TECHNICAL SUPPORT DOCUMENT: 

ENERGY EFFICIENCY PROGRAM 

FOR CONSUMER PRODUCTS AND 

COMMERCIAL AND INDUSTRIAL EQUIPMENT:

COMMERCIAL CLOTHES WASHERS 

February 21, 2014

U.S. Department of Energy

Assistant Secretary 

Office of Energy Efficiency and Renewable Energy 

Building Technologies Program 

Appliances and Commercial Equipment Standards 

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

by staff members of 

Navigant Consulting, Inc. 

and 

Lawrence Berkeley National Laboratory
1-i

CHAPTER 1. INTRODUCTION 

TABLE OF CONTENTS 

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

1.2 OVERVIEW OF STANDARDS FOR COMMERCIAL CLOTHES WASHERS ......... 1-1

1.3 PROCESS FOR SETTING ENERGY CONSERVATION STANDARDS ................... 1-1

1.4 STRUCTURE OF THE DOCUMENT ............................................................................ 1-5

LIST OF TABLES 

Table 1.3.1 Analyses Under the Process Rule ........................................................................ 1-4
1-1

CHAPTER 1. INTRODUCTION 

1.1 PURPOSE OF THE DOCUMENT 

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

analyses and results supporting the information presented in the Notice of Proposed Rulemaking 

(NOPR) for commercial clothes washers. This TSD reports on the activities and analyses 

conducted in the period following the publication of the framework document for this 

rulemaking. 

1.2 OVERVIEW OF STANDARDS FOR COMMERCIAL CLOTHES WASHERS 

 Title III of the Energy Policy and Conservation Act of 1975 (42 U.S.C.6291, et seq; 

“EPCA”), Pub. L. 94-163, sets forth a variety of provisions designed to improve energy 

efficiency. (All references to EPCA refer to the statute as amended through the American Energy 

Manufacturing Technical Corrections Act, Pub. L. 112-210 (Dec. 18, 2012)). Part C of title III, 

which for editorial reasons was re-designated as Part A-1 upon incorporation into the U.S. Code 

(42 U.S.C. 6311–6317, as codified), establishes the "Energy Conservation Program for Certain 

Industrial Equipment." These include commercial clothes washers, the subject of this 

rulemaking. (42 U.S.C. 6311(1)(H)). 

 The Energy Policy Act of 2005 (EPACT) established the first energy conservation 

standards for commercial clothes washers. (42 U.S.C. 6313(e)(1)) EPACT directed DOE to 

conduct two rulemakings to determine whether the established standards should be amended. 

DOE published its first final rule amending commercial clothes washer standards on January 8, 

2010 (“January 2010 final rule”), which apply to commercial clothes washers manufactured on 

or after January 8, 2013. EPACT required the second final rule to be published by January 1, 

2015, which will apply to commercial clothes washers manufactured three years after the date on 

which the final amended standard is published. (42 U.S.C. 6313(e)(2)(B)) This current 

rulemaking will satisfy the requirement to publish the second final rule by January 1, 2015. 

Compliance with any amended standards would be required three years after the date of 

publication of the final standards. 

1.3 PROCESS FOR SETTING ENERGY CONSERVATION STANDARDS 

 Under EPCA, when DOE is studying new or amended standards, it must consider, to the 

greatest extent practicable, the following seven factors (42 U.S.C. 6295 (o)(2)(B)(i) and 

6316(a)):
1-2

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

products; 

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

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

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

of the standard; 

4) any lessening of the utility or the performance of the products likely to result from the 

imposition of the standard; 

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

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

6) the need for national energy conservation; and 

7) other factors the Secretary considers relevant. 

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

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

 DOE considers stakeholder participation to be an important part of the process for setting 

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

notices), DOE actively encourages the participation and interaction of all stakeholders during the 

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

during subsequent comment periods, interactions among stakeholders provide a balanced 

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

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

standard, it must first solicit comments on the proposed standard. (42 U.S.C. 6295(m)(2)(B); 

6295(p)(2); and 6316(a)) Any new or amended standard must be designed to achieve significant 

additional conservation of energy and be technologically feasible and economically justified. (42 

U.S.C. 6295(o)(2)(A) and 6316(a)) To determine whether economic justification exists, DOE 

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

exceed its burdens to the greatest extent practicable, weighing the seven factors listed above. (42 

U.S.C. 6295 (o)(2)(B)(i) and 6316(a)) 

 After the publication of the framework document, the energy conservation standards 

rulemaking process involves three additional, formal public notices, which DOE publishes in the 

Federal Register. The first of the rulemaking notices is a notice of public meeting (NOPM), 

which is designed to publicly vet the models and tools used in the preliminary rulemaking and to 

facilitate public participation before the next stage. The second notice is the notice of proposed 

rulemaking (NOPR), which presents a discussion of comments received in response to the 
1-3

NOPM and the preliminary analyses and analytical tools; analyses of the impacts of potential 

amended energy conservation standards on consumers, manufacturers, and the Nation; DOE’s 

weighting of these impacts of amended energy conservation standards; and the proposed energy 

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

discussion of the comments received in response to the NOPR; the revised analyses; DOE’s 

weighting of these impacts; the amended energy conservation standards DOE is adopting for the 

product; and the compliance dates of the amended energy conservation standards. 

 DOE initiated the current rulemaking on August 13, 2012 by publishing a notice 

announcing the availability of the framework document, the Energy Conservation Standards 

Rulemaking Framework Document for Commercial Clothes Washers. In this notice, DOE also 

announced a public meeting and requested comment on the matters raised in the framework 

document. 77 FR 48108 (August 13, 2012). The framework document described the procedural 

and analytical approaches that DOE anticipated using to evaluate energy conservation standards 

for commercial clothes washers and identified various issues to resolve during the rulemaking. 

The NOPM is available at http://www.regulations.gov/#!documentDetail;D=EERE-2012-BTSTD-0020-0002 and the framework document is available at 

http://www.regulations.gov/#!documentDetail;D=EERE-2012-BT-STD-0020-0001.

 On September 24, 2012, DOE held the framework document public meeting and 

discussed the issues detailed in the framework document. DOE also described the analyses that it 

planned to conduct during the rulemaking, listed in Table 1.3.1. Through the public meeting, 

DOE sought feedback from interested parties on these subjects and provided information 

regarding the rulemaking process that DOE would follow. Interested parties discussed the 

following major issues at the public meeting: rulemaking schedule; test procedure revisions; 

product classes; technology options; efficiency levels; and approaches for each of the analyses 

performed by DOE as part of the rulemaking process. The comments received since publication 

of the August 2012 notice, including those received at the September 2012 framework public 

meeting, have contributed to DOE's proposed resolution of the issues in this rulemaking as they 

pertain to commercial clothes washers. 
1-4

Table 1.3.1 Analyses Under the Process Rule 

Preliminary Analyses1 NOPR Final Rule

Market and technology assessment Revised preliminary analyses Revised NOPR analyses

Screening analysis Life-cycle cost sub-group 

analysis

Engineering analysis Manufacturer impact analysis

Energy use determination Utility impact analysis

Markups for equipment price 

determination

Emissions analysis

Life-cycle cost and payback period 

analysis

Employment impact analysis

Shipments analysis Regulatory impact analysis

National impact analysis

Preliminary manufacturer impact analysis

1. In the current rulemaking, DOE conducted the analyses listed under Preliminary 

Analyses as part of the NOPR analysis. 

 Following the framework meeting, DOE gathered additional information, held 

discussions with manufacturers, performed product testing and teardowns, and performed the 

various analyses described in the framework document, including the engineering, life-cycle 

cost, payback period, manufacturer impact, and national impact analyses. The results of these 

analyses are presented in the NOPR and accompanying TSD chapters. 

 As part of the information gathering and sharing process, DOE organized and held 

interviews with manufacturers of the commercial clothes washers considered in this rulemaking 

as part of the engineering analysis. DOE selected companies that represented production of all 

types of products on the market, ranging from small to large manufacturers, and included the 

Association of Home Appliance Manufacturers (AHAM) member companies. DOE had four 

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

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

(3) provide an opportunity, early in the rulemaking process, to express manufacturers’ concerns 

to DOE; and (4) foster cooperation between manufacturers and DOE. DOE incorporated the 

information gathered during the engineering interviews with manufacturers into its engineering 

analysis (chapter 5) and the manufacturer impact analysis (chapter 12). 

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

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

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

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

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

standards at various levels on product shipments. All of these spreadsheets are available on the 
1-5

DOE website for commercial clothes washers at: 

http://www1.eere.energy.gov/buildings/appliance_standards/product.aspx/productid/46.

 

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

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

provided in such a format. 

1.4 STRUCTURE OF THE DOCUMENT 

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

the following chapters and appendices: 

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

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

document. 

Chapter 2 Analytical Framework: describes the rulemaking process. 

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

considered products and the technologies available for increasing 

product efficiency. 

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

efficiency of the considered products, and determines which technology 

options are viable for consideration in the engineering analysis. 

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

relationship between increased manufacturer price and increased 

efficiency. 

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

for converting manufacturer prices to customer product costs.

Chapter 7 Energy and Water Use Analysis: discusses the process used for 

generating energy-use estimates for the considered products as a 

function of standard levels. 

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

standards on individual customers and users of the products and 

compares the LCC and PBP of products with and without higher 

efficiency standards. 
1-6

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

year analysis period that is used in performing the national impact 

analysis (NIA). 

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

national net present value of total consumer costs and savings, expected 

to result from specific, potential energy conservation standards for 

residential clothes washers. 

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

different subgroups of consumers. 

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

finances and profitability of product manufacturers. 

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

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

as well as carbon dioxide emissions. 

Chapter 14 Monetization of Emission Reductions Benefits 

Chapter 15 Utility Impact Analysis: discusses certain effects of the considered 

standards on electric and gas utilities. 

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

national employment. 

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

alternatives to efficiency standards. 

Appendix 5-A AHAM Data Request 

Appendix 6-A Detailed Data for Markups 

Appendix 7-A Load Usage Factor in Commercial Laundry Applications 

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

Appendix 8-B Uncertainty and Variability 

Appendix 8-C Lifetime Distributions

Appendix 9-A Relative Price Elasticity of Demand for Clothes Washers 
1-7

Appendix 10-A Full-Fuel-Cycle Multipliers

Appendix 10-B User Instructions for Shipments and National Impact Analysis 

Spreadsheets 

Appendix 10-C National Energy Savings and Net Present Value Using Alternative 

Growth Scenarios 

Appendix 10-D National Net Present Value of Consumer Benefits Using Alternative 

Product Price Forecasts 

Appendix 12-A Manufacturer Interview Guide 

Appendix 12-B Government Regulatory Impact Model 

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

Order 12866 

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

Analysis Under Executive Order 12866 

Appendix 17-A Regulatory Impact Analysis: Supporting Materials
2-i

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

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

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

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

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

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

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

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

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

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

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

2.13 MONETIZATION OF EMISSIONS REDUCTION BENEFITS ................................. 2-12

2.14 UTILITY IMPACT ANALYSIS ................................................................................... 2-13

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

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

LIST OF FIGURES 

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) and 6316(a) of the Energy Policy and Conservation Act (EPCA), 

Pub. L. 94-163, 42 U.S.C. 6291 et seq. requires the U.S. Department of Energy (DOE) to set 

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

that is technologically feasible and economically justified. This chapter describes the general 

analytical framework that DOE uses in developing such standards. The analytical framework is a 

description of the methodology, the analytical tools, and the relationships among the various 

analyses that are part of this rulemaking. 

 

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

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

Inputs” and “Key Outputs” show how the analyses fit into the rulemaking process, and how the 

analyses relate to each other. Key inputs are the types of data and information that the analyses 

require. Some key inputs exist in public databases; DOE collects other inputs from interested 

parties or other knowledgeable experts within the field. Key outputs are analytical results that 

feed directly into the standards-setting process. Dotted lines connecting analyses show types of 

information that feed from one analysis to another. 

 In this rulemaking, DOE proceeded directly to the Notice of Proposed Rulemaking 

(NOPR) stage after publishing the framework document; therefore, the analyses listed within the 

Preliminary Analysis period were conducted as part of the NOPR analysis. 
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

Product Price

Determination Life-Cycle Cost and 

Payback Period 

Analyses

National Impact

Analysis

Energy Use

Revise Preliminary

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

• Product Designs

• 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

•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 (ANO)

• Efficiency/Performance

• Commercial Products

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

Product Designs

Utility Impact

Analysis 

Notice of Proposed Rulemaking (NOPR)

Final Rule

Approaches Key Inputs Analyses Key Outputs

Market and Technology

Assessment

Screening Analysis

Engineering Analysis

Product Price

Determination Life-Cycle Cost and 

Payback Period 

Analyses

National Impact

Analysis

Energy Use

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

• Product Designs

• 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

•Retail Price Collection and

Analysis

• Manufacturer Prices

• Average Costs

• Manufacturer Financial Data

Design Options

Product Classes

Retail 

Prices

Design Energy/ Use

Publication of Preliminary Analyses

• Efficiency/Performance

• Commercial Products

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

Product Designs
2-3

 The analyses performed for this NOPR and reported in this technical support document 

(TSD) are listed below. 

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

existing technology options, including prototype designs. 

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

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

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

safety. 

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

manufacturer’s cost of achieving increased efficiency. 

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

production cost (MPC) to the cost to the consumer. 

 An analysis of the annual energy and water use of the considered products. 

 Life-cycle cost (LCC) and payback period (PBP) analyses to calculate, at the consumer 

level, the discounted savings in operating costs (minus maintenance and repair costs) 

throughout the estimated average lifetime of the covered products, compared to any 

increase in purchase and installation cost likely to result directly from imposition of a 

given standard. 

 A shipments analysis to forecast shipments of residential clothes washers, which are then 

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

and future manufacturer cash flows. 

 A national impact analysis (NIA) to assess aggregate impacts at the national level of 

potential energy conservation standards for the considered products, as measured by the 

NPV of total consumer economic impacts and the national energy savings (NES). 

 A manufacturer impact analysis to assess the potential impacts of energy conservation 

standards on manufacturers’ capital conversion expenditures, marketing costs, shipments, 

and research and development costs. 

 

 An LCC subgroup analysis to evaluate variations in consumer characteristics that might 

cause a standard to affect particular consumer subpopulations, such as low-income 

households, differently than the overall population. 
2-4

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

manufacturers and to calculate impacts on competition, employment, and manufacturing 

capacity. 

 A utility impact analysis to estimate the effects of proposed standards on electric, gas, or 

oil utilities. 

 An employment impact analysis to assess the aggregate impacts on national employment. 

 An environmental impact analysis to estimate the effects of amended standards on 

emissions of carbon dioxide (CO2), sulfur dioxide (SO2), nitrogen oxides (NOX), and 

mercury (Hg). 

 A regulatory impact analysis to examine major alternatives to proposed amended energy 

conservation standards that potentially could achieve substantially the same regulatory 

goal at a lower cost. 

 

 DOE developed this analytical framework and documented its initial findings in the 

Energy Conservation Standards Rulemaking Framework Document for Commercial Clothes 

Washers (the framework document). DOE announced the availability of the framework 

document in a Notice of Public Meeting and Availability of a Framework Document published in 

the Federal Register on August 13, 2012. 77 FR 48108. DOE presented the analytical approach 

to interested parties during a public meeting on September 24, 2012. The framework document is 

available at 

http://www.regulations.gov/#!documentDetail;D=EERE-2012-BT-STD-0020-0001.

 In response to the publication of the framework document and the associated public 

meeting, DOE received comments from interested parties regarding its analytical approach. In 

the NOPR, DOE: (1) summarizes the key comments received from interested parties and 

describes DOE’s responses to those comments; and (2) summarizes any significant changes in 

the analytical approach made since publishing the framework document. This NOPR TSD 

contains details of the final analyses conducted for residential clothes washers. 
2-5

2.2 MARKET AND TECHNOLOGY ASSESSMENT 

 The market and technology assessment characterizes the relevant product markets and 

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

2.2.1 Market Assessment 

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

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

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

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

information. The subjects addressed in the market assessment include manufacturers, trade 

associations, and the quantities and types of products sold and offered for sale. DOE examined 

both large and small and foreign and domestic manufacturers. DOE also examined publicly 

available data from the key trade association for this product category. DOE reviewed shipment 

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

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

provides additional details on the market and technology assessment. 

2.2.2 Technology Assessment 

 

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

prototype designs as inputs to determine what technologies manufacturers use to attain higher 

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

for consideration. Initially, these technologies encompass all those it believes are technologically 

feasible. 

 DOE developed its list of technologically feasible design options through consultation 

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

papers. Since many options for improving product efficiency are available in existing units, 

product literature and direct examination provided additional information. 

Chapter 3 of the TSD includes the detailed list of all the technology options.

2.3 SCREENING ANALYSIS 

 The screening analysis examines various technologies as to whether they: (1) are 

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

adverse impact on product utility or availability; and (4) have adverse impacts on health and 

safety. DOE developed an initial list of efficiency-enhancement options from the technologies 

identified as technologically feasible in the technology assessment. Then DOE, in consultation 

with interested parties, reviewed the list to determine if these options are practicable to 

manufacture, install, and service, would adversely affect product utility or availability, or would 

have adverse impacts on health and safety. In addition, DOE removed from the list technology 
2-6

options that lack energy consumption data as well as technology options whose energy 

consumption could not be adequately measured by existing DOE test procedures. In the 

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

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

analysis. 

2.4 ENGINEERING ANALYSIS 

The engineering analysis establishes the relationship between the manufacturing 

production cost (MPC) and the efficiency for each class of products. The purpose of the analysis 

is to estimate the incremental MPCs for a product that would result from increasing efficiency 

levels above the level of the baseline model in each product class. This relationship serves as the 

basis for cost/benefit calculations in terms of 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 the manufacturing 

production costs, the cost-efficiency curves, and the impact of efficiency improvements on the 

considered products. 

 The engineering analysis considered technologies not eliminated in the screening 

analysis, although certain technologies were not analyzed due to negligible incremental 

efficiency improvements or the inability of the existing DOE test procedures to measure any 

reduction in energy use. DOE considered the remaining technologies, designated as design 

options, in developing the cost-efficiency curves. 

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

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

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

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

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

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

materials derived from tear-downs of the product being analyzed. DOE used a combination of 

these approaches for this rulemaking, as described in further detail in Chapter 5. 

2.5 MARKUPS ANALYSIS

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

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

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

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

the change in the manufacturer sales price of higher efficiency models (the incremental cost 

increase) to the change in the retailer or distributor sales price. 
2-7

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

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

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

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

provides details on DOE’s development of markups.

2.6 ENERGY AND WATER USE ANALYSIS 

DOE performed an energy and water use analysis to assess the energy and water savings 

potential from higher efficiency levels, providing the basis for the energy and water savings 

values used in the LCC and subsequent analyses. The goal of the energy and water use 

characterization is to generate a range of energy and water use values that reflects actual product 

use. Chapter 7 of this TSD provides more detail about DOE’s approach for characterizing energy 

and water use. 

2.7 LIFE-CYCLE COST AND PAYBACK PERIOD ANALSYIS 

New energy conservation standards usually decrease a product’s operating expenses, and 

usually increase its price. DOE analyzes the net effect of amended standards on consumers by 

evaluating the net life cycle cost (LCC). To evaluate the net LCC, DOE uses the cost-efficiency 

relationship derived in the engineering analysis and the energy and water costs derived from the 

energy and water use analysis. Inputs to the LCC calculation include the installed cost of a 

product to the consumer (consumer purchase price plus installation cost), operating expenses 

(energy expenses and maintenance costs), the lifetime of the unit, and a discount rate. 

 Because the installed cost of a product typically increases while operating costs typically 

decrease under new standards, there is a time in the life of products having higher-than-baseline 

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

the incremental first cost of purchasing the product. The time required for products to reach this 

cost-equivalence point is known as the payback period (PBP). 

 Recognizing that several inputs to determining consumer LCC and PBP are either 

variable or uncertain, DOE uses Monte Carlo simulation and probability distributions to model 

both the uncertainty and variability in inputs. DOE uses Microsoft Excel 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.

 Chapter 8 of this TSD describes the results from the LCC and PBP analyses. 
2-8

2.8 SHIPMENTS ANALYSIS

 Forecasts of product shipments are needed to calculate the national impacts of standards 

on energy use, NPV, and future manufacturer cash flows. DOE developed shipment forecasts 

based on an analysis of key market drivers. In DOE’s shipments model, shipments of products 

are driven by new construction, stock replacements, and other types of purchases. 

The shipments models take an accounting approach, tracking market shares of each 

product class and the vintage of units in the existing stock. Stock accounting uses product 

shipments 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 calculations of both the NES and 

NPV, because operating costs for any year depend on the age distribution of the stock. 

 DOE also considers the impacts on shipments from changes in product purchase price 

and operating cost associated with higher energy efficiency levels. Chapter 9 of this TSD 

provides additional details on the shipments analysis.

2.9 NATIONAL IMPACT ANALYSIS 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

before the revised standard takes effect. 

2.9.1 National Energy and Water Savings 

 The inputs for determining the national energy and water savings (NES) for each product 

class are: (1) annual energy and water consumption per unit, (2) shipments, (3) product stock, (4) 

national energy and water consumption, and (5) site-to-source conversion factors for energy. 
2-9

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

stock, of each product class (by vintage, or age) by the unit energy and water consumption (also 

by vintage). DOE calculated annual NES based on the difference in national energy and water 

consumption for the base case (without new efficiency standards) and for each efficiency 

standard being considered. DOE estimated energy consumption and savings based on site energy 

consumption, which it then converted to source energy. DOE estimated water consumption and 

savings based on site water use. DOE did not use a conversion factor for water because no such 

factor has been developed. Cumulative energy and water savings are the sum of the NES for 

each year. 

2.9.2 Net Present Value of Consumer Benefit 

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

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

factor, (4) present value of costs, and (5) present value of savings. DOE calculated net savings 

each year as the difference in total savings in operating costs and total increases in installed costs 

between the base case and each standards case. DOE calculated savings over the life of each 

product class, accounting for differences in yearly energy rates. DOE calculated NPV as the 

difference between the present value of operating cost savings and the present value of total 

installed costs. DOE used a discount factor based on real discount rates of 3 percent and 7 

percent to discount future costs and savings to present values. 

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

standards case (i.e., after standards take effect). Because the more efficient products bought in 

the standards case usually cost more than products bought in the base case, cost increases appear 

as negative values in the NPV. 

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

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

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

each vintage that survive in a given year. 

2.10 CONSUMER SUBGROUP ANALYSIS 

The consumer subgroup analysis evaluates economic impacts on selected groups of 

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

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

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

using the LCC spreadsheet model. Chapter 11 of this TSD describes the consumer subgroup 

analysis.
2-10

2.11 MANUFACTURER IMPACT ANALYSIS 

The MIA assesses the impacts of new energy conservation standards on manufacturers of 

the considered products. Potential impacts include financial effects, both quantitative and 

qualitative, that might lead to changes in the manufacturing practices for these products. DOE 

identified these potential impacts through interviews with manufacturers and other interested 

parties. 

 DOE conducted the MIA in three phases, and further tailored the analytical framework 

based on interested parties’ comments. In Phase I, an industry profile was created to characterize 

the industry, and a preliminary MIA was conducted 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. Industry and subgroup 

cash flow and NPV were assessed through use of the Government Regulatory Impact Model 

(GRIM). Then impacts on competition, manufacturing capacity, employment, and cumulative 

regulatory burden were assessed based on manufacturer interview feedback and discussions. 

DOE discusses its findings from the MIA in chapter 12 of the TSD. 

2.12 EMISSIONS ANALYSIS 

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

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

potential energy conservation standards for the considered products. In addition, DOE will 

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

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

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

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

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

gases. 

DOE 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. 
2-11

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 

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

CSAPR.1

 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 

 

1

 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. 
2-12

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

standards in the States where emissions are not capped. 

 The MATS limit mercury emissions from power plants, but they do not include emissions 

caps and, as such, DOE’s energy conservation standards would likely reduce Hg emissions. 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. 

2.13 MONETIZATION OF EMISSIONS REDUCTION BENEFITS 

In Chapter 14 of the TSD, DOE explains how it plans to monetize the benefits associated 

with 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 emissions of 

CO2 emissions, DOE uses the most current Social Cost of Carbon (SCC) values developed by an 

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

resulting from greenhouse gas (GHG) emissions, including, but not limited to, net agricultural 

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

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

change will raise serious questions of science, economics, and ethics. But with full regard for the 

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

social benefits of reductions in GHG emissions. 

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

benefits resulting from reduced CO2 emissions in 2015, expressed in 2012$, were $12.9, $40.8, 

$62.2, and $117.0 per metric ton avoided.2For emission reductions that occur in later years, 

these values grow over time. Additionally, the interagency group determined that a range of 

 

2

Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, Technical Model Update for 

the Social Cost of Carbon (SCC). Interagency Working Group on Social Cost of Carbon, United States Government, 

Internal EPA Draft, February 13, 2013. 
2-13

values from 7 percent to 23 percent should be used to adjust the global SCC to calculate 

domestic effects, although DOE gives preference to consideration of the global benefits of 

reducing CO2 emissions. To calculate a present value of the stream of monetary values, DOE 

discounts the values in each of the four cases using the discount rates that had been used to 

obtain the SCC values in each case. 

DOE recognizes that scientific and economic knowledge continues to evolve rapidly as to 

the contribution of CO2 and other GHG to changes in the future global climate and the potential 

resulting damages to the world economy. Thus, these values are subject to change. 

DOE also estimates the potential monetary benefit of reduced NOX emissions resulting from 

the standard levels it considers. For NOx emissions, available estimates suggest a very wide range of 

monetary values, ranging from $468 to $4,809 per ton in 2012$.3In accordance with U.S. Office 

of Management and Budget (OMB) guidance, DOE conducts two calculations of the monetary 

benefits derived using each of the economic values used for NOx, one using a real discount rate 

of 3 percent and another using a real discount rate of 7 percent.4

DOE did not monetize estimates of SO2 and Hg reduction in this rulemaking. 

2.14 UTILITY IMPACT ANALYSIS 

 The utility impact analysis estimates the effects of amended energy conservation 

standards on installed electricity generation capacity and electricity generation. For this analysis, 

DOE adapted NEMS, which is a large multi-sectoral, partial-equilibrium model of the U.S. 

energy sector that the EIA has developed throughout the past decade, primarily for preparing 

EIA’s AEO. In previous rulemakings, a variant of NEMS (currently termed NEMS-BT, BT 

referring to DOE’s Building Technologies Program), was developed to better address the 

specific impacts of an energy conservation standard. NEMS, which is available in the public 

domain, produces a widely recognized baseline energy forecast for the United States. The typical 

NEMS outputs include forecasts of electricity and natural gas sales, prices, and electric 

generating capacity. 

 DOE conducts the utility impact analysis as a scenario that departs from the latest Annual 

Energy Outlook reference case. In other words, the energy savings impacts from amended energy 

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

AEO reference case. Chapter 15 describes the results of the utility impact analysis. 

 

3

 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. 

4

 OMB, Circular A-4: Regulatory Analysis (Sept. 17, 2003). 
2-14

2.15 EMPLOYMENT IMPACT ANALYSIS 

New or amended energy conservation standards can impact employment both directly 

and indirectly. Direct employment impacts are changes in the number of employees at the plants 

that produce the covered products, and at the affiliated distribution and service companies, 

resulting from the adoption of new standards. DOE evaluated direct employment impacts in the 

MIA. Indirect employment impacts may result from expenditures shifting between goods (the 

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

occur due to the adoption of standards. Chapter 16 describes the results of the employment 

impact analysis. 

 DOE investigated the combined direct and indirect employment impacts of standards 

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

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

Planning, Budget, and Analysis, estimates the employment and income effects energy-saving 

technologies produced in buildings, industry, and transportation. In comparison with simple 

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

economic impacts of energy conservation investments. 

2.16 REGULATORY IMPACT ANALYSIS 

 DOE prepared a regulatory impact analysis (RIA) under Executive Order 12866, 

“Regulatory Planning and Review,” 58 FR 51735, October 4, 1993, which was subject to review 

under the Executive Order by the Office of Information and Regulatory Affairs (OIRA) at the 

Office of Management and Budget. The RIA evaluated non-regulatory alternatives to standards, 

in terms of their ability to achieve significant energy savings in the considered products at a 

reasonable cost, and compared the effectiveness of each one to the effectiveness of the adopted 

standards. Chapter 17 of this TSD describes the results of the regulatory impact analysis. 

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. DOE based its assessment on the 

actual impacts of any such initiatives to date, but also considered information presented 

regarding the impacts that any existing initiative might have in the future. 
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 ................................................................................. 3-2

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

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

3.5.2 Coin Laundry Association ............................................................................................... 3-3

3.5.3 Multi-housing Laundry Association ................................................................................ 3-4

3.6 MANUFACTURER INFORMATION ........................................................................... 3-4

3.6.1 Manufacturers and Market Shares ................................................................................... 3-4

3.6.2 Mergers and Acquisitions ................................................................................................ 3-4

3.6.3 Small Business Impacts ................................................................................................... 3-5

3.6.4 Distribution Channels ...................................................................................................... 3-5

3.7 REGULATORY PROGRAMS ....................................................................................... 3-6

3.7.1 Energy Policy Act of 2005 ............................................................................................... 3-7

3.7.2 Energy Independence and Security Act of 2007 .............................................................. 3-7

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

3.7.4 Canadian Energy Conservation Standards ....................................................................... 3-8

3.7.5 International Standby Power Regulatory Programs ....................................................... 3-10

3.8 VOLUNTARY PROGRAMS ........................................................................................ 3-10

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

3.8.2 Consortium for Energy Efficiency ................................................................................. 3-11

3.8.3 Federal Energy Management Program .......................................................................... 3-11

3.8.4 Manufacturer Tax Credits for Energy-Efficient Appliances ......................................... 3-12

3.8.5 Rebates for Highly Energy-Efficient Products .............................................................. 3-13

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

3.9.1 New Multi-Family Unit Starts ....................................................................................... 3-13

3.9.2 Unit Shipments............................................................................................................... 3-14

3.9.3 AHAM Data ................................................................................................................... 3-15

3.9.4 Imports and Exports ....................................................................................................... 3-16

3.10 HISTORICAL EFFICIENCIES..................................................................................... 3-17

3.11 PRODUCT RETAIL PRICES ....................................................................................... 3-18

3.12 INDUSTRY COST STRUCTURE ................................................................................ 3-20

3.13 TECHNOLOGY ASSESSMENT .................................................................................. 3-20

3.13.1 Product Operations and Components ............................................................................. 3-20

3.13.2 Technology Options ....................................................................................................... 3-22

3.13.2.1 Adaptive water fill ....................................................................................... 3-23

3.13.2.2 Advanced agitation concepts for top-loading machines .............................. 3-23

3.13.2.3 Capacity increase ......................................................................................... 3-24

3.13.2.4 Direct-drive motor ....................................................................................... 3-24
3-ii

3.13.2.5 Higher spin speeds to reduce remaining moisture content .......................... 3-25

3.13.2.6 Increased motor efficiency ........................................................................... 3-26

3.13.2.7 Ozonated laundering .................................................................................... 3-26

3.13.2.8 Polymer bead cleaning ................................................................................. 3-26

3.13.2.9 Spray rinse ................................................................................................... 3-27

3.13.2.10 Temperature-differentiated pricing controls ................................................ 3-27

3.13.2.11 Thermostatically controlled mixing valves .................................................. 3-27

3.13.2.12 Water circulation loop .................................................................................. 3-28

3.13.3 Energy Efficiency .......................................................................................................... 3-28

LIST OF TABLES 

Table 3.6.1 Major and Other Commercial Clothes Washer Manufacturers ................................ 3-4

Table 3.7.1 Energy Conservation Standards for Commercial Clothes Washers Established 

by EPACT 2005 ................................................................................................... 3-7

Table 3.7.2 Energy Conservation Standards for Commercial Clothes Washers Established 

by January 2010 Final Rule ................................................................................. 3-7

Table 3.7.3 Current Canadian Energy Conservation Standards for Household-Style 

Commercial Clothes Washers .............................................................................. 3-9

Table 3.7.4 Current Canadian Standard Levels for High-Efficiency Household-Style 

Commercial Clothes Washers .............................................................................. 3-9

Table 3.7.5 EU Ecodesign Requirements for Standby and Off Mode ....................................... 3-10

Table 3.8.1 ENERGY STAR Criteria for Commercial Clothes Washers ................................. 3-11

Table 3.8.2 Manufacturer Tax Credits for Energy-Efficient Clothes Washers, 2011-2013 ...... 3-13

Table 3.9.1 New Multi-Family Housing Unit Starts in the United States from 2000-2012 ...... 3-14

Table 3.9.2 Industry Shipments of Commercial Clothes Washers 2002–2011 ......................... 3-15

Table 3.9.3 AHAM Commercial Clothes Washer Shipment-Weighted Average Efficiency 

Data Submittal ................................................................................................... 3-15

Table 3.9.4 AHAM Commercial Clothes Washer Market Share Efficiency Data Submittal .... 3-16

Table 3.9.5 Import and Export Data for Commercial Clothes Washers, 2003-2013 ................. 3-17

Table 3.10.1 Energy Efficiency Trends for Commercial Clothes Washers, 2002-2011 ........... 3-18

Table 3.11.1 Commercial Clothes Washer Retail Prices ........................................................... 3-19

Table 3.13.1 Design Options Removed from Consideration ..................................................... 3-22

Table 3.13.2 Technology Options for Residential Clothes Washers ......................................... 3-23

LIST OF FIGURES 

Figure 3.6.1 Distribution Channel for Commercial Clothes Washers ......................................... 3-6

Figure 3.9.1 Multi-Family Housing Unit Starts, 2000–2012 ..................................................... 3-14

Figure 3.9.2 Annual CCW Shipments 2002-2011 ..................................................................... 3-15

Figure 3.9.3 Import and Export Trends for Commercial Clothes Washers, 2003-2013 ............ 3-17

Figure 3.10.1 Energy Efficiency Trends for Commercial Clothes Washers, 2002-2011 .......... 3-18

Figure 3.13.1 Distribution of Commercial Clothes Washers by MEF, November 2013 ........... 3-29
3-iii

Figure 3.13.2 Distribution of Commercial Clothes Washers by WF, November 2013 ............. 3-29
3-1

CHAPTER 3. MARKET AND TECHNOLOGY ASSESSMENT 

3.1 INTRODUCTION 

This chapter provides a profile of the commercial clothes washer (CCW) industry 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 

The Energy Policy and Conservation Act of 1975, as amended, defines commercial 

clothes washers as follows: 

The term “commercial clothes washer” means a soft-mount, front-loading or soft-mount, 

top-loading clothes washer that: 

A) has a clothes compartment that: 

i) for horizontal-axis clothes washers, is not more than 3.5 cubic feet; and 

ii) for vertical-axis clothes washers, is not more than 4.0 cubic feet; and 

B) is defined for use in: 

i) applications in which the occupants of more than 1 household will be using the 

clothes washer, such as multi-family housing common areas and coin laundries; 

or 

ii) other commercial applications. 

(42 U.S.C. 6311(21)) 

3.3 PRODUCT CLASSES 

DOE separated commercial clothes washers into product classes and has formulated 

separate energy conservation standards for each class. The criteria for separation into different 

product classes are: (1) type of energy used; (2) capacity; or (3) other performance-related 

features such as those that provide utility to the consumer, or others deemed appropriate by the 

Secretary that would justify the establishment of a separate energy conservation standard. (42 

U.S.C. 6295 (q) and 6316(a)) 

For the existing energy conservation standards, DOE defines two product classes for 

commercial clothes washers based on location of access: 
3-2

 Top-loading 

 Front-loading 

 In the August 13, 2012 framework document, DOE proposed to maintain this product 

class distinction for the current rulemaking. Interested parties submitted comments either 

supporting DOE’s proposal to maintain this product class structure or encouraging DOE to 

reconsider the product class structure by establishing a single product class for all CCWs. As 

described in full detail in the NOPR, DOE proposes to maintain separate product classes for toploading and front-loading CCWs for this rulemaking. 

3.4 PRODUCT TEST PROCEDURES

 The Energy Policy and Conservation Act (EPCA), Pub. L. 94-163, 42 U.S.C. 6291 et seq, 

as amended, mandates that the test procedures for commercial clothes washers shall be the same 

as the test procedures established by DOE for residential clothes washers (RCW). (42 U.S.C. 

6314(a)(8)). The DOE test procedures for commercial clothes washers currently being 

manufactured is codified at title 10 of the Code of Federal Regulations (CFR) part 430, subpart 

B, appendix J1 (hereafter, “appendix J1”). On March 7, 2012, DOE published a final rule 

amending its test procedures for residential clothes washers (“March 2012 final rule”). (77 FR 

13888) The March 2012 final rule included minor amendments to appendix J1 and also 

established a new test procedure at appendix J2 (hereafter, “appendix J2”). 

 Beginning March 7, 2015, manufacturers of commercial clothes washers may use either 

appendix J1 or appendix J2 to demonstrate compliance with the current standards established by 

the January 2010 final rule. Manufacturers using appendix J2 would be required to use the 

conversion equations proposed in a separate test procedure NOPR to translate the measured 

efficiency metrics into equivalent appendix J1 values.1The use of appendix J2 would be required 

to demonstrate compliance with any amended energy conservation standards to be published in a 

final rule by January 1, 2015, and the conversion equations would no longer be used at that time.

 In the March 2012 final rule, DOE incorporated standby and off mode energy use with 

active mode energy use into an integrated metric, integrated modified energy factor (IMEF),

contained in the new appendix J2 test procedure. Appendix J2 also retains provisions for 

calculating modified energy factor (MEF). As described in greater detail in the NOPR, in this 

rulemaking, DOE proposes amended standards based on MEF as measured using appendix J2, 

which DOE designates as MEFJ2.

 

1

 DOE published a test procedure NOPR for commercial clothes washers on February 11, 2014 (79 FR 8112). 

Additional details are available at DOE’s rulemaking webpage: 

http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx?ruleid=86. All rulemaking documents 

are also available at www.regulations.gov, under Docket # EERE-2013-BT-TP-0002. 
3-3

 As described further in chapter 5 of this TSD, DOE also considered the Association of 

Home Appliance Manufacturers (AHAM) test method HLW-1-2010, “Performance Evaluation 

Procedures for Household Clothes Washers” (referred to here as “AHAM HLW-1-2010”), in 

order to evaluate potential impacts on cleaning and rinsing performance as a result of more 

stringent standard levels. AHAM HLW-1-2010 is typically used to measure the performance of 

residential clothes washers; however, given the similarity of CCWs covered in this rulemaking to 

RCWs, and through consultation with CCW manufacturers, DOE determined that AHAM HLW1-2010 would be appropriate to use for comparison testing of CCWs. Specifically, DOE 

performed the soil/stain removal, rinsing effectiveness, and sand removal tests provided in 

HLW-1-2010. 

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 commercial 

clothes washer industry, DOE researched various associations available to manufacturers, 

suppliers, and users of such equipment. 

 DOE identified several trade groups that support, or have an interest in, the residential 

cooking product and/or CCW industries, including the Association of Home Appliance 

Manufacturers (AHAM), the Coin Laundry Association (CLA), and the Multi-housing Laundry 

Association (MLA). 

3.5.1 Association of Home Appliance Manufacturers 

 AHAM,1formed 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; an active communications program; and 

technical services and research. In addition, AHAM conducts other market and consumer 

research studies and publishes periodic industry reports. AHAM also develops and maintains 

technical standards for various appliances to provide uniform, repeatable procedures for 

measuring specific product characteristics and performance features. 

3.5.2 Coin Laundry Association 

 The CLA2is a trade association representing the 30,000 coin laundry owners in the 

United States and around the world. Since 1960, the CLA has provided education and service to 

the entrepreneurs of the coin laundry industry, promoted their business interests, and promoted 

the industry to the general public. 
3-4

3.5.3 Multi-housing Laundry Association 

 The MLA,3founded in 1959, is a trade association of operator and supplier companies 

providing professional laundry services for the multi-housing industry. The MLA works for a 

favorable legislative and regulatory climate for the industry, facilitates the exchange of technical 

and business information among members, and demonstrates the value of the industry to target 

markets. 

3.6 MANUFACTURER INFORMATION 

3.6.1 Manufacturers and Market Shares 

 DOE estimates that there are five domestic and two foreign manufacturers of the types of 

commercial clothes washers covered by this rulemaking. DOE estimates that two major 

manufacturers hold over 90 percent of the market share of commercial clothes washers covered 

by DOE standards: Alliance Laundry Systems LLC2

and Whirlpool Corporation3. Other 

manufacturers include AB Electrolux,4 Continental Girbau, Inc., General Electric, and LG. Table 

3.6.1 lists these manufacturers. 

Table 3.6.1 Major and Other Commercial Clothes Washer Manufacturers 

Major Manufacturers Other Manufacturers

Alliance Laundry Systems AB Electrolux

Whirlpool Continental Girbau

General Electric

LG

3.6.2 Mergers and Acquisitions 

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

March 31, 2006, in chapter 3 of the TSD accompanying the January 2010 final rule.4 DOE is not 

aware of any significant mergers or acquisitions in the commercial clothes washer market since 

then. 

 

2

 Alliance Laundry Systems markets the following brands: Speed Queen, UniMac, Huebsch, and IPSO. 

3

 Whirlpool also markets the Maytag brand. Whirlpool merged with Maytag on March 31, 2006. 

4

 AB Electrolux also markets the Wascomat brand. 
3-5

3.6.3 Small Business Impacts 

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

energy conservation standards. Based on the size standards published by the Small Business 

Association (SBA) (65 FR 30840, May 15, 2000, as amended at 67 FR 52602, Aug. 13, 2002; 74 

FR 46313, Sept. 9, 2009), to be categorized as a small business manufacturer of commercial 

clothes washers under North American Industry Classification System (NAICS) 333318, “Other 

commercial and service industry machinery manufacturing,” a commercial laundry equipment 

manufacturer and its affiliates may employ a maximum of 1000 employees. The 1000-employee 

threshold includes all employees in a business’s parent company and any other subsidiaries. 

Using this classification in conjunction with a search of industry databases and the SBA member 

directory, DOE did not identify any manufacturers of commercial clothes washers that qualify as 

small businesses. 

DOE identified one low-volume manufacturer (LVM) of commercial clothes washers. 

Unlike small business manufacturers, there is no employment limit associated with LVMs. 

Instead, LVMs are characterized by their low overall production volumes relative to their 

competitors, often associated with specialization within a singular industry. In 2012, the LVM 

derived 98 percent of its revenues from the sale of laundry equipment and service parts, while, 

for its main competitor, this percentage was 30 percent. Within the clothes washer segment, 

DOE estimates that the LVM derived 88 percent of its clothes washer equipment revenues from 

the sale of commercial clothes washers covered by this rulemaking. Because the commercial 

clothes washer industry itself is characterized by low total shipments, with less than 200,000 

units sold annually in the U.S., the concentration of this manufacturer’s business in this industry 

qualifies them as an LVM. Where the LVM operates at a much smaller scale and does not 

manufacture products across a broad range of industries, this rulemaking could have 

disproportionate impacts on the LVM compared to its large, diversified competitors. 

Accordingly, DOE performed an in-depth analysis of the issues relating to the commercial 

clothes washer LVM. The manufacturer subgroup analysis is discussed in greater detail in the 

NOPR and in chapter 12 of the NOPR TSD. 

3.6.4 Distribution Channels 

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

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

channels for commercial clothes washers from publicly available sources and through 

discussions with manufacturers. 

 For commercial clothes washers, DOE believes the distribution channels identified 

during the prior rulemaking remain relevant for this current rulemaking. Figure 3.6.1 illustrates 

the CCW distribution channels identified during the prior rulemaking, which were originally 

described in a 1998 Consortium for Energy Efficiency (CEE) report describing their 

Commercial, Family-Sized Clothes Washer Initiative.5
3-6

Figure 3.6.1 Distribution Channel for Commercial Clothes Washers 

 The 1998 CEE report states that the CCW market can be divided into three areas, 

including: (1) laundromats; (2) private multi-family housing; and (3) large institutions (e.g., 

military barracks, universities, and housing authorities.) Most large institutions and a majority of 

private multifamily housing (between 50 and 90 percent) do not purchase clothes washers 

directly. Rather, these organizations lease their laundry space to a third party known as a route 

operator. Route operators supply laundry equipment and maintain facilities in exchange for a 

percentage of the laundry revenue. Laundromats and some private building managers, 

conversely, will purchase clothes washers directly from distributors. The main difference 

between route operators and distributors is the length of service provided to their clients. Route 

operators provide on-going support, while distributor support ends at the point of sale. 

3.7 REGULATORY PROGRAMS 

The following section details current regulatory programs mandating energy conservation 

standards for CCWs. 
3-7

3.7.1 Energy Policy Act of 2005 

 The Energy Policy Act of 2005 (EPACT 2005), P.L. 109-58, amended EPCA to expand 

DOE’s energy conservation program to include commercial clothes. This legislation established 

the first energy conservation standards for CCWs, shown in Table 3.7.1, without consideration of 

separate product classes. These standards became effective January 1, 2007. 

Table 3.7.1 Energy Conservation Standards for Commercial Clothes Washers Established 

by EPACT 2005 

Metric Standard Effective 

January 1, 2007

Modified Energy Factor (MEF), ft3/kwh/cycle 1.26 (minimum)

Water Factor (WF), gal/cycle/ft39.5 (maximum)

 EPACT 2005 further directed DOE to conduct two rulemaking cycles to determine 

whether to amend these standards: the first final rule was required by January 1, 2010, which was 

fulfilled by the January 2010 final rule. Table 3.7.2 shows the amended standard levels from the 

January 2010 final rule, which became effective January 8, 2013. 

Table 3.7.2 Energy Conservation Standards for Commercial Clothes Washers Established 

by January 2010 Final Rule 

Standards Effective January 8, 2013

Equipment Class Modified Energy Factor

(cu.ft./kWh/cycle)

Water Factor

(gal/cycle/cu.ft)

Top-Loading ≥ 1.60 ≤ 8.5

Front-Loading ≥ 2.00 ≤ 5.5

 The second final rule is required by January 1, 2015, which will be fulfilled by the 

current rulemaking. (42 U.S.C. 6313(e))

3.7.2 Energy Independence and Security Act of 2007 

The Energy Independence and Security Act of 2007 (EISA 2007), Pub. L. No. 110-142,

amended EPCA to require any final rule for new or amended energy conservation standards for 

consumer products promulgated after July 1, 2010 to address standby mode and off mode energy 

use. (42 U.S.C. 6295(gg)(3)) This requirement applies to residential clothes washers but does not 

apply to commercial clothes washers, which are covered under DOE’s energy conservation 

program for certain industrial equipment (42 U.S.C. 6311-6317). Specifically, when DOE 

adopts a standard for a covered consumer product after July 1, 2010, it must, if justified by the 

criteria for adoption of standards under EPCA (42 U.S.C. 6295(o)), incorporate standby mode 

and off mode energy use into the standard, or, if that is not feasible, adopt a separate standard for 

such energy use for that product. (42 U.S.C. 6295(gg)(3)(A)-(B)) In the March 2012 final rule, 

DOE incorporated standby and off mode energy use with active mode energy use into an 
3-8

integrated metric, integrated modified energy factor (IMEF), contained in the new appendix J2

test procedure. Appendix J2 also retains provisions for calculating modified energy factor 

(MEFJ2). As described above, DOE proposes amended standards based on MEFJ2, which does 

not incorporate standby and off mode power.

3.7.3 State Energy Conservation Standards

Prior to the passage of EPACT 2005, several States proposed and adopted State-level 

efficiency regulations for commercial clothes washers that are identical, or very similar, to 

EPACT 2005 regulations. Complete details of these State-level regulations are available in 

chapter 3 of the TSD accompanying the January 2010 final rule.6

The EPACT 2005 energy and 

water use standards for CCWs preempt any State efficiency standards since they became 

effective January 1, 2007. DOE is not aware of any State-level energy conservation programs for 

commercial clothes washers since the standards from EPACT 2005 became effective.

3.7.4 Canadian Energy Conservation Standards 

The Canadian government regulates the efficiency of household-style commercial clothes 

washers. The standard levels are contained in Table 11 of the Canadian Standard Association 

(CSA) C360-03 test method (“CSA C360-03”), Energy performance, water consumption and 

capacity of household clothes washers.

7 CSA C360-03 defines household-style commercial 

clothes washers as follows: 

Household style commercial clothes washer means a soft-mounted front-loading or softmounted top-loading clothes washer that is designed for use in—(i) Applications in 

which the occupants of more than one household will be using the clothes washer, such 

as multi-family housing common areas and coin laundries; or (ii) other commercial 

applications. 

Table 3.7.3 shows the currently applicable regulations for household-style commercial clothes 

washers from Table 11 of CSA C360-03. These standards are harmonized with the U.S. 

standards that became effective January 8, 2013. 
3-9

Table 3.7.3 Current Canadian Energy Conservation Standards for Household-Style 

Commercial Clothes Washers 

Standards Effective January 8, 2013

Equipment Class

Modified Energy Factor,

L/kWh/cycle

(cu.ft./kWh/cycle)

Water Factor,

L/cycle/L

(gal/cycle/cu.ft)

Vertical-axis ≥ 45.31

(≥ 1.6)

≤ 1.13

(≤ 8.5)

Horizontal-axis ≥ 56.63

(≥ 2.0)

≤ 0.73

(≤ 5.5)

 Canada also defines voluntary standard levels for high-efficiency household-style 

commercial clothes washers in Table 13 of CSA C360-03. These standards, shown in Table 

3.7.4, are consistent with the U.S. ENERGY STAR version 6.0 standards as described later in 

this chapter. These high-efficiency standard levels are not defined by product class. 

Table 3.7.4 Current Canadian Standard Levels for High-Efficiency Household-Style 

Commercial Clothes Washers 

Standards Effective February 1, 2013

Equipment Class

Modified Energy Factor,

L/kWh/cycle

(cu.ft./kWh/cycle)

Water Factor,

L/cycle/L

(gal/cycle/cu.ft)

Household-style commercial 

clothes washers

≥ 62.3

(≥ 2.2)

≤ 0.6

(≤ 4.5)

 Canada has recently revised its C360 test method, designated CSA C360-13, to 

harmonize with DOE’s appendix J2 test procedure. CSA C360-13 contains a slightly modified 

definition of household-style commercial clothes washers, defined as follows: 

Household style commercial clothes washer means a soft-mounted front-loading or softmounted top-loading clothes washer that— 

(1) Has a clothes container compartment that--

(i) For horizontal-axis clothes washers, is not more than 3.5 cubic feet; and 

(ii) For vertical-axis clothes washers, is not more than 4.0 cubic feet; and 

(2) is designed for use in applications in which the occupants of more than one household 

will be using the clothes washer, such as multi-family housing common areas and coin 

laundries. 

 CSA C360-13 does not yet contain updated standards for household-style commercial 

clothes washers based on appendix J2. DOE believes that such standards may be harmonized 

with the final standards promulgated as a result of this rulemaking, and would be incorporated 

into CSA C360-13 shortly thereafter. 
3-10

3.7.5 International 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 watt (W) requirement for all 

consumer electrical products (unless specifically excluded) in standby mode. The IEA also stated 

that IEC 62301 provides an internationally sanctioned definition and test procedure for standby 

power which is now widely specified and used.8

The European Union 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 household clothes washers. DOE does not believe that these regulations would 

apply to commercial clothes washers. On December 18, 2008, the Commission of the European 

Communities published Commission Regulation (EC) No 1275/2008 containing Ecodesign 

requirements for standby and off mode electric power consumption of electrical and electronic 

household and office equipment. Table 3.7.5 presents the requirements for energy consumption 

in standby and off mode power.9

Table 3.7.5 EU Ecodesign Requirements for Standby and Off Mode 

Mode Maximum Power (Watts)

Tier 1 (effective January 07, 2010)

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 (effective January 07, 2013)

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 several voluntary programs promoting commercial clothes washers in the United 

States. Many programs, including ENERGY STAR, CEE, and the Federal Energy Management 

Program (FEMP), establish voluntary energy conservation standards for these products. 
3-11

3.8.1 ENERGY STAR 

ENERGY STAR, a voluntary labeling program backed by the U.S. Environmental 

Protection Agency (EPA) and DOE, identifies energy efficient products through a qualification 

process. To qualify, a product must exceed Federal minimum standards by a specified amount, or 

if no Federal standard exists, exhibit selected energy saving features. The ENERGY STAR 

program works to recognize the top quartile of products on the market, meaning that 

approximately 25 percent of products on the market meet or exceed the ENERGY STAR levels. 

ENERGY STAR specifications exist for several products, including commercial clothes washers. 

 ENERGY STAR criteria exist for commercial clothes washers with capacities greater 

than 1.6 cubic feet.10 New criteria for CCWs became effective on February 1, 2013, shown 

below in Table 3.8.1. The ENERGY STAR program does not distinguish between product 

classes for CCWs. 

Table 3.8.1 ENERGY STAR Criteria for Commercial Clothes Washers 

Standards Effective February 1, 2013

Equipment Type Modified Energy Factor

(cu.ft./kWh/cycle)

Water Factor

(gal/cycle/cu.ft)

Commercial Clothes Washers ≥ 2.2 ≤ 4.5

3.8.2 Consortium for Energy Efficiency 

The CEE11 develops initiatives for its North American members to promote the 

manufacture and purchase of energy efficient products and services. The goal of the organization 

is to induce lasting structural and behavioral changes in the marketplace, resulting in the 

increased adoption of energy efficient technologies. CEE has historically maintained a 

Commercial Clothes Washer Initiative that encourages the purchase and use of energy and water 

efficient commercial clothes washers. Given recent changes to federal minimum efficiency 

standards and ENERGY STAR criteria for commercial clothes washers, the CEE Commercial 

Clothes Washer Specification is not currently active as of the publication date of this NOPR. As 

a result, publication of the associated qualifying product list is also temporarily suspended. 

According to information on the CEE website, the specification will be reinstated once the 

revised levels are finalized through the CEE specification revision process. 

3.8.3 Federal Energy Management Program 

DOE’s FEMP12 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 

commercial clothes washers. 
3-12

 The National Energy Conservation Act (P.L. 95-619), Executive Order 13423, and 

Federal Acquisition Regulations Subpart 23.2 and 53.223 require that Federal agencies purchase 

ENERGY STAR-qualified products. Currently, the FEMP recommendations for commercial 

clothes washers are equivalent to the ENERGY STAR standard levels described above. 

3.8.4 Manufacturer Tax Credits for Energy-Efficient Appliances 

EPACT 2005 amended the Internal Revenue Code of 1986 (26 U.S.C. 1 et seq.) to 

establish a tax credit to manufacturers for the production in the United States of energy efficient 

CCWs. (26 U.S.C. 45M(b)(1)(B)) A credit of $100 was established for CCWs manufactured in 

the calendar year 2006 or 2007 which met the ENERGY STAR qualifying criteria in effect in 

2007. The total credit for CCWs was equal to an applicable credit amount multiplied by the 

eligible production, subject to a $75 million limit, reduced by the amount of credit taken in 

previous years. The eligible production for CCWs equaled the number of appliances produced in 

a year divided by the average number of appliances produced during the preceding 3-calendaryear period.

The Energy Improvement and Extension Act of 2008 (Pub. L. 110-343; Oct. 3, 2008) 

further amended the Internal Revenue Code of 1986 to modify and extend the manufacturer tax 

credits for CCWs to apply to equipment produced in calendar years 2008, 2009, or 2010. (26 

U.S.C. 45M(b)(2)) Eligible production was calculated over a 2-calendar-year period and the total 

credit limit calculation was reduced by the amount of credit taken in prior years beginning after 

December 31, 2007. Further, the aggregate credit amount did not apply to CCWs meeting or 

exceeding 2.2 MEF/4.5 WF. (26 U.S.C. 45M(c)(2) and (e)) 

The Tax Relief, Unemployment Insurance Reauthorization, and Job Creation Act of 2010 

(Pub. L. 111-312) and the American Taxpayer Relief Act of 2012 (Pub. L. 112-240) further 

extended the manufacturing tax credits for all clothes washers, including CCWs, through 2013. 

(26 U.S.C. 45M(b)(2 )(B Table 3.8.2 shows the applicable tax credit amounts based on MEF and 

WF. Although CCWs are eligible to receive these tax credits, DOE’s market survey conducted 

for this rulemaking indicates that no top-loading or front-loading CCWs on the market achieve 

the MEF and WF levels required to receive the tax credits. 
3-13

Table 3.8.2 Manufacturer Tax Credits for Energy-Efficient Clothes Washers, 2011-2013

Equipment Type Modified Energy Factor

(cu.ft./kWh/cycle)

Water Factor

(gal/cycle/cu.ft)

Credit

Amount

Top-Loading Clothes Washers1≥ 2.2 ≤ 4.5 $175

Top-Loading Clothes Washers ≥ 2.4 ≤ 4.2 $225

Front-Loading Clothes Washer ≥ 2.8 ≤ 3.5 $225

1. Effective only in 2011 

3.8.5 Rebates for Highly Energy-Efficient Products 

 States, electric utilities, municipalities, and other organizations promote the purchase of 

highly energy-efficient commercial clothes washers through consumer rebates. Typically, these 

programs offer rebates for products meeting existing ENERGY STAR efficiency levels. DOE 

jointly manages the Database of State Incentives for Renewables & Efficiency (DSIRE), which 

lists the financial incentives available in each state for purchasing energy-efficient products, 

including CCWs.13

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 commercial clothes washer 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 Multi-Family Unit Starts 

Trends in multi-family unit starts can affect shipments of commercial clothes washers.

Table 3.9.1 and Figure 3.9.1 show the number of new multi-family housing units started in the 

United States from 2000-2012.

14 Over the 5-year period from 2000–2005, multi-family unit 

starts remained stable at around 300,000 units annually. Between 2005 and 2009, multi-family 

unit starts decreased significantly to around 100,000 units annually. Since 2009, multi-family 

unit starts have increased steadily, reaching almost 250,000 in 2012.
3-14

Table 3.9.1 New Multi-Family Housing Unit Starts in the United States from 2000-2012

Year

MultiUnit

Housing 

Starts

(000’s)

Figure 3.9.1 Multi-Family Housing Unit Starts, 2000–2012

2000 299

2001 293

2002 308

2003 315

2004 303

2005 311

2006 293

2007 277

2008 266

2009 98

2010 104

2011 167

2012 234

3.9.2 Unit Shipments 

AHAM provided shipment estimates during the last rulemaking for the period 2002-

2005, and during the current rulemaking for the period 2010-2011, as presented in Table 3.9.2

and Figure 3.9.2 below. According to the data, shipments of CCWs peaked in 2003 at 191,000 

units, and declined to 161,000 in 2011. AHAM provided the breakdown between top-loading and 

front-loading CCWs for the period 2010-2011, as shown below. 

0

50

100

150

200

250

300

350

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Housing Units Started (thousands)

Year
3-15

Table 3.9.2 Industry Shipments of Commercial Clothes Washers 2002–2011

Shipments (000’s)

Figure 3.9.2 Annual CCW Shipments 2002-2011

Year Total

TopLoadFrontLoad

2002 175 n/a n/a

2003 191 n/a n/a

2004 178 n/a n/a

2005 177 n/a n/a

2006 n/a n/a n/a

2007 n/a n/a n/a

2008 n/a n/a n/a

2009 n/a n/a n/a

2010 164 104 60

2011 161 107 54

3.9.3 AHAM Data 

 AHAM provide shipment-weighted average efficiency levels disaggregated by product 

class for 2010 and 2011, as shown in Table 3.9.3. 

Table 3.9.3 AHAM Commercial Clothes Washer Shipment-Weighted Average Efficiency 

Data Submittal

Top-Loading Front-Loading

Year Weighted MEF Weighted WF Weighted MEF Weighted WF

2010 1.36 9.10 2.19 4.68

2011 1.32 9.33 2.31 4.43

 AHAM also provided market share efficiency data for top-loading and front-loading 

commercial clothes washers for 2010 and 2011, as shown in Table 3.9.4. The submitted values 

represent the market shares for clothes washers up to the listed efficiency levels.

0

50

100

150

200

250

2000 2002 2004 2006 2008 2010 2012

Annual Shipments (thousands)

Year

Total

Top-Loading

Front-Loading
3-16

Table 3.9.4 AHAM Commercial Clothes Washer Market Share Efficiency Data Submittal 

Top-Loading Front-Loading

Efficiency Bins Market Share (%) Efficiency Bins Market Share (%)

MEF WF 2010 2011 MEF WF 2010 2011

1.26 9.5 82% 80% 1.72 8.0 1% 0%

1.42 9.5 18% 20% 1.80 7.5 1% 0%

1.60 8.5 0% 0% 2.00 5.5 28% 28%

2.20 5.1 68% 34%

2.40 4.0 0% 38%

2.60 3.7 0% 0%

Notes: 

1. Models with MEF/WF data not meeting the criteria for both MEF and WF for any of the Efficiency Bins in 

the table above were included in the Efficiency Bin for which the value of the lesser efficient, MEF or WF, 

is satisfied. For example, a model with a MEF = 2.01 and WF = 5.6 would have been included in the 

1.80/7.5 Efficiency Bin. 

2. The percentages for the Front-Loading may not add up to 100% due to the absence of the current Federal 

Minimum Standard levels from the MEF/WF Efficiency Bins listed in the table. 

3.9.4 Imports and Exports 

DOE obtained import and export data from the U.S. International Trade Commission’s 

DataWeb database.15 Table 3.9.5 and Figure 3.9.3 show the number of coin-operated commercial 

clothes washer imports and exports for the period 2003-2013. The quantity of exports is

substantially greater than imports. Exports have generally decreased from a peak of around 

120,000 units in 2005 to around 56,000 units in 2013. Imports have generally increased from 

around 3,800 units in 2003 to around 6,500 units in 2013. 
3-17

Table 3.9.5 Import and Export Data for Commercial Clothes Washers, 2003-2013

Unit Shipments

Figure 3.9.3 Import and Export Trends for Commercial 

Clothes Washers, 2003-2013

Year Imports Exports

2003 3,780 112,110

2004 3,793 109,307

2005 3,499 120,856

2006 2,113 94,841

2007 4,581 88,382

2008 3,543 77,195

2009 2,858 54,513

2010 6,264 46,006

2011 5,701 52,391

2012 12,037 56,409

2013 6,543 56,227

3.10 HISTORICAL EFFICIENCIES 

The average efficiency of new commercial clothes washers has steadily increased since 

2002. Table 3.10.1 and Figure 3.10.1 show the shipment-weighted trend in MEF from 2002-

2011. This data was provided by AHAM during the last rulemaking for the period 2002-2005, 

and during the current rulemaking for the period 2010-2011.

0

20

40

60

80

100

120

140

Shipments (thousands)

Year

Exports

Imports
3-18

Table 3.10.1 Energy Efficiency Trends for Commercial Clothes Washers, 2002-2011

Shipment-Weighted 

MEF

Figure 3.10.1 Energy Efficiency Trends for 

Commercial Clothes Washers, 2002-2011

Year Total

TopLoadFrontLoad

2002 1.30 n/a n/a

2003 1.33 n/a n/a

2004 1.33 n/a n/a

2005 1.41 n/a n/a

2006 n/a n/a n/a

2007 n/a n/a n/a

2008 n/a n/a n/a

2009 n/a n/a n/a

2010 1.66 n/a n/a

2011 1.65 1.36 2.19

2012 1.30 1.32 2.31

3.11 PRODUCT RETAIL PRICES 

DOE conducted a limited search of retail prices for CCWs by contacting several CCW 

distributors by phone and by gathering pricing data for CCW models available for sale on the 

Internet. Table 3.11.1 summarizes the data collected by DOE. Where DOE obtained multiple 

prices for the same model, the average price is shown in the table. 

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

2000 2002 2004 2006 2008 2010 2012

Weighted MEF

Year

Total

Top-Loading

Front-Loading
3-19

Table 3.11.1 Commercial Clothes Washer Retail Prices 

Manufacturer Model Product Class

Control Panel 

Type

Payment System 

Type Vendor Price

GE WNRD2050G Top-Loading No Vend Display None AppliancesDirect.com $682 

Alliance Laundry Systems SWNSX2PP112TW01 Top-Loading No Vend Display Coin Box Appliances Direct $909 

Alliance Laundry Systems SWNBC2PP112TW01 Top-Loading Vend Price Display Coin Box Appliances Direct $1,052 

Alliance Laundry Systems SWNLC2PP112TW01 Top-Loading Vend Price Display Coin Box Appliances Direct $1,086 

Whirlpool CAE2743BQ Top-Loading No Vend Display Coin Slide Home Depot $764 

Whirlpool MHN30PDBWW Front-Loading Vend Price Display Coin Box Appliances Connection $1,579 

Whirlpool CHW8990AW Front-Loading Vend Price Display Coin Box Whirlsource.com $1,225 

Alliance Laundry Systems SFNLCFSP* Front-Loading Vend Price Display Coin Box Yankee Equipment $1,900 

Alliance Laundry Systems SFNNCFSP* Front-Loading Vend Price Display Coin Box Yankee Equipment $1,900 

LG GCW1069* Front-Loading No Vend Display None Daniels Equipment $1,695 

GE WNRD2050G Top-Loading No Vend Display None AppliancesDirect.com $682 

Alliance Laundry Systems SWNSX2PP112TW01 Top-Loading No Vend Display Coin Box Appliances Direct $909 
3-20

 Based on its market survey, DOE determined that retail price is largely a function of the 

type of loading (i.e., top-loading or front-loading) as well as whether the unit offers a control 

panel that displays vend price. Vend price display units would be primarily sold to Laundromats 

and multi-family housing laundries; whereas units with no vend price display would be used 

mostly in other commercial applications where fare is not charged. 

 As discussed further in chapter 8 of the TSD, DOE used manufacturing costs developed 

for specific standard levels in combination with manufacturer and distributor markups, sales 

taxes, and installation costs to establish the total installed cost differences between baseline and 

more efficient commercial clothes washers. DOE used the above range in current retail price 

differences between top-loading and front-loading clothes washers as an indication of the 

reasonableness of the total installed cost estimates that it developed for residential clothes 

washers. 

3.12 INDUSTRY COST STRUCTURE 

 Chapter 12 of this TSD provides a detailed financial analysis of the industry cost 

structure for commercial clothes washers. This analysis identifies key financial inputs including 

cost of capital, working capital, depreciation, and capital expenditures. 

3.13 TECHNOLOGY ASSESSMENT 

 The purpose of the technology assessment is to understand how the product uses energy 

and potential changes that could reduce energy consumption. DOE typically uses information 

about “technology options”—existing technologies and prototype designs and concepts—to 

identify technologies product manufacturers could use to attain higher energy efficiency levels. 

3.13.1 Product Operations and Components 

In preparation for the screening and engineering analyses, DOE prepared a brief 

description of the characteristics and operation of commercial clothes washers. These 

descriptions provide a basis for understanding the technologies used to improve product 

efficiency. 

Commercial clothes washers are appliances designed to clean clothes by using a solution 

of soap and/or detergent and water with mechanical agitation. Clothes washers use electricity to 

operate one or more pumps and to power an electric motor that agitates and spins the clothes. 

Clothes are washed, rinsed, and spun within a tub that is inside a cabinet. Because clothes 

washers require heated water for hot and warm water cycles, a separate water heater is typically 

needed to supply hot water. Most of the energy used by clothes washers is for water heating. 

Water is fed into the clothes washer through electrically operated water valves, one connected to 

a hot water supply line and a second connected to a cold water supply line. 
3-21

Traditional top-loading clothes washers have an opening on the top of the cabinet, 

covered by a door, which gives access to the inner basket where the load to be washed is placed. 

The inner basket is typically perforated and is surrounded by a larger outer tub which holds the 

water when the machine is running. The inner basket typically contains an agitator along a 

vertical axis, which undergoes a reversing circular motion. The motion of the agitator, which is 

powered by an electric motor, circulates the clothes vertically from the bottom to the top of the 

basket. The spinning action of the inner basket and the drain pump are also powered by the 

motor. Traditional top-loading clothes washers use more water than other types of washers, and 

therefore, consume more energy during heated wash cycles. They also typically extract less 

water from laundry during the spin cycle, which results in longer drying time and greater overall 

energy consumption. 

Non-agitator top-loading clothes washers may have a “wash plate” rather than an agitator 

to move the clothes within the basket. These top-loading clothes washers function by partially 

filling the tub with water for wash and rinse cycles and using very high spin speeds to extract 

rinse water from the clothes during the spin cycle. These top-loading clothes washers are more 

efficient in terms of water and energy than traditional top-loaders, but their high spin speeds that 

reduce drying time (and thus dryer energy consumption) may wrinkle clothing more than 

traditional top-loader spin speeds. These machines work best with low-foaming, high efficiency 

detergent. 

Front-loading clothes washers utilize a cylindrical tub or drum rotating on a horizontal or 

nearly-horizontal axis to wash clothes. Clothes are usually loaded along the axis of the 

cylindrical drum, which has led to this type of clothes washer to be called a front-loader. The 

clothes are cleaned by tumbling them in the water (i.e., clothes are lifted to the top of the drum 

by the rotation of the cylinder and then dropped into the water below.) The cylindrical drum is 

only partially filled with water for wash and rinse cycles. High spin speeds are used to extract the 

water from the clothes during the spin cycles. These clothes washers are typically more efficient 

in terms of water and energy than traditional top-loaders, but as with non-agitator top-loading 

clothes washers, their high spin speeds may wrinkle clothing. 

Manufacturers typically base CCWs on existing RCW platforms. This simplifies 

fabrication and assembly (e.g. commercial and residential clothes washers can be assembled on 

the same assembly line), and helps reduce the fixed costs associated with tooling, overhead etc. 

for the (typically) much lower CCW manufacturing volumes. However, some CCW components 

are selectively upgraded to make them more rugged, reliable, and vandal-resistant. Furthermore, 

the user interface is usually simplified (presenting the commercial user with fewer wash choices 

than a residential user) and the control system is designed to interface with various payment 

systems (ranging from coin slides to magnetic card readers). Lastly, the wash cycles may be 

redesigned to allow satisfactory operation in an approximately 30-minute wash cycle.
3-22

3.13.2 Technology Options 

In order to gain a deeper understanding of the technological improvements used to 

increase the efficiency of commercial clothes washers, DOE identified several possible 

technologies and examined the most common improvements used in today’s market. 

 For the technology assessment for this rulemaking, DOE considered a subset of the 

design options considered during the previous CCW standards rulemaking. Based on information 

published in recent trade publications, technical reports, manufacturer literature, and through 

product teardowns, DOE has observed that the results of the technology screening analysis 

performed during the previous rulemaking remain largely relevant for this rulemaking. After 

further analysis of the both the CCW and RCW markets and discussions with manufacturers of 

both CCWs and RCWs, DOE concluded that some of the technologies previously included in the 

analysis are not considered “technology options” for improving efficiency because they have 

negligible or no impact on the energy efficiency of a commercial clothes washer. Table 3.13.1

lists the specific technologies DOE eliminated from consideration and provides a brief 

justification for each. 

Table 3.13.1 Design Options Removed from Consideration 

Design Option Removed 

from Consideration Justification for Removal

Adaptive control systems 

(not including adaptive water 

fill controls)

DOE is not aware of any adaptive control systems for commercial clothes washers 

other than adaptive water fill controls, which are considered a separate design 

option. In addition, DOE has not found any indications of adaptive control 

systems being used to achieve energy savings in clothes washers. 

Horizontal-axis design This would be considered a design option only if DOE were to eliminate the 

product class distinction based on axis of loading. However, DOE has proposed 

retaining two separate product classes, top-loading and front-loading.

Improved horizontal-axis 

washer drum design

DOE does not have any data to suggest that changes to a horizontal-axis drum 

design could provide improvements in efficiency (e.g. drum shape or hole 

patterns).

Low standby power controls DOE is proposing amended energy standards based on the MEFJ2 metric, which 

would exclude measurement of standby and off mode power.

Reduced thermal mass DOE has not found any data indicating potential energy savings due to reduced 

thermal mass of the wash basket or outer wash tub. DOE believes any such energy 

savings would be negligible.

Silver ion injection DOE has determined that silver ion injection technology is no longer available on 

the market for residential clothes washers and has not been considered as a 

technology option for commercial clothes washers.

Tighter tub tolerance DOE’s engineering analyses for prior rulemakings have indicated that 

manufacturers have already achieved the maximum feasible reduction in spacing 

between the wash basket and outer was tub, leaving little, if any, room for 

improvement.

 Based on comments received from interested parties in response to the framework 

document, DOE added temperature-differentiated pricing controls as a technology option for 

consideration. Table 3.13.2 shows the complete list of technology options considered by DOE in 
3-23

the technology assessment. Detailed descriptions of each technology option are provided after 

the table. 

Table 3.13.2 Technology Options for Residential Clothes Washers 

1. Adaptive water fill

2. Advanced agitation concepts for top-loading machines 

3. Capacity increase

4. Direct-drive motor 

5. Higher spin speeds to reduce remaining moisture content 

6. Motor efficiency improvements

7. Ozonated laundering

8. Polymer bead cleaning

9. Spray rinse or similar water-reducing rinse technology 

10. Temperature-differentiated pricing controls

11. Thermostatically controlled mixing valves 

12. Water recirculation loop

3.13.2.1 Adaptive water fill 

 Adaptive water fill controls incorporate advanced control technologies to sense the size 

of the clothing load and adjust the water level accordingly. This technology option can overcome 

the tendency of consumers to manually select a water level greater than required for a given load. 

The most common implementation is a water level pressure sensor, which measures the pressure 

of the pooled water at the bottom of the tub. During the water fill portion of the cycle, dry 

clothing absorbs most of the incoming water. As the clothes become saturated, water begins 

pooling in the bottom of the wash tub. Once the pooled water reaches a predetermined height, the 

pressure will trigger the sensor to shut off the water flow. 

 

 This technology is used primarily to save water; however, energy is also saved by 

reducing the amount of hot water used in the wash cycle. DOE testing indicates that for toploading clothes washers, adaptive water fill controls can provide to up 50% energy and water 

savings compared to clothes washers with manual water fill controls. Nearly all front-load 

clothes washers already implement adaptive water fill controls, but higher efficiency can be 

achieved by implementing more sensitive water level pressure sensors. DOE estimates that such 

sensors can provide around 10% additional energy and water savings for those machines that 

already have adaptive water fill control. 

3.13.2.2 Advanced agitation concepts for top-loading machines 

 Advanced agitation designs replace the standard agitator found in traditional top-loading 

clothes washers. The most common implementation of this technology is a rotating “impeller” 

wash plate at the bottom of the drum. A traditional agitator requires clothing to be fully 

suspended in water; as the agitator rotates, the agitator vanes catch the clothing and move the 

garments through the watear. A rotating wash plate, however, requires a much lower water level 
3-24

inside the wash tub to clean the clothing properly. The wet clothing load sits on top of the wash 

plate, and as the wash plate rotates, raised fins catch the clothing along the bottom of the wash 

tub to rotate the garments. If the wash tub contains too much water and the clothing becomes 

suspended in water, the wash plate is unable to rotate the clothing effectively. DOE believes that 

the lack of a standard agitator may encourage machine overloading in a coin laundry 

environment, and that this technology is more susceptible to producing poorer wash performance 

when overloaded compared to a traditional agitator design. 

 As with adaptive water fill controls, impeller wash plates are used primarily to save 

water; however, energy is also saved by reducing the amount of hot water used in the wash 

cycle. In addition, the absence of the agitator increases the available capacity for the same size 

wash tub. Advanced agitation systems add considerable complexity to the control strategies for 

top-loading clothes washers.

 DOE has identified only one top-loading commercial clothes washer, manufactured by 

Whirlpool, that uses an advanced agitation concept. This clothes washer uses a rotating wash 

plate in combination with a vane-less agitator column, which is intended primarily to prevent 

over-loading of the machine by the end user. 

DOE testing indicates that the use of impeller wash plates can directly contribute around 

10% energy and water savings in top-loading clothes washers. 

3.13.2.3 Capacity increase 

Implementing a larger tub capacity can contribute to improved efficiency because a larger 

amount of clothing can be washed using an incremental increase in the quantity of water that is 

less than the incremental increase in capacity, therefore reducing the amount of water and energy 

per pound of clothing. DOE estimates that increased capacity can contribute around 10-15% 

energy and water savings. 

 End-users typically value larger capacity clothes washers, so increases in tub capacity 

provide increased end-user utility. However, a larger clothes container capacity may be 

considered a detriment to coin-operated laundry owners and route operators because a larger 

capacity tub may result in fewer wash cycles performed by the end-user customer. In competitive 

markets, coin-operated laundries may not be able to sustain higher vend fares to compensate for 

the lower number of “turns” per day. In addition, based on discussions with manufacturers, 

larger tub capacities may encourage the over-loading of machines by end-user customers. 

Because of these perceived negative impacts on utility, unlike the residential clothes washer 

market, manufacturers may not be willing to aggressively increase tub volumes in order to 

achieve higher efficiency levels. 

3.13.2.4 Direct-drive motor 

A baseline clothes washer typically uses a relatively simple and inexpensive single-speed 

or dual-speed capacitor-start induction motor, in combination with a mechanical transmission, 
3-25

and sometimes a pulley belt. A direct-drive motor can replace this conventional 

motor/transmission system. Rather than using a belt and/or transmission, a direct-drive motor is 

mounted directly to the wash tub along the axis of rotation, thus directly driving the rotation of 

the wash tub and eliminating transmission and/or gearbox losses. A direct drive motor for a 

clothes washer is typically a variable-speed, electronically-controlled permanent magnet motor. 

 DOE has observed increasingly widespread use of direct-drive motors in the residential 

clothes washer market, particularly for front-loading clothes washers and high-efficiency toploading clothes washers. DOE has identified only one front-loading commercial clothes washer, 

manufactured by LG, that uses a direct drive motor.

DOE testing indicates that direct-drive permanent magnet motors can reduce a clothes 

washer’s motor energy consumption by up to 50 percent; however, because motor energy 

represents such a small portion of overall clothes washer energy, total energy is reduced by 

roughly five percent.

3.13.2.5 Higher spin speeds to reduce remaining moisture content 

 The clothes washer test procedure requires measuring the remaining moisture content of 

the clothing load, which is calculated as: 

WI

WIWC

RMC  )(

where Wc is the weight of the test cloth immediately after completion of the washer spin cycle, 

and Wi is the “bone dry” weight of the test cloth. The RMC measurement is used to determine 

the estimated drying energy that would be needed to dry the clothes in an electric clothes dryer 

after the wash cycle is complete. The drying energy is included in the MEF equation to 

encourage manufacturers to reduce the remaining moisture content of the clothing coming out of 

the clothes washer, since that will subsequently result in clothes dryer energy savings. 

 The most common ways to reduce the RMC include increasing the spin speed of the 

wash basket for the final spin cycle, or using a longer spin cycle. Since increasing the duration of 

the spin cycle would result in a longer total cycle time, which would negatively impact consumer 

utility, manufacturers focus mainly on increasing the spin speed of the final spin cycle. 

Implementing faster spin speeds requires more advanced motor technologies, including 

electronic feedback sensing; stronger tub materials such as stainless steel; more robust bearings; 

and consumer safety protections such as locking lids. 

 Since mechanical water removal is more energy efficient than using heat to evaporate it, 

overall energy consumption for the combined wash and dry process is reduced. DOE testing 

indicates that faster spin speeds can reduce the calculated drying energy by 30% or more. 

Because drying energy constitutes a majority of the total energy captured in the MEF metric, this 

translates to 15-20% total energy savings. 
3-26

3.13.2.6 Increased motor efficiency 

Around 10 percent of the total energy factored into the MEF metric is used by the electric 

motor. The typical clothes washer has a 1⁄2 –2⁄3 horsepower single-speed or dual-speed capacitorstart induction motor. Drive system efficiency can be increased by using a buck-boost power 

factor correction circuit (PFC). This reduces inverter complexity and cost by controlling inverter 

output voltage through changing DC bus voltage without affecting performance. Using a PFC 

circuit can improve efficiency by more than 1.5 times from that of a single-phase induction 

motor.16 Three-phase induction motors have also been shown to consume 50 percent less power 

than universal commutator motors through better control of motor slip.17

3.13.2.7 Ozonated laundering 

 Ozonated laundering systems are available for large-scale commercial or institutional 

multi-load clothes washers. An ozone laundry system consists of a separate wall-mounted unit 

that pumps ambient air through an ozone generator, which is then directly injected into the wash 

water. The ozone may be injected into the cold water line or directly into the clothes washer 

through a port located in the clothes washer sump. Once in the water, the ozone reacts with 

insoluble soils, making them soluble, after which the mechanical action of the washing separates 

the soils from the fabric. Ozone also acts as a natural disinfectant, providing sanitization in 

colder water temperatures. 

 Ozone laundry technology saves energy because the water is not heated for lightly to 

moderately soiled laundry. For heavily soiled laundry, temperatures of 100–110 °F are used, 

which are lower than the temperatures required in a conventional washer. The reduced water 

temperature results in energy savings of up to 89 percent. Also, since fewer chemicals are used, 

the rinse time is minimized, thereby reducing the total cycle time and water consumption.18

3.13.2.8 Polymer bead cleaning 

Polymer bead cleaning uses the absorbent properties of nylon polymer beads to clean 

clothes. The nylon beads are added to the wash drum with a small amount of water and detergent 

to loosen the dirt or stains on the clothing. The clothes are tumbled with the polymer beads for 

around 45 minutes. The polarity of the nylon polymer attracts stains from the clothing. Under 

humid conditions, the polymer becomes absorbent. Dirt is attracted to the surface and then 

absorbed into the center of the bead. At the end of the cycle, the polymer beads are separated 

from the clothing through an inner drum/outer drum rotation process. 

 This technology was developed by Xeros Ltd. in the United Kingdom, and the company 

has recently introduced this technology to the U.S. market. Research conducted by Xeros in the 

United Kingdom has shown that this technology could reduce water consumption by up to 90 

percent compared to a traditional clothes washer. Including lower temperature cleaning and 

reduced detergent consumption, total laundry energy consumption could be reduced by up to 40 

percent.19
3-27

3.13.2.9 Spray rinse 

 Traditional vertical-axis clothes washers immerse the clothing completely in water during 

the wash and rinse phases of the cleaning cycle. Spray-rinse systems eliminate the immersion 

requirement by spraying rinse water into the drum while the wash basket is rotating. The 

centrifugal force of the rotating basket pins the clothes against the outer walls and subsequently 

forces the rinse liquid through the clothes. In the process, the rinsing liquid picks up remaining 

suds and transports them into the sump, from which they are drained. Since immersion or partial 

immersion is no longer necessary, less rinse water is required. 

 On clothes washers that provide only a cold rinse option, spray rinse technology provides 

water savings but no energy savings. On clothes washers that provide a warm rinse option, spray 

rinse will provide energy savings by reducing the quantity of hot water consumed during the 

rinse portion of the cycle. DOE testing indicates that spray rinse technology can provide around 

25% water savings. 

3.13.2.10 Temperature-differentiated pricing controls 

 Temperature-differentiated pricing controls offer the potential to incentivize energy 

savings by providing favorable vend pricing for lower-temperature settings. End-users may thus 

opt for lower-temperature settings than they might otherwise have selected under 

undifferentiated pricing. DOE’s market analysis confirmed the availability of this feature on 

multiple commercial clothes washer models from multiple manufacturers. DOE is not aware of 

any information regarding the extent to which this feature changes the temperature selection 

frequencies of end users.

3.13.2.11 Thermostatically controlled mixing valves 

Thermostatically controlled water valves (TCWVs) are clothes washer inlet valves that 

have the ability to sense and adjust the hot and cold supply water. This technology option 

achieves energy savings by more accurately controlling inlet water temperature for hot and warm 

fills. In a typical non-thermostatically-controlled water inlet system, two solenoid valves are used 

on a timed basis; one valve opens for hot water fills while the other opens for cold water fills. 

Both solenoid valves are opened if a warm water setting is selected. 

 Energy can be saved with a TCWV by either reducing the hot water temperature and/or 

reducing the warm water temperature. For example, the TCWV could be used to lower the hot 

wash temperature from 135 °F to 130 °F by mixing hot water (at 135 °F test inlet conditions) 

with cold water. Warm water temperature could be similarly reduced. The energy savings can 

vary widely depending on the test standard’s specified inlet hot water temperature, the selected 

temperature of the warm water, and the amount of hot water consumed.

The TCWV technology option has the potential to overstate the real-world energy 

savings, depending on the proximity of the water heater to the installed clothes washer. When 

tested under laboratory conditions, the clothes washer is typically close to the hot water source, 
3-28

whereas this is generally not the case in a home laundry setting. A large distance between the hot 

water source and the clothes washer can create a significant temperature drop due to line losses. 

Because of these line losses, a clothes washer with a TCWV may use more hot water than 

predicted on the basis of laboratory testing. Similarly, in a real-world application, savings will 

also vary with the temperature of incoming cold water. The unheated water temperature will vary 

with both geographic location and time of year. More energy would likely be saved in cases of 

warmer “cold” supply water, as a TCWV would then reduce the amount of time a hot water 

valve is open. The amount of savings is also dependent on the set point of the TCWV, the water 

heater set point, the temperature selection of the wash (hot or warm cycle), and the location of 

the sensing thermostat controlling the inlet water valves.

3.13.2.12 Water circulation loop 

 Water recirculation pumps are used in some high-efficiency clothes washers to 

recirculate water out of the bottom sump and spray it back into the wash drum during certain 

parts of the cycle. This reduces the amount of unusable standing water in the sump and reduces 

the amount of new water that must be periodically introduced into the clothes washer. 

 DOE testing indicates that water recirculation pumps can provide water and energy 

savings of around 5-10%.

3.13.3 Energy Efficiency 

In preparation for the screening and engineering analyses, DOE gathered data on the 

energy efficiency of commercial clothes washers available in the marketplace in November 

2013. While this section is not intended to provide a complete characterization of the energy 

efficiency of all commercial clothes washers currently available and in use, it does provide an 

overview of the vast majority of units available currently on the market. 

DOE used its Compliance Certification Database to identify CCW models on the 

market.20 As of November 2013, DOE’s database contains 253 CCW models. DOE excluded any 

models in the database that do not achieve the new standard levels effective January 8, 2013. A 

large number of these models belong to the same basic model. DOE defines a “basic model” as 

all units of a given type of covered product (or class thereof) manufactured by one manufacturer, 

having the same primary energy source, and which have essentially identical electrical, physical, 

and functional (or hydraulic) characteristics that affect energy consumption, energy efficiency, 

water consumption, or water efficiency. (10 CFR 430.2) After filtering out non-unique basic 

models and models that do not achieve the January 8, 2013 standards, DOE identified 8 toploading basic models and 11 front-loading basic models available on the market. 

Figure 3.13.1 displays the distribution of commercial clothes washers as a function of 

MEF, as measured using appendix J1. As shown in the figure, no overlap exists between the 

MEF ratings of top-loading and front-loading CCWs. Top-loading MEF values range from 1.60 

to 1.85, and front-loading MEF values range from 2.20 to 2.60. 
3-29

 

Figure 3.13.1 Distribution of Commercial Clothes Washers by MEF, November 2013 

Figure 3.13.2 displays the distribution of commercial clothes washers as a function of 

WF, as measured using appendix J1. As shown in the figure, no overlap exists between the WF 

ratings of top-loading and front-loading CCWs. Top-loading WF values range from 6.2 to 8.5, 

and front-loading WF values range from 3.7 to 4.5. 

Figure 3.13.2 Distribution of Commercial Clothes Washers by WF, November 2013 

0

1

2

3

4

5

6

1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7

Frequency

MEF (based on Appendix J1)

Top-Loading

Front-Loading

0

1

2

3

4

5

3.03.43.84.24.65.05.45.86.26.67.07.47.88.28.6

Frequency

WF (based on Appendix J1)

Top-Loading

Front-Loading
3-30

REFERENCES 

 

1

 Association of Home Appliance Manufacturers website: www.aham.org. 

2

 Coin Laundry Association website: For more information, visit www.coinlaundry.org 

3

 Multi-Housing Laundry Association website: http://www.mla-online.com/

4

 Technical Support Document accompanying the January 8, 2010 final rule is available at 

http://www.regulations.gov/#!documentDetail;D=EERE-2006-STD-0127-0118.

5

 CEE, Commercial Family-Sized Washers: An Initiative Description, 1998. No longer 

accessible online. 

6

 Technical Support Document accompanying the January 8, 2010 final rule is available at 

http://www.regulations.gov/#!documentDetail;D=EERE-2006-STD-0127-0118.

7

 CSA C360-13 available at http://shop.csa.ca/en/canada/energy-efficiency/c360-

13/invt/27019792013. The CSA C360-03 test method has been superseded by C360-13 and 

is no longer available online. 

8

 Internation Energy Agency website: http://www.iea.org/. 

9

 Commission of the European Communities. 2008. “Commission Regulation (EC) No. 

1275/2008 of 17 December 2008 implementing Directive 2005/32/EC of the European 

Parliament and of the Council with regard to ecodesign requirements for standby and off 

mode electric power consumption of electrical and electronic household and office 

equipment.” December 18, 2008. Available online at http://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2008:339:0045:0052:EN:PDF (Accessed 

December 2009)

10 ENERGY STAR criteria for commercial clothes washers: 

http://www.energystar.gov/index.cfm?c=clotheswash.pr_crit_clothes_washers 

11 Consortium for Energy Efficiency website: http://www.cee1.org.

12 Federal Energy Management Program website: http://www.eere.energy.gov/femp. 

13 Database of State Incentives for Renewables & Efficiency website: www.dsireusa.org

14 U.S. Census Bureau. “New Residential Construction.” Accessed online at 

http://www.census.gov/construction/nrc/. 
3-31

 

15 U.S. International Trade Commission. “USITC Interactive Tariff and Trade DataWeb.” 

Accessed online at http://dataweb.usitc.gov, Feb. 2014. 

16 Hoghani, J.S. and Heidari, M. “High Efficient Low Cost Induction Motor Drive for 

Residential Applications.” 2006 International Symposium of Power Electronics, Electrical 

Drives, Automation and Motion. IEEE. pp. 1399-1402. May 26, 2006.

17 Bourgeois, J.-M. “Energy Saving Motor Controls for Washing Machines.” Electronik. Vol. 

50, No. 12, pp. 58-61. July 12, 2001. 

18 “Cold Facts: Textile/Linen Wear Study of Traditional versus EcoTex Ozone Laundry Wash 

Practices.” Clearwater Tech, LLC. Available at http://www.cwtozone.com. 

19 Xeros Bead Cleaning website: http://www.xeroscleaning.com/

20 DOE Compliance Certification Database: http://www.regulations.doe.gov/certificationdata/CCMS-79222370561.html
4-i

CHAPTER 4. SCREENING ANALYSIS 

TABLE OF CONTENTS 

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

4.2 DISCUSSION OF TECHNOLOGY OPTIONS .............................................................. 4-1

4.2.1 Screened-Out Technology Options .................................................................................. 4-1

4.2.1.1 Ozonated laundering ...................................................................................... 4-3

4.2.1.2 Plastic particle cleaning ................................................................................. 4-3

4.2.2 Remaining Design Options .............................................................................................. 4-4

LIST OF TABLES 

Table 4.2.1 Retained Design Options for Commercial Clothes Washers ............................... 4-4
4-1

CHAPTER 4. SCREENING ANALYSIS 

4.1 INTRODUCTION 

 This chapter discusses the screening analysis conducted by the U.S. Department of 

Energy (DOE) of the technology options identified in the market and technology assessment for 

commercial clothes washers (CCW) (chapter 3 of this technical support document (TSD)). In the 

market and technology assessment, DOE presented an initial list of technology options that can 

be used to reduce energy consumption of CCWs. The goal of the screening analysis is to identify 

any technologies that will be eliminated from further consideration in the rulemaking analyses. 

The candidate technology options are assessed based on DOE analysis as well as inputs 

from stakeholders including manufacturers, trade organizations, and energy efficiency advocates. 

Technology options that are judged to be viable approaches for improving energy efficiency are 

retained as inputs to the subsequent engineering analysis, and are designated as design options. 

Technology options that are not incorporated in commercial clothes washer equipment or in 

working prototypes or that fail to meet certain criteria regarding practicability to manufacture, 

install and service, impacts on equipment utility or availability, or 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 36974 (July 15, 1996). The rationale for either screening out or 

retaining each technology option is detailed in the following sections. 

4.2 DISCUSSION OF TECHNOLOGY OPTIONS 

For commercial clothes washers, the screening criteria specified in section 4.1 were 

applied to the technology options to either retain or eliminate each technology from the 

engineering analysis. DOE derived the list of technology options as a subset of options 

considered in the previous CCW energy conservations standards rulemaking. DOE has included 

temperature-differentiated pricing controls as an additional technology option based on 

comments received in response to the framework document for this rulemaking. 

