert a counterbalancing downward pressure on retailer profit 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 categories: 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 each sector. Generally, an FFCR of 

less than 40 percent indicates that a sector is not concentrated, and an FFCR of more than 70

percent indicates that a sector is highly concentrated.d, e

The FFCR of the sub-sector of appliance sales within each channel is equal to the sector

FFCR times the percent of total sales within each channel accounted for by major appliances. As 

shown in Table 6.3.3, the results indicate that appliance sales in electronics and appliance stores, 

household appliance stores, building materials and supplies dealers, and general merchandise 

stores have a FFCR well under the 40-percent threshold. Moreover, the electronics and appliance 

stores sector includes a sub-sector of household appliance stores. Because this sub-sector 

contains many stores, it has a FFCR of only 16.8 percent. 

 d University of Maryland University College. AMBA 607 course materials. (Last accessed August 18, 2012.) 

<http://info.umuc.edu/mba/public/AMBA607/IndustryStructure.html> e Quick MBA. Economics—industry concentration. (Last accessed August 18, 2012.)

<http://www.quickmba.com/econ/micro/indcon.shtml>
6-8

Table 6.3.3 Electronics and Appliance Stores: Concentration by Four Large Firms

Sector

Four-Firm 

Concentration Ratio

% of sector sales

Percent of Sales 

Accounted for by 

Major 

Appliances %

Four-Firm 

Concentration Ratio

% of major appliance 

sales

Electronics and 

appliance stores 44.3 39.4 17.5

Sub-sector of

household 

appliance stores 

16.8 - -

Building materials and 

supplies dealers 36.7 15.8 5.8

General merchandise 

stores 65.6 35.4 23.2

 Sources: U.S. Economic Census. Establishment and Firm Size: Concentration by Largest Firms for the United 

States. 2002. U.S. Economic Census. Product Lines: Kings of Business by Broad Product Line. 2002.

*Note: It is assumed here that sales of major appliance are distributed uniformly within all firms in each sector. 

6.4 SALES TAXES

Sales tax comprises state and local sales taxes that are applied to the consumer product 

price. The sales tax is a multiplicative factor that increases the consumer product price. DOE 

derived state and local taxes from data provided by the Sales Tax Clearinghouse.

5

Table 6.4.1

DOE derived 

population-weighted average tax values for each Census division and large State, as well as the 

U.S. population-weighted average tax of 7.1 percent ( ).
6-9

Table 6.4.1 Average Sales Tax Rates by Census Division and Large State

Census Division/State Population Tax Rate %

New England 14,492,360 5.64

Mid Atlantic 21,564,041 6.62

East North Central 46,519,084 6.85

West North Central 20,639,751 7.06

South Atlantic 41,167,090 6.26

East South Central 18,553,961 7.93

West South Central 11,304,323 8.46

Mountain 22,373,411 6.80

Pacific 12,799,425 5.25

New York State 19,465,197 8.45

California 37,691,912 8.20

Texas 25,674,681 8.00

Florida 19,057,542 6.65

U.S. Average 7.11

6.5 SUMMARY OF MARKUPS

Table 6.5.1 summarizes the markups at each stage in the distribution channel for 

microwave ovens plus the average sales tax.

Table 6.5.1 Summary of Markups

Markup Baseline Incremental

Manufacturer 1.26

Retailer 1.45 1.17

Sales tax 0.071

Total 1.96 1.58
6-10

REFERENCES

 

1 Association of Home Appliance Manufacturers. AHAM 2003 Fact Book. 2003. AHAM: 

Washington, D.C. p. 25.

2 Security Exchange Commission. SEC 10-K Reports. Various dates, 2002–2005. Security 

Exchange Commission. www.sec.gov/. (Accessed May 2013.)

3 U.S. Census Bureau. 2002 Economic Census, Business Expenses Survey, Retail Trade, 

Household Appliance Stores. 2002. U.S. Census Bureau: Washington, D.C. 

www.census.gov/econ/bes/ . <www.census.gov/prod/ec02/be0200i1.pdf. (Accessed May 

2013.)

4 Dale, L., D. Millstein, K. Coughlin, R. Van Buskirk, G. Rosenquist, A. Lekov, and S.

Bhuyan. An Analysis of Price Determination and Markups in the Air-Conditioning and 

Heating Equipment Industry. January 2004. Lawrence Berkeley National Laboratory: 

Berkeley, CA. Report No. LBNL-52791. 

5 Sales Tax Clearinghouse, Inc. State Sales Tax Rates Along with Combined Average City 

and County Rates. thestc.com/STrates.stm. (Accessed May 2013.)
7-i

CHAPTER 7. ENERGY USE ANALYSIS

TABLE OF CONTENTS

7.1 INTRODUCTION ........................................................................................................... 7-1

7.2 CONSUMER SAMPLE................................................................................................... 7-1

7.3 STANDBY POWER CONSUMPTION.......................................................................... 7-1

7.3.1 Annual Energy Consumption by Efficiency Level.......................................................... 7-3

7.3.2 Variability of Annual Energy Consumption.................................................................... 7-4

LIST OF TABLES

Table 7.2.1 Statistics Regarding RECS 2009 Sample............................................................. 7-1

Table 7.3.1 Number of Meals Cooked per Day Based on RECS 2009 Sample...................... 7-3

Table 7.3.2 RECS 2009 Microwave Oven Use Percentage .................................................... 7-3

Table 7.3.3 Microwave-Only Ovens and Countertop Convection Microwave Ovens: 

Annual Energy Consumption by Standby Power Level ...................................... 7-4

Table 7.3.4 Built-In and Over-the-Range Convection Microwave Ovens: Annual 

Energy Consumption by Standby Power Level................................................... 7-4

LIST OF FIGURES

Figure 7.3.2 Range of Annual Energy Use for Microwave-Only Ovens and Countertop 

Convection Microwave Ovens............................................................................. 7-5

Figure 7.3.3 Range of Annual Energy Use for Built-In and Over-the-Range Convection 

Ovens ................................................................................................................... 7-5
7-1

CHAPTER 7. ENERGY USE ANALYSIS

7.1 INTRODUCTION

To carry out the life-cycle cost (LCC) and payback period (PBP) analysis described in 

chapter 8, the U.S. Department of Energy (DOE) must determine the energy savings that

consumers will experience from more efficient products. This chapter describes how DOE 

determined the annual standby energy consumption of microwave ovens at various efficiency 

levels.

The engineering analysis, described in chapter 5, reports energy use based on the DOE 

test procedure. This test provides standardized results that serve as the basis for comparing the 

performance of appliances under uniform conditions. Actual field usage of course varies 

depending on the conditions under which an appliance is operated. 

To establish a reasonable range of energy consumption for microwave ovens used in the 

field, DOE primarily used data from the Energy Information Administration (EIA)’s 2009

Residential Energy Consumption Survey (RECS 2009).1 RECS is a national sample survey of 

housing units that collects statistical information on the consumption of and expenditures for 

energy in housing units, along with data on energy-related characteristics of the housing units 

and occupants. RECS 2009, which collected data on 12,083 housing units, was constructed by 

EIA to be a national representation of the household population in the United States.

7.2 CONSUMER SAMPLE

For the microwave oven sample, DOE used the subset of RECS 2009 records that met the 

criterion that the household had a microwave oven. Because RECS 2009 provides no means of 

determining the type of microwave oven in a given household, DOE used the same sample for 

both product classes. The RECS reported saturation of microwave ovens in 2009 was 88 percent.

Table 7.2.1 summarizes the data DOE on microwave oven ownership obtained from REC 2009.

Table 7.2.1 Statistics Regarding RECS 2009 Sample

Product 

Class Selection Criterion No. of Records

No. of U.S. Households 

Represented million

1 and 2 Has microwave = Yes 11,616 109.0

7.3 STANDBY POWER CONSUMPTION

To estimate the annual energy use associated with standby power (ESP) for both product 

classes, DOE used the following expression, in which the standby power is multiplied by the 

number of hours in a year the microwave oven is in standby mode.
7-2

ESP = PSP × HoursSP

Where:

PSP = standby power (watts), and

HoursSP = hours in a year the microwave oven is in standby mode (hours).

The annual standby hours equal the total hours in a year less the number of hours the 

microwave oven is in active operation. DOE used the following expression to determine the 

hours of active operation (HoursACTIVE).

HoursACTIVE = HoursNOPR ×UseRECS

Where:

HoursNOPR = average microwave operating hours from the notice of proposed 

rulemaking (NOPR), and

UseRECS = relative usage factor.

In the 2008 NOPR for cooking products, DOE determined the average hours of operation 

for microwaves (HoursNOPR) to be 71 hours per year.2 DOE has no reason to believe that this 

number has changed. 

For each household, RECS 2009 provides information on the frequency of microwave 

oven usage and the number of hot meals prepared per day. DOE used the following expression to 

calculate the RECS usage factor (UseRECS) for each household in the sample.

( ) UseRECS MealsRECS FreqRECS UseAVG = × /

Where:

MealsRECS = RECS reported household hot meals per day,

FreqRECS = RECS reported microwave use frequency, and

UseAVG = weighted average usage factor.

The weighted average usage factor was calculated using the following expression.

∑

∑ × × =

RECS

AVG AVG RECS

AVG Weight

Meals Freq Weight Use

Where:

MealsAVG = average household hot meals per day,

FreqAVG = average microwave use frequency, and
7-3

Weight = RECS household weight.

The RECS 2009 data for MealsRECS and FreqRECS fall into one of the bins shown in Table 

7.3.1 and Table 7.3.2, respectively. For each question, DOE assumed a uniform or triangular 

distribution within the boundaries of each bin and randomly assigned a value from within the 

appropriate range to each sample household. For calculating the average usage factor (UseAVG),

DOE used the average value from the corresponding bin.

Table 7.3.1 Number of Meals Cooked per Day Based on RECS 2009 Sample

Bin Frequency of Microwave 

Oven Use

Meals per Day

Min. Max.

0 Doesn't cook/never cooks 0.0 0.0

1 Three or more times a day 3.0 4.0

2 Two times a day 1.0 3.0

3 Once a day 0.5 1.5

4 A few times each week 0.2 0.8

5 About once a week 0.1 0.2

6 Less than once a week 0.0 0.1

Table 7.3.2 RECS 2009 Microwave Oven Use Percentage

Bin Amount of Food Cooked in 

Microwave

Use Percentage %

Min. Max.

1 Most or all 90 100

2 About half 25 75

3 Some or very little 20 30

4 Used only for snacks, 

defrosting, or reheating food 5 25

7.3.1 Annual Energy Consumption by Efficiency Level

Table 7.3.3 and Table 7.3.4 present the average annual energy consumption by efficiency 

level for both product classes.
7-4

Table 7.3.3 Microwave-Only Ovens and Countertop Convection Microwave Ovens: 

Annual Energy Consumption by Standby Power Level

Efficiency 

Level

Standby

Power W

Energy Use

kWh/yr

Baseline 4.00 34.76

1 2.00 17.38

2 1.50 13.03

3 1.00 8.69

4 0.02 0.17

Table 7.3.4 Built-In and Over-the-Range Convection Microwave Ovens: Annual Energy 

Consumption by Standby Power Level

Efficiency 

Level

Standby

Power W

Energy Use

kWh/yr

Baseline 4.50 39.10

1 3.70 32.15

2 2.70 23.46

3 2.20 19.12

4 0.04 0.35

7.3.2 Variability of Annual Energy Consumption

Figure 7.3.1 and Figure 7.3.2 show the range of average annual energy consumption by 

efficiency level for both product classes. Because variation among households in use of 

microwave ovens for cooking has a minor effect on the number of standby hours, the range in 

standby energy use is very small.
7-5

0

5

10

15

20

25

30

35

40

Level 0 Level 1 Level 2 Level 3 Level 4

Annual Energy Use (kWh)

Efficiency Level

Microwave and MWO-Convection CT Average Annual Energy Use

Average; Median; Box 25%-75%; Whisker 5%-95%

median

average

Figure 7.3.2 Range of Annual Energy Use for Microwave-Only Ovens and 

Countertop Convection Microwave Ovens

0

5

10

15

20

25

30

35

40

45

Level 0 Level 1 Level 2 Level 3 Level 4

Annual Energy Use (kWh)

Efficiency Level

Built-In and Over-the-Range Convection Ovens

Average Annual Energy Use

Average; Median; Box 25%-75%; Whisker 5%-95%

median

average

Figure 7.3.3 Range of Annual Energy Use for Built-In and Over-the-Range 

Convection Ovens
7-6

REFERENCES

1. U.S. Department of Energy (DOE)–Energy Information Administration (EIA).

Residential Energy Consumption Survey: Preliminary 2009 Public Use Microdata File. 

October 2011. EIA: Washington, D.C. 

www.eia.gov/consumption/residential/data/2009/index.cfm#microdata. (Accessed May 

2013.)

2. DOE–Energy Efficiency and Renewable Energy. Energy Conservation Program for 

Consumer Products: Notice of Proposed Rulemaking for Residential Dishwashers, 

Dehumidifiers, and Cooking Products, and Commercial Clothes Washers. Federal 

Register, October 17, 2008. Vol. 73, no. 202, pp. 62034–62134.

www1.eere.energy.gov/buildings/appliance_standards/residential/pdfs/home_appl_nopr_

fr.pdf . (Accessed May 2013.)
8-i

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 ................ 8-7

8.2.1 Inputs to Total Installed Cost........................................................................................... 8-8

8.2.1.2 Forecasting Future Product Prices................................................................. 8-9

8.2.1.3 Baseline Manufacturer Cost......................................................................... 8-11

8.2.1.4 Standard-Level Manufacturer Cost Increases.............................................. 8-11

8.2.1.5 Overall Markup............................................................................................ 8-12

8.2.1.6 Installation Cost ........................................................................................... 8-12

8.2.1.7 Total Installed Cost...................................................................................... 8-12

8.2.2 Inputs to Operating Cost ................................................................................................ 8-13

8.2.2.2 Annual Energy Consumption....................................................................... 8-14

8.2.2.3 Residential Electricity Prices....................................................................... 8-15

8.2.2.4 Energy Price Trends..................................................................................... 8-17

8.2.2.5 Repair and Maintenance Costs..................................................................... 8-17

8.2.2.6 Product Lifetime .......................................................................................... 8-17

8.2.3 Discount Rates............................................................................................................... 8-19

8.2.4 Compliance Date of Standard ........................................................................................ 8-23

8.2.5 Product Energy Efficiency in the Base Case ................................................................. 8-23

8.3 RESULTS OF LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS........... 8-24

8.3.1 Summary of Results....................................................................................................... 8-24

8.3.1.2 Distributions of Impacts............................................................................... 8-25

8.3.1.3 Range of Impacts ......................................................................................... 8-28

8.4 REBUTTABLE PAYBACK PERIOD.......................................................................... 8-32

8.4.1 Inputs.............................................................................................................................. 8-32

8.4.2 Results............................................................................................................................ 8-32

LIST OF TABLES

Table 8.1.1 Microwave-Only Ovens and Countertop Convection Microwave Ovens: 

Summary of Inputs to LCC and PBP Input Summary......................................... 8-6

Table 8.1.2 Built-In and Over the Range Convection Microwave Ovens: Summary of 

Inputs to LCC and PBP........................................................................................ 8-7

Table 8.2.1 Inputs to Total Installed Cost............................................................................... 8-8

Table 8.2.2 Baseline Manufacturer Costs............................................................................. 8-11

Table 8.2.3 Standard-Level Manufacturer Cost Increases.................................................... 8-12

Table 8.2.4 Microwave Ovens: Overall Markups................................................................. 8-12

Table 8.2.5 Total Installed Costs .......................................................................................... 8-13

Table 8.2.6 Inputs to Operating Cost .................................................................................... 8-13
8-ii

Table 8.2.7 Annual Energy Consumption............................................................................. 8-15

Table 8.2.8 Average Residential Electricity Prices............................................................... 8-16

Table 8.2.9 Lifetime Parameters........................................................................................... 8-19

Table 8.2.10 Types of Household Debt and Equity by Percentage Shares............................. 8-20

Table 8.2.11 Average Nominal Interest Rates for Household Debt ....................................... 8-21

Table 8.2.12 Average Real Effective Interest Rates for Household Debt .............................. 8-21

Table 8.2.13 Average Nominal and Real Interest Rates for Household Equity ..................... 8-22

Table 8.2.14 Average Interest on Household Debt and Equity .............................................. 8-22

Table 8.2.15 Microwave-Only and Countertop Convection Microwave Ovens: BaseCase Market Shares............................................................................................ 8-23

Table 8.2.16 Built-In and Over-the-Range Convection Microwave Ovens: Base-Case 

Market Shares .................................................................................................... 8-24

Table 8.3.1 Microwave-Only and Countertop Convection Microwave Ovens: LCC and 

PBP Results........................................................................................................ 8-25

Table 8.3.2 Built-In and Over-the-Range Convection Microwave Ovens: LCC and 

PBP Results........................................................................................................ 8-25

Table 8.4.1 Rebuttable Payback Periods............................................................................... 8-33

LIST OF FIGURES

Figure 8.1.1 Flow Diagram of Inputs for Determining LCC and PBP .................................... 8-4

Figure 8.2.1 Residential Electricity Price Trends................................................................... 8-17

Figure 8.2.2 Weibull Function for MWO Lifetime................................................................ 8-19

Figure 8.3.1 Microwave-Only and Countertop Convection Microwave Ovens: BaseCase LCC Distribution....................................................................................... 8-26

Figure 8.3.2 Built-In and Over-the-Range Convection Microwave Ovens: Base-Case 

LCC Distribution ............................................................................................... 8-26

Figure 8.3.3 Microwave-Only and Countertop Convection Microwave Ovens: LCC 

Savings Distribution for TSL 3.......................................................................... 8-27

Figure 8.3.4 Built-In and Over-the-Range Convection Microwave Ovens: LCC Savings 

Distribution for TSL 3 ....................................................................................... 8-27

Figure 8.3.5 Microwave-Only and Countertop Convection Microwave Ovens: PBP 

Distribution for TSL 3 ....................................................................................... 8-28

Figure 8.3.6 Built-In and Over-the-Range Convection Microwave Ovens: PBP 

Distribution for TSL 3 ....................................................................................... 8-28

Figure 8.3.7 Microwave-Only and Countertop Convection Microwave Ovens: Range of 

Average LCC Savings........................................................................................ 8-29

Figure 8.3.8 Built-In and Over the Range Convection Microwave Ovens: Range of 

Average LCC Savings........................................................................................ 8-30

Figure 8.3.9 Microwave-Only and Countertop Convection Microwave Ovens: Range of 

Average Payback Period.................................................................................... 8-31

Figure 8.3.10 Built-In and Over the Range Convection Microwave Ovens: Range of 

Average Payback Period.................................................................................... 8-31
8-1

CHAPTER 8. LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

8.1 INTRODUCTION

This chapter describes the U.S. Department of Energy’s (DOE)’s methodology for 

analyzing the economic impacts of new energy efficiency standards on individual consumers.

The effect of standards for microwave oven standby power on individual consumers includes a 

change in operating expense (usually decreased) and a change in purchase price (usually 

increased). This chapter describes three metrics DOE used to determine the effect of standards 

on individual consumers.

• 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 products through lower 

operating costs.

• Rebuttable payback period is a special case of the PBP. Whereas LCC and PBP are 

estimated given 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 calculations 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 here using a series of Microsoft Excel spreadsheets, which are accessible 

on the Internet:

(www1.eere.energy.gov/buildings/appliance_standards/residential/cooking_products.html)

Details and instructions for using the spreadsheets are presented in appendix 8A. 

8.1.1 General Approach to Analysis

DOE calculates life-cycle cost (LCC), the total consumer expense over the life of an 

appliance, by the following equation.

( ) ∑= + = +

N

t

t

t

r

OC LCC IC

1 1

Where:

LCC = life-cycle cost in dollars,

IC = total installed cost in dollars,

∑ = sum over the appliance lifetime, from year 1 to year N,

N = lifetime of the appliance in years,
8-2

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

∆

∆ =

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 using 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 microwave ovens. By 

developing household samples, DOE was able to perform the LCC and PBP calculations 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, DOE used the DOE Energy Information Administration’s

(EIA’s) 2009 Residential Energy Consumption Survey (RECS 2009) to develop household 

samples for microwave-only and convection microwave ovens.1 The EIA constructed RECS 

2009, which consists of 12,083 housing units, to be a national representation of the household 

population in the United States. DOE used the subset of RECS 2009 records in which the 

household had a microwave oven. Because RECS 2009 provides no means of determining the 

type of microwave oven in a given household, DOE used the same sample for both product 
8-3

classes. DOE used RECS to establish the variability of annual appliance use and of energy 

prices. DOE was able to assign unique annual hours of operation to each household in the 

sample. The variability among households in annual appliance use and/or energy pricing 

contributes to the range of LCCs and PBPs calculated for any particular trial standard level

(TSL).

DOE displays the LCC and PBP results as distributions of impacts compared to baseline 

conditions. Results, which are presented at the end of this chapter, 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 and PBP for each TSL 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 the 

operating cost. 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 cost

associated with producing products that meet a given TSL.

• 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 the operating cost are listed below.

• Product energy consumption: The on- site energy use associated with operating the 

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 2012 (AEO2012) 2

• 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.

to forecast energy prices for the results presented in this chapter.

• Lifetime: The age at which the product is retired from service. 
8-4

• Discount rate: The rate at which DOE discounts future expenditures to establish their 

present value. 

The data inputs to PBP are the total installed cost of the product to the consumer for each 

energy efficiency level and the annual (first-year) operating expenditures for each TSL. The 

inputs to total installed cost are the product cost plus the installation cost. The inputs to operating 

costs are the annual energy cost, the annual repair cost, and the annual maintenance cost. The 

PBP uses the same inputs as the LCC analysis, except 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 conservation standard takes effect. The energy price 

DOE used in the PBP calculation was the price projected for that year. Discount rates are also 

not required for the simple PBP calculation.

Figure 8.1.1 depicts the relationships among the 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 the final outputs (the LCC and PBP).

Figure 8.1.1 Flow Diagram of Inputs for Determining LCC and PBP

Baseline

Manufacturer

Cost

Std-Level

Manufacturer

Cost

Manufacturer

Markup

Retailer or

Distributor

Markup

Sales Tax

Installation

Cost

Energy

Consumption

 Product

Price

Lifetime

Operating

Expense

Annual Energy

Expense

Lifetime Repair and

Maintenance

Cost

Discount Rate

Energy Price

Trends

Annual

Operating

Expense

Payback

Period

Life-Cycle

Cost

Energy Prices

Total Installed

Cost
8-5

Table 8.1.1 and Table 8.1.2 summarize the input values that DOE used to calculate the 

LCC and PBP for each microwave oven product class. DOE used single-point values to 

characterize all of the inputs to total cost, but characterized several of the inputs to operating cost 

using probability distributions that capture the input’s uncertainty and/or variability. For those 

inputs characterized with probability distributions, the values provided in the tables are average 

or typical values.
8-6

Table 8.1.1 Microwave-Only Ovens and Countertop Convection Microwave Ovens: 

Summary of Inputs to LCC and PBP Input Summary

Input Efficiency 

Level

Average or Typical 

Value Characterization

2016 Baseline 

manufacturer cost 

2011$

Baseline 4 W* standby = $120.35 Single-point value

2016 Standardlevel manufacturer 

cost increase 2011$

TSL 1 2 W standby = $0.26 Single-point value

TSL 2 1.5 W standby = $0.38 Single-point value

TSL 3 1 W standby = $3.28 Single-point value

TSL 4 0.02 W standby = $6.23 Single-point value

Retailer markup All

Baseline = 1.45

Single-point value Incremental = 1.17

Manufacturer 

markup All 1.298 Single-point value

Sales tax All 7.1% Variability based on region

Annual energy use 

kWh

Baseline 34.76 Variability based on usage

TSL 1 17.38 Variability based on usage

TSL 2 13.03 Variability based on usage

TSL 3 8.69 Variability based on usage

TSL 4 0.17 Variability based on usage

2016 average 

energy prices 

2011$

All 12 cents/kWh† Variability based on region

Repair and 

maintenance costs All No cost increase with efficiency

No cost increase with 

efficiency

Installation costs All No cost increase with 

efficiency

No cost increase with 

efficiency

Lifetime All 10.91 years Weibull distribution

Discount rate All 4.9% Custom distribution

Energy price trend All AEO2012 reference case Single-point values

* W = watts.

† kWh = kilowatt-hour.
8-7

Table 8.1.2 Built-In and Over the Range Convection Microwave Ovens: Summary of 

Inputs to LCC and PBP 

Input Efficiency 

Level Average or Typical Value Characterization

2016 Baseline 

manufacturer cost 

2011$

Baseline 4.5 W* standby = $260.41 Single-point value

2016 Standardlevel manufacturer 

cost increase 2011$

TSL 1 3.7 W standby = $0.06 Single-point value

TSL 2 2.7 W standby = $0.08 Single-point value

TSL 3 2.2 W standby = $5.01 Single-point value

TSL 4 0.04 W standby = $5.86 Single-point value

Retailer markup All

Baseline = 1.45

Single-point value Incremental = 1.17

Manufacturer 

markup All 1.298 Single-point value

Sales tax All 7.1% Variability based on region

Annual energy use 

kWh

Baseline 39.10 Variability based on usage

TSL 1 32.15 Variability based on usage

TSL 2 23.46 Variability based on usage

TSL 3 19.12 Variability based on usage

TSL 4 0.35 Variability based on usage

2016 average 

energy prices 

2011$

All 12 cents/kWh† Variability based on region

Repair and 

maintenance costs All No cost increase with efficiency

No cost increase with 

efficiency

Installation costs All No cost increase with 

efficiency

No cost increase with 

efficiency

Lifetime All 10.91 years Weibull distribution

Discount rate All 4.9% Custom distribution

Energy price trend All AEO2012 reference case Single-point values

* W = watts.

† kWh = kilowatt-hour.

8.2 INPUTS TO LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

Because DOE gathered most of its data for the LCC and PBP analysis in 2012, DOE 

expresses dollar values in 2011$.
8-8

8.2.1 Inputs to Total Installed Cost 

DOE uses the following equation to define the total installed cost.

IC = CPC + INST

Where:

CPC = consumer product cost (i.e., consumer cost for the product only), and 

INST = installation cost or the consumer cost to install the product.

The product cost is based on how the consumer purchases the product. As discussed in 

chapter 6, DOE defined markups and sales taxes for converting manufacturing costs into 

consumer product costs. Table 8.2.1 summarizes the inputs for determining total installed cost.

Table 8.2.1 Inputs to Total Installed Cost

Baseline manufacturer cost

Standard-level manufacturer cost

Distribution chain markups

Sales tax (replacement applications)

Installation cost

The baseline manufacturer cost is the cost incurred by the manufacturer to produce 

products meeting existing minimum efficiency standards. Standard-level manufacturer cost

increases are the change in manufacturer cost associated with producing products at a standard 

level. Markups and sales tax convert the manufacturer cost to a consumer product cost. The

installation cost is the cost to the consumer of installing the product and 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.

DOE calculated the total installed cost for baseline products based on the following 

equation.

MFG OVERALL BASE BASE

BASE BASE BASE

COST MU INST

IC CPC INST

= × +

= +

_

Where:

ICBASE = baseline total installed cost,

CPCBASE = consumer product cost for baseline models, 

INSTBASE = baseline installation cost,

COSTMFG = manufacturer cost for baseline models, and
8-9

MUOVERALL_BASE = baseline overall markup (product of manufacturer markup, baseline 

retailer or distributor markup, and sales tax).

DOE calculated the total installed cost for standard-level products based on the following 

equation.

( ) ( )

( ) ( )

( ) 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

= + ∆ × + ∆

= + + ∆ + ∆

= + ∆ + + ∆

= +

_

Where:

ICSTD = standard-level total installed cost,

CPCSTD = consumer product cost for standard-level models, 

INSTSTD = standard-level installation cost,

CPCBASE = consumer product cost for baseline models, 

ΔCPCSTD = change in product cost for standard-level models,

INSTBASE = baseline installation cost,

ΔINSTSTD = change in installation cost for standard-level models,

ICBASE = baseline total installed cost,

ΔCOSTMFG = change in manufacturer cost for standard-level models, 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 that 

DOE used to calculate the total installed cost for microwave ovens.

8.2.1.2 Forecasting Future Product Prices

Historical price data for certain appliances and equipment that have been subject to 

energy conservation standards indicate that the assumption of constant real prices and costs may

often overestimate long-term trends in appliance and equipment prices. Economic literature and 

historical data suggest that the real costs of the products may trend downward over time in 

response to “learning” or “experience” curves. A draft paper, Using the Experience Curve 

Approach for Appliance Price Forecasting, posted on the DOE web site at 

www1.eere.energy.gov/buildings/appliance_standards/pdfs/experience_curve_appliance_price_f

orecasting_3-16-11.pdf, summarizes the currently available data and literature that are relevant 

to price forecasts for selected appliances and equipment.

In light of the data and DOE’s aim to improve the accuracy and robustness of its 

analyses, DOE has decided to project future product prices by incorporating learning over time, 

consistent with the analysis in the available literature. DOE used this approach to derive learning 

rates to forecast future prices of microwave ovens at the considered efficiency levels.
8-10

An extensive literature discusses the learning or experience curve phenomenon, typically 

based on observations in the manufacturing sector. Based on the experience curve approach, the 

real cost of production is related to the cumulative production, or experience, with a product. To 

explain the empirical relationship, the theory of technology learning is used to substantiate a 

decline in the cost of producing a given product as firms accumulate experience with the 

technology. A common functional relationship for modeling the evolution of production costs is:

Y = aX-b

Where:

a = the initial price (or cost), 

b = a positive constant known as the learning rate parameter, 

X = cumulative production, and 

Y = 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), and is given by:

LR = 1 – 2-b

In typical learning curve formulations, the LR parameter is derived using two historical 

data series: product price (or cost), and cumulative production, which is a function of shipments 

throughout a long period.

To derive an LR parameter for microwave ovens, DOE obtained historical Producer Price 

Index (PPI) data for electric cooking products from the Bureau of Labor Statistics. DOE 

understands that electric cooking products encompass much more than microwave ovens; 

however, because no PPI data specific to microwave ovens were available, DOE used PPI data 

for electric cooking products as representative of microwave ovens. Additionally, shipments of 

microwave oven have become a significant part of the overall shipments of electric household 

cooking product since 1975. For electric cooking products, DOE used PPI data spanning 1969–

2011. An inflation-adjusted price index for electric cooking products was calculated by dividing 

the PPI series by the Consumer Price “all items” index for the same years.

DOE assembled a time-series of annual shipments for 1969–2009 for electric household 

cooking products (including microwave ovens, cooktops, and ranges), and for 1972–2009 for 

microwave ovens from data submittals from AHAM, AHAM Fact Books, and Appliance 

Magazine. The shipments data were used to estimate cumulative shipments (production).

To estimate a learning 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

,
8-11

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.

Fitting the model to microwave oven shipments, he parameter values obtained are:

Po = 13.08−1.54

+1.74 (95% confidence), and

b = 0.406±0.024 (95% confidence).

The estimated learning rate (defined as the fractional reduction in price expected from 

each doubling of cumulative production) is 10.1±1.7% (95-percent confidence). For the LCC and 

PBP analysis, DOE used the central value, along with the shipments forecast for 2011–2016, to 

estimate the price of microwave ovens in 2016.

DOE’s derivation of learning rates for microwave ovens is described in further detail in 

appendix 8C.

8.2.1.3 Baseline Manufacturer Cost

DOE developed the baseline manufacturer costs for both product classes of microwave

ovens as described in the engineering analysis (described in chapter 5). Baseline manufacturer 

costs are shown in Table 8.2.2.

Table 8.2.2 Baseline Manufacturer Costs

Product Class

Standby Power

W

Manufacturer 

Cost 2011$

Microwave-only and countertop 

convection microwave ovens 4.00 115.92

Built-in and over-the-range 

convection microwave ovens 2.50 250.83

8.2.1.4 Standard-Level Manufacturer Cost Increases

DOE used a combination of cost data submitted by AHAM and a reverse-engineering 

analysis to develop manufacturer cost increases associated with increases in microwave oven

efficiency. Refer to chapter 5 for details. Table 8.2.3 presents the standard-level manufacturer 

cost increase for each considered efficiency level.
8-12

Table 8.2.3 Standard-Level Manufacturer Cost Increases

Microwave-Only and Countertop 

Convection Microwave Ovens

Built-In and Over-the-Range 

Convection Microwave Ovens

Standby 

Power W

Manufacturer Cost 

Increase 2011$

Standby 

Power W

Manufacturer Cost 

Increase 2011$

2.00 0.25 3.70 0.05

1.50 0.37 2.70 0.07

1.00 3.16 2.20 4.82

0.02 6.00 0.04 5.65

8.2.1.5 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 8.2.4 shows the 

overall baseline and incremental markups for microwave ovens. Refer to chapter 6 for details.

Table 8.2.4 Microwave Ovens: Overall Markups

Markup Baseline Incremental

Manufacturer 1.298

Retailer 1.45 1.17

Sales tax 0.071

Total 2.02 1.63

8.2.1.6 Installation Cost

DOE determined that any installation costs would not be affected by increased efficiency 

levels. A similar finding was made in the previous rulemaking conducted for microwave ovens.

3

8.2.1.7 Total Installed Cost

Total installed cost is the sum of the consumer product cost and installation cost. Table 

8.2.5 presents the total installed costs for the two product classes of microwave ovens. Prices and 

costs are presented at the baseline level and each energy efficiency level.
8-13

Table 8.2.5 Total Installed Costs

Microwave-Only and 

Countertop Convection 

Microwave Ovens

Built-In and Over-the-Range 

Convection Microwave Ovens

Standby

Power W

Total Installed 

Cost 2011$

Standby 

Power W

Total Installed 

Cost 2011$

4.00 232.68 4.50 503.47

2.00 233.09 3.70 503.56

1.50 233.27 2.70 503.59

1.00 237.80 2.20 511.28

0.02 242.40 0.04 512.62

8.2.2 Inputs to Operating Cost

DOE defines the operating cost (OC) by the following equation:

OC = EC + RC + MC

Where:

EC = energy expenditure associated with operating the product, 

RC = repair cost associated with component failure, and 

MC = cost for maintaining product operation.

Table 8.2.6 shows the inputs for determining the annual operating costs and their 

discounted value over the product lifetime.

Table 8.2.6 Inputs to Operating Cost

Annual energy consumption

Energy prices and price trends

Repair and maintenance costs

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 

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 calculated the annual operating cost for baseline products based on the following 

equation.
8-14

OCBASE = AECBASE × PRICEENERGY + RCBASE + MCBASE ( )

Where:

OCBASE = operating cost for baseline product,

AECBASE = annual energy consumption for baseline product, 

PRICEENERGY = energy price,

RCBASE = repair cost associated with component failure for baseline product, and

MCBASE = cost for maintaining operation of baseline product,

DOE calculated the annual operating cost for standard-level products based on the 

following equation.

OCSTD = AECSTD × PRICEENERGY + RCSTD + MCSTD ( )

Where:

OCSTD = operating cost for standard-level product,

AECSTD = annual energy consumption for standard-level product, 

PRICEENERGY = energy price,

RCSTD = repair cost associated with component failure for standard-level product, 

and

MCSTD = cost for maintaining operation of standard-level product.

The rest of this section provides information about each of the above input variables that 

DOE used to calculate the operating costs for both product classes of microwave ovens.

8.2.2.2 Annual Energy Consumption

As described in chapter 7 and in section 8.1.1, from RECS 2009 DOE developed a sample 

of individual households that use microwave ovens. By developing a household sample, 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. The annual 

energy consumption values in Table 8.2.7 are averages. DOE captured the variability in energy 

consumption when it conducted its LCC and PBP analysis. Table 8.2.7 provides the average 

annual energy consumption by efficiency level for each product class.
8-15

Table 8.2.7 Annual Energy Consumption

Standard 

Level

Microwave-Only and Countertop 

Convection Microwave Ovens

Built-In and Over-the-Range 

Convection Microwave Ovens

Standby 

Power W

Average Energy 

Consumption 

kWh/year

Standby 

Power W

Average Energy 

Consumption 

kWh/year

Baseline 4.00 34.76 4.50 39.10 

1 2.00 17.38 3.70 32.15 

2 1.50 13.03 2.70 23.46 

3 1.00 8.69 2.20 19.12 

4 0.02 0.17 0.04 0.35 

8.2.2.3 Residential Electricity Prices

DOE derived electricity prices for each of 27 RECS Reportable Domain categories. 

Using these data, DOE analyzed the variability of electricity prices at the regional level. 

DOE used data from EIA Form 8614 to estimate electricity prices for residential 

consumers in each of the 27 geographic areas. Those data, published annually, include annual 

electricity sales in kilowatt hours (kWh); revenues from electricity sales; and number of 

consumers in the residential, commercial, and industrial sectors for every utility that serves final 

consumers. DOE calculated average residential electricity prices in two steps.

1. For each utility, an average residential price was estimated by dividing residential 

revenues by residential sales.

2. An average regional price was calculated, weighting each utility that has customers in 

a region by the number of residential consumers served in that region.

Table 8.2.8 shows the average residential electricity price calculated for each geographic 

region.
8-16

Table 8.2.8 Average Residential Electricity Prices 

Geographic Area

Average Price 

2011$/kWh

Connecticut, Maine, New 

Hampshire, Rhode Island, Vermont $0.137

Massachusetts $0.138

New York $0.179

New Jersey $0.169

Pennsylvania $0.122

Illinois $0.120

Indiana, Ohio $0.097

Michigan $0.129

Wisconsin $0.131

Iowa, Minnesota, North Dakota, 

South Dakota $0.106

Kansas, Nebraska $0.099

Missouri $0.094

Virginia $0.108

Delaware, DC, Maryland, West 

Virginia $0.128

Georgia $0.104

North Carolina, South Carolina $0.106

Florida $0.118

Alabama, Kentucky, Mississippi $0.101

Tennessee $0.095

Arkansas, Louisiana, Oklahoma $0.093

Texas $0.120

Colorado $0.114

Idaho, Montana, Utah, Wyoming $0.090

Arizona $0.113

Nevada, New Mexico $0.120

California $0.153

Alaska, Hawaii, Oregon, 

Washington $0.109

Source: EIA Form 861 for 2010.
8-17

8.2.2.4 Energy Price Trends

DOE used EIA’s price forecasts to estimate future trends in electricity prices. To arrive at 

prices in future years, it multiplied the average prices listed in Table 8.2.8 by the forecast of 

annual average price changes in EIA’s AEO2012 reference case.

2 To estimate the trend after 

2035, DOE followed past guidelines that EIA provided to the Federal Energy Management 

Program, using the average rate of change during 2020–2035.

DOE calculated LCC and PBP based on three separate projections from the AEO2012:

reference case, low economic growth, and high economic growth. Those three cases reflect the 

uncertainty regarding economic growth during the forecast period. Figure 8.2.1 shows the three 

projected trends in residential electricity prices based on the three AEO2012 cases. 

Figure 8.2.1 Residential Electricity Price Trends

8.2.2.5 Repair and Maintenance Costs

DOE included no changes in repair or maintenance costs for microwave ovens that 

exceed baseline efficiency.

8.2.2.6 Product Lifetime

RECS1 records the presence of various appliances in each household. That data,

combined with data from the U.S. Census’s American Housing Survey,5 which surveys all 

housing including vacant and second homes, enabled DOE to estimate the stock of microwaves 

in all households. By combining the results of both surveys with the known history of appliance 
8-18

shipments (collected from Appliance Magazine or directly from manufacturer trade 

associations), DOE estimated the percentage of appliances of a given age still in operation. 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.a 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 in a 

particular fashion. 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 = appliance age;

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 shows the Weibull retirement 

function for microwave ovens. The results of DOE’s analysis are shown in Table 8.2.9.

 

a For reference on the Weibull distribution, see sections 1.3.6.6.8 and 8.4.1.3 of the NIST/SEMATECH e-Handbook 

of Statistical Methods. <www.itl.nist.gov/div898/handbook/> (Last accessed August 21, 2012.)
8-19

Figure 8.2.2 Weibull Function for MWO Lifetime

Table 8.2.9 Lifetime Parameters

Product

Weibull Parameters

Average

years

Alpha (scale) Beta (shape)

Microwave oven 10.91 4.75 10.28

8.2.3 Discount Rates

The discount rate is the rate at which DOE discounts future expenditures to establish their 

present value. DOE derived discount rates for the LCC and PBP analyses from estimates of the 

cost to finance the purchase of the considered products. Following financial theory, the financial 

cost of raising funds to purchase products can be interpreted as: (1) the financial cost of any debt 

incurred to purchase a product, or (2) the opportunity cost of any equity used to purchase a 

product. 

Discount Rates for Residential Consumers

The purchase of products installed in new homes entails different finance costs for 

consumers than does the purchase of appliances bought directly from retailers. For microwave 

and convection ovens, however, DOE believes that few products are installed by builders in new 

homes. Households use various methods to finance the purchase of major appliances. In 

principle, one could estimate the interest rates on the actual financing methods used to purchase 

appliances. However, the shares of different financing methods in total appliance purchases are 

unknown.
8-20

DOE’s approach involved identifying all possible debt or asset classes that might be used 

to purchase/lease the considered products, including household assets that might be affected 

indirectly.b

 DOE excluded debt from primary mortgages and the equity of assets considered 

non-liquid (such as retirement accounts), because those are unlikely to be used to purchase 

products. DOE estimated the average percentage shares of the various types of debt and equity in 

the average U.S. household using data from the Federal Reserve Board’s Survey of Consumer 

Finances (SCF) for 1989, 1992, 1995, 1998, 2001, 2004, and 2007.6 Table 8.2.10 shows the 

average percentages of each considered type of debt or equity. DOE derived the mean 

percentages of each source of financing throughout the 7 years surveyed.

Table 8.2.10 Types of Household Debt and Equity by Percentage Shares

Type of Debt or Equity 1989 % 1992 % 1995 % 1998 % 2001 % 2004 % 2007 % Mean %

Home equity loan 4.3 4.5 2.7 2.8 2.8 4.4 4.6 3.7

Credit card 1.6 2.1 2.6 2.2 1.7 2.0 2.4 2.1

Other installment loan 2.8 1.7 1.4 1.7 1.1 1.3 1.1 1.6

Other residential loan 4.4 6.9 5.2 4.3 3.1 5.8 7.1 5.3

Other line of credit 1.1 0.6 0.4 0.2 0.3 0.5 0.3 0.5

Checking account 5.8 4.7 4.9 3.9 3.6 4.2 3.4 4.4

Savings or money market 

account 19.2 18.8 14.0 12.8 14.2 15.1 13.0 15.3

Certificate of deposit 14.5 11.7 9.4 7.0 5.4 5.9 6.5 8.6

Savings bond 2.2 1.7 2.2 1.1 1.2 0.9 0.7 1.4

Bonds 13.8 12.3 10.5 7.0 7.9 8.4 6.7 9.5

Stocks 22.4 24.0 25.9 36.9 37.5 28.0 28.6 29.0

Mutual funds 8.0 11.1 20.9 20.1 21.3 23.4 25.5 18.6

Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Sources: Federal Reserve Board. Survey of Consumer Finances (SCF) for 1989, 1992, 1995, 1998, 2001, 2004, and 

2007.

DOE estimated interest or return rates associated with each type of equity and debt. The 

source for interest rates for loans, credit cards, and lines of credit was the Federal Reserve 

Board’s SCF for 1989, 1992, 1995, 1998, 2001, 2004, and 2007. Table 8.2.11 shows the average 

nominal rates in each year and the inflation factors used to calculate real rates. DOE calculated 

effective interest rates for home equity loans in a similar manner as for mortgage rates, because 

interest on both such loans is tax deductible. Table 8.2.12 shows the average effective real rates 

in each year and the mean rate across years. Because the interest rates for each type of household 

debt reflect economic conditions throughout numerous years, they are expected to be 

representative of rates that may be in effect in 2018. 

 b An indirect effect would arise if a household sold assets in order to pay off a loan or credit card debt that might 

have been used to finance the appliance purchase.
8-21

Table 8.2.11 Average Nominal Interest Rates for Household Debt 

Type of Debt 1989 % 1992 % 1995 % 1998 % 2001 % 2004 % 2007 % Mean %

Home equity loan 11.5 9.6 9.6 9.8 8.7 5.7 7.9 9.0

Credit card* - - 14.2 14.5 14.2 11.7 12.6 3.4

Other installment loan 9.0 7.8 9.3 7.8 8.7 7.4 10.4 8.6

Other residential loan 8.8 7.6 7.7 7.7 7.5 6.0 6.3 7.4

Other line of credit 14.8 12.7 12.4 11.9 14.7 8.8 12.7 12.6

Inflation rate 4.82 3.01 2.83 1.56 2.85 2.66 2.85

Sources: Federal Reserve Board. Survey of Consumer Finances (SCF) for 1989, 1992, 1995, 1998, 2001, 2004, and 

2007.

* No data on interest rates available for credit cards in 1989 or 1992.

Table 8.2.12 Average Real Effective Interest Rates for Household Debt

Type of Debt 1989 % 1992 % 1995 % 1998 % 2001 % 2004 % 2007 % Mean %

Home equity loan 3.8 4.3 4.4 5.8 3.8 1.9 2.1 3.0

Credit card* - - 11.0 12.7 11.1 9.1 3.3 3.9

Other installment loan 4.9 5.8 7.0 6.6 6.1 5.4 9.7 10.7

Other residential loan 4.0 4.7 4.8 6.0 4.6 3.3 3.4 4.4

Other line of credit 9.6 9.4 9.3 10.2 11.7 6.0 9.7 9.4

Sources: Federal Reserve Board. Survey of Consumer Finances (SCF) for 1989, 1992, 1995, 1998, 2001, 

2004, and 2007.

* No data on interest rates available for credit cards in 1989 or 1992.

No similar rate data are available from the SCF for classes of assets, so the Department 

derived that information from national historical data. The interest rates associated with 

certificates of deposit,7 savings bonds,8 and bonds (AAA corporate bonds)9 were collected from 

Federal Reserve Board time-series data for 1977–2011. DOE assumed rates on checking 

accounts to be zero. Rates on savings and money market accounts came from Cost of Savings 

Index data covering 1984–2009.

10 The rates for stocks are the annual returns on the Standard and 

Poor’s 500 for 1977–2009.

11

Table 8.2.13

Rates for mutual funds are a weighted average of the stock rates 

(two-thirds weight) and the bond rates (one-third weight) in each year for 1977–2011. 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 . 

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 2018.
8-22

Table 8.2.13 Average Nominal and Real Interest Rates for Household Equity 

Type of Equity Average 

Nominal Rate 

%

Average Real 

Rate 

%

Checking account - 0.0

Savings and money market accounts 5.1 2.1

Certificate of deposit 6.1 2.0

Savings bond 7.4 3.3 

Bonds 8.2 4.0

Stocks 11.8 7.6

Mutual funds 10.4 6.1

Table 8.2.14 summarizes the mean real effective rates of each type of equity or debt. 

DOE determined the average percentage of each type of debt and asset using SCF data for 1989, 

1992, 1995, 1998, 2001, 2004, and 2007. Each year of SCF data provides the percentages of 

debts and assets for U.S. households. DOE averaged those percentages for the 7 years of survey 

data to arrive at the percentages shown in Table 8.2.14. The average rate across all types of 

household debt and equity, weighted by the percentages of each type, is 5.0 percent.

Table 8.2.14 Average Interest on Household Debt and Equity 

Type of Debt or Equity Average Percentage of 

Household Debt plus Equity

%*

Mean Effective Real Rate 

%**

Home equity loan 3.7 3.9 

Credit card 2.1 10.7 

Other installment loan 1.6 6.0 

Other residential loan 5.3 4.4 

Other line of credit 0.5 8.8 

Checking account 4.4 0.0 

Savings and money market account 15.3 2.1

Certificate of deposit 8.6 2.0

Savings bond 1.4 3.3 

Bonds 9.5 4.0

Stocks 29.0 7.6

Mutual funds 18.6 6.1

Total/weighted-average discount rate 100.0 5.0

* Not including primary mortgage or retirement accounts.

** Adjusted for inflation and, for home equity loans, tax deduction of interest.

DOE developed a normal probability distribution of interest rates for each asset type by 

using the mean value and standard deviation from the distribution. To account for variation 
8-23

among households, DOE sampled a rate for each household from the distributions for the 

appropriate asset class.

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 new energy efficiency standards for microwave 

ovens manufactured in, or imported into, the United States is April 1, 2016. DOE calculated the 

LCC for all consumers as if each would purchase a new product in 2016.

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 potential efficiency levels, in its LCC analysis DOE considered the projected distribution of 

efficiencies for products that consumers purchase under the base case (the case without new or 

amended energy efficiency standards). DOE refers to this distribution of product energy 

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 sample 

household. The energy efficiency distributions that DOE used in the LCC analysis are described 

below. For this final rule, DOE used the efficiency distributions calculated in the previous 

microwave oven rulemaking.12

Table 8.2.15 and Table 8.2.16 present recent market shares of the efficiency levels for 

microwave-only and convection microwave ovens. Because there is no evidence of a change in 

average efficiency in recent years, DOE assumed that the shares in 2014 are the same. 

Table 8.2.15 Microwave-Only and Countertop Convection Microwave Ovens: Base-Case 

Market Shares

Standard 

Level

Standby 

Power W

Market 

Share %

Baseline 4.00 46.2

1 2.00 34.6

2 1.50 19.2

3 1.00 0

4 0.02 0
8-24

Table 8.2.16 Built-In and Over-the-Range Convection Microwave Ovens: Base-Case 

Market Shares 

Standard 

Level

Standby 

Power W

Market 

Share %

Baseline 4.50 100

1 3.70 0

2 2.70 0

3 2.20 0

4 0.04 0

8.3 RESULTS OF LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

This section presents the results of the LCC and PBP analysis for microwave-only and 

convection microwave ovens. As discussed in section 8.1.1, DOE’s approach to the LCC 

analysis relied on developing samples of consumers who 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 Monte Carlo simulation to perform the LCC calculations for the consumers

in the sample. For each set of sample consumers using the product in each product class, DOE 

calculated the average LCC and LCC savings and the median and average PBP for each trial

standard level (TSL).

DOE calculated LCC savings and PBPs relative to the base-case products that it assigned 

to the sample 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 standard level and the LCC of the baseline product

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 

consumer selected from the sample. A consumer was selected based on its weight (i.e., how 

representative that particular consumer was of other consumers in the distribution). Each LCC 

and PBP calculation also sampled from the probability distributions that DOE developed to 

characterize many of the inputs to the analysis.

Based on the Monte Carlo simulations, for each TSL DOE calculated the share of 

consumers experiencing a net LCC benefit, a net LCC cost, and no effect. DOE considered a 

consumer to receive no effect at a given standard level if DOE assigned it a base-case 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 and Table 8.3.2 show the LCC and PBP results by TSL for each microwave

oven product class. The TSLs correspond to the energy efficiency levels included in the 
8-25

engineering analysis. With regard to the PBPs, DOE determined the median values by excluding 

those users not affected by the standard.

Table 8.3.1 Microwave-Only and Countertop Convection Microwave Ovens: LCC and 

PBP Results

TSL

Standby

Power

W

Life-Cycle Cost 2011$ Life-Cycle Cost Savings

Median 

Payback 

Period 

years

Average 

Installed 

Cost

Average

Lifetime 

Operating 

Cost*

Average 

LCC

Average 

Savings 

2011$

% Users Having

Net

Cost

No 

Impact

Net 

Benefit

Baseline 4.00 $ 234 $ 35 $ 269 NA 0 100% 0% NA

1 2.00 $ 234 $ 18 $ 252 $ 8 0 54% 46% 0.2

2 1.50 $ 234 $ 13 $ 247 $ 11 0 19% 81% 0.3

3 1.00 $ 239 $ 9 $ 248 $ 11 12% 0% 88% 3.5

4 0.02 $ 243 $ 0 $ 244 $ 15 5% 0% 95% 3.5

* Discounted

Table 8.3.2 Built-In and Over-the-Range Convection Microwave Ovens: LCC and PBP 

Results

TSL

Standby 

Power

W

Life-Cycle Cost 2011$ Life-Cycle Cost Savings

Median 

Payback 

Period 

years

Average 

Installed 

Cost

Average

Lifetime 

Operating 

Cost*

Average 

LCC

Average 

Savings 

2011$

% Users Having

Net

Cost

No 

Impact

Net 

Benefit

Baseline 4.50 $ 506 $ 40 $ 545 NA 0% 100% 0% NA

1 3.70 $ 506 $ 33 $ 538 $ 7 0% 0% 100% 0.1

2 2.70 $ 506 $ 24 $ 529 $ 16 0% 0% 100% 0.1

3 2.20 $ 513 $ 19 $ 533 $ 12 0% 0% 100% 3.3

4 0.04 $ 515 $ 0 $ 515 $ 30 0% 0% 100% 2.0

* Discounted

8.3.1.2 Distributions of Impacts 

The figures in this section show the distribution of LCCs in the base case for each 

product class. Also presented for one standard level are figures showing the distribution of LCC 

impacts and the distribution of PBPs. The figures are presented as frequency charts that show the 

distribution of LCCs, LCC impacts, and PBPs 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 and Figure 8.3.2 show the base-case LCC

distributions for microwave-only and convection microwave ovens.
8-26

Figure 8.3.1 Microwave-Only and Countertop Convection Microwave 

Ovens: Base-Case LCC Distribution

Figure 8.3.2 Built-In and Over-the-Range Convection Microwave Ovens: 

Base-Case LCC Distribution

Standard-Level Distribution of Impacts. Figure 8.3.3 and Figure 8.3.4 are examples of

frequency charts showing the distribution of LCC savings for microwave-only and convection

microwave ovens at TSL 3. DOE can generate frequency charts similar to those shown for every 

TSL.
8-27

Figure 8.3.3 Microwave-Only and Countertop Convection Microwave Ovens: LCC 

Savings Distribution for TSL 3 

Figure 8.3.4 Built-In and Over-the-Range Convection Microwave Ovens: LCC 

Savings Distribution for TSL 3

Figure 8.3.5 and Figure 8.3.6 are examples of frequency charts showing the distribution 

of PBPs for microwave-only and convection microwave ovens at TSL 3. DOE can generate 

frequency charts similar to those shown for every standard level.
8-28

Figure 8.3.5 Microwave-Only and Countertop Convection Microwave

Ovens: PBP Distribution for TSL 3 

Figure 8.3.6 Built-In and Over-the-Range Convection Microwave Ovens: 

PBP Distribution for TSL 3 

8.3.1.3 Range of Impacts

Figure 8.3.7 and Figure 8.3.8 show the range of LCC savings for the TSLs considered for 

microwave-only and convection microwave ovens. For each standard level, the top and the 
8-29

bottom of the box indicate the 75th and 25th percentiles, respectively. The bar at the middle of the 

box indicates the median; 50 percent of the households have lifecycle cost savings above this 

value. The “whiskers” at the bottom and the top of the box indicate the 5th and 95th percentiles. 

The small box shows the average LCC savings for each standard level.

Figure 8.3.7 Microwave-Only and Countertop Convection

Microwave Ovens: Range of Average LCC Savings
8-30

Figure 8.3.8 Built-In and Over the Range Convection Microwave 

Ovens: Range of Average LCC Savings

Figure 8.3.9 and Figure 8.3.10 show the range of PBPs for all TSLs considered for 

microwave-only and convection microwave ovens. For each efficiency level, the top and the 

bottom of the box in the figure indicate the 75th and 25th percentiles, respectively. The bar at the 

middle of the box indicates the median: 50 percent of the households have a payback period

above this value. The horizontal lines above and below each box indicate the 95th and 5th

percentiles, respectively. The small box indicates the average PBP for each TSL.
8-31

Figure 8.3.9 Microwave-Only and Countertop Convection

Microwave Ovens: Range of Average Payback 

Period

Figure 8.3.10 Built-In and Over the Range Convection Microwave 

Ovens: Range of Average Payback Period
8-32

8.4 REBUTTABLE PAYBACK PERIOD

DOE presents rebuttable PBPs to provide the legally established rebuttable presumption 

that an energy efficiency standard is economically justified if the additional product costs 

attributed to the standard are less than three times the value of the first-year energy cost savings.

(42 U.S.C. §6295 (o)(2)(B)(iii))

The basic equation for rebuttable PBP is the same as that shown for the PBP in section 

8.1.1. Unlike the analyses described in section 8.2, however, the rebuttable PBP is not based on 

the use of household samples and probability distributions. The rebuttable PBP is based not on

distributions but 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 from the probability distribution to determine the rebuttable PBP.

Other than the use of single-point values, the most notable difference between the 

distribution PBP and the rebuttable PBP is the latter’s reliance on the DOE test procedure to 

determine a product’s annual energy consumption. The following sections identify the 

differences, if any, between the annual energy consumptions determined for the distribution PBP 

and the rebuttable PBP.

DOE based the annual energy consumption values that it used to determine the rebuttable 

PBP for microwave ovens on the number of operating hours per year specified in previous 

microwave rulemaking.

8.4.1 Inputs

Inputs for the rebuttable PBP differ from the distribution PBP in that the calculation uses 

discrete values, rather than distributions. Note that for the calculation of distribution PBP, 

because inputs for the determination of total installed cost were based on single-point values, 

only the variability and/or uncertainty in the inputs for determining operating cost contributed to 

variability in the distribution PBPs. The following summarizes the single-point values that DOE 

used in determining the rebuttable PBP:

• Manufacturing costs, markups, sales taxes, and installation costs were all based on the 

single-point values used in the distributional LCC and PBP analysis.

• Energy prices were based on national average values for the year that new standards 

will take effect.

• An average discount rate or lifetime is not required in the rebuttable PBP calculation.

• The effective date of the standard is assumed to be 2014. 

8.4.2 Results

DOE calculated rebuttable PBPs for each standard level relative to the distribution of 

product energy efficiencies estimated for the base case. In other words, DOE did not determine 

the rebuttable PBP relative to the baseline energy efficiency level, but relative to the distribution 

of product energy efficiencies DOE determined for the base case (i.e., the case without new 
8-33

energy conservation standards). Table 8.4.1 presents the rebuttable PBPs for microwave-only

and convection microwave ovens.

Table 8.4.1 Rebuttable Payback Periods

Microwave-Only and 

Countertop 

Convection

Microwave Ovens

Built-In and Overthe-Range 

Convection

Microwave Ovens

Standby 

Power W

PBP

years

Standby 

Power W

PBP

years

2.00 0.2 3.70 0.1

1.50 0.3 2.70 1.1

1.00 3.5 2.20 3.3

0.02 3.5 0.04 2.0
8-34

REFERENCES

 

1 U.S. Department of Energy (DOE)–Energy Information Administration (EIA). 

Residential Energy Consumption Survey: 2009 RECS Survey Data—Public Use 

Microdata File. October 2011. EIA: Washington, D.C. 

www.eia.gov/consumption/residential/data/2009/#microdata. (Accessed May 2013.)

2 DOE–EIA. Annual Energy Outlook 2012 with Projections to 2035. June 25, 2012. 

Washington, D.C. www.eia.gov/forecasts/aeo/. (Accessed May 2013.)

3 DOE–Energy Efficiency and Renewable Energy. Energy Conservation Program for 

Consumer Products: Technical Support Document: Energy Efficiency Program for 

Consumer Products and Commercial and Industrial Equipment, Residential Dishwashers, 

Dehumidifiers, and Cooking Products, and Commercial Clothes Washers. 2009. 

Washington, D.C. www.regulations.gov/#!documentDetail;D=EERE-2006-STD-0127-

0004. (Accessed May 2013.)

4 DOE–EIA. EIA Form 861: Customer Count Data by Utility. 2010. 

www.eia.doe.gov/cneaf/electricity/page/eia861.html. (Accessed May 2013.)

5 U.S. Census Bureau–Housing and Household Economic Statistics Division. American 

Housing Survey National Data. 2009. HUD. 

www.huduser.org/portal/datasets/ahs/ahsdata09.html. (Accessed May 2013.)

6 The Federal Reserve Board. Survey of Consumer Finances. 1989, 1992, 1995, 1998, 

2001, 2004, 2007. www.federalreserve.gov/pubs/oss/oss2/scfindex.html. (Accessed May 

2013.)

7 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, 2011. Washington, D.C. 

www.federalreserve.gov/releases/H15/data.htm. (Accessed May 2013.)

8 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, 2011. Washington, 

D.C. www.federalreserve.gov/releases/H15/data.htm. (Accessed May 2013.)

9 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, 

2011. Washington, D.C. www.federalreserve.gov/releases/H15/data.htm. (Accessed May 

2013.)
8-35

 

10 Mortgage-X - Mortgage Information Service, Cost of Savings Index (COSI) Index 

History, 2011. mortgage-x.com/general/indexes/default.asp. (Accessed May 2013.)

11 Damodaran Online Data Page, Historical Returns on Stocks, Bonds and Bills-United 

States, 2011. Damodaran. pages.stern.nyu.edu/~adamodar/ . (Accessed May 2013.)

12 DOE–Energy Efficiency and Renewable Energy. Energy Conservation Program for 

Consumer Products: Technical Support Document: Energy Efficiency Program for 

Consumer Products and Commercial and Industrial Equipment, Residential Dishwashers, 

Dehumidifiers, and Cooking Products, and Commercial Clothes Washers. Chapter 8 LifeCycle Cost and Payback Period Analysis. 2009. Washington, D.C. 

www1.eere.energy.gov/buildings/appliance_standards/residential/pdfs/home_appliances

_tsd/chapter_8.pdf. (Accessed May 2013.)
9-i 

CHAPTER 9. SHIPMENTS ANALYSIS 

TABLE OF CONTENTS 

9.1 INTRODUCTION ........................................................................................................... 9-1

9.2 METHODOLOGY BEHIND SHIPMENTS MODEL METHODOLOGY .................... 9-1

9.3 DATA INPUTS AND MODEL CALIBRATION .......................................................... 9-3

9.3.1 Historical Shipments ........................................................................................................ 9-3

9.3.2 Markets and Model Calibration ....................................................................................... 9-4

9.3.2.1 New Construction .......................................................................................... 9-5

9.3.2.2 Replacements ................................................................................................. 9-6

9.3.3 Base-Case Shipments ....................................................................................................... 9-7

9.4 EFFECT OF INCREASED PURCHASE PRICE ON SHIPMENTS ............................. 9-8

9.5 AFFECTED STOCK ..................................................................................................... 9-11

9.6 EFFECT OF TRIAL STANDARD LEVEL ON SHIPMENTS FORECAST .............. 9-11

LIST OF TABLES 

Table 9.3.1 Microwave Ovens: Historical Shipments, Domestic plus Imports ...................... 9-4

Table 9.3.2 Microwave Ovens: Historical Market Saturations ............................................... 9-6

Table 9.4.1 Change in Relative Price Elasticity After a Purchase Price Change ................. 9-10

LIST OF FIGURES 

Figure 9.3.1 Microwave Ovens: Survival and Retirement Functions ...................................... 9-7

Figure 9.3.2 Microwave Ovens: Historical and Base-Case Shipments Forecast ..................... 9-7

Figure 9.3.3 Microwave Ovens: Disaggregated Shipments Forecast for Base Case ............... 9-8

Figure 9.6.1 Shipments Forecasts for Base Case and TSL 4 ................................................. 9-12
9-1

CHAPTER 9. SHIPMENTS ANALYSIS 

9.1 INTRODUCTION 

 Estimates of future product shipments are a necessary input to calculations of the 

national energy savings (NES) and net present value (NPV), as well as to the manufacturer 

impact analysis. This chapter describes the data and methods the U.S. Department of Energy 

(DOE) used to forecast annual product shipments and presents results for microwave ovens

being considered for this standards rulemaking. Because DOE did not have shipment data for 

both product classes, total shipments of microwaves were estimated and then separated by 

market share.

 To estimate shipments for microwaves, DOE used a shipments model that is calibrated 

with historical shipments data. The shipments model estimates shipments to specific market 

segments, the results for which are then aggregated to estimate total product shipments. To 

estimate the impacts of potential standard levels on product shipments, the shipments model 

accounts for the combined effects of changes in purchase price, annual operating cost, and 

household income on the consumer purchase decision. 

 The shipments model was developed as a part of the national impacts analysis (NIA) 

spreadsheet. Appendix 10A discusses how to access the NIA spreadsheet and provides basic 

instructions for its use. 

 The rest of this chapter explains the shipments models in more detail. Section 9.2

presents the methodology behind the shipments model; section 9.3 describes the data inputs and 

model calibration; section 9.4 discusses impacts on shipments from changes in product purchase 

price; section 9.5 discusses the affected stock; and section 9.6 presents the shipments forecast for 

the maximum trial standard level (TSL) that DOE considered. 