4.2.1 Screened-Out Technology Options 

The following section describes the 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), Pub. L. 94-

163, 42 U.S.C. 6291 et seq. (42 U.S.C. 6311-6317) EPCA provides criteria for prescribing new 
4-2

or amended standards, which will achieve the maximum improvement in energy efficiency that 

the Secretary of Energy determines is technologically feasible. (42 U.S.C. 6295(o)(2)(A) and 

6316(a)) It also establishes guidelines for determining whether a standard is economically 

justified. (42 U.S.C. 6295(o)(2)(B) and 6316(a)) Pursuant to 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

standard. Under the guidelines, at the outset of a rulemaking, 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 commercially available 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 commercially available 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 or product availability. If a technology is determined to 

have significant adverse impact on the utility of the equipment to significant subgroups of 

consumers, or results in the unavailability of any covered equipment type with performance 

characteristics (including reliability), features, size, capacities, and volumes that are substantially 

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

considered further. 

(4) Impacts on Health or Safety. If it is determined that a technology will have 

significant adverse impacts on health or safety, it will not be considered further. 

 Based on these factors, DOE screened out ozonated laundering and polymer bead 

cleaning for the reasons that follow. 
4-3

4.2.1.1 Ozonated laundering 

DOE has tentatively concluded that ozonated laundering presents unacceptable problems 

with the following EPCA screening criteria:

(1) Technological feasibility. Although ozone laundering systems are commercially 

available as add-on systems for large-scale commercial or institutional multi-load clothes 

washers, DOE is unaware of any instances of this technology being incorporated into any 

commercially available products or prototypes for the type of commercial clothes washers 

covered by DOE standards, which include primarily single-load soft-mounted clothes washers 

intended for use in coin-operated laundries and multi-family housing settings. 

 (2) Practicability to manufacture, install, and service. DOE believes that it would not 

be practicable to manufacture, install and service this technology on the scale necessary to serve 

the U.S. commercial clothes washer market at the time of the effective date of an amended 

standard. 

 (4) Impacts on Health or Safety. Because this technology has not been implemented in 

coin-operated laundries our multi-family housing settings, DOE is unable to assess whether 

ozone laundry technology would have any adverse impacts on end-user health or safety. 

4.2.1.2 Plastic particle cleaning 

DOE has tentatively concluded that plastic particle cleaning presents unacceptable 

problems with the following EPCA screening criteria: 

(1) Technological feasibility. Although plastic particle cleaning systems are 

commercially available for large-scale commercial or institutional multi-load clothes washers, 

DOE is unaware of any instances of this technology being incorporated into any commercially 

available products or working prototypes for the type of commercial clothes washers covered by 

DOE standards, which include primarily single-load soft-mounted clothes washers intended for 

use in coin-operated laundries and multi-family housing settings. 

 (2) Practicability to manufacture, install, and service. Plastic particle cleaning is still 

in the research stage for single-load residential-style clothes washers, so DOE believes that it 

would not be practicable to manufacture, install and service this technology on the scale 

necessary to serve the relevant market at the time of the effective date of an amended standard. 

In addition, the requirements for the potential purchase, handling, reusing, recycling, and/or 

disposal of the plastic particles are unknown.

 (3) Impacts on product utility or product availability. DOE is unable to assess the 

potential impact of plastic particle cleaning on product utility. DOE notes that as currently 

implemented, plastic particle cleaning systems are operated by trained staff in large-scale 
4-4

commercial or institutional laundry settings. Because of the significantly different operating 

protocol compared to commercial clothes washers currently on the market, DOE does not believe 

that such technology could be used by untrained users, which would include most users of coinoperated laundries and multi-family housing laundry equipment. DOE is also unable to evaluate 

impacts on cleaning performance compared to current commercial clothes washer products on 

the market.

4.2.2 Remaining Design Options 

Table 4.2.1 lists the technology options for commercial clothes washers that were 

retained by DOE and subsequently designated as design options.

Table 4.2.1 Retained Design Options for Commercial Clothes Washers 

1. Adaptive water fill

2. Advanced agitation concepts for top-loading machines 

3. Capacity increase

4. Direct-drive motor 

5. Higher spin speeds to reduce remaining moisture content 

6. Motor efficiency improvements

7. Spray rinse or similar water-reducing rinse technology 

8. Temperature-differentiated pricing controls

9. Thermostatically controlled mixing valves 

10. Water recirculation loop
4-5

REFERENCES 

(None) 
5-i

CHAPTER 5. ENGINEERING ANALYSIS 

TABLE OF CONTENTS 

5.1 INTRODUCTION ........................................................................................................... 5-1

5.2 TECHNOLOGIES UNABLE TO BE INCLUDED IN THE ANALYSIS ..................... 5-2

5.3 PRODUCT CLASSES ANALYZED .............................................................................. 5-2

5.4 EFFICIENCY LEVELS................................................................................................... 5-3

5.4.1 Efficiency Metrics ............................................................................................................ 5-3

5.4.2 Appendix J1/Appendix J2 Efficiency Metric Translations .............................................. 5-5

5.4.3 Baseline Units .................................................................................................................. 5-6

5.4.4 Higher Efficiency Levels ................................................................................................. 5-6

5.4.5 Maximum Technologically Feasible Efficiency Levels .................................................. 5-7

5.5 METHODOLOGY OVERVIEW .................................................................................... 5-7

5.5.1 AHAM Data Request ....................................................................................................... 5-7

5.5.2 Product Testing Methodology.......................................................................................... 5-8

5.5.3 Product Teardown Methodology ..................................................................................... 5-8

5.5.3.1 Selection of Units ........................................................................................... 5-9

5.5.3.2 Generation of Bill of Materials ...................................................................... 5-9

5.5.3.3 Cost Structure of the Spreadsheet Models ..................................................... 5-9

5.5.3.4 Cost Model and Definitions ......................................................................... 5-11

5.5.3.5 Cost Model Assumptions ............................................................................. 5-12

5.5.4 Manufacturer Interviews ................................................................................................ 5-12

5.6 ANALYSIS AND RESULTS ........................................................................................ 5-12

5.6.1 AHAM Data ................................................................................................................... 5-12

5.6.2 Product Testing Results ................................................................................................. 5-12

5.6.2.1 Active Mode Testing.................................................................................... 5-14

5.6.2.2 Standby Mode and Off Mode Power ........................................................... 5-18

5.6.2.3 Cleaning Performance Testing ..................................................................... 5-19

5.6.3 Product Teardowns ........................................................................................................ 5-34

5.6.3.1 Top-Loading Commercial Clothes Washers ................................................ 5-34

5.6.3.2 Front-Loading Commercial Clothes Washers ............................................. 5-37

5.6.4 Cost-Efficiency Curves .................................................................................................. 5-41

5.6.4.1 Top-Loading Cost-Efficiency Curves .......................................................... 5-41

5.6.4.2 Front-Loading Cost-Efficiency Curves ........................................................ 5-42

LIST OF TABLES 

Table 5.4.1 Revisions to the Clothes Washer Test Procedure that Significantly Affect 

Efficiency Metrics ................................................................................................ 5-4

Table 5.4.2 Appendix J1 and Appendix J2 Efficiency Level Translations for Top-Loading 

CCWs ................................................................................................................... 5-5
5-ii

Table 5.4.3 Appendix J1 and Appendix J2 Efficiency Level Translations for FrontLoading CCWs..................................................................................................... 5-6

Table 5.5.1 Major Manufacturing Processes ............................................................................. 5-10

Table 5.6.4 Top-Loading Commercial Clothes Washer Test Unit Features .............................. 5-13

Table 5.6.5 Front-Loading Commercial Clothes Washer Test Unit Features ........................... 5-14

Table 5.6.6 Commercial Clothes Washer Standby Power Measurements................................. 5-19

Table 5.6.7 Production Cost Distribution for Baseline Top-Loading CCW .............................. 5-35

Table 5.6.8 Materials Cost Distribution for Baseline Top-Loading CCW ................................ 5-35

Table 5.6.9 Baseline Front-Load Clothes Washer Production Cost Distribution ...................... 5-39

Table 5.6.10 Baseline Front-Load Clothes Washer Materials Cost Distribution ...................... 5-39

Table 5.6.11 Incremental Manufacturing Costs for Top-Loading CCWs Based on MEFJ2 ...... 5-41

Table 5.6.12 Incremental Manufacturing Costs for Front-Loading CCWs Based on MEFJ2 .... 5-43

LIST OF FIGURES

Figure 5.5.1 Manufacturing Cost Assessment Stages ................................................................ 5-10

Figure 5.6.1 Relationship Between Equivalent MEFJ2 (Appendix J2) and Rated MEF 

(Appendix J1) for Top-Loading CCWs ............................................................. 5-15

Figure 5.6.2 Relationship Between Equivalent IWF (Appendix J2) and Rated WF 

(Appendix J1) for Top-Loading CCWs ............................................................. 5-16

Figure 5.6.3 Relationship Between Equivalent MEFJ2 (Appendix J2) and Rated MEF 

(Appendix J1) for Front-Loading CCWs ........................................................... 5-17

Figure 5.6.4 Relationship Between Equivalent IWF (Appendix J2) and Rated WF 

(Appendix J1) for Front-Loading CCWs ........................................................... 5-18

Figure 5.6.5 AHAM Soil/Stain Test Strip ................................................................................. 5-20

Figure 5.6.6 Total Cleaning Score vs. Appendix J2 MEFJ2 Efficiency Level for TopLoading CCWs................................................................................................... 5-22

Figure 5.6.7 Total Cleaning Score vs. Appendix J2 IWF Efficiency Level for TopLoading CCWs................................................................................................... 5-22

Figure 5.6.8 Total Cleaning Score vs. Cycle Time for Top-Loading CCWs ............................ 5-23

Figure 5.6.9 Total Cleaning Score vs. Appendix J2 MEFJ2 Efficiency Level for FrontLoading CCWs................................................................................................... 5-24

Figure 5.6.10 Total Cleaning Score vs. Appendix J2 IWF Efficiency Level for FrontLoading CCWs................................................................................................... 5-24

Figure 5.6.11 Total Cleaning Score vs. Cycle Time for Front-Loading CCWs ........................ 5-25

Figure 5.6.12 Rinse Score vs. Appendix J2 MEFJ2 Efficiency Level for Top-Loading 

CCWs ................................................................................................................. 5-26

Figure 5.6.13 Rinse Score vs. Appendix J2 IWF Efficiency Level for Top-Loading 

CCWs ................................................................................................................. 5-27

Figure 5.6.14 Rinse Score vs. Cycle Time for Top-Loading CCWs ......................................... 5-28

Figure 5.6.15 Rinse Score vs. Appendix J2 MEFJ2 Efficiency Level for Front-Loading 

CCWs ................................................................................................................. 5-29

Figure 5.6.16 Rinse Score vs. Appendix J2 IWF Efficiency Level for Front-Loading 

CCWs ................................................................................................................. 5-29
5-iii

Figure 5.6.17 Rinse Score vs. Cycle Time for Front-Loading CCWs ....................................... 5-30

Figure 5.6.18 Sand Removal Score vs. Appendix J2 MEFJ2 Efficiency Level for TopLoading CCWs................................................................................................... 5-31

Figure 5.6.19 Sand Removal Score vs. Appendix J2 IWF Efficiency Level for TopLoading CCWs................................................................................................... 5-32

Figure 5.6.20 Sand Removal Score vs. Appendix J2 MEFJ2 Efficiency Level for FrontLoading CCWs................................................................................................... 5-33

Figure 5.6.21 Sand Removal Score vs. Appendix J2 IWF Efficiency Level for FrontLoading CCWs................................................................................................... 5-33

Figure 5.6.22 Production Cost Distribution for Baseline Top-Loading CCW .......................... 5-35

Figure 5.6.23 Baseline Top-Load Standard Clothes Washer Materials Cost Distribution ........ 5-35

Figure 5.6.24 Production Cost Distribution for Efficiency Level 1 Front-Loading 

Commercial Clothes Washer ............................................................................. 5-39

Figure 5.6.25 Materials Cost Distribution for Efficiency Level 1 Front-Loading 

Commercial Clothes Washer ............................................................................. 5-39

Figure 5.6.26 Cost-Efficiency Curve Based on MEFJ2 for Top-Loading Commercial 

Clothes Washer .................................................................................................. 5-41

Figure 5.6.27 Cost-Efficiency Curve Based on MEFJ2 for Front-Loading Commercial 

Clothes Washer .................................................................................................. 5-43
5-1

CHAPTER 5. ENGINEERING ANALYSIS 

5.1 INTRODUCTION 

After conducting the screening analysis, the U.S. Department of Energy (DOE) 

performed an engineering analysis based on the remaining design options. The engineering 

analysis consists of estimating the energy and water consumption and costs of commercial 

clothes washers at various levels of increased efficiency. 

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 cost and energy 

efficiency data, which DOE received from the Association of Home Appliance Manufacturers 

(AHAM) and qualified and supplemented through teardown analysis and manufacturer 

interviews. The primary output of the engineering analysis is a set of cost-efficiency curves. 

 DOE typically structures its engineering analysis around one of three methodologies, 

described as follows: 

(1) Design-option approach, which provides the incremental costs of adding to a baseline 

model design options that will improve its efficiency; 

(2) Efficiency-level approach, which provides the relative costs of achieving increases in 

energy efficiency levels, without regard to the particular design options used to achieve 

such increases; and 

(3) Cost-assessment (or reverse-engineering) approach, which provides “bottom-up” 

manufacturing cost assessments for achieving various levels of increased efficiency, 

based on detailed data regarding costs for parts and material, labor, shipping/packaging, 

and investment for models that operate at particular efficiency levels. 

DOE conducted the engineering analyses for the top-loading and front-loading product 

classes using a combination of the efficiency-level approach and cost-assessment approach. The 

cost-assessment approach provides an accurate means for estimating a single manufacturer’s 

incremental manufacturing costs for achieving various levels of increased efficiency. This 

approach involves physically disassembling commercially available products to develop costefficiency relationships for each manufacturer’s product lines. Because each manufacturer may 

choose a different path to achieve higher levels of efficiency, an efficiency-level approach 

produces an industry-wide cost-efficiency relationship for each product class. DOE developed 

cost-efficiency relationships for the top-loading and front-loading product classes by calculating 

the market-weighted average of the individual cost-efficiency relationships it developed for each 

manufacturer. 
5-2

5.2 TECHNOLOGIES UNABLE TO BE INCLUDED IN THE ANALYSIS 

 In performing the engineering analysis, DOE did not consider for analysis certain 

technologies that met the screening criteria, described in chapter 4 of this TSD, but were unable 

to be evaluated for one or more of the following reasons: (1) data are not available to evaluate 

the energy efficiency characteristics of the technology; (2) available data suggested that the 

efficiency benefits of the technology would be negligible; or (3) certain technologies cannot be 

measured according to the conditions and methods specified in the DOE clothes washer test 

procedure (10 CFR 430, subpart B, appendix J2; hereafter, “appendix J2”).

 As described in the Notice of Proposed Rulemaking (NOPR), interested parties suggested 

adding temperature-differentiated pricing controls to the list of design options for consideration. 

Such controls would offer the potential to incentive energy savings by providing favorable vend 

pricing for lower-temperature wash/rinse settings. DOE’s market analysis confirmed the 

availability of this feature on multiple clothes washer models from multiple manufacturers. 

However, DOE’s current test procedure at appendix J2 uses a fixed set Temperature Use Factors 

(TUFs), which represent the percentage of time an end-user would select each wash/rinse 

temperature selection available on the clothes washer. Because the TUFs in the test procedure 

are fixed, a clothes washer with temperature-differentiated pricing controls would be tested the 

same way as an identical clothes washer without temperature-differentiated pricing controls. 

Therefore, the energy savings of this technology cannot be measured according to the conditions 

and methods specified in the DOE clothes washer test procedure. Accordingly, DOE did not 

analyze this technology option in its NOPR analysis. 

5.3 PRODUCT CLASSES ANALYZED 

DOE separates commercial clothes washers into product classes and has formulated 

separate energy conservation standards for each class. The criteria for separation into different 

product classes are: (1) type of energy used; (2) capacity; or (3) other performance-related 

features such as those that provide utility to the consumer, or others deemed appropriate by the 

Secretary that would justify the establishment of a separate energy conservation standard. (42 

U.S.C. 6295 (q) and 6316(a)) 

 As discussed in chapter 3 of this TSD, DOE has separated commercial clothes washers 

into the following two product classes for this rulemaking: 

 Top-loading 

 Front-loading 
5-3

5.4 EFFICIENCY LEVELS 

5.4.1 Efficiency Metrics 

The energy conservation standard levels for commercial clothes washers are currently 

defined by two factors—modified energy factor (MEF) and water factor (WF). MEF is 

calculated as the clothes container capacity in cubic feet divided by the sum, expressed in 

kilowatt-hours (kWh), of: (1) the total weighted per-cycle hot water energy consumption; (2) the 

total weighted per-cycle machine electrical energy consumption; and (3) the per-cycle energy 

consumption for removing moisture from a test load. WF is calculated as the weighted per-cycle 

water consumption of the cold wash/cold rinse cycle divided by the clothes container capacity in 

cubic feet. 

 The equations for calculating MEF and WF are as follows: 

where: 

C= Clothes container capacity (cubic feet) 

HET = Total weighted per-cycle hot water energy consumption (kWh) 

MET = Total weighted per-cycle machine electrical energy consumption (kWh) 

DE= Per-cycle energy consumption for removal of moisture from test load (kWh) 

where: 

QT= Total weighted per-cycle water consumption of cold wash/cold rinse cycles (gallons) 

C = Clothes container capacity (cu.ft.).

 On March 7, 2012, DOE published a final rule amending its test procedures for 

residential clothes washers (“March 2012 final rule”). (77 FR 13888) The March 2012 final rule 

included minor amendments to the test procedure at 10 CFR 430, subpart B, appendix J1 and 

also established a new test procedure at appendix J2. Beginning March 7, 2015, manufacturers of 

commercial clothes washers may use either appendix J1 or appendix J2 to demonstrate 

compliance with the current standards established by the January 2010 final rule. Manufacturers 

using appendix J2 would be required to use the conversion equations proposed in a separate test 

procedure NOPR to translate the measured efficiency metrics into equivalent appendix J1 

MEF =

C

HET + MET + DE

WF =

QT

C
5-4

values.1The use of appendix J2 would be required to demonstrate compliance with any amended 

energy conservation standards to be published in a final rule by January 1, 2015, and the 

conversion equations would no longer be used at that time. 

 In the March 2012 final rule, DOE incorporated standby and off mode energy use with 

active mode energy use into an integrated metric, integrated modified energy factor (IMEF),

contained in the new appendix J2 test procedure. Appendix J2 also retains provisions for 

calculating modified energy factor, which DOE designates as MEFJ2. As described in greater 

detail in the NOPR, DOE proposes amended energy standards based on MEFJ2 as measured 

using appendix J2. 

 Table 5.4.1 shows the major revisions included in the amended test procedure that 

significantly affect the calculated efficiency metrics for commercial clothes washers. 

Table 5.4.1 Revisions to the Clothes Washer Test Procedure that Significantly Affect 

Efficiency Metrics 

Factors Affecting MEF Appendix J1 Appendix J2

Capacity measurement

For top-loading clothes 

washers: Innermost diameter 

of the tub cover. (Defined as 

“Fill Level 3” in DOE 

guidance document2)

For top-loading clothes 

washers: Uppermost edge of the 

rotating portion, including any 

balance ring. (Defined as “Fill 

Level 2” in DOE guidance 

document)

Dryer energy calculation

Based on Load Adjustment 

Factor (LAF) of 0.52 

multiplied by maximum test 

load weight

Based on weighted average load 

size according to existing Load 

Usage Factors (LUF).

Dryer Usage Factor (DUF) DUF = 0.84 DUF = 0.91

Factors Affecting WF / IWF Appendix J1 (WF) Appendix J2 (IWF)

Capacity measurement See above. See above.

Water Consumption

Based on water usage of 

Cold Wash / Cold Rinse 

cycles only.

Based on weighted average of 

water usage of all Wash / Rinse 

temperature combinations

 

1

 DOE published a test procedure NOPR for commercial clothes washers on February 11, 2014 (79 FR 8112). 

Additional details are available at DOE’s rulemaking webpage: 

http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx?ruleid=86. All rulemaking documents 

are also available at www.regulations.gov, under Docket # EERE-2013-BT-TP-0002. 

2

 DOE issued guidance in May 2010 on what is considered the clothes container for purposes of measuring clothes 

container capacity. The guidance is available at the residential clothes washer rulemaking website, 

http://www1.eere.energy.gov/guidance/detail_search.aspx?IDQuestion=568&pid=2&spid=1
5-5

 As described in the NOPR, DOE is proposing amended water efficiency standards for 

commercial clothes washers based on the IWF metric, which incorporates water consumption 

from all the temperature cycles included as part of the energy test cycle in appendix J2. DOE 

believes that the IWF metric provides a more representative measure of water consumption than 

the WF metric. The equation for IWF is as follows:

where: 

QT= Total weighted per-cycle water consumption of all wash cycles (gallons) 

C = Clothes container capacity (cu.ft.).

Current CCW product ratings are based on MEF and WF as measured using appendix J1. 

To define energy efficiency levels based on MEFJ2 and IWF as measured using appendix J2, 

DOE first conducted testing on a representative sample of CCWs, spanning the entire range of 

available efficiency levels, to determine each model’s corresponding appendix J2 rating. DOE 

then established baseline and higher efficiency levels based on the appendix J2 MEFJ2 and IWF 

ratings. 

For convenience, throughout this TSD, DOE displays both the appendix J1 and appendix 

J2 ratings corresponding to each efficiency level.

5.4.2 Appendix J1/Appendix J2 Efficiency Metric Translations 

Based on the test results described in section 5.6.2 of this TSD chapter, DOE translated 

the appendix J1 MEF/WF efficiency levels into appendix J2 MEFJ2/IWF efficiency levels for 

consideration in the NOPR analysis. Table 5.4.2 shows the resulting efficiency level translations 

for top-loading CCWs. Table 5.4.3 shows the resulting efficiency level translations for frontloading CCWs. 

Table 5.4.2 Appendix J1 and Appendix J2 Efficiency Level Translations for Top-Loading 

CCWs 

Efficiency Level Efficiency Level 

Source

Appendix J1 Metrics Appendix J2 Metrics

MEF WF MEFJ2 IWF

Baseline DOE Standard 1.60 8.5 1.15 8.9

Level 1 Gap Fill 1.70 8.4 1.35 8.8

Level 2 Maximum Available 1.85 6.9 1.55 6.9

IWF =

QT

C
5-6

Table 5.4.3 Appendix J1 and Appendix J2 Efficiency Level Translations for Front-Loading 

CCWs 

Efficiency Level Efficiency Level 

Source

Appendix J1 Metrics Appendix J2 Metrics

MEF WF MEFJ2 IWF

Baseline DOE Standard 2.00 5.5 1.65 5.2

Level 1 CEE Tier 2 2.20 4.5 1.80 4.5

Level 2 CEE Tier 3 2.40 4.0 2.00 4.1

Level 3 Maximum Available 2.60 3.7 2.20 3.9

 The following sections describe the baseline and higher efficiency levels in greater detail.

5.4.3 Baseline Units 

DOE selects baseline units as reference points for each product class. 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 energy conservation standards and provides basic 

consumer utility. DOE uses the baseline units in the engineering analysis and the LCC and PBP 

analysis. To determine energy savings and changes in price, DOE compares each higher energy 

efficiency design option with the baseline unit. 

DOE established the baseline level for the top-loading product class based on the 1.60 

MEF and the 8.5 WF requirements specified by current Federal energy conservation standards, 

which became effective for commercial clothes washers manufactured on or after January 8, 

2013. For the front-loading product class, DOE established the baseline level based on the 2.00 

MEF and 5.5 WF requirements specified by current Federal energy conservation standards.

5.4.4 Higher Efficiency Levels 

For the top-loading and front-loading product classes, DOE considered efficiency levels 

higher than baseline levels based on the Consortium for Energy Efficiency (CEE) former Tier 

levels for commercial clothes washers.3

The highest efficiency levels were defined by the 

maximum available technology that DOE could identify on the market. For the top-loading 

product class, DOE added an intermediate “gap fill” levels between the baseline and maximum 

available efficiency levels. 

 

3

 CEE has historically maintained a Commercial Clothes Washer Initiative that encourages the purchase and use of 

energy and water efficient commercial clothes washers. Given recent changes to federal minimum efficiency 

standards and ENERGY STAR criteria for commercial clothes washers, the CEE Commercial Clothes Washer 

Specification is not currently active as of the publication date of this NOPR. As a result, publication of the 

associated qualifying product list is also temporarily suspended. 
5-7

5.4.5 Maximum Technologically Feasible Efficiency Levels 

Under EPCA, DOE is required to consider the maximum technologically feasible level 

(“max-tech”). DOE determines max-tech levels based on technologies that are either 

commercially available or have been demonstrated as working prototypes. If the max-tech design 

meets DOE’s screening criteria, DOE considers the design in further analysis. DOE also 

considers consumer utility and availability of features, which may be met by a niche product, as 

required by EPCA. 

DOE identified the max-tech levels for top-loading and front-loading commercial clothes 

washers based on the maximum performance of products currently on the market in the United 

States. DOE has observed that the max-tech units on the market use a combination of reduced 

water volumes, reduced water temperatures, extended cycle times, higher spin speeds. DOE is 

not aware of any additional design options that could be used to increase the efficiency beyond 

the max-tech levels without causing potential negative effects on consumer utility. Nor is DOE 

aware of any working prototype clothes washers that exceed the efficiency levels of the max-tech 

units on the market in the United States. Therefore, DOE has tentatively determined that the 

“max available” efficiency levels identified for top-loading and front-loading commercial clothes 

washers correspond to the maximum technologically feasible efficiency levels. 

5.5 METHODOLOGY OVERVIEW 

DOE conducted energy and water testing, cleaning and rinsing performance testing, and 

standby power testing on a representative sample of top-loading and front-loading CCWs 

spanning a range of efficiencies and manufacturers. The objective of this testing was to gain 

insights into the relative performance differences among the CCW models available on the 

market. DOE conducted its testing at an independent laboratory with significant experience 

performing clothes washer testing. Following testing, DOE performed detailed product 

teardowns and cost modeling to generate cost-efficiency curves for each product class. Finally, 

DOE conducted interviews with commercial clothes washer manufacturers to better understand 

and explain the differences among manufacturers in baseline unit construction, design strategies 

for achieving higher efficiency levels, and overall cost structures. 

5.5.1 AHAM Data Request 

 In support of this rulemaking effort, DOE requested incremental cost data from AHAM 

for commercial clothes washers. The AHAM data request is presented in Appendix 5-A to this 

TSD. The data represent the average incremental production cost to improve a baseline unit to a 

specified efficiency level. In addition, DOE requested shipments, shipment-weighted average 

efficiency, and market share efficiency data. 
5-8

5.5.2 Product Testing Methodology 

 DOE conducted performance testing on a range of CCW models for the following 

purposes: 

 Determine equivalent MEFJ2 and IWF ratings using the new appendix J2 test procedure; 

 Verify performance trends that are apparent in the publicly available data; 

 Develop a better understanding of the operational characteristics of CCWs; and 

 Evaluate potential impacts on cleaning and rinsing performance as a result of proposed 

higher efficiency levels. 

 DOE conducted energy and water testing, cleaning and rinsing performance testing, and 

standby power testing on a representative sample of top-loading and front-loading CCWs 

spanning a range of efficiencies and manufacturers. DOE conducted its testing at an independent 

laboratory with significant experience performing clothes washer testing. DOE used the 

following test procedures for its testing: 

 10 CFR 430, subpart B, Appendix J1, 

 10 CFR 430, subpart B, Appendix J2 

 AHAM HLW-1-2010, Performance Evaluation Procedures for Household Clothes 

Washers4

Within AHAM HLW-1-2010, DOE performed the following tests: 

 Soil/Stain Removal (Section 6) 

 Sand Removal (Section 8) 

 Rinsing Effectiveness Test (Annex E) 

Section 5.6.2 provides detailed results of this testing. 

5.5.3 Product Teardown Methodology 

Other than obtaining detailed manufacturing costs directly from a manufacturer, the most 

accurate method for determining the production cost of a product is to disassemble representative 

units piece-by-piece and estimate the material, labor, and overhead costs associated with each 

component using a process commonly called a physical teardown. A supplementary method, 

called a catalog teardown, uses published manufacturer catalogs and supplementary component 

data to estimate the major physical differences between a product that has been physically 

disassembled and another similar product. DOE performed physical teardown analysis on both 

top-loading and front-loading commercial clothes washers. The teardown methodology is 

explained in the following sections.

 

4

 Available from AHAM at http://www.aham.org/ht/d/ProductDetails/sku/4045-110-140/from/714/pid/
5-9

5.5.3.1 Selection of Units 

DOE generally adopts the following criteria for selecting units for teardown analysis: 

 The selected products should span the full range of efficiency levels for each product class 

under consideration; 

 Within each product class, the selected products should, if possible, come from the same 

manufacturer and belong to the same product platform; 

 The selected products should, if possible, come from manufacturers with large market shares 

in that product class, although the highest efficiency products are chosen irrespective of 

manufacturer; and 

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

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

5.5.3.2 Generation of Bill of Materials 

The end result of each teardown is a structured bill of materials (BOM), which describes 

each product part and its relationship to the other parts, in the estimated order of assembly. The 

BOMs describe each fabrication and assembly operation in detail, including the type of value—

added equipment needed (e.g., stamping presses, injection molding machines, spot-welders, etc.) 

and the estimated cycle times associated with each conversion step. The result is a thorough and 

explicit model of the production process. 

Materials in the BOM are divided between raw materials that require conversion steps to 

be made ready for assembly, while purchased parts are typically delivered ready for installation. 

The classification into raw materials or purchased parts is based on DOE’s previous industry 

experience, recent information in trade publications, and discussions with original equipment 

manufacturers (OEMs). For purchased parts, the purchase price is based on volume-variable 

price quotations and detailed discussions with suppliers. 

For parts fabricated in-house, the prices of the underlying “raw” metals (e.g., tube, sheet 

metal) are estimated on the basis of 5-year averages to smooth out spikes in demand. Other 

“raw” materials such as plastic resins, insulation materials, etc. are estimated on a current-market 

basis. The costs of raw materials are based on manufacturer interviews, quotes from suppliers, 

secondary research, and by subscriptions to publications including the American Metals Market 

(AMM).5Past price quotes are indexed using applicable Bureau of Labor Statistics producer 

price index tables as well as AMM monthly data. 

5.5.3.3 Cost Structure of the Spreadsheet Models 

The manufacturing cost assessment methodology uses a detailed, component-focused 

technique for rigorously calculating the manufacturing cost of a product (direct materials, direct 

 

5

 For information on American Metals Market, please visit: www.amm.com. 
5-10

labor and some overhead costs). Figure 5.5.1 shows the three major steps in generating the 

manufacturing cost. 

Figure 5.5.1 Manufacturing Cost Assessment Stages 

The first step in the manufacturing cost assessment is the creation of a complete and 

structured BOM from the disassembly of the units selected for teardown. The units are 

dismantled, and each part is characterized according to weight, manufacturing processes used, 

dimensions, material, and quantity. The BOM incorporates all materials, components, and 

fasteners with estimates of raw material costs and purchased part costs. Assumptions on the 

sourcing of parts and in-house fabrication are based on industry experience, information in trade 

publications, and discussions with manufacturers. Interviews and plant visits are 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. Some typical processes are listed in Table 

5.5.1.

Table 5.5.1 Major Manufacturing Processes 

Fabrication Finishing Assembly/Joining Quality 

Control

Fixturing

Stamping/Pressing

Brake Forming

Cutting and 

Shearing

Insulating

Turret Punch

Tube Forming

Enameling

Washing

Powder 

Coating

De-burring

Polishing

Refrigerant 

Charging

Adhesive Bonding

Spot Welding

Seam Welding

Packaging

Inspecting & 

Testing

Fabrication process cycle times for each part made in-house are estimated and entered 

into the BOM. Based on estimated assembly and fabrication time requirements, the labor content 

of each appliance can be estimated. 

Cycle requirements for fabrication steps are similarly aggregated by fabrication machine 

type while accounting for dedicated vs. non-dedicated machinery and/or change-over times (die 

swaps in a press, for example). Once the cost estimate for each teardown unit are finalized, a 

detailed summary is prepared for relevant components, subassemblies and processes. The BOM 

thus details all aspects of unit costs: material, labor, and overhead. 
5-11

Design options used in units subject to teardown are noted in the summary sheet of each 

cost model and are cost-estimated individually. Thus, various implementations of design options 

can be accommodated, ranging from assemblies that are entirely purchased to units that are made 

entirely from raw materials. Hybrid assemblies, consisting of purchased parts and parts made on 

site are thus also accommodated. 

5.5.3.4 Cost Model and Definitions 

The cost model is based on production activities and divides factory costs into the 

following categories: 

 Materials: Purchased parts (i.e., motors, valves, etc.), raw materials, (i.e., cold rolled 

steel, copper tube, etc.), and indirect materials that are used for processing and 

fabrication. 

 Labor: Fabrication, assembly, indirect, and supervisor labor. Fabrication and assembly 

labor cost are burdened with benefits and supervisory costs. 

 Overhead: Equipment, tooling, and building depreciation, as well as utilities, equipment 

and tooling maintenance, insurance, and property taxes. 

Cost Definitions 

Because there are many different accounting systems and methods to monitor costs, DOE 

defined the above terms as follows: 

 Direct material: Purchased parts (out-sourced) plus manufactured parts (made in-house 

from raw materials). 

 Indirect material: Material used during manufacturing (e.g., welding rods, adhesives). 

 Fabrication labor: Labor associated with in-house piece manufacturing. 

 Assembly labor: Labor associated with final assembly. 

 Supervisory labor: Labor associated with fabrication and assembly basis. Assigned on a 

span basis (x number of employees per supervisor) that depends on the industry. 

 Indirect labor: Labor costs that scale with fabrication and assembly labor. These included 

the cost of technicians, manufacturing engineering support, stocking, etc. that are 

proportional to all other labor. 

 Equipment depreciation: Money allocated to pay for initial equipment installation and 

replacement as the production equipment is amortized. All depreciation is assigned in a 

linear fashion and affected equipment life depends on the type of equipment. 

 Tooling depreciation: Cost for initial tooling (including non-recurring engineering and 

debugging of the tools) and tooling replacement as it wears out or is rendered obsolete. 

 Building depreciation: Money allocated to pay for the building space and the conveyors 

that feed and/or make up the assembly line.

 Utilities: Electricity, gas, telephones, etc. 

 Maintenance: Annual money spent on maintaining tooling and equipment. 

 Insurance: Appropriated as a function of unit cost. 

 Property Tax: Appropriated as a function of unit cost. 
5-12

5.5.3.5 Cost Model Assumptions 

As discussed in the previous section, assumptions about manufacturer practices and cost 

structure played an important role in estimating the final product cost. In converting physical 

information about the product into cost information, DOE reconstructed manufacturing processes 

for each component using internal expertise and knowledge of the methods used by the industry. 

Site visits allowed DOE to confirm its cost model assumptions through direct observation of the 

manufacturing plant, as well as through manufacturer interviews, reviews of current Bureau of 

Labor Statistics data, etc. 

5.5.4 Manufacturer Interviews 

DOE understands that there is variability among manufacturers in baseline units, design 

strategies, and cost structures. To better understand and explain these variances, DOE conducted 

manufacturer interviews. These confidential interviews provided a deeper understanding of the 

various combinations of technologies used to increase residential clothes washer efficiency, and 

their associated manufacturing costs. Sample questions asked during the interviews are contained 

in appendix 12A.

During the interviews, DOE also gathered information about the capital expenditures 

required to increase the efficiency of the baseline units to various efficiency levels (i.e., 

conversion capital expenditures by efficiency or energy-use level). The interviews provided 

information about the size and the nature of the capital investments. DOE also requested 

information about the depreciation method used to expense the conversion capital. The 

manufacturer impact analysis in chapter 12 includes a discussion of this information obtained 

during manufacturer interviews. 

5.6 ANALYSIS AND RESULTS 

5.6.1 AHAM Data 

 In its data submittal to DOE, AHAM stated that it has not collected incremental cost data 

from CCW manufacturers and therefore cannot provide that data to DOE. 

5.6.2 Product Testing Results 

 DOE tested a total of 10 commercial clothes washer models in support of this 

rulemaking: five top-loading and five front-loading units. DOE also conducted teardowns on 

these 10 clothes washer models. DOE selected clothes washer models for testing and teardowns 

based on the proposed efficiency levels and the range of product efficiencies available on the 

market. Table 5.6.1 lists the major features of the top-loading CCWs observed at each efficiency 

level. Table 5.6.2 lists the major features of front-loading CCWs observed at each efficiency 

level. The efficiency levels listed in these tables correspond to the levels shown above in Table 
5-13

5.4.2 and Table 5.4.3. Note that for top-loading Units #2 and #3, the Efficiency Level changes 

depending on whether Appendix J1 or Appendix J2 is used. The next section below provides 

further details regarding the test result differences between Appendix J1 and Appendix J2. 

Table 5.6.1 Top-Loading Commercial Clothes Washer Test Unit Features 

Feature Unit #1 Unit #2 Unit #3 Unit #4 Unit #5

Appendix J2 Efficiency 

Level (MEFJ2/IWF)

Baseline

(1.15/8.9)

Baseline

(1.15/8.9)

Level 1

(1.35/8.8)

Level 1

(1.35/8.8)

Level 2

(1.55/6.9)

Appendix J1Efficiency 

Level (MEF/WF)

Baseline

(1.60/8.5)

Level 1

(1.70/8.4)

Baseline

(1.15/8.9)

Level 1

(1.70/8.4)

Level 2

(1.85/6.9)

Efficiency Level WF 

(Appendix J2 IWF) [gal/ 

ft3]

8.5 (8.9) 8.4 (8.8) 8.5 (8.9) 8.4 (8.8) 6.9 (6.9)

Rated Drum Capacity (ft3) 3.5 3.3 3.3 3.3 2.9

RMC (%) 50.8% 48.1% 49.5% 48.0% 47.2%

Control Panel Type No vend 

display

Vend 

price 

display + 

advanced 

features1

No vend 

display

Vend 

price 

display

No vend 

display

Payment System Type

None 

(“push to 

start”)

Coin box Coin box Coin box Coin slide

Advanced Auditing or 

Remote

Communication Features

No Yes No No No

Water Fill Control Manual Manual Automatic 

Fixed 

Automatic 

Fixed 

Automatic 

Adaptive

Agitator Type Standard Standard Standard Standard LowProfile

Internal Heater No No No No No

1. Advanced features may include dynamic or cycle-based pricing controls, built-in logging capabilities, and 

remote auditing features. 
5-14

Table 5.6.2 Front-Loading Commercial Clothes Washer Test Unit Features 

Feature Unit #1 Unit #2 Unit #3 Unit #4 Unit #5

Appendix J2 Efficiency 

Level (MEFJ2/IWF)

Level 1

(1.80/4.5)

Level 1

(1.80/4.5)

Level 2

(2.00/4.1)

Level 2

(2.00/4.1)

Level 3

(2.20/3.9)

Appendix J1 Efficiency 

Level (MEF/WF)

Level 1

(2.20/4.5)

Level 1

(2.20/4.5)

Level 2

(2.40/4.0)

Level 2

(2.40/4.0)

Level 3

(2.60/3.7)

Rated Drum Capacity (ft3) 2.8 3.1 2.8 3.1 3.6

RMC (%) 39.6% 37.9% 39.6% 35.5% 33.4%

Control Panel Type

Vend 

price 

display + 

advanced 

features1

Vend 

price 

display

Vend 

price 

display + 

advanced 

features1

Vend 

price 

display

No vend 

display

Payment System Type Coin box Coin box Coin box Coin box

None 

(“push to 

start”)

Advanced Auditing or 

Remote

Communication Features

Yes No Yes No No

Water Fill Control Automatic 

Adaptive

Automatic 

Adaptive

Automatic 

Adaptive

Automatic 

Adaptive

Automatic 

Adaptive

Internal Heater No No No No No

1. Advanced features may include dynamic or cycle-based pricing controls, built-in logging capabilities, and 

remote auditing features. 

5.6.2.1 Active Mode Testing 

DOE conducted testing on a representative sample of five top-loading and five frontloading CCWs using both appendix J1 and appendix J2. DOE used the results from these tests to 

determine each model’s appendix J2 MEFJ2/IWF ratings in relation to its appendix J1 MEF/WF 

ratings. These relationships were then used to develop the equivalent Appendix J2 values for 

each Appendix J1 efficiency level shown in Table 5.4.2 and Table 5.4.3. 

The DOE test procedure consists of running different load sizes of preconditioned test 

cloth in the clothes washer at various temperature settings. For all clothes washers, the minimum 

test load is 3 pounds of test cloth. Average and maximum load sizes are specified based on the 

clothes washer capacity. During the energy test cycles, the total kWh of electric energy 

consumed by the clothes washer is measured, in addition to the cold and hot water consumption. 
5-15

The measurements also include the “bone dry”6 weight of the test load and the weight 

immediately after the completion of certain energy test cycles for the RMC calculation. 

Figure 5.6.1 shows a plot of the equivalent MEFJ2 values (using appendix J2) associated 

with the rated MEF values (using appendix J1) for each of the top-loading CCWs in the test 

sample. For reference, the plot also indicates the associated efficiency levels used for the 

rulemaking analysis, based on appendix J2 MEFJ2 ratings. 

Figure 5.6.1 Relationship Between Equivalent MEFJ2 (Appendix J2) and Rated MEF 

(Appendix J1) for Top-Loading CCWs 

Figure 5.6.1 shows that top-loading clothes washers with manual water fill controls have 

lower relative appendix J2 MEFJ2 ratings compared to similarly-rated units (based on appendix 

J1) with automatic water fill controls.7 As a result, some manual water fill units that would have 

been considered higher-efficiency models under appendix J1 ratings are considered baseline 

models under appendix J2 ratings. 

The primary driver of the lower relative efficiency ratings under appendix J2 for units 

with manual water fill controls is the amended calculation of drying energy in section 4.3 of 

 

6

 “Bone dry” means a condition of a load of test clothes which has been dried in a dryer at maximum temperature for 

a minimum of 10 minutes, removed and weighed before cool down, and then dried again for 10-minute periods until 

the final weight change of the load is 1 percent or less. 

7

 The term “automatic” water fill used here refers to water fill control systems that determine the water fill level 

without requiring user intervention or actions. This includes “adaptive” water fill control systems and “fixed” water 

fill control systems, available on some commercial clothes washers, that provide a fixed water level for all load sizes 

and no water fill selector or water fill control settings available to the user. Clothes washers with fixed water fill 

control systems are tested in the same manner as clothes washers with adaptive water fill control systems. 

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.5 1.6 1.7 1.8 1.9 2.0

Equivalent MEFJ2 (Appendix J2)

Rated MEF (Appendix J1)

(Higher MEF is more efficient)

Manual Water Fill

Automatic Water Fill

Baseline: 1.15 MEFJ2

Level 1: 1.35 MEFJ2

Level 2: 1.55 MEFJ2
5-16

appendix J2. In appendix J2, the drying energy is calculated using the weighted average load 

size, with the weightings corresponding to the Load Usage Factors (LUF) for manual water fill 

machines. Whereas, in section 4.3 of appendix J1, the drying energy is calculated using an 

adjusted load size equal to 0.52 x Maximum Load Size. The calculated drying energy using 

appendix J2 is a greater quantity than under appendix J1; thus resulting in a lower MEFJ2 value. 

For units with adaptive fill controls, the difference between the weighted average load size in 

appendix J2 and the adjusted load size in appendix J1 is much less; therefore, the difference in 

drying energy is not as pronounced as with manual water fill units. 

Figure 5.6.2 shows a plot of the equivalent IWF values (using appendix J2) associated 

with the rated WF values (using appendix J1) for each of the top-loading CCWs in the test 

sample. For reference, the plot also indicates the associated efficiency levels used for the 

rulemaking analysis, based on appendix J2 IWF ratings. 

Figure 5.6.2 Relationship Between Equivalent IWF (Appendix J2) and Rated WF 

(Appendix J1) for Top-Loading CCWs 

Figure 5.6.3 shows a plot of the equivalent MEFJ2 values (using appendix J2) associated 

with the rated MEF values (using appendix J1) for each of the front-loading CCWs in the test 

sample. For reference, the plot also indicates the associated efficiency levels used for the 

rulemaking analysis, based on appendix J2 MEFJ2 ratings. 

6.0

6.5

7.0

7.5

8.0

8.5

9.0

6.0 6.5 7.0 7.5 8.0 8.5 9.0

Equivalent IWF (Appendix J2)

Rated WF (Appendix J1)

(Lower IWF is more efficient)

Manual Water Fill

Automatic Water Fill

Baseline: 8.9 IWF

Level 1: 8.8 IWF

Level 2: 6.9 IWF
5-17

Figure 5.6.3 Relationship Between Equivalent MEFJ2 (Appendix J2) and Rated MEF 

(Appendix J1) for Front-Loading CCWs 

As noted in the NOPR discussion, DOE is not aware of any front-loading CCWs on the 

market with a rating of 2.00 MEF, which corresponds to the current minimum standard level. As 

a result, DOE used data from the prior CCW rulemaking to represent the 2.00 MEF level, 

indicated in Figure 5.6.3 as a “virtual model.” To calculate the equivalent appendix J2 MEFJ2 for 

the virtual model, DOE assumed that a model with a 2.00 MEF rating under appendix J1 would 

have the same measured capacity, machine electrical, and hot water heating energy under 

appendix J2. The only change would be the drying energy calculation, as described above. 

Figure 5.6.4 shows a plot of the equivalent IWF values (using appendix J2) associated 

with the rated WF values (using appendix J1) for each of the front-loading CCWs in the test 

sample. For reference, the plot also indicates the associated efficiency levels used for the 

rulemaking analysis, based on appendix J2 IWF ratings. 

1.5

1.6

1.7

1.8

1.9

2.0

2.1

2.2

2.3

1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8

Equivalent MEFJ2 (Appendix J2)

Rated MEF (Appendix J1)

(Higher MEF is more efficient)

Tested Models

Virtual Model

Baseline: 1.65 MEFJ2

Level 1: 1.80 MEFJ2

Level 2: 2.00 MEFJ2

Level 3: 2.20 MEFJ2
5-18

Figure 5.6.4 Relationship Between Equivalent IWF (Appendix J2) and Rated WF 

(Appendix J1) for Front-Loading CCWs 

 

5.6.2.2 Standby Mode and Off Mode Power 

 As described in greater detail in the NOPR, DOE proposes amended standards for 

commercial clothes washers based on MEFJ2, which does not incorporate standby and off mode 

power. As part of its investigation of standby power for CCWs, DOE measured standby and off 

mode power for all 10 clothes washers selected in the test sample, using the methodology 

provided in Appendix J2, which references International Electrotechnical Commission (IEC) 

Standard 62301 Second Edition. 

 CCW manufacturers offer a variety of display and payment functionalities that can be 

selected independent of the basic model selected. The standby power associated with these 

different display and payment functionalities varies widely. The lowest standby power levels are 

associated with models having no vend price display and no coin or card payment options (often 

referred to as “push-to-start” models). These models are typically used in multi-family housing 

facilities offering free laundry, or in other commercial applications not requiring fare payment. 

Such models are not suitable for coin-operated laundry or most multi-family housing facilities. 

The highest standby power levels are associated with models having a digital vend price display, 

coin or debit card payment system, and advanced features such as dynamic or cycle-based 

pricing controls, built-in logging capabilities, and remote auditing features. These models are 

typically used in coin-operated laundries located in competitive markets. 

 The results of the standby and off mode measurements are shown in Table 5.6.3 below.

3.0

3.5

4.0

4.5

5.0

5.5

6.0

3.0 3.5 4.0 4.5 5.0 5.5 6.0

Equivalent IWF (Appendix J2)

Rated WF (Appendix J1)

(Lower IWF is more efficient)

Tested Models

Virtual Model

Baseline: 5.2 IWF

Level 1: 4.5 IWF

Level 2: 4.1 IWF

Level 3: 3.9 IWF
5-19

Table 5.6.3 Commercial Clothes Washer Standby Power Measurements 

Product 

Class

Rated 

MEF 

(ft3/kWh) Control Panel Type

Payment 

System 

Type

Power 

Consumpti

on (W)

TopLoading

1.60 No vend display Push-to-start 2.84

1.65 No vend display Coin slot 5.44

1.70 Vend price only Coin slot 10.21

1.75 Vend price display + advanced 

features1 Coin slot 11.77

1.85 No vend display Coin slide 0.88

FrontLoading

2.24 Vend price display + advanced 

features1 Coin slot 11.60

2.24 Vend price only Coin slot 7.10

2.46 Vend price display + advanced 

features1 Coin slot 11.30

2.49 Vend price only Coin slot 7.70

2.60 Vend price only Push-to-start 5.80

1. Advanced features may include dynamic or cycle-based pricing controls, built-in logging capabilities, and 

remote auditing features. 

5.6.2.3 Cleaning Performance Testing 

 To investigate potential impacts on cleaning performance, rinsing performance, and solid 

particle removal, DOE performed additional testing on each model using AHAM’s HLW-1-2010 

test method: Performance Evaluation Procedures for Household Clothes Washers (hereafter, 

“AHAM HLW-1-2010”). Specifically, DOE performed the soil/stain removal, rinsing 

effectiveness, and sand removal tests provided in HLW-1-2010. For each clothes washer, DOE 

tested the maximum load size specified in appendix J2, rounded to the nearest pound, using the 

warm wash/cold rinse cycle. DOE believes that the maximum load size is particularly relevant to 

commercial clothes washer owners and operators because end-users often overload the machines 

in order to limit their total laundry cost. 

 DOE consulted with a number of manufacturers who indicated that AHAM HLW-1-2010 

would be the most appropriate test method to determine relative cleaning performance across 

different commercial clothes washer models. DOE recognizes that AHAM HLW-1-2010 is 

typically used to measure the performance of residential clothes washers, but given the 

similarities in physical construction, DOE believes the test procedure is also appropriate for 

commercial clothes washers covered by DOE standards. DOE also acknowledges that the 

commercial clothes washer industry has not agreed upon acceptable ranges of performance 
5-20

characteristics; therefore, DOE’s test results should be used for relative comparison purposes 

only. 

Cleaning Performance 

DOE performed the Soil/Stain Removal test within section 6 of AHAM HLW-1-2010 to 

measure relative cleaning performance among the CCW test sample units. AHAM HLW-1-2010 

states that the purpose of the Soil/Stain Removal test is to evaluate the performance of household 

clothes washers in removing representative soils and stains from fabric. For each clothes washer, 

DOE tested the maximum load size specified in appendix J2, rounded to the nearest pound, and 

used the warm wash/cold rinse cycle. 

 The AHAM HLW-1-2010 Soil/Stain Removal test consists of running a wash cycle with 

user-defined test load size and machine settings, using AHAM base load materials, a defined 

number of soil/stain strips, and a defined quantity of powdered AHAM detergent. The AHAM 

base load materials consist of 100% cotton sheets, towels, and pillowcases. The soil/stain strips 

consist of square test fabric swatches measuring 4.7 in. x 4.7 in., carrying five different types of 

standard stains in addition to a sixth unstained swatch. The swatches are attached to each other, 

forming a test strip with the different stains in the following order: 

1. Unsoiled 

2. Pigment/sebum 

3. Carbon black/mineral oil. 

4. Blood. 

5. Chocolate and milk. 

6. Red wine 

Figure 5.6.5 shows the soil/stain strips, including typical packaging material. The photo includes 

a printed copy of the AHAM HLW-1-2010 test procedure for scale. 

Figure 5.6.5 AHAM Soil/Stain Test Strip 
5-21

 Multiple stain strips are attached to individual towels comprising the test load. AHAM 

HLW-1-2010 defines the number of test strips based on the total load size. Three replications of 

the Soil/Stain Removal test are performed on each clothes washer, using fresh soil/stain strips for 

each replication. 

 After a single wash cycle replication is performed, the soil/stain test strips are removed 

from the base load and measured using a tristimulus colorimeter. The unsoiled swatches are 

removed and used for the Rinsing Effectiveness test, as described in the next section of this 

chapter. The reflectance values of each stain swatch are measured; higher reflectance represents 

better stain removal. For each stain type, the reflectance values measured from all soil/stain test 

strips are averaged to produce an average reflectance score, yw lab for each stain. For each stain 

type, an Individual Cleaning Score, CSi, is obtained, expressed as: 

where for each soil/stain: 

ymax lot = post-wash reflectance as determined by the strip manufacturer using the 

calibration wash treatment defined in Annex A.A.7.1 of HLW-1-2010; 

yw lab = post-wash reflectance as measured by laboratory. 

CSi is determined for each stain type after each wash cycle replication. After the three 

replications have been performed, the three CSi scores for each stain type are averaged, to 

produce a mean CSi score for each stain type. A Total Cleaning Score, CSt, is then determined by 

averaging the mean CSi scores for all five stain types and multiplying by 100: 

 The Total Cleaning Score represents cleaning performance as a percentage of that 

achieved by a reference “maximum” wash cycle determined by the calibration wash treatment 

performed by the stain strip manufacturer. The Total Cleaning Score may be less than or greater 

than 100%. A higher Total Cleaning Score represents better cleaning performance. 

 Figure 5.6.6 shows the Total Cleaning Score for each top-loading CCW included in the 

test sample as a function of each washer’s appendix J2 MEFJ2 efficiency level. Figure 5.6.7

shows the Total Cleaning Score for each top-loading CCW as a function of each washer’s 

appendix J2 IWF efficiency level. As shown in both figures, some of the higher-efficiency 

clothes washers in the test sample achieve better Total Cleaning Scores than the baseline models. 

DOE believes that one of the factors that may contribute to higher-efficiency models achieving 

better Total Cleaning Scores is that using a smaller quantity of wash water in high-efficiency 

models increases the concentration of detergent during the wash portion of the cycle; thus 

enhancing stain removal. The proposed standard levels in the NOPR correspond to Efficiency 

Level 1. 

CSi =

yw lab

ymax lot

CSt =

 CSi × 100 5

i=1

5
5-22

Figure 5.6.6 Total Cleaning Score vs. Appendix J2 MEFJ2 Efficiency Level for TopLoading CCWs 

 

Figure 5.6.7 Total Cleaning Score vs. Appendix J2 IWF Efficiency Level for Top-Loading 

CCWs 

 Figure 5.6.8 shows the Total Cleaning Score for each top-loading CCW as a function of 

each washer’s cycle time, as measured during the AHAM HLW-1-2010 test. DOE notes that 

these cycle times may not correspond to the same cycle times when conducting the DOE 

80

82

84

86

88

90

92

94

96

1.1 1.2 1.3 1.4 1.5 1.6

AHAM Total Cleaning Score

Appendix J2 MEFJ2 Efficiency Level (cu.ft./kWh/cycle)

Total Cleaning Score vs. Appendix J2 MEFJ2 Efficiency Level

(Higher cleaning score is better)

Baseline

Level 1

Level 2

80

82

84

86

88

90

92

94

96

6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5

AHAM Total Cleaning Score

Appendix J2 IWF Efficiency Level (gal/cycle/cu.ft.)

Total Cleaning Score vs. Appendix J2 IWF Efficiency Level

(Higher cleaning score is better)

Baseline

Level 1

Level 2
5-23

appendix J2 test procedure, due to differences in the base load material that may affect any 

adaptive control strategies used throughout the wash cycle. However, DOE believes that the 

cycle times shown in the figure would be representative of consumer usage in a coin-operated 

laundry or multi-family housing laundry setting. As shown in the figure, one of the CCW models 

at Efficiency Level 1 achieves a better cleaning performance score than baseline models, at a 

cycle time of just under 30 minutes. 

Figure 5.6.8 Total Cleaning Score vs. Cycle Time for Top-Loading CCWs

 Figure 5.6.9 shows the Total Cleaning Score for each front-loading CCW included in the 

test sample as a function of each washer’s appendix J2 MEFJ2 efficiency level. Figure 5.6.10

shows the Total Cleaning Score for each front-loading CCW as a function of each washer’s 

appendix J2 IWF efficiency level. Both figures indicate a general trend of higher efficiency 

levels resulting in reduced cleaning performance scores; however, DOE notes that one Level 2 

clothes washer achieves better a better cleaning score than both Level 1 clothes washers. DOE 

believes that this clothes washer may utilize advanced control strategies to maintain cleaning 

performance at the higher efficiency level. The proposed standard levels in the NOPR 

correspond to Efficiency Level 2. 

80

82

84

86

88

90

92

94

96

25 30 35 40 45 50

AHAM Total Cleaning Score

Cycle Time (min)

Total Cleaning Score vs. Cycle Time

(Higher cleaning score is better)

Baseline

Level 1

Level 2
5-24

Figure 5.6.9 Total Cleaning Score vs. Appendix J2 MEFJ2 Efficiency Level for FrontLoading CCWs 

Figure 5.6.10 Total Cleaning Score vs. Appendix J2 IWF Efficiency Level for FrontLoading CCWs 

 Figure 5.6.11 shows the Total Cleaning Score for each front-loading CCW as a function 

of each washer’s cycle time, as measured during the AHAM HLW-1-2010 test. DOE notes that 

these cycle times may not correspond to the same cycle times when conducting the DOE 

appendix J2 test procedure, due to differences in the base load material that may affect any 

80

82

84

86

88

90

92

94

96

1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3

AHAM Total Cleaning Score

Appendix J2 MEFJ2 Efficiency Level (cu.ft./kWh/cycle)

Total Cleaning Score vs. Appendix J2 MEFJ2 Efficiency Level

(Higher cleaning score is better)

Level 1

Level 2

Level 3

80

82

84

86

88

90

92

94

96

3.7 3.8 3.9 4.0 4.1 4.2 4.3 4.4 4.5 4.6 4.7

AHAM Total Cleaning Score

Appendix J2 IWF Efficiency Level (gal/cycle/cu.ft.)

Total Cleaning Score vs. Appendix J2 IWF Efficiency Level

(Higher cleaning score is better)

Level 1

Level 2

Level 3
5-25

adaptive control strategies used throughout the wash cycle. However, DOE believes that the 

cycle times shown in the figure would be representative of consumer usage in a coin-operated 

laundry or multi-family housing laundry setting. DOE notes a lack of any discernible trend of 

cleaning performance as a function of cycle time. As shown in the figure, one of the models at 

Efficiency Level 2 achieves the best cleaning performance score with a cycle time just over 36 

minutes. The cycle times for the Level 1 models are approximately 31 and 34 minutes. DOE 

believes that the highest cleaning performance score of the Level 2 model may be achieved in 

part through a slightly longer wash cycle time. Based on conversations with CCW 

manufacturers, DOE believes that a 36-minute cycle remains within the range of acceptable 

cycle times for coin-operated laundry owners and multi-family housing laundry operators.

Figure 5.6.11 Total Cleaning Score vs. Cycle Time for Front-Loading CCWs 

Rinsing Performance 

 The AHAM Rinsing Effectiveness test consists of measuring the concentration of 

detergent remaining in the unstained swatches after performing the Soil/Stain Removal tests 

described in the previous section. This is accomplished by measuring the UV absorbance at the 

wavelength particular to LAS (surfactant), a key ingredient of the detergent with a known linear 

relationship to the quantity of detergent mixture. Using a concentration vs. absorbance curve 

developed as part of this procedure, the absorbance values are then converted into detergent 

concentrations, which together with test solution weight data, yields detergent quantities. 

 After a single wash cycle replication is performed, the unsoiled test strips are removed 

and each strip is placed into an individual bottle with 100ml distilled water. After all the test 

strips have been bottled, they are placed on a laboratory shaker for 20 minutes. Following that, 

80

82

84

86

88

90

92

94

96

28 30 32 34 36 38 40

AHAM Total Cleaning Score

Cycle Time (min)

Total Cleaning Score vs. Cycle Time

(Higher cleaning score is better)

Level 1

Level 2

Level 3
5-26

samples of the fluid from each bottle are then collected and measured in a UV 

spectrophotometer. The measured values are translated into detergent concentrations using the 

detergent concentration curves developed at the beginning of the procedure. The detergent 

concentration is then used to determine the amount of detergent recovered per gram of swatch 

material. The final Rinse Score is then calculated as the amount of detergent, in grams, 

remaining in a pound of test load. A lower Rinse Score represents better rinsing performance. 

 Figure 5.6.12 shows the Rinse Score for each top-loading CCW included in the test 

sample as a function of each washer’s appendix J2 MEFJ2 efficiency level. Figure 5.6.13 shows 

the Rinse Score for each top-loading CCW as a function of each washer’s appendix J2 IWF 

efficiency level. As demonstrated in both figures, DOE notes a lack of any clear trend in rinsing 

performance as a function of efficiency level. For example, DOE notes that the most efficient 

Level 2 model achieves comparable rinsing scores with the current baseline models. DOE 

believes that a major factor affecting the rinse score is the quantity of water used during the rinse 

portion of the cycle. To maintain rinsing performance in higher-efficiency models, 

manufacturers may use relatively more water during the rinse portion of the cycle while reducing 

total water usage overall. The proposed standard levels in the NOPR correspond to Efficiency 

Level 1. 

Figure 5.6.12 Rinse Score vs. Appendix J2 MEFJ2 Efficiency Level for Top-Loading CCWs 

0.5

0.6

0.7

0.8

0.9

1.0

1.1 1.2 1.3 1.4 1.5 1.6

AHAM Rinse Score

Appendix J2 MEFJ2 Efficiency Level (cu.ft./kWh/cycle)

Rinse Score vs. Appendix J2 MEFJ2 Efficiency Level

(Lower rinse score is better)

Baseline

Level 1

Level 2
5-27

Figure 5.6.13 Rinse Score vs. Appendix J2 IWF Efficiency Level for Top-Loading CCWs 

 Figure 5.6.14 shows the Rinse Score for each top-loading CCW as a function of each 

washer’s cycle time, as measured during the AHAM HLW-1-2010 test. DOE notes a general 

trend of better Rinse Scores as a function of increased cycle time; however, DOE notes that the 

increases in cycle time are not directly correlated with efficiency level. As shown in the figure, 

one of the baseline models achieves a Rinse Score just over 0.7 with a cycle time of 

approximately 30 minutes. The Level 1 models achieve slightly higher rinse scores (just under 

0.8) with similar cycle times. The most efficient Level 2 model achieves a better rinse score of 

less than 0.7, but at an increased cycle time of around 39 minutes. The proposed standard levels 

in the NOPR correspond to Efficiency Level 1. Based on trends in the figure, DOE believes that 

manufacturers should be able to achieve Rinse Scores comparable to current baseline models 

with cycle times between 30-35 minutes. Based on conversations with CCW manufacturers, 

DOE believes that a cycle time range of 30-35 minutes is within the range of acceptable cycle 

times for coin-operated laundry owners and multi-family housing laundry operators. 

0.5

0.6

0.7

0.8

0.9

1.0

6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5

AHAM Rinse Score

Appendix J2 IWF Efficiency Level (gal/cycle/cu.ft.)

Rinse Score vs. Appendix J2 IWF Efficiency Level

(Lower rinse score is better)

Baseline

Level 1

Level 2
5-28

Figure 5.6.14 Rinse Score vs. Cycle Time for Top-Loading CCWs 

 Figure 5.6.15 shows the Rinse Score for each front-loading CCW included in the test 

sample as a function of each washer’s appendix J2 MEFJ2 efficiency level. Figure 5.6.16 shows 

the Rinse Score for each front-loading CCW as a function of each washer’s appendix J2 IWF 

efficiency level. As demonstrated in both figures, DOE notes a lack of any clear trend in rinsing 

performance as a function of efficiency level. The proposed standard levels in the NOPR 

correspond to Efficiency Level 2. DOE notes that one of the Level 2 models achieves the lowest 

(best) Rinse Score of the entire test sample, at around 0.8. 

0.5

0.6

0.7

0.8

0.9

1.0

25 30 35 40 45 50

AHAM Rinse Score

Cycle Time (min)

Rinse Score vs. Cycle Time

(Lower rinse score is better)

Baseline

Level 1

Level 2
5-29

Figure 5.6.15 Rinse Score vs. Appendix J2 MEFJ2 Efficiency Level for Front-Loading 

CCWs 

Figure 5.6.16 Rinse Score vs. Appendix J2 IWF Efficiency Level for Front-Loading CCWs 

 Figure 5.6.17 shows the Rinse Score for each front-loading CCW as a function of each 

washer’s cycle time, as measured during the AHAM HLW-1-2010 test. Similar to the Total 

Cleaning Scores for front-loading CCWs, DOE notes a lack of a discernible trend of cleaning 

performance as a function of cycle time. As shown in the figure, one of the models at Efficiency 

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4

1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3

AHAM Rinse Score

Appendix J2 MEFJ2 Efficiency Level (cu.ft./kWh/cycle)

Rinse Score vs. Appendix J2 MEFJ2 Efficiency Level

(Lower rinse score is better)

Level 1

Level 2

Level 3

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4

3.7 3.8 3.9 4.0 4.1 4.2 4.3 4.4 4.5 4.6 4.7

AHAM Rinse Score

Appendix J2 IWF Efficiency Level (gal/cycle/cu.ft.)

Rinse Score vs. Appendix J2 IWF Efficiency Level

(Lower rinse score is better)

Level 1

Level 2

Level 3
5-30

Level 2 achieves the best rinsing performance score with a cycle time just over 36 minutes. The 

cycle times for the Level 1 models are approximately 31 and 34 minutes. Based on conversations 

with CCW manufacturers, DOE believes that a 36-minute cycle remains within the range of 

acceptable cycle times for coin-operated laundry owners and multi-family housing laundry 

operators. 

Figure 5.6.17 Rinse Score vs. Cycle Time for Front-Loading CCWs 

Sand Removal Performance 

DOE performed the Sand Removal test within section 8 of AHAM HLW-1-2010, the 

purpose of which is to evaluate the ability of a clothes washers to remove insoluble and heavierthan-water soils from clothes and the system. For each clothes washer, DOE tested the maximum 

load size specified in appendix J2, rounded to the nearest pound, and used the warm wash/cold 

rinse cycle. The Sand Removal test is performed separately from the Soil/Stain Removal and 

Rinsing Effectiveness tests. Three replications of the test are performed. 

 The AHAM HLW-1-2010 Sand Removal test consists of running a wash cycle with userdefined test load size and machine settings, using AHAM base load materials, a defined quantity 

of powdered AHAM detergent, and 50 grams of sand added to the load. During the wash cycle, 

sand is collected through a filter placed at the end of the clothes washer drain hose. Upon 

completion of the wash cycle, the test load is removed and any sand particles remaining in the 

clothing load are captured and measured. The Sand Remaining score, SR, is calculated as the 

quantity of sand remaining in the load (in grams) divided by the amount of sand added to the 

load (50 grams). A lower SR score represents better sand removal performance.

 During testing, some sand may remain trapped in the clothes washer after each 

replication. Therefore, after each replication, “cleanup cycles” with no load are performed to 

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4

28 30 32 34 36 38 40

AHAM Rinse Score

Cycle Time (min)

Rinse Score vs. Cycle Time

(Lower rinse score is better)

Level 1

Level 2

Level 3
5-31

flush out any sand remaining inside the washer. However, in some cases, despite running 

multiple cleanup cycles, not all the sand was accounted for after each replication of the test. 

Therefore, the second and third replications of each test may contain sand carried over from prior 

tests. DOE notes that because of these uncertainties, it is important to look at the SR scores from 

the three replications individually, rather than averaging them together. 

 Figure 5.6.18 shows the Sand Removal Score for each top-loading CCW included in the 

test sample as a function of each washer’s appendix J2 MEF J2 efficiency level. Figure 5.6.19

shows the Sand Removal Score for each top-loading CCW as a function of each washer’s 

appendix J2 IWF efficiency level. As shown in both figures, current baseline clothes washers 

achieve a minimum sand removal score of 2%; Level 1 washers achieve a minimum score of 

around 4%, and Level 2 washers achieve a minimum score of 6%. The upper bound of the range 

is around 16% at all efficiency levels. The proposed standard levels in the NOPR correspond to 

Efficiency Level 1. 

Figure 5.6.18 Sand Removal Score vs. Appendix J2 MEFJ2 Efficiency Level for TopLoading CCWs 

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

1.1 1.2 1.3 1.4 1.5 1.6

AHAM Sand Removal Score

Appendix J2 MEFJ2 Efficiency Level (cu.ft./kWh/cycle)

Sand Removal Score vs. Appendix J2 MEFJ2 Efficiency Level

(Lower sand removal score is better)

Baseline

Level 1

Level 2
5-32

Figure 5.6.19 Sand Removal Score vs. Appendix J2 IWF Efficiency Level for Top-Loading 

CCWs 

 Figure 5.6.20 shows the Sand Removal Score for each front-loading CCW included in the 

test sample as a function of each washer’s appendix J2 MEF J2 efficiency level. Figure 5.6.21

shows the Sand Removal Score for each front-loading CCW as a function of each washer’s 

appendix J2 IWF efficiency level. As shown in both figures, current Level 1 clothes washers 

achieve sand removal scores ranging from 7-17%; Level 2 washers achieve scores ranging from 

5-10%; and Level 3 washers achieve scores ranging from 12-14%. The proposed standard levels 

in the NOPR correspond to Efficiency Level 2. 

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5

AHAM Sand Removal Score

Appendix J2 IWF Efficiency Level (cu.ft./kWh/cycle)

Sand Removal Score vs. Appendix J2 IWF Efficiency Level

(Lower sand removal score is better)

Baseline

Level 1

Level 2
5-33

Figure 5.6.20 Sand Removal Score vs. Appendix J2 MEFJ2 Efficiency Level for FrontLoading CCWs 

Figure 5.6.21 Sand Removal Score vs. Appendix J2 IWF Efficiency Level for FrontLoading CCWs 

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3

AHAM Sand Removal Score

Appendix J2 MEFJ2 Efficiency Level (cu.ft./kWh/cycle)

Sand Removal Score vs. Appendix J2 MEFJ2 Efficiency Level

(Lower sand removal score is better)

Level 1

Level 2

Level 3

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

3.7 3.8 3.9 4.0 4.1 4.2 4.3 4.4 4.5 4.6 4.7

AHAM Sand Removal Score

Appendix J2 IWF Efficiency Level (cu.ft./kWh/cycle)

Sand Removal Score vs. Appendix J2 IWF Efficiency Level

(Lower sand removal score is better)

Level 1

Level 2

Level 3
5-34

5.6.3 Product Teardowns 

As part of its reverse-engineering process, DOE conducted teardowns of commercial 

clothes washers to identify design features and corresponding manufacturing costs that are 

associated with successively higher efficiency levels. The clothes washer teardown analysis 

performed for this analysis included five top-loading and five front-loading models. Details of 

the teardown results are as follows.

5.6.3.1 Top-Loading Commercial Clothes Washers 

Top-Loading Baseline Construction (1.15 MEFJ2 / 8.9 IWF) 

The baseline top-loading CCW is equipped with electronic controls that allow the user to 

select specific cycle settings, including wash/rinse temperature and load size. Temperature is 

controlled using a thermal cutout on the water inlet assembly, which consists of two solenoid 

valves. The load size selector switch determines the fill level for a wash cycle, which is 

controlled by a pressure switch. Between 2-4 load size selections may be available on baseline 

units. 

The motor is typically a 1⁄2-horsepower permanent split capacitor (PSC) motor which 

drives the drum and the agitator. On some baseline models, this motor also drives the drain pump 

by reversing direction, while other manufacturers opt to include a dedicated drain pump instead. 

The baseline wash basket consists of either an enameled steel or stainless steel cylinder with a 

circular plate attached to the bottom to form a drum. From the center of the bottom plate a 

smaller cylinder rises to guide the drive shaft into the agitator and to center the wash basket 

inside the tub. Snapped onto the top rim of the drum is a plastic balance ring, which is partially 

filled with a saline solution. Covering the balance ring is a plastic cover, often referred to as the 

tub cover. 

 The outer wrapper of the baseline unit consists of stamped metal panels which may be 

made of pre-painted steel or coated post-fabrication with powder-coat paint. The top panel 

houses the lid, which is a smaller stamped piece of sheet metal on hinges. One of these hinges 

triggers a limit switch that cuts power to the motor when the lid is opened. The top panel and lid 

are typically enameled to provide greater scratch resistance.

 Based upon product teardowns, DOE developed the following baseline production and 

materials cost distributions for a typical top-loading CCW. Table 5.6.4 and Figure 5.6.22 show 

the baseline production cost distributions; and Table 5.6.5 and Figure 5.6.23 show the baseline 

materials cost distribution. Depending on the manufacturer and the production volume, the 

depreciation costs may vary from those shown in the figures, which assume a “green-field” site. 
5-35

Table 5.6.4 Production Cost Distribution for Baseline Top-Loading CCW 

Cost Category Total Cost

($2012)

Figure 5.6.22 Production Cost Distribution for Baseline 

Top-Loading CCW

Raw Materials $73.97 

Purchased Parts $161.21 

Assy Labor Cost $18.15 

Fab Labor Cost $27.63 

Indirect Labor Cost $2.38 

Supervision Cost $15.89 

Tooling Dep. Cost $59.41 

Indirect Process Costs $25.85 

Equipment Dep. Cost $50.09 

Building Dep. Cost $21.20 

Maintenance $52.48 

Utility Cost $5.38 

Property Tax $4.62 

Insurance $4.15 

Total $522.42 

Table 5.6.5 Materials Cost Distribution for Baseline Top-Loading CCW 

Sub-Assembly

Total 

Materials 

Cost ($2012)

Figure 5.6.23 Baseline Top-Load Standard Clothes 

Washer Materials Cost Distribution

Motor/Pump/Capacitor $61.06 

Control Assy $37.93 

Gear Box/Transmission $29.97 

Tub/Drum $28.85 

Outer Wrapper $24.95 

Suspension $15.63 

Door Assy $10.45 

Main Wiring Harness $9.90 

Packaging $9.68 

Water Temp. Mgmt. $3.91 

Final Assy $2.87 

Total $235.19

Raw Material 

Costs, $73.97 

Purchased Parts 

Material Costs, 

$161.21 

Fab Labor Cost, $27.63 Assy Labor Cost, $18.15 

Supervision Cost, $2.38 

Indirect Labor Cost, 

$15.89 

Equipment Dep. Cost, 

$59.41 

Tooling Dep. Cost, 

$25.85 

Building Dep. Cost, 

$50.09 

Indirect Process Costs, 

$21.20 

Maintenance Costs, 

$52.48 

Utility Costs, $5.38 

Property Tax, $4.62 Insurance, $4.15 

Motor/Pump/Capacitor, 

$61.06 

Control Assy, 

$37.93 

Gear Box/Transmission, 

$29.97 

Tub/Drum, $28.85 

Outer Wrapper, $24.95 

Suspension, $15.63 

Door Assy, $10.45 

Main Wiring Harness, 

$9.90 

Packaging, $9.68 

Water Temperature 

Management, $3.91 

Final Assy, $2.87 
5-36

Construction at Higher Efficiency Levels 

 Based on the design options retained from the screening analysis (see chapter 4 of this 

TSD), the reverse-engineering analyses, and discussions with manufacturers, DOE developed 

manufacturing costs associated with various design features necessary to achieve higher 

efficiencies. The following are the design changes DOE believes would be necessary to meet 

each higher efficiency level, which were subsequently modeled to obtain incremental 

manufacturing cost estimates. 

Top-Loading Efficiency Level 1 (1.35 MEFJ2 / 3.8 IWF) 

Based on characteristics of units selected for teardown and based on discussions with 

manufacturers, DOE research suggests that Efficiency Level (EL) 1 is typically achieved in toploading commercial clothes washers through the following changes: 

1. Automatic Water Fill Control 

Through its observations and discussions with manufacturers, DOE believes that in 

moving from the baseline level to EL 1, manufacturers would likely replace manual water fill 

controls, in which the end-user selects the fill level, with automatic water fill controls, in which 

the machine automatically selects the water fill level. The test procedure assumes a different set 

of Load Usage Factors (LUF) for manual and automatic water fill machines. For manual water 

fill machines, the LUF for the maximum load size is 72% (with the minimum load size TUF as 

28%), whereas for automatic water fill machines, the maximum load size LUF is 12% (with the 

average load size TUF as 74%, and minimum load size TUF as 14%). 

2. Reduced Hot Water Temperatures 

DOE believes that in addition to using automatic water fill controls, manufacturers may 

slightly reduce the temperature of the hot and warm water settings compared to the baseline unit. 

Top-Loading Efficiency Level 2 (1.55 MEFJ2 / 6.9 IWF) 

 DOE research suggests that manufacturers would likely significantly change the design 

platform of a top-loading commercial clothes washer to reach EL 2. A CCW at EL 2 is likely to 

employ the same features as one at EL 1, and would incorporate the following additional 

features: 

1. Improved Fill Level Control 

Manufacturers would likely improve the automatic fill control at EL2, which would 

include more accurate water level sensors and a more sophisticated control scheme for 

determining optimal wash water levels. The improved pressure sensor and control logic would 

allow the clothes washer to more accurately determine the load size based on the measured 

volume of water introduced to the clothes container and the transient water pressure measured at 

the bottom of the tub, which is affected by the load size. These controls would reduce excess 

water consumption by filling the wash basket with the minimum volume of water needed to soak 

the load. 
5-37

2. Improved Water Flow Control 

 At EL 2, manufacturers would likely use a combination of electronically controlled 

thermistors and solenoid valves to achieve more precise water flow control. The thermistors 

sense the internal water temperatures, and the control system actively adjusts the solenoid valves 

to maintain the desired internal wash temperature. 

3. Spray Rinse 

 Manufacturers would likely employ a spray rinse at EL 2. Spray rinse significantly 

decreases the volume of water used during the rinse cycle. The clothes washers at the baseline 

level and EL 1 typically use a deep rinse, during which the tub is filled with water to a level in 

which the clothes are submerged, agitated, and then drained to remove soap suds. A spray rinse 

uses high-pressure jets of water to rinse the load while the basket is spinning, without requiring 

the clothes to be immersed. 

 

4. Increased Capacity 

At EL2, manufacturers would likely increase capacity slightly by around 0.2 cubic feet 

above the capacity of the baseline and EL1 units. 

5. Low-Profile Agitator Design 

DOE believes manufacturers would likely move to a low-profile agitator design at EL 2. 

The low-profile agitator design consists of a rotating flat plate at the bottom of the wash basket, 

compared to the standard agitator, which is a finned cylinder that rises axially into the wash 

basket. The low-profile agitator is designed to manually agitate the clothing using less water than 

a traditional agitator in a washer with deep water fills. 

6. Improved motor efficiency 

At EL2, manufacturers are likely to use a more efficient motor and transmission system 

that reduces the machine electrical energy consumption by almost 50%. 

5.6.3.2 Front-Loading Commercial Clothes Washers 

Front-Loading Baseline Construction (1.65 MEFJ2 / 5.5 IWF) 

DOE is unaware of any front-loading commercial clothes washers currently available on 

the market that are at or above the baseline efficiency level but do not meet EL 1. For this 

reason, DOE established the baseline construction for a front-loading clothes washer at EL 1. 

Front-Loading Efficiency Level 1 Construction (1.80 MEFJ2 / 4.5 IWF) 

The EL1 front-loading commercial clothes washer is equipped with basic electronic 

controls that allow the user to select specific cycle settings, including wash cycle and wash/rinse 

temperature. Typically up to four wash temperature selections are offered, and the temperature is 

controlled using two water inlet solenoid valves combined with a third solenoid to control the 

flow of the mixed water. From the inlet solenoid assembly, the water flows through a detergent 
5-38

dispenser tray before entering the wash basket. The water fill level for a wash cycle is

automatically controlled using a pressure switch. 

 The internal tub/drum assembly of the baseline front-loading clothes washer is supported 

by three or four suspension legs attached to a sturdy metal base plate. Two springs attached to 

the upper sides of the outer wrapper may also support the tub/drum assembly from above. The 

tub/drum assembly consists of an internal rotating basket and a stainless steel or plastic outer 

wash tub. The basket is made of a stainless steel cylinder with an attached front ring and back 

plate. Attached to the back plate is a cast aluminum connector piece that secures the drive shaft. 

Molded into the back half of the tub is a cast iron hub that houses two bearings, which guide the 

drive shaft. A variable speed motor powers the drive shaft using a drive belt and drive wheel. 

The motor is attached to the back base plate. 

The outer wrapper of the baseline unit typically consists of individual side, back, and top 

panels made of stamped cold-rolled steel (CRS) that are clinched together. The front panel, 

which is a smaller stamped piece of CRS, houses the door assembly. A large piece of tempered 

glass with a plastic frame and handle make up the door assembly, which is attached to the front 

panel by a hinge. A door latch inside the front panel locks the door shut during the wash cycle. 

Attached to the front of the internal wash tub is a door seal gasket. This is a flexible ring that 

extends forward from the wash tub to create a water-tight seal when the door is shut. 

 Based on product teardowns, DOE developed the following baseline production and 

materials cost distributions for a typical front-loading CCW. Table 5.6.6 and 

Figure 5.6.24 show the total cost distribution, and Table 5.6.7 and Figure 5.6.25 show the 

materials cost distribution. Depending on the manufacturer and the production volume, the 

depreciation costs may vary from those shown in the figures, which assume a green-field site. 

Raw Material Costs, 

$151.10 

Purchased Parts 

Material Costs, 

$282.48 

Assy Labor Cost, 

$28.46 

Fab Labor Cost, 

$40.08 

Supervision Cost, 

$3.56 

Indirect Labor Cost, 

$23.80 

Equipment Dep. Cost, 

$72.99 

Tooling Dep. Cost, 

$51.55 

Building Dep. Cost, 

$62.92 

Indirect Process 

Costs, $10.73 

Maintenance Costs, 

$77.49 

Utility Costs, $9.33 

Property Tax, $7.33 Insurance, $6.57 
5-39

Table 5.6.6 Baseline Front-Load Clothes Washer Production Cost Distribution 

Cost Category

Total 

Cost 

($2012)

Figure 5.6.24 Production Cost Distribution for Efficiency 

Level 1 Front-Loading Commercial Clothes Washer

Purchased Parts $151.10 

Raw Materials $282.48 

Assy Labor Cost $28.46 

Indirect Labor Cost $40.08 

Fab Labor Cost $3.56 

Supervision Cost $23.80 

Tooling Dep. Cost $72.99 

Indirect Process Costs $51.55 

Equipment Dep. Cost $62.92 

Building Dep. Cost $10.73 

Utility Costs $77.49 

Property Tax $9.33 

Maintenance Costs $7.33 

Insurance Costs $6.57 

Total $828.40 

Table 5.6.7 Baseline Front-Load Clothes Washer Materials Cost Distribution 

Sub-Assembly

Total 

Materials 

Cost ($2012)

Figure 5.6.25 Materials Cost Distribution for Efficiency 

Level 1 Front-Loading Commercial Clothes Washer 

Tub/Drum $139.79 

Control Assy $104.61 

Motor/Pump/Capacitor $58.50 

Outer Wrapper $52.51 

Door Assy $19.39 

Main Wiring Harness $16.14 

Packaging $15.04 

Water Temp Mgmt. $13.19 

Suspension $10.66 

Final Assy $3.74 

Total $433.58

Raw Material Costs, 

$151.10 

Purchased Parts 

Material Costs, 

$282.48 

Assy Labor Cost, 

$28.46 

Fab Labor Cost, 

$40.08 

Supervision Cost, 

$3.56 

Indirect Labor Cost, 

$23.80 

Equipment Dep. Cost, 

$72.99 

Tooling Dep. Cost, 

$51.55 

Building Dep. Cost, 

$62.92 

Indirect Process 

Costs, $10.73 

Maintenance Costs, 

$77.49 

Utility Costs, $9.33 

Property Tax, $7.33 Insurance, $6.57 

Tub/Drum, $139.79 

Control Assy, 

$104.61 

Motor/Pump/Capacitor, 

$58.50 

Outer Wrapper, $52.51 

Door Assy, $19.39 

Main Wiring Harness, 

$16.14 

Packaging, $15.04 

Water Temperature 

Management, $13.19 

Suspension, $10.66 Final Assy, $3.74 
5-40

Construction at Higher Efficiency Levels 

 Based on the design options retained from the screening analysis (see chapter 4 of this 

TSD), the reverse-engineering analyses, and discussions with manufacturers, DOE developed 

manufacturing costs associated with various design features necessary to achieve higher 

efficiencies. The following are the design changes DOE believes would be necessary to meet 

each higher efficiency level, which were subsequently modeled to obtain incremental 

manufacturing cost estimates. 

Front-Loading Efficiency Level 2 (2.00 MEFJ2 / 4.1 IWF) 

Based on characteristics of units selected for teardown and based on discussions with 

manufacturers, DOE research suggests that EL2 is typically achieved in front-loading 

commercial clothes washers through the following changes: 

1. Reduced Hot Water Temperatures 

DOE believes that manufacturers may reduce the temperature of the hot and warm water 

settings compared to the EL1 unit. 

2. Improved Fill Level Control 

DOE believes manufacturers would use reduced water fill levels compared to the EL2 

unit. The reduction in water fill levels would be achieved through changes to the overall control 

strategy; no hardware changes would be implemented. 

Front-Loading Efficiency Level 3 (2.20 MEFJ2 / 3.9 IWF) 

DOE research suggests that manufacturers would likely significantly change the design 

platform of a front-loading commercial clothes washer to reach EL 3. A CCW at EL 3 is likely to 

employ the same features as one at EL 1, and would incorporate the following additional 

features: 

1. Increased Capacity 

 At EL2, manufacturers would likely increase capacity by around 0.6 cubic feet above the 

capacity of the EL1and EL2 units. 

2. Direct Drive Motor 

 Manufacturers would likely use a direct drive motor on their front-loading clothes 

washers at EL 3. This would replace the standard variable-speed motor, drive belt, and drive 

wheel used on the clothes washers at lower efficiency levels. 

3. Increased Maximum Spin Speed 

 Manufacturers would likely increase the maximum spin speed for a front-loading clothes 

washer at EL 3. DOE believes the spin speed would be roughly 100 RPM higher than the spin 

speed at EL 1 and EL 2. 
5-41

4. Modified Control Scheme 

 DOE believes that some manufacturers would likely adjust control parameters to improve 

efficiency at EL 3. Through its active mode testing, DOE observed that longer spin cycles and 

lower wash temperature set-points correspond to lower RMC values and less hot water 

consumption, respectively. Neither of these changes is associated with any physical design 

change to the clothes washer, but they may impact consumer utility.

5.6.4 Cost-Efficiency Curves 

Based upon product teardowns and cost modeling, DOE developed the following costefficiency relationships for top-loading and front-loading commercial clothes washers.

5.6.4.1 Top-Loading Cost-Efficiency Curves

For top-loading commercial clothes washers, DOE developed incremental manufacturing 

costs by tearing down units, and creating a cost model at each efficiency level. DOE started with 

the baseline unit cost model, and at each higher efficiency level, added in the observed changes 

associated with improving efficiency. By doing this, DOE excluded the costs of any nonefficiency related components from the more efficient units. The more efficient units are 

generally sold at a higher price point, and sometimes include more complex control panels, 

payment options, or other user interface features that are not necessarily efficiency-related. Table 

5.6.8 and Figure 5.6.26 show the incremental manufacturing costs from DOE’s reverseengineering analysis. 

Table 5.6.8 Incremental Manufacturing Costs for Top-Loading CCWs Based on MEFJ2

Appendix J2 

Efficiency Level 

Incremental 

Costs ($2012)

Figure 5.6.26 Cost-Efficiency Curve Based on MEFJ2 for 

Top-Loading Commercial Clothes Washer

Baseline

(1.15 MEFJ2 / 

8.9 IWF)

-

EL 1

(1.35 MEFJ2 / 

8.8 IWF)

$0.00

EL 2

(1.55 MEFJ2 /

6.9 IWF)

$38.89

$ 0

$10

$20

$30

$40

$50

1.1 1.2 1.3 1.4 1.5 1.6 1.7

Incremental Cost (2012$)

Appendix J2 MEF (cu.ft./kWh/cycle)
5-42

Table 5.6.8 and Figure 5.6.26 show that the incremental cost to achieve EL1 is $0. This is 

consistent with information gained through product testing and through manufacturer interviews. 

As described above, the design options used to achieve EL1 are control-based strategies that do 

not require any additional hardware costs. 

 The incremental cost to achieve EL2 is much more significant at $38.89. As described 

above, a more significant design platform change is required to achieve EL2, resulting in 

additional hardware costs over the EL1 design. 

 DOE is confident in its estimates of the incremental costs for the units it torn down. 

However, DOE is aware that these values are only representative of the small number of units 

torn down, and do not necessarily characterize the entire top-loading commercial clothes washer 

market nor the expected typical costs at the time of the effective date of new standards.

5.6.4.2 Front-Loading Cost-Efficiency Curves

 For front-loading commercial clothes washers, DOE tore down units at all efficiency 

levels, except for the baseline level because DOE is unaware of any front-loading CCWs 

available at that level. 

 DOE observed that manufacturers each have unique EL1 units depending on their 

segment of the market. To account for this, DOE developed incremental cost curves for each 

manufacturer according to their respective product offerings. For a manufacturer only producing 

front-loading CCWs at higher efficiency levels, the incremental costs associated with the 

efficiency levels below that manufacturer’s baseline is assumed to be zero. A single costefficiency curve was then calculated from a market share-weighted average of the individual 

curves. The DOE incremental costs shown in Table 5.6.9 and Figure 5.6.27 show the market 

share-weighted average of the incremental cost curves. 
5-43

Table 5.6.9 Incremental Manufacturing Costs for Front-Loading CCWs Based on MEFJ2

Appendix J2 

Efficiency 

Level 

Incremental 

Costs ($2012)

Figure 5.6.27 Cost-Efficiency Curve Based on MEFJ2 for 

Front-Loading Commercial Clothes Washer

Baseline

(1.65 MEFJ2 / 

5.2 IWF)

-

EL 1

(1.80 MEFJ2 / 

4.5 IWF)

-

EL 2

(2.00 MEFJ2 / 

4.1 IWF)

$0.31

EL 3

(2.20 MEFJ2 / 

3.9 IWF)

$19.55

Table 5.6.9 and Figure 5.6.27 show that the incremental cost to achieve EL1 is $0, and 

the cost to achieve EL2 is relatively small at $0.31. This is consistent with information gained 

through product testing and through manufacturer interviews. As described above, the design 

options used to achieve EL1 and EL2 are largely control-based strategies that do not require any 

additional hardware costs. 

 The incremental cost to achieve EL3 is more significant at $19.55. As described above, a 

more significant design platform change is required to achieve EL3, resulting in additional 

hardware costs over the EL2 design. 

 DOE is confident in its estimates of the incremental costs for the units it torn down. 

However, DOE is aware that these values are only representative of the small number of units 

torn down, and do not necessarily characterize the entire front-loading commercial clothes 

washer market nor the expected typical costs at the time of the effective date of new standards.

$ 0

$ 5

$10

$15

$20

$25

1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5

Incremental Cost (2012$)

Appendix J2 MEF (cu.ft./kWh/cycle)
CHAPTER 6. MARKUPS FOR EQUIPMENT PRICE DETERMINATION

TABLE OF CONTENTS

6.1 INTRODUCTION ...............................................................................................................2

6.2 DISTRIBUTION CHANNELS ...........................................................................................2

6.3 MARKUP CALCULATION PROCEDURE ......................................................................3

6.3.1 Manufacturer Markups.............................................................................................. 3

6.3.2 Distributor Markups .................................................................................................. 4

6.3.2.1 Baseline Distributor Markup ............................................................................. 5

6.3.2.2 Incremental Distributor Markup ........................................................................ 5

6.4 SALES TAXES ...................................................................................................................9

6.5 SUMMARY OF MARKUPS ............................................................................................10

LIST OF TABLES

Table 6.3.1 Major Appliance Manufacturer Gross Margins and Markups .................................4

Table 6.3.2 Data for Baseline Markup Calculation: Machinery, Equipment and 

Supplies Merchant Wholesalers (2007) ...................................................................5

Table 6.3.3 Data for Incremental Markup Calculation: Machinery, Equipment and 

Supplies Merchant Wholesalers ...............................................................................7

Table 6.3.4 Concentration by Four Firms for Commercial Clothes Washer 

Distributors and Route Operators ............................................................................8

Table 6.4.1 Average Sales Tax Rates by Censes Division and Large State ...............................9

Table 6.5.1 Summary of Markups ............................................................................................10

LIST OF FIGURES

Figure 6.2.1 Channel for Commercial Clothes Washers ..............................................................2

6-i 
CHAPTER 6. MARKUPS FOR EQUIPMENT PRICE DETERMINATION

6.1 INTRODUCTION

To carry out the engineering and life-cycle cost (LCC) analyses, the U.S. Department of 

Energy (DOE) needed to determine the cost to the customer of a baseline product and the cost of 

more-efficient units. By applying a multiplier called a “markup” to the manufacturers’ 

equipment costs estimated in the engineering analysis, DOE estimated the consumer prices for 

baseline models and more-efficient equipment. 

6.2 DISTRIBUTION CHANNELS 

The appropriate markups for determining the consumer equipment price depend on the 

type of distribution channels through which products move from manufacturers to purchasers. 

At each point in the distribution channel, companies mark up the price of the equipment to cover 

their business costs and profit margin. For commercial clothes washers, DOE based the 

distribution channels (see Figure 6.2.1) on data developed by the Consortium for Energy 

Efficiency (CEE).

Source: CEE, 1998 

Figure 6.2.1 Channel for Commercial Clothes Washers

2 
The CEE states that the relevant portions of the commercial, family-sized clothes washer 

market can be divided into three areas: laundromats, private multi-family housing, and large 

institutions. Included as part of large institutions are military barracks, universities, housing 

authorities, lodging establishments, and health care facilities. For these three market areas, the 

CEE data indicate that an overwhelming majority of commercial clothes washers are sold 

through either distributors or route operators. Laundromats generally purchase their equipment 

through distributors, while multi-family housing and large institutions generally lease their 

equipment from route operators. For purposes of developing the markups for commercial 

clothes washers, DOE based its calculations primarily on data for the distribution channel that 

involves only distributors. DOE assumed that the markups and the resulting consumer 

equipment prices determined for the distribution channel involving distributors would be 

representative of the prices paid by consumers acquiring their equipment from route operators. 

6.3 MARKUP CALCULATION PROCEDURE

At each point in the distribution channel, companies mark up the price of the equipment to 

cover their business costs and profit margin. In financial statements, gross margin is the difference 

between the company revenue and the company cost of sales or cost of goods sold (CGS). Inputs for 

calculating the gross margin are all corporate costs—including overhead costs (sales, general, and 

administration), research and development (R&D) and interest expenses, depreciation, and taxes—

and profits. In order for sales of a product to contribute positively to company cash flow, the 

product’s markup must be greater than the corporate gross margin. Individual products may 

command a lower or higher markup, depending on their perceived added value and the competition 

they face from similar products in the market. 

In developing markups for manufacturers and distributors, DOE obtained data about the 

revenue, CGS, and expenses of firms that produce and sell the products of interest. DOE’s 

approach categorizes the expenses into two categories: fixed costs, which are fixed labor and 

occupancy expenses that increase in proportion to the amount of labor required to produce or sell 

the product, and variable costs, which are variable operating costs that do not scale with labor and 

vary in proportion to CGS. Together, fixed and variable costs represent the gross margin. 

6.3.1 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) with the following equation: 

MUMFG = CGSMFG + GMMFG

CGSMFG

3 
Where:

MUMFG = manufacturer markup, 

CGSMFG = manufacturer’s cost of goods sold or manufacturer production cost (MPC), 

and

GMMFG = manufacturer’s gross margin. 

The manufacturer’s CGS (or MPC) plus its gross margin 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 three publicly traded manufacturers 

primarily engaged in appliance manufacturing and whose combined product range includes 

commercial clothes washers (Table 6.3.1). 

Table 6.3.1 Major Appliance Manufacturer Gross Margins and Markups

Mfr A Mfr B Mfr C

Average Net Revenues (Million) $359 $5,443 $10,000

Corporate Gross Margin 26.5% 14.5% 22.3%

Markup 1.36 1.17 1.29

6.3.2 Distributor Markups

DOE based the distributor markups for commercial clothes washers on financial data for 

the sector Machinery, Equipment and Supplies Merchant Wholesalers from the 2007 U.S. 

Census Business Expenses Survey (BES), which is the most recent available survey.1 This sector 

includes the subsector Laundry Machinery, Equipment, and Supplies, Commercial, Merchant 

Wholesalers, which specifically sells commercial clothes washers. However, as few data are 

available for this detailed subsector, DOE relied on the 2007 BES data for Machinery, 

Equipment and Supplies Merchant Wholesalers to estimate distributor markups. 

DOE organized the financial data into statements that break down cost components 

incurred by firms in this category. DOE assumes that the income statements faithfully represent 

the various average costs incurred by distributors. Although firms in this sector handle multiple 

commodity lines, the data provide the most accurate available indication of expenses for selling 

commercial clothes washers. 

The 2007 BES data provided for Machinery, Equipment and Supplies Merchant 

Wholesalers only contain total sales and detailed operating expenses. In order to construct a 

complete data set to estimate markups, DOE needed to estimate CGS and gross margin. The 

1997 Business Expenses Survey provides total sales, gross margin, and detailed operating 

expenses of Machinery, Equipment and Supplies Merchant Wholesalers.2

 DOE found that gross 

4 
margin as a percent of sales has been roughly constant in this category from 1992 to 2010, 

averaging 27.1%.3 Therefore, DOE assumed that the fractions of CGS and gross margin as a 

percent of sales in 2007 are the same as in 1997. Following this assumption, DOE calculated the 

CGS, gross margin, and net profit for Machinery, Equipment and Supplies Merchant 

Wholesalers from the 2007 BES. 

6.3.2.1 Baseline Distributor Markup

The baseline markup relates the manufacturer sales price of baseline products to the 

distributor sales price. DOE considers baseline models to be equipment sold under existing 

market conditions (i.e., without new energy efficiency standards). DOE calculated the baseline 

markup (MUBASE) for distributors as an average markup using the following equation: 

 

MUBASE = CGSDist + GMDist

CGSDist

Where:

MUBASE = baseline distributor markup, 

CGSDist = distributor’s cost of goods sold, and

GMDist = distributor’s gross margin. 

 

Table 6.3.2 shows the calculation of the baseline distributor markup. 

Table 6.3.2 Data for Baseline Markup Calculation: Machinery, Equipment and Supplies 

Merchant Wholesalers (2007) 

Kind of business item

Amount 

($million)

Sales $328,820 

Cost of Goods Sold (CGS) $239,710 

Gross Margin (GM) $89,110 

Baseline Markup = (CGS+GM)/CGS 1.37

Source: U.S. Census, 2007 Business Expenses Survey (for Sales) and 1997 Business Expenses Survey (for CGS and 

GM shares)

6.3.2.2 Incremental Distributor Markup

Incremental markups are coefficients that relate the change in the manufacturer sales 

price of higher efficiency models to the change in the distributor sales price. DOE considers 

5 
higher efficiency models to be equipment sold under market conditions with new efficiency 

standards. The incremental markup reflects a situation in which the distributor faces an increase 

in CGS for a particular product due to new or amended standards. 

 Unfortunately, empirical evidence regarding appliance distributor markup practices when 

a product increases in cost (due to increased efficiency or other factors) is lacking. DOE 

understands that real-world markup practices will vary depending on the market conditions faced 

by distributors, on the magnitude of the change in CGS associated with an efficiency increase 

and on any associated changes in wholesale costs. Pricing may also involve rules of thumb that 

are difficult to know and to incorporate into DOE’s analysis. 

 Given the uncertainty about actual markup practices used by appliance distributors, DOE 

uses an approach that reflects the following two key concepts:

 

1. Changes in the efficiency of the goods sold are not expected to increase economic profits. 

Thus, DOE calculates markups/gross margins to allow cost recovery for wholesale

companies in the distribution chain (including changes in the cost of capital) without 

changes in company profits. 

2. Efficiency improvements impact some distribution costs but not others. DOE sets 

markups and prices to cover the distribution costs expected to change with efficiency but 

not the distribution costs 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 distributor markups, DOE divides distributors’ operating 

expenses into two categories: (1) those that do not change when CGS increases due to amended 

efficiency standards (“fixed”), and (2) those that increase proportionately with CGS (“variable”). 

DOE defines fixed costs to include labor and occupancy expenses, because these costs are not 

likely to increase as a result of a rise in CGS due to amended efficiency standards. All other 

expenses, as well as the net profit, are assumed to vary in proportion to CGS. Although it is 

possible that some of the other expenses may not scale with CGS, DOE is inclined to take a more 

conservative position and include these as variable costs. Note that under DOE’s approach, a 

high fixed-cost component yields a low incremental markup. 

DOE calculated the incremental markup (MUINCR) for distributors using the following 

equation: 

MUINCR = CGSDist + VCDist

CGSDist

Where:

MUINCR = incremental distributor markup, 

CGSDist = distributor’s cost of goods sold, and 

6 
VCDist = distributor’s variable costs.

 

Table 6.3.3 shows the breakdown of operating expenses using the 2007 BES data. The 

incremental markup is calculated as 1.18.

Table 6.3.3 Data for Incremental Markup Calculation: Machinery, Equipment and 

Supplies Merchant Wholesalers

Amount 

($million)

Sales 328,820 

Cost of Goods Sold (CGS) 239,710 

Gross Margin (GM) 89,110 

Labor & Occupancy Expenses (“Fixed”)

Annual payroll 32,871 

Employer costs for fringe benefits 6,638 

Contract labor costs, including temporary help 572 

Purchased communication services 808 

Purchased repairs and maintenance to machinery and equipment 763 

Purchased repairs and maintenance to buildings, structures, and offices 462 

Lease and rental payments for machinery and equipment 492 

Lease and rental payments for buildings, offices, stores 2,655 

Purchased electricity 583 

Purchased fuels (except motor fuels) 220 

Water, sewer, refuse removal, and other utility payments 111 

Fixed Subtotal: 46,175 

Other Operating Expenses & Profit (“Variable”)

Expensed equipment 329 

Purchases of packaging materials and containers 390 

Purchases of other materials, parts, and supplies (not for resale) 1,321 

Expensed purchases of software 178 

Data processing and other purchased computer services 275 

Commission expense 1,213 

Purchased transportation, shipping and warehousing services 2,446 

Purchased advertising and promotional services 1,211 

Purchased professional and technical services 1,404 

Depreciation and amortization charges 2,461 

Taxes and license fees 917 

Other operating expenses 2 6,448 

Net profit before taxes 24,342 

Variable Subtotal: 42,935

Incremental Markup = (CGS+Total Other Operating Expenses and Profit)/CGS 1.18

Source: U.S. Census, 2007 Business Expenses Survey; U.S. Census, 1997 Business Expenses Survey

 By dividing expenses into fixed and variable components, the incremental markup 

approach envisions that distributors cover costs without changing profits. Although distributors 

may be able to reap higher profits for a time, DOE’s approach assumes that competition in the 

appliance distributor market, combined with relatively inelastic demand (i.e., the demand is not 

7 
expected to decrease significantly with a relatively small increase in price), will tend to pressure 

margins back down. 

 To measure the degree of competition, DOE estimated the four firm concentration ratio 

(FFCR) of commercial clothes washer sales in the two sectors included in the distribution 

channels: distributors and route operators. The FFCR represents the market share of the four 

largest firms in the relevant industry. Generally, an FFCR of less than 40% indicates that the 

industry is not concentrated, and an FFCR of more than 70% indicates that an industry is highly 

concentrated.5,6 

 The 2007 Economic Census reports the FFCR of merchant wholesalers (including 

laundry equipment distributors) and service providers (including route operators) in aggregate 

and for many detailed subsectors.7, 8

 The FFCR of commercial clothes washer sales is equal to 

the sector FFCR times the percent of total sales within each sector accounted for by major 

appliances, which are estimated from the financial reports of distributors and route 

operators.

9, 10, 11, 12 DOE approximates the weighted aggregate FFCR for commercial clothes 

washer sales by weighting these sector FFCRs by the market share of each sector (Table 6.3.4). 

 

Table 6.3.4 Concentration by Four Firms for Commercial Clothes Washer Distributors 

and Route Operators

Sector

Four Firm 

Concentration Ratio

(%of Sector Sales)

% of Revenue 

Accounted for 

by CCW

Four Firm 

Concentration Ratio

(% of CCW Sales) 

Market 

Share (%

of CCW 

Sales)

Distributors 60.3 85 51.3 33.3

Route operators 26.5 5.1 1.4 66.6

Weighted 

Aggregate FFCR 18.0 

Source: 2007 Economic Census (Note that distributors are represented by the sector NAICS 4238503 “Laundry and 

dry-cleaning equipment and supplies merchant wholesalers” and route operators are represented by the sector 

NAICS 81231 “coin-operated laundries and drycleaners”)

DOE’s estimation of the weighted average FFCR for commercial clothes washer sales is 

further supported by a 2011 IBISWorld Industry Report, which states that concentration in the 

sector is low.13 Note that the sector is referred to in the IBISWorld report as “Janitorial 

Equipment Supply Wholesaling,” rather than “Service establishment equipment and supplies 

merchant wholesalers” as in the U.S. Economic Census. 

8 
6.4 SALES TAXES

The sales tax represents state and local sales taxes that are applied to the consumer 

equipment price. The sales tax is a multiplicative factor that increases the consumer equipment 

price.

DOE derived state and local taxes from data provided by the Sales Tax Clearinghouse

and population data from the U.S. Census.14 DOE derived population-weighted average tax 

values for each Census “Reportable Domain” (large states and groups of states), as shown in 

Table 6.4.1. 

Table 6.4.1 Average Sales Tax Rates by Censes Division and Large State 

Reportable Domain State(s) Tax Rate

(February 2013)

1 CT, ME, NH, RI, VT 5.11%

2 MA 6.25%

3 NY 8.40%

4 NJ 6.95%

5 PA 6.40%

6 IL 8.05%

7 IN, OH 6.87%

8 MI 6.00%

9 WI 5.45%

10 IA, MN, ND, SD 6.86%

11 KS, NB 7.22%

12 MO 6.60%

13 VA 5.00%

14 DE, DC, MD, WV 6.87%

15 GA 7.10%

16 NC, SC 6.93%

17 FL 6.65%

18 AL, KY, MS 5.09%

19 TN 9.45%

20 AK, LA, OK 8.51%

21 TX 7.95%

22 CO 6.15%

9 
23 IA, MT, UT, WY 5.29%

24 AZ 8.15%

25 NV, NM 7.31%

26 CA 8.40%

27 AK, HI, OR, WA 5.28%

 

DOE then derived a U.S. average tax value of 7.04% for commercial clothes washers 

based on the product’s saturation within each Census division.15 

6.5 SUMMARY OF MARKUPS

Table 6.5.1 summarizes the markups at each stage in the distribution channel and the 

average sales tax.

Table 6.5.1 Summary of Markups 

Markup Baseline Incremental

Manufacturer 1.285

Distributor 1.37 1.18

Sales Tax 1.074

Overall 1.89 1.62

10 
REFERENCES

1. U.S. Census Bureau, Economic Census, Business Expenses Survey, Wholesale Trade, 

Machinery, Equipment and Supplies Merchant Wholesalers, 2007. (Last accessed 

February, 2013.) 

<http://www.census.gov/wholesale/www/historic_releases/annual_historic_releases.html

>

2. U.S. Census Bureau, Economic Census, Business Expenses Survey, Wholesale Trade, 

Machinery, Equipment and Supplies Merchant Wholesalers, 1997. Washington, D.C. 

3. U.S. Census Bureau, 2010 Annual Wholesale Trade Survey. 2010: Washington, D.C. 

http://www2.census.gov/wholesale/pdf/awts/2010_awts_salesinv_nomsbo_table2.pdf

4. Dale, L., D. Millstein, K. Coughlin, R. V. Buskirk, G. Rosenquist, A. Lekov, and S. 

Bhuyan, An Analysis of Price Determination and Markups in the Air-Conditioning and 

Heating Equipment Industry, 2004. Lawrence Berkeley National Laboratory. 

5. University of Maryland University College, Note on Industry Structure, 2002. (Last 

accessed February, 2013.) 

<http://info.umuc.edu/mba/public/AMBA607/IndustryStructure.html>

6. QuickMBA, Industry Concentration, 2010. (Last accessed February, 2013.) 

<http://www.quickmba.com/econ/micro/indcon.shtml>

7. U.S. Census Bureau, 2007 Economic Census, Establishment and Firm Size: (Including 

Legal Form of Organization), Wholesale Trade Subject Series, 2007. 

8. U.S. Census Bureau, 2007 Economic Census, Establishment and Firm Size: (Including 

Legal Form of Organization), Other Services (Except Public Administration) Subject 

Series, 2007. 

9. Mac-Gray Corporation, Form 10-K Annual Report Pursuant to Section 13 of 15(d) of the 

Securities Exchange Act of 1934, 2011. (Last accessed November, 2012.) 

<http://www.sec.gov/Archives/edgar/data/1038280/000104746912002365/a2207800z10-

k.htm#ds11601_item_8._financial_statements_and_supplementary_data>

10. Coinmach Corporation, 10-K Annual Report for Fiscal Year 2005, 2005 

<http://www.sec.gov/Archives/edgar/data/91693/000095012305007218/y09552e10vk.ht

m#106>

11. Coinmach Corporation, 10-K Annual Report for Fiscal Year 2007, 2007.

<http://www.sec.gov/Archives/edgar/data/1282858/000095012307008319/y35770e10vk.

htm>

11 
12. Alliance Laundry Systems, Alliance Laundry Holdings LLC Reports 2004 Sales and 

Earnings, 2005. Ripon, W.I. 

13. Culbert, K., Janitorial Equipment Supply Wholesaling in the US, 2011. <ibisworld.com> 

14. Sales Tax Clearinghouse, I., State sales tax rates along with combined average city and 

county rates, 2013. 2013.) <http://thestc.com/STrates.stm>

15. U.S. Energy Information Administration, Residential Energy Consumption Survey, U.S. 

Department of Energy, Editor. 2009. 

12 
7-i

CHAPTER 7. ENERGY AND WATER USE DETERMINATION

TABLE OF CONTENTS

7.1 INTRODUCTION ........................................................................................................... 7-1

7.2 PER-CYCLE ENERGY AND WATER CONSUMPTION BY EFFICIENCY 

LEVEL ............................................................................................................................. 7-1

7.3 AVERAGE ANNUAL ENERGY AND WATER CONSUMPTION BY 

EFFICIENCY LEVEL ..................................................................................................... 7-5

7.4 VARIABILITY OF ANNUAL ENERGY AND WATER CONSUMPTION .............. 7-10

LIST OF TABLES

Table 7.2.1 Load Usage Factors from the Test Procedure ...................................................... 7-2

Table 7.2.2 Top-Loading Commercial Clothes Washers: Per-Cycle Energy and Water 

Use by Efficiency Level....................................................................................... 7-5

Table 7.2.3 Front-Loading Commercial Clothes Washers: Per-Cycle Energy and 

Water Use by Efficiency Level ............................................................................ 7-5

Table 7.3.1 Commercial Clothes Washer Usage Data ............................................................ 7-6

Table 7.3.2 Top-Loading Commercial Clothes Washers, Multi-Family Application: 

Annual Energy and Water Use by Efficiency Level ............................................ 7-8

Table 7.3.3 Top-Loading Commercial Clothes Washers, Laundromat Application: 

Annual Energy and Water Use by Efficiency Level ............................................ 7-9

Table 7.3.4 Front-Loading Commercial Clothes Washers, Multi-Family Application: 

Annual Energy and Water Use by Efficiency Level ............................................ 7-9

Table 7.3.5 Front-Loading Commercial Clothes Washers, Laundromat Application: 

Annual Energy and Water Use by Efficiency Level ............................................ 7-9

LIST OF FIGURES

Figure 7.4.1 Probability Distribution for Commercial Clothes Washer Usage in MultiFamily Applications ........................................................................................... 7-10

Figure 7.4.2 Probability Distribution for Commercial Clothes Washer Usage in 

Laundromat Applications................................................................................... 7-11
7-1

CHAPTER 7. ENERGY AND WATER USE DETERMINATION

7.1 INTRODUCTION

 To carry out the life-cycle cost (LCC) and payback period (PBP) calculations described 

in Chapter 8, the U.S. Department of Energy (DOE) needed to determine the operating cost 

savings to consumers from more-efficient equipment. The LCC and PBP analysis requires data 

on annual energy and water use because, along with energy and water prices, DOE uses these 

data to establish the most significant component of consumer operating costs. (Maintenance and 

repair costs are the other contributors to operating cost.) This chapter describes how DOE 

determined the annual energy and water consumption of commercial clothes washers. 

7.2 PER-CYCLE ENERGY AND WATER CONSUMPTION BY EFFICIENCY 

LEVEL

 DOE established the annual energy and water consumption of commercial clothes 

washers by multiplying the per-cycle energy and water use by the number of cycles per year. 

Commercial clothes washer per-cycle energy consumption is composed of three 

components: water-heating energy, machine energy, and drying energy. The machine energy is 

composed of the motor energy needed for turning an agitator or rotating the wash basket. Based 

on language in the Energy Policy Act of 2005 (EPACT 2005), DOE is required to rate 

commercial clothes washers with the same test procedure established for residential clothes 

washers.1

 The DOE test procedures for commercial clothes washers currently being 

manufactured is codified at Title 10 of the Code of Federal Regulations (CFR) part 430, subpart 

B, appendix J1. In March 2012, DOE published a final rule amending its test procedures for 

residential clothes washers. (77 FR 13888) The March 2012 final rule included minor 

amendments to appendix J1 and also established a new test procedure at appendix J2 (hereafter, 

“appendix J2”).2 

 The DOE test procedure provides the following equations to calculate the total per-cycle 

clothes washer energy consumption: 

ET

T

CYCLE c ME D

e

HE

CW ++×=

 Where:

 

CWCYCLE = total commercial clothes washer per-cycle energy consumption,

HET = per-cycle water heating consumption (kWh/cycle), 

e = efficiency of electric water heater (100%) or gas water heater (75%),

c = conversion factor if water heater is gas (3,412 Btu/kWh),
7-2

MET = per-cycle machine electrical energy consumption (kWh/cycle), and 

DE = per-cycle energy consumption for removal of moisture from test load, i.e.,

dryer energy consumption (kWh/cycle). 

DOE determined the per-cycle machine energy use from the tests results of the 

considered models, performed according to the current DOE test procedure (77 FR 13888, Mar. 

7, 2012). It determined the per-cycle clothes drying energy use by using remaining moisture 

content (RMC) values contained in the costs and efficiencies data set developed in the 

engineering analysis. The energy required to remove moisture from clothes, i.e., the dryer 

energy, is a significant component of total clothes washer energy consumption. The equation 

used to determine this energy component is as described in the current DOE test procedure. 

 The following equation from the current DOE test procedure calculates the per-cycle 

energy consumption for removing moisture: 

DE ((LUF W ) (LUFavg Wavg ) (LUF W )) ́ ́+ ́+ ́= ( ) RMC %4 DEF  ́ ́ DUF _

max max min min

Where:

DE = per-cycle energy consumption for removal of moisture from test load, 

LUF = load usage factor, 

W = weight of test load [pounds (lb)/cycle], 

RMC = moisture content that remains after washing (lb),

DEF = nominal energy required for a clothes dryer to remove moisture from clothes 

(0.5kWh/lb), and 

DUF = dryer usage factor, a percentage of washer loads dried in a clothes dryer (0.91). 

 For clothes washers with manual fill control systems, only the LUFmax and LUFmin terms 

are used. For clothes washers with adaptive fill control systems, all three terms are included.

DOE implements the Load Usage Factor, as described in the current test procedure. According to

the test procedure description, LUFmax, LUFmin, and LUFavg are described in the Table 7.2.1. 

Table 7.2.1 Load Usage Factors from the Test Procedure

LUFmax LUFavg LUFmin

Manual Fill 0.72 - 0.28

Adaptive Fill 0.12 0.74 0.14

Since the test procedure describes the load usage for a typical residential application, 

DOE received comments on possible differences in loading in a commercial setting. DOE 

examined this issue by analyzing data from a study that monitored energy and water usage in 

multiple sites.a DOE found a pattern based on water usage in 6 sites to establish an alternative 

 

a PNNL 2008. Cal-UCONS Commercial Laundry Program Measurement and Evaluation - PNWD-3910. 
7-3

LUF distribution for the range of load usage factors. However, since this pattern does not have 

sufficient geographic coverage to conclusively establish a new usage factor, DOE has 

implemented an optional LUF as a sensitivity in the energy and water use calculations. The 

optional LUF for adaptive fill category of 0.26, 0.54, and 0.20 for Lmax, Lavg, and Lmin captures 

possible divergence in the load usage factor in the field. Details of this estimation are provided in 

Appendix 7-A. 

 

 When considering gas drying, DOE multiplies the dryer energy consumption by a gas 

correction factor to account for the additional energy a gas dryer requires relative to an electric 

dryer. The following equation determines per-cycle energy consumption for gas drying. 

DG GCF ×= DE

 Where:

DG = per-cycle energy consumption for removal of moisture from test load due to 

a gas dryer, i.e., dryer energy consumption (kWh/cycle), and 

GCF = gas correction factor = 1.12 3, 

 DE = per-cycle energy consumption for removal of moisture from test load, i.e.,

dryer energy consumption (kWh/cycle). 

 DOE previously used a modified energy factor (MEF) and a water factor (WF) to 

establish the energy and water efficiencies of clothes washers. The following equations 

determine the MEF and WF: 

CWCYCLE

C

MEF =

C

Q WF T =

Where:

C = container capacity [cubic feet (cu ft)],

CWCYCLE = total commercial clothes washer per-cycle energy consumption, and

 QT = per-cycle water consumption [gallons (gal)/cycle].

The existing test procedure was revised to include self-cleaning and non-active mode 

energy use.4 The revised test procedure uses an integrated modified energy factor (IMEF) that 

includes non-active and self-cleaning mode energy use, and this analysis takes those revisions 

into account by also using the IMEF, which is defined as: 
7-4

IMEF = C / (CWcycle +cyclic self-cleaning energy +cyclic off-mode energy)

The cyclic self-cleaning and cyclic off-mode energy are expressed in units of kWh/cycle 

and are obtained by dividing the annual energy use for each of those two modes by the number 

of wash cycles per year.

 

 The amended energy efficiency standards proposed in this rulemaking are based on the 

MEFJ2 metric. In the framework document, DOE stated it would consider establishing amended 

energy efficiency standards for commercial clothes washers on the IMEF metric, which would 

incorporate standby and off mode power. As described in Chapter 3, DOE performed an in-depth 

evaluation of the standby and off mode power characteristics of a representative sample of 

commercial clothes washers and has tentatively determined that establishing amended standards 

for commercial clothes washers based on an integrated energy metric would not be technically 

feasible. Instead, DOE proposes amended standards based on MEFJ2, which does not incorporate 

standby and off mode power. The amended water efficiency standards proposed in this 

rulemaking are based on the IWF metric contained in appendix J2, as the IWF metric provides a 

more representative measure of water consumption than the WF metric. For further discussion on 

metrics, please refer to Chapter 3.

 

As discussed in Chapter 5, DOE analyzed specific efficiency levels for top-loading and 

front-loading commercial clothes washers. Table 7.2.2 and Table 7.2.3 show the efficiency levels 

for top-loading and front-loading commercial clothes washers, respectively, and their 

corresponding per-cycle energy and water use. Per-cycle energy use is disaggregated into water 

heating, machine, and clothes drying. DOE determined the per-cycle clothes drying energy use 

by first establishing the RMC based on the engineering data. DOE then calculated the per-cycle 

clothes-drying energy use using the equation that determines the per-cycle energy consumption 

for the removal of moisture. DOE determined the per-cycle machine energy use from the 

engineering analysis based on test data. With the per-cycle clothes drying and machine energy 

known, DOE determined the per-cycle water-heating energy use by first determining the total 

per-cycle energy use (the clothes container volume divided by the MEFJ2) and then subtracting 

from it the per-cycle clothes-drying and machine energy. The water-heating energy use assumes 

the use of an electric water heater. DOE determined the per-cycle hot water use by dividing the 

per-cycle water-heating energy use by an assumed temperature rise of 75 oF (21.1 oC) and a 

specific heat of 0.0024 kWh/gal-oF (4.186 joule/gram-oC). DOE determined the total water use 

by multiplying the WF by the clothes container volume.
7-5

Table 7.2.2 Top-Loading Commercial Clothes Washers: Per-Cycle Energy and Water 

Use by Efficiency Level

Level

MEFJ2 IWF

RMC

Energy Use Water Use

Machine Dryer Water Heat† Hot Total 

cu.ft./kWh/cyc gal/cu.ft. kWh/cyc kWh/cyc kWh/cyc gal/cyc gal/cyc

Baseline 1.15 8.90 49.0% 0.220 2.08 0.391 2.2 27.6

1 1.35 8.80 48.0% 0.210 1.66 0.427 2.4 27.3

2 1.55 6.90 47.0% 0.100 1.71 0.324 1.8 22.8

† Based on the use of an electric water heater at 100% efficiency.

Table 7.2.3 Front-Loading Commercial Clothes Washers: Per-Cycle Energy and Water 

Use by Efficiency Level

Level

MEFJ2 IWF

RMC

Energy Use Water Use

Machine Dryer Water Heat† Hot Total 

cu.ft./kWh/cyc gal/cu.ft. kWh/cyc kWh/cyc kWh/cyc gal/cyc gal/cyc

Baseline 1.65 5.20 42.0% 0.110 1.10 0.264 1.5 14.6

1 1.80 4.50 39.0% 0.080 1.080 0.269 1.5 13.1

2 2.00 4.10 38.0% 0.100 1.100 0.134 0.7 11.9

3 2.20 3.90 36.0% 0.070 1.070 0.421 2.3 14.0

† Based on the use of an electric water heater at 100% efficiency.

7.3 AVERAGE ANNUAL ENERGY AND WATER CONSUMPTION BY 

EFFICIENCY LEVEL

 DOE determined the average annual energy and water consumption by multiplying the 

per-cycle energy and water consumption by the number of cycles per year. Because the 

predominant applications of commercial clothes washers are in multi-family buildings and 

laundromats, DOE focused only on these two building applications to determine the appropriate 

number of commercial clothes washer cycles per year. Other applications include lodging 

establishments (e.g., hotels and motels), inpatient health care facilities, and nursing homes. 

Relative to multi-family buildings and laundromats, these other applications are a small segment 

of the market. Therefore, DOE believes it is not critical to the analysis to accurately characterize 

commercial clothes washer usage for these applications. 

Table 7.3.1 shows the usage of commercial clothes washers from several studies. The last 

column of the table shows the variation in usage from each of the studies. Note that the variation 

shown for the City of Toronto and Multi-Housing Laundry Association (MLA) studies is based 

on the range of average values for either specific buildings or cities. Thus, if individual machine 
7-6

data were made available, the variation in usage would likely be much greater than indicated by 

the average data. For multi-family applications, the usage ranges from 1 to 6 cycles per day.

Assuming each study has equal representation, the average value from all studies performed on 

multi-family applications is 3 cycles per day. The sparse data on commercial clothes washer use 

in laundromats show a usage variation of 2.8 to 6 cycles per day with an average value of 4.1 

cycles per day. Based on 365 days per year of use, the minimum, average, and maximum usage 

data for multi-family applications of 1.5, 3.0, and 4.3 cycles per day yield 548, 1,095, and 1,570 

cycles per year, respectively. For laundromats, the minimum, average, and maximum usage data 

of 2.8, 4.1, and 6.0 cycles per day yield 1,022, 1,497, and 2,190 cycles per year, respectively. As 

a comparison, DOE assumed that residential clothes washers have an annual average usage of 

295 cycles per year.

Table 7.3.1 Commercial Clothes Washer Usage Data

Study Application Population Usage per Machine

(cycles/day)

Variation

(cycles/day)

Multi-Family Buildings

City of Toronto 

(1999)5

Multifamily coinoperated (6 buildings, 

945 apartments, 39 

machines)

3 family;

1 mixed;

1 seniors & 

singles; and 1 

seniors

4.3: metered 3.3 to 6.0 (variation 

based on average 

values from 6 

buildings) 

SCE/PNNL (2000)6 Multifamily coinoperated (12 machines)Seniors 2.2 : metered 1 to 4

MLA (2002)7 Multifamily coinoperated

(4 buildings in 4 cities, 

50 common area 

machines)

General 2.9: metered *

2.7: coin * 

4.5: survey * 

2.6 to 3.1 (variation 

based on average 

values from 4 cities)

Wisconsin/

PNNL (2004)8

Multifamily coinoperated (9 machines)Apartments & 

Condos

1.5: metered ** 1 to 3

Lightwash/

Equipoise (2004)9

Multifamily & 

Businesses

Not specified. 3: businesses & multifamily 

<10 units, industry value 

***

4: multifamily ≥10 units, 

industry value ***

Not available.

PG&E Cal-UCONS

(2008)10

Multifamily Not specified. 2.6: metered 1.1 to 3.9 (variation 

based on average 

values from 5 metered 

property sites)

SCGC, SDG&E CalUCONS (2008)11Multifamily Not specified. 4.2: metered 3.3 to 5.4 (variation 

based on average 

values from 5 metered 

property sites)

Alliance for Water 

Efficiency

Multifamily Not specified. 2: estimated
7-7

Study Application Population Usage per Machine

(cycles/day)

Variation

(cycles/day)

Laundromats

Lightwash/

Equipoise (2004)10

Laundromat & 

Institutional

Not specified. 6: industry value *** Not available.

Alliance for Water 

Efficiency

Laundromat Not specified. 4.8: estimated Not available.

San Diego County 

Water Authority 

(2006)12

Laundromat Not specified. 2.8: metered. 2.3 to 4.7 (variation 

based on average 

values from 3 metered 

sites)

PG&E Cal-UCONS 

(2008) 11

Laundromat Not specified. 4.8: metered 4.2 to 5.3 (variation 

based on average 

values from top-load 

and front-load 

washing machines in 

one metered site.

SCGC and SDG&E 

Cal-UCONS (2008)12

Laundromat Not specified. 3.5: metered 2.4 to 4.6 (variation 

based on average 

values from top-load 

and front-load 

washing machines in 

one metered site.

Sutter, M. (2006)13 Laundromat Not specified. 3.0: metered 2.97±0.70 

Coin-op (2013)14 Laundromat Not specified. 3.6: survey data Not available.

* Usage per machine values derived based on water usage of 32 gallons per cycle.

** Report states that multifamily industry average is 2.5 cycles per day and attributes the lower reported usage per

machine value to a lower ratio of tenants to machines.

*** Usage per machine values are “industry values.” Study says that survey data in report may be unreliable.

DOE calculated commercial clothes washer annual energy consumption with the 

following equation: 

CWANNUAL = CWCYCLE × N

 Where:

CWANNUAL = total annual commercial clothes washer energy consumption, 

CWCYCLE = total per-cycle commercial clothes washer energy consumption, and 

N = representative average commercial clothes washer use.

DOE calculated commercial clothes washer annual water consumption with the following 

equation: 
7-8

CWANNUAL WATER = CWCYCLE WATER × N

 Where:

CWANNUAL WATER = total annual commercial clothes washer water consumption, 

CWCYCLE WATER = total per-cycle commercial clothes washer water consumption, and 

N = representative average commercial clothes washer use.

Table 7.3.2 and Table 7.3.3 show the annual energy and water consumption for multifamily buildings and laundromats, respectively, for top-loading washers. Table 7.3.4 and Table 

7.3.5 show the annual energy and water consumption for multi-family buildings and 

laundromats, respectively, for front-loading washers. The electric and gas water heating and 

drying consumption in Table 7.3.2 through Table 7.3.5 are weighted by the share of buildings 

that use electric and gas water heaters and dryers. Based on data from the Residential Energy 

Consumption Survey (RECS) 2009 15 and Consortium for Energy Efficiency (CEE), 16 the shares 

of electric and gas water heating and drying in multi-family applications are: 25 percent and 75 

percent electric and gas water heating, respectively, and 40 percent and 60 percent electric and 

gas drying, respectively. Also based on CEE data, 100 percent of laundromats use gas water 

heating and drying.The energy and water consumption values provided are based on average use 

cycles of 3.0 cycles per day for multi-family buildings and 4.1 cycles per day for laundromats. 

The annual water-heating and clothes-drying energy consumption reflects the use of either an 

electric or a gas water heater.

Table 7.3.2 Top-Loading Commercial Clothes Washers, Multi-Family Application:

Annual Energy and Water Use by Efficiency Level

Level

MEFJ2 IWF

Annual Energy Use*

Annual 

Water Use

Water Heating** Drying

Electric Gas Electric Gas Machine

cu.ft./kWh/cyc gal/cu.ft. kWh/yr MMBtu/yr kWh/yr MMBtu/yr kWh/yr 1000 gal/year

Baseline 1.15 8.90 107.1 1.46 913.0 5.23 240.9 30.21

1 1.35 8.80 138.6 1.89 692.0 3.97 230.0 29.87

2 1.55 6.90 108.7 1.48 690.9 3.96 91.9 24.26

* Annual usage based on 3.0 cycles per day (1,095 cycles per year).

** Electric and gas water heating based on water heater efficiencies of 100% for electric and 75% for gas.
7-9

Table 7.3.3 Top-Loading Commercial Clothes Washers, Laundromat Application: 

Annual Energy and Water Use by Efficiency Level

Level

MEFJ2 IWF

Annual Energy Use*

Annual 

Water Use

Water Heating** Drying

Electric Gas Electric Gas Machine

cu.ft./kWh/cyc gal/cu.ft. kWh/yr MMBtu/yr kWh/yr MMBtu/yr kWh/yr 1000 gal/year

Baseline 1.15 8.90 0.0 2.66 0.0 11.92 329.3 41.30

1 1.35 8.80 0.0 3.45 0.0 9.04 314.4 40.84

2 1.55 6.90 0.0 2.70 0.0 9.02 133.0 33.17

* Annual usage based on 4.1 cycles per day (1,497 cycles per year).

** Electric and gas water heating based on water heater efficiencies of 100% for electric and 75% for gas.

Table 7.3.4 Front-Loading Commercial Clothes Washers, Multi-Family Application:

Annual Energy and Water Use by Efficiency Level

Level

MEFJ2 IWF

Annual Energy Use*

Annual 

Water Use

Water Heating** Drying

Electric Gas Electric Gas Machine

cu.ft./kWh/cyc gal/cu.ft. kWh/yr MMBtu/yr kWh/yr MMBtu/yr kWh/yr 1000 gal/year

Baseline 1.65 5.20 88.9 1.21 552.9 3.17 120.5 15.94

1 1.80 4.50 87.9 1.20 516.1 2.96 87.6 14.10

2 2.00 4.10 51.4 0.70 501.3 2.87 95.7 12.85

3 2.20 3.90 119.3 1.63 439.0 2.52 61.2 14.12

* Annual usage based on 3.0 cycles per day (1,095 cycles per year).

** Electric and gas water heating based on water heater efficiencies of 100% for electric and 75% for gas.

Table 7.3.5 Front-Loading Commercial Clothes Washers, Laundromat Application: 

Annual Energy and Water Use by Efficiency Level

Level

MEFJ2 IWF

Annual Energy Use*

Annual 

Water Use

Water Heating** Drying

Electric Gas Electric Gas Machine

cu.ft./kWh/cyc gal/cu.ft. kWh/yr MMBtu/yr kWh/yr MMBtu/yr kWh/yr 1000 gal/year

Baseline 1.65 5.20 0.0 2.21 0.0 7.22 164.7 21.80

1 1.80 4.50 0.0 2.19 0.0 6.74 119.8 19.28

2 2.00 4.10 0.0 1.28 0.0 6.55 136.7 17.57

3 2.20 3.90 0.0 2.97 0.0 5.73 90.1 19.30

* Annual usage based on 4.18 cycles per day (1,497 cycles per year).

** Electric and gas water heating based on water heater efficiencies of 100% for electric and 75% for gas.
7-10

7.4 VARIABILITY OF ANNUAL ENERGY AND WATER CONSUMPTION

 DOE determined the variability in annual energy and water consumption using the 

average usage data from Table 7.3.1. From the table, there are nine average usage values for 

multi-family buildings ranging from a low of 1.5 cycles per day to a high of 4.3 cycles per day.

For laundromats, the low and high values are 2.8 and 6.0 cycles per day, respectively. 

DOE weighted the usage from each study to vary the annual energy and water 

consumption of commercial clothes washers when it conducted the LCC and PBP analysis. As 

shown in Figure 7.4.1and Figure 7.4.2, DOE weighted the usage studies equally for multi-family 

and laundromats applications. As will be described in chapter 8 on the LCC and PBP analysis, 

DOE used the usage variability to vary the annual energy and water consumption for multifamily and laundromat applications when it conducted the LCC and PBP analysis. 

Figure 7.4.1 Probability Distribution for Commercial Clothes Washer Usage in 

Multi-Family Applications

0.00

0.02

0.04

0.06

0.08

0.10

1 1.5 2 2.5 3 3.5 4 4.5

Probability

Usage (cycles/day)

Wisc/PNNL

Alliance

SCE/PNNL

PG&E

MLA

Equip<10

Equip>=10

SCGC, et al.

Toronto
7-11

Figure 7.4.2 Probability Distribution for Commercial Clothes Washer Usage in 

Laundromat Applications

 

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5

Probability

Usage (cycles/day)

San Diego

Sutter, M.

SCGC & SDG&E; Coin-op 

PG&E; Alliance

Equip
7-12

REFERENCES

 

1

 Energy Policy Act of 2005, Title 1—Energy Efficiency, Subtitle C—Energy Efficient 

Products, Sec. 136., Energy conservation standards for commercial equipment. 

2

 U.S. Federal Register. Energy Conservation Standards for Certain Consumer Products and 

Commercial and Industrial Equipment, Final Rule; technical amendment, October 18, 2005. 

70 FR 60407. 

3

 Biermayer, P.J. Preliminary Engineering Analysis for Clothes Washers, October 1996. 

Berkeley, CA. LBNL-47889: Appendix D. 

4

 U.S. Office of the Federal Register. Title 10, Code of Federal Regulations, Part 430–Energy 

Conservation Program for Certain Commercial and Industrial Product, Subpart B, Appendix 

JI—Residential Clothes Washers. 2010. Washington, D.C. 

5

 City of Toronto Works and Emergency Services and Toronto Community Housing 

Corporation. Multi-Unit Residential Clothes Washer Replacement Pilot Project 1999, May, 

2003. Toronto, Ontario, Canada. 

6

 G. P. Sullivan, J. W. Currie, T.C. Hillman, and G.B. Parker, Southern California Edison 

High-Performance Clothes Washer Demonstration at Leisure World Laguna Woods, Final 

Report, December 2000. Prepared for Southern California Edison Company by Battelle 

Pacific Northwest National Laboratory, Richland, WA. 

7

 National Research Center, Inc., A National Study of Water & Energy Consumption in 

Multifamily Housing, November 2002. Prepared for the Multi-Housing Laundry Association 

by National Research Center, Inc, Boulder, CO. 

8

 Sullivan, G.P., G. B. Parker, and J. W. Currie. Wisconsin Focus on Energy Resource Efficient 

Laundry (REL) Room Demonstration, Final Report, June 2004. Prepared for Wisconsin 

Focus on Energy by Battelle Pacific Northwest National Laboratory, Richland, WA. Report 

No. PNWD-3445. 

9

 Equipoise Consulting, Inc. Final Report for Local Energy Efficiency Program No. 148-02 

Energy Solutions’ 2002/03 LightWash Program, June 30, 2004. Prepared by Equipoise 

Consulting Incorporated.

10 PNNL 2008a. Cal-UCONS Commercial Laundry Program Measurement and Evaluation: 

 Pacific Gas and Electric Final Report, PNWD-3910. Prepared by Battelle. 

11 PNNL 2008b. Cal-UCONS Commercial Laundry Program Measurement and Evaluation: 

Pacific Gas and Electric Final Report, PNWD-3910. Prepared by Battelle. 

12 San Diego County Water Authority. Report on the Monitoring and Assessment of Water 
7-13

 

 Savings from the Coin-operated Multi-Load Clothes Washers Voucher Initiative Program, 

 October 2006. Prepared by Water Management, Inc. 

13 Sutter, M., 2006, Estimating Commercial Clothes Washer Use in California Coin Laundry 

Stores, 2006 ACEEE Summer Study on Energy Efficiency in Buildings.

<http://www.eceee.org/conference_proceedings/ACEEE_buildings/2006/Panel_9/p9_29/paper> 

14 America Coin-Op. State of the Industry, 2012 – 2013. April 2013. 

 <http://www.americancoinop.com/> 

15 U.S. Energy Information Administration, Residential Energy Consumption Survey 2009. 

 Washington, DC. 

 < http://www.eia.gov/consumption/residential/data/2009/> 

16 Consortium for Energy Efficiency. Commercial, Family-Sized Washers: An Initiative

Description of the Consortium for Energy Efficiency, 1998. Boston, MA. 

 <http://www.cee1.org/com/cwsh/cwsh-main.php3> 
CHAPTER 8. LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

TABLE OF CONTENTS

8.1 INTRODUCTION ........................................................................................................... 8-1

8.1.1 General Approach for LCC and PBP Analysis ................................................................ 8-1

8.1.2 Overview of LCC and PBP Inputs ................................................................................... 8-2

8.2 LIFE-CYCLE COST INPUTS ........................................................................................ 8-6

8.2.1 Total Installed Cost Inputs ............................................................................................... 8-6

8.2.1.1 Baseline Manufacturer Cost ........................................................................... 8-8

8.2.1.2 Markups ......................................................................................................... 8-9

8.2.1.3 Overall Markup .............................................................................................. 8-9

8.2.1.4 Standard-Level Manufacturer Cost Increases .............................................. 8-10

8.2.1.5 Installation Cost ........................................................................................... 8-10

8.2.1.6 Total Installed Cost ...................................................................................... 8-11

8.2.1.7 Future Product Prices ................................................................................... 8-12

8.2.2 Operating Cost Inputs .................................................................................................... 8-14

8.2.2.1 Annual Energy and Water Consumption ..................................................... 8-16

8.2.2.2 Energy and Water Prices .............................................................................. 8-19

8.2.2.3 Energy and Water Price Trends ................................................................... 8-25

8.2.2.4 Repair and Maintenance Costs ..................................................................... 8-27

8.2.3 Product Lifetime ............................................................................................................ 8-28

8.2.4 Discount Rates ............................................................................................................... 8-30

8.2.5 Effective Date of Standard ............................................................................................. 8-33

8.2.6 Equipment Assignment for the Base Case ..................................................................... 8-33

8.3 PAYBACK PERIOD INPUTS ...................................................................................... 8-35

8.4 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS ...................................... 8-35

8.4.1 Base Case LCC Distributions ........................................................................................ 8-36

8.4.2 Standard-Level Distributions of LCC Impacts .............................................................. 8-39

8.4.3 Standard-Level PBP Distributions ................................................................................. 8-39

8.4.4 LCC and PBP Results .................................................................................................... 8-40

8.5 REBUTTABLE PAYBACK PERIOD .......................................................................... 8-47

8.5.1 Metric ............................................................................................................................. 8-47

8.5.2 Inputs.............................................................................................................................. 8-47

8.5.3 Results ............................................................................................................................ 8-48

LIST OF TABLES

Table 8.1.1 LCC and PBP Input Summary ............................................................................. 8-5

Table 8.2.1 Inputs for Total Installed Cost ............................................................................. 8-7

Table 8.2.2 Commercial Clothes Washers: Baseline Manufacturer Costs ............................. 8-8

Table 8.2.3 Summary of Average Markups ............................................................................ 8-9

8-i 
Table 8.2.4 Overall Markups ................................................................................................ 8-10

Table 8.2.5 Top-Loading Commercial Clothes Washers: Standard-Level Manufacturer 

Cost Increases .................................................................................................... 8-10

Table 8.2.6 Front-Loading Commercial Clothes Washers: Standard-Level 

Manufacturer Cost Increases.............................................................................. 8-10

Table 8.2.7 Commercial Clothes Washers: Baseline Installation Costs ............................... 8-11

Table 8.2.8 Top-Loading Commercial Clothes Washers: Consumer Equipment Prices, 

Installation Costs, and Total Installed Costs ...................................................... 8-11

Table 8.2.9 Front-Loading Commercial Clothes Washers: Consumer Equipment 

Prices, Installation Costs, and Total Installed Costs .......................................... 8-12

Table 8.2.10 Inputs for Operating Cost................................................................................... 8-15

Table 8.2.11 Top-Loading Commercial Clothes Washers, Multi-Family Application: 

Annual Energy and Water Use by Efficiency Level .......................................... 8-17

Table 8.2.12 Top-Loading Commercial Clothes Washers, Laundromat Application: 

Annual Energy and Water Use by Efficiency Level .......................................... 8-18

Table 8.2.13 Front-Loading Commercial Clothes Washers, Multi-Family Application: 

Annual Energy and Water Use by Efficiency Level .......................................... 8-18

Table 8.2.14 Front-Loading Commercial Clothes Washers, Laundromat Application: 

Annual Energy and Water Use by Efficiency Level .......................................... 8-19

Table 8.2.15 Average Commercial Electricity Prices in 2011 ................................................ 8-20

Table 8.2.16 Average Commercial Electricity Prices for Commercial Clothes Washers 

in 2012 ............................................................................................................... 8-21

Table 8.2.17 Average Commercial Natural Gas Prices in 2012 ............................................. 8-23

Table 8.2.18 Average Per-Unit-Volume Water and Waste Water Prices in 2012 .................. 8-25

Table 8.2.19 Top-Loading Commercial Clothes Washer Annualized Repair Costs .............. 8-28

Table 8.2.20 Front-Loading Commercial Clothes Washer Annualized Repair Costs ............ 8-28

Table 8.2.21 Commercial Clothes Washers: Product Lifetime Estimates and Sources ........ 8-29

Table 8.2.22 Commercial Clothes Washers: Average, Minimum, and Maximum 

Product Lifetimes ............................................................................................... 8-29

Table 8.2.23 Data for the Calculation of Weighted-Average Cost of Capital for Sectors 

that Purchase Commercial Clothes Washers ..................................................... 8-32

Table 8.2.24 Weighted Average Cost of Capital for Sectors that Purchase Commercial 

Clothes Washers................................................................................................. 8-32

Table 8.2.25 Top-Loading Commercial Clothes Washers: Base Case Market Shares ........... 8-34

Table 8.2.26 Front-Loading Commercial Clothes Washers: Base Case Market Shares......... 8-34

Table 8.4.1 Top-Loading Commercial Clothes Washers, Multi-Family Application: 

LCC and PBP Results ........................................................................................ 8-41

Table 8.4.2 Top-Loading Commercial Clothes Washers, Laundromat Application: 

LCC and PBP Results ........................................................................................ 8-41

Table 8.4.3 Front-Loading Commercial Clothes Washers, Multi-Family Application: 

LCC and PBP Results ........................................................................................ 8-41

Table 8.4.4 Front-Loading Commercial Clothes Washers, Laundromat Application: 

LCC and PBP Results ........................................................................................ 8-42

Table 8.5.1 Top-Loading Commercial Clothes Washers, Multi-Family Application: 

Rebuttable Payback Periods ............................................................................... 8-48

8-ii
Table 8.5.2 Top-Loading Commercial Clothes Washers, Laundromat Application: 

Rebuttable Payback Periods ............................................................................... 8-49

Table 8.5.3 Front-Loading Commercial Clothes Washers, Multi-Family Application: 

Rebuttable Payback Periods ............................................................................... 8-49

Table 8.5.4 Front-Loading Commercial Clothes Washers, Laundromat Application: 

Rebuttable Payback Periods ............................................................................... 8-49

LIST OF FIGURES

Figure 8.1.1 Flow Diagram of Inputs for the Determination of LCC and PBP ....................... 8-4

Figure 8.2.1 Historic Nominal and Deflated Producer Price Indexes for Commercial 

Laundry and Drycleaning Machinery ................................................................ 8-13

Figure 8.2.2 Historic Nominal and Deflated Producer Price Indexes for Household 

Laundry Equipment ........................................................................................... 8-14

Figure 8.2.3 Electricity Price Trends...................................................................................... 8-26

Figure 8.2.4 Natural Gas Price Trends ................................................................................... 8-26

Figure 8.2.5 Water Price Trend .............................................................................................. 8-27

Figure 8.4.1 Top-Loading Commercial Clothes Washers, Multi-Family Application: 

Base Case LCC Distribution .............................................................................. 8-37

Figure 8.4.2 Top-Loading Commercial Clothes Washers, Laundromat Application: 

Base Case LCC Distribution .............................................................................. 8-37

Figure 8.4.3 Front-Loading Commercial Clothes Washers, Multi-Family Application: 

Base Case LCC Distribution .............................................................................. 8-38

Figure 8.4.4 Front-Loading Commercial Clothes Washers, Laundromat Application: 

Base Case LCC Distribution .............................................................................. 8-38

Figure 8.4.5 Front-Loading Commercial Clothes Washers, Multi-Family Application: 

Distribution of LCC Impacts for EL3 ................................................................ 8-39

Figure 8.4.6 Front-loading Commercial Clothes Washers, Multi-Family Application: 

Distribution of PBPs for EL 3 ............................................................................ 8-40

Figure 8.4.7 Range of LCC Savings for Top-Loading Commercial Clothes Washers, 

Multi-Family ...................................................................................................... 8-43

Figure 8.4.8 Range of LCC Savings for Top-Loading Commercial Clothes Washers, 

Laundromat ........................................................................................................ 8-43

Figure 8.4.9 Range of LCC Savings for Front-Loading Commercial Clothes Washers, 

Multi-Family ...................................................................................................... 8-44

Figure 8.4.10 Range of LCC Savings for Front-Loading Commercial Clothes Washers, 

Laundromat ........................................................................................................ 8-44

Figure 8.4.11 Range of Payback Periods for Top-Loading Commercial Clothes Washers, 

Multi-Family ...................................................................................................... 8-45

Figure 8.4.12 Range of Payback Periods for Top-Loading Commercial Clothes Washers, 

Laundromat ........................................................................................................ 8-45

Figure 8.4.13 Range of Payback Periods for Front-Loading Commercial Clothes 

Washers, Multi-Family ...................................................................................... 8-46

Figure 8.4.14 Range of Payback Periods for Front-Loading Commercial Clothes 

Washers, Laundromat ........................................................................................ 8-46

8-iii
CHAPTER 8. LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

8.1 INTRODUCTION

 This chapter describes the Department of Energy (DOE)’s methodology for analyzing the 

economic impacts of possible energy efficiency standards on individual consumers. The effect 

of standards 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 in the consumer analysis 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 expense and operating costs (including energy expenditures). DOE 

discounts future operating costs to the time of purchase, and sums them over the lifetime 

of the equipment. 

• Payback period (PBP) measures the amount of time it takes customers to recover the 

assumed higher purchase price of more energy-efficient equipment through lower 

operating costs.

• Rebuttable payback period is a special case of the PBP. Where LCC and PBP are 

estimated over a range of inputs reflecting actual conditions, rebuttable payback period is 

based on laboratory conditions, specifically DOE test procedure inputs. 

 Inputs to the LCC and PBP are discussed in sections 8.2 and 8.3, respectively, of this 

chapter. Results for the LCC and PBP are presented in section 8.4. The rebuttable PBP is 

discussed in section 8.5. 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 (http://www.eere.energy.gov/buildings/appliance_standards/). 

Details and instructions for using the spreadsheets are discussed in Appendix 8A. 

8.1.1 General Approach for LCC and PBP Analysis 

 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 in the inputs using Monte Carlo simulation and probability 

distributions. A detailed explanation of Monte Carlo simulation and the use of probability 

distributions is contained in Appendix 8A. DOE developed LCC and PBP spreadsheet models 

incorporating both Monte Carlo simulation and probability distributions by using Microsoft 

Excel® spreadsheets combined with Crystal Ball® (a commercially available add-in program). 

 As described in Chapter 7, DOE established the variability and uncertainty in energy and 

water use by defining the uncertainty and variability in the usage (in cycles per day) of the 

8-1
equipment. As will be described later in this chapter, the variability and uncertainty in energy 

and water pricing are characterized by regional differences in energy and water prices.

 DOE displays the LCC and PBP results as distributions of impacts compared to the 

baseline conditions. Results are presented at the end of this chapter and are based on 10,000 

samples per Monte Carlo simulation run. To illustrate the implications of the analysis, DOE 

generated a frequency chart depicting the variation in LCC and PBP for each standard level 

considered. 

8.1.2 Overview of LCC and PBP Inputs

 The LCC is the total consumer expense over the life of the equipment, including purchase 

expense and operating expense (including energy expenditures). DOE discounts future operating 

expenses to the time of purchase and sums them over the lifetime of the equipment. The PBP is 

the change in purchase expense due to an increased efficiency standard divided by the change in 

annual operating expense that results from the standard. It represents the number of years it will 

take the customer to recover the increased purchase expense through decreased operating 

expenses. 

 DOE categorizes inputs to the LCC and PBP analysis as follows: (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:

• Baseline manufacturer cost: The costs incurred by the manufacturer to produce 

equipment meeting existing minimum efficiency standards. 

• Standard-level manufacturer cost increases: The change in manufacturer cost associated 

with producing equipment to meet a particular standard level. 

• Markups and sales tax: The markups and sales tax associated with converting the 

manufacturer cost to a consumer equipment price. The markups and sale tax are 

described in detail in Chapter 7, Markups for Equipment Price Determination. 

• Installation cost: The cost to the consumer of installing the equipment. The installation 

cost represents all costs required to install the equipment other than the marked-up 

consumer equipment price. The installation cost includes labor, overhead, and any 

miscellaneous materials and parts. Thus, the total installed cost equals the consumer 

equipment price plus the installation cost. 

The primary inputs for calculating the operating cost are:

• Equipment energy and water consumption: The equipment energy consumption is the 

site energy use associated with operating the equipment. The water consumption is the 

8-2
site water use associated with operating the equipment. Chapter 7, Energy and Water 

Use Determination, details how DOE determined the equipment energy and water 

consumption based on various data sources. 

• Equipment efficiency: The equipment efficiency dictates the equipment energy and water 

consumption associated with standard-level equipment (i.e., equipment with efficiencies 

greater than baseline equipment). Chapter 7, Energy and Water Use Determination, 

details how energy and water consumption change with increasing equipment efficiency. 

 

• Energy and water prices: Energy and water prices are the prices paid by consumers for 

energy (i.e., electricity, gas, or oil) and water. DOE determined current energy prices 

based on data from the DOE- EIA. DOE determined water prices based on data from the 

American Water Works Association (AWWA).

• Energy and water price trends: DOE used the EIA Annual Energy Outlook 2013

(AEO2013) to forecast energy prices into the future. For the results presented in this 

chapter, DOE used the AEO2013 reference case to forecast future energy prices. DOE 

used consumer price index data specific to water and sewerage maintenance from the 

Bureau of Labor Statistics as the basis for its water price trend.

• 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 equipment. 

• Lifetime: The age at which the equipment is retired from service. 

• Discount rate: The rate at which DOE discounted future expenditures to establish their 

present value. 

Figure 8.1.1 graphically depicts the relationships between the installed cost and operating 

cost inputs for the calculation of the LCC and PBP. In the figure below, the yellow boxes 

indicate the inputs, the green boxes indicate intermediate outputs, and the blue boxes indicate the 

final outputs (the LCC and PBP). 

8-3
Figure 8.1.1 Flow Diagram of Inputs for the Determination of LCC and PBP

Table 8.1.1 summarizes the input values that DOE used to calculate the LCC and PBP. 

Each table summarizes the total installed cost inputs and the operating cost inputs including the 

lifetime, discount rate, and energy and water price trends. DOE characterized all of the total cost 

inputs with single-point values, but characterized several of the operating cost inputs with 

probability distributions that capture the input’s uncertainty and/or variability. For those inputs 

characterized with probability distributions, the values provided in the following tables are the 

average or typical values. Also listed in the following tables is the section of the technical 

support document (TSD) where more detailed information on the inputs can be found. 

Energy and 

Water Use

Energy and 

Water Prices

From Energy 

and Water Use 

Analysis

(Use is a 

function of 

Efficiency)

Installation 

Cost

Equipment 

Price

From 

Engineering 

Analysis

(Price is a 

function of 

Efficiency)

From 

Markups 

Analysis

Baseline 

Manufacturer 

Price

Std-Level 

Manufacturer 

Price

Consumer 

Retail Price 

Markup

Sales Tax

Repair Cost

Maintenance 

Cost

Annual Energy 

and Water 

Expense

Lifetime

Discount Rate

Energy and 

Water Price

Annual 

Operating 

Expense

Total Installed 

Cost

Lifetime 

Operating 

Expense

Payback Life-Cycle 

Cost

8-4
Table 8.1.1 LCC and PBP Input Summary

Input

Product 

Class Average or Typical Value Characterization

TSD Section 

Reference

Total Installed Cost Inputs

Baseline Manufacturer 

Cost

TopLoading 1.15 MEF/8.90 IWF = $522.42 Single-Point Value

8.2.1.2 FrontLoading 1.65 MEF/5.20 IWF = $828.40 Single-Point Value

Standard-Level 

Manufacturer Cost 

Increase

TopLoading1.35 MEF/8.80 IWF = $0.00

1.55 MEF/6.90 IWF = $39.39 Single-Point Value

8.2.1.3 FrontLoading

1.80 MEF/4.50 IWF = $0.00

2.00 MEF/4.10 IWF = $0.31

2.20 MEF/3.90 IWF = $19.55 Single-Point Value

Manufacturer Markup Both 1.29 Single-Point Value 6.4

Distributor Markup Both Baseline = 1.3

Incremental = 1.18 Single-Point Value 6.5 

Sales Tax Both 1.0711 Single-Point Value 6.6

Installation Cost Both $223.00 Single-Point Value 8.2.1.5

Operating Cost Inputs

Usage Both Multi-Family = 3.0 cycles/day

Laundromat = 4.1 cycles/day

Uniform distribution:

Multi-Family = 1.0 to 6.0cyc/day

Laundromat = 2.8 to 6 cyc/day

7.3, 7.4

Annual Electricity Use

TopLoading

Baseline use*:

Multi-Family = 1,237kWh 

Laundromat = 326 kWh

Variability based on usage

7.3

FrontLoading

Baseline use*:

Multi-Family = 748 kWh 

Laundromat = 163 kWh

Variability based on usage

Annual Gas Use

TopLoading

Baseline use*:

Multi-Family = 6.6 MMBtu

Laundromat = 14.5 MMBtu

Variability based on usage

7.3

FrontLoading

Baseline use*:

Multi-Family = 4.3 MMBtu

Laundromat = 9.3 MMBtu

Variability based on usage

Annual Water Use

TopLoading

Baseline use*:

Multi-Family = 29.6 103 gallon

Laundromat = 40.9 103 gallon

Variability based on usage

7.3

FrontLoading

Baseline use*:

Multi-Family = 15.6 103 gallon

Laundromat = 21.6 103 gallon

Variability based on usage

Energy Prices Both Elec = 10.4 ¢/kWh (average)

Gas = 8.74 $/MMBtu (average) Variable based on location 8.2.2.2

Water and Wastewater 

Prices Both

Water = 3.54 $/103 gallon

(average)

Wastewater = 4.48 $/103 gallon

(average)

Variable based on region 8.2.2.2

Repair and 

Maintenance Costs Both Annualized repair cost =

1⁄2 Equipment price / Lifetime Single-Point Value 8.2.2.4

Lifetime Both Multi-Family = 11.25 years

Laundromat = 7.125 years Weibull distribution 8.2.3

8-5
Discount Rate Both 6.0% Custom distribution 8.2.4

Energy Price Trend Both AEO 2013 Reference Case Two sensitivities:

High & Low Growth Cases 8.2.2.3

Water and Wastewater 

Price Trend Both Bureau of Labor Statistics:Water and sewerage CPI Single forecast 8.2.2.3

* Annual use based on electric water heating and electric clothes drying

** Annual use provided for baseline product only. Annual use decreases with increased product efficiency.

8.2 LIFE-CYCLE COST INPUTS

Life-cycle cost is the total customer expense over the life of an appliance, including 

purchase expense and operating costs (including energy expenditures). DOE discounts future 

operating costs to the time of purchase, and sums them over the lifetime of the equipment. DOE 

defines LCC by the following equation: 

( ) ∑= +

+=

N

t

t

t

r

OC ICLCC

1 1

where:

LCC = Life-cycle cost in dollars,

IC = Total installed cost in dollars,

∑ = Sum over the lifetime, from year 1 to year N,

N = Lifetime of appliance in years,

OC = Operating cost in dollars,

r = Discount rate, and 

t = Year for which operating cost is being determined.

DOE expresses dollar values in 2012$. 

 The following sections discuss total installed cost, operating cost, lifetime, and discount 

rate.

8.2.1 Total Installed Cost Inputs

DOE defines the total installed cost using the following equation:

EQPIC += INST

where:

EQP = Equipment price (i.e., customer price for the equipment only), expressed in 

dollars, and 

INST = Installation cost or the customer price to install equipment (i.e., the cost for 

labor and materials), also in dollars.

8-6
 The equipment price is based on how the consumer purchases the equipment. As 

discussed in Chapter 7, Markups for Equipment Price Determination, DOE defined markups and 

sales taxes for converting manufacturing costs into consumer equipment prices. 

Table 8.2.1 summarizes the inputs for the determination of total installed cost.

Table 8.2.1 Inputs for Total Installed Cost 

Baseline Manufacturer Cost

Standard-Level Manufacturer Cost

Manufacturer Markup

Retailer or Distributor Markup

Sales Tax

Installation Cost

The baseline manufacturer cost is the cost incurred by the manufacturer to produce 

equipment meeting existing minimum efficiency standards. Standard-level manufacturer cost 

increases are the change in manufacturer cost associated with producing equipment at a standard 

level. Markups and sales tax convert the manufacturer cost to a consumer equipment price. The 

installation cost is the cost to the consumer of installing the equipment and represents all costs 

required to install the equipment other than the marked-up consumer equipment price. The 

installation cost includes labor, overhead, and any miscellaneous materials and parts. Thus, the 

total installed cost equals the consumer equipment price plus the installation cost. 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 EQP INST

= × +

+=

_

where:

ICBASE = Baseline total installed cost, 

EQPBASE = Consumer equipment price for baseline models, 

INSTBASE = Baseline installation cost,

COSTMFG = Manufacturer cost for baseline models, and 

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: 

8-7
( ) ( )

( ) ( )

( ) BASE MFG OVERALL INCR STD

BASE BASE STD STD

BASE STD BASE STD

STD STD STD

IC COST MU INST

EQP INST EQP INST

EQP EQP INST INST

IC EQP INST

∆+= × ∆+

= + ∆+∆+

= +∆+ ∆+

+=

_

where:

ICSTD = Standard-level total installed cost, 

EQPSTD = Consumer equipment price for standard-level models, 

INSTSTD = Standard-level installation cost,

EQPBASE = Consumer equipment price for baseline models, 

ΔEQPSTD = Change in equipment price 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 = Incremental overall markup (product of manufacturer markup, 

incremental retailer or distributor markup, and sales tax). 

 The remainder of this section provides information about each of the above input 

variables that DOE used to calculate the total installed cost for commercial clothes washers. 

 

8.2.1.1 Baseline Manufacturer Cost

DOE developed the manufacturer costs for commercial clothes washer units as described 

in chapter 5, Engineering Analysis. Based on a manufacturer markup of 1.29, as determined in 

section 6.2 of Chapter 6, Markups for Equipment Price Determination, DOE arrived at a baseline 

manufacturer cost. Table 8.2.2 presents the baseline manufacturer cost as well as the associated 

baseline modified energy factor and water factor.

Table 8.2.2 Commercial Clothes Washers: Baseline Manufacturer Costs

Product Class

Baseline Modified 

Energy Factor

(cu.ft./kWh/cyc)

Baseline Integrated

Water Factor

(gal/cu.ft.)

Baseline Manufacturer 

Cost

(2012$)

Top-Loading 1.15 8.90 $522.42

Front-Loading 1.65 5.20 $828.40

8-8
8.2.1.2 Markups 

For a given distribution channel, the overall markup is the value determined by 

multiplying all the associated markups and the applicable sales tax together to arrive at a single 

overall distribution chain markup value. The overall markup is multiplied times the baseline or 

standard-compliant manufacturer cost to arrive at the price paid by the customer. Because there 

are baseline and incremental markups associated with the wholesaler and mechanical and general

contractors, the overall markup is also divided into a baseline markup (i.e., a markup used to 

convert the baseline manufacturer price into a customer price) and an incremental markup (i.e., a 

markup used to convert a standard-compliant manufacturer cost increase due to an efficiency 

increase into an incremental customer price). Markups can differ depending on whether the 

equipment is being purchased for a new construction installation or is being purchased to replace 

existing equipment. DOE developed the overall baseline markups and incremental markups for 

both new construction and replacement applications as a part of the markups analysis (chapter 6 

of the TSD).

Table 8.2.3 displays baseline and incremental markups used to calculate customer price from 

manufacturer cost; Table 8.2.3 presents the values used for the distribution channels involving 

wholesalers and contractors, as well as the national accounts distribution channels. Because the 

relative importance of new construction and replacements in total shipments varies among the 

equipment classes, the total markup varies as well (Table 8.2.3).

Table 8.2.3 Summary of Average Markups 

Baseline Markup Incremental Markup

Manufacturer 1.1 to 1.36 (see Table 6.3.1 in chapter 6)

Wholesaler 1.36 1.10

Mechanical Contractor 

(new construction/replacement) 1.42/1.50 1.14/1.20 

General Contractor 

(new construction only) 1.31 1.19 

Sales Tax (replacement only) 1.07 1.07

8.2.1.3 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 commercial clothes washers. Refer to Chapter 7, 

Markups for Equipment Price Determination, for details. 

8-9
Table 8.2.4 Overall Markups

Markup Baseline Incremental

Manufacturer 1.285

Distributor 1.37 1.18

Sales Tax 1.0711

Overall 1.89 1.62

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 commercial clothes washer manufacturer cost increases associated with 

increases in product standard levels. Refer to Chapter 5, Engineering Analysis, for details. 

Table 8.2.5 and Table 8.2.6 present the standard-level manufacturer cost increases as well as the 

associated modified energy factors and water factors for top-loading and front-loading washers, 

respectively. 

Table 8.2.5 Top-Loading Commercial Clothes Washers: Standard-Level Manufacturer 

Cost Increases

Standard Level

Modified

Energy Factor 

(cu.ft./kWh/cyc)

Integrated Water

Factor 

(gal/cu.ft.)

Standard-Level

Manufacturer Cost Increase

(2012$)

Baseline 1.15 8.90 -

1 1.35 8.80 $0.00

2 1.55 6.90 $38.39

Table 8.2.6 Front-Loading Commercial Clothes Washers: Standard-Level Manufacturer 

Cost Increases

Standard Level

Modified

Energy Factor

(cu.ft./kWh/cyc)

Integrated Water

Factor 

(gal/cu.ft.)

Standard-Level

Manufacturer Cost Increase

(2012$)

Baseline 1.65 5.20 -

1 1.80 4.50 $0.00

2 2.00 4.10 $0.31

3 2.20 3.90 $19.55

8.2.1.5 Installation Cost 

DOE derived baseline installation costs for commercial clothes washers from data in the 

RS Means Mechanical Cost Data, 2013.

1

 This book provides estimates on the labor required to 

8-10
install commercial clothes washers. Table 8.2.7 summarizes the nationally representative 

average costs associated with the installation of a four-cycle, coin operating, commercial clothes 

washer as presented in RS Means Mechanical Cost Data. Table 8.2.7 provides both bare costs 

(i.e., costs before O&P) and installation costs including O&P. DOE determined that installation 

costs would not be impacted with increased standard levels. 

Table 8.2.7 Commercial Clothes Washers: Baseline Installation Costs 

Bare Costs (2013$) Including Overhead & Profit (2013$) 

Installation Type Material Labor Total Total Material* Labor**

Average $1,250 $149 $1,399 $1,600 $1,374 $225

Average (2012$) $223

* Material costs including O&P equal bare costs plus 10% profit.

** DOE derived labor cost including O&P by subtracting material with O&P from total with O&P.

Source: RS Means, Mechanical Cost Data, 2013.

8.2.1.6 Total Installed Cost 

The total installed cost is the sum of the consumer equipment price and the installation 

cost. Refer back to section 8.2.1 to see the equations that DOE used to calculate the total 

installed cost for baseline and standard-level products. 

Table 8.2.8 and Table 8.2.9present the consumer equipment price, installation costs, and 

total installed costs for top-loading and front-loading washers, respectively. Prices and costs are

presented at the baseline level and each standard level. 

Table 8.2.8 Top-Loading Commercial Clothes Washers: Consumer Equipment Prices, 

Installation Costs, and Total Installed Costs 

Standard 

Level

Modified

Energy Factor

(cu.ft./kWh/cyc)

Integrated 

Water 

Factor 

(gal/cu.ft.)

Equipment 

Price

(2012$)

Installation 

Cost 

(2012$)

Total Installed 

Cost

(2012$)

Baseline 1.15 8.90 $1,028 $223 $1,251

1 1.35 8.80 $1,028 $223 $1,251.

2 1.55 6.90 $1,090 $223 $1,313

8-11
Table 8.2.9 Front-Loading Commercial Clothes Washers: Consumer Equipment Prices, 

Installation Costs, and Total Installed Costs 

Standard 

Level

Modified

Energy Factor

(cu.ft./kWh/cyc)

Integrated 

Water 

Factor 

(gal/cu.ft.)

Equipment 

Price

(2012$)

Installation 

Cost 

(2012$)

Total Installed 

Cost

(2012$)

Baseline 1.65 5.20 $1,630 $223 $1,853

1 1.80 4.50 $1,630 $223 $1,853

2 2.00 4.10 $1,631 $223 $1,854

3 2.20 3.90 $1,662 $223 $1,885

8.2.1.7 Future Product Prices

DOE obtained historical PPI data for commercial laundry and drycleaning machinery

spanning the time period 1982-2012 from the Bureau of Labor Statistics’ (BLS).a The PPI data 

reflect nominal prices, adjusted for product quality changes. An inflation-adjusted (deflated) 

price index for commercial laundry and drycleaning machinery was calculated by dividing the 

PPI series by the Gross Domestic Product Chained Price Index (see ).

a

 Series ID PCU3333183333182; http://www.bls.gov/ppi/ 

8-12

 
Figure 8.2.1 Historic Nominal and Deflated Producer Price Indexes for 

Commercial Laundry and Drycleaning Machinery

 From the early-1980s to early-1990s, the deflated price index for commercial laundry and 

drycleaning machinery was decreasing slowy. Since 1993, the deflated price index for this 

industry has risen consistently and only till the last third year (2010), the prices started to stay

flat. The rising prices we observed in commercial laundry and drycleaning machinery industry 

from early-2000s to 2010 could partly attribute to the concurrent price increase in copper and 

steel products which are heavily used in commercial clothes washers; however, it is uncertain 

what was cauing the price to rise between 1993 to early-2000s. Although the prices in this 

industry remain roughly constant in the last three years, given the uncertain cuase of the longlasting increasing price trend and the slowdown in global economic activity in recent years, DOE 

believes that the extent to how the trends will continue is very uncertain. Additionally, in terms 

of the types of commerial clothes washers considered in this rulemaking, they are largely similar 

to the residential clothes washers. Hence, DOE also referred to the household laundry equipment 

PPI for comparison and found that the deflated household laundry equipment PPI have actually 

been decreasing consistenly from 1947 to 2012, as shown in . b

b

 Series ID PCU335224335224P; http://www.bls.gov/ppi/ 

0.75

0.80

0.85

0.90

0.95

1.00

1.05

0.0

50.0

100.0

150.0

200.0

250.0

1982198419861988199019921994199619982000200220042006200820102012

Deflated PPI ($2012; 2012=1)

Nominal PPI (1982=100)

Year

Nominal Commercial Laundry & Drycleaning Machinery PPI (2012=1)

Defalted Commercial Laundry & Drycleaning Machinery PPI (2012=1)

8-13

 
Figure 8.2.2 Historic Nominal and Deflated Producer Price Indexes for 

Household Laundry Equipment

 Given the above considerations, DOE decided to use constant prices as the default price 

assumption to project future commercial clothes washer prices. Thus, projected prices for the 

LCC and PBP analysis are equal to the 2012 values for each efficiency level in each product 

class.

8.2.2 Operating Cost Inputs

DOE defines the operating cost by the following equation: 

OC EC WC RC +++= MC

where:

EC = Energy expenditure associated with operating the equipment, 

WC = For dishwashers and commercial clothes washers, the water expenditure 

associated with operating the equipment, 

RC = Repair cost associated with component failure, and 

MC = Service cost for maintaining equipment operation. 

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

1947195019531956195919621965196819711974197719801983198619891992199519982001200420072010

Deflated PPI (2012=1)

Nominal PPI (1979=100)

Nominal Household Laundry PPI Deflated Household Laundry PPI (2012=1)

8-14
Table 8.2.10 shows the inputs for determining the operating costs. The inputs listed in 

Table 8.2.10 are also necessary for determining lifetime operating expenses, which include the 

energy price trends (and water price trends), product lifetime, discount rate, and effective date of 

the standard. 

Table 8.2.10 Inputs for Operating Cost

Annual Energy (and Water) Consumption

Energy and Water Prices

Repair and Maintenance Costs

Energy and Water Price Trends

Product Lifetime

Discount Rate

Effective Date of Standard

The annual energy consumption is the site energy use associated with operating the 

equipment. The annual water consumption, which is applicable to commercial clothes washers, 

is the site water use associated with operating the equipment. The annual energy (and water) 

consumption vary with the product efficiency. That is, the energy and water consumption 

associated with standard-level equipment (i.e., equipment with efficiencies greater than baseline 

equipment) are less than the consumptions associated with baseline equipment. Energy and 

water prices are the prices paid by consumers for energy (i.e., electricity, gas, or oil) and water. 

Multiplying the annual energy and water consumption by the energy and water prices yields the 

annual energy cost and water cost, respectively. Repair costs are associated with repairing or 

replacing components that have failed. Maintenance costs are associated with maintaining the 

operation of the equipment. DOE used energy and water price trends to forecast energy and 

water prices into the future and, along with the product lifetime and discount rate, to establish the 

lifetime energy and water costs. The product lifetime is the age at which the equipment is retired 

from service. The discount rate is the rate at which DOE discounted future expenditures to 

establish their present value. DOE calculated the operating cost for baseline products based on 

the following equation: 

BASE ENERGY BASE WATER BASE BASE

BASE BASE BASE BASE BASE

AEC PRICE AWC PRICE RC MC

OC EC WC RC MC

×= + × ++

+++=

where:

OCBASE = Baseline operating cost, 

ECBASE = Energy expenditure associated with operating the baseline equipment, 

WCBASE = For commercial clothes washers, the water expenditure associated with 

operating the baseline equipment,

8-15
RCBASE = Repair cost associated with component failure for the baseline 

equipment, 

MCBASE = Service cost for maintaining baseline equipment operation,

AECBASE = Annual energy consumption for baseline equipment, 

PRICEENERGY = Energy price,

AWCBASE = Annual water consumption for baseline equipment, and

PRICEWATER = Water price.

DOE calculated the operating cost for standard-level products based on the following equation: 

( ) ( )

( ) ( ) BASE STD BASE STD

BASE STD ENERGY BASE STD WATER

STD ENERGY STD WATER STD STD

STD STD STD STD STD

RC RC MC MC

AEC AEC PRICE AWC AWC PRICE

AEC PRICE AWC PRICE RC MC

OC EC WC RC MC

∆++∆++

= ×∆ + ×∆

×= ×+ ++

+++=

__ __

where: 

OCSTD = Standard-level operating cost, 

ECSTD = Energy expenditure associated with operating standard-level equipment, 

WCSTD = For commercial clothes washers, the water expenditure associated with 

operating standard-level equipment,

RCSTD = Repair cost associated with component failure for standard-level 

equipment, 

MCSTD = Service cost for maintaining standard-level equipment operation, 

AECSTD = Annual energy consumption for standard-level equipment, 

PRICEENERGY = Energy price,

AWCSTD = Annual water consumption for standard-level equipment,

PRICEWATER = Water price,

ΔAECSTD = Change in annual energy consumption caused by standard-level

equipment, 

ΔAWCSTD = Change in annual water consumption caused by standard-level 

equipment, 

ΔRCSTD = Change in repair cost caused by standard-level equipment, and 

ΔMCSTD = Change in maintenance cost caused by standard-level equipment. 

 The remainder of this section provides information about each of the above input 

variables that DOE used to calculate the operating costs for cooking products, dishwashers, 

dehumidifiers, and commercial clothes washers. 

8.2.2.1 Annual Energy and Water Consumption

 Chapter 6, Energy and Water Use Determination, details how DOE determined the 

annual energy and water consumption for baseline and standard-level products. 

8-16
DOE characterized the variability of commercial clothes washer usage based on several 

studies. For both multi-family as well as laundromat applications, DOE equally weighted each 

study to establish the variability. Refer back to Chapter 7, section 7.4 to review how DOE 

characterized commercial clothes washer usage variability.

The tables presented below are based on the energy and water use determination analysis 

described in Chapter 7. Keep in mind that the annual energy and water consumption values in 

the tables below are averages. DOE captured the variability in energy (and water) consumption 

when it conducted its LCC and PBP analysis. 

Table 8.2.11and Table 8.2.12provide the average annual energy and water consumption 

by efficiency level for top-loading commercial clothes washers. Table 8.2.13 and Table 8.2.14

provide the average annual energy and water consumption by efficiency level for front-loading 

commercial clothes washers. DOE presents annual consumption based on two applications: 

multi-family housing and laundromats. 

Table 8.2.11 Top-Loading Commercial Clothes Washers, Multi-Family Application: 

Annual Energy and Water Use by Efficiency Level

Standard 

Level

MEF IWF

Annual Energy Use*

Annual 

Water Use

Water Heating** Drying***

Electric Gas Electric Gas Machine

cu.ft./kWh/cyc gal/cu.ft. kWh/yr MMBtu/yr kWh/yr MMBtu/yr kWh/yr 1000 gal/year

Baseline 1.15 8.90 107.1 1.46 913.0 5.23 240.9 30.21

1 1.35 8.80 138.6 1.89 692.0 3.97 230.0 29.87

2 1.55 6.90 108.7 1.48 690.9 3.96 91.9 24.26

* Annual usage based on 3.0 cycles per day (1,095 cycles per year).

** Electric and gas water heating based on water heater efficiencies of 100% for electric and 75% for gas. Water 

heater consumption is weighted by the share of buildings with electric (25%) and gas (75%) water heaters. 

*** Dryer consumption is weighted by the share of buildings with electric (40%) and gas (60%) dryers.

8-17
Table 8.2.12 Top-Loading Commercial Clothes Washers, Laundromat Application: 

Annual Energy and Water Use by Efficiency Level

Standard 

Level

MEF IWF

Annual Energy Use*

Annual 

Water Use

Water Heating** Drying***

Electric Gas Electric Gas Machine

cu.ft./kWh/cyc gal/cu.ft. kWh/yr MMBtu/yr kWh/yr MMBtu/yr kWh/yr 1000 gal/year

Baseline 1.15 8.90 0.0 2.66 0.0 11.92 329.3 41.30

1 1.35 8.80 0.0 3.45 0.0 9.04 314.4 40.84

2 1.55 6.90 0.0 2.70 0.0 9.02 133.0 33.17

* Annual usage based on 4.1 cycles per day (1,497 cycles per year).

** Electric and gas water heating based on water heater efficiencies of 100% for electric and 75% for gas. Water 

heater consumption is weighted by the share of buildings with electric (0%) and gas (100%) water heaters. 

*** Dryer consumption is weighted by the share of buildings with electric (0%) and gas (100%) dryers.

Table 8.2.13 Front-Loading Commercial Clothes Washers, Multi-Family Application: 

Annual Energy and Water Use by Efficiency Level

Standard 

Level

MEF IWF

Annual Energy Use*

Annual 

Water Use

Water Heating** Drying***

Electric Gas Electric Gas Machine

cu.ft./kWh/cyc gal/cu.ft. kWh/yr MMBtu/yr kWh/yr MMBtu/yr kWh/yr 1000 gal/year

Baseline 1.65 5.20 88.9 1.21 552.9 3.17 120.5 15.94

1 1.80 4.50 87.9 1.20 516.1 2.96 87.6 14.10

2 2.00 4.10 51.4 0.70 501.3 2.87 95.7 12.85

3 2.20 3.90 119.3 1.63 439.0 2.52 61.2 14.12

* Annual usage based on 3.0 cycles per day (1,095 cycles per year).

** Electric and gas water heating based on water heater efficiencies of 100% for electric and 75% for gas. Water 

heater consumption is weighted by the share of buildings with electric (25%) and gas (75%) water heaters. 

*** Dryer consumption is weighted by the share of buildings with electric (40%) and gas (60%) dryers.

8-18
Table 8.2.14 Front-Loading Commercial Clothes Washers, Laundromat Application: 

Annual Energy and Water Use by Efficiency Level

Standard 

Level

MEF IWF

Annual Energy Use*

Annual 

Water Use

Water Heating** Drying***

Electric Gas Electric Gas Machine

cu.ft./kWh/cyc gal/cu.ft. kWh/yr MMBtu/yr kWh/yr MMBtu/yr kWh/yr 1000 gal/year

Baseline 1.65 5.20 0.0 2.21 0.0 7.22 164.7 21.80

1 1.80 4.50 0.0 2.19 0.0 6.74 119.8 19.28

2 2.00 4.10 0.0 1.28 0.0 6.55 136.7 17.57

3 2.20 3.90 0.0 2.97 0.0 5.73 90.1 19.30

* Annual usage based on 4.1 cycles per day (1,497 cycles per year).

** Electric and gas water heating based on water heater efficiencies of 100% for electric and 75% for gas. Water 

heater consumption is weighted by the share of buildings with electric (0%) and gas (100%) water heaters. 

*** Dryer consumption is weighted by the share of buildings with electric (0%) and gas (100%) dryers.

8.2.2.2 Energy and Water Prices

 

DOE derived energy prices for 27 geographic areas in the U.S. and derived water prices for the 

four Census regions. Using these data, DOE analyzed the variability of energy and water prices 

at the regional level for commercial clothes washers. 

DOE characterized energy and water price regional variability with probability 

distributions. It based the probability associated with each regional energy and water price on 

the population weight of each region. 

 The methodology that DOE used for deriving the energy and water prices is presented 

below. Included are tables that summarize the regional energy and water prices for each product. 

Energy Prices

DOE derived average commercial electricity prices from data that are published annually 

based on EIA Form 862

. Those data include annual electricity sales in kilowatt-hours, revenues 

from electricity sales, and number of customers in the residential, commercial, and industrial 

sectors, for every utility serving final consumers. DOE calculated commercial electricity prices 

for each of the 27 U.S. geographic areas to accordance with RECS 2009 geographic areas. The 

calculation of an average commercial electricity price proceeds in two steps:

1. For each utility, estimate an average commercial price by dividing the commercial 

revenues by commercial sales.

2. Calculate a regional average price, weighting each utility with customers in a region by 

the number of commercial consumers served in that region. 

8-19
Table 8.2.15 shows the results for each geographic region. 

Table 8.2.15 Average Commercial Electricity Prices in 2011 

Geographic Area 2011$/kWh

1 Connecticut, Maine, New Hampshire, 

Rhode Island, Vermont $0.108

2 Massachusetts $0.120

3 New York $0.133

4 New Jersey $0.120

5 Pennsylvania $0.088

6 Illinois $0.079

7 Indiana, Ohio $0.081

8 Michigan $0.106

9 Wisconsin $0.105

10 Iowa, Minnesota, North Dakota, South 

Dakota $0.085

11 Kansas, Nebraska $0.092

12 Missouri $0.083

13 Virginia $0.083

14 Delaware, District of Columbia, 

Maryland, West Virginia $0.094

15 Georgia $0.101

16 North Carolina, South Carolina $0.088

17 Florida $0.100

18 Alabama, Kentucky, Mississippi $0.099

19 Tennessee $0.105

20 Arkansas, Louisiana, Oklahoma $0.083

21 Texas $0.102

22 Colorado $0.096

23 Idaho, Montana, Utah, Wyoming $0.077

24 Arizona $0.099

25 Nevada, New Mexico $0.094

26 California $0.134

27 Alaska , Hawaii , Oregon, Washington $0.105

Source: EIA Form 861.

Table 8.2.16 shows the national average commercial electricity prices for commercial 

clothes washers based on the relative residential consumer weight of each geographic area. DOE 

used 2012 population estimates from the U.S. Census as a proxy to estimate how the national 

saturation of commercial clothes washers was distributed over the 27 geographic areas.3

 

Because DOE conducted the LCC and PBP analysis in 2012$, all electricity prices are in 2012$. 

8-20
To perform the necessary monetary conversion, DOE used the CPI to convert the electricity 

prices from 2011$ to 2012$. 

Table 8.2.16 Average Commercial Electricity Prices for Commercial Clothes Washers in 

2012 

Geographic Area 2012$/kWh Saturation

1 Connecticut, Maine, New Hampshire, 

Rhode Island, Vermont $0.110 2.5%

2 Massachusetts $0.123 2.1%

3 New York $0.136 6.2%

4 New Jersey $0.122 2.8%

5 Pennsylvania $0.089 4.1%

6 Illinois $0.081 4.1%

7 Indiana, Ohio $0.082 5.8%

8 Michigan $0.108 3.1%

9 Wisconsin $0.107 1.8%

10 Iowa, Minnesota, North Dakota, South 

Dakota $0.086 3.2%

11 Kansas, Nebraska $0.094 1.5%

12 Missouri $0.085 1.9%

13 Virginia $0.085 2.6%

14 Delaware, District of Columbia, 

Maryland, West Virginia $0.096 2.4%

15 Georgia $0.103 3.2%

16 North Carolina, South Carolina $0.089 4.6%

17 Florida $0.102 6.2%

18 Alabama, Kentucky, Mississippi $0.101 3.9%

19 Tennessee $0.107 2.1%

20 Arkansas, Louisiana, Oklahoma $0.085 3.6%

21 Texas $0.104 8.3%

22 Colorado $0.098 1.7%

23 Idaho, Montana, Utah, Wyoming $0.079 1.9%

24 Arizona $0.101 2.1%

25 Nevada, New Mexico $0.096 1.5%

26 California $0.136 12.1%

27 Alaska , Hawaii , Oregon, Washington $0.107 4.7%

Source: EIA Form 861.

Commercial Natural Gas Prices

DOE obtained the data for the natural gas price calculation from the EIA publication 

Natural Gas Prices.

4

 This publication includes a compilation of monthly natural gas delivery 

volumes and average consumer prices by State, for residential, commercial, and industrial 

8-21
customers. The Department used the complete annual data for 2012 to calculate an average 

annul price for each area. The calculation of average prices proceeds in two steps:

1. Calculate the annul price for each State using a simple average over the appropriate 

months. 

2. Calculate a regional price, weighting each State in a region by its population. 

This method differs from the method used to calculate electricity prices because EIA does not 

provide consumer- or utility-level data on gas consumption and prices. Table 8.2.17 shows the 

national average commercial natural gas prices for commercial clothes washers based on the 

relative commercial consumer weight of each geographic area. DOE used 2012 population 

estimates from the U.S. Census as a proxy to estimate how the national saturation of commercial 

clothes washers was distributed over the 27 geographic areas. 

8-22
Table 8.2.17 Average Commercial Natural Gas Prices in 2012 

Geographic Area 2012$/MMBtu Saturation

1 Connecticut, Maine, New Hampshire, 

Rhode Island, Vermont $10.06 2.5%

2 Massachusetts $10.05 2.1%

3 New York $7.28 6.2%

4 New Jersey $8.17 2.8%

5 Pennsylvania $10.39 4.1%

6 Illinois $8.64 4.1%

7 Indiana, Ohio $7.36 5.8%

8 Michigan $8.52 3.1%

9 Wisconsin $6.82 1.8%

10 Iowa, Minnesota, North Dakota, South

Dakota $6.40 3.2%

11 Kansas, Nebraska $8.13 1.5%

12 Missouri $10.29 1.9%

13 Virginia $8.57 2.6%

14 Delaware, District of Columbia, 

Maryland, West Virginia $10.81 2.4%

15 Georgia $9.70 3.2%

16 North Carolina, South Carolina $8.35 4.6%

17 Florida $10.04 6.2%

18 Alabama, Kentucky, Mississippi $9.70 3.9%

19 Tennessee $8.15 2.1%

20 Arkansas, Louisiana, Oklahoma $8.76 3.6%

21 Texas $6.66 8.3%

22 Colorado $7.56 1.7%

23 Idaho, Montana, Utah, Wyoming $7.12 1.9%

24 Arizona $9.35 2.1%

25 Nevada, New Mexico $6.88 1.5%

26 California $6.87 12.1%

27 Alaska , Hawaii , Oregon, Washington $12.89 4.7%

Source: EIA, Natural Gas Prices. 

 

8-23
Water Prices

DOE obtained water price data from the year 2012 from the Water and Wastewater Rate 

Survey conducted by Raftelis Financial Consultants and the American Water Works 

Association.5

 The survey covers approximately 290 water utilities and 214 wastewater utilities, 

with each industry analyzed separately. The water survey includes, for each utility, the cost to 

consumers of purchasing a given volume of water. In this case, the data include a division of the 

total consumer cost into fixed and volumetric charges. The calculations use only the volumetric 

charge to calculate water prices, since only this charge would be affected by a change in water 

consumption. Including the fixed charge in the average would lead to a slightly higher water 

price.

The Water and Wastewater Rate Survey provides prices separately for residential and 

non-residential customers. For wastewater utilities, the format is similar, but the cost refers to 

the cost of treating a given volume of wastewater. In both surveys, the non-residential sector is 

divided into four sub-sectors based on the average monthly volume of water delivered or, 

equivalently, the size of the delivery pipe. The sub-sectors are named non-manufacturing, 

commercial, industrial 1, and industrial 2. DOE’s analysis uses the commercial category to 

estimate prices for commercial consumers. 

A sample of 290 utilities is not large enough to calculate regional prices for all U.S. 