9.2 METHODOLOGY BEHIND SHIPMENTS MODEL METHODOLOGY 

 DOE developed a national stock model for estimating annual shipments 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 for 

microwave ovens are installations in newly constructed homes and replacements. 

jRpljShip NI j)()()(

p p p

 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.
9-2

 DOE’s shipments model takes 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 microwave ovens. Estimated sales attributable to replacements in a given 

year therefore are equal to the total stock of the appliance minus the sum of the appliances sold 

in previous years that remain in the stock. As described in chapter 8, section 8.2.2.6, DOE 

determined the useful service life of microwave ovens. DOE then estimated how long the 

appliance is likely to remain in stock. The following equation represents how DOE estimated 

replacement shipments. 

)1()( ∑ ∑ )(

0

1

__

_

jStockjRpl Ship Rtr ageprob

ageMax

age

j

Nj

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). 

 Stock accounting takes product shipments, a retirement function, and initial in-service 

product stock as inputs to estimate 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 standards case 

scenario that produces increasing efficiency over time, whereby older, less efficient units may 

have higher operating costs, while younger, more-efficient units will have lower operating costs. 

In the case of an early replacement scenario, retirements due to early replacement will depend on 

the age of the units that are subject to early replacement. 

DOE estimated replacements using product retirement functions that it developed based 

on product lifetimes. DOE based the retirement function on a Weibull distribution for the 

product lifetime. The shipments model assumes 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 a Weibull 

equation: 

e

x

xP )(

 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 = appliance age; 

α = scale parameter, which would be the decay length in an exponential 

distribution; 
9-3

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. 

 The retirement probability is the difference in the survival function from one year to 

another. 

 DOE calculated total in-service stock of a product by integrating historical shipments 

data starting from a specific year. The start year depends 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= )j(Ship)age,j(Stock

_

 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 following equation describes the 

accounting of the existing in-service stock of units: 

)(1),()1,1(

_

agejStock agejStock ageprobRtr

 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, that 

fraction being determined by a retirement probability function, probRtr(age), which is described 

in section 9.3. Most replacements are made when a product wears out and fails. Over time, some 

of the units will be retired and removed from the stock, triggering the shipment of a new unit. 

Because the products considered in this rulemaking are common appliances, replacements 

constitute the majority of shipments. 

9.3 DATA INPUTS AND MODEL CALIBRATION 

 The sections below describe the data inputs and market segments considered for 

microwaves. 

9.3.1 Historical Shipments 

 DOE used data on historical shipments (domestic shipments plus imports) to calibrate its 

shipments model for microwave ovens. DOE’s sources for historical shipments data were (1) 

AHAM Fact Books for 2000,12003,

2

2005,

3

and 20094covering 1989−2009, and (2) Appliance 
9-4

Magazine5for 1981−1988.aTable 9.3.1 summarizes the historical data on microwave oven 

shipments. 

Table 9.3.1 Microwave Ovens: Historical Shipments, Domestic plus Imports 

Year Shipments 

millions

Year Shipments 

millions

1980 3.41 1995 8.16

1981 4.18 1996 8.77

1982 3.85 1997 9.14

1983 5.60 1998 10.37

1984 8.63 1999 11.42

1985 10.28 2000 12.64

1986 11.76 2001 13.45

1987 11.91 2002 13.31

1988 10.38 2003 14.27

1989 9.78 2004 15.53

1990 7.69 2005 13.86

1991 6.62 2006 13.69

1992 7.19 2007 11.85

1993 7.24 2008 11.34

1994 8.57 2009 9.63

Source for 1981–1988: Statistical Review. Appliance Magazine. 2009.

Source for 1989–2009: AHAM Fact Books 2000, 2003, 2005, and 2009.

9.3.2 Markets and Model Calibration 

 The market for microwave ovens is comprised primarily of replacement units for 

products that have been retired from service. Total microwave oven shipments are represented by 

the following equation:

jRpljShip NI j)()()( MWO MWO MWO

 Where: 

ShipMWO (j) = total shipments of microwave ovens in year j, 

RplMWO (j) = replacement shipments in year j, 

NIMWO (j) = shipments to new households in year j, and 

 The sections below discuss these markets in further detail. 

 

a

 Shipments estimates from Appliance Magazine included exports. Therefore, DOE reduced total shipments by 12.3 

percent (the average percentage of exports for 1989–1993 based on the 5-year average difference between AHAM 

Fact Book 2000 and Appliance Magazine) to estimate total domestic shipments plus imports. 
9-5

9.3.2.1 New Construction 

 DOE estimated shipments to new construction using two inputs: new housing forecasts 

and market saturation data. New housing includes newly constructed single- and multi-family 

units, referred to as new housing completions, and mobile home placements. For new housing 

completions and mobile home placements, DOE used actual data through 2009, and adopted the 

projections from the DOE Energy Information Administration’s (EIA’s) Annual Energy Outlook 

2012 (AEO2012) for 2009–2035.6

Table 9.3.2 presents historical market saturations of microwave ovens based on various 

sources, including AHAM’s 2009 Fact Book,4 Appliance Magazine,5 NFO WorldGroup,7and 

EIA’s RECS for 1993,8

1997,92001,10 2005,

11 and 2009.12 The table presents market saturations 

for the overall housing stock, new households only, and households that have two or more 

microwave ovens. 

 Because the forecast of shipments for the new housing market depends on the saturation 

of microwave ovens in new housing, DOE focused its attention on the market saturations for new 

housing as well as the saturations of housing having multiple microwave ovens. According to 

RECS, microwave oven saturation in new housing for 1993, 1997, 2001, 2005, and 2009 was 92 

percent, 87.5 percent, 93.3 percent, 90.3 percent, and 100 percent, respectively. According to 

NFO, households having more than one microwave oven represented 3.7 percent, 4.9 percent, 

and 4.1 percent of the housing market for 1990, 1996, respectively. DOE incremented the 

saturation levels for new housing estimated by RECS by the amounts provided by NFO to 

estimate new housing saturations. As a result, DOE determined the effective saturation of 

microwave ovens in new housing to be 95.7 percent, 92.4 percent, 97.4 percent, and 94.6 percent 

for 1993, 1997, 2001, and 2005 respectively. Because the 2009 RECS survey did not include a 

large enough sample of new homes with microwaves, DOE decided to retain the previous 

saturation of 94.6 percent to estimate shipments to new construction for future years. 
9-6

Table 9.3.2 Microwave Ovens: Historical Market Saturations 

Year

Overall Household %

New 

Households %

Households with 

≥ 2 Ovens %

AHAM* Appl† NFO‡ RECS

§ RECS§ NFO‡

1978 7.2

1982 25.6

1983 33.3

1987 65.9

1990 82.7% 81.1 3.9

1991 85.2

1992 84.5

1993 85.5 84.1 92.0

1994 88.8

1995 88.9

1996 90.6 88.7 4.9

1997 91.0 83.0 87.5

1998 91.3

1999 93.1

2000 95.6

2001 90.7 95.7 91.8 86.1 93.3 4.1

2002 95.0

2003 95.0

2004 95.5

2005 89.3 87.9 90.3

2006

2007

2008

2009 95.9 100

Sources:

* AHAM 2005 Fact Book.

† The Saturation Picture. Appliance Magazine. September 2009.

‡ NFO WorldGroup, 2001.

§ DOE-EIA. RECS, 1993, 1997, 2001, 2005, and 2009.

9.3.2.2 Replacements 

 DOE used an accounting method that tracks the total stock of units by vintage to 

determine shipments to the replacement market. DOE estimated a stock of microwave ovens by 

vintage by integrating historical shipments starting from 1980. Over time, some units are retired 

and removed from the stock, triggering the shipment of a replacement unit. Depending on the 

vintage, a certain percentage of each type of unit will fail and need to be replaced. To determine 

when a microwave oven fails, DOE used a product survival function based on a lifetime 

distribution having an average value of 10.91 years. For a more complete discussion of 

microwave lifetimes, refer to section 8.2.2.5 of chapter 8. Figure 9.3.1 shows the survival and 

retirement function that DOE used to estimate replacement shipments. 
9-7

Figure 9.3.1 Microwave Ovens: Survival and Retirement Functions 

9.3.3 Base-Case Shipments 

Figure 9.3.2 shows the forecasted shipments in the base case (i.e., the case without new 

energy efficiency standards) and the historical shipments DOE used to calibrate the forecast. 

Figure 9.3.2 Microwave Ovens: Historical and Base-Case Shipments 

Forecast 

0

2

4

6

8

1 0

1 2

1 4

1 6

1 8

2 0

1972 1982 1992 2002 2012 2022 2032 2042

Shipments (million)

Year

Historical Shipments

Modeled Shipments
9-8

Figure 9.3.3 presents forecasted shipments of microwave ovens in the base case; 

shipments are disaggregated into those due to retirements and those going to new housing. Note 

that replacement shipments comprise 85 percent to 92 percent of total microwave oven 

shipments during 2006–2035. The percentage of total shipments represented by replacements is 

high because the microwave oven market is fairly mature. 

Figure 9.3.3 Microwave Ovens: Disaggregated Shipments Forecast for Base 

Case 

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 conducted 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 considered the decreases in operating costs from higher energy 

efficiency and changes over time in household income. Appendix 9A explains the method DOE 

used to quantify the effects of the above variables. 

In the literature, DOE found only a few studies of appliance markets that are relevant to 

this rulemaking analysis and identified no studies that use time-series data of product prices and 

shipments after 1980. The information that can be summarized from the literature suggests that 

the demand for appliances is price-inelastic. Other information in the literature suggests that 

appliances are a normal good, such that rising incomes increase the demand for appliances. 
9-9

Finally, the literature suggests that consumers use relatively high implicit discount ratesb when 

comparing appliance prices and operating costs. 

 DOE found insufficient data on product purchase price and operating cost to perform a 

thorough analysis of dynamic changes in the appliance market. Rather, it used purchase price and 

efficiency data specific to residential refrigerators, clothes washers, and room air conditioners 

during 1980–2002 to evaluate broad market trends and conduct simple regression analyses. The

data indicate that there has been an increase in appliance shipments and a decrease in appliance 

purchase price and operating costs during the period. Household income also increased during 

this time. To simplify the analysis, DOE combined the available economic information into one 

variable, termed the relative price, and used this variable in an analysis of market trends, as well 

as to conduct a regression analysis. The relative price is defined using the following expression. 

Income

PP PVOC

Income

TP

RP

+

= =

Where: 

RP = relative price, 

TP = total price, 

Income = household income, 

PP = appliance purchase price, and 

PVOC = present value of operating cost. 

In the above equation, DOE used an implicit discount rate of 37 percent to determine the 

present value of operating costs. 

 DOE’s analysis of market trendssuggests that the relative price elasticity of demand for 

the three appliances is relatively inelastic (that is, less than 1.0). DOE’s regression analysis 

suggests that the relative price elasticity of demand, averaged for the three appliances, is -0.34. 

Thus a relative price increase of 10 percent results in a shipments decrease of 3.4 percent. Note 

that, because the relative price elasticity incorporates the effects of three factors (purchase price, 

operating cost, and household income), the effect of any single factor is mitigated by changes to

the other two. 

 The relative price elasticity of -0.34 is consistent with estimates in the literature. 

Nevertheless, DOE stresses that the measure is based on a small data set subject to simple 

statistical analysis. More importantly, the measure is based on an assumption that economic 

variables, including purchase price, operating costs, and household income, explain most of the 

trend in appliances per household in the United States since 1980. Changes in appliance quality 

and consumer preferences may have occurred during that period, but DOE did not account for 

them in this analysis. Despite these uncertainties, DOE believes that its estimate of the relative 

 

b

 A high implicit discount rate with regard to operating costs means that consumers do not put much economic value 

on the operating cost savings realized from more efficient appliances. Consumers are much more concerned with 

the higher purchase prices. 
9-10

price elasticity of demand provides a reasonable assessment of the impact that purchase price, 

operating cost, and household income have on product shipments. 

 Because DOE’s forecasts of shipments and national impacts due to standards consider 

30-year period, it needed to consider how the relative price elasticity is affected once a new 

standard takes effect. DOE considered the relative price elasticity provided above to be a shortterm value. It was unable to identify sources specific to household durable goods, such as 

appliances, to indicate how short- and long-term price elasticities differ. To estimate how the 

relative price elasticity changes over time, therefore, DOE relied on a study pertaining to 

automobiles.13, 14 That study showed that the automobile price elasticity of demand changes 

following a change in purchase price. With increasing years after the purchase price change, the 

price elasticity becomes more inelastic until it reaches a terminal value around the tenth year 

after the price change. Table 9.4.1 shows the relative change in the price elasticity of demand for 

automobiles over time. DOE developed a time-series of relative price elasticities for home 

appliances based on the relative change in the automobile price elasticity of demand. For years 

not shown in Table 9.4.1, DOE performed a linear interpolation to obtain the relative price

elasticity. 

Table 9.4.1 Change in Relative Price Elasticity After a Purchase Price Change 

Change in elasticity 

relative to first year

Number of Years After Price Change

1 2 3 5 10 20

1.00 0.78 0.63 0.44 0.35 0.33

Relative price elasticity -0.34 -0.26 -0.21 -0.16 -0.12 -0.11

 Based on the following equation, DOE estimated standards case shipments by 

incorporating the impact of the relative price into the base-case shipments forecast. Note that in 

the equation below, the relative price and the relative price elasticity are functions of the year,

because both change with time. 

)( )( )()(1)()(

_

_ _ _ _

jRpljShip NI jejMj RP j

pSTD pBASE pBASE pBASE RP

Where: 

ShipSTD_p(j) = total shipments of product p in year j under the standards case, 

RplBASE_p(j) = units of product p retired and replaced in year j under the base case, 

NIBASE_p(j) = number of new home installations of product p in year j under the base 

case, 

MBASE_p(j) = first-time owners market M of product p in year j under the base case, 

eRP(j)= relative price elasticity in year j (equals -0.34 for year 1), and 

ΔRP(j)= change in relative price due to a standard level in year j. 
9-11

9.5 AFFECTED STOCK 

 The affected stock is the in-service stock of a product that is affected by a standard level. 

In addition to the forecast of product shipments under both the base case and the standards case, 

the affected stock (which represents the difference in the appliance stock between the base case 

and the standards case) is a key output of DOE’s shipments models. The affected stock quantifies 

the effect that new product shipments have on the appliance stock because of a standard level. 

Therefore, the affected stock consists of those in-service units that are purchased in or after the 

year the standard takes effect, as described by the following equation. 

∑

_

1

_

)()( )(

Stdj yr

age

p p

jShipjStockAff

p ageStock

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 for 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 2016. Thus, all 

appliances purchased starting in 2016 are affected by the standard level. 

9.6 EFFECT OF TRIAL STANDARD LEVEL ON SHIPMENTS FORECAST 

This section compares the base-case shipments forecast with the forecast for the 

maximum trial standard level (TSL) that DOE considered for microwave oven standby power. 

For all TSLs, shipments are forecasted to decrease—the effects from the increase in product 

purchase prices offsets the effects from decreased operating costs, resulting in a net decrease in 

shipments. 

Figure 9.6.1 shows the effects on shipments during 2016–2045 that would arise from the 

maximum TSL DOE considered for microwave oven standby power for both product classes 

(TSL 4). In Figure 9.6.1, the difference between the shipments forecast for the base case and the 

TSL illustrates the annual shipments reduction caused by the TSL. The other TSLs have a 

smaller effect on shipments than does TSL 4. 
9-12

Figure 9.6.1 Shipments Forecasts for Base Case and TSL 4 
9-13

REFERENCES 

 

1 Association of Home Appliance Manufacturers (AHAM). AHAM 2000 Fact Book. 2000. 

AHAM: Washington, D.C. 

2 AHAM. AHAM 2003 Fact Book. 2003. AHAM: Washington, D.C. 

3 AHAM. AHAM 2005 Major Appliance Fact Book. June 2006. AHAM: Washington, D.C. 

4 AHAM . AHAM 2009 Fact Book. 2009. AHAM: Washington, D.C. 

5 The Life Expectancy/Replacement Picture. Appliance Magazine. 2009. 64(9): pp. 65-66. 

6 U.S. Department of Energy (DOE)–Energy Information Administration (EIA). Annual 

Energy Outlook 2012 with Projections to 2035. June 25, 2012. Washington, D.C. 

www.eia.gov/forecasts/aeo/. (Accessed May 2013.) 

7 AHAM. Average Useful Life of Major Home Appliances. September 1996. AHAM: 

Washington, D.C. Prepared by NFO Research, Inc. www.aham.org. (Accessed May 

2013.) 

8 U.S. Department of Energy (DOE)–Energy Information Administration (EIA). 

Residential Energy Consumption Survey: 1993 Public Use Data Files.

www.eia.doe.gov/emeu/recs/93publicuse.html. (Accessed May 2013.) 

9 DOE-EIA. Residential Energy Consumption Survey: 1997 RECS Public Use Data. EIA: 

Washington, D.C www.eia.doe.gov/emeu/recs/public.html. (Accessed May 2013.) 

10 DOE-EIA. Residential Energy Consumption Survey: 2001 RECS Public Use Microdata 

Files. 2004. EIA: Washington, D.C. 

www.eia.doe.gov/emeu/recs/recs2001/publicuse2001.html. (Accessed May 2013.) 

11 DOE-EIA. Residential Energy Consumption Survey: 2005 RECS Public Use Microdata 

Files. February 2009. EIA: Washington, D.C. 

www.eia.doe.gov/emeu/recs/recspubuse05/pubuse05.html. (Access May 2013.) 

12 DOE-EIA. Residential Energy Consumption Survey: 2009 RECS Survey Data—Public 

Use Microdata File. October 2011. EIA: Washington, D.C. 

www.eia.gov/consumption/residential/data/2009/#microdata. (Access May 2013.) 

13 Hymans, S. Consumer Durable Spending: Explanation and Prediction. Brookings Papers 

on Economic Activity. 1971. 1971(1): pp. 234–239.

14 Dale, L., and K.S. Fujita. An Analysis of the Price Elasticity of Demand of Household 

Appliances.2008. Lawrence Berkeley National Laboratory: Berkeley, CA. Report No. 

LBNL 326E. www.osti.gov/bridge/servlets/purl/929429-Vm1S9X/929429.pdf. (Access 

May 2013.) 
10-i

CHAPTER 10. NATIONAL IMPACT ANALYSIS

TABLE OF CONTENTS

10.1 INTRODUCTION ......................................................................................................... 10-1

10.2 TRIAL STANDARD LEVELS ..................................................................................... 10-1

10.3 FORECASTED EFFICIENCY LEVELS...................................................................... 10-2

10.4 NATIONAL ENERGY SAVINGS ............................................................................... 10-4

10.4.1 Definition ....................................................................................................................... 10-4

10.4.2 Inputs.............................................................................................................................. 10-5

10.4.2.1 Shipments and Product Stock ...................................................................... 10-5

10.4.2.2 Annual Energy Consumption per Unit ........................................................ 10-6

10.4.2.3 National Annual Energy Consumption........................................................ 10-7

10.4.2.4 Energy Site-to-Source Conversion Factors.................................................. 10-7

10.5 NET PRESENT VALUE............................................................................................... 10-8

10.5.1 Definition ....................................................................................................................... 10-8

10.5.2 Inputs............................................................................................................................ 10-10

10.5.2.1 Total Installed Cost per Unit...................................................................... 10-10

10.5.2.2 Annual Operating Cost Savings per Unit................................................... 10-15

10.5.2.3 Total Increases in Product Cost ................................................................. 10-15

10.5.2.4 Total Savings in Annual Operating Cost ................................................... 10-15

10.5.2.5 Discount Factors ........................................................................................ 10-15

10.5.2.6 Present Value of Costs............................................................................... 10-16

10.5.2.7 Present Value of Savings........................................................................... 10-16

10.6 NATIONAL ENERGY SAVINGS AND NET PRESENT VALUE RESULTS........ 10-16

10.6.2 Results for National Energy Savings........................................................................... 10-17

10.6.3 Results for Net Present Value ...................................................................................... 10-18

LIST OF TABLES

Table 10.2.1 Trial Standard Levels for Microwave Oven Standby Mode and Off-Mode 

Energy Use......................................................................................................... 10-2

Table 10.3.1 Microwave-Only Ovens and Countertop Convection Microwave Ovens: 

Distributions of Standby Energy Consumption in 2016 for Base and 

Standards Cases ................................................................................................. 10-3

Table 10.3.2 Built-In and Over-the-Range Convection Microwave Ovens: Distributions 

of Standby Energy Consumption in 2016 for Base and Standards Cases.......... 10-4

Table 10.4.1 Microwave-Only and Countertop Convection Microwave Ovens: 

Shipments-Weighted Average per-Unit Annual Energy Consumption in 

2016.................................................................................................................... 10-6

Table 10.4.2 Built-In and Over-the-Range Convection Microwave Ovens: ShipmentsWeighted Average per-Unit Annual Energy Consumption in 2016.................. 10-6

Table 10.5.1 Shipments-Weighted Average Product Costs in 2016 for Base and 

Standards Cases ............................................................................................... 10-11
10-ii

Table 10.6.1 Inputs to National Energy Savings and Net Present Value Calculations......... 10-17

Table 10.6.2 Microwave-Only Ovens and Countertop Convection Microwave Ovens: 

Cumulative National Energy Savings for 2016-2045...................................... 10-18

Table 10.6.3 Built-In and Over-the-Range Convection Microwave Ovens: Cumulative 

National Energy Savings for 2016-2045.......................................................... 10-18

Table 10.6.4 Microwave-Only Ovens and Countertop Convection Microwave Ovens: 

Cumulative Net Present Value of Consumer Benefit ...................................... 10-19

Table 10.6.5 Built-In and Over the Range Convection Microwave Ovens: Cumulative 

Net Present Value of Consumer Benefit.......................................................... 10-20

LIST OF FIGURES

Figure 10.4.1 Marginal Site-to-Source Conversion Factors for Electricity ............................. 10-8

Figure 10.5.1 Microwave-Only Ovens and Countertop Convection Microwave Ovens: 

Average Product Costs over the Forecast Period with the default price 

factor index ...................................................................................................... 10-12

Figure 10.5.2 Built-In and Over-the-Range Convection Microwave Ovens: Average 

Product Costs over the Forecast Period with the default price factor index.... 10-12

Figure 10.5.3 Equipment Price Factor Indexes for Default and Sensitivity Cases................ 10-13

Figure 10.5.4 Microwave-Only Ovens and Countertop Convection Microwave Ovens: 

Average Product Costs over the Forecast Period with and without the 

default price index and constant prices............................................................ 10-14

Figure 10.5.5 Built-In and Over-the-Range Convection Microwave Ovens: Average 

Product Costs over the Forecast Period with the default price factor index 

and constant prices........................................................................................... 10-14

Figure 10.6.1 Annual Costs and Savings for TSL 3............................................................... 10-19
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 national impacts of new energy conservation standards for microwave oven standby 

mode and off-mode power. To estimate those impacts, DOE calculated the quantity and net value 

to consumers of future national energy savings (NES) and the net present value (NPV) of 

consumer benefits resulting from the various trial standard levels (TSLs). Results described here 

include (1) national energy consumption and savings, (2) monetary value of national energy 

savings associated with each TSL, (3) increased total installed costs nationwide due to TSLs, and 

(4) the NPV of consumer benefits (difference between value of energy savings and increased 

total installed costs). 

DOE determined both the NES and the NPV for all the TSLs considered for microwave 

oven standby mode and off-mode power. All calculations were performed using a Microsoft 

Excel spreadsheet model. The spreadsheet model, referred to as the national impact analysis 

(NIA) model, incorporates the calculations of NES and NPV, along with input from the 

shipments model described in chapter 9. Details and instructions for using the NIA model are 

provided in appendix 10A.

Chapter 9 provides a detailed description of the shipments model DOE used to forecast 

future purchases of microwave ovens. Chapter 9 describes consumers’ sensitivities (otherwise 

known as elasticities) related to total installed cost, operating costs, and household income, and 

how DOE captured them within the model. 

10.2 TRIAL STANDARD LEVELS

DOE analyzed the benefits and burdens of several trial standard levels (TSLs) for 

microwave oven standby mode and off-mode energy use. As shown in Table 10.2.1, DOE 

considered four TSLs. TSL 1, which corresponds to the first candidate standard level (CSL) from 

each product class, represents the standby power level that involves the least significant design 

change. TSL 4 corresponds to the max-tech CSL (the current maximum microwave oven standby 

technology, or the lowest available standby power). TSLs 2 and 3 are intermediate levels 

between TSLs 1 and 4.
10-2

Table 10.2.1 Trial Standard Levels for Microwave Oven Standby Mode and Off-Mode

Energy Use

TSL

Standby Power W

Product Class 1: MicrowaveOnly and Countertop 

Convection Microwave Ovens

Product Class 2: Built-In and 

Over-the-Range Convection

Microwave Ovens

1 2.00 3.70

2 1.50 2.70

3 1.00 2.20

4 0.02 0.04

10.3 FORECASTED EFFICIENCY LEVELS

A key component of DOE’s estimates of NES and NPV is the standby power 

consumption forecasted over time for the base case (without new standards) and each potential 

standards case (with new standards). The forecasted values represent the annual shipmentsweighted standby power consumption of microwave ovens throughout the forecast period (i.e., 

from the assumed compliance date of a new standard to 30 years after that date, or 2016–2045).

For calculating NES, per-unit annual energy consumption is a direct function of product standby 

power consumption. For calculating NPV, two inputs that depend on the level of standby power 

consumption are needed: per-unit total installed cost and per-unit annual operating cost. Per-unit 

total installed cost is a direct function of standby power consumption, because units that have

lower standby energy consumption tend to cost more. The per-unit annual operating cost is also a 

function of standby power consumption. The inputs to the calculation of NES and NPV are 

discussed in detail in sections 10.4 and 10.5, respectively.

DOE used base-case standby energy levels from the previous microwave oven 

rulemaking to predict standby mode and off-mode energy consumption for microwave ovens in 

the base case for all years in the forecast. The standby energy consumption values for microwave 

ovens in the base case are described further in section 8.2.5 of chapter 8. In forecasting base-case 

energy consumption for microwave ovens, DOE assumed there would be no increase in the 

shipments-weighted efficiency and no change in the distribution of product efficiencies until the 

end of the forecast period (2045). Although DOE recognizes that product efficiencies may 

change in the future, there are no historical data for microwave ovens to indicate how 

efficiencies have changed in the past or may change in the future.

To forecast standby energy consumption levels for the standards cases, DOE assumed a 

“roll-up” scenario that incorporates the shipments-weighted standby energy levels for the 

compliance year (2016). DOE assumed that standby energy consumption levels that did not meet 

the standard under consideration would “roll up” to meet the new standard. Also, DOE assumed 

that all products having standby energy consumption levels that exceed the standard under 

consideration would not be affected. DOE made the same assumptions regarding increases in 

efficiency under the standards cases as under the base case; namely that forecasted efficiencies 

remain at the 2016 efficiency levels until the end of the forecast period. Although frozen trends 
10-3

may not exactly predict efficiencies in the future, DOE considers that maintaining a frozen 

efficiency for the base and standards cases allows for a reasonable estimate of the effect of

standards on product standby energy consumption.

Table 10.3.1and Table 10.3.2 show the distributions of product annual energy 

consumption levels and standby power in 2016 in the base and standards cases that DOE 

examined for microwave ovens. DOE maintained the shipments-weighted standby power and 

annual energy use shown in the following tables throughout the forecast period, 2016–2045. 

Table 10.3.1 Microwave-Only Ovens and Countertop Convection Microwave Ovens: 

Distributions of Standby Energy Consumption in 2016 for Base and 

Standards Cases

TSL Standby 

Power W

Annual Energy 

Use kWh/yr

Market Share %

Base 

Case

Trial Standard Level

1 2 3 4

Baseline 4.00 34.76 46

1 2.00 17.38 35 81

2 1.50 13.03 19 19 100

3 1.00 8.69 0 0 0 100

4 0.02 0.17 0 0 0 0 100

Shipments-weighted standby power 2.83 1.90 1.50 1.00 0.02

Shipments-weighted annual energy use 24.6 16.5 13.0 8.7 0.17
10-4

Table 10.3.2 Built-In and Over-the-Range Convection Microwave Ovens: Distributions of 

Standby Energy Consumption in 2016 for Base and Standards Cases

TSL Standby 

Power W

Annual Energy 

Use kWh/yr

Market Share %

Base 

Case

Trial Standard Level

1 2 3 4

Baseline 4.50 39.1 100

1 3.70 32.1 0 100

2 2.70 23.5 0 0 100

3 2.20 19.1 0 0 0 100

4 0.04 0.35 0 0 0 0 100

Shipments-weighted standby power 4.50 3.70 2.70 2.20 0.04

Shipments-weighted annual energy use 39.1 32.1 23.5 19.1 0.35

10.4 NATIONAL ENERGY SAVINGS

DOE calculates annual national energy savings (NES) and cumulative NES throughout 

the forecast period, which extends from 2016 to 2045. Positive values of NES represent energy 

savings, meaning national energy consumption under standards is lower than in the base case.

10.4.1 Definition

As the following equation indicates, DOE calculates annual NES as the difference 

between two projections: a base case (without new standards) and a standards case (with new 

standards). 

BASE STD NESy AEC AEC _ =

Cumulative NES is the sum of annual energy savings throughout the forecast period, 

which extends from the assumed compliance date of a new standard, 2016, to 30 years after the 

effective date (2045). This calculation is represented by the following equation.

NEScum = ∑NES y

DOE calculated national annual energy consumption by multiplying the number, or 

stock, of microwave ovens (by vintage) by the associated unit energy consumption (also by 

vintage). National annual energy consumption is calculated by the following equation.

= ∑ V ×UECV AEC STOCK
10-5

For the above expressions, DOE defined the various quantities as follows.

AEC = national annual energy consumption each year summed over vintages of the 

product stock, STOCKV (quads);

NES = annual national energy savings (quads);

STOCKV = stock of product (millions of units) of vintage V surviving in the year for 

which DOE is calculating annual energy consumption;

UECV = annual energy consumption per product in kilowatt-hours (kWh);

V = year in which the product was purchased as a new unit; and 

y = year in the forecast (2016–2045).

The stock of a product depends on annual shipments and the lifetime of the product. As 

described in chapter 9, DOE projected shipments under the base and each standards case. DOE 

determined that shipments under the standards cases would be slightly lower than under the base 

case, because more efficient products have slightly higher purchase prices. In other words, DOE 

believes that higher purchase prices cause some consumers to forego purchasing new products, 

resulting in dropped, or displaced, shipments.

To avoid including savings attributable to shipments displaced because of standards, 

DOE used the projected standards-case shipments and, in turn, the standards-case stock, to 

calculate the annual energy consumption for the base case.

10.4.2 Inputs

The inputs to the calculation of national energy savings (NES) are:

• shipments;

• product stock (STOCKV);

• annual energy consumption per unit (UEC);

• national annual energy consumption (AEC); and

• a site-to-source conversion factor (src_conv).

10.4.2.1 Shipments and Product Stock

DOE forecasted shipments of each product class under the base case and each standards 

case. Several factors affect forecasted shipments, including purchase cost, operating cost, and 

household income. As noted earlier, the increased cost of more efficient products causes some 

consumers to forego buying the products. Consequently, shipments forecasted under the 

standards cases are lower than under the base case. The method DOE used to calculate and 

generate the shipments forecasts 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. The NIA shipments model tracks the number of units shipped each year. 

DOE assumes that products have an increasing probability of retiring as they age. The 

probability of survival as a function of years since purchase is the survival function. Chapter 9 
10-6

provides additional details on the survival function that DOE used for both product classes of 

microwave ovens.

10.4.2.2 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). Because annual per-unit energy consumption depends 

directly on energy efficiency, DOE used the shipments-weighted energy efficiencies annual 

energy use for the base and standards cases (presented in section 10.3), along with the annual 

energy use data presented in chapter 8, to estimate the shipments-weighted average annual perunit energy consumption under the base and standards cases.

As noted in section 10.3, DOE assumed that forecasted energy efficiencies in the base 

and standards cases remain constant at 2016 levels. Because annual per-unit energy consumption 

is a function of energy efficiency, DOE held the values shown in Table 10.4.1 and Table 10.4.2

constant throughout the forecast period.

Table 10.4.1 Microwave-Only and Countertop Convection Microwave Ovens: ShipmentsWeighted Average per-Unit Annual Energy Consumption in 2016

TSL Standby 

Power W

Annual Energy 

Use kWh/yr

Baseline 4.00 34.8

1 2.00 17.4

2 1.50 13.0

3 1.00 8.69

4 0.02 0.17

Table 10.4.2 Built-In and Over-the-Range Convection Microwave Ovens: ShipmentsWeighted Average per-Unit Annual Energy Consumption in 2016

TSL Standby 

Power W

Annual Energy 

Use kWh/yr

Baseline 4.50 39.1

1 3.70 32.1

2 2.70 23.5

3 2.20 19.1

4 0.04 0.35
10-7

10.4.2.3 National Annual Energy Consumption

National annual energy consumption is the product of the annual energy consumption per 

unit and the number of units of each vintage. This calculation accounts for differences in unit 

energy consumption from year to year. The equation for determining annual energy 

consumption, shown in section 10.3, is repeated here. 

= ∑ V ×UECV AEC STOCK

In determining national annual energy consumption, DOE initially calculates annual 

energy consumption at the site and then applies a conversion factor to calculate primary energy 

consumption (see section 10.4.2.4). 

10.4.2.4 Energy Site-to-Source Conversion Factors

In determining national annual energy consumption, DOE initially calculates the annual 

energy consumption at the site (for electricity, the energy in kilowatt-hours (kWh) consumed at 

the household or establishment). To account for losses associated with the generation, 

transmission, and distribution of electricity, DOE then applies a conversion factor to the site 

energy consumption to calculate primary (source) energy consumption. The site-to-source 

conversion factor is a multiplicative factor used to convert site energy consumption into primary 

or source energy consumption, expressed in quadrillion British thermal units (quads). DOE used 

annual site-to-source conversion factors based on the version of the National Energy Modeling 

System (NEMS)a that corresponds to the Energy Information Administration’s (EIA) Annual 

Energy Outlook 2012 (AEO2012).1

Figure 10.4.1

The factors are marginal values, which represent the 

response of the system to an incremental decrease in consumption. For electricity, the conversion 

factors change over time in response to projected changes in generation sources (i.e., the types of 

power plants projected to provide electricity to the Nation). shows the site-tosource conversion factors DOE applied from 2005 to the end of the forecast period. DOE 

assumed that conversion factors remain at 2030 values throughout the rest of the forecast period.

 a For more information on NEMS, refer to the U.S. Department of Energy–Energy Information Administration 

documentation. A useful summary is National Energy Modeling System: An Overview 2000, DOE/EIA0581(2000), March 2000. EIA approves use of the name NEMS to describe only an official version of the model 

without any modification to code or data. Because this analysis entails some minor code modifications and the 

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

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

NEMS-BT previously was called NEMS-BRS.
10-8

0

2000

4000

6000

8000

10000

12000

14000

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Marginal Site-to-Source Conversion Factor for 

Electricty (Btu/kWh)

Figure 10.4.1 Marginal Site-to-Source Conversion Factors for 

Electricity

10.5 NET PRESENT VALUE

DOE calculated the net present value (NPV) of the increased product cost and reduced

operating cost associated with the difference between the base case and each potential standards 

case for both microwave oven product classes examined for standby mode and off-mode energy 

use.

10.5.1 Definition

The NPV is the value in the present of a time-series of costs and savings. The NPV is 

described by the following equation.

NPV PVS PVC _ =

Where:

PVS = present value of savings in operating cost, and 

PVC = present value of increased total product cost to consumers. 
10-9

DOE determined the PVS and PVC according to the following expressions.

PVS = ∑OCSy × DFy

PVC = ∑TICy × DFy

Where:

OCS = total annual savings in operating cost each year summed over vintages of the 

product stock, STOCKV;

TIC = total annual increases in product cost each year summed over years of the product 

shipments, SHIPy;

DF = discount factor in each year; and 

y = year in the forecast.

DOE calculated the total annual consumer savings in operating cost by multiplying the 

number or stock of a given product class (by vintage) by its per-unit operating cost savings (also 

by vintage). DOE calculated the total annual increases in consumer product cost by multiplying 

the number or shipments of the given product class (by vintage) by its per-unit increase in 

consumer product cost (also by vintage). The calculation of total annual operating cost savings 

and total annual product cost increases is represented by the following equations.

y ∑ V UOCSV OCS = STOCK ×

y ∑ y UTICy TIC = SHIP ×

Where:

STOCKV = stock of products of vintage V that survive in the year for which DOE 

calculated annual energy consumption;

UOCSV = annual per-unit savings in operating cost;

V = year in which the product was purchased as a new unit;

SHIPy = shipments of products in year y; and

UTICy = annual per-unit increase in installed product cost in year y.

DOE determined the total increased product cost for each year from the compliance date 

of the new standard (2016) to 2045. DOE determined the present value of operating cost savings 

for each year from the compliance date of the standard to the year when all units purchased by 

2045 have been retired. DOE calculated costs and savings as the difference between a standards 

case and a base case without new standards. 
10-10

DOE developed a discount factor from the national discount rate and the number of years 

between the present (i.e., year to which the sum is being discounted) and the year in which the

costs and savings occur. The NPV is the sum over time of the discounted net savings.

10.5.2 Inputs

The inputs to calculation of the net present value (NPV) are: 

• average annual product cost;

• average annual savings in operating cost;

• total annual increases in product cost;

• total annual savings in operating cost; 

• discount factor;

• present value of costs; and

• present value of savings.

The increase in total annual product cost is equal to the annual change in the average 

annual product cost (difference between base case and standards case) multiplied by the 

shipments forecasted in the standards case. As with the calculation of the NES, DOE did not 

calculate total annual product costs using base-case shipments. To avoid including savings due to 

displaced shipments (when consumers decide not to buy higher-cost products), DOE used the 

standards-case projection of shipments and, in turn, the standards-case stock, to calculate product 

costs. 

The total annual savings in operating costs are equal to the change in annual operating 

cost (difference between base case and standards case) per unit multiplied by the shipments

forecasted for the standards case.

10.5.2.1 Total Installed Cost per Unit

The per-unit total installed cost of each product class is described in chapter 8 as a 

function of product energy efficiency. Because the per-unit total annual installed cost depends 

directly on energy efficiency, DOE used the shipments-weighted energy efficiencies of the base 

and standards cases described in section 10.3, in combination with the total installed costs 

developed in chapter 8, section 8.2.1.7, to estimate the shipments-weighted average annual perunit total installed cost under the base and standards cases.

DOE based average consumer costs for microwave oven on average manufacturer costs 

plus average overall markup values. Using the relationship between microwave oven standby 

mode and off-mode energy use and total installed cost presented in chapter 8, section 8.2.1.7, 

DOE derived the per-unit total installed cost based on the shipments-weighted energy 

efficiencies that correspond to the base case and each standards case for the two product classes.

For the Final Rule, DOE applied the price forecasting method described in chapter 8, 

section 8.2.1.2, to forecast the prices of microwave ovens sold in each year of the forecast period 
10-11

(2016–2045). The price in each year is a function of the cumulative production of microwave 

ovens forecast for that year. 

DOE derived a price factor index to forecast prices in each future year. The default index 

value in a given year is a function of the cumulative production forecast through that year, which 

is based on the shipments forecast described in chapter 9. DOE applied the same value to 

forecast prices for each microwave oven product class at each considered efficiency level.

For microwave ovens, the estimated fractional reduction in price expected from each 

doubling of cumulative production is 10.1 percent. Table 10.5.1 shows the costs in 2016.

Table 10.5.1 Shipments-Weighted Average Product Costs in 2016 for Base and Standards

Cases

Base 

Case

Trial Standards Level

1 2 3 4

Microwave-Only Ovens and Countertop Convection

Microwave Ovens

Shipments-weighted annual energy use kWh 34.9 17.4 13.1 8.72 0.17

Average product cost 2011$ 234 234 234 238 243

Built-In and Over-the-Range Convection Microwave 

Ovens

Shipments-weighted annual energy use kWh 39.2 32.3 23.5 19.2 0.35

Average product cost 2011$ 505 505 507 513 514

Figures 10.5.1 and 10.5.2 show how the average product costs for the base case and each 

TSL decline over the forecast period with the default price factor index. Figure 10.5.1 pertains 

to product class 1, microwave-only ovens and countertop convection microwave ovens, while 

Figure 10.5.2 pertains to product class 2, built-in and over-the-range convection microwave 

ovens.
10-12

Figure 10.5.1 Microwave-Only Ovens and Countertop Convection Microwave 

Ovens: Average Product Costs over the Forecast Period with the 

default price factor index

Figure 10.5.2 Built-In and Over-the-Range Convection Microwave Ovens: 

Average Product Costs over the Forecast Period with the default 

price factor index
10-13

DOE recognizes that there is uncertainty in its estimates of product price forecasts. To 

provide an indication of potential long-term changes in the forecast, DOE performed price-trend 

fits to two periods in the historical data, equal to the first and second half of the total period. For 

electric cooking equipment in general, the selected periods were 1969–1989 and 1990–2010. 

Appendix 8C shows the estimated learning parameters for those periods. DOE also performed a 

sensitivity calculation using data specific to microwave ovens.

In addition to using learning parameters, DOE examined the case involving a constant 

real price sensitivity. DOE also used an index forecasted for AEO2012, one based on the 

“chained price index—other consumer durable goods except ophthalmic.” That index is the most 

disaggregated category that includes appliances. To develop an inflation-adjusted index, DOE 

normalized the above index with the “chained price index—gross domestic product” forecasted 

for AEO2012.

Figure 10.5.3 shows the default price factor index and the indexes that corresponding to 

each of the sensitivities for microwave ovens. All indexes are adjusted for inflation. 

Figure 10.5.3 Equipment Price Factor Indexes for Default and Sensitivity Cases

Figures 10.5.4 and 10.5.5 show the average product costs for the base case and each TSL 

over the forecast period with the default price factor index and one of the sensitivities, constant 

prices. Comparing the average product costs with and without a price factor index demonstrates 

the impact of using price factor index on forecasted product costs. Figure 10.5.4 pertains to 

product class 1, microwave-only ovens and countertop convection microwave ovens, while 

Figure 10.5.4 pertains to product class 2, built-in and over-the-range convection microwave 

ovens.
10-14

Figure 10.5.4 Microwave-Only Ovens and Countertop Convection Microwave 

Ovens: Average Product Costs over the Forecast Period with and 

without the default price index and constant prices

Figure 10.5.5 Built-In and Over-the-Range Convection Microwave Ovens: 

Average Product Costs over the Forecast Period with the default 

price factor index and constant prices
10-15

The NIA results reported in this chapter are based on the default LR. With cumulative 

shipments through 2045 projected to reach 1,280 million (compared with 522 million in 2010), 

the price is projected to drop to 0.64 times the 2010 value. Appendix 10B presents annualized 

NPV results using the other price factor indexes.

10.5.2.2 Annual Operating Cost Savings per Unit

Per-unit annual operating cost represents the annual cost for energy for each unit. DOE 

determined the savings in per-unit annual energy cost for each product class by multiplying the 

savings in per-unit annual energy consumption by the appropriate energy price. 

Estimates of per-unit annual energy consumption for the base case and each standards 

case were presented in section 10.4.2.2. DOE forecasted energy prices based on EIA’s AEO2012.

Energy prices and price trends are described in chapter 8. 

10.5.2.3 Total Increases in Product Cost

The total annual increase in product cost for any given standards case is the product of 

the average cost increase per unit due to the standard and the number of units of each vintage 

shipped. The equation for determining the total annual increase in product cost for a given 

standards case, shown in section 10.5.1, is repeated here.

y ∑ y UTICy TIC = SHIP ×

10.5.2.4 Total Savings in Annual Operating Cost

The total annual savings in operating cost for any given standards case is the product of 

the annual savings in operating cost per unit attributable to the standard and the number of units 

of each vintage. This method accounts for differences in annual operating cost savings from year 

to year. The equation for determining the total annual savings in operating cost for a given 

standards case, which was presented in section 10.5.1, is repeated here.

∑ V UOCSV OCS = STOCK ×

10.5.2.5 Discount Factors

DOE multiplies monetary values for future years by a discount factor to determine their 

present value. The discount factor (DF) is described by the equation:

( ) (1 )

1

y yp r

DF − + =

Where:

r = discount rate, 

y = year of the monetary value, and 

yP = year in which the present value is being determined.
10-16

Although DOE used consumer discount rates to determine the life-cycle cost (LCC) of 

microwave ovens, 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.

2

10.5.2.6 Present Value of Costs

DOE defined the present year as 2013.

The present value of increased installed costs is the annual increase in installed cost for 

each year (i.e., the difference between the standards case and base case), discounted to the 

present and summed over the period for which DOE is considering the installed products (from 

the compliance date of energy conservation standards through 30 years thereafter).

The increase in total installed cost refers to both product and installation costs associated 

with the more energy efficient products purchased in the standards case compared to the base 

case. DOE calculated annual increases in installed cost as the difference in total cost of new 

products installed each year, multiplied by the shipments in the standards case.

10.5.2.7 Present Value of Savings

The present value of savings in operating cost is the annual savings in operating cost (i.e., 

the difference between the base case and standards case), discounted to the present and summed 

over the period that begins with the compliance date of standards and ends when the last installed 

unit is retired from service.

Savings represent decreases in operating costs (including costs for energy, repair, and 

maintenance) associated with the more energy efficient products purchased in a standards case 

compared to the base case. Total annual savings in operating cost are the savings per unit 

multiplied by the number of units of each vintage that survive in a particular year. Because a 

product consumes energy throughout its lifetime, the energy consumption for units installed in 

past years includes energy consumed until the unit is retired from service.

10.6 NATIONAL ENERGY SAVINGS AND NET PRESENT VALUE RESULTS

The NIA model provides estimates of the NES and NPV that would result from standards 

at various efficiency levels. The inputs to the NIA model were discussed in sections 10.4.2

(inputs to NES) and 10.5.2 (inputs to NPV). DOE generated the NES and NPV results using a 

Microsoft Excel spreadsheet. Details and instructions for using the spreadsheet are provided in 

Appendix 10A, User Instructions for the National Impact Analysis Spreadsheet.

Table 10.6.1summarizes inputs to the NIA model. The data source for each input also is 

noted.
10-17

Table 10.6.1 Inputs to National Energy Savings and Net Present Value Calculations

Input Data Description

Shipments Annual shipments from shipments model (chapter 9).

Compliance date of standard 2016

Energy efficiency in base case Shipments-weighted unit energy consumption (UEC)

determined for each year. 

Energy efficiency in standards 

cases 

“Roll-up” scenario assumed for determining shipmentsweighted UEC for each standards case.

Annual energy consumption per 

unit

Annual weighted-average values are a function of 

shipments-weighted UEC.

Learning rates See chapter 8 and appendix 8C.

Total installed cost per unit Annual weighted-average values are a function of 

efficiency level. 

Energy cost per unit Annual weighted-average values are a function of the 

annual UEC and energy prices (see chapter 8 for energy 

prices).

Escalation of energy prices Based on EIA AEO2012 forecasts (to 2035) and on 

extrapolation after 2035 (see chapter 8).

Energy site-to-source conversion 

factor

Conversion, which differs yearly, is generated by 

DOE/EIA’s NEMS program (a time-series conversion 

factor that includes electric generation, transmission, and 

distribution losses).

Discount rate 3 percent and 7 percent real.

Present year 2013.

10.6.2 Results for National Energy Savings 

This section provides results of NES calculations for standards at each efficiency level

analyzed for the two product classes of microwave ovens. NES results, which are cumulative 

through 2045, are shown as primary energy savings. Because DOE based the inputs to the NIA 

model on weighted-average values, results are discrete point values, rather than a distribution of 

values as produced by the LCC and payback period analysis. DOE reports both undiscounted and 

discounted values of energy savings. Discounted energy savings represent a policy perspective in 

which energy savings in the future are less significant than energy savings in the present.
10-18

Table 10.6.2 Microwave-Only Ovens and Countertop Convection Microwave Ovens: 

Cumulative National Energy Savings for 2016-2045

Trial Standard 

Level

Standby 

Power W

National 

Energy Savings 

quads

1 2.00 0.24

2 1.50 0.35

3 1.00 0.47

4 0.02 0.72

Table 10.6.3 Built-In and Over-the-Range Convection Microwave Ovens: Cumulative 

National Energy Savings for 2016-2045

Trial Standard 

Level

Standby 

Power W

National 

Energy Savings 

quads

1 3.70 0.00

2 2.70 0.00

3 2.20 0.01

4 0.04 0.01

10.6.3 Results for Net Present Value 

This section provides the results of calculating the NPV of consumer benefits for 

standards at each considered efficiency level for the two product classes of microwave ovens. 

Results, which are cumulative, are shown as the discounted value of the net savings in dollar 

terms. 

The present value of increased total installed cost is the total annual increase in installed 

cost (the difference between a standards case and the base case), discounted to the present and 

summed throughout the period for which DOE evaluated the effects of standards.

Savings are decreases in operating cost associated with the more energy efficient

products purchased in the standards case compared to the base case. Total savings in operating 

cost are the savings per unit multiplied by the number of units of each vintage (i.e., year of 

manufacture) that survive in a particular year. The operating cost includes expenditures until the 

last unit purchased during the forecast period is retired from service.

As an example, Figure 10.6.1 illustrates the net costs and benefits for both product 

classes under TSL 3. DOE can use analysis data to produce figures similar to the one presented

below for each TSL. The annual product cost is the sum of the increase in the total installed cost 

for products purchased each year during the forecast period. The annual savings in operating cost 

is the savings for products operating in each year. The NPV is the difference between the 

cumulative annual discounted savings and the cumulative annual discounted costs. 
10-19

Figure 10.6.1 Annual Costs and Savings for TSL 3

Table 10.6.4 and Table 10.6.5 present the cumulative NPV of consumer benefits for both 

product classes of microwave ovens. Benefits are provided for each TSL at both a 3-percent and 

a 7-percent discount rate.

Table 10.6.4 Microwave-Only Ovens and Countertop Convection Microwave Ovens: 

Cumulative Net Present Value of Consumer Benefit

Trial 

Standard 

Level

Standby 

Power W

Net Present Value

7% Discount Rate

billion 2011$

3% Discount Rate

billion 2011$

1 2.00 1.13 2.32

2 1.50 1.61 3.31

3 1.00 1.51 3.34

4 0.02 2.00 4.56
10-20

Table 10.6.5 Built-In and Over the Range Convection Microwave Ovens: Cumulative Net 

Present Value of Consumer Benefit

Trial 

Standard 

Level

Standby 

Power W

Net Present Value

7% Discount Rate

billion 2011$

3% Discount Rate

billion 2011$

1 3.70 0.01 0.02

2 2.70 0.02 0.05

3 2.20 0.02 0.04

4 0.04 0.04 0.09

The NPV values in this chapter were calculating using the default price forecast. DOE 

considered three price forecasting sensitivities in addition to the default rate of 10.1 percent: (1) a 

constant price forecast; (2) a high decline forecast; and (4) an AEO2012 chained-price index 

forecast. Appendix 10C presents NPV results for each sensitivity case. 
10-21

REFERENCES

 

1 U.S. Department of Energy–Energy Information Administration (EIA). Annual Energy 

Outlook 2012 with Projections to 2035. June 25, 2012. EIA: Washington, D.C. 

www.eia.gov/forecasts/aeo/. (Accessed May 2013.)

2 The White House Office of Management and Budget. Circular A-4: Regulatory Analysis: 

Section E: identifying and measuring benefits and costs. September 17, 2003. Office of 

Management and Budget: Washington, D.C.

www.whitehouse.gov/omb/circulars_a004_a-4/. (Accessed May 2013.)
11-i

CHAPTER 11. CONSUMER SUB-GROUP ANALYSIS

TABLE OF CONTENTS

11.1 INTRODUCTION ......................................................................................................... 11-1

11.2 DEFINITIONS OF SUB-GROUPS............................................................................... 11-1

11.2.1 Senior-Only Households................................................................................................ 11-1

11.2.2 Low-Income Households............................................................................................... 11-1

11.3 INPUTS TO LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS FOR 

SUB-GROUPS............................................................................................................... 11-2

11.4 RESULTS ...................................................................................................................... 11-3

11.4.1 Senior-Only Households................................................................................................ 11-3

11.4.2 Low-Income Households............................................................................................... 11-4

LIST OF TABLES

Table 11.2.1 RECS 2009 Definitions of Low-Income Households by Yearly Income .......... 11-2

Table 11.3.1 Household Population........................................................................................ 11-3

Table 11.3.2 Weighted-Average Annual Energy Use for Microwave-Only and 

Countertop Convection Microwave Ovens (kWh)............................................. 11-3

Table 11.3.3 Weighted-Average Annual Energy Use for Built-In and Over-the-Range 

Convection Microwave Ovens (kWh)................................................................ 11-3

Table 11.4.1 Microwave-Only and Countertop Convection Microwave Ovens: LCC and 

PBP Results for Senior-Only Households ......................................................... 11-4

Table 11.4.2 Built-In and Over-the-Range Convection Microwave Ovens: LCC and 

PBP Results for Senior-Only Households ......................................................... 11-4

Table 11.4.3 Microwave-Only and Countertop Convection Microwave Ovens: LCC and 

PBP Results for Low-Income Households ........................................................ 11-5

Table 11.4.4 Built-In and Over-the-Range Convection Microwave Ovens: LCC and 

PBP Results for Low-Income Households ........................................................ 11-5
11-1

CHAPTER 11. CONSUMER SUB-GROUP ANALYSIS

11.1 INTRODUCTION

Chapter 8 describes the life-cycle cost (LCC) and payback period (PBP) analysis, which

examines effects of energy conservation standards on the U.S. population. In analyzing the 

potential effects of new or amended standards on residential consumers, the U.S. Department of 

Energy (DOE) further evaluates the impact on identifiable groups of consumers (i.e., sub-groups) 

that may be disproportionately affected by a national standard level. The consumer sub-group 

analysis evaluates impacts by analyzing the LCC and PBPs for sub-groups of residential 

consumers. 

For new standards related to microwave oven standby mode and off mode, DOE 

identified two consumer sub-groups that warranted further study: (a) households composed of 

people 65 years of age or older (senior-only); and (b) households at or below the poverty line 

(low-income). 

DOE determined the effect on consumer sub-groups for two product classes: (1) 

microwave-only ovens and countertop convection microwave ovens and (2) built-in and overthe-range convection microwave ovens. To perform the consumer sub-group analysis, DOE used

the LCC spreadsheet model, which allows for the examination of particular consumer subgroups. Chapter 8 explains in detail the model inputs used in determining LCC and PBP.

This chapter identifies the two consumer sub-groups in further detail and gives the results 

of the LCC and PBP analysis for the considered sub-groups.

11.2 DEFINITIONS OF SUB-GROUPS 

In the case of microwave ovens, DOE analyzed two sub-groups: (1) low-income 

households and (2) households occupied by senior citizens. The two sub-groups are defined 

below.

11.2.1 Senior-Only Households

The occupants of senior-only households are all at least 65 years of age. Based on the 

DOE Energy Information Administration’s (EIA’s) 2009 Residential Energy Consumption 

Survey (RECS 2009), senior-only households comprise 17 percent of the country’s households.

1

11.2.2 Low-Income Households

As defined in the RECS survey, low-income households are considered to be those at or 

below the poverty line. The poverty line varies with household size, age of head of household, 

and family income. Table 11.2.1 summarizes the income level baselines for selecting low-
11-2

income households from the RECS 2009 sample. The RECS survey classifies 15 percent of the

country’s households as low-income.

Table 11.2.1 RECS 2009 Definitions of Low-Income Households by Yearly Income

Household 

Size

Average Income 2009$

48 Contiguous 

States and D.C. Alaska Hawaii

1 10,830 13,530 12,460

2 14,570 18,210 16,760

3 18,310 22,890 21,060

4 22,050 27,570 25,360

5 25,790 32,250 29,660

6 29,530 36,930 33,960

7 33,270 41,610 38,260

8 37,010 46,290 42,560

For households in the 48 contiguous states and D.C., add 3,740 for each additional person.

For households in Alaska, add 4,680 for each additional person.

For households in Hawaii, add 4,300 for each additional person.

11.3 INPUTS TO LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS FOR

SUB-GROUPS

Table 11.3.1 summarizes the household population for microwave-only and convection

ovens. Table 11.3.2 and Table 11.3.3 summarize the weighted-average annual energy use for the 

households examined in the consumer sub-group analysis. Those values are compared against

the weighted-average values for the national sample.
11-3

Table 11.3.1 Household Population 

All Microwave Ovens

Population Count Weight

National 11,616 108,995,609

Senior-only 1756 17,784,266

Senior-only % 15.1 16.3

Low-income 1554 15,574,932

Low-income % 13.4 14.3

Table 11.3.2 Weighted-Average Annual Energy Use for Microwave-Only and Countertop 

Convection Microwave Ovens (kWh)

Trial Standard 

Level

All

Households

SeniorOnlyLowIncome

Baseline 34.76 34.76 34.69

1 17.38 17.38 17.35

2 13.03 13.03 13.01

3 8.69 8.69 8.67 

4 0.17 0.17 0.17

Table 11.3.3 Weighted-Average Annual Energy Use for Built-In and Over-the-Range 

Convection Microwave Ovens (kWh)

Trial Standard 

Level

All

Households

SeniorOnlyLowIncome

Baseline 39.10 39.10 39.03

1 32.15 32.15 32.09

2 23.46 23.46 23.42

3 19.12 19.12 19.08

4 0.35 0.35 0.35

11.4 RESULTS

Table 11.4.1 through Table 11.4.4 summarize results of DOE’s LCC and PBP analysis 

performed for the two identified sub-groups, senior-only and low-income households. The tables

provide the average installed price; average lifetime operating cost (discounted); average lifecycle cost; average life-cycle cost savings; the percentage of consumers who are burdened with 

net costs, realize net savings, or are not affected; and the median payback period.

11.4.1 Senior-Only Households

Table 11.4.1 and Table 11.4.2 summarize the analytical results for standby and off-mode 

efficiency levels for both product classes of microwave ovens for senior-only households.
11-4

Table 11.4.1 Microwave-Only and Countertop Convection Microwave Ovens: LCC and 

PBP Results for Senior-Only Households

Standby

Power

W

Life-Cycle Cost 2011$ Life-Cycle Cost Savings Median 

Payback 

Period

years

Average 

Installed 

Price

Average 

Operating 

Cost

Average 

LCC

Average 

Savings 

2011$

% Households Having

Net

Cost

No 

Impact*

Net 

Benefit

4.00 234 35 268

2.00 234 17 251 8 0 54 46 0.2 

1.50 234 13 247 11 0 19 81 0.3 

1.00 239 9 247 11 13 0 87 3.5 

0.02 243 0 243 15 5 0 95 3.5 

* “No impact” means that the base-case product assigned to the household has an efficiency greater than the level 

indicated, so the household is not affected.

Table 11.4.2 Built-In and Over-the-Range Convection Microwave Ovens: LCC and PBP 

Results for Senior-Only Households

Standby 

Power

W

Life-Cycle Cost 2011$ Life-Cycle Cost Savings Median 

Payback 

Period

years

Average 

Installed 

Price

Average 

Operating 

Cost

Average 

LCC

Average 

Savings 

2011$

% Households Having

Net

Cost

No 

Impact*

Net 

Benefit

4.50 505 39 544 NA 0 100 0 NA

3.70 505 32 538 7 0 0 100 0.1

2.70 505 23 529 16 0 0 100 0.1

2.20 513 19 532 12 0 0 100 3.3

0.04 514 0 515 30 0 0 100 2.0

* “No impact” means that the base-case product assigned to the household has an efficiency greater than the level 

indicated, so the household is not affected.

11.4.2 Low-Income Households

Table 11.4.3 and Table 11.4.4 summarize the analytical results for standby and off-mode 

efficiency levels for both product classes of microwave ovens for low-income households.
11-5

Table 11.4.3 Microwave-Only and Countertop Convection Microwave Ovens: LCC and 

PBP Results for Low-Income Households

Standby

Power

W

Life-Cycle Cost 2011$ Life-Cycle Cost Savings Median 

Payback 

Period

years

Average 

Installed 

Price

Average 

Operating 

Cost

Average 

LCC

Average 

Savings 

2011$

% Households Having

Net

Cost

No 

Impact*

Net 

Benefit

4.00 234 35 269 NA 0 100 0 NA

2.00 234 17 252 8 0 54 46 0.2

1.50 235 13 248 11 0 19 81 0.3

1.00 239 9 248 11 13 0 87 3.5

0.02 244 0 244 15 5 0 95 3.5

* “No impact” means that the base-case product assigned to the household has an efficiency greater than the level 

indicated, so the household is not affected.

Table 11.4.4 Built-In and Over-the-Range Convection Microwave Ovens: LCC and PBP 

Results for Low-Income Households

Standby 

Power

W

Life-Cycle Cost 2011$ Life-Cycle Cost Savings Median 

Payback 

Period

years

Average 

Installed 

Price

Average 

Operating 

Cost

Average 

LCC

Average 

Savings 

2011$

% Households Having

Net

Cost

No 

Impact*

Net 

Benefit

4.50 506 39 545 NA 0 100 0 NA

3.70 506 32 538 7 0 0 100 0.7

2.70 506 23 530 15 0 0 100 1.2

2.20 514 19 533 12 0 0 100 3.4

0.04 515 0 516 29 0 0 100 2.0

* “No impact” means that the base-case product assigned to the household has an efficiency greater than the level 

indicated, so the household is not affected.
11-6

REFERENCE

 

1 U.S. Department of Energy–Energy Information Administration (EIA). Residential 

Energy Consumption Survey: 2009 RECS Survey Data—Public Use Microdata File. 