Census divisions and large States (for comparison, the EIA Form 861 data include more than 

3000 utilities). For this reason, DOE calculated regional values at the Census region level 

(Northeast, South, Midwest, and West). The calculation of average per-unit-volume prices 

proceeds in three steps:

1. For each utility, calculate the per-unit-volume price by dividing the total volumetric cost 

by the volume delivered. 

2. Calculate a State-level average price by weighting each utility in a given State by the 

number of commercial consumers it serves. 

3. Calculate a regional average by combining the State-level averages, weighting each by 

the population of that State. This third step helps reduce any bias in the sample that may 

occur due to relative under-sampling of large States.

 The results of the calculation for the commercial sector are presented inTable 8.2.18. 

The price units in the table are dollars per thousand gallons ($/tg). 

8-24
Table 8.2.18 Average Per-Unit-Volume Water and Waste Water Prices in 2012 

Commercial

Region 2012$/tg Water 2012$/tg Wastewater

Northeast $3.85 $6.48

Midwest $3.28 $3.91

South $3.28 $4.67

West $3.98 $4.69

Source: Raftelis, Raftelis Survey, 2012. 

 

 

8.2.2.3 Energy and Water Price Trends

 DOE used price forecasts by the EIA to estimate the trends in natural gas, oil, and 

electricity prices. To arrive at prices in future years, it multiplied the average prices described in 

the preceding section (section 8.2.2.2) by the forecast of annual average price changes in EIA’s 

AEO 2013.

6

 To estimate the trend after 2040, DOE followed past guidelines provided to the 

Federal Energy Management Program (FEMP) by EIA and used the average rate of change 

during 2025–2040. 

The Department set up its analysis to calculate LCC and PBP using three separate 

projections: Reference, Low Economic Growth, and High Economic Growth. These three cases 

reflect the uncertainty of economic growth in the forecast period. The high and low growth 

cases show the projected effects of alternative growth assumptions on energy markets. Figure 

8.2.1 and Figure 8.2.2 show the commercial electricity and natural gas price trends, respectively, 

based on the three projections. For the LCC results presented in section 8.4, DOE used only the 

energy price forecasts from the AEO 2013 Reference Case. 

0.90

0.95

1.00

1.05

1.10

1.15

1.20

1.25

1.30

1.35

Electricity Price Trend (2012=100)

Reference Case

High Case

Low Case

8-25
Figure 8.2.3 Electricity Price Trends

Figure 8.2.4 Natural Gas Price Trends

 To estimate the future trend for water and wastewater prices, DOE used data on the 

historic trend in the national water price index (U.S. city average) from 1970 through 2012.7

DOE extrapolated the future trend based on the linear growth from 1970 to 2012. But rather than 

use the extrapolated trend to forecast the prices for the eight years after 2012, DOE used the 

annual growth rate between 2012 and 2021 to project water price trend. Otherwise, forecasted 

prices for this eight-year time period would have been much lower than the price in 2012. 

Beyond the eight-year time period, DOE used the extrapolated trend to forecast prices out to the 

year 2047. Figure 8.2.3 shows the historical and projected water price trend data. DOE used the 

forecasted data to estimate water and wastewater prices for commercial clothes washers.

0.90

1.10

1.30

1.50

1.70

1.90

2.10

2.30

Natural Gas Price Trend (2012=100)

Reference Case

High Case

Low Case

8-26
Figure 8.2.5 Water Price Trend

8.2.2.4 Repair and Maintenance Costs

Typically, small incremental changes in product efficiency incur no, or only very small, 

changes in repair and maintenance costs over baseline products. However, equipment with 

efficiencies that are significantly higher than the baseline are more likely to incur higher repair 

and maintenance costs because its higher complexity and higher part count typically increases 

the cumulative probability of failure. 

 The Whirlpool Corporation estimates that the unit shipments of horizontal-axis 

commercial clothes washers are less than half that of vertical-axis machines while their inwarranty repair costs are double that of vertical-axis machines.8

 This suggests that the repair of 

horizontal-axis machines is four times as costly as that of vertical-axis machines. Because inwarranty repair costs, based on Whirlpool’s experience, are greater for horizontal-axis machines 

that vertical-axis machines, DOE estimated that commercial clothes washer repair costs would 

increase as a function of equipment efficiency. DOE utilized an algorithm developed for central 

air conditioners and heat pumps to estimate repair cost increases.

9

 This algorithm calculates 

annualized repair costs by dividing the half the equipment retail price by the equipment lifetime 

as show in the expression below: 

LIFE

EQP RCANNUAL

× = 5.0

where:

RCANNUAL = Annualized repair cost associated with equipment, 

EQP = Consumer equipment price, and 

y = 8.502x - 16753

R2 = 0.9558

0.0

100.0

200.0

300.0

400.0

500.0

600.0

700.0

800.0 Water Price CPI Trend (1982-1984=100)

Historical Water Price CPI

Extrapolated Water Price CPI 

Trend

Linear (Historical Water Price 

CPI)

8-27
LIFE = Product lifetime.

Table 8.2.19 and Table 8.2.20 show for each product class and each product application 

(multi-family buildings and laundromats) the annualized repair costs for the commercial clothes 

washer baseline and efficiency levels. Annualized repair costs for each product application 

differ because commercial clothes washer lifetime is different for each application. (Product 

lifetimes are presented below is section 8.2.3.) The tables also present the consumer equipment 

prices for the baseline level and each efficiency level. 

Table 8.2.19 Top-Loading Commercial Clothes Washer Annualized Repair Costs

Level

Modified

Energy Factor

(cu.ft./kWh/cyc)

Integrated 

Water 

Factor 

(gal/cu.ft.)

Equipment 

Price

(2012$)

Annualized Repair Cost (2012$)

Multi-Family* Laundromat*

Baseline 1.15 8.90 $1,008.84 $47 $73

1 1.35 8.80 $1,008.84 $47 $73

2 1.55 6.90 $1,070.01 $50 $78

* Average product lifetime: Multi-family = 11.3 years; Laundromat = 7.1 years.

Table 8.2.20 Front-Loading Commercial Clothes Washer Annualized Repair Costs

Level

Modified

Energy Factor

(cu.ft./kWh/cyc)

Integrated 

Water 

Factor 

(gal/cu.ft.)

Equipment 

Price

(2012$)

Annualized Repair Cost (2012$)

Multi-Family* Laundromat*

Baseline 1.65 5.20 $1,599.71 $74 $116

1 1.80 4.50 $1,599.71 $74 $116

2 2.00 4.10 $1,600.20 $74 $116

3 2.20 3.90 $1,630.86 $76 $118

* Average product lifetime: Multi-family = 11.3 years; Laundromat = 7.1 years.

8.2.3 Product Lifetime

 DOE used only primary sources of data to estimate product lifetimes. DOE considered 

the sources listed in Table 8.2.21 to estimate product lifetime. 

8-28
Table 8.2.21 Commercial Clothes Washers: Product Lifetime Estimates and Sources

Lifetime (years) Source

7 to 10 CEE (1998) 5

Top-Loading (12 to 14 lbs.): 5 to 8*

Front-Loading (18 to 50 lbs.): 10 to 15** CLA10

8 to 9*** ACEEE (2001)11

5 (high-use)* to 13 (low-use)** Southern California Edison (2000)12

15** † CALMAC (2000)13

* Used to establish laundromat lifetime only.

** Used to establish multi-family lifetime only.

*** Depending on the usage rate, the life can be shorter.

† Based on engineering judgment.

Because DOE conducted the LCC analysis based on analyzing two product 

applications—multi-family and laundromats—it established separate lifetimes for each product 

application. Because clothes washers in multi-family applications are used less frequently than 

in laundromats, DOE decided to use only those sources that indicated lifetimes of seven years or 

greater for multi-family applications. For laundromats applications, where clothes washers 

receive heavier use, DOE decided to use only those sources that indicated lifetimes of 10 years 

or less. DOE then took an average of the lifetime estimates that met the above criteria. For 

multi-family applications the average lifetime turned out to be 11.3 years, while the average 

lifetime for laundromats was 7.1 years. DOE used the low estimate for the above applicable 

sources to establish the minimum product lifetime for each product application. For multi-family 

applications the minimum estimate is 7 years and for laundromats it is 5 years. To establish the 

maximum product lifetime, DOE took the difference between the minimum and average values 

(4.3 years for multi-family and 2.1 years for laundromats) and added it to the average product 

lifetime. The minimum, average, and maximum lifetime estimates for each product application 

are shown inTable 8.2.22. 

Table 8.2.22 Commercial Clothes Washers: Average, Minimum, and Maximum Product 

Lifetimes

Product

Minimum

years

Average

years

Maximum

years

Multi-Family

Laundromats

7.0

5.0

11.3

7.1

15.5

9.3

DOE characterized the clothes washer product lifetimes for each product application with 

Weibull distributions. Appendix 8C presents the Weibull distributions for commercial clothes 

washers that DOE used in the LCC and PBP analysis.

8-29
8.2.4 Discount Rates

 The discount rate is the rate at which future expenditures and savings are discounted to 

establish their present value.The cost of capital is commonly used to estimate the present value 

of cash flows to be derived from a typical company project or investment. Following financial 

theory, the finance cost of raising funds for expenditure can be interpreted as: (1) the financial 

cost of any debt incurred, or (2) the opportunity cost of any equity. Similarly, the value of cost 

savings can be interpreted as: (1) the cost avoided by decreasing debt, or (2) the return earned 

from increasing equity holdings. Most companies use both debt and equity capital to fund 

investments, so their cost of capital is the weighted average of the cost to the firm of equity and 

debt financing.10 DOE defines the discount rate as the weighted average cost of capital (WACC), 

calculated as the weighted average of the cost of equity financing and the cost of debt financing, 

as estimated from financial data for publicly traded firms in the sectors that purchase commercial 

clothes washers.

For commercial clothes washers, DOE derived the discount rate from the cost of capital 

of publicly traded firms in the sectors that purchase commercial clothes washers.14 DOE 

estimated the cost of capital of these firms as the weighted average of the cost of equity 

financing and the cost of debt financing. The costs of debt and equity financing are usually 

obtainable from publicly available data about the following types of companies in the sectors that 

purchase commercial clothes washers: 

• personal services (including laundromats), 

• educational services (which buy washers for dormitories), 

• hotels, and 

• real estate investment trusts (REITs) (i.e., building owners, including owners of 

apartment complexes). 

 Damodaran Online is a widely used source of information about company debt and 

equity financing for most types of firms, and was the source of data for this analysis on 

educational services, hotels, and REITs.15 Since Damodaran Online does not include data for 

firms in the personal services sector (SIC 7200), DOE used data from Ibbotson’s Associates for 

this sector.16 Ibbotson Associates is a leading authority on asset allocation with expertise in 

capital market expectations and portfolio implementation.

DOE estimated the cost of equity using the capital asset pricing model (CAPM).17 The 

CAPM assumes that the cost of equity (ke) for a particular company is proportional to the 

systematic risk faced by that company, where high risk is associated with a high cost of equity 

and low risk is associated with a low cost of equity. The systematic risk facing a firm is 

determined by several variables: the risk coefficient of the firm (β), the expected return on riskfree assets (Rf), and the equity risk premium (ERP). The risk coefficient of the firm indicates the 

risk associated with that firm relative to the price variability in the stock market. The expected 

return on risk-free assets is defined by the yield on long-term government bonds. The ERP

represents the difference between the expected stock market return and the risk-free rate. The 

8-30
cost of equity financing is estimated using the following equation, where the variables are 

defined as above: 

Rk fe ( ) β ×+= ERP

where:

 

ke = Cost of equity, 

Rf = Expected return on risk-free assets,

β = Risk coefficient of the firm, and 

ERP = Equity risk premium. 

 The cost of debt financing (kd) is the interest rate paid on money borrowed by a company. 

The cost of debt is estimated by adding a risk adjustment factor (Ra) to the risk-free rate. This 

risk adjustment factor depends on the variability of stock returns represented by standard 

deviations in stock prices. So for firm i, the cost of debt financing is: 

+= RRk aifdi

where:

 

kd = Cost of debt financing for firm, i, 

Rf = Expected return on risk-free assets, and

Rai = Risk adjustment factor to risk-free rate for firm, i. 

DOE estimates the weighted-average cost of capital (WACC) using the following 

equation: 

×+×= wkwkWACC ddee

where:

WACC = Weighted average cost of capital,

we = Proportion of equity financing, and 

wd = Proportion of debt financing. 

Several parameters of the cost of capital equations can vary substantially over time, and 

therefore the estimates can vary with the time period over which data is selected and the 

technical details of the data averaging method. For guidance on the time period for selecting and 

averaging data for key parameters and the averaging method, DOE used Federal Reserve 

methodologies for calculating these parameters. In its use of the CAPM, the Federal Reserve 

uses a forty-year period for calculating discount rate averages, utilizes the gross domestic 

product price deflator for estimating inflation, and considers the best method for determining the 

8-31
risk free rate as one where “the time horizon of the investor is matched with the term of the riskfree security.” 18

 

 Damodaran Online is a widely used source of information about company debt and 

equity financing for most types of firms.19 By taking a forty-year geometric average of 

Damodaran Online data, DOE found for this analysis the following risk free rates for 2010-2012 

(Table 8.2.24). DOE also estimated the ERP by calculating the difference between risk free rate 

and stock market return for the same time period. Since Damodaran Online does not include data 

for firms in the personal services sector (SIC 7200), DOE used 2008 data from Ibbotson’s Associates 

for this sector.16 Ibbotson Associates is a leading authority on asset allocation with expertise in 

capital market expectations and portfolio implementation. 

 The values of the parameters used in the calculations are shown in Table 8.2.23 below. 

Table 8.2.23 Data for the Calculation of Weighted-Average Cost of Capital for Sectors 

that Purchase Commercial Clothes Washers

Sector β Rf ERP Ra we wd

Personal Services 0.53 5.20% 5.10% 0.80% 67.40% 32.60%

Educational Services 0.85 5.20% 5.10% 2.33% 86.79% 13.21%

Hotels 1.71 5.20% 5.10% 2.17% 67.37% 32.63%

REIT 1.40 5.20% 5.10% 1.17% 74.55% 25.45%

By adjusting for the influence of inflation, DOE estimates the real weighted average cost 

of capital, or discount rate, for each sector. DOE then aggregates the sector-level real weighted 

average costs of capital to estimate the discount rate for each of the four ownership types in the 

commercial clothes washer analysis. An overall average is estimated by weighting each sector’s 

discount rate by its estimated share of the commercial clothes washer market. These values are 

presented in Table 8.2.24. While WACC values for any sector may trend higher or lower over 

substantial periods of time, these values represent a private sector cost of capital that is averaged 

over major business cycles. 

Table 8.2.24 Weighted Average Cost of Capital for Sectors that Purchase Commercial 

Clothes Washers

Sector Discount Rate Market Share

Personal Services 4.7% 14.0%

Educational Services 5.4% 9.7%

Hotels 6.9% 9.7%

R.E.I.T. 6.2% 66.6%

Average 6.0% 100%

 

8-32
8.2.5 Effective Date of Standard

The effective date is the future date when a new standard becomes operative. Based on 

DOE’s implementation report for energy conservation standards activities submitted pursuant to 

Section 136 of the Energy Policy Act of 2005, a final rule for the commercial clothes washers

being considered for this standards rulemaking is scheduled for completion no later than January 

1, 2015. Therefore, the effective date of any new energy efficiency standards for these products 

will be three years after the final rule is published, which is January 1, 2018. The Department 

calculated the LCC for all consumers as if they each would purchase a new piece of equipment 

in the year the standard takes effect. 

8.2.6 Equipment Assignment for the Base Case

For purposes of conducting the LCC analysis, DOE analyzed standard levels relative to a 

baseline efficiency level. However, some consumers already purchase products with efficiencies 

greater than the baseline levels. Thus, to accurately estimate the percentage of consumers that 

would be affected by a particular standard level, DOE took into account the distribution of 

product efficiencies currently in the marketplace. In other words, rather than analyzing the 

impacts of a particular standard level assuming that all consumers are currently purchasing 

products at the baseline level, DOE conducted the analysis by taking into account the full breadth 

of product efficiencies that consumers purchase under the base case (i.e., the case without new 

energy efficiency standards).

DOE took into account the mix of commercial clothes washer efficiencies by 

characterizing the current mix of product efficiencies as a probability distribution. In other 

words, as DOE performed the Monte Carlo simulation, each standard level analyzed was 

evaluated against the distribution of product efficiencies in the base case. 

 Rather than providing market share data, AHAM provided shipment-weighted 

efficiencies of all commercial clothes washers.20 That is, shipment-weighted efficiencies were 

not provided separately for the top-loading and front-loading product classes. For the year 2005, 

the shipment-weighted efficiencies of commercial clothes washers are 1.41 modified energy 

factor (MEF) and 10.91 water factor (WF). Based on the shipment-weighted efficiency data, 

DOE was able to derive the market share of each product class. DOE assumed a bi-modal 

market share distribution consisting of only top-loading equipment meeting current minimum 

efficiency standards of 1.26 MEF/9.5 WF and top-loading equipment with an efficiency of 2.00 

MEF/5.5 WF. Thus, by using a shipment-weighted efficiency of 1.41 MEF, DOE determined 

that the market share of 1.26 MEF/9.5 WF washers is 79.7 percent and the market share of 2.00 

MEF/5.5 WF washers is 20.3 percent.c

 DOE interprets this result as indicating that in the year 

2005, 79.7 percent of the market consists of top-loading washers while 20.3 percent is comprised 

of front-loading washers. 

c DOE did not consider the shipment-weighted average WF of 10.91 in performing the market share calculation.

8-33

 
 Subsequent to 2005, energy efficiency tax incentives in the Energy Policy Act of 2005 

and extensions as well as new provisions in the Economic Stabilization Act of 2008 have 

resulted in more front-loading washers being produced by manufacturers. DOE reviewed the 

U.S. Securities and Exchange Commission (SEC) 10K report of the low volume manufacturer 

(LVM) of commercial clothes washers and determined that manufacturer tax credits in this 

recent federal legislation have resulted in significantly increased sales of the front-loading 

washers for the LVM. When accounting for the LVM’s market share, the increase in frontloading sales results in a current market share of 30 percent for front-loading washers. Although 

tax credits are set to expire after 2010, DOE estimates that the tax credits would permanently 

transform the market so that front-loading washers would continue to comprise 30 percent of the 

market over the entire forecast period.

 Knowing the market share of each product class does not provide the distribution of 

equipment efficiencies within each class. Therefore, DOE relied on anther data source—the 

California Energy Commission (CEC).21 As discussed in Section 3.14.3 of Chapter 3, Market 

and Technology Assessment, the CEC publishes the MEFs and WFs of commercial clothes 

washers sold in California. Although the range in efficiencies is relatively wide, the distribution 

is based on the number of available models being sold rather than the percent of shipments being 

sold. Without any other data available, DOE assumed that the distribution of available models in 

the CEC data were representative of actual shipments sold in the U.S. Table 8.2.25 and Table 

8.2.26 present the market shares of the efficiency levels in the base case for top-loading and 

front-loading washers, respectively. The tables represent the equipment that establishments 

would have purchased in the year 2018 in the absence of new standards. 

Table 8.2.25 Top-Loading Commercial Clothes Washers: Base Case Market Shares

Standard Level MEF IWF Market Share

Baseline 1.15 8.90 99.5%*

1 1.35 8.80 0.3%**

2 1.55 6.90 0.3%***

* Represents units with the following MEF and WF ratings: 1.15≤ MEF < 1.35; 8.9 ≥ IWF > 8.8.

** Represents units with the following MEF and WF ratings: 1.35 ≤ MEF < 1.55; 8.8 ≥ IWF > 6.9.

*** Represents units with the following MEF and WF ratings: MEF ≥ 1.55; IWF ≤ 6.9.

Table 8.2.26 Front-Loading Commercial Clothes Washers: Base Case Market Shares 

Standard Level MEF WF Market Share

Baseline 1.65 5.20 28.0%*

1 1.80 4.50 34.0%**

2 2.00 4.10 38.0%***

3 2.20 3.90 0.0%†

* Represents units with the following MEF and WF ratings: 1.65 ≤ MEF < 1.80; 5.2 ≥ IWF > 4.5.

** Represents units with the following MEF and WF ratings: 1.80 ≤ MEF < 2.00; 4.5 ≥ IWF > 4.1.

*** Represents units with the following MEF and WF ratings: 2.00 ≤ MEF < 2.20; 4.9 ≥ IWF > 3.9.

† Represents units with the following MEF and WF ratings: MEF ≥ 2.35; IWF ≤ 3.9.

8-34
8.3 PAYBACK PERIOD INPUTS 

 The payback period is the amount of time it takes the consumer to recover the assumed 

higher purchase expense of more energy-efficient equipment as a result of lower operating costs. 

Numerically, the PBP is the ratio of the increase in purchase expense (i.e., from a less efficient 

design to a more efficient design) to the decrease in annual operating expenditures. This type of 

calculation is known as a “simple” payback period, because it does not take into account changes 

in operating expense over time or the time value of money; i.e., the calculation is done at an 

effective discount rate of zero percent. 

 The equation for PBP is: 

OC

IC PBP

∆

∆ =

where:

 ΔIC = Difference in the total installed cost between the more efficient standard level 

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 mean that the increased total installed cost is not recovered in reduced operating 

expenses.

 

 The data inputs to PBP are the total installed cost of the equipment to the consumer for 

each efficiency level and the annual (first year) operating expenditures for each standard level. 

The inputs to the total installed cost are the equipment price and the installation cost. The inputs 

to the operating costs are the annual energy (and water) cost, the annual repair cost, and the 

annual maintenance cost. The PBP uses the same inputs as the LCC analysis as described in 

section 8.2, except that energy (and water) price trends and discount rates are not required. Since 

the PBP is a “simple” payback, the required energy price is only for the year in which a new 

standard is to take effect—in this case, the year 2018. 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.

8.4 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS

This section presents the LCC and PBP results for commercial clothes washers. DOE’s 

approach for conducting the LCC analysis relied on characterizing the uncertainty and variability 

of many of the inputs to the analysis with probability distributions. DOE used a Monte Carlo 

simulation technique to perform the LCC calculations. DOE calculated the average LCC and 

8-35
LCC savings and the median and average PBP for each of the candidate standard levels. These 

standard levels are also referred to as candidate standard levels (CSL). 

DOE calculated LCC savings and PBPs relative to the base case equipment that it 

assigned to commercial consumers. As discussed in section 8.2.6, DOE assigned base case 

equipment that is more efficient than some of the CSLs. For that reason, the average LCC 

impacts are not equal to the difference between the LCC of a specific and the LCC of the 

baseline equipment. 

 In the subsections below, DOE presents figures showing the distribution of LCCs in the 

base case for each product class. Also presented below for a specific 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. The LCC and PBP calculations were performed 10,000 

times by sampling from the probability distributions that DOE developed to characterize many of 

the inputs. 

Based on the Monte Carlo simulations that DOE performed, for each standard-level, 

DOE calculated the share of commercial consumers with a net LCC benefit, with a net LCC cost, 

and with no impact. DOE considered a commercial user to receive no impact at a given if DOE 

assigned it base case equipment that is the same as or has higher efficiency than the CSL. To 

illustrate the range of LCC and PBP impacts among the commercial clothes washer users, the 

sections below present figures that provide such information for each product class. 

8.4.1 Base Case LCC Distributions

Figure 8.4.1and Figure 8.4.2 show the frequency charts for the base case LCC for both 

product applications of top-loading commercial clothes washers. Figure 8.4.3 and Figure 8.4.4

show the frequency charts for the base case LCC for both product applications offront-loading 

commercial clothes washers.The figures below show the mean LCC of the base case distribution 

as well as the full range of LCCs.

8-36
Figure 8.4.1 Top-Loading Commercial Clothes Washers, Multi-Family 

Application: Base Case LCC Distribution 

Figure 8.4.2 Top-Loading Commercial Clothes Washers, Laundromat 

Application: Base Case LCC Distribution 

8-37
Figure 8.4.3 Front-Loading Commercial Clothes Washers, Multi-Family 

Application: Base Case LCC Distribution 

Figure 8.4.4 Front-Loading Commercial Clothes Washers, Laundromat 

Application: Base Case LCC Distribution 

8-38
8.4.2 Standard-Level Distributions of LCC Impacts

 Figure 8.4.5 is an example of a frequency chart showing the distribution of LCC 

differences for the case of EL3 for front-loading washers, multi-family product application. In 

the figure, a text box next to a vertical line at that value on the x-axis shows the mean change in 

LCC (a savings of $12.51 in the example here). The note, “Certainty is 54.39% from $0 to 

+Infinity,” means that 54.39 percent of owners of CCW units will have LCC savings or not be 

impacted due to the EL compared to the base case. The large ‘spike’ in Figure 8.4.5 represents 

the percentage of consumers that are not impacted by an increase in the EL, i.e., consumers that 

already use commercial clothes washers with efficiencies greater than or equal to the EL. Refer 

back to section 8.2.6 on the distribution of product efficiencies under the base case. DOE can 

generate a frequency chart like the one shown in Figure 8.4.5 for every EL and each product 

application. 

Figure 8.4.5 Front-Loading Commercial Clothes Washers, Multi-Family

Application: Distribution of LCC Impacts for EL3 

8.4.3 Standard-Level PBP Distributions

Figure 8.4.6 is an example of a frequency chart showing the distribution of payback 

periods of EL 3 for front-loading washer, multi-family product application. The entire 

distribution of PBP value above 0 indicates that all consumers are impacted by an increase in the 

EL, i.e., consumers are currently using front-loading commercial clothes washers with 

efficiencies lower than then EL. Refer back to section 8.2.6 on the distribution of product 

8-39
efficiencies under the base case. DOE can generate a frequency chart like the one shown in 

Figure 8.4.6 for every EL and each product application. 

Figure 8.4.6 Front-loading Commercial Clothes Washers, Multi-Family 

Application: Distribution of PBPs for EL 3 

8.4.4 LCC and PBP Results

Table 8.4.1 and Table 8.4.2 show the LCC and PBP results for both product applications 

of top-loading commercial clothes washers. Table 8.4.3 and Table 8.4.4 show the LCC and PBP 

results for both product applications of front-loading commercial clothes washers. For example, 

in the case of the front-loading washer, multi-family application, EL 2 (2.00 MEF/4.10 I WF)

shows an average LCC savings of $285. Note that for EL 2, approximately 39 percent of 

consumers in 2018 are assumed to already be using a commercial clothes washer in the base case 

at EL 2 and thus have zero savings due to the standard. With regard to the PBPs shown below, 

DOE determined the median and average values by excluding the percentage of households not 

impacted by the standard. For example, in the case of EL 2 for front-loading washers, about 39 

percent of the consumers did not factor into the calculation of the median and average PBP. 

8-40
Table 8.4.1 Top-Loading Commercial Clothes Washers, Multi-Family Application: LCC 

and PBP Results

CSL MEF/WF

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

(years)

Average 

Installed 

Price

Average 

Operating 

Cost

Average 

LCC

Average 

Savings

Households with

Median Average

Net

Cost

No 

Impact

Net 

Benefit

Baseline 1.15 / 

8.90 $1,251 $4,617 $5,868 - - - - - - 

1 1.35 / 

8.80 $1,251 $4,350 $5,601 $268 0% 1% 99% 0.0 0.0

2 1.55 / 

6.90 $1,313 $3,708 $5,022 $845 0% 0% 100% 0.6 0.7

Table 8.4.2 Top-Loading Commercial Clothes Washers, Laundromat Application: LCC 

and PBP Results

CSL MEF/WF

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

(years)

Average 

Installed 

Price

Average 

Operating 

Cost

Average 

LCC

Average 

Savings

Households with

Median Average

Net

Cost

No 

Impact

Net 

Benefit

Baseline 1.15 / 

8.90 $1,251 $3,924 $5,176 - - - - - - 

1 1.35 / 

8.80 $1,251 $3,767 $5,018 $158 0% 1% 99% 0.0 0.0

2 1.55 / 

6.90 $1,313 $3,181 $4,494 $681 0% 0% 100% 0.6 0.5

Table 8.4.3 Front-Loading Commercial Clothes Washers, Multi-Family Application: 

LCC and PBP Results

CSL MEF/WF

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

(years)

Average 

Installed 

Price

Average 

Operating 

Cost

Average 

LCC

Average 

Savings

Households with

Median Average

Net

Cost

No 

Impact

Net 

Benefit

Baseline 1.65 / 

5.20 $1,853 $2,987 $4,840 - - - - - - 

1 1.80 / 

4.50 $1,853 $2,749 $4,602 $238 0% 73% 27% 0.0 0.0

2 2.00 / 

4.10 $1,854 $2,555 $4,409 $298 0% 39% 61% 0.0 0.0

3 2.20 / 

3.90 $1,885 $2,700 $4,585 $8 46% 0% 53% 3.7 3.9

8-41
Table 8.4.4 Front-Loading Commercial Clothes Washers, Laundromat Application: 

LCC and PBP Results

CSL MEF/WF

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

(years)

Average 

Installed 

Price

Average 

Operating 

Cost

Average

LCC

Average 

Savings

Households with

Median Average

Net

Cost

No 

Impact

Net 

Benefit

Baseline 1.65 / 

5.20 $1,853 $2,604 $4,458 - - - - - - 

1 1.80 / 

4.50 $1,853 $2,398 $4,251 $204 0% 73% 27% 0.0 0.0

2 2.00 / 

4.10 $1,854 $2,242 $4,096 $245 0% 39% 61% 0.0 0.0

3 2.20 / 

3.90 $1,885 $2,381 $4,266 -$19 72% 0% 28% 7.3 8.0

Figure 8.4.7 and Figure 8.4.8 show the range of LCC savings for the CSLs for both 

product applications of top-loading commercial clothes washers. Figure 8.4.9 and Figure 8.4.10

show the range of LCC savings for the ELs for both product applications of front-loading 

commercial clothes washers. For each EL, the top and the bottom of the box indicate the 75th

and 25th percentiles, respectively. The bar at the middle of the box indicates the median; 50 

percent of the households have LCC 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 EL. 

Figure 8.4.11 and Figure 8.4.12 show the range of PBPs for top-loading commercial 

clothes washers. Figure 8.4.13 and Figure 8.4.14 show the range of PBPs for front-loading 

commercial clothes washers. In these figures, households which are not impacted by an increase 

in the standard are not included (this situation applies to all ELs except EL 3 for front-loading 

washers).

8-42
Figure 8.4.7 Range of LCC Savings for Top-Loading Commercial 

Clothes Washers, Multi-Family 

Figure 8.4.8 Range of LCC Savings for Top-Loading Commercial 

Clothes Washers, Laundromat 

0

200

400

600

800

1,000

1,200

1,400

1,600

Level 1 Level 2

Life Cycle Cost Savings ($)

Efficiency Level

median average

0

200

400

600

800

1,000

1,200

Level 1 Level 2

Life Cycle Cost Savings ($)

Efficiency Level

median average

8-43
Figure 8.4.9 Range of LCC Savings for Front-Loading 

Commercial Clothes Washers, Multi-Family 

Figure 8.4.10 Range of LCC Savings for Front-Loading 

Commercial Clothes Washers, Laundromat 

-400

-200

0

200

400

600

800

Level 1 Level 2 Level 3

Life Cycle Cost Savings ($)

Efficiency Level

median average

-300

-200

-100

0

100

200

300

400

500

600

Level 1 Level 2 Level 3

Life Cycle Cost Savings ($)

Efficiency Level

median average

8-44
Figure 8.4.11 Range of Payback Periods for Top-Loading 

Commercial Clothes Washers, Multi-Family

Figure 8.4.12 Range of Payback Periods for Top-Loading 

Commercial Clothes Washers, Laundromat 

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Level 1 Level 2

Payback Period (years)

Efficiency Level

median average

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Level 1 Level 2

Payback Period (years)

Efficiency Level

median average

8-45
Figure 8.4.13 Range of Payback Periods for Front-Loading 

Commercial Clothes Washers, Multi-Family

Figure 8.4.14 Range of Payback Periods for Front-Loading 

Commercial Clothes Washers, Laundromat 

0

2

4

6

8

10

12

Level 1 Level 2 Level 3

Payback Period (years)

Efficiency Level

median average

0

5

10

15

20

25

Level 1 Level 2 Level 3

Payback Period (years)

Efficiency Level

median average

8-46
8.5 REBUTTABLE PAYBACK PERIOD

 DOE presents rebuttable PBPs to provide the legally established rebuttable presumption 

that an energy efficiency standard is economically justified if the additional product costs 

attributed to the standard are less than three times the value of the first-year energy cost savings. 

(42 U.S.C. §6295 (o)(2)(B)(iii)) 

8.5.1 Metric

 The basic equation for rebuttable PBP is the same as that shown in section 8.3, Payback 

Period Inputs. Unlike the analyses described in sections 8.2 and 8.3, however, the rebuttable 

PBP is not based on the use of household samples and probability distributions. Rather than 

distributions, the rebuttable PBP is based on discrete single-point values. For example, while 

DOE uses a probability distribution of regional energy prices in the distributional payback period 

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 and water consumption. The following identifies the 

differences between the annual energy and water consumptions determined for the distribution 

PBP and the rebuttable PBP. 

 DOE based the annual energy and water consumption values that it used to determine the 

rebuttable PBP on the number of cycles per year specified in the DOE test procedure.22 The 

commercial clothes washer test procedure cites the residential clothes washer test procedure to 

establish efficiency ratings as well as annual energy and water consumption. As a result, the 

annual number of use cycles, 295 cycles per year, for determining the annual energy and water 

consumption of commercial clothes washers is representative of residential use, not commercial 

use. Because residential use is significantly lower than the average usage for commercial 

applications—1460 cycles per year in multi-family buildings and 2190 cycles per year in 

laundromats—the average annual energy and water consumption used to determine rebuttable 

PBP will be significantly less than the consumption associated with actual usage. As a result, the 

rebuttable PBP will be significantly longer than the distribution PBPs for both multi-family and 

laundromat applications. 

8.5.2 Inputs

 Inputs for the rebuttable PBP differ from the distribution PBP in that the calculation uses 

discrete values, rather than distributions, for inputs. 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 the determination of the rebuttable PBP. 

8-47
• Manufacturing costs, markups, sales taxes, and installation costs were all based on the 

single-point values used in the distributional LCC and PBP analysis. 

• As described in section 8.5.1, annual energy and water consumption is based on the DOE 

test procedure.

• Energy and water prices are based on national average values for the year that new 

standards are assumed to 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 2013. 

8.5.3 Results

DOE calculated rebuttable PBPs for each CSL relative to the distribution of product 

efficiencies assumed for the base case (refer back to section 8.2.6 for more details on the base 

case efficiency distributions for each product). In other words, DOE did not determine the 

rebuttable PBP relative to the baseline efficiency level, but relative to the current distribution of 

product efficiencies DOE determined for the base case (i.e., the case without new standards). 

The following sections present the results for the sets of products being analyzed for this 

standards rulemaking. 

Table 8.5.1 and Table 8.5.2 present the rebuttable PBPs for top-loading commercial 

clothes washers under both product applications; multi-family buildings and laundromats. Table 

8.5.3 and Table 8.5.4 present the rebuttable PBPs for front-loading commercial clothes washers 

under both product applications; multi-family buildings and laundromats. 

Table 8.5.1 Top-Loading Commercial Clothes Washers, Multi-Family Application: 

Rebuttable Payback Periods

CSL MEF IWF

PBP

years

Baseline 1.15 8.90 -

1 1.35 8.80 0

2 1.55 6.90 2.3

8-48
Table 8.5.2 Top-Loading Commercial Clothes Washers, Laundromat Application: 

Rebuttable Payback Periods

CSL MEF IWF

PBP

years

Baseline 1.15 8.90 -

1 1.35 8.80 0

2 1.55 6.90 2.72

Table 8.5.3 Front-Loading Commercial Clothes Washers, Multi-Family Application: 

Rebuttable Payback Periods

CSL MEF IWF

PBP

years

Baseline 1.65 5.20 -

1 1.80 4.50 0

2 2.00 4.10 0.04

3 2.20 3.90 8.73

Table 8.5.4 Front-Loading Commercial Clothes Washers, Laundromat Application: 

Rebuttable Payback Periods

CSL MEF IWF

PBP

years

Baseline 1.65 5.20 -

1 1.80 4.50 0

2 2.00 4.10 0.05

3 2.20 3.90 11.24

8-49
REFERENCES

1. RS Means, Mechanical Cost Data, 36st Annual Edition, 2013. Kingston, MA. 

2. U.S. Department of Energy–Energy Information Administration, Form EIA-861 for 2011, 

2012. (Last accessed November, 2013.) <http://www.eia.gov/electricity/data/eia861>

3. U.S. Census Bureau, Population Estimates, National Totals: Vintage 2012, (Last 

accessed November, 2013.) 

<http://www.census.gov/popest/data/national/totals/2012/index.html>

4. U.S. Department of Energy-Energy Information Administration, Natural Gas Prices, 

2013. Washington, DC. <http://www.eia.gov/dnav/ng/ng_pri_sum_dcu_nus_m.htm> 

5. Raftelis Financial Consultants Inc., 2012 RFC/AWWA Water and Wastewater Rate 

Survey, 2013. Charlotte, NC, Kansas City, MO, and Pasadena, CA. 

<http://www.raftelis.com/ratessurvey.html> 

6. U.S. Department of Energy-Energy Information Administration, Annual Energy Outlook 

2013 with Projections to 2040, 2013. Washington, DC. 

<http://www.eia.gov/forecasts/aeo/> 

7. U.S. Department of Labor-Bureau of Labor Statistics, Consumer Price Indexes, Item: 

Water and sewerage maintenance, Series Id: CUUR0000SEHG01, U.S. city average (not 

seasonally adjusted), 2013. Washington, DC. <http://www.bls.gov/cpi/home.htm#data> 

8. Whirlpool Corporation, Comment of Whirlpool Corporation Regarding the ANOPR for 

Cooking Products and Commercial Clothes Washers. 

9. U.S. Department of Energy-Energy Efficiency and Renewable Energy, Technical Support 

Document: Energy Efficiency Standards for Consumer Products: Residential Central Air 

Conditioners and Heat Pumps Including: Regulatory Impact Analysis. Chapter 5, LifeCycle Cost and Payback Analysis, May, 2002. Washington, D.C. 

<http://www1.eere.energy.gov/buildings/appliance_standards/residential/ac_central_1000

_r.html> 

10. Coin Laundry Association, Industry Overview. Downers Grove, IL. 

<http://www.coinlaundry.org/resources/industryoverview.cfm> 

11. Southern California Edison, 2004-2005 Database for Energy Efficiency Resources 

(DEER) Update Study, Final Report, December, 2005. Prepared for Southern California 

Edison by Itron, Inc. Vavcouver, WA. <http://www.calmac.org/publications/2004-

05_DEER_Update_Final_Report-Wo.pdf> 

8-50
12. Sullivan, G. P., J. W. Currie, T. C. Hillman, and G. B. Parker, High-Performance Clothes 

Washer Demonstration at Leisure World Laguna Woods Final Report, December, 2000. 

Prepared for Southern California Edison Company by Battelle Pacific Northwest 

Laboratory. Richland, WA. <http://eere.pnl.gov/building-technologies/doc/scefinal%202-

22-01.pdf> 

13. California Measurement Advisory Committee, CALMAC Protocols, Appendix F, 

Effective Useful Live Values for Major Energy Efficiency Measures, September, 2000. 

<http://www.calmac.org/events/APX_F.pdf> 

14. Ibbotsons Associates, Cost of Capital 2001 Yearbook, Data Through March 2001, 2001. 

Chicago, IL. 

15. Damodaran Online, The Data Page: Cost of Capital by Industry Sector.

2012<http://pages.stern.nyu.edu/~adamodar> 

16. Ibbotsons Associates, Statistics for SIC code 72, 2008. <http://www.ibbotson.com> 

17. Ibbotsons Associates, SBBI Valuation Edition 2001 Yearbook, 2001. Chicago, IL. 

18. The Federal Reserve Board, Federal Reserve Bank Services Private Sector Adjustment 

Factor, 2005. Washington, DC. 

19. Damodaran Online, The Data Page: Cost of Capital by Industry Sector, 2008, 2010, 

2011, 2012. <http://pages.stern.nyu.edu/~adamodar/> 

20. Association of Home Appliance Manufacturers, AHAM Data on Commercial Clothes 

Washers for DOE Efficiency Standards Rulemaking. 2012, Letter to Building 

Technologies Program,U.S. Department of Energy. 

21. California Energy Commission, Appliances Databases – Clothes Washers, 2009. (Last 

accessed November, 2013.) 

<http://www.energy.ca.gov/appliances/database/excel_based_files/Clothes_Washers/>

22. U.S. Office of the Federal Register, Code of Federal Regulations, Title 10, Part 431 - 

Energy Conservation Program for Certain Commercial and Industrial Equipment, 

Subpart I—Commercial Clothes Washers, January 1, 2007. Washington, DC. 

8-51
9-i

CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION ........................................................................................................... 9-1

9.2 SHIPMENTS MODEL METHODOLOGY .................................................................... 9-1

9.3 DATA INPUTS AND MODEL CALIBRATION .......................................................... 9-3

9.3.1 Historical Shipments ........................................................................................................ 9-5

9.3.2 Markets and Model Calibration ....................................................................................... 9-7

9.3.2.1 New Construction .......................................................................................... 9-7

9.3.2.2 Replacements ............................................................................................... 9-10

9.3.2.3 Model Calibration—Non-Replacements ..................................................... 9-11

9.3.2.4 Base Case Shipments ................................................................................... 9-12

9.3.3 Market Share Forecast ................................................................................................... 9-14

9.4 PURCHASE PRICE AND OPERATING COST IMPACTS ....................................... 9-16

9.5 AFFECTED STOCK ..................................................................................................... 9-17

9.6 RESULTS ...................................................................................................................... 9-18

LIST OF TABLES

Table 9.3.1 Commercial Clothes Washers: Historical Shipments (1972 – 2011) .................... 9-6

Table 9.3.3 Commercial Clothes Washers: Historical New Multi-Family Building ................ 9-9

Table 9.3.4 Multi-Family In-Unit Washer Saturations .............................................................. 9-9

Table 9.3.5 Commercial Clothes Washers: Historical New Multi-Family Building .............. 9-12

Table 9.4.1 Price Elasticity Decrease after CCW Price Change .............................................. 9-17

Table 9.6.1 Top-Loading Commercial Clothes Washers: Cumulative Shipments 

Impacts .................................................................................................................. 9-18

Table 9.6.2 Front-Loading Commercial Clothes Washers: Cumulative Shipments ............... 9-19

LIST OF FIGURE

Figure 9.3.1 Commercial Clothes Washer Shipments and Trend in Quantity Index ............... 9-7

Figure 9.3.2 Historical and Forecasted Multi-Family Housing Starts (millions) ..................... 9-8

Figure 9.3.3 Commercial Laundry Saturation in New Multi-Family Housing....................... 9-10

Figure 9.3.4 Commercial Clothes Washers: Survival and Retirement Functions ................. 9-11

Figure 9.3.5 Commercial Clothes Washers: Historical and Base Case Shipments ............... 9-13

Figure 9.3.6 Commercial Clothes Washers: Disaggregated Base Case ................................. 9-13

Figure 9.3.7 Historical Market Share of Top-loading Units ................................................... 9-14

Figure 9.3.8 Forecast of Market Share in Base Case and Standards Case TSL 3 for ............. 9-15

Figure 9.6.1 Top-Loading Commercial Clothes Washers: Base Case and TS Shipments 

Forecasts ............................................................................................................ 9-19

Figure 9.6.2 Front-Loading Commercial Clothes Washers: Base Case and TSL 

Shipments Forecasts........................................................................................... 9-20
9-1

CHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION

 Product shipments estimates are a necessary input to the national energy savings (NES) 

and net present value (NPV) calculations. Shipments are also a necessary input to the 

manufacturer impact analysis (MIA), which the U.S. Department of Energy (DOE) conducts for 

its notice of proposed rulemaking (NOPR). This chapter describes DOE’s methodology for 

projecting annual shipments and presents results for commercial clothes washers.

DOE estimated shipments for commercial clothes washers with a shipments model. DOE

calibrated the shipments model against historical shipments. For purposes of estimating the 

impacts of prospective trial standard levels (TSLs) on product shipments, the shipments model 

accounts for the combined effects of changes in purchase price and annual operating cost on the 

product purchase decision. 

The shipments model considers specific market segments to estimate shipments, the 

results of which are then aggregated to estimate total product shipments. In the case of 

commercial clothes washers, DOE accounted for two market segments: (1) shipments due to 

new construction, and (2) replacements of retired units from existing buildings. 

The shipments model is a Microsoft Excel spreadsheet that is accessible on the Internet 

(http://www.eere.energy.gov/buildings/appliance_standards/). Appendix 10-B discusses how to 

access the shipments model contained in the NIA spreadsheet, and provides basic instructions for 

using them. The rest of this chapter explains the shipments model in more detail. Section 9.2 

presents the shipments model methodology; section 9.3 describes the data inputs and the model 

calibration; section 9.4 discusses impacts on shipments from changes in equipment purchase 

price and operating cost; section 9.5 discusses the affected stock; and section 9.6 presents the 

results for different TSL scenarios.

9.2 SHIPMENTS MODEL METHODOLOGY

DOE developed a national stock model for estimating annual shipments for commercial

clothes washers. The model considers market segmentation as a distinct input to the shipments 

projection. As represented by the following equation, the two primary market segments are new 

installations and replacements. 

Ship j Rpl j NI j)()()( p p += p

 Where:
9-2

Shipp(j) = total shipments of product p in year j, 

Rplp(j) = units of product p retired and replaced in year j, and 

NIp(j) = number of new installations of product p in year j. 

As section 9.3.2.3 discusses, DOE also considered non-replacements as a factor for most 

of the products to calibrate its shipments model to historical shipments data.

In principle, each market segment and each product class responds differently to both the 

base case demographic and economic trends and to the implementation of standards. 

Furthermore, retirements, early replacements, and efficiency trendsa are dynamic and can vary 

among product classes. Rather than simply extrapolating a current shipments trend, the base 

case shipments analysis (i.e., the case without new standards) uses driver input variables, such as 

construction forecasts and product lifetime distributions, to project sales in each market segment. 

Thus, DOE’s shipments model assumes that construction--i.e., new multi-family housing units--

drives new installations. In each year, the product shipments from the new construction market 

segment are equal to the number of new multi-family housing units built times the purchase rate, 

which is determined by the product class market share and the market saturation of the product 

under considerationb. 

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 

model estimates shipments due to replacements using sales in previous years and assumptions 

about the lifetime of the equipment. Therefore, estimated sales due to replacements in a given 

year are equal to the total stock of the appliance minus the sum of the appliances sold in previous 

years that still remain in the stock. DOE determined the useful service life of each appliance to 

estimate how long the appliance is likely to remain in stock. The following equation represents 

how DOE estimated replacement shipments.

Rplp (j) = Stockp (j − 1) − � � Shipj 

j−1

j=N

ageMax

age=0

× probRtr (age)

 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

 

a

 Efficiency trends affect shipments only in the standards case. A change in the efficiency distribution of the 

stock results in a change in the purchase price and operating cost and, therefore, produces a purchase price and 

operating cost impact on the shipments. This is discussed later in the chapter in section 9.4. 

b

 In this case, market saturation of the product is related to saturation of in-home clothes washer units in multifamily households.
9-3

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 and provides an estimate of the age distribution of in-service product

stocks for all years. The age distribution of in-service product stocks is a key input to both the 

NES and NPV calculations—the operating costs for any year depend on the age distribution of 

the stock. The dependence of operating cost on the equipment age distribution occurs under a 

TSL that produces increasing efficiency over time, where older, less efficient units may have 

higher operating costs, while younger, more-efficient units will have lower operating costs. 

DOE calculated total in-service stock of equipment by integrating historical shipments 

data starting from a specific year. The start year depended on the historical data available for 

the product. As units are added to the in-service stock, some of the older ones retire and exit the 

stock. To estimate future shipments, DOE developed a series of equations that define the 

dynamics and accounting of in-service stocks. For new units, the equation is: 

)1()1,( _ Stock j age == Ship j

 Where: 

Stock(j, age) = the population of in-service units of a particular age,

j = year for which the in-service stock is being estimated, and

 Ship (j) = number of units purchased in year j. 

The previous equation states that the number of one-year-old units is simply equal to the 

number of new units purchased the previous year. The slightly more complicated equations 

(e.g., the following equation) are those that describe the accounting of the existing in-service 

stock of units: 

Stock(j + 1, age + 1) = Stock(j, age) × [1 − probRtr(age)]

In this equation, as the year is incremented from j to j+1, the age is also incremented 

from age to age+1. With time, a fraction of the in-service stock is removed, and that fraction is 

determined by a retirement probability function, probRtr(age), which is described in section 10.3. 

Because commercial clothes washers are considered to be common appliances that have been 

used by U.S. consumers for a long time, replacements typically constitute the majority of 

shipments. Most replacements are made when equipment wears out and fails. 

9.3 DATA INPUTS AND MODEL CALIBRATION 

As discussed previously, shipments are driven primarily by two market segments: new 

construction and replacements. DOE used historical shipments as the basis for calibrating its 
9-4

shipments model. Because new construction shipments and replacements were not enough to 

account for all product shipments, DOE developed another factor to calibrate its shipments 

model. This factor, retired units not replaced, represents a small share of total shipments. 

Shipments to the different market segments are discussed below.

 DOE designed its shipments model for commercial clothes washers by developing a 

single shipments model for all washers destined for the two most predominant product 

applications—multi-family buildings and laundromats—and then disaggregating the shipments 

into its two product classes—top-loading and front-loading washers. DOE developed a crossprice elasticity to estimate the market shares of the considered product classes through the 

forecast period. The Cross-Price elasticity is covered in section 9.3.3. 

DOE’s approach for estimating shipments relies on using aggregate historical shipments 

data for all commercial clothes washers. Tracking of product stock and vintage relies on a 

retirement function, which describes the likelihood that equipment installed in a certain year will 

still be in service in any subsequent year. Once a product reaches the end of its useful life it is 

removed from service. In most cases, retirement is followed by replacement, which is a key 

driver of further shipments. The difference between replacements and total shipments for the 

following year provides the shipments going into new construction. The output includes units 

going into both laundromats and multi-family apartment buildings. 

DOE based its shipments model on the following three assumptions: (1) all product 

shipments for new construction are driven by the new multi-family housing market, (2) the 

relative market shares of the two product applications, laundromats and common-area laundry 

facilities in multi-family housing, are constant over time, and (3) the U.S. Census Bureau’s 

quantity index data can be used to validate the shipments trend observed in the historical data. 

DOE’s first assumption is based on the premise that multi-family occupants who do not 

have their own personal washers and dryers must either wash their laundry in the common area 

laundry facility within their building or in a laundromat. Because the vast majority of people 

that use laundromats live in multi-family buildings, the construction of any new laundromats 

would seem to be driven by new multi-family customers. Thus, DOE believes it is reasonable to 

expect that new multi-family construction starts capture all new product shipments, regardless of 

whether the units are destined for multi-family buildings or laundromats. 

 With regard to the second assumption, i.e., constant market shares, DOE did not have 

sufficient data to project whether the market shares of laundromats and multi-family housing will 

vary over time. Therefore, DOE assumed that market shares will remain constant at current 

levels. DOE relied on a report from the Consortium for Energy Efficiency (CEE) to estimate 

current market shares. Based on the CEE report, multi-family buildings and laundromats 

comprise 85 percent and 15 percent of the commercial clothes washer market, respectively.1

 

Although the CEE report was published more than 10 years ago, it continues to the most reliable 

data source for developing a market split for commercial clothes washer shipments that was 

available. 
9-5

 The third assumption pertains to the use of the U.S Bureau of Economic Analysis’ 

quantity index data to validate historical shipments data.2 The Census Bureau’s quantity index 

for commercial laundry equipment includes shipments of commercial laundry products beyond 

those covered by this rulemaking. Because disaggregated quantity index data were not available, 

DOE assumed that the market share of commercial clothes washers covered under this 

rulemaking relative to all commercial laundry equipment was constant over the period 1977–

2011. Based on this assumption, DOE derived quantity index data specific to the commercial 

clothes washers of interest to this rulemaking and used these data to validate the historical 

shipments. 

9.3.1 Historical Shipments

DOE used historical shipments data (i.e., domestic shipments) to calibrate its shipments 

model. It used two sources to establish historical shipments: (1) data the Association of Home 

Appliance Manufacturers (AHAM) provided for the period 2010–20113 and (2) data for the 

period 1972–20054

 from the last rulemaking. DOE used a five-year moving average to estimate

shipments for the period 2006–2009. Table 9.3.1 summarizes the historical commercial clothes 

washer shipments data. 
9-6

Table 9.3.1 Commercial Clothes Washers: Historical Shipments (1972 – 2011) 

Year Shipments (thousands) Year Shipments (thousands)

1972 167 1992 188

1973 167 1993 190

1974 166 1994 205

1975 167 1995 209

1976 167 1996 232

1977 167 1997 241

1978 166 1998 265

1979 164 1999 239

1980 170 2000 215

1981 171 2001 194

1982 163 2002 175

1983 163 2003 191

1984 167 2004 178

1985 176 2005 177

1986 186 2006 175

1987 194 2007 172

1988 213 2008 169

1989 215 2009 167

1990 225 2010 164

1991 193 2011 161

Sources: 2010–2011: AHAM Data Submittal, 2012; 2006–2009, DOE interpolated values; 1972–2005: DOE 

Standards for Commercial Clothes Washers, Technical Support Document, 2010.

 

 As indicated in Table 9.3.1, there was a significant drop in commercial clothes washer 

shipments between 1998 and 2011. Therefore, DOE validated the shipments data through a 

review of the U.S. Bureau of Economic Analysis’ quantity index data.2 DOE compared the 

historical shipments data against the quantity index for commercial laundry equipment 

(represented by North American Industry Classification System (NAICS) code 333312). As 

shown in Figure 9.3.1 below, the commercial laundry quantity index tracks the shipments trend 

closely. Thus, DOE concluded that the downward trend observed in the historical shipments 

between 1998 and 2011 was real. 
9-7

Figure 9.3.1 Commercial Clothes Washer Shipments and Trend in Quantity Index

9.3.2 Markets and Model Calibration

The market for commercial clothes washers primarily consists of replacement units for 

equipment that has been retired and units for new housing. Total commercial clothes washers 

shipments are represented by the following equation: 

Ship j Rpl j NI j)()()( CCW = CCW + CCW

 Where: 

ShipCCW (j) = total shipments of commercial clothes washers in year j, 

RplCCW (j) = replacement shipments in year j, and 

NICCW (j) = shipments due to new construction in year j. 

The following sections discuss both of these markets in further detail. The model 

calibration is also discussed. 

9.3.2.1 New Construction 

To project shipments to the new construction market, DOE assumed shipments are driven 

solely by multi-family construction starts. Implicit in this assumption is the fact that a certain 

percentage of multi-family residents will need to wash their laundry in either a common-area 

laundry facility (within the multi-family building) or a laundromat. 

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0

20

40

60

80

100

120

140

160

Shipments (millions)

Quantity Index (2000 =100)

Quantity Index Historical Shipments QI-based Shipments

Pre-1988 

shipments

based on QI

Decling 

trend since 

1999
9-8

 To estimate shipments to new construction, DOE used projections of multi-family 

housing starts coupled with commercial clothes washer saturation data. In other words, to 

project the shipments for new construction for any given year, DOE multiplied the multi-housing 

projections by the projected saturation of commercial clothes washers for new multi-family 

housing. Because DOE used only multi-family housing starts as a driver for new construction 

shipments, the saturation represents the fulfilled washer demand in both the multi-family housing 

and laundromat markets. 

Figure 9.3.2 presents historical and projected new multi-family housing starts based on 

the DOE Energy Information Administration (EIA)’s Annual Energy Outlook 2013 for the period 

2013–2040.5 The AEO typically provides three scenarios of multi-family housing starts: the 

Reference case, the High Economic Growth case, and the Low Economic Growth case. All three 

housing starts projections are presented in Figure 9.3.2. DOE used only the projections from the 

Reference case to estimate its shipments to new construction. For the period 2041−2047, DOE 

froze completions at the level in the year 2040 for all three economic projections. 

Figure 9.3.2 Historical and Forecasted Multi-Family Housing Starts (millions)

 

 DOE deduced historical commercial laundry saturations for the new construction market 

based on the remaining portion of historical shipments that did not serve as replacement units. 

(The determination of replacement shipments is discussed in section 9.3.2.2.) Table 9.3.3 shows 

the historical saturation of commercial clothes washers in new multi-family buildings.

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

Housing Starts (millions)

Reference

High

Low
9-9

Table 9.3.2 Commercial Clothes Washers: Historical New Multi-Family Building 

 Saturations 

Year Saturation

1990 18.6%

1991 10.8%

1992 9.8%

1993 11.6%

1994 15.6%

1995 11.9%

1996 17.0%

1997 19.0%

1998 23.7%

Average 15.3%

 DOE assumed that the average saturation over the period 1990−1998 of 15.3 percent 

would likely change during the period 1999 – 2011, due to the increasing saturation of in-unit 

washers in multi-family units as shown by historical data from the Census Bureau’s American 

Housing Survey (AHS).6 DOE utilized the in-unit washer trends in multi-family buildings to 

estimate the associated trends in the use of commercial clothes washers in multi-family common 

area laundry facilities. 

 Table 9.3.4 shows the saturation of in-unit washers in both the multi-family stock and 

new construction from 1997 to 2011. Between 1997 and 2011, the annual growth rate of 

saturation of in-unit washers in new multi-family construction (i.e., residential washers used in 

newly constructed multi-family units) is 0.68 percent. Therefore, DOE assumed that the annual 

growth rate of saturation of commercial clothes washers is -0.68 percent during the period 2000 

– 2011. DOE also assumed that the average saturation over the period 1999 – 2011 of 14.7 

percent would remain constant over the period 2012–2047. 

 

Table 9.3.3 Multi-Family In-Unit Washer Saturations 

In-Unit Washer Saturation

Multi-Family Housing 1997 1999 2001 2003 2005 2007 2009 2011 

New Construction

70.4% 64.4% 68.7% 68.1% 73.4% 73.5% 78.1% 77.41% 

Stock 32.3% 32.7% 35.6% 36.1% 37.3% 37.5% 39.1% 39.56% 

Source: AHS, 1997–2011.

 Figure 9.3.3 shows the historical variation in the new construction saturation as well a

the projected saturation for the years after 1998. 
9-10

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

Commercial Laundry Saturation in New 

Multi-Family Housing (%)

 

 Figure 9.3.3 Commercial Laundry Saturation in New Multi-Family Housing

9.3.2.2 Replacements

 DOE determined shipments to the replacement market using an accounting method that 

tracks the total stock of units by vintage. DOE estimated a stock of commercial clothes washers 

by vintage by integrating historical shipments starting from the year 1972. Over time, some 

units will be retired and removed from stock, thereby 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 unit fails, DOE used a survival function based on the product 

lifetime distributions of the two product applications considered in the analysis—laundromats 

and multi-family buildings. DOE based the retirement function on a Weibull probability 

distribution for the product lifetime. 

 For the commercial clothes washer shipments model, DOE combined the maximum and 

minimum age values for the two product applications based on the market share of each 

application (85 percent for multi-family housing and 15 percent for laundromats). The survival 

function that DOE used had an average lifetime of 10.6 years with minimum and maximum 

values of 6.7 years and 14.6 years, respectively. For a more complete discussion of commercial 

clothes washer lifetimes, refer back to section 8.2.3 of Chapter 8. 

 Figure 9.3.4 shows the survival function that DOE used to estimate replacement 

shipments. 

 
9-11

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

% Surviving by Age

 Figure 9.3.4 Commercial Clothes Washers: Survival and Retirement Functions

9.3.2.3 Model Calibration—Non-Replacements

To account for the significant drop in shipments after 1998, and to reconcile the historical 

shipments with the accounting model, DOE assumed every retired unit is not replaced 

immediately. The market share of retired units not replaced was relatively large due to the 

significant drop in shipments after 1998 (as discussed in section 9.3.1). Starting in 1999 and 

extending to 2011, DOE estimated the share of retired units that were not replaced. The share of 

retired units not replaced fluctuated between 16 and 42 percent. Table 9.3.5 shows the share of 

retired units not replaced between 1999–2011.
9-12

Table 9.3.4 Commercial Clothes Washers: Historical New Multi-Family Building 

 Saturations 

Year % of Retired units not replaced

1999 0.0%

2000 16.2%

2001 26.7%

2002 37.8%

2003 29.0%

2004 37.1%

2005 37.2%

2006 40.9%

2007 39.5%

2008 41.6%

2009 40.5%

2010 32.8%

2011 31.8%

Average 31.6%

To determine the share of retired units not replaced after the year 2011, DOE reviewed 

data from the American Housing Survey. 

 As evident from Table 9.3.4, the saturation of in-unit washers in new construction has 

increased by approximately 10 percent, while the saturation in the multi-family building stock 

has increased by approximately 23 percent. Because the saturation of in-unit washers in new 

construction has increased relatively slowly, there must be another driver for the increase of inunit washers in the building stock. DOE postulates that the growth in in-unit washer saturations 

in the multi-family stock over the last ten years was caused by conversions of rental property to 

condominiums, resulting in the gradual phase out or non-replacement of failed commercial 

clothes washers in common-area laundry facilities. Based on this apparent trend, DOE used the 

average percent of non-replacements over the 1999–2011 timeframe—31.6 percent—and 

maintained it over the entire projection period of 2012–2047. 

9.3.2.4 Base Case Shipments

 Figure 9.3.5 shows the projected commercial clothes washer shipments in the base case 

(i.e., the case without new energy efficiency standards) and the historical shipments DOE used to 

calibrate the projection. To be clear, all commercial clothes washer shipments, including both 

top-loading and front-loading units, are represented in the shipments shown in Figure 9.3.5. 
9-13

0.00

0.05

0.10

0.15

0.20

0.25

Shipments (millions)

Year

Replacement Modeled Shipments

New Construction Modeled Shipments

 Figure 9.3.5 Commercial Clothes Washers: Historical and Base Case Shipments 

 Forecast

Figure 9.3.6 presents projected base case commercial clothes washer shipments 

disaggregated into shipments due to retirements and shipments for new construction. The 

unusual jump in shipments going from 2012 to 2013 is driven primarily by the new construction 

starts in multifamily housing. 

 Figure 9.3.6 Commercial Clothes Washers: Disaggregated Base Case 

 Shipments Projection 

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

0.26

0.28

Shipments (million)

Year

Historical Shipments

Projected Total Shipments
9-14

9.3.3 Market Share Forecast

 DOE projected the change in market share between the front-loading and top-loading 

units in the base case on the basis of available market share data. For the base case projection, 

DOE fit a curve to estimate the top-loading market share using data submitted by AHAM for the 

period between 2005 and 2011, as shown in Figure 9.3.7. 

 Figure 9.3.7 Historical Market Share of Top-loading Units 

 

 

 

 For the standards case, both available data and cross-price elasticities were used to 

forecast market share changes. Due to insufficient data, DOE was not able to estimate cross-price 

impacts on the market share of top-loading and front-loading commercial clothes washers and instead 

relies on its recent analysis performed for residential clothes washers.7

 In order to estimate the impact of price changes on market share, DOE applied a logistic 

regression method to regress market share on price variables and other attributes. The basic form of a 

logistic regression is: 

pi = � 1

1 + e−�β0+β1x1,i+⋯+βkxk,i�

�

 With some simple operations, the model can be transformed to an equivalent log-linear form: 

ln � pi

1 − pi

� β0 + β1x1,i + ⋯ + βkxk,i

y = 5E+239x-72.62

R2 = 0.8972

0%

20%

40%

60%

80%

100%

2000 2002 2004 2006 2008 2010 2012 2014

Market Share 

y = 0.8104 x-0.116

R2 = 0.92
9-15

 In this case, is the market share of either top- or front-loading clothes washers, and is the 

ratio of clothes washer market shares, i.e., the relative probability of the market share of top- and

front-loading clothes washers.

 These regression results were used to derive the cross-price impact of a change in the 

front-loading washer price on the top-loading market share (and vice versa). For example, these 

results suggest that a 10% increase in the price of front-loading washers would lead to a 10.7% 

decrease in top-loading washers’ market share, holding other variables constant and measured as 

changes from the reference case using average values for each variable. In this case, the frontloading cross-price impact (percent change in top-loading market share over percent change in 

front-loading price) is 1.07.c Similarly, these results indicate that a 20% price increase for toploading washers would yield a 21.49 % increase in front-loading market share, measured as 

changes from the variable means. Thus, in this example, the top-loading washer cross-price 

impact is also 1.07. See the residential clothes washers 2012 Direct Final Rule Technical 

Support Document for additional details.7

 Figure 9.3.8 shows forecasted base and standards case market share of commercial 

clothes washers disaggregated by the two product types. The figures show the standards case for 

top-loading and front-loading clothes washers, TSL 3. Given the direct correlation between price 

and market share, as long as the price changes the market share will also continue to change.

 

Figure 9.3.8 Forecast of Market Share in Base Case and Standards Case TSL 3 for 

 Front-Loading (EL 3) and for Top-Loading (EL 2) 

 

c Price elasticity varies according to the reference price and market share – typically measured as changes from 

mean values for these variables in the data set. In DOE’s data set, the reference top-loading washer price is 

$403.78, the reference front-loading washer price is $823.83, the corresponding reference top-loading washer 

market share is 71%, and the corresponding reference front washer market share is 29%.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Market Share (%)

Market Share of Front- and Top-loading Washers

Front Loader - Base Case Top Loader - Base Case

Front Loader - Standard Top Loader - Standard
9-16

9.4 PURCHASE PRICE AND OPERATING COST IMPACTS

 

 DOE conducted a literature review to establish the likely range of values for the price 

elasticity of demand for commercial clothes washers. The price elasticity of demand describes

the responsiveness of the quantity purchased of a product to a change in its price and can be 

generically defined as the ceteris paribus percent change in shipments given a 1% change in 

price, or: 

ed = P0

Q0

∗

∂Q

∂P

 where P and Q represent price and shipments quantity, respectively.

8

 DOE believes commercial clothes washers to be highly price inelastic and applies a 

default price elasticity of demand of zero for this product, meaning that any cost and price 

increases resulting from efficiency standards are unlikely to substantially affect the quantity of 

washers demanded. As these are somewhat niche products, estimates of commercial clothes 

washer price elasticity were not found in the literature, and DOE does not have the necessary 

data to perform an accurate independent calculation of price elasticity. Instead, the value of zero 

is based on DOE’s understanding of the commercial clothes washer market and distribution 

channels, particularly in relation to the demand for laundry services. 

 To validate the use of a price elasticity of zero, DOE estimated an implied commercial 

clothes washer price elasticity based on known variables describing the market for laundry 

services. The implied price elasticity is approximated as follows:

Implied ed,CCW = ed,LaundrySvc ∗ � PriceCCW

PriceLaundrySvc�

 Where: 

 = the price elasticity of demand for commercial clothes washers,

 = the average price elasticity of demand for laundry service,

 = the commercial clothes washers wholesale price (or cost), and

 = the price (or cost) of providing laundry service. 

 DOE estimated an implied price elasticity of demand for commercial clothes washers of 

approximately -0.03 to -0.12 based on laundry equipment distribution channels and the market 

for laundry services. These values are provided as alternatives to the default price elasticity of 

zero.

 Additionally, price elasticity for a given product is not constant over time; numerous 

studies have demonstrated decreases in price elasticity for durable goods as the period of time 

since a price change increases.9, 10,11 Similarly, decreases in price elasticity have been 

demonstrated through the stages of a product’s life cycle.

12 DOE projects that any non-zero 
9-17

commercial clothes washer price elasticity will decrease over time, following the proportional 

decreases demonstrated for automobiles (Table 9.4.1).

Table 9.4.1 Price Elasticity Decrease after CCW 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 

Implied Price Elasticity (low) -0.031 -0.024 -0.015 -0.007 -0.002 -0.001

Implied Price Elasticity (high) -0.123 -0.096 -0.060 -0.028 -0.010 -0.003

Source: Hymans 1979 and DOE calculations. 

 Based on the following equation, DOE estimated standards case shipments by 

incorporating the impact of the price into the base case shipments projection. Note that in the 

equation below, the price and the price elasticity are functions of the year, because both change 

with time. 

)( ( )( ) ( )()(1)()( ) _

_ _ _ _ Ship j Rpl j NI jejMj RP j pSTD = BASE p + BASE p + BASE p RP D ́

 Where:

ShipSTD_p(j) = total shipments under the standards case of product p in year j, 

RplBASE_p(j) = units of product p under the base case retired and replaced in year j, 

NIBASE_p(j) = number of new construction installations under the base case of product p

in year j, 

MBASE_p(j) = units installed in Market M under the base case of product p in year j (M

represents early replacements for commercial clothes washers),

ed(j)= price elasticity in year j (equals -0.031 or -0.123 for year 1), and 

ΔP(j)=change in price due to a standard level in year j. 

9.5 AFFECTED STOCK

The affected stock is the in-service stock of the appliance or product that is affected by a 

TSL. In addition to the projection of product shipments under both the base case and the 

standards case, the affected stock (which represents the difference in the appliance stock for the 

base case and the standards case) is a key output of DOE’s shipments models. The affected 

stock quantifies the impact that new product shipments have on the appliance stock due to a 

TSL. Therefore, the affected stock consists of those in-service units that are purchased in or after 

the year the standard has taken effect, as described by the following equation:
9-18

∑

_

1

_

)()( )(

j Std yr

age

Aff p p jShipjStock 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 in this equation, to calculate the affected stock, DOE must define the effective 

date of the standard. For the NES and NPV results presented in Chapter 11, DOE assumed that 

new energy efficiency standards will become effective in the year 2018. Thus, all appliances 

purchased starting on the first day of the year 2018 are affected by the standard level. 

9.6 RESULTS

 The following sections show the shipments projections for the various TSLs that DOE 

considered for each of the products. The TSLs are identified and described in chapter 10. 

Table 9.6.1 and Table 9.6.2 show the cumulative shipments impacts over the period 

2018–2047 caused by each of the TSLs considered for top-loading and front-loading commercial 

clothes washers, respectively. Figure 9.6.1compares the shipments projections for the base case 

and each of the three TSLs for top-loading washers. In Figure 9.6.1, the difference between the 

base case and TSL shipments projections depicts the annual shipments reductions caused by 

cross-price elasticities. Figure 9.6.2 compares the shipments projections for the base case and 

each of the three TSLs for front-loading washers. 

Table 9.6.1 Top-Loading Commercial Clothes Washers: Cumulative Shipments Impacts 

 Caused by Standards over the Period 2018–2047 

TSL

MEFJ2/IWF (cu.ft/kWh/cycle 

gal/cu.ft)

Cumulative Impacts

(thousands)

1 1.35/8.80 0.00

2 1.35/8.80 0.62

3 1.55/6.90 -86.46
9-19

Table 9.6.2 Front-Loading Commercial Clothes Washers: Cumulative Shipments 

 Impacts Caused by Standards over the Period 2018–2047 

TSL

MEFJ2/IWF (cu.ft/kWh/cycle 

gal/cu.ft)

Cumulative Impacts

(thousands)

1 1.80/4.50 0.00

2 2.00/4.10 -0.62

3 2.20/3.90 86.46

 Figure 9.6.1 Top-Loading Commercial Clothes Washers: Base Case and TS 

 Shipments Forecasts

0.10

0.10

0.10

0.11

0.11

0.11

0.12

0.12

0.12

0.13

Shipments (million)

Year

TSL 3 Base Case

TSL 2 TSL 1
9-20

 Figure 9.6.2 Front-Loading Commercial Clothes Washers: Base Case and TSL 

 Shipments Forecasts

0.05

0.06

0.06

0.07

0.07

0.08

0.08

0.09

0.09

0.10

0.10

0.11

0.11

0.12

Shipments (million)

Year

TSL 3 Base Case

TSL 2 TSL 1
9-21

REFERENCES

 

1

 Consortium for Energy Efficiency. Commercial Family-Sized Washers: An Initiative 

Description of the Consortium for Energy Efficiency, 1998. Boston, MA. 

2

 U.S. Department of Commerce-Bureau of Economic Analysis. Industry Economic 

 Accounts, Gross-Domestic-Product-(GDP)-by-Industry-Data, 1998-2011 NAICS Data: 

 GDPbyInd_SHIP_NAICS and SIC Data: GDPbyInd_SHIP_SIC, Commercial Laundry 

 Quantity Index Data, NAICS code 333312. Washington, DC. 

 <http://www.bea.gov/industry/gdpbyind_data.htm> 

3 Association of Home Appliance Manufacturers. AHAM Data on Commercial Clothes 

 Washers for DOE Efficiency Standards Rulemaking, November 12, 2012. Letter to Building 

Technologies Program, U.S. Department of Energy. 

4

 U.S. Department of Energy, Chapter 10. Shipment Analysis, Commercial Clothes Washers 

Final Rule Technical Support Document, EERE-2006-STD-0127 COMMENT 69.1, 2010-

01-19. 

 <http://www.regulations.gov/#!documentDetail;D=EERE-2006-STD-0127-0118> 

5

 U.S. Department of Energy-Energy Information Administration. Annual Energy Outlook 

2013 with Projections to 2040, April 2013. Washington, DC. DOE/EIA-0383(2013). 

 <http://www.eia.gov/forecasts/aeo/pdf/0383(2013).pdf> 

6

 U.S. Census Bureau, Housing and Household Economic Statistics Division. The American 

Housing Survey, 1997—2011. Washington, DC. 

<http://www.census.gov/housing/ahs/data/national.html> 

7 U.S. Department of Energy, Technical Support Document: Energy Efficiency Program for 

 Consumer Products and Commercial and Industrial Equipment: Residential Clothes 

 Washers (Direct Final Rule), Building Technologies Program, Editor. 2012: Washinton, D.C. 

8 Varian, H. R., Intermediate Microeconomics: A Modern Approach. 1999. Vol. 5th Edition. 

 W.W. Norton & Company: New York, London. 

9

 Hymans, S., Consumer Durable Spending: Explanation and Prediction. Brookings Papers on 

 Economic Activity, 1971. 1971(1): pp. 234-239. 

10 Fibrich, G., A. Gavious, and O. Lowngart, The Dynamics of Price Elasticity of Demand in 

 the Presence of Reference Price Effects. Journal of the Academy of Marketing Science, 

 2005. 33(1): pp. 66-78. 

11 Gowrisankaran, G. and M. Rysman, Dynamics of Consumer Demand for New Durable 

 Goods. 2009, National Bureau of Economic Research Working Paper: Cambridge, MA. 
9-22

 12 Lilien, G. L. and E. Yoon, An Exploratory Analysis of the Dynamic Behavior of Price 

 Elasticity over the Product Life Cycle: An Empirical Analysis of Industrial Chemical 

 Products. Issues in Pricing, 1988: pp. 261-408. 
10-i

CHAPTER 10: NATIONAL IMPACT ANALYSIS

TABLE OF CONTENTS

10.1 INTRODUCTION ......................................................................................................... 10-1

10.2 TRIAL STANDARD LEVELS ..................................................................................... 10-1

10.3 BASE CASE AND STANDARDS CASE PROJECTED EFFICIENCIES .................. 10-2

10.4 NATIONAL ENERGY AND WATER SAVINGS ...................................................... 10-4

10.4.1 Definition ....................................................................................................................... 10-4

10.4.2 National Energy Savings Inputs..................................................................................... 10-6

10.4.2.1 Annual Energy and Water Consumption per Unit ....................................... 10-6

10.4.2.2 Shipments ..................................................................................................... 10-8

10.4.2.3 Equipment Stock .......................................................................................... 10-8

10.4.2.4 National Annual Energy and Water Consumption ...................................... 10-8

10.4.2.5 Site-to-Power Plant Energy Use Factors...................................................... 10-8

10.4.2.6 Full-Fuel-Cycle Energy Factors ................................................................... 10-9

10.5 NET PRESENT VALUE ............................................................................................. 10-10

10.5.1 Net Present Value Definition ....................................................................................... 10-10

10.5.2 Net Present Value Inputs ............................................................................................. 10-11

10.5.2.1 Total Installed Cost per Unit ...................................................................... 10-12

10.5.2.2 Annual Operating Cost Savings per Unit................................................... 10-13

10.5.2.3 Total Annual Installed Cost Increases ....................................................... 10-14

10.5.2.4 Total Annual Operating Cost Savings ....................................................... 10-14

10.5.2.5 Discount Factors ........................................................................................ 10-15

10.5.2.6 Present Value of Costs ............................................................................... 10-15

10.5.2.7 Present Value of Savings ........................................................................... 10-16

10.6 NATIONAL ENERGY AND WATER SAVINGS AND NET PRESENT 

VALUE RESULTS ...................................................................................................... 10-16

10.6.1 National Energy Savings and Net Present Value Input Summary ............................... 10-16

10.6.2 National Energy and Water Savings Results ............................................................... 10-17

10.6.3 Annual Costs and Savings ........................................................................................... 10-18

10.6.4 Net Present Value Results ............................................................................................ 10-18

LIST OF TABLES

Table 10.2.1 Trial Standard Levels for Commercial Clothes Washers .................................. 10-2

Table 10.3.1 Top-Loading Commercial Clothes Washers: Base Case and Standards 

Case Efficiency Distributions in 2018 ............................................................... 10-4

Table 10.3.2 Front-Loading Commercial Clothes Washers: Base Case and Standards 

Case Efficiency Distributions in 2018 ............................................................... 10-4

Table 10.4.1 National Energy Saving Inputs .......................................................................... 10-6
10-ii

Table 10.4.2 Top-Loading Commercial Clothes Washers: Base Case and Standards 

Case Shipment-Weighted Average Per-Unit Annual Energy and Water 

Consumption in 2018 ......................................................................................... 10-7

Table 10.4.3 Front-Loading Commercial Clothes Washers: Base Case and Standards 

Case Shipment-Weighted Average Per-Unit Annual Energy and Water 

Consumption in 2018 ......................................................................................... 10-7

Table 10.4.4 Full-Fuel-Cycle Energy Multipliers (Based on AEO 2013) ............................ 10-10

Table 10.5.1 Net Present Value Inputs ................................................................................. 10-12

Table 10.5.2 Top-Loading Commercial Clothes Washers: Base Case and Standards 

Case Shipment-Weighted Average Per-Unit Total Installed Costs ................. 10-12

Table 10.5.3 Front-Loading Commercial Clothes Washers: Base Case and Standards 

Case Shipment-Weighted Average Per-Unit Total Installed Costs ................. 10-13

Table 10.5.4 Top-Loading Commercial Clothes Washers: Base Case and Standards 

Case Shipment-Weighted Average Per-Unit Annualized Repair Costs in 

2018.................................................................................................................. 10-14

Table 10.5.5 Front-Loading Commercial Clothes Washers: Base Case and Standards 

Case Shipment-Weighted Average Per-Unit Annualized Repair Costs in 

2018.................................................................................................................. 10-14

Table 10.6.1 NES and NPV Inputs ....................................................................................... 10-17

Table 10.6.2 Commercial Clothes Washers: Cumulative NES and NWS Results .............. 10-17

Table 10.6.3 Commercial Clothes Washers: Cumulative NPV Results based on SevenPercent and Three-Percent Discount Rates ...................................................... 10-19

LIST OF FIGURES

Figure 10.4.1 Site-to-Power Plant Energy Use Factor for Commercial Clothes Washers ....... 10-9

Figure 10.6.1 Non-Discounted Annual Installed Cost Increases and Annual Operating 

Cost Savings for Top-Loading Clothes Washers, TSL 2 ................................. 10-18
10-1

CHAPTER 10. NATIONAL IMPACT ANALYSIS

10.1 INTRODUCTION

This chapter describes the method for estimating national impacts for commercial clothes 

washers, i.e., the quantity and net value to consumers of future national energy savings (NES) 

and water savings from possible trial standard levels (TSLs). Results described here include: 

(1) national energy and water consumption and savings, (2) monetary value of energy savings to 

the Nation due to TSLs, (3) increased total installed costs to the Nation due to TSLs, and (4) the 

net present value (NPV) of energy savings (difference between value of energy savings and 

increased total installed costs). When NES is discussed throughout this chapter, it includes 

national water savings in addition to national energy savings. 

 The U.S. Department of Energy (DOE) determined both the NES and NPV for all of the 

TSLs considered for commercial clothes washers. It performed all calculations for each of the 

considered products using a Microsoft Excel spreadsheet model, which is accessible on the 

Internet (http://www.eere.energy.gov/buildings/appliance_standards/). The spreadsheet model, 

referred to as the National Impact Analysis (NIA) model, combined the calculations for 

determining the NES and NPV as well as input from the relevant shipments model. Details and 

instructions for using each of the NIA models are provided in Appendix 10B. 

 The important facets of national energy consumption include: (1) shipments of the 

equipment, (2) customer response to changes in total installed cost (i.e., purchase price plus 

installation costs), operating expense, and (3) voluntary programs promoting higher energy 

efficiency of equipment.

Chapter 9 provides a detailed description of the shipments models that DOE used to 

project future purchases of commercial clothes washers. Included in the discussion are detailed 

descriptions of customers’ sensitivities to total installed cost, operating expense, and price 

elasticities, and how DOE captured them within the model. 

 Concerning voluntary programs, although they may increase the share of energy efficient 

equipment prior to the implementation date of any new TSL, DOE was not able to obtain 

information that quantified how such programs affect equipment efficiencies on a national basis. 

Consequently, DOE did not explicitly incorporate the impact of market-based initiatives into the 

shipments projections detailed in chapter 9. 

10.2 TRIAL STANDARD LEVELS 

 DOE analyzed the benefits and burdens of three TSLs for commercial clothes washers. 

These TSLs were developed using combinations of efficiency levels for the front-loading and 

top-loading product classes analyzed by DOE. Table 10.2.1 presents the TSLs and the 
10-2

corresponding efficiency levels for CCW. TSL 3 represents the maximum technologically 

feasible (“max-tech”) improvements in energy efficiency for commercial clothes washers. TSL 2 

was chosen as an intermediate level between TSL3 and TSL1 to represent the additional 

efficiency level that has been analyzed for Front-loading equipment class. TSL 1 was set at 

efficiency level 1 for both Front-loading and Top-loading equipment class. 

Table 10.2.1 Trial Standard Levels for Commercial Clothes Washers

Equipment Type

Efficiency Levels (MEFJ2/IWF)

Baseline

FL: EL0

TL: EL0

TSL1 TSL2 TSL3

FL: EL1

TL:EL1

FL: EL2

TL:EL1

FL: EL3

TL:EL2

Front Loading CCW Units 1.65/5.5 1.80/4.5 2.00/4.0 2.20/3.7 

Top Loading CCW Units 1.15/8.9 1.35/8.8 1.35/8.8 1.55/6.9 

10.3 BASE CASE AND STANDARDS CASE PROJECTED EFFICIENCIES 

A key component of DOE’s estimates of NES and NPV is the energy efficiencies 

projected over time for the base case (without new standards) and each of the standards cases 

(with new standards). The projected efficiencies represent the annual shipment-weighted energy 

efficiency of the products under consideration over the projection period (i.e., from the assumed 

effective date of a new standard to 30 years after the standard becomes effective). Because key 

inputs to the calculation of the NES and NPV are dependent on the estimated efficiencies, these 

efficiencies are of great importance to the analysis. In the case of the NES, the per-unit annual 

energy and water consumption is a direct function of product efficiency. Chapter 7, Energy and 

Water Use Determination, describes how the per-unit energy and water consumption vary as a 

function of equipment efficiency. With regard to the NPV, two inputs, the per-unit total installed 

cost and the per-unit annual operating cost, are dependent on efficiency. The per-unit total 

installed cost is a direct function of efficiency, while the per-unit annual operating cost, because 

it is a function of the per-unit annual and water consumption, is indirectly dependent on product 

efficiency. Section 8.2.1 of chapter 8, Life-Cycle Cost (LCC) and Payback Period (PBP) 

Analysis, describes how per-unit total installed costs vary as a function of efficiency for each of 

the considered products. The above NES and NPV inputs, as well as all other inputs to the 

calculation of the NES and NPV, are also discussed in sections 10.3.1 and 10.4.1. 

DOE based the development of the product efficiencies in the base case on the 

assignment of equipment efficiencies in the base case for the year 2011. In other words, DOE 

determined the distribution of product efficiencies currently in the marketplace to develop a 

shipment-weighted efficiency for the year 2011. For a complete discussion of the assignment of 

efficiencies in the base case for each of the considered products, refer to section 8.2.6 of chapter 

8. 
10-3

 Using the shipment-weighted efficiency for the year 2011 as a starting point, DOE 

developed base case projected efficiencies based on assumptions regarding future efficiency 

growth. For the period spanning 2012–2018 (2018 being the assumed effective date of a new 

standard), DOE assumed that there would be no growth in the shipment-weighted efficiency (i.e.,

no change in the distribution of product efficiencies). Because there are no historical data to 

indicate how product efficiencies have changed over time, DOE estimated that projected

efficiencies would remain frozen at the 2018 efficiency level until the end of the projection 

period (30 years after the assumed effective date—the year 2047). Although DOE recognizes 

the possibility that product efficiencies may change over time, DOE does not want to speculate 

as to how these product efficiencies may change without historical information. 

 For its determination of standards-case projected efficiencies, DOE assumed a “roll-up” 

scenario to establish the shipment-weighted efficiency for the year that standards are assumed to 

become effective (i.e., 2018). DOE assumed that product efficiencies in the base case that did 

not meet the standard under consideration would “roll up” to meet the new standard level. Also, 

DOE assumed that all product efficiencies in the base case that were above the standard under 

consideration would not be affected. Using the shipment-weighted efficiency in the year 2018 as 

a starting point, DOE developed standards-case projected efficiencies based on assumptions 

regarding future efficiency growth. DOE made the same assumptions regarding standards-case 

efficiency growth rates as for the base case— namely that projected efficiencies would remain 

frozen at the 2018 efficiency level until the end of the projection period. By maintaining the 

same growth rate for projected efficiencies in the standards case as in the base case (i.e., zero 

growth), DOE retained a constant efficiency difference or gap between the two cases over the 

length of the projection period. Although frozen trends may not reflect what happens to base 

case and standards case product efficiencies in the future, DOE believes that maintaining a 

frozen efficiency difference between the base case and standards case provides a reasonable 

estimate of the impact that standards have on product efficiency. 

Table 10.3.1 and Table 10.3.2 show the base case and TSL product efficiency 

distributions in the year 2018, corresponding to the integrated modified energy factor (MEFJ2) 

and integrated water factor (IWF) of top-loading and front-loading clothes washing equipment, 

respectively, that DOE used in its national impact analysis for commercial clothes washers. The 

TSLs are composed of efficiency levels analyzed in the LCC and PBP analysis. The TSLs are 

also shown in these tables. Note that DOE considered all the candidate standard levels in the 

TSLs that it developed for commercial clothes washers. Also included in the table below is the 

shipment-weighted energy factor (SWEF) associated with the base case and each TSL. 
10-4

Table 10.3.1 Top-Loading Commercial Clothes Washers: Base Case and Standards 

Case Efficiency Distributions in 2018 

CSL TSL

Efficiencies

Market Shares

Base Case 

Trial Standard Level

MEFJ2 IWF 1 2 3

Baseline - 1.15 8.90 99.5% 0.0% 0.0% 0.0%

1 1, 2 1.35 8.80 0.3% 99.8% 99.8% 0.0%

2 3 1.55 6.90 0.3% 0.3% 0.3% 100.0%

SWEF (MEF) 1.15 1.35 1.35 1.55 

SWEF (IWF) 8.89 8.80 8.80 6.90 

Table 10.3.2 Front-Loading Commercial Clothes Washers: Base Case and Standards 

Case Efficiency Distributions in 2018 

CSL TSL

Efficiencies

Market Shares

Base Case 

Trial Standard Level

MEFJ2 IWF 1 2 3

Baseline - 1.65 5.20 28.0% 0.0% 0.0% 0.0%

1 1 1.80 4.48 34.0% 62.0% 0.0% 0.0%

2 2 2.00 4.13 38.0% 38.0% 100.0% 0.0% 

3 3 2.20 3.92 0.0% 0.0% 0.0% 100.0%

SWEF (MEFJ2) 1.83 1.88 2.00 2.20

SWEF (IWF) 4.55 4.35 4.13 3.92

Based on the assumption of a zero-percent growth rate in the projected efficiencies for 

the base case and all standards cases, DOE maintained the SWEFs shown above in Table 10.3.1

and Table 10.3.2throughout the entire projection period, i.e., from 2018 to 2047. 

10.4 NATIONAL ENERGY AND WATER SAVINGS

DOE calculated the national energy and water savings associated with each potential 

standard for commercial clothes washers. DOE calculated cumulative energy savings throughout 

the forecast period, which extends from 2018 to 2047. The equations shown calculate energy 

savings; DOE used similar equations to calculate water savings.

10.4.1 Definition 

 As shown in the following equation, DOE calculates annual national energy and water 

savings as the difference between two projections: a base case (without new standards) and a 
10-5

standards case (with new standards). Positive values of NES correspond to energy savings (i.e.,

national energy consumption with standards is less than national energy consumption in the base 

case).

BASE STD NESy AEC AEC _ =

Cumulative energy and water savings are the sum over the projection period, extending 

from the assumed effective date—i.e., 2018—to 30 years after the effective date—i.e., 2047—of 

the analysis period. The calculation is represented by the following equations: 

NEScumulative = NESy

NWScumulative = NWS y

DOE calculated the national annual energy and water consumption by multiplying the 

number or stock of the given product (by vintage) by its unit energy and water consumption 

(UEC or UWC; also by vintage). The calculation of the national annual energy and water 

consumption is represented by the following equation: 

y = V UECV AEC STOCK

y = V UWCV AWC STOCK

For the above expressions, DOE defined the various quantities as follows:

AEC = annual national energy consumption each year in quadrillion British thermal 

units (Btus)–or quads—summed over vintages of the product stock, 

STOCKV, 

AWC= annual national water consumption each year in trillion gallons—summed 

over vintages of the product stock, STOCKV, 

NES = annual national energy savings (quads), 

NWS = annual national water savings (gallons), 

STOCKV = stock of product (millions of units) of vintage V surviving in the year for 

which DOE calculated annual energy consumption (vintages range from five 

to approximately 15.5 years, depending on the retirement function of the 

washer based on its product application),

UECV = annual energy consumption per product in either kilowatt-hours (kWh) or 

million Btus (MMBtu) (electricity and gas consumption are converted from 
10-6

site energy to source energy (quads) by applying a time-dependent 

conversion factor), 

UWCV = annual water consumption per product in gallons, 

V = year in which the product was purchased as a new unit, and 

y = year in the projection (i.e., 2018 to 2047).

 The stock of equipment is dependent on annual shipments and the lifetime of the 

equipment. As described in chapter 9, DOE conducted shipments projections under the base 

case and standards cases. DOE determined that the shipments projections under the standards 

cases were slightly lower than those in the base case projection, due to the higher purchase prices 

of the more-efficient equipment. In other words, DOE believes that the higher purchase prices 

would cause some consumers to forego new equipment purchases. 

 DOE assumed that any drop in shipments caused by standards would result in the 

purchase of used machines. Therefore, DOE explicitly accounted for the energy and water 

consumption of the used washers in the standards cases. 

10.4.2 National Energy Savings Inputs 

Table 10.4.1 lists the inputs for the determination of NES.

Table 10.4.1 National Energy Saving Inputs 

Annual Energy and Water Consumption per Unit (UEC and UWC)

Shipments

Equipment Stock (STOCKV)

National Annual Energy and Water Consumption (AEC and AWC)

Site-to-Source Conversion Factor (src_conv)

10.4.2.1 Annual Energy and Water Consumption per Unit

DOE presented the per-unit annual energy and water consumption as a function of 

product efficiency in chapter 7, Energy and Water Use Determination, and section 8.2.2.1 of 

chapter 8. Because the per-unit annual energy and water consumption is directly dependent on 

efficiency, DOE used the base case and standards case SWEFs presented in section 10.3, in 

combination with the annual energy and water use data presented in chapters 7 and 8, to estimate 

the shipment-weighted average annual per-unit energy and water consumption under the base 

case and standards cases. 

 Using the relationship between clothes washerMEFJ2and IWF and annual energy and 

water consumption described in chapter 8, Table 10.4.2 and Table 10.4.3 present the per-unit 

annual electrical and gas energy use, and water consumption, based on the SWEFs 

corresponding to the base case and each TSL for top-loading and front-loading washers, 

respectively. Because establishments that use commercial clothes washers use either electric or 

gas water heaters and dryers, the per-unit annual energy use values in these tables are provided 
10-7

by fuel type. The energy and water use values shown in these tables are weighted-average 

values reflecting the market share of washers in multi-family buildings (85 percent) and 

laundromats (15 percent), as provided by the Consortium for Energy Efficiency (CEE).1

 The 

water-heating and drying energy consumption data reflect the percentage of gas and electric 

water heaters and dryers in the combined multi-family and laundromat market. Based on data 

from the CEE, in the combined market, 22 and 78 percent of water heaters are electric and gas, 

respectively, while 34 and 66 percent of dryers are electric and gas, respectively.3 

 As described in chapter 9, section 9.3, DOE assumed that market share of washers in 

multi-family buildings and laundromats will remain constant at current levels. 

Table 10.4.2 Top-Loading Commercial Clothes Washers: Base Case and Standards 

Case Shipment-Weighted Average Per-Unit Annual Energy and Water 

Consumption in 2018 

Base 

Case

Trial Standard Level

1 2 3

SWEF (MEFJ2) 1.15 1.35 1.35 1.55

SWEF (IWF) 8.89 8.80 8.80 6.90

Annual 

Energy 

Use

Electric (kWh/yr) 1,099 930 930 763 

Gas (MMBtu/yr) 7.74 6.74 6.74 6.28 

Annual Water Use (1000 gal/ yr) 31.31 30.96 30.96 25.16

Table 10.4.3 Front-Loading Commercial Clothes Washers: Base Case and Standards 

Case Shipment-Weighted Average Per-Unit Annual Energy and Water 

Consumption in 2018 

Base 

Case

Trial Standard Level

1 2 3

SWEF (MEFJ2) 1.83 1.88 2.00 2.2

SWEF (IWF) 4.55 4.35 4.13 3.92

Annual 

Energy 

Use

Electric (kWh/yr) 600 582 561 530 

Gas (MMBtu/yr) 4.62 4.55 4.14 4.75 

Annual Water Use (1000 gal/ yr) 14.66 14.13 13.32 14.64 

As noted in section 10.2, DOE assumed that projected efficiencies in the base case and standards 

cases would remain frozen at the efficiency levels in 2018. Therefore, as the per-unit annual 

energy and water consumption are a function of efficiency, DOE held the values shown in Table 

10.4.2 and Table 10.4.3 constant over the projection period (2018–2047).
10-8

10.4.2.2 Shipments

DOE projected shipments for the base case and all standards cases. Several factors, 

including total installed costs (purchase price plus installation costs), operating cost, and 

equipment lifetime, all impact projected shipments. Chapter 9, Shipments Analysis, describes, in 

detail, the method DOE used to calculate and generate the shipments projections for each 

product class. 

10.4.2.3 Equipment Stock

The equipment stock in a given year is the number of products shipped from earlier years 

that survive in the given year. The NIA models keep track of the number of units shipped each 

year. DOE assumes that the 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. See chapter 

9, Shipments Analysis, for further details on the survival functions that DOE used in its analysis. 

10.4.2.4 National Annual Energy and Water Consumption

 The national annual energy and water consumption (AEC or AWC) is the product of the 

annual energy and water consumption per unit and the number of units of each vintage. This 

approach accounts for differences in unit energy consumption from year to year. The equation 

for determining the annual energy consumption was shown in section 10.3.1 and is repeated 

here. As stated previously, the equation for water consumption are the same as the equation for 

energy consumption. 

 

= V UECV AEC STOCK

 In determining national annual energy consumption, DOE initially calculated the annual 

energy consumption at the site and then applied a conversion factor to calculate primary energy 

consumption (see section 10.3.2.5). 

10.4.2.5 Site-to-Power Plant Energy Use Factors

 In determining national annual energy consumption, DOE initially calculated the annual 

energy consumption at the site (for electricity, the energy in kWh consumed at the household or 

establishment). It then used site energy consumption to calculate primary (source) energy 

consumption by applying a conversion factor to account for losses associated with the 

generation, transmission, and distribution of electricity. The site-to-power plant conversion 

factor is a multiplicative factor used to convert site energy consumption into primary or source 

energy consumption, expressed in quads (quadrillion Btus). DOE used annual site-to-power plant 
10-9

conversion factors based on the version of the National Energy Modeling System (NEMS)a that 

corresponds to EIA’s Annual Energy Outlook 2013 (AEO 2013).2 The factors are marginal

values, which represent the response of the system to an incremental decrease in consumption. 

For electricity, the conversion factors change over time in response to projected changes in 

generation sources (i.e., the types of power plants projected to provide electricity to the Nation). 

Figure 10.4.1 shows the site-to-power plant factors from 2018 to the end of the forecast period. 

The value reported in AEO for year 2040 (the last year available in AEO) was extrapolated 

through the end of the projection period. 

Figure 10.4.1 Site-to-Power Plant Energy Use Factor for Commercial Clothes Washers

10.4.2.6 Full-Fuel-Cycle Energy Factors

 The full-fuel-cycle (FFC) measure includes point-of-use (site) energy, the energy losses 

associated with generation, transmission, and distribution of electricity, and the energy consumed 

in extracting, processing, and transporting or distributing primary fuels. To complete the fullfuel-cycle by encompassing the energy consumed in extracting, processing, and transporting or 

distributing primary fuels, which we refer to as “upstream” activities, DOE developed FFC 

multipliersb

 using the data and projections generated by the National Energy Modeling System 

 

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

Administration documentation. A useful summary is National Energy Modeling System: An Overview 2000, 

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

the model without any modification to code or data. Because 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 was previously called NEMS-BRS.

b FFC multipliers discussed in this chapter relate to the upstream part of the FFC process.

7.400

7.600

7.800

8.000

8.200

8.400

8.600

8.800

mn Btu/MWh
10-10

(NEMS) used for AEO 2013. The AEO provides extensive information about the energy system, 

including projections of future oil, natural gas and coal supply, energy use for oil and gas field 

and refinery operations, and fuel consumption and emissions related to electric power 

production. This information can be used to define a set of parameters representing the energy 

intensity of energy production. 

 Table 10.4.4 shows the FFC energy multipliers used for commercial clothes washers for 

selected years. The method used to calculate FFC energy multipliers is described in appendix 10-

A. 

Table 10.4.4 Full-Fuel-Cycle Energy Multipliers (Based on AEO 2013)

2018 2020 2025 2030 2035 2040

Electricity

(power plant energy use) 1.041 1.041 1.040 1.040 1.041 1.040 

Natural Gas (site) 1.102 1.101 1.100 1.098 1.099 1.100 

10.5 NET PRESENT VALUE

10.5.1 Net Present Value Definition

The NPV is the value in the present of a time series of costs and savings. The NPV is 

described by the equation: 

NPV PVCPVS

_ =

 Where:

PVS = present value of operating cost savings (including energy, water, repair, and 

maintenance costs), and 

PVC = present value of increased total installed costs (including equipment and 

installation). 

 DOE determined the PVS and PVC according to the following expressions: 

PVS ∑  ́= DFOCS yy

PVC ∑TIC  ́= DFyy

DOE calculated the total annual operating cost savings by multiplying the number or 

stock of the given product (by vintage) by its per-unit operating cost savings (also by vintage). 

DOE calculated the total annual installed cost increases by multiplying the number or stock of 
10-11

the given product (by vintage) by its per-unit total installed cost increase (also by vintage). The 

calculation of the annual operating costs savings and total annual installed cost increases is 

represented with the following equations: 

OCSy = ∑STOCKV UOCSV

TICy = ∑SHIPy UTICy

For the above expressions, DOE defined the various quantities as follows: 

OCSy = total annual operating cost savings in a given year y summed over vintages 

of the product stock, STOCKV, 

TICy = total annual installed cost in year y summed over shipments the product in 

year y, SHIPy, 

DFy = discount factor in year y, 

STOCKV = stock of product (millions of units) of vintage V surviving in the year for 

which DOE calculated annual energy consumption (vintages range from five 

to approximately 15.5 years, depending on the retirement function of the 

washer based on its product application),

UOCSV = annual operating cost savings per unit of vintage V, 

UTICy = annual total installed cost increase per unit in year y, 

V = year in which the product was purchased as a new unit, and 

y = year in the projection (i.e., 2018 to 2047).

DOE determined the PVC for each year, from the effective date of the standard to the 

year 2047. It determined the PVS for each year, from the effective date of the standard to the 

year when units purchased in 2047 retire. DOE calculated costs and savings as the difference 

between a standards case and a base case (i.e., without new standards). 

DOE calculated a discount factor from the 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 Net Present Value Inputs

Table 10.5.1 summarizes the inputs to the NPV calculation. 
10-12

Table 10.5.1 Net Present Value Inputs

Total Installed Cost per Unit

Annual Operating Cost Savings per Unit

Total Annual Installed Cost Increases

Total Annual Operating Costs

Discount Factor

Present Value of Costs

Present Value of Savings

The increase in the total annual installed cost is equal to the annual change in the per-unit 

total installed cost (difference between base case and standards case) multiplied by the shipments 

projected in the standards case. As with the calculation of the NES, DOE assumed that any drop 

in shipments caused by standards would result in the purchase of used machines. 

The total annual operating cost savings are equal to the change in the annual operating 

costs (difference between base case and standards case) per unit multiplied by the shipments 

projected in the standards case. The annual operating cost includes energy, water, repair, and 

maintenance costs.

10.5.2.1 Total Installed Cost per Unit 

DOE first presented the per-unit total installed cost as a function of product efficiency in 

section 8.2.1.5 of chapter 8. Because the per-unit total annual installed cost is directly dependent 

on efficiency, DOE used the base case and standards case SWEFs presented in section 10.2, in 

combination with the total installed costs presented in chapter 8, to estimate the shipmentweighted average annual per-unit total installed cost under the base case and standards cases. 

The total installed cost includes both the equipment price and the installation cost. DOE 

based average equipment prices on average manufacturer costs multiplied by average overall 

markup values. Using the relationship between commercial clothes washer MEFJ2 and WF and 

total installed cost presented in chapter 8, Table 10.5.2 and Table 10.5.3 show the per-unit total 

installed cost based on the SWEFs corresponding to the base case and each TSL for top-loading 

and front-loading washers, respectively. 

Table 10.5.2 Top-Loading Commercial Clothes Washers: Base Case and Standards 

Case Shipment-Weighted Average Per-Unit Total Installed Costs 

Base Case Trial Standard Level

1 2 3

SWEF (MEFJ2) 1.15 1.35 1.35 1.55

SWEF (IWF) 8.89 8.80 8.80 6.90

Total Installed Cost (2012$) $1,251 $1,251 $1,251 $1,313 
10-13

Table 10.5.3 Front-Loading Commercial Clothes Washers: Base Case and Standards 

Case Shipment-Weighted Average Per-Unit Total Installed Costs 

Base Case Trial Standard Level

1 2 3

SWEF (MEFJ2) 1.83 1.88 2.00 2.20

SWEF (IWF) 4.55 4.35 4.13 3.92

Total Installed Cost (2012$) $1,853 $1,853 $1,854 $1,885 

As noted in section 10.2, DOE assumed that projected efficiencies in the base case and 

TSLs remain frozen at the efficiency levels in 2018. Therefore, because the per-unit total 

installed costs are a function of efficiency, DOE held the values constant over the projection 

period (2018–2047).

10.5.2.2 Annual Operating Cost Savings per Unit

The per-unit annual operating cost includes the energy, water, repair, and maintenance 

costs. 

Annual Energy and Water Costs 

DOE determined the per-unit annual energy and water cost savings by taking the per-unit 

annual energy (and water) consumption savings developed for each product and multiplying it by 

the appropriate energy (and water) price. 

 For all of the considered products, estimates of the per-unit annual energy and water 

consumption for the base case and each standards case were presented earlier in section 10.3.2.1. 

As described previously, DOE projected the per-unit annual energy (and water) consumption for 

the base case and each standards case for all products by freezing the consumption at levels 

estimated for the year 2018. 

 Energy and water prices are described in section 8.2.2.2 of chapter 8. DOE projected 

energy prices based on EIA’s AEO2013. DOE projected water prices based on trends in the 

national water price index as provided by the Bureau of Labor Statistics.3

 The energy and water 

price trends are described in section 8.2.2.3 of chapter 8. 

Annual Repair and Maintenance Costs 

 As described in section 8.2.2.4 of chapter 8, DOE estimated increased repair and 

maintenance costs for commercial clothes washers. DOE estimated that repair costs would 

increase as a function of equipment efficiency. Using the relationship between clothes washer 

IMEF and IWF and repair described in chapter 8, Table 10.5.4 and Table 10.5.5 present the perunit annualized repair cost based on the SWEFs corresponding to the base case and each TSL for 

top-loading and front-loading washers, respectively. The repair costs are weighted-average 

values reflecting the market share of washers in multi-family buildings (85 percent) and 

laundromats (15 percent), as provided by the Consortium for Energy Efficiency (CEE). 
10-14

Table 10.5.4 Top-Loading Commercial Clothes Washers: Base Case and Standards 

Case Shipment-Weighted Average Per-Unit Annualized Repair Costs in 

2018 

Base 

Case

Trial Standard Level

1 2 3

SWEF (MEFJ2) 1.15 1.35 1.35 1.55

SWEF (WF) 8.89 8.80 8.80 6.90

Annualized Repair Cost (2012$) $51 $51 $51 $54

Table 10.5.5 Front-Loading Commercial Clothes Washers: Base Case and Standards 

Case Shipment-Weighted Average Per-Unit Annualized Repair Costs in 

2018 

Base Case

Trial Standard Level

1 2 3

SWEF (MEFJ2) 1.83 1.88 2.00 2.20

SWEF (WF) 4.55 4.35 4.13 3.92

Annualized Repair Cost (2012$) $81 $81 $81 $82

As noted in section 11.2, DOE assumed that projected efficiencies in the base case and 

TSLs remain frozen at the efficiency levels in 2013. Therefore, because the per-unit total 

installed costs are a function of efficiency, DOE held the values constant over the projection 

period (2018–2047).

10.5.2.3 Total Annual Installed Cost Increases

The total annual installed cost increase for any given standards case is the product of the 

total installed cost increase per unit due to the standard and the number of units of each vintage. 

This approach accounts for differences in total installed cost from year to year. The equation for 

determining the total annual installed cost increase for a given standards case was shown in 

section 10.4.1 and is repeated here. 

 

TIC = ∑STOCKV UTICV

As noted earlier for commercial clothes washers, DOE accounted for the total installed 

cost of those establishments that were forced into purchasing used clothes washers due to 

standards. Therefore, the total annual installed cost increase takes into account the total installed 

cost of the used washers that were purchased in lieu of the purchase of new washers.

10.5.2.4 Total Annual Operating Cost Savings 

 The total annual operating cost savings for any given standards case is the product of the 

annual operating cost savings per unit due to the standard and the number of units of each 

vintage. This approach accounts for differences in annual operating cost savings from year to 

year. The equation for determining the total annual operating cost savings for a given standards 

case was shown previously and is repeated here. 
10-15

 

OCS =∑STOCKV UOCSV

As noted earlier, DOE accounted for the energy and water use of those establishments 

that were forced into purchasing used washers due to standards. Therefore, the total annual 

operating cost savings take into account the energy and water costs of the used washers that were 

purchased in lieu of the purchase of new washers. 

10.5.2.5 Discount Factors 

DOE multiplies monetary values in future years by a discount factor to determine the 

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. 

DOE estimated national impacts with both a three-percent and a seven-percent real 

discount rate as the average real rate of return on private investment in the U.S. economy. It 

used these discount rates in accordance with the Office of Management and Budget (OMB)’s 

guidance to Federal agencies on the development of regulatory analysis (OMB Circular A-4, 

September 17, 2003), and section E., “Identifying and Measuring Benefits and Costs,” therein. 

DOE defines the present year as 2013. 

10.5.2.6 Present Value of Costs

The present value of increased installed costs is the annual installed cost increase in each 

year (i.e., the difference between the standards case and base case), discounted to the present, 

and summed for the time period over which DOE is considering the installation of equipment 

(i.e., from the effective date of standards, 2018, to the year 2047).

The increase in total installed cost refers to both equipment cost and installation cost 

associated with the higher energy efficiency of equipment purchased in the standards case 

compared to the base case. DOE calculated annual increases in installed costs as the difference 

in total installed cost for new equipment purchased each year, multiplied by the shipments in the 

standards case.
10-16

10.5.2.7 Present Value of Savings 

The present value of operating cost savings is the annual operating cost savings (i.e., the 

difference between the base case and standards case) discounted to the present and summed over 

the period from the effective date, 2018, to the time when the last unit installed in 2047 is retired 

from service.

Savings are decreases in operating costs (including electricity, repair, and maintenance 

costs) associated with the higher energy efficiency of equipment purchased in the standards case 

compared to the base case. Total annual operating cost savings are the savings per unit 

multiplied by the number of units of each vintage surviving in a particular year. Equipment 

consumes energy over its entire lifetime and, for units purchased in 2047, the consumption 

includes energy consumed until the unit is retired from service. 

10.6 NATIONAL ENERGY AND WATER SAVINGS AND NET PRESENT 

VALUE RESULTS

 The NIA Model provides estimates of the NES and NPV due to various candidate 

standards levels. The inputs to the NIA model have been discussed earlier in sections 10.3.2 

(NES Inputs) and 10.4.2 (NPV Inputs). DOE generated the NES and NPV results using a 

Microsoft Excel® spreadsheet, which is accessible on the Internet 

(http://www.eere.energy.gov/buildings/appliance_standards/). Details and instructions for using 

the spreadsheet are provided in Appendix 10B. 

10.6.1 National Energy Savings and Net Present Value Input Summary

Table 10.6.1 summarizes the inputs to the NIA model. For each input a brief description 

of the data source is given. 
10-17

Table 10.6.1 NES and NPV Inputs

Input Data Description

Shipments Annual shipments from Shipments Model. (See chapter 9.)

Effective Date of Standard 2018.

Base Case Forecasted Efficiencies SWEF determined in the year 2011. SWEF held constant over 

forecast period of 2011–2047. (See section 10.2.)

Standards Case Efficiencies Roll-up scenario assumed for determining SWEF in the year 2018 for 

each standards case and for each of the considered products. SWEF 

held constant over forecast period of 2018–2047. (See section 10.2.)

Annual Energy Consumption per Unit Annual weighted-average values are a function of SWEF. (See 

section 10.3.2.1.)

Total Installed Cost per Unit Annual weighted-average values are a function of SWEF. (See 

section 10.4.2.1.)

Energy and Water Cost per Unit Annual weighted-average values are a function of the annual energy 

consumption per unit and energy (and water) prices. (See section 

8.2.2.3 of chapter 8 for energy and water prices.)

Repair Cost and Maintenance Cost per Unit Annual weighted-average values are a function of SWEF. (See 

section 8.2.2.4 of chapter 8 for repair and maintenance costs.)

Escalation of Energy and Water Prices Energy Prices: EIA AEO2013 forecasts (to 2040) and extrapolation to 

2047. (See section 8.2.2.3 of chapter 8.)

Water Prices: Linear extrapolation off historical trend in national 

water price index. (See section 8.2.2.3 of chapter 8.)

Energy Site-to-Source Conversion Conversion varies yearly and is generated by DOE/EIA’s NEMS* 

program (a time series conversion factor; includes electric generation, 

transmission, and distribution losses).

Discount Rate 3 percent and 7 percent real.

Present Year Future expenses are discounted to year 2013.

* Chapter 15on the Utility Impact Analysis and the Environmental Assessment Report provide more detail on NEMS.

10.6.2 National Energy and Water Savings Results 

 The following sections provide NES results and national water savings (NWS) results for 

the TSLs analyzed for commercial clothes washers. NES results are cumulative to 2047 and are 

shown as primary energy savings. NWS results are expressed in trillions of gallons. DOE based 

the inputs to the NIA model on weighted-average values, yielding results that are discrete point 

values, rather than a distribution of values as in the LCC and PBP Analysis. 

Table 10.6.2 shows the NES and NWS results for the TSLs analyzed for commercial 

clothes washers. The results are based on the estimate that multi-family buildings and 

laundromats comprise 85 percent and 15 percent of the commercial clothes washer market, 

respectively. 

Table 10.6.2 Commercial Clothes Washers: Cumulative NES and NWS Results

Trial Standard Levels Top-load Front-load

TSL 1 TSL 2 TSL 3 TSL 1 TSL 2 TSL3

Source Energy Saving 2018 to 2047 (quads) 0.09 0.09 0.16 0.01 0.02 0.01

Water Saving (2018 to 2047) (trillion gallon) 0.01 0.01 0.22 0.01 0.04 -0.01
10-18

10.6.3 Annual Costs and Savings 

To illustrate the basic inputs to the NPV calculations, Figure 10.6.1 presents the nondiscounted annual installed cost increases and annual operating cost savings at the national level 

for TSL 3 for top-loading washers. The figure also shows the net savings, which is the 

difference between the savings and costs for each year. The annual equipment cost is the 

increase in the total installed cost for products purchased each year over the period 2018–2047. 

The annual operating cost savings is the savings in operating costs for products operating in each 

year. The NPV is the difference between the cumulative annual discounted savings and the 

cumulative annual discounted costs. DOE could create figures like the one shown below for 

each of the standard cases.

Figure 10.6.1 Non-Discounted Annual Installed Cost Increases and Annual Operating 

Cost Savings for Top-Loading Clothes Washers, TSL 2 

10.6.4 Net Present Value Results

 The following sections provide NPV results for the candidate standards levels considered 

for the product classes of commercial clothes washers. Results are cumulative and are shown as 

the discounted value of these savings in dollar terms. DOE based the inputs to the NIA model on 

weighted-average values, yielding results that are discrete point values, rather than a distribution 

of values as in the LCC and PBP Analysis. 

-0.0200

0.0300

0.0800

Billion 2012$

Operating Cost Savings

Increased Equipment Cost

Net Savings
10-19

The present value of increased total installed costs is the total annual installed cost 

increase (i.e., the difference between the standards case and base case), discounted to the present, 

and summed over the time period in which DOE evaluates the impact of standards (i.e., from the 

effective date of standards, 2018, to the year 2047).

 Savings are decreases in operating costs (including energy and water) associated with the 

higher energy efficiency of equipment purchased in the standards case compared to the base 

case. Total operating cost savings are the savings per unit multiplied by the number of units of 

each vintage (i.e., the year of manufacture) surviving in a particular year. Energy and water 

consumption are taken into account over the equipment’s entire lifetime. . For units purchased 

through 2047, the operating cost includes energy and water consumed until the last unit is retired 

from service. 

Table 10.6.3 presents the NPV results for the trial standard levels considered commercial 

clothes washers. As with the NES results, NPV results are presented by TSL. 

Table 10.6.3 Commercial Clothes Washers: Cumulative NPV Results based on SevenPercent and Three-Percent Discount Rates

Trial Standard Levels Top-load Front-load

TSL 1 TSL 2 TSL 3 TSL 1 TSL 2 TSL 3

NPV @ 3% (billion 2012$) 0.60 0.59 2.13 0.12 0.34 -0.13

NPV @ 7% (billion 2012$) 0.26 0.26 0.91 0.05 0.14 -0.06
10-20

REFERENCES

1. Consortium for Energy Efficiency, Commercial Family-Sized Washers: An Initiative 

Description of the Consortium for Energy Efficiency, 1998. Boston, MA. 

2. Energy Information Administration, Annual Energy Outlook 2013 with Projections to 

2040, 2013. Washington, DC. Report No. DOE/EIA-0383. 

<http://www.eia.gov/forecasts/aeo/pdf/0383(2013).pdf> 

3. U.S. Department of Labor-Bureau of Labor Statistics, Consumer Price Indexes, Item: 

Water and sewerage maintenance, Series Id: CUUR0000SEHG01, U.S. city average (not 

seasonally adjusted), 2013. Washington, DC. <http://www.bls.gov/cpi/home.htm#data> 
11-i

CHAPTER 11. LIFE-CYCLE COST SUBGROUP ANALYSIS

TABLE OF CONTENTS

11.1 INTRODUCTION ......................................................................................................... 11-1

11.2 SUBGROUP DEFINITION........................................................................................... 11-1

11.3 INPUTS TO THE SUBGROUP ANALYSIS FOR SMALL BUSINESSES ............... 11-1

11.3.1 Small Business Statistics for Commercial Laundry....................................................... 11-1

11.3.2 Small Company Discount Rates .................................................................................... 11-3

11.4 RESULTS FOR THE CONSIDERED COMMERCIAL CLOTHES WASHER 

SUBGROUP .................................................................................................................. 11-5

LIST OF TABLES

Table 11.3.1 Coin-operated laundries & drycleaners: Number of Establishments and 

Receipts by Nonemployer and Employer Status ............................................... 11-2

Table 11.3.2 Establishment and Firm Size: Legal Form of Organization .............................. 11-2

Table 11.3.3 Establishment and Firm Size: Receipts/Revenue Size of Establishments ......... 11-3

Table 11.3.4 Small Company Discount Rates and Small Cap Premium ................................ 11-4

Table 11.4.1 Top-Loading Commercial Clothes Washers: LCC and PBP Results for 

Small Companies ............................................................................................... 11-5

Table 11.4.2 Front -Loading Commercial Clothes Washers: LCC and PBP Results for 

Small Companies ............................................................................................... 11-6
11-1

CHAPTER 11. LIFE-CYCLE COST SUBGROUP ANALYSIS

11.1 INTRODUCTION

 Chapter 8 describes the Life-Cycle Cost (LCC) and Payback Period (PBP) Analysis that 

examines impacts of energy efficiency standards on the U.S. population. In analyzing the 

potential impact of new or amended standards on residential and commercial consumers, DOE 

further evaluates the impact on identifiable groups of consumers (i.e., subgroups) that may be 

disproportionately affected by a national standard level. The LCC subgroup analysis evaluates 

impacts by analyzing the LCC and PBPs for subgroups commercial consumers. For commercial 

clothes washers, DOE identified one consumer subgroup: small businesses that own commercial 

laundries. 

DOE determined the impact on consumer subgroups for commercial clothes washers

using the LCC Spreadsheet Model, which allows for the examination of particular consumer 

subgroups. For the small business subgroup, consumer discount rates different than the general 

population were used. The inputs for the considered subgroup are described in section 11.3. 

11.2 SUBGROUP DEFINITION

 DOE analyzed the potential effects of standards on one subgroup: small commercial 

clothes washer businesses. 

For small commercial clothes washer businesses, DOE analyzed the potential impacts of 

standards by conducting the analysis with different discount rates, as small businesses do not 

have the same access to capital as larger businesses. As described below, DOE estimated that for 

businesses purchasing commercial clothes washers, small companies have an average discount 

rate which is 3.5 percent higher than the industry average. 

11.3 INPUTS TO THE SUBGROUP ANALYSIS FOR SMALL BUSINESSES 

11.3.1 Small Business Statistics for Commercial Laundry

The small business administration (SBA) defines a small business by its annual receipts 

or its number of employees. Coin-operated laundries and drycleaners are considered small if its 

annual revenue is less than $7 million.1

 This industry is comprised of: (1) establishments primarily engaged in operating facilities 

with coin-operated or similar self-service laundry and dry-cleaning equipment for customer use 
11-2

on the premises and (2) establishments primarily engaged in supplying and servicing coinoperated or similar self-service laundry and dry cleaning equipment for customer use in places of 

business operated by others, such as apartments and dormitories. Table 11.3.1 provides the 

statistics on coin-operated laundromats and drycleaners.2

Table 11.3.1 Coin-operated laundries & drycleaners: Number of Establishments and 

Receipts by Non-employer and Employer Status

All Firms Non-employers* Employers

Establishments

Receipts 

($1,000) Establishments

Receipts 

($1,000) Establishments

Receipts 

($1,000)

25,087 4,249,811 14,126 896,158 10,961 3,353,653

* Non-employers are establishments or firms with no paid employees.

Source: U.S. Census Bureau, 2007 Economic Census. Other Services (Except Public Administration): Subject 

Series – Estab and Firm Size: Receipts/Revenue Size of Establishments for the United States: 2007.

 The following two tables provide further detail on the establishment firm size and 

receipts and revenues. Table 11.3.2 provides the establishment firm size by the type of legal 

organization.3

 Table 11.3.3 provides the firm size by the amount of sales per year.4

Table 11.3.2 Establishment and Firm Size: Legal Form of Organization 

Legal form of organization

Number of 

Establishments

Receipts/Revenue

($1,000)

Number of 

employees

All establishments (employers) 10,961 3,353,653 40,309

Corporations 6,871 2,494,268 26,589

Individual proprietorships 2,631 -- --

Partnerships 1,456 456,629 6,001

Other legal forms of organization 3 -- --

Source: U.S. Census Bureau, 2007 Economic Census. Other Services (Except Public Administration): Subject 

Series – Estab and Firm Size: Legal Form of Organization for the United States: 2007.
11-3

Table 11.3.3 Establishment and Firm Size: Receipts/Revenue Size of Establishments

Meaning of sales/receipts/revenue size categories 

or establishments operated the entire year

Number of 

establishments

Receipts/

Revenue 

($1,000)

Number of 

employees

All establishments (employers) 10,961 3,353,653 40,309

Establishments operated for the entire year 9,396 3,149,554 37,610

Less than $100,000 2,244 140,825 3,776

$100,000 to $249,999 3,750 596,516 11,289

$250,000 to $499,999 1,739 547,216 8,335

$500,000 to $999,999 730 360,393 5,537

$1,000,000 to $2,499,999 350 201,981 2,926

$2,500,000 to $4,999,999* 114 88,630 1,023

$5,000,000 to $9,999,999 204 100,194 705

$10,000,000 or more 265 -- --

* Note that the bins reported by the Census Bureau do not allow DOE to specifically identify establishments with 

revenues of less than $7 million; $5 million is used as a cut off instead.

Source: U.S. Census Bureau, 2007 Economic Census. Other Services (Except Public Administration): Subject 

Series – Estab and Firm Size: Receipts/Revenue Size of Firms for the United States: 2007.

 Based on the data in Table 11.3.3, approximately 95 percent of establishments (operated 

for the entire year) have receipts/revenues of less than $5 million. But only 61 percent of 

industry receipts/revenues are earned by establishments that make less than $5 million. 

Therefore, even though almost all establishments are small, a significant percent of the revenues 

are captured by large establishments (i.e., those that earn at least $5 million). Because small 

establishments capture less than 70 percent of total industry revenues, small establishments do 

not represent the entire industry and should be analyzed as a subgroup. 

11.3.2 Small Company Discount Rates 

To calculate discount rates for small companies that purchase commercial clothes 

washers, DOE used the same methodology as the general population of commercial clothes 

washer consumers as presented in Chapter 8, section 8.2.4.3. Although the methodology is 

appropriate, the capital asset pricing model (CAPM)a

 used in section 8.2.4 for the general 

population underestimates the cost of capital for small companies. In CAPM, the risk premium β

is used to account for the higher returns associated with greater risk. However, for small 

companies, particularly very small companies, historic returns have been significantly higher 

than the CAPM equation predicts. This additional return can be accounted for by adding a size 

premium to the cost of equity for small firms:

Rk fe ( ) ERP + ́+= S

ke = Cost of equity, 

 

a See 8.2.4.3 for more extensive description of CAPM and its parameters.
11-4

Rf = Expected return on risk-free assets,

β = Risk coefficient of the firm, 

ERP = Equity risk premium, and 

S = Size Premium.

DOE obtained size premium data from Ibbotson Associates’ Stocks, Bonds, Bills, and 

Inflation 2009 Yearbook.

5

 For the period of 1926-2008, the average size premium for the 

smallest companies in all industries is 5.81%, implying that on average, historic performance of 

small companies has been 5.81% higher than the CAPM estimate of the small company cost of 

equity.b

DOE calculated the real weighted average cost of capital (as described in chapter 8) using 

the cost of equity including a size premium for small companies instead of the CAPM cost of 

equity.2

 DOE estimates that small companies have average discount rates 1.26% higher than the 

sector average in the educational services sector, 1.47% higher than the sector average in the 

hotels/gaming sector, and 2.50% higher than the sector average in the REIT sector (see Table 

11.3.4). Because there was insufficient data to estimate it separately, DOE assumes that the 

small company discount rate premium for Personal Services is the same as the average for all 

small companies in educational services, hotels/gaming, and REIT (1.74%). 

Table 11.3.4 Small Company Discount Rates and Small Cap Premium

Sector

Discount Rate

Average Std Dev

Small Company 

Discount Rate 

Premium

Educational 

Services 

Entire Sector 5.37% 0.35%

Small Companies 6.63% 3.61% 1.26%

Hotel/Gaming Entire Sector 6.94% 0.41%

Small Companies 8.41% 2.68% 1.47%

REIT Entire Sector 6.19% 0.06%

Small Companies 8.69% 2.03% 2.50%

Personal 

Services

Entire Sector 4.67% -- 1.74% Small Companies 6.41% --

In chapter 8, DOE estimated the average discount rates to be 5.37% for educational services 

customers, 6.94% for hotel/gaming customers, 6.19% for REIT customers, and 4.67% for 

personal services customers. Applying the small capitalization discount rate premiums presented 

in Table 11.3.4, the average small business discount rate is 6.63% in the educational services 

 

b In this calculation, “small companies” are defined as the decile of companies with the lowest market capitalization 

(considered either Decile 10 or Decile 1, according to different sources); the Ibbotson Associates’ definition of 

Decile 10 includes companies with market capitalization of less than or equal to $218.53 million.1
11-5

sector, 8.41% in the hotel/gaming sector, 8.69% in the REIT sector, and 6.41% in the personal 

services sector.

11.4 RESULTS FOR THE CONSIDERED COMMERCIAL CLOTHES WASHER 

SUBGROUP

Table 11.4.1 and Table 11.4.2 below summarize the top-loading commercial clothes 

washer LCC results for multi-family establishments and commercial laundries that are defined as 

a small business. For the two efficiency levels for top-loading washers and the three efficiency

levels for front-loading washers, the LCC results provide the average or mean LCC, the relative 

LCC savings or costs, and the percentage of consumers that are burdened with net costs, realize

net savings, or incur no impact.

 

Table 11.4.1 Top-Loading Commercial Clothes Washers: LCC and PBP Results for 

Small Companies

Candidate 

Standard 

Level TSL

Life-Cycle Cost Life-Cycle Cost Savings

Payback Period 

(Years)**

Average 

Installed 

Price

Average 

Operati

ng Cost

Average 

LCC

Average 

Savings

Customers with

Net Cost Median Average

No 

Impact*

Net 

Benefit

Multi-Family Application

Baseline - $1,251 $4,069 $5,320 - - - - - -

1 1, 2 $1,251 $3,833 $5,084 $236 0% 1% 99% 0.0 0.0

2 3 $1,313 $3,268 $4,582 $737 0% 0% 100% 0.61 0.66

Commercial Laundry Application

Baseline - $1,251 $3,596 $4,847 - - - - - -

1 1, 2 $1,251 $3,451 $4,702 $144 0% 1% 99% 0.0 0.0

2 3 $1,313 $2,914 $4,228 $618 0% 0% 100% 0.56 0.53

* “No impact” means that the base case forecast commercial clothes washer assigned to the building has greater 

efficiency than the level indicated, so the household is not affected. 

** Based on the payback calculation, a very small change in operating cost can result in a few extremely large 

paybacks, which will skew the average payback. In these cases, median is probably a better indicator.
11-6

Table 11.4.2 Front -Loading Commercial Clothes Washers: LCC and PBP Results for 

Small Companies

Candidate 

Standard 

Level TSL

Life-Cycle Cost Life-Cycle Cost Savings

Payback Period 

(Years)**

Average 

Installed 

Price

Average 

Operati

ng Cost

Average 

LCC

Average 

Savings

Customers with

Net Cost Median Average

No 

Impact*

Net 

Benefit

Multi-Family Application

Baseline - $1,853 $2,634 $4,487 - - - - - -

1 1 $1,853 $2,424 $4,278 $209 0% 73% 27% 0.00 0.00

2 2 $1,854 $2,254 $4,108 $262 0% 39% 61% 0.02 0.02

3 3 $1,885 $2,381 $4,266 $3 49% 0% 51% 3.66 3.92

Commercial Laundry Application

Baseline - $1,853 $2,387 $4,240 - - - - -

1 1 $1,853 $2,198 $4,051 $187 0% 73% 27% 0.00 0.00

2 2 $1,854 $2,055 $3,909 $225 0% 39% 61% 0.01 0.02

3 3 $1,885 $2,182 $4,067 -$20 72% 0% 28% 7.33 7.95

* “No impact” means that the base case forecast commercial clothes washer assigned to the building has greater efficiency 

than the level indicated, so the household is not affected. 

** Based on the payback calculation, a very small change in operating cost can result in a few extremely large paybacks, 

which will skew the average payback. In these cases, median is probably a better indicator.

The small company subgroups shows a similar trend in average LCC differences and 

consumer impacts (i.e., percentage of consumers significantly or insignificantly impacted) as the 

overall sample. In general, the benefits of moving to a higher efficiency are less in the case of the 

small business subgroups. 
11-7

REFERENCES

 

1

 U.S. Government Printing Office. Title 13: Business Credit and Assistance, Part 121—Small 

Business Size Regulations, Section 121.201—Small Business Size Standards by NAICS 

Industry, Subsector 812—Personal and Laundry Services, 812310—Coin-Operated 

Laundries and Drycleaners, 2013. Washington, DC. 

 <http://www.ecfr.gov/cgibin/retrieveECFR?gp=&SID=49c0a05ac640b0e9d6f8946ceb23bf45&n=13y1.0.1.1.17.1&r=

SUBPART&ty=HTML#13:1.0.1.1.17.1.268.10 > 

2

 U.S. Census Bureau, 2007 Economic Census. Industry Statistics Sampler, NAICS 81231, 

Coin-operated laundries and drycleaners, 2007. Washington, DC. 

<http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_20

07_US_00A1&prodType=table> 

3

 U.S. Census Bureau, 2007 Economic Census. Sector 81: Other Services (Except Public 

Administration): Subject Series - Estab & Firm Size: Legal Form of Organization for the 

United States: 2007. Washington, DC. 

<http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_20

07_US_81SSSZ7&prodType=table> 

4

 U.S. Census Bureau, 2007 Economic Census. Sector 81: Other Services (Except Public 

Administration): Subject Series - Estab & Firm Size: Receipts/Revenue Size of 

Establishments for the United States: 2007. Washington, DC. 

<http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_20

07_US_81SSSZ4&prodType=table> 

5

 Ibbotsons Associates. Stocks, Bonds, Bills, and Inflation 2007 Yearbook, 2007. Chicago, IL. 
CHAPTER 12. MANUFACTURER IMPACT ANALYSIS

12.1 INTRODUCTION ............................................................................................. 12-1

12.2 METHODOLOGY ............................................................................................ 12-1

12.2.1 Phase I: Industry Profile..................................................................................... 12-2

12.2.2 Phase II: Industry Cash-Flow Model and Interview Guide ............................... 12-2

12.2.2.1 Industry Cash-Flow Analysis ............................................................... 12-2

12.2.2.2 Interview Guide ................................................................................... 12-3

12.2.3 Phase III: Subgroup Analysis............................................................................. 12-3

12.2.3.1 Manufacturing Interviews .................................................................... 12-3

12.2.3.2 Revised Industry Cash-Flow Analysis ................................................. 12-3

12.2.3.3 Manufacturer Subgroup Analysis ........................................................ 12-4

12.2.3.4 Manufacturing Capacity Impact .......................................................... 12-5

12.2.3.5 Employment Impact ............................................................................. 12-5

12.2.3.6 Cumulative Regulatory Burden ........................................................... 12-6

12.3 MANUFACTURER IMPACT ANALYSIS KEY ISSUES .............................. 12-6

12.3.1 Market Model Challenges .................................................................................. 12-6

12.3.2 Consumer Behavior ........................................................................................... 12-7

12.3.3 Customer Satisfaction Impacts .......................................................................... 12-7

12.3.4 Technology Limits ............................................................................................. 12-7

12.3.5 Competitive Impacts .......................................................................................... 12-8

12.4 GRIM INPUTS AND ASSUMPTIONS ............................................................ 12-8

12.4.1 Overview of the GRIM ...................................................................................... 12-8

12.4.2 Sources for GRIM Inputs ................................................................................... 12-9

12.4.2.1 Corporate Annual Reports ................................................................... 12-9

12.4.2.2 Standard and Poor’s Credit Ratings ..................................................... 12-9

12.4.2.3 Shipment Model ................................................................................... 12-9

12.4.2.4 Engineering Analysis ......................................................................... 12-10

12.4.2.5 Manufacturer Interviews .................................................................... 12-10

12.4.3 Financial Parameters ........................................................................................ 12-10

12.4.4 Corporate Discount Rate .................................................................................. 12-11

12.4.5 Trial Standard Levels ....................................................................................... 12-13

12.4.6 NIA Shipment Forecast.................................................................................... 12-13

12.4.6.1 Base-Case Shipments Forecast .......................................................... 12-14

12.4.6.2 Standards-Case Shipments Forecast .................................................. 12-14

12.4.7 Production Costs .............................................................................................. 12-15

12.4.8 Conversion Costs and Stranded Assets ............................................................ 12-16

12.4.8.1 Commercial Clothes Washer Conversion Costs ................................ 12-16

12.4.9 Markup Scenarios ............................................................................................ 12-17

12.4.9.1 Preservation of Gross Margin Percentage Markup Scenario ............. 12-18

12.4.9.2 Preservation of EBIT Markup Scenario ............................................. 12-18

12.5 INDUSTRY FINANCIAL IMPACTS ............................................................ 12-19

12.5.1 Introduction ...................................................................................................... 12-19

12.5.2 Commercial Clothes Washer Industry Financial Impacts ............................... 12-20

12-i 
12.6 IMPACTS ON THE LOW-VOLUME COMMERCIAL CLOTHES WASHER 

MANUFACTURER ........................................................................................ 12-22

12.7 OTHER IMPACTS .......................................................................................... 12-24

12.7.1 Employment ..................................................................................................... 12-24

12.7.2 Production Capacity ......................................................................................... 12-27

12.7.3 Cumulative Regulatory Burden ....................................................................... 12-27

12.7.3.1 DOE Regulations for Other Products Produced by Commercial Clothes 

Washer Manufacturers ......................................................................... 12-28

12.7.3.2 Other Regulations That Could Impact Commercial Clothes Washer 

Manufacturers ...................................................................................... 12-28

12.8 CONCLUSION ................................................................................................ 12-29

12.8.1 Commercial Clothes Washers .......................................................................... 12-29

LIST OF TABLES

Table 12.2.2 SBA and NAICS Classification of Small Businesses Potentially Affected by 

This Rulemaking ................................................................................................ 12-5

Table 12.4.1 Financial Parameters based on 2006–2012 Weighted Company Financial 

Data .................................................................................................................. 12-10

Table 12.4.2 Cost of Equity Calculation ...................................................................... 12-12

Table 12.4.3 Cost of Debt Calculation......................................................................... 12-12

Table 12.4.4 Trial Standard Levels for Commercial Clothes Washers ....................... 12-13

Table 12.4.6 Total Base-Case 2018 NIA Shipments ................................................... 12-13

Table 12.4.7 Base-Case Distribution of Efficiencies for Front-Loading Commercial 

Clothes Washers in 2018 ................................................................................. 12-14

Table 12.4.8 Base-Case Distribution of Efficiencies for Top-Loading Commercial 

Clothes Washers in 2018 ................................................................................. 12-14

Table 12.4.9 MPC Breakdown for Equipment Class 1 – Front-Loading Commercial 

Clothes Washers............................................................................................... 12-15

Table 12.4.10 MPC Breakdown for Equipment Class 2 - Top-Loading Commercial 

Clothes Washers............................................................................................... 12-16

Table 12.4.34 Product Conversion Costs for Equipment Class 1 – Front-Loaders by EL

.......................................................................................................................... 12-17

Table 12.4.35 Product Conversion Costs for Equipment Class 2 – Top-Loaders by EL 12-

17

Table 12.4.36 Capital Conversion Costs for Equipment Class 1 – Front-Loaders by EL12-

17

Table 12.4.37 Capital Conversion Costs for Equipment Class 2 – Top-Loaders by EL . 12-

17

Table 12.4.40 Preservation of EBIT Markups for Equipment Class 1: Front-Loading 

Commercial Clothes Washers .......................................................................... 12-18

Table 12.4.41 Preservation of EBIT Markups for Equipment Class 2: Top-Loading 

Commercial Clothes Washers .......................................................................... 12-18

Table 12.5.1 Manufacturer Impact Analysis for Commercial Clothes Washers – 

Preservation of Gross Margin Percentage Markup Scenario ........................... 12-20

12-ii
Table 12.5.2 Manufacturer Impact Analysis for Commercial Clothes Washers – 

Preservation of EBIT Markup Scenario ........................................................... 12-21

Table 12.7.1 Potential Changes in the Total Number of Domestic Commercial Clothes 

Washer Production Workers in 2018 ............................................................... 12-26

Table 12.7.2 Other DOE and Federal Actions Affecting the Commercial Clothes Washer 

Industry ............................................................................................................ 12-28

LIST OF FIGURES

Figure 12.4.1 Using the GRIM to Calculate Cash Flow ................................................ 12-8

Figure 12.5.1 Annual Industry Net Cash Flows for Commercial Clothes Washers 

(Preservation of Gross Margin Markup Scenario) ........................................... 12-21

Figure 12.5.2 Annual Industry Net Cash Flows for Commercial clothes washers 

(Preservation of EBIT Markup Scenario) ........................................................ 12-22

Figure 12.7.1 Commercial Clothes Washer Domestic Production Employment by Year

.......................................................................................................................... 12-26

12-iii
CHAPTER 12. MANUFACTURER IMPACT ANALYSIS

12.1 INTRODUCTION

In determining whether a standard is economically justified, the U.S. Department of 

Energy (DOE) is required to consider “the economic impact of the standard on the manufacturers 

and on the consumers of the products subject to such a standard.” (42 U.S.C. 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 more stringent energy conservation standards 

on manufacturers of commercial clothes washers, and assess the impact of such standards on 

direct employment and manufacturing capacity. 

The MIA has both quantitative and qualitative aspects. The quantitative part of the MIA 

primarily relies on the Government Regulatory Impact Model (GRIM), an industry cash-flow 

model adapted to the equipment classes covered 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 discounted industry annual cashflows over the analysis period. The model estimates the financial impact of more stringent 

energy conservation standards 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 

preliminary research directed at characterizing the commercial clothes washer manufacturing

industry. This research involved collecting data on market share, sales volumes and trends, 

pricing, employment, and the industry financial structure. 

In Phase II, “Industry Cash Flow Model and Interview Guide,” DOE created a framework 

GRIM to analyze the economic impact of amended energy conservation standards on the 

commercial clothes washer manufacturing industry as a whole. The DOE also developed a 

manufacturer interview guide to gather additional information on the potential impacts on 

manufacturers in Phase III. 

In Phase III, “Subgroup Impact Analysis,” DOE interviewed manufacturers representing 

an estimated 98 percent of the commercial clothes washer market. Interviewees included 

manufacturers with various market shares and product focus, providing a representative crosssection of the industry. During interviews, DOE discussed financial topics specific to each 

manufacturer and obtained each manufacturer’s view of the industry. The interviews provided 

DOE with valuable information for evaluating the impacts of amended energy conservation 

standards on manufacturer cash flows, investments, and employment. 

12-1 
12.2.1 Phase I: Industry Profile

In Phase I of the MIA, DOE prepared a profile of the commercial clothes washer industry 

that built upon the market and technology assessment prepared for this rulemaking. (See chapter 

3 of this NOPR Technical Support Document (TSD)). Before initiating the detailed impact 

studies, DOE collected information on the present and past market structure and characteristics 

of the industry, tracking trends in market share, product attributes, product shipments, 

manufacturer markups, and the cost structure for various manufacturers. 

The profile also included a top-down analysis of manufacturers in the industry using 

Security and Exchange Commission (SEC) 10–K filings,

a

 Standard & Poor’s (S&P) stock 

reports,b and corporate annual reports released by both public and privately held companies. 

DOE used this and other publicly available information to derive preliminary financial inputs for 

the GRIM (e.g. revenues; cost of goods sold; depreciation; selling, general and administrative 

expenses (SG&A); and research and development (R&D) expenses). 

12.2.2 Phase II: Industry Cash-Flow Model and Interview Guide

Phase II focused on the financial impacts of amended energy conservation standards on 

the commercial clothes washer manufacturing industry as a whole. Amended energy 

conservation standards can affect manufacturer cash flows in three distinct ways: (1) by creating 

a need for increased investment, (2) by raising production costs per unit, and (3) by altering 

revenue due to higher per-unit prices and/or possible changes in sales volumes. DOE created a 

framework GRIM to analyze the economic impact of amended energy conservation standards on 

the commercial clothes washer manufacturing industry as a whole. In preparing the GRIM, DOE 

used the financial values derived during Phase I and the shipment assumptions from the NIA. 

Additionally, DOE prepared a written guide for manufacturer interviews to collect additional 

data critical to developing other inputs for the GRIM. 

12.2.2.1 Industry Cash-Flow Analysis 

The GRIM uses several factors to determine a series of annual cash flows over a period

from the announcement year of the amended energy conservation standards until thirty years 

after the standards’ compliance date. INPV is the sum of these annual cash flows discounted by 

the industry weighted average cost of capital. Inputs to the GRIM include the manufacturing 

costs, markups, and shipment forecasts developed in other analyses as well as the industry 

weighted average financial parameters developed in Phase I. DOE derived the manufacturing 

costs from the engineering analysis as presented in chapter 5 of this NOPR TSD, information 

provided by the industry, publicly available financial reports, and interviews with manufacturers. 

To examine the range of possible impacts, DOE developed alternative markup scenarios based 

on discussions with manufacturers. DOE’s shipments analysis, presented in chapter 9of the 

NOPR TSD, provided the basis for the shipment projections. DOE derived the financial 

parameters using publicly available reports and revised them using information received during 

confidential manufacturer interviews. DOE used the GRIM to compare INPV in the base case 

a

 Available online at www.sec.gov. b

 Available online at www2.standardandpoors.com. 

12-2 

 
with INPV at various TSLs (the standards cases). The difference in INPV between the base and 

standards cases represents the financial impact of the amended standard on manufacturers.

12.2.2.2 Interview Guide

During Phase III of the MIA, DOE interviewed manufacturers of commercial clothes 

washers to gather information on the effects of amended energy conservation on revenues and 

finances, direct employment, capital assets, and industry competitiveness. Before the interviews, 

DOE distributed an interview guide to representatives of each participating manufacturer. The 

interview guide provided a starting point to help identify relevant issues and understand the 

impacts of amended energy conservation standards on individual manufacturers or subgroups of 

manufacturers. The information DOE received from these meetings is protected by nondisclosure agreements and resides with DOE’s contractors. The topics covered as part of these 

interviews include (1) key issues related to this rulemaking; (2) engineering and life cycle cost; 

(3) manufacturer markups and profitability; (4) financial parameters; and (5) conversion costs. 

The interview guide is presented in appendix 12-A. 

12.2.3 Phase III: Subgroup Analysis 

While conducting the MIA, DOE interviewed a representative cross-section of 

commercial clothes washer manufacturers. The MIA interviews broadened the discussion to 

include business-related topics. DOE sought to isolate key issues and concerns, to obtain 

feedback from industry on the approaches used in the GRIM, and to identify key manufacturer 

subgroups for analysis. 

12.2.3.1 Manufacturing Interviews

The information gathered in Phase I and the cash-flow analysis performed in Phase II are 

supplemented with information gathered from manufacturer interviews in Phase III. The 

interview process provides an opportunity for interested parties to express their views on 

important issues privately, allowing confidential or sensitive information to be considered in the 

rulemaking process. 

DOE used these interviews to tailor the GRIM to reflect the unique financial 

characteristics of the commercial clothes washer manufacturing industry. Companies with 

various market shares and product focus were interviewed to provide a representation of the 

industry. Interviews were scheduled well in advance to provide every opportunity for key 

individuals to be available for comment. Although a written response to the questionnaire was 

acceptable, DOE sought interactive interviews, as they help to clarify responses and identify 

additional issues. The resulting information provides valuable inputs to the GRIM. 

12.2.3.2 Revised Industry Cash-Flow Analysis 

In Phase II of the MIA, DOE provided manufacturers with preliminary financial figures 

for review and evaluation. During the interviews, DOE requested comments on the values it 

selected for the parameters. DOE revised its industry cash-flow model based on this feedback. 

Section 12.4.3 provides more information on how DOE calculated the parameters. 

12-3 
12.2.3.3 Manufacturer Subgroup Analysis 

Using average cost assumptions to develop an industry cash flow estimate is not adequate 

for assessing differential impacts among subgroups of manufacturers. Small, low-volume 

manufacturers, or manufacturers exhibiting a cost structure that differs significantly from the 

industry average could be more negatively affected. 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 stakeholders to suggest what 

subgroups or characteristics are the most appropriate for the analysis. 

Small-Business Manufacturers 

DOE used the Small Business Administration (SBA) small business size standards as 

amended by the Office of Management and Budget on January 1, 2012, and effective January 7, 

2013, and the North American Industry Classification System (NAICS) code, presented in Table 

12.2.2, to determine whether any small entities would be affected by the rulemaking.c

 For the 

equipment classes under review, the SBA bases its small business definition on the total number 

of employees for a business including the total employee count of a parent company and its 

subsidiaries. An aggregated business entity with fewer employees than the listed limit is 

considered a small business. 

As there is currently an energy conservation standard for commercial clothes washers, 

DOE used its own publically available Compliance Certification Database1 as a starting point to 

identify manufacturers of commercial clothes washers. Additionally, DOE used public 

certification databases provided by the California Energy Commission (CEC)2 and ENERGY 

STAR3 to identify commercial clothes washer manufacturers. DOE then checked this list of 

commercial clothes washer manufacturers against the employee limit for small businesses using 

reports from vendors such as Dun & Bradstreet. DOE also consulted publicly available data from 

the Small Business Association (SBA) to determine the presence of any additional small 

businesses in the industry. Further, DOE asked interested parties and industry representatives if 

they were aware of other small business manufacturers and checked any companies identified 

against the small business criteria. 

For the Framework document, DOE considered businesses with primary NAICS code 

333312, “Commercial Laundry, Dry Cleaning, and Pressing Machine Manufacturing.”A search 

of companies operating under this 2007 NAICS code indicated that there was potentially one 

small business that manufactures commercial clothes washers covered by this rulemaking. DOE 

has since confirmed that this small business is not a manufacturer, and thus will not be 

considered for the purposes of this small business analysis. In 2012, commercial laundry 

manufacturing was rolled into NAICS code 333318, “Other Commercial and Service Industry 

c The size standards are available on the SBA’s website at www.sba.gov/content/table-small-business-sizestandards.

12-4 

 
Machinery Manufacturing,” d

 DOE has updated its search for small business manufacturers, 

using this new NAICS classification and corresponding size limit provided by the Small 

Business Administration. During this updated research, DOE did not identify any manufacturer 

of commercial clothes washers that qualifies as a small business as specified by the SBA

employee limits. 

Table 12.2.1 SBA and NAICS Classification of Small Businesses Potentially Affected by 

This Rulemaking

Industry Description Revenue Limit Employee Limit NAICS

Commercial Laundry, Dry-cleaning, and Pressing 

Machine Manufacturing N/A 500 333312

Other Commercial and Service Industry Machinery 

Manufacturing N/A 1,000 333318

Low-Volume Manufacturers

While no small business manufacturers of commercial clothes washers were identified 

during interviews, one manufacturer was identified as having significant commercial clothes 

washer market share, but much lower revenues than its diversified competitors. This 

manufacturer’s business is highly concentrated in the production of both commercial and 

residential laundry products. Still, it is not a small business per the SBA definition for 

manufacturers of commercial clothes washers. DOE subsequently assigned this manufacturer its 

own sub-category, the “Low-Volume Manufacturer” (LVM), to evaluate the disproportionate 

impacts of amended standards for commercial clothes washers on this manufacturer. The 

analysis of impacts on this subgroup is found in section 12.6. 

12.2.3.4 Manufacturing Capacity Impact 

One possible outcome of amended energy conservation standards is the obsolescence of 

existing manufacturing assets, including tooling and production equipment. The manufacturer 

interview guide contains a series of questions to help identify impacts of amended standards on 

manufacturing capacity, specifically capacity utilization and plant location decisions in the 

United States and North America, with and without amended standards; the ability of 

manufacturers to upgrade or remodel existing facilities to accommodate the new requirements; 

the nature and value of any stranded assets; and estimates for any one-time changes to existing 

plant, property, and equipment (PPE). DOE’s estimates of the one-time capital changes and 

stranded assets affect the annual cash flow projections in the GRIM. These estimates can be 

found in section 12.4.8; DOE’s discussion of the capacity impact can be found in section 12.7.2. 

12.2.3.5 Employment Impact 

The impact of amended energy conservation standards on employment is an important 

consideration in the rulemaking process. To assess how domestic direct employment patterns 

might be affected, the interviews explored current employment trends in the commercial clothes 

washer industry. The interviews also solicited manufacturer views on changes in employment 

d NAICS code 333318 includes all manufacturing of commercial and service industry equipment, except optical 

instruments and lenses, and photographic and photocopying equipment, and has a size limit of 1,000 employees, as 

defined by the Small Business Administration. 

12-5 

 
patterns that may result from more stringent standards. The employment impacts section of the 

interview guide focused on current employment levels associated with manufacturers at each 

production facility, expected future employment levels with and without amended energy 

conservation standards, and differences in workforce skills and issues related to the retraining of 

employees. The employment impacts are reported in section 12.7.1. 

12.2.3.6 Cumulative Regulatory Burden

DOE seeks to mitigate the overlapping effects of regulation on manufacturers of 

commercial clothes washers. These effects may be the result of other regulatory actions 

affecting commercial clothes washers, or of amended energy conservation standards for other 

products and equipment made by the same manufacturers. DOE identified regulations relevant to 

commercial clothes washer manufacturers using its own research and discussions with 

manufacturers. A discussion of the cumulative regulatory burden of energy conservation 

standards and the impact on manufacturers of multiple, product-specific regulatory actions can 

be found in section 12.7.3. 

12.3 MANUFACTURER IMPACT ANALYSIS KEY ISSUES

Each MIA interview starts by asking: “What are the key issues for your company 

regarding this energy conservation standards rulemaking?” This question prompts manufacturers 

to identify the issues they feel DOE should explore and discuss further during the interview. The 

following section describes key issues manufacturers cited for all equipment classes under 

review. 

12.3.1 Market Model Challenges

The majority of the manufacturers interviewed emphasized that the profit structure of the 

commercial clothes washer market fundamentally opposes increased levels of product efficiency. 

Manufacturers sell their products either to route-operators or to distributors. Route-operators then 

lease the machines to multi-family housing unit owners under 5-year to 15-year contracts, and 

typically provide a 1-2 day service guarantee on their machines. In contrast, distributors sell 

commercial clothes washers to owners of laundromats. 

The profits of both route-operators and laundromat owners are driven by throughput, which 

is maximized by small capacity machines with short cycle times (less than 35 minutes). In 

addition to maximizing throughput, one manufacturer noted that consistency of cycle times is 

necessary for ensuring the correct number of washers and dryers in a given premise or 

laundromat. Thus, commercial clothes washer manufacturers are constrained by capacity and 

cycle time limits in any effort to increase the efficiency of their machines. 

Also, due to the length of route-operators lease contracts with their customers, if energy 

efficiency improvements necessitate an increase in manufacturing selling price, any required 

replacement of units before lease contracts are expired will likely squeeze route-operators’ 

profits, as they will not be able to pass-through increased unit costs to lessees. One manufacturer 

asserted that in instances when route-operators and laundromat owners pass-through the costs of 

energy efficiency improvements, this will have a disproportionate negative impact on lower 

12-6 
income populations, as the majority of commercial laundry users tend to be from lower income 

groups. 

Finally, most manufacturers affirm that higher efficiency machines are more complex and 

will require more time and money to repair. Additionally, efficiency changes, such as reduced 

water level, are likely to be ill-received by end users and will lead to increases in service calls 

and failures. Both outcomes will again potentially cut into route-operator and laundromat owner 

profits. 

12.3.2 Consumer Behavior 

All manufacturers noted that energy efficiency efforts are inherently less effective in the 

commercial clothes washer market than in markets for residential appliances, including 

residential clothes washers. This is due to the usage patterns of commercial clothes washer end

users, reflecting the fact that end users: 1) do not own the machines, and 2) pay by the load to use 

machines. Such usage patterns include putting too much soap into machines (leading to “suds 

lock”), overfilling machines with oversized loads, choosing to use hot water when unnecessary, 

and washing clothes twice to deal with suds lock. 

Platform changes and reduced water levels of higher efficiency products exacerbate these 

issues. One manufacturer notes that there is a steep learning curve for end users relating to 

adaptation to low-water machines. For instance, end users should be using high efficiency 

detergents in recommended quantities, but are unlikely to do so. As mentioned in the previous 

section, concerns that machines are not functioning properly will lead to increased service calls.

Another manufacturer noted that end user dissatisfaction with high efficiency products may drive 

the need for selectable cycle modifiers, which would allow end users to choose less efficient 

settings in order to reach an acceptable level of cleaning performance to resolve the performance 

issues caused by their incorrect usage of the machines. 

12.3.3 Customer Satisfaction Impacts

All of the manufacturers interviewed expressed concerns that future energy conservation 

standards would have an adverse impact on cleaning performance and reliability, and would thus 

compromise consumer utility. One manufacturer noted that reaching the max tech level would 

require higher spin speeds, which would decrease the reliability of the product. Two 

manufacturers expressed concerns that the max tech level for top loaders pushes the boundary of 

acceptable water level in terms of both cleaning performance and market acceptance. The lower

water level of max tech products would necessitate lighter loads in order to maintain cleaning 

performance. A lighter load size requirement would contradict consumer tendencies to overload 

machines, as mentioned in section 12.3.2. 

12.3.4 Technology Limits 

One manufacturer stated that technology has not changed since the last commercial clothes 

washers standard came into effect in January 2013, which places limits on the types of efficiency 

12-7 
improvements that can be made realistically. Another manufacturer affirms that products 

currently at the max tech are not truly commercial products. 

12.3.5 Competitive Impacts

Manufacturers expressed concerns relating to competitive impacts caused by future energy 

conservation standards. One manufacturer noted that a conservative approach to meeting 

amended energy conservation standards would result in a competitive disadvantage for them 

relative to other manufacturers. Also, the LVM predicts that it will be impacted 

disproportionately by any commercial clothes washer standard since its revenue is so closely tied 

to commercial clothes washers. More detail is provided on the impact on the LVM in section 

12.6 of this TSD. 

12.4 GRIM INPUTS AND ASSUMPTIONS

The GRIM serves as the main tool for assessing the impacts on industry due to amended 

energy conservation standards. DOE relies on several sources to obtain inputs for the GRIM. 

Data and assumptions from these sources are then fed into an accounting model that calculates 

the industry cash flow both with and without amended energy conservation standards. 

12.4.1 Overview of the GRIM

The basic structure of the GRIM, illustrated in Figure 12.4.1, is an annual cash flow 

analysis that uses manufacturer prices, manufacturing costs, shipments, and industry financial 

information as inputs, and accepts a set of regulatory conditions such as changes in costs, the 

need for additional investments, and changes in 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 to 2047, 30 years after the compliance year of the rulemaking. 

The model calculates the INPV by summing the stream of annual discounted cash flows during 

this period.4

Figure 12.4.1 Using the GRIM to Calculate Cash Flow 

The GRIM projects cash flows using standard accounting principles and compares INPV 

between the base-case and the standard-case scenarios. The difference in INPV between the base 

case and the standard case(s) represents the estimated financial impact of the amended energy 

12-8 
conservation standards on manufacturers. Appendix 12-B provides more technical details and 

user information for the GRIM. 

12.4.2 Sources for GRIM Inputs

The GRIM uses several different sources for data inputs in determining industry cash 

flow. These sources include corporate annual reports, company profiles, U.S. Census data, credit 

ratings, the shipments model, the engineering analysis, and the manufacturer interviews. 

12.4.2.1 Corporate Annual Reports

Corporate annual reports for publicly held companies are freely available to the general 

public through the SEC as filings of Form 10-K. Additionally, some privately held companies 

publish annual financial reports on their corporate websites. DOE developed initial financial 

inputs to the GRIM by examining the publicly available annual reports of companies primarily 

engaged in the manufacture of home appliances whose combined product range includes 

commercial clothes washers. As these companies do not provide detailed information about their 

individual product lines, DOE used the aggregate financial information at the corporate level in 

developing its initial estimates of the financial parameters to be used in the GRIM. In doing so, 

DOE assumes that the industry-average figures calculated for these companies were 

representative of manufacturing for commercial clothes washers. These figures were later revised 

using feedback from interviews to be representative of the commercial clothes washer 

manufacturing industry. DOE used corporate annual reports to derive the following initial inputs 

to the GRIM: 

• Tax rate; 

• Working capital; 

• SG&A; 

• R&D; 

• Depreciation; 

• Capital expenditures; and

• Net PPE. 

12.4.2.2 Standard and Poor’s Credit Ratings

S&P provides independent credit ratings, research, and financial information. DOE relied 

on S&P reports to determine the industry’s average cost of debt when calculating the weighted 

average cost of capital.

12.4.2.3 Shipment Model

The GRIM used shipment projections derived from DOE’s shipments model in the 

national impact analysis (NIA). The model relied on historical shipments data for commercial 

clothes washers. Chapter 10 of the NOPR TSD describes the methodology and analytical model 

DOE used to forecast shipments. 

12-9 
12.4.2.4 Engineering Analysis 

DOE conducted the engineering analysis for this rulemaking using the efficiency-level 

approach, combined with the cost-assessment approach, to develop a cost for each efficiency 

level for commercial clothes washers. During this analysis, DOE used a manufacturing cost 

model to develop manufacturer production cost (MPC) estimates for commercial clothes 

washers. The analysis yielded the labor, materials, overhead, and total production costs for 

products at each efficiency level. The engineering analysis also estimated a manufacturer markup 

to determine the manufacturer selling price (MSP) for each product at every efficiency level. 

Chapter 5 of the NOPR TSD describes the engineering analysis in detail.

12.4.2.5 Manufacturer Interviews

As part of the MIA, DOE conducted interviews with a representative cross-section of 

manufacturers. Through these discussions, DOE obtained information to determine and verify 

GRIM input assumptions. Key topics discussed during the interviews and reflected in the GRIM 

include: 

• Capital conversion costs (one-time investments in PPE);

• Product conversion costs (one-time investments in research, product development, 

testing, and marketing);

• Product cost structure, or the portion of the MPCs related to materials, labor, 

overhead, and depreciation costs; 

• Projected total shipment and shipment distribution mix; and

• MPCs estimated in the engineering analysis.

12.4.3 Financial Parameters

In the manufacturer interviews, DOE used the financial parameters from 2006 to 2012 for 

three manufacturers of commercial clothes washers which command a combined market share of 

over 95 percent as a starting point for determining the commercial clothes washer industry 

financial parameters. The industry financial parameters were determined by weighting each 

manufacturer’s individual financial parameters by their respective market share, and correcting 

for the fraction of the market that was not represented. Table 12.4.1 below shows the data used to 

determine the initial financial parameter estimates.

Table 12.4.1 Financial Parameters based on 2006–2012 Weighted Company Financial Data 

Parameter Industry Weighted

Average Manufacturer A Manufacturer B Manufacturer C 

Tax Rate 

(% of Taxable Income) 29.2 34.8 23.5 17.6

Working Capital 

(% of Revenue) 9.5 15.7 3.2 2.4

SG&A 

(% of Revenue) 11.7 14.1 9.2 17.2

12-10
R&D 

(% of Revenue) 2.5 2.3 2.7 1.7

Depreciation 

(% of Revenue) 3.7 4.3 3.1 4.9

Capital Expenditures 

(% of Revenue) 2.1 2.1 2.1 5.6

Net Property, Plant, and 

Equipment 

(% of Revenues)

16.0 15.0 16.9 22.6

During interviews, manufacturers were asked to provide their own figures for the 

parameters listed in Table 12.4.1. Where applicable, DOE adjusted the financial parameters

according to the manufacturers’ feedback.

12.4.4 Corporate Discount Rate 

A company’s assets are financed by a combination of debt and equity, and the weighted

average cost of capital (WACC) represents the minimum rate of return necessary to cover the 

debt and equity obligations manufacturers use to finance operations. The WACC is the total cost 

of debt and equity weighted by their respective proportions in the capital structure of the 

company. 

DOE estimated the WACC for the commercial clothes washer manufacturing industry 

based on three 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 

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. In practice, investors use a variety of 

different maturity T-Bills to estimate the risk-free rate. DOE used the 10-year T-Bill return 

because it captures long-term inflation expectations and is less volatile than short-term rates. The 

risk-free rate is estimated to be approximately 5.2 percent, which is the average 10-year T-Bill

return between 1928 and 2012. 

Risk premium is the difference between the expected return on stocks and the risk-free

rate of return. DOE used the average annual return on the S&P 500 between 1928 and 2012 as 

the expected return on stocks to arrive at an estimated market risk premium of 6.1 percent. 

12-11
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.

Values for Beta are only available for publicly traded companies.

DOE used the capital asset pricing model to calculate the cost of equity for three 

commercial clothes washer manufacturers whose combined market share is over 95 percent. 

DOE determined that the industry-average cost of equity for the commercial clothes washer

industry is 15.7 percent (see Table 12.4.2). 

Table 12.4.2 Cost of Equity Calculation 

Parameter Industry Weighted 

Average

Manufacturer 

A 

Manufacturer 

B 

Manufacturer 

C 

(a) Average Beta 1.72 1.75 1.75 1.03

(b) Yield on 10 Year T-Bill 

(1928–2012) (%) 5.16 

(c) Market Risk Premium 

(1928–2012) (%) 6.10 

Cost of Equity (b) + [(a)*(c)] 

(%) 15.67 

Equity/Total Capital (%) 59.67 45.87 69.20 75.54

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 three manufacturers by using S&P and other estimates of 

corporate credit ratings and adding the relevant spread to the risk-free rate. 

Since proceeds from debt issuance are tax deductible, DOE adjusted the gross cost of 

debt by the industry-average tax rate to determine the net cost of debt for the industry. DOE 

determined that the after-tax industry-average cost of debt for the commercial clothes washer

industry is 4.9 percent. Table 12.4.3 presents the derivation of the cost of debt and the capital 

structure of the industry (i.e. the debt ratio (debt/total capital)).

Table 12.4.3 Cost of Debt Calculation 

Parameter

Industry

Weighted 

Average

Manufacturer 

A 

Manufacturer 

B 

Manufacturer 

C 

S&P Bond Rating -- BBB BBB BBB

(a) Yield on 10 year T-Bill 

(1927–2011) (%) 5.16 

12-12
(b) Gross Cost of Debt (%) 6.76 6.76 6.76 6.76

(c) Tax Rate (%) 28.14 34.82 23.53 20.54

Net Cost of Debt (b) x[1-(c)] 

(%) 4.86 

Debt/Total Capital (%) 40.33 54.13 30.80 24.46

Correcting for an inflation rate of 3.1 percent over the analysis period, DOE’s calculated 

value for the commercial clothes washer industry’s inflation-adjusted WACC and the initial 

estimate of the discount rate is 8.2 percent. During interviews, manufacturers were asked to 

provide their estimate of the discount rate. Subsequently, DOE adjusted the discount rate to 8.6 

percent. 

12.4.5 Trial Standard Levels 

DOE developed TSLs to analyze the impact on manufacturers of amended energy 

efficiency standards for two equipment classes of commercial clothes washers. Table 12.4.4

presents the TSLs and the corresponding equipment class efficiency levels based on Modified 

Energy Factor (10 CFR part 430, subpart B, appendix J2). 

TSL 3 is comprised of the max-tech efficiency levels for both equipment classes. TSL 2

is comprised of efficiency level 2 for front-loading commercial clothes washers and efficiency 

level 1 for top-loading commercial clothes washers. TSL 1 is comprised of efficiency level 1 for 

each equipment class.

Table 12.4.4 Trial Standard Levels for Commercial Clothes Washers 

Equipment Class Base Case TSL 1 TSL 2 TSL 3

Front-Loading 

Commercial Clothes Washers

Efficiency Level Baseline EL 1 EL 2 EL 3

MEF (using Appendix J2) 1.65 1.8 2.0 2.2

Top-Loading

Commercial Clothes Washers 

Efficiency Level Baseline EL 1 EL 1 EL 2

MEF (using Appendix J2) 1.15 1.35 1.35 1.55

12.4.6 NIA Shipment Forecast

The GRIM estimates manufacturer revenues based on total-unit-shipment forecasts and the 

distribution of these values by efficiency level. Changes in the efficiency mix at each standard 

level are a key driver of manufacturer finances. For this analysis, the GRIM used commercial 

clothes washer shipment data from the NIA. Chapter 9 of the NOPR TSD explains DOE’s 

calculations of total shipments in detail. Table 12.4.6 shows total shipments forecasts for both 

equipment classes of commercial clothes washers in 2018, the year new standards for 

commercial clothes washers would take effect. 

Table 12.4.5 Total Base-Case 2018 NIA Shipments 

12-13
Equipment Class Total Industry Shipments

Front-Loading

Commercial Clothes Washers 78,112

Top-Loading

Commercial Clothes Washers 115,566

12.4.6.1 Base-Case Shipments Forecast

As part of the shipment analysis, DOE estimated the distribution of shipments by 

efficiency level for both equipment classes of commercial clothes washers. DOE held the basecase energy efficiency distribution constant throughout the forecast period. Table 12.4.7 and 

Table 12.4.8 show the base-case distributions of shipments by efficiency level estimated in the 

NIA for the commercial clothes washer equipment classes. 

Table 12.4.6 Base-Case Distribution of Efficiencies for Front-Loading Commercial Clothes 

Washers in 2018 

Efficiency Level Baseline EL 1 EL 2 EL 3

MEF 1.65 1.8 2 2.2

% of Shipments 28.0% 34.0% 38.0% 0.0% 

Table 12.4.7 Base-Case Distribution of Efficiencies for Top-Loading Commercial Clothes 

Washers in 2018 

Efficiency Level Baseline EL 1 EL 2

MEF 1.15 1.35 1.55

% of Shipments 99.5% 0.25% 0.25% 

12.4.6.2 Standards-Case Shipments Forecast

To examine the impact of amended energy conservation standards on shipments, which 

in turn affects the INPV, DOE used the base-case shipments described in the previous section as 

a point of comparison for shipments forecast in the standards case. For each TSL described in the 

standards case, DOE used the shipments forecasts developed in the NIA for commercial clothes 

washers. DOE assumed the presence of cross-price elasticity in determining efficiency 

distributions for the standards case, meaning that a portion of shipments moves from one 

equipment class to the other as the former becomes comparatively more expensive to produce. 

The remaining portion of shipments for products that fall below the amended energy 

conservation standards are assumed to “roll-up” to the new standards efficiency level for a given 

12-14
equipment class on the compliance datee and thereafter. 

As in the shipments analysis, DOE assumed no relative price elasticity in the commercial 

clothes washer market, meaning that amended energy conservation standards that increase the 

first cost of commercial clothes washers would lead to no change the number of total shipments.

12.4.7 Production Costs 

Changes in the MPCs of commercial clothes washers can affect revenues, gross margins, 

and cash flow of the industry, making product cost data key GRIM inputs for DOE’s analysis. In 

the engineering analysis, DOE created separate cost curves for the two commercial clothes 

washer equipment classes using data from tear-downs to develop both the baseline MPCs and the 

incremental costs that correspond to the proposed design options. Generally, manufacturing 

higher efficiency products is more costly than manufacturing baseline products due to the use of 

more complex components. 

The cost model disaggregated the MPCs at each efficiency level into material, labor, 

overhead, and depreciation. For materials, DOE used the incremental component and raw 

material costs that correspond to the proposed design options at each EL. For labor, DOE 

estimated the labor contribution at each EL by examining how the proposed design options may 

influence manufacturing and assembly practices. For depreciation, DOE used a depreciation 

value that is consistent with historical information in SEC 10-Ks. The remainder of total 

overhead was allocated to factory overhead. 

Later, manufacturers validated these estimates and assumptions during interviews. DOE 

used the resulting MPCs and cost breakdowns as described in section 12.4.2.4 above, and further 

detailed in chapter 5 of the NOPR TSD, for each efficiency level analyzed in the GRIM.

The MSP is comprised of production costs (the direct manufacturing costs or MPCs), nonproduction costs (indirect costs including SG&A), and profit. DOE calculated the MSPs for 

commercial clothes washers by multiplying the MPCs by the appropriate manufacturer markup 

for that product. Table 12.4.9 and Table 12.4.10 show the production cost estimates used in the 

GRIM for the representative equipment classes for commercial clothes washers. 

Table 12.4.8 MPC Breakdown for Equipment Class 1 – Front-Loading Commercial 

Clothes Washers 

EL MEF WF Materials Labor Depreciation Overhead MPC Mfr. 

Markup MSP

EL1 1.80 4.5 $433.6 $95.93 $31.9 $267.0 $828.4 1.285 $1,064.5

EL2 2.00 4.0 $433.9 $95.88 $31.9 $267.0 $828.7 1.285 $1,064.9

EL3 2.20 3.7 $449.4 $97.56 $32.7 $268.3 $848.0 1.285 $1,089.6

e The estimated compliance date for the commercial clothes washer energy conservation standard is 2018.

12-15

 
Table 12.4.9 MPC Breakdown for Equipment Class 2 - Top-Loading Commercial Clothes 

Washers 

EL MEF WF Materials Labor Depreciation Overhead MPC Mfr. 

Markup MSP

EL1 1.35 8.8 $235.2 $64.1 $20.1 $203.0 $522.4 1.285 $671.3

EL2 1.55 6.9 $268.7 $74.0 $21.6 $197.0 $561.3 1.285 $721.3

12.4.8 Conversion Costs and Stranded Assets

Amended energy conservation standards typically cause manufacturers to incur one-time 

conversion costs to bring their production facilities and equipment designs into compliance with 

new regulations. For the MIA, DOE classified these one-time conversion costs into two major 

groups: product conversion costs and capital conversion costs. Product conversion costs are 

investments in research, development, testing, marketing, and other non-capitalized costs 

focused on making equipment designs that comply with the amended energy conservation 

standard. Capital conversion costs are investments in property, plant, and equipment needed to 

adapt or change existing production facilities so that new equipment designs can be fabricated

and assembled. In the instance where changes to energy conservation standards result in the 

obsolescence of manufacturing capital, the un-depreciated value of any obsolete equipment is 

considered a stranded asset. In addition to product and capital conversion costs, stranded assets 

also factor into the GRIM’s calculation of annual cash flows. The following sections describe 

the inputs DOE used in the GRIM in greater detail. 

12.4.8.1 Commercial Clothes Washer Conversion Costs 

DOE based its estimates of the conversion costs required to meet each TSL on 

confidential information received during manufacturer interviews. DOE asked manufacturers to 

estimate their investments in product development and new manufacturing capital and their 

anticipated stranded capital assets at various efficiency levels. DOE then reviewed public 

information in the DOE Compliance and Certification, California Energy Commission (CEC), 

and ENERGY STAR product databases, as well as manufacturer websites to understand which 

products manufacturers would need to upgrade at each efficiency level. DOE mapped 

manufacturers’ estimates of investments and stranded assets to a composite database of products 

to disaggregate their reporting and allocate the estimated costs across the various equipment 

classes and efficiency levels that each representative company manufactures. From this, DOE 

also developed an average cost estimate to bring non-compliant products in each equipment class 

into compliance at each efficiency level. DOE applied these average cost estimates to the 

remaining products in the database to arrive the compliance costs required by the remainder of 

industry. These figures were then added to the disaggregated estimates of individual 

manufacturer conversion costs to arrive at a total industry estimate of product and capital 

conversion costs and stranded assets for each efficiency level of each equipment class. 

Table 12.4.34 through 12.4.37 show DOE’s estimates of the product and capital 

12-16
conversion costs necessary for each commercial clothes washer equipment class at each 

efficiency level identified. None of the manufacturers interviewed provided comment regarding 

potential stranded assets.

Table 12.4.10 Product Conversion Costs for Equipment Class 1 – Front-Loaders by EL 

EL (J2 MEF) Product Conversion Costs (Millions 2012$)

EL1 (1.80) $0.0

EL2 (2.00) $0.3

EL3 (2.20) $11.9

Table 12.4.11 Product Conversion Costs for Equipment Class 2 – Top-Loaders by EL 

EL (J2 MEF) Product Conversion Costs (Millions 2012$)

EL1 (1.35) $9.9

EL2 (1.55) $50.6

Table 12.4.12 Capital Conversion Costs for Equipment Class 1 – Front-Loaders by EL 

EL (J2 MEF) Capital Conversion Costs (Millions 2012$)

EL1 (1.80) $0.0

EL2 (2.00) $0.0

EL3 (2.20) $24.5

Table 12.4.13 Capital Conversion Costs for Equipment Class 2 – Top-Loaders by EL 

EL (J2MEF) Capital Conversion Costs (Millions 2012$)

EL1 (1.35) $0.0

EL2 (1.55) $38.7

12.4.9 Markup Scenarios

MSP is equal to MPC times a manufacturer markup. The MSP includes direct 

manufacturing production costs (i.e., material, labor, depreciation, and overhead estimated in 

DOE’s MPCs) and all non-production costs (i.e., SG&A, R&D, and interest), along with profit.

In the base case, DOE used the same baseline markups calculated in the engineering 

analysis for both equipment classes. In the standards case, DOE modeled two markup scenarios 

to represent the uncertainty about the potential impacts on prices and profitability following the 

implementation of amended energy conservation standards: (1) a preservation of gross margin 

percentage markup scenario, and (2) a preservation of earnings before interest and taxes (EBIT) 

markup scenario. Modifying these markups from the base case to the standards cases yields 

different sets of impacts on manufacturers by changing industry revenue and cash flow. 

To calculate the baseline manufacturer markup, DOE evaluated publicly available 

financial information for manufacturers whose product offerings include commercial clothes 

washers. During manufacturer interviews, DOE received feedback supporting the calculated 

12-17
1.285 baseline manufacturer markup. For both GRIM markup scenarios, DOE assumed a 

predominantly flat markup structure, placing no premium on higher efficiency products. 

12.4.9.1 Preservation of Gross Margin Percentage Markup Scenario

The preservation of gross margin percentage markup scenario assumes that the baseline 

markup of 1.285 is maintained for all products in the standards case. As production costs 

increase with efficiency, this scenario implies that the absolute dollar markup will increase as 

well. This scenario represents the upper bound of industry profitability as manufacturers are able 

to fully mark up and pass through higher production costs to their customers.

12.4.9.2 Preservation of EBIT Markup Scenario

DOE also modeled the preservation of EBIT markup scenario to estimate a lower bound 

of profitability for the industry. This is similar to the preservation of gross margin percentage 

markup scenario with the exception that in the standards case, minimally compliant products lose 

a fraction of the baseline markup. The lower markup for minimally compliant products is derived 

by calibrating the markup for minimally compliant products such that industry-wide EBIT in the 

year after standards go into effect matches EBIT of the same year in the base case. This scenario 

represents a more substantial impact to the commercial clothes washer industry in the form of 

reduced gross margin percentage as manufacturers vie to maintain the lowest possible prices for 

marginally compliant products while securing the same level of EBIT they saw prior to new and 

amended standards. 

While all compliant products receive the 1.285 markup in the preservation of gross 

margin percentage markup scenario, Table 12.4.40 through Table 12.4.41 list the calibrated 

markups used in the preservation of EBIT markup scenario. 

Table 12.4.14 Preservation of EBIT Markups for Equipment Class 1: Front-Loading 

Commercial Clothes Washers

EL J2 MEF Minimally Compliant EL

EL 1 EL 2 EL 3

EL 1 1.80 1.285 

EL 2 2.00 1.285 1.285 

EL 3 2.20 1.285 1.285 1.279

Table 12.4.15 Preservation of EBIT Markups for Equipment Class 2: Top-Loading 

Commercial Clothes Washers

EL J2 MEF Minimally Compliant EL

EL 1 EL 2

EL 1 1.35 1.285 

12-18
EL 2 1.55 1.285 1.279

12.5 INDUSTRY FINANCIAL IMPACTS

Using the inputs and scenarios described in the previous sections, DOE used the GRIM to 

estimate the financial impacts on the commercial clothes washer industry. The MIA uses two key 

financial metrics: INPV and annual cash flows. The main results of the MIA are reported in this 

section. 

12.5.1 Introduction

The INPV measures the industry value and is used in the MIA to compare the economic 

impacts of different TSLs in the standards case. The INPV is different from DOE’s NPV, which 

is applied to the U.S. economy at large. The INPV is specific to the commercial clothes washer

manufacturing industry, and is the sum of all annual net cash flows discounted at the industry’s 

WACC. The GRIM for the commercial clothes washer industry models cash flows from 2014 to 

2047. This timeframe models both the short-term impacts on the industry from the 

announcement of the standard until the compliance date in 2018, and a long-term assessment 

over the 30-year analysis period immediately thereafter. 

In the MIA, DOE compares the INPV at the base case (no amended energy conservation 

standards) to that at each TSL in the standards case. The difference between the base case and a 

standards case INPV is an estimate of the economic impacts that implementing that particular 

TSL would have on the industry. For the commercial clothes washer industry, DOE examined 

the two markup scenarios described above: the preservation of gross margin percentage markup 

scenario and the preservation of EBIT markup scenario. DOE’s estimates of INPV for the full 

analysis period (2014 – 2047) for the base case and at each TSL in the standards case are 

presented in Table 12.5.1and Table 12.5.2 below. While INPV is useful for evaluating the longterm effects of amended energy conservation standards, short-term changes in cash flow are also 

important indicators of the industry’s financial situation. For example, a large investment over 

one or two years could strain the industry’s capital reserves and cash flow. Consequently, the 

sharp drop in financial performance could cause investors to flee, even if recovery is possible. 

Thus, a short-term disturbance can have long-term effects that the INPV cannot capture. To get 

an idea of the behavior of annual net cash flows, Figure 12.5.1 and Figure 12.5.2 below present 

the annual net or free cash flows from 2014 through 2047 for the base case and each TSL in the 

standards case. 

Annual cash flows are discounted to the base year, 2014. Between 2014 and the 2018 

compliance date, cash flows are driven by the level of conversion costs and the portion of these 

investments made each year. After the standard announcement date (i.e., the publication date of 

the final rule), industry cash flows begin to decline as companies use their financial resources to 

prepare for the amended energy conservation standard. The more stringent the amended energy 

conservation standard, the greater the impact on industry cash flows in the years leading up to 

the compliance date, as product conversion costs lower cash flows from operations and capital 

12-19
conversion costs increase outlays of cash for capital expenditures. 

Free cash flow in the year the amended energy conservation standards take effect is 

driven by two competing factors. In addition to capital and product conversion costs, amended 

energy conservation standards could create stranded assets, i.e., the residual un-depreciated value 

of tooling and equipment that would have enjoyed longer use if the energy conservation standard 

had not made them obsolete. In this year, manufacturers write down the remaining book value of 

existing tooling and equipment, the value of which is affected by the amended energy 

conservation standards. This one time write down acts as a tax shield that mitigates decreases in 

cash flow from operations in the year of the write-down. In this year, there is also an increase in 

working capital that reduces cash flow from operations. A large increase in working capital can 

be attributed to more costly production components and materials, higher inventory carrying to 

sell more expensive products, and higher accounts receivable for more expensive products. 

Depending on these two competing factors, cash flow can either be positively or negatively 

affected in the year the standard takes effect. 

In the years following the compliance date of the standard, the impact on cash flow 

depends on the operating revenue. Under the preservation of gross margin percentage markup 

scenario, more stringent TSLs typically have a positive impact on cash flows relative to the base 

case because in marking up more costly equipment, manufacturers are able to earner higher 

operating profit, which increases cash flow from operations. There is very little impact on cash 

flow from operations under the preservation of EBIT scenario because this scenario is calibrated 

to have the same earnings before interest and taxes in the standards case at each TSL as the base 

case as in the year after the standard takes effect. In this scenario production costs increase, but 

EBIT remains approximately equal to the base case, effectively decreasing profit margins as a 

percentage of revenue. 

12.5.2 Commercial Clothes Washer Industry Financial Impacts

Table 12.5.1 and Table 12.5.2 provide the INPV estimates for the commercial clothes 

washer manufacturing industry. Figure 12.5.1 and Figure 12.5.2 present the annual net cash 

flows for the commercial clothes washer manufacturing industry for each of the markup 

scenarios.

Table 12.5.1 Manufacturer Impact Analysis for Commercial Clothes Washers – 

Preservation of Gross Margin Percentage Markup Scenario

Base Case

Trial Standard Level

1 2 3 

INPV (2012$ millions) $124.2 $118.3 $118.2 $33.0 

Change in 

INPV

(2012$ millions) - ($5.9) ($6.0) ($91.2) 

(%) - -4.7% -4.9% -73.4% 

12-20
*For tables in section 12.5, values in parenthesis indicate negative numbers 

Table 12.5.2 Manufacturer Impact Analysis for Commercial Clothes Washers – 

Preservation of EBIT Markup Scenario

Base Case

Trial Standard Level

1 2 3 

INPV (2012$ millions) $124.2 $118.3 $118.2 $28.8 

Change in 

INPV

(2012$ millions) - ($5.9) ($6.0) ($95.4)

(%) - -4.7% -4.9% -76.8%

*For tables in section 12.5, values in parenthesis indicate negative numbers

 

Figure 12.5.1 Annual Industry Net Cash Flows for Commercial Clothes Washers 

(Preservation of Gross Margin Markup Scenario)

12-21
 

Figure 12.5.2 Annual Industry Net Cash Flows for Commercial clothes washers 

(Preservation of EBIT Markup Scenario)

12.6 IMPACTS ON THE LOW-VOLUME COMMERCIAL CLOTHES WASHER 

MANUFACTURER 

One LVM of commercial clothes washers believes it will be disproportionately affected 

by any energy efficiency regulation in the commercial clothes washer industry. This business is 

concentrated in one specific market segment (commercial laundry) and is at least one order of 

magnitude smaller than its diversified competitors. Due to this combination of market 

concentration and size, the viability of this LVM could be at risk depending on the TSL chosen. 

Small and low-volume manufacturers typically pay higher prices for components because 

of smaller purchasing volumes while their large competitors receive volume discounts. Despite 

the fact that the commercial clothes washer industry as a whole is a low-volume industry, larger 

manufacturers, with a significantly larger proportion of washer revenues derived from the sale of 

residential clothes washers, are able to purchase components in high quantities due to the 

similarities in commercial and residential clothes washer component parts. 

DOE used the annual reports from both the LVM and its larger diversified competitors to 

analyze the extent to which impacts on the LVM may be disproportionate. In its 2009 10-K, the

LVM’s major competitor, whose products offerings are “primarily for home use,” reported 

laundry segment revenues of approximately one-third its total annual revenues. Component parts 

that are typically used across both residential and commercial clothes washers include outer 

sheet metal, doors and lid hardware, wash basket clothes container, outer plastic wash tub, 

suspension hardware, drain pump, motor and transmission assembly. DOE estimated LVM 

segment revenues and shipments from publically available sources (see Table 12.6.1). In contrast 

to the larger commercial clothes washer manufacturers, the LVM’s washer revenues are mainly 

12-22
generated from commercial washers; residential clothes washers only account for an estimated 

12 percent of the LVM’s washer revenues and an estimated 2 percent of total annual... 

Because design options associated with TSL1 and TSL2 for both equipment classes relate to 

control changes and different cycle options rather than material changes to the equipment itself, 

DOE assumes that lack of bulk purchasing power would only disadvantage the LVM relative to 

its major competitor if the max-tech level is chosen.

Table 12.6.1 DOE Estimates of LVM Segment Revenues and Shipments

2012 

Revenues 

($MM)

% of 

Washer 

Revenues

% of 

Total 

Revenues

Shipment 

Volume by 

Sales Channel

% of 

Washer 

Shipments

Commercial Top-Loader $59 62% 12% 60,762 68%

Front-Loader $25 26% 5% 13,662 15%

Residential Top-Loader $10 11% 2% 14,267 16%

Front-Loader $1 1% 0% 633 1%

All Washers $95 100% 19% 89,324 100%

 

 In addition to component costs, conversion costs associated with product development 

and new manufacturing capital are likely to be disproportionately burdensome for the 

commercial clothes washer LVM. DOE assumes for all manufacturers that the same line would 

be used to build both residential and commercial clothes washers for a given washer type. 

Hence, the upfront capital and product conversion costs associated with standards compliance, as 

well as depreciation costs are spread over the entire production volume, not just production 

volumes of commercial equipment. Although commercial clothes washer shipment volumes are 

comparable between the LVM and its larger competitor, the LVM’s overall clothes washer 

production volume is at least two orders of magnitude smaller than its major competitor. 

Accordingly, the opportunity for the LVM to recoup upfront investments in product development 

is substantially smaller than its competitor. Similarly, depreciated manufacturing capital can only 

be spread across a disproportionately lower volume of shipments, contributing to higher per-unit 

production costs. 