October 2011. EIA: Washington, D.C. 

www.eia.gov/consumption/residential/data/2009/#microdata. (Accessed May 2013.)
12-i

CHAPTER 12. MANUFACTURER IMPACT ANALYSIS

TABLE OF CONTENTS

12.1 INTRODUCTION ......................................................................................................... 12-1

12.2 METHODOLOGY ........................................................................................................ 12-1

12.2.1 Phase I: Industry Profile..................................................................................... 12-2

12.2.2 Phase II: Analysis Framework and Interview Guides........................................ 12-2

12.2.2.1 Analysis Framework ............................................................................ 12-2

12.2.2.2 Interview Guide ................................................................................... 12-3

12.2.3 Phase III: Manufacturer Interviews, Revised Industry Cash Flow Analysis, and 

Additional Analyses....................................................................................................... 12-3

12.2.3.1 Manufacturer Interviews...................................................................... 12-3

12.2.3.2 Revised Industry Cash Flow Analysis................................................. 12-4

12.2.3.3 Manufacturer Subgroup Analysis........................................................ 12-4

12.2.3.4 Small Business Manufacturer Subgroup.............................................. 12-5

12.2.3.5 Manufacturing Capacity Impact .......................................................... 12-5

12.2.3.6 Employment Impact............................................................................. 12-5

12.2.3.7 Cumulative Regulatory Burden ........................................................... 12-6

12.3 MANUFACTURER IMPACT ANALYSIS KEY ISSUES.......................................... 12-6

12.3.1 Consumer Utility................................................................................................ 12-6

12.3.2 Profitability ........................................................................................................ 12-6

12.4 GRIM INPUTS AND ASSUMPTIONS........................................................................ 12-7

12.4.1 Overview of the GRIM ...................................................................................... 12-7

12.4.2 Sources of GRIM Inputs.................................................................................... 12-7

12.4.2.1 Corporate Annual Reports ................................................................... 12-8

12.4.2.2 Standard and Poor Credit Ratings........................................................ 12-8

12.4.2.3 Dun and Bradstreet Reports................................................................. 12-8

12.4.2.4 Shipments Model ................................................................................. 12-8

12.4.2.5 Engineering Analysis........................................................................... 12-9

12.4.2.6 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-Shipments Forecast.................................................................................. 12-13

12.4.6.1 Base-Case Shipments Forecast .......................................................... 12-13

12.4.6.2 Standards-Case Shipments Forecast .................................................. 12-14

12.4.6.3 Price Elasticity of Demand Shipments Scenario ............................... 12-15

12.4.6.4 Economic Growth Shipments Scenario ............................................. 12-15

12.4.7 Production Costs.............................................................................................. 12-16

12.4.8 Conversion Costs............................................................................................. 12-17

12.4.8.1 Capital Conversion Costs................................................................... 12-17

12.4.8.2 Product Conversion Costs.................................................................. 12-18

12.4.9 Markups ........................................................................................................... 12-20

12.4.9.1 Preservation of Gross Margin Percentage.......................................... 12-20
12-ii

12.4.9.2 Preservation of Gross Margin in Absolute Dollars............................ 12-20

12.5 MICROWAVE OVEN INDUSTRY FINANCIAL IMPACTS .................................. 12-21

12.5.1 Impacts on Industry Net Present Value ........................................................... 12-22

12.5.2 Impacts on Annual Cash Flow......................................................................... 12-24

12.6 OTHER IMPACTS...................................................................................................... 12-28

12.6.1 Employment..................................................................................................... 12-28

12.6.1.1 Methodology...................................................................................... 12-28

12.6.2 Production Capacity......................................................................................... 12-30

12.6.3 Cumulative Regulatory Burden ....................................................................... 12-31

12.6.3.1 Standby Power Requirements............................................................ 12-31

12.6.3.2 Additional Federal Energy Conservation Standards.......................... 12-32

12.6.3.3 Restriction of Hazardous Substance Directive .................................. 12-35

12.6.3.4 International Energy Conservation Standards ................................... 12-36

12.7 CONCLUSIONS.......................................................................................................... 12-37

LIST OF TABLES

Table 12.4.1 GRIM Financial Parameters Based on 2005–2011 Weighted Company Financial 

Data ..................................................................................................................... 12-10

Table 12.4.2 Cost of Equity Calculation.................................................................................. 12-11

Table 12.4.3 Cost of Debt Calculation..................................................................................... 12-12

Table 12.4.4 Product Class 1 Standby Power Efficiency Levels and TSLs............................ 12-12

Table 12.4.5 Product Class 2 Standby Power Efficiency Levels and TSLs............................ 12-13

Table 12.4.6 Total NIA-Shipments Forecast in 2016.............................................................. 12-13

Table 12.4.7 Base-Case Standby Power Distribution of Efficiencies for Product Class 1 in 2016

............................................................................................................................. 12-13

Table 12.4.8 Base-Case Standby Power Distribution of Efficiencies for Product Class 2 in 2016

............................................................................................................................. 12-14

Table 12.4.9 Distribution of Standby Power Product Class 1 Shipments in the Standards Case in 

2016 – Rollup Scenario....................................................................................... 12-14

Table 12.4.10 Distribution of Standby Power Product Class 2 Shipments in the Standards Case in 

2016 – Rollup Scenario....................................................................................... 12-15

Table 12.4.11 Base-Case Standby Power Production Costs (2011$) used in the GRIM for 

Product Class 1 ................................................................................................... 12-16

Table 12.4.12 Base-Case Standby Power Production Costs (2011$) used in the GRIM for 

Product Class 2 ................................................................................................... 12-16

Table 12.4.13 Standby Power Capital Conversion Costs for Product Class 1 by TSL ........... 12-18

Table 12.4.14 Standby Power Capital Conversion Costs for Product Class 2 by TSL ........... 12-18

Table 12.4.15 Per Platform Assumptions for Capital Conversion Costs (Product Class 1 and 

Product Class 2).................................................................................................. 12-18

Table 12.4.16 Standby Power Product Conversion Costs for Product Class 1 by TSL .......... 12-19

Table 12.4.17 Standby Power Product Conversion Costs for Product Class 2 by TSL .......... 12-19

Table 12.4.18 Per-Platform Assumptions for Product Conversion Costs (Product Class 1)... 12-19
12-iii

Table 12.4.19 Per Platform Assumptions for Product Conversion Costs (Product Class 2)... 12-20

Table 12.4.20 Product Class 1 Preservation of Gross Margin in Absolute Dollars Manufacturer 

Markups .............................................................................................................. 12-21

Table 12.4.21 Product Class 2 Preservation of Gross Margin in Absolute Dollars Manufacturer 

Markups .............................................................................................................. 12-21

Table 12.5.1 Product Class 1 Manufacturer Impact Analysis Under the Preservation of Gross 

Margin Percentage Markup Scenario.................................................................. 12-22

Table 12.5.2 Product Class 1 Manufacturer Impact Analysis Under the Preservation of Gross 

Margin in Absolute Dollars Markup Scenario.................................................... 12-22

Table 12.5.3 Product Class 2 Manufacturer Impact Analysis Under the Preservation of Gross 

Margin Percentage Markup Scenario.................................................................. 12-23

Table 12.5.4 Product Class 2 Manufacturer Impact Analysis Under the Preservation of Gross 

Margin in Absolute Dollars Markup Scenario.................................................... 12-23

Table 12.5.5 Manufacturer Impact Analysis Under the Preservation of Gross Margin Percentage 

Markup Scenario................................................................................................. 12-23

Table 12.5.6 Manufacturer Impact Analysis Under the Preservation of Gross Margin in Absolute 

Dollars Markup Scenario for Product Class 1 and 2 Combined......................... 12-24

Table 12.6.1 Industry Estimated Employment Impacts in 2016 for Product Classes 1 and 2 

Combined............................................................................................................ 12-29

Table 12.6.2 Other DOE and Federal Actions Affecting Microwave Oven Manufacturers ... 12-33

LIST OF FIGURES

Figure 12.4.1 Using the GRIM to Calculate Cash Flow............................................................ 12-7

Figure 12.5.1 Annual Net Cash Flows for Product Class 1 (Preservation of Gross Margin 

Percentage Markup Scenario, RP Elasticity 0.34).............................................. 12-24

Figure 12.5.2 Annual Net Cash Flows Product Class 1 (Preservation of Gross Margin in 

Absolute Dollars Markup Scenario, RP Elasticity 0.34) .................................... 12-25

Figure 12.5.3 Annual Net Cash Flows Product Class 2 (Preservation of Gross Margin Percentage 

Markup Scenario, RP Elasticity 0.34)................................................................. 12-25

Figure 12.5.4 Annual Net Cash Flows Product Class 2 (Preservation of Gross Margin in 

Absolute Dollars Markup Scenario. RP Elasticity 0.34) .................................... 12-26

Figure 12.5.5 Annual Net Cash Flows for Product Class 1 and Product Class 2 Combined 

(Preservation of Gross Margin Percentage Markup Scenario, RP Elasticity 0.34) 12-

26

Figure 12.5.6 Annual Net Cash Flows for Product Class 1 and Product Class 2 Combined 

(Preservation of Gross Margin in Absolute Dollars Markup Scenario, RP Elasticity 

0.34) .................................................................................................................... 12-27

Figure 12.6.1 Total Microwave Ovens Industry Domestic Employment by Year for Product 

Classes 1 and 2 Combined .................................................................................. 12-30

12-1

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. 6295(o)(2)(B)(i)) 

The statute 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 energy conservation standards for standby 

mode and off mode power on manufacturers of microwave ovens and to assess the impact of 

such standards on 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), a discounted cash-flow 

model that is adapted to reflect the industry affected by 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), which is the sum of industry cash flows over 

the analysis period, discounted using the industry weighted average cost of capital. The model 

estimates the financial impact of energy conservation standards on each industry 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 

characterizing the microwave oven industry by collecting data on manufacturer market share, 

sales volumes, pricing, employment, and financial structure. In Phase II, “Framework for 

Analysis and Interview Guides,” DOE developed a framework GRIM for analysis of industry 

cash flows. Using publicly available information developed in Phase I, DOE adapted the 

GRIM’s generic structure to facilitate analysis of new standby power requirements for 

microwave oven manufacturers. For the purposes of this analysis, two product classes of 

microwave ovens were identified:

• Product Class 1: Microwave-Only Ovens and Countertop Combination Microwave 

Ovens

• Product Class 2: Built-In and Over-the-Range Combination Microwave Ovens.

In Phase III, “Manufacturer Interviews and Revised Industry Cash Flow Analysis,” DOE 

interviewed three microwave oven manufacturers with varying product focus and market shares,

providing a cross-section of the industry. During these interviews, DOE discussed financial 

topics specific to each manufacturer and obtained each manufacturer’s view of the industry as a 

whole. The interviews provided valuable information that DOE used to evaluate the impacts of 

new standby mode and off mode standards on manufacturers’ cash flows, investment 
12-2

requirements, and employment levels. The following subsections describe in detail the steps 

DOE took in developing the information on which it based the MIA.

12.2.1 Phase I: Industry Profile

In Phase I of the MIA, DOE prepared a profile of the microwave oven industry that built 

upon the market and technology assessment prepared for this rulemaking (see chapter 3 of this 

Technical Support Document (TSD)). Before initiating the detailed impact studies, DOE 

collected quantitative and qualitative financial information, as well as current and historical

market data. This included estimated market shares, corporate operating ratios, wages, 

employment, and financial ratios for several home appliance manufacturers. The industry profile 

included a top-down cost analysis of microwave oven manufacturers that DOE used to derive 

cost and preliminary financial inputs for the GRIM (e.g., revenues; material; labor; overhead; 

depreciation; selling, general and administrative expenses (SG&A); and research and 

development (R&D) expenses).

DOE also used public information to further calibrate its initial characterization of the 

microwave oven industry, including Securities and Exchange Commission (SEC) 10–K reports, 

Standard & Poor’s (S&P) credit ratings, corporate annual reports, and U.S. Census Bureau’s 

2010 Annual Survey of Manufactures (2010 ASM). In addition, DOE relied on information from 

its engineering analysis, life-cycle cost analysis, and analysis of markups to determine product

prices to characterize the industry. DOE also interviewed manufacturers of microwave ovens. 

These preliminary interviews were designed to solicit key concerns, insights into industry 

structure, and other relevant information to consider in subsequent analysis.

12.2.2 Phase II: Analysis Framework and Interview Guides

In Phase II, DOE developed a framework GRIM to analyze industry cash-flows and

prepared written guides to facilitate manufacturer interviews. Phase II focused on the financial 

impacts of new standby mode and off mode standards on the microwave oven industry.

12.2.2.1 Analysis Framework

The GRIM uses several factors to determine a series of annual cash flows starting with 

the announcement of the standard and extending over a 30 year period following the effective 

date of the standard. These factors include annual expected revenues; costs of sales; SG&A and 

R&D expenses; taxes; and capital expenditures. New standards can affect manufacturer cash 

flows in three distinct ways. They may: (1) require additional investment, (2) raise production 

costs, and (3) impact revenues through higher prices and possibly lower shipments. DOE 

developed a framework GRIM to analyze industry cash flows and assess the financial impacts of 

energy conservation standards on manufacturers of microwave ovens. The GRIM’s primary 

output is the INPV, which is the sum of industry annual cash flows, discounted at the industry 

weighted average cost of capital. The impact of new standby mode and off mode standards is the 

difference between the INPV in the base case and at each TSL represented in the standards cases.

Inputs to the GRIM include manufacturing costs, shipments forecasts, and price trends

developed in other analyses as well as markups, conversion costs, and industry financial 
12-3

parameters developed specifically for the MIA. For preliminary GRIM inputs, DOE estimated 

industry financial parameters and manufacturer markups from publicly available financial 

reports. DOE derived the manufacturing costs from the engineering analysis (see chapter 5 of 

this TSD), shipments from the shipments analysis (see chapter 9 of this TSD), and price trends 

from the Life-Cycle Cost analysis (chapter 8 of this TSD). Later, DOE revised these inputs based 

on feedback received during manufacturer interviews.

12.2.2.2 Interview Guide

To develop other key GRIM inputs including product and capital conversion costs, and to 

gather additional information on the anticipated effects of energy conservation standards on 

revenues, direct employment, capital assets, and industry competitiveness, DOE conducted

interviews with manufacturers of microwave ovens. Prior to the interviews, DOE distributed a

standard interview guide which included questions about current organizational characteristics, 

industry infrastructure, manufacturer cash-flows, as well as anticipated impacts to competition,

domestic manufacturing employment, and manufacturing capacity. The interview guide also 

served to help identify sub-groups of manufacturers and key issues faced by the industry as a 

result of new energy conservation standards. The interview guides used to conduct the 

manufacturer interviews are found in appendix 12A of this TSD.

12.2.3 Phase III: Manufacturer Interviews, Revised Industry Cash Flow Analysis, and 

Additional Analyses

In Phase III, DOE interviewed manufacturers representing approximately twenty percent

of microwave oven sales. Many of these same companies also participated in interviews for the 

engineering analysis. However, the MIA interviews broadened the discussion from primarily 

technology-related issues to include business-related topics. These interviews served to gather 

feedback from industry on the approaches used in the GRIM, to isolate key issues and concerns, 

and to inform additional analyses pertaining to manufacturer impacts. DOE revised the industry 

cash flow analysis based on information gained through these interviews. The following sections 

summarize the methodology and findings of this assessment.

12.2.3.1 Manufacturer Interviews

The information gathered in Phase I and Phase II is supplemented with insights gained 

through interviews with manufacturers during Phase III. The interview process plays a key role 

in the manufacturer impact analysis, since it provides an opportunity for interested parties to 
12-4

express their views privately on important issues, allowing confidential or sensitive information 

to be considered in the rulemaking process.

DOE prepared two different interview guides—one for microwave oven manufacturers

dealing with energy factor standards and one for microwave oven manufacturers regarding 

standby power requirements.

a

12.2.3.2 Revised Industry Cash Flow Analysis

DOE used information gained through these interviews to develop

additional inputs for the GRIM. DOE contacted companies from its database of manufacturers, 

and conducted detailed interviews with all manufacturers that agreed to participate. In the end, 

DOE interviewed three microwave oven manufacturers with varying product focus and market 

shares. Interviews were scheduled well in advance in order to provide every opportunity for key 

individuals to be available for comment. Although a written response to the questionnaire was

acceptable, DOE preferred an interactive interview process because it helped clarify responses 

and provide the opportunity to identify additional issues.

During interviews, DOE provided manufacturers with preliminary industry financial 

parameters and requested comment and suggestions regarding the proposed values. Information 

gained during manufacturer interviews also served as the basis for DOE’s estimates of both 

product and capital conversion costs. Upon completion of the interviews, DOE revised its 

industry cash flow models based on the feedback provided. More information on how DOE 

calculated the parameters for the GRIM is found in section 12.4.3 of this chapter.

12.2.3.3 Manufacturer Subgroup Analysis

Certain subgroups of manufacturers must be analyzed apart from the rest of the industry.

Manufacturers exhibiting a cost structure that differs significantly from the industry average

could be more negatively affected if average cost assumptions are used to develop an industry 

cash flow estimate. Ideally, DOE would consider the impact on every manufacturer individually; 

however, it typically uses the results of the industry characterization to group manufacturers 

exhibiting similar characteristics. During the interview process, DOE discussed the potential 

subgroups and subgroup members that have been identified for the analysis. DOE looked to the 

manufacturers and other interested parties to suggest what subgroups or characteristics are the 

most appropriate for the analysis. For this rulemaking, DOE did not identify any subgroups

which required a separate analysis of the impacts on INPV. 

 

a DOE published its final rule setting energy factor microwave oven standards on April 8, 2009. 

Please see: www.regulations.gov/#!documentDetail;D=EERE-2006-STD-0127-0004.
12-5

12.2.3.4 Small Business Manufacturer Subgroup

DOE used the small business size standards published on November 5, 2010, as 

amended, by the Small Business Administration (SBA) to determine whether any small entities 

would be required to comply with the rule (codified at 13 CFR Part 121). The size standards are 

listed by North American Industry Classification System (NAICS) code and industry description.

DOE used the SBA limits for NAICS code 335221 (Household Cooking Appliance 

Manufacturing) to determine if any small business manufacturers would be affected by this 

rulemaking. The SBA bases its small business definition on the total number of employees for a 

business, its subsidiaries, and its parent companies. Whenever an aggregated business entity has 

less than the listed number of employees, it is considered a small business. For NAICS code 

335221, the SBA size limit is a total of 750 employees. 

DOE surveyed the Association of Home Appliance Manufacturers (AHAM) member’s

directory to identify manufacturers of microwave oven products. DOE also asked stakeholders 

and AHAM representatives within the industry if they were aware of any small business 

manufacturers. DOE then consulted publicly available data, purchased company reports from 

vendors such as Dun and Bradstreet, and contacted manufacturers, as needed, to determine if 

they meet the SBA’s definition of a small business manufacturing facility and have their 

manufacturing facilities located within the United States. Based on this analysis, DOE did not 

identify any small business manufacturers of microwave ovens. 

12.2.3.5 Manufacturing Capacity Impact

One of the significant outcomes of energy conservation standards could be the 

consequential obsolescence of existing manufacturing assets, including tooling and investment.

The manufacturer interview guide has a series of questions to help identify impacts on 

manufacturing capacity, specifically capacity utilization and plant location decisions in the 

United States and North America with and without a standard; the ability of manufacturers to 

upgrade or remodel existing facilities to accommodate the new requirements due to energy 

conservation standards; the nature and value of stranded assets, if any; and estimates for any onetime restructuring and other charges, where applicable.

12.2.3.6 Employment Impact

The impact of energy conservation standards on employment is an important 

consideration in the rulemaking process. To assess how domestic employment patterns might be 

affected, the interviews explored current employment trends in the microwave oven

manufacturing industry. In addition, the interviews solicited manufacturer views on changes in 

employment patterns that may result from more stringent standard levels. The employment 

impacts section of the interview guide focused on current employment levels associated with 

manufacturers at each of their production facilities, expected future employment levels with and 

without energy conservation standards for standby mode and off mode, and differences in 

workforce skills and issues related to the retraining of employees.
12-6

12.2.3.7 Cumulative Regulatory Burden

DOE recognizes and seeks to mitigate the overlapping effects on manufacturers of energy 

conservation standards and other Federal regulatory actions affecting the same products. DOE 

analyzed and considered the impact on manufacturers of multiple, product-specific regulatory 

actions. Based on its own research and discussions with manufacturers, DOE identified several

regulations relevant to microwave oven manufacturers including Federal energy conservation 

standards for other products manufactured by these manufacturers and other environmental 

regulations.

12.3 MANUFACTURER IMPACT ANALYSIS KEY ISSUES

Each MIA starts by asking: “What are the key issues for your company regarding this 

energy conservation standards rulemaking?” This question opens up dialogue with 

manufacturers, enabling them to identify the issues that they feel DOE should explore and 

discuss further during the interview. The following section describes key issues mentioned for all 

product classes under review. Manufacturers indicated that, for the most part, the risks associated 

with these issues increase with more stringent TSLs. 

12.3.1 Consumer Utility

All manufacturers of microwave ovens expressed concerns that a new standby power 

requirement could reduce consumer utility. For example, one manufacturer expressed the 

position that because all cooking sensors require standby power, depending on the level chosen 

by DOE, a new standby power requirement could lead to the elimination of cooking sensors from 

its products. However, multiple manufacturers stated that they already use or are in the process 

of adopting cooking sensors that require no standby power. Another potential impact on 

consumer utility related to cooking sensors mentioned by manufacturers was waiting for sensors

to activate or a hard off switch causing microwave ovens to have a delayed start. 

On a related note, manufacturers stated that low standby power requirements could 

impact display and control choices, which also impact consumer utility. Manufacturers indicated 

that some other features that provide consumer utility (i.e., a clock or nightlight) could also be 

impacted by standby mode and off mode standards since these features consume standby power. 

12.3.2 Profitability

Manufacturers of microwave ovens stated that standby mode and off mode standards 

have the potential to hurt profitability. Manufacturers typically earn a premium on higher-end 

models, while low-end models are commodity products. The premium on high-end products 

would be squeezed if standards eliminated product differentiators (such as larger, premium

displays) or forced high-end features to lower end models (such as cooking sensors). 

Manufacturers indicated that standards have the potential to eliminate remaining U.S. production 

due to already tight margins, especially if standards impact consumer utility. 
12-7

12.4 GRIM INPUTS AND ASSUMPTIONS

The GRIM serves as the main tool for assessing the impact on industry due standby mode 

and off mode energy conservation standards. DOE relies on several sources to obtain inputs for 

the GRIM. Data and assumptions from these sources are then fed into an accounting model that 

details the industry cash flow both with and without new 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 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 a series of annual cash 

flows, beginning with the base year of the analysis, 2013, and continuing explicitly through 

2045. The model calculates the INPV by summing the stream of annual discounted cash flows 

during this period and adding a terminal value.

1

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 scenario and each of the standards-case scenarios, 

evaluating changes induced by the energy conservation standards. The difference in INPV 

between the base case and the standards cases represents the estimated financial impact of the

energy conservation standard on manufacturers. Appendix 12B provides more technical details

and user information for the GRIM.

12.4.2 Sources of GRIM Inputs

The GRIM uses several different sources for data inputs in determining the cash flows for 

the industry, including corporate annual reports, company profiles, Census data, credit ratings, 

the shipments model, the engineering analysis, and manufacturer interviews.
12-8

12.4.2.1 Corporate Annual Reports

Corporate annual reports to the SEC (10-Ks) provided many of the financial inputs to the 

GRIM.2 These reports exist for publicly held companies and are freely available to the general 

public. DOE developed average financial inputs to the GRIM by examining the annual SEC 10-

K reports filed by publicly-traded manufacturers primarily engaged in appliance manufacturing 

and whose combined product range includes microwave ovens. Because these companies are 

typically diversified, producing a range of different appliances, an industry average was assumed 

by DOE to be representative for the manufacture of each type of appliance. In determining 

financial parameters for the industry, DOE weighted corporate financial information contained in 

the SEC 10-Ks by each company’s estimated market share to arrive at industry-weighted

averages. DOE used corporate annual reports to derive the following GRIM inputs:

• Tax rate,

• Working capital,

• SG&A,

• R&D expenses,

• Depreciation,

• Capital expenditures, and

• Net property, plant, and equipment (PPE).

DOE also used information from company SEC 10-K reports to calibrate the GRIM’s 

operating profit margin against the industry weighted average.

12.4.2.2 Standard and Poor Credit Ratings

S&P provides independent credit ratings, research, and financial information. S&P 

reports are available for a nominal fee. DOE relied on S&P reports and similar sources to 

determine the industry’s average cost of debt for the cost of capital calculation.

12.4.2.3 Dun and Bradstreet Reports

Dun and Bradstreet (D&B) provides independent research regarding company cash 

flows, revenues, employees, and credit-worthiness. Besides conducting manufacturer interviews, 

DOE used D&B reports to profile several manufacturers which had the potential to be small 

businesses. 

12.4.2.4 Shipments Model

The GRIM used shipment projections derived from DOE’s shipments model in the 

national impact analysis. The model relied on historical shipment data for microwave ovens from 

AHAM. Chapter 9 of this TSD describes the methodology and analytical model DOE used to 

forecast shipments.
12-9

12.4.2.5 Engineering Analysis

During the engineering analysis, DOE used data submitted from AHAM, manufacturer 

interviews, as well as product testing and teardowns to develop manufacturing cost estimates for 

Product Class 1 (Microwave-Only Ovens and Countertop Combination Microwave Ovens) and 

Product Class 2 (Built-In and Over-the-Range Combination Microwave Ovens).

12.4.2.6 Manufacturer Interviews

During the course of the MIA, DOE conducted interviews with manufacturers of 

microwave ovens. Through these discussions, DOE obtained information, which it used to 

determine and verify GRIM input assumptions for each industry. Key topics discussed during the 

interviews and reflected in the GRIM include:

• Capital conversion costs (one-time investments in property, plant, and equipment);

• Product conversion costs (one-time investments in research, product development, 

testing, and marketing);

• Stranded assets (write downs of un-depreciated manufacturing capital rendered 

obsolete by the rulemaking);

• Manufacturer production costs (as estimated in the engineering analysis);

• Product cost structure (the allocation of manufacturer production costs related to 

materials, labor, overhead, and depreciation; and

• Projected total shipments and the distribution of shipments across the various 

efficiency levels.

12.4.3 Financial Parameters

Table 12.4.1 provides financial parameters for six public companies engaged in 

manufacturing and selling microwave ovens. The values listed are averages over a seven-year 

period (2005–2011).
12-10

Table 12.4.1 GRIM Financial Parameters Based on 2005–2011 Weighted Company 

Financial Data

Parameter

IndustryWeighted 

Average

Manufacturer

A B C D E F

Tax Rate

(% of Taxable Income) 28.2 41.1 35.2 16.4 20.5 45.1 24.2

Working Capital

(% of Revenue) 6.9 9.8 11.7 13.7 1.9 7.0 3.4

SG&A

(% of Revenue) 15.4 18.6 8.1 19.4 17.5 10.8 7.5

R&D

(% of Revenues) 1.2 1.6 1.1 1.5 0.9 1.6 0.6

Depreciation

(% of Revenues) 4.7 3.4 2.2 7.8 5.1 3.6 3.1

Capital Expenditures

(% of Revenues) 4.8 4.8 1.6 12.2 2.8 4.2 2.2

Net Property, Plant, and 

Equipment

(% of Revenues)

27.3 20.4 23.8 35.6 24.4 34.2 17.0

These companies constitute approximately 77 percent of the microwave oven industry.

For other companies in the industry, public data was not available and hence not used to

calculate the parameters. The values used in the GRIM, therefore, represent the industry’s 

weighted average estimates using financial data from six publicly traded manufacturers. During 

manufacturer interviews, microwave ovens manufacturers were asked to review the parameters 

listed in Table 12.4.1 and comment on how accurately they reflect the microwave oven 

manufacturing industry. Where applicable, the parameters were then adjusted for the GRIM.

12.4.4 Corporate Discount Rate

DOE used the weighted-average cost of capital (WACC) for the industry 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 cost of debt and cost of equity weighted by their respective proportions in the 

capital structure of the industry. DOE estimated the WACC for the microwave oven industry

based on several representative companies, using the following formula:

WACC = (After-Tax Cost of Debt x Debt Ratio) + (Cost of Equity x Equity Ratio)

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 = Risk Free Rate of Return + (β x Risk Premium)
12-11

where:

Risk Free Rate of Return is the rate of return on a “safe” benchmark investment, typically 

considered the short-term Treasury bill (T-Bill) yield.

Risk Premium is the difference between the expected return on stocks and the riskless rate.

Beta (β) is 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 microwave oven industry is 13.6

percent as calculated in Table 12.4.2.

Table 12.4.2 Cost of Equity Calculation

Parameter

IndustryWeighted 

Average

Manufacturer

A B C D E F

(1) Average Beta 1.39 0.89 1.26* 2.63* 1.03 1.32 1.67

(2) Yield on 10 Year TBill (1928–2011) (%) 5.20

(3) Market Risk 

Premium (1928–2011)

(%)

6.01

Cost of Equity (2) +

[(1)*(3)] (%) 13.55

Equity/Total Capital (%) 71.86 71.71 28.96 96.51 75.54 65.19 70.77

* Estimated Beta

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 all six manufactures. DOE used bond ratings from S&P 

or a similar source to estimate the manufacturers’ cost of debt by 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 a long-term Treasury bond return (10-year 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 5.20 percent, which is the average 10-year Treasury bond return 

over the period of 1927 to 2011.
12-12

In order to calculate the cost of debt for the industry, DOE added the industry-weighted 

average spread to the average T-Bill yield over the same period. 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 12.4.3 presents the derivation of the cost 

of debt. Also shown is the capital structure of the industry, i.e. the debt ratio (debt/total capital).

Table 12.4.3 Cost of Debt Calculation

Parameter

IndustryWeighted 

Average

Manufacturer

A B C D E F

S&P Bond Rating -- A- BBB A BBB BB+ BBB

(1) Yield on 10 year TBill (1927–2011) (%) 5.20%

(2) Gross Cost of Debt 

(%) 6.88% 6.30% 6.80% 6.20% 6.80% 8.20% 6.80%

(3) Tax Rate (%) 28.24% 41.06

%

35.22

%

16.40

%

20.54

%

45.10

%

24.22

%

Net Cost of Debt (2)x(1-

(3)) (%) 4.94%

Debt/Total Capital (%) 28.14% 28.29

%

71.04

% 3.49% 24.46%

34.81

%

29.23

%

DOE estimated the microwave oven industry weighted average WACC to be 

approximately 11.1 percent. Subtracting an inflation rate of 3.1 percent between 1927 and 2011, 

the inflation adjusted WACC, and the corporate discount rate used in the GRIM, is 8.0 percent.

12.4.5 Trial Standard Levels

DOE developed TSLs for microwave oven standby mode and off mode energy 

conservation standards. The efficiency levels used in the GRIM and other analyses are presented 

in Table 12.4.4 and Table 12.4.5.

Table 12.4.4 Product Class 1 Standby Power Efficiency Levels and TSLs

Product Class 1 Watts Description

Baseline 4.00 Baseline

TSL 1 2.00

FEMP Procurement Efficiency 

Recommendation

TSL 2 1.50 Gap Fill

TSL 3 1.00 IEA 1-Watt Program

TSL 4 0.02 Max Tech
12-13

Table 12.4.5 Product Class 2 Standby Power Efficiency Levels and TSLs

Product Class 2 Watts Description

Baseline 4.50 Baseline

TSL 1 3.70

FEMP Procurement Efficiency 

Recommendation

TSL 2 2.70 Gap Fill

TSL 3 2.20 IEA 1-Watt Program

TSL 4 0.04 Max Tech

12.4.6 NIA-Shipments Forecast

The GRIM estimates manufacturer revenues based on total-unit-shipment forecasts and 

the distribution of these shipments by efficiency level. Changes in the efficiency mix at each 

standard level are a key driver of manufacturer finances. For this analysis, the GRIM used the 

national impact analysis (NIA) shipments forecasts for microwave ovens from 2013 to 2045.

Only the shipments in 2013 and beyond have an impact on INPV because 2013 is the base year 

to which future cash flows are discounted. Further explanation of approaches and calculations of 

total shipments can be found in the shipment analysis section of this TSD (chapter 9). Total

shipments forecasted in the shipment analysis for all efficiency levels in 2016, the year new 

standards for microwave oven standby and off mode power consumption would become 

effective are shown in Table 12.4.6 and are further detailed below.

Table 12.4.6 Total NIA-Shipments Forecast in 2016

Product Class Total Industry Shipments*

Product Class 1 14,267,710

Product Class 2 144,118

* Estimates are rounded to the nearest whole number

12.4.6.1 Base-Case Shipments Forecast

As part of the shipment analysis, DOE estimated the shipment distribution by efficiency level 

for Product Class 1 and Product Class 2. Table 12.4.7 and Table 12.4.8 show the base-case 

distributions of shipments by TSL estimated in the NIA for various product classes for 2016.

Table 12.4.7 Base-Case Standby Power Distribution of Efficiencies for Product Class 1 in 

2016

Efficiency Level Baselin

e

EL 1 EL 2 EL 3 EL 4

Standby Power 

(W) 4.00 2.00 1.50 1.00 0.02

% of Shipments 46.2% 34.6% 19.2% 0.0% 0.0%
12-14

Table 12.4.8 Base-Case Standby Power Distribution of Efficiencies for Product Class 2 in 

2016

Efficiency Level Baselin

e

EL 1 EL 2 EL 3 EL 4

Standby Power 

(W) 4.50 3.70 2.70 2.20 0.04

% of Shipments 100.0% 0.0% 0.0% 0.0% 0.0%

12.4.6.2 Standards-Case Shipments Forecast

To examine the effects of energy conservation standards on shipments, which affect the 

INPV, DOE used the base-case shipments described in the previous section. For the standards 

case, DOE assumed shipments at lower efficiencies would roll up to the minimally compliant 

efficiency levels in response to more stringent energy conservation standards. This scenario 

assumes that demand for high efficiency equipment is a function of its price without regards for 

the standard level. Table 12.4.9 and Table 12.4.10 show the distributions of efficiencies for the 

various equipment classes in 2016 under the roll-up scenario.

Table 12.4.9 Distribution of Standby Power Product Class 1 Shipments in the Standards 

Case in 2016 – Rollup Scenario

Efficiency 

Level Baseline EL 1 EL 2 EL 3 EL 4

Standby Power 

(W) 4.00 2.00 1.50 1.00 0.02

Baseline 46.2% 34.6% 19.2% 0.0% 0.0%

TSL 1 80.8% 19.2% 0.0% 0.0%

TSL 2 100.0% 0.0% 0.0%

TSL 3 100.0% 0.0%

TSL 4 100.0%
12-15

Table 12.4.10 Distribution of Standby Power Product Class 2 Shipments in the Standards 

Case in 2016 – Rollup Scenario

Efficiency 

Level Baseline EL 1 EL 2 EL 3 EL 4

Standby Power 

(W) 4.50 3.70 2.70 2.20 0.04

Baseline 100.0% 0.0% 0.0% 0.0% 0.0%

TSL 1 100.0% 0.0% 0.0% 0.0%

TSL 2 100.0% 0.0% 0.0%

TSL 3 100.0% 0.0%

TSL 4 100.0%

12.4.6.3 Price Elasticity of Demand Shipments Scenario

In the GRIM, DOE modeled a shipment scenario that considers the impacts of changes in

relative prices on consumer demand for each product. As described in chapter 9 of this TSD, this 

shipment scenario reflects how the combined effects of increases in purchase price and decreases 

in operating costs due to energy conservation standards may affect shipments. DOE calculated 

the relative price elasticity of demand over time to determine how shipments would likely 

change following standards. Under the “price elasticity scenario,” the effects from the increase in 

product purchase prices offset the effects from decreased operating costs, resulting in a net 

decrease in shipments. For the INPV impacts described in this chapter, DOE uses the shipments 

scenario that includes the effects of price elasticity, though results using the shipments scenario 

without the relative price elasticity can also be generated in the GRIM. See chapter 9 of this TSD 

for more details on the different shipment scenarios considered. 

12.4.6.4 Economic Growth Shipments Scenario

In the GRIM, DOE modeled three shipment scenarios that consider the impacts of 

changes in the overall U.S. economy. These shipment scenarios are based on the Energy 

Information Administration’s (EIA) 2012 Annual Energy Outlook Report (AEO2012).3

AEO2012 provides three scenarios: the Reference case, the High Economic Growth case, and the 

Low Economic Growth case. DOE used the Reference case for economic growth when 

generating the results (impact on INPV) that are described in this chapter. However, the user still 

has the option to examine other economic growth scenarios and their impact on INPV in the 

GRIM. Further, it is also possible for the user to combine the scenarios with and without price 

elasticity with each economic scenario in the GRIM. See chapter 9 of this TSD for more details 

on the economic growth shipment scenarios.
12-16

12.4.7 Production Costs

Changes in production costs impact revenues and gross profits. As shown in the 

engineering analysis (chapter 5 of this TSD), products that are more efficient typically cost more 

to produce than baseline products. For the MIA, DOE used the manufacturing production costs

(MPCs) derived in the engineering analysis, with appropriate production volume estimates. For 

instance, more efficient products sold under existing energy conservation standards are 

manufactured at lower production volumes than products at the baseline for standby mode power 

consumption. Enacting more stringent energy conservation standards will increase production 

volumes for more efficient units.

As described in chapter 5 of this TSD, the costs for Product Class 1 and Product Class 2 

were calculated using data supplied by AHAM and product testing and teardowns. For this final 

rule, DOE updated the MPCs it estimated for the supplemental notice of proposed rulemaking 

(SNOPR) published on February 14, 2012 (77 FR 8526) (hereinafter referred to as the February

2012 SNOPR), to 2011$ using the Producer Price Index (PPI). For the GRIM, DOE estimated 

the proportion of costs associated with materials, labor, overhead, and depreciation using 

information provided by AHAM4 , the U.S. Census Bureau’s Current Industry Reports (CIR)

5

and the 2010 ASM. Table 12.4.11 and Table 12.4.12 provide the MPC results used in the GRIM 

for microwave ovens.

Table 12.4.11 Base-Case Standby Power Production Costs (2011$) used in the GRIM for 

Product Class 1

TSL (W) Labor Material Overhead Depreciatio

n

MPC 

Baseline 

(4.00) $10.63 $101.28 $1.08 $7.36 $120.35

TSL 1 (2.00) $10.63 $101.54 $1.08 $7.36 $120.61

TSL 2 (1.50) $10.63 $101.66 $1.08 $7.36 $120.73

TSL 3 (1.00) $10.63 $104.56 $1.08 $7.36 $123.63

TSL 4 (0.02) $10.63 $107.51 $1.08 $7.36 $126.58

Table 12.4.12 Base-Case Standby Power Production Costs (2011$) used in the GRIM for 

Product Class 2

TSL (W) Labor Material Overhead Depreciation MPC

Baseline 

(4.50) $23.00 $219.15 $2.33 $15.93 $260.41

TSL 1 (3.70) $23.00 $219.21 $2.33 $15.93 $260.47

TSL 2 (2.70) $23.00 $219.23 $2.33 $15.93 $260.49

TSL 3 (2.20) $23.00 $224.16 $2.33 $15.93 $265.42

TSL 4 (0.04) $23.00 $225.02 $2.33 $15.93 $266.27
12-17

12.4.8 Conversion Costs

Energy conservation standards typically cause manufacturers to incur one-time

conversion costs to bring their production facilities and product designs into compliance with 

new regulations. For the purpose of the MIA, DOE classified these one-time conversion costs 

into two major groups: (1) capital conversion costs, and (2) product conversion costs. Capital 

conversion costs are one-time investments in property, plant, and equipment to adapt or replace 

existing production facilities so that new product designs that meet the new standard can be 

fabricated and assembled. Product conversion costs are one-time investments in research, 

development, testing, and marketing focused on making product designs comply with new 

energy conservation standards. The following sections describe in greater detail the inputs DOE

used in the GRIM.

12.4.8.1 Capital Conversion Costs

DOE evaluated the level of capital investment needed to comply with the energy 

conservation standards for standby power consumption at each TSL. DOE estimated the portion 

of products on the market that will have to be redesigned to meet each TSL. The portion of 

products on the market requiring redesign increases with each higher TSL.

DOE used a platform approach to estimate the capital conversion costs. For standby 

mode and off mode standards, DOE used information submitted during manufacturer interviews, 

conversations with industry experts, and product catalogs to estimate the percentage of platforms 

at each TSL that would require minor tooling changes to accommodate more efficient 

components, as well as the cost of the tooling. DOE scaled the per platform conversion costs 

presented by individual manufacturers to account for all necessary conversion costs across the 

industry.

Because the design options for Product Class 1 and Product Class 2 are similar at each 

TSL, DOE used the same per-platform costs for both product classes. DOE also used the same 

per platform cost as in the February 2012 SNOPR, but converted each cost to 2011$ using the 

PPI. To segment the total capital conversion costs between Product Class 1 and Product Class 2, 

DOE used the same split as the shipments in the NIA. 

Substituting components to reduce standby power does not require major changes to 

microwave enclosures or cavities because these design options all have standard mounting 

features and locations. Table 12.4.13 and Table 12.4.14 summarize the breakdown of capital 

conversion costs by TSL for energy conservation standards for standby mode and off mode. 

Table 12.4.15 shows the per-platform capital cost assumptions DOE made for the tooling 

changes required for each platform that did not meet the required efficiency. 
12-18

Table 12.4.13 Standby Power Capital Conversion Costs for Product Class 1 by TSL

TSL (W) Total Industry Capital Conversion Costs (Millions 

2011$)

Baseline (4.00) 0.0

TSL 1 (2.00) 3.9

TSL 2 (1.50) 4.3

TSL 3 (1.00) 4.7

TSL 4 (0.02) 7.8

Table 12.4.14 Standby Power Capital Conversion Costs for Product Class 2 by TSL

TSL (W) Total Industry Capital Conversion Costs (Millions 

2011$)

Baseline (4.50) 0.0

TSL 1 (3.70) 0.0*

TSL 2 (2.70) 0.0*

TSL 3 (2.20) 0.0*

TSL 4 (0.04) 0.1

*Conversion Costs are less than $50,000.

Table 12.4.15 Per Platform Assumptions for Capital Conversion Costs (Product Class 1 

and Product Class 2)

TSL Per-Platform Tooling Capital Cost Assumption (2011$)

TSL 1 $158,058

TSL 2 $173,864

TSL 3 $189,669

TSL 4 $316,116

12.4.8.2 Product Conversion Costs

DOE conducted interviews with manufacturers to better understand the many steps that 

manufacturers have to take before they can release new or improved products. Multiple 

manufacturers estimated that the time and resources spent on research and development, testing, 

and certification of products would be significant depending on the design path chosen to 

achieve higher efficiency. The magnitude of the required investments varies by manufacturer and 

the TSL analyzed.

For Product Class 1 and Product Class 2, DOE estimated the engineering, reliability 

testing, and product development expenses per platform necessary at each TSL. DOE used the 

same per-platform cost as in the February 2012 SNOPR, but converted these product and capital 

conversion costs to 2011$ using the PPI. To segment the total product conversion costs between 

Product Class 1 and Product Class 2, DOE used the same split as the shipments in the NIA. DOE 

multiplied the estimate of these development costs per platform by its approximation of the 

number of platforms available in the United States that would require changes to meet the 

efficiency standards at each TSL.
12-19

For standby power, conversion costs become more expensive at higher TSLs as the 

complexity of further lowering standby power increases, substantially driving up engineering and 

product testing time. There is a large jump in conversion expenses at TSL 4 because the design 

options included at this efficiency level impact other functions of every platform. Table 12.4.16

and Table 12.4.17 summarize the breakdown of product conversion costs by TSL for PC 1 and 

PC 2 for standby mode and off mode standards. Table 12.4.18 and Table 12.4.19 show the perplatform product conversion cost assumptions DOE made for each platform that did not meet the 

required efficiency. 

Table 12.4.16 Standby Power Product Conversion Costs for Product Class 1 by TSL

TSL (W) Total Industry Product Conversion Costs (Millions 

2011$)

Baseline (4.00) $0.0

TSL 1 (2.00) $16.7

TSL 2 (1.50) $30.0

TSL 3 (1.00) $38.0

TSL 4 (0.02) $73.4

Table 12.4.17 Standby Power Product Conversion Costs for Product Class 2 by TSL

TSL (W) Total Industry Product Conversion Costs (Millions 

2011$)

Baseline (4.50) $0.0

TSL 1 (3.70) $0.2

TSL 2 (2.70) $0.3

TSL 3 (2.20) $0.4

TSL 4 (0.04) $0.7

Table 12.4.18 Per-Platform Assumptions for Product Conversion Costs (Product Class 1) 

TSL (W)

Per-Platform 

Engineering 

Cost 

Assumption

(2011$)

Per-Platform 

Reliability Cost 

Assumption

(2011$)

Per-Platform 

Product 

Development 

Expenses 

Assumption

(2011$)

Extraordinary 

Marketing 

Expenses per 

Platform

(2011$)

TSL 1 (2.00) $52,686 $52,686 $26,343 $0

TSL 2 (1.50) $105,372 $105,372 $26,343 $0

TSL 3 (1.00) $158,058 $105,372 $26,343 $9,879

TSL 4 (0.02) $210,744 $210,744 $52,686 $105,372
12-20

Table 12.4.19 Per Platform Assumptions for Product Conversion Costs (Product Class 2) 

TSL (W)

Per-Platform 

Engineering 

Cost 

Assumption 

(2011$)

Per-Platform 

Reliability Cost 

Assumption 

(2011$)

Per-Platform 

Product 

Development 

Expenses 

Assumption 

(2011$)

Extraordinary 

Marketing 

Expenses per 

Platform 

(2011$)

TSL 1 (3.70) $52,686 $52,686 $26,343 $0

TSL 2 (2.70) $105,372 $105,372 $26,343 $0

TSL 3 (2.20) $158,058 $105,372 $26,343 $0

TSL 4 (0.04) $210,744 $210,744 $52,686 $105,372

12.4.9 Markups

To understand how baseline and more efficient products are differentiated, DOE 

reviewed manufacturer catalogs and utilized information gathered from manufacturers. To 

estimate the prices at which manufacturers sell their products, DOE applied markups to the 

production costs it had developed earlier. DOE used markup scenarios to set the range of 

expected product prices following energy conservation standards. For each product class, DOE

used the markup scenarios that best characterized the prevailing markups and described the range 

of market responses as a result of energy conservation standards for standby mode and off mode.

DOE analyzed two distinct markup scenarios: (1) preservation of gross margin percentage, and

(2) preservation of gross margin in absolute dollars.

12.4.9.1 Preservation of Gross Margin Percentage

The gross margin is defined as revenue less the cost of goods sold. Under the 

preservation of gross margin percentage scenario in the GRIM, DOE applied a single uniform 

“gross margin percentage” markup to MPCs across all efficiency levels. As production costs

increase by efficiency level, this scenario implies that the absolute dollar markup will also 

increase. DOE used a manufacturer markup of 1.298, which covers SG&A expenses, R&D 

expenses, interest, and profit. This markup is consistent with the manufacturer markup DOE 

assumed in the base case for the GRIM. Most manufacturers stated it is optimistic to assume that 

they could maintain their gross margin percentages despite production cost increases due to new 

standby mode and off mode standards. Given this feedback, DOE assumes this scenario 

represents a high bound to industry profitability under an energy conservation standard.

12.4.9.2 Preservation of Gross Margin in Absolute Dollars

The implicit assumption behind the “preservation of gross margin in absolute dollars” 

markup scenario is that in response to the standards the industry will lower its selling price of 

minimally compliant products to maintain a price competitive product offering. This lower MSP

relative to the MPC translates into a lower markup as the gross margin percentage will be lower.

The industry would do so by passing through part of the increase in production costs while 

increased R&D and SG&A reduce profits. DOE implemented this scenario in the GRIM by 

calibrating the markups for each TSL to yield the same gross margin in dollars in the year 
12-21

standards become effective in both the base case and standards cases. Table 12.4.20 and Table 

12.4.21 show the manufacturer markups for Product Class 1 and Product Class 2 under the 

Preservation of Gross Margin in Absolute Dollars markup scenario. 

Table 12.4.20 Product Class 1 Preservation of Gross Margin in Absolute Dollars 

Manufacturer Markups 

TSL (W) Baseline (4.00 

W)

EL 1 (2.00 

W)

EL 2 (1.50 

W)

EL 3 (1.00 

W)

EL 4 (0.02 

W)

Baseline 

(4.00) 1.2980 1.2980 1.2980 1.2980 1.2980

TSL 1 

(2.00) - 1.2977 1.2980 1.2980 1.2980

TSL 2 

(1.50) - - 1.2976 1.2980 1.2980

TSL 3 

(1.00) - - - 1.2926 1.2980

TSL 4 

(0.02) - - - - 1.2878

Table 12.4.21 Product Class 2 Preservation of Gross Margin in Absolute Dollars 

Manufacturer Markups

TSL (W) Baseline (4.50 

W)

EL 1 (3.70 

W)

EL 2 (2.70 

W)

EL 3 (2.20 

W)

EL 4 (0.04 

W)

Baseline 

(4.50) 1.2980 1.2980 1.2980 1.2980 1.2980

TSL 1 

(3.70) - 1.2980 1.2980 1.2980 1.2980

TSL 2 

(2.70) - - 1.2979 1.2980 1.2980

TSL 3 

(2.20) - - - 1.2940 1.2980

TSL 4 

(0.04) - - - - 1.2933

12.5 MICROWAVE OVEN INDUSTRY FINANCIAL IMPACTS

Using the inputs and assumptions described in the previous sections, the GRIM modeled 

the financial impacts of new standby and off mode power consumption standards on the 

microwave oven industry. The following sections detail additional inputs and assumptions for 

microwave ovens. The main results of the MIA are also reported in this section. The MIA results 

consist of two key financial metrics: INPV and annual cash flows.
12-22

12.5.1 Impacts on Industry Net Present Value

The INPV measures the industry value and is used in the MIA to compare the economic 

impacts of different TSLs. For the microwave oven industry, the GRIM estimated cash flows 

between 2013 and 2045, consistent with the forecast period used in the national impact analysis 

(chapter 10 of this TSD).

In the MIA, DOE compares the INPV of the base case (no new standby mode and off 

mode standards) to that at each TSL. The difference between the base-case INPV and a standards 

case INPV is an estimate of the economic impacts that implementing that particular TSL would 

have on the entire industry. For the microwave oven industry, DOE examined the two markup 

scenarios described in the manufacturer markup section above. Table 12.5.1 through Table 

12.5.6 provide the INPV estimates for the microwave oven industry under the different 

scenarios.

Table 12.5.1 Product Class 1 Manufacturer Impact Analysis Under the Preservation of 

Gross Margin Percentage Markup Scenario 

INPV Change in INPV Change in INPV

Units Millions 2011$ Millions 2011$ %

Base Case 1,356.8 - -

TSL 1 1,341.9 -14.9 -1.1

TSL 2 1,332.5 -24.3 -1.8

TSL 3 1,317.3 -39.5 -2.9

TSL 4 1,281.4 -75.4 -5.6

Parentheses indicate negative (-) values.

Table 12.5.2 Product Class 1 Manufacturer Impact Analysis Under the Preservation of 

Gross Margin in Absolute Dollars Markup Scenario

INPV Change in INPV Change in INPV

Units Millions 2011$ Millions 2011$ %

Base Case 1,356.8 - -

TSL 1 1,339.7 -17.1 -1.3

TSL 2 1,328.6 -28.2 -2.1

TSL 3 1,261.6 -95.2 -7.0

TSL 4 1,174.0 -182.8 -13.5

Parentheses indicate negative (-) values.
12-23

Table 12.5.3 Product Class 2 Manufacturer Impact Analysis Under the Preservation of 

Gross Margin Percentage Markup Scenario

INPV Change in INPV Change in INPV

Units Millions 2011$ Millions 2011$ %

Base Case 29.7 - -

TSL 1 29.5 -0.1 -0.5

TSL 2 29.4 -0.2 -0.8

TSL 3 29.2 -0.5 -1.5

TSL 4 28.9 -0.8 -2.5

Parentheses indicate negative (-) values.

Table 12.5.4 Product Class 2 Manufacturer Impact Analysis Under the Preservation of 

Gross Margin in Absolute Dollars Markup Scenario

INPV Change in INPV Change in INPV

Units Millions 2011$ Millions 2011$ %

Base Case 29.7 - -

TSL 1 29.5 -0.2 -0.5

TSL 2 29.4 -0.3 -0.9

TSL 3 28.3 -1.4 -4.6

TSL 4 27.8 -1.8 -6.1

Parentheses indicate negative (-) values.

Table 12.5.5 Manufacturer Impact Analysis Under the Preservation of Gross Margin 

Percentage Markup Scenario

INPV Change in INPV Change in INPV

Units Millions 2011$ Millions 2011$ %

Base Case 1,386.5 - -

TSL 1 1,371.4 -15.1 -1.1

TSL 2 1,361.9 -24.6 -1.8

TSL 3 1,346.5 -40.0 -2.9

TSL 4 1,310.3 -76.1 -5.5

Parentheses indicate negative (-) values.
12-24

Table 12.5.6 Manufacturer Impact Analysis Under the Preservation of Gross Margin in 

Absolute Dollars Markup Scenario for Product Class 1 and 2 Combined

INPV Change in INPV Change in INPV

Units Millions 2011$ Millions 2011$ %

Base Case 1,386.5 - -

TSL 1 1,369.2 -17.3 -1.2

TSL 2 1,358.0 -28.5 -2.1

TSL 3 1,289.9 -96.6 -7.0

TSL 4 1,201.9 -184.6 -13.3

Parentheses indicate negative (-) values.

12.5.2 Impacts on Annual Cash Flow

DOE reports the annual free cash flows from 2013 through 2045 for each TSL. Figure 

12.5.1 through Figure 12.5.6 present the annual net cash flows for the base case and each of the 

four standby mode and off mode TSLs for the microwave oven industry for a period of fifteen 

years starting from the announcement of the standard, assuming the different markup scenarios.

Figure 12.5.1 Annual Net Cash Flows for Product Class 1 (Preservation of Gross Margin 

Percentage Markup Scenario, RP Elasticity 0.34)
12-25

Figure 12.5.2 Annual Net Cash Flows Product Class 1 (Preservation of Gross Margin in 

Absolute Dollars Markup Scenario, RP Elasticity 0.34)

Figure 12.5.3 Annual Net Cash Flows Product Class 2 (Preservation of Gross Margin 

Percentage Markup Scenario, RP Elasticity 0.34)
12-26

Figure 12.5.4 Annual Net Cash Flows Product Class 2 (Preservation of Gross Margin in 

Absolute Dollars Markup Scenario. RP Elasticity 0.34)

Figure 12.5.5 Annual Net Cash Flows for Product Class 1 and Product Class 2 Combined 

(Preservation of Gross Margin Percentage Markup Scenario, RP Elasticity 0.34)
12-27

Figure 12.5.6 Annual Net Cash Flows for Product Class 1 and Product Class 2 Combined 

(Preservation of Gross Margin in Absolute Dollars Markup Scenario, RP Elasticity 0.34)

Prior to the compliance date of the energy conservation standard, cash flows are driven 

by the level of capital and product conversion costs and the portion of these investments spent 

each year. After the standard announcement date, industry free cash flows decline as companies 

use their financial resources to prepare for the new standard. The more stringent the energy 

conservation standard, the greater the negative impact on industry cash flows in the years prior to 

the compliance date. Since the capital and conversion costs are the same regardless of the 

markup scenario, the impacts in the years prior to the compliance date of the standards are the 

same in both markup scenarios. Since DOE assumed that standards compliant microwave ovens 

would be based on existing products, all existing manufacturing capital is left in place and there 

is no one-time return of cash in the compliance year resulting from a write-down of stranded 

assets.

The markup scenario has a large impact on industry cash flows beginning in the 

compliance year of the standard. The preservation of gross margin percentage markup scenario 

restores the original (baseline) cash flow within approximately two years of the compliance date.

Higher average selling prices increase operating profit, thereby increasing free cash flow, while 

lower unit sales have the opposite effect. For the preservation of gross margin in absolute dollars 

markup scenario, cash flows increase for all TSLs in 2016, as they did in the previous scenario.

However, after 2016, higher TSLs have a lower cash flow relative to the base case because 

manufacturers only recover a portion of their increased costs through increased selling prices.

The magnitude of the negative cash flow impact under the preservation of gross margin in 

absolute dollars scenario depends on the incremental cost of standards-compliant products. The 

higher the relative cost, the larger the impacts on operating revenue and cash flow in the years 

following the compliance date of the standard.
12-28

12.6 OTHER IMPACTS

12.6.1 Employment

12.6.1.1 Methodology

To quantitatively assess the impacts of energy conservation standards on microwave oven

manufacturing employment, DOE used the GRIM to estimate the domestic labor expenditures 

and number of employees in the base case and at each TSL. DOE used statistical data from the 

Census Bureau’s 2010 ASM and 2010 CIR, the results of the engineering analysis, and interviews 

with manufacturers to estimate the inputs necessary to calculate industry-wide labor expenditures 

and domestic employment levels. 

To calculate the annual domestic labor expenditures, DOE used the 2010 CIR to estimate 

the percentage of domestic microwave oven sales that are produced in the United States. DOE 

estimated that 1.3 percent of microwave ovens that are sold in the United States are 

manufactured domestically as well. DOE multiplied the total annual labor expenditures by the 

rate of domestic production to calculate domestic labor expenditures at each TSL. The domestic 

annual labor expenditures at each TSL were converted to domestic production employment 

levels by dividing production labor expenditures by the annual payment per production worker 

(production worker hours times the labor rate found in the 2010 ASM)

b and correcting for the 

cost of benefits.

The impacts on domestic employment due to standards can be assessed by comparing the 

employment results in the base case to the results at each TSL. In the GRIM, the employment 

results represent domestic production workers that are impacted by this rulemaking. Workers 

involved in manufacturing or supporting products for exports would not be impacted and are not 

included as part of the labor impacts.

The domestic industry labor expenditure for microwave oven production workers in 2016 

is approximately $2.0 million. Using the $15.64 wage rate and 1,891 production hours per year 

per employee found in the 2010 ASM, the GRIM estimates there are approximately 68

production employees involved in manufacturing microwave ovens covered by this rulemaking 

 

b 2010 ASM labor rates and production hours per year per employee are very similar to figures 

reported in the engineering analysis. DOE used 2010 ASM figures to ensure a consistent set of 

publicly available data is used for the manufacturing employment analysis.
12-29

in the United States. In addition, DOE estimates that 9 indirect employees in the United States 

support microwave oven production. The annual domestic employment impacts due to energy 

conservation standards are shown in further detail on the employment analysis spreadsheet of the 

microwave oven GRIM.

Table 12.6.1 illustrates the impact of new standby mode and off mode standards on 

employment levels at each TSL for the microwave oven industry calculated by the GRIM.

Table 12.6.1 Industry Estimated Employment Impacts in 2016 for Product Classes 1 and 2 

Combined

Base 

Case TSL 1 TSL 2 TSL 3 TSL 4

Total Number of Domestic 

Microwave Oven Production 

Employees in 2016

68 68 68 68 67

Total Number of Domestic 

Microwave Oven Non-Production 

Employees in 2016

9 9 9 9 9

Total Number of Domestic 

Microwave Oven Employees in 

2016

77 77 77 77 76

Change in Total Number of 

Domestic Microwave Oven 

Production Employees in 2016 Due 

to Standards

- 0.0% 0.0% 0.0% -1.3%

Figure 12.6.1 shows annual domestic employment levels for each TSL calculated in the 

GRIM for standby mode and off mode standards.
12-30

Figure 12.6.1 Total Microwave Ovens Industry Domestic Employment by Year for Product 

Classes 1 and 2 Combined

Nearly 99 percent of microwave ovens sold in the United States are imported. The GRIM 

finds that TSL1 and TSL2 maintain the same domestic employment as in the baseline in 2016. 

However, the GRIM calculates a small decrease in domestic labor relative to the baseline for 

TSL3 and TSL4. Because per unit labor costs are assumed to be the same regardless of 

efficiency level, the total labor expenditure only decreases at higher TSLs because of the price

elasticity of demand, which decreases overall shipments. However, because so few products are 

manufactured in the United States, the overall impact on domestic manufacturing employment is 

small.

This analysis of the impact to domestic production employment ignores the possible 

relocation of domestic jobs to lower-labor-cost countries, which may occur independently of new 

standards or may be influenced by the level of investments required by new standards. Since 

most microwave ovens are already imported and the employment impacts in the GRIM are 

small, the actual impacts on domestic employment would be mostly impacted if any 

manufacturer decided to shift remaining domestic production to lower-cost-countries.

12.6.2 Production Capacity

Based on manufacturer feedback provided during interviews, energy conservation 

standards will not significantly affect production capacity. As with conventional cooking 

products, any necessary microwave oven redesigns involve component switches that will not 

change the fundamental assembly of the equipment. However, manufacturers anticipate minor 

changes to tooling at all TSLs for more efficient standby power. For all efficiency levels, the 

most significant conversion costs are research and development, testing, and certification, which 
12-31

do not affect production line capacity. Thus, DOE believes manufacturers will be able to 

maintain manufacturing capacity levels and continue to meet market demand through the 

implementation of standby and off mode energy conservation standards for microwave ovens.

12.6.3 Cumulative Regulatory Burden

While any one regulation may not impose a significant burden on manufacturers, the 

combined effects of several impending regulations may have serious consequences for some 

manufacturers, groups of manufacturers, or an entire industry. Assessing the impact of a single 

regulation may overlook this cumulative regulatory burden. For the cumulative regulatory 

burden analysis, DOE describes other significant product-specific regulations that could affect

microwave oven manufacturers that will take effect 3 years before or 3 years after the 

compliance date of the amended energy conservation standards for these productsc

.

Companies which produce a wide range of regulated products may be faced with more 

capital and product development expenditures than competitors with a narrower scope of 

products. Regulatory burdens can prompt companies to exit the market or reduce their product 

offerings, potentially reducing competition. In addition to the energy conservation standards for

microwave oven standby mode and off mode, several other Federal regulations and pending 

regulations apply to these products and other equipment produced by the same manufacturers.

DOE recognizes that each regulation can significantly impact manufacturers’ financial 

operations. The following sections provide a qualitative discussion of some of these regulations 

and standards. 

12.6.3.1 Standby Power Requirements

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

Section 325 of the Energy Policy and Conservation Act (EPCA) to require DOE to regulate 

standby mode and off mode energy consumption as part of an energy conservation standard for 

all covered products for which a final rule is adopted after July 10, 2010. For this rulemaking, 

EISA 2007 specifies that the test procedure for residential ranges and ovens and microwave 

ovens be amended to include measurement of standby mode and off mode energy consumption, 

taking into consideration the most current version of International Electrotechnical 

 

c The compliance date for energy conservation standards for microwave oven standby mode and 

off mode is 3 years from the date of publication of the final rule. 
12-32

Commission’s (IEC) Standard 62301, “Household electrical appliances – Measurement of 

standby power” (IEC Standard 62301).

Besides today’s standard which sets a minimum standby mode and off mode requirement, 

manufacturers are concerned that the EISA 2007 requirement that all covered products measure 

standby power and off mode will impose a heavy burden on their testing facilities going forward.

In addition, because the test procedure change will affect all covered products, manufacturers 

expressed a concern that this had the potential to create many overlapping regulatory compliance 

costs in the future.

12.6.3.2 Additional Federal Energy Conservation Standards

Besides the energy conservation regulations on microwave ovens, several other Federal 

regulations and pending regulations apply to other products and equipment produced by the same 

manufacturers. Table 12.6.2 lists the Federal regulations that could also affect manufacturers of 

the microwave oven industry in the 3 years leading up to and the 3 years preceding the 

compliance date of the amended energy conservation standards for these products. It must be 

noted that while the list of products is long, the amount of cumulative burden on any particular 

firm is extremely variable since the product scope of each company is different.
12-33

Table 12.6.2 Other DOE and Federal Actions Affecting Microwave Oven Manufacturers

Regulation

Approximate 

Compliance 

Date*

Number of 

Impacted 

Companies 

from the 

Market and 

Technology 

Assessment 

(MTA) (See 

Chapter 3)

Estimated Total 

Industry Conversion 

Costs

Commercial Clothes Washers 2013& 2018* 2 $20.4 million (2008$)d

Battery Chargers and External 

Power Supplies 2013* 5 N/A††

Residential Dishwashers 2013 5 93.9 million (2010$)e

Residential Furnaces & 

Residential Central Air 

Conditioners and Heat Pumps

2013 

(Furnaces); 

2015 (Central 

AC & Heat 

Pumps)

4 46.0 million (2009$)f

Room Air Conditioners 2014 4 $171 million (2009$)g

 

d Estimated industry conversion expenses relate to the 2013 Compliance Date and were 

published in the TSD for the January 2010 commercial clothes washers final rule. 75 FR 1122. 

The TSD for the 2010 commercial clothes washers final rule can be found at: 

www.regulations.gov/#!documentDetail;D=EERE-2006-STD-0127-0118. e The TSD for Residential Dishwashers Direct Final Rule can be found at: 

www.regulations.gov/#!documentDetail;D=EERE-2011-BT-STD-0060-0007. f Forty-six million includes $43.2 million conversion costs for active mode standard (TSL 4) and 

$2.8 million conversion costs for standby and off modes standard (TSL 2). The TSD and GRIM 

for Residential Furnaces and Central Air Conditioners and Heat Pumps Direct Final Rule from 

2011 can be found at: www.regulations.gov/#!documentDetail;D=EERE-2011-BT-STD-0011-

0012 and www.regulations.gov/#!documentDetail;D=EERE-2011-BT-STD-0011-0010. g The TSD for Residential Clothes Dryers and Room Air Conditioners Direct Final Rule from 

2011 can be found at: www.regulations.gov/#!documentDetail;D=EERE-2007-BT-STD-0010-

0053.
12-34

Residential Refrigerators and 

Freezers 2014 8 $1,243 million (2009$)h

Regulation

Approximate 

Compliance 

Date*

Number of 

Impacted 

Companies 

from the 

Market and 

Technology 

Assessment 

(MTA) (See 

Chapter 3)

Estimated Total 

Industry Conversion 

Costs

Fluorescent Lamp Ballasts 2014 1 $74 million (2010$)i

Residential Clothes Dryers 2015 4 $95 million (2009$)j

Residential Clothes Washers 2015 & 2018 4 $418.5 million (2010$)

Commercial Refrigeration 

Equipment 2016* 1 N/A††

Automatic Ice Makers 2017* 1 N/A††

Residential Furnace Fans 2018* 4 N/A††

Wine Chillers & Misc. 

Refrigeration 2018* 4 N/A††

*The dates listed are an approximation. The exact dates are pending final DOE action.

† Energy conservation standards and compliance dates for residential boilers can be found at 10 

CFR 430.32(e)(2)(ii)-(iv). 

†† For energy conservation standards for rulemakings awaiting DOE final action, DOE does not 

have a finalized estimated total industry conversion cost. 

††† 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 

 

h The TSD for Refrigerator, Refrigerator-Freezer and Freezers Final Rule can be found at: 

www.regulations.gov/#!documentDetail;D=EERE-2008-BT-STD-0012-0128. i

The TSD for Fluorescent Lamp Ballasts can be found at: 

www.regulations.gov/#!documentDetail;D=EERE-2007-BT-STD-0016-0067. j

The TSD for Residential Clothes Dryers and Room Air Conditioners Direct Final Rule from 

2011 can be found at: www.regulations.gov/#!documentDetail;D=EERE-2007-BT-STD-0010-

0053.
12-35

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.