 During an interview, the LVM indicated that it could not manufacture top-loading or 

front-loading washers at the proposed max-tech level with its existing manufacturing capital and 

platform constraints. If DOE were to set the standard at the max-tech level, the LVM believes 

that a “green field” design for front-loaders would likely be required. Similarly, for top-loaders, 

the LVM asserts that it does not have the technology to reach the max-tech level, and it would be 

forced to adapt its business model, possibly ceasing production of commercial clothes washers 

altogether, sourcing from abroad, shifting production abroad, or some combination thereof. All 

of these changes could have nontrivial impacts on manufacturing capacity and employment in 

the commercial clothes washer industry. Due to its relative concentration in the top-loading 

commercial washer segment, if the LVM no longer offers top-loading washers, it would likely 

cease commercial clothes washer production altogether. DOE estimates that, currently, the 

LVM’s top-loading washers account for more than half of the company’s commercial clothes 

washer revenues and more than two-thirds of its commercial clothes washer shipments. To shift 

12-23
all top-loading commercial clothes washers to front-loading washers at current production 

volumes would require substantial investments that the company may not be able to justify. In 

addition, the LVM derives an estimated 88 percent of its clothes washer revenue from 

commercial clothes washers, so its sales in the residential clothes washer market would be too 

low to justify continuing any top-loading clothes washer manufacturing. 

In terms of access to the capital required to cover the conversion costs associated with 

reaching the max-tech levels, the LVM would likely be forced to take on additional debt, 

whereas its larger competitors would be better equipped to fund purchases with existing cash 

flow from operations. Additionally, since the recession of 2007 and 2008, small business lending 

has dropped substantially due to a combination of tightened lending standards, increasing 

collateral requirements and reduced focus on small business credit markets. Thus, the LVM 

generally has access to disproportionately lesser capital than its larger competitors. 

In summary, DOE recognizes that depending on the TSL selected, amended energy 

conservation standards may have disproportionate impacts on the LVM relative to its larger 

diversified competitors. In this industry, larger manufacturers may have a competitive advantage 

compared to the LVM due to their production volumes, focus on the residential market, and

ability to access capital and to purchase component parts at a discount. 

12.7 OTHER IMPACTS

12.7.1 Employment

For commercial clothes washers, DOE used the GRIM to estimate the domestic labor 

expenditures and number of domestic production workers in the base case and at each TSL from 

2014 to 2047. DOE used statistical data from the most recent U.S Census Bureau’s 2010 Annual 

Survey of Manufactures (ASM), the results of the engineering analysis, and interviews with 

manufacturers to determine the inputs necessary to calculate industry-wide labor expenditures 

and domestic employment levels. Labor expenditures for the manufacture of a product are a 

function of the labor intensity of the product, the sales volume, and an assumption that wages in 

real terms remain constant.

 In the GRIM, DOE used the labor content of each product and the manufacturing 

production costs from the engineering analysis to estimate the annual labor expenditures in the 

commercial clothes washer industry. DOE used census data and interviews with manufacturers 

to estimate the portion of the total labor expenditures that is attributable to domestic labor.

The production worker estimates in this section only cover workers up to the linesupervisor level who are directly involved in fabricating and assembling a product within an 

Original Equipment Manufacturer (OEM) facility. Workers performing services that are closely 

associated with production operations, such as material handling with a forklift, are also included 

as production labor. DOE’s estimates account only for production workers who manufacture the 

specific products covered by this rulemaking.

The employment impacts shown in Table 12.7.1 represent the potential production 

employment that could result following amended energy conservation standards. The upper end 

12-24
of the results in this table estimates the total potential increase in the number of production 

workers after amended energy conservation standards. To calculate the total potential increase, 

DOE assumed that manufacturers continue to produce the same scope of covered products in 

domestic production facilities and domestic production is not shifted to lower-labor-cost 

countries. Because there is a risk of manufacturers evaluating sourcing decisions in response to 

amended energy conservation standards, the lower end of the range of employment results in 

Table 12.7.1 includes the estimated total number of U.S. production workers in the industry who 

could lose their jobs if all existing production were moved outside of the United States. 

While the results present a range of employment impacts following the compliance date 

of amended energy conservation standards, the discussion below also includes a qualitative 

evaluation of the likelihood of negative domestic production employment impacts at the various 

TSLs. 

Using the GRIM, DOE forecasts the domestic labor expenditure for commercial clothes 

washer production labor in 2018 will fall approximately within the range of $12.4 to $13.5 

million, depending on the TSL chosen. Using the $18.74 hourly wage rate including fringe 

benefits and 1,973 production hours per year per employee found in the 2010 ASM, DOE

estimates there will be 334 to 364 domestic production workers involved in the manufacturing of

commercial clothes washers in 2018, the year in which amended standards would go into effect 

for TSL 1, TSL 2, and TSL 3. In addition, DOE estimates that 271 to 295 non-production 

employees in the United States will support commercial clothes washer production.f

 

Approximately 83 percent of commercial clothes washers sold in the United States are 

manufactured domestically. The employment tab of the commercial clothes washer GRIM 

contains more detailed information on the annual domestic employment impacts.

Table 12.7.1 illustrates the range of potential impacts of amended energy conservation 

standards on domestic production employment levels at each TSL for the commercial clothes 

washer market. 

f As defined in the 2010 ASM, production workers number include “workers (up through the line-supervisor 

level) engaged in fabricating, processing, assembling, inspecting, receiving, storing, handling, packing, 

warehousing, shipping (but not delivering), maintenance, repair, janitorial and guard services, product 

development, auxiliary production for plant's own use (e.g., power plant), recordkeeping, and other services 

closely associated with these production operations at the establishment covered by the report. Employees 

above the working-supervisor level are excluded from this item.” Non-production workers are defined as 

“employees of the manufacturing establishment including those engaged in factory supervision above the linesupervisor level. It includes sales (including driver-salespersons), sales delivery (highway truck drivers and

their helpers), advertising, credit, collection, installation and servicing of own products, clerical and routine 

office functions, executive, purchasing, financing, legal, personnel (including cafeteria, medical, etc.), 

professional, and technical employees. Also included are employees on the payroll of the manufacturing 

establishment engaged in the construction of major additions or alterations utilized as a separate work force.” 

12-25

 
Table 12.7.1 Potential Changes in the Total Number of Domestic Commercial Clothes 

Washer Production Workers in 2018 

Baseline TSL 1 TSL 2 TSL 3

Total Number of Domestic Production 

Workers in 2018 

(without changes in production locations)

334 334 334 364 

Potential Changes in Domestic 

Production Workers in 2018* - 0 - 

(334) 

0 - 

(334) 

30 - 

(334) 

*DOE presents a range of potential employment impacts. Numbers in parentheses indicate negative numbers.

Figure 12.7.1 below shows total annual domestic employment levels for each TSL 

calculated by the GRIM.

Figure 12.7.1 Commercial Clothes Washer Domestic Production Employment by Year 

Due to the fact that production employment expenditures are assumed to be a fixed 

percentage of COGS and the MPCs typically increase with more efficient products, labor tracks 

the increased prices in the GRIM. As efficiency of washers increases, so does the complexity of 

the machines, generally requiring more labor to produce. As previously discussed, for TSL 1, 

there is no change in MPCs from the base case, and, for TSL 2, there is a small increase in MPCs 

for front-loaders offset by a shift in shipments from front-loaders to top-loaders. As a result, 

DOE expects that there would be no employment impacts among domestic commercial clothes 

washer manufacturers for TSL 1 and TSL 2. For TSL 3, the GRIM predicts an increase in 

domestic employment following amended standards based on the increase in complexity and 

relative price of the equipment. 

Approximately 83 percent of commercial clothes washers are currently produced 

domestically. In the commercial clothes washer industry, 100 percent of top-loaders are 

12-26
manufactured domestically, while a much larger share of front-loaders are produced abroad. The 

actual impacts on domestic employment after standards would be different than estimated if any 

U.S. manufacturer decided to shift remaining U.S. production to lower cost countries. The 

proposed standard could result in losing all 334 production workers if all U.S. manufacturers 

source standards-compliant washers or shift U.S. production abroad. However, feedback from 

manufacturers during NOPR interviews supports the notion that top-loading commercial clothes 

washers will continue to be produced domestically following amended energy conservation 

standards, unless the max-tech level is chosen.

12.7.2 Production Capacity 

According to the majority of commercial clothes washer manufacturers, new energy 

conservation standards could potentially impact manufacturers’ production capacity depending 

on the efficiency level required. For TSL 1 and TSL 2, the most significant conversion costs are 

the research and development, testing, and certification of products with more-efficient 

components, which does not affect production line capacity. Thus, DOE believes manufacturers 

will be able to maintain manufacturing capacity levels and continue to meet market demand 

under new energy conservation standards as long as manufacturers continue to offer top-loading 

and front-loading washers. 

 

 However, a very high efficiency standard for top-loading clothes washers could cause 

certain manufacturers to abandon further domestic production of top-loading clothes washers 

after the effective date, and choose instead to cease production of commercial clothes washers 

altogether, which could possibly lead to a permanently lower production capacity within the 

commercial clothes washer industry.

12.7.3 Cumulative Regulatory Burden 

While any one regulation may not impose a significant burden on manufacturers, the 

combined effects of several impending regulations may have serious consequences for some 

manufacturers, groups of manufacturers, or an entire industry. Regulatory burdens can prompt 

companies to exit the market or reduce their equipment offerings, potentially reducing 

competition. Smaller companies in particular can be affected by regulatory costs since these 

companies have lower sales volumes over which they can amortize the costs of meeting new 

regulations. Assessing the impact of a single regulation may overlook this cumulative regulatory 

burden. A proposed standard is not economically justified if it contributes to an unacceptable 

level of cumulative regulatory burden. 

For the cumulative regulatory burden analysis, DOE looks at other significant productspecific regulations that will take effect 3 years before or after the 2018 compliance date of the 

amended energy conservation standards for commercial clothes washers. In addition to amended 

energy conservation regulations, several other Federal regulations apply to commercial clothes 

washers. While this analysis focuses on the impacts on manufacturers born of other Federal 

requirements, DOE also has described a number of other non-Federal regulations in section 

12.7.3.2 because it recognizes that these regulations also impact the equipment covered by this 

rulemaking. 

12-27
12.7.3.1 DOE Regulations for Other Products Produced by Commercial Clothes 

Washer Manufacturers

Companies that produce a wide range of regulated products and equipment may face

more capital and product development expenditures than competitors with a narrower scope of 

products and equipment. The majority of commercial clothes washer manufacturers also produce 

other appliances and commercial refrigeration equipment. In addition to the amended energy 

conservation standards for commercial clothes washers, these manufacturers contend with 

several other Federal regulations and pending regulations that apply to other products and 

equipment. DOE recognizes that each regulation can significantly affect a manufacturer’s 

financial operations. Multiple regulations affecting the same manufacturer can quickly strain 

manufacturers’ profits and possibly cause an exit from the market. Table 12.7.2 lists the other 

DOE energy conservation standards that could also affect manufacturers of commercial clothes 

washers in the 3 years leading up to and after the compliance date of amended energy 

conservation standards for this equipment. 

Table 12.7.2 Other DOE and Federal Actions Affecting the Commercial Clothes Washer

Industry

Regulation

Approximate 

Compliance 

Date*

Number of Impacted 

Companies 

Estimated Total Industry 

Conversion Costs

Residential Clothes Washers 2015 & 2018 4 $418.5 M

Residential Clothes Dryers 2015 4 $95.4 M

Microwave Ovens 2016 4 $43.1 M

Miscellaneous Refrigeration 2018 3 N/A† 

Dehumidifiers 2019 4 N/A† 

*The dates listed are an approximation. The exact dates are pending final DOE action.

† For energy conservation standards for rulemakings awaiting DOE final action, DOE does not have a finalized 

estimated total industry conversion cost. 

12.7.3.2 Other Regulations That Could Impact Commercial Clothes Washer

Manufacturers

While the cumulative regulatory burden focuses on the impacts on manufacturers of other 

Federal requirements, in this section DOE has described a number of other regulations below 

that could also impact the commercial clothes washers covered by this rulemaking. 

Other Federal Standards 

One manufacturer expressed concerns about the conflict minerals directive included in 

the Dodd-Frank Act of 2010 (H.R. 4173, section 1502)g

. When promulgated, this rule will 

require that U.S. companies report and make public available their use of certain minerals from 

g http://www.sec.gov/rules/proposed/2010/34-63547fr.pdf. 

12-28

 
the Democratic Republic of the Congo and adjoining countries. This may require manufacturers 

to make investments in new tracking systems to ensure visibility throughout their supply chain. 

State Energy Conservation and Environmental Standards 

Manufacturers indicated that state-level regulations, especially in California, contribute to 

overall cumulative regulatory burden. These regulations include state energy efficiency 

standards, for example, proposed standards for commercial clothes dryers in California. Other 

state-level regulations that may impact commercial clothes washer manufacturers include 

product and packaging, and extended producer responsibility regulations. Additionally, 

manufacturers expressed concerns over future compliance costs associated with green chemistry 

laws and chemical bans, such as those initiated and proposed as part of the California Green 

Chemistry Initiative.h

 

International Energy Conservation Standards 

During the interview process, one manufacturer cited international electromagnetic 

compatibility (EMC) standards as a contributor to cumulative regulatory burden. Such standards 

serve to standardize the EMC performance in terms of emissions and radio interference of 

various electrical products, including household appliances. i

12.8 CONCLUSION

The following section summarizes the scenarios DOE believes are most likely to capture 

the range of impacts on commercial clothes washer manufacturers at each TSL in the standards 

case. While these scenarios bound the range of the most plausible impacts on manufacturers, 

some circumstances could cause manufacturers to experience impacts outside this range.

12.8.1 Commercial Clothes Washers 

At TSL 1, DOE estimates the impact on INPV for manufacturers of commercial clothes 

washers to be $5.9 million, or a change in INPV of -4.7 percent under either markup scenario. At 

this TSL, industry free cash flow is estimated to decrease by approximately 30.2 percent to $6.3 

million, compared to the base-case value of $9.1 million in the year before the compliance date 

(2017). 

TSL 1 represents an improvement in MEF (as determined using appendix J2) from the 

baseline level of 1.65 to 1.80 (ft3

/kWh/cycle) for front-loading equipment and an improvement in 

MEF from the baseline level of 1.15 to 1.35 (ft3/kWh/cycle) for top-loading equipment. The lack 

of difference in results between the two markup scenarios at TSL 1 can be traced to the fact that 

for both equipment classes, the baseline MPCs and the MPCs at TSL 1 are the very same. For 

front-loading equipment, this is because the 1.8 MEF (as determined using appendix J2) products 

(on which the EL 1 standard is based) are in fact the lowest efficiency front-loading equipment 

h http://www.mofo.com/green-chemistry/

i

http://en.wikipedia.org/wiki/List_of_EMC_directives

12-29

 
available on the market today. As such, TSL 1 would have no impact on the front-loading 

market. Similarly, the design options associated with EL 1 for top-loading equipment relate to 

control changes and different cycle options, rather than material changes to the equipment itself. 

In turn, there are product conversion costs associated with the research and development needed 

to make these changes, but no changes in the per unit production costs. Given these conditions, 

the entirety of impacts on INPV at TSL 1 can be attributed to the $9.9 million in product 

conversion costs for top-loading equipment. 

At TSL 2, DOE estimates the impact on INPV for manufacturers of commercial clothes 

washers to be $6.0 million, or a change in INPV of -4.9 percent under either markup scenario. At 

this TSL, industry free cash flow is estimated to decrease by approximately 31.2 percent to $6.2 

million, compared to the base-case value of $9.1 million in the year before the compliance date 

(2017). 

TSL 2 represents an improvement in MEF from the baseline level of 1.65 to 2.00 

(ft3

/kWh/cycle) for front-loading equipment and an improvement in MEF from the baseline level 

of 1.15 to 1.35 (ft3

/kWh/cycle) for top-loading equipment. Much like TSL 1, the lack of 

difference in results between the two markup scenarios at TSL 2 can be traced to the fact that the 

baseline MPCs and the MPCs at TSL 2 are very close for front-loading equipment, and the very 

same for top-loading equipment. For front-loading equipment, this is because the 2.0 MEF EL 

(as determined using appendix J2) only requires minor changes to baseline equipment needed to 

enable slightly faster spin speeds. The standard level for top-loading equipment at TSL 2 is the 

same at TSL 1, and again, simply relates to control changes and different cycle options, rather 

than material changes to the equipment itself. Because there are no substantive changes to MPCs 

for either equipment class, much as in TSL 1, nearly all of the impacts on INPV at TSL 2 can be 

attributed to the $10.2 million in product conversion costs. 

At TSL 3, DOE estimates impacts on INPV for manufacturers of automatic commercial

clothes washers to range from -$91.2 million to -$95.4 million, or a change in INPV of -73.4 

percent to -76.8 percent. At this TSL, industry free cash flow is estimated to decrease by over 

500 percent to -$36.8 million, compared to the base-case value of $9.1 million in the year before 

the compliance date (2017). 

TSL 3 represents an improvement in MEF from the baseline level of 1.65 to 2.20 (ft3

/kWh/cycle)

for equipment class 1 and an improvement in MEF from the baseline level of 1.15 to 1.55 

(ft3

/kWh/cycle) for equipment class 2. Unlike TSL 1 and TSL 2, the efficiency levels specified at 

TSL 3 would require substantial redesigns of products in both equipment classes. The design 

options proposed at these efficiency levels include switching to direct-drive motors, hung 

suspension, non-traditional agitation, and increasing the tub capacity – all of which require major 

platform overhauls and significant changes to manufacturing capital. These design options do 

not contribute to substantially different MPCs, but the conversion costs associated with product 

development and testing, as well as the investments in manufacturing capital including retooling 

of tubs and agitators dramatically impact the INPV in the order of $125.6 million industry-wide. 

12-30
REFERENCES

1

 U.S. Department of Energy. Compliance Certification Database. Last accessed September 3, 

 2013. Available online at: http://www.regulations.doe.gov/certification-data/. 

2

 California Energy Commission. Appliance Efficiency Database. Last accessed September 3, 

 2013. Available online at: http://www.appliances.energy.ca.gov/. 

3 ENERGY STAR. Commercial Clothes Washers. Last accessed September 11, 2013. 

Available online at : 

http://www.energystar.gov/index.cfm?fuseaction=find_a_product.showProductGroup&pgw_

code=CCW. 

4 McKinsey & Company, Inc. Valuation: Measuring and Managing the Value of 

Companies, 3rd Edition, Copeland, Koller, Murrin. New York: John Wiley & Sons, 2000. 

12-31

 
CHAPTER 13. EMISSIONS IMPACT ANALYSIS

TABLE OF CONTENTS

13.1 INTRODUCTION ......................................................................................................... 13-1

13.2 AIR QUALITY REGULATIONS AND EMISSIONS IMPACTS ............................... 13-2

13.3 POWER SECTOR EMISSIONS FACTORS ................................................................ 13-3

13.4 UPSTREAM FACTORS ............................................................................................... 13-4

13.5 EMISSIONS IMPACT RESULTS ................................................................................ 13-5

LIST OF TABLES

Table 13.3.1 Power Plant Emissions Factors for Commercial Refrigeration End Use* ........ 13-4

Table 13.4.1 Electricity Upstream Emissions Factors ............................................................ 13-4

LIST OF FIGURES

Figure 13.5.1 CO2 Total Emissions Reduction ........................................................................ 13-6

Figure 13.5.2 SO2 Total Emissions Reduction ......................................................................... 13-6

Figure 13.5.3 NOx Total Emissions Reduction ........................................................................ 13-7

Figure 13.5.4 Hg Total Emissions Reduction .......................................................................... 13-7

Figure 13.5.5 N2O Total Emissions Reduction ........................................................................ 13-8

Figure 13.5.6 CH4 Total Emissions Reduction ........................................................................ 13-8

13-i 
CHAPTER 13. EMISSIONS IMPACT ANALYSIS

13.1 INTRODUCTION

The emissions analysis consists of two components. The first component estimates the 

effect of potential energy conservation standards on power sector emissions of carbon dioxide 

(CO2), nitrogen oxides (NOX), sulfur dioxide (SO2) and mercury (Hg). The second component 

estimates the impacts of a potential standard on emissions of two additional greenhouse gases, 

methane (CH4) and nitrous oxide (N2O), as well as the reductions to emissions of all species due 

to “upstream” activities in the fuel production chain. These upstream activities comprise 

extraction, processing, and transporting fuels to the site of combustion. The associated emissions are 

referred to as upstream emissions. Together, these emissions account for the full-fuel-cycle 

(FFC), in accordance with DOE’s FFC Statement of Policy. 76 FR 51282 (Aug. 18, 2011). 

The analysis of most power sector emissions uses marginal emissions intensity factors 

derived from runs of DOE’s NEMS-BT model, described in Chapter 15. DOE used the version 

of NEMS based on the Annual Energy Outlook 2013 (AEO 2013).1 Each annual version of 

NEMS incorporates the projected impacts of existing air quality regulations on emissions. AEO 

2013 generally represents current Federal and State legislation and final implementation 

regulations in place as of the end of December 2012. Where applicable, site emissions of CO2

and NOX are estimated using emissions intensity factors from a publication of the Environmental 

Protection Agency (EPA).2

Combustion emissions of CH4 and N2O (from power plants and from upstream 

combustion) are estimated using emissions intensity factors published by the EPA, GHG 

Emissions Factors Hub.a

 

The FFC upstream emissions are estimated based on the methodology developed by 

Coughlin (2013).3

 The upstream emissions include emissions from fuel combustion during 

extraction, processing and transportation of fuel, and “fugitive” emissions (direct leakage to the 

atmosphere) of CH4 and CO2. 

The emissions intensity factors are expressed in terms of physical units per MWh or 

MMBtu of site energy savings. Total emissions reductions are estimated using the energy 

savings calculated in the national impact analysis (chapter 10). 

For CH4 and N2O, DOE also presents results in terms of units of carbon dioxide 

equivalent (CO2e). Gases are converted to CO2e by multiplying by the gas' global warming 

potential (GWP) over a 100 year time horizon. Based on the Fourth Assessment Report of the 

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

13-1 

 
Intergovernmental Panel on Climate Change,b

 DOE used GWP values of 25 for CH4 and 298 for 

N2O.

13.2 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 in those States and D.C. 70 FR 25162 (May 12, 2005). CAIR 

was remanded to EPA by the U.S. Court of Appeals for the District of Columbia Circuit (D.C. 

Circuit) but parts of it remained in effect. On July 6, 2011 EPA issued a replacement for CAIR, 

the Cross-State Air Pollution Rule (CSAPR). 76 FR 48208 (August 8, 2011). On August 21, 

2012, the D.C. Circuit issued a decision to vacate CSAPR. See EME Homer City Generation, 

LP v. EPA, 696 F.3d 7, 38 (D.C. Cir. 2012). The court ordered EPA to continue administering 

CAIR. The AEO 2013 emissions factors used for this analysis assume that CAIR remains a 

binding regulation through 2040. 

The attainment of emissions caps is typically flexible among affected Electric Generating 

Units (EGUs) and is enforced through the use of emissions allowances and tradable permits. 

Under existing EPA regulations, any excess SO2 emissions allowances resulting from the lower 

electricity demand caused by the imposition of an efficiency standard could be used to permit 

offsetting increases in SO2 emissions by any regulated EGU. In past rulemakings, DOE 

recognized that there was uncertainty about the effects of efficiency standards on SO2 emissions 

covered by the existing cap-and-trade system, but it concluded that no reductions in power sector 

emissions would occur for SO2 as a result of standards.

Beginning in 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 

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 2013 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

b Forster, P., V. Ramaswamy, P. Artaxo, T. Berntsen, R. Betts, D.W. Fahey, J. Haywood, J. Lean, D.C. Lowe, G. 

Myhre, J. Nganga, R. Prinn,G. Raga, M. Schulz and R. Van Dorland. 2007: Changes in Atmospheric Constituents 

and in Radiative Forcing. In Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to 

the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. S. Solomon, D. Qin, M. 

Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller, Editors. 2007. Cambridge University 

Press, Cambridge, United Kingdom and New York, NY, USA. p. 212.

13-2 

 
emissions when electricity demand decreases (e.g., as a result of energy efficiency standards). 

Emissions will be far below the cap established by CAIR, so it is unlikely that excess SO2

emissions allowances resulting from the lower electricity demand would be needed or used to 

permit offsetting increases in SO2 emissions by any regulated EGU. Therefore, DOE believes 

that efficiency standards will reduce SO2 emissions in 2015 and beyond. 

CAIR established a cap on NOx emissions in 28 eastern States and the District of 

Columbia. Energy conservation standards are expected to have little effect on NOx emissions in 

those States covered by CSAPR because excess NOx emissions allowances resulting from the 

lower electricity demand could be used to permit offsetting increases in NOx emissions.

However, standards would be expected to reduce NOx emissions in the States not affected by 

CAIR, so DOE estimated NOx emissions reductions from potential standards for those States. 

 The MATS limit mercury emissions from power plants, but they do not include 

emissions caps and, as such, DOE’s energy conservation standards would likely reduce Hg 

emissions. DOE estimated mercury emissions reductions using the NEMS-BT based on AEO 

2013, which incorporates the MATS. 

13.3 POWER SECTOR EMISSIONS FACTORS 

The analysis of power sector emissions uses marginal emissions intensity factors derived 

from runs of DOE’s NEMS-BT model, using the version updated to the Annual Energy Outlook 

2013 (AEO 2013). To model the impact of a standard, DOE inputs a reduction to annual energy 

demand for the corresponding end use in the appropriate start year. The NEMS-BT model is run 

with the decremented energy demand to determine the modified build-out of capacity, fuel use 

and power sector emissions. A marginal emissions intensity factor is defined by dividing the 

reduction in the total emissions of a given pollutant by the reduction in total generation (in 

billion kWh). DOE uses the site energy savings multiplied by a T&D loss factor to estimate the 

reduction in generation for each TSL. Details on the approach used may be found in Coughlin 

(2013).3

 

Table 13.3.1 presents the power plant emissions factors for selected years. These power 

plant emissions factors are derived from the emissions factors of the plant types used to supply 

electricity for commercial refrigeration, which DOE assumes will be the same to provide 

electricity for commercial clothes washers. The average factors for each year take into account 

the projected shares of each of the sources in total electricity generation. The marginal 

calculation based on the NEMS-BT model accounts for the fact that NOx emissions are capped 

in some States. 

13-3 
Table 13.3.1 Power Plant Emissions Factors for Commercial Refrigeration End Use* 

Unit** 2016 2020 2025 2030 2035 2040

CO2 kg/MWh 598.1 598.1 558.6 524.4 497.0 309.4

SO2 g/MWh 570.4 570.4 690.6 732.5 453.6 523.6

NOx g/MWh -902.0 -902.0 -417.8 -217.9 -69.7 43.2

Hg g/MWh 0.00146 0.00146 0.00039 0.00090 0.00067 0.00061

N2O g/MWh 6.9 7.2 7.2 7.1 7.1 6.9

CH4 g/MWh 48.3 50.2 50.1 49.6 49.2 47.8

* DOE assumes these emissions factors to be the same as the emissions factors resulting from providing electricity 

for commercial clothes washers.

** Refers to MWh of site electricity savings.

13.4 UPSTREAM FACTORS 

The upstream emissions accounting uses the same approach as the upstream energy 

accounting described in appendix 10-A. See also Coughlin (2013).3

 When demand for a 

particular fuel is reduced, there is a corresponding reduction in the emissions from combustion of 

that fuel at either the building site or the power plant. The associated reduction in energy use for 

upstream activities leads to further reductions in emissions. These upstream emissions are 

defined to include the combustion emissions from the fuel used upstream, the fugitive emissions 

associated with the fuel used upstream, and the fugitive emissions associated with the fuel used 

on site. 

Fugitive emissions of CO2 occur during oil and gas production, but are small relative to 

combustion emissions. They comprise about 2.5% of total CO2 emissions for natural gas and 

1.7% for petroleum fuels. Fugitive emissions of methane occur during oil, gas and coal 

production. Combustion emissions of CH4 are very small, while fugitive emissions (particularly 

for gas production) may be relatively large. Hence, fugitive emissions make up over 99% of total 

methane emissions for natural gas, about 95% for coal, and 93% for petroleum fuels. 

 Upstream emissions factors account for both fugitive emissions and combustion 

emissions in extraction, processing, and transport of primary fuels. For ease of application in its 

analysis, DOE developed all of the emissions factors using site (point of use) energy savings in 

the denominator. Table 13.4.1 presents the electricity upstream emissions factors for selected 

years. The caps that apply to power sector NOx emissions do not apply to upstream combustion 

sources. 

Table 13.4.1 Electricity Upstream Emissions Factors

Unit* 2015 2020 2025 2030 2035 2040

CO2 kg/MWh 28.5 27.3 26.9 26.8 26.9 26.3

SO2 g/MWh 4.9 5.3 5.3 5.2 5.2 5.1

NOx g/MWh 361 340 334 333 336 329

Hg g/MWh 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001

N2O g/MWh 0.25 0.25 0.25 0.25 0.24 0.24

CH4 g/MWh 2142 2025 2008 2025 2057 1999

* Refers to MWh of site electricity savings.

13-4 
13.5 EMISSIONS IMPACT RESULTS

Table 13.5.1 presents the estimated cumulative emissions reductions for the lifetime of 

top-loading and front-loading commercial clothes washers sold in 2018-2047 for each TSL.

Table 13.5.1 Cumulative Emissions Reduction for Top-Loading and Front-Loading 

Commercial Clothes Washers Trial Standard Levels 

 

Trial Standard 

Level

1 2 3

Power Sector Emissions

CO2 (million metric tons) 4.53 5.37 8.16

SO2 (thousand tons) 3.96 4.32 8.61

NOX (thousand tons) 1.22 1.71 1.17

Hg (tons) 0.00 0.01 0.01

N2O (thousand tons) 0.07 0.07 0.14

CH4 (thousand tons) 0.40 0.44 0.83

Upstream Emissions

CO2 (million metric tons) 0.41 0.50 0.69

SO2 (thousand tons) 0.04 0.04 0.08

NOX (thousand tons) 6.04 7.39 9.96

Hg (tons) 0.00 0.00 0.00

N2O (thousand tons) 0.00 0.00 0.00

CH4 (thousand tons) 40.37 49.69 65.33

Total Emissions

CO2 (million metric tons) 4.94 5.87 8.84

SO2 (thousand tons) 3.99 4.36 8.69

NOX (thousand tons) 7.26 9.10 11.14

Hg (tons) 0.00 0.01 0.01

N2O (thousand tons) 0.07 0.08 0.15

N2O (thousand tons CO2eq) 20.37 22.57 43.25

CH4 (thousand tons) 40.77 50.14 66.16

CH4 (thousand tons CO2eq) 1.02 1.25 1.65

13-5 
Figure 13.5.1 through Figure 13.5.6 show the annual reductions for total emissions for 

each type of emission from each TSL. The reductions reflect the lifetime impacts of equipment 

sold in 2018-2047. 

Figure 13.5.1 CO2 Total Emissions Reduction 

Figure 13.5.2 SO2 Total Emissions Reduction 

0

50

100

150

200

250

300

350

400

201820232028203320382043204820532058206320682073

kt

CO2 Emissions Reduction

TSL 1 TSL 2 TSL 3

0

50

100

150

200

250

300

350

400

201820232028203320382043204820532058206320682073

ton

SO2 Emissions Reduction

TSL 1 TSL 2 TSL 3

13-6 
Figure 13.5.3 NOx Total Emissions Reduction 

Figure 13.5.4 Hg Total Emissions Reduction 

-50

0

50

100

150

200

250

300

350

400

450

500

201820232028203320382043204820532058206320682073

ton

NOx Emissions Reduction

TSL 1 TSL 2 TSL 3

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

201820232028203320382043204820532058206320682073

ton

Hg Emissions Reduction

TSL 1 TSL 2 TSL 3

13-7 
Figure 13.5.5 N2O Total Emissions Reduction

Figure 13.5.6 CH4 Total Emissions Reduction 

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

201820232028203320382043204820532058206320682073

ton

N2OEmissions Reduction

TSL 1 TSL 2 TSL 3

0

500

1000

1500

2000

2500

201820232028203320382043204820532058206320682073

ton

CH4 Emissions Reduction

TSL 1 TSL 2 TSL 3

13-8 
REFERENCES

1. Energy Information Administration, Annual Energy Outlook 2013 with Projections to 

2040, 2013. Washington, DC. <http://www.eia.gov/forecasts/aeo/pdf/0383(2013).pdf> 

2. U.S. Environmental Protection Agency, Compilation of Air Pollutant Emission Factors, 

AP-42, Fifth Edition, Volume I: Stationary Point and Area Sources. 1998. 

http://www.epa.gov/ttn/chief/ap42/index.html

3. Coughlin, K., Projections of Full-Fuel-Cycle Energy and Emissions Metrics, 2013. 

Lawrence Berkeley National Laboratory. Report No. LBNL-6025E. 

13-9 
CHAPTER 14. MONETIZATION OF EMISSION REDUCTIONS BENEFITS 

TABLE OF CONTENTS

14.1 INTRODUCTION ......................................................................................................... 14-1

14.2 MONETIZING CARBON DIOXIDE EMISSIONS ..................................................... 14-1

14.2.1 Social Cost of Carbon .................................................................................................... 14-1

14.2.2 Monetizing Carbon Dioxide Emissions ......................................................................... 14-2

14.2.3 Current Approach and Key Assumptions ...................................................................... 14-2

14.3 VALUATION OF OTHER EMISSIONS REDUCTIONS ........................................... 14-5

14.4 RESULTS ...................................................................................................................... 14-5

LIST OF TABLES

Table 14.2.1 Annual SCC Values from 2010 Interagency Report, 2010–2050 (in 2007 

dollars per metric ton of CO2) ............................................................................ 14-4

Table 14.2.2 Annual SCC Values from 2013 Interagency Update, 2010–2050 (in 2007 

dollars per metric ton of CO2) ............................................................................ 14-4

Table 14.4.1 Estimates of Present Value of CO2 Emissions Reduction for Commercial 

Clothes Washers Trial Standard Levels ............................................................. 14-6

Table 14.4.2 Estimates of Domestic Present Value of CO2 Emissions Reduction for 

Commercial Clothes Washers Trial Standard Levels ........................................ 14-7

Table 14.4.3 Estimates of Present Value of NOX Emissions Reduction for Commercial 

Clothes Washers Trial Standard Levels ............................................................. 14-7

14-i 
CHAPTER 14. MONETIZATION OF EMISSION REDUCTIONS BENEFITS 

14.1 INTRODUCTION

As part of its assessment of energy conservation standards, DOE estimated the 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 monetary values used for each of these emissions and presents the benefits estimates 

considered. 

14.2 MONETIZING CARBON DIOXIDE EMISSIONS

14.2.1 Social Cost of Carbon 

The social cost of carbon (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 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.

As part of the interagency process that developed these SCC estimates, technical experts 

from numerous agencies met on a regular basis to explore the technical literature in relevant 

fields, discuss key model inputs and assumptions, and consider public comments. 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.

14-1 
14.2.2 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 report from the National Research 

Council1

 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 (or costs from increased) 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 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. 

 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.

2

 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. 

14.2.3 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 (IAMs) 

commonly used to estimate the SCC: the FUND, DICE, and PAGE models.a These models are 

frequently cited in the peer-reviewed literature and were used in the last assessment of the 

a The models are described in appendix 14-A of the TSD.

14-2 

 
Intergovernmental Panel on Climate Change. Each model was given equal weight in the SCC 

values that were developed. 

 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: (1) climate sensitivity; 

(2) socio-economic and emissions trajectories; and (3) 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 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. 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 14.2.1 presents the values in the 2010 interagency group report,b

 which is 

reproduced in appendix 14-A of the TSD.

The SCC values used for this analysis were generated using the most recent versions of 

the three integrated assessment models that have been published in the peer-reviewed literature, 

as described in the 2013 update3 from the interagency working group (revised November 2013).c

Table 14.2.2 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 14-B 

of the TSD. 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. For the years after 2050, DOE applied the average annual growth rate of the SCC 

estimates in 2040−2050 associated with each of the four sets of values. 

b Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866. Interagency Working Group 

on Social Cost of Carbon, United States Government, February 2010. 

http://www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/Social-Cost-of-Carbon-for-RIA.pdf. c Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866. 

Interagency Working Group on Social Cost of Carbon, United States Government. May 2013; revised November 

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

14-3 

 
Table 14.2.1 Annual SCC Values from 2010 Interagency Report, 2010–2050 (in 2007 

dollars per metric ton of 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 14.2.2 Annual SCC Values from 2013 Interagency Update, 2010–2050 (in 2007 

dollars per metric ton of CO2) 

Year

Discount Rate %

5 3 2.5 3

Average Average Average 95th Percentile

2010 11 32 51 89

2015 11 37 57 109

2020 12 43 64 128

2025 14 47 69 143

2030 16 52 75 159

2035 19 56 80 175

2040 21 61 86 191

2045 24 66 92 206

2050 26 71 97 220

 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 

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. 

14-4 
 In summary, in considering the potential global benefits resulting from reduced CO2

emissions, DOE used the values from the 2013 interagency report, escalated to 2012$ using the 

GDP price deflator. For each of the four cases specified, the values used for emissions in 2015 

are $11.8, $39.7, $61.2, and $117 per metric ton avoided. 

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, the interagency report notes that damages from future emissions should be discounted at 

the same rate as that used to calculate the SCC estimates themselves to ensure internal 

consistency. Thus, DOE discounted the values in each of the four cases using the specific 

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

14.3 VALUATION OF OTHER EMISSIONS REDUCTIONS

DOE considered the potential monetary benefit of reduced NOX emissions from the TSLs 

it considered. As noted in chapter 13, new or amended energy conservation standards would 

reduce NOX emissions in those 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. Estimates of 

monetary value for reducing NOx from stationary sources range from $468 to $4,809 per ton in 

2012$.4

 DOE calculated monetary benefits using a medium value for NOX emissions of $2,639 

per short ton (in 2012$), and real discount rates of 3 percent and 7 percent. 

DOE is still evaluating appropriate values to use to monetize avoided SO2 and Hg 

emissions. It did not monetize these emissions for this analysis. 

14.4 RESULTS

Table 14.4.1 presents the global values of CO2 emissions reductions for each considered 

TSL. DOE calculated domestic values as a range from 7 percent to 23 percent of the global 

values, and these results are presented in Table 14.4.2. 

Table 14.4.3 presents the present value of cumulative NOX emissions reductions for each 

TSL, calculated using the average dollar-per-ton values and seven-percent and three-percent 

discount rates. 

14-5 
Table 14.4.1 Estimates of Present Value of CO2 Emissions Reduction for Commercial 

Clothes Washers Trial Standard Levels

TSL

SCC Case*

5% discount 

rate, average*

3% discount

rate, average*

2.5% discount 

rate, average*

3% discount 

rate, 95th

percentile*

Million 2012$ 

Power Sector Energy Emissions

1 30.1 139 222 431 

2 35.5 165 262 509 

3 54.4 251 400 777 

Upstream Emissions

1 2.65 12.4 19.9 38.5 

2 3.22 15.1 24.2 46.8 

3 4.43 20.8 33.2 64.4 

Total Emissions

1 32.7 152 242 469

2 38.7 180 287 556

3 58.8 272 434 841 

* For each of the four cases, the corresponding global SCC value for emissions in 2015 is $11.8, 

$39.7, $61.2, and $117 per metric ton (2012$).

14-6 
Table 14.4.2 Estimates of Domestic Present Value of CO2 Emissions Reduction for 

Commercial Clothes Washers Trial Standard Levels

TSL

SCC Case*

5% discount 

rate, average*

3% discount 

rate, average*

2.5% discount 

rate, average*

3% discount 

rate, 95th

percentile*

Million 2012$ 

Power Sector Energy Emissions

1 2.1 to 6.9 9.8 to 32.1 15.5 to 51.1 30.1 to 99.0

2 2.5 to 8.2 11.5 to 37.9 18.4 to 60.4 35.6 to 117.1

3 3.8 to 12.5 17.6 to 57.8 28.0 to 92.1 54.4 to 178.7 

Upstream Emissions

1 0.2 to 0.6 0.9 to 2.9 1.4 to 4.6 2.7 to 8.9 

2 0.2 to 0.7 1.1 to 3.5 1.7 to 5.6 3.3 to 10.8 

3 0.3 to 1.0 1.5 to 4.8 2.3 to 7.6 4.5 to 14.8 

Total Emissions

1 2.3 to 7.5 10.6 to 34.9 16.9 to 55.6 32.8 to 107.9

2 2.7 to 8.9 12.6 to 41.4 20.1 to 65.9 38.9 to 127.8

3 4.1 to 13.5 19.1 to 62.6 30.3 to 99.7 58.9 to 193.5

* For each of the four cases, the corresponding SCC value for emissions in 2015 is $11.8, $39.7, 

$61.2, and $117 per metric ton (2012$).

Table 14.4.3 Estimates of Present Value of NOX Emissions Reduction for Commercial 

Clothes Washers Trial Standard Levels

TSL 3% discount rate 7% discount rate

Million 2012$ 

Power Sector Emissions

1 1.18 0.257 

2 1.77 0.496 

3 0.63 -0.304 

Upstream Emissions

1 7.93 3.60 

2 9.66 4.36 

3 13.1 5.93 

Total Emissions

1 9.10 3.85

2 11.4 4.86 

3 13.7 5.63

14-7 
REFERENCES

1. National Research Council, Hidden Costs of Energy: Unpriced Consequences of Energy 

Production and Use, 2009. National Academies Press. Washington, DC. 

<http://www.nap.edu/catalog.php?record_id=12794> 

2. Interagency Working Group on Social Cost of Carbon, Social Cost of Carbon for 

Regulatory Impact Analysis under Executive Order 12866, February, 2010. United States 

Government. <http://www.whitehouse.gov/sites/default/files/omb/inforeg/foragencies/Social-Cost-of-Carbon-for-RIA.pdf> 

3. Interagency Working Group on Social Cost of Carbon, Technical Update of the Social 

Cost of Carbon for Regulatory Impact Analysis under Executive Order 12866, May 2013. 

United States Government. 

http://www.whitehouse.gov/sites/default/files/omb/inforeg/social_cost_of_carbon_for_ria

_2013_update.pdf

4. U.S. Office of Management and Budget (OMB), 2006 Report to Congress on the Costs 

and Benefits of Federal Regulations and Unfunded Mandates on State, Local, and Tribal 

Entities, 2006. Washington, DC. 

<http://www.whitehouse.gov/sites/default/files/omb/assets/omb/inforeg/2006_cb/2006_c

b_final_report.pdf> 

14-8 
CHAPTER 15. UTILITY IMPACT ANALYSIS

TABLE OF CONTENTS

15.1 INTRODUCTION ......................................................................................................... 15-1

15.2 METHODOLOGY ........................................................................................................ 15-1

15.3 UTILITY IMPACT RESULTS ..................................................................................... 15-3

15.3.1 Installed Capacity........................................................................................................... 15-3

15.3.2 Electricity Generation .................................................................................................... 15-6

15.3.3 Results Summary ......................................................................................................... 15-10

LIST OF TABLES

Table 15.3.1 Summary of Utility Impact Results ................................................................. 15-10

LIST OF FIGURES

Figure 15.3.1 Total Electric Capacity Reduction ..................................................................... 15-3

Figure 15.3.2 Coal Capacity Reduction ................................................................................... 15-4

Figure 15.3.3 Nuclear Capacity Reduction .............................................................................. 15-4

Figure 15.3.4 Gas Combined Cycle Capacity Reduction ......................................................... 15-5

Figure 15.3.5 Peaking Capacity Reduction .............................................................................. 15-5

Figure 15.3.6 Renewables Capacity Reduction........................................................................ 15-6

Figure 15.3.7 Total Generation Reduction ............................................................................... 15-7

Figure 15.3.8 Coal Generation Reduction ................................................................................ 15-7

Figure 15.3.9 Nuclear Generation Reduction ........................................................................... 15-8

Figure 15.3.10 Gas Combined Cycle Generation Reduction ..................................................... 15-8

Figure 15.3.11 Peaking Generation Reduction .......................................................................... 15-9

Figure 15.3.12Renewables Generation Reduction .................................................................... 15-9

15-i 
CHAPTER 15. UTILITY IMPACT ANALYSIS

15.1 INTRODUCTION

In the utility impact analysis, the U.S. Department of Energy (DOE) analyzes the changes 

in electric installed capacity and power generation that result for each trial standard level (TSL).

The utility impact analysis uses 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). DOE uses a variant 

of this model, referred to as NEMS-BT,b to account for selected utility impacts from energy 

conservation standards. DOE’s analysis consists of a comparison between model results for the 

most recent AEO Reference Case and for cases in which energy use is decremented to reflect the 

impacts of standards. For the analysis of standards for top-loading and front-loading commercial 

clothes washers DOE used the version of NEMS based on AEO 2013.

2

 

NEMS-BT has a number of advantages that have led to its use in the analysis of energy 

conservation standards: 

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

due to the exposure and scrutiny each AEO receives. 

• NEMS-BT is updated each year, with each edition of the AEO, to reflect changes 

in energy prices, supply trends, regulations, etc. 

• The comprehensiveness of NEMS-BT permits the modeling of interactions 

among the various energy supply and demand sectors. 

15.2 METHODOLOGY 

DOE uses NEMS-BT to estimate the marginal impacts of reduction in energy demand on 

the energy supply sector. In principle, marginal values should provide a better estimate of the 

actual impact of energy conservation standards. In practice, the numerical differences between 

marginal and average values may turn out to be smaller than the intrinsic uncertainties in the 

AEO.

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

documentation. A useful summary is National Energy Modeling System: An Overview.

1

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). 

15-1 

 
NEMS uses predicted growth in demand for each end use to build up a projection of the 

total electric system load growth. The system load shapes are converted internally to load 

duration curves, which are then used to estimate the most cost-effective additions to capacity. 

When electricity demand deviates from the AEO reference case, in general there are three interrelated effects: the annual generation (terawatt hours, TWh) from the stock of electric generating 

capacity changes, the total generation capacity itself (gigawatts, GW) may change, and the mix 

of capacity by fuel type may change. Each of these effects can vary for different types of end 

use. The change in total generating capacity is sensitive to the degree to which the end-use is 

peak coincident, while the capacity mix is sensitive to the hourly load shape associated with the 

end use. 

To model the impact of a standard, DOE inputs a reduction to annual energy demand for 

the corresponding end use in the appropriate start year. The NEMS-BT model is run with the 

decremented energy demand to determine the modified build-out of capacity and total 

generation. Regional effects of a standard can be accounted for by defining the energy demand 

decrement as a function of census division. 

The output of the NEMS-BT analysis includes the effective marginal heat rate (ratio of 

the change in energy consumption in quads to the change in generation in TWh), and the 

capacity reduction by plant type for a given reduction in total generation. DOE uses the site 

energy savings multiplied by a transmission-and-distribution (T&D) loss factor to estimate the 

reduction in generation for each TSL. The relationship between a reductionc in electricity 

generation (TWh) and the reduction in capacity (GW) is estimated based on the output of 

NEMS-BT model runs using the end-use specific energy demand decrement. Details on the 

approach used may be found in Coughlin (2013).3

NEMS-BT provides output for the following capacity types: coal, nuclear, 

combined cycle (natural gas), renewable sources, oil and natural gas steam, 

combustion turbine/diesel, pumped storage, fuel cells, and distributed generation (natural gas). 

DOE grouped oil and natural gas steam and combustion turbine/diesel into a “peaking” category, 

and grouped pumped storage, fuel cells, and distributed generation (natural gas) into an “other” 

category.

In general, energy conservation standards impact primarily fossil combustion (coal, 

natural gas and diesel) and renewables. Pumped storage and nuclear power are very insensitive 

to small changes in demand, while fuel cells and distributed generation make up a very small 

fraction (less than 1 percent) of the generation capacity base.

For the analysis of top-loading and front-loading commercial clothes washers, DOE 

derived factors based on the plant types used to supply electricity for commercial refrigeration 

equipment. DOE assumes that commercial clothes washers operate during the same period of the 

day as do commercial refrigeration equipment. 

c These reductions are defined relative to the AEO Reference case.

15-2 

 
15.3 UTILITY IMPACT RESULTS

 This section presents results of the analysis for all of the capacity types except “Other”, 

for which the impacts are very small.

15.3.1 Installed Capacity 

The figures in this section show the changes in U.S. electricity installed capacity that 

result for each TSL by major plant type for selected years. The changes have been calculated 

based on factors (megawatts (MW) of capacity reduction per gigawatt hours (GWh) of 

generation reduction) estimated from a NEMS-BT model run that simulated a decrement in 

energy demand for the load shape corresponding to commercial refrigeration. DOE assumes 

commercial clothes washers operate during the same period of the day as do commercial 

refrigeration equipment. Note that a positive change means a reduction in capacity under a TSL. 

 

Figure 15.3.1 Total Electric Capacity Reduction

0

20

40

60

80

100

120

201820232028203320382043204820532058206320682073

MW

Total

TSL 1 TSL 2 TSL 3

15-3 
Figure 15.3.2 Coal Capacity Reduction 

Figure 15.3.3 Nuclear Capacity Reduction

0

2

4

6

8

10

12

14

201820232028203320382043

2048

20532058206320682073

MW

Coal

TSL 1 TSL 2 TSL 3

0.0

0.5

1.0

1.5

2.0

2.5

201820232028203320382043204820532058206320682073

MW

Nuclear

TSL 1 TSL 2 TSL 3

15-4 
Figure 15.3.4 Gas Combined Cycle Capacity Reduction

Figure 15.3.5 Peaking Capacity Reduction 

0

5

10

15

20

25

201820232028203320382043204820532058206320682073

MW

NGCC

TSL 1 TSL 2 TSL 3

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

201820232028203320382043204820532058206320682073

MW

Peaking

TSL 1 TSL 2 TSL 3

15-5 
Figure 15.3.6 Renewables Capacity Reduction

15.3.2 Electricity Generation

The figures in this section show the annual change in electricity generation that result for 

each TSL by plant type. The change by capacity type has been calculated based on shares 

(percentage of generation reduction for each capacity type over total generation reduction) 

estimated from a NEMS-BT model run that simulated a decrement in energy demand for the load 

shape corresponding to commercial refrigeration. DOE assumes commercial clothes washers 

operate during the same period of the day as do commercial refrigeration equipment. Note that a 

positive change means a reduction in generation under a TSL. 

0

10

20

30

40

50

60

70

80

90

201820232028203320382043

2048

20532058206320682073

MW

Renewable

TSL 1 TSL 2 TSL 3

15-6 
Figure 15.3.7 Total Generation Reduction

Figure 15.3.8 Coal Generation Reduction 

0

100

200

300

400

500

600

201820232028203320382043204820532058206320682073

GWh

Total

TSL 1 TSL 2 TSL 3

0

10

20

30

40

50

60

70

80

90

201820232028203320382043204820532058206320682073

GWh

Coal

TSL 1 TSL 2 TSL 3

15-7 
Figure 15.3.9 Nuclear Generation Reduction

Figure 15.3.10 Gas Combined Cycle Generation Reduction

0

2

4

6

8

10

12

14

16

18

20

201820232028203320382043204820532058206320682073

GWh

Nuclear

TSL 1 TSL 2 TSL 3

0

50

100

150

200

250

300

350

201820232028203320382043204820532058206320682073

GWh

NGCC

TSL 1 TSL 2 TSL 3

15-8 
Figure 15.3.11 Peaking Generation Reduction

Figure 15.3.12 Renewables Generation Reduction

-10

-5

0

5

10

15

20

20182023202820332038

2043

204820532058206320682073

GWh

Peaking

TSL 1 TSL 2 TSL 3

0

50

100

150

200

250

201820232028203320382043204820532058206320682073

GWh

Renewable

TSL 1 TSL 2 TSL 3

15-9 
15.3.3 Results Summary 

Table 15.3.1 presents a summary of the utility impact results estimated for top-loading 

and front-loading commercial clothes washers. 

Table 15.3.1 Summary of Utility Impact Results

TSL

1 2 3

Installed Capacity Reduction (MW)

2020 2.25 2.43 4.84

2025 8.79 9.51 18.9

2030 19.6 21.3 42.4

2035 30.4 33.1 66.0

2040 50.7 55.5 111

Electricity Generation Reduction (GWh)

2020 61.7 66.6 133

2025 148 161 320

2030 198 215 429

2035 213 232 463

2040 217 237 473

15-10
REFERENCES

1. U.S. Department of Energy-Energy Information Administration, National Energy 

Modeling System: An Overview, 2009. Report No. DOE/EIA-0581(2009). 

<http://www.eia.gov/oiaf/aeo/overview/> 

2. Energy Information Administration, Annual Energy Outlook 2013 with Projections to 

2040, 2013. Washington, DC. <http://www.eia.gov/forecasts/aeo/pdf/0383(2013).pdf> 

3. Coughlin, K., H. Shen, P. Chan, B. McDevitt, and A. Sturges, Marginal Utility Sector 

Impacts of Reduced Electricity Demand, 2012. 

15-11
CHAPTER 16. EMPLOYMENT IMPACT ANALYSIS

TABLE OF CONTENTS

16.1 INTRODUCTION ......................................................................................................... 16-1

16.2 ASSUMPTIONS ............................................................................................................ 16-1

16.3 METHODOLOGY ........................................................................................................ 16-1

16.4 SHORT-TERM RESULTS ............................................................................................ 16-2

16.5 LONG-TERM RESULTS .............................................................................................. 16-3

LIST OF TABLES

Table 16.4.1 Commercial clothes washer Net National Short-term Change in 

Employment (Number of Jobs) .......................................................................... 16-3

16-i 
CHAPTER 16. EMPLOYMENT IMPACT ANALYSIS

16.1 INTRODUCTION

DOE’s employment impact analysis is designed to estimate indirect national job creation 

or elimination resulting from possible standards, due to reallocation of the associated 

expenditures for purchasing and operating commercial clothes washers. Job increases or 

decreases reported in this chapter are separate from the direct commercial clothes washer

production sector employment impacts reported in the manufacturer impact analysis (Chapter 

12), and reflect the employment impact of efficiency standards on all other sectors of the 

economy. 

16.2 ASSUMPTIONS

 DOE expects energy conservation standards to decrease energy consumption, and 

therefore to reduce energy expenditures. The savings in energy expenditures may be spent on 

new investment or not at all (i.e., they may remain “saved”). The standards may increase the 

purchase price of products, including the retail price plus sales tax, and increase installation 

costs. 

The ImSET input/output econometric model of the U.S. economy, this analysis estimated 

the short-term effect of these expenditure impacts on net economic output and employment. 

DOE intends this analysis to quantify the indirect employment impacts of these expenditure 

changes. It evaluated direct employment impacts at manufacturers’ facilities in the manufacturer 

impact analysis (see Chapter 12). 

 DOE notes that ImSET is not a general equilibrium forecasting model, and understands 

the uncertainties involved in projecting employment impacts, especially changes in the later 

years of the analysis.1 Because ImSET does not incorporate price changes, the employment 

effects predicted by ImSET would over-estimate the magnitude of actual job impacts over the 

long run for this rule. Since input/output models do not allow prices to bring markets into 

equilibrium, they are best used for short-run analysis. DOE therefore includes a qualitative 

discussion of how labor markets are likely to respond in the longer term. In future rulemakings, 

DOE may consider the use of other modeling approaches for examining long run employment 

impacts. 

16.3 METHODOLOGY

 The Department based its analysis on an input/output model of the U.S. economy that 

estimates the effects of standards on major sectors of the economy related to buildings and the 

net impact of standards on jobs. The Pacific Northwest National Laboratory developed the 

model, ImSET 3.1.12

 (Impact of Sector Energy Technologies) as a successor to ImBuild3, a 

special-purpose version of the IMPLAN4

 national input/output model. ImSET estimates the 

employment and income effects of building energy technologies. In comparison with simple 

16-1 
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 products. The increased cost of products leads to higher employment in 

the product manufacturing sectors and lower employment in other economic sectors. Second, 

commercial firm and residential spending are redirected from utilities toward firms that supply 

production inputs. Third, 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 commercial clothes washer manufacturing 

sector estimated in Chapter 12 using the Government Regulatory Impact Model (GRIM). The 

methodologies used and the sectors analyzed in the ImSET and GRIM models are different. 

16.4 SHORT-TERM RESULTS

The results in this section refer to impacts of commercial clothes washer standards 

relative to the base case. DOE disaggregated the impact of standards on employment into three 

component effects: increased capital investment costs, decreased energy costs, and changes in 

operations and maintenance costs. DOE presents the summary impact. 

16-2 
 Conceptually, one can consider the impact of the rule in its first year on three aggregate 

sectors, the commercial clothes washer 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 level). By raising energy efficiency, the rule generally increases the purchase 

price of commercial clothes washers; 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, freeing up this money to be spent in other sectors. The 

reduction in electricity demand causes a reduction in employment in that sector. Finally, based 

on the net impact of increased expenditures on commercial clothes washers 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 (e.g. as more workers are hired they consume more goods, which generates more 

employment; the converse is true for workers laid off). 

Table 16.4.1 present the modeled net employment impact from the rule in 2015, rounded 

to the nearest hundred jobs. As most commercial clothes washers are produced domestically, 

DOE does not model the impact of import spending for this product. 

Table 16.4.1 Commercial Clothes Washer Net National Short-term Change in 

Employment (Number of Jobs) 

Trial 

Standard 

Level

2015 2020 

TSL 1 10 80

TSL 2 20 80

TSL 3 -20 110

 For context, the Office of Management of Budget currently assumes that the 

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.

16.5 LONG-TERM RESULTS

Over the long term DOE expects the energy savings to consumers to increasingly 

dominate the increase in product costs, resulting in increased aggregate savings to consumers. As 

a result, DOE expects demand for electricity to decline over time and demand for other goods to 

increase. Since the electricity generation sector is relatively capital intensive compared to the 

consumer goods sector, the net effect will be an increase in labor demand. In equilibrium, this 

16-3 
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 in general be negligible over time due to the small magnitude of the short-term effects 

presented in Table 16.4.1. The ImSET model projections, assuming no price or wage effects 

until 2020, are included in the second column of Table 16.4.1. 

 

16-4 
REFERENCES

1. Scott, M., JM Roop, RW Schultz, DM Anderson, KA Cort, The Impact of DOE Building 

Technology Energy Efficiency Programs on U.S. Employment, Income, and Investment.

Energy Economics, 2008. 30(5): pp. 2283-2301 

2. Roop, J. M., M. J. Scott, and R. W. Schultz, ImSET: Impact of Sector Energy 

Technologies, 2005. Pacific Northwest National Laboratory. Richland, WA. Report No. 

PNNL- 15273. 

3. Scott, M. J., D. J. Hostick, and D. B. Belzer, ImBuild: Impact of Building Energy 

Efficiency Programs, April, 1998. Pacific Northwest National Laboratory. Richland, WA. 

Report No. PNNL-11884. Prepared for the U.S. Department of Energy under Contract 

DE-AC06-76RLO 1830. 

4. Minnesota IMPLAN Group Inc., IMPLAN Professional: User's Guide, Analysis Guide, 

Data Guide, 1997. Stillwater, MN. 

5. Office of Management and Budget, Fiscal Year 2012 Mid-Session Review: Budget of the 

U.S. Government, Washington, DC. 

<http://www.whitehouse.gov/sites/default/files/omb/budget/fy2012/assets/12msr.pdf> 

16-5 
17-i

CHAPTER 17. REGULATORY IMPACT ANALYSIS 

TABLE OF CONTENTS

17.1 INTRODUCTION ................................................................................................... 17-1

17.2 NON-REGULATORY POLICIES .......................................................................... 17-1

17.2.1 Methodology ............................................................................................................ 17-2

17.2.2 Assumptions Regarding Non-Regulatory Policies .................................................. 17-3

17.2.3 Policy Interactions ................................................................................................... 17-4

17.3 NON-REGULATORY POLICY ASSUMPTIONS ................................................ 17-4

17.3.1 No New Regulatory Action ..................................................................................... 17-4

17.3.2 Consumer Rebates ................................................................................................... 17-4

17.3.2.1 Methodology .......................................................................................... 17-4

17.3.2.2 Analysis.................................................................................................. 17-6

17.3.3 Consumer Tax Credits ............................................................................................. 17-8

17.3.4 Manufacturer Tax Credits ...................................................................................... 17-12

17.3.5 Voluntary Energy Efficiency Targets .................................................................... 17-13

17.3.6 Early Replacement ................................................................................................. 17-14

17.3.7 Bulk Government Purchases .................................................................................. 17-18

17.4 IMPACTS OF NON-REGULATORY ALTERNATIVES ................................... 17-20

LIST OF TABLES

Table 17.1.1 Non-Regulatory Alternatives to National Standards ...................................... 17-1

Table 17.2.1 Efficiency Levels for Trial Standard Levels for Commercial Clothes 

Washers Equipment Classes ........................................................................... 17-3

Table 17.3.1 Benefit/Cost Ratios Without and With Rebates for Commercial Clothes 

Washer ............................................................................................................ 17-7

Table 17.3.2 Market Penetrations in 2018 Attributable to Consumer Rebates for 

Commercial Clothes Washers ......................................................................... 17-8

Table 17.3.3 Oregon Tax Credits Claimed for Residential Clothes Washers from 2000 

to 2011 ............................................................................................................ 17-9

Table 17.3.4 Percentages of Increase in Total Shipments of Commercial Clothes 

Washers Attributed to Tax Credits, Rebates and other Market Effects ........ 17-11

Table 17.3.5 Market Penetrations in 2018 Attributable to Consumer Tax Credits for 

Commercial Clothes Washers ....................................................................... 17-11

Table 17.3.6 Market Penetrations in 2018 Attributable to Manufacturer Tax Credits 

for Commercial Clothes Washers ................................................................. 17-13

Table 17.3.7 Market Penetrations in 2018 Attributable to Voluntary Energy 

Efficiency Targets for Commercial Clothes Washers .................................. 17-14

Table 17.3.8 Market Penetrations in 2018 Attributable to Early Replacements for 

Commercial Clothes Washers ....................................................................... 17-18
17-ii

Table 17.3.9 Market Penetrations in 2018 Attributable to Bulk Government 

Purchasing of Commercial Clothes Washers................................................ 17-20

Table 17.4.1 Impacts of Non-Regulatory Alternatives for Front-Loading Commercial 

Clothes Washers (TSL 2) .............................................................................. 17-22

Table 17.4.2 Impacts of Non-Regulatory Alternatives for Top-Loading Commercial 

Clothes Washers (TSL 2) .............................................................................. 17-23

LIST OF FIGURES

Figure 17.3.1 Market Penetration Curves for Commercial Clothes Washers ....................... 17-8

Figure 17.3.2 Base Case and Policy Case Shipments Projects for the Early 

Replacement Policy Case (thousand units) ................................................... 17-17

Figure 17.4.1 Market Penetration of Front-Loading Commercial Clothes Washers 

Meeting the Target Level in Policy Cases (TSL 2) ...................................... 17-21

Figure 17.4.2 Market Penetration of Top-Loading Commercial Clothes Washers 

Meeting the Target Level in Policy Cases (TSL 2) ...................................... 17-21
17-1

CHAPTER 17. REGULATORY IMPACT ANALYSIS

17.1 INTRODUCTION

 The U.S. Department of Energy (DOE) has determined that energy conservation 

standards for consumers of commercial clothes washers constitute an “economically significant 

regulatory action” under Executive Order (E.O.) 12866, Regulatory Planning and Review. 58 FR 

51735 (October 4, 1993). This RIA, which DOE has prepared pursuant to E.O. 12866, evaluates 

potential non-regulatory alternatives, comparing the costs and benefits of each to those of the 

proposed standards. 58 FR 51735, 51741. 

 For this analysis, DOE used an integrated National Impact Analysis (NIA)-RIA model 

built on the NIA model discussed in Chapter 11 for its analysis. 

 

DOE identified seven non-regulatory policy alternatives that possibly could provide 

incentives for the same energy efficiency levels as the ones in the trial standard levels (TSL) for 

the equipment that are the subject of this rulemaking. The non-regulatory policy alternatives 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 to the 

effectiveness of the proposed standard. 

Table 17.1.1 Non-Regulatory Alternatives to National Standards 

No New Regulatory Action

Consumer Rebates

Consumer Tax Credits

Manufacturer Tax Credits

Voluntary Energy Efficiency Targets

Early Replacement

Bulk Government Purchases

 

Sections 17.2 and 17.3 discuss the analysis of six selected policies listed above

(excluding the alternative of no new regulatory action). Section 17.4 presents the results of the 

policy alternatives. 

17.2 NON-REGULATORY POLICIES

 This section describes the method DOE used to analyze the energy savings and cost 

effectiveness of the non-regulatory policy alternatives for commercial clothes washers. This 

section also describes the assumptions underlying the analysis. 
17-2

17.2.1 Methodology 

DOE used its integrated NIA-RIA spreadsheet model to calculate the national energy 

savings (NES) and net present value (NPV) associated with each non-regulatory policy 

alternative. Chapter 11 of the technical support document (TSD) describes the NIA spreadsheet 

model. Appendix 17-A, discusses the NIA-RIA integrated model approach. 

DOE quantified the effect of each alternative on the purchase of equipment that meet the 

efficiency levels corresponding to the levels set for each TSL. After establishing the quantitative 

assumptions underlying each alternative, DOE appropriately revised inputs to the NIA-RIA

spreadsheet model. The primary model inputs revised were market shares of equipment meeting 

the target efficiency levels set for each TSL. The shipments of equipment for any given year 

reflect a distribution of efficiency levels. DOE assumed, for each TSL, that new energy 

efficiency standards would affect 100 percent of the shipments of equipment that did not meet 

the TSL target level in the base case,a whereas the non-regulatory policies would affect a smaller 

percentage of those shipments. DOE made certain assumptions about the percentage of 

shipments affected by each alternative policy. DOE used those percentages to calculate the 

shipment-weighted average energy consumption and costs of commercial clothes washers

attributable to each policy alternative. 

Increasing the efficiency of equipment often increases its average installed cost. 

However, operating costs generally decrease because energy consumption declines. DOE 

therefore calculated an NPV for each non-regulatory alternative in the same way it did for the 

proposed standards. In some policy scenarios, increases in total installed cost are mitigated by 

government rebates or tax credits. Because DOE assumed that consumers would re-pay credits 

and rebates in some way (such as additional taxes), DOE did not include rebates or tax credits as 

a consumer benefit when calculating national NPV. DOE’s analysis also excluded any 

administrative costs for the non-regulatory policies; including such costs would decrease the 

NPVs slightly.

 

The following are key measures for evaluating the impact of each alternative. 

National Energy Savings (NES), given in quadrillion Btus (quads), describes the 

cumulative national primary energy saved over the lifetime of equipment purchased 

during the 30-year analysis period starting in the effective date of the policy (2018-

2047). 

Net Present Value (NPV), represents the value of net monetary savings in 2013, 

expressed in 2012$, from equipment purchased during the 30-year analysis period 

starting in the effective date of the policy (2018-2047). DOE calculated the NPV as 

the difference between the present value of installed equipment cost and operating 

 

a The base case for the NIA is a market-weighted average energy efficiency calculated from units at several 

efficiency levels.
17-3

expenditures in the base case and the present value of those costs in each policy case. 

DOE calculated operating expenses (including energy costs) for the life of equipment. 

17.2.2 Assumptions Regarding Non-Regulatory Policies

The effects of non-regulatory policies are by nature uncertain because they depend on 

program implementation, marketing efforts, and on consumers’ responses to a program. Because 

the projected effects depend on assumptions regarding the rate of consumer participation, they 

are subject to more uncertainty than are the impacts of mandatory standards, which DOE 

assumes will meet with full compliance. To increase the robustness of the analysis, DOE 

conducted a literature review regarding each non-regulatory policy and consulted with 

recognized experts to gather information on similar incentive programs that have been 

implemented in the United States. By studying experiences with the various types of programs,

DOE sought to make credible assumptions regarding potential market impacts. Section 17.3

presents the sources DOE relied on in developing assumptions about each alternative policy and 

reports DOE’s conclusions as they affected the assumptions that underlie the modeling of each 

alternative policy. 

 Each non-regulatory policy that DOE considered would improve the average efficiency 

of new commercial clothes washers relative to their base case efficiency scenario (which 

involves no new regulatory action). The analysis considered that each alternative policy would 

induce consumers to purchase units having the same efficiency level as required by the proposed 

standards (the target level) set for each TSL. As opposed to the standards case, however, the 

policy cases may not lead to 100 percent market penetration of units that meet the target level. 

Table 17.2.1 shows the efficiency level stipulated in the proposed standards for 

commercial clothes washers. 

Table 17.2.1 Efficiency Levels for Trial Standard Levels for Commercial Clothes Washers

Equipment Classes 

Equipment Type

Efficiency Levels (MEFJ2/IWF)

Baseline

FL: EL0

TL: EL0

TSL1 TSL2 TSL3

FL: EL1

TL:EL1

FL: EL2

TL:EL1

FL: EL3

TL:EL2

Front Loading CCW Units 1.65/5.5 1.80/4.5 2.00/4.0 2.20/3.7 

Top Loading CCW Units 1.15/8.9 1.35/8.8 1.35/8.8 1.55/6.9 

 DOE assumed that the effects of non-regulatory policies would last from the effective 

date of standards—2018—through the end of the analysis period, which is 2047. 
17-4

17.2.3 Policy Interactions 

DOE calculated the effects of each non-regulatory policy separately from those of the 

other policies. In practice, some policies are most effective when implemented in combination, 

such as early replacement implemented with consumer rebates, or early replacement 

implemented with bulk government purchases. However, DOE attempted to make conservative 

assumptions to avoid double-counting policy impacts. The resulting policy impacts are not 

additive; the combined effect of several or all policies cannot be inferred from summing their 

results. 

Section 17.4 presents graphs that show the market penetration estimated under each nonregulatory policy for the commercial clothes washer equipment classes. 

17.3 NON-REGULATORY POLICY ASSUMPTIONS

 The following subsections describe DOE’s analysis of the impacts of the six nonregulatory policy alternatives to proposed standards for commercial clothes washers. (Because 

the alternative of No New Regulatory Action has no energy or economic impacts, essentially 

representing the NIA base case, DOE did not perform any additional analysis for that 

alternative.) DOE developed estimates of the market penetration of high-efficiency equipment 

both with and without each of the non-regulatory policy alternatives. 

17.3.1 No New Regulatory Action 

 The case in which no new regulatory action is taken with regard to the energy efficiency 

of commercial clothes washers constitutes the base case, as described in Chapter 11, National 

Impact Analysis. The base case provides the basis of comparison for all other policies. By 

definition, no new regulatory action yields zero NES and an NPV of zero dollars. 

17.3.2 Consumer Rebates

DOE considered the scenario in which the Federal government would provide financial 

incentives in the form of rebates to consumers for purchasing energy-efficient appliances. This 

policy provides a consumer rebate for purchasing commercial clothes washers that operate at (or 

above) the same efficiency as stipulated in each TSL (target level).

17.3.2.1 Methodology 

DOE based its evaluation methodology for consumer rebates on a comprehensive study 

of California’s potential for achieving energy efficiency. The study, performed by XENERGY, 
17-5

Inc.,b summarized experiences with various utility rebate programs.1 XENERGY’s analytical 

method utilized graphs, or penetration curves, that estimate the market penetration of a 

technology based on its benefit/cost (B/C) ratio. DOE consulted with experts and reviewed other 

methods of estimating the effect of consumer rebate programs on the market penetration of 

efficient technologies. The other methods, developed after the referenced XENERGY report was 

published,2, 3, 4, 5, 6, 7 used different approaches: other economic parameters (e.g., payback 

period), expert surveys, or model calibration based on specific utility program data rather than 

multi-utility data. Some models in use by energy efficiency program evaluation experts were so 

client-specific that generic relationships between economic parameters and consumer response 

could not be established.5

DOE decided that the most appropriate available method for this RIA 

analysis was the XENERGY approach of penetration curves based on B/C ratio, which 

incorporates lifetime operating cost savings. 

XENERGY’s model estimates market impacts induced by financial incentives based on 

the premise that two types of information diffusion drive the adoption of new technologies. 

Internal sources of information encourage consumers to purchase new equipment primarily 

through word-of-mouth from early adopters. External sources affect consumer purchase 

decisions through marketing efforts and information from outside the consumer group. Appendix 

17-A contains additional details on internal and external information diffusion. 

XENERGY’s model equation accounts for the influences of both internal and external 

sources of information by superimposing the two components. Combining the two mechanisms 

for information diffusion, XENERGY’s model generates a set of penetration (or implementation) 

curves for a policy measure. XENERGY calibrated the curves based on participation data from 

utility rebate programs. The curves illustrate the increased penetration (i.e., increased market 

share) of efficient equipment driven by consumer response to changes in B/C ratio induced by 

rebate programs. The penetration curves depict various diffusion patterns based on perceived 

market barriers (from no-barriers to extremely-high-barriers) to consumer purchase of highefficiency equipment. DOE adjusted the XENERGY former penetration curves based on expert 

advice founded on more recent utility program experience.5, 8

 

DOE modeled the effects of a consumer rebate policy for commercial clothes washers by 

determining, for each TSL, the increase in market penetration of equipment meeting the target 

level relative to their market penetration in the base case. It used the interpolation method 

presented in Blum et al (2011)9 to create customized penetration curves based on relationships 

between actual base case market penetrations and actual B/C ratios. To inform its estimate of 

B/C ratios provided by a rebate program DOE performed a thorough nationwide search for 

existing rebate programs for commercial clothes washers. It gathered data on utility or agency 

rebates throughout the nation for this equipment, and used this data to calibrate the customized 

penetration curves it developed for front-loading and top-loading commercial clothes washers so 

 

b XENERGY is now owned by KEMA, Inc. (www.kema.com) 
17-6

they can best reflect the market barrier level faced by each equipment class. Section 17.3.2.2 

shows the interpolated curves used in the analysis. 

17.3.2.2 Analysis 

 For the two commercial clothes washer equipment classes it analyzed, DOE estimated the 

effect of increasing its B/C ratio via a rebate that would pay all of the increased installed cost of 

a unit that met the target efficiency level compared to one meeting the baseline efficiency level.

c

To inform its estimate of an appropriate rebate amount, DOE performed a thorough nationwide 

search for existing rebate programs for commercial clothes washers. It gathered data from a 

sample of utility and agency rebate programs that includes 38 rebates for commercial clothes 

washers initiated by 26 utilities or agencies in various Statesd. (Appendix 17-A, identifies the 

rebate programs.) To represent the rebate level, DOE used the simple average of the rebate 

amounts in these programs. DOE assumed that rebates would remain in effect at the same levels 

throughout the forecast period (2018–2047). 

For each of the two commercial clothes washer equipment classes, DOE first calculated 

the B/C ratio without a rebate using the difference in total installed costs (C) and lifetime 

operating cost savings (B) between the unit meeting the target level and the baseline unit. It then

calculated the B/C ratio given a rebate for the unit meeting the target efficiency level. Because 

the rebate reduced the incremental cost, the unit receiving the rebate had a larger B/C ratio. Table 

17.3.1 shows the effect of consumer rebates on the B/C ratio for each TSL. 

 

c The baseline technology for each product class is defined in the engineering analysis, Chapter 5, as the technology 

that represents the basic characteristics of products in that class. A baseline unit typically is one that just meets 

current Federal energy conservation standards and provides basic consumer utility. 

d Some rebates for commercial clothes washers are offered by electric and/or gas utilities, some by water utilities 

and, in some service territories, by both energy and water utilities. DOE calculated the average rebate amount from 

the total (combined) rebate amount available to the consumer.
17-7

Table 17.3.1 Benefit/Cost Ratios Without and With Rebates for Commercial Clothes 

Washer 

Trial Standard Levels

Front-Loading 1 2 3

B/C Ratio Without Rebate infinite 715.8 7.7

Rebate Amount (2012$) 158.16 158.16 158.16

B/C Ratio With Rebate infinite infinite infinite

Calculated Market Barriers Curve Extra-High Extra-High Extra-High

Top-Loading 1 2 3

B/C Ratio Without Rebate infinite infinite 12.5

Rebate Amount (2012$) 158.16 158.16 158.16

B/C Ratio With Rebate infinite infinite infinite

Calculated Market Barriers Curve Extra-High Extra-High Extra-High

 DOE used these B/C ratios along with the penetration curves shown in Figure 17.3.1 to 

estimate the percentage of consumers who would purchase commercial clothes washers that meet 

the target level both with and without a rebate incentive. The penetration curves calculated by 

DOE to represent the market behavior for front-loading and top-loading commercial clothes 

washers at each TSL are indicated in Table 17.3.1. 

Front-Loading Top-Loading

TSL 1 TSL 2
17-8

TSL 3

Figure 17.3.1 Market Penetration Curves for Commercial Clothes Washers

For each equipment class, DOE next estimated the percent increases represented by the 

change in penetration rate shown on the corresponding penetration curve. It then added this

percent increase to the market share of units that meet the target level in the base case to obtain 

the market share of units that meet the target level in the rebate policy case. Table 17.3.2

summarizes the market shares for commercial clothes washers that meet the target level in 2018. 

DOE used the resulting annual increases in market shares as inputs to represent the rebate policy 

case scenario in its NIA-RIA model. Appendix 17-A, shows the annual market share increases 

due to this policy. Section 17.4 presents the resulting market penetration trends for the policy 

case of consumer rebates for commercial clothes washers. 

Table 17.3.2 Market Penetrations in 2018 Attributable to Consumer Rebates for 

Commercial Clothes Washers

Trial Standard Levels

Front-Loading 1 2 3

Base-Case Market Share 34.0% 38.0% 0.0%

Policy Case Market Share 50.0% 50.0% 50.0%

Increased Market Share 16.0% 12.0% 50.0%

Top-Loading 1 2 3

Base-Case Market Share 0.3% 0.3% 0.3%

Policy Case Market Share 50.0% 50.0% 50.0%

Increased Market Share 49.8% 49.8% 49.8%

17.3.3 Consumer Tax Credits

In preparing its assumptions to estimate the effects of tax credits on consumer purchases

of commercial clothes washers, DOE reviewed tax credit programs that have been offered at the 

Federal level for energy-efficient appliances. The Energy Policy Act of 2005 (EPACT 2005) 

included Federal tax credits for consumers who purchase energy-efficient equipment.10 Those 

tax credits were in effect in 2006 and 2007, expired in 2008, were reinstated for 2009–2010 by 

the American Recovery and Reinvestment Act of 2009 (ARRA), extended by Congress for 2011 

with some modifications, and expired at the end of 2011.11, 12 The American Taxpayer Relief 
17-9

Act of 2012 extended, with some modifications, residential tax credits for air conditioners, heat 

pumps, furnaces, and water heaters placed in service between January 1, 2012 and December 31, 

2013.13 DOE reviewed Internal Revenue Service data on the numbers of taxpayers who claimed 

the tax credits during tax years 2006 and 2007. DOE also reviewed data from an earlier Federal 

energy conservation tax credit program in place in the 1980s. However, DOE did not find data 

specific enough to commercial clothes washers to warrant its analysis for the consumer tax 

credits policy case. Appendix 17-A contains more information on Federal consumer tax credits. 

DOE estimated the effects of tax credits on consumer purchases using data from Oregon 

State’s tax credit programs for energy-efficient appliances. (Additional discussion of State tax 

credits for Oregon and other states is in Appendix 17-A.) Oregon has had consumer tax credits 

for efficient appliances for several years.e

 DOE reviewed Oregon’s business energy tax credit 

(BETC) for commercial clothes washers. Oregon’s BETC would typically cover 35–50 percent 

of the incremental cost of the efficient equipment.f The majority of these credits was claimed for 

laundries and rental dwellings, and included commercial clothes washers as well as other 

appliances. However, ODOE did not track the types of appliances involved nor the number of 

machines per project; the annual counts are the total number of projects, with each project 

involving one or more appliances. DOE was therefore unable to use the data to estimate market 

percentages for specific appliances, i.e. commercial clothes washers. However, DOE did note the 

trend of increasing numbers of credits claimed from 1998 through 2006 for residential clothes 

washers, which contributed to its assumptions regarding the trend line for this policy scenario. 

Table 17.3.3 Oregon Tax Credits Claimed for Residential Clothes Washers from 2000 to 

2011 

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Tax Credits* 8.2 12.2 16.2 19.7 18.5 23.4 25.5 23.6 23.8 23.3 29.7 24.6

Shipments** 86.4 88.3 91.0 97.7 102.0 101.8 106.5 118.2 137.6 145.9 155.1 142.1

Percentage*** 9.5 13.8 17.8 20.2 18.2 23.0 24.0 20.0 17.3 16.0 19.1 17.3

* Number of tax credits claimed (thousand units). 

** Shipments of clothes washers to Oregon (thousand units). 

*** Percentage of shipments to Oregon with tax credits.

Because tax credits have not been available for commercial clothes washers, DOE 

analyzed the Oregon residential tax credit data for residential clothes washers to inform the 

analysis of the tax credit policy for commercial clothes washers. DOE obtained data from the 

Oregon Department of Energy (ODOE) on the number of efficient appliances for which tax 

credits were claimed by Oregon residential taxpayers for the years 1998 through 2012, including 

 

e

 The District of Columbia (Washington D.C.) also passed a piece of 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.

f

 The business tax credit is taken over five years: 10 percent in the first and second years and 5 percent each year 

thereafter; the taxpayer may carry the unused credit forward up to eight years. Those with eligible project costs of 

$20,000 or less may take the tax credit in one year.
17-10

those for residential clothes washers.

14 Since the tax credit for clothes washers expired at the end 

of 2011, DOE assumed that the 2012 credits in ODOE’s database were for systems sold prior to 

2012. DOE also obtained shipments data for clothes washers by state (including Oregon) for the 

years 2000 through 2012 from the Association of Home Appliance Manufacturers (AHAM).15 It 

then calculated the percentages of clothes washers sold in Oregon for which taxpayers claimed 

credits for the years 2000 through 2011. Table 17.3.3 shows the number of Oregon residential 

tax credits claimed for clothes washers as percentages of total annual shipments these claims 

represent for the years 2000–2011. DOE used those percentages to estimate the percentage of 

shipments of commercial clothes washers that would meet the efficiency level at each TSL due 

to a tax credit program for 2018 through 2029. 

Not all of these high-efficiency clothes washers, however, would likely be purchased due 

to the tax credit program alone. Some taxpayers would be motivated by utility rebate incentives, 

and others would be "free riders" who would purchase 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, in its previous analysis DOE reviewed a report 

prepared by KEMA on efficient clothes washers in the Northwest. KEMA compared the 

penetration of “ultra high efficiency” (UHE)g clothes washers in Oregon and Washington, two 

neighboring States that have similar utility rebate programs for residential clothes washers.

16 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 relied on this conclusion to estimate the increase in market share of high-efficiency 

clothes washers due to a consumer tax credit program only. For each equipment class, DOE first 

removed the potential contributions of rebates and free riders to that increase. For rebates, DOE 

used the same market penetration it estimated above for the rebate policy case; for free riders 

DOE used the base case market share. Since both the rebate policy case and the base case market 

shares are constant, the resulting percentages for front-loading and top-loading commercial 

clothes washers for the years 2018 through 2029 have different values yet same shape. They both 

present an increasing trend for the first seven years and a decreasing trend for the remaining five 

years. DOE did some interpolations to smooth the shapes of the two market penetration curves 

for the twelve initial years, and extrapolated the decreasing trend of market share for years 

beyond 2029 until that share reaches zero. Table 17.3.4 shows the percentages of increase in total 

shipments attributed to the tax credits themselves, to utility rebates, and to other market 

influences/free riders for 2018 through 2029 for each TSL. 

 

g

 Units meeting specifications of minimum modified energy factor (MEFJ2) of 2.20 and maximum water factor 

(WF) of 6.5.
17-11

Table 17.3.4 Percentages of Increase in Total Shipments of Commercial Clothes Washers 

Attributed to Tax Credits, Rebates and other Market Effects 

2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029

TSL 1

Front-Loading

Rebates 4.7% 6.9% 8.9% 10.1% 9.1% 11.5% 12.0% 10.0% 8.7% 8.0% 9.6% 8.7%

Market effects 3.2% 4.7% 6.1% 6.9% 6.2% 7.8% 8.2% 6.8% 5.9% 5.4% 6.5% 5.9%

Tax Credits 1.5% 2.2% 2.9% 3.2% 2.9% 3.7% 3.8% 3.2% 2.8% 2.6% 3.1% 2.8%

Top-Loading

Rebates 4.7% 6.9% 8.9% 10.1% 9.1% 11.5% 12.0% 10.0% 8.7% 8.0% 9.6% 8.7%

Market effects 0.0% 0.0% 0.0% 0.1% 0.0% 0.1% 0.1% 0.1% 0.0% 0.0% 0.0% 0.0%

Tax Credits 4.7% 6.9% 8.9% 10.1% 9.0% 11.4% 11.9% 10.0% 8.6% 7.9% 9.5% 8.6%

TSL 2

Front-Loading

Rebates 4.7% 6.9% 8.9% 10.1% 9.1% 11.5% 12.0% 10.0% 8.7% 8.0% 9.6% 8.7%

Market effects 3.6% 5.2% 6.8% 7.7% 6.9% 8.7% 9.1% 7.6% 6.6% 6.1% 7.3% 6.6%

Tax Credits 1.1% 1.7% 2.1% 2.4% 2.2% 2.8% 2.9% 2.4% 2.1% 1.9% 2.3% 2.1%

Top-Loading

Rebates 4.7% 6.9% 8.9% 10.1% 9.1% 11.5% 12.0% 10.0% 8.7% 8.0% 9.6% 8.7%

Market effects 0.0% 0.0% 0.0% 0.1% 0.0% 0.1% 0.1% 0.1% 0.0% 0.0% 0.0% 0.0%

Tax Credits 4.7% 6.9% 8.9% 10.1% 9.0% 11.4% 11.9% 10.0% 8.6% 7.9% 9.5% 8.6%

TSL 3

Front-Loading

Rebates 4.7% 6.9% 8.9% 10.1% 9.1% 11.5% 12.0% 10.0% 8.7% 8.0% 9.6% 8.7%

Market effects 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Tax Credits 4.7% 6.9% 8.9% 10.1% 9.1% 11.5% 12.0% 10.0% 8.7% 8.0% 9.6% 8.7%

Top-Loading

Rebates 4.7% 6.9% 8.9% 10.1% 9.1% 11.5% 12.0% 10.0% 8.7% 8.0% 9.6% 8.7%

Market effects 0.0% 0.0% 0.0% 0.1% 0.0% 0.1% 0.1% 0.1% 0.0% 0.0% 0.0% 0.0%

Tax Credits 4.7% 6.9% 8.9% 10.1% 9.0% 11.4% 11.9% 10.0% 8.6% 7.9% 9.5% 8.6%

Table 17.3.5 Market Penetrations in 2018 Attributable to Consumer Tax Credits for 

Commercial Clothes Washers

Trial Standard Levels

Front-Loading 1 2 3

Base-Case Market Share 34.0% 38.0% 0.0%

Policy Case Market Share 35.7% 39.3% 5.2%

Increased Market Share 1.7% 1.3% 5.2%

Top-Loading 1 2 3

Base-Case Market Share 0.3% 0.3% 0.3%

Policy Case Market Share 5.5% 5.5% 5.5%

Increased Market Share 5.2% 5.2% 5.2%
17-12

Table 17.3.5 summarizes DOE’s assumptions for commercial clothes washers regarding 

the market penetration of units in 2018 that meet the efficiency level at each TSL given a 

consumer tax credit. The increased market shares attributable to consumer tax credits shown in 

Table 17.3.3 were used as inputs in the NIA-RIA model. Appendix 17-A, Table 17-A.2.1, shows 

the annual market share increases due to this policy. Section 17.4 presents the resulting market 

penetration trends for the policy case of consumer tax credits for commercial clothes washers 

that meet the efficiency level for each TSL. 

17.3.4 Manufacturer Tax Credits

To analyze the potential effects of a policy that offers tax credits to manufacturers that 

produce commercial clothes washers that meet the target efficiency level at each TSL, DOE 

attempted to investigate manufacturer response to the Energy Efficient Appliance Credits for 

manufacturers mandated by EPACT 2005.17 Those manufacturer tax credits have been in effect 

for dishwashers, clothes washers and refrigerators produced beginning in 2009. (Appendix 17-A 

presents details on Federal manufacturer tax credits.) DOE was unable to locate data from the 

Internal Revenue Service or other sources on manufacturer response to the Federal credits. 

DOE then estimated the effects of tax credits to manufacturers based on its previous 

analysis of consumer participation in tax credits. DOE incorporated previous research that 

disaggregated the effect of rebates and tax credits into a direct price effect, which derives from 

the savings in purchase price, and an announcement effect, which is independent of the amount 

of the incentive.18,19 The announcement effect derives from the credibility that a technology 

receives from being included in an incentive program, as well as changes in product marketing 

and modifications in markup and pricing. DOE assumed that rebate and consumer tax credit 

policies would encompass both direct price effects and announcement effects, and that half of the 

increase in market penetration associated with either policy would be due to the direct price 

effect and half to the announcement effect. DOE then assumed that a manufacturer tax credit 

would lead manufacturers to pass on some of their reduced costs to consumers, causing a direct 

price effect. However, no announcement effect would occur because the program would not be 

visible to consumers.h Because the direct price effect is approximately equivalent to the 

announcement effect,

18 DOE estimated that a manufacturer tax credit would induce half the 

number of consumers assumed to take advantage of a consumer tax credit to purchase more 

efficient equipment. DOE thus applied 50 percent to the increase in market penetrations 

estimated above for consumer tax credits (See Table 17.3.5) and used the resulting market 

penetrations to represent the impacts from manufacturer tax credits. Table 17.3.6 summarizes 

DOE’s assumptions for commercial clothes washers regarding the market penetration of units in 

2018 meeting the target efficiency level at each TSL given a manufacturer tax credit.

 

h Note that this is a conservative assumption, since it is possible that manufacturers or utility/agency efficiency 

programs might promote the models for which manufacturers increase production due to the tax credits, which in 

turn might induce some announcement effect. However, DOE found no data on such programs on which to base an 

estimate of the magnitude of this possible announcement effect on consumer behavior.
17-13

Table 17.3.6 Market Penetrations in 2018 Attributable to Manufacturer Tax Credits for 

Commercial Clothes Washers

Trial Standard Levels

Front-Loading 1 2 3

Base-Case Market Share 34.0% 38.0% 0.0%

Policy Case Market Share 34.8% 38.6% 2.6%

Increased Market Share 0.8% 0.6% 2.6%

Top-Loading 1 2 3

Base-Case Market Share 0.3% 0.3% 0.3%

Policy Case Market Share 2.9% 2.9% 2.9%

Increased Market Share 2.6% 2.6% 2.6%

The increased market shares attributable to a manufacturer tax credit shown in Table 

17.3.6 were used as inputs in the NIA-RIA model. Appendix 17-A, shows the annual market 

share increases due to this policy. Section 17.4 presents the resulting market penetration trends 

for the policy case of manufacturer tax credits for commercial clothes washers. 

17.3.5 Voluntary Energy Efficiency Targets 

For each equipment class, DOE assumed that voluntary energy efficiency targets would 

be achieved as manufacturers gradually stopped producing units that operated below the 

efficiency level set for each TSL. DOE assumed that the impetus for phasing out production of 

low-efficiency units would be a program similar to the ENERGY STAR labeling program 

conducted by the Environmental Protection Agency (EPA) and DOE. The ENERGY STAR 

program specifies the minimum energy efficiencies that various products must have to receive 

the ENERGY STAR label. ENERGY STAR encourages consumers to purchase efficient 

products via marketing that promotes consumer label recognition, various incentive programs 

that adopt the ENERGY STAR specifications, and manufacturers’ promotion of their qualifying 

appliances. ENERGY STAR projects market penetration of compliant appliances and estimates 

the percentage of sales of compliant appliances that are attributable to the ENERGY STAR 

program. 

Researchers have analyzed the ENERGY STAR program’s effects on sales of several 

consumer products. Program efforts generally involve a combination of information 

dissemination and utility or agency rebates. The analyses have been based on State-specific data 

on percentages of shipments of various appliances that meet ENERGY STAR specifications. The 

analyses generally have concluded that the market penetration of ENERGY STAR-qualifying 

appliances is higher in regions or States where ancillary promotional programs have been 

active.

20, 21, 22

DOE based its estimates of market penetration on its previous estimates of the impact of 

a voluntary energy efficiency targets program on shipments of commercial clothes washers. In 

the absence of data on the impacts of the existing ENERGY STAR program for commercial 

clothes washers, in its prior analysis23 DOE estimated the percentage of market shares 
17-14

attributable to the existing ENERGY STAR program for residential clothes washers for 1996 –

2025. DOE assumed that commercial clothes washers would experience the same trend of annual 

increases in market penetration beginning in 2018 as the one presented by residential clothes 

washers starting in 1997. DOE added those percent increases to the market shares of commercial 

clothes washers that met the target level in the RIA base case for each TSL, starting in 2018, to 

obtain the annual market shares of units meeting the efficiency level set for each TSL in the 

voluntary energy efficiency targets policy case. Table 17.3.7 summarizes DOE’s assumptions for 

commercial clothes washers regarding the market penetration of units in 2018 that, given 

voluntary energy efficiency targets, would meet the target efficiency level set for each TSL. 