Additional investments necessary to meet these potential standards could have significant 

impacts on manufacturers of the covered products. However, DOE has limited data on the 

importance of these other regulated products for manufacturers of microwave ovens. Differences 

in market shares and manufacturing processes of other regulated products for each manufacturer

could cause varying degrees of burdens on these manufacturers. Therefore, DOE only estimated 

the cost of compliance to meet other energy conservation standards for regulated products if 

DOE had published a final rule.

12.6.3.3 Restriction of Hazardous Substance Directive

According to manufacturers, the Restriction of Hazardous Substance Directivek (RoHS) 

will have some global impact on manufacturing of electrical and electronic equipment. Under the 

directive, all manufacturers are banned from placing on the European Union (EU) market new 

electrical and electronic equipment containing more than agreed-upon levels of lead, cadmium, 

mercury, hexavalent chromium, polybrominated biphenyl and polybrominated diphenyl ether 

flame retardants. Although there is no Federal regulation on RoHS, California has passed SB 20: 

Electronic Waste Recycling Act of 2003. Under this law, California limits the amount of 

hazardous substances included in the RoHS directive that can be sold in California.

In addition, several northeastern states have placed a ban on any product that contains 

more than 1 ounce of mercury. This primarily affects mercury-based gas valves found in 

commercial and residential conventional cooking products. Manufacturers of conventional 

cooking products have said that these bans and RoHS will have some impacts in the way the 

electronic components in the cooktops and ovens are incorporated into the design. Currently, 

these manufacturers have electronic components in the cooktop and oven designs that contain 

substances banned by the RoHS and mercury limit passed by northeastern states. However, 

manufacturers do not anticipate these restrictions will significantly impact the cooking products 

industry as a whole.

 

k Further information about RoHS can be found at:

http://eur-lex.europa.eu/LexUriServ/site/en/oj/2003/l_037/l_03720030213en00190023.pdf. 

(Accessed May 2013.)
12-36

A significant worry to manufacturers is the uncertainty about the future of RoHS. One 

manufacturer stated that California’s resumption of the RoHS initiative demonstrates the ease 

with which any state could selectively choose to ban its products. One manufacturer stated that 

there are number of exemptions found in RoHS which must be periodically renewed by the EU.

Manufacturers use a number of these exempted substances in their products and are not sure if 

the exemptions will be renewed. Lastly, confirming RoHS-compliance will place additional 

testing burdens on manufacturers.

Most manufacturers interviewed for this rulemaking are already compliant with the 

RoHS directive. The most significant cumulative regulatory burden for gas cooking appliance 

manufacturers is a state-by-state restriction on mercuryl

12.6.3.4 International Energy Conservation Standards

, which affects the gas valves used in 

their appliances. Most gas cooking appliance manufacturers have already eliminated mercury 

switches or already have plans in place to do so. However, all appliance manufacturers are 

concerned about potential restrictions of other hazardous substances in the future, such as fire 

protection materials, which could be costly to remove from existing products.

Canada does set energy conservation standards for cooking products. However, these 

standards are less stringent than those of the United States. Manufacturers stated their concern 

that Canada will begin to regulate products at the provincial level. Unlike the United States, 

Canada does not have Federal preemption clauses for efficiency standards so province-byprovince standards could create a patchwork of standards and result in even greater testing costs.

DOE surveyed foreign regulatory and non-regulatory programs, identifying Korea’s estandby program, Australia’s standby program, and Japan’s Top Runner Program. All of them 

aim to meet or exceed the goals of the International Energy Agency (IEA)’s One-Watt program 

to lower standby power consumption below 1 watt (W) for microwave ovens.

m

 

l

For example, the Interstate Mercury Education & Reduction Clearinghouse (IMERC) is a 

coalition of Northeast states coordinating the banning of products containing mercury. Please

see:

Korea’s estandby program currently has a voluntary labeling program for microwave ovens with less than 

www.newmoa.org/prevention/mercury/imerc.cfm. (Accessed May 2013). m IEA Energy Information Centre, Standby Power Use and the IEA “1-Watt Plan”. Please see: 

http://greenshorenstein.info/pdf/Standby%20Power%20Fact%20Sheet%20-%20IEA%20-

%20April%202007.pdf. (Accessed May 2013.)
12-37

1 W of standby power. The program is currently transitioning to a mandatory 1 W maximum 

standby limit by the year 2010. Australia has plans to implement a mandatory 1 W standard by 

the year 2012. In accordance with Japan’s Top Runner Program, Japanese appliance 

manufacturers made a voluntary declaration to reduce standby power consumption of microwave 

ovens without a timer to as close to 0 watts as possible and that of microwave ovens with a timer 

to 1 W or less.

12.7 CONCLUSIONS

The following sections summarize the different impacts for the scenarios DOE believes 

are most likely to capture the range of impacts on microwave oven manufacturers as a result of 

energy conservation standards for standby mode and off mode. DOE also notes that while these 

scenarios bound the range of most plausible impacts on manufacturers, potentially there could be 

circumstances that lead manufacturers to experience impacts outside of this range. 

To assess the lower end of the range of potential impacts for the microwave oven 

industry, DOE considered the scenario reflecting the preservation of gross margin percentage. As 

production cost increases with efficiency, this scenario implies manufacturers will be able to 

maintain gross margins as a percentage of revenues. To assess the higher end of the range of 

potential impacts for the microwave oven industry, DOE considered the scenario reflecting 

preservation of gross margin in absolute dollars. Under this scenario, DOE assumed that the 

industry can maintain its gross margins in absolute dollars after the standard compliance date.

The industry would do so by passing through its increased costs to customers without increasing 

its gross margin in absolute dollars. 

TSL 1 represents an improvement in standby power from the baseline level of 4.0 W to 

2.0 W for Product Class 1 and an improvement in standby power from the baseline level of 4.5 

W to 3.7 W for Product Class 2. At TSL 1, the impact on INPV and cash flow varies depending 

on the manufacturers’ ability to pass on increases in MPCs to their customers. DOE estimated 

the impacts in INPV at TSL 1 to range -$15.1 million to -$17.3 million, or a change in INPV of -

1.1 percent to -1.2 percent. At this level, the industry cash flow decreases by approximately 6.0 

percent, to $99.7 million, compared to the base-case value of $106.1 million in the year leading 

up to the standards.

TSL 2 represents an improvement in standby power from the baseline level of 4.0 W to 

1.5 W for Product Class 1 and an improvement in standby power from the baseline level of 4.5 

W to 2.7 W for Product Class 2. At TSL 2, the impact on INPV and cash flow would be similar 

to TSL 1 and depend on whether manufacturers can fully recover the increases in MPCs from 

their customers. DOE estimated the impacts in INPV at TSL 2 to range from -$24.6 million to -

$28.5 million, or a change in INPV of -1.8 percent to -2.1 percent. At this level, the industry cash 

flow decreases by approximately 9.7 percent, to $95.8 million, compared to the base-case value 

of $106.1 million in the year leading up to the standards.

TSL 3 represents an improvement in standby power from the baseline level of 4.0 W to 

1.0 W for Product Class 1 and an improvement in standby power from the baseline level of 4.5 
12-38

W to 2.2 W for Product Class 2. At TSL 3, the impact on INPV and cash flow continues to vary 

depending on the manufacturers and their ability to pass on increases in MPCs to their 

customers. DOE estimated the impacts in INPV at TSL 3 to range from approximately -$40.0

million to -$96.6 million, or a change in INPV of -2.9 percent to -7.0 percent. At this level, the 

industry cash flow decreases by approximately 12.0 percent, to $93.4 million, compared to the 

base-case value of $106.1 million in the year leading up to the standards.

TSL 4 represents an improvement in standby power from the baseline level of 4.0 W to 

0.02 W for Product Class 1 and an improvement in standby power from the baseline level of 4.5 

W to 0.04 W for Product Class 2. At TSL 4, DOE estimated the impacts in INPV to range from 

approximately -$76.1 million to -$184.6 million, or a change in INPV of -5.5 percent to -13.3

percent. At this level, the industry cash flow decreases by approximately 22.7 percent, to $82.0

million, compared to the base-case value of $106.1 million in the year leading up to the 

standards. At higher TSLs, manufacturers have a harder time fully passing on larger increases in 

MPCs to their customers. At TSL 4, the conversion costs are higher than the other TSLs because 

the design of all microwave platforms must be more significantly altered.

For new standby mode and off mode energy conservation standards, conversion costs 

increase at higher TSLs as the complexity of further lowering standby power increases, 

substantially driving up engineering time and also increasing the testing and product 

development time. If the increased production costs are fully passed on to consumers (the 

preservation of gross margin percentage scenario), the operating revenue from higher prices is 

still not enough to overcome the negative impacts from the substantial conversion costs. The 

incremental costs are small for each TSL, meaning the positive impact on cash flows is small 

compared to the conversion costs required to achieve these efficiencies. As a result of the small 

incremental costs and large conversion expenses, INPV is negative for all TSLs under the 

preservation of gross margin percentage scenario. If the incremental costs are not fully passed 

along to customers (the preservation of gross margin in absolute dollars scenario), the negative 

impacts on INPV are amplified at each TSL.
12-39

REFERENCES

 

1 McKinsey & Company, Inc. Valuation: Measuring and Managing the Value of

Companies, 3rd Edition, Copeland, Koller, Murrin. New York: John Wiley & Sons, 2000.

2 Securities and Exchange Commission, Annual 10-K Reports, Various Years,

Washington D.C., www.sec.gov.

3 U.S. Department of Energy–Energy Information Administration. Annual Energy Outlook 2010 

with Projections to 2035. April 2010. U.S. DOE–EIA: Washington, D.C. DOE/EIA-0383(2010).

4 Association of Home Appliance Manufacturers. AHAM 2005 Fact Book, 2005. Washington, 

DC.

5 U.S. Census Bureau, July 2010,“Table 2 – Quantity and Value of Shipments of Refrigeration, 

Air-Conditioning, and Warm Air Heating Equipment: 2010 and 2009.” 2006–2010 Current 

Industrial Reports (CIR). 
13-i

CHAPTER 13. EMPLOYMENT IMPACT ANALYSIS

TABLE OF CONTENTS

13.1 INTRODUCTION ......................................................................................................... 13-1

13.2 ASSUMPTIONS............................................................................................................ 13-1

13.3 METHODOLOGY ........................................................................................................ 13-1

13.4 SHORT-TERM RESULTS............................................................................................ 13-2

13.5 LONG-TERM IMPACTS.............................................................................................. 13-3

LIST OF TABLES

Table 13.4.1 Net National Short-term Change in Employment (number of jobs).................. 13-3
13-1

CHAPTER 13. EMPLOYMENT IMPACT ANALYSIS

13.1 INTRODUCTION

DOE’s employment impact analysis is designed to estimate indirect national job creation 

or elimination resulting from possible standards, due to reallocation of the associated 

expenditures for purchasing and operating microwave ovens. Job increases or decreases reported 

in this chapter are separate from the direct microwave sector employment impacts reported in 

Chapter 12, and reflect the employment impact of efficiency standards on all other sectors of the 

economy. DOE examined the combined employment impacts for Microwave-only Ovens and 

Countertop Combination Ovens (Product Class 1) and Over-the-range Combination Ovens 

(Product Class 2).

13.2 ASSUMPTIONS

DOE expects energy conservation standards to decrease energy consumption, and 

therefore to reduce energy expenditures. The savings in energy expenditures may be spent on 

new investment or not at all (i.e., they may remain “saved”). The standards may increase the 

purchase price of appliances, including the retail price plus sales tax, and increase installation 

costs. 

Using an input/output econometric model of the U.S. economy, this analysis estimated 

the short-term effect of these expenditure impacts on net economic output and employment. 

DOE intends this analysis to quantify the indirect employment impacts of these expenditure 

changes. It evaluated direct employment impacts at manufacturers’ facilities in the manufacturer 

impact analysis (see chapter 12).

DOE notes that ImSET is not a general equilibrium forecasting model, and understands 

the uncertainties involved in projecting employment impacts, especially changes in the later 

years of the analysis.1 Because ImSET does not incorporate price changes, the employment 

effects predicted by ImSET would over-estimate the magnitude of actual job impacts over the 

long run for this rule. Since input/output models do not allow prices to bring markets into 

equilibrium, they are best used for short-run analysis. DOE therefore include a qualitative 

discussion of how labor markets are likely to respond in the longer term. In future rulemakings, 

DOE may consider the use of other modeling approaches for examining long run employment 

impacts.

13.3 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 
13-2

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

economic impacts of energy-efficiency investments in buildings.

In an input/output model, the level of employment in an economy is determined by the 

relationship of different sectors of the economy and the spending flows among them. Different 

sectors have different levels of labor intensity and so changes in the level of spending (e.g., due 

to the effects of an efficiency standard) in one sector of the economy will affect flows in other 

sectors, which affects the overall level of employment.

ImSET uses a 187-sector model of the national economy to predict the economic effects 

of residential and commercial buildings technologies. ImSET collects estimates of initial 

investments, energy savings, and economic activity associated with spending the savings 

resulting from standards (e.g., changes in final demand in personal consumption, business 

investment and spending, and government spending). It provides overall estimates of the change 

in national output for each input-output sector. The model applies estimates of employment and 

wage income per dollar of economic output for each sector and calculates impacts on national 

employment and wage income.

Energy-efficiency technology primarily affects the U.S. economy along three spending 

pathways. First, general investment funds are diverted to sectors that manufacture, install, and 

maintain energy-efficient appliances. The increased cost of appliances leads to higher 

employment in the appliance manufacturing sectors and lower employment in other economic 

sectors. Second, commercial firm and residential spending are redirected from utilities toward 

firms that supply production inputs. Third, electric utility sector investment funds are released 

for use in other sectors of the economy. When consumers use less energy, electric utilities 

experience relative reductions in demand which leads to reductions in utility sector investment 

and employment.

DOE also notes that the employment impacts estimated with ImSET for the entire 

economy differ from the employment impacts in the microwave 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. 

13.4 SHORT-TERM RESULTS

The results in this section refer to impacts of microwave oven 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 anticipates no change in operations and maintenance costs for 

microwave ovens. DOE presents the summary impact.

Conceptually, one can consider the impact of the rule in its first year on three aggregate 

sectors, the microwave production sector, the energy generation sector, and the general consumer 

good sector (as mentioned above ImSET’s calculations are made at a much more disaggregate 
13-3

level). By raising energy efficiency, the rule increases the purchase price of microwaves, this

increase in expenditures causes an increase in employment in this sector. At the same time, the 

improvements in energy efficiency reduce consumer expenditures on electricity. The reduction in 

electricity demand causes a reduction in employment in that sector. Finally, based on the net 

impact of increased expenditures on microwaves and reduced expenditures on electricity, 

consumer expenditures on everything else are either positively or negatively affected, increasing 

or reducing jobs in that sector accordingly. The model also captures any indirect jobs created or 

lost by changes in consumption due to changes in employment (as more workers are hired they 

consume more goods, which generates more employment, the converse is true for workers laid 

off).

Table 13.4.1 presents the modeled net employment impact from the rule in 2015. As 

mentioned in Chapter 12, 1% of microwaves are produced domestically and 99% are imported. 

The net employment impact estimate is sensitive to assumptions regarding the return to the U.S. 

economy of money spent on imported microwaves. The two scenarios bounding the ranges 

presented in Table 13.4.1 represent situations in which none of the money spent on imported 

microwaves returns to the U.S. economy and all of the money spent on imported microwaves 

returns to the U.S. economy (low and high bounds, respectively). The U.S. trade deficit in recent 

years suggests that between 50% and 75% of the money spent on imported microwaves is likely 

to return, with employment impacts falling within the ranges presented below.

Table 13.4.1 Net National Short-term Change in Employment (number of jobs)

Trial Standard Level 2016 2020

1 65 to 88 400 to 422

2 43 to 112 527 to 593

3 -551 to 17 153 to 697

4 -1426 to -102 -339 to 935

For context, OMB currently projects that the office unemployment rate may decline to 6.9% in 

2014 and drop further to 5.3% in 2017.5 The unemployment rate in 2017 is projected to be close 

to “full employment.” When an economy is at full employment any effects on net employment 

are likely to be transitory as workers change jobs, rather than enter or exit longer-term 

employment.

13.5 LONG-TERM IMPACTS

Due to the short payback period of energy efficiency improvements mandated by this 

rule, over the long term DOE expects the energy savings to consumers to increasingly dominate 

the increase in appliance costs, resulting in increased aggregate savings to consumers. As a 

result, DOE expects demand for electricity to decline over time and demand for other goods to 

increase. 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 
13-4

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 

will be negligible over time due to the small magnitude of the short-term effects presented in 

Table 13.4.1. The ImSET model projections, assuming no price or wage effects until 2020, are 

included in the second column of Table 13.4.1.
13-5

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 2012 Mid-Session Review: Budget of the 

U.S. Government, Washington, DC. 

www.whitehouse.gov/sites/default/files/omb/budget/fy2012/assets/12msr.pdf. (Accessed 

May 2013)
14-i

CHAPTER 14. UTILITY IMPACT ANALYSIS

TABLE OF CONTENTS

14.1 INTRODUCTION ......................................................................................................... 14-1

14.2 METHODOLOGY ........................................................................................................ 14-1

14.3 RESULTS ...................................................................................................................... 14-2

LIST OF TABLES

Table 14.3.1 AEO 2012 Reference Case Forecast.................................................................. 14-3

Table 14.3.2 Residential Microwave Oven Standby Power and Off Modes Trial 

Standard Level 1 Forecast.................................................................................. 14-4

Table 14.3.3 Residential Microwave Oven Standby Power and Off Modes Trial 

Standard Level 2 Forecast.................................................................................. 14-4

Table 14.3.4 Residential Microwave Oven Standby Power and Off Modes Trial 

Standard Level 3 Forecast.................................................................................. 14-5

Table 14.3.5 Residential Microwave Oven Standby Power and Off Modes Trial 

Standard Level 4 Forecast.................................................................................. 14-5

Table 14.3.6 Reduction in Electric Generating Capacity in 2045 Under Microwave 

Oven Standby Power and Off Modes Trial Standard Levels............................. 14-6
14-1

CHAPTER 14. UTILITY IMPACT ANALYSIS

14.1 INTRODUCTION

DOE analyzed the effects of its amended standard levels on the electric utility industry using 

a variant 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 AEO for 2012 (AEO2012) forecasts energy supply and demand 

through 2040.

1 DOE used a variant of this model, referred to as NEMS-BT,b

The utility impact analysis reports the changes in electric installed capacity and generation 

that result for each trial standard level (TSL) by plant type, as well as changes in residential 

electricity consumption. 

to account for the 

impacts of standby power energy conservation standards for microwave ovens. DOE’s utility impact 

analysis consists of a comparison between model results for the AEO2012 Reference Case and for 

cases in which standards are in place, and applies the same basic set of assumptions as the AEO2012. 

The AEO2012 reference case corresponds to medium economic growth.

NEMS-BT has several advantages that have led to its adoption as the forecasting tool in the 

analysis of energy conservation standards. NEMS-BT uses a set of assumptions that are well known 

and fairly transparent, due to the exposure and scrutiny each AEO receives. In addition, the 

comprehensiveness of NEMS-BT permits the modeling of interactions among the various energy 

supply and demand sectors, producing a complete picture of the effects of energy conservation 

standards. Perhaps most importantly, NEMS-BT can be used to estimate marginal effects, which 

yield a better estimate of the actual impact of energy conservation standards than considering only 

average effects.

14.2 METHODOLOGY

NEMS provides reference case load shapes for several end uses. The model uses predicted 

growth in demand for each end use to build up a projection of the total electric system load growth 

for each region, which it uses in turn to predict the necessary additions to capacity. DOE uses 

NEMS-BT to account for the implementation of energy conservation standards by decrementing the 

appropriate reference case load shape. For microwave oven standby power the refrigeration end use 

 

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 2003, DOE/EIA-0581(2003), 

March, 2003. 

b DOE/EIA approves use of the name NEMS to describe only an official version of the model without any 

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

under various policy scenarios that are variations on DOE/EIA assumptions, DOE refers to it by the name NEMSBT (BT is DOE’s Building Technologies Program, under whose aegis this work has been performed). NEMS-BT 

was previously called NEMS-BRS.
14-2

was decremented. The refrigeration end use is the best representation of a baseline energy load 

which is characteristic of the standby power in microwave ovens. The energy conservation 

decrement is also divided amongst the nine U.S. Census divisions based upon the share of energy end 

use consumption in each division, as given in NEMS.

DOE used the site energy savings developed in the national impact analysis (chapter 10) for 

each TSL as input to NEMS-BT. The magnitude of the energy decrement that would be required for 

NEMS-BT to produce stable results out of the range of numerical noise is larger than the highest 

efficiency standard under consideration. Therefore, DOE estimated results corresponding to each 

TSL using interpolation. DOE ran higher energy use reduction levels in NEMS-BT, representing 

multipliers of each TSL, and used these outputs to linearly interpolate the results to estimate actual 

changes in generation and capacity due to the standard.

Although the current time horizon of NEMS-BT is 2040, other parts of the energy 

conservation standards analysis extend through the year 2045. It is not feasible to extend the forecast 

period of NEMS-BT for the purposes of this analysis, nor does DOE/EIA have an approved method 

for extrapolation of many outputs beyond 2040. While it might seem reasonable to make simple 

linear extrapolations of results, in practice this is not advisable because outputs could be 

contradictory. An analysis of various trends sufficiently detailed to guarantee consistency is beyond 

the scope of this work, and, in any case, would involve a great deal of uncertainty. Therefore, all 

extrapolations beyond 2040 are simple replications of year 2040 results. To emphasize the 

extrapolated results wherever they appear, they are shaded in gray to distinguish them from actual 

NEMS-BT results.

14.3 RESULTS

This utility impact analysis reports NEMS-BT forecasts for residential sector electricity 

consumption, total electricity generation by fuel type, and installed electricity generation capacity by 

fuel type. Results are presented in five-year increments through 2040. Beyond 2040, an extrapolation 

through 2045 for each TSL represents a simple replication of the 2040 results. 

The results from the AEO2012 Reference Case are shown in Table 14.3.1. The results for 

microwave oven standby power TSLs are presented in Tables 14.3.2 through 14.3.5. Each table 

shows forecasts using interpolated results, as described in section 14.2, for total U.S. electricity 

generation and installed capacity.

The considered TSLs reduce electricity consumption compared to the AEO2012 Reference 

Case. The electricity savings predicted by the NIA Model range from 0.071 to 0.216 percent of total 

residential electricity consumption in the year 2040.
14-3

Table 14.3.1 AEO 2012 Reference Case Forecast

NEMS-BT Results: AEO2012 Reference Case

2010 2015 2020 2025 2030 2035 2040

Residential Sector Energy Consumption 1

Electricity Sales (TWh)2 1,451 1,392 1,454 1,533 1,626 1,718 1,831

Natural Gas (EJ) 5.34 5.25 5.22 5.15 5.10 5.02 4.98

Other (EJ) 1.74 1.60 1.53 1.47 1.43 1.39 1.36

Natural Gas (Quads) 5.06 4.97 4.95 4.88 4.84 4.76 4.72

Other (Quads)3 1.65 1.52 1.45 1.39 1.35 1.31 1.29

Total U.S. Electric Generation 4

Coal (TWh) 1,851 1,581 1,671 1,786 1,841 1,897 1,924

Gas (TWh) 982 1,129 1,112 1,138 1,267 1,394 1,473

Petroleum (TWh) 37 28 28 29 29 30 30

Nuclear (TWh) 807 830 887 917 914 887 964

Renewables (TWh) 429 562 614 662 702 760 842

Total (TWh)5 4,106 4,131 4,312 4,533 4,754 4,968 5,233

Installed Generating Capacity 6

Coal (GW) 318 286 281 282 284 285 288

Other Fossil (GW)7 461 467 475 495 525 568 598

Nuclear (GW) 101 104 111 115 114 111 121

Renewables (GW) 134 163 168 176 184 201 225

Total (GW)8 1,013 1,019 1,036 1,068 1,107 1,165 1,232

1

Comparable to Table A2 of AEO2012: Energy Consumption, Residential

2

Comparable to Table A8 of AEO2012: Electricity Sales by Sector

3

Includes distillate fuel, kerosene, LPG, coal, and renewable energy

4

Comparable to Table A8 of AEO2012: Electric Generators and Cogenerators

5

Excludes "Other Gaseous Fuels" cogenerators and "Other" cogenerators

6

Comparable to Table A9 of AEO2012: Electric Generators and Cogenerators Capability

7

Includes "Other Gaseous Fuels" cogenerators

8

Excludes Pumped Storage and Fuel Cells
14-4

Table 14.3.2 Residential Microwave Oven Standby Power and Off Modes Trial Standard Level 

1 Forecast

Table 14.3.3 Residential Microwave Oven Standby Power and Off Modes Trial Standard Level 

2 Forecast

NEMS-BT Results: Difference from AEO2012 Reference Case

2015 2020 2025 2030 2035 2040 2015 2020 2025 2030 2035 2040 2041 2045

Residential Sector Energy Consumption Residential Sector Energy Consumption

Electricity Sales (TWh) 1,392 1,453 1,531 1,625 1,716 1,829 Electricity Sales (TWh) 0.00 -0.55 -1.08 -1.19 -1.24 -1.29 -1.30 -1.35

Total U.S. Electric Generation Total U.S. Electric Generation

Coal (TWh) 1,581 1,671 1,786 1,841 1,896 1,924 Coal (TWh) -0.05 -0.31 -0.44 -0.42 -0.47 -0.44 -0.44 -0.44

Gas (TWh) 1,129 1,111 1,137 1,266 1,393 1,472 Gas (TWh) -0.03 -0.19 -0.58 -0.66 -0.64 -0.59 -0.59 -0.59

Petroleum (TWh) 28 28 29 29 30 30 Petroleum (TWh) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Nuclear (TWh) 830 887 917 914 887 963 Nuclear (TWh) 0.00 -0.04 -0.05 -0.05 -0.08 -0.24 -0.24 -0.24

Renewables (TWh) 562 614 662 702 759 842 Renewables (TWh) -0.02 -0.08 -0.12 -0.15 -0.14 -0.18 -0.18 -0.18

Total (TWh) 4,131 4,311 4,532 4,752 4,966 5,231 Total (TWh) -0.09 -0.62 -1.19 -1.29 -1.33 -1.45 -1.45 -1.45

Installed Generating Capacity Installed Generating Capacity

Coal (GW) 286 281 282 283 285 288 Coal (GW) -0.02 -0.04 -0.06 -0.06 -0.07 -0.07 -0.07 -0.07

Other Fossil (GW) 467 475 495 525 568 598 Other Fossil (GW) 0.00 -0.01 -0.05 -0.06 -0.10 -0.09 -0.09 -0.09

Nuclear (GW) 104 111 115 114 111 121 Nuclear (GW) 0.00 0.00 -0.01 -0.01 -0.01 -0.03 -0.03 -0.03

Renewables (GW) 163 168 175 184 201 225 Renewables (GW) -0.01 -0.01 -0.02 -0.03 -0.04 -0.05 -0.05 -0.05

Total (GW) 1,019 1,036 1,067 1,107 1,165 1,232 Total (GW) -0.03 -0.07 -0.14 -0.16 -0.21 -0.24 -0.24 -0.24

Extrapolation

NEMS-BT Results: Difference from AEO2012 Reference Case

2015 2020 2025 2030 2035 2040 2015 2020 2025 2030 2035 2040 2041 2045

Residential Sector Energy Consumption Residential Sector Energy Consumption

Electricity Sales (TWh) 1,392 1,453 1,531 1,624 1,716 1,829 Electricity Sales (TWh) 0.00 -0.80 -1.56 -1.72 -1.79 -1.87 -1.88 -1.95

Total U.S. Electric Generation Total U.S. Electric Generation

Coal (TWh) 1,581 1,671 1,786 1,841 1,896 1,923 Coal (TWh) -0.07 -0.45 -0.63 -0.61 -0.67 -0.64 -0.64 -0.64

Gas (TWh) 1,129 1,111 1,137 1,266 1,393 1,472 Gas (TWh) -0.04 -0.28 -0.83 -0.96 -0.92 -0.85 -0.85 -0.85

Petroleum (TWh) 28 28 29 29 30 30 Petroleum (TWh) 0.00 0.00 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01

Nuclear (TWh) 830 887 917 914 887 963 Nuclear (TWh) 0.00 -0.05 -0.07 -0.08 -0.12 -0.35 -0.35 -0.35

Renewables (TWh) 562 614 662 702 759 842 Renewables (TWh) -0.02 -0.11 -0.17 -0.21 -0.20 -0.26 -0.26 -0.26

Total (TWh) 4,131 4,311 4,531 4,752 4,966 5,231 Total (TWh) -0.13 -0.90 -1.71 -1.87 -1.92 -2.10 -2.10 -2.10

Installed Generating Capacity Installed Generating Capacity

Coal (GW) 286 281 282 283 285 288 Coal (GW) -0.03 -0.06 -0.09 -0.09 -0.09 -0.10 -0.10 -0.10

Other Fossil (GW) 467 475 495 525 568 598 Other Fossil (GW) -0.01 -0.02 -0.08 -0.09 -0.14 -0.13 -0.13 -0.13

Nuclear (GW) 104 111 115 114 111 121 Nuclear (GW) 0.00 -0.01 -0.01 -0.01 -0.02 -0.04 -0.04 -0.04

Renewables (GW) 163 168 175 184 201 225 Renewables (GW) -0.01 -0.02 -0.03 -0.04 -0.06 -0.08 -0.08 -0.08

Total (GW) 1,019 1,036 1,067 1,107 1,165 1,232 Total (GW) -0.04 -0.11 -0.21 -0.23 -0.31 -0.35 -0.35 -0.35

Extrapolation
14-5

Table 14.3.4 Residential Microwave Oven Standby Power and Off Modes Trial Standard Level 

3 Forecast

Table 14.3.5 Residential Microwave Oven Standby Power and Off Modes Trial Standard Level 

4 Forecast

NEMS-BT Results: Difference from AEO2012 Reference Case

2015 2020 2025 2030 2035 2040 2015 2020 2025 2030 2035 2040 2041 2045

Residential Sector Energy Consumption Residential Sector Energy Consumption

Electricity Sales (TWh) 1,392 1,453 1,530 1,624 1,715 1,828 Electricity Sales (TWh) 0.00 -1.10 -2.15 -2.37 -2.46 -2.57 -2.60 -2.69

Total U.S. Electric Generation Total U.S. Electric Generation

Coal (TWh) 1,581 1,671 1,785 1,840 1,896 1,923 Coal (TWh) -0.09 -0.62 -0.87 -0.84 -0.93 -0.88 -0.88 -0.88

Gas (TWh) 1,129 1,111 1,137 1,265 1,393 1,472 Gas (TWh) -0.06 -0.38 -1.15 -1.32 -1.27 -1.17 -1.17 -1.17

Petroleum (TWh) 28 28 29 29 30 30 Petroleum (TWh) 0.00 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01

Nuclear (TWh) 830 887 917 914 887 963 Nuclear (TWh) 0.00 -0.07 -0.10 -0.11 -0.16 -0.48 -0.48 -0.48

Renewables (TWh) 562 614 662 702 759 842 Renewables (TWh) -0.03 -0.15 -0.23 -0.29 -0.28 -0.36 -0.36 -0.36

Total (TWh) 4,131 4,311 4,530 4,751 4,965 5,230 Total (TWh) -0.18 -1.23 -2.36 -2.57 -2.65 -2.90 -2.90 -2.90

Installed Generating Capacity Installed Generating Capacity

Coal (GW) 286 281 282 283 285 288 Coal (GW) -0.04 -0.09 -0.12 -0.13 -0.13 -0.13 -0.13 -0.13

Other Fossil (GW) 467 475 495 525 568 598 Other Fossil (GW) -0.01 -0.02 -0.11 -0.12 -0.19 -0.18 -0.18 -0.18

Nuclear (GW) 104 111 115 114 111 121 Nuclear (GW) 0.00 -0.01 -0.01 -0.01 -0.02 -0.06 -0.06 -0.06

Renewables (GW) 163 168 175 184 201 225 Renewables (GW) -0.01 -0.03 -0.04 -0.05 -0.08 -0.11 -0.11 -0.11

Total (GW) 1,019 1,036 1,067 1,107 1,165 1,232 Total (GW) -0.06 -0.15 -0.28 -0.32 -0.43 -0.48 -0.48 -0.48

Extrapolation

NEMS-BT Results: Difference from AEO2012 Reference Case

2015 2020 2025 2030 2035 2040 2015 2020 2025 2030 2035 2040 2041 2045

Residential Sector Energy Consumption Residential Sector Energy Consumption

Electricity Sales (TWh) 1,392 1,452 1,529 1,622 1,714 1,827 Electricity Sales (TWh) 0.00 -1.69 -3.31 -3.64 -3.79 -3.95 -3.99 -4.13

Total U.S. Electric Generation Total U.S. Electric Generation

Coal (TWh) 1,581 1,671 1,785 1,840 1,895 1,923 Coal (TWh) -0.14 -0.96 -1.34 -1.30 -1.43 -1.36 -1.36 -1.36

Gas (TWh) 1,129 1,111 1,136 1,265 1,392 1,471 Gas (TWh) -0.08 -0.59 -1.77 -2.03 -1.96 -1.79 -1.79 -1.79

Petroleum (TWh) 28 28 29 29 30 30 Petroleum (TWh) 0.00 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01

Nuclear (TWh) 830 887 917 914 887 963 Nuclear (TWh) 0.00 -0.11 -0.15 -0.17 -0.24 -0.74 -0.74 -0.74

Renewables (TWh) 562 613 662 702 759 842 Renewables (TWh) -0.05 -0.23 -0.36 -0.44 -0.43 -0.55 -0.55 -0.55

Total (TWh) 4,131 4,310 4,529 4,750 4,963 5,228 Total (TWh) -0.28 -1.90 -3.63 -3.96 -4.07 -4.45 -4.45 -4.45

Installed Generating Capacity Installed Generating Capacity

Coal (GW) 286 281 282 283 285 288 Coal (GW) -0.07 -0.14 -0.18 -0.19 -0.20 -0.20 -0.20 -0.20

Other Fossil (GW) 467 475 495 525 568 598 Other Fossil (GW) -0.01 -0.04 -0.16 -0.18 -0.30 -0.27 -0.27 -0.27

Nuclear (GW) 104 111 115 114 111 121 Nuclear (GW) 0.00 -0.01 -0.02 -0.02 -0.03 -0.10 -0.10 -0.10

Renewables (GW) 163 168 175 184 201 225 Renewables (GW) -0.02 -0.04 -0.07 -0.08 -0.13 -0.17 -0.17 -0.17

Total (GW) 1,019 1,035 1,067 1,107 1,164 1,232 Total (GW) -0.10 -0.23 -0.43 -0.48 -0.66 -0.74 -0.74 -0.74

Extrapolation
14-6

Table 14.3.6 presents the estimated reduction in electricity generating capacity in 2045

for the TSLs that DOE considered in this rulemaking.

Table 14.3.6 Reduction in Electric Generating Capacity in 2045 Under Microwave Oven 

Standby Power and Off Modes Trial Standard Levels

TSL 1 TSL 2 TSL 3 TSL 4

MWO Standby Power

Gigawatts

0.241 0.348 0.480 0.737
14-7

REFERENCES

1. Energy Information Administration, Annual Energy Outlook 2012 Reference Case 

Service Report, 2012. Washington, DC. Report No. DOE/EIA-0383(2012). 

www.eia.gov/forecasts/aeo/. (Accessed May 2013)
15-i

CHAPTER 15. EMISSIONS IMPACT ANALYSIS

TABLE OF CONTENTS

15.1 INTRODUCTION ......................................................................................................... 15-1

15.2 AIR QUALITY REGULATIONS AND EMISSIONS IMPACTS............................... 15-1

15.3 METHODOLOGY ........................................................................................................ 15-2

15.4 EMISSIONS IMPACT RESULTS................................................................................ 15-3

LIST OF TABLES

Table 15.4.1 Power Sector Emissions Forecast from AEO2012 Reference Case................... 15-3

Table 15.4.2 Power Sector Emissions Impacts for Microwave Oven Standby Power 

TSLs................................................................................................................... 15-4
15-1

CHAPTER 15. EMISSIONS IMPACT ANALYSIS

15.1 INTRODUCTION

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. 

DOE conducted the emissions impact analysis using the NEMS-BT model based on EIA’s 

Annual Energy Outlook 2012 (AEO 2012).

1 EIA prepares the Annual Energy Outlook using the 

National Energy Modeling System (NEMS).

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

regulations on emissions. AEO 2012 generally represents current Federal and State legislation and 

final implementation regulations available as of the end of December 2011.

15.2 AIR QUALITY REGULATIONS AND EMISSIONS IMPACTS

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. 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. On July 6, 2011 EPA issued a replacement for CAIR, the Cross-State Air Pollution Rule 

(CSAPR). 76 FR 48208 (August 8, 2011). The AEO 2012 NEMS used for this analysis assumes 

the implementation of CSAPR.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 

 

a On December 30, 2011, the D.C. Circuit stayed the new rules while a panel of judges reviews them, and told EPA 

to continue enforcing CAIR. See EME Homer City Generation, LP v. EPA, Order, No. 11-1302, Slip Op. at *2 

(D.C. Cir. Dec. 30, 2011). On August 21, 2012, the D.C. Circuit vacated CSAPR and related Federal 

Implementation Plans that would have superseded the State Implementation Plans that EPA typically approves for 

compliance with Clean Air Act stationary source regulations. See EME Homer City Generation, LP v. EPA, No. 

11-1302, 2012 WL 3570721 at *24 (D.C. Cir. Aug. 21, 2012). The court required EPA to continue administering 

CAIR. See id. The AEO 2012, however, had been finalized prior to both these decisions. DOE understands, 

however, that CAIR and CSAPR are similar with respect to their effect on emissions impacts of energy efficiency 

standards.
15-2

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 2015, however, SO2 emissions will fall as a result of the Mercury and Air 

Toxics Standards (MATS) for power plants, which were announced by EPA on December 21, 

2011. 77 FR 9304 (Feb. 16, 2012).b 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 2012 assumes that, in 

order to continue operating, coal plants must have either flue gas desulfurization or dry sorbent 

injection systems installed by 2015. Both technologies, which are used to reduce acid gas 

emissions, also reduce SO2 emissions. Under the MATS, NEMS shows a reduction in SO2

emissions when electricity demand decreases (e.g., as a result of energy efficiency standards). 

Emissions will be far below the cap that would be established by CSAPR, 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 2015 and beyond.

CSAPR 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 

CSAPR. Therefore, DOE estimated NOx emissions reductions from potential standards in those 

States.

The MATS limit mercury emissions from power plants, but they do not include 

emissions caps and, as such, DOE’s energy conservation standards would likely reduce Hg 

emissions. For this rulemaking, DOE estimated mercury emissions reductions using the NEMSBT based on AEO 2012, which incorporates the MATS.

15.3 METHODOLOGY 

The impact of the energy conservation standard on power sector air emissions is 

determined by calculating the difference between emissions projected in the NEMS-BT 

reference case run and emissions projected in the NEMS-BT policy run. The policy run was 

decremented with site energy savings resulting from the standard which were developed in the 

national impact analysis (chapter 10). The impact of the standard on power sector emissions was 

determined for each of the four TSLs (see chapter 14 for a description of the NEMS-BT model).

 

b On July 20, 2012, EPA announced a partial stay, for a limited duration, of the effectiveness of national new source 

emission standards for hazardous air pollutants from coal- and oil-fired electric utility steam generating units. 

http://www.epa.gov/airquality/powerplanttoxics/pdfs/20120727staynotice.pdf
15-3

All extrapolations beyond 2040 are simple replications of year 2040 results. To emphasize 

the extrapolated results wherever they appear, they are shaded in gray to distinguish them from actual 

NEMS-BT results.

15.4 EMISSIONS IMPACT RESULTS

Table 15.4.1 shows AEO2012 Reference Case power plant emissions in selected years. 

The Reference Case emissions are the emissions shown by the NEMS-BT model to result if none 

of the TSLs are promulgated (the base case). 

Table 15.4.1 Power Sector Emissions Forecast from AEO2012 Reference Case

NEMS-BT Results 2015 2020 2025 2030 2035 2040

CO2 (million metric tons) 2,011 2,067 2,179 2,258 2,330 2,358

NOX (million tons) 1.79 1.87 1.92 1.94 1.96 1.97

SO2 (million tons) 1.26 1.31 1.55 1.62 1.71 1.74

Hg (tons) 6.4 6.7 7.2 7.5 7.9 8.0

Table 15.4.2 shows the estimated changes in power plant emissions of CO2, NOX, SO2,

and Hg in selected years for each of the TSLs considered in this rulemaking. As in Table 15.4.1, 

values for CO2 are given in metric tons, while values for NOX, SO2, and Hg are given in short 

tons. Over the time period 2016 through 2045, cumulative CO2, NOX, SO2, and Hg emissions are 

reduced compared to the Reference case for all TSLs. For comparison, the cumulative power 

sector emissions in the Reference case, over the same thirty year period, are 67,037 Mt for CO2, 

57,696 kt for NOx, 47,079 kt for SO2, and 223 tons for Hg.
15-4

Table 15.4.2 Power Sector Emissions Impacts for Microwave Oven Standby Power TSLs

NEMS-BT Results* Difference from AEO2012 Reference Case

Total

2015 2020 2025 2030 2035 2040 2041 2045 2016-2045

Standard Level 1

CO2 (Mt/yr) -0.06 -0.37 -0.75 -0.79 -0.78 -0.71 -0.71 -0.71 -19.2

NOx (kt/yr) -0.05 -0.34 -0.67 -0.68 -0.66 -0.59 -0.59 -0.59 -16.5

SO2 (1,000 tons/yr) -0.03 -0.24 -0.54 -0.56 -0.57 -0.52 -0.52 -0.52 -13.7

Hg (ton/yr) 0.000 -0.001 -0.002 -0.002 -0.002 -0.002 -0.002 -0.002 -0.048

Standard Level 2

CO2 (Mt/yr) -0.08 -0.54 -1.09 -1.14 -1.13 -1.03 -1.03 -1.03 -27.8

NOx (1,000 tons/yr) -0.07 -0.49 -0.96 -0.98 -0.95 -0.86 -0.86 -0.86 -23.8

SO2 (1,000 tons/yr) -0.05 -0.34 -0.77 -0.81 -0.83 -0.76 -0.76 -0.76 -19.8

Hg (ton/yr) 0.000 -0.002 -0.003 -0.003 -0.002 -0.003 -0.003 -0.003 -0.069

Sandard Level 3

CO2 (Mt/yr) -0.11 -0.75 -1.50 -1.57 -1.55 -1.42 -1.42 -1.42 -38.3

NOx (1,000 tons/yr) -0.10 -0.67 -1.32 -1.35 -1.31 -1.18 -1.18 -1.18 -32.8

SO2 (1,000 tons/yr) -0.07 -0.47 -1.06 -1.12 -1.14 -1.04 -1.04 -1.04 -27.3

Hg (ton/yr) 0.000 -0.003 -0.004 -0.004 -0.003 -0.004 -0.004 -0.004 -0.095

Standard Level 4

CO2 (Mt/yr) -0.17 -1.15 -2.31 -2.41 -2.38 -2.18 -2.18 -2.18 -58.8

NOx (1,000 tons/yr) -0.15 -1.04 -2.04 -2.07 -2.01 -1.82 -1.82 -1.82 -50.4

SO2 (1,000 tons/yr) -0.11 -0.73 -1.64 -1.72 -1.75 -1.60 -1.60 -1.60 -41.9

Hg (ton/yr) 0.000 -0.004 -0.006 -0.005 -0.005 -0.006 -0.006 -0.006 -0.146

*CO2 results are in metric tons, NOX, SO2, and Hg results are in short tons.

Extrapolation
15-5

REFERENCES

1. U.S. Department of Energy–Energy Information Administration. 2012. Annual Energy 

Outlook 2012. Washington, DC. www.eia.gov/forecasts/aeo/. (Accessed May 2013.)
16-i

CHAPTER 16. MONETIZATION OF EMISSION REDUCTIONS BENEFITS 

TABLE OF CONTENTS

16.1 INTRODUCTION ......................................................................................................... 16-1

16.2 MONETIZING CARBON DIOXIDE EMISSIONS..................................................... 16-1

16.2.1 Social Cost of Carbon .................................................................................................... 16-1

16.2.1.1 Monetizing Carbon Dioxide Emissions..................................................... 16-2

16.2.2 Current Approach and Key Assumptions...................................................................... 16-2

16.3 VALUATION OF OTHER EMISSIONS REDUCTIONS ........................................... 16-5

16.4 RESULTS ...................................................................................................................... 16-5

LIST OF TABLES

Table 16.2-1 Annual SCC Values from 2010 Interagency Report, 2010–2050 (in 2007 

dollars per metric ton CO2)................................................................................ 16-4

Table 16.2-2 Annual SCC Values from 2013 Interagency Update, 2010–2050 (in 2007 

dollars per metric ton CO2)................................................................................ 16-4

Table 16.4.1 Microwave Ovens: Estimates of Global Present Value of CO2 Emissions 

Reduction Under Trial Standard Levels ............................................................ 16-6

Table 16.4.2 Microwave Ovens: Estimates of Domestic Present Value of CO2

Emissions Reduction Under Trial Standard Levels........................................... 16-6

Table 16.4.3 Microwave Ovens: Estimates of Present Value of NOX Emissions 

Reduction Under Trial Standard Levels ............................................................ 16-7
16-1

CHAPTER 16. MONETIZATION OF EMISSION REDUCTIONS BENEFITS

16.1 INTRODUCTION

As part of its assessment of energy conservation standards, DOE considered the estimated 

monetary benefits likely to result from the reduced emissions of carbon dioxide (CO2) and 

nitrogen oxides (NOX) that are expected to result from each of the TSLs considered. This chapter

summarizes the basis for the estimated monetary values used for each of these emissions and 

presents the benefits estimates considered.

16.2 MONETIZING CARBON DIOXIDE EMISSIONS

16.2.1 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. 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. Estimates of the SCC are provided in dollars per metric 

ton of carbon dioxide. 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.

Under section 1(b)(6) of Executive Order 12866, “Regulatory Planning and Review,” 58 

FR 51735 (Oct. 4, 1993), 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 presented here is to 

allow agencies to incorporate the monetized social benefits of reducing CO2 emissions into costbenefit 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.

As part of the interagency process that developed the 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.
16-2

16.2.1.1 Monetizing Carbon Dioxide Emissions

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 

Research Council 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. Most Federal 

regulatory actions can be expected to have marginal impacts on global emissions. For such 

policies, the agency can estimate the benefits from reduced emissions in any future year 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 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. 

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$) of $55, $33, $19, $10, and $5 per metric ton of CO2. These 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 this preliminary effort were presented in 

several proposed and final rules.

16.2.2 Current Approach and Key Assumptions

After the release of the interim values, the interagency group reconvened on a regular 

basis to generate improved SCC estimates, which were considered for this proposed rule. 

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 FUND, DICE, and PAGE models. These models are frequently 

cited in the peer-reviewed literature and were used in the last assessment of the 

Intergovernmental Panel on Climate Change. Each model was given equal weight in the SCC 

values that were developed.
16-3

Each model takes a slightly different approach to model how changes in emissions result 

in changes in economic damages. 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, 

socio-economic 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 socio-economic 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 sets of SCC values for use in regulatory analyses. 

Three sets of values are based on the average SCC from three integrated assessment models, at 

discount rates of 2.5 percent, 3 percent, and 5 percent. The fourth set, which represents the 95thpercentile SCC estimate across all three models at a 3-percent discount rate, is included to 

represent higher-than-expected impacts from climate change further out in the tails of the SCC 

distribution. The 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 preference is given to consideration of the global 

benefits of reducing CO2 emissions. Table 16.2-1 presents the values in the 2010 interagency 

group report, which is presented in appendix 16-A.

a

The SCC values used in this TSD were generated using the most recent versions of the 

three integrated assessment models that have been published in the peer-reviewed literature.

b

Table 16.2-2 shows the updated sets of SCC estimates in five year increments from 2010 to 

2050. The full set of annual SCC estimates between 2010 and 2050 is reported in appendix 16-B. 

The central value that emerges is the average SCC across models at the 3 percent discount rate. 

However, for purposes of capturing the uncertainties involved in regulatory impact analysis, the 

interagency group emphasizes the importance of including all four sets of SCC values.

 a Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866. Interagency Working Group 

on Social Cost of Carbon, United States Government, 2010.

b 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, April 2013.
16-4

Table 16.2-1 Annual SCC Values from 2010 Interagency Report, 2010–2050 (in 2007 

dollars per metric ton CO2)

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

Table 16.2-2 Annual SCC Values from 2013 Interagency Update, 2010–2050 (in 2007 

dollars per metric ton CO2)

Year

Discount Rate %

5 3 2.5 3

Average Average Average 95th Percentile

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

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 Research Council report mentioned 

above 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. There are 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 interagency group intends to periodically 
16-5

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 summary, in considering the potential global benefits resulting from reduced CO2

emissions, DOE used the values from the 2013 interagency draft report, which is reprinted in 

appendix 16-B of this TSD, adjusted to 2011$ using the Gross Domestic Product price deflator.

For each of the four cases specified, the values used for emissions in 2016 were $12.6, $41.1, 

$63.2, and $119 per metric ton avoided (values expressed in 2011$). DOE derived values after 

2050 using the growth rate for the 2040-2050 period in the interagency update.

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.

16.3 VALUATION OF OTHER EMISSIONS REDUCTIONS

DOE considered the potential monetary benefit of reduced NOX emissions from the TSLs 

it considered. As noted in chapter 15, new or amended energy conservation standards would 

reduce NOX emissions in those 22 States that are not affected by caps. DOE estimated the 

monetized value of NOX emissions reductions resulting from each of the TSLs considered based 

on environmental damage estimates found in the relevant scientific literature. Available 

estimates suggest a very wide range of monetary values, ranging from $468 to $4,809 per ton in 

2012$).c In accordance with OMB guidance, DOE calculated a range of monetary benefits using 

each of the economic values for NOX and real discount rates of 3 percent and 7 percent..

d

DOE is still evaluating appropriate values to use to monetize avoided SO2 and Hg 

emissions. It did not monetize these emissions for the MWO final rule.

16.4 RESULTS

Table 16.4.1 presents the global values of CO2 emissions reductions at each TSL. DOE 

calculated domestic values as a range from 7 percent to 23 percent of the global values, and those 

results are presented in Table 16.4.2.

 c For additional information, refer to 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. d

 OMB, Circular A-4: Regulatory Analysis (Sept. 17, 2003).
16-6

Table 16.4.1 Microwave Ovens: Estimates of Global Present Value of CO2 Emissions 

Reduction Under Trial Standard Levels 

TSL

Million 2011$

5% Discount 

Rate, 

Average*

3% Discount 

Rate, 

Average*

2.5% Discount 

Rate, Average*

3% Discount 

Rate, 95th

Percentile*

1 $128 $592 $942 $1,815

2 $185 $855 $1,360 $2,621

3 $255 $1,179 $1,876 $3,615

4 $392 $1,812 $2,882 $5,554

* Columns are labeled by the discount rate used to calculate the SCC and whether it is an average value or drawn 

from a different part of the distribution. Values presented in the table are based on escalating 2007$ to 2011$ for 

consistency with other values presented in this notice, and incorporate the escalation of the SCC over time.

Table 16.4.2 Microwave Ovens: Estimates of Domestic Present Value of CO2

Emissions Reduction Under Trial Standard Levels

TSL

5% Discount 

Rate, 

Average*

Million 2011$

3% Discount 

Rate, 

Average*

2.5% Discount 

Rate, Average*

3% Discount 

Rate, 95th

Percentile*

1 9 to 29 41 to 136 66 to 217 127 to 417

2 13 to 43 60 to 197 95 to 313 183 to 603

3 18 to 59 83 to 271 131 to 431 253 to 831

4 27 to 90 127 to 417 202 to 663 389 to 1277

* Domestic values are presented as a range between 7% and 23% of the global values.

** Columns are labeled by the discount rate used to calculate the SCC and whether it is an average value or drawn 

from a different part of the distribution. Values presented in the table are based on escalating 2007$ to 2011$ for 

consistency with other values presented in this notice, and incorporate the escalation of the SCC over time.

Table 16.4.3 presents the cumulative monetary value of the economic benefits associated 

with NOX emissions reductions for each TSL, calculated using seven-percent and three-percent 

discount rates.
16-7

Table 16.4.3 Microwave Ovens: Estimates of Present Value of NOX Emissions 

Reduction Under Trial Standard Levels

TSL 3% discount rate 7% discount rate

million 2011$

1

million 2011$

$22.3 $11.0

2 $32.3 $15.8

3 $44.5 $21.8

4 $68.4 $33.6
17-i

CHAPTER 17. REGULATORY IMPACT ANALYSIS 

TABLE OF CONTENTS 

17.1 INTRODUCTION ......................................................................................................... 17-1

17.2 NON-REGULATORY POLICIES ................................................................................ 17-2

17.2.1 Methodology .................................................................................................................. 17-2

17.2.2 Policy Assumptions ....................................................................................................... 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 Financial Incentives Policies .......................................................................................... 17-4

17.3.2.1 Consumer Rebates ....................................................................................... 17-5

17.3.2.2 Consumer Tax Credits ................................................................................. 17-8

17.3.2.3 Manufacturer Tax Credits .......................................................................... 17-11

17.3.3 Voluntary Energy Efficiency Targets .......................................................................... 17-11

17.3.4 Early Replacement ....................................................................................................... 17-13

17.3.5 Bulk Government Purchases ........................................................................................ 17-14

17.4 NON-REGULATORY POLICY IMPACTS ............................................................... 17-15

17.5 RESULTS SUMMARY FOR NON-REGULATORY ALTERNATIVES ................. 17-16

LIST OF TABLES 

Table 17.1.1 Policy Alternatives to National Standards ......................................................... 17-1

Table 17.2.1 Trial Standard Level 3 for Microwave Ovens ................................................... 17-3

Table 17.3.1 Benefit/Cost Ratios for Microwave Oven Proposed Standard and Rebate 

Policy Case......................................................................................................... 17-6

Table 17.3.2 Oregon Tax Credits Claimed for Residential Dishwashers from 1998 to 

2006 and their Attribution for 2006 ................................................................. 17-10

Table 17.3.3 Oregon Tax Annual Increased Market Shares of Microwave Ovens at 

Target Efficiency Levels from Consumer Tax Credits .................................... 17-10

Table 17.3.4 Annual Increased Market Shares of Microwave Ovens at Target 

Efficiency Level from Voluntary Efficiency Targets ...................................... 17-12

Table 17.5.1 Non-Regulatory Alternatives for Product Class 1 with Target Level at TSL 

3........................................................................................................................ 17-17

Table 17.5.2 Non-Regulatory Alternatives for Product Class 2 with Target Level at TSL 

3........................................................................................................................ 17-17

LIST OF FIGURES 

Figure 17.3.1 Market Penetration Curve for Microwave Ovens (product class 1) .................. 17-7
17-ii

Figure 17.3.2 Market Penetration Curve for Microwave Ovens (product class 2) .................. 17-7

Figure 17.4.1 Penetration Rates of Units Meeting the Target Level for Microwave Ovens.. 17-16
17-1

CHAPTER 17. REGULATORY IMPACT ANALYSIS 

17.1 INTRODUCTION 

 The Department of Energy (DOE) has determined that energy conservation standards for 

microwave ovens constitute an “economically significant regulatory action” under Executive 

Order (E.O.) 12866 “Regulatory Planning and Review.” 58 FR 51735 (October 4, 1993). 

Therefore, DOE’s proposed energy conservation standards require a regulatory impact analysis 

(RIA), which involves an evaluation of non-regulatory alternatives to the standards. This 

document evaluates several possible alternatives to the proposed standards, and compares the 

costs and benefits of each to the proposed standards. As described in section 17.2.2 of this report, 

the proposed standards for microwave ovens the proposed standards are those in TSL 3. 

 Under the Process Rule (Procedures for Consideration of New or Revised Energy 

Conservation Standards for Consumer Products, 61 FR 36974 (July 15, 1996); 10 CFR Part 430, 

Subpart C, Appendix A), DOE is committed to continually explore non-regulatory alternatives to 

standards. This RIA, which DOE has prepared pursuant to E.O. 12866, is subject to review under 

the Executive Order by the Office of Management and Budget (OMB)’s Office of Information 

and Regulatory Affairs (OIRA). 58 FR 51735. 

DOE identified six major non-regulatory alternatives to standards as representing feasible 

policy options to achieve consumer product energy efficiency for the appliance products that are 

the subject of this rulemaking. These are listed in Table 17.1.1. DOE evaluated each alternative 

in terms of its ability to achieve significant energy savings at a reasonable cost, and compared 

the effectiveness of each one to the effectiveness of the proposed standards. 

Table 17.1.1 Policy Alternatives to National Standards 

No New Regulatory Action

Consumer Rebates

Consumer Tax Credits

Manufacturer Tax Credits

Voluntary Energy Efficiency Targets

Early Replacement

Bulk Government Purchases
17-2

17.2 NON-REGULATORY POLICIES 

17.2.1 Methodology 

 This section describes the approach DOE used to analyze non-regulatory policies for 

microwave ovens. 

 To calculate the national energy savings and the net present value (NPV) corresponding 

to each policy alternative, DOE used its national impact analysis (NIA) spreadsheet models. (See 

Chapter 10 of the technical support document (TSD) for a description of the NIA spreadsheet 

models.) To compare each alternative to the proposed standards, DOE quantified the effect of 

each alternative on the purchase of microwave ovens meeting the target levels, which are defined 

as the efficiency levels in the proposed standards. Once it had made the quantitative assumptions 

for each alternative policy, DOE made the appropriate revisions to the inputs in the NIA 

spreadsheet models. The main model inputs that DOE revised were market shares of equipment 

at target efficiencies, shipment-weighted average annual energy consumption, and equipment 

replacement rates. The shipments for any given year are comprised of a distribution of efficiency 

levels. DOE assumed that standards would affect 100 percent of the shipments, while the nonregulatory policies would affect a smaller percentage of the shipments. In each policy case, DOE 

made particular assumptions about the percentage of shipments impacted by the policy under 

analysis. DOE then calculated the shipment-weighted average energy consumption and costs 

using these market shares. 

 A shift in the market share of higher efficiency units may increase the average installed 

cost of energy-consuming equipment. Operating costs will generally decrease due to a decline in 

energy consumption. Therefore, DOE calculated an NPV for non-regulatory alternatives in the 

same way as it did for the proposed standards. In some scenarios, total installed cost increases 

are partially mitigated by government rebates or tax credits. However, DOE assumed that credits 

and rebates would be paid for by consumers in another form (such as additional taxes), and 

therefore did not include them as a consumer benefit for the purposes of calculating the national 

NPV. DOE did not consider administrative costs for any of the non-regulatory policies in its 

analysis. Inclusion of such costs would decrease their NPVs by a small amount. 

 The key measures of the impact of each alternative are: 

• National energy savings in quadrillion Btus (quads): Cumulative national primary energy 

savings for equipment bought in the period from the effective date of the policy case 

(2016) to the year 2045. 

• Net present value: The value of net monetary savings from equipment bought in the 

period from the effective date of the policy case (2016) to the year 2045. DOE calculated 

the NPV as the difference between the present value of equipment and operating 

expenditures (including energy) in the base case and the present value of expenditures in 
17-3

each alternative policy case. DOE calculated operating expenses (including energy) for 

the life of the equipment. 

17.2.2 Policy Assumptions 

 The impacts of non-regulatory policies are by nature uncertain, since they depend on 

program implementation and marketing efforts and the subsequent consumer behavior response. 

The projected impacts depend on the assumptions regarding the consumer participation rate, and 

are therefore subject to more uncertainty than the impacts of mandatory standards, which DOE 

assumes will have full compliance. To increase the robustness of the analysis, DOE conducted a 

literature review on each non-regulatory policy and consulted with key experts to gather 

information on similar incentive programs that have already been implemented in the U.S. By 

studying field experience with sample programs of each type, DOE sought to make credible 

assumptions of their potential market impacts. Section 17.3 below reports the conclusions from 

this research as they apply to the policy modeling assumptions and includes the corresponding 

literature citations. 

 Each of the policy alternatives that DOE considered to the proposed standards would 

improve the average efficiency of new microwave ovens relative to their base cases (no new 

regulatory action). The analysis considered that each alternative policy would induce residential 

or commercial consumers to purchase units at the same efficiency levels as required by the 

proposed standards, or the target levels. In contrast to the proposed standards, however, their 

market penetration rate in the alternative policy cases may not be 100 percent. 

 The proposed standards for microwave ovens are those in TSL 3, as shown in Table 

17.2.1. 

Table 17.2.1 Trial Standard Level 3 for Microwave Ovens 

No. Product Class Proposed Energy Conservation Standards

1 Microwave oven with or without thermal elements Maximum standby power = 1.0 Watt

2 Over-the-range combination ovens Maximum standby power = 2.2 watts

 DOE assumed that the non-regulatory policy impacts would last from the effective date 

for proposed standards for microwave ovens—2016—through the end of the analysis period, 

2045.

17.2.3 Policy Interactions 

 DOE calculated the impacts of each regulatory policy separately from those of the other 

policies. In actual practice, certain policies are often most effective when implemented in 

combination to provide incentives, such as early replacement with consumer rebates, or early 

replacement with bulk government purchases. DOE attempted to make conservative assumptions 

to avoid double-counting policy impacts. Therefore, the policy impacts reported below are not 
17-4

additive; the combined impact of several or all of the policies may not be inferred from adding 

the results together. 

 Section 17.3 presents graphs showing market penetration or shipment-weighted 

efficiency parameters for each of the non-regulatory policy cases for microwave ovens. 

17.3 NON-REGULATORY POLICY ASSUMPTIONS 

17.3.1 No New Regulatory Action 

 The case in which no new regulatory action is taken with regard to microwave oven 

efficiency constitutes the base case scenario described in Chapter 10 on the national impact 

analysis. This case defines the basis of comparison for all other scenarios. By definition, no new 

regulatory action yields zero energy savings and an NPV of zero dollars. 

17.3.2 Financial Incentives Policies 

 DOE considered scenarios in which the Federal government would provide two types of 

financial incentives: rebates and tax credits. The government could provide consumers with a 

rebate for purchasing an energy efficient appliance meeting the target level for each product. Tax 

credits could be offered to consumers who purchased target-level microwave ovens. The 

government could also provide tax credits to manufacturers to offset costs associated with 

producing such equipment. 

 DOE’s evaluation of consumer rebates used a comprehensive study of the potential for 

energy efficiency in California performed by Xenergy, Inc., which summarized experience with 

various utility rebate programs.1

 This analysis method is based on curves that estimate the 

market penetration of a technology based on its benefit/cost (B/C) ratio. DOE consulted with 

experts and reviewed several other methods of estimating market penetration of efficient 

technologies due to consumer rebate programs that were developed since the referenced Xenergy 

report was published.2,

3,4,5

 However, these methods were based either on other economic 

parameters (payback period) or on expert surveys predicting penetration of a new technology 

over time. Therefore, DOE decided to use the penetration curve method based on B/C ratio, 

which incorporates lifetime operating cost savings, and was calibrated with utility rebate 

program participation results. 

 Xenergy’s information diffusion model estimates market impacts induced by financial 

incentives for energy efficient appliances. The basic premise of this model is that information 

diffusion drives technology adoption. The model is formulated to characterize the influences of 

both internal and external sources of information on consumer behavior by superimposing two 

components in the equation, each capturing the effect of one of two different types of 

information source. The effects of these two types of information diffusion mechanisms are 

different. Internal sources of information influence consumers to purchase new products due 
17-5

mainly to word-of-mouth from early adopters, while external information sources influence 

consumers to change their adoption decisions as a result of marketing efforts and information 

coming from outside the consumer group. (Appendix 17A of the TSD contains further details on 

modeling these influences.) 

 Xenergy’s model combined these two information diffusion mechanisms and generated a 

set of measure “implementation curves” or penetration curves, which Xenergy calibrated using 

evaluation data from utility rebate programs. Consumer response to rebate incentives appears to 

be a combination of the two information source types. The penetration curves illustrate the 

increased penetration (i.e., increased market share) of efficient equipment as a result of consumer 

response to B/C ratio changes induced by a specific rebate program. The penetration curves are 

used to depict various diffusion patterns based on perceived barriers to consumer purchase of 

high-efficiency equipment. There are penetration curves for varying levels of market barriers, 

from “no barriers” to “extremely high barriers.” These curves provide a means to study the 

impact of changing the B/C ratio, by reducing the initial equipment cost through financial 

incentives, on the consumer participation rate. 

 DOE based its estimates of the impacts of consumer tax credits on actual program 

experience with State tax credits in Oregon. DOE studied State tax credits in Montana as well. 

DOE also attempted to determine residential consumer participation due to the Federal appliance 

tax credits, which were mandated by the Energy Policy Act of 2005 (EPACT 2005). For the 

manufacturer tax credits policy, DOE attempted to investigate manufacturer participation due to 

the efficient equipment tax credits from EPACT 2005. Both the Federal consumer and 

manufacturer credits were in effect in 2006 and 2007. Unfortunately, the Internal Revenue 

Service (IRS) had not yet published data on taxpayer response to either of these tax credits. 

 DOE also incorporated previous research that had differentiated the impact of tax credits 

into the “direct price effect,” which arises from the incremental equipment cost savings, and the 

“announcement effect,” which is independent of the credit amount.6,

7

 The announcement effect 

derives from the credibility that a particular technology receives from its inclusion in an 

incentive program, as well as changes in product marketing strategy, and the resulting 

modifications in markups and pricing. DOE assumed that the direct price effect and the 

announcement effect would apply to the consumer tax credit policy as well as the consumer 

rebate policy, and that half of the increases in market penetration associated with either policy 

would be due to the direct price effect and half to the announcement effect. 

17.3.2.1 Consumer Rebates 

 DOE modeled the impact of the consumer rebate policy by determining the increase in 

market penetration of target-level equipment relative to its market penetration in the base case. 

DOE assumed a rebate value of 4.00 dollars, which would cover 78 percent (for product class 1) 

and 51 percent (for product class 2) of the incremental installed cost between a microwave 

meeting the baseline efficiency level and a unit meeting the target efficiency. DOE assumed the 

rebates would remain in effect until they had transformed the markets so that the shift in market 
17-6

share of efficient units seen in the first year of the programs would be maintained throughout the 

forecast period (2016–2045). 

 DOE first calculated the B/C ratio for the baseline unit without a rebate. It then calculated 

another B/C ratio for the unit meeting the target level, with a rebate, relative to the baseline unit. 

Because of the incremental cost reductions due to the rebates, the B/C ratios for the rebate policy 

unit were larger. Table 17.3.1 shows the benefits as lifetime operating cost savings, incremental 

installed costs without rebates and with rebates, and B/C ratios without rebates and with rebates. 

Table 17.3.1 Benefit/Cost Ratios for Microwave Oven Proposed Standard and Rebate 

Policy Case 

PC 1 PC 2

Benefit (Lifetime Operating Cost Savings) $31.04 $23.80

Incremental Installed Cost (Increased Installed Cost) $5.14 $7.84

B/C Ratio with No Rebate 6.0 3.0

Rebate Amount $4.00 $4.00

Adjusted Incremental Installed Cost (Increased Installed Cost after Rebate) $1.14 $3.84

B/C Ratio for Rebate Policy Case 27.2 6.2

 DOE then used the B/C ratios with the penetration curves shown in Figure 17.3.1 to 

estimate the increased percentage of consumers who would purchase the units that meet the 

policy target levels if given a rebate incentive. For microwave ovens, DOE chose the “no 

barriers” curve. The incremental cost between the baseline and the TSL is minimal, and there are 

no product utility issues that would cause barriers to consumer acceptance. Figures 17.3.1 and 

17.3.2 show the increase in penetration rates of target-level units as a function of their higher 

B/C ratios. Using the method discussed above, DOE estimated that the market share of 

equipment meeting the policy target due to a rebate policy would increase by 11.7 percent for 

product class 1 and 11.9 percent for product class 2. 

 To calculate the impacts of this policy, DOE adjusted the market shares of microwave 

ovens at the target efficiencies in its NIA model to represent the policy case scenarios. 
17-7

Figure 17.3.1 Market Penetration Curve for Microwave Ovens (product class 1) 

Figure 17.3.2 Market Penetration Curve for Microwave Ovens (product class 2) 

0 %

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 100

Penetration Rate

Benefit/Cost Ratio

No Barriers No Rebate With Rebate

0 %

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 100

Penetration Rate

Benefit/Cost Ratio

No Barriers No Rebate With Rebate
17-8

17.3.2.2 Consumer Tax Credits 

 To analyze this policy, DOE studied taxpayer participation in tax credit programs for 

energy efficient appliances, which were available at both the Federal and the State levels. DOE

analyzed a consumer tax credit program whose credit amounts would be similar to the 

percentage of equipment costs covered by existing State tax credits. Because tax credits have not 

been available for microwave ovens, DOE analyzed Oregon's residential tax credit data for an 

analogous product—dishwashers. 

 EPACT 2005 included Federal tax credits for consumers who installed efficient air 

conditioners or heat pumps, gas or oil furnaces, furnace fans, and gas, oil, or electric heat pump 

water heaters in new or existing homes.8 While these credits were available during tax years 

2006 and 2007, as mentioned above, the IRS had not published data on the numbers of taxpayers 

taking these credits. Appendix 17A gives details of the equipment covered and the Federal tax 

credit amounts for residential appliances. 

 The States of Oregon and Montana have had consumer tax credits for efficient appliances 

for several years.a DOE based most of its estimates on Oregon's experience with this policy. 

Oregon’s Residential Energy Tax Credit (RETC) was created in 1977. The Oregon legislature 

expanded the RETC program in 1997 to include home appliances and participation in the 

program increased significantly after they became eligible.b

 In response to changes in the 

appliance market, the RETC program updates its lists of eligible model numbers monthly, and 

makes occasional adjustments to the maximum tax credit dollar amount for each appliance. 

Oregon offers tax credits on residential refrigerators, clothes washers, and dishwashers.9 The tax 

credit is either an amount noted on the list of qualifying appliancescor 25 percent of the purchase 

price of the appliance, whichever is less. 

 Montana has had an Energy Conservation Installation Credit for residential energy 

conservation measures since 1998.10

 The tax credit covers a variety of residential energy and 

water efficiency installations, including ENERGY STAR heating/cooling equipment, water 

heaters, low-flow showerheads and faucets, and light fixtures and controls. The amount of the 

credit increased in 2002 from 5 percent of equipment costs (up to $150) to 25 percent (up to 

$500). DOE obtained data from the Montana Department of Revenue (DER) on the numbers of 

tax credits claimed from 1998 through 2006.11 However the DER did not have data 

disaggregated by appliance, so the number of tax credits claimed for large household appliances 

could not be distinguished from those for plumbing products. DOE analyzed the available data 

and noted that the number of tax credits claimed increased steadily from 1998 through 2006, 

which contributed to its assumptions for this policy scenario. 

 

a

 The District of Columbia (Washington D.C.) passed legislation entitled the “Residential Energy Conservation Tax 

Credit Act Of 2005” for consumer tax credits, but did not appropriate the necessary funding and so the credits did 

not go into effect. 

b

 The program added fuel cells in 1999 and high-efficiency heat pump systems, furnaces, and boilers in 2002. 

c

 These lists change frequently, and generally require units that exceed ENERGY STAR specifications -- for 

example units that meet Consortium for Energy Efficiency (CEE) specifications. 
17-9

DOE obtained data from the Oregon Department of Energy (ODOE) on the number of 

efficient appliance tax credits claimed by Oregon residential taxpayers for the years 1998 

through 2006, as well as the credits claimed by appliance type for the years 2000 to 2006.12 

DOE also obtained Oregon appliance shipments data for clothes washers and dishwashers for 

2006.13,

14

 The number of credits claimed generally increased each year, although there were 

some fluctuations that ODOE attributed to changes in qualifying models. DOE assumed that a 

tax credit program would be designed to provide consistent consumer information and slightly 

adjusted the ODOE data for the years 2003 and 2004 to provide a steady trend line. DOE 

calculated that the number of clothes washer tax credits claimed comprised 24 percent of the 

total number of Oregon clothes washer shipments in 2006. However, not all of these highefficiency clothes washers were likely purchased due to the tax credit program alone. Some 

taxpayers would have been motivated by utility rebate incentives, and others would be "free 

riders" who would have purchased efficient appliances without a tax credit or rebate incentive 

but still claimed the tax credit. To better understand this interaction between tax credits, rebates, 

and free riders/market impacts, DOE reviewed a report prepared by KEMA on efficient clothes 

washers in the Northwest. KEMA compared the penetration of “ultra high efficiency” (UHE)d

clothes washers in Oregon and Washington, two neighboring States that have similar utility 

rebate programs for residential clothes washers. 15

 The study attributed the difference in market 

share of these UHE units (50 percent for Oregon versus 15 percent for Washington) to the 

Oregon tax credits. DOE estimated that 38 percent of the tax credits claimed (9 percent of total 

shipments) were actually due to rebates. To estimate the free riders, DOE used the market 

penetration of the baseline commercial clothes washer units, which was 9.2 percent of total 

shipments (38 percent of the number of tax credit claims). DOE then estimated that the 

remaining 24 percent of the Oregon clothes washer tax credits claimed for the years 1998–2005 

(or 5.8 percent of total shipments) were attributable to tax credits alone. 

Microwave Ovens 

The percentage of total annual dishwasher shipments comprised by tax credit claims 

declined in 2005 and 2006, which ODOE attributed to changes in the listings of eligible models. 

Observing that clothes washer tax credits claimed generally rose each year between 1998 and 

2006, and also that Montana's tax credit claims increased annually during the same period (see 

below), DOE assumed that the tax credit program for this policy would be designed for 

increasing annual participation and adjusted the dishwasher percentages for the years 2004 

through 2006 to provide a steady trend line. For 2006, the estimated percentage of total 

shipments comprised by tax credit claims was 14 percent. DOE did not find data comparing 

Oregon and Washington’s efficient dishwasher sales. To estimate the portions of these tax credit 

claims that were likely due to utility rebates and to market impacts, DOE reviewed a report by 

Itron on utility rebate programs in California, which compared the percentage of free riders in 

residential clothes washer and dishwasher rebate programs in 2005.16 Itron found that the market 

penetration rate of ENERGY STAR dishwashers was much higher than that of ENERGY STAR 

clothes washers and the percentage of free riders was greater for dishwasher programs than for 

clothes washer programs. Also, DOE estimated that the incremental cost from the baseline was 

 

d

 Units meeting specifications of minimum modified energy factor (MEF) of 2.20 and maximum water factor (WF) 

of 6.5. 
17-10

much smaller for dishwashers than for clothes washers. Thus DOE estimated that market impacts 

(part of which are due to free riders) were responsible for a higher percentage of dishwasher tax 

credit claims than for residential clothes washer credit claims. DOE estimated that 49 percent of 

Oregon's dishwasher tax credits claimed, or 6.9 percent of total dishwasher shipments, were due 

to market impacts. It estimated that utility rebates were responsible for 32 percent of tax credits 

claimed, or 4.5 percent of total shipments. DOE attributed the remaining 20 percent of tax credits 

claimed, or 2.8 percent of total shipments, to tax credits. DOE then took 20 percent of the 

number of Oregon dishwasher tax credits claimed for the years 1998–2005 to estimate the impact 

attributable to tax credits for those years. 

 Table 17.3.2 shows the number of Oregon residential tax credits claimed for dishwashers 

as percentages of total annual shipments these claims represent for the years 1998–2006. It also 

shows the percentages of total shipments attributed to the tax credits themselves, to utility 

rebates, and to other market influences/free riders for 2006, and the percentage attributed to tax 

credits for 1998–2005. 

Table 17.3.2 Oregon Tax Credits Claimed for Residential Dishwashers from 1998 to 2006 

and their Attribution for 2006 

1998 1999 2000 2001 2002 2003 2004 2005 2006

Tax Credits Claimed 4% 7% 9% 10% 12% 14% 12% 7.5% 7.6%

Tax Credits Claimed, Adjusted 4% 7% 9% 10% 12% 14% 14% 14% 14%

Due to Tax Credits 0.7% 1.4% 1.8% 2.0% 2.4% 2.8% 2.8% 2.8% 2.8%

Due to Rebates NA NA NA NA NA NA NA NA 4.5%

Due to Market NA NA NA NA NA NA NA NA 6.9%

 

 DOE estimated that, due to consumer tax credits, the market share of target-efficiency 

microwave ovens would increase by the same percentages shown for dishwashers for the first 

nine years and remain constant at 2.8 percent thereafter. Table 17.3.3 shows the resulting market 

share increases. 

Table 17.3.3 Oregon Tax Annual Increased Market Shares of Microwave Ovens at Target 

Efficiency Levels from Consumer Tax Credits 

Year of Program Annual Market Share Increase

2016 0.7%

2017 1.4%

2018 1.8%

2019 2.0%

2020 2.4%

2021 2.8%

2022 2.8%

2023 2.8%

2024 2.8%

2025–2045 2.8%
17-11

17.3.2.3 Manufacturer Tax Credits 

 EPACT 2005 provides Energy Efficient Appliance Credits to manufacturers for 

production of high-efficiency refrigerators, clothes washers, and dishwashers at the Federal level 

through the IRS.17

 Manufacturers receive these credits only for the increase in production of 

qualifying appliances over a three-year rolling baseline. Each manufacturer is limited to a certain 

amount for all credits under this provision. The credits were available for models produced in 

2006 and 2007. Legislation pending in Congress would extend the manufacturer tax credits in 

modified form through 2010.18 Appendix 17A gives details of the equipment covered and the 

Federal tax credit amounts for appliance manufacturers. 

 DOE assumed that a manufacturer tax credit program would effectively result in a lower 

price to the consumer by an amount equivalent to that provided by consumer tax credits as 

described above. Because these tax credits would go directly to manufacturers, DOE assumed 

that manufacturers would pass on the reduced costs to consumers, causing the direct price effect. 

However, DOE assumed that the announcement effect would not occur because the program 

would not be visible to consumers. Since the direct price effect is approximately equivalent to 

the announcement effect,6 DOE estimated that half of the consumers assumed to take advantage 

of consumer tax credits would purchase more-efficient products due to a manufacturer tax credit 

program. DOE thus applied 50 percent to the market penetrations attributed to Oregon’s 

consumer tax credits in the year 2006 for dishwashers and used the resulting market penetrations 

to represent the impact of manufacturer tax credits for all years of the analysis period. DOE 

assumed the impact of this policy would be to permanently transform the market so that the 

market share increase seen in the first year of the program would be maintained throughout the 

forecast period. 

 DOE estimated that this policy could increase the market share of efficient microwave 

ovens by 1.4 percent annually over the base case at the TSL. Because of the small incremental 

cost between the baseline and TSL for this product, a manufacturer tax credit might be easier to 

implement than a consumer tax credit for microwave ovens. 

17.3.3 Voluntary Energy Efficiency Targets 

 For microwave ovens DOE assumed that voluntary targets would be achieved through 

manufacturer participation in a gradual phase-out of production of units below their respective 

TSLs. DOE assumed that this would be achieved by a program similar to the ENERGY STAR 

endorsement labeling program conducted by the Environmental Protection Agency (EPA) and 

DOE. The ENERGY STAR program sets minimum energy efficiency specifications for various 

products. ENERGY STAR encourages consumer adoption of these products through marketing 

to promote consumer label recognition, adoption of the specifications by various efficiency 

incentive programs, and manufacturer production and promotion of ENERGY STAR-compliant 

appliances. ENERGY STAR prepares projections of market penetration of compliant appliances, 

and estimates the percentage of the sales of those compliant appliances that are attributed to the 

ENERGY STAR program. 
17-12

 Several reports have analyzed the impacts of ENERGY STAR programs for specific 

products, which are generally based on a combination of information dissemination and utility or 

agency rebates. These studies base their analysis on the ENERGY STAR statewide data on 

percentages of each appliance’s shipments that meet the ENERGY STAR specifications. These 

analyses have generally concluded that the market penetration of ENERGY STAR-qualifying 

appliances is higher in regions or States where such promotional programs have been 

active.15,16,

19,20,21

 There has been no ENERGY STAR program for microwave ovens. Because of the low 

incremental costs and the small overall operating cost savings opportunity at the TSL, DOE 

assumed that an ENERGY STAR effort for this product would focus on an information program. 

DOE assumed that such a voluntary program would induce similar market penetration behavior 

as that of televisions with cathode ray tube (CRT) screens, for which there was an ENERGY 

STAR specification, since energy savings for both products are achieved by reducing standby 

power. DOE used estimates of the increased market penetration due to the ENERGY STAR 

program for CRT televisions during its first six years (1998–2003), applying those percentage 

increases to microwaves for the first six years of the analysis period, from 2016 through 2021. 

Beginning in 2004, televisions with liquid crystal display (LCD) screens dominated the market, 

so DOE did not use the ENERGY STAR forecast for televisions from 2004 on. Instead, it 

calculated the average market penetration of all consumer electronics products in 2003—58 

percent—and assumed this level could eventually be achieved by a voluntary program for 

microwave ovens.22 Thus DOE estimated that, from 2021 through 2025, the market penetration 

of microwave ovens meeting the TSL would increase an additional 1.7 percentage points per 

year, reaching 58 percent by 2025. After 2025 DOE assumed that market penetration would 

remain at that level through the remainder of the forecast. Table 17.3.4 shows the annual 

projected market share increases of the targeted units due to the programs resulting from this 

voluntary efficiency targets policy. 

Table 17.3.4 Annual Increased Market Shares of Microwave Ovens at Target Efficiency 

Level from Voluntary Efficiency Targets

Year of Program Microwave Ovens*

2016 15%

2017 37%

2018 44%

2019 43%

2020 42%

2021 47%

2022 49%

2023 52%

2024 55%

2025–2045 58%

* Percentages in each column refer to shares of the eligible market.
17-13

17.3.4 Early Replacement 

 Early replacement refers to the replacement of microwave ovens before the ends of their 

useful lives. The purpose of this policy is to replace old, inefficient equipment with higherefficiency units. The economic feasibility of early replacement depends on the vintage of the unit 

being replaced, the cost for the new equipment, and the energy cost savings. 

 There has been limited experience with early replacement programs for appliances in the 

U.S. However, DOE studied several reports to inform its analysis of this policy. One report 

detailed the Connecticut Appliance Retirement Program (ARP) conducted in 2004.23

 Another 

was an electric energy efficiency potentials study performed for the State of Vermont in 2006.24 

DOE had also performed an earlier study on Federal potential for early retirement of appliances 

under EPACT 1992. 

The Connecticut ARP was conducted from June through December 2004 by Nexus 

Market Research, Inc. and RLW Analytics, Inc. for Northeast Utilities–Connecticut Light and 

Power and the United Illuminating Company’s State programs. The ARP intended to assist 

utility customers in Connecticut to overcome barriers to recycling room air conditioners (RACs), 

secondary refrigerators, and freezers. The program picked up used appliances at customers’ 

homes or at turn-in events, paid participants to retire their units, and educated customers about 

the costs of running older appliances. In addition, the program paid incentives to consumers to 

replace their RACs with ENERGY STAR-qualified units. DOE considered the RAC program to 

most closely resemble the early replacement policy scenario, since these consumers were 

replacing primary units rather than retiring second units. Nexus’ report on ARP results estimated 

the number of RACs retired by program participants, the percentage of those replaced with an 

ENERGY STAR model, and the number of RACs replaced by non-participants during the 

program duration, using program data and surveys. According to their analysis, about seven 

percent of all RACs retired during the program were retired through the ARP, and 63 percent of 

those were replaced with an ENERGY STAR model. This meant that the program directly 

resulted in early replacement with a more efficient unit of about four percent of the total eligible 

RAC population. DOE assumed that this type of program most likely eliminates second 

ownership of the RAC, thus shortening its useful service life. 

 In 2006, GDS Associates, Inc. conducted an electricity and peak demand potentials study 

for energy efficiency and fuel conversion measures for the State of Vermont. GDS developed a 

special “early retirement” scenario with all residential appliances replaced during the four-year 

period from 2006 to 2009, and analyzed similar early replacements in the commercial sector. 

They estimated achievable market penetrations assuming that consumers would receive a 

financial incentive equal to 50 percent of the incremental cost of the measure in most programs. 

GDS assumed an 80 percent penetration limit for the program. It estimated a maximum 

achievable annual program participant level of two percent of applicable single-family or 

multifamily homes in 2005. 

 DOE also reviewed a study it conducted in the 1990s, under EPACT 1992, which 

analyzed the feasibility of a Federal program to promote early replacement of appliances.25 This 
17-14

study identified policy options for early replacement that included a direct national early 

replacement program, replacement of Federally-owned appliances, promotion through equipment 

manufacturers, consumer incentives, incentives to utilities, and building regulations.e

 For this analysis, DOE considered a program that targets the units in the stock that have 

efficiency levels lower than the policy target efficiency level and encourages their early 

replacement with products at the target level. The program affects a portion of units in the stock 

in 2016 that would be replaced at the end of their useful or service life (at the “natural” 

replacement rate) without the effects of this policy, under which they would be replaced sooner. 

Shipments in 2016 and beyond are not affected by this program and remain at base case 

efficiency levels. 

 Based on their average service lives, 11 percent of microwaves are replaced annually; 

these are their natural replacement rates. For this policy study, DOE assumed that an additional 

percentage of microwave ovens in the existing stock in 2016, the first year of the analysis period, 

would be replaced by models meeting the target levels. For each product, DOE modeled this 

policy by replacing four percent per year of the units in the stock in addition to those being 

replaced at the end of their service lives (using the estimated percentage from the Connecticut 

study since it was based on actual program experience). DOE assumed that these early 

replacement programs would last as long as it took to completely replace all of the eligible 

microwave ovens that were in the stock in the year that the program began (2016). After the 

2016 stock had been completely replaced, there would be no additional impacts from this policy. 

17.3.5 Bulk Government Purchases 

 DOE assumed that a bulk government purchase policy would encourage Federal, State, 

and local governments to purchase equipment meeting the target efficiency levels. Aggregating 

public sector demand could provide a market signal to manufacturers and vendors that some of 

their largest customers sought suppliers with products that met an efficiency target at favorable 

prices. This program also could induce “market pull” impacts through manufacturers and 

vendors achieving economies of scale for high-efficiency products. 

While there have been several bulk government purchasing initiatives at the Federal, 

State, and municipal levels, most of these programs have not tracked data on number of 

purchases or degree of compliance with procurement specifications. In many cases, procurement 

programs are decentralized, being part of larger State or regional initiatives. At the Federal level, 

 

e

 The analysis concluded that, while cost-effective opportunities for early replacement exist, a widespread Federal 

early replacement program was not economically justified. Because premature retirement means that a unit may be 

replaced by an appliance less efficient than the eventual replacement would probably have been, energy savings 

would be smaller than anticipated. Early replacement programs could increase sales volatility in the long run by 

encouraging a temporary increase in production followed by a lull in demand. Early replacement could be 

economical in localities with high energy cost conditions or environmental constraints, when replacement appliances 

are much more efficient than existing stock, or when a major technology breakthrough has recently occurred, 

creating the need for a ready market. 
17-15

the Federal Energy Management Program (FEMP) has performed studies of savings potential for 

its procurement specifications for appliances and other equipment on which DOE based its 

assumptions for this policy. Yet FEMP does not track purchasing data, due to the complexity of 

the purchasing systems, number of vendors, etc. There is evidence of increasing interest and 

activity in “green purchasing" on the State, county, and municipal levels. While many of these 

programs target office equipment, the existence of a growing infrastructure for efficient 

purchasing specifications indicates that such impacts that DOE attributed to bulk government 

purchasing programs are feasible.26,

27

 For microwave ovens, DOE assumed that government agencies, such as the Department 

of Housing and Urban Development (HUD), would administer such a program. At the Federal 

level, this program could be incorporated into the existing FEMP program. FEMP has 

procurement guidelines for Federal government equipment purchasing and Federal construction 

requirements include these guidelines for installing or replacing equipment.Error! Bookmark 

not defined. There are currently no FEMP procurement guidelines for microwave ovens. 

 DOE assumed that this policy would impact a subset of housing units for which 

government agencies purchase or influence the purchase of microwave ovens. This subset would 

consist mainly of public housing and housing on military bases. To represent this subset, DOE 

considered publicly owned housing identified in the American Housing Survey (AHS) for 2005, 

which was 2.0 million households, or about 1.6 percent of all U.S. households.28 (Based on the 

AHS, there 124.4 million U.S. households.29) According to the 2001 Residential Energy 

Consumption Survey (RECS 2001), 76 percent of publicly owned households used microwave 

ovens.30

 DOE assumed that the same percentage of publicly owned housing units would operate 

microwave ovens. Hence, DOE estimated that 1.2 percent of US households are publicly owned 

households using microwave ovens, and thus constitute the eligible population to which this 

policy would apply. Of government-owned residential buildings, DOE’s life-cycle cost and 

payback period analysis estimated that, by the start year of 2016, none had microwave ovens at 

efficiency levels meeting the amended energy conservation standards. 

 Based on the above percentages, at the end of the first year of the policy case (2016), 

DOE estimated that eight percent of shipments of government-purchased microwave ovens 

would be equipment meeting the target levels due to existing bulk government purchase 

programs. By 2026, DOE estimated that bulk government purchase programs would increase 

their market share to 80 percent. Thus, DOE modeled the enhanced bulk government purchase 

program assuming that an annual eight-percent market share increase would be maintained over 

10 years starting in 2016. Section 17.4 below presents the resulting efficiency trends for the bulk 

government purchase policy case for microwave ovens. 

17.4 NON-REGULATORY POLICY IMPACTS 

 Figure 17.4.1 shows the market share impacts (increased penetration rates) for 

microwave ovens for each policy case. In the base case (i.e., the case without standards or non-
17-16

regulatory programs), no products are forecasted to meet the target level. All policy cases 

increase the market share of products meeting the target level, with voluntary energy efficiency 

targets being the most effective. As a reminder, the proposed standards (not shown in Figure 

17.4.2) would result in a 100 percent penetration of products meeting the target level. 

Figure 17.4.1 Penetration Rates of Units Meeting the Target Level for Microwave 

Ovens 

17.5 RESULTS SUMMARY FOR NON-REGULATORY ALTERNATIVES 

 Tables 17.5.1 and 17.5.2 show the national energy savings and NPV resulting from the 

various non-regulatory alternative policy cases, when the efficiency target levels are equal to the 

proposed standard levels. The case in which no regulatory action is taken with regard to 

microwave ovens constitutes the base case (or "No Action") scenarios, in which energy savings 

and NPV are zero by definition. For comparison, the tables include the impacts of the proposed 

energy conservation standards. The NPV amounts shown in Tables 17.5.1 and 17.5.2 are based 

on two discount rates (seven percent and three percent real). 

0 %

10%

20%

30%

40%

50%

60%

70%

2012 2017 2022 2027 2032 2037 2042 2047

Percent Units Meeting Target Level

Rebate

Consumer Tax Credit

Manufacturer Tax Credit

Voluntary Efficiency Target

Early Replacement

Bulk Govt Purchase
17-17

Table 17.5.1 Non-Regulatory Alternatives for Product Class 1 with Target Level at TSL 3 

Policy Alternatives

Primary 

Energy 

Savings

(quads)

Net Present Value*

(billion 2011$)

7% discount rate 3% discount rate

No New Regulatory Action 0.0 0.0 0.0

Consumer Rebates 0.056 0.178 0.393

Consumer Tax Credits 0.020 0.073 0.162

Manufacturer Tax Credits 0.011 0.041 0.088

Voluntary Energy Efficiency Targets 0.370 1.305 2.884

Early Replacement 0.016 0.086 0.134

Bulk Government Purchases 0.006 0.021 0.050

Proposed Standards At TSL 3 0.475 1.522 3.358

* Net present value (NPV) is the value of a time series of costs and savings. DOE determined the NPV from 2016 

to 2045 in billions of 2011$.

Table 17.5.2 Non-Regulatory Alternatives for Product Class 2 with Target Level at TSL 3 

Policy Alternatives

Primary 

Energy 

Savings

(quads)

Net Present Value*

(billion 2011$)

7% discount rate 3% discount rate

No New Regulatory Action 0.0 0.0 0.0

Consumer Rebates 0.001 0.002 0.005

Consumer Tax Credits 0.000 0.000 0.001

Manufacturer Tax Credits 0.000 0.000 0.001

Voluntary Energy Efficiency Targets 0.003 0.008 0.020

Early Replacement 0.000 0.001 0.001

Bulk Government Purchases 0.000 0.000 0.000

Proposed Standards At TSL 3 0.006 0.017 0.038

* Net present value (NPV) is the value of a time series of costs and savings. DOE determined the NPV from 2016 

to 2045 in billions of 2011$.
17-18

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 Titus, E. and S. Feldman. Tracking the Effectiveness of Energy-Efficient Appliance 

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Proceedings. 2003. Seattle, WA, August 20-22. International Energy Program Evaluation 

Conference. Pp. 1087-1098.

22 Sanchez, M., C. Webber, R. Brown, and G. Homan. 2007 Status Report - Savings Estimates 

for the ENERGY STAR® Voluntary Labeling Program. 2007. Lawrence Berkeley National 

Laboratory: Berkeley CA. LBNL-56380. 
17-20

 

23 Nexus and RLW Analytics. Impact, Process, and Market Study of the Connecticut Appliance 

Retirement Program: Overall Report, Final. 2005. Submitted to Northeast Utilities–

Connecticut Light and Power and the United Illuminating Company. Nexus Market 

Research, Inc. and RLW Analytics, Inc.: Cambridge, MA. 

24 GDS Associates, Inc. Vermont Electric Energy Efficiency Potential Study: Final Report. 

2007. Prepared for the Vermont Department of Public Service. GDS Associates, Inc.: 

Marietta, GA. 

25 McMahon, J. E. and J. P. Harris. Early Replacement of Appliances. 1994. Unpublished 

report for US Department of Energy. Lawrence Berkeley National Laboratory: Berkeley, 

CA. 

26 Smith , N. Personal communication. National Association of State Procurement Officials 

(NAPSO), Lexington, KY. Telephone conversation with Barbara Atkinson, LBNL. April, 

2008. 

27 Responsible Purchasing Network. (Last accessed December 10, 2010.) 

<http://www.responsiblepurchasing.org/> 

28 HUD. A Picture of Subsidized Households – 2000. US Department of Housing and Urban 

Development. (Last accessed December 10, 2010.) 

 <http://www.huduser.org/picture2000/> 

29 U.S. Census Bureau, Housing and Household Economic Statistics Division. The American 

Housing Survey, 2005. Washington, DC. (Last accessed December 10, 2010.) 

<http://www.census.gov/hhes/www/housing/ahs/ahs.html> 

30 U.S. Department of Energy—Energy Information Administration. Residential Energy 

Consumption Survey: Household Energy Consumption and Expenditures 2001. 2001. 

Washington, DC. (Last accessed December 10, 2010.) 

 <http://www.eia.doe.gov/emeu/recs/public.html> 
5A-i

APPENDIX 5A. AHAM DATA SUBMITTAL

TABLE OF CONTENTS

5A.1 INTRODUCTION .................................................................................................. 5A-1

5A.2 AHAM MICROWAVE OVEN DATA SUBMITTAL.......................................... 5A-1

LIST OF FIGURES

Figure 5A.2.1 AHAM Standby Power Test Data ................................................................. 5A-6
5A-1

APPENDIX 5A. AHAM DATA SUBMITTAL

5A.1 INTRODUCTION

This appendix presents the shipments and incremental cost data submitted to the U.S. 

Department of Energy (DOE) by the Association of Home Appliance Manufacturers (AHAM) in 

support of the engineering analysis for this rulemaking. See chapter 5 of the technical support 

document (TSD) for details of the engineering analysis for which these data were used.

5A.2 AHAM MICROWAVE OVEN DATA SUBMITTAL

AHAM submitted the following microwave efficiency and standby test data and report to 

DOE on October 23, 2006.

October 23, 2006

Mr. Michael Rivest

Managing Director

Navigant Consulting, Inc.

1801 K Street, NW, Suite 500

Washington, D.C. 20006- 1301

Subject: AHAM Test Data on Microwave Ovens 

Summary: 

The Association of Home Appliance Manufacturers is the trade association representing the 

manufacturers of over 95% of the microwave ovens sold in the U.S. To assist the Department of Energy 

in its rulemaking for Microwave Ovens (MWOs), AHAM commissioned the independent testing at 

Intertek/ETL of 22 randomly selected MWOs (provided by 7 different manufacturers). The ovens 

represented a broad spectrum of units presently in the market place, and varied by size, power rating, 

components, and features.
5A-2

The tests were conducted using the DOE test procedures prescribed in 10 CFR Part 430 Appendix I, 

which references IEC Standard 705-1988 with Amendment 2-1993. The results of the tests are shown in 

the file embedded below. Each unit was tested 3 times. The average Energy Factor of the 3 runs for each 

unit is highlighted in yellow in the embedded file.

[File reproduced at the end of this letter]

The test results show that there is a very narrow range in energy efficiency between the least and most 

efficient unit (54.8% minimum to 61.8% maximum - less than a 13% spread). Using the annual useful 

cooking energy output of 79.8kWh/yr, as determined by DOE in its September 8, 1998 rulemaking, this 

range in efficiencies results in a difference of only about 16 kWh/yr (which is about $1.60/yr) between the 

least and most efficient unit.

The September 8, 1998 Final Rule on cooking products states that, “DOE has determined that there would 

be no significant conservation of energy for ... microwave ovens, and standards would not be 

economically justified.”a

 We believe the test data and information provided herein confirms that an

efficiency standard for MWOs is still not warranted.

DOE considered many design options for microwave ovens in its 1998 rule, and after extensive analysis, 

it was determined that no design options were technology feasible or economically justifiable. There 

have been no technological or economic breakthroughs since the previous determination which would 

change the previous conclusion. 

Factors and Features Impacting Efficiency:

There are many factors that impact the efficiency of MWOs, and many different features that are driven

by the demands of the marketplace. These features determine the need for components that consume 

electrical energy or absorb microwave energy during operation. Common components that affect the 

energy efficiency are shown in the table below:

Turntable motor Waveguide & cavity Magnetron tube

Stirrer motor Glass turntable High Voltage Transformer

Cavity lamp Turntable roller High Voltage Diode

Fan motor Turntable hub High Voltage Capacitor

Display, relays, & electronics Stirrer blade

Many of the components listed above may impact the energy consumption differently, depending upon 

the specific oven features and application. For example, an Over-The-Range (OTR) oven may require a 

larger cooling fan motor than a counter-top oven. This is because the ambient air at the OTR air inlet is at 

an elevated temperature due to the range beneath being in operation.

Wattage of the cavity lamp is another example. The consumer desires brightly illuminated oven cavities. 

Increasing the wattage of the lamp by only 10 watts could lower the efficiency of an oven by about 0.5%. 

Some manufacturers may choose higher wattage lamps for product differentiation.

 

a 63 Fed. Reg. 48,038 (Tuesday, September 8, 1998).
5A-3

Manufacturers do not optimize the efficiency of their designs around the DOE test procedure, which calls 

for a simplified method of heating one liter of water in the center of the oven. Rather, ovens are designed 

to heat various sizes, shapes, and types of food most efficiently and evenly. Again, consumer needs are 

the key drivers on MWO design.

For example, designers often use a stirrer to improve cooking performance as determined by Consumer 

Union testing in categories such as thermal uniformity and defrosting. The stirrer is a rotating device 

used to vary how the electromagnetic field is applied to the food in the oven cavity. As the stirrer sweeps 

through its rotation, the magnetron’s frequency and efficiency also change. The net effect is to improve 

cooking performance for the consumer, in exchange for a small decrease in oven efficiency.

Design Options:

In DOE’s March 15, 2006 Framework Document, several design options are listed for microwave ovens 

as possible considerations to improve energy efficiency. We offer the following comments on those 

design options:

• Add insulation – This is not a practical option, as it would not improve the Energy Factor. The 

DOE test is only about 45 seconds in duration and the temperature of the oven walls does not 

increase during use, thus no significant energy would be saved by adding insulation. It is likely 

that the net result would not even be measurable.

• Reflective surfaces – Manufacturers are already using surface finishes to optimize efficiency. If 

DOE is referring to the possibility of adding metallic plates inside the oven cavity to adjust cavity 

impedance to the highest oscillation impedance of the magnetron, this is best accomplished by 

better oven design without such metallic plates.

• More efficient fan – A typical fan only draws about 30 watts, resulting in less than 2% of the total 

energy consumed during the DOE test. Therefore, improving the fan efficiency by going to more 

expensive motors would at best improve the energy factor by less than 0.5%. Thus more efficient 

fans motors are not economically justified.

• Improve efficiency of magnetron – The magnetron tube is the heart of the MWO. As such, 

considerable engineering effort has already gone into optimizing their efficiency, and there is 

very little difference in available technologies. Virtually all magnetrons are the same. A review 

of magnetron manufacturers’ specifications shows that the typical efficiency is about 73%, with 

only a plus or minus 2% variance. Thus, there is no available technology for improvement.

• Improve efficiency of the power supply – There are two types of high voltage power supplies that 

are used in MWOs. The predominant type is the inductive capacitance transformer, known as the 

LC power supply, which has an efficiency of about 82%. The other type is the inverter type, 

which is more expensive, and has an efficiency of about 84%. (General purpose transformers 

have higher efficiencies, but the need for stable output power in MWOs requires the use of 

special transformers which have inherently lower efficiencies.) 

Three of the units tested utilized an inverter type power supply, whereas all others used the more 

typical LC transformer. Even though inverter power supplies are slightly more efficient than LC 

types, the Energy Factors of the 3 units with an inverter were right around the median of all the 

units tested. They ranged around 56% to 59%, with the average of the 3 units being 58%. 
5A-4

Therefore, the use of an inverter as a design option does not necessarily improve efficiency. We 

see no cost effective opportunity for improving the efficiency of the power supply. 

• Eliminate or improve ceramic stirrer cover – Virtually no residential solo-function microwave 

ovens still use a ceramic stirrer. Almost all MWOs use a polypropylene or mica film, which are 

low energy loss materials resulting in less losses than the ceramic type. Thus this design option 

has already been optimized.

• Modify wave guide – We are not aware of any improvement to the wave guide that would result 

in any significant increase in energy efficiency. Losses associated with the wave guide are 

typically less than 0.5% of the overall energy consumption.

• Dual magnetrons – This would not be an effective way to improve energy efficiency since 

magnetrons require energy to heat the magnetron heater. This energy is not converted to 

microwave energy, and thus is wasted. Thus, the more magnetrons used, the more wasted energy 

is consumed, not to mention the additional cost of extra magnetrons.

Standby Power:

Standby power was measured on all the ovens tested, in accordance with IEC Standard 62301-2005, 

Household electrical appliances – Measurement of standby power. The measurements ranged between 

1.5 and 5.8 watts, with an average of 2.9 watts. Each watt of standby power is approximately 8.7 kWh/yr, 

which amounts to about 85 cents/yr. Even if DOE limited the standby power of MWOs to 2 watts, it 

would only result in less than a dollar per year savings on average per unit.

There are three major types of displays used in microwave ovens today. They are LED (light emitting 

diodes), VFD (vacuum fluorescent display) and LCD (liquid crystal display). Typically, LCD without 

back-lighting uses the least amount of energy while VFD uses the most. Energy consumed by LCD 

would depend on whether back-lighting is present or not. And if present, the number of LEDs used for 

back-lighting is a major factor in energy consumption.

For LEDs, the number of segments, color and operating temperature has an impact on its energy 

consumption. The size of the display (number of digits and icons) also affects the energy consumed by 

the display. For VFDs, the size of display is a major factor in its energy consumption. 

The type of display used, its size, and type of back-lighting, etc., all have a direct impact on the product’s 

appeal to the consumer. Thus, while an LCD without back-lighting may be most energy efficient, it may 

not have as much consumer appeal as a VFD, which is typically the best from a legibility perspective. 

Manufacturers (driven by consumer/market desires) want the flexibility to produce microwave ovens 

using the various displays described above in order to provide products with market differentiation, in 

turn meeting the diverse needs of consumers. This is the reason for the variance in the test results for 

measured standby power. Therefore, AHAM recommends that standby power not be considered as a 

separate prescriptive requirement. 

Conclusion:

All the ovens we tested were within a relatively narrow efficiency range without any current energy 

standards in effect. The tests indicated a range in efficiencies that results in a difference of only about 16 
5A-5

kWh/yr (which is about $1.60/yr) between the least and most efficient unit. This narrow range of 

efficiencies is a result of optimization of current cost effective design options and market competition to 

produce high quality microwave ovens at low prices for the consumer, while still being able to offer 

ample product features and differentiation. 

Efficiency standards for microwave ovens are not justified, as very little energy savings would result. 

The cost and risk of modifying today’s well-performing products with questionable design options should 

not be underestimated. Microwave ovens are much more efficient than conventional cooking products, 

and their use should be encouraged, not discouraged by adding cost to the products for very little overall 

energy savings benefit.

Therefore, we recommend that a “No Standard, Standard” be re-established for these products and 

that significant resources not be expended to conduct unnecessary analyses. Sufficient data and 

precedent exists to support and affirm DOE’s previous conclusion that standards for MWOs are not 

justified. DOE and industry resources would be better spent on expediting other rulemakings.

Thank you for the opportunity to provide this information. Please feel free to contact me with any 

questions that you might have. 

Sincerely,

Lawrence R. Wethje, P.E.

Vice President, Technical Services
5A-6

Figure 5A.2.1 AHAM Standby Power Test Data

Client: AHAM

Project No.: 3100653

Microwave Oven Re-Testing: 09/20-22/06

Tested per IEC 705-1988 and Amendment 2-1993

Unit

No.

Rated

Volume

(ft3)

Humidity

Sensor

(Y/N)

Rated 

Watts

Mw ,

(g)

Mc,

(g)

T0,

(oC)

T1,

(oC)

T2,

(oC)

t,

(Sec.) Win

Watts @ 

20 Sec.

P,

(Watts)

EF,

(%)

SP,**

(Watts)

1000 515 20.8 10.0 19.5 52 18.9 1292 753.6 58.3

1000 515 19.6 10.4 19.7 52 18.8 1286 749.7 58.3

1000 515 19.9 10.0 19.1 52 18.8 1292 725.8 56.2

57.6

1005 515 19.5 10.4 19.9 35 18.4 1875 1147.3 61.2

1000 515 21.9 11.7 21.3 35 18.4 1876 1140.7 60.8

995 515 20.8 10.5 20.2 35 18.3 1872 1146.8 61.3

61.1

1000 515 18.9 9.8 20.0 42 20.0 1730 1028.7 59.5

1000 515 19.5 10.1 20.3 42 20.1 1735 1025.5 59.1

1000 515 19.9 10.4 20.3 42 19.7 1705 991.3 58.1

58.9

1000 515 19.5 10.1 19.9 42 19.6 1696 981.3 57.9

1000 515 20.7 10.5 20.2 42 19.7 1704 961.6 56.4

995 515 20.5 10.5 20.3 42 19.4 1678 969.9 57.8

57.4

1000 515 20.7 10.8 20.4 42 19.1 1604 953.8 59.5

1000 515 19.3 10.1 19.8 42 19.1 1602 972.4 60.7

1000 515 19.2 9.9 19.8 42 19.1 1607 993.4 61.8

60.7

1000 515 19.1 10.1 20.1 35 19.6 1946 1209.2 62.1

1005 515 19.2 10.1 20.1 35 19.6 1951 1213.9 62.2

1000 515 21.1 10.8 20.8 35 19.6 1950 1192.4 61.1

61.8

1000 515 19.2 9.8 18.8 32 17.6 1988 1171.9 59.0

995 515 20.4 10.2 19.4 32 17.6 1986 1183.6 59.6

1000 515 20.8 10.8 19.8 32 17.5 1984 1163.4 58.6

59.1

1000 515 21.3 11.0 20.4 34 18.7 2010 1145.6 57.0

1000 515 19.6 10.6 19.9 34 18.8 2010 1149.3 57.2

1000 515 20.0 10.7 19.9 34 18.8 2010 1131.6 56.3

56.8

1005 515 18.7 9.3 18.6 34 18.8 2008 1149.7 57.3

995 515 19.3 9.8 19.3 34 18.7 2006 1164.0 58.0

1000 515 19.2 9.7 19.3 34 19.1 2009 1183.5 58.9

2.2 Y Average: 58.1

1.2 Y 1300 Average: 2.0

1.6 Y 1200 Average: 3.7

1.6 N

9

8 Average:

1.5

3.0

3.9

4.2

Average:

Average:

Average:

Average:

1.2 N 1.8

1250 2.2

1250 2.9

Average:

N 1200

1.6*

1000

1.6*

1.5

Y 1000

N 800

N 1000

1 0.8

3

4

5

2

6

7
5A-7

1000 515 19.6 10.0 19.8 60 20.2 1216 685.4 56.4

1000 515 21.8 11.8 21.3 60 20.3 1218 659.2 54.1

1000 515 21.7 11.8 21.4 60 20.0 1210 667.7 55.2

55.2

1000 515 19.7 10.1 20.3 60 20.0 1192 716.3 60.1

1000 515 21.6 11.4 21.4 60 19.6 1180 696.3 59.0

1000 515 21.7 11.1 21.0 60 19.6 1176 685.6 58.3

59.1

1000 515 21.8 11.3 21.0 42 19.8 1690 958.4 56.7

1000 515 19.2 10.1 19.9 42 20.1 1712 984.5 57.5

1000 515 19.6 10.0 19.6 42 19.6 1668 957.0 57.4

57.2

1000 515 18.5 9.1 19.2 38 20.9 1934 1121.2 58.0

1000 515 19.1 9.9 20.1 38 20.9 1959 1135.8 58.0

1000 515 19.1 9.9 19.8 38 20.6 1909 1099.2 57.6

57.8

1000 515 19.8 8.9 19.0 42 19.9 1670 998.2 59.8

1000 515 21.7 11.3 21.1 42 20.3 1686 970.5 57.6

1000 515 21.0 10.4 20.4 42 20.2 1683 990.4 58.8

58.7

1000 515 18.9 9.7 20.0 42 19.7 1672 1038.7 62.1

1000 515 19.1 10.1 20.3 42 19.7 1675 1029.8 61.5

1000 515 20.4 10.2 20.4 42 19.8 1676 1016.8 60.7

61.4

1000 515 19.5 10.0 20.0 52 21.0 1421 809.6 57.0

1000 515 21.7 11.2 21.1 52 20.6 1402 791.9 56.5

1000 515 21.6 11.0 20.8 52 20.6 1402 782.1 55.8

56.4

995 515 19.6 10.5 20.3 35 18.7 1878 1175.6 62.6

1000 515 21.1 11.1 20.7 35 18.6 1874 1143.3 61.0

1000 515 21.0 10.6 20.2 35 18.6 1875 1138.1 60.7

61.4

1000 515 21.3 10.6 20.7 42 20.8 1740 1000.4 57.5

1000 515 19.4 9.7 19.8 42 20.8 1745 1011.2 57.9

1005 515 20.0 10.5 20.3 42 20.8 1745 985.1 56.5

57.3

1000 515 19.4 9.2 18.8 42 19.9 1668 950.6 57.0

1000 515 20.7 10.3 19.7 42 19.8 1669 926.3 55.5

1000 515 21.3 11.6 20.8 42 19.8 1668 911.8 54.7

55.7

1000 515 20.0 10.7 20.2 38 21.1 1936 1049.1 54.2

1005 515 20.0 9.6 19.4 38 21.1 1941 1078.0 55.5

1000 515 20.1 10.0 19.6 38 20.8 1922 1051.8 54.7

54.8

1000 515 20.2 10.6 20.3 44 20.5 1630 924.1 56.7

995 515 21.1 11.1 20.6 44 20.5 1628 894.3 54.9

1000 515 19.9 10.4 19.9 44 20.2 1623 904.0 55.7

55.8

Legend:

P - is the microwave power output, in watts

M w - is the mass of the water, in grams

M c - is the mass of the container, in grams

T0 - is the ambient temperature, in degrees Celsius

T1 - is the initial temperature of the water, in degrees Celsius

T2 - is the final temperature of the water, in degrees Celsius

t - is the heating time, in seconds, excluding the magnetron filament heating-up time

Average:

Average:

Y 1000

1000

N 1000

Average:

Average:

Average:

Average:

0.7 N

Y 1100

1000

Average:

Average:

Average:

N

DAMAGED UNIT!!!!!

5.8

22 1.4* N 950 2.6

1.5* Y 1100 Average:

Average:

21

4.4

20 1.0 N 1000 1.7

19 1.5

2.0

18 1.5 N -

1.2 N 1200 Average:

Average:

17

1.8

16 0.8* Y 800 5.7

15 1.1

3.6

14 0.9*

Not

Listed 1000 2.0

13 2.0

0.7 700 2.0

2.1

1.9

700

12 1.6 N

10

11
5A-8

Win - is the energy input, in watt-hours

EF - is the Energy Factor, or efficiency, in percent

SP - is the standby power, in watts 

All tests were performed at 120 Vac, 60 Hz

* - Physical measurement performed by ETL

** - M easured with the microwave clock set to 12:00 and with door closed.
5B-i

APPENDIX 5B. MICROWAVE OVEN STANDBY POWER TESTING 

REGRESSION METHODOLOGY

TABLE OF CONTENTS

5B.1 INTRODUCTION ...................................................................................................5B-1

5B.2 METHODOLOGY ..................................................................................................5B-1

LIST OF TABLES

Table 5B.2.1 Active Element Lookup Table .....................................................................5B-1

Table 5B.2.2 Sample Regression Table.............................................................................5B-2

Table 5B.2.3 Sample Regression Output...........................................................................5B-2

Table 5B.2.4 Sample Integration Table.............................................................................5B-3
5B-1

APPENDIX 5B. MICROWAVE OVEN STANDBY POWER TESTING 

REGRESSION METHODOLOGY

5B.1 INTRODUCTION

The U.S. Department of Energy (DOE) considered alternate testing methods to determine 

standby power over a shorter period than 12 hours that were highly representative in order to 

reduce the test burden on manufacturers. Thus, DOE developed an algorithm by which the 

standby power consumption and line voltage are measured as the clock is cycled through all the 

possible digit combinations (in terms of active elements) and then a regression analysis is 

performed to quantify the impact of the number of lit elements (by digit) and voltage on power 

consumption. The results are then integrated across the number of minutes that each active 

element combination is “on” through the course of the 12 hours.

5B.2 METHODOLOGY

The test method would require that the tester enter the following 18 clock combinations 

at 15 second intervals into the microwave oven clock display: 1:11, 1:17, 1:14, 1:15, 1:16, 1:18, 

1:41, 1:51, 1:01, 1:55, 7:55, 4:55, 5:55, 6:55, 8:55, 11:55, 12:55, and 10:55. For each 

programmed time, the average line voltage and standby power consumption over a 15-second 

period are measured. The tester also determines, for each clock time displayed, the total number 

of active elements. For the purpose of calculating the active number of elements for each digit 

displayed on the microwave oven display, DOE developed the following simplified lookup table.

Table 5B.2.1 Active Element Lookup Table

Digit Displayed Number of Active Elements

Blank / None 0

0 6

1 2

2 5

3 5

4 4

5 5

6 6

7 3

8 7

9 6

The results are then used as inputs to the regression analysis, as shown for illustrative 

purposes with example data in Table 5B.2.2 below. The regression uses five input factors: the 

number of active elements for each digit (AE1 through AE4, assuming a four-digit display) and 
5B-2

the average line voltage in volts alternating-current (VAC), while the standby power 

consumption in watts (W) is the output.

Table 5B.2.2 Sample Regression Table

Clock Time Average Voltage (VAC)

Number of Active Elements by Digit

1 Average Standby Power (W) st 2nd 3rd 4th

1:11 119.718 0 2 2 2 1.16308

1:17 119.8 0 2 2 4 1.22024

1:14 119.8128 0 2 2 4 1.22018

1:15 119.5997 0 2 2 5 1.23941

1:16 119.7364 0 2 2 6 1.2659

1:18 119.7421 0 2 2 7 1.28572

1:41 119.7179 0 2 4 2 1.21773

1:51 119.9457 0 2 5 2 1.24857

1:01 119.9266 0 2 6 2 1.2708

1:55 119.8723 0 2 5 5 1.32908

7:55 119.8025 0 4 5 5 1.38373

4:55 119.7811 0 4 5 5 1.3812

5:55 119.6811 0 5 5 5 1.40734

6:55 119.7248 0 6 5 5 1.43133

8:55 119.758 0 7 5 5 1.45239

11:55 119.7374 2 2 5 5 1.38162

12:55 119.6646 2 5 5 5 1.46521

10:55 119.6489 2 6 5 5 1.48858

The regression results consist of six coefficients: AE1-AE4, voltage, and the intercept. A 

sample Microsoft Excel regression result is shown below in Table 5B.2.3.

Table 5B.2.3 Sample Regression Output

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.998071

R Square 0.996146

Adjusted R 0.99454

Standard E 0.009197

Observatio 18

ANOVA

df SS MS F ignificance F

Regression 5 0.26235 0.05247 620.3353 4.78E-14

Residual 12 0.001015 8.46E-05

Total 17 0.263365

Coefficientstandard Erro t Stat P-value Lower 95%Upper 95%Lower 95.0%Upper 95.0%

Intercept -2.989038 0.434515 -6.879024 1.7E-05 -3.935764 -2.042311 -3.935764 -2.042311

Volts 0.035709 0.00358 9.975633 3.68E-07 0.02791 0.043508 0.02791 0.043508

AE1 0.047066 0.003238 14.5357 5.57E-09 0.040011 0.054121 0.040011 0.054121

AE2 0.030723 0.001635 18.79208 2.88E-10 0.027161 0.034286 0.027161 0.034286

AE3 0.030894 0.001898 16.28024 1.52E-09 0.02676 0.035029 0.02676 0.035029

AE4 0.032807 0.00169 19.41726 1.97E-10 0.029126 0.036488 0.029126 0.036488
5B-3

The five input factors are input into a small integration table which accounts for how 

many minutes each digit is displaying a given number of active elements per day. A sample 

integration table appears as Table 5B.2.4 below. 

Table 5B.2.4 Sample Integration Table

Active 

Elements

Minutes the Number of Active Elements are on For a 

Given Digit per Day Standby Energy 

AE1 AE2 AE3 AE4 Consumption

2 360 240 240 144 0.913 Wh

3 0 0 0 0 0.000 Wh

4 0 240 240 288 1.367 Wh

5 0 480 720 432 3.661 Wh

6 0 360 240 432 2.743 Wh

7 0 120 0 144 0.808 Wh

Voltage 28.292 Wh

Intercept -4.207 Wh

Average Daily Total 33.577 Wh

1.399 W Yearly 12.2557 kWh

The integration table then predicts the average standby power in W, the daily total watthours (Wh) consumed, and the yearly standby energy consumption in kilowatt-hours (kWh), 

assuming stable 120 VAC input power.
5C-i

APPENDIX 5C. MICROWAVE OVEN DISPLAY CONFIGURATIONS

TABLE OF CONTENTS

5C.1 INTRODUCTION ...................................................................................................5C-1

5C.2 DISPLAY CONFIGURATIONS ............................................................................5C-1

LIST OF FIGURES

Figure 5C.2.1 Numeric Display ..........................................................................................5C-1

Figure 5C.2.2 Alphanumeric Display .................................................................................5C-1

Figure 5C.2.3 Dot-Matrix Array Display............................................................................5C-2
5C-1

APPENDIX 5C. MICROWAVE OVEN DISPLAY CONFIGURATIONS

5C.1 INTRODUCTION

The U.S. Department of Energy (DOE) reviewed various configurations of microwave 

oven displays in order to gain a better understanding of how the number of active elements, and 

hence display power consumption, can vary as a function of the digits or characters being 

displayed. Such an analysis helped DOE to determine the optimal method to measure a 

representative average standby power, given the possible variation in standby power during 

testing.

5C.2 DISPLAY CONFIGURATIONS

The simplest displays are the numeric variety, which can only show the time as a series 

of digits. A simplified representation can be found below in Figure 5C.2.1, which shows four 

individual digits. Each digit may have up to seven active elements (for the purpose of this 

discussion, an element is considered “active” whenever the consumer can view it in contrast to 

the background.) Thus, each digit has a changing number of active elements as the clock time 

advances or a cooking timer counts down. 

Figure 5C.2.1 Numeric Display

More sophisticated displays can represent alphanumeric characters via additional 

elements. A simplified version of such a display appears below in Figure 5C.2.2. Such displays 

usually have more than four digits, allowing an original equipment manufacturer (OEM) to scroll 

messages to a microwave oven user.

Figure 5C.2.2 Alphanumeric Display

Digit Individual Element
5C-2

The most flexible displays use dot matrix arrays. Such higher-resolution displays allow a 

microwave oven OEM to represent most Western fonts and some Asian fonts with acceptable 

clarity, allowing the most effective communication with a user. Changeable microwave oven 

controller daughter-cards allow an OEM to readily customize the microwave oven language to 

suit local markets. The array shown below in Figure 5C2.3 is a simplified example. 

Figure 5C.2.3 Dot-Matrix Array Display
5D-i

APPENDIX 5D. MICROWAVE OVEN DESIGN PATHWAYS

TABLE OF CONTENTS

5D.1 INTRODUCTION ..................................................................................................... 5-1

5D.2 PRODUCT TYPES.................................................................................................... 5-1

5D.3 DESIGN PATHWAYS.............................................................................................. 5-2

5D.4 MODEL-BASED WEIGHTING............................................................................... 5-2

5D.5 RESULTS .................................................................................................................. 5-2

LIST OF TABLES

Table 5.1 Product Class 1 Design Options and Model-Based Share by Product Type ............... 5-3

Table 5.2 Product Class 2 Design Options and Model-Based Share by Product Type ............... 5-5
5-1

APPENDIX 5D. MICROWAVE OVEN DISPLAY CONFIGURATIONS

5D.1 INTRODUCTION

The U.S. Department of Energy (DOE) reviewed various configurations of microwave 

ovens to determine likely design approaches which manufacturers would implement in order to 

achieve different standby power levels. After characterizing the sequence of design options that 

would be incorporated (i.e., “design pathways”), DOE weighted the estimated costs of the design 

options by the model-based share of each product type in Product Class 1 and Product Class 2 in 

order to estimate the market-average incremental cost for each standby power level.

5D.2 PRODUCT TYPES

DOE identified five microwave oven features that affect standby power, and thus the 

choice of design options necessary for achieving a specific standby power level. The features 

include:

1. Installation Configuration – whether the product is designed for countertop or over-therange use. DOE considered a product designed for built-in applications to be equivalent 

to over-the-range units based on the requirements for thermal management.

2. Heating Technology – whether the product heats or cooks food by means of microwave 

energy only, or by microwave plus convection (and possibly other) means of cooking.

3. Display Type – the display, which typically includes a clock, may be a liquid crystal 

display (LCD) with or without backlighting, a light emitting diode (LED) display, or a 

vacuum fluorescent display (VFD).

4. Sensor Cooking Capability – whether or not the product incorporates a cooking sensor to 

automatically detect the end of a cooking process

5. Magnetron Power Supply – certain products incorporate an inverter-based high-voltage 

power supply for the magnetron, which adds standby power compared to a typical 

inductive capacitance inverter.

DOE reviewed the features for 540 stock-keeping units (SKUs) which DOE determined 

were on the market in the United States as of October 2012, of which 459 were classified as 

belonging to Product Class 1 and 81 as belonging to Product Class 2. DOE identified 25 unique 

combinations of the five design features among the units classified in Product Class 1. Each 

combination was designated as a product type. In addition, DOE identified six product types in 

Product Class 2.
5-2

5D.3 DESIGN PATHWAYS

In the market and technology assessment and screening analysis, DOE identified multiple 

design options as means to reduce standby power. (See chapters 3 and 4 of the final rule 

technical support Document (TSD) for further information.) These design options included :

1. Lower-power display technologies

2. Zero or low-standby power cooking sensors

3. Improved power supply and control board options

4. Automatic power-down

For the development of the design pathways, DOE considered improved power supply 

and control board options to consist of the use of a switch-mode power supply in place of a 

conventional linear power supply and/or the use of high-efficiency electromechanical or solidstate relays in place of low-efficiency electromechanical ones.

DOE then considered different ways in which the design options could be sequentially 

implemented to achieve successively lower standby power consumption. The standby power 

consumption and incremental manufacturing cost associated with each design option in the 

pathway were summed to develop cost-efficiency relationships for each approach. In total, DOE 

identified 14 pathways that could be utilized by the manufacturer. For each product type, DOE 

selected the lowest-cost pathway that meets the required standby power levels as the one most 

likely to be implemented by manufacturers.

5D.4 MODEL-BASED WEIGHTING

For each product class, DOE calculated the percentage of the total number of models that 

were represented by each product type. These percentages were used as a proxy for the market 

share, and were assigned as weighting factors to the costs for each product type at the various 

standby power levels. The result is that product types with many available models will most 

heavily influence the incremental costs, and those design pathways can be considered as the most 

typical that manufacturers will use.

5D.5 RESULTS

The following tables present the product types that DOE observed to be on the market for 

each of the two product classes, along with the most likely design pathway to achieve each 

standby power level and the product type’s model-based share.
5-3

Table 5.1 Product Class 1 Design Options and Model-Based Share by Product Type

Type

Microwave Oven Features Design Options Implemented SKU 

Share 

Installation Convection? Display Type (%)*

Cooking 

Sensor?

Magnetron 

Supply SL 1 SL 2 SL 3 SL 4

1 Countertop No LED No Conventional

Same as 

Baseline

Same as 

Baseline

Switch-Mode 

Power Supply

Auto PowerDown 21.4%

2 Countertop No LED Yes Conventional

Zero-Standby 

Sensor Same as SL 1

SL 2 + SwitchMode Power 

Supply

Auto PowerDown 7.2%

3 Countertop No LED Yes Inverter

Zero-Standby 

Sensor & 

Switch-Mode 

Power Supply Same as SL 1

SL 2 + High 

Efficiency 

Relays, 

Inverter 

Shutoff Relay

Auto PowerDown 0.7%

4 Countertop Yes LED No Conventional

Same as 

Baseline

Switch-Mode 

Power Supply Same as SL 2

Auto PowerDown 1.1%

5 Countertop Yes LED Yes Conventional

Zero-Standby 

Sensor

SL 1 + SwitchMode Power 

Supply Same as SL 2

Auto PowerDown 0.7%

6

Over-theRange/Built-In No LED No ConventionalSame as 

Baseline

Switch-Mode 

Power Supply Same as SL 2

Auto PowerDown 8.3%

7

Over-theRange/Built-In No LED Yes ConventionalZero-Standby 

Sensor

SL 1 + SwitchMode Power 

Supply Same as SL 2

Auto PowerDown 2.2%

9 Countertop No LCD No Conventional

Same as 

Baseline

Same as 

Baseline

Switch-Mode 

Power Supply

Auto PowerDown 0.2%

10 Countertop No LCD Yes Conventional

Zero-Standby 

Sensor Same as SL 1

SL 2 + SwitchMode Power 

Supply

Auto PowerDown 0.7%

11 Countertop Yes LCD Yes Conventional

Zero-Standby 

Sensor

SL 1 + SwitchMode Power 

Supply Same as SL 2

Auto PowerDown 0.4%

12

Over-the 

Range/Built-In No LCD No Conventional

Same as 

Baseline

Switch-Mode 

Power Supply Same as SL 2

Auto PowerDown 0.9%

13 Countertop No Backlit LCD No Conventional

Same as 

Baseline

Switch-Mode 

Power Supply Same as SL 2

Auto PowerDown 0.7%
5-4

Type

Microwave Oven Features Design Options Implemented SKU 

Share 

Installation Convection? Display Type (%)*

Cooking 

Sensor?

Magnetron 

Supply SL 1 SL 2 SL 3 SL 4

14 Countertop No Backlit LCD Yes Conventional

Zero-Standby 

Sensor

SL 1 + SwitchMode Power 

Supply Same as SL 2

Auto PowerDown 3.5%

15 Countertop No Backlit LCD Yes Inverter

Zero-Standby 

Sensor & 

Switch-Mode 

Power Supply Same as SL 1

SL 2 + High 

Efficiency 

Relays, 

Inverter 

Shutoff Relay

Auto PowerDown 5.2%

16 Countertop Yes Backlit LCD No Conventional

Same as 

Baseline

Switch-Mode 

Power Supply Same as SL 2

Auto PowerDown 0.9%

17 Countertop Yes Backlit LCD Yes Conventional

Zero-Standby 

Sensor & 

Switch-Mode 

Power Supply Same as SL 1

Auto PowerDown Same as SL 3 0.9%

18 Countertop Yes Backlit LCD Yes Inverter

Zero-Standby 

Sensor & 

Switch-Mode 

Power Supply

SL 1 + High 

Efficiency 

Relays, 

Inverter 

Shutoff Relay

Auto PowerDown Same as SL 3 0.9%

19

Over-theRange/Built-In No Backlit LCD No ConventionalSwitch-Mode 

Power Supply Same as SL 1

Auto PowerDown Same as SL 3 1.1%

20

Over-theRange/Built-In No Backlit LCD Yes Conventional

Zero-Standby 

Sensor & 

Switch-Mode 

Power Supply Same as SL 1

Auto PowerDown Same as SL 3 6.5%

21 Countertop No VFD Yes Conventional

Zero-Standby 

Sensor & 

Switch-Mode 

Power Supply Same as SL 1

Auto PowerDown Same as SL 3 7.6%

22 Countertop Yes VFD No Conventional

Switch-Mode 

Power Supply Same as SL 1

Auto PowerDown Same as SL 3 0.4%

23 Countertop Yes VFD Yes Conventional

Zero-Standby 

Sensor & 

Switch-Mode 

Power Supply Same as SL 1

Auto PowerDown Same as SL 3 2.8%
5-5

Type

Microwave Oven Features Design Options Implemented SKU 

Share 

Installation Convection? Display Type (%)*

Cooking 

Sensor?