Table 17.3.7 Market Penetrations in 2018 Attributable to Voluntary Energy Efficiency 

Targets for Commercial Clothes Washers

Trial Standard Levels

Front-Loading 1 2 3

Base-Case Market Share 34.0% 38.0% 0.0%

Policy Case Market Share 34.5% 38.5% 0.5%

Increased Market Share 0.5% 0.5% 0.5%

Top-Loading 1 2 3

Base-Case Market Share 0.3% 0.3% 0.3%

Policy Case Market Share 0.7% 0.7% 0.7%

Increased Market Share 0.5% 0.5% 0.5%

DOE estimated that the program developed in support of the voluntary energy efficiency 

targets policy would increase market shares of efficient units. Appendix 17-A shows the annual 

market share increases due to this policy used as inputs to the NIA-RIA model. Section 17.4

presents the resulting market penetration trends for the policy case of voluntary energy efficiency 

targets for commercial clothes washers.

17.3.6 Early Replacement 

The non-regulatory policy of early replacement refers to a program to replace commercial 

clothes washers before the end of their useful lives. The purpose of such a policy is to replace 

old, inefficient units by units at the target efficiency level defined for each TSL. DOE examined 

several resources to inform its analysis of the policy. One report detailed the Connecticut 

Appliance Retirement Program (ARP), which was conducted in 2004.24 Another was a 2006 

study of the potentials for electric energy efficiency performed for the State of Vermont.25 

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 purpose of the ARP was to 

help Connecticut utility customers 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 
17-15

the costs of operating older appliances. In addition, the program provided consumers with 

financial incentives to replace inefficient RACs with ENERGY STAR-qualified units. 

Nexus/RLW used program data and surveys to estimate the number of RACs retired by ARP 

participants, the percentage of retired units that were replaced with an ENERGY STAR model, 

and the number of RACs replaced by non-participants during the program. According to the 

Nexus/RLW analysis, about 7 percent of all RACs retired during the program were retired 

through the ARP, and 63 percent of those were replaced with ENERGY STAR models. Thus the 

program resulted directly in about 4 percent of total eligible RAC consumers deciding on early 

replacement of inefficient units. 

 In 2006, GDS Associates, Inc. conducted a study for the State of Vermont of the potential 

for reducing electricity use and peak demand through energy efficiency and fuel conversion 

measures. The study took an aggressive, multi-program approach, one aspect of which was early 

replacement of appliances. GDS considered that under the program residential appliances, 

including RACs, would be replaced during 4 years (2006–2009). GDS estimated achievable 

market penetrations assuming that consumers would receive a financial incentive equal to 50 

percent of the incremental cost of each program measure. GDS assumed a maximum 80-percent 

penetration. For early replacement of RACs, GDS estimated a maximum achievable participation 

of 5 percent of eligible single-family or multi-family homes in the year before the program began 

(2005). 

DOE also reviewed a study it had conducted in the 1990s, under EPACT 1992, which 

analyzed the effectiveness of Federal programs that supported early retirement of appliances.

26

The study identified policy options for early replacement that included a direct national program; 

replacement of Federally-owned appliances; and promotion through product manufacturers, 

consumer incentives, incentives to utilities, and building regulations.i

Historically, DOE modeled the effects of the early replacement policy by increasing by 4 

percent per year the retirement rate of units that were in the stock in the first year of the analysis 

period. DOE chose the 4-percent rate from the Connecticut study because it was based on actual 

program experience. DOE assumed that the early replacement program would continue until it 

had facilitated the replacement of all eligible units in the stock in the year the program began. 

Shipments of new units in the year the program began and beyond would not be affected by the 

program, but would remain at base-case efficiency levels. After the stock of inefficient units was

completely replaced, the policy would produce no additional impacts. 

 

i

The analysis concluded that, although cost-effective opportunities for early replacement exist, a widespread Federal 

program was not justified economically. Because early retirement means that a unit may be replaced by an 

appliance less efficient than the eventual replacement would have been, energy savings would be less than 

anticipated. Early replacement programs also could increase long-term sales volatility by encouraging a temporary 

increase in production, followed by a lull in demand. Early replacement could be economical in localities subject to 

high energy costs or environmental constraints; when replacement appliances are much more efficient than existing 

stock; or when a major technology breakthrough has occurred, creating the need for a ready market. 
17-16

 For the commercial clothes washer RIA analysis, DOE analyzed a program designed to 

target installed units with efficiency levels lower than the target level set for each TSL and 

designed to encourage their early replacement with equipment that performs at that target level. 

Rather than relying on a fixed rate of increase in retirements, DOE based its estimates on the 

economic feasibility of early replacements. The feasibility depends on the energy efficiency of 

the unit being replaced, the purchase cost of a new efficient unit, and the energy cost savings.

DOE performed the feasibility analysis of early replacements for front-loading and top-loading 

commercial clothes washers.

DOE started the analysis estimating the stock of existing commercial clothes washers by 

vintage for each equipment class. To estimate the stock by vintage DOE relied on the historical 

shipments from 1989 to 2011 used in the NIA (Chapter 10). DOE then used the same lifetime 

distributions used in the NIA (Chapter 10) to estimate the existing stock by vintage for each of 

the 30 years that comprise the analysis period. As a second step to this analysis, DOE estimated 

historical market efficiency distributions. DOE applied the historical market efficiency 

distributions to the stock by vintage that DOE projected. It then estimated: (a) the market average 

unit lifetime energy cost of a commercial clothes washer from the existing stock, and (b) the unit 

lifetime energy cost of a replacement, efficient commercial clothes washer. DOE calculated both 

values as the discounted sum of the corresponding stream of energy costs, taking into account the 

age of the unit in the replacement year and a stream of annual probabilities that the unit will be in 

operation until the expected end of its life. The difference between the two lifetime energy costs 

DOE calculated reflects the estimated energy cost savings that a consumer is likely to enjoy from 

the early replacement of units in the existing stock by more efficient units. DOE calculated these 

savings for each of the 30 years in the analysis period for commercial clothes washers from all 

vintages in the existing stock.

Savings from early replacements, however, come at a cost. Anticipating the time of 

replacement of a unit in stock either (a) prevents consumers from realizing financial benefits 

from investing the money they would pay for the new, replacement unit; or (b) imposes financial 

costs to those consumers who do not hold funds – and therefore would have to loan – to purchase 

the new, replacement unit. In either case, there is an economic value in delaying the time of 

replacement. DOE calculated such value for commercial clothes washers from all vintages in the 

existing stock and for each year in the 30-year analysis period. 

While the energy cost savings are an incentive for early replacing a unit in the existing 

stock (benefits from replacing), the financial costs underlying the early replacement (benefits 

from waiting) may hinder the initiative. DOE considered the net-result from the benefits from 

replacing and the benefits from waiting as an indicator of the decision that consumers would 

make towards anticipating the replacement of a front-loading and a top-loading commercial 

clothes washer in the existing stock by a more efficient unit rated at the target efficiency level set 

for each TSL. The comparison of the two benefits shows that the early replacement of a unit 

would be cost-effective for front-loading units after their seventh year of operation, and for all 

top-loading units. 
17-17

Based on the stock by vintage that DOE projected over the 30-year analysis period, and 

the net-results from the benefits from replacing and the benefits from waiting for each vintage, 

DOE projected the number of potential front-loading and top-loading commercial clothes 

washers in the existing stock to be (early) replaced in each of the 30 years of the analysis period. 

For this projection DOE further considered that, because of lack of information on the benefits 

from early replacements or any transaction costs involved in the process of early replacing a unit 

in operation, not all consumers would be informed to or willing to decide for the replacement. To 

estimate the share of the market that would take such initiative DOE used the customized 

penetration curves it developed to support the analysis of the rebate policy case described above. 

DOE assumed for this policy case the same monetary incentive and market barrier level it 

estimated for each equipment class when calculating the impacts from rebates. As for the 

benefit-cost ratio, DOE used the ratio of the benefits from replacing divided by the benefits from 

waiting. 

 The units in the existing stock projected to be replaced early represent additional 

shipments of front-loading and top-loading units rated at the target efficient level of each TSL. 

Figure 17.3.2 shows DOE’s projections of shipments of front-loading and top-loading 

commercial clothes washers in response to an early replacement policy. An early replacement 

policy would create a fairly immediate jump in shipments of equipment that meet the target 

efficiency level relative to the base case. High-efficiency units would be brought quickly into the 

stock, leading to an immediate gain in the market share of efficient units compared to the base 

case. However, as opposed to the policy cases discussed previously, an early replacement policy 

results in market shares of efficient units returning to base-case percentages as the eligible 

market is depleted. Additionally, because units removed early from the stock would have been 

replaced later (at the ends of their useful lives) without the program, the number of shipments in 

later years drops slightly below the base-case shipments forecast. 

Front-Loading Top-Loading

Figure 17.3.2 Base Case and Policy Case Shipments Projections for the Early 

Replacement Policy Case (thousand units)

DOE used the additional shipments for early replacements to calculate the increase in 

market penetration of more efficient units that would result from an early replacement policy. 

0.000

0.020

0.040

0.060

0.080

0.100

0.120

0.140

0.160

201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044204520462047

Base Case Policy Case

0.000

0.020

0.040

0.060

0.080

0.100

0.120

0.140

0.160

201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044204520462047

Base Case Policy Case
17-18

Table 17.3.8 summarizes DOE’s assumptions for commercial clothes washers regarding the 

market penetration of units in 2018 meeting the target efficiency level at each TSL given early 

replacement incentives.

Table 17.3.8 Market Penetrations in 2018 Attributable to Early Replacements for 

Commercial Clothes Washers

Trial Standard Levels

Front-Loading 1 2 3

Base-Case Market Share 34.0% 38.0% 0.0%

Market Share After Policy Implementation 35.4% 39.4% 2.1%

Increased Market Share After Policy Implementation 1.4% 1.4% 2.1%

Top-Loading 1 2 3

Base-Case Market Share 0.3% 0.3% 0.3%

Market Share After Policy Implementation 13.7% 13.7% 15.8%

Increased Market Share After Policy Implementation 13.4% 13.4% 15.6%

The resulting increased market shares attributable to early replacements shown in Table 

17.3.8 were used as inputs in the NIA-RIA model. Appendix 17-A shows the annual market 

share increases due to this policy. Section 17.4 presents the resulting market penetration trends 

and cumulative savings for the policy case of early replacement for commercial clothes washers. 

17.3.7 Bulk Government Purchases 

 Bulk government purchases can lead to Federal, State, and local governments purchasing 

large quantities of equipment that meet the target efficiency level. Combining the market 

demands of multiple public sectors also can provide a market signal to manufacturers and 

vendors that some of their largest customers seek equipment that meet an efficiency target at 

favorable prices. Such a program also can induce “market pull,” whereby manufacturers and 

vendors would achieve economies of scale for high efficiency equipment. 

 Most of the previous bulk government purchase (procurement) initiatives at the Federal, 

State, and municipal levels have not tracked data on numbers of purchases or degree of 

compliance with procurement specifications. In many cases, procurement programs are 

decentralized, being part of larger State or regional initiatives. DOE based its assumptions 

regarding the effects of this policy on studies the Federal Energy Management Program (FEMP) 

performed regarding the savings potential of its procurement specifications for appliances and 

other equipment. FEMP, however, does not track purchasing data, because of the complex range 

of purchasing systems, large number of vendors, and so on. States, counties, and municipalities 

have demonstrated increasing interest and activity in “green purchasing." Although many of the 

programs target office equipment, the growing infrastructure for developing and applying 

efficient purchasing specifications indicates that bulk government purchase programs are 

feasible.

27, 28 
17-19

 DOE assumed that government agencies would administer bulk purchasing programs for 

commercial clothes washers. At the federal level, this type of program could modify the current 

FEMP procurement guidelines for commercial clothes washers, which refer to the ENERGY 

STAR requirements for commercial clothes washers29. DOE reviewed its own previous research 

on the potential for market transformation through bulk government purchases. Its major study 

analyzed several scenarios based on the assumption that 20 percent of Federal equipment 

purchases in 2000 already incorporated energy efficiency requirements based on FEMP 

guidelines. One scenario in the DOE report showed energy efficient purchasing ramping up 

during 10 years from 20 percent to 80 percent of all Federal purchases.30 Based on this study, 

DOE estimated that a bulk government purchase program instituted within a 10-year period 

would result in at least 80 percent of government-purchased commercial clothes washers meeting 

target efficiency levels.

 To estimate the impact of a bulk government purchase program for this equipment, DOE 

first determined the shipments of commercial clothes washers that go to government-owned 

housing. A report published by Lawrence Berkeley National Laboratory (LBNL)31 in 2012 

estimated that around 3000-3200 commercial clothes washers were purchased annually for 

Federal buildings from 2005 through 2011. This corresponds to an average of 1.9% of all 

shipments of commercial clothes washers in the same period. DOE applied the estimated 1.9% of 

shipments to the forecast of market penetration of more efficient front-loading and top-loading 

commercial clothes washers. DOE estimated that, for each TSL, starting in 2018 each year of a 

bulk government purchase policy would result in an increasing percent of shipments of 

government-purchased units beyond the base case that would meet target efficiency levels. DOE 

estimated that within 10 years (by the end of 2027), bulk government purchasing programs 

would result in 80 percent of the front-loading and top-loading commercial clothes washers 

market for government-owned commercial buildings meeting the target level of each TSL. DOE 

modeled the bulk government purchase program assuming that the market share for each 

equipment class achieved in 2027 would be maintained throughout the rest of the analysis

period. 

Table 17.3.9 summarizes DOE’s assumptions for commercial clothes washers regarding 

the market penetration of units in 2018 meeting, for each TSL, the target efficiency level given 

bulk government purchasing. 
17-20

Table 17.3.9 Market Penetrations in 2018 Attributable to Bulk Government Purchasing of 

Commercial Clothes Washers

Trial Standard Levels

Front-Loading 1 2 3

Base-Case Market Share 34.0% 38.0% 0.0%

Market Share After Policy Implementation 34.7% 38.8% 0.1%

Increased Market Share After Policy Implementation 0.7% 0.8% 0.1%

Top-Loading 1 2 3

Base-Case Market Share 0.3% 0.3% 0.3%

Market Share After Policy Implementation 0.4% 0.4% 0.4%

Increased Market Share After Policy Implementation 0.2% 0.2% 0.2%

The resulting increased market shares attributable to bulk government purchasing shown 

in Table 17.3.9 were used as inputs in the NIA-RIA model. Appendix 17-A shows the annual 

market share increases due to this policy that DOE used as inputs to the NIA-RIA model. Section 

17.4 below presents the resulting market penetration trends for the policy case of bulk 

government purchase of commercial clothes washers.

17.4 IMPACTS OF NON-REGULATORY ALTERNATIVES 

Figure 17.4.1 and Figure 17.4.2 show the effects of each non-regulatory policy on market 

penetration for front-loading and top-loading commercial clothes washers. Relative to the base 

case, the policy cases increase the market shares that meet the target level. Recall the proposed 

standards (not shown in the figures) would result in a 100-percent market penetration of 

equipment that meets the target efficiency level. 
17-21

Figure 17.4.1 Market Penetration of Front-Loading Commercial Clothes 

Washers Meeting the Target Level in Policy Cases (TSL 2)

Figure 17.4.2 Market Penetration of Top-Loading Commercial Clothes 

Washers Meeting the Target Level in Policy Cases (TSL 2)

Table 17.4.1 and Table 17.4.2 show the national energy savings and net present value for 

six non-regulatory policies analyzed in detail for commercial clothes washers. The target level 

0%

10%

20%

30%

40%

50%

60%

70%

20182019

2020202120222023202420252026202720282029203020312032

2033203420352036203720382039

20402041204220432044204520462047

Consumer Rebate

Consumer Tax Credit

Manufacturer Tax Credit

Voluntary Efficiency Targets

Govt Bulk Purchasing

Early Replacement

Base Case

0%

10%

20%

30%

40%

50%

60%

70%

201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044204520462047

Consumer Rebate

Consumer Tax Credit

Manufacturer Tax Credit

Voluntary Efficiency Targets

Govt Bulk Purchasing

Early Replacement

Base Case
17-22

for each policy equals the efficiency levels proposed for standards in TSL 2. The case in which 

no regulatory action is taken with regard to commercial clothes washers constitutes the base case

(or "No New Regulatory Action" scenario), in which NES and NPV are zero by definition. For 

comparison, the tables include the impacts of the proposed standards. Energy savings are given 

in quadrillion British thermal units (quads). The NPVs shown in Table 17.4.1 and Table 17.4.2

are based on two discount rates, 7 percent and 3 percent. 

 The policies with the highest projected cumulative energy savings are consumer rebates 

and voluntary energy efficiency targets. Early replacements result in negative NPV for both 

equipment classes and at both discount rates. The increase in shipments induced by the policy 

outweighs the benefits from the higher energy efficiency achieved in the policy case, in relation 

to the base case, during the first years of the analysis period. For the remaining years, the 

decrease in shipments potentiates the benefits from the higher energy efficiency. However, even 

at 3 percent discount rate, the positive effects from the last years of the analysis period do not 

counterbalance the negative effects observed over the initial years. NPV would become zero for 

front-loading commercial clothes washers at 1.8 percent discount rate, and for top-loading at 1.3 

percent discount rate.

Table 17.4.1 Impacts of Non-Regulatory Alternatives for Front-Loading Commercial 

Clothes Washers (TSL 2) 

Policy Alternative

Primary Energy 

Savings quads

Net Present Value*

billion 2012$

7% Discount 

Rate

3% Discount 

Rate

Consumer Rebates 0.004 0.028 0.067

Consumer Tax Credits 0.000 0.004 0.008

Manufacturer Tax Credits 0.000 0.002 0.004

Voluntary Energy Efficiency Targets 0.008 0.042 0.113

Early Replacement 0.002 -0.060 -0.033

Bulk Government Purchases 0.001 0.003 0.008

Proposed Standards 0.023 0.145 0.344

*For equipment shipped 2018 – 2047
17-23

Table 17.4.2 Impacts of Non-Regulatory Alternatives for Top-Loading Commercial 

Clothes Washers (TSL 2) 

Policy Alternative

Primary Energy 

Savings quads

Net Present Value*

billion 2012$

7% Discount 

Rate

3% Discount 

Rate

Consumer Rebates 0.043 0.130 0.298

Consumer Tax Credits 0.005 0.021 0.039

Manufacturer Tax Credits 0.002 0.010 0.020

Voluntary Energy Efficiency Targets 0.018 0.045 0.116

Early Replacement 0.003 -0.138 -0.099

Bulk Government Purchases 0.001 0.003 0.007

Proposed Standards 0.085 0.260 0.594

*For equipment shipped 2018 – 2047
17-24

REFERENCES

1. Rufo, M. and F. Coito, California's Secret Energy Surplus: The Potential for Energy 

Efficiency, 2002. XENERGY Inc. (Last accessed November, 2013.) 

<http://www.issuelab.org/resource/californias_secret_energy_surplus_the_potential_for_

energy_efficiency>

2. ICF International, Arizona Public Service Energy Efficient Market Potential Study, 2007. 

Fairfax, VA. 

3. Mosenthal, P., Telephone Conversation with Barbara Atkinson of LBNL, personal 

communication. Optimal Energy. January, 2008. 

4. Lee, A., Telephone Conversation with Barbara Atkinson of LBNL, personal 

communication. Quantec, LLC. January, 2008. 

5. Rufo, M., Telephone Conversation with Barbara Atkinson of LBNL, personal 

communication. Itron, Inc. January 2008 and March 2009. 

6. Itron Inc. and KEMA Inc., California Energy Efficiency Potential Study, 2008. (Last 

accessed November, 2013.) <http://calmac.org/publications/PGE0264_Final_Report.pdf>

7. Global Energy Partners LLC, AmerenUE Demand Side Management (DSM) Market 

Potential Study, 2010. (Last accessed November, 2013.) 

<http://www.ameren.com/sites/aue/Environment/Renewables/Documents/AmerenUEVol

ume2MarketResearchReport2.pdf>

8. Coito, F., E-mail to Barbara Atkinson of LBNL, personal communication. KEMA, Inc. 

June, 2010. 

9. Blum, H., B. Atkinson, and A. Lekov, A Framework for Comparative Assessments of 

Energy Efficiency Policy Measures, 2011. (Last accessed November, 2013.) 

<http://efficiency.lbl.gov/drupal.files/ees/A%20Framework%20for%20Comparative%20

Assessments%20of%20Energy%20Efficiency%20Policy%20Measures_LBNL4749E.pdf>

10. Energy Policy Act of 2005, in 119 STAT. 594 Public Law 109-58. Section 1333, 26 USC 

25C note,. 2005. http://www.gpo.gov/fdsys/pkg/BILLS-109hr6pcs/pdf/BILLS109hr6pcs.pdf

11. American Recovery and Reinvestment Act of 2009, 2009. Government Printing Office. 

(Last accessed November, 2013.) <http://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=111_cong_bills&docid=f:h1enr.pdf>
17-25

12. Tax Incentives Assistance Project, Consumer Tax Incentives: Home Heating & Cooling 

Equipment., 2011. (Last accessed November, 2013.) 

<www.energytaxincentives.org/consumers/heating-cooling.php>

13. Tax Incentives Assistance Project, The Tax Incentives Assistance Project (TIAP), 2013.

(Last accessed November, 2013.) <http://energytaxincentives.org/>

14. Barker, D., personal communication. December 2007–April 2008. 

15. AHAM, Estimated Distribution Sales by State for Room Air Conditioners - 2006, 2006.

Association of Home Appliance Manufacturers (AHAM). (Last accessed November, 

2013.) <http://www.aham.org/ht/d/ProductDetails/sku/BDSTATE/from/5256/pid/>

16. KEMA Inc., Consumer Product Market Progress Evaluation Report 3, 2007. Prepared 

for Northwest Energy Efficiency Alliance. Portland, OR. Report No. 07-174. 

17. Tax Incentives Assistance Project, Manufacturers Incentives, (Last accessed November, 

2013.) <http://energytaxincentives.org/builders/appliances.php>

18. Lawrence Berkeley National Laboratory, Energy End-Use Forecasting: Analysis of Tax 

Credits for Efficient Equipment, 1998. LBNL Technology Evaluation Modeling and 

Assessment Group (Last accessed November, 2013.) 

<http://enduse.lbl.gov/Projects/TaxCredits.html>

19. Train, K., Customer Decision Study: Analysis of Residential Customer Equipment 

Purchase Decisions, 1994. Prepared for Southern California Edison by Cambridge 

Systematics, Pacific Consulting Services, The Technology Applications Group, and 

California Survey Research Services. 

20. Feldman, S., L. Hoegfen, L.Wilson-Wright, and A. Li, Modeling the Effects of U.S. 

ENERGY STAR® Appliance Programs. In Energy Savings: What Works and Who 

Delivers?, May 30-June 4, 2005. ECEEE Summer Study Proceedings. Mandelieu La 

Napoule, France. 

<http://www.eceee.org/conference_proceedings/eceee/2005c/Panel_4/4228feldman/paper

> 

21. Rosenberg, M., The Impact of Regional Programs on the Market Share of Energy Star® 

Appliances, August 20-22, 2003. Seattle, WA. Published in the 2003 International Energy 

Program Evaluation Conference Proceedings. 

22. Titus, E. and S. Feldman, Tracking the Effectiveness of Energy-Efficient Appliance 

Programs in the U.S., August 20-22, 2003. In 2003 International Energy Program 

Evaluation Conference Proceedings. Seattle, WA. 
17-26

23. Technical Support Document: Energy Efficiency Standards for Commercial Clothes 

Washers, 2010. U.S. Department of Energy - Energy Efficiency & Renewable Energy. 

(Last accessed November, 2013.) 

<http://www.regulations.gov/#!documentDetail;D=EERE-2006-STD-0127-0118>>

24. Nexus and RLW Analytics, Impact, Process, and Market Study of the Connecticut 

Appliance Retirement Program: Overall Report, Final, 2005. (Last accessed November, 

2013.) <http://www.ctsavesenergy.org/files/Appliance%20Retirement%2012-05.pdf>

25. GDS Associates Inc., Vermont Electric Energy Efficiency Potential Study: Final Report, 

2007. (Last accessed November, 2013.) 

<http://publicservice.vermont.gov/energy/vteefinalreportjan07v3andappendices.pdf>

26. McMahon, J. E. and J.P. Harris, Early Replacement of Appliances, 1994. Lawrence 

Berkeley National Laboratory. Berkeley, CA. Unpublished report for U.S. Department of 

Energy. 

27. Smith, N., Telephone conversation with Barbara Atkinson of LBNL, personal 

communication. National Association of State Procurement Officials. April, 2008. 

28. Responsible Purchasing Network, (Last accessed November, 2013.) 

<www.responsiblepurchasing.org/>

29. Federal Energy Management Program (FEMP), Covered Products Category: Clothes 

Washers (Commercial), (Last accessed November, 2013.) 

<https://www1.eere.energy.gov/femp/technologies/eep_purchasingspecs.html>

30. Harris, J. and F. Johnson, Potential Energy, Cost, and CO2 Savings from EnergyEfficient Government Purchase. ACEEE: Summer Study on Energy Efficiency in 

Buildings, 2000: pp. 147-166 

31. Fujita, K. S. and M. Taylor, Achieved and Potential Energy Savings through Energy 

Efficient Procurement, 2012. Lawrence Berkeley National Laboratory. Report No. 

LBNL-5737E. 

<http://ees.ead.lbl.gov/sites/all/files/achieved_and_potential_energy_savings_through_en

ergy_efficient_procurement_lbnl-5737e.pdf> 
5-A-i

APPENDIX 5-A. AHAM DATA REQUEST 

TABLE OF CONTENTS 

5-A.1 DRAFT ENGINEERING ANALYSIS DATA REQUEST SHEETS ...................5-A-1

5-A.2 MANUFACTURER IMPACT ANALYSIS QUESTIONS: .................................5-A-5

LIST OF TABLES 

Table 5-A.1Commercial Clothes Washer Shipments, Domestic plus Imports, in 

Thousands of Units ....................................................................................5-A-2

Table 5-A.2 Shipment-Weighted Average Efficiency Data ....................................................5-A-3

Table 5-A.3 Market Share Efficiency Data* ...........................................................................5-A-3

Table 5-A.4 Aggregate Manufacturer Cost Data: Front-Loading Commercial Clothes 

Washers ......................................................................................................5-A-4
5-A-1

APPENDIX 5-A. AHAM DATA REQUEST 

5-A.1 DRAFT ENGINEERING ANALYSIS DATA REQUEST SHEETS 

DOE seeks average incremental production cost to take basic models in the categories shown 

from the current DOE minimum efficiency level (or proposed baseline level) to the specified 

efficiency level. For those product classes where more than one basic model may exist, please 

indicate the minimum and maximum incremental costs that would be incurred across the array of 

basic models. 

The data sheets are divided by product and contain tables requesting shipment and manufacturer 

cost data. The shipment-weighted energy use data of Table A-2 should be based on the DOE 

clothes washer test procedure (10 CFR 430, subpart B, appendix J1, J2). 

Shipments

For commercial clothes washers, the AHAM 2005 Fact Book offers historical shipments data, 

but the information is not disaggregated by product class. As shown in the “shipment request” 

tables below, DOE hopes to collect both shipments data and shipment-weighted average 

efficiency data dating back to 1995. In addition, DOE hopes to collect market share efficiency 

data (i.e., data on the distribution of equipment shipments by efficiency) for each product class. 

Manufacturer Costs

Incremental cost data (in U.S. dollars) include the materials, labor, and overhead needed to take 

basic models from the current minimum DOE baseline efficiency standard to each higher 

efficiency level. The depreciation of the conversion capital expenditures is an important 

component of the overhead for DOE to understand. Therefore, DOE is requesting information 

about conversion capital expenditures by efficiency level. 

DOE requests notification from those interested parties planning to submit data by the close of 

the framework comment period. 

Please provide aggregated industry data for the following tables. 
5-A-2

Table 5-A.1Commercial Clothes Washer Shipments, Domestic plus Imports, in Thousands 

of Units 

Year Total Top-Loading % Imported Front-Loading % Imported

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011
5-A-3

Table 5-A.2 Shipment-Weighted Average Efficiency Data 

Top-Loading Front-Loading

Year EF / MEF / IMEF* WF / IWF† EF / MEF / IMEF* WF / IWF†

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

* Please specify whether energy factor (EF), modified energy factor (MEF), or integrated 

modified energy factor (IMEF). 

† Please specify whether water factor (WF) or integrated water factor (IWF). 

Table 5-A.3 Market Share Efficiency Data* 

Top-Loading Front-Loading

Efficiency Bins Market Share†

(Percent) Efficiency Bins Market Share†(Percent)

MEF WF 2009 2010 2011 MEF WF 2009 2010 2011

1.26 9.5 1.72 8.0

1.42 9.5 1.80 7.5

1.60‡ 8.5‡ 2.00‡ 5.5‡

2.20 5.1

2.40 4.0

2.60 3.7

* Efficiency bins correspond to those used during the prior CCW rulemaking in addition to the 

proposed TSLs for this rulemaking. 

† Total market shares should equal 100%. 

‡ January 8, 2013 DOE standard. 
5-A-4

Table 5-A.4 Aggregate Manufacturer Cost Data: Front-Loading Commercial Clothes 

Washers 

Efficiency Level Baseline Level 1 Level 2 Level 3

MEF 2.00 2.20 2.40 2.60

WF 5.5 4.5 4.0 3.7

Average Incremental Costs ($ Per Unit)*

Direct Material 0

Direct Labor 0

Overhead#0

Minimum Incremental Costs ($ Per Unit)*

Direct Material 0

Direct Labor 0

Overhead#0

Maximum Incremental Costs ($ Per Unit)*

Direct Material 0

Direct Labor 0

Overhead#0

Conversion Capital Expenditures ($, Millions)

Building CAPX 0

Tooling/ Equipment CAPX 0

R&D 0

Marketing 0

* Incremental costs per unit should be reported relative to the baseline unit’s cost. 

# Depreciation on the conversion capital expenditure should NOT be included in incremental 

overhead. 

Direct material – Costs of raw materials including scrap that can be traced to final or end 

products. Direct material costs do not include indirect material costs, which are attributed to 

supplies that may be used in the production process but are not assigned to final products (e.g., 

lubricating oil for production machinery). 

Direct labor – The earnings of workers who assemble parts into a finished good or operate 

machines in the production process. Direct labor includes the fringe benefits of direct laborers, 

such as group health care, as well as overtime pay. Direct labor does not include indirect labor, 

which is defined as the earnings of employees who do not work directly in assembling 

equipment, such as supervisors, janitors, stockroom personnel, and forklift operators. 

Overhead – Factory overhead excluding depreciation. Factory overhead includes indirect labor, 

downtime, set-up costs, indirect material, expendable tools, maintenance, property taxes, 

insurance on assets, and utility costs. Factory overhead does not include selling, general, and 

administrative costs (SG&A); R&D; interest; or profit (accounted for by DOE separately). 
5-A-5

5-A.2 MANUFACTURER IMPACT ANALYSIS QUESTIONS: 

 What fraction of the Commercial Clothes Washer manufacturing industry is represented by 

AHAM? Please indicate if this is by quantity or value of shipments. 

 Are you aware of, or do you represent any small business or low volume manufacturers? Please 

identify them. 

 In the absence of new standards, do you expect any industry consolidation? 

 Could new standards disproportionately advance or harm the competitive positions of some 

firms? 

 Could new standards result in disproportionate economic or performance penalties for particular 

consumer/user subgroups? 

 Beyond price and energy efficiency, could new standards result in products that will be more or 

less desirable to consumers due to changes in product functionality, utility, or other features? 

 What distribution channels are used from the manufacturer to the retail outlet? What is the share 

of product going through each distribution channel? 
6-A-i

APPENDIX 6-A. DETAILED DATA FOR MARKUPS 

TABLE OF CONTENTS

6-A.1 INTRODUCTION ................................................................................................. 6-A-1

LIST OF TABLES

Table 6-A.1.1 Sales Tax Rate by State.............................................................................. 6-A-1
6-A-1

APPENDIX 6-A. DETAILED DATA FOR MARKUPS

6-A.1 INTRODUCTION

In this appendix, DOE provides state-level sales tax data used in equipment price determination. 

Population by state was obtained from the U.S. Census Bureau.1 State and local tax rates were 

obtained from the Sales Tax Clearinghouse website.2

Table 6-A.1.1 Sales Tax Rate by State

Domain State Census 

Region

Population,

July 2009 (1000s)

State and Local 

Tax Rate

1 Connecticut 1 3518.288 6.4%

1 Maine 1 1318.301 5.0%

1 New Hampshire 1 1324.575 0.0%

1 Rhode Island 1 1053.209 7.0%

1 Vermont 1 621.76 6.1%

2 Massachusetts 1 6593.587 6.3%

3 New York 1 19541.45 8.4%

4 New Jersey 1 8707.739 7.0%

5 Pennsylvania 1 12604.77 6.4%

6 Illinois 2 12910.41 8.1%

7 Indiana 2 6423.113 7.0%

7 Ohio 2 11542.65 6.8%

8 Michigan 2 9969.727 6.0%

9 Wisconsin 2 5654.774 5.5%

10 Iowa 2 3007.856 6.9%

10 Minnesota 2 5266.214 7.2%

10 North Dakota 2 646.844 5.9%

10 South Dakota 2 812.383 5.4%

11 Kansas 2 2818.747 8.0%

11 Nebraska 2 1796.619 6.0%

12 Missouri 2 5987.58 6.6%

13 Virginia 3 7882.59 5.0%

14 Delaware 3 885.122 0.0%

14 Maryland 3 5699.478 6.0%

14 West Virginia 3 1819.777 6.1%

15 Georgia 3 9829.211 7.1%

16 North Carolina 3 9380.884 6.9%

16 South Carolina 3 4561.242 7.1%
6-A-2

17 Florida 3 18537.97 6.7%

Domain State Census 

Region

Population,

July 2009 (1000s)

State and Local 

Tax Rate

18 Alabama 3 4708.708 8.6%

18 Kentucky 3 4314.113 0.0%

18 Mississippi 3 2951.996 7.0%

19 Tennessee 3 6296.254 9.5%

20 Arkansas 3 2889.45 8.4%

20 Louisiana 3 4492.076 8.8%

20 Oklahoma 3 3687.05 8.4%

21 Texas 3 24782.3 8.0%

22 Colorado 4 5024.748 6.2%

23 Idaho 4 1545.801 6.1%

23 Montana 4 974.989 0.0%

23 Utah 4 2784.572 6.7%

23 Wyoming 4 544.27 5.4%

24 Arizona 4 6595.778 8.2%

25 Nevada 4 2643.085 7.9%

25 New Mexico 4 2009.671 6.6%

26 California 4 36961.66 8.4%

27 Alaska 4 698.473 1.4%

27 Hawaii 4 1295.178 4.4%

27 Oregon 4 3825.657 0.0%

27 Washington 4 6664.195 8.9%
6-A-3

REFERENCES

1. U.S. Census Bureau, The 2012 Statistical Abstract, 2012. (Last accessed May, 2013.) 

<http://www.census.gov/compendia/statab/cats/population.html>

2. The Sales Tax Clearinghouse, 2013. (Last accessed May, 2013.) 

<https://thestc.com/STRates.stm>
1

APPENDIX 7-A. LOAD USAGE FACTOR IN COMMERCIAL LAUNDRY 

APPLICATIONS

TABLE OF CONTENTS

7-A.1 INTRODUCTION .........................................................................................................2

7-A.2 METHODOLOGY AND DATA ...................................................................................2

7-A.2.1 Computation of gallons per cycle ..................................................................................3

7-A.3 FINDINGS .....................................................................................................................3

LIST OF TABLES

Table 7-A.1.1 Load Usage Factors from the Test Procedure ....................................................2

Table 7-A.2.1 Cal UCONS Commercial Laundry Program: Number of monitored 

days for each washer ..........................................................................................3

Table 7-A.3.1 Load Usage Factors from the Cal-UCONS Commercial Laundry 

Program ..............................................................................................................4

LIST OF FIGURES

Figure 7-A.3.1 Distribution of Load Usage Factors .......................................................................4
2

APPENDIX 7-A. LOAD USAGE FACTOR IN COMMERCIAL LAUNDRY 

APPLICATIONS

7-A.1 INTRODUCTION

 The purpose of this appendix is to clarify how the Department of Energy (DOE) made its 

estimates regarding the load usage factor (LUF) to address potential differences in loading 

practices between residential clothes washer usage and commercial clothes washer usage. 

DOE’s current approach for estimating dryer energy use is based on the existing test 

procedure for residential clothes washers. The approach utilizes the following equation to 

calculate the per-cycle energy consumption for removing moisture – 

DE ((LUF W ) (LUFavg Wavg ) (LUF W ))××+×+×= ( ) RMC %4 DEF ×× DUF _

max max min min

Where:

LUF = load usage factor, 

W = weight of test load [pounds (lb)/cycle], 

RMC = moisture content that remains after washing (lb),

DEF = nominal energy required for a clothes dryer to remove moisture from clothes 

(0.5kWh/lb), and 

DUF = dryer usage factor, a percentage of washer loads dried in a clothes dryer (0.91). 

DOE implements the Load Usage Factor, as described in the current test procedure. As 

per this description, LUFmax, LUFmin, and LUFavg are established as described below: 

Table 7-A.1.1 Load Usage Factors from the Test Procedure

LUFmax LUFavg LUFmin

Manual Fill 0.72 - 0.28

Adaptive Fill 0.12 0.74 0.14

Since the test procedure describes the load usage for a typical residential application, 

DOE analyzed data from available studies to establish loading practices in a commercial setting. 

7-A.2 METHODOLOGY AND DATA

 For the purposes of understanding loading patterns, DOE looked at total water 

consumption as a proxy for loading. DOE collected data from the Cal-UCONS Commercial 

Laundry Program, which monitored clothes washers from different coin-operated laundromats or 

multifamily laundry rooms within the Southern California Gas Company (SCG) and the San 
3

Diego Gas and Electric Company (SDG&E) service territories.1 The study conducted in situ 

monitoring of 50 clothes washers at selected properties, either common area laundry in multifamily buildings or coin-operated laundromats. The CCW units included 21 clothes washers 

located at two commercial coin-operated laundromats and 32 clothes washers at four multifamily properties, for a total of 53 clothes washers. Of these, only 6 coin-operated laundromats 

monitored water usage. The monitored data included hot and cold water gallons dispersed at 5 

minute intervals with indicators of a completion of a cycle. In order to assess loading, DOE only 

examined those washers that had gallons recorded for both cold and hot water. For all sites, only 

six clothes washers had data for gallons of hot and cold water. Two of these clothes washers 

were from the same site (CCW#3 and CCW#4). Table 7-A.2.1 provides the number of days of 

data for each clothes washer. 

Table 7-A.2.1 Cal UCONS Commercial Laundry Program: Number of monitored days 

for each washer

CCW #1 CCW #2 CCW#3 CCW#4 CCW#5 CCW#6

9 13 12 12 35 35

7-A.2.1 Computation of gallons per cycle 

 A cycle was flagged as a beginning of the cycle if the number of gallons for hot or cold 

water during that interval were greater than zero for the current interval and equal to zero for 

both cold and hot water for at least the previous two consecutive 5-minute intervals. The amount 

of hot and cold water gallons were then summed until at least two intervals following the current 

interval recorded zero gallons for both hot and cold water. This interval was then flagged as the 

completion of the cycle. 

DOE further categorized each cycle into different buckets based on their total gallons 

used during that cycle. The Load Usage Factor is described in the current test procedure. 

7-A.3 FINDINGS

Table 7-A.3.1 shows the different Load Usage Factors for the six different clothes 

washers we analyzed from the Cal-UCONS Commercial Laundry Program. These are compared 

to the Load Usage Factors from DOE’s test procedure. The distribution of LUFmin varies from 

7% to 36% across all six clothes washers. A wide range of values is also apparent for LUFavg and 

LUFmax. Figure 7-A.3.1 shows the distribution for all clothes washers as well. CCW#1, 

CCW#5, and CCW#6 are all located in multifamily homes. All other clothes washers are located 

in coin-operated Laundromats. Variability may also be due to different clothes washing 

behaviors of residents living multifamily buildings or customers washing at coin-operated 

Laundromats. However, we do not have enough data here to conclude this. Thus the variability 

of the data is problematic. 

 In addition, CCW#5 and CCW#6 are monitored for at least 20 more days than the rest of 

the clothes washers. This may explain why they have a couple of washing cycles with a high 
4

water usage, but the majority of their washing loads are around the average amount of water per 

cycle. Furthermore, all of these clothes washers are located in the Southern California area, 

which may not be representative of the U.S. population. However, since analysis of the CalUCONS Commercial Laundry Program does represent a section of the U.S population, DOE 

considers it for a sensitivity analysis. For default LUF values, DOE decided to use the load usage 

factors from the current test procedure. 

Table 7-A.3.1 Load Usage Factors from the Cal-UCONS Commercial Laundry 

Program

DOE TP CCW 

#1 

CCW 

#2 

CCW 

#3 

CCW 

#4 

CCW 

#5 

CCW 

#6 

Average 

across 

CCWs

LUFmin 0.14 0.10 0.26 0.14 0.07 0.36 0.29 0.20

LUFavg 0.74 0.69 0.53 0.43 0.37 0.60 0.61 0.54

LUFmax 0.12 0.22 0.21 0.43 0.56 0.04 0.10 0.26

Figure 7-A.3.1 Distribution of Load Usage Factors 

0%

10%

20%

30%

40%

50%

60%

70%

80%

LUFmin LUFavg LUFmax

Frequency

DOE TP CCW #1 CCW #2 CCW #3 CCW #4 CCW #5 CCW #6
5

References

1. Pacific Northwest National Laboratory (PNNL), Cal-UCONS Commercial 

Laundry Program Measurement and Evaluation, 2008. Report No. PNWD-3910. 
8A-i

APPENDIX 8-A. USER INSTRUCTIONS FOR LIFE-CYCLE COST AND 

PAYBACK PERIOD SPREADSHEET 

TABLE OF CONTENTS

8-A.1 DEFINITIONS ....................................................................................................... 8-A-1

8-A.2 BASIC INSTRUCTIONS ...................................................................................... 8-A-2
8-A-1

APPENDIX 8-A. USER INSTRUCTIONS FOR LIFE-CYCLE COST AND PAYBACK 

PERIOD SPREADSHEET

8-A.1 DEFINITIONS

The interested reader can examine and reproduce detailed results of the U.S. Department 

of Energy’s (DOE’s) life-cycle cost (LCC) and payback period (PBP) analysis for commercial 

clothes washers by using Microsoft Excel spreadsheets available on DOE’s website at

http://www1.eere.energy.gov/buildings/appliance_standards/residential/clothes_washers.html. 

To fully execute the spreadsheets requires both Microsoft Excel and Crystal Ball software. Both 

applications are commercially available. Crystal Ball is available at www.decisioneering.com. 

The latest version of the workbook, which is posted on the DOE website, was tested 

using Microsoft Excel 2007. The LCC and PBP workbook for commercial clothes washers 

comprises the following worksheets.

Summary Presents the results of an analysis in terms of average LCC, LCC

savings, and median, average, and undefined PBP for both frontand top-loading washers. A table includes, for each efficiency level

considered, installed price; lifetime operating cost; LCC; average 

savings; and the percentage of customers that would incur a net 

cost, no impact, or net savings from each standard level. The user 

can stipulate three parameters for a simulation run: whether the 

AEO energy price trend reflects an economic case that is reference, 

low-growth, or high-growth (reference is default); and the number 

of simulation runs to be performed within a range of 1000–10,000 

(10,000 is default). 

LCC & Payback Lists the input values used to calculate LCC and PBP. Parameters 

include fuel type for water heating and clothes drying; annual 

numbers of cycles or loads; product lifetime; the discount rate 

applied to costs and savings; energy and water prices; the basecase efficiency distribution; product price and total installed cost; 

and energy and water use. The result is annual and cumulative cash

flow for both front- and top-loading clothes washers operating at 

each efficiency level.

Energy & Water Use Contains per-cycle energy and water use data at each efficiency 

level broken down into washer machine energy use, dryer energy 

use (gas or electric); water heating energy use (electric or gas); and 

both total and hot water use. Identifies the parameters exported to 

the LCC and PBP analysis.
8-A-2

Base-Case Efficiency Gives the market shares for efficiency levels in the base case.

Distribution

Equipment Prices Develops total installed cost for clothes washers in 2012$. This 

sheet provides baseline and incremental manufacturer costs, retail 

price, sales tax, and installation cost for both product classes and 

each efficiency level. Includes the assumptions used about 

markups and sales tax.

Energy & Water Prices Contains the regional prices in 2012$ for electricity, gas, and water

used in the LCC and PBP analysis. 

Energy Price Trends Contains the electricity and natural gas price trends for the 

reference, high, and low economic growth scenarios based on AEO 

2013. 

Water Price Trend Contains the historical water price trend that is used to forecast 

future water price. 

Discount Rate Contains data from which an average discount rate and a 

distribution of discount rates are determined. 

Forecast Cells Gives details regarding base-case efficiency distributions for both 

classes of clothes washers. Median, minimum, maximum, and 

average values are given, along with 5th, 25th, 50th, 75th, and 95th

percentile values. Included are product prices and details of the 

LCC and PBP (LCC savings in terms of money, energy, and water 

and the percentages of customers that would experience a net cost, 

no impact, or net savings from each efficiency level). 

Lifetime Presents the average lifetime, in years, for front- and top-loading 

commercial clothes washers, the Weibull parameters used for the 

survival function, and a graph of the Weibull retirement function

for residential clothes washers. 

8-A.2 BASIC INSTRUCTIONS

Basic instructions for operating the LCC spreadsheet are provided below. 

1. After downloading the LCC file from DOE’s website, use Microsoft Excel to open 

it. At the bottom of the workbook, click on the tab for the sheet labeled Summary. 

2. Use Excel’s “View/Zoom” command in the top menu bar to change the size of the 

display so that it fits your monitor. 
8-A-3

3. Use the graphical interface in the spreadsheet to choose parameters or enter data. 

You can change the default choices for the three inputs listed under "User Input" 

(energy price trend, start year, and number of simulation runs). To change a default 

input, select the desired value from the drop-down choices by the input box. 

4. After selecting the desired parameters, click the “Run” button. The spreadsheet will 

minimize until the simulation is complete, and will then re-open with the updated 

results.
8B-i

APPENDIX 8-B. UNCERTAINTY AND VARIABILITY 

TABLE OF CONTENTS

8-B.1 INTRODUCTION ................................................................................................. 8-B-1

8-B.2 UNCERTAINTY ................................................................................................... 8-B-1

8-B.3 VARIABILITY ...................................................................................................... 8-B-1

8-B.4 APPROACHES TO UNCERTAINTY AND VARIABILITY ............................. 8-B-2

8-B.5 PROBABILITY ANALYSIS AND THE USE OF CRYSTAL BALL................. 8-B-2

LIST OF FIGURES 

Figure 8-B.5.1 Normal Probability Distribution ................................................................ 8-B-3

Figure 8-B.5.2 Triangular Probability Distribution............................................................ 8-B-3

Figure 8-B.5.3 Uniform Probability Distribution ............................................................... 8-B-3
8-B-1

APPENDIX 8-B. UNCERTAINTY AND VARIABILITY

8-B.1 INTRODUCTION

 Analysis of a potential energy efficiency standard involves calculating effects, for 

example, the effect of a standard on consumer life-cycle cost (LCC). 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 or model; and (3) provide numerical values for each quantity. In the 

simplest case, the equation is unambiguous (it contains all relevant quantities and no others), 

each quantity has a single numerical value, and the calculation produces a single value. 

Unambiguousness and precision are rarely the case, however. In most cases, the model and/or 

the numerical values for each quantity in the model are not completely known (i.e., there is 

uncertainty) or the model and/or the numerical values for each quantity in the model depend 

upon other conditions (i.e., there is variability).

 Thorough analysis involves accounting for uncertainty and variability. Although the 

simplest analysis involves a single numerical value for each quantity in the calculation, 

arguments can arise about the appropriate value for each quantity. Explicit analysis of 

uncertainty and variability provides more complete information to the decision-making process.

8-B.2 UNCERTAINTY

 When making observations of past events or speculating about the future, imperfect 

knowledge is the rule rather than the exception. For example, the energy consumed by a 

particular type of appliance (such as the average residential clothes washer) is not recorded 

directly, but rather estimated based on available information. Even direct laboratory 

measurements have a margin of error. When estimating numerical values expected for quantities 

at some future date, the exact outcome rarely is known. 

8-B.3 VARIABILITY

Variability in the calculation of a quantity means that different applications or situations 

produce different numerical values. Specifying an exact value for a quantity may be difficult 

because the value depends on something else. For example, the annual number of cycles for a 

commercial clothes washer application depends on the specific circumstances and behavior of 

the user. Variability makes specifying an appropriate population value more difficult, because no 

one value is likely to be representative of the entire population. Surveys can be helpful here, and 

analysis of surveys can relate the variable of interest (e.g., cycles per day) to other variables that 

are better known or easier to forecast (e.g., average number of users of a laundry facility on a 

given day).
8-B-2

8-B.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 based on 

electricity costs of 6, 11, 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 energy 

rate at which the consumer achieves savings in operating costs 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 

characterized by variability (e.g., electricity rates in different regions), surveys can be used to 

generate a frequency distribution of numerical values (e.g., the number of customers subject to

electricity rates at particular levels) to estimate the probability of each value. For quantities 

characterized by uncertainty, statistical or subjective measures can be used to provide 

probabilities (e.g., manufacturing cost to improve energy efficiency to a given level may be 

estimated to be $10 ± $3). 

 The major disadvantage of the probability approach is that it requires more information, 

namely information about the shapes and magnitudes of the variability and uncertainty of each 

quantity. The advantage of the probability approach is that it provides more information about 

the outcome of the calculations; that is, it provides the probability that the outcome will be in a 

particular range. 

 Scenario and probability analysis provide some indication of the robustness of a policy 

given the identified uncertainties and variability. A policy is robust when the impacts are 

acceptable over a wide range of possible conditions. 

8-B.5 PROBABILITY ANALYSIS AND THE USE OF CRYSTAL BALL

 To quantify the uncertainty and variability that exist in inputs to the engineering, LCC, 

and payback period analyses, DOE used Microsoft Excel spreadsheets combined with Crystal 

Ball, a commercially available add-in software, to conduct probability analyses. The probability 

analyses used Monte Carlo simulation and probability distributions. 
8-B-3

Simulation refers to any analytical method meant to duplicate 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 outcome. Spreadsheet risk analysis uses both a spreadsheet model and 

simulation to automatically analyze the effect of varying inputs on outputs of the modeled 

system. One type of spreadsheet simulation is Monte Carlo simulation, which randomly 

generates values for uncertain variables numerous times. Monte Carlo simulation was named for 

Monte Carlo, Monaco, where the primary attractions are casinos containing games of chance. 

Games of chance such as roulette wheels, dice, and slot machines, exhibit random behavior. The 

random behavior in games of chance is similar to how Monte Carlo simulation selects variable 

values at random to simulate a model. When you roll a die, you know that a 1, 2, 3, 4, 5, or 6 will 

come up, but you do not know which number for any particular roll. So too with 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 (a variable that has a range of possible values), a probability 

distribution is used to define possible values. The type of distribution selected is based on the 

conditions surrounding that variable. Types of probability distributions include the following. 

Figure 8-B.5.1 Normal Probability Figure 8-B.5.2 Triangular Figure 8-B.5.3 Uniform Probability

Distribution Probability Distribution 

Distribution

During a simulation, multiple scenarios are calculated by sampling values repeatedly 

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 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. 

NORMAL TRIANGULAR UNIFORM
8-C-i

APPENDIX 8-C. LIFETIME DISTRIBUTIONS 

TABLE OF CONTENTS

8-C.1 INTRODUCTION ................................................................................................. 8-C-1

8-C.2 DERIVATION OF WEIBULL DISTRIBUTION PARAMETERS ..................... 8-C-1

LIST OF TABLES

Table 8-C.1.1 Commercial Clothes Washers .................................................................... 8-C-2

LIST OF FIGURES 

Figure 8-C.2.1 Fraction of the Commercial Clothes Washers Failing, Multi- Family ....... 8-C-3

Figure 8-C.2.2 Cumulative Lifetime Length of Commercial Clothes Washers

Multi-Family .............................................................................................. 8-C-3

Figure 8-C.2.3 Fraction of the Commercial Clothes Washers Failing, Laundromat .......... 8-C-4

Figure 8-C.2.4 Cumulative Lifetime Length of Commercial Clothes Washers

Laundromat ................................................................................................ 8-C-4

 
8-C-1

APPENDIX 8-C. LIFETIME DISTRIBUTIONS

8-C.1 INTRODUCTION

The U.S. Department of Energy (DOE) characterized the lifetime of both product classes

of clothes washers being considered for new energy efficiency standards (front-loading and toploading). DOE characterized commercial clothes washer lifetimes using a Weibull probability 

distribution that ranged from the minimum to maximum lifetime estimates, as described in 

chapter 8, section 8.2.3. The Weibull distribution is recommended for evaluating lifetime data, 

because it can be shaped to match low, most likely (or average), and high values. The probability 

of exceeding the high value is contained in the long tail of the Weibull distribution.

1,2

8-C.2 DERIVATION OF WEIBULL DISTRIBUTION PARAMETERS

Weibull distributions utilize available data to assign low, average, and high values to a 

random variable that has unknown distribution parameters. DOE applied Weibull distributions to 

product lifetime data to derive low, average, and high lifetime values, along with a percentile

containing a high value. A similar approach is described in a technical note to the software 

Crystal Ball, which uses a most likely value in place of an average value.3 The Weibull 

distribution can be defined as: 

 Where: 

 L = location, 

 α = scale, and 

 β = shape. 

 The cumulative distribution therefore is: 

 Based on available data, Weibull distribution parameters are specified as follows.

1. The output deviates must be greater than the expert opinion of low value. 

2. The average, Xavg, must be equal to the average value from the available data. 

3. The high value, xb, must correspond to some particular percentile point (e.g., 95 

percent or 90 percent). 
8-C-2

The values for the parameters in the equations were determined using the approach 

outlined in Crystal Ball’s technical note.

3

 

Crystal Ball can be used to check a solution by specifying a Weibull distribution that has 

the calculated parameters (location, scale, and shape) in an assumption cell, then generating a 

forecast that equals that assumption. The forecast histogram and statistics will confirm whether

the Weibull distribution matches the desired shape. 

This solution can be checked using Crystal Ball by specifying a Weibull distribution with the 

calculated parameters (location, scale, and shape) in an assumption cell and generate a forecast 

that equals the assumption. Forecast histogram and statistics verify that the Weibull distribution 

matches the desired shape.

Table 8C.2.1 shows the average, minimum, maximum lifetime, and maximum percentile 

values used to determine the Weibull distribution parameters alpha and beta. For commercial 

clothes washers, DOE developed two lifetime estimates based on product application—one for 

multi-family applications and another for Laundromats. DOE estimated that product lifetimes 

did not vary based on whether the washer was top-loading or front-loading. DOE estimated that 

the maximum lifetime percentile for both product applications was 99 percent. 

Table 8-C.1.1 Commercial Clothes Washers

Product Application

Expert Opinion Values

Weibull 

Parameters

Minimum

years

Average

years

Maximum

years

Maximum

percentile

%

Alpha 

(scale)

Beta 

(shape)

Multi-Family 7 11.3 15.5 99 4.782 2.655

Laundromat 5 7.1 9.3 99 2.391 2.655

Figures 8C.2.1 to 8C.2.4 show the Weibull distribution as well as the cumulative Weibull 

distribution for each commercial clothes washer product application. 
8-C-3

 Figure 8-C.2.1 Fraction of the Commercial Clothes Washers Failing, Multi- Family

 Figure 8-C.2.2 Cumulative Lifetime Length of Commercial Clothes Washers, 

 Multi-Family

0%

5%

10%

15%

20%

25%

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Age of Equipment (Years)

Probability of Failure(%)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Age of Equipment (Years)

Probability (%)
8-C-4

 Figure 8-C.2.3 Fraction of the Commercial Clothes Washers Failing, Laundromat 

 Figure 8-C.2.4 Cumulative Lifetime Length of Commercial Clothes Washers, 

Laundromat 

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

0 1 2 3 4 5 6 7 8 9 10 11

Age of Equipment (Years)

Probability of Failure (%)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 1 2 3 4 5 6 7 8 9 10 11

Age of Equipment (Years)

Probability (%)
8-C-5

REFERENCES

1. Barnes, P. R., J. W. Van Dyke, B. W. McConnell, S. M. Cohn, and S. L. Purucker, The 

Feasibility of Replacing or Upgrading Utility Distribution Transformers During Routine 

Maintenance, 1995. Oak Ridge National Laboratory. Oak Ridge, TN. Report No. Report 

No. ORNL-6804/R1. <http://www.ornl.gov/~webworks/cpr/v823/rpt/78562.pdf> 

2. Karr, T., Making the Most of Life Test Data. Appliance Magazine, 2003. 

<http://www.appliancemagazine.com/print.php?article=197&zone=1&first=1> 

3. Crystal Ball, Technote: Derivation of Weibull Distribution Parameters When the 

Minimum, Most Likely Value, and a Percentile are Known, 2007. (Last accessed 

November, 2013.) <http://www.crystalball.com/support/simulation/cbl_gen_021A.html>
<<-i

APPENDIX 9-A RELATIVE PRICE ELASTICITY OF DEMAND FOR CLOTHES 

WASHERS

TABLE OF CONTENTS

9-A.1 INTRODUCTION ................................................................................................. 9-A-1

9-A.2 IMPLIED PRICE ELASTICITY ........................................................................... 9-A-1

9-A.2.1 Implied Price Elasticity Estimation Method .......................................................... 9-A-2

9-A.2.2 Price Elasticity Over Time ..................................................................................... 9-A-4

9-A.2.3 Discussion of Relevant Economic Theory ............................................................. 9-A-4

9-A.3 PRICE IMPACTS ON THE MARKET SHARE OF FRONT AND TOP 

LOADER CLOTHES WASHERS ........................................................................ 9-A-6

LIST OF TABLES

Table 9-A.2.1 Price elasticity of demand for laundry equipment and other appliances 

and durables ............................................................................................... 9-A-2

Table 9-A.2.2 Route Operator Revenue by Business Activity: Coinmach ($ million) ..... 9-A-3

Table 9-A.2.3 Price Elasticity Decrease after CCW Price Change ................................... 9-A-4
9-A-1

Appendix 9-A. RELATIVE PRICE ELASTICITY OF DEMAND FOR CLOTHES 

WASHERS

9-A.1 INTRODUCTION

This appendix describes the methods used by DOE to estimate price elasticity of demand 

and cross-price impacts for commercial clothes washers.

9-A.2 IMPLIED PRICE ELASTICITY

The price elasticity of demand describes the responsiveness of the quantity purchased of 

a product to a change in its price, and can be generically defined as the ceteris paribus percent 

change in shipments given a 1% change in price, or: 

ed = 

P0

Q0

∗

∂Q

∂P

where P and Q represent price and shipments quantity, respectively.1

 Goods for which a 1% change in price leads to a greater than 1% change in quantity 

demanded are considered “elastic,” while goods for which a 1% change in price leads to a less 

than 1% change in quantity demanded are “inelastic.” The more elastic a given product, the more 

a change in cost that translates into a change in price will alter the demand for the product.

DOE believes that CCW are highly price inelastic and applies a default price elasticity of 

demand of zero for this product, meaning that any cost and price increases resulting from 

efficiency standards are unlikely to substantially affect the quantity of CCW demanded. As 

CCW are a somewhat niche product, estimates of CCW price elasticity were not found in the 

literature, and DOE does not have the necessary data to perform an accurate independent 

calculation of CCW price elasticity. Instead, the value of zero is based on DOE’s understanding 

of the CCW market and distribution channels, particularly in relation to the demand for laundry 

services. 

In the case of CCW, consumer price elasticity of demand for laundry services, rather than 

laundry equipment is most relevant. However, in the absence of any specific estimates of

laundry service price elasticity, laundry equipment price elasticity can provide useful insights. 

Table 1 compares the price elasticity of demand for residential laundry equipment and other 

durable goods, demonstrating that demand for clothes dryers and clothes washers tends to be 

somewhat less elastic than for other appliances and durables. Inelastic demand for laundryrelated products suggests that demand for laundry services is likely inelastic as well. As 
9-A-2

demonstrated in Table 1, laundry equipment appears to be somewhat less price elastic than other 

types of appliances and durables.

Table 9-A.2.1 Price elasticity of demand for laundry equipment and other appliances 

and durables

Product Price elasticity of demand Source

Residential Clothes Dryer -0.14 Golder and Tellis 2

Residential Clothes Washer -0.27 Dale and Fujita 3

Dishwasher -0.43 Dale and Fujita 3

Dishwasher -0.42 Golder and Tellis 2

Room Air Conditioner -0.37 Golder and Tellis 2

Refrigerator -0.37 Revelt and Train 4

Refrigerator -0.35 Dale and Fujita 3

Automobiles -1.07 Hymans 5

Automobiles -0.36 Hymans 5

9-A.2.1 Implied Price Elasticity Estimation Method

To validate the use of a price elasticity of zero, DOE estimates an implied CCW price 

elasticity based on known variables describing the market for laundry services. CCW price 

elasticity is approximated as follows:

Implied ed,CCW = ed,LaundrySvc ∗ � PriceCCW

PriceLaundrySvc�

where, 

ed,CCW = price elasticity of demand for commercial clothes washers,

ed,LaundrySvc = average price elasticity of demand for laundry service,

PriceCCW = CCW wholesale price (or cost), and

PriceLaundrySvc = price (or cost) of providing laundry service.

Based on review of the literature, an exact value of the price elasticity of demand was not 

identified, but a likely range was established: − 1.0 < ed,LaundrySvc ≤ −0.5. Schettkat and 

Yocarini (2006) note a range of -0.53 to -0.47 (averaging -0.5) for the average price elasticity of 

service demand in the U.S.6 Laundry service is expected to be more of a necessity than luxury, 

and thus the price elasticity of demand is modeled as inelastic, with a bounding value of -1.0. 

For example, Kemp (1998) provides a discussion of the relationship between luxury-necessity 

status and price elasticity for a variety of goods and services.7

 The provision of laundry services involves other aspects, including equipment and 

services, beyond the simple rental or sale of a clothes washer. As described by Coinmach 

Corporation, a major route operator, in a recent 10-K annual financial report to the SEC, route 

operator core business activities center around “leasing laundry rooms from building owners and 
9-A-3

property management companies, installing and servicing laundry equipment, collecting 

revenues generated from laundry machines and operating retail laundromats;” additionally, route 

operators “rent laundry machines and other household appliances to property owners, managers 

of multi family housing properties, individuals and corporate entities,” and may engage in 

laundry equipment distribution.8

Coinmach reports revenue by individual business activities in their SEC 10-K report: route, 

rental, and distribution.8, 9 “Route” refers primarily to laundry room leasing and management 

activities; “rental” refers to revenue from leasing clothes washers and dryers; “distribution” 

refers to equipment wholesaling activities. 

Route activities related to laundry room or laundromat design, installation, and maintenance are 

not likely to be substantially altered by a change in the price of CCW, but rental and distribution 

revenue are more directly influenced by CCW features and costs. A route operator generally 

both rents CCW to a building owner and manages the laundry space.10

Table 9-A.2.2 Route Operator Revenue by Business Activity: Coinmach ($ million) 

Business Activity FY 2007 FY 2006 FY 2005 FY 2004

Route 492.1 481.7 472.5 469.6

Rental 38.4 36.1 34.4 32.6

Distribution 24.8 25.7 31.7 28.9

Rental % of total 6.9% 6.6% 6.4% 6.1%

Rental +Distribution % 

of total 11.4% 11.4% 12.3% 11.6% 

Source: Coinmach Corporation (2005; 2007)

 From Coinmach’s ratios of the route, rental, and distribution revenue categories in 2004 - 

2007, DOE estimates that PriceCCW

PriceLaundrySvc

 = 0.061 to 0.123. 

Combining the above data inputs, 

Implied ed,CCW low = ed,LaundrySvc ∗ � PriceCCW

PriceLaundrySvc� = −0.5 ∗ 0.061 = −0.031

Implied ed,CCW high = ed,LaundrySvc ∗ � PriceCCW

PriceLaundrySvc� = −1.0 ∗ 0.123 = −0.123

DOE estimates an implied price elasticity of demand for CCW of approximately -0.03 to 

-0.12 based on the laundry equipment distribution channels and market for laundry services. 

DOE notes that values in this range are quite low, and feels that this exercise supports the use of 

a price elasticity of zero.

 A further factor to consider is that CCW price is in some cases not readily apparent to the 

building owner. CCW price is also unknown by the end user of the product, whose demand for 

laundry services drives building owner demand for laundry equipment and laundry room or 

laundromat management services. CCW are generally leased at a monthly rate, so the building 
9-A-4

owner is exposed to only a portion of the total CCW cost increase, based on the ratio of their 

lease term to the expected CCW useful life. If the CCW price is not visible, the consumer will 

not have a price signal to react to. As the implied price elasticity estimate assumes consumer 

awareness of the CCW price, it is likely an overestimation.

9-A.2.2 Price Elasticity Over Time

 Price elasticity for a given product is not constant over time; numerous studies have 

demonstrated decreases in price elasticity for durable goods as the period of time since a price 

change increases.5, 11, 12 Similarly, decreases in price elasticity have been demonstrated through 

the stages of a product’s life cycle.

13 DOE projects that any non-zero CCW price elasticity will 

decrease over time, following the proportional decreases demonstrated for automobiles (Table 9-

A.2.3).5

Table 9-A.2.3 Price Elasticity Decrease after CCW 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 

Implied Price Elasticity (low) -0.031 -0.024 -0.015 -0.007 -0.002 -0.001

Implied Price Elasticity (high) -0.123 -0.096 -0.060 -0.028 -0.010 -0.003

Source: Hymans (1971) and DOE calculations

While estimated to be small initially, the CCW implied price elasticity of demand further 

approaches zero in the years following any price change due to standards. DOE believes that 

using a price elasticity of zero, rather than a small non-zero price elasticity, will introduce at 

most a negligible increase in the probability of error in downstream analyses. An alternative 

scenario using the value of -0.12 is also available as a sensitivity analysis.

9-A.2.3 Discussion of Relevant Economic Theory

 Demand for commercial clothes washers can be viewed as a form of derived demand; 

what end users want are laundry services, what apartment and laundromat owners want is an 

avenue to provide laundry services, of which the commercial clothes washer as defined in this 

rulemaking is only one input. Other inputs include clothes dryers and other laundry equipment, 

water and energy, building space, change machines, and labor for maintenance and management. 

While many discussions of derived demand in the literature come from the realm of labor 

economics and focus on the relationship between demand for labor and demand for a final 

product whose inputs include labor and capital and possibly other inputs, many if the major 

insights can still be applied to the case of demand for an input (CCW) and demand for a service 

whose inputs include CCW and the other inputs described above.
9-A-5

 The four key Marshallian rules of derived demand, as restated by Pigou (1932), are as 

follows:14

1) "The demand for anything is likely to be more elastic, the more readily substitutes for 

that thing can be obtained." (p. 682) 

2) "The demand for anything is likely to be less elastic, the less important is the part played 

by the cost of that thing in the total cost of some other thing, in the production of 

which it is employed." (p. 683) 

3) "The demand for anything is likely to be more elastic, the more elastic is the supply of 

co-operant agents of production." (p. 684) 

4) "The demand for anything is likely to be more elastic, the more elastic is the demand for 

any further thing which it contributes to produce." (p. 685) 

 In the preceding treatment of CCW price elasticity, DOE draws largely on rules 2 and 4, 

for which some relevant data is available. Below, the rules are discussed in the context of CCW.

 Considering rule 1, from the end-user viewpoint some substitutes for laundry services 

provided by covered CCW in common area laundry facilities or laundromats may exist. It is 

possible that some end-users could instead purchase and use a residential clothes washer, 

assuming that their housing unit has appropriate laundry hook-ups. An additional possibility is 

that end-users may begin to favor types of CCW not covered by this rule if there is a sizeable 

increase in the cost of using a residential-style CCW but not of a larger hard-mount CCW. These 

are fairly similar though imperfect substitutes; they can potentially accomplish the purpose of 

providing laundry services, but each involves additional factors that differ from the covered 

CCW. From the service provider’s perspective, residential-style soft-mount CCW and other 

CCW types may or may not be interchangeable, depending on the space they have available. 

The effect of end-user’s preferences for certain washer types will be limited by physical building 

constraints. While there are some substitutes available for covered CCW, DOE does not believe 

that they will be so readily interchanged as to strongly influence the overall price elasticity of 

demand for CCW. 

Hamermesh (1993) rephrases rule 2: “the demand for an input that accounts for a small 

share of costs will be less elastic, other things equal, because the scale effect will be very 

small.”15 As the cost of CCW equipment is only a small portion of the total cost of providing 

laundry services (as demonstrated by the relative shares of route operator revenue from 

equipment rental/sale and other services), rule 2 suggests that this fact supports an inelastic 

demand for CCW. This is incorporated into the estimation equation in the factor PriceCCW

PriceLaundrySvc

. 

Restating rule 3 in terms of the present example, demand elasticity for CCW is expected 

to be more inelastic if the process of providing laundry services makes use of other inputs that 

are highly inelastic in supply. Other inputs, in this case, include other laundry equipment, labor, 

capital, and building space. DOE does not have sufficient information on the elasticity of supply 

for the other inputs of production to directly incorporate this factor into the estimation equation; 
9-A-6

DOE believes that the influence of these other factor is unlikely to overturn the conclusion that 

CCW price elasticity of demand is quite inelastic.

Regarding rule 4, the demand for CCW is likely to be less elastic, the less elastic the 

demand for laundry services. DOE believes laundry services to be more of a necessity than 

luxury, leading to the factor − 1.0 < ed,LaundrySvc ≤ −0.5 incorporated into the estimation equation. 

9-A.3 PRICE IMPACTS ON THE MARKET SHARE OF FRONT AND TOP 

LOADER CLOTHES WASHERS

While DOE expects commercial clothes washers in aggregate to be highly price-inelastic, 

substitution between types of washers (i.e. front- and top-loading) may occur if the change in 

price due to standards is not uniform across types. The cross-price impact is estimated to model 

likely switching across product classes.

Due to insufficient data, DOE was not able to estimate cross-price impacts on the market 

share of top-loading and front-loading commercial clothes washers, and instead relies on its recent 

analysis performed for residential clothes washers.16 Analysis methods and results are summarized 

below. 

 In order to estimate the impact of price changes on market share, DOE applied a logistic 

regression method to regress market share on price variables and other attributes. The basic form of a 

logistic regression is: 

pi = � 1

1 + e−�β0+β1x1,i+⋯+βkxk,i�

�

 With some simple operations, the model can be transformed to an equivalent log-linear form: 

ln � pi

1 − pi

� β0 + β1x1,i + ⋯ + βkxk,i

In this case, pi is the market share of either top- or front-loading clothes washers, and 

pi

1−pi

is the ratio of clothes washer market shares, i.e., the relative probability of the market share 

of top and front-loading clothes washers. The explanatory variables include price-related 

variables, washer size and other washer attributes. The logistic regression model equation 

estimated in DOE’s residential clothes washer analysis is shown below.

ln � MarketShareFront

1 − MarketShareFront

� = −2.985386 + 0.0035452 ∗ PriceTop + 4.24347 ∗ �SizeFront

SizeTop �

Where:
9-A-7

MarketShareFront

1−MarketShareFront

 = relative probability of the market share of front- and top-loading clothes 

washers,

PriceTop = weighted monthly average price of top-loaders, 

PriceFront = weighted monthly average price of front-loaders, and 

SizeFront

SizeTop

 = ratio between the monthly weighted average of front-loaders tub volume 

and the monthly weighted average of top-loaders tub volume. 

The equation indicates that front loader market share is positively correlated with top 

loader price and size and negatively correlated with front loader price (all variables in this 

regression were statistically significant; the R2

 in the regression was 0.88). 

The price coefficients suggest that top- and front-loading washers are substitutes for each 

other. The positive coefficient associated with the size variable indicates that capacity is a 

desirable clothes washer feature for most consumers.

These regression results were used to derive the cross price impact of a change in the 

front loading washer price on the top loading market share (and vice versa). For example, these 

results suggest that a 10% increase in the price of front loader washers would lead to a 10.7 % 

decrease in top-loader market share, holding other variables constant and measured as changes 

from the reference case using average values for each variable. In this case, the front-loading 

cross-price impact (percent change in top-loading market share over percent change in frontloading price) is 1.07. Note that price elasticity varies according to the reference price and 

market share, typically measured as changes from mean values for these variables in the data set. 

In DOE’s dataset, the reference top-loading washer price is $403.78, the reference front-loading 

washer price is $823.83, the corresponding reference top-loading washer market share is 71% 

and the corresponding reference front washer market share is 29%. 

Similarly, these results indicate that a 20% price increase for top-loading washers would yield a 

21.49 % increase in front-loading market share, measured as changes from the variable means. 

Thus, in this example the top-loader cross-price impact is also 1.07. See the residential clothes 

washers 2012 Direct Final Rule Technical Support Document for additional details.16
9-A-8

REFERENCES

1. Varian, H. R., Intermediate Microeconomics: A Modern Approach. 1999. Vol. 5th 

Edition. W.W. Norton & Company: New York, London. 

2. Golder, P. and G. Tellis, Beyond Diffusion: An Affordability Model of the Growth of 

New Consumer Durables. Journal of Forecasting, 1998. 17: pp. 259-280 

3. Dale, L. and K. S. Fujita, An Analysis of the Price Elasticity of Demand for Household 

Appliances, 2008. Lawrence Berkeley National Laboratory. 

4. Revelt, D. and K. Train, Mixed Logit with Repeated Choices: Household's Choice of 

Appliance Efficiency Level. Review of Economics and Statistics, 1998. 80(4): pp. 647-

657 <http://www.mitpressjournals.org/doi/pdfplus/10.1162/003465398557735> 

5. Hymans, S., Consumer Durable Spending: Explanation and Prediction. Brookings Papers 

on Economic Activity, 1971. 1971(1): pp. 234-239 

6. Schettkat, R. and L. Yocarini, The shift to services employment: A review of the 

literature. Structural Change and Economic Dynamics, 2006. 17: pp. 127-147 

7. Kemp, S., Perceiving luxury and necessity. Journal of Economic Psychology, 1998. 19: 

pp. 591-606 

8. Coinmach Corporation, 10-K Annual Report for Fiscal Year 2005, 2005. SEC EDGAR. 

(Last accessed November 19, 2012.) 

<http://www.sec.gov/Archives/edgar/data/91693/000095012305007218/y09552e10vk.ht

m#106>

9. Coinmach Corporation, 10-K Annual Report for Fiscal Year 2007, 2007. SEC EDGAR. 

(Last accessed November 20, 2012.) 

<http://www.sec.gov/Archives/edgar/data/1282858/000095012307008319/y35770e10vk.

htm>

10. Mac-Gray Corporation, 10-K Annual Report for Fiscal Year 2011, 2011. SEC EDGAR. 

(Last accessed November 20, 2012.) 

<http://www.sec.gov/Archives/edgar/data/1038280/000104746912002365/a2207800z10-

k.htm#ds11601_item_8._financial_statements_and_supplementary_data>

11. Fibrich, G., A. Gavious, and O. Lowngart, The Dynamics of Price Elasticity of Demand 

in the Presence of Reference Price Effects. Journal of the Academy of Marketing Science, 

2005. 33(1): pp. 66-78 

12. Gowrisankaran, G. and M. Rysman, Dynamics of Consumer Demand for New Durable 

Goods. 2009, National Bureau of Economic Research Working Paper: Cambridge, MA. 
9-A-9

13. Lilien, G. L. and E. Yoon, An Exploratory Analysis of the Dynamic Behavior of Price 

Elasticity over the Product Life Cycle: An Empirical Analysis of Industrial Chemical 

Products. Issues in Pricing, 1988: pp. 261-408 

14. Pigou, A. C., The Economics of Welfare. 4 ed. 1932. MacMillian: London. 682-687. 

15. Hamermesh, D., Labor Demand. 1993. Princeton University Press: Princeton, N.J. 

16. U.S. Department of Energy, Technical Support Document: Energy Efficiency Program 

for Consumer Products and Commercial and Industrial Equipment: Residential Clothes 

Washers (Direct Final Rule), Building Technologies Program, Editor. 2012: Washinton, 

D.C. 
1

APPENDIX 10-A. FULL-FUEL-CYCLE MULTIPLIERS

TABLE OF CONTENTS

10-A.1 INTRODUCTION ............................................................................................... 10-A-1

10-A.2 METHODOLOGY .............................................................................................. 10-A-1

10-A.3 FULL-FUEL-CYCLE ENERGY MULTIPLIERS ............................................. 10-A-3

LIST OF TABLES

Table 10-A.2.1 Dependence of FFC Parameters on AEO Inputs ..................................... 10-A-3

Table 10-A.3.1 Full Fuel Cycle Energy Multipliers (Based on AEO 2013) ..................... 10-A-3
10-A-1

APPENDIX 10-A. FULL-FUEL-CYCLE MULTIPLIERS

10-A.1 INTRODUCTION

 This appendix summarizes the methods used to calculate full-fuel-cycle (FFC) energy 

savings expected to result from potential standards. The FFC measure includes point-of-use (site)

energy, the energy losses associated with generation, transmission, and distribution of electricity, 

and the energy consumed in extracting, processing, and transporting or distributing primary 

fuels. DOE’s traditional approach encompassed only site energy and the energy losses associated 

with generation, transmission, and distribution of electricity.1

 Per DOE’s 2011 Statement of 

Policy for Adopting Full Fuel Cycle Analyses, DOE now uses FFC measures of energy use and 

emissions in its energy conservation standards analyses. This appendix summarizes the methods 

used to incorporate the full-fuel-cycle impacts into the analysis.

This analysis uses several different terms to reference energy use. The physical sources of 

energy are the primary fuels such as coal, natural gas, liquid fuels, etc. Primary energy is equal to 

the heat content (Btu) of the primary fuels used to provide an end-use service. Site energy use is 

defined as the energy consumed at the point-of-use in a building or industrial process. Where 

natural gas and petroleum fuels are consumed at the site (for example in a furnace), site energy is 

identical to primary energy, with both equal to the heat content of the primary fuel consumed. 

For electricity, site energy is measured in kWh. In this case the primary energy is equal to the 

quads of primary energy required to generate and deliver the site electricity. This primary energy 

is calculated by multiplying the site kWh times the site-to-power plant energy use factor, given 

in chapter 10. For the FFC analysis, the upstream energy use is defined as the energy consumed 

in extracting, processing, and transporting or distributing primary fuels. FFC energy use is the 

sum of primary plus upstream energy use. 

Both primary fuels and electricity are used in upstream activities. The treatment of 

electricity in fuel cycle analysis must distinguish between electricity generated by fossil fuels 

and uranium, and electricity generated from renewable fluxes (wind, solar and hydro). For the 

former, the upstream fuel cycle impacts are derived from the amount of fuel consumed at the 

power plant. For the latter, no fuel per se is used, so there is no upstream component. 

10-A.2 METHODOLOGY 

The mathematical approach is discussed in the paper A Mathematical Analysis of Full Fuel 

Cycle Energy Use,

2

 and details on the fuel production chain analysis are presented in the paper 

Projections of Full Fuel Cycle Energy and Emissions Metrics.

3 The text below provides a brief 

summary of the methods used to calculate FFC energy.

When all energy quantities are normalized to the same units, the FFC energy use can be 

represented as the product of the primary energy use and an FFC multiplier. The FFC multiplier 

is defined mathematically as a function of a set of parameters representing the energy intensity 
10-A-2

and material losses at each production stage. These parameters depend only on physical data, so

the calculations do not require any assumptions about prices or other economic data. While in 

general these parameter values may vary by geographic region, for this analysis national 

averages are used. 

 In the notation below, the indices x and y are used to indicate fuel type, with x=c for coal, 

x=g for natural gas, x=p for petroleum fuels, x=u for uranium and x=r for renewable fluxes. The 

fuel cycle parameters are:

• ax is the quantity of fuel x burned per unit of electricity output, on average, for grid 

electricity. The calculation of ax includes a factor to account for transmission and 

distribution system losses. 

• by is the amount of grid electricity used in production of fuel y, in MWh per physical unit 

of fuel y.

• cxy is the amount of fuel x consumed in producing one unit of fuel y. 

• qx is the heat content of fuel x (MBtu/physical unit) 

• zx(s) is the emissions intensity for fuel x (mass of pollutant s per physical unit of x)

The parameters are calculated as a function of time with an annual time step; hence, a time 

series of annual values is used to estimate the FFC energy and emissions savings in each year of 

the analysis period. Fossil fuel quantities are converted to energy units using the heat content 

factors qx. To convert electricity in kWh to primary energy units, on-site electricity consumption 

is multiplied by the site-to-power plant energy use factor. The site-to-power plant energy use 

factor is defined as the ratio of the total primary energy consumption by the electric power sector 

(in quadrillion Btu’s) divided by the total electricity generation in each year.

The FFC multiplier is denoted μ (mu). A separate multiplier is calculated for each fuel used 

on site. A multiplier is also calculated for electricity reflecting the fuel mix used in its generation. 

The multipliers are dimensionless numbers that are applied to primary energy savings to obtain 

the FFC energy savings. The upstream component of the energy savings is proportional to (μ-1). 

The fuel type is denoted by a subscript on the multiplier μ. 

For DOE’s appliance standards energy savings estimates, the fuel cycle analysis 

methodology is designed to make use of data and projections published in the Annual Energy 

Outlook (AEO). Table 10-A.2.1 provides a summary of the AEO data used as inputs to the 

different parameter calculations. The AEO does not provide all the information needed to 

estimate total energy use in the fuel production chain. Reference three describes the additional 

data sources used to complete the analysis. However, the time dependence in the FFC multipliers 

arises exclusively from variables taken from the AEO. The FFC analysis for CUAC and CUHP

equipment used data from AEO 2013.

4
10-A-3

Table 10-A.2.1 Dependence of FFC Parameters on AEO Inputs

Parameter Fuel AEO Table Variables 

qx all Conversion Factors MMBtu per physical unit

ax all

Electricity Supply, Disposition, 

Prices, and Emissions Generation by fuel type

Energy Consumption by Sector and 

Source

Electric power sector energy 

consumption

bc, cnc, cpc coal

Coal Production by Region and 

Type

Production by coal type and 

sulfur content

bp, cnp, cpp petroleum 

Refining Industry Energy 

Consumption Refining only energy use

Liquid Fuels Supply and Disposition Crude supply by source

International Liquids Supply and 

Disposition Crude oil imports

Oil and Gas Supply Crude oil domestic production

cnn

natural 

gas

Oil and Gas Supply US dry gas production

Natural Gas Supply, Disposition and 

Prices Pipeline, lease and plant fuel

zx all

Electricity Supply, Disposition,

Prices and Emissions Power sector emissions

10-A.3 FULL-FUEL-CYCLE ENERGY MULTIPLIERS 

FFC energy multipliers are presented in Table 10-A.3.1 for selected years. For the analysis 

period beyond 2040, the last year in the AEO 2013 projection, the 2040 value was held constant. 

The multiplier for electricity reflects the shares of various primary fuels in total electricity 

generation over the forecast period. 

Table 10-A.3.1 Full-Fuel-Cycle Energy Multipliers (Based on AEO 2013)

2019 2020 2025 2030 2035 2040

Electricity 1.041 1.041 1.040 1.040 1.041 1.040

Natural Gas 1.101 1.101 1.100 1.098 1.099 1.100

Petroleum Fuels 1.139 1.140 1.148 1.158 1.166 1.168
10-A-4

REFERENCES

1. United States - Office of the Federal Register, Federal Register, Volume 76, Number 160, 

August 18, 2011. UNT Digital Library. Washington D.C. . 

2. Coughlin, K., A Mathematical Analysis of Full Fuel Cycle Energy Use. Energy, 2012. 

37(1): pp. 698-708 

<http://www.sciencedirect.com/science/article/pii/S0360544211006803> 

3. Coughlin, K., Projections of Full-Fuel-Cycle Energy and Emissions Metrics, 2013. 

Lawrence Berkeley National Laboratory. Report No. LBNL-6025E. 

4. Energy Information Administration, Annual Energy Outlook 2013 with Projections to 

2040, 2013. Washington, DC. <http://www.eia.gov/forecasts/aeo/pdf/0383(2013).pdf> 
10A-i

APPENDIX 10-B. USER INSTRUCTIONS FOR SHIPMENTS AND NATIONAL 

IMPACT ANALYSIS SPREADSHEETS

TABLE OF CONTENTS

10-B.1 INTRODUCTION ............................................................................................... 10-B-1

10-B.2 BASIC INSTRUCTIONS .................................................................................... 10-B-2
10-B-1

APPENDIX 10-B. USER INSTRUCTIONS FOR SHIPMENTS AND NATIONAL

IMPACT ANALYSIS SPREADSHEETS 

10-B.1 INTRODUCTION

The interested reader can examine and reproduce detailed results of the U.S. Department 

of Energy’s (DOE’s) shipments and national impact analysis (NIA) for commercial clothes 

washers using Microsoft Excel spreadsheets that are available on DOE’s website.

<http://www.eere.energy.gov/buildings/appliance_standards/> 

The latest version of the Microsoft Excel shipments and NIA workbook, which is posted 

on the DOE website, was tested using Microsoft Excel 2007. To execute the spreadsheet requires 

Microsoft Excel 2003 or a later version. The NIA spreadsheet performs calculations to forecast 

the change in national energy use and net present value due to an energy conservation standard. 

The energy use and associated costs and savings attributable to a given standard are determined 

first by calculating the shipments and then the energy use and costs for all products shipped 

under that standard. The differences between results under the standard case and the base case

then can be compared and the nationwide energy savings and net present values (NPVs) 

determined. 

The shipments and NIA workbook for both front- and top-loading residential clothes 

washers comprises the following worksheets. 

Input and Summary This sheet contains user input selections under “User Inputs” and a 

summary table, Cumulative Energy Savings and NPV for the 

selected standard level efficiency distribution. The sheet contains 

the efficiency levels being considered for commercial clothes 

washers and the associated incremental prices. This sheet also 

contains efficiency weighted average energy and water use and 

equipment price for the base and standards cases for all the product 

classes.

LCC Input This sheet contains the inputs from the Life-cycle cost analysis. 

Efficiency Distribution This sheet contains base and standards case efficiency trends. 

Historical Shipment This sheet contains data for historical sales of the equipment. 

Base Case This sheet mainly performs the calculations leading up to the 

estimation of total commercial clothes washer shipments for the 

base case. The sheet starts with the stock accounting of the 

equipment and uses the survival function to calculate the surviving 

stock. It then performs calculations of replacements, and shipments 

going into new housing, thus yielding total shipments. 
10-B-2

Standards Case Contains stock accounting of the equipment that helps calculate 

annual commercial clothes washer shipments estimates for the 

standards case. 

Base Case Calc. This sheet calculates the estimation of shipments for selected 

product class by taking cross-price elasticity into account for the 

base case. It also calculates the energy and water consumption and 

operating cost for selected product class. 

Standards Case Calc. This sheet estimates shipments for selected product class by taking 

cross-price elasticity into account for the standards case. It also 

calculates the energy and water savings and cost savings. The 

energy and cost savings in a single year are the difference between 

the base case energy and water use and costs for that year and the 

standard case energy and water use and costs in the same year. 

Housing Projections This sheet provides projected new housing construction starts by 

housing type. 

Fuel Prices This worksheet contains projected average electricity, gas and 

water prices for the three economic scenarios. 

Heat Rates The sheet contains the marginal site to source conversion factors 

that are used in the source energy savings calculations.

Lifetime This sheet contains the probability of survival of a commercial 

clothes washer unit at a given age of the unit. The sheet also 

provides the average lifetime of a unit. 

10-B.2 BASIC INSTRUCTIONS

Basic instructions for operating the NIA spreadsheets are as follows:

1. Once the NIA spreadsheets have been downloaded from the Web, open the file using 

Excel. At the bottom, click on the tab for the worksheet Input and Summary. 

2. Use Excel’s View/Zoom commands at the top menu bar to change the size of the display 

to make it fit your monitor.

3. The user can change the model parameters listed in the box labelled “User Inputs”. The 

parameters are: 
10-B-3

a. Economic Growth: To the change value, use the drop-down arrow and select the 

desired Growth level (Reference, High, or Low).

b. Discount Rate: To the change value, type in the desired Discount Rate.

c. Relative Price Elasticity: To change the applicable elasticity, use the drop-down 

menu and select the desired value. 

d. Base Case Efficiency Trend: To change value, use the drop-down menu and select 

the desired efficiency growth value for desired product class (Frozen, 1% growth, 

or 2% growth).

e. Standards Case Efficiency Trend: To change value, use the drop-down menu and 

select the desired efficiency growth value for desired product class (Frozen, 1% 

growth, or 2% growth).

4. Once the parameters have been set, the results are automatically updated and are reported 

in the “National Impact Summary” table for each product class to the right of the “User 

Inputs” box. 
10- -C-i

APPENDIX 10-C. NATIONAL ENERGY SAVINGS AND NET PRESENT VALUE USING 

ALTERNATIVE GROWTH SCENARIOS 

TABLE OF CONTENTS

10-C.1 INTRODUCTION .........................................................................................................1

10-C.2 NIA RESULTS FOR HIGH ECONOMIC GROWTH SCENARIO ............................2

10-C.3 NIA RESULTS FOR LOW ECONOMIC GROWTH SCENARIO .............................3

LIST OF TABLES

Table 10-C.2.1 Cumulative National Energy Savings in Quads, National Water 

Savings in Trillion Gallons, High Economic Growth Scenario .........................2

Table 10-C.2.2 Cumulative Net Present Value of Consumer Benefits, High Economic 

Growth Scenario ................................................................................................3

Table 10-C.3.1 Cumulative National Energy Savings in Quads, Low Economic 

Growth Scenario ................................................................................................3

Table 10-C.3.2 Cumulative Net Present Value of Consumer Benefits, Low Economic 

Growth Scenario ................................................................................................3

LIST OF FIGURE

Figure 10-C.1.1 Housing Starts Forecast Under Alternative AEO2013 Economic 

Growth Scenarios ...............................................................................................1

Figure 10-C.1.2 Average Commercial Electricity Price Forecasts under Alternative 

AEO2013 Economic Growth Scenarios ............................................................2
10-C-1

APPENDIX 10-C. NATIONAL ENERGY SAVINGS AND NET PRESENT VALUE USING 

ALTERNATIVE GROWTH SCENARIOS

10-C.1 INTRODUCTION

This appendix presents national energy savings (NES) and net present value (NPV) results using 

inputs from alternative economic growth scenarios. The scenarios use the energy price and 

housing starts forecasts in the High Economic Growth case and the Low Economic Growth case 

from EIA’s Annual Energy Outlook 20131 (AEO 2013).1

Figure 10C.1.1 and Figure 10C.1.2 show the forecasts for housing starts and commercial 

electricity prices under the different economic growth scenarios. AEO2013 provides a forecast to 

2040. To estimate the trend after 2040, DOE followed guidelines that the EIA had provided to 

the Federal Energy Management Program, which called for using the average rate of change for

electricity during 2025–2040. 

Figure 10-C.1.1 Housing Starts Forecast Under Alternative 

AEO2013 Economic Growth Scenarios

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

Housing Starts (millions)

Reference

High

Low
10-C-2

Figure 10-C.1.2 Average Commercial Electricity Price Forecasts 

under Alternative AEO2013 Economic Growth 

Scenarios

10-C.2 NIA RESULTS FOR HIGH ECONOMIC GROWTH SCENARIO

Table 10-C.2.1 Cumulative National Energy Savings in Quads, National Water 

Savings in Trillion Gallons, High Economic Growth Scenario

Trial Standard 

Level

Efficiency Level Combination NES NWS

Front-loading Top-loading quads trillion gal

1 1 1 0.12 0.03

2 2 1 0.14 0.06

3 3 2 0.21 0.26
10-C-3

Table 10-C.2.2 Cumulative Net Present Value of Consumer Benefits, High Economic 

Growth Scenario

Trial Standard 

Level

Efficiency Level Combination 3% 7%

Front-loading Top-loading billion 

2012$

billion 

2012$

1 1 1 0.93 0.39

2 2 1 1.21 0.51

3 3 2 2.55 1.06

10-C.3 NIA RESULTS FOR LOW ECONOMIC GROWTH SCENARIO

Table 10-C.3.1 Cumulative National Energy Savings in Quads, Low Economic Growth 

Scenario

Trial Standard 

Level

Efficiency Level Combination NES NWS

Front-loading Top-loading quads trillion gal

1 1 1 0.08 0.02

2 2 1 0.10 0.04

3 3 2 0.15 0.18

Table 10-C.3.2 Cumulative Net Present Value of Consumer Benefits, Low Economic 

Growth Scenario

Trial Standard 

Level

Efficiency Level Combination 3% 7%

Front-loading Top-loading billion 

2012$

billion 

2012$

1 1 1 0.62 0.27

2 2 1 0.82 0.36

3 3 2 1.75 0.75
10-C-4

REFERENCES

 

1. U.S. Department of Energy-Energy Information Administration, Annual Energy Outlook 

2013 with Projections to 2040, 2013. Washington, DC. 