Magnetron 

Supply SL 1 SL 2 SL 3 SL 4

24

Over-theRange/Built-In No VFD No ConventionalSwitch-Mode 

Power Supply Same as SL 1

Auto PowerDownAuto PowerDown 3.1%

25

Over-theRange/Built-In No VFD Yes Conventional

Zero-Standby 

Sensor & 

Switch-Mode 

Power Supply Same as SL 1

Auto PowerDownAuto PowerDown 21.8%

* Numbers do not add to 100% due to rounding

Table 5.2 Product Class 2 Design Options and Model-Based Share by Product Type

Type

Microwave Oven Features Design Options Implemented SKU 

Share 

Installation Convection? Display Type (%)

Cooking 

Sensor?

Magnetron 

Supply SL 1 SL 2 SL 3 SL 4

1

Over-theRange/Built-In Yes LED No ConventionalSwitch-Mode 

Power Supply Same as SL 1 Same as SL 2

Auto PowerDown 6.2%

2

Over-theRange/Built-In Yes LED Yes Conventional

Zero-Standby 

Sensor & 

Switch-Mode 

Power Supply Same as SL 1 Same as SL 2

Auto PowerDown 4.9%

3

Over-theRange/Built-In Yes LCD Yes ConventionalZero-Standby 

Sensor

Switch-Mode 

Power Supply Same as SL 2

Auto PowerDown 3.7%

4

Over-theRange/Built-In Yes Backlit LCD Yes Conventional

Zero-Standby 

Sensor & 

Switch-Mode 

Power Supply Same as SL 1

Auto PowerDown Same as SL 3 16.1%

5

Over-theRange/Built-In Yes VFD No ConventionalSwitch-Mode 

Power Supply Same as SL 1

Auto PowerDown Same as SL 3 1.2%

6

Over-theRange/Built-In Yes VFD Yes Conventional

Zero-Standby 

Sensor & 

Switch-Mode 

Power Supply Same as SL 1

Auto PowerDown Same as SL 3 67.9%
7A-i

APPENDIX 7A. VARIABLES AND VALUES FROM RESIDENTIAL ENERGY 

CONSUMPTION SURVEY

TABLE OF CONTENTS

7A.1 INTRODUCTION .................................................................................................. 7A-1

7A.2 SAMPLE DETERMINATION............................................................................... 7A-1

7A.3 VARIABLE RESPONSE CODES IN 2009 DATABASE..................................... 7A-2

LIST OF TABLES

Table 7A.2.1 Selection of RECS 2009 Records................................................................ 7A-1

Table 7A.2.2 Variables Tracked in RECS 2009 ............................................................... 7A-2

Table 7A.3.1 Variable Response Codes in RECS 2009.................................................... 7A-3
7A-1

APPENDIX 7A. VARIABLES AND VALUES FROM RESIDENTIAL ENERGY 

CONSUMPTION SURVEY 

7A.1 INTRODUCTION

The U.S. Department of Energy (DOE) created a database containing a subset of the 

records and variables from DOE’s Energy Information Administration's (EIA's) 2009 Residential 

Energy Consumption Survey (RECS 2009).

1 DOE used this RECS subset in its life-cycle cost 

analysis for both product classes of microwave ovens: (1) microwave-only ovens and countertop 

convection microwave ovens, and (2) built-in and over-the-range convection microwave ovens. 

This appendix explains the abbreviations for the variable names and defines the variable values. 

7A.2 SAMPLE DETERMINATION

RECS 2009 does not distinguish between the two product classes of microwave ovens. 

Therefore, the subset of RECS 2009 households used to represent both product class 1

(microwave-only ovens and countertop convection microwave ovens) and product class 2 (builtin and over-the-range convection microwave ovens) met the following criterion.

• The household had a microwave.

The RECS 2009 variables used to match the selection criteria are listed in Table 7A.2.1.

Table 7A.2.1 Selection of RECS 2009 Records

Product 

Class

Selection 

Criterion

No. of 

Records

No. of U.S. 

Households 

Represented

million

No. Households with 

Microwave Oven

million

Percent of 

Households with 

Microwave 

Oven

1 and 2 MICRO = 1 11,616 113.6 109.0 96%

Source: EIA. 2009 RECS Survey Data: Table HC3.1 Appliances in U.S. homes, by housing unit type, 2009.

Table 7A.2.2 lists all the variables associated with a household microwave oven that 

RECS 2009 tracked. Table 7A.2.2 shows the variables used in the life-cycle cost analysis.
7A-2

Table 7A.2.2 Variables Tracked in RECS 2009

Variable Description

Location Variables

DIVISION Census division

DIVISION + LRG STATE* Combined large state (LRGSTATE) and census division (DIVISION)

REPORTABLE_DOMAIN Reportable states and groups of states

Variables for Household Characteristics

AMTMICRO Amount of food cooked in the microwave

DOEID DOE 4-digit identification number

NUMMEAL Hot meal preparation frequency

NWEIGHT Final weight

YEARMADE Residence build year

* Not among RECS 2009 variables.

7A.3 VARIABLE RESPONSE CODES IN 2009 DATABASE 

Table 7A.3.1 provides the response codes for all RECS 2009 variables used in the 

household sample.
7A-3

Table 7A.3.1 Variable Response Codes in RECS 2009

Variable Definition

AMTMICRO Amount of food cooked in the microwave

1 = Most or all

2 = About half

3 = Some or very little

4 = Used only for snacks, defrosting, or reheating food

DIVISION Census division

1 = New England

2 = Middle Atlantic

3 = East North Central

4 = West North Central

5 = South Atlantic

6 = East South Central

7 = West South Central

8 = Mountain

9 = Pacific

DIVISION + LRG STATE* Combined large state (LRGSTATE) and census division (DIVISION)

1 = New England

2 = Middle Atlantic (not including New York)

3 = East North Central

4 = West North Central

5 = South Atlantic (not including Florida)

6 = East South Central

7 = West South Central (not including Texas)

8 = Mountain

9 = Pacific (not including California)

10 = New York

11 = California

13 = Texas

14 = Florida
7A-4

Variable Definition

REPORTABLE_DOMAIN 1 = Connecticut, Maine, New Hampshire, Rhode Island, Vermont 

 2 = Massachusetts

 3 = New York

 4 = New Jersey

 5 = Pennsylvania

 6 = Illinois

 7 = Indiana, Ohio

 8 = Michigan

 9 = Wisconsin

10 = Iowa, Minnesota, North Dakota, South Dakota

11 = Kansas, Nebraska

12 = Missouri

13 = Virginia

14 = Delaware, District of Columbia, Maryland, West Virginia;

15 = Georgia

16 = North Carolina, South Carolina

17 = Florida

18 = Alabama, Kentucky, Mississippi

19 = Tennessee

20 = Arkansas, Louisiana, Oklahoma

21 = Texas

22 = Colorado

23 = Idaho, Montana, Utah, Wyoming

24 = Arizona

25 = Nevada, New Mexico

26 = California

27 = Alaska, Hawaii, Oregon, Washington

DOEID DOE 4-digit identification number

NHSLDMEM Number that live in household 

NUMMEAL Hot meal preparation frequency

1 = Three or more times a day

2 = Two times a day

3 = Once a day

4 = A few times a week

5 = About once a week

6 = Less than once a week

0 = Doesn’t cook/never cooks

NWEIGHT Final weight

* Not among RECS 2009 variables.
7A-5

REFERENCES

1. U.S. Department of Energy–Energy Information Administration (EIA). Residential 

Energy Consumption Survey: 2009 RECS Survey Data. October 2011. EIA: Washington, 

D.C. Available online at

 www.eia.gov/consumption/residential/data/2009/; 

www.eia.gov/consumption/residential/data/2009/#microdata.

 (Accessed September 2012.)
8A-i

APPENDIX 8A. USER INSTRUCTIONS FOR THE LIFE-CYCLE COST 

ANALYSIS SPREADSHEET

TABLE OF CONTENTS

8A.1 USER INSTRUCTIONS ........................................................................................ 8A-1

8A.2 STARTUP............................................................................................................... 8A-1

8A.3 DESCRIPTION OF LIFE-CYCLE COST WORKSHEETS ................................. 8A-1

8A.4 BASIC INSTRUCTIONS FOR OPERATING THE LIFE-CYCLE COST 

SPREADSHEET..................................................................................................... 8A-2
8A-1

APPENDIX 8A. USER INSTRUCTIONS FOR THE LIFE-CYCLE COST ANALYSIS 

SPREADSHEET

8A.1 USER INSTRUCTIONS

The results obtained through the U.S. Department of Energy's (DOE's) life-cycle cost 

analysis for microwave ovens can be examined and reproduced using the Microsoft Excel 

spreadsheets available on DOE's Home Appliances Rulemaking website at

www.eere.energy.gov/buildings/appliance_standards /. Spreadsheets are available on the 

Building Technologies Program–Cooking Products website at

www1.eere.energy.gov/buildings/appliance_standards/residential/cooking_products.html.

8A.2 STARTUP

DOE’s spreadsheet enables users to perform life-cycle cost (LCC) and payback period 

(PBP) analyses for both product classes.

To examine the spreadsheet, DOE assumes that the user has access to a personal 

computer having a hardware configuration capable of running Windows NT/2000/XP. All LCC 

spreadsheets require Microsoft Excel 2000 or later installed under the Windows operating 

system. Because certain variables inside the worksheets are defined as distributions, the user’s 

computer requires a copy of Crystal Ball (a commercially available add-on program).

8A.3 DESCRIPTION OF LIFE-CYCLE COST WORKSHEETS

Each worksheet represents a conceptual component within the LCC calculation. To 

facilitate navigability and identify how worksheets are related, each worksheet contains an area 

on the extreme left that shows the variables imported to and exported from the current 

worksheet. Each LCC spreadsheet contains the following worksheets.

Summary The Summary worksheet contains results of LCC and PBP simulation 

runs for each design option and product class.

Statistics The Statistics worksheet contains statistics for each design option and 

product class.

LCC & PB Calcs The LCC&PB Calcs worksheet shows the results of LCC calculations

for all efficiency levels based on data from the 2009 Residential Energy 

Consumption Survey (RECS 2009).

1

Rebuttable PBP The Rebuttable PBP worksheet contains the calculations of total and 

incremental manufacturer costs, retail prices, installation costs, repair 

and maintenance costs, energy use calculations, and simple payback 
8A-2

period for each efficiency level.

Equipment Cost This worksheet calculates the retail price values used as inputs to the

calculation of LCC in the Summary worksheet.

RECS Households The RECS Households worksheet contains the RECS 2009 household 

data for each product class.

Base Case Eff Dist The Base Case Eff Dist worksheet determines the efficiency of the basecase unit.

Energy Prices The Energy Prices worksheet calculates energy prices.

Energy Price Trends The Energy Price Trends worksheet shows future energy price trends.

Discount Rate The Discount Rate worksheet contains the distributions of discount rates 

for replacement and new units.

Lifetime The Lifetime worksheet contains the distribution of lifetimes for 

products in each product class.

8A.4 BASIC INSTRUCTIONS FOR OPERATING THE LIFE-CYCLE COST 

SPREADSHEET 

Basic instructions for operating the LCC spreadsheet are given below.

1. After downloading the LCC spreadsheet from the Internet, open the file using 

Microsoft Excel. Click Enable Macro

2. Use Excel's 

when prompted, and then click on the tab for 

the Summary worksheet.

View/Zoom

3. You can change the parameters listed under USER INPUT on the Summary

worksheet. There are four drop-down boxes and one command button. The default 

parameters and associated options are given below.

commands at the top menu bar to change the size of the 

display to fit your monitor.

a. Energy Price Trend

b.

: Defaults to “AEO 2012–reference.” To change the input, 

use the drop-down menu and select the desired trend (reference, low, or high).

Start Year

c.

: Defaults to “2014.” To change the value, use the drop-down menu

and select the desired year.

# of Trials

d.

: Defaults to “10,000.” To change the value, use the drop-down 

menu and select the desired number of trials (100; 1,000; 2,000; 5,000;

10,000; or 50,000).

Analysis Group: Defaults to “National.” To analyze a subgroup, use the dropdown menu and select the desired subgroup.
8A-3

4. To run the Crystal Ball simulation, click the run button (you must re-run after 

changing any parameters). The spreadsheet will then be minimized. You can monitor 

the progress of the simulation by watching the count of iterations in the bottom-left 

corner of the screen. When the simulation is finished, the spreadsheet will re-open.

Additional information can be found in the Statistics and Summary worksheets.
8A-4

REFERENCES

1. U.S. Department of Energy–Energy Information Administration (EIA). Residential 

Energy Consumption Survey: 2009 RECS Survey Data. October 2011. EIA: Washington, 

D.C. Available online at 

 www.eia.gov/consumption/residential/data/2009/.

 (Accessed September 2012.)
8B-i

APPENDIX 8B. UNCERTAINTY AND VARIABILITY

TABLE OF CONTENTS

8B.1 INTRODUCTION ...................................................................................................8B-1

8B.2 UNCERTAINITY....................................................................................................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

FIGURE

Figure 8B.5.1 Normal, Triangular, Uniform, Weibull, and Custom Probability 

Distributions.................................................................................................8B-3
8B-1

APPENDIX 8B. UNCERTAINTY AND VARIABILITY

8B.1 INTRODUCTION

Analysis of energy conservation standards involves calculating 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, complete clarity

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 known completely (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. Although 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 more completely inform the decision-making process.

8B.2 UNCERTAINITY

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. microwave oven) is not recorded directly, but 

rather estimated based on 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 when calculating a quantity, different applications or situations 

produce different numerical values. Specifying an exact value for a quantity may be difficult if

the value depends on something else. For example, the number of hours a household operates a

microwave oven depends on the specific circumstances and behaviors of the occupants (e.g., 

number of persons, personal habits about food preparation). Variability makes specifying an 

appropriate population value difficult because any one value is unlikely to be representative of 

the entire population. Surveys can be helpful in understanding variability, 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. Numerous

calculations are performed, which provide some indication of the extent to which the result 

depends on the assumptions. For example, the LCC of an appliance could be calculated for 

energy rates of 2, 8, and 14 cents per kilowatt-hour.

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 LCC is reduced, holding all other inputs constant. That is, the 

crossover point is the energy rate at which the consumer achieves savings in operating cost that 

more than compensate for the increased purchase price. 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 

that contain variability (e.g., electricity rates among households), survey data can be used to 

generate a frequency distribution of numerical values (e.g., the number of households that 

experience electricity rates at particular levels) to estimate the probability of each value. For 

quantities that contain 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

than required for the scenario approach, namely information about the shapes and magnitudes of 

the variability and uncertainty in 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 fall within a particular range. 

Scenario and probability analyses provide some indication of the robustness of a 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 in inputs to the engineering, LCC, and 

payback period analyses, DOE used Microsoft Excel spreadsheets combined with Crystal Ball, a 

commercially available add-in software product, 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 difficult to reproduce. Without the aid of 

simulation, a spreadsheet model will reveal only 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 performs a calculation repeatedly based 

on randomly generating values for uncertain variables again and again. 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 randomly selects variable values to simulate a model. When you roll a die, you know 

that a 1, 2, 3, 4, 5, or 6 will come up, but you do not know which for any particular roll. The 

same condition applies to variables that have a known range of values but an uncertain value for 

any particular time or event (e.g., product lifetime, discount rate, and installation cost). 

For each uncertain variable (one that has a range of possible values), possible values can 

be defined using a probability distribution. The type of distribution selected is based on the 

conditions surrounding that variable. Probability distribution types include those shown 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. Crystal Ball simulations can 

consist of as many trials (or scenarios) as desired—hundreds or even thousands. During a single 
8B-4

trial, Crystal Ball randomly selects a value from the defined possibilities (the range and shape of 

the probability distribution) for each uncertain variable and then recalculates the spreadsheet.
8D-i

APPENDIX 8D. DISTRIBUTIONS USED FOR DISCOUNT RATES

TABLE OF CONTENTS

8D.1 INTRODUCTION .................................................................................................. 8D-1

8D.2 DISTRIBUTION OF MORTGAGE INTEREST RATES ..................................... 8D-1

8D.3 DISTRIBUTION OF RATES FOR DEBT CLASSES CONSIDERED FOR 

REPLACEMENT PRODUCTS ............................................................................. 8D-2

8D.4 DISTRIBUTION OF RATES FOR EQUITY CLASSES CONSIDERED FOR 

REPLACEMENT PRODUCTS ............................................................................. 8D-5

LIST OF FIGURES

Figure 8D.2.1 Distribution of Interest Rates on New Home Mortgages............................ 8D-1

Figure 8D.3.1 Distribution of Interest Rates for Home Equity Loans............................... 8D-2

Figure 8D.3.2 Distribution of Interest Rates for Credit Cards........................................... 8D-3

Figure 8D.3.3 Distribution of Interest Rates for Installment Loans................................... 8D-3

Figure 8D.3.4 Distribution of Interest Rates for Other Residence Loans.......................... 8D-4

Figure 8D.3.5 Distribution of Interest Rates for Other Lines of Credit Loans.................. 8D-4

Figure 8D.4.1 Distribution of Annual Rates of Return on CDs......................................... 8D-5

Figure 8D.4.2 Distribution of Annual Rates of Return on Savings Bonds........................ 8D-6

Figure 8D.4.3 Distribution of Annual Rates of Return on Corporate AAA Bonds........... 8D-6

Figure 8D.4.4 Distribution of Annual Rates on Savings Accounts ................................... 8D-7

Figure 8D.4.5 Distribution of Annual Rates of Return on S&P 500 ................................. 8D-7

Figure 8D.4.6 Distribution of Annual Rates of Return on Mutual Funds.......................... 8D-8
8D-1

APPENDIX 8D. DISTRIBUTIONS USED FOR DISCOUNT RATES

8D.1 INTRODUCTION

The U.S. Department of Energy (DOE) derived discount rates for the life-cycle cost 

analysis (described in chapter 8) using data on interest or return rates for various types of

consumer debt and equity. To account for variation in rates for each type of debt or equity, DOE 

sampled a rate for each household in the sample from a distribution of rates for each debt and 

equity type. This appendix describes the distributions of rates DOE used.

8D.2 DISTRIBUTION OF MORTGAGE INTEREST RATES

Figure 8D.2.1 shows the distribution of real interest rates for new home mortgages. The 

data source DOE used for mortgage interest rates is the Federal Reserve Board’s Survey of 

Consumer Finances (SCF) in 1989, 1992, 1995, 1998, 2001, 2004, and 2007.1 Using the 

appropriate SCF data for each year, DOE adjusted the nominal mortgage interest rate for each 

relevant household for mortgage tax deduction and inflation. In cases where the effective interest 

rate was equal to or less than the inflation rate (resulting in a negative real interest rate), DOE set 

the real effective interest rate to zero.

0%

5%

10%

15%

20%

25%

30%

0.5%1.6%2.5%3.5%4.4%5.4%6.4%7.4%8.5%9.5%

10.5%11.6%12.4%13.5%14.4%16.7%22.9%

Probability

Real Interest Rate

Figure 8D.2.1 Distribution of Interest Rates on New Home 

Mortgages
8D-2

8D.3 DISTRIBUTION OF RATES FOR DEBT CLASSES CONSIDERED FOR 

REPLACEMENT PRODUCTS

Figure 8D.3.1 through Figure 8D.3.5 show the distributions of real interest rates for 

various types of debt used to finance replacement microwave and combination ovens. The data 

source for the interest rates for home equity loans, credit cards, installment loans, other residence 

loans, and other lines of credit was the Federal Reserve Board’s SCF in 1989, 1992, 1995, 1998, 

2001, 2004, and 2007.

1 DOE used the annual inflation rate in each year to adjust the nominal 

rates to real rates. For home equity loans, DOE calculated effective interest rates in a similar 

manner as for mortgage rates, because interest on both types of loans is tax deductible.

0%

5%

10%

15%

20%

25%

30%

0.4%1.5%2.6%3.5%4.5%5.4%6.4%7.5%8.5%9.5%

10.6%11.8%12.7%13.7%14.7%16.7%17.5%42.5%

Probability

Real Interest Rate

Figure 8D.3.1 Distribution of Interest Rates for Home Equity 

Loans
8D-3

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

0.0%2.5%4.5%6.7%8.7%

10.6%12.6%14.7%16.4%18.5%20.5%22.5%24.6%26.3%28.4%36.9%

Probability

Real Interest Rate

Figure 8D.3.2 Distribution of Interest Rates for Credit Cards

0%

5%

10%

15%

20%

25%

30%

35%

40%

0.0%2.4%4.5%6.7%8.6%

10.6%12.5%14.7%16.5%18.5%20.5%22.3%24.6%26.6%28.5%34.1%43.6%47.2%

Probability

Real Interest Rate

Figure 8D.3.3 Distribution of Interest Rates for Installment Loans
8D-4

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

0.2%1.5%2.5%3.5%

4.5%5.5%

6.5%7.5%8.6%9.4%

10.6%11.6%

12.6%14.7%15.7%16.6%17.9%

Probability

Real Interest Rate

Figure 8D.3.4 Distribution of Interest Rates for Other Residence 

Loans

0%

2%

4%

6%

8%

10%

12%

14%

0.1%2.5%4.5%

6.7%

8.7%

10.5%12.5%14.7%16.5%18.5%20.2%22.1%25.4%

Probability

Real Interest Rate

Figure 8D.3.5 Distribution of Interest Rates for Other Lines of 

Credit Loans
8D-5

8D.4 DISTRIBUTION OF RATES FOR EQUITY CLASSES CONSIDERED FOR 

REPLACEMENT PRODUCTS

Figure 8D.4.1 through Figure 8D.4.6 show the distributions of real interest rates for 

various types of equity used to finance replacement microwave and combination ovens. Data for 

equity classes are not available from the Federal Reserve Board’s SCF, so DOE derived data for 

the equity classes from national-level historical data. The interest rates associated with 

certificates of deposit (CDs),2 savings bonds,3 and AAA corporate bonds4were derived from 

Federal Reserve Board time-series data covering 1977–2009. DOE assumed rates on checking 

accounts to be zero. Rates on savings and money market accounts were derived from Cost of 

Savings Index data covering 1984–2009.5 The rates for stocks are the annual returns on the 

Standard and Poor’s (S&P) 500 from 1977 to 2009.6 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 

from 1977 to 2009. DOE used the annual inflation rate in each year to adjust the nominal rates to 

real rates.

-2%

0%

2%

4%

6%

8%

197719791981198319851987198919911993199519971999200120032005

2007

20092011

Annual Rate of Return

Year

Figure 8D.4.1 Distribution of Annual Rates of Return on CDs
8D-6

-2%

0%

2%

4%

6%

8%

197719791981198319851987198919911993199519971999200120032005200720092011

Annual Rate of Return

Year

Figure 8D.4.2 Distribution of Annual Rates of Return on Savings 

Bonds

-2%

0%

2%

4%

6%

8%

197719791981198319851987198919911993199519971999200120032005200720092011

Annual Rate of Return

Year

Figure 8D.4.3 Distribution of Annual Rates of Return on 

Corporate AAA Bonds
8D-7

-2%

0%

2%

4%

6%

8%

19841986198819901992199419961998200020022004200620082010

Annual Rate of Return

Year

Figure 8D.4.4 Distribution of Annual Rates on Savings Accounts

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

197719791981198319851987198919911993199519971999200120032005200720092011

Annual Rate of Return

Year

Figure 8D.4.5 Distribution of Annual Rates of Return on S&P 500
8D-8

-30%

-20%

-10%

0%

10%

20%

30%

197719791981198319851987198919911993199519971999200120032005200720092011

Annual Rate of Return

Year

Figure 8D.4.6 Distribution of Annual Rates of Return on Mutual 

Funds
8D-9

REFERENCES

1. Board of Governors of the Federal Reserve System. Research Resources: Survey of 

Consumer Finances. 1989, 1992, 1995, 1998, 2001, 2004, and 2007. Available online at

 www.federalreserve.gov/pubs/oss/oss2/scfindex.html. (Accessed September 2012.)

2. Board of Governors of the Federal Reserve System. 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. 2010.

Available online at

 www.federalreserve.gov/releases/H15/data.htm. (Accessed September 2012.)

3. Board of Governors of the Federal Reserve System. 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. 2010. Available online at

 www.federalreserve.gov/releases/H15/data.htm. (Accessed September 2012.)

4. Board of Governors of the Federal Reserve System. 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. 2010. Available online at 

 www.federalreserve.gov/releases/H15/data.htm. (Accessed September 2012.)

5. Mortgage-X–Mortgage Information Service. Cost of Savings Index (COSI) Index History.

2010. Available online at

 mortgage-x.com/general/indexes/default.asp. (Accessed September 2012.)

6. Damodaran Online. Historical Returns on Stocks, Bonds and Bills–United States. Use 

link for Updated Data: Data Sets. 2012. A. Damodaran, New York University. Available 

online at

 pages.stern.nyu.edu/~adamodar/. (Accessed September 2012.)
9A-i

APPENDIX 9A. RELATIVE PRICE ELASTICITY OF DEMAND FOR 

APPLIANCES

TABLE OF CONTENTS

9A.1 INTRODUCTION .................................................................................................. 9A-1

9A.2 LITERATURE REVIEW ....................................................................................... 9A-1

9A.2.1 Price ........................................................................................................................ 9A-1

9A.2.2 Income..................................................................................................................... 9A-1

9A.2.3 Appliance Efficiency and Discount Rates .............................................................. 9A-2

9A.3 VARIABLES DESCRIBING THE MARKET FOR REFRIGERATORS, 

CLOTHES WASHERS, AND DISHWASHERS .................................................. 9A-2

9A.3.4 Physical Household/Appliance Variables............................................................... 9A-3

9A.3.5 Economic variables................................................................................................. 9A-3

9A.4 REGRESSION ANALYSIS OF VARIABLES AFFECTING APPLIANCE 

SHIPMENTS .......................................................................................................... 9A-4

9A.4.1 Broad Trends........................................................................................................... 9A-5

9A.4.2 Specification of Model............................................................................................ 9A-7

9A.4.3 Discussion of Model ............................................................................................... 9A-8

9A.4.4 Analysis Results...................................................................................................... 9A-8

9A.4.4.1 Individual Appliance Model ................................................................. 9A-8

9A.4.4.2 Combined Appliance Model................................................................. 9A-9

9A.4.5 Additional Regression Specifications and Results ............................................... 9A-10

9A.4.5.1 Lower Consumer Discount Rate......................................................... 9A-10

9A.4.5.2 Disaggregated Variables..................................................................... 9A-11

9A.5 LONG RUN IMPACTS........................................................................................ 9A-11

9A.6 SUMMARY.......................................................................................................... 9A-12

9A.7 DATA USED IN THE ANALYSIS ..................................................................... 9A-13

LIST OF TABLES

Table 9A.2.1 Estimates of the Impact of Price, Income and Efficiency on 

Automobile and Appliance Sales................................................................ 9A-2

Table 9A.3.1 Physical Household/Appliance Variables................................................... 9A-3

Table 9A.3.2 Economic Variables.................................................................................... 9A-4

Table 9A.4.1 Estimate of Total Price Elasticity of Demand............................................. 9A-6

Table 9A.4.2 Tabular Estimate of Relative Price Elasticity of Appliance Demand......... 9A-7

Table 9A.4.3 Individual Appliance Model Results .......................................................... 9A-9

Table 9A.4.4 Combined Appliance Model Result............................................................ 9A-9

Table 9A.4.5 Combined and Individual Results, 20 percent discount rate..................... 9A-10

Table 9A.4.6 Disaggregated Regression Results, 37 percent discount rate.................... 9A-11

Table 9A.5.1 Change in Price Elasticity of Demand for Automobiles following a 

Purchase Price Change.............................................................................. 9A-12
9A-ii

Table 9A.5.2 Change in Relative Price Elasticity for Home Appliances following a 

Purchase Price Change.............................................................................. 9A-12

LIST OF FIGURE

Figure 9A.3.1 Trends in Appliance Shipment, Housing Starts and Replacements............ 9A-4

Figure 9A.4.1 Residual Shipments and Appliance Price ................................................... 9A-6
9A-1

APPENDIX 9A. RELATIVE PRICE ELASTICITY OF DEMAND FOR APPLIANCES

9A.1 INTRODUCTION

This appendix summarizes DOE’s study of the price elasticity of demand for home 

appliances, including refrigerators, clothes washers and dishwashers. DOE chose this particular 

set of appliances because of the availability of data to determine a price elasticity. Section 9A.2 

reviews the existing economics literature describing the impact of economic variables on the sale 

of durable goods. Section 9A.3 describes the market for home appliances and the changes that 

have occurred over the past 20 years. In section 9A.4, DOE summarizes the results of its

regression analysis and presents estimates of the price elasticity of demand for the three 

appliances. In section 9A.5, DOE presents development of an ‘effective’ purchase price 

elasticity. DOE’s interpretation of its results is presented in section 9A.6. Finally, section 9A.7 

describes the data used in DOE’s analysis.

9A.2 LITERATURE REVIEW

Relatively few studies measure the impact of price, income and efficiency on the sale of 

household appliances. This section briefly reviews the literature that describes the likely 

importance of these variables on the purchase of household appliances.

9A.2.1 Price

DOE reviewed many studies that sought to measure the impact of price on sales in a 

dynamic market. One study of the automobile market prior to 1970 finds the price elasticity of 

demand to decline over time. The author explains this as the result of buyers delaying purchases 

after a price increase but eventually making the purchase (Table 9A.2.1).1 A contrasting study of 

household white goods also prior to 1970, finds the elasticity of demand to increase over time as 

more price-conscious buyers enter the market.2 An analysis of refrigerator market survey data 

finds that consumer purchase probability decreases with survey asking price.3 Estimates of the 

price elasticity of demand for different brands of the same product tend to vary. A review of 41 

studies of the impact of price on market share found the average price elasticity to be -1.75.4

9A.2.2 Income

The 

average estimate of price elasticity of demand reported in these studies is -0.33 in the appliance 

market and -0.47 in the combined automobile and appliance markets.

Higher income households are more likely to own household appliances.5 The impact of 

income on appliance shipments is explored in two econometric studies of the automobile and 

appliance markets.1,2 The average income elasticity of demand is 0.50 in the appliance study 

cited in the literature review, much larger in the automobile study (Table 9.A.2.1).
9A-2

9A.2.3 Appliance Efficiency and Discount Rates

Many studies estimate the impact of appliance efficiency on consumers’ choice of

appliance. Typically, this impact is summarized by the implicit discount rate; that is, the rate 

consumers use to compare future savings in appliance operating costs against a higher initial 

purchase price of an appliance. One early and much cited study concludes that consumers use a 

20 percent implicit discount rate when purchasing room air conditioners (Table 9A.2.1).6 A 

survey of several studies of different appliances suggests that the consumer implicit discount rate 

has a broad range and averages about 37 percent.

7

Table 9A.2.1 Estimates of the Impact of Price, Income and Efficiency on Automobile and 

Appliance Sales

Durable Good

Price 

Elasticity

Income 

Elasticity

Brand 

Price 

Elasticity

Implicit 

Discount 

Rate Model

Data 

Years

Time 

Period

Automobiles1 -1.07 3.08 - - Linear Regression, stock 

adjustment - Short run

Automobiles1 -0.36 1.02 - - Linear Regression, stock 

adjustment - Long run

Clothes Dryers2 -0.14 0.26 - - Cobb-Douglas, diffusion 1947-1961 Mixed

Room Air 

Conditioners2 -0.378 0.45 - - Cobb-Douglas, diffusion 1946-1962 Mixed

Dishwashers2 -0.42 0.79 - - Cobb-Douglas, diffusion 1947-1968 Mixed

Refrigerators3 -0.37 - - 39% Logit probability, survey 

data 1997 Short run

Various4 - - -1.769 - Multiplicative regression - Mixed

Room Air 

Conditioners5 - - -1.72 - Non-linear diffusion 1949-1961 Short run

Clothes Dryers5 - - -1.32 - Non-linear diffusion 1963-1970 Short run

Room Air 

Conditioners6 - - - 20% Qualitative choice, survey data - -

Household 

Appliances7 - - - 37%10 Assorted - -

Sources: 1 S. Hymens. 1971; 2 P. Golder and G. Tellis, 1998; 3 D. Revelt and K. Train, 1997; 4 G. Tellis, 1988; 5 D. Jain and R. Rao; 6 J. Hausman; 7 K. Train, 1985.

Notes: 8 Logit probability results are not directly comparable to other elasticity estimates in this table.

9 Average brand price elasticity across 41 studies.

10 Averaged across several household appliance studies referenced in this work.

9A.3 VARIABLES DESCRIBING THE MARKET FOR REFRIGERATORS, 

CLOTHES WASHERS, AND DISHWASHERS

In this section DOE evaluates variables that appear to account for refrigerator, clothes 

washer and dishwasher shipments, including physical household/appliance variables and 

economic variables.
9A-3

9A.3.4 Physical Household/Appliance Variables

Several variables influence the sale of refrigerators, clothes washers and dishwashers.

The most important for explaining appliance sales trends are the annual number of new 

households formed (housing starts) and the number of appliances reaching the end of their 

operating life (replacements). Housing starts influence sales because new homes are often 

provided with, or soon receive, new appliances, including dishwashers and refrigerators.

Replacements are correlated with sales because new appliances are typically purchased when old 

ones wear out. In principle, if households maintain a fixed number of appliances, shipments 

should equal housing starts plus appliance replacements. 

9A.3.5 Economic variables

Appliance price, appliance operating cost and household income are important economic 

variables affecting shipments. Low prices and costs encourage household appliance purchases 

and a rise in income increases householder ability to purchase appliances. In principle, changes 

in economic variables should explain changes in the number of appliances per household.

During a 1980–2002 study period, annual shipments grew 69 percent for clothes washers, 

81 percent for refrigerators and 105 percent for dishwashers (Table 9A.3.1). This rising 

shipments trend is explained in part by housing starts, which increased 6 percent and by 

appliance replacements, which rose between 49 percent and 90 percent, depending on the 

appliance, over the period (Table 9A.3.1).a For mature markets such as these, replacements 

exceed appliance sales associated with new housing construction.

Table 9A.3.1 Physical Household/Appliance Variables

Shipments1 (millions) Housing Starts2 (millions) Replacements3 (millions)

Appliance 1980 2002 Change 1980 2002 Change 1980 2002 Change

Refrigerators 5.124 9.264 81% 1.723 1.822 6% 3.93 5.84 49%

Clothes Washers 4.426 7.492 69% 1.723 1.822 6% 3.66 5.50 50%

Dishwashers 2.738 5.605 105% 1.723 1.822 6% 1.99 3.79 90% 1

Shipments: Number of units sold. Sources: AHAM Fact Book and Appliance Magazine. 2

Housing Starts: Annual number of new homes constructed. Source: U.S. Census. 3

Replacements: Average of annual lagged shipments, with lag equal to expected appliance operating life, ± 5 years.

Shipments increased somewhat more rapidly than housing starts and replacements. This 

is shown by comparing the beginning and end points of lines that represent “starts plus 

replacements” (uppermost solid line in Figure 9A.3.1) and “shipments” (diamond linked line in 

Figure 9A.3.1). In 1980 the “shipment” line begins below the “starts plus replacements” line. In 

2002, the “shipments” line ends above the “starts plus replacements” line. This more rapid 

 

a Appliance replacements are determined from the expected operating life of refrigerators (19 years), clothes 

washers (14 years), and dishwashers (12 years) and from past shipments. Replacements are further discussed in 

section 9A.3. The dishwasher lifetime used in this analysis does not match the dishwasher used in the primary 

analysis. 
9A-4

increase in shipments, compared to housing starts plus replacements, suggests that the appliance 

per household ratio increased over the study period. 

Figure 9A.3.1 Trends in Appliance Shipment, Housing Starts and Replacements

Economic variables, including price, cost and income, may explain this increase in 

appliances per household. Over the period, appliance prices decreased 40 percent to 50 percent, 

operating costs fell between 33 percent and 72 percent, and median household income rose 16 

percent (Table 9A.3.2).

Table 9A.3.2 Economic Variables

Price1 (1999$) Operating Cost2 (1999$) Household Income3 (1999$)

Appliance 1980 2002 Change 1980 2002 Change 1980 2002 Change

Refrigerators 1208 726 -40% 333 94 -72% 37,447 43,381 16%

Clothes Washers 779 392 -50% 262 175 -33% 37,447 43,381 16%

Dishwashers 713 369 -48% 183 95 -48% 37,447 43,381 16% 1

Price: Shipment weighted retail sales price. Sources: AHAM Fact Book and Appliance Magazine. 2

Operating Cost: Annual electricity price times electricity consumption. Source: AHAM Fact Book. 3

Income: Mean Household income. Source: U.S. Census.

9A.4 REGRESSION ANALYSIS OF VARIABLES AFFECTING APPLIANCE 

SHIPMENTS

Few data are available to estimate the impact of economic variables on the demand for 

appliances. Industry operating cost data is incomplete—appliance energy use data are available 

for only 12 years of the 1980-2002 study period. Industry price data are also incomplete—

available for only 8 years of the study period for each of the appliances.

The lack of data suggests that regression analysis can at best evaluate broad data trends, 

utilizing relatively few explanatory variables. This section begins by describing broad trends 

apparent in the economic and physical household data sets and then specifies a simple regression 

Refrigerator Shipments

0

1

2

3

4

5

6

7

8

9

10

1980 1984 1988 1992 1996 2000

Millions 

New Housing

Retirements

Shipments

Clothes Washer Shipments

0

1

2

3

4

5

6

7

8

1980 1984 1988 1992 1996 2000

Millions 

Dishwasher Shipments

0

1

2

3

4

5

6

1980 1984 1988 1992 1996 2000

Millions 
9A-5

model to measure these trends, making assumptions to minimize the number of explanatory 

variables. Finally, results of the regression analysis are presented along with an estimate of the 

price elasticity of demand for appliances. In section 9A.4.5, DOE presents the results of 

regression analysis performed with more complex models, which are used to test assumptions 

underlying the simple model. These results support the specification of the simple model and the 

price elasticity of appliance demand estimated with that model.

9A.4.1 Broad Trends 

In this section DOE reviews trends in the physical household and economic data sets and 

posits a simple approach for estimating the price elasticity of appliance demand. As noted above, 

the physical household variables (housing starts and appliance replacements) explain most of the 

variability in appliance shipments during the study period (1980-2002).

b DOE assumes the rest 

of the variability in shipments (referred to as “residual shipments”) is explained by economic 

variables. Below, DOE presents a tabular method for measuring price elasticities.

To illustrate this tabular approach, DOE defines two new variables—residual shipments 

and total price. Residual shipments are defined as the difference between shipments and physical 

household demand (starts plus replacements). Total price, represented by the following equation,

is defined as appliance price plus the present value of lifetime appliance operating cost:

c

TP = PP + PVOC

where:

TP = Total price,

PP = Appliance purchase price, and

PVOC = Present value of operating cost.

Over the study period, residual shipments increased in proportion to total shipments by

30 percent for refrigerators, 19 percent for clothes washers, and 23 percent for dishwashers. At 

the same time, total prices declined 47 percent, 45 percent and 48 percent for refrigerators, 

clothes washers, and dishwashers, respectively. Assuming that total price explains the entire 

change in per household appliance usage, a rough estimate is calculated of the total price 

elasticity of demand equal to -0.48 for refrigerators, -0.32 for clothes washers and -0.37 for 

dishwashers (Table 9A.4.1).

 

b A log regression of the form: Shipments = a + b • Housing Starts + c • Retirements, indicates that these two 

variables explain 89 percent of the variation in refrigerator shipments, 97 percent of the variation in clothes washer 

shipments, and 97 percent of the variation in dishwasher shipments.

c Present value operating cost is calculated assuming a 19-year operating life for refrigerators, 14-year operating life 

for clothes washers, and a 12-year operating life for dishwashers. A 37 percent discount rate is used to sum annual 

operating costs into a present value operating cost.
9A-6

Table 9A.4.1 Estimate of Total Price Elasticity of Demand

Residual Shipments (millions) Total Price (199$)

Appliance 1980 2002 Difference Change 1980 2002 Change Elasticity

Refrigerators -0.5 1.6 2.1 30% 1541 820 -61% -0.48

Clothes Washers -1.0 0.2 1.1 19% 1042 567 -59% -0.32

Dishwashers -1.0 -0.01 1.0 23% 896 464 -64% -0.37

The negative correlation between total price and residual shipments suggested by these 

negative price elasticities is illustrated in a graph of residual shipments on the y-axis and total 

price on the x-axis (Figure 9A.4.1).

Yellow points are observed price data; red points are interpolated price data.

Figure 9A.4.1 Residual Shipments and Appliance Price

Household income rose during the study period, making it easier for households to 

purchase appliances. Assuming that a rise in income has a similar impact on shipments as a 

decline in price, the impact of income is incorporated by defining a third variable, termed 

relative price, which is calculated as total price divided by household income and represented by 

the following equation.

d

Income

TP

RP =

where:

RP = Relative price,

TP = Total price, and

Income = Household income.

 

d Recall that the income elasticity of demand cited in the literature review is 0.50 and the price elasticity of demand 

cited in the review averages -0.35. This suggests that combining the effects of income and price will yield an 

elasticity less negative than price elasticity alone.

Refrigerators

400

800

1200

1600

2000

-2.0 -1.0 0.0 1.0 2.0

Total Price

Residual Shipments 

Clothes Washers

400

600

800

1000

1200

-1.5 -1.0 -0.5 0.0 0.5

Total Price

Residual Shipments 

Dishwashers

200

400

600

800

1000

-2.0 -1.5 -1.0 -0.5 0.0 0.5

Total Price

Residual Shipments 
9A-7

The percent decline in relative price for the three appliances divided by the percent 

decline in residual shipments suggests a rough estimate of relative price elasticity equal to -0.40 

for refrigerators, -0.26 for clothes washers and -0.30 for dishwashers (Table 9A.4.2).

Table 9A.4.2 Tabular Estimate of Relative Price Elasticity of Appliance Demand

Residual Shipments (millions) Relative Price (1999$)

Appliance 1980 2002 Change 1980 2002 Change Elasticity

Refrigerators -0.532 1.597 30% 0.041 0.019 -74% -0.40

Clothes Washers -0.953 0.174 19% 0.028 0.013 -72% -0.26

Dishwashers -0.974 -0.005 23% 0.024 0.011 -76% -0.30

9A.4.2 Specification of Model

The limited price data suggest it is appropriate to use a simple regression model to 

estimate the impact of economic variables on shipments, using few explanatory variables. The 

following equation, chosen for this analysis, includes one physical household variable (housing 

starts plus replacements) and one relative price variable (the sum of purchase price plus 

operating cost, divided by income). 

Ship = a + b× RP + c ×[Starts + Rplc] Eq. 9A.1

where:

Ship = Quantity of appliance sold,

RP = Relative price,

Starts = Number of new homes, and

Rplc = Number of appliances at the end of their operating life.

The natural logs are taken of all variables so that the estimated coefficients for each 

variable in the model may be interpreted as the percent change in shipments associated with the 

percent change in the variable. Thus, the coefficient b in this model is interpreted as the relative

price elasticity of demand for the three appliances.

DOE used the following combined regression equation to estimate an average price 

elasticity of demand across the three appliances, using pooled data in a single regression. A 

combined regression specification is justified, given the limited data available and the similarity 

in price and shipment behavior across appliances (see Figure 9A.4.1). Thus, the model 

represented by the combined regression equation is considered the basic model in DOE’s

analysis of appliance shipments.

Ship = a + b× RP + c ×[Starts + Rplc]+ d ×CW + e × DW Eq. 9A.2

where:

CW = Quantity of clothes washers sold, and
9A-8

DW = Quantify of dishwashers sold.

9A.4.3 Discussion of Model 

The most important assumption used to specify this model is that changes in economic 

variables over the study period—income, price, and operating cost—are responsible for all 

observed growth in residual appliance shipments. In other words, DOE assumes no impact from

other possible factors, such as changing consumer preferences or increases in the quality of 

appliances. This assumption seems unlikely, but without additional data, the impact of this 

assumption on the price elasticity of demand cannot be measured. DOE effectively assumes that 

changes in consumer preferences and appliance characteristics, while affecting which models are 

purchased, have relatively little impact on the total number of appliances purchased in a year.

Three additional assumptions used to specify this model deserve comment. The relative

price variable is specified in the model, assuming that (1) the correct implicit discount rate is 

used to combine appliance price and operating cost and that (2) rising income has the same 

impact on shipments as falling total price. The “starts + replacements” variable is specified, 

assuming (3) that starts and replacements have similar impacts on shipments.

To investigate the first assumption about discount rates, DOE calculated “present value 

operating cost” using a 20 percent implicit discount rate and performed a second regression 

analysis based on the models described in equations 9A.1 and 9A.2. The results of this analysis, 

presented in section 9A.4.5, indicate that the elasticity of relative price is fairly insensitive to 

changes in the discount rate.

To investigate the second and third assumptions, DOE specified a regression model 

separating income from total price and replacements from starts, thereby adding two additional 

explanatory variables to the basic model as shown in the following equation:

Ship = a + b ×TP + c × Incone + d × Start + e× Rplc + f ×CW + g × DW Eq. 9A.3

The results of the regression analysis of this model are presented in section 9A.4.5. These 

results suggest that the elasticity of total price (coefficient b) is relatively insensitive to changes 

in the treatment of income and “starts + replacements” in the model.

9A.4.4 Analysis Results

The following sections describe results of analyses using both the individual and 

combined models for appliances and the effects of a lower consumer discount rate and 

disaggregated variables.

9A.4.4.1 Individual Appliance Model

The individual appliance regression equations are specified in the following equation.
9A-9

Ship = a + b× RP + c ×[Starts + Rplc]

In regression analysis of this model, the elasticity of relative price (b) is estimated to be

-0.40 for refrigerators, -0.31 for clothes washers and -0.32 for dishwashers (Table 9A.4.3), 

averaging -0.35. These elasticities are similar to those reported in the literature survey for 

appliances (Table 9A.2.1). They are remarkably similar to the price elasticity calculated using a 

tabular approach (Table 9A.4.2).

The estimated coefficient associated with the “starts + replacements” variable is close to 

one. A coefficient equal to one for this variable would imply that, holding economic variables 

constant, shipments increase in direct proportion to an increase in “starts + replacements.” The 

high R-squared values (above 95) and t-statistics (above 5) in the results provide a measure of 

confidence in this analysis, despite the very small data set.

Table 9A.4.3 Individual Appliance Model Results

Refrigerator Clothes Washer Dishwasher

Variable Coefficient t-stat Coefficient t-stat Coefficient t-stat

Intercept -1.51 -7.26 -1.47 -8.23 -2.08 -16.78

Relative Price -0.40 -6.60 -0.31 -5.69 -0.32 -7.03

Starts + Replacements 1.05 5.90 1.08 6.41 1.35 11.46

R2 0.954 0.954 0.975

Observations 23 23 23

9A.4.4.2 Combined Appliance Model

The combined appliance regression equation is specified in the following equation. 

Ship = a + b× RP + c ×[Starts + Rplc]+ d ×CW + e × DW

This regression analysis indicates that the model fits the existing shipments data well 

(high R-squared) and that the variables included in the model are statistically significant (Table 

9A.4.4). Estimated with this model, the elasticity of relative price is -0.34, close to the average 

value estimated in the individual appliance models (-0.35). It is also similar to elasticity 

estimates reported in the literature survey and calculated using the tabular approach in Table 

9A.4.2. 

Table 9A.4.4 Combined Appliance Model Result

Variable Coefficient t-statistic

Intercept -1.60 -15.54

Relative Price -0.34 -10.74

Starts + Replacements 1.21 13.95

CW -0.20 -9.04

DW -0.32 -6.58

R2 0.983

Observations 69
9A-10

9A.4.5 Additional Regression Specifications and Results

As described in section 9A.4.3, DOE used three assumptions to specify its appliance 

models. The first, made to aggregate appliance price and operating cost, is that the implicit price 

variable in the basic regression model is specified using a 37 percent implicit discount rate. The 

second states that the implicit price variable is defined assuming that rising income has the same 

impact on shipments as falling total price. The third states that the “starts + replacements”

variable is defined assuming that housing starts have a similar impact on shipments as appliance 

replacements. 

9A.4.5.1 Lower Consumer Discount Rate

To investigate the first assumption about discount rates, DOE calculated “present value

operating cost” using a 20 percent implicit discount rate and performed a second regression 

analysis based on the models described in equations 9A.1 and 9A.2. The estimated coefficient 

associated with the relative price variable in these regressions is almost identical to the 

coefficients estimated for the same variable based on a 37 percent implicit discount rate. The 

elasticity of relative price calculated using a 20 percent discount rate is -0.33 in the combined 

regression and averages -0.35 for the three appliances (Table 9A.4.5). The elasticity of price

calculated using a 37 percent discount rate is -0.34 in the combined regression and averages -

0.35 for the three appliances. DOE concludes from this analysis that the elasticity of relative

price is fairly insensitive to changes in the discount rate.

Table 9A.4.5 Combined and Individual Results, 20 percent discount rate

Three Appliances

Variable Coefficient t-Stat

Intercept -1.53 -14.61

Total Price / Income -0.33 -10.69

Starts + Retirements 1.20 13.65

CW -0.18 -8.69

DW -0.32 -6.57

R2 0.982

Observations 69

Refrigerator Clothes Washers Dishwasher

Variable Coefficient t-Stat Coefficient t-Stat Coefficient t-Stat

Intercept -1.36 -6.26 -1.41 -7.49 -2.04 -17.23

Total Price / Income -0.38 -6.50 -0.32 -5.29 -0.33 -7.30

Starts + Retirements 1.04 5.73 1.06 5.83 1.34 11.64

R2 0.953 0.950 0.977

Observations 23 23 23
9A-11

9A.4.5.2 Disaggregated Variables

To investigate the second and third assumptions, DOE constructed a regression model 

that separates income from total price and replacements from starts, thus adding two additional 

explanatory variables to the basic model (as shown earlier as Eq. 9A.3 and shown below).

Ship = a + b ×TP + c × Income + d × Start + e× Rplc + f ×CW + g × DW

The estimated coefficient associated with the total price variable in these regressions is 

almost identical to the coefficients estimated for the relative price variable reported above. The 

elasticity of total price in the above equation is -0.36 in the combined appliance regression and 

averages -0.35 for the three appliances (Table 9A.4.6). The elasticity of relative price based on 

the model described in equation 9A.2 is -0.34 in the combined regression (Table 9A.4.4) and 

averages -0.35 across the individual appliances (Table 9A.4.3). DOE concludes that the price 

elasticity calculated in this analysis is relatively insensitive to the specification of household 

income and “starts + replacements” variables in the model.

Table 9A.4.6 Disaggregated Regression Results, 37 percent discount rate

9A.5 LONG RUN IMPACTS

As noted above in Table 9A.2.1, the literature review provides price elasticities over short 

and long time periods, also referred to as short run and long run price elasticities. As noted in the 

first two rows of Table 9A.2.1, one source (i.e., Hymans) shows that the price elasticity of 

demand is significantly different over the short run and long run for automobiles.

1 Because 

DOE’s forecasts of shipments and national impacts due to standards is over a 30-year time 

period, consideration must be given to how the relative price elasticity is affected once a new 

standard takes effect.

Three Appliances

Variable Coefficient t-Stat

Intercept -2.92 -1.26

Income 0.58 2.92

Total Price -0.36 -7.06

Housing Starts 0.44 10.02

Retirements 0.62 8.12

CW -0.24 -9.25

DW -0.46 -7.68

R2 0.985

Observations 69

Refrigerator Clothes Washers Dishwasher

Variable Coefficient t-Stat Coefficient t-Stat Coefficient t-Stat

Intercept -6.19 -2.24 -6.64 -1.63 1.00 0.23

Income 0.89 3.80 0.87 2.31 0.20 0.52

Total Price -0.35 -5.48 -0.27 -2.51 -0.43 -5.18

Housing Starts 0.41 7.38 0.25 3.29 0.62 8.24

Retirements 0.56 6.06 0.56 2.09 0.65 5.86

R2 0.984 0.958 0.979

Observations 23 23 23
9A-12

DOE considers the relative price elasticities determined above in section 9A.4 to be short 

run elastcities. DOE was unable to identify sources specific to household durable goods, such as 

appliances, to indicate how short run and long run price elasticities differ. Therefore, to estimate 

how the relative price elasticity changes over time, DOE relied on the Hymans study pertaining 

to automobiles. Based on the Hymans study, Table 9A.5.1 shows how the automobile price 

elasticity of demand changes in the years following a purchase price change. With increasing 

years after the price change, the price elasticity becomes more inelastic until it reaches a terminal 

value around the tenth year after the price change.

Table 9A.5.1 Change in Price Elasticity of Demand for Automobiles following a Purchase 

Price Change

Years Following Price Change

1 2 3 5 10 20

Price Elasticity of Demand -1.20 -0.93 -0.75 -0.55 -0.42 -0.40

Relative Change in 

Elasticity to 1st year

1.00 0.78 0.63 0.46 0.35 0.33

Source: Hymans, 1971.

Based on the relative change in the automobile price elasticity of demand shown in Table 

9A.5.1, DOE developed a time series of relative price elasticities for home appliances. Table 

9A.5.2 presents the time series.

Table 9A.5.2 Change in Relative Price Elasticity for Home Appliances following a 

Purchase Price Change

Years Following Price Change

1 2 3 5 10 20

Relative Change in 

Elasticity to 1st year

1.00 0.78 0.63 0.46 0.35 0.33

Relative Price Elasticity -0.34 -0.26 -0.21 -0.16 -0.12 -0.11

9A.6 SUMMARY

This appendix describes the results of a literature search, tabular analysis, and regression 

analyses of the impact of price and other variables on appliance shipments. In the literature, DOE

found only a few studies of appliance markets that are relevant to this analysis and no studies

after 1980 using time series price and shipments data. The information that can be summarized 

from the literature suggests that the demand for appliances is price inelastic. Other information in 

the literature suggests that appliances are a normal good, such that rising incomes increase the 

demand for appliances. Finally, the literature suggests that consumers use relatively high implicit 

discount rates, when comparing appliance prices and appliance operating costs. 

There are too few price and operating cost data available to perform complex analysis of 

dynamic changes in the appliance market. In this analysis, DOE used data available for 
9A-13

refrigerators, clothes washers, and dishwashers to evaluate broad market trends and perform 

simple regression analysis.

These data indicate an increase in appliance shipments and a decline in appliance price 

and operating cost over the study period 1980-2002. Household income has also risen during this 

time. To simplify the analysis, DOE combined the available economic information into one 

variable, termed relative price, and used that variable in a tabular analysis of market trends and a 

regression analysis.

DOE’s tabular analysis of trends in the number of appliances per household suggests that 

the price elasticity of demand for the three appliances is inelastic. Our regression analysis of 

these same variables suggests that the relative price elasticity of demand is -0.34. The price 

elasticity is consistent with estimates in the literature. Nevertheless, DOE stresses that the 

measure is based on a small data set, using very simple statistical analysis. More important, the 

measure is based on an assumption that economic variables, including price, income and 

operating costs, explain most of the trend in appliances per household in the United States since 

1980. Changes in appliance quality and consumer preferences may have occurred during this 

period, but they are not accounted for in this analysis.

9A.7 DATA USED IN THE ANALYSIS

• Appliance Shipments are defined as the annual number of units shipped in millions. These 

data were collected from the Association of Home Appliance Manufacturers (AHAM)

8 and 

Appliance Magazine9 as annual values for each year, 1980–2002. AHAM was used for the 

period 1989–2002 while Appliance Magazine was used for the period 1980–1988.

• Appliance Price is defined as the shipments weighted retail sales price of the unit in 1999 

dollars. Price values for 1980, 1985, 1986, 1991, 1993, 1994, 1998, and 2002 were collected 

from AHAM Fact Books.

10 Price values for other years were interpolated from these eight 

years of data.

• Housing Starts data were collected from the U.S. Census construction statistics (C25 

reports) as annual values for each year, 1980–2002.11

• Replacements, driven by equipment retirements, are estimated with the assumption that 

some fraction of sales arise from consumers replacing equipment at the end of its useful life.

Since each appliance has a different expected lifespan (19 years for refrigerators12, 14 years 

for clothes washers13, 12 years for dishwashers14), replacements are calculated differently for 

each appliance type. Replacements are estimated as the average of shipments 14–24 years 

previous for refrigerators, 9–19 years previous for clothes washers, and 7–17 years previous 

for dishwashers. Historical shipments data were collected from AHAM and Appliance 

Magazine.
9A-14

• Annual Electricity Consumption (UEC) is defined as the energy consumption of the unit in 

kilowatt-hours. Electricity consumption depends on appliance capacity and efficiency. These 

data were provided by AHAM for 1980, 1990–1997 and 1999–2002.15 Data were 

interpolated in the years for which data were not available.

• Operating Cost is the present value of the electricity consumption of an appliance over its 

expected lifespan. The lifespans of refrigerators, clothes washers and dishwashers are 

assumed to be 19, 14, and 12 years respectively. Discount rates of 20 percent6 and 37

percent16 were used, producing similar estimates of price elasticity. A study by Hausman 

recommended a discount rate of “about 20 percent” in its introduction and presented results 

ranging from 24.1 percent to 29 percent based on his calculations for room air conditioners.

A study by Train suggests a range of implicit discount rates averaging 35 percent for 

appliances.

• Income: Median annual household income in 2003 dollars. These data were collected for 

each year, 1980–2002, from Table H-6 of the U.S. Census.

17
9A-15

REFERENCES

 1 S. Hymans. Consumer Durable Spending: Explanation and Prediction, Brookings Papers on 

Economic Activity, 1971. Vol. 1971, No. 1, pp. 234-239.

2 P. Golder and G. Tellis. Beyond Diffusion: An Affordability Model of the Growth of New 

Consumer Durables, Journal of Forecasting, 1998.

3 D. Revelt and K. Train. Mixed Logit with Repeated Choices: Household's Choice of 

Appliance Efficiency Level, Review of Economics and Statistics, 1997.

4 G. Tellis. The Price Elasticity of Selective Demand: A Meta-Analysis of Econometric 

Models of Sales, Journal of Marketing Research, 1988.

5 U.S. Department of Energy-Energy Information Agency. The Effect of Income on Appliances 

in U.S. Households, 2002. Washington, DC. Available online at 

www.eia.doe.gov/emeu/recs/appliances/appliances.html.

6 J. Hausman. Individual discount rates and the purchase and utilization of energy using 

durables, The Bell Journal of Economics, 1979.

7 K. Train. Discount rates in consumer' energy related decisions: a review of the literature, 

Energy, 1985.

8 Association of Home Appliance Manufacturers. AHAM Fact Books, 2000 and 2003. 

Washington, DC.

9 Statistical Review. Appliance Magazine, April, 1990.

10 Association of Home Appliance Manufacturers. AHAM Fact Books, 1987, 1988, 1993, 1995, 

2000, and 2003. Washington, DC.

11 U.S. Census Bureau. Current Construction Reports, C25 – New One-Family Houses Sold.

Washington, DC. Available online at

 www.census.gov/prod/www/decennial.html.

12 Duemling, Reubin. Product Life of Refrigerators, 1999. University of California, Berkeley, 

Energy and Resources Group masters thesis.

13 U.S. Department of Energy-Office of Building Research and Standards. Final Rule Technical 

Support Document (TSD): Energy Efficiency Standards for Consumer Products: Clothes 

Washers, December, 2000. Washington, DC. Chapter 7.
9A-16

 14 U.S. Department of Energy-Office of Codes and Standards. Technical Support Document: 

Energy Conservation Standards for Consumer Products: Dishwashers, Clothes Washers, and 

Clothes Dryers, December, 1990. Washington, DC. DOE/CE-02999P. Chapter 4.

15 Association of Home Appliance Manufacturers. AHAM 2003 Fact Book, 2003. Washington, 

DC.

16 K.E. Train, and Atherton, T. Rebates, loans, and customers’ choice of appliance efficiency 

level: combining stated- and revealed-preference data, The Energy Journal, 1995.

17 U.S. Census Bureau. Income, Historical Income Tables – Households, Table H-6.

Washington, DC. Available online at

 www.census.gov/hhes/www/income/data/historical/index.html.
10A-i

APPENDIX 10A. USER INSTRUCTIONS FOR THE NATIONAL IMPACTS

ANALYSIS SPREADSHEET

TABLE OF CONTENTS

10A.1 USER INSTRUCTIONS ...................................................................................... 10A-1

10A.2 STARTUP............................................................................................................. 10A-1

10A.3 DESCRIPTION OF NATIONAL IMPACT ANALYSIS WORKSHEETS........ 10A-1

10A.4 BASIC INSTRUCTIONS FOR OPERATING THE NATIONAL IMPACT 

ANALYSIS SPREADSHEET.............................................................................. 10A-2
10A-1

APPENDIX 10A. USER INSTRUCTIONS FOR SHIPMENTS AND NATIONAL 

IMPACT ANALYSIS SPREADSHEET MODEL

10A.1 USER INSTRUCTIONS

The results of DOE's analysis of product shipments and national impacts can be 

examined and replicated using the Microsoft Excel spreadsheets accessible on the Internet from 

DOE's Building Technologies Program webpage for Appliance Standards: Cooking Products.

www1.eere.energy.gov/buildings/appliance_standards/residential/cooking_products.html

10A.2 STARTUP

The spreadsheet enables users to perform the national impact analysis (NIA) for energy 

efficiency standards for microwave oven standby and off-mode power. To examine the 

spreadsheets, the user needs access to a personal computer that has a hardware configuration 

capable of running Windows NT/2000/XP. The spreadsheets require Microsoft Excel 2000 or 

later, installed under the Windows operating system.

10A.3 DESCRIPTION OF NATIONAL IMPACT ANALYSIS WORKSHEETS

The NIA spreadsheets perform calculations to forecast the change in national energy use 

and net present value of financial impacts attributable to a revised energy conservation standard.

The energy use and associated costs for a given standard are determined by first calculating the 

shipments and then by calculating the energy use and costs for all microwave ovens shipped 

under that standard. The differences between national energy use and net present value under the 

standards and base case can be compared and the overall energy savings and net present values 

determined. The NIA spreadsheet model consists of the following worksheets.

Input and Summary The Input and Summary sheet contains the user's input selections and a 

summary table for each product class.

Charts The Charts sheet contains various charts and other information.

Summary (All TSLs) The Summary (All TSLs) worksheet contains results for all trial standard 

levels (TSLs) under various cost scenarios.

ROCIS The ROCIS worksheet contains the ROCIS results table.

Base Case The Base Case sheet contains a tabular summary and chart of the annual 

shipments forecast for each product class if no new standards are enacted, 

along with calculations of energy and operating costs.

Standards Case The Standards Case sheet contains a tabular summary and chart of the 
10A-2

annual shipments forecast for each product class at each TSL, along with 

calculations of energy and operating costs.

Learning Rates The Learning Rates worksheet contains the default learning rate along 

with other learning rate sensitivities.

Housing Projections The Housing Projections sheet contains housing forecasts based on the 

Energy Information Administration's Annual Energy Outlook for 2012 

(AEO2012).

Historical Shipment The Historical Shipment sheet contains data on historical shipments.

Lifetime The Lifetime sheet calculates the distribution of lifetime expectancies.

Elec Prices The Elec Prices sheet contains electricity price projections based on

AEO2012.

10A.4 BASIC INSTRUCTIONS FOR OPERATING THE NATIONAL IMPACT 

ANALYSIS SPREADSHEET

Basic instructions for operating the NIA spreadsheet are given below.

1. After downloading the NIA spreadsheet file from the Internet, open the file using 

Excel. Click Enable Macro

2. Use Excel's 

when prompted and then click on the tab for the National 

Impacts Summary Analysis worksheet.

View/Zoom

3. You can change the parameters listed under USER INPUTS on the Summary

worksheet. The default parameters and associated options are given below.

commands in the top menu bar to change the size of the 

display to make it fit your monitor.

a. TSL Level

b.

(cell I3): Defaults to "3." To change the TSL analyzed, change the 

value of this cell. Acceptable values are 1 through 4.

Discount Rate

c.

: Defaults to "7 percent." To change, click on cell C5 and choose 3

percent.

Year to Discount to

d.

: Defaults to "2011." To change value, click on cell C6 and set 

the desired year.

Economic Growth

e.

: Defaults to "reference case." To change the value, use the 

pull-down menu and select the desired trend (reference, low, or high).

Market Impact

f.

: Defaults to "-0.34." To change the price elasticity, use the pulldown menu and select the desired level (RP elasticity = -0.34 or No Impact).

Learning Case: Defaults to "default." To change the learning rate utilized, use the 

pull-down menu to select the desired rate.

The results are updated automatically and reported in the national impact summary table 

for each product class. Annualized values are presented as well.
10B-i

APPENDIX 10B. NATIONAL ENERGY SAVINGS AND NET PRESENT VALUE

USING ALTERNATIVE ECONOMIC GROWTH SCENARIOS

TABLE OF CONTENTS

10B.1 INTRODUCTION .................................................................................................10B-1

10B.2 RESULTS USING ALTERNATIVE GROWTH SCENARIOS ..........................10B-2

LIST OF TABLES

Table 10B.2.1 Cumulative National Energy Savings and Net Present Value of 

Consumer Benefits: High Economic Growth Case ...................................10B-2

Table 10B.2.2 Cumulative National Energy Savings and Net Present Value of 

Consumer Benefits: Low Economic Growth Case ....................................10B-3

LIST OF FIGURES

Figure 10B.1.1 Forecast Housing Starts Under Three AEO2012 Economic Growth 

Scenarios....................................................................................................10B-1

Figure 10B.1.2 Forecast Average Residential Electricity Price Under Three AEO2012

Economic Growth Scenarios......................................................................10B-2
10B-1

APPENDIX 10B. NATIONAL ENERGY SAVINGS AND NET PRESENT VALUE

USING ALTERNATIVE ECONOMIC GROWTH SCENARIOS

10B.1 INTRODUCTION

This appendix presents results for national energy savings (NES) and net present 

value (NPV) using inputs from alternative economic growth scenarios. The scenarios use 

the energy price and housing starts forecasts in the high and the low economic growth 

cases from The Energy Information Administration's (EIA’s) Annual Energy Outlook 

2012 (AEO2012).1

Figure 10B.1.1 shows the forecasts for housing starts under each of the three AEO 

economic growth scenarios. Figure 10B.1.2 shows the three forecasts for residential 

electricity prices. AEO2012 provides forecasts to 2035. To estimate the trend after 2035, 

DOE followed guidelines that the EIA provided to the Federal Energy Management 

Program, calling for using the average rate of change for electricity prices during 2020–

2035.

Figure 10B.1.1 Forecast Housing Starts Under Three 

AEO2012 Economic Growth Scenarios
10B-2

Figure 10B.1.2 Forecast Average Residential Electricity 

Price Under Three AEO2012 Economic

Growth Scenarios

10B.2 RESULTS USING ALTERNATIVE GROWTH SCENARIOS

This section presents tables that show NIA results under high and low AEO 

economic growth cases. NIA results for the reference case are presented in chapter 10. 

Table 10B.2.1 shows the cumulative NES and the NPV of consumer benefits under 

AEO's high economic growth case. Table 10B.2.2 shows those results under the lowgrowth scenario. 

Table 10B.2.1 Cumulative National Energy Savings and Net Present Value of 

Consumer Benefits: High Economic Growth Case

Trial 

Standard 

Level

NES 

quads

NPV billion 2011$

7% Discount 

Rate

3% Discount 

Rate

1 0.22 1.13 2.22

2 0.32 1.58 3.11

3 0.44 2.03 4.04

4 0.67 2.54 5.23
10B-3

Table 10B.2.2 Cumulative National Energy Savings and Net Present Value of 

Consumer Benefits: Low Economic Growth Case

Trial 

Standard 

Level

NES 

quads

NPV billion 2011$

7% Discount 

Rate

3% Discount 

Rate

1 0.20 0.92 1.76

2 0.28 1.27 2.46

3 0.39 1.62 3.17

4 0.60 1.98 4.00
10B-4

REFERENCE

 

1 U.S. Department of Energy–Energy Information Administration (EIA). Annual 

Energy Outlook 2012 with Projections to 2035. June 25, 2012. EIA: Washington, 

D.C. Available online at

 http://www.eia.gov/forecasts/aeo/. (Accessed September 2012.)
10C-i

APPENDIX 10C. NATIONAL NET PRESENT VALUE AND EMISSIONS 

REDUCTIONS USING ALTERNATIVE PRODUCT PRICE SCENARIOS

TABLE OF CONTENTS

10C.1 INTRODUCTION .................................................................................................10C-1

10C.2 COMBINED RESULTS USING ALTERNATIVE LEARNING RATES...........10C-3

LIST OF TABLES

Table 10C.1.1 Factors Affecting Microwave Oven Prices................................................10C-2

Table 10C.2.1 Microwave Oven Standby Power: Annualized Present Value of 

Consumer Impacts (3-Percent Discount Rate) and Annualized Present 

Value of Monetized Benefits from CO2 and NOX Emissions 

Reductions (3-Percent Discount Rate) Under Alternative Price 

Scenarios....................................................................................................10C-4

Table 10C.2.2 Microwave Oven Standby Power: Annualized Present Value of 

Consumer Impacts (7-Percent Discount Rate) and Annualized Present 

Value of Monetized Benefits from CO2 and NOX Emissions 

Reductions (Discount Rates of 3 percent for CO2 and 7 percent for 

NOX) Under Alternative Price Scenarios...................................................10C-5
10C-1

APPENDIX 10C. NATIONAL NET PRESENT VALUE AND EMISSIONS 

REDUCTIONS USING ALTERNATIVE PRODUCT PRICE SCENARIOS

10C.1 INTRODUCTION

The U.S. Department of Energy (DOE) investigated the effects of experience curves and 

other factors on the combined net present value (NPV) of consumer benefits for each trial 

standard level (TSL) considered for microwave oven standby power consumption. The NPV 

results for product prices that are presented in chapter 10 are based on a learning rate of 28.9

percent (the default learning rate) for the manufacture of electric cooking products. The

experience curves are functions relating the cost of production to quantity produced (typically 

cumulative production). The learning rate (LR) derived from the experience curve functions 

represents the cost reduction factor associated with economies of scale ,capital investment and 

automation in the particular industry. Detailed experience curve analysis is documented in 

appendix 8C. DOE considered five sensitivities: (1) a low learning rate; (2) a high learning rate;

(3) a learning rate based on data for microwave ovens only, (4) an AEO2011 chained price index 

forecast, and (5) no learning rate, which assumes constant real prices throughout the forecast 

period. Refer to appendix 8C for details regarding the development of learning rates.

DOE derived a price factor index, with 2011 equal to 1, to forecast prices in each future 

year of the analysis period. The default index value in a given year is a function of the LR and 

the cumulative production forecast through that year, which is based on the shipments forecast 

described in chapter 9. Table 10C.1.1 presents the factors DOE used to forecast product prices 

for microwave ovens (MWOs) under each considered sensitivity. 
10C-2

Table 10C.1.1 Factors Affecting Microwave Oven Prices

Year

Default LR

(28.9%)

Low LR

(19.2%)

High LR

(37.0%)

LR with MWO 

Data Only

(39.6%)

AEO2011

Chained Price 

Index Forecast

No LR 

(constant 

real prices)

2010 1.000 1.000 1.000 1.000 1.000 1.000

2011 0.981 0.986 0.979 0.968 0.989 1.000

2012 0.963 0.971 0.958 0.938 0.978 1.000

2013 0.946 0.958 0.939 0.909 0.964 1.000

2014 0.930 0.945 0.920 0.883 0.949 1.000

2015 0.914 0.933 0.903 0.859 0.932 1.000

2016 0.900 0.921 0.886 0.837 0.916 1.000

2017 0.886 0.910 0.871 0.817 0.899 1.000

2018 0.873 0.900 0.857 0.799 0.883 1.000

2019 0.861 0.890 0.842 0.781 0.866 1.000

2020 0.848 0.880 0.828 0.763 0.848 1.000

2021 0.836 0.870 0.814 0.745 0.829 1.000

2022 0.823 0.860 0.801 0.728 0.810 1.000

2023 0.812 0.850 0.788 0.711 0.791 1.000

2024 0.800 0.841 0.775 0.696 0.772 1.000

2025 0.790 0.832 0.763 0.681 0.754 1.000

2026 0.779 0.824 0.752 0.667 0.736 1.000

2027 0.770 0.816 0.741 0.654 0.719 1.000

2028 0.760 0.808 0.731 0.642 0.701 1.000

2029 0.751 0.800 0.721 0.630 0.684 1.000

2030 0.742 0.793 0.711 0.618 0.667 1.000

2031 0.733 0.785 0.702 0.607 0.651 1.000

2032 0.725 0.778 0.692 0.596 0.634 1.000

2033 0.716 0.771 0.683 0.585 0.619 1.000

2034 0.708 0.764 0.674 0.575 0.604 1.000

2035 0.700 0.757 0.665 0.565 0.589 1.000

2036 0.692 0.751 0.657 0.555 0.575 1.000

2037 0.685 0.745 0.649 0.546 0.561 1.000

2038 0.678 0.739 0.641 0.537 0.547 1.000

2039 0.670 0.733 0.633 0.529 0.534 1.000

2040 0.663 0.727 0.626 0.521 0.521 1.000

2041 0.657 0.721 0.618 0.513 0.508 1.000

2042 0.650 0.715 0.611 0.505 0.496 1.000

2043 0.643 0.709 0.604 0.497 0.484 1.000

The results presented here combine the NPV of consumer savings, calculated for each 

TSL using both 3-percent and 7-percent discount rates, with the present value of the potential 

economic benefits resulting from reduced CO2 and NOX emissions. The economic benefits from 

reduced CO2 emissions were calculated using a social cost of carbon (SCC) value of $41.1/metric 

ton in 2016 (in 2011$) for CO2, increasing at 3 percent per year, and a discount rate of 3 percent. 

The economic benefits from reduced NOX emissions were calculated using a value of $2,567/ton 

(in 2011$), which is the average of the low and high values used in DOE’s analysis, and either a

3-percent or 7-percent discount rate. Chapter 16 provides information regarding the derivation of 
10C-3

the values assigned to emissions reductions. All results refer to lifetime impacts of products 

shipped during 2014–2043.

The results presented here are annualized values. DOE used a two-step process to convert 

the time-series of costs and benefits into annualized values. First, DOE calculated a present value

for the time-series of costs and benefits in 2011, the year used for discounting the NPV of total 

consumer costs and savings. In that calculation DOE used discount rates of 3 percent and 7

percent for all costs and benefits except for the value of CO2 reductions. For the latter, DOE used 

the discount rate appropriate for each SCC time-series. From that present value, DOE calculated 

the fixed annual payment during a 30-year period, starting in 2011, that yields the same present 

value. The fixed annual payment is the annualized value. Annualized values do not imply that 

the time-series of cost and benefits from which those annualized values were determined would

actually be a steady stream of payments.

10C.2 COMBINED RESULTS USING ALTERNATIVE LEARNING RATES

Table 10C.2.1 and Table 10C.2.2 present annualized present values of consumer impacts 

and monetized benefits from reductions in CO2 and NOX for microwave oven standby power at 

each TSL considered in this rulemaking. The values are based on alternative forecasts of product 

prices as determined primarily by learning rates (LRs). Table 10C.2.1 presents results for 

consumer impacts and emissions reductions at 3-percent discount rates. Table 10C.2.2 provides 

results for consumer impacts at a 7-percent discount rate and for emissions reductions at a 3-

percent rate for CO2 and a 7-percent discount rate for NOX.
10C-4

Table 10C.2.1 Microwave Oven Standby Power: Annualized Present Value of Consumer Impacts (3-Percent Discount 

Rate) and Annualized Present Value of Monetized Benefits from CO2 and NOX Emissions Reductions

(3-Percent Discount Rate) Under Alternative Price Scenarios

Trial 

Standard 

Level Parameter

Billion 2011$

Default LR

(28.9%)

Low LR

(19.2%)

High LR

(37.0%)

LR with MWO 

Data Only

(39.6%)

AEO2011

Chained Price 

Index Forecast

No Learning

(constant 

real prices)

1

Incremental installed cost 0.002 0.002 0.002 0.002 0.002 0.003

Operating cost savings 0.103 0.103 0.103 0.103 0.103 0.103

Value of emissions 

reductions

0.030 0.030 0.030 0.030 0.030 0.030

Net present value 0.131 0.131 0.131 0.131 0.131 0.131

2

Incremental installed cost 0.007 0.008 0.007 0.006 0.007 0.009

Operating cost savings 0.149 0.149 0.149 0.149 0.149 0.149

Value of emissions 

reductions

0.044 0.044 0.044 0.044 0.044 0.044

Net present value 0.186 0.186 0.186 0.187 0.187 0.184

3

Incremental installed cost 0.022 0.023 0.021 0.019 0.020 0.027

Operating cost savings 0.205 0.205 0.205 0.205 0.205 0.205

Value of emissions 

reductions

0.061 0.061 0.061 0.061 0.061 0.061

Net present value 0.244 0.243 0.245 0.247 0.245 0.238

4

Incremental installed cost 0.079 0.083 0.076 0.068 0.074 0.099

Operating cost savings 0.314 0.314 0.314 0.314 0.314 0.314

Value of emissions 

reductions

0.093 0.093 0.093 0.093 0.093 0.093

Net present value 0.328 0.323 0.340 0.373 0.298 0.308

Note: Parentheses indicate negative (-) values.
10C-5

Table 10C.2.2 Microwave Oven Standby Power: Annualized Present Value of Consumer Impacts (7-Percent Discount 

Rate) and Annualized Present Value of Monetized Benefits from CO2 and NOX Emissions Reductions 

(Discount Rates of 3 percent for CO2 and 7 percent for NOX) Under Alternative Price Scenarios

Trial 

Standard 

Level Parameter

Billion 2011$

Default LR

(28.9%)

Low LR

(19.2%)

High LR

(37.0%)

LR with MWO 

Data Only

(39.6%)

AEO2011

Chained Price 

Index Forecast

No Learning

(constant 

real prices)

1

Incremental installed cost 0.002 0.002 0.002 0.002 0.002 0.003

Operating cost savings 0.084 0.084 0.084 0.084 0.084 0.084

Value of emissions 

reductions

0.030 0.030 0.030 0.030 0.030 0.030

Net present value 0.112 0.112 0.112 0.112 0.112 0.111

2

Incremental installed cost 0.007 0.007 0.007 0.006 0.007 0.009

Operating cost savings 0.121 0.121 0.121 0.122 0.121 0.121

Value of emissions 

reductions

0.044 0.044 0.044 0.044 0.044 0.044

Net present value 0.158 0.158 0.159 0.159 0.159 0.157

3

Incremental installed cost 0.020 0.021 0.020 0.018 0.019 0.025

Operating cost savings 0.167 0.167 0.167 0.167 0.167 0.167

Value of emissions 

reductions

0.060 0.060 0.060 0.060 0.060 0.060

Net present value 0.207 0.206 0.207 0.209 0.208 0.202

4

Incremental installed cost 0.075 0.078 0.072 0.066 0.071 0.090

Operating cost savings 0.256 0.256 0.256 0.256 0.256 0.256

Value of emissions 

reductions

0.092 0.092 0.092 0.092 0.092 0.092

Net present value 0.273 0.270 0.276 0.282 0.277 0.258

Note: Parentheses indicate negative (-) values.
12A-i

APPENDIX 12A. MANUFACTURER IMPACT ANALYSIS INTERVIEW GUIDES

TABLE OF CONTENTS

12A.1 MICROWAVE OVENS (ENERGY FACTOR STANDARDS) ......................... 12A-1

12A.2 MICROWAVE OVENS (STANDBY POWER STANDARDS)......................... 12A-5
12A-1

APPENDIX 12A. MANUFACTURER IMPACT ANALYSIS INTERVIEW GUIDES

12A.1 MICROWAVE OVENS (ENERGY FACTOR STANDARDS)

Cooking Products Rulemaking 

Manufacturer Impact Analysis Interview Guide: Microwave Ovens

March 3, 2008
12A-2

1 KEY ISSUES 

The U.S. Department of Energy (DOE) is currently reviewing a set of efficiency levels for 

Microwave Ovens (MWOs). The efficiency levels are presented below as they apply to the 

single product class. In responding to this questionnaire, please refer to the energy factor (EF) 

values described below.

Table 1.1 Efficiency Levels (EF %)

Product 

Class Baseline EfficiencyLevel 1

Efficiency 

Level 2

Efficiency 

Level 3

Efficiency 

Level 4

Microwave 

Ovens 55.70 58.60 58.80 59.70 60.20

1.1 What are the key issues for your company regarding MWOs related to energy conservation 

standards and this rulemaking?

1.2 For the issues identified, how significant are they for each efficiency level?

1.3 How can we most effectively incorporate these issues in the Manufacturer Impact Analysis

(MIA)?

2 SHIPMENT PROJECTION AND MARKET SHARE

2.1 Would you expect your market share to change once standards become effective? Does 

your outlook change with higher efficiency levels?

2.2 How would you expect shipments to change for the industry as a whole as a function of 

standards and why?

3 CONVERSION COSTS 

An increase in energy conservation standards may cause your company to incur one-time 

capital and product conversion costs. DOE calls these conversion costs because they are 

the costs of changing your business to comply with new energy conservation standards.

3.1 What level of conversion costs do you anticipate incurring under the different efficiency 

levels? Refer to the tables below to provide your company’s estimates. Please provide 

additional qualitative information to help us understand the nature of your investments.

Table 3.1 MWO Conversion Costs

Standard Level Conversion Capital 

Expenditures

Product Conversion 

Expenses

Stranded Assets

Baseline (55.70)

Level 1 (58.60)

Level 2 (58.80)

Level 3 (59.70)

Level 4 (60.20)
12A-3

4 PROFITABILITY AND MANUFACTURING CAPACITY

One of the primary objectives of the MIA is to assess the impact of efficiency levels on industry 

profitability and changes in manufacturing capacity.

4.1 Does your company have any MWO manufacturing facilities located in the United States? 

How would you expect shipments to change for the industry as a whole as a function of 

standards and why? 

4.2 Do you expect any changes in your estimated profitability following the new energy 

conservation standard? If so, please explain why.

4.3 How would the imposition of new energy conservation standards affect your 

manufacturing capacity at your production facilities? Would any facilities be closed, 

downsized, added, or upgraded?

5 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 products or 

industry.

5.1 Are there other recent or impending regulations that MWO manufacturers face (from DOE 

or otherwise)? What level of investment are you expecting to incur as a result of these 

regulations?

5.2 Under what circumstances would you be able to coordinate any expenditures related to 

other regulations thereby lessening the cumulative burden?

6 DIRECT EMPLOYMENT IMPACT ASSESSMENT

The impact of new 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 

MWO production employment and solicit manufacturer views on how domestic employment 

patterns might be affected by new energy conservation standards.

6.1 Would your domestic employment levels change under new energy conservation 

standards? If so, please explain how they would change.

6.2 Would the workforce skills necessary under new energy conservation standards require 

extensive retraining or replacement of employees?

7 CONSOLIDATION

New 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 Justice 

Department are both interested in any potential reduction in competition that would result from a 

new energy conservation standard.
12A-4

7.1 In the absence of new standards, do you expect any industry consolidation? Please 

describe your expectations.

7.2 How would new standards affect your ability to compete?

7.3 Could new standards disproportionately advance or harm the competitive positions of 

some firms (e.g., small businesses)?
12A-5

12A.2 MICROWAVE OVENS (STANDBY POWER STANDARDS)

Cooking Products Rulemaking

Standby Power Interview Guide: Microwave Ovens

INTRODUCTION:

The U.S. Department of Energy (DOE) is currently reviewing energy efficiency standards for 

cooking products. Of high interest are the standby power requirements of microwave ovens. As a 

result, the Department invites manufacturers to participate in this questionnaire to help the 

Department better understand the issues regarding standby power, as well as any potential 

impacts on consumers that standby power legislation could have. 

The Department tested 32 microwave ovens for sale in the United States within the past year. 

The sample included a wide range of input powers and cavity sizes to investigate cooking 

efficiency and standby power requirements. DOE subsequently published its findings at the 

December 17, 2007 Advance Notice of Proposed Rulemaking (ANOPR) meeting in Washington 

DC. Due to comments submitted to DOE during and after this meeting, the Department would 

like to solicit additional feedback from manufacturers. 

During testing, DOE noted that microwave ovens with cooking sensors tend to consume more 

standby power than those without cooking sensors. DOE also noted how display size and 

technology and the associated controller could influence standby power requirements. At the end 

of the testing cycle, every microwave oven was completely disassembled. DOE noted a wide 

variety of display types and cooking sensors, even if most other aspects of microwave 

construction have been largely standardized. This questionnaire will thus focus on the 

controllers, displays, and power supplies.

Cooking Sensors

DOE would like to understand why manufacturers incorporate cooking sensors into their 

microwave ovens, and factors influencing the choice of a specific sensor type from among the 

range of available technologies.

1.1 On a unit shipment-basis, what percentage of microwave ovens that you sell into the 

United States market feature cooking sensors?

1.2 Do you have any data on the percentage of consumers that use the sensor function? Is 

there any data available quantifying the energy savings associated with the use of a 

cooking sensor?
12A-6

1.3 What sensor technologies do you use for microwave ovens sold in the United States? 

What are the standby requirements of these sensors? Are there sensor technologies 

available that require less power in standby that can be applied in microwave ovens?

Displays

DOE also noted a wide range of standby power consumption based on the display technology. 

For example, displays based on vacuum-fluorescent displays tended to consume more power 

than comparable-sized displays based on light emitting diodes. DOE noted three display types in 

its market sample, consisting of light-emitting diodes (LEDs), liquid crystal displays (with or 

without LED backlighting), and vacuum-fluorescent displays.

1.4 What is the consumer utility associated with each type of display? Do consumers demand 

displays with specific technologies?

1.5 What are the power requirements for each type of display assuming a comparable size and 

general capability (complexity of the characters that a display can show)? What are the 

relative costs associated with each type of display?

Controllers and Power Supplies

While cooking sensors and displays consumed the most standby power, DOE also noted that 

controllers and the control boards also consume standby power. The Department recognizes that 

there may be tradeoffs between standby power consumption and the ability of control systems to 

tackle complex tasks.

1.6 What consumer utility is associated with microwave oven controllers? Could controller 

attributes or functionalities be impacted by reducing microwave oven standby 

requirements? If so, how?

1.7 Are lower-power versions of these controllers available? What control board design 

options are available for reducing standby power? At what cost? 

1.8 What is the range of efficiencies for controller power supplies? Can higher efficiency 

power supplies be implemented? At what cost?

Potential Standby Power Standard Levels

DOE surveyed foreign regulatory and non-regulatory programs, identifying Korea’s e-standby 

program, Australia’s standby program, and Japan’s Top Runner Program as pursuing reductions 

in standby power consumption through support for the International Energy Agency’s One-Watt 
12A-7

program to lower standby power consumption below 1 watt (W) for microwave ovensa, among 

others. DOE is also aware of zero-watt-standby microwave ovens in Japan that feature cooking 

sensors.

1.9 What design changes from current US products would be required if the following standby 

standard level were established?

• 4 W/h

• 2 W/h

• 1.5 W/h

• 1 W/h

• 0 W/h

• What would be the manufacturing cost impacts of these changes?

• Are there any impacts on consumer utility as standby power levels are lowered? If 

so, what are they, and at what standby power levels do they occur?

Standby Power Standards Manufacturer Impacts

DOE understands that by establishing standby power standards, manufacturer could potentially 

incur one-time capital and product conversion costs.

b

 DOE calls these conversion costs because 

they are the costs of changing your business to comply with new standby power standards. In 

addition, DOE would like to understand possible effects of new standby power standards on 

industry shipments, profitability, and manufacturing capacity.

What level of conversion costs do you anticipate incurring under different standby power 

standard level? Refer to the tables below to provide your company’s estimates. Please provide 

additional qualitative information to help us understand the nature of your investments.

 

a IEA Energy Information Centre, Standby Power Use and the IEA “1-Watt Plan”

http://www.iea.org/textbase/subjectqueries/standby.asp

b Capital and product conversion costs include capital conversion expenditures, product conversion expenditures and 

stranded assets. Capital Conversion Expenditures – One time investments in plant, property, and equipment (PPE) 

necessitated by an energy conservation standard. These may be completely incremental to existing PPE or they 

could involve replacement of existing PPE. Product Conversion Expenditures – One-time expenses in research, 

product development, testing, and marketing necessitated by an appliance energy conservation standard. Stranded 

Assets – Investments such as equipment or tooling that become obsolete as a result of new regulation.
12A-8

Table 1.1 MWO Conversion Costs

Standard Level Conversion Capital 

Expenditures

Product Conversion 

Expenses

Stranded Assets

4 W/h

2 W/h

1.5 W/h

1 W/h

0 W/h

1.10 How would you expect shipments to change for the industry as a whole as a function of 

standby power standards and why?

1.11 Do you expect any changes in your estimated profitability following new standby power 

standard? If so, please explain why.

1.12 How would the imposition of new standby power standards affect your manufacturing 

capacity at your production facilities? Would any facilities be closed, downsized, added, 

or upgraded?

Standby Test Procedure

As part of the analysis, DOE reviewed the existing DOE cooking efficiency test procedure and 

possible standby test procedures that could be incorporated. For standby testing, DOE is 

considering adopting by reference the IEC 62301 standby test procedurec

. 

1.13 Do you currently conduct microwave oven standby power testing? If so, which test 

procedure do you use?

1.14 How do you feel about the incorporation of IEC 62301 to measure standby with the 

provision that a 12-hour (or possibly 24-hour) test be performed with the clock set to a 

specific starting time?

 

c See http://iec.org for further details
12B-i

APPENDIX 12B. GOVERNMENT REGULATORY IMPACT MODEL OVERVIEW

TABLE OF CONTENTS

12B.1 INTRODUCTION AND PURPOSE.................................................................... 12B-1

12B.2 MODEL DESCRIPTION ..................................................................................... 12B-1

12B.3 DETAILED CASH FLOW EXAMPLE............................................................... 12B-4
12B-1

APPENDIX 12B. GOVERNMENT REGULATORY IMPACT MODEL

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 value of the industry or manufacturers(s) following 

a regulation or a series of regulations. The model structure also allows an analysis of multiple 

products with regulations taking effect over a period of time, and of multiple regulations on the 

same products.

Industry net present value is defined, for the purpose of this analysis, as the discounted 

sum of industry free cash flows plus a discounted terminal value. The model calculates the actual 

cash flows by year and then determines the present value of those cash flows both without an 

amended energy conservation standard (i.e., the base case) and under different trial standard 

levels (TSLs).

Output from the model consists 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 (see section 12.B.3).

Unit Sales: Total annual shipments for the industry were obtained from the National Impact 

Analysis Spreadsheet;

Revenues: Annual revenues - computed by multiplying unit prices at each efficiency level by 

the appropriate manufacturer markup;

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; 

Material: The portion of COGS that includes materials;
12B-2

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;

Depreciation: Annual depreciation computed as a percentage of Revenues. While included in 

overhead, the depreciation is shown as a separate line item;

Stranded Assets: In the compliance year of the standard, a one-time write-off of net property, 

plant, and equipment (PPE) assets to account for the book value of these assets that would 

have enjoyed a longer life if not for the standard;

Standard SG&A: Selling, general, and administrative costs are computed as a percentage of 

Revenues;

R&D: the GRIM separately accounts for ordinary research and development (R&D) as a 

percentage of Revenues;

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 the effective date;

Earnings Before Interest and Taxes (EBIT): Includes profit before deductions for interest 

paid and taxes;

EBIT as a Percentage of Sales (EBIT/Revenues): the GRIM calculates EBIT as a percentage 

of sales to compare with the industry’s average reported in financial statements;

Taxes: Taxes on EBIT are calculated by multiplying the tax rate contained in Major 

Assumptions by EBIT;

Net Operating Profits After Taxes (NOPAT): Computed by subtracting Cost of Goods Sold, 

SG&A, R&D, Product Conversion Costs, and Taxes from Revenues;

NOPAT repeated: NOPAT is repeated in the Statement of Cash Flows;

Depreciation repeated: Depreciation and Stranded Assets are added back in the Statement of 

Cash Flows because they are non-cash expenses;

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;

Cash Flow From Operations: Calculated by taking NOPAT, adding back non-cash items 

such as a Depreciation, and subtracting out Change in Working Capital;
12B-3

Ordinary Capital Expenditures: Ordinary investments in property, plant, and equipment to 

maintain and replace existing production assets, computed as a percentage of Revenues;

Capital Conversion Costs: Capital conversion costs are one-time investments in property, 

plant, and equipment to adapt or change existing production facilities so that new product

designs can be fabricated and assembled under the new regulation;

Capital Investment: Total investments in property, plant, and equipment are computed by 

adding Ordinary Capital Expenditures and Capital Conversion Costs;

Free Cash Flow: Annual cash flow from operations and investments; computed by 

subtracting Capital Investment from Cash Flow from Operations;

Terminal Value: Estimate of the continuing value of the industry after 2045. Computed by 

growing the Free Cash Flow in year 2045 at a constant rate in perpetuity;

Present Value Factor: Factor used to calculate an estimate of the present value of an amount 

to be received in the future;

Discounted Cash Flow: Free Cash Flows multiplied by the Present Value Factor. For 2045

the discounted cash flow includes the discounted Terminal Value; and

Industry Value thru 2045: The sum of Discounted Cash Flows.
12B-4

12B.3 DETAILED CASH FLOW EXAMPLE

Ancmt Yr Std Yr

Industry Income Statement 2012 2013 2014 2015 2016 2017 2018 2019 2020

Revenues $ 2,370.1 $ 2,300.1 $ 2,362.5 $ 2,319.4 $ 2,270.8 $ 2,160.0 $ 2,083.1 $ 2,099.0 $ 2,184.1

- Materials $ 1,537.0 $ 1,491.7 $ 1,532.1 $ 1,504.2 $ 1,497.5 $ 1,424.4 $ 1,373.7 $ 1,384.2 $ 1,440.3

- Labor $ 161.0 $ 156.3 $ 160.5 $ 157.6 $ 148.1 $ 140.9 $ 135.9 $ 136.9 $ 142.5

- Depreciation $ 111.5 $ 108.3 $ 111.2 $ 109.2 $ 102.6 $ 97.6 $ 94.1 $ 94.8 $ 98.7

- Overhead $ 16.3 $ 15.8 $ 16.3 $ 16.0 $ 15.0 $ 14.3 $ 13.8 $ 13.9 $ 14.4

- Standard SG&A $ 365.5 $ 354.7 $ 364.3 $ 357.6 $ 350.2 $ 333.1 $ 321.2 $ 323.7 $ 336.8

- R&D $ 28.4 $ 27.6 $ 28.4 $ 27.8 $ 27.2 $ 25.9 $ 25.0 $ 25.2 $ 26.2

- Product Conversion Costs $ - $ 18.2 $ 25.4 $ 29.1 $ 1.5 $ - $ - $ - $ -

- Stranded Assets $ - $ - $ - $ - $ - $ - $ - $ - $ -

Earnings Before Interest and Taxes (EBIT) $ 150.2 $ 127.6 $ 124.3 $ 117.9 $ 128.7 $ 123.8 $ 119.4 $ 120.3 $ 125.2

EBIT/Revenues 6.3% 5.5% 5.3% 5.1% 5.7% 5.7% 5.7% 5.7% 5.7%

- Taxes $ 42.4 $ 36.0 $ 35.1 $ 33.3 $ 36.3 $ 35.0 $ 33.7 $ 34.0 $ 35.4

Net Operating Profit after Taxes (NOPAT) $ 107.8 $ 91.6 $ 89.2 $ 84.6 $ 92.3 $ 88.9 $ 85.7 $ 86.3 $ 89.8

Cash Flow Statement 

NOPAT $ 107.8 $ 91.6 $ 89.2 $ 84.6 $ 92.3 $ 88.9 $ 85.7 $ 86.3 $ 89.8

+ Depreciation $ 111.5 $ 108.3 $ 111.2 $ 109.2 $ 102.6 $ 97.6 $ 94.1 $ 94.8 $ 98.7

+ Loss on Disposal of Stranded Assets $ - $ - $ - $ - $ - $ - $ - $ - $ -

- Change in Working Capital $ - $ (4.8) $ 4.3 $ (3.0) $ (3.3) $ (7.6) $ (5.3) $ 1.1 $ 5.9

Cash Flows from Operations $ 219.3 $ 204.6 $ 196.1 $ 196.8 $ 198.3 $ 194.1 $ 185.1 $ 180.1 $ 182.7

- Ordinary Capital Expenditures $ 114.0 $ 110.6 $ 113.6 $ 111.6 $ 109.2 $ 103.9 $ 100.2 $ 101.0 $ 105.1

- Capital Conversion Costs $ - $ 2.0 $ 2.8 $ 3.2 $ - $ - $ - $ - $ -

Free Cash Flow $ 105.3 $ 92.0 $ 79.7 $ 82.0 $ 89.1 $ 90.2 $ 84.9 $ 79.1 $ 77.6

Discounted Cash Flow

Free Cash Flow $ 105.3 $ 92.0 $ 79.7 $ 82.0 $ 89.1 $ 90.2 $ 84.9 $ 79.1 $ 77.6

Terminal Value $ - $ - $ - $ - $ - $ - $ - $ - $ -

Present Value Factor 0.000 1.000 0.926 0.857 0.793 0.734 0.680 0.629 0.582

Discounted Cash Flow $ - $ 92.0 $ 73.8 $ 70.3 $ 70.7 $ 66.2 $ 57.7 $ 49.8 $ 45.2

INPV at TSL 4 $ 1,201.9
16A-i

APPENDIX 16A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT 

ANALYSIS UNDER EXECUTIVE ORDER 12866

TABLE OF CONTENTS

16A.1 EXECUTIVE SUMMARY .................................................................................. 16A-1

16A.2 MONETIZING CARBON DIOXIDE EMISSIONS............................................ 16A-2

16A.3 SOCIAL COST OF CARBON VALUES USED IN PAST REGULATORY 

ANALYSES.......................................................................................................... 16A-4

16A.4 APPROACH AND KEY ASSUMPTIONS ......................................................... 16A-5

16A.4.1 Integrated Assessment Models ............................................................................. 16A-5

16A.4.2 Global versus Domestic Measures of SCC......................................................... 16A-11

16A.4.3 Valuing Non-CO2 Emissions.............................................................................. 16A-13

16A.4.4 Equilibrium Climate Sensitivity ......................................................................... 16A-13

16A.4.5 Socioeconomic and Emissions Trajectories........................................................ 16A-16

16A.4.6 Discount Rate...................................................................................................... 16A-19

16A.5 REVISED SCC ESTIMATES ............................................................................ 16A-26

16A.6 LIMITATIONS OF THE ANALYSIS ............................................................... 16A-32

16A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPACTS AND 

DAMAGE FUNCTIONS ................................................................................... 16A-34

16A.8 CONCLUSION................................................................................................... 16A-37

16A.9 ANNEX............................................................................................................... 16A-43

16A.9.1 Other (non-CO2) gases....................................................................................... 16A-44

16A.9.2 Extrapolating Emissions Projections to 2300 ..................................................... 16A-46

LIST OF TABLES

Table 16A.1.1 Social Cost of CO2, 2010 – 2050 (in 2007 dollars) .................................. 16A-2

Table 16A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity 

Distributions............................................................................................ 16A-14

Table 16A.4.2 Socioeconomic and Emissions Projections from Select EMF-22 

Reference Scenarios................................................................................ 16A-18

Table 16A.5.1 Disaggregated Social Cost of CO2 Values by Model, Socioeconomic 

Trajectory, and Discount Rate for 2010 (in 2007 dollars)...................... 16A-29

Table 16A.5.2 Social Cost of CO2, 2010 – 2050 (in 2007 dollars) ................................ 16A-31

Table 16A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates 

between 2010 and 2050 .......................................................................... 16A-31

Table 16A.7.1 Probabilities of Various Tipping Points from Expert Elicitation............ 16A-35

Table 16A.9.1 Annual SCC Values: 2010–2050 (in 2007 dollars) ................................ 16A-43

Table 16A.9.2 2010 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton 

CO2) ........................................................................................................ 16A-53

Table 16A.9.3 2010 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton 

CO2) ........................................................................................................ 16A-54
16A-ii

Table 16A.9.4 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton 

CO2) ........................................................................................................ 16A-55

Table 16A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates............. 16A-56

LIST OF FIGURES

Figure 16A.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.......................................................................................... 16A-10

Figure 16A.4.2 Annual Consumption Loss for Lower Temperature Changes in DICE, 

FUND, and PAGE .................................................................................. 16A-11

Figure 16A.4.3 Estimates of the Probability Density Function for Equilibrium Climate 

Sensitivity (°C) ....................................................................................... 16A-16

Figure 16A.5.1 Level of Global GDP across EMF Scenarios ......................................... 16A-30

Figure 16A.9.2 Sulfur Dioxide Emission Scenarios........................................................ 16A-46

Figure 16A.9.3 Global Population, 2000-2300 (Post-2100 extrapolations assume the 

population growth rate changes linearly to reach a zero growth rate by 

2200.) ...................................................................................................... 16A-48

Figure 16A.9.4 World GDP, 2000-2300 (Post-2100 extrapolations assume GDP per 

capita growth declines linearly, reaching zero in the year 2300) ........... 16A-49

Figure 16A.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)................................................. 16A-50

Figure 16A.9.6 Global Net Land Use CO2 Emissions, 2000-2300 (Post-2100 

extrapolations assume emissions decline linearly, reaching zero in the 

year 2200) ............................................................................................... 16A-50

Figure 16A.9.7 Global Non-CO2 Radiative Forcing, 2000-2300 (Post-2100 

extrapolations assume constant non-CO2 radiative forcing after 2100) . 16A-51

Figure 16A.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).................................. 16A-52

Figure 16A.9.9 Histogram of Global SCC Estimates in 2010 (2007$/ton CO2), by 

discount rate ............................................................................................ 16A-55
16A-1

APPENDIX 16A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT 

ANALYSIS UNDER EXECUTIVE ORDER 12866a

16A.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
16A-2

The interagency group selected four SCC values for use in regulatory analyses. Three 

values are based on the average SCC from three integrated assessment models, at discount rates 

of 2.5, 3, and 5 percent. The fourth value, which represents the 95th percentile SCC estimate 

across all three models at a 3 percent discount rate, is included to represent higher-than-expected 

impacts from temperature change further out in the tails of the SCC distribution.

Table 16A.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

16A.2 MONETIZING CARBON DIOXIDE EMISSIONS

The SCC is an estimate of the monetized damages associated with an incremental 

increase in carbon emissions in a given year. We report estimates of the SCC in dollars per 

metric ton of carbon dioxide throughout this document.b

 

When attempting to assess the incremental economic impacts of carbon dioxide 

emissions, the analyst faces a number of serious challenges. A recent report from the National 

Academies of Science (NRC 2009) points out that any assessment will suffer from uncertainty, 

speculation, and lack of information about (1) future emissions of greenhouse gases, (2) the 

effects of past and future emissions on the climate system, (3) the impact of changes in climate 

on the physical and biological environment, and (4) the translation of these environmental 

impacts into economic damages. As a result, any effort to quantify and monetize the harms 

associated with climate change will raise serious questions of science, economics, and ethics and 

should be viewed as provisional. 

 b In this document, we present all values of the SCC as the cost per metric ton of CO2 emissions. Alternatively, one 

could report the SCC as the cost per metric ton of carbon emissions. The multiplier for translating between mass of 

CO2 and the mass of carbon is 3.67 (the molecular weight of CO2 divided by the molecular weight of carbon = 44/12 

= 3.67). 
16A-3

Despite the serious limits of both quantification and monetization, SCC estimates can be 

useful in estimating the social benefits of reducing carbon dioxide emissions. Under Executive 

Order 12866, agencies are required, to the extent permitted by law, “to assess both the costs and 

the benefits of the intended regulation and, recognizing that some costs and benefits are difficult 

to quantify, propose or adopt a regulation only upon a reasoned determination that the benefits of 

the intended regulation justify its costs.” The purpose of the SCC estimates presented here is to 

make it possible for agencies to incorporate the social benefits from reducing carbon dioxide 

emissions into cost-benefit analyses of regulatory actions that have small, or “marginal,” impacts 

on cumulative global emissions. Most federal regulatory actions can be expected to have 

marginal impacts on global emissions. 

For such policies, the benefits from reduced (or costs from increased) emissions in any 

future year can be estimated by multiplying the change in emissions in that year by the SCC 

value appropriate for that year. The net present value of the benefits can then be calculated by 

multiplying each of these future benefits by an appropriate discount factor and summing across 

all affected years. This approach assumes that the marginal damages from increased emissions 

are constant for small departures from the baseline emissions path, an approximation that is 

reasonable for policies that have effects on emissions that are small relative to cumulative global 

carbon dioxide emissions. For policies that have a large (non-marginal) impact on global 

cumulative emissions, there is a separate question of whether the SCC is an appropriate tool for 

calculating the benefits of reduced emissions; we do not attempt to answer that question here.

An interagency group convened on a regular basis to consider public comments, explore 

the technical literature in relevant fields, and discuss key inputs and assumptions in order to 

generate SCC estimates. Agencies that actively participated in the interagency process include 

the Environmental Protection Agency, and the Departments of Agriculture, Commerce, Energy, 

Transportation, and Treasury. This process was convened by the Council of Economic Advisers 

and the Office of Management and Budget, with active participation and regular input from the 

Council on Environmental Quality, National Economic Council, Office of Energy and Climate 

Change, and Office of Science and Technology Policy. The main objective of this process was 

to develop a range of SCC values using a defensible set of input assumptions that are grounded 

in the existing literature. In this way, key uncertainties and model differences can more 

transparently and consistently inform the range of SCC estimates used in the rulemaking process. 

The interagency group selected four SCC estimates for use in regulatory analyses. For 

2010, these estimates are $5, $21, $35, and $65 (in 2007 dollars). The first three estimates are 

based on the average SCC across models and socioeconomic and emissions scenarios at the 5, 3, 

and 2.5 percent discount rates, respectively. The fourth value is included to represent the higherthan-expected impacts from temperature change further out in the tails of the SCC distribution. 

For this purpose, we use the SCC value for the 95th percentile at a 3 percent discount rate. The 

central value is the average SCC across models at the 3 percent discount rate. For purposes of 

capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance 

and value of considering the full range. These SCC estimates also grow over time. For instance, 

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.
16A-4

It is important to emphasize that the interagency process is committed to updating these 

estimates as the science and economic understanding of climate change and its impacts on 

society improves over time. Specifically, we have set a preliminary goal of revisiting the SCC 

values within two years or at such time as substantially updated models become available, and to 

continue to support research in this area. In the meantime, we will continue to explore the issues 

raised in this document and consider public comments as part of the ongoing interagency 

process.

16A.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 
16A-5

estimates. All of these values were assumed to increase at 3 percent annually to represent growth 

in incremental damages over time as the magnitude of climate change increases.

These interim values represent the first sustained interagency effort within the U.S. 

government to develop an SCC for use in regulatory analysis. The results of this preliminary 

effort were presented in several proposed and final rules and were offered for public comment in 

connection with proposed rules, including the joint EPA-DOT fuel economy and CO2 tailpipe 

emission proposed rules.

16A.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.

16A.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

 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).

 These models are frequently cited in the peer-
16A-6

reviewed literature and used in the IPCC assessment. Each model is given equal weight in the 

SCC values developed through this process, bearing in mind their different limitations (discussed 

below).

 

These models are useful because they combine climate processes, economic growth, and 

feedbacks between the climate and the global economy into a single modeling framework. At 

the same time, they gain this advantage at the expense of a more detailed representation of the 

underlying climatic and economic systems. DICE, PAGE, and FUND all take stylized, reducedform approaches (see NRC 2009 for a more detailed discussion; see Nordhaus 2008 on the 

possible advantages of this approach). Other IAMs may better reflect the complexity of the 

science in their modeling frameworks but do not link physical impacts to economic damages. 

There is currently a limited amount of research linking climate impacts to economic damages, 

which makes this exercise even more difficult. Underlying the three IAMs selected for this 

exercise are a number of simplifying assumptions and judgments reflecting the various modelers’ 

best attempts to synthesize the available scientific and economic research characterizing these 

relationships.

The three IAMs translate emissions into changes in atmospheric greenhouse 

concentrations, atmospheric concentrations into changes in temperature, and changes in 

temperature into economic damages. The emissions projections used in the models are based on 

specified socioeconomic (GDP and population) pathways. These emissions are translated into 

concentrations using the carbon cycle built into each model, and concentrations are translated 

into warming based on each model’s simplified representation of the climate and a key 

parameter, climate sensitivity. Each model uses a different approach to translate warming into 

damages. Finally, transforming the stream of economic damages over time into a single value 

requires judgments about how to discount them.

Each model takes a slightly different approach to model how changes in emissions result 

in changes in economic damages. In PAGE, for example, the consumption-equivalent damages 

in each period are calculated as a fraction of GDP, depending on the temperature in that period 

relative to the pre-industrial average temperature in each region. In FUND, damages in each 

period also depend on the rate of temperature change from the prior period. In DICE, 

temperature affects both consumption and investment. We describe each model in greater detail 

here. In a later section, we discuss key gaps in how the models account for various scientific and 

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 
16A-7

parameters are handled deterministically and represented by fixed constants; in PAGE, most 

parameters are represented by probability distributions. FUND was also run in a mode in which 

parameters were treated probabilistically.

The sensitivity of the results to other aspects of the models (e.g. the carbon cycle or 

damage function) is also important to explore in the context of future revisions to the SCC but 

has not been incorporated into these estimates. Areas for future research are highlighted at the 

end of this document.

The DICE Model

The DICE model is an optimal growth model based on a global production function with 

an extra stock variable (atmospheric carbon dioxide concentrations). Emission reductions are 

treated as analogous to investment in “natural capital.” By investing in natural capital today 

through reductions in emissions—implying reduced consumption—harmful effects of climate 

change can be avoided and future consumption thereby increased. 

For purposes of estimating the SCC, carbon dioxide emissions are a function of global 

GDP and the carbon intensity of economic output, with the latter declining over time due to 

technological progress. The DICE damage function links global average temperature to the 

overall impact on the world economy. It varies quadratically with temperature change to capture 

the more rapid increase in damages expected to occur under more extreme climate change, and is 

calibrated to include the effects of warming on the production of market and nonmarket goods 

and services. It incorporates impacts on agriculture, coastal areas (due to sea level rise), “other 

vulnerable market sectors” (based primarily on changes in energy use), human health (based on 

climate-related diseases, such as malaria and dengue fever, and pollution), non-market amenities 

(based on outdoor recreation), and human settlements and ecosystems. The DICE damage 

function also includes the expected value of damages associated with low probability, high 

impact “catastrophic” climate change. This last component is calibrated based on a survey of 

experts (Nordhaus 1994). The expected value of these impacts is then added to the other market 

and non-market impacts mentioned above.

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 
16A-8

reduce GDP in future years. In contrast, GDP is exogenous in FUND and PAGE, so damages in 

any given year do not propagate forward.d

The PAGE Model

PAGE2002 (version 1.4epm) treats GDP growth as exogenous. It divides impacts into 

economic, non-economic, and catastrophic categories and calculates these impacts separately for 

eight geographic regions. Damages in each region are expressed as a fraction of output, where 

the fraction lost depends on the temperature change in each region. Damages are expressed as 

power functions of temperature change. The exponents of the damage function are the same in 

all regions but are treated as uncertain, with values ranging from 1 to 3 (instead of being fixed at 

2 as in DICE).

PAGE2002 includes the consequences of catastrophic events in a separate damage subfunction. Unlike DICE, PAGE2002 models these events probabilistically. The probability of a 

“discontinuity” (i.e., a catastrophic event) is assumed to increase with temperature above a 

specified threshold. The threshold temperature, the rate at which the probability of experiencing 

a discontinuity increases above the threshold, and the magnitude of the resulting catastrophe are 

all modeled probabilistically.

Adaptation is explicitly included in PAGE. Impacts are assumed to occur for temperature 

increases above some tolerable level (2°C for developed countries and 0°C for developing 

countries for economic impacts, and 0°C for all regions for non-economic impacts), but 

adaptation is assumed to reduce these impacts. Default values in PAGE2002 assume that the 

developed countries can ultimately eliminate up to 90 percent of all economic impacts beyond 

the tolerable 2°C increase and that developing countries can eventually eliminate 50 percent of 

their economic impacts. All regions are assumed to be able to mitigate 25 percent of the noneconomic impacts through adaptation (Hope 2006). 

The FUND Model

Like PAGE, the FUND model treats GDP growth as exogenous. It includes separately 

calibrated damage functions for eight market and nonmarket sectors: agriculture, forestry, water, 

energy (based on heating and cooling demand), sea level rise (based on the value of land lost and 

the cost of protection), ecosystems, human health (diarrhea, vector-borne diseases, and 

cardiovascular and respiratory mortality), and extreme weather. Each impact sector has a 

different functional form, and is calculated separately for sixteen geographic regions. In some 

impact sectors, the fraction of output lost or gained due to climate change depends not only on 

 d Using the default assumptions in DICE 2007, this effect generates an approximately 25 percent increase in the 

SCC relative to damages calculated by fixing GDP. In DICE2007, the time path of GDP is endogenous.

Specifically, the path of GDP depends on the rate of saving and level of abatement in each period chosen by the 

optimizing representative agent in the model. We made two modifications to DICE to make it consistent with EMF 

GDP trajectories (see next section): we assumed a fixed rate of savings of 20%, and we 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.
16A-9

the absolute temperature change but also on the rate of temperature change and level of regional 

income.e

 In the forestry and agricultural sectors, economic damages also depend on CO2

concentrations.

Tol (2009) discusses impacts not included in FUND, noting that many are likely to have a 

relatively small effect on damage estimates (both positive and negative). However, he 

characterizes several omitted impacts as “big unknowns”: for instance, extreme climate 

scenarios, biodiversity loss, and effects on economic development and political violence. With 

regard to potentially catastrophic events, he notes, “Exactly what would cause these sorts of 

changes or what effects they would have are not well-understood, although the chance of any one 

of them happening seems low. But they do have the potential to happen relatively quickly, and if 

they did, the costs could be substantial. Only a few studies of climate change have examined 

these issues.”

Adaptation is included both implicitly and explicitly in FUND. Explicit adaptation is 

seen in the agriculture and sea level rise sectors. Implicit adaptation is included in sectors such 

as energy and human health, where wealthier populations are assumed to be less vulnerable to 

climate impacts. For example, the damages to agriculture are the sum of three effects: (1) those 

due to the rate of temperature change (damages are always positive); (2) those due to the level of 

temperature change (damages can be positive or negative depending on region and temperature); 

and (3) those from CO2 fertilization (damages are generally negative but diminishing to zero). 

Adaptation is incorporated into FUND by allowing damages to be smaller if climate 

change happens more slowly. The combined effect of CO2 fertilization in the agricultural sector, 

positive impacts to some regions from higher temperatures, and sufficiently slow increases in 

temperature across these sectors can result in negative economic damages from climate change.

Damage Functions

To generate revised SCC values, we rely on the IAM modelers’ current best judgments of 

how to represent the effects of climate change (represented by the increase in global-average 

surface temperature) on the consumption-equivalent value of both market and non-market goods 

(represented as a fraction of global GDP). We recognize that these representations are 

incomplete and highly uncertain. But given the paucity of data linking the physical impacts to 

economic damages, we were not able to identify a better way to translate changes in climate into 

net economic damages, short of launching our own research program. 

The damage functions for the three IAMs are presented in Figures 16A.4.1 and 16A.4.2, 

using the modeler’s default scenarios and mean input assumptions. There are significant 

differences between the three models both at lower (figure 16A.4.2) and higher (figure 16A.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).
16A-10

Figure 16A.4.1 Annual Consumption Loss as a Fraction of Global 

GDP in 2100 Due to an Increase in Annual Global 

Temperature in the DICE, FUND, and PAGE 

modelsf

The lack of agreement among the models at lower temperature increases is underscored 

by the fact that the damages from FUND are well below the 5th percentile estimated by PAGE, 

while the damages estimated by DICE are roughly equal to the 95th percentile estimated by 

PAGE. This is significant because at higher discount rates we expect that a greater proportion of 

the SCC value is due to damages in years with lower temperature increases. For example, when 

the discount rate is 2.5 percent, about 45 percent of the 2010 SCC value in DICE is due to 

damages that occur in years when the temperature is less than or equal to 3 °C. This increases to 

approximately 55 percent and 80 percent at discount rates of 3 and 5 percent, respectively.

These differences underscore the need for a thorough review of damage functions—in 

particular, how the models incorporate adaptation, technological change, and catastrophic 

damages. Gaps in the literature make modifying these aspects of the models challenging, which

highlights the need for additional research. As knowledge improves, the Federal government is 

 f The x-axis represents increases in annual, rather than equilibrium, temperature, while the y-axis represents the 

annual stream of benefits as a share of global GDP. Each specific combination of climate sensitivity, 

socioeconomic, and emissions parameters will produce a different realization of damages for each IAM. The 

damage functions represented in Figures 1A and 1B are the outcome of default assumptions. For instance, under 

alternate assumptions, the damages from FUND may cross from negative to positive at less than or greater than 3 

°C.
16A-11

committed to exploring how these (and other) models can be modified to incorporate more 

accurate estimates of damages.

Figure 16A.4.2 Annual Consumption Loss for Lower Temperature 

Changes in DICE, FUND, and PAGE

16A.4.2 Global versus Domestic Measures of SCC

Because of the distinctive nature of the climate change problem, we center our current 

attention on a global measure of SCC. This approach is the same as that taken for the interim 

values, but it otherwise represents a departure from past practices, which tended to put greater 

emphasis on a domestic measure of SCC (limited to impacts of climate change experienced 

within U.S. borders). As a matter of law, consideration of both global and domestic values is 

generally permissible; the relevant statutory provisions are usually ambiguous and allow 

selection of either measure.g

Global SCC

Under current OMB guidance contained in Circular A-4, analysis of economically 

significant proposed and final regulations from the domestic perspective is required, while 

analysis from the international perspective is optional. However, the climate change problem is 

highly unusual in at least two respects. First, it involves a global externality: emissions of most 

greenhouse gases contribute to damages around the world even when they are emitted in the 

 g It is true that federal statutes are presumed not to have extraterritorial effect, in part to ensure that the laws of the 

United States respect the interests of foreign sovereigns. But use of a global measure for the SCC does not give 

extraterritorial effect to federal law and hence does not intrude on such interests.
16A-12

United States. Consequently, to address the global nature of the problem, the SCC must 

incorporate the full (global) damages caused by GHG emissions. Second, climate change 

presents a problem that the United States alone cannot solve. Even if the United States were to 

reduce its greenhouse gas emissions to zero, that step would be far from enough to avoid 

substantial climate change. Other countries would also need to take action to reduce emissions if 

significant changes in the global climate are to be avoided. Emphasizing the need for a global 

solution to a global problem, the United States has been actively involved in seeking 

international agreements to reduce emissions and in encouraging other nations, including 

emerging major economies, to take significant steps to reduce emissions. When these 

considerations are taken as a whole, the interagency group concluded that a global measure of 

the benefits from reducing U.S. emissions is preferable. 

When quantifying the damages associated with a change in emissions, a number of 

analysts (e.g., Anthoff, et al. 2009a) employ “equity weighting” to aggregate changes in 

consumption across regions. This weighting takes into account the relative reductions in wealth 

in different regions of the world. A per-capita loss of $500 in GDP, for instance, is weighted 

more heavily in a country with a per-capita GDP of $2,000 than in one with a per-capita GDP of 

$40,000. The main argument for this approach is that a loss of $500 in a poor country causes a 

greater reduction in utility or welfare than does the same loss in a wealthy nation. 

Notwithstanding the theoretical claims on behalf of equity weighting, the interagency group 

concluded that this approach would not be appropriate for estimating a SCC value used in 

domestic regulatory analysis.h

 For this reason, the group concluded that using the global (rather 

than domestic) value, without equity weighting, is the appropriate approach.

Domestic SCC

As an empirical matter, the development of a domestic SCC is greatly complicated by the 

relatively few region- or country-specific estimates of the SCC in the literature. One potential 

source of estimates comes from the FUND model. The resulting estimates suggest that the ratio 

of domestic to global benefits of emission reductions varies with key parameter assumptions.

For example, with a 2.5 or 3 percent discount rate, the U.S. benefit is about 7-10 percent of the 

global benefit, on average, across the scenarios analyzed. Alternatively, if the fraction of GDP 

lost due to climate change is assumed to be similar across countries, the domestic benefit would 

be proportional to the U.S. share of global GDP, which is currently about 23 percent.i

On the basis of this evidence, the interagency workgroup determined that a range of 

values from 7 to 23 percent should be used to adjust the global SCC to calculate domestic 

effects. Reported domestic values should use this range. It is recognized that these values are 

 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.
16A-13

approximate, provisional, and highly speculative. There is no a priori reason why domestic 

benefits should be a constant fraction of net global damages over time. Further, FUND does not 

account for how damages in other regions could affect the United States (e.g., global migration, 

economic and political destabilization). If more accurate methods for calculating the domestic 

SCC become available, the Federal government will examine these to determine whether to 

update its approach.

16A.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. 

16A.4.4 Equilibrium Climate Sensitivity

Equilibrium climate sensitivity (ECS) is a key input parameter for the DICE, PAGE, and 

FUND models.j It is defined as the long-term increase in the annual global-average surface 

temperature from a doubling of atmospheric CO2 concentration relative to pre-industrial levels 

(or stabilization at a concentration of approximately 550 parts per million (ppm)). Uncertainties 

in this important parameter have received substantial attention in the peer-reviewed literature.

 j The equilibrium climate sensitivity includes the response of the climate system to increased greenhouse gas 

concentrations over the short to medium term (up to 100-200 years), but it does not include long-term feedback 

effects due to possible large-scale changes in ice sheets or the biosphere, which occur on a time scale of many 

hundreds to thousands of years (e.g. Hansen et al. 2007).
16A-14

The most authoritative statement about equilibrium climate sensitivity appears in the 

Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC):

Basing our assessment on a combination of several independent lines of evidence...including 

observed climate change and the strength of known feedbacks simulated in [global climate 

models], we conclude that the global mean equilibrium warming for doubling CO2, or 

‘equilibrium climate sensitivity’, is likely to lie in the range 2 °C to 4.5 °C, with a most likely 

value of about 3 °C. Equilibrium climate sensitivity is very likely larger than 1.5 °C. k

 

For fundamental physical reasons as well as data limitations, values substantially higher 

than 4.5 °C still cannot be excluded, but agreement with observations and proxy data is 

generally worse for those high values than for values in the 2 °C to 4.5 °C range. (Meehl et 

al., 2007, p 799)

After consulting with several lead authors of this chapter of the IPCC report, the 

interagency workgroup selected four candidate probability distributions and calibrated them to 

be consistent with the above statement: Roe and Baker (2007), log-normal, gamma, and Weibull. 

Table 16A.4.1 included below gives summary statistics for the four calibrated distributions.

Table 16A.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:

 

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.
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(1) a median equal to 3°C, to reflect the judgment of “a most likely value of about 3 °C”;l

(2) two-thirds probability that the equilibrium climate sensitivity lies between 2 and 4.5 °C; 

and

(3) zero probability that it is less than 0°C or greater than 10°C (see Hegerl et al. 2006, p. 

721).

We selected the calibrated Roe and Baker distribution from the four candidates for two 

reasons. First, the Roe and Baker distribution is the only one of the four that is based on a 

theoretical understanding of the response of the climate system to increased greenhouse gas 

concentrations (Roe and Baker 2007, Roe 2008). In contrast, the other three distributions are 

mathematical functions that are arbitrarily chosen based on simplicity, convenience, and general 

shape. The Roe and Baker distribution results from three assumptions about climate response: 

(1) absent feedback effects, the equilibrium climate sensitivity is equal to 1.2 °C; (2) feedback 

factors are proportional to the change in surface temperature; and (3) uncertainties in feedback 

factors are normally distributed. There is widespread agreement on the first point and the second 

and third points are common assumptions. 

Second, the calibrated Roe and Baker distribution better reflects the IPCC judgment that 

“values substantially higher than 4.5°C still cannot be excluded.” Although the IPCC made no 

quantitative judgment, the 95th percentile of the calibrated Roe & Baker distribution (7.1 °C) is 

much closer to the mean and the median (7.2 °C) of the 95th percentiles of 21 previous studies 

summarized by Newbold and Daigneault (2009). It is also closer to the mean (7.5 °C) and 

median (7.9 °C) of the nine truncated distributions examined by the IPCC (Hegerl, et al., 2006) 

than are the 95th percentiles of the three other calibrated distributions (5.2-6.0 °C).

Finally, we note the IPCC judgment that the equilibrium climate sensitivity “is very 

likely larger than 1.5°C.” Although the calibrated Roe & Baker distribution, for which the 

probability of equilibrium climate sensitivity being greater than 1.5°C is almost 99 percent, is not 

inconsistent with the IPCC definition of “very likely” as “greater than 90 percent probability,” it 

reflects a greater degree of certainty about very low values of ECS than was expressed by the 

IPCC. 

 l Strictly speaking, “most likely” refers to the mode of a distribution rather than the median, but common usage 

would allow the mode, median, or mean to serve as candidates for the central or “most likely” value and the IPCC 

report is not specific on this point. For the distributions we considered, the median was between the mode and the 

mean. For the Roe and Baker distribution, setting the median equal to 3°C, rather than the mode or mean, gave a 95th

percentile that is more consistent with IPCC judgments and the literature. For example, setting the mean and mode 

equal to 3°C produced 95th percentiles of 5.6 and 8.6 °C, respectively, which are in the lower and upper end of the 

range in the literature. Finally, the median is closer to 3°C than is the mode for the truncated distributions selected 

by the IPCC (Hegerl, et al., 2006); the average median is 3.1 °C and the average mode is 2.3 °C, which is most 

consistent with a Roe and Baker distribution with the median set equal to 3 °C.
16A-16

Figure 16A.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 16A.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

16A.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.
16A-17

To accomplish this task in a transparent way, we decided to rely on the recent Stanford 

Energy Modeling Forum exercise, EMF-22. EMF-22 uses ten well-recognized models to 

evaluate substantial, coordinated global action to meet specific stabilization targets. A key 

advantage of relying on these data is that GDP, population, and emission trajectories are 

internally consistent for each model and scenario evaluated. The EMF-22 modeling effort also is 

preferable to the IPCC SRES due to their age (SRES were developed in 1997) and the fact that 3 

of 4 of the SRES scenarios are now extreme outliers in one or more variables. Although the 

EMF-22 scenarios have not undergone the same level of scrutiny as the SRES scenarios, they are 

recent, peer-reviewed, published, and publicly available.

To estimate the SCC for use in evaluating domestic policies that will have a small effect 

on global cumulative emissions, we use socioeconomic and emission trajectories that span a 

range of plausible scenarios. Five trajectories were selected from EMF-22 (see Table 16A.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 (i.e., CO2-only concentrations of 425 – 484 ppm or a radiative forcing of 3.7 W/m2) in 

2100, a lower-than-BAU trajectory.n

 Out of the 10 models included in the EMF-22 exercise, we 

selected the trajectories used by MiniCAM, MESSAGE, IMAGE, and the optimistic scenario 

from MERGE. For the BAU pathways, we used the GDP, population, and emission trajectories 

from each of these four models. For the 550 ppm CO2e scenario, we averaged the GDP, 

population, and emission trajectories implied by these same four models. 

 

n Such an emissions path would be consistent with widespread action by countries to mitigate GHG emissions, 

though it could also result from technological advances. It was chosen because it represents the most stringent case 

analyzed by the EMF-22 where all the models converge: a 550 ppm, not to exceed, full participation scenario.
16A-18

Table 16A.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.
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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.

16A.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 

 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.
16A-20

temperatures, as represented in each of the three IAMs. Then we discount the stream of future 

damages to its present value in the year when the additional unit of emissions was released using 

the selected discount rate, which is intended to reflect society's marginal rate of substitution 

between consumption in different time periods. 

For rules with both intra- and intergenerational effects, agencies traditionally employ 

constant discount rates of both 3 percent and 7 percent in accordance with OMB Circular A-4. 