<http://www.eia.gov/forecasts/aeo/> 
10-D-1

APPENDIX 10-D. NATIONAL NET PRESENT VALUE OF CONSUMER 

BENEFITS USING ALTERNATIVE PRODUCT PRICE FORECASTS 

TABLE OF CONTENTS

10-D.1 INTRODUCTION ............................................................................................... 10-D-1

10-D.2 ALTERNATIVE COMMERCIAL CLOTHES WASHER PRICE TREND 

SCENARIOS ....................................................................................................... 10-D-1

10-D.2.1 Exponential Fit Approach (Increasing Price Scenario) ....................................... 10-D-2

10-D.2.2 Annual Energy Outlook 2013 Price Forecast ( Decreasing Price Scenario) ....... 10-D-3

10-D.2.3 Summary .............................................................................................................. 10-D-3

10-D.3 NET PRESENT VALUE RESULTS USING ALTERNATIVE PRODUCT 

PRICE TRENDS .................................................................................................. 10-D-4

LIST OF TABLES

Table 10-D.2.1 Price Trend Sensitivities .......................................................................... 10-D-3

Table 10-D.3.1 NPV Using Price Trend, Discounted at 3 Percent, for Commercial 

Clothes Washers (billion 2012$) ............................................................. 10-D-4

Table 10-D.3.2 NPV Using Price Trend, Discounted at 7 Percent, for Commercial 

Clothes Washers (billion 2012$) ............................................................. 10-D-5

LIST OF FIGURES

Figure 10-D.2.1 Deflated Commercial Laundry and Drycleaning Machinery PPI versus 

Year, with Exponential Fit from 1982 to 2012 ........................................ 10-D-2

Figure 10-D.2.3 Commercial Clothes Washer Price Forecast Indexes ................................. 10-D-4
10-D-1 

APPENDIX 10-D. NATIONAL NET PRESENT VALUE OF CONSUMER 

BENEFITS USING ALTERNATIVE PRODUCT PRICE FORECASTS 

10-D.1 INTRODUCTION

 DOE used a constant price assumption for commercial clothes washers’ default price 

projection in the NIA described in Chapter 10. In order to investigate the impact of different 

product price forecasts on the consumer net present value (NPV) for the considered TSLs for 

commercial clothes washers, DOE also considered two alternative price trends for a sensitivity 

analysis. This appendix describes the alternative price trends and compares NPV results for these 

scenarios with the default price projection. 

In recent rulemakings for several residential products, DOE has used the experience 

curve method to derive learning rates to forecast future prices. In the experience curve method, 

the real cost of production is related to the cumulative production, or experience, with a 

manufactured product. That experience usually is measured in terms of cumulative production. 

As experience (production) accumulates, the cost of producing the next unit decreases. The 

percentage reduction in cost that occurs with each doubling of cumulative production is known 

as the learning rate. A recent report from Lawrence Berkeley National Laboratory by Taylor and 

Fujita provides an overview of some of the major findings of the academic literature on learning 

curves, and describes the application of a component-based learning curve approach (by the 

Environmental Protection Agency and the National Highway Transportation Safety 

Administration) and a price-based learning curve approach (by DOE) in regulatory impact 

assessment.1

 For some commercial and industrial products, there are insufficient data to apply a pricebased learning curve approach, particularly with respect to cumulative production. In such cases, 

DOE used a constant price assumption for the default price projection in the NIA, but made use 

of price indexes that are relevant for the product in question to derive alternative price trends for 

sensitivity analysis.a Because DOE is using motor price trends as a proxy for furnace fan price 

trends, this approach was used for furnace fans.

 

10-D.2 ALTERNATIVE COMMERCIAL CLOTHES WASHER PRICE 

TREND SCENARIOS

DOE considered two alternative price trends for a sensitivity analysis. One of these used 

an exponential fit on the deflated Producer Price Index (PPI) for commercial laundry and 

drycleaning machinery, and the other is based on the “deflator— industrial equipment” that was 

forecasted for EIA’s Annual Energy Outlook 2013 (AEO2013). 

 

a See appendix 10C of the final rule TSD for distribution transformers. 

http://www.regulations.gov/#!documentDetail;D=EERE-2010-BT-STD-0048-0760
10-D-2 

10-D.2.1 Exponential Fit Approach (Increasing Price Scenario)

For the increasing price scenario, DOE used an inflation-adjusted commercial laundry 

and drycleaning machinery Producer Price Index (PPI) spanning the time period of 1982-2012 

from the Bureau of Labor Statistics’ (BLS) to fit an exponential model with year as the 

explanatory variable.b The PPI data reflect nominal prices, adjusted for product quality changes. 

An inflation-adjusted (deflated) price index for commercial laundry and drycleaning machinery

was calculated by dividing the PPI series by the Gross Domestic Product Chained Price Index. In 

this case, the exponential function takes the form of: 

Y = a ∙ ebX 

where Y is the commercial clothes washer price index, X is the time variable, a is the constant 

and b is the slope parameter of the time variable. 

To estimate these exponential parameters, a least-square fit was performed on the 

inflation-adjusted commercial laundry and drycleaning machinery price index versus year from 

1982 to 2012. The fitted regresion is shown in Figure 10-D-1. 

Figure 10-D.2.1 Deflated Commercial Laundry and Drycleaning Machinery PPI versus 

Year, with Exponential Fit from 1982 to 2012 

The regression performed as an exponential trend line fit results in an R-square of 0.74, 

which indicates a decent fit to the data. The final estimated exponential function is:

 

b Series ID PCU3333183333182; http://www.bls.gov/ppi/

y = 4E-05e0.005x

R2 = 0.7439

0.820

0.840

0.860

0.880

0.900

0.920

0.940

0.960

0.980

1.000

1.020

1980 1990 2000 2010 2020

Price Index (2012=1)

Year

Y=Commercial Laundry & 

Drycleaning Machinery 

Deflated PPI

Expon. (Y=Commercial 

Laundry & Drycleaning 

Machinery Deflated PPI)
10-D-3 

Y = 3.92 × 10−5 ∙ e0.005X 

DOE then derived a price factor index for this scenario, renormalized with 2012 equal to 

1, to project prices in each future year in the analysis period considered in the NIA. The index 

value in a given year is a function of the exponential parameter and year. 

10-D.2.2 Annual Energy Outlook 2013 Price Forecast ( Decreasing Price Scenario) 

DOE also examined a forecast based on the “deflator— industrial equipment” that was 

forecasted for AEO2013 out to 2040. AEO 2013 is the most current price trend projection that 

DOE acquired. This index is the most disaggregated category that includes commercial laundry 

and drycleaning machinery. To develop an inflation-adjusted index, DOE normalized the above 

index with the “chained price index—gross domestic product” forecasted for AEO2013, with 

2012 equal to 1. To extend the price index beyond 2040, DOE used the average annual price 

growth rate in 2031 to 2040. 

10-D.2.3 Summary

Table 10-D.2.1 shows the summary of the average annual rates of change for the product 

price index in each scenario. Figure 10-D-2 shows the resulting price trends under all scenarios. 

Table 10-D.2.1 Price Trend Sensitivities

Sensitivity Price Trend Average Annual Rate 

of Change %

Medium (Default) Constant Price Projection 0.0

Decreasing Price 

Scenario

AEO2011-- “deflator— industrial equipment” -0.35 

Increasing Price 

Scenario

Exponential Fit using PPI data from 1982 to 

2012 0.50 
10-D-4 

Figure 10-D.2.2 Commercial Clothes Washer Price Forecast Indexes

10-D.3 NET PRESENT VALUE RESULTS USING ALTERNATIVE 

PRODUCT PRICE TRENDS 

 This section presents the NPV results using the alternative product price forecast for each 

key product class. The results are only slightly sensitive to the alternative product price forecasts 

for TSL 3. 

Table 10-D.3.1 NPV Using Price Trend, Discounted at 3 Percent, for Commercial 

Clothes Washers (billion 2012$) 

Price Trend Product Class Trial Standard Levels

TSL1 TSL 2 TSL 3 

Default Top-loading 0.596 0.594 2.131

Front-loading 0.120 0.344 -0.132

Decreasing Price Top-loading 0.596 0.594 2.130

Front-loading 0.120 0.344 -0.132

Increasing Price Top-loading 0.596 0.594 2.127

Front-loading 0.120 0.344 -0.134

0.000

0.200

0.400

0.600

0.800

1.000

1.200

1.400

201220142016201820202022202420262028203020322034203620382040204220442046

Price Factor Index (2012=1)

Year

Constant Price Index 

(Default)

PPI Exponential Fit (1982 to 

2012) (Increasing Price)

AEO 2013 -- Industrial 

Equipment (Decreasing 

Price)
10-D-5 

Table 10-D.3.2 NPV Using Price Trend, Discounted at 7 Percent, for Commercial 

Clothes Washers (billion 2012$) 

Price Trend Product Class Trial Standard Levels

TSL1 TSL 2 TSL 3 

Default Top-loading 0.261 0.260 0.910

Front-loading 0.051 0.145 -0.060

Decreasing Price Top-loading 0.261 0.260 0.909

Front-loading 0.051 0.145 -0.060

Increasing Price Top-loading 0.261 0.260 0.908

Front-loading 0.051 0.145 -0.061
12A-i 

APPENDIX 12A. MANUFACTURER IMPACT ANALYSIS INTERVIEW GUIDE 

TABLE OF CONTENTS 

12A.1 COMMERCIAL CLOTHES WASHERS .....................................................................1
12A-1

APPENDIX 12A. MANUFACTURER IMPACT ANALYSIS INTERVIEW GUIDE 

12A.1 COMMERCIAL CLOTHES WASHERS 

Commercial Clothes Washer Rulemaking 

Manufacturer Impact Analysis Interview Guide for 

Commercial Clothes Washers 

 

May 1, 2013
12A-2

The U.S. Department of Energy (DOE) conducts the manufacturer impact analysis (MIA) as part 

of the rulemaking process for amended energy conservation standards for commercial clothes 

washers (CCWs). In this analysis, DOE uses publicly available information and information 

provided by manufacturers during interviews to assess possible impacts on manufacturers due to 

amended energy conservation standards. 

The DOE is currently reviewing a set of efficiency levels for front-loading CCWs. The 

efficiency levels are presented below as they apply to the front-loading CCWs. For top-loading 

CCWs, the DOE is seeking input relative to the range of efficiency levels that have been certified 

in the DOE’s Compliance Certification Database since the previous standard went into effect on 

January 8th, 2013. In responding to this questionnaire, please refer to the modified energy factor 

(MEF) and Water Factor (WF) values described below. 

DOE is requesting information on the product classes in the table below.

Table 1.1 Efficiency Levels Under Consideration for Front-Loading Commercial Clothes 

Washer Analysis 

Efficiency Level

Efficiency Level 

Source

MEF 

ft3/kwh/cycle

WF 

gal/cycle/ft3

Baseline DOE Standard 2 5.5

1 CEE Tier 2 2.2 4.5

2 CEE Tier 3 2.4 4

3 Maximum Available 2.6 3.7

Table 1.2 Efficiency Levels for Top-Loading Commercial Clothes Washers Listed in DOE 

Compliance Certification Database 

Efficiency Levels Listed in DOE Certification 

Database

MEF 

ft3/kwh/cycle

WF 

gal/cycle/ft3

Baseline 1.6 8.5

1 1.65 8.4

2 1.7 8.4

3 1.75 8.4

4 1.85 6.85

 

1 KEY ISSUES 

1.1 In general, what are the key issues for your company regarding amended energy 

conservation standards for commercial clothes washers? 
12A-3

1.2 Are any of the issues more or less significant for the front-loading versus top-loading 

product classes? 

1.3 Do any of the issues become more significant at higher efficiency levels? 

1.4 Do you have any suggestions for incorporating any of these issues into the DOE’s 

engineering analysis or manufacturing impact model? 

2 COMPANY OVERVIEW AND ORGANIZATIONAL CHARACTERISTICS 

DOE is interested in understanding manufacturer impacts at the plant or profit center level 

directly pertinent to commercial clothes washer production. However, the context within which 

this profit center operates and the details of plant production are not always readily available 

from public sources. Understanding the organizational setting around the commercial clothes 

washer industry profit center will help DOE understand the probable future of the manufacturing 

activity with and without amended energy conservation standards. 

2.1 What percentage of your commercial clothes washer manufacturing corresponds to each 

product class, in terms of both revenue and shipments? Please indicate if you do not manufacture 

products in any given product class. Please also indicate whether you purchase your commercial 

clothes washers from other manufacturers, and whether the factory that supplies the products is 

located in the United States.

Table 12.1 Commercial Clothes Washer Revenue and Shipment Volumes by Product Class 

Product Class 

Number 

Product 

Type

2012 

Revenue

2012 

Shipments

% 

Made

% 

Bought

% Made in 

U.S.

1

FrontLoading

2

TopLoading

2.2 What is your company’s approximate market share in the commercial clothes washer 

market, for each product class?

3 ENGINEERING AND LIFE-CYCLE COST ANALYSIS 

3.1 Please describe the key design features associated with products at the baseline efficiency 

level as well as design features and associated costs that would be necessary to reach the

incremental efficiency levels listed below.
12A-4

Front-Loading Commercial Clothes Washer Incremental Costs 

MEF

(ft3/kWh/cycle)

WF

(gal/cycle/ft3)

Estimated

Incremental 

Cost 

Description of Design Options

2.0 5.5 --

2.2 4.5

2.4 4

2.6 3.7

Top-Loading Commercial Clothes Washer Incremental Costs (Exploratory Efficiency 

Levels*) 

MEF

(ft3/kWh/cycle)

WF

(gal/cycle/ft3)

Estimated 

Incremental 

Cost 

Description of Design Options

1.60 8.5 --

1.65 8.4

1.7 8.4

1.75 8.4

1.85 6.85

*These top-loading efficiency levels are exploratory only. Since the effective date of new standards on January 

8, 2013, top-loading models at these efficiency levels have been certified to DOE and listed in DOE’s 

Compliance Certification Database. Information obtained for each of these efficiency levels will help DOE 

define the specific top-loading efficiency levels to be considered during this rulemaking. 

3.1.a Are the proposed efficiency levels appropriate? 

3.1.b Are the “max tech” efficiency levels appropriate? If not, can you suggest more 

appropriate max-tech levels? 

DOE published a revised clothes washer test procedure, “Appendix J2”, which will be required 

for use beginning March 7, 2015. Among other changes, Appendix J2 does the following: 

 Incorporates standby power energy into a new metric, Integrated Modified Energy Factor 

(IMEF)

 Incorporates the water consumption of all wash cycle temperatures into a new metric, 

Integrated Water Factor (IWF) 

 Amends the capacity measurement for top-loading clothes washers

 Amends the definition of the energy test cycle 

DOE has proposed to translate current MEF/WF standards into their equivalent IMEF/IWF 

values based on comparison testing of clothes washer models currently on the market. Any new 

standards as a result of this rulemaking will be defined in terms of IMEF and IWF. 
12A-5

3.2 Please comment on any issues related to the new DOE clothes washer test procedure as 

applied to commercial clothes washers:

3.2.a Standby Power 

Is the standby power measurement procedure in Appendix J2 appropriate for measuring 

the standby power associated with all types of control systems available on commercial 

clothes washers? 

Is there a relationship between standby power energy consumption and the overall 

efficiency level? (i.e. Does standby power energy consumption tend to increase or 

decrease as overall machine efficiency increases?) 

Will the inclusion of standby power affect the design options currently used to reach 

higher efficiencies? 

3.2.b Capacity Measurement Method 

Will the revised capacity measurement method for top-loading clothes washers affect the 

design options you use to reach higher efficiencies? 

3.2.C Energy Test Cycle Definition 

How will the changes to the energy test cycle definition change how your clothes washers 

are tested? Will this revised definition limit the design options available for reaching 

higher efficiencies? 

3.2.d Consumer Use Factors 

Are the consumer usage factors within the test procedure consistent with actual consumer 

use of commercial clothes washers? These include the following: 

 Temperature use factors 

 Load usage factors 

 Dryer usage factor

Can you provide any non-proprietary data related to consumer use of different cycles? 

4 MARKUPS AND PROFITABILITY 

One of the primary objectives of the MIA is to assess the impact of amended energy 

conservation standards on industry profitability. In this section, DOE would like to understand 

the current markup structure of the industry and how amended energy conservation standards 

would impact your company’s markup structure and profitability. 

DOE will estimate the manufacturer production costs for the two product classes of commercial 

clothes washers. DOE defines manufacturer production cost as all direct costs associated with 

manufacturing a product: direct labor, direct materials, overhead, and depreciation. The 
12A-6

manufacturer markup is a multiplier applied to manufacturer production cost to cover nonproduction costs, such as SG&A and R&D, as well as profit. It does not reflect a “profit 

margin.” 

The manufacturer production cost times the manufacturer markup equals the manufacturer 

selling price. Manufacturer selling price is the price manufacturers charge their first customers, 

but does not include additional costs along the distribution channels. 

DOE estimated a baseline markup of 1.27 for commercial clothes washers. 

4.1 Is the 1.27 baseline markup representative of an average industry markup?

4.2 Please comment on the baseline markups DOE calculated as compared to your 

company’s baseline markups for the commercial clothes washer product classes.

Table 4.2 Commercial Clothes Washer Baseline Manufacturer Markups by Product Class 

Product 

Class Product Type

Estimated 

Baseline 

Markup

Manufacturer Comments or Revised Estimates

1 Top-Loading 1.27

2 Front-Loading 1.27

4.3 How are markups and margins determined in this industry? How would revised 

standards potentially impact this pricing/margin structure? Please indicate if profit levels vary 

between product classes or product line. If yes, please explain why.

4.4 One of the possible scenarios DOE uses to model impacts on industry profitability is the 

impact of commoditization of premium products. Because the market disruption caused by 

standards can alter the pricing of premium products, DOE is interested in understanding if 

efficiency is a feature that earns a premium. Within each product class, do markups vary by 

efficiency level? If yes, please provide estimates for your markups by product class and 

efficiency level in Table 4.2 and Table 4.3. 

Table 4.2 Estimated Markups for Front-Loading Commercial Clothes Washers 

Efficiency

Level

MEF 

ft3/kwh/cycle

WF 

gal/cycle/ft3 Estimated Markup Manufacturer Comments

1 2.20 4.50

2 2.40 4.00

3 2.60 3.70
12A-7

Table 4.3 Estimated Markups for Top-Loading Commercial Clothes Washers (Exploratory 

Efficiency Levels) 

Efficiency

Level

MEF 

ft3/kwh/cycle

WF 

gal/cycle/ft3 Estimated Markup Manufacturer Comments

1 1.65 8.4

2 1.7 8.4

3 1.75 8.4

4 1.85 6.85

4.5 What factors or product attributes besides efficiency affect the profitability of 

commercial clothes washers within a product class?

4.6 Would you expect changes in your estimated profitability following an amended energy 

conservation standard? If so, please explain why. 

4.7 What is the structure of your distribution channels and how does that influence your 

markup? Does that change between product classes or across the industry?

5 FINANCIAL PARAMETERS 

DOE’s contractor has developed a draft model of the commercial clothes washer industry 

financial performance called the Government Regulatory Impact Model (GRIM), using publicly 

available data. However, this public information might not be reflective of manufacturing at the 

commercial clothes washer profit center. This section attempts to understand the financial 

parameters for commercial clothes washer manufacturing and how your company’s financial 

situation could differ from the industry aggregate picture. 

5.1 In order to accurately collect information about commercial clothes washer 

manufacturing, please compare your financial parameters to the GRIM parameters tabulated 

below.
12A-8

Table 5.3 Financial Parameters for Commercial Clothes Washer Manufacturing 

GRIM Input Definition

Industry 

Estimated

Value (%)

Your Actual (If 

Different from 

DOE’s 

Estimate)

Income Tax 

Rate

Corporate effective income tax paid (percentage of 

earnings before taxes, EBT) 29.2%

Discount Rate

Weighted average cost of capital (inflationadjusted weighted average of corporate cost of 

debt and return on equity)

8.0%

Working 

Capital

Current assets less current liabilities (percentage of 

revenues) 9.5%

Net PPE Net plant property and equipment (percentage of 

revenues) 16.0%

SG&A Selling, general, and administrative expenses 

(percentage of revenues) 11.7%

R&D Research and development expenses (percentage 

of revenues) 2.5%

Depreciation Amortization of fixed assets (percentage of 

revenues) 3.7%

Capital 

Expenditures

Outlay of cash to acquire or improve capital assets 

(percentage of revenues, not including acquisition 

or sale of business units)

2.1%

Cost of Goods 

Sold

Includes material, labor, overhead, and 

depreciation (percentage of revenues) 79.5%

5.2 Do any of the financial parameters in Table 5.3 change based on product class? Please 

describe any differences. 

5.3 Do any of the financial parameters in Table 5.3 change for a particular subgroup of 

manufacturers? Please describe any differences. 

5.4 DOE accounts for one time product and capital conversion costs including research and 

development, as well as capital expenditures for facility changes and the depreciation of these 

fixed assets. Beyond these short term changes in cost structure, how would you expect an 

amended energy conservation standard to impact any of the financial parameters for the industry 

over time?
12A-9

6 CONVERSION COSTS 

Amended energy conservation standards may cause your company to incur capital and product 

conversion costs to redesign existing products and make changes to existing production lines to 

be compliant with the amended energy conservation standard. Depending on their magnitude, the 

conversion costs can have a substantial impact on the outputs used by DOE to evaluate the 

industry impacts. Understanding the nature and magnitude of the conversion costs is critical 

portion of the MIA. The MIA considers two types of conversion costs: 

 Capital conversion costs are one-time investments in plant, property, and equipment 

(PPE) necessitated by an amended energy conservation standard. These may be 

incremental changes to existing PPE or the replacement of existing PPE. Included are 

expenditures on buildings, equipment, and tooling. 

 Product conversion costs are costs related research, product development, testing, 

marketing and other costs for redesigning products necessitated by an amended energy 

conservation standard. 

DOE asks a number of questions to understand the nature and magnitude of your expected 

capital and product conversion costs. 

Table 6.1 through Table 6.4 show the MEF and WF for the different efficiency levels analyzed in 

the Engineering Analysis for the product categories covered by this rulemaking. 

6.1 Please provide estimates for your capital conversion costs by product class and efficiency 

level in Table 6.1 and Table 6.2. In the description column, DOE is interested in understanding 

the kinds of changes that would need to be implemented to production lines and production 

facilities at each efficiency level. Where applicable, please quantify the number and cost of new 

production equipment, etc. that would be required to implement the specified design changes. 
12A-10

Table 6.4 Expected Capital Conversion Costs for Front-Loading Commercial Clothes 

Washers 

Efficiency

Level

MEF 

ft3/kwh/cycle

WF 

gal/cycle/ft3

Total Capital 

Conversion Costs Description

1 2.20 4.50

2 2.40 4.00

3 2.60 3.70

Table 6.5 Expected Capital Conversion Costs for Top-Loading Commercial Clothes 

Washers (Exploratory Efficiency Levels) 

Efficiency

Level

MEF 

ft3/kwh/cycle

WF 

gal/cycle/ft3

Total Capital 

Conversion Costs Description

1 1.65 8.4

2 1.7 8.4

3 1.75 8.4

4 1.85 6.85

6.2 Would the changes in question 6.1 be similar across all of your production lines and 

factories for each product class?

6.3 Are there certain efficiency levels that would require relatively minor changes to existing 

products? Are there certain efficiency levels where the capital or product conversion costs 

significantly increase over the previous efficiency levels? Would your answer change for 

different product classes? Please describe these changes qualitatively. 

6.4 For each of the product categories shown in Table 6.1 and Table 6.2, which efficiency 

level changes could be made within existing platform designs and which would result in major 

product redesigns?

6.5 For the efficiency levels put forth, which design options would require only minor 

changes to production lines, major changes to production lines, substantial modifications to 

existing facilities, or the development of entirely new manufacturing facilities? 

6.6 What level of product conversion costs would you expect to incur at each of these 

efficiency levels for each product class? Please provide your estimates in Table 6.3 and Table 6.4 

considering such expenses as product development expenses, prototyping, testing, certification, 

and marketing. In the description column, please describe the assumptions behind the estimates 

provided. 
12A-11

Table 6.3 Expected Product Conversion Costs for Front-Loading Commercial Clothes 

Washers 

Efficiency

Level

MEF 

ft3/kwh/cycle

WF 

gal/cycle/ft3

Total Product 

Conversion Costs Description

1 2.20 4.50

2 2.40 4.00

3 2.60 3.70

Table 6.4 Expected Product Conversion Costs for Top-Loading Commercial Clothes 

Washers (Exploratory Efficiency Levels) 

Efficiency

Level

MEF 

ft3/kwh/cycle

WF 

gal/cycle/ft3

Total Product 

Conversion Costs Description

1 1.65 8.4

2 1.7 8.4

3 1.75 8.4

4 1.85 6.85

6.7 Please provide additional qualitative information to help DOE understand the types and 

nature of your investments, including the plant and tooling changes and the product development 

effort required at different efficiency levels.

7 CUMULATIVE REGULATORY BURDEN 

In assessing the impact to industry, DOE seeks to understand the cumulative regulatory burden 

facing manufacturers. Cumulative regulatory burden refers to the financial burden that stems 

from overlapping effects of new or revised DOE standards and/or other regulatory actions 

affecting the same product or industry. In this regard, DOE will consider other regulations 

(beyond energy efficiency) such as materials regulations and building codes that will adversely 

impact the cost to either the manufacturer or consumer. 

7.1 In assessing cumulative regulatory burden, DOE considers the impacts of other 

regulations for which the effective starting date falls within a six year timeframe extending from 

three years before to three years after the proposed commercial clothes washer standards become 

effective. Below is a list of other relevant regulations that could affect manufacturers of 

commercial clothes washers. Please provide any comments on the listed regulations and provide 

an estimate for your expected compliance cost.
12A-12

Table 7.6 Other Regulations Identified by DOE 

Regulation Estimated or Actual 

Effective Date(s) Comments Expected Expense for Compliance (Millions $)

Residential central air conditioners 

and heat pumps energy 

conservation standards

2013 & 2015 211.6

Residential clothes washer energy 

conservation standards 2015 & 2018 418.5

Residential clothes dryer energy 

conservation standards 2015 95.4

Wine chiller and misc. 

refrigeration energy conservation 

standards

2018

Residential furnace fans energy 

conservation standards 2018

Dehumidifier energy conservation 

standards 2019

7.2 Are there any other recent or impending regulations that commercial clothes washer 

manufacturers face (from DOE or otherwise)? If so, please identify the regulation, the 

corresponding effective dates, and your expected compliance cost. 

7.3 Under what circumstances would you be able to coordinate any expenditure related to 

these other regulations with an amended energy conservation standard?

8 DIRECT EMPLOYMENT IMPACT ASSESSMENT 

The impact of amended energy conservation standards on employment is an important 

consideration in the rulemaking process. This section of the interview guide seeks to explore 

current trends in commercial clothes washer manufacturer employment and solicit manufacturer 

views on how domestic employment patterns might be affected by amended energy conservation 

standards. 

8.1 Where are your commercial clothes washer manufacturing facilities that produce 

products for the United States located? What types of products are manufactured at each 

location? Please provide annual shipment figures for your company’s commercial clothes washer 

manufacturing at each location by product class. Please also provide employment levels at each 

of these facilities.
12A-13

Table 8.7 Commercial Clothes Washer Revenue and Shipment Volumes by Product Class 

Facility Location Product Types Manufactured Employees Annual 

Shipments

Example Sheboygan, WI Commercial Clothes Washers 200 100,000

1

2

3

4

5

8.2 Would your domestic employment levels be expected to change significantly under 

amended energy conservation standards? If so, please explain how they would change if higher 

efficiency levels are required.

8.3 Would the workforce skills necessary under amended energy conservation standards 

require extensive retraining or replacement of employees at your manufacturing facilities?

8.4 Would amended energy conservation standards require extensive retraining of your 

service/field technicians? If so, could you expand on how your service infrastructure would be 

impacted in general as a result of amended energy conservation standards?

9 MANUFACTURING CAPACITY AND NON-US SALES 

9.1 How would amended energy conservation standards impact your company’s 

manufacturing capacity?

9.2 For any design changes that would require new production equipment, please describe 

how much downtime would be required. What impact would downtime have on your business? 

Are there any design changes that could not be implemented before the compliance date of the 

final rule for certain product classes?

9.3 What percentage of your company’s commercial clothes washer sales is made within the 

United States? 

9.4 What percentage of your U.S. production of commercial clothes washers is exported? 

9.5 Are there any foreign companies with North American production facilities?

9.6 Would amended energy conservation standards impact your domestic vs. foreign 

manufacturing or sourcing decisions? Is there an efficiency level that would cause you to move 

domestic production facilities outside the U.S.?
12A-14

10 IMPACT ON COMPETITION 

Amended energy conservation standards can alter the competitive dynamics of the market. This 

can include prompting companies to enter or exit the market, or to merge. DOE and the 

Department of Justice are both interested in any potential reduction in competition that would 

result from an amended energy conservation standard. 

10.1 How would amended energy conservation standards affect your ability to compete in the 

marketplace? Would the effects on your company be different than others in the industry?

10.2 Would you expect your market share to change if amended energy conservation standards 

become effective?

10.3 Do any firms hold intellectual property that gives them a competitive advantage 

following amended energy conservation standards? 

10.4 How would industry competition change as a result of amended energy conservation 

standards?

11 IMPACTS ON SMALL BUSINESS 

11.1 The Small Business Administration (SBA) denotes a small business in the commercial 

clothes washer manufacturing industry as having less than 1,000 total employees, including the 

parent company and all subsidiaries.a By this definition, is your company considered a small 

business?

11.2 Are there any reasons that a small business manufacturer might be at a disadvantage 

relative to a larger business under amended energy conservation standards? Please consider such 

factors as technical expertise, access to capital, bulk purchasing power for materials/components, 

engineering resources, and any other relevant issues.

11.3 To your knowledge, are there any small businesses for which the adoption of amended 

energy conservation standards would have a particularly severe impact? If so, why?

11.4 To your knowledge, are there any niche manufacturers or component manufacturers for 

which the adoption of amended energy conservation standards would have a particularly severe 

impact? If so, why?

 

a

 DOE uses the small business size standards published on January 1, 2012, as amended, by the SBA to determine 

whether a company is a small business. To be categorized as a small business, a manufacturer of “other commercial 

and service industry machinery manufacturing” (NAICS #333318, which includes commercial clothes washer 

manufacturers) and its affiliates may employ a maximum of 1,000 employees. The 1,000 employee threshold 

includes all employees in a business’s parent company and any other subsidiaries. 
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 on manufacturers. The basic mode of analysis is to 

estimate the change in value of the industry 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 

annual cash flows 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 such as changes in costs and investments. 

The analysis is separated into two major sections: 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 2047. Computed by 

growing the Free Cash Flow in year 2047 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 2047

the discounted cash flow includes the discounted Terminal Value; and 

Industry Value thru 2047: The sum of Discounted Cash Flows.
12B-4

12B.3 DETAILED CASH FLOW EXAMPLE 

 Base Yr Ancmt Yr Std Yr 

Industry Income Statement 2013 2014 2015 2016 2017 2018 2019 2020

Revenues 77.9$ $ 76.3 $ 77.6 $ 79.5 $ 82.3 $ 83.2 $ 83.703 $ 84.1

- Materials 31.7$ $ 31.1 $ 31.6 $ 32.4 $ 33.5 $ 33.9 $ 34.104 $ 34.3

- Labor 7.0$ $ 6.9 $ 7.0 $ 7.2 $ 7.4 $ 7.5 $ 7.536 $ 7.6

- Depreciation 2.3$ $ 2.3 $ 2.3 $ 2.4 $ 2.5 $ 2.5 $ 2.511 $ 2.5

- Overhead 19.5$ $ 19.1 $ 19.5 $ 19.9 $ 20.6 $ 20.9 $ 20.987 $ 21.1

- Standard SG&A 8.8$ $ 8.6 $ 8.7 $ 9.0 $ 9.3 $ 9.4 $ 9.432 $ 9.5

- R&D 2.1$ $ 2.1 $ 2.1 $ 2.2 $ 2.2 $ 2.3 $ 2.273 $ 2.3

- Product Conversion Costs -$ -$ $ 0.1 $ 0.1 $ 0.1 $ 0.0 -$ -$ 

- Stranded Assets -$ -$ -$ -$ -$ -$ -$ -$ 

Earnings Before Interest and Taxes (EBIT) 6.4$ $ 6.3 $ 6.3 $ 6.4 $ 6.6 $ 6.8 $ 6.9 $ 6.9

EBIT/Revenues 8.2% 8.2% 8.1% 8.1% 8.0% 8.2% 8.20% 8.2%

- Taxes 1.9$ $ 1.8 $ 1.8 $ 1.9 $ 1.9 $ 2.0 $ 2.0 $ 2.0

Net Operating Profit after Taxes (NOPAT) 4.5$ $ 4.4 $ 4.5 $ 4.5 $ 4.7 $ 4.8 $ 4.9 $ 4.9

Cash Flow Statement 

NOPAT 4.5$ $ 4.4 $ 4.5 $ 4.5 $ 4.7 $ 4.8 $ 4.9 $ 4.9

+ Depreciation 2.3$ $ 2.3 $ 2.3 $ 2.4 $ 2.5 $ 2.5 $ 2.5 $ 2.5

+ Loss on Disposal of Stranded Assets -$ -$ -$ -$ -$ -$ -$ -$ 

- Change in Working Capital -$ $ (0.1) $ 0.1 $ 0.1 $ 0.2 $ 0.1 $ 0.0 $ 0.0

Cash Flows from Operations 6.9$ $ 6.8 $ 6.7 $ 6.8 $ 7.0 $ 7.3 $ 7.3 $ 7.4

- Ordinary Capital Expenditures 2.3$ $ 2.3 $ 2.3 $ 2.4 $ 2.5 $ 2.5 $ 2.5 $ 2.5

- Capital Conversion Costs -$ -$ -$ -$ -$ -$ -$ -$ 

Free Cash Flow 4.5$ $ 4.6 $ 4.4 $ 4.4 $ 4.5 $ 4.8 $ 4.8 $ 4.9

Discounted Cash Flow

Free Cash Flow 4.5$ $ 4.6 $ 4.4 $ 4.4 $ 4.5 $ 4.8 $ 4.8 $ 4.9

Terminal Value -$ -$ -$ -$ -$ -$ -$ -$ 

Present Value Factor 0.000 1.000 0.921 0.848 0.781 0.720 0.663 0.611

Discounted Cash Flow -$ $ 4.6 $ 4.0 $ 3.7 $ 3.5 $ 3.4 $ 3.2 $ 3.0

INPV at TSL 2 $ 66.2
APPENDIX 14-A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT 

ANALYSIS UNDER EXECUTIVE ORDER 12866

TABLE OF CONTENTS

14-A.1 EXECUTIVE SUMMARY ................................................................................. 14-A-1

14-A.2 MONETIZING CARBON DIOXIDE EMISSIONS ........................................... 14-A-2

14-A.3 SOCIAL COST OF CARBON VALUES USED IN PAST REGULATORY 

ANALYSES ......................................................................................................... 14-A-4

14-A.4 APPROACH AND KEY ASSUMPTIONS ........................................................ 14-A-5

14-A.4.1 Integrated Assessment Models ............................................................................ 14-A-5

14-A.4.2 Global versus Domestic Measures of SCC ........................................................ 14-A-11

14-A.4.3 Valuing Non-CO2 Emissions ............................................................................. 14-A-13

14-A.4.4 Equilibrium Climate Sensitivity ........................................................................ 14-A-13

14-A.4.5 Socioeconomic and Emissions Trajectories....................................................... 14-A-16

14-A.4.6 Discount Rate ..................................................................................................... 14-A-19

14-A.5 REVISED SCC ESTIMATES ........................................................................... 14-A-26

14-A.6 LIMITATIONS OF THE ANALYSIS .............................................................. 14-A-32

14-A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPACTS AND 

DAMAGE FUNCTIONS .................................................................................. 14-A-34

14-A.8 CONCLUSION .................................................................................................. 14-A-37

14-A.9 ANNEX.............................................................................................................. 14-A-43

14-A.9.1 Other (non-CO2) gases ...................................................................................... 14-A-44

14-A.9.2 Extrapolating Emissions Projections to 2300 .................................................... 14-A-46

LIST OF TABLES

Table 14-A.1.1 Social Cost of CO2, 2010 – 2050 (in 2007 dollars) ................................. 14-A-2

Table 14-A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity 

Distributions ........................................................................................... 14-A-14

Table 14-A.4.2 Socioeconomic and Emissions Projections from Select EMF-22 

Reference Scenarios ............................................................................... 14-A-18

Table 14-A.5.1 Disaggregated Social Cost of CO2 Values by Model, Socioeconomic 

Trajectory, and Discount Rate for 2010 (in 2007 dollars) ..................... 14-A-29

Table 14-A.5.2 Social Cost of CO2, 2010 – 2050 (in 2007 dollars) ............................... 14-A-31

Table 14-A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates 

between 2010 and 2050 ......................................................................... 14-A-31

Table 14-A.7.1 Probabilities of Various Tipping Points from Expert Elicitation ........... 14-A-35

Table 14-A.9.1 Annual SCC Values: 2010–2050 (in 2007 dollars) ............................... 14-A-43

Table 14-A.9.2 2010 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton 

CO2) ....................................................................................................... 14-A-53

Table 14-A.9.3 2010 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton 

CO2) ....................................................................................................... 14-A-54

14-A-i
Table 14-A.9.4 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton 

CO2) ....................................................................................................... 14-A-55

Table 14-A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates ............ 14-A-56

LIST OF FIGURES 

Figure 14-A.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......................................................................................... 14-A-10

Figure 14-A.4.2 Annual Consumption Loss for Lower Temperature Changes in DICE, 

FUND, and PAGE ................................................................................. 14-A-11

Figure 14-A.4.3 Estimates of the Probability Density Function for Equilibrium Climate 

Sensitivity (°C) ...................................................................................... 14-A-16

Figure 14-A.5.1 Level of Global GDP across EMF Scenarios ........................................ 14-A-30

Figure 14-A.9.2 Sulfur Dioxide Emission Scenarios ....................................................... 14-A-46

Figure 14-A.9.3 Global Population, 2000-2300 (Post-2100 extrapolations assume the 

population growth rate changes linearly to reach a zero growth rate by 

2200.) ..................................................................................................... 14-A-48

Figure 14-A.9.4 World GDP, 2000-2300 (Post-2100 extrapolations assume GDP per 

capita growth declines linearly, reaching zero in the year 2300) .......... 14-A-49

Figure 14-A.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) ................................................ 14-A-50

Figure 14-A.9.6 Global Net Land Use CO2 Emissions, 2000-2300 (Post-2100 

extrapolations assume emissions decline linearly, reaching zero in the 

year 2200) .............................................................................................. 14-A-50

Figure 14-A.9.7 Global Non-CO2 Radiative Forcing, 2000-2300 (Post-2100 

extrapolations assume constant non-CO2 radiative forcing after 2100) 14-A-51

Figure 14-A.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) ................................. 14-A-52

Figure 14-A.9.9 Histogram of Global SCC Estimates in 2010 (2007$/ton CO2), by 

discount rate ........................................................................................... 14-A-55

14-A-ii
APPENDIX 14-A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT 

ANALYSIS UNDER EXECUTIVE ORDER 12866a

14-A.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

14-A-1

 
The interagency group selected four SCC values for use in regulatory analyses. Three 

values are based on the average SCC from three integrated assessment models, at discount rates 

of 2.5, 3, and 5 percent. The fourth value, which represents the 95th percentile SCC estimate 

across all three models at a 3 percent discount rate, is included to represent higher-than-expected 

impacts from temperature change further out in the tails of the SCC distribution. 

 

Table 14-A.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

14-A.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). 

14-A-2

 
Despite the serious limits of both quantification and monetization, SCC estimates can be 

useful in estimating the social benefits of reducing carbon dioxide emissions. Under Executive 

Order 12866, agencies are required, to the extent permitted by law, “to assess both the costs and 

the benefits of the intended regulation and, recognizing that some costs and benefits are difficult 

to quantify, propose or adopt a regulation only upon a reasoned determination that the benefits of 

the intended regulation justify its costs.” The purpose of the SCC estimates presented here is to 

make it possible for agencies to incorporate the social benefits from reducing carbon dioxide 

emissions into cost-benefit analyses of regulatory actions that have small, or “marginal,” impacts 

on cumulative global emissions. Most federal regulatory actions can be expected to have 

marginal impacts on global emissions. 

For such policies, the benefits from reduced (or costs from increased) emissions in any 

future year can be estimated by multiplying the change in emissions in that year by the SCC 

value appropriate for that year. The net present value of the benefits can then be calculated by 

multiplying each of these future benefits by an appropriate discount factor and summing across 

all affected years. This approach assumes that the marginal damages from increased emissions 

are constant for small departures from the baseline emissions path, an approximation that is 

reasonable for policies that have effects on emissions that are small relative to cumulative global 

carbon dioxide emissions. For policies that have a large (non-marginal) impact on global 

cumulative emissions, there is a separate question of whether the SCC is an appropriate tool for 

calculating the benefits of reduced emissions; we do not attempt to answer that question here. 

An interagency group convened on a regular basis to consider public comments, explore 

the technical literature in relevant fields, and discuss key inputs and assumptions in order to 

generate SCC estimates. Agencies that actively participated in the interagency process include 

the Environmental Protection Agency, and the Departments of Agriculture, Commerce, Energy, 

Transportation, and Treasury. This process was convened by the Council of Economic Advisers 

and the Office of Management and Budget, with active participation and regular input from the 

Council on Environmental Quality, National Economic Council, Office of Energy and Climate 

Change, and Office of Science and Technology Policy. The main objective of this process was 

to develop a range of SCC values using a defensible set of input assumptions that are grounded 

in the existing literature. In this way, key uncertainties and model differences can more 

transparently and consistently inform the range of SCC estimates used in the rulemaking process. 

The interagency group selected four SCC estimates for use in regulatory analyses. For 

2010, these estimates are $5, $21, $35, and $65 (in 2007 dollars). The first three estimates are 

based on the average SCC across models and socioeconomic and emissions scenarios at the 5, 3, 

and 2.5 percent discount rates, respectively. The fourth value is included to represent the higherthan-expected impacts from temperature change further out in the tails of the SCC distribution. 

For this purpose, we use the SCC value for the 95th percentile at a 3 percent discount rate. The 

central value is the average SCC across models at the 3 percent discount rate. For purposes of 

capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance 

and value of considering the full range. These SCC estimates also grow over time. For instance, 

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. 

14-A-3
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. 

14-A.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 

14-A-4
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. 

14-A.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. 

14-A.4.1 Integrated Assessment Models 

We rely on three integrated assessment models (IAMs) commonly used to estimate the 

SCC: the FUND, DICE, and PAGE models.c

 These models are frequently cited in the peerc 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).

14-A-5

 
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 

14-A-6
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 

14-A-7
reduce GDP in future years. In contrast, GDP is exogenous in FUND and PAGE, so damages in 

any given year do not propagate forward.d 

The PAGE Model

PAGE2002 (version 1.4epm) treats GDP growth as exogenous. It divides impacts into 

economic, non-economic, and catastrophic categories and calculates these impacts separately for 

eight geographic regions. Damages in each region are expressed as a fraction of output, where 

the fraction lost depends on the temperature change in each region. Damages are expressed as 

power functions of temperature change. The exponents of the damage function are the same in 

all regions but are treated as uncertain, with values ranging from 1 to 3 (instead of being fixed at 

2 as in DICE). 

PAGE2002 includes the consequences of catastrophic events in a separate damage subfunction. Unlike DICE, PAGE2002 models these events probabilistically. The probability of a 

“discontinuity” (i.e., a catastrophic event) is assumed to increase with temperature above a 

specified threshold. The threshold temperature, the rate at which the probability of experiencing 

a discontinuity increases above the threshold, and the magnitude of the resulting catastrophe are 

all modeled probabilistically.

 Adaptation is explicitly included in PAGE. Impacts are assumed to occur for temperature 

increases above some tolerable level (2°C for developed countries and 0°C for developing 

countries for economic impacts, and 0°C for all regions for non-economic impacts), but 

adaptation is assumed to reduce these impacts. Default values in PAGE2002 assume that the 

developed countries can ultimately eliminate up to 90 percent of all economic impacts beyond 

the tolerable 2°C increase and that developing countries can eventually eliminate 50 percent of 

their economic impacts. All regions are assumed to be able to mitigate 25 percent of the noneconomic impacts through adaptation (Hope 2006). 

The FUND Model 

Like PAGE, the FUND model treats GDP growth as exogenous. It includes separately 

calibrated damage functions for eight market and nonmarket sectors: agriculture, forestry, water, 

energy (based on heating and cooling demand), sea level rise (based on the value of land lost and 

the cost of protection), ecosystems, human health (diarrhea, vector-borne diseases, and 

cardiovascular and respiratory mortality), and extreme weather. Each impact sector has a 

different functional form, and is calculated separately for sixteen geographic regions. In some 

impact sectors, the fraction of output lost or gained due to climate change depends not only on 

d Using the default assumptions in DICE 2007, this effect generates an approximately 25 percent increase in the 

SCC relative to damages calculated by fixing GDP. In DICE2007, the time path of GDP is endogenous. 

Specifically, the path of GDP depends on the rate of saving and level of abatement in each period chosen by the 

optimizing representative agent in the model. We made two modifications to DICE to make it consistent with EMF 

GDP trajectories (see next section): we assumed a fixed rate of savings of 20%, and we re-calibrated the exogenous 

path of total factor productivity so that DICE would produce GDP projections in the absence of warming that 

exactly matched the EMF scenarios.

14-A-8

 
the absolute temperature change but also on the rate of temperature change and level of regional 

income.e In the forestry and agricultural sectors, economic damages also depend on CO2

concentrations. 

 Tol (2009) discusses impacts not included in FUND, noting that many are likely to have a 

relatively small effect on damage estimates (both positive and negative). However, he 

characterizes several omitted impacts as “big unknowns”: for instance, extreme climate 

scenarios, biodiversity loss, and effects on economic development and political violence. With 

regard to potentially catastrophic events, he notes, “Exactly what would cause these sorts of 

changes or what effects they would have are not well-understood, although the chance of any one 

of them happening seems low. But they do have the potential to happen relatively quickly, and if 

they did, the costs could be substantial. Only a few studies of climate change have examined 

these issues.”

Adaptation is included both implicitly and explicitly in FUND. Explicit adaptation is 

seen in the agriculture and sea level rise sectors. Implicit adaptation is included in sectors such 

as energy and human health, where wealthier populations are assumed to be less vulnerable to 

climate impacts. For example, the damages to agriculture are the sum of three effects: (1) those 

due to the rate of temperature change (damages are always positive); (2) those due to the level of 

temperature change (damages can be positive or negative depending on region and temperature); 

and (3) those from CO2 fertilization (damages are generally negative but diminishing to zero). 

Adaptation is incorporated into FUND by allowing damages to be smaller if climate 

change happens more slowly. The combined effect of CO2 fertilization in the agricultural sector, 

positive impacts to some regions from higher temperatures, and sufficiently slow increases in 

temperature across these sectors can result in negative economic damages from climate change.

Damage Functions 

To generate revised SCC values, we rely on the IAM modelers’ current best judgments of 

how to represent the effects of climate change (represented by the increase in global-average 

surface temperature) on the consumption-equivalent value of both market and non-market goods 

(represented as a fraction of global GDP). We recognize that these representations are 

incomplete and highly uncertain. But given the paucity of data linking the physical impacts to 

economic damages, we were not able to identify a better way to translate changes in climate into 

net economic damages, short of launching our own research program. 

 The damage functions for the three IAMs are presented in 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).

14-A-9

 
Figure 14-A.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.

14-A-10

 
committed to exploring how these (and other) models can be modified to incorporate more 

accurate estimates of damages.

Figure 14-A.4.2 Annual Consumption Loss for Lower Temperature 

Changes in DICE, FUND, and PAGE

14-A.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.

14-A-11

 
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. 

14-A-12

 
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. 

14-A.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. 

14-A.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).

14-A-13

 
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 14-A.4.1 included below gives summary statistics for the four calibrated distributions.

Table 14-A.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.

14-A-14

 
(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.

14-A-15

 
Figure 14-A.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 

14-A.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.

14-A-16

 
To accomplish this task in a transparent way, we decided to rely on the recent Stanford 

Energy Modeling Forum exercise, EMF-22. EMF-22 uses ten well-recognized models to 

evaluate substantial, coordinated global action to meet specific stabilization targets. A key 

advantage of relying on these data is that GDP, population, and emission trajectories are 

internally consistent for each model and scenario evaluated. The EMF-22 modeling effort also is 

preferable to the IPCC SRES due to their age (SRES were developed in 1997) and the fact that 3 

of 4 of the SRES scenarios are now extreme outliers in one or more variables. Although the 

EMF-22 scenarios have not undergone the same level of scrutiny as the SRES scenarios, they are 

recent, peer-reviewed, published, and publicly available. 

To estimate the SCC for use in evaluating domestic policies that will have a small effect 

on global cumulative emissions, we use socioeconomic and emission trajectories that span a 

range of plausible scenarios. Five trajectories were selected from EMF-22 (see Table 14-A.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.

14-A-17

 
Table 14-A.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.

14-A-18

 
 There are a number of caveats. First, EMF BAU scenarios represent the modelers’ 

judgment of the most likely pathway absent mitigation policies to reduce greenhouse gas 

emissions, rather than the wider range of possible outcomes. Nevertheless, these views of the 

most likely outcome span a wide range, from the more optimistic (e.g. abundant low-cost, lowcarbon energy) to more pessimistic (e.g. constraints on the availability of nuclear and 

renewables).p Second, the socioeconomic trajectories associated with a 550 ppm CO2e 

concentration scenario are not derived from an assessment of what policy is optimal from a 

benefit-cost standpoint. Rather, it is indicative of one possible future outcome. The emission 

trajectories underlying some BAU scenarios (e.g. MESSAGE’s 612 ppm) also are consistent 

with some modest policy action to address climate change.q We chose not to include 

socioeconomic trajectories that achieve even lower GHG concentrations at this time, given the 

difficulty many models had in converging to meet these targets.

For comparison purposes, the Energy Information Agency in its 2009 Annual Energy 

Outlook projected that global carbon dioxide emissions will grow to 30.8, 35.6, and 40.4 

gigatons in 2010, 2020, and 2030, respectively, while world GDP is projected to be $51.8, $71.0 

and $93.9 trillion (in 2005 dollars using market exchange rates) in 2010, 2020, and 2030, 

respectively. These projections are consistent with one or more EMF-22 scenarios. Likewise, 

the United Nations’ 2008 Population Prospect projects population will grow from 6.1 billion 

people in 2000 to 9.1 billion people in 2050, which is close to the population trajectories for the 

IMAGE, MiniCAM, and MERGE models. 

In addition to fossil and industrial CO2 emissions, each EMF scenario provides 

projections of methane, nitrous oxide, fluorinated greenhouse gases, and net land use CO2

emissions out to 2100. These assumptions also are used in the three models while retaining the 

default radiative forcings due to other factors (e.g. aerosols and other gases). See the Annex for 

greater detail.

14-A.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.

14-A-19

 
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 

14-A-20
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. 

14-A-21
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

 A measure of 

the post-tax risky rate for investments whose returns are positively correlated with overall equity 

r The literature argues for a risk-free rate on government bonds as an appropriate measure of the consumption rate of 

interest. Arrow (2000) suggests that it is roughly 3-4 percent. OMB cites evidence of a 3.1 percent pre-tax rate for 

10-year Treasury notes in the A-4 guidance. Newell and Pizer (2003) find real interest rates between 3.5 and 4 

percent for 30-year Treasury securities. 

s The positive approach reflects how individuals make allocation choices across time, but it is important to keep in 

mind that we wish to reflect preferences for society as a whole, which generally has a longer planning horizon.

14-A-22

 
market returns can be obtained by adjusting pre-tax rates of household returns to risky 

investments (approximately 7 percent) for taxes, which yields a real rate of roughly 5 percent.t 

The Ramsey Equation

Ramsey discounting also provides a useful framework to inform the choice of a discount 

rate. Under this approach, the analyst applies either positive or normative judgments in selecting 

values for the key parameters of the Ramsey equation: η (coefficient of relative risk aversion or 

elasticity of the marginal utility of consumption) and ρ (pure rate of time preference).u

 These are 

then combined with g (growth rate of per-capita consumption) to equal the interest rate at which 

future monetized damages are discounted: ρ + η∙g.v In the simplest version of the Ramsey 

model, with an optimizing representative agent with perfect foresight, what we are calling the 

“Ramsey discount rate,” ρ + η∙g, will be equal to the rate of return to capital, i.e., the market 

interest rate.

A review of the literature provides some guidance on reasonable parameter values for the 

Ramsey discounting equation, based on both prescriptive and descriptive approaches. 

• η. Most papers in the climate change literature adopt values for η in the range of 0.5 to 3 

(Weitzman cites plausible values as those ranging from 1 to 4), although not all authors 

articulate whether their choice is based on prescriptive or descriptive reasoning.w 

Dasgupta (2008) argues that η should be greater than 1 and may be as high as 3, since η 

equal to 1 suggests savings rates that do not conform to observed behavior. 

t Cambell et al (2001) estimates that the annual real return from stocks for 1900-1995 was about 7 percent. The 

annual real rate of return for the S&P 500 from 1950 – 2008 was about 6.8 percent. In the absence of a better way to 

population-weight the tax rates, we use the middle of the 20 – 40 percent range to derive a post-tax interest rate 

(Kotlikoff and Rapson 2006).

u The parameter ρ measures the pure rate of time preference: people’s behavior reveals a preference for an 

increase in utility today versus the future. Consequently, it is standard to place a lower weight on utility in the 

future. The parameter η captures diminishing marginal utility: consumption in the future is likely to be higher than 

consumption today, so diminishing marginal utility of consumption implies that the same monetary damage will 

cause a smaller reduction of utility for wealthier individuals, either in the future or in current generations. If η = 0, 

then a one dollar increase in income is equally valuable regardless of level of income; if η = 1, then a one percent 

increase in income is equally valuable no matter the level of income; and if η > 1, then a one percent increase in 

income is less valuable to wealthier individuals. 

v In this case, g could be taken from the selected EMF socioeconomic scenarios or alternative assumptions about the 

rate of consumption growth.

w Empirical estimates of η span a wide range of values. A benchmark value of 2 is near the middle of the range of 

values estimated or used by Szpiro (1986), Hall and Jones (2007), Arrow (2007), Dasgupta (2006, 2008), Weitzman 

(2007, 2009), and Nordhaus (2008). However, Chetty (2006) developed a method of estimating η using data on 

labor supply behavior. He shows that existing evidence of the effects of wage changes on labor supply imposes a 

tight upper bound on the curvature of utility over wealth (CRRA < 2) with the mean implied value of 0.71 and 

concludes that the standard expected utility model cannot generate high levels of risk aversion without contradicting 

established facts about labor supply. Recent work has jointly estimated the components of the Ramsey equation. 

Evans and Sezer (2005) estimate η = 1.49 for 22 OECD countries. They also estimate ρ = 1.08 percent per year 

using data on mortality rates. Anthoff, et al. (2009b) estimate η = 1.18, and ρ = 1.4 percent. When they multiply the 

bivariate probability distributions from their work and Evans and Sezer (2005) together, they find η = 1.47, and ρ = 

1.07. 

14-A-23

 
• 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. n ethical choice linked to The choice of η has also been posed a 

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.)

14-A-24

 
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. 

14-A-25

 
mentioned, the consumption rate of interest is the correct discounting concept to use when future 

damages from elevated temperatures are estimated in consumption-equivalent units. Further, 3 

percent roughly corresponds to the after-tax riskless interest rate. The upper value of 5 percent is 

included to represent the possibility that climate damages are positively correlated with market 

returns. Additionally, this discount rate may be justified by the high interest rates that many 

consumers use to smooth consumption across periods. 

The low value, 2.5 percent, is included to incorporate the concern that interest rates are 

highly uncertain over time. It represents the average certainty-equivalent rate using the meanreverting and random walk approaches from Newell and Pizer (2003) starting at a discount rate 

of 3 percent. Using this approach, the certainty equivalent is about 2.2 percent using the random 

walk model and 2.8 percent using the mean reverting approach.aa Without giving preference to a 

particular model, the average of the two rates is 2.5 percent. Further, a rate below the riskless 

rate would be justified if climate investments are negatively correlated with the overall market 

rate of return. Use of this lower value also responds to certain judgments using the prescriptive 

or normative approach and to ethical objections that have been raised about rates of 3 percent or 

higher. 

14-A.5 REVISED SCC ESTIMATES 

Our general approach to estimating SCC values is to run the three integrated assessment 

models (FUND, DICE, and PAGE) using the following inputs agreed upon by the interagency 

group: 

• A Roe and Baker distribution for the climate sensitivity parameter bounded between 0

and 10 with a median of 3 °C and a cumulative probability between 2 and 4.5 °C of twothirds. 

• Five sets of GDP, population, and carbon emissions trajectories based on EMF-22. 

• Constant annual discount rates of 2.5, 3, and 5 percent. 

Because the climate sensitivity parameter is modeled probabilistically, and because PAGE and 

FUND incorporate uncertainty in other model parameters, the final output from each model run 

is a distribution over the SCC in year t. 

For each of the IAMs, the basic computational steps for calculating the SCC in a particular year t

are:

1. Input the path of emissions, GDP, and population from the selected EMF-22 

scenarios, and the extrapolations based on these scenarios for post-2100 years.

2. Calculate the temperature effects and (consumption-equivalent) damages in each 

year resulting from the baseline path of emissions. 

aa Calculations done by Pizer et al. using the original simulation program from Newell and Pizer (2003).

14-A-26

 
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, 

14-A-27
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 14-A.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. 

14-A-28
Table 14-A.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 

14-A-29

 
The SCC estimates from FUND are sensitive to differences in emissions paths but 

relatively insensitive to differences in GDP paths across scenarios, while the reverse is true for 

DICE and PAGE. This likely occurs because of several structural differences among the models. 

Specifically in DICE and PAGE, the fraction of economic output lost due to climate damages 

increases with the level of temperature alone, whereas in FUND the fractional loss also increases 

with the rate of temperature change. Furthermore, in FUND increases in income over time 

decrease vulnerability to climate change (a form of adaptation), whereas this does not occur in 

DICE and PAGE. These structural differences among the models make FUND more sensitive to 

the path of emissions and less sensitive to GDP compared to DICE and PAGE. 

Figure 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 14-A.5.1 Level of Global GDP across EMF Scenarios 

Table 14-A.5.2 shows the four selected SCC values in five-year increments from 2010 to 

2050. Values for 2010, 2020, 2030, 2040, and 2050 are calculated by first combining all outputs 

(10,000 estimates per model run) from all scenarios and models for a given discount rate. Values 

for the years in between are calculated using 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

14-A-30

 
Table 14-A.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 14-A.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 14-A.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 

14-A-31
calculated using a SCC based on a 5 percent discount rate also should be discounted back to the 

analysis year using a 5 percent discount rate.cc 

14-A.6 LIMITATIONS OF THE ANALYSIS

As noted, any estimate of the SCC must be taken as provisional and subject to further 

refinement (and possibly significant change) in accordance with evolving scientific, economic, 

and ethical understandings. During the course of our modeling, it became apparent that there are 

several areas in particular need of additional exploration and research. These caveats, and 

additional observations in the following section, are necessary to consider when interpreting and 

applying the SCC estimates.

Incomplete treatment of non-catastrophic damages. The impacts of climate change are 

expected to be widespread, diverse, and heterogeneous. In addition, the exact magnitude of these 

impacts is uncertain because of the inherent complexity of climate processes, the economic 

behavior of current and future populations, and our inability to accurately forecast technological 

change and adaptation. Current IAMs do not assign value to all of the important physical, 

ecological, and economic impacts of climate change recognized in the climate change literature 

(some of which are discussed above) because of lack of precise information on the nature of 

damages and because the science incorporated into these models understandably lags behind the 

most recent research. Our ability to quantify and monetize impacts will undoubtedly improve 

with time. But it is also likely that even in future applications, a number of potentially significant 

damage categories will remain non-monetized. (Ocean acidification is one example of a 

potentially large damage from CO2 emissions not quantified by any of the three models. Species 

and wildlife loss is another example that is exceedingly difficult to monetize.) 

Incomplete treatment of potential catastrophic damages. There has been considerable 

recent discussion of the risk of catastrophic impacts and how best to account for extreme 

scenarios, such as the collapse of the Atlantic Meridional Overturning Circulation or the West 

Antarctic Ice Sheet, or large releases of methane from melting permafrost and warming oceans. 

Weitzman (2009) suggests that catastrophic damages are extremely large—so large, in fact, that 

the damages from a low probability, catastrophic event far in the future dominate the effect of 

the discount rate in a present value calculation and result in an infinite willingness-to-pay for 

mitigation today. However, Nordhaus (2009) concluded that the conditions under which 

Weitzman's results hold “are limited and do not apply to a wide range of potential uncertain 

scenarios." 

Using a simplified IAM, Newbold and Daigneault (2009) confirmed the potential for 

large catastrophe risk premiums but also showed that the aggregate benefit estimates can be 

highly sensitive to the shapes of both the climate sensitivity distribution and the damage function 

at high temperature changes. Pindyck (2009) also used a simplified IAM to examine highcc However, it is possible that other benefits or costs of proposed regulations unrelated to CO2 emissions will be 

discounted at rates that differ from those used to develop the SCC estimates. 

14-A-32

 
impact, low-probability risks, using a right-skewed gamma distribution for climate sensitivity as 

well as an uncertain damage coefficient, but in most cases found only a modest risk premium. 

Given this difference in opinion, further research in this area is needed before its practical 

significance can be fully understood and a reasonable approach developed to account for such 

risks in regulatory analysis. (The next section discusses the scientific evidence on catastrophic 

impacts in greater detail.)

Uncertainty in extrapolation of damages to high temperatures: The damage functions in 

these IAMs are typically calibrated by estimating damages at moderate temperature increases 

(e.g., DICE was calibrated at 2.5 °C) and extrapolated to far higher temperatures by assuming 

that damages increase as some power of the temperature change. Hence, estimated damages are 

far more uncertain under more extreme climate change scenarios. 

Incomplete treatment of adaptation and technological change: Each of the three 

integrated assessment models used here assumes a certain degree of low- or no-cost adaptation. 

For instance, Tol assumes a great deal of adaptation in FUND, including widespread reliance on 

air conditioning; so much so, that the largest single benefit category in FUND is the reduced 

electricity costs from not having to run air conditioning as intensively (NRC 2009). 

Climate change also will increase returns on investment to develop technologies that 

allow individuals to cope with adverse climate conditions, and IAMs to do not adequately 

account for this directed technological change.dd For example, scientists may develop crops that 

are better able to withstand higher and more variable temperatures. Although DICE and FUND 

have both calibrated their agricultural sectors under the assumption that farmers will change land 

use practices in response to climate change (Mastrandrea, 2009), they do not take into account 

technological changes that lower the cost of this adaptation over time. On the other hand, the 

calibrations do not account for increases in climate variability, pests, or diseases, which could 

make adaptation more difficult than assumed by the IAMs for a given temperature change. 

Hence, models do not adequately account for potential adaptation or technical change that might 

alter the emissions pathway and resulting damages. In this respect, it is difficult to determine 

whether the incomplete treatment of adaptation and technological change in these IAMs 

understate or overstate the likely damages.

Risk aversion: A key question unanswered during this interagency process is what to 

assume about relative risk aversion with regard to high-impact outcomes. These calculations do 

not take into account the possibility that individuals may have a higher willingness to pay to 

reduce the likelihood of low-probability, high-impact damages than they do to reduce the 

likelihood of higher-probability, but lower-impact, damages with the same expected cost. (The 

inclusion of the 95th percentile estimate in the final set of SCC values was largely motivated by 

this concern.) If individuals do show such a higher willingness to pay, a further question is 

whether that fact should be taken into account for regulatory policy. Even if individuals are not 

risk-averse for such scenarios, it is possible that regulatory policy should include a degree of 

risk-aversion. 

dd However these research dollars will be diverted from whatever their next best use would have been in the absence 

of climate change (so productivity/GDP would have been still higher).

14-A-33

 
Assuming a risk-neutral representative agent is consistent with OMB’s Circular A-4, 

which advises that the estimates of benefits and costs used in regulatory analysis are usually 

based on the average or the expected value and that “emphasis on these expected values is 

appropriate as long as society is ‘risk neutral’ with respect to the regulatory alternatives. While 

this may not always be the case, [analysts] should in general assume ‘risk neutrality’ in [their] 

analysis.” 

Nordhaus (2008) points to the need to explore the relationship between risk and income 

in the context of climate change across models and to explore the role of uncertainty regarding 

various parameters in the results. Using FUND, Anthoff et al (2009) explored the sensitivity of 

the SCC to Ramsey equation parameter assumptions based on observed behavior. They conclude

that “the assumed rate of risk aversion is at least as important as the assumed rate of time 

preference in determining the social cost of carbon.” Since Circular A-4 allows for a different 

assumption on risk preference in regulatory analysis if it is adequately justified, we plan to 

continue investigating this issue.

14-A.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 

14-A-34
permafrost. Many of these tipping points are estimated to have thresholds between about 3 °C 

and 5 °C (Lenton et al., 2008). Probabilities of several of these tipping points were assessed 

through expert elicitation in 2005–2006 by Kriegler et al. (2009); results from this study are 

highlighted in Table 14-A.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 14-A.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 

14-A-35
crossing at least one of their primary climatic tipping points in a scenario with temperatures 

about 2-4 °C warmer than pre-Industrial levels in 2100. 

It is important to note that crossing a climatic tipping point will not necessarily lead to an 

economic catastrophe in the sense used in the IAMs. A tipping point is a critical threshold across 

which some aspect of the Earth system starts to shifts into a qualitatively different state (for 

instance, one with dramatically reduced ice sheet volumes and higher sea levels). In the IAMs, a 

catastrophe is a low-probability environmental change with high economic impact. 

Failure to incorporate inter-sectoral and inter-regional interactions

The damage functions do not fully incorporate either inter-sectoral or inter-regional 

interactions. For instance, while damages to the agricultural sector are incorporated, the effects 

of changes in food supply on human health are not fully captured and depend on the modeler’s 

choice of studies used to calibrate the IAM. Likewise, the effects of climate damages in one 

region of the world on another region are not included in some of the models (FUND includes 

the effects of migration from sea level rise). These inter-regional interactions, though difficult to 

quantify, are the basis for climate-induced national and economic security concerns (e.g., 

Campbell et al., 2007; U.S. Department of Defense 2010) and are particularly worrisome at 

higher levels of warming. High-end warming scenarios, for instance, project water scarcity 

affecting 4.3-6.9 billion people by 2050, food scarcity affecting about 120 million additional 

people by 2080, and the creation of millions of climate refugees (Easterling et al., 2007; 

Campbell et al., 2007). 

Imperfect substitutability of environmental amenities

Data from the geological record of past climate changes suggests that 6 °C of warming 

may have severe consequences for natural systems. For instance, during the Paleocene-Eocene 

Thermal Maximum about 55.5 million years ago, when the Earth experienced a geologically 

rapid release of carbon associated with an approximately 5 °C increase in global mean 

temperatures, the effects included shifts of about 400-900 miles in the range of plants (Wing et 

al., 2005), and dwarfing of both land mammals (Gingerich, 2006) and soil fauna (Smith et al., 

2009). 

The three IAMs used here assume that it is possible to compensate for the economic 

consequences of damages to natural systems through increased consumption of non-climate 

goods, a common assumption in many economic models. In the context of climate change, 

however, it is possible that the damages to natural systems could become so great that no 

increase in consumption of non-climate goods would provide complete compensation (Levy et 

al., 2005). For instance, as water supplies become scarcer or ecosystems become more fragile 

and less bio-diverse, the services they provide may become increasingly more costly to replace. 

Uncalibrated attempts to incorporate the imperfect substitutability of such amenities into IAMs 

(Sterner and Persson, 2008) indicate that the optimal degree of emissions abatement can be 

considerably greater than is commonly recognized. 

14-A-36
14-A.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. 

 

14-A-37
REFERENCES

Andronova, N., and M. Schlesinger. 2001. “Objective estimation of the probability density 

function for climate sensitivity.” J. Geophys. Res., 106(D19), 22605–22611. 

Annan, J., et al., 2005. “Efficiently constraining climate sensitivity with paleoclimate

simulations.” Scientific Online Letters on the Atmosphere, 1, 181–184. 

Anthoff D, C. Hepburn, and R. Tol. 2009a. “Equity Weighting and the Marginal Damage Costs 

of Climate Change.” Ecological Economics 68:836-849. 

Anthoff, D., R. Tol, and G. Yohe. 2009b. “Risk aversion, time preference, and the social cost of 

carbon.” Environmental Research Letters 4: 024002 (7pp). 

Arrow, K. 2007. “Global climate change: a challenge to policy.” Economist’s Voice 4(3):Article 

2. 

Arrow, K. 2000. “A Comment on Cooper.” The World Bank Research Observer. vol 15, no. 2. 

Arrow, K., et al. 1996. Benefit-Cost Analysis in Environmental, Health, and Safety Regulation: A 

Statement of Principles. Washington, D.C., AEI Press. pp. 13-14. 

Arrow, K.J., et al. 1996. ”Intertemporal equity, discounting and economic efficiency,” in 

Climate Change 1995: Economic and Social Dimensions of Climate Change, Contribution of 

Working Group III to the Second Assessment Report of the Intergovernmental Panel on Climate 

Change. 

Campbell, J., P. Diamond, and J. Shoven. 2001. “Estimating the Real Rate of Return on Stocks 

Over the Long Term.” Presented to the Social Security Advisory Board. August. 

Campbell, K. et al. 2007. The age of consequences: The foreign policy and national security 

implications of global climate change. Center for Strategic & International Studies, 119 pp. 

Castles, I. and D. Henderson. 2003. “The IPCC Emission Scenarios: An Economic-Statistical 

Critique.” Energy and Environment 14(2-3): 159-185. 

Chetty, R. 2006. “A New Method of Estimating Risk Aversion.” American Economic Review

96(5): 1821–1834. 

Dasgupta P. 2006. “Comments on the Stern Review’s economics of climate change.” University 

of Cambridge working paper. 

Dasgupta P. 2008. “Discounting climate change.” Journal of Risk and Uncertainty 37:141-169. 

Easterling, W., et al. 2007. Climate Change 2007: Impacts, Adaptation, and Vulnerability. 

Intergovernmental Panel on Climate Change, 976 pp. 

14-A-38
Evans D., and H. Sezer. 2005. “Social discount rates for member countries of the European 

Union.” J. Econ. Stud. 32 47–59. 

Forest, C., et al. 2002. “Quantifying uncertainties in climate system properties with the use of 

recent observations.” Science 295, 113. 

Forest, D., P. Stone, and A. Sokolov. 2006. “Estimated PDFs of climate system properties 

including natural and anthropogenic forcings.” Geophys. Res. Lett., 33, L01705. 

 

Forster, P., and J. Gregory. 2006. “The climate sensitivity and its components diagnosed from 

Earth radiation budget data.” J. Clim., 19, 39–52. 

Frame, D., et al. 2005. “Constraining climate forecasts: The role of prior assumptions.” Geophys. 

Res. Lett., 32, L09702.

Gingerich, P. 2006. “Environment and evolution through the Paleocene-Eocene thermal 

maximum.” Trends Ecol. Evol. 21: 246-253. 

Gollier, C. 2008. “Discounting with fat-tailed economic growth.” Journal of Risk and 

Uncertainty 37:171-186. 

Gollier, C. and M. Weitzman (2009). "How Should the Distant Future be Discounted When 

Discount Rates are Uncertain?" Harvard University, mimeo, Nov 2009. 

Gregory, J., et al. 2002a. “An observationally based estimate of the climate sensitivity.” J. Clim., 

15(22), 3117–3121. 

Groom, B., Koundouri, P., Panipoulou, E., Pantelidis, T. 2006. “An econometric approach to 

estimating long-run discount rates.” Journal of Applied Econometrics. 

Hall R, and C. Jones . 2007. “The value of life and the rise in health spending.” Quarterly 

Journal of Economics 122(1):39-72. 

Hansen, J.,M. Sato, P. Kharecha, G. Russell, D. W. Lea and M. Siddall. 2007. “Climate change 

and trace gases.” Phil. Trans. Roy. Soc. A 365: 1925-1954. 

Hegerl G, et al. 2007. “Understanding and attributing climate change.” in Solomon S, et al. (eds) 

Climate Change 2007: the physical science basis. Contribution of Working Group I to the 

Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: 

Cambridge University Press.

Hegerl, G., T. Crowley, W. Hyde, and D. Frame. 2006. “Constraints on climate sensitivity from 

temperature reconstructions of the past seven centuries.” Nature 440. 

14-A-39
Holtsmark, B., and K. Alfsen. 2005. “PPP Correction of the IPCC Emission Scenarios – Does it 

Matter?” Climatic Change 68(1-2): 11-19. 

Hope C. 2006. “The marginal impact of CO2 from PAGE2002: an integrated assessment model 

incorporating the IPCC’s five reasons for concern.” The Integrated Assessment Journal 6(1):19-

56. 

Hope C. 2008. “Optimal carbon emissions and the social cost of carbon under uncertainty.” The 

Integrated Assessment Journal 8(1):107-122. 

Intergovernmental Panel on Climate Change (2007). “Summary for Policymakers.” In Climate 

Change 2007: The Physical Science Basis, Contribution of Working Group I to the Fourth 

Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University 

Press.

Just, R., D. Hueth, and A. Schmitz. 2004. The Welfare Economics of Public Policy. Glos UK: 

Edward Elgar Publishing Limited. 

 

Knutti, R., T. Stocker, F. Joos, and G. Plattner. 2002. “Constraints on radiative forcing and future 

climate change from observations and climate model ensembles.” Nature, 416, 719–723. 

Kriegler, E. et al. 2009. “Imprecise probability assessment of tipping points in the climate 

system.” Proc. Natl. Acad. Sci. 106: 5041-5046. 

Kotlikoff, L. and D. Rapson. 2006. “Does It Pay, at the Margin, to Work and Save? – Measuring 

Effective Marginal Taxes on Americans’ Labor Supply and Saving.” National Bureau of 

Economic Research, Working Paper, No. 12533. 

Le Treut H., et al. 2007. “Historical Overview of Climate Change.” in Solomon et al., Climate 

Change 2007. 

Lenton, T., et al. 2008. “Tipping elements in the Earth’s climate system.” Proc. Natl. Acad. Sci.

105: 1786-1793. 

Levy, M., et al. 2005. “Ecosystem conditions and human well-being.” In: Ecosystems and 

Human Well-being: Current State and Trends, Volume 1. [R. Hassan, R. Scholes, and N. Ash, 

eds.] Washington: Island Press. pp. 123-164. 

Lind, R. 1990. “Reassessing the Government’s Discount Rate Policy in Light of New Theory 

and Data in a World Economy with a High Degree of Capital Mobility.” Journal of 

Environmental Economics and Management 18, S-8-S-28. 

Mastrandre, M. 2009. “Calculating the benefits of climate policy: Examining the assumptions of 

Integrated Assessment Models.” Pew Center on Global Climate Change Working Paper, 60 pp. 

Meehl, G, et al. 2007. “Global Climate Projections.” in Solomon et al., Climate Change 2007. 

14-A-40
National Research Council (2009). Hidden Costs of Energy: Unpriced Consequences of Energy 

Production and Use. National Academies Press.

Newbold S, Daigneault A. 2009. “Climate response uncertainty and the benefits of greenhouse 

gas emissions reductions.” Environmental and Resource Economics 44:351-377. 

Newell, R., and W. Pizer. 2003. Discounting the distant future: how much do uncertain rates 

increase valuations? Journal of Environmental Economics and Management 46: 52-71. 

Nordhaus, W. 1994. “Expert Opinion on Climate Change.” American Scientist 82: 45-51. 

Nordhaus, W. 2007a. Accompanying notes and documentation on development of DICE-2007 

model: notes on DICE-2007.delta.v8 as of September 21, 2007. 

Nordhaus, W. 2007b. “Alternative measures of output in global economic-environmental 

models: Purchasing power parity or market exchange rates?” Energy Economics 29: 349-372. 

Nordhaus W. 2008. A Question of Balance: Weighing the Options on Global Warming Policies. 

New Haven, CT: Yale University Press.

Nordhaus, W. 2009. “An Analysis of the Dismal Theorem. Cowles Foundation Discussion 

Paper. No. 1686. January. 

Nordhaus W., and Boyer J. 2000. Warming the World: Economic Models of Global Warming. 

Cambridge, MA: MIT Press.

Pindyck, R. 2009. “Uncertain Outcomes and Climate Change Policy.” NBER Working Paper, 

No. 15259. August. 

Ramsey, F. 1928. “A Mathematical Theory of Saving.” The Economic Journal 38(152): 543-

559. 

Roe, G. 2008. “Feedbacks, timescales, and seeing red.” Annual Review of Earth and Planetary 

Sciences 37:5.1-5.23. 

Roe, G., and M. Baker. 2007. “Why is climate sensitivity so unpredictable?” Science 318:629-

632. 

Schneider von Deimling, T., H. Held, A. Ganopolski, and S. Rahmstorf. 2006. “Climate 

sensitivity estimated from ensemble simulations of glacial climate.” Clim. Dyn., 27, 149–163. 

Smith, J. et al. 2009. “Transient dwarfism of soil fauna during the Paleocene-Eocene Thermal 

Maximum.” Proc. Natl. Acad. Sci. 106: 17665-17660. 

Stern, N., et al. (2006), Stern Review: The Economics of Climate Change, HM Treasury, London. 

14-A-41
Stern N. 2008. “The economics of climate change.” American Economic Review 98(2):1-37. 

Sterner, T., and U. Persson. 2008. An even Sterner review: Introducing relative prices into the 

discounting debate. Rev. Env. Econ. Pol. 2: 61-76. 

Summers, L., and R. Zeckhauser. 2008. “Policymaking for Prosperity.” Journal of Risk and 

Uncertainty 37: 115-140. 

Szpiro, G. 1986. “Measuring Risk Aversion: An Alternative Approach.” The Review of 

Economics and Statistics 68(1): 156-9. 

Tol, R. 2002a. “Estimates of the damage costs of climate change. Part I: benchmark estimates.” 

Environmental and Resource Economics 21:47-73. 

Tol, R. 2002b. “Estimates of the damage costs of climate change. Part II: dynamic estimates.” 

Environmental and Resource Economics 21:135-160. 

Tol, R. 2006. “Exchange Rates and Climate Change: An Application of FUND.” Climatic 

Change 75(1-2): 59-80. 

Tol, R. 2009. “An analysis of mitigation as a response to climate change.” Copenhagen 

Consensus on Climate. Discussion Paper.

U.S. Department of Defense. 2010. Quadrennial Defense Review Report. February. 

Warren, R., et al. 2006. “Spotlight Impact Functions in Integrated Assessment.” Tyndall Center 

for Climate Change Research, Working Paper 91. 

Weitzman, M. 2009. “On modeling and interpreting the economics of catastrophic climate 

change.” Review of Economics and Statistics 91:1-19. 

Weitzman, M. 2007. “A review of The Stern Review of the Economics of Climate Change.” 

Journal of Economic Literature 45:703-724. 

Weitzman, M. 1999. In Portney P.R. and Weyant J.P. (eds.), Discounting and Intergenerational 

Equity, Resources for the Future, Washington, D.C. 

Weitzman, M. 1998. “Why the Far-Distant Future Should Be Discounted at Its Lowest Possible 

Rate.” Journal of Environmental Economics and Management 36 (3): 201-208. 

Wing, S. et al. 2005. “Transient floral change and rapid global warming at the Paleocene-Eocene 

boundary.” Science 310: 993-996. 

14-A-42
14-A.9 ANNEX

Table 14-A.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. 

14-A-43
14-A.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.

14-A-44

 
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.

14-A-45

 
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 14-A.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. 

14-A.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 

14-A-46
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

14-A-47

 
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 14-A.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

14-A-48
Figure 14-A.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

14-A-49
Figure 14-A.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 14-A.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

14-A-50

 
Figure 14-A.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

14-A-51
Figure 14-A.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

14-A-52
Table 14-A.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

14-A-53
Table 14-A.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

14-A-54
Table 14-A.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 14-A.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%

14-A-55
Table 14-A.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

14-A-56
14-B-i

APPENDIX 14-B. TECHNICAL UPDATE OF SOCIAL COST OF CARBON FOR 

REGULATORY IMPACT ANALYSIS UNDER EXECUTIVE ORDER 12866

TABLE OF CONTENTS

14-B.1 PREFACE ............................................................................................................ 14-B-2

14-B.2 PURPOSE ............................................................................................................ 14-B-2

14-B.3 SUMMARY OF MODEL UPDATES................................................................. 14-B-3

14-B.3.1 DICE .................................................................................................................... 14-B-3

14-B.3.1.1 Carbon Cycle Parameters .............................................................. 14-B-3

14-B.3.1.2 Sea Level Dynamics ...................................................................... 14-B-4

14-B.3.1.3 Re-calibrated Damage Function .................................................... 14-B-5

14-B.3.2 FUND ................................................................................................................... 14-B-5

14-B.3.2.1 Space Heating ................................................................................ 14-B-6

14-B.3.2.2 Sea Level Rise and Land Loss ...................................................... 14-B-6

14-B.3.2.3 Agriculture ..................................................................................... 14-B-7

14-B.3.2.4 Temperature Response Model ....................................................... 14-B-7

14-B.3.2.5 Methane ......................................................................................... 14-B-7

14-B.3.3 PAGE ................................................................................................................... 14-B-8

14-B.3.3.1 Sea Level Rise ............................................................................... 14-B-8

14-B.3.3.2 Revised Damage Function to Account for Saturation ................... 14-B-8

14-B.3.3.3 Regional Scaling Factors ............................................................... 14-B-8

14-B.3.3.4 Probability of a Discontinuity ....................................................... 14-B-9

14-B.3.3.5 Adaptation ..................................................................................... 14-B-9

14-B.3.3.6 Other Noteworthy Changes ......................................................... 14-B-10

14-B.4 REVISED SCC ESTIMATES ........................................................................... 14-B-10

14-B.5 OTHER MODEL LIMITATIONS OR RESEARCH GAPS ............................ 14-B-13

LIST OF TABLES

Table 14-B.4.1 Revised Social Cost of CO2, 2010 – 2050 (in 2007 dollars per ton of 

CO2) ....................................................................................................... 14-B-12

Table 14-B.4.2 Average Annual Growth Rates of SCC Estimates between 2010 and 

2050........................................................................................................ 14-B-13

Table A1. Annual SCC Values: 2010-2050 (2007$/ton CO2) ............................................. 14-B-16

Table A2. 2020 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton CO2) ..... 14-B-17

Table A3. 2020 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2) ....... 14-B-17

Table A4. 2020 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton CO2) ........ 14-B-18

LIST OF FIGURES

Figure 14-B.4.2 Distribution of SCC Estimates for 2020 (in 2007$ per ton CO2) .......... 14-B-12
14-B-2

APPENDIX 14-B. TECHNICAL UPDATE OF SOCIAL COST OF CARBON FOR 

REGULATORY IMPACT ANALYSIS UNDER EXECUTIVE ORDER 12866

14-B.1 PREFACE

 The following text is reproduced almost verbatim from the May 2013 report (revised 

November 2013) of the Interagency Working Group on the Social Cost of Carbon of the United 

States Government.

a

 Minor changes were made to the working group's report to make it more 

consistent with the rest of this technical support document. Annex B describes the revisions that 

were made to the May 2013 report. 

14-B.2 PURPOSE

The purpose of this document is to update the schedule of social cost of carbon (SCC)b

estimates from the 2010 interagency technical support document (TSD) (Interagency Working 

Group on Social Cost of Carbon 2010).1 E.O. 13563 commits the Administration to regulatory 

decision making “based on the best available science.”c Additionally, the interagency group 

recommended in 2010 that the SCC estimates be revisited on a regular basis or as model updates 

that reflect the growing body of scientific and economic knowledge become available.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 solely on the latest peer-reviewed version of the models, 

replacing model versions that were developed up to ten years ago in a rapidly evolving field. It 

does not revisit other assumptions with regard to the discount rate, reference case socioeconomic 

and emission scenarios, or equilibrium climate sensitivity. Improvements in the way damages are 

modeled are confined to those that have been incorporated into the latest versions of the models 

by the developers themselves in the peer-reviewed literature. The Environmental Protection 

Agency (EPA), in collaboration with other Federal agencies such as the Department of Energy 

(DOE), continues to investigate potential improvements to the way in which economic damages 

associated with changes in CO2 emissions are quantified. 

 

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

Interagency Working Group on Social Cost of Carbon, United States Government. May 2013; revised November 

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

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. c http://www.whitehouse.gov/sites/default/files/omb/inforeg/eo12866/eo13563_01182011.pdf

d See p. 1, 3, 4, 29, and 33 (Interagency Working Group on Social Cost of Carbon 2010).1
14-B-3

Section 3 summarizes the major updates relevant to SCC estimation that are contained in 

the new versions of the integrated assessment models released since the 2010 interagency report. 

Section 4 presents the updated schedule of SCC estimates for 2010 – 2050 based on these 

versions of the models. Section 5 provides a discussion of recent workshops to support 

improvements in SCC estimation.

14-B.3 SUMMARY OF MODEL UPDATES

This section briefly summarizes changes integrated into the most recent versions of the 

three integrated assessment models (IAMs) used by the interagency group in 2010. We focus on 

describing those model updates that are relevant to estimating the social cost of carbon. For 

example, both the DICE and PAGE models now include an explicit representation of sea level 

rise damages. Other revisions to PAGE include: updated adaptation assumptions, revisions to 

ensure damages are constrained GDP, updated regional scaling of damages, and a revised 

treatment of potentially abrupt shifts in climate damages. In the most recent version of DICE, 

the model’s simple carbon cycle has been updated to be more consistent with a relatively more 

complex climate model. The FUND model includes updated damage functions for sea level rise 

impacts, the agricultural sector, and reduced space heating requirements, as well as changes to 

the response of temperature to the buildup of GHG concentrations and the inclusion of indirect 

effects of methane emissions. Changes made to parts of the models that are superseded by the 

interagency working group’s modeling assumptions – regarding climate sensitivity, discounting, 

and socioeconomic variables – are not discussed. 

14-B.3.1 DICE

Changes in the DICE model relevant for the SCC estimates developed by the interagency 

working group include: 1) updated parameter values for the carbon cycle model, 2) an explicit 

representation of sea level dynamics, and 3) a re-calibrated damage function that includes an 

explicit representation of economic damages from sea level rise. Changes were also made to 

other parts of the DICE model—including the equilibrium climate sensitivity parameter, the rate 

of change of total factor productivity, and the elasticity of the marginal utility of consumption—

but these components of DICE are superseded by the interagency working group’s assumptions 

and so will not be discussed here. More details on DICE2007 can be found in Nordhaus (2008)2

and on DICE2010 in Nordhaus (2010)3 and the associated on-line appendix containing 

supplemental information. 

14-B.3.1.1 Carbon Cycle Parameters

DICE uses a three-box model of carbon stocks and flows to represent the accumulation 

and transfer of carbon among the atmosphere, the shallow ocean and terrestrial biosphere, and 

the deep ocean. These parameters are “calibrated to match the carbon cycle in the Model for the 
14-B-4

Assessment of Greenhouse Gas Induced Climate Change (MAGICC)” (Nordhaus 2008 p 44).2e

Carbon cycle transfer coefficient values in DICE2010 are based on re-calibration of the model to 

match the newer version of MAGICC (Nordhaus 2010 p 2).3 For example, in DICE2010 in each 

decade, 12 percent of the carbon in the atmosphere is transferred to the shallow ocean, 4.7 

percent of the carbon in the shallow ocean is transferred to the atmosphere, 94.8 percent remains 

in the shallow ocean, and 0.5 percent is transferred to the deep ocean. For comparison, in DICE 

2007, 18.9 percent of the carbon in the atmosphere is transferred to the shallow ocean each 

decade, 9.7 percent of the carbon in the shallow ocean is transferred to the atmosphere, 85.3 

percent remains in the shallow ocean, and 5 percent is transferred to the deep ocean. 

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. 

14-B.3.1.2 Sea Level Dynamics

A new feature of DICE2010 is an explicit representation of the dynamics of the global 

average sea level anomaly to be used in the updated damage function (discussed below). This 

section contains a brief description of the sea level rise (SLR) module; a more detailed 

description can be found on the model developer’s website.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.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. The contribution to SLR in 

each period is proportional to the difference between the previous period’s sea level anomaly and 

 

e MAGICC is a simple climate model initially developed within the U.S. National Center for Atmospheric Research 

that has been used heavily by the Intergovernmental Panel on Climate Change (IPCC) to emulate projections from 

much more sophisticated state of the art earth system simulation models (Randall et al. 2007).4

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

 
14-B-5

the equilibrium sea level anomaly, where the constant of proportionality increases with the 

temperature anomaly in the current period. 

The contribution to SLR from the melting of the Antarctic ice sheet is -0.001 meters per 

decade when the temperature anomaly is below 3 °C and increases linearly to a maximum rate of 

0.025 meters per decade at a temperature anomaly of 6 °C. 

14-B.3.1.3 Re-calibrated Damage Function

Economic damages from climate change in the DICE model are represented by a 

fractional loss of gross economic output in each period. A portion of the remaining economic 

output in each period (net of climate change damages) is consumed and the remainder is invested 

in the physical capital stock to support future production, so each period’s climate damages will 

reduce consumption in that period and in all future periods due to the lost investment. The 

fraction of output in each period that is lost due to climate change impacts is represented as one 

minus a fraction, which is one divided by a quadratic function of the temperature anomaly, 

producing a sigmoid (“S”-shaped) function. The loss function in DICE2010 has been expanded 

by adding a quadratic function of SLR to the quadratic function of temperature. In DICE2010 the 

temperature anomaly coefficients have been recalibrated to avoid double-counting damages from 

sea level rise that were implicitly included in these parameters in DICE2007. 

The aggregate damages in DICE2010 are illustrated by Nordhaus (2010 p 3),3 who notes 

that “...damages in the uncontrolled (baseline) [i.e., reference] case ... in 2095 are $12 trillion, 

or 2.8 percent of global output, for a global temperature increase of 3.4 oC above 1900 levels.” 

This compares to a loss of 3.2 percent of global output at 3.4 oC in DICE2007. However, in 

DICE2010 (as downloaded from the homepage of William Nordhaus), annual damages are lower 

in most of the early periods but higher in later periods of the time horizon than would be 

calculated using the DICE2007 damage function. Specifically, the percent difference between 

damages in the base run of DICE2010 and those that would be calculated using the DICE2007 

damage function starts at +7 percent in 2005, decreases to a low of -14 percent in 2065, then 

continuously increases to +20 percent by 2300 (the end of the interagency analysis time horizon), 

and to +160 percent by the end of the model time horizon in 2595. The large increases in the far 

future years of the time horizon are due to the permanence associated with damages from sea 

level rise, along with the assumption that the sea level is projected to continue to rise long after 

the global average temperature begins to decrease. The changes to the loss function generally 

decrease the interagency working group SCC estimates slightly, all else equal.

14-B.3.2 FUND

FUND version 3.8 includes a number of changes over the previous version 3.5 used in 

the interagency report. Documentation supporting FUND and the model’s source code for all 

versions of the model is available from the model authors.h Notable changes, due to their impact 

 

h http://www.fund-model.org/. This report uses version 3.8 of the FUND model, which represents a modest update 

to the most recent version of the model to appear in the literature (version 3.7) (Anthoff and Tol, 2013).7

 For the 

purpose of computing the SCC, the relevant changes are associated with improving consistency with IPCC AR4 by 
14-B-6

on the estimates of expected SCC, are adjustments to the space heating, agriculture, and sea level 

rise damage functions in addition to changes to the temperature response function and the 

inclusion of indirect effects from methane emissions.i

 We discuss each of these in turn. 

14-B.3.2.1 Space Heating

In FUND, the damages associated with the change in energy needs for space heating are 

based on the estimated impact due to one degree of warming. These baseline damages are scaled 

based on the forecasted temperature anomaly’s deviation from the one degree benchmark and 

adjusted for changes in vulnerability due to economic and energy efficiency growth. In FUND 

3.5, the function that scales the base year damages adjusted for vulnerability allows for the 

possibility that in some simulations the benefits associated with reduced heating needs may be an 

unbounded convex function of the temperature anomaly. In FUND 3.8, the form of the scaling 

has been modified to ensure that the function is everywhere concave, meaning that for every 

simulation there will exist an upper bound on the benefits a region may receive from reduced 

space heating needs. The new formulation approaches a value of two in the limit as the 

temperature anomaly increases, or in other words, assuming no decrease in vulnerability, the 

reduced expenditures on space heating at any level of warming will not exceed two times the 

reductions experienced at one degree of warming. Since the reduced need for space heating 

represents a benefit of climate change in the model, or a negative