As Circular A-4 acknowledges, however, the choice of discount rate for intergenerational 

problems raises distinctive problems and presents considerable challenges. After reviewing 

those challenges, Circular A-4 states, “If your rule will have important intergenerational benefits 

or costs you might consider a further sensitivity analysis using a lower but positive discount rate 

in addition to calculating net benefits using discount rates of 3 and 7 percent.” For the specific 

purpose of developing the SCC, we adapt and revise that approach here.

Arrow et al. (1996) outlined two main approaches to determine the discount rate for 

climate change analysis, which they labeled “descriptive” and “prescriptive.” The descriptive 

approach reflects a positive (non-normative) perspective based on observations of people’s 

actual choices—e.g., savings versus consumption decisions over time, and allocations of savings 

among more and less risky investments. Advocates of this approach generally call for inferring 

the discount rate from market rates of return “because of a lack of justification for choosing a 

social welfare function that is any different than what decision makers [individuals] actually use” 

(Arrow et al. 1996). 

One theoretical foundation for the cost-benefit analyses in which the social cost of carbon 

will be used—the Kaldor-Hicks potential-compensation test—also suggests that market rates 

should be used to discount future benefits and costs, because it is the market interest rate that 

would govern the returns potentially set aside today to compensate future individuals for climate 

damages that they bear (e.g., Just et al. 2004). As some have noted, the word “potentially” is an 

important qualification; there is no assurance that such returns will actually be set aside to 

provide compensation, and the very idea of compensation is difficult to define in the 

intergenerational context. On the other hand, societies provide compensation to future 

generations through investments in human capital and the resulting increase in knowledge, as 

well as infrastructure and other physical capital.

The prescriptive approach specifies a social welfare function that formalizes the 

normative judgments that the decision-maker wants explicitly to incorporate into the policy 

evaluation—e.g., how inter-personal comparisons of utility should be made, and how the welfare 

of future generations should be weighed against that of the present generation. Ramsey (1928), 

for example, has argued that it is “ethically indefensible” to apply a positive pure rate of time 

preference to discount values across generations, and many agree with this view. 

Other concerns also motivate making adjustments to descriptive discount rates. In 

particular, it has been noted that the preferences of future generations with regard to 

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 
16A-21

market distortions and uncertainties or inefficiencies in intergenerational transfers of wealth, 

which in the Kaldor-Hicks logic are presumed to compensate future generations for damage (a 

potentially controversial assumption, as noted above) (Arrow et al. 1996, Weitzman 1999).

Further, a legitimate concern about both descriptive and prescriptive approaches is that 

they tend to obscure important heterogeneity in the population. The utility function that 

underlies the prescriptive approach assumes a representative agent with perfect foresight and no 

credit constraints. This is an artificial rendering of the real world that misses many of the 

frictions that characterize individuals’ lives and indeed the available descriptive evidence 

supports this. For instance, many individuals smooth consumption by borrowing with credit 

cards that have relatively high rates. Some are unable to access traditional credit markets and 

rely on payday lending operations or other high-cost forms of smoothing consumption. Whether 

one puts greater weight on the prescriptive or descriptive approach, the high interest rates that 

credit-constrained individuals accept suggest that some account should be given to the discount 

rates revealed by their behavior.

We draw on both approaches but rely primarily on the descriptive approach to inform the 

choice of discount rate. With recognition of its limitations, we find this approach to be the most 

defensible and transparent given its consistency with the standard contemporary theoretical 

foundations of benefit-cost analysis and with the approach required by OMB’s existing guidance. 

The logic of this framework also suggests that market rates should be used for discounting future 

consumption-equivalent damages. Regardless of the theoretical approach used to derive the 

appropriate discount rate(s), we note the inherent conceptual and practical difficulties of 

adequately capturing consumption trade-offs over many decades or even centuries. While 

relying primarily on the descriptive approach in selecting specific discount rates, the interagency 

group has been keenly aware of the deeply normative dimensions of both the debate over 

discounting in the intergenerational context and the consequences of selecting one discount rate 

over another. 

Historically Observed Interest Rates

In a market with no distortions, the return to savings would equal the private return on 

investment, and the market rate of interest would be the appropriate choice for the social 

discount rate. In the real world risk, taxes, and other market imperfections drive a wedge 

between the risk-free rate of return on capital and the consumption rate of interest. Thus, the 

literature recognizes two conceptual discount concepts—the consumption rate of interest and the 

opportunity cost of capital. 

According to OMB’s Circular A-4, it is appropriate to use the rate of return on capital 

when a regulation is expected to displace or alter the use of capital in the private sector. In this 

case, OMB recommends Agencies use a discount rate of 7 percent. When regulation is expected 

to primarily affect private consumption—for instance, via higher prices for goods and services—

a lower discount rate of 3 percent is appropriate to reflect how private individuals trade-off 

current and future consumption. 
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The interagency group examined the economics literature and concluded that the 

consumption rate of interest is the correct concept to use in evaluating the benefits and costs of a 

marginal change in carbon emissions (see Lind 1990, Arrow et al 1996, and Arrow 2000). The 

consumption rate of interest also is appropriate when the impacts of a regulation are measured in 

consumption (-equivalent) units, as is done in the three integrated assessment models used for 

estimating the SCC. 

Individuals use a variety of savings instruments that vary with risk level, time horizon, 

and tax characteristics. The standard analytic framework used to develop intuition about the 

discount rate typically assumes a representative agent with perfect foresight and no credit 

constraints. The risk-free rate is appropriate for discounting certain future benefits or costs, but 

the benefits calculated by IAMs are uncertain. To use the risk-free rate to discount uncertain 

benefits, these benefits first must be transformed into "certainty equivalents," that is the 

maximum certain amount that we would exchange for the uncertain amount. However, the 

calculation of the certainty-equivalent requires first estimating the correlation between the 

benefits of the policy and baseline consumption.

If the IAM projections of future impacts represent expected values (not certaintyequivalent values), then the appropriate discount rate generally does not equal the risk-free rate. 

If the benefits of the policy tend to be high in those states of the world in which consumption is 

low, then the certainty-equivalent benefits will be higher than the expected benefits (and vice 

versa). Since many (though not necessarily all) of the important impacts of climate change will 

flow through market sectors such as agriculture and energy, and since willingness to pay for 

environmental protections typically increases with income, we might expect a positive (though 

not necessarily perfect) correlation between the net benefits from climate policies and market 

returns. This line of reasoning suggests that the proper discount rate would exceed the riskless 

rate. Alternatively, a negative correlation between the returns to climate policies and market 

returns would imply that a discount rate below the riskless rate is appropriate.

This discussion suggests that both the post-tax riskless and risky rates can be used to 

capture individuals’ consumption-equivalent interest rate. As a measure of the post-tax riskless 

rate, we calculate the average real return from Treasury notes over the longest time period 

available (those from Newell and Pizer 2003) and adjust for Federal taxes (the average marginal 

rate from tax years 2003 through 2006 is around 27 percent).r

 This calculation produces a real 

interest rate of about 2.7 percent, which is roughly consistent with Circular A-4’s 

recommendation to use 3 percent to represent the consumption rate of interest.s

 r The literature argues for a risk-free rate on government bonds as an appropriate measure of the consumption rate of 

interest. Arrow (2000) suggests that it is roughly 3-4 percent. OMB cites evidence of a 3.1 percent pre-tax rate for 

10-year Treasury notes in the A-4 guidance. Newell and Pizer (2003) find real interest rates between 3.5 and 4 

percent for 30-year Treasury securities. 

 A measure of 

the post-tax risky rate for investments whose returns are positively correlated with overall equity 

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.
16A-23

market returns can be obtained by adjusting pre-tax rates of household returns to risky 

investments (approximately 7 percent) for taxes, which yields a real rate of roughly 5 percent.t

 

The Ramsey Equation

Ramsey discounting also provides a useful framework to inform the choice of a discount 

rate. Under this approach, the analyst applies either positive or normative judgments in selecting 

values for the key parameters of the Ramsey equation: η (coefficient of relative risk aversion or 

elasticity of the marginal utility of consumption) and ρ (pure rate of time preference).u These are 

then combined with g (growth rate of per-capita consumption) to equal the interest rate at which 

future monetized damages are discounted: ρ + η∙g.v In the simplest version of the Ramsey 

model, with an optimizing representative agent with perfect foresight, what we are calling the 

“Ramsey discount rate,” ρ + η∙g, will be equal to the rate of return to capital, i.e., the market 

interest rate.

A review of the literature provides some guidance on reasonable parameter values for the 

Ramsey discounting equation, based on both prescriptive and descriptive approaches. 

• η. Most papers in the climate change literature adopt values for η in the range of 0.5 to 3 

(Weitzman cites plausible values as those ranging from 1 to 4), although not all authors 

articulate whether their choice is based on prescriptive or descriptive reasoning.w

 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).

 

Dasgupta (2008) argues that η should be greater than 1 and may be as high as 3, since η

equal to 1 suggests savings rates that do not conform to observed behavior. 

u The parameter ρ measures the pure rate of time preference: people’s behavior reveals a preference for an 

increase in utility today versus the future. Consequently, it is standard to place a lower weight on utility in the 

future. The parameter η captures diminishing marginal utility: consumption in the future is likely to be higher than 

consumption today, so diminishing marginal utility of consumption implies that the same monetary damage will 

cause a smaller reduction of utility for wealthier individuals, either in the future or in current generations. If η = 0, 

then a one dollar increase in income is equally valuable regardless of level of income; if η = 1, then a one percent 

increase in income is equally valuable no matter the level of income; and if η > 1, then a one percent increase in 

income is less valuable to wealthier individuals. 

v In this case, g could be taken from the selected EMF socioeconomic scenarios or alternative assumptions about the 

rate of consumption growth.

w Empirical estimates of η span a wide range of values. A benchmark value of 2 is near the middle of the range of 

values estimated or used by Szpiro (1986), Hall and Jones (2007), Arrow (2007), Dasgupta (2006, 2008), Weitzman 

(2007, 2009), and Nordhaus (2008). However, Chetty (2006) developed a method of estimating η using data on 

labor supply behavior. He shows that existing evidence of the effects of wage changes on labor supply imposes a 

tight upper bound on the curvature of utility over wealth (CRRA < 2) with the mean implied value of 0.71 and 

concludes that the standard expected utility model cannot generate high levels of risk aversion without contradicting 

established facts about labor supply. Recent work has jointly estimated the components of the Ramsey equation. 

Evans and Sezer (2005) estimate η = 1.49 for 22 OECD countries. They also estimate ρ = 1.08 percent per year 

using data on mortality rates. Anthoff, et al. (2009b) estimate η = 1.18, and ρ = 1.4 percent. When they multiply the 

bivariate probability distributions from their work and Evans and Sezer (2005) together, they find η = 1.47, and ρ = 

1.07. 
16A-24

• ρ. With respect to the pure rate of time preference, most papers in the climate change 

literature adopt values for ρ in the range of 0 to 3 percent per year. The very low rates

tend to follow from moral judgments involving intergenerational neutrality. Some have 

argued that to use any value other than ρ = 0 would unjustly discriminate against future 

generations (e.g., Arrow et al. 1996, Stern et al. 2006). However, even in an intergenerational setting, it may make sense to use a small positive pure rate of time 

preference because of the small probability of unforeseen cataclysmic events (Stern et al. 

2006).

• g. A commonly accepted approximation is around 2 percent per year. For the 

socioeconomic scenarios used for this exercise, the EMF models assume that g is about 

1.5-2 percent to 2100. 

Some economists and non-economists have argued for constant discount rates below 2 

percent based on the prescriptive approach. When grounded in the Ramsey framework, 

proponents of this approach have argued that a ρ of zero avoids giving preferential treatment to 

one generation over another. The choice of η has also been posed as an ethical choice linked to 

the value of an additional dollar in poorer countries compared to wealthier ones. Stern et al. 

(2006) applies this perspective through his choice of ρ = 0.1 percent per year, η = 1 and g = 1.3 

percent per year, which yields an annual discount rate of 1.4 percent. In the context of 

permanent income savings behavior, however, Stern’s assumptions suggest that individuals 

would save 93 percent of their income.x

Recently, Stern (2008) revisited the values used in Stern et al. (2006), stating that there is 

a case to be made for raising η due to the amount of weight lower values place on damages far in 

the future (over 90 percent of expected damages occur after 2200 with η = 1). Using Stern’s 

assumption that ρ = 0.1 percent, combined with a η of 1.5 to 2 and his original growth rate, 

yields a discount rate of greater than 2 percent. 

We conclude that arguments made under the prescriptive approach can be used to justify 

discount rates between roughly 1.4 and 3.1 percent. In light of concerns about the most 

appropriate value for η, we find it difficult to justify rates at the lower end of this range under the 

Ramsey framework. 

Accounting for Uncertainty in the Discount Rate

While the consumption rate of interest is an important driver of the benefits estimate, it is 

uncertain over time. Ideally, we would formally model this uncertainty, just as we do for climate 

sensitivity. Weitzman (1998, 2001) showed theoretically and Newell and Pizer (2003) and 

Groom et al. (2006) confirm empirically that discount rate uncertainty can have a large effect on 

net present values. A main result from these studies is that if there is a persistent element to the 

 x Stern (2008) argues that building in a positive rate of exogenous technical change over time reduces the implied 

savings rate and that η at or above 2 are inconsistent with observed behavior with regard to equity. (At the same 

time, adding exogenous technical change—all else equal—would increase g as well.)
16A-25

uncertainty in the discount rate (e.g., the rate follows a random walk), then it will result in an 

effective (or certainty-equivalent) discount rate that declines over time. Consequently, lower 

discount rates tend to dominate over the very long term (see Weitzman 1998, 1999, 2001; 

Newell and Pizer 2003; Groom et al. 2006; Gollier 2008; Summers and Zeckhauser 2008; and 

Gollier and Weitzman 2009). 

The proper way to model discount rate uncertainty remains an active area of research. 

Newell and Pizer (2003) employ a model of how long-term interest rates change over time to 

forecast future discount rates. Their model incorporates some of the basic features of how 

interest rates move over time, and its parameters are estimated based on historical observations 

of long-term rates. Subsequent work on this topic, most notably Groom et al. (2006), uses more 

general models of interest rate dynamics to allow for better forecasts. Specifically, the volatility 

of interest rates depends on whether rates are currently low or high and the variation in the level 

of persistence over time.

While Newell and Pizer (2003) and Groom et al (2006) attempt formally to model 

uncertainty in the discount rate, others argue for a declining scale of discount rates applied over 

time (e.g., Weitzman 2001, and the UK’s “Green Book” for regulatory analysis). This approach 

uses a higher discount rate initially, but applies a graduated scale of lower discount rates further 

out in time.y

 A key question that has emerged with regard to both of these approaches is the 

trade-off between potential time inconsistency and giving greater weight to far future outcomes 

(see the EPA Science Advisory Board’s recent comments on this topic as part of its review of 

their Guidelines for Economic Analysis).z

The Discount Rates Selected for Estimating SCC

In light of disagreement in the literature on the appropriate market interest rate to use in 

this context and uncertainty about how interest rates may change over time, we use three 

discount rates to span a plausible range of certainty-equivalent constant discount rates: 2.5, 3, 

and 5 percent per year. Based on the review in the previous sections, the interagency workgroup 

determined that these three rates reflect reasonable judgments under both descriptive and 

prescriptive approaches.

The central value, 3 percent, is consistent with estimates provided in the economics 

literature and OMB’s Circular A-4 guidance for the consumption rate of interest. As previously 

 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. 
16A-26

mentioned, the consumption rate of interest is the correct discounting concept to use when future 

damages from elevated temperatures are estimated in consumption-equivalent units. Further, 3 

percent roughly corresponds to the after-tax riskless interest rate. The upper value of 5 percent is 

included to represent the possibility that climate damages are positively correlated with market 

returns. Additionally, this discount rate may be justified by the high interest rates that many 

consumers use to smooth consumption across periods.

The low value, 2.5 percent, is included to incorporate the concern that interest rates are 

highly uncertain over time. It represents the average certainty-equivalent rate using the meanreverting and random walk approaches from Newell and Pizer (2003) starting at a discount rate 

of 3 percent. Using this approach, the certainty equivalent is about 2.2 percent using the random 

walk model and 2.8 percent using the mean reverting approach.aa

16A.5 REVISED SCC ESTIMATES

 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.

Our general approach to estimating SCC values is to run the three integrated assessment 

models (FUND, DICE, and PAGE) using the following inputs agreed upon by the interagency 

group:

• A Roe and Baker distribution for the climate sensitivity parameter bounded between 0 

and 10 with a median of 3 °C and a cumulative probability between 2 and 4.5 °C of twothirds.

• Five sets of GDP, population, and carbon emissions trajectories based on EMF-22.

• Constant annual discount rates of 2.5, 3, and 5 percent.

Because the climate sensitivity parameter is modeled probabilistically, and because PAGE and 

FUND incorporate uncertainty in other model parameters, the final output from each model run 

is a distribution over the SCC in year t. 

For each of the IAMs, the basic computational steps for calculating the SCC in a particular year t

are:

1. Input the path of emissions, GDP, and population from the selected EMF-22 

scenarios, and the extrapolations based on these scenarios for post-2100 years.

2. Calculate the temperature effects and (consumption-equivalent) damages in each 

year resulting from the baseline path of emissions. 

 

aa Calculations done by Pizer et al. using the original simulation program from Newell and Pizer (2003).
16A-27

a. In PAGE, the consumption-equivalent damages in each period are 

calculated as a fraction of the EMF GDP forecast, depending on the 

temperature in that period relative to the pre-industrial average 

temperature in each region. 

b. In FUND, damages in each period depend on both the level and the rate of 

temperature change in that period. 

c. In DICE, temperature affects both consumption and investment, so we 

first adjust the EMF GDP paths as follows: Using the Cobb-Douglas 

production function with the DICE2007 parameters, we extract the path of 

exogenous technical change implied by the EMF GDP and population 

paths, then we recalculate the baseline GDP path taking into account 

climate damages resulting from the baseline emissions path. 

3. Add an additional unit of carbon emissions in year t. (The exact unit varies by 

model.)

4. Recalculate the temperature effects and damages expected in all years beyond t

resulting from this adjusted path of emissions, as in step 2. 

5. Subtract the damages computed in step 2 from those in step 4 in each year. 

(DICE is run in 10-year time steps, FUND in annual time steps, while the time 

steps in PAGE vary.)

6. Discount the resulting path of marginal damages back to the year of emissions 

using the agreed upon fixed discount rates.

7. Calculate the SCC as the net present value of the discounted path of damages 

computed in step 6, divided by the unit of carbon emissions used to shock the 

models in step 3. 

8. Multiply by 12/44 to convert from dollars per ton of carbon to dollars per ton of 

CO2 (2007 dollars) in DICE and FUND. (All calculations are done in tons of CO2 

in PAGE).

The steps above were repeated in each model for multiple future years to cover the time horizons 

anticipated for upcoming rulemaking analysis. To maintain consistency across the three IAMs, 

climate damages are calculated as lost consumption in each future year. 

It is important to note that each of the three models has a different default end year. The 

default time horizon is 2200 for PAGE, 2595 for DICE, and 3000 for the latest version of FUND. 

This is an issue for the multi-model approach because differences in SCC estimates may arise 

simply due to the model time horizon. Many consider 2200 too short a time horizon because it 

could miss a significant fraction of damages under certain assumptions about the growth of 

marginal damages and discounting, so each model is run here through 2300. This step required a 

small adjustment in the PAGE model only. This step also required assumptions about GDP, 
16A-28

population, and greenhouse gas emission trajectories after 2100, the last year for which these 

data are available from the EMF-22 models. (A more detailed discussion of these assumptions is 

included in the Annex.)

This exercise produces 45 separate distributions of the SCC for a given year, the product 

of 3 models, 3 discount rates, and 5 socioeconomic scenarios. This is clearly too many separate 

distributions for consideration in a regulatory impact analysis. 

To produce a range of plausible estimates that still reflects the uncertainty in the 

estimation exercise, the distributions from each of the models and scenarios are equally weighed 

and combined to produce three separate probability distributions for SCC in a given year, one for 

each assumed discount rate. These distributions are then used to define a range of point estimates 

for the global SCC. In this way, no IAM or socioeconomic scenario is given greater weight than 

another. Because the literature shows that the SCC is quite sensitive to assumptions about the 

discount rate, and because no consensus exists on the appropriate rate to use in an 

intergenerational context, we present SCCs based on the average values across models and 

socioeconomic scenarios for each discount rate. 

The interagency group selected four SCC values for use in regulatory analyses. Three 

values are based on the average SCC across models and socioeconomic and emissions scenarios 

at the 2.5, 3, and 5 percent discount rates. The fourth value is included to represent the higherthan-expected economic impacts from climate change further out in the tails of the SCC 

distribution. For this purpose, we use the SCC value for the 95th percentile at a 3 percent discount 

rate. (The full set of distributions by model and scenario combination is included in the Annex.) 

As noted above, the 3 percent discount rate is the central value, and so the central value that 

emerges is the average SCC across models at the 3 percent discount rate. For purposes of 

capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance 

and value of considering the full range.

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 16A.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. 
16A-29

Table 16A.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 
16A-30

The SCC estimates from FUND are sensitive to differences in emissions paths but 

relatively insensitive to differences in GDP paths across scenarios, while the reverse is true for 

DICE and PAGE. This likely occurs because of several structural differences among the models. 

Specifically in DICE and PAGE, the fraction of economic output lost due to climate damages 

increases with the level of temperature alone, whereas in FUND the fractional loss also increases 

with the rate of temperature change. Furthermore, in FUND increases in income over time 

decrease vulnerability to climate change (a form of adaptation), whereas this does not occur in 

DICE and PAGE. These structural differences among the models make FUND more sensitive to 

the path of emissions and less sensitive to GDP compared to DICE and PAGE. 

Figure 16A.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 16A.5.1 Level of Global GDP across EMF Scenarios

Table 16A.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
16A-31

Table 16A.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 16A.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 16A.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 
16A-32

calculated using a SCC based on a 5 percent discount rate also should be discounted back to the 

analysis year using a 5 percent discount rate.cc

16A.6 LIMITATIONS OF THE ANALYSIS

 

As noted, any estimate of the SCC must be taken as provisional and subject to further 

refinement (and possibly significant change) in accordance with evolving scientific, economic, 

and ethical understandings. During the course of our modeling, it became apparent that there are 

several areas in particular need of additional exploration and research. These caveats, and 

additional observations in the following section, are necessary to consider when interpreting and 

applying the SCC estimates.

Incomplete treatment of non-catastrophic damages. The impacts of climate change are 

expected to be widespread, diverse, and heterogeneous. In addition, the exact magnitude of these 

impacts is uncertain because of the inherent complexity of climate processes, the economic 

behavior of current and future populations, and our inability to accurately forecast technological 

change and adaptation. Current IAMs do not assign value to all of the important physical, 

ecological, and economic impacts of climate change recognized in the climate change literature 

(some of which are discussed above) because of lack of precise information on the nature of 

damages and because the science incorporated into these models understandably lags behind the 

most recent research. Our ability to quantify and monetize impacts will undoubtedly improve 

with time. But it is also likely that even in future applications, a number of potentially significant 

damage categories will remain non-monetized. (Ocean acidification is one example of a 

potentially large damage from CO2 emissions not quantified by any of the three models. Species 

and wildlife loss is another example that is exceedingly difficult to monetize.) 

Incomplete treatment of potential catastrophic damages. There has been considerable 

recent discussion of the risk of catastrophic impacts and how best to account for extreme 

scenarios, such as the collapse of the Atlantic Meridional Overturning Circulation or the West 

Antarctic Ice Sheet, or large releases of methane from melting permafrost and warming oceans. 

Weitzman (2009) suggests that catastrophic damages are extremely large—so large, in fact, that 

the damages from a low probability, catastrophic event far in the future dominate the effect of 

the discount rate in a present value calculation and result in an infinite willingness-to-pay for 

mitigation today. However, Nordhaus (2009) concluded that the conditions under which 

Weitzman's results hold “are limited and do not apply to a wide range of potential uncertain 

scenarios." 

Using a simplified IAM, Newbold and Daigneault (2009) confirmed the potential for 

large catastrophe risk premiums but also showed that the aggregate benefit estimates can be 

highly sensitive to the shapes of both the climate sensitivity distribution and the damage function 

at high temperature changes. Pindyck (2009) also used a simplified IAM to examine high-

 

cc However, it is possible that other benefits or costs of proposed regulations unrelated to CO2 emissions will be 

discounted at rates that differ from those used to develop the SCC estimates. 
16A-33

impact, low-probability risks, using a right-skewed gamma distribution for climate sensitivity as 

well as an uncertain damage coefficient, but in most cases found only a modest risk premium. 

Given this difference in opinion, further research in this area is needed before its practical 

significance can be fully understood and a reasonable approach developed to account for such 

risks in regulatory analysis. (The next section discusses the scientific evidence on catastrophic 

impacts in greater detail.)

Uncertainty in extrapolation of damages to high temperatures: The damage functions in 

these IAMs are typically calibrated by estimating damages at moderate temperature increases 

(e.g., DICE was calibrated at 2.5 °C) and extrapolated to far higher temperatures by assuming 

that damages increase as some power of the temperature change. Hence, estimated damages are 

far more uncertain under more extreme climate change scenarios. 

Incomplete treatment of adaptation and technological change: Each of the three 

integrated assessment models used here assumes a certain degree of low- or no-cost adaptation. 

For instance, Tol assumes a great deal of adaptation in FUND, including widespread reliance on 

air conditioning; so much so, that the largest single benefit category in FUND is the reduced 

electricity costs from not having to run air conditioning as intensively (NRC 2009). 

Climate change also will increase returns on investment to develop technologies that 

allow individuals to cope with adverse climate conditions, and IAMs to do not adequately 

account for this directed technological change.dd For example, scientists may develop crops that 

are better able to withstand higher and more variable temperatures. Although DICE and FUND 

have both calibrated their agricultural sectors under the assumption that farmers will change land 

use practices in response to climate change (Mastrandrea, 2009), they do not take into account 

technological changes that lower the cost of this adaptation over time. On the other hand, the 

calibrations do not account for increases in climate variability, pests, or diseases, which could 

make adaptation more difficult than assumed by the IAMs for a given temperature change. 

Hence, models do not adequately account for potential adaptation or technical change that might 

alter the emissions pathway and resulting damages. In this respect, it is difficult to determine 

whether the incomplete treatment of adaptation and technological change in these IAMs 

understate or overstate the likely damages.

Risk aversion: A key question unanswered during this interagency process is what to 

assume about relative risk aversion with regard to high-impact outcomes. These calculations do 

not take into account the possibility that individuals may have a higher willingness to pay to 

reduce the likelihood of low-probability, high-impact damages than they do to reduce the 

likelihood of higher-probability, but lower-impact, damages with the same expected cost. (The 

inclusion of the 95th percentile estimate in the final set of SCC values was largely motivated by 

this concern.) If individuals do show such a higher willingness to pay, a further question is 

whether that fact should be taken into account for regulatory policy. Even if individuals are not 

risk-averse for such scenarios, it is possible that regulatory policy should include a degree of 

risk-aversion.

 dd However these research dollars will be diverted from whatever their next best use would have been in the absence 

of climate change (so productivity/GDP would have been still higher).
16A-34

Assuming a risk-neutral representative agent is consistent with OMB’s Circular A-4, 

which advises that the estimates of benefits and costs used in regulatory analysis are usually 

based on the average or the expected value and that “emphasis on these expected values is 

appropriate as long as society is ‘risk neutral’ with respect to the regulatory alternatives. While 

this may not always be the case, [analysts] should in general assume ‘risk neutrality’ in [their] 

analysis.” 

Nordhaus (2008) points to the need to explore the relationship between risk and income 

in the context of climate change across models and to explore the role of uncertainty regarding 

various parameters in the results. Using FUND, Anthoff et al (2009) explored the sensitivity of 

the SCC to Ramsey equation parameter assumptions based on observed behavior. They conclude

that “the assumed rate of risk aversion is at least as important as the assumed rate of time 

preference in determining the social cost of carbon.” Since Circular A-4 allows for a different 

assumption on risk preference in regulatory analysis if it is adequately justified, we plan to 

continue investigating this issue.

16A.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 
16A-35

permafrost. Many of these tipping points are estimated to have thresholds between about 3 °C 

and 5 °C (Lenton et al., 2008). Probabilities of several of these tipping points were assessed 

through expert elicitation in 2005–2006 by Kriegler et al. (2009); results from this study are 

highlighted in Table 16A.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 16A.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 16A.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 
16A-36

crossing at least one of their primary climatic tipping points in a scenario with temperatures 

about 2-4 °C warmer than pre-Industrial levels in 2100. 

It is important to note that crossing a climatic tipping point will not necessarily lead to an 

economic catastrophe in the sense used in the IAMs. A tipping point is a critical threshold across 

which some aspect of the Earth system starts to shifts into a qualitatively different state (for 

instance, one with dramatically reduced ice sheet volumes and higher sea levels). In the IAMs, a 

catastrophe is a low-probability environmental change with high economic impact.

Failure to incorporate inter-sectoral and inter-regional interactions

The damage functions do not fully incorporate either inter-sectoral or inter-regional 

interactions. For instance, while damages to the agricultural sector are incorporated, the effects 

of changes in food supply on human health are not fully captured and depend on the modeler’s 

choice of studies used to calibrate the IAM. Likewise, the effects of climate damages in one 

region of the world on another region are not included in some of the models (FUND includes 

the effects of migration from sea level rise). These inter-regional interactions, though difficult to 

quantify, are the basis for climate-induced national and economic security concerns (e.g., 

Campbell et al., 2007; U.S. Department of Defense 2010) and are particularly worrisome at 

higher levels of warming. High-end warming scenarios, for instance, project water scarcity 

affecting 4.3-6.9 billion people by 2050, food scarcity affecting about 120 million additional 

people by 2080, and the creation of millions of climate refugees (Easterling et al., 2007; 

Campbell et al., 2007).

Imperfect substitutability of environmental amenities

Data from the geological record of past climate changes suggests that 6 °C of warming 

may have severe consequences for natural systems. For instance, during the Paleocene-Eocene 

Thermal Maximum about 55.5 million years ago, when the Earth experienced a geologically 

rapid release of carbon associated with an approximately 5 °C increase in global mean 

temperatures, the effects included shifts of about 400-900 miles in the range of plants (Wing et 

al., 2005), and dwarfing of both land mammals (Gingerich, 2006) and soil fauna (Smith et al., 

2009).

The three IAMs used here assume that it is possible to compensate for the economic 

consequences of damages to natural systems through increased consumption of non-climate 

goods, a common assumption in many economic models. In the context of climate change, 

however, it is possible that the damages to natural systems could become so great that no 

increase in consumption of non-climate goods would provide complete compensation (Levy et 

al., 2005). For instance, as water supplies become scarcer or ecosystems become more fragile 

and less bio-diverse, the services they provide may become increasingly more costly to replace. 

Uncalibrated attempts to incorporate the imperfect substitutability of such amenities into IAMs 

(Sterner and Persson, 2008) indicate that the optimal degree of emissions abatement can be 

considerably greater than is commonly recognized. 
16A-37

16A.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. 
16A-38

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16A-43

16A.9 ANNEX

Table 16A.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. 
16A-44

16A.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.
16A-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.
16A-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 16A.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. 

16A.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 
16A-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
16A-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 16A.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
16A-49

Figure 16A.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]

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
16A-50

Figure 16A.9.5 Global Fossil and Industrial CO2 Emissions, 2000-

2300 (Post-2100 extrapolations assume growth rate 

of CO2 intensity (CO2/GDP) over 2090-2100 is 

maintained through 2300)

Note: In the fifth scenario, 2000-2100 emissions are equal to the average of the emissions under 

the 550 ppm CO2e, full-participation, not-to-exceed scenarios considered by each of the four 

models. 

Figure 16A.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
16A-51

Figure 16A.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
16A-52

Figure 16A.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
16A-53

Table 16A.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
16A-54

Table 16A.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
16A-55

Table 16A.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 16A.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%
16A-56

Table 16A.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
i

APPENDIX 16B

TECHNICAL UPDATE OF THE SOCIAL COST OF CARBON FOR 

REGULATORY IMPACT ANALYSIS UNDER EXECUTIVE ORDER 12866

TABLE OF CONTENTS

16B.1 PREFACE......................................................................................................................1

16B.2 EXECUTIVE SUMMARY ...........................................................................................1

16B.3 PURPOSE......................................................................................................................2

16B.4 SUMMARY OF MODEL UPDATES...........................................................................3

16B.4.1 DICE ..............................................................................................................................4

16-B.4.1.1 Carbon Cycle Parameters............................................................................ 4

16-B.4.1.2 Sea Level Dynamics ................................................................................... 5

16-B.4.1.3 Re-calibrated Damage Function ................................................................. 6

16B.4.2 FUND.............................................................................................................................6

16-B.4.2.1 Space Heating ............................................................................................. 7

16-B.4.2.2 Sea Level Rise and Land Loss.................................................................... 7

16-B.4.2.3 Agriculture .................................................................................................. 7

16-B.4.2.4 Transient Temperature Response................................................................ 8

16-B.4.2.5 Methane....................................................................................................... 8

16B.4.3 PAGE.............................................................................................................................9

16-B.4.3.1 Sea Level Rise............................................................................................. 9

16-B.4.3.2 Revised Damage Function to Account for Saturation ................................ 9

16-B.4.3.3 Regional Scaling Factors ............................................................................ 9

16-B.4.3.4 Probability of a Discontinuity................................................................... 10

16-B.4.3.5 Adaptation................................................................................................. 10

16-B.4.3.6 Other Noteworthy Changes....................................................................... 11

16B.5 REVISED SCC ESTIMATES .....................................................................................11

16B.6 OTHER MODEL LIMITATIONS AND RESEARCH GAPS ...................................14

ANNEX A ADDITIONAL TABLES

LIST OF TABLES

Table 16B.5.1 Revised Social Cost of CO2, 2010 – 2050 (in 2007 dollars per ton of 

CO2) .................................................................................................................12

Table 16B.5.2 Average Annual Growth Rates of SCC Estimates between 2010 and 

2050..................................................................................................................13

Table 16B.6.1 Annual SCC Values: 2010-2050 (2007$/metric ton CO2)...............................17

Table 16B.6.2 2020 Global SCC Estimates at 2.5 Percent Discount Rate 

(2007$/metric ton CO2)....................................................................................18

Table 16B.6.3 2020 Global SCC Estimates at 3 Percent Discount Rate (2007$/metric 

ton CO2)...........................................................................................................18

Table 16B.6.4 2020 Global SCC Estimates at 5 Percent Discount Rate (2007$/metric 

ton CO2)...........................................................................................................19
ii

LIST OF FIGURES

Figure 16B.5.2 Distribution of SCC Estimates for 2020 (in 2007$ per metric ton CO2).........13
1

APPENDIX 16B. TECHNICAL UPDATE OF THE SOCIAL COST OF 

CARBON FOR REGULATORY IMPACT ANALYSIS UNDER 

EXECUTIVE ORDER 12866

16B.1 PREFACE

The following text is reproduced from the April 2013 report of the Interagency Working 

Group on Social Cost of Carbon, United States Government, titled “Technical Update of the 

Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866.”a

16B.2 EXECUTIVE SUMMARY

Minor 

changes were made to the working group's report format and reference style to make it more

consistent with the rest of this technical support document.

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 impact cumulative global emissions. 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.

The interagency process that developed the original U.S. government’s SCC estimates is 

described in the 2010 interagency technical support document (TSD) (Interagency Working 

Group on Social Cost of Carbon 2010). Through that process 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 (IAMs), 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.

While acknowledging the continued limitations of the approach taken by the interagency 

group in 2010, this document provides an update of the SCC estimates based on new versions of 

each IAM (DICE, PAGE, and FUND). It does not revisit other interagency modeling decisions 

(e.g., with regard to the discount rate, reference case socioeconomic and emission scenarios, or 

 

a 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 Management and Budget, Office of Science and 

Technology Policy, Department of the Treasury
2

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 SCC estimates using the updated versions of the models are higher than those 

reported in the 2010 TSD. By way of comparison, the four 2020 SCC estimates reported in the 

2010 TSD were $7, $26, $42 and $81 (2007$). The corresponding four updated SCC estimates 

for 2020 are $12, $43, $65, and $129 (2007$). The model updates that are relevant to the SCC 

estimates include: an explicit representation of sea level rise damages in the DICE and PAGE 

models; updated adaptation assumptions, revisions to ensure damages are constrained by GDP,

updated regional scaling of damages, and a revised treatment of potentially abrupt shifts in 

climate damages in the PAGE model; an updated carbon cycle in the DICE model; and updated 

damage functions for sea level rise impacts, the agricultural sector, and reduced space heating

requirements, as well as changes to the transient response of temperature to the buildup of GHG 

concentrations and the inclusion of indirect effects of methane emissions in the FUND model.

The SCC estimates vary by year, and the following table summarizes the revised SCC estimates 

from 2010 through 2050.

Table 16B.2.1 Revised Social Cost of CO2, 2010–2050 (in 2007 dollars per metric ton 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

16B.3 PURPOSE

The purpose of this document is to update the schedule of social cost of carbon (SCC)b

1

estimates from the 2010 interagency technical support document (TSD) (Interagency Working 

Group on Social Cost of Carbon 2010). The 2010 TSD is reproduced in appendix 16-A.

E.O. 13563 commits the Administration to regulatory decision making “based on the best 

available science.”c

 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).

Additionally, the interagency group recommended in 2010 that the SCC 

c http://www.whitehouse.gov/sites/default/files/omb/inforeg/eo12866/eo13563_01182011.pdf
3

estimates be revisited on a regular basis or as model updates that reflect the growing body of 

scientific and economic knowledge become available.d 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 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 agencies participating in the interagency working 

group continue to investigate potential improvements to the way in which economic damages 

associated with changes in CO2 emissions are quantified. 

Section 16b.4 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 16b.5 presents the updated schedule of SCC estimates for 2010 –

2050 based on these versions of the models. Section 16b.6 provides a discussion of other model 

limitations and research gaps.

16B.4 SUMMARY OF MODEL UPDATES

This section briefly summarizes changes to 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, as 

summarized in Table 16B.4.1. 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 by GDP, updated regional 

scaling of damages, and a revised treatment of potentially abrupt shifts in climate damages. The 

DICE model’s simple carbon cycle has been updated to be more consistent with a 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 

transient 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 equilibrium climate 

sensitivity, discounting, and socioeconomic variables – are not discussed here but can be found 

in the references provided in each section below.

Table 16B.4.1: Summary of Key Model Revisions Relevant to the Interagency SCC

IAM Version used 

in 2010 

Interagency 

New 

Version 

Key changes relevant to interagency SCC 

 d See p. 1, 3, 4, 29, and 33 (Interagency Working Group on Social Cost of Carbon 2010).1
4

Analysis 

DICE 2007 2010 Updated calibration of the carbon cycle 

model and explicit representation of sea level 

rise (SLR) and associated damages. 

FUND 3.5 

(2009) 

3.8 

(2012) 

Updated damage functions for space heating, 

SLR, agricultural impacts, changes to 

transient response of temperature to buildup 

of GHG concentrations, and inclusion of 

indirect climate effects of methane. 

PAGE 2002 2009 Explicit representation of SLR damages, 

revisions to damage function to ensure 

damages do not exceed 100% of GDP, 

change in regional scaling of damages, 

revised treatment of potential abrupt 

damages, and updated adaptation 

assumptions. 

16B.4.1 DICE

DICE 2010 includes a number of changes over the previous 2007 version used in the 

2010 interagency report. The model changes that are 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 The 

DICE2010 model and documentation is also available for download from the homepage of 

William Nordhaus.

16-B.4.1.1 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).2e

3

Carbon cycle transfer coefficient values in DICE2010 are based on re-calibration of the model to 

match the 2009 version of MAGICC (Nordhaus 2010 p 2). For example, in DICE2010, in each 

decade, 12 percent of the carbon in the atmosphere is transferred to the shallow ocean, 4.7 

 e MAGICC is a simple climate model initially developed by 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 more 

sophisticated state of the art earth system simulation models (Randall et al. 2007).4
5

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.

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.

16-B.4.1.2 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.f 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 (AR4).

4 g 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 for temperature anomalies 

between 1 o

C and 3.5 °C. 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 between 3 °C and 6 

°C to a maximum rate of 0.025 meters per decade at a temperature anomaly of 6 °C.

 f 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.

g For a review of post-IPCC AR4 research on sea level rise, see Nicholls et al. (2011)5 and NAS (2011).6
6

16-B.4.1.3 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 economic production, so each period’s climate 

damages will reduce consumption in that period and in all future periods due to the lost 

investment. The fraction of output in each period that is lost due to climate change impacts is 

represented as one minus a fraction, which is one divided by a quadratic function of the 

temperature anomaly, producing a sigmoid (“S”-shaped) function.h The loss function in 

DICE2010 has been expanded by adding a quadratic function of SLR to the quadratic function of 

temperature. In DICE2010 the temperature anomaly coefficients have been recalibrated to avoid 

double-counting damages from sea level rise that were implicitly included in these parameters in 

DICE2007. 

The aggregate damages in DICE2010 are illustrated by Nordhaus (2010 p 3),3 who notes 

that “...damages in the uncontrolled (baseline) [i.e., reference] case ... in 2095 are $12 trillion, 

or 2.8 percent of global output, for a global temperature increase of 3.4 o

C above 1900 levels.” 

This compares to a loss of 3.2 percent of global output at 3.4 oC in DICE2007. However, in 

DICE2010 annual damages are lower in most of the early periods of the modeling horizon but 

higher in later periods 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 given that relative increases in damages in later periods are discounted more heavily, all 

else equal.

16B.4.2 FUND

FUND version 3.8 includes a number of changes over the previous version 3.5 (Narita et 

al. 2010) used in the 2010 interagency report. Documentation supporting FUND and the model’s 

source code for all versions of the model is available from the model authors.i

 h The model and documentation, including formulas, are available on the author’s

Notable changes, 

due to their impact on the SCC estimates, are adjustments to the space heating, agriculture, and 

webpage at http://www.econ.yale.edu/~nordhaus/homepage/RICEmodels.htm.

i

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 (between 3.7 to 3.8) 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.
7

sea level rise damage functions in addition to changes to the temperature response function and 

the inclusion of indirect effects from methane emissions.j

16-B.4.2.1 Space Heating

We discuss each of these in turn.

In FUND, the damages associated with the change in energy needs for space heating are 

based on the estimated impact due to one degree of warming. These baseline damages are scaled 

based on the forecasted temperature anomaly’s deviation from the one degree benchmark and 

adjusted for changes in vulnerability due to economic and energy efficiency growth. In FUND 

3.5, the function that scales the base year damages adjusted for vulnerability allows for the 

possibility that in some simulations the benefits associated with reduced heating needs may be an 

unbounded convex function of the temperature anomaly. In FUND 3.8, the form of the scaling 

has been modified to ensure that the function is everywhere concave and that 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 of large temperature anomalies, 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.

16-B.4.2.2 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 

convex function of sea level rise, thereby assuming that the slope of the shore line increases

moving inland. The effect of this change is to typically reduce the vulnerability of some regions 

to sea level rise based land loss, thereby lowering the expected SCC estimate.

k

16-B.4.2.3 Agriculture

In FUND, the damages associated with the agricultural sector are measured as 

proportional to the sector’s value. The fraction is bounded from above by one and is made up of 

three additive components that represent the effects from carbon fertilization, the rate of 

 

j

The other damage sectors (water resources, space cooling, land loss, migration, ecosystems, human health, and 

extreme weather) were not significantly updated. k For stability purposes this report also uses an update to the model which assumes that regional coastal protection 

measures will be built to protect the most valuable land first, such that the marginal benefits of coastal protection is 

decreasing in the level of protection following Fankhauser (1995).
8

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 coefficients of this loss function 

are modeled as the ratio of two random normal variables. This specification had the potential for 

unintended extreme behavior as draws from the parameter in the denominator approached zero 

or went negative. In FUND 3.8, the coefficients are drawn directly from truncated normal 

distributions so that they remain in the range [0,∞) and (−∞,0], respectively, ensuring the correct 

sign and eliminating the potential for divide by zero errors. 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 decrease the range of the distribution while 

spreading out the distributions’ mass over the remaining range relative to the previous version. 

The net effect of this change on the SCC estimates is difficult to predict. 

16-B.4.2.4 Transient Temperature Response 

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 

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, a 

relationship first noted by Hansen et al. (1985) based on the heat uptake of the deep ocean. 

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.

16-B.4.2.5 Methane

The IPCC AR4 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 methane emissions on its own lifetime. The 

radiative forcing associated with atmospheric methane has also been increased by 40% to 

account for its net impact on ozone production and stratospheric water vapor. All else equal, the 

effect of this increased radiative forcing will be to increase the estimated SCC values, due to 

greater projected temperature anomaly.
9

16B.4.3 PAGE

PAGE09 (Hope 2013)

9 includes a number of changes from PAGE2002, the version used 

in the 2010 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.l

10

More details on PAGE09 can be found in Hope (2011a, 

2011b, 2011c). , 11, 12 A description of PAGE2002 can be found in Hope (2006).13

16-B.4.3.1 Sea Level Rise

While PAGE2002 aggregates all damages into two categories – economic and noneconomic impacts - PAGE09 adds a third explicit category: damages from sea level rise. In the 

previous version of the model, damages from sea level rise were subsumed by the other damage 

categories. In PAGE09, sea level damages increase less than linearly with sea level under the 

assumption that land, people, and GDP are more concentrated in low-lying shoreline areas. 

Damages from the economic and non-economic sector were adjusted to account for the 

introduction of this new category. 

16-B.4.3.2 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 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.

16-B.4.3.3 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 developed countries, and higher 

damages in developing countries. 

 l

Because several changes in the PAGE model are structural (e.g., the addition of sea level rise and treatment of 

discontinuity), it is not possible to assess the direct impact of each change on the SCC in isolation as done for the 

other two models above.
10

16-B.4.3.4 Probability of a Discontinuity

In PAGE2002, the damages associated with a “discontinuity” (nonlinear extreme event) 

were modeled as an expected value. Specifically, a stochastic probability of a discontinuity was 

multiplied by the damages associated with a discontinuity to obtain an expected value, and this 

was added to the economic and non-economic impacts. 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 either 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.

16-B.4.3.5 Adaptation

As in PAGE2002, adaptation is available to help mitigate any climate change impacts 

that 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 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 2°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 2°C by 50-90 

percent after 20 years. Beyond 2°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.
11

16-B.4.3.6 Other Noteworthy Changes

Two other changes in the model are worth noting. There is a change in the way the model 

accounts for decreased CO2 absorption on land and in the ocean as temperature rises. PAGE09 

introduces a linear feedback from global mean temperature to the percentage gain in the excess 

concentration of CO2, capped at a maximum level. In PAGE2002, 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 PAGE2002, 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, to 

capture relatively greater changes in temperature forecast to be experienced at higher latitudes.

16B.5 REVISED SCC ESTIMATES

The updated versions of the three integrated assessment models were run using the same 

methodology detailed in the 2010 TSD (Interagency Working Group on Social Cost of Carbon 

2010).

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 IPCC AR4, and three constant discount rates of 

2.5, 3, and 5 percent.

As was previously the case, the use of three models, three discount rates, and five 

scenarios produces 45 separate distributions for the global SCC. The approach laid out in the 

2010 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 and 

additional summary statistics for the 2020 values is available in the Annex.) As noted in the 

2010 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” (Interagency Working Group on 

Social Cost of Carbon 2010, 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 16B.5.1 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. 
12

Values for the years in between are calculated using linear interpolation. The full set of revised 

annual SCC estimates between 2010 and 2050 is reported in Annex A.

Table 16B.5.1Revised 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 2010 TSD due to the changes to the models outlined in the previous section. By 

way of comparison, the 2020 SCC estimates reported in the original TSD were $7, $26, $42 and 

$81 (2007$) (Interagency Working Group on Social Cost of Carbon 2010). Figure 16B.5.2

illustrates where the four SCC values for 2020 fall within the full distribution for each discount 

rate based on the combined set of runs for each model and scenario (150,000 estimates in total 

for each discount rate). In general, the distributions are skewed to the right and have long tails. 

The Figure also shows that the lower the discount rate, the longer the right tail of the distribution.
13

Figure 16B.5.2 Distribution of SCC Estimates for 2020 (in 2007$ per metric 

ton CO2)

As was the case in the 2010 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 compute the cost of a marginal ton emitted in the future by running the models for a 

set of perturbation years out to 2050. Table 16B.5.2 illustrates how the growth rate for these four 

SCC estimates varies over time.

Table 16B.5.2 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 2010

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.
14

Under current OMB guidance contained in Circular A-4, analysis of economically 

significant proposed and final regulations from the domestic perspective is required, while 

analysis from the international perspective is optional. However, the climate change problem is 

highly unusual in at least two respects. First, it involves a global externality: emissions of most 

greenhouse gases contribute to damages around the world even when they are emitted in the 

United States. Consequently, to address the global nature of the problem, the SCC must 

incorporate the full (global) damages caused by GHG emissions. Second, climate change 

presents a problem that the United States alone cannot solve. Even if the United States were to 

reduce its greenhouse gas emissions to zero, that step would be far from enough to avoid 

substantial climate change. Other countries would also need to take action to reduce emissions if 

significant changes in the global climate are to be avoided. Emphasizing the need for a global 

solution to a global problem, the United States has been actively involved in seeking 

international agreements to reduce emissions and in encouraging other nations, including 

emerging major economies, to take significant steps to reduce emissions. When these 

considerations are taken as a whole, the interagency group concluded that a global measure of 

the benefits from reducing U.S. emissions is preferable. For additional discussion, see the 2010

TSD.

16B.6 OTHER MODEL LIMITATIONS AND RESEARCH GAPS

The 2010 interagency SCC TSD 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 inter-sectoral linkages are 

modeled. While the new version of the models discussed above offer some improvements in 

these areas, further work remains warranted. The 2010 TSD 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 research on modeling and valuation of climate impacts that can potentially improve

SCC estimation in the future.
15

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from PAGE09 are higher than those from PAGE2002. Climatic Change 117: 531-543

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Cambridge Judge Business School Working Paper No. 4/2011. Available online at
16

 http://www.jbs.cam.ac.uk/research/working_papers/2011/wp1104.pdf.

11. Hope, C., The Social Cost of CO2 from the PAGE09 Model. 2011, Cambridge Judge 

Business School Working Paper No. 5/2011. Avaiable online at

 http://www.jbs.cam.ac.uk/research/working_papers/2011/wp1105.pdf.

12. Hope, C., New Insights from the PAGE09 Model: The Social Cost of CO2. 2011, 

Cambridge Judge Business School Working Paper No. 8/2011. Avaiable online at

http://www.jbs.cam.ac.uk/research/working_papers/2011/wp1108.pdf.

13. Hope, C., The Marginal Impact of CO2 from PAGE2002: An Integrated Assessment

Model Incorporating the IPCC’s Five Reasons for Concern. The Integrated Assessment 

Journal, 2006. 6(1): pp. 19-56 
17

ANNEX A:

ADDITIONAL TABLES

Table 16B.6.1 Annual SCC Values: 2010-2050 (2007$/metric 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
18

Table 16B.6.2 2020 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/metric 

ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenariom 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 16B.6.3 2020 Global SCC Estimates at 3 Percent Discount Rate (2007$/metric 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

 m See 2010 TSD for a description of these scenarios.
19

5th Scenario -22 -10 -5 2 10 13 20 33 42 63

Table 16B.6.4 2020 Global SCC Estimates at 5 Percent Discount Rate (2007$/metric 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

Table 16A.5.5: Additional Summary Statistics of 2020 Global SCC Estimates

Discount 5.0% 3.0% 2.5%

Statistic: Mea Varian Skewne Kurtos Mea Varian Skewne Kurtos Mea Varian Skewne Kurtos

DICE 12 26 2 15 38 409 3 24 57 1097 3 30

PAGE 22 1616 5 32 71 14953 4 22 101 29312 4 23

FUND 3 560 -170 35222 21 22487 -85 18842 36 68055 -46 13105
20

ANNEX B

SOCIAL COST OF CARBON FOR REGULATORY IMPACT ANALYSIS UNDER 

EXECUTIVE ORDER 12866n

 n
17-A-i

APPENDIX 17-A. REGULATORY IMPACT ANALYSIS SUPPORTING 

MATERIAL 

TABLE OF CONTENTS

17-A.1 MARKET PENETRATION CURVES DEVELOPED BY XENERGY............17-A-1

17-A.2 IMPACTS OF CONSUMER TAX CREDITS....................................................17-A-2

17-A.3 EPACT 2005 FEDERAL TAX CREDITS FOR CONSUMERS AND 

MANUFACTURERS OF RESIDENTIAL APPLIANCES................................17-A-3

17-A.3.1 Tax Credits for Consumers of Residential Appliances........................................17-A-3

17-A.3.2 Tax Credits for Manufacturers of Energy Efficient Appliances..........................17-A-4

LIST OF FIGURES

Figure 17-A.1.1 Comparison of Exponential and Logistic Curves Showing External and 

Internal Influences on Consumers ...........................................................17-A-2
17-A-1

APPENDIX 17-A. SUPPORTING MATERIAL: REGULATORY IMPACT ANALYSIS

17-A.1 MARKET PENETRATION CURVES DEVELOPED BY XENERGY

Xenergy, Inc., developed a re-parameterized, mixed-source model of information 

diffusion to estimate market impacts induced by financial incentives for energy efficient 

appliances. The basic premise of this mixed-source model is that information diffusion drives 

technology adoption. There is extensive economic literature discussing the diffusion process of 

new products as technologies evolve. Some of the literature focuses primarily on the 

development of analytical models of diffusion patterns of new products for individual consumers 

or for technologies from competing firms.1, 2, 3 One study records researchers’ attempts to 

investigate underlying factors that drive diffusion processes.4 Because of the distinct 

characteristics of diverse new products, few studies have conclusively developed a universally 

applicable model. Some key findings, however, seem to have gained wide recognition in 

academia and industry. 

First, all potential users may adopt new technologies, regardless of their economic 

benefits and technological merits. Many products face a ceiling on their adoption rate. Second, 

not all adopters purchase new products at the same time. Some act quickly after the introduction 

of a new product, while others respond slowly, waiting for products to become more mature. 

Third, diffusion processes can be approximately characterized by asymmetric S-curves, depicting 

three stages of diffusion: starting, accelerating, and decreasing as the adoption ceiling is 

reached.

One important diffusion model, the epidemic model, is used widely in marketing and 

social studies on diffusion. It assumes that (1) all consumers value the benefits of a new product 

identically and (2) the cost of a new product is constant or declines monotonically over time. 

What induces a consumer to purchase the new product is information about the availability and 

benefits of the product. In other words, it is information diffusion that drives individual 

consumers’ adoption of new product.3 By superimposing a logistic function on an exponential 

function, the model incorporates information diffusion from both internal sources (news spread 

by word of mouth from early adopters) and external sources (the “announcement effect” by 

government, other institutions, or commercial advertising).1, 4

 

The relative degree of influence by internal and external sources determines the general 

shape of the diffusion curve for the specific product.1, 4 For instance, if the adoption of a product 

is influenced more by external sources of information diffusion (announcement effect) than by 

internal sources (word of mouth among early adopters to prospective adopters), the rate of 

diffusion at the beginning of the diffusion process is much higher. This effect reflects the 

immediate information exposure to a significant number of prospective adopters brought about 

by external sources, in contrast to the more gradual exposure of early adopters, a small 

proportion of the population, to other prospective adopters. Graphically speaking, a relatively 

dominant external source of information diffusion gives an immediate jump-start to the adoption 
17-A-2

of a new product in the first years, forming a concave curve with respect to the Y axis (the 

exponential curve in Figure 17-A.1.1). Adoption of a new product influenced more strongly by 

internal sources of information diffusion (such as a social network formed by prospective 

adopters) may start with a few early adopters and increase gradually as the number of adopters 

increases. This process forms a convex curve (the logistic curve in Figure 17-A.1.1).

Figure 17-A.1.1 Comparison of Exponential and Logistic Curves Showing External and 

Internal Influences on Consumers

17-A.2 IMPACTS OF CONSUMER TAX CREDITS

Because there have been no consumer tax credits for microwave ovens, DOE analyzed 

data from Oregon's Residential Energy Tax Credit (RETC) for dishwashers as an analogous 

kitchen product. DOE calculated that for 2006 Oregon taxpayers claimed credits for 14 percent 

of dishwasher shipments to the State. DOE found no data comparing sales of efficient 

dishwashers in Oregon and Washington. To estimate how many of the tax credit claims likely 

were due to utility rebates and to market impacts, DOE reviewed a report by Itron on utility 

rebate programs in California. That report compared the percentage of residential clothes washer 

and dishwasher rebate programs in 2005.5 Free riders are consumers who would have purchased 

efficient appliances without a tax credit or rebate incentive but claimed the credit. Itron found 

that the penetration rate of ENERGY STAR dishwashers (85 percent) was much higher than that 
17-A-3

of ENERGY STAR clothes washers (40 percent). Itron also found that the percentage of free 

riders was greater for dishwasher programs than for clothes washer programs. DOE’s previous 

estimates showed that the incremental cost compared to a baseline unit was much smaller for 

high efficiency dishwashers than for high efficiency clothes washers,6, 7 which could explain the 

higher penetration rate for efficient dishwashers. An incentive program for a product having a 

higher market share would tend to have more free riders. Based on these various factors, DOE 

estimated that market impacts (partly due to free riders) were responsible for a higher percentage 

of tax credit claims for dishwashers than for residential clothes washers. 

Itron’s report contained two estimates of free riders for ENERGY STAR dishwashers in 

2005. DOE took the simple average of the supplier estimate of 93 percent and the self-reported 

consumer estimate of 59 percent, yielding 76 percent. DOE also developed a simple average of 

estimated free riders for residential clothes washers, averaging 77 percent from suppliers and 43 

percent from consumers to yield 60 percent. DOE assumed that the difference in numbers of free 

riders between the two products would be on the order of the difference between the two 

estimates (16 percent).

DOE interpreted the higher percentage of consumers purchasing dishwashers as free 

riders, and the lower percentage of taxpayers influenced primarily by rebates, to estimate that 49 

percent of Oregon's 2006 dishwasher tax credits claimed (6.9 percent of total dishwasher 

shipments to the State) were due to market impacts. It estimated that utility rebates were 

responsible for 32 percent of tax credits claimed (4.5 percent of total shipments). DOE attributed 

the remaining 20 percent of tax credits claimed (2.8 percent of total shipments) to the ability of 

the consumer to claim a tax credit.

17-A.3 EPACT 2005 FEDERAL TAX CREDITS FOR CONSUMERS AND 

MANUFACTURERS OF RESIDENTIAL APPLIANCES 

This section describes the products that qualified for Federal tax credits for consumers 

and manufacturers of energy efficient residential appliances under EPACT 2005.

17-A.3.1 Tax Credits for Consumers of Residential Appliances

EPACT 2005 included Federal tax credits for consumers who installed efficient air 

conditioners or heat pumps; gas or oil furnaces; furnace fans; and/or gas, oil, or electric heat 

pump water heaters in new or existing homes.8,9,10 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). 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.

Residential appliances must meet the following specifications, as applicable, to qualify 

for Federal tax credits.
17-A-4

• Furnaces and Boilers:

o Until December 31, 2010: Natural gas & propane furnaces must meet an Annual 

Fuel Use Efficiency (AFUE) 95 or higher, oil furnaces and gas, oil and propane 

boilers must meet an AFUE of 90 or better. 

o From January 1 through December 31, 2011: Natural gas & propane furnaces 

must meet an Annual Fuel Use Efficiency (AFUE) 95 or higher, oil furnaces and 

gas, oil and propane boilers must meet an AFUE of 95 or better. In addition, the 

credit only covers costs up to $150.

• Central Air Conditioning Units: 

o Until December 31, 2010: Central air conditioning units and air-source heat 

pumps must meet the highest tier standards set by the Consortium for Energy 

Efficiency (CEE) as of February 17, 2009, which in most cases requires a 

Seasonal Energy Efficiency Ratio (SEER) of 16. SEER measures performance 

throughout the cooling season. 

o Until December 31, 2011: same specifications, the credit only covers costs up to 

$300.

• Fans for heating and cooling systems: 

o Until December 31, 2010: fan uses no more than 2% of total heating system 

energy use, as defined by DOE test procedure.

o From January 1 through December 31, 2011: $50 for any advanced main air 

circulating fan

• Water Heaters: 

o Until December 31, 2010: 

 Gas or propane water heaters—Energy Factor of at least 0.82, or a thermal 

efficiency of at least 90%

 Heat pump water heaters—Energy Factor of at least 2.0

o From January 1 through December 31, 2011: same specifications, the credit only 

covers costs up to $300.

17-A.3.2 Tax Credits for Manufacturers of Energy Efficient Appliances

EPACT 2005 provided Federal Energy Efficient Appliance Credits to manufacturers that 

produced high-efficiency refrigerators, clothes washers, and dishwashers in 2006 and 2007.11

The Emergency Economic Stabilization Act of 200812 amended the credits and extended them 

through 2010. The credits were extended again to 2011 with modifications in the eligibility 

requirements. Manufacturers who produce these appliances receive the credits for increasing 

their production of qualifying appliances. The credits were available for models produced in 

2008, 2009, and 2010 and 2011. 

The credit amounts applied to each unit manufactured. The credit to manufacturers of 

qualifying clothes washers, refrigerators and dishwashers is 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 are not subject to the cap. The credit to manufacturers is capped at $25 million for 
17-A-5

2011. The most efficient refrigerators (35%) and clothes washers (2.8 MEF/3.5 WCF) are 

exempted from this cap.13
17-A-6

REFERENCES

 1 Geroski, P.A. Models of Technology Diffusion. Research Policy. 2000. 29: pp.603–625.

2 Hall, B. H. and B. Khan, Adoption of New Technology. Working Paper No. E03-330, 2003. 

 Department of Economics, University of California, Berkeley, CA. 

3 Lekvall. P and C. Wahlbin, "A Study of Some Assumptions Underlying Innovation Diffusion 

Functions". Swedish Journal of Economics, 1973

4 Van den Bulte, C., "Want to Know How Diffusion Speed Varies Across Countries and 

Products? Try a Bass Model". Product Development & Management Association, 2002. XXVI

(4).

5 Itron and KEMA. 2004/2005 Statewide Residential Retrofit Single-Family Energy Efficiency 

Rebate Evaluation.2007. Prepared for the California Public Utilities Commission, Pacific Gas 

and Electric Company, San Diego Gas and Electric Company, Southern California Edison, and 

Southern California Gas Company. Itron, Inc., and KEMA, Inc.: Oakland, CA. CPUC-Id#: 

1115-04.

6 U.S. Department of Energy. Technical Support Document: Residential Dishwashers, 

Dehumidifiers, and Cooking Products and Commercial Clothes Washers. November 2007. 

Washington, D.C. Chapter 8. Available online at 

www.eere.energy.gov/buildings/appliance_standards/residential/home_appl_tsd.html. 

(Accessed October 2012.)

7 U.S. Department of Energy. Clothes Washer Technical Support Document. December 2000. 

Washington, D.C. Chapter 7. Available online at 

http://www.eere.energy.gov/buildings/appliance_standards/residential/clwash_0900_r.html.

(Accessed October 2012.) 

8 Tax Incentives Assistance Project, Consumer Tax Incentives: Home Heating & Cooling 

 Equipment, 2011. Available online at

 www.energytaxincentives.org/consumers/heating-cooling.php. (Accessed October 2012.)

9 Energy Policy Act of 2005. 2005. Public Law 109–58–Aug. 8, 2005. Available online at

www.gpo.gov/fdsys/pkg/PLAW-109publ58/pdf/PLAW-109publ58.pdf. 

(Accessed October 2012.)

10 Consortium for Energy Efficiency, CEE High-Efficiency Specification: Central Air 

 Conditioners and Air Source Heat Pumps, January 1, 2009. Available online at

 www.cee1.org/resid/rs-ac/res-ac_specs.pdf. (Accessed October 2012.)
17-A-7

 11 U.S. Department of the Treasury—Internal Revenue Service. Energy Efficient Appliance 

Credit Form 8909. Available online at

 www.irs.gov/pub/irs-pdf/f8909.pdf. (Accessed October 2012.)

12 Emergency Economic Stabilization Act of 2008, Available online at 

 www.gpo.gov/fdsys/pkg/PLAW-110publ343/pdf/PLAW-110publ343.pdf.

 (Accessed October 2012.)

13 Tax Incentives Assistance Project, Manufacturers Incentives, Available online at 

 energytaxincentives.org/builders/appliances.php. (Accessed October 2012.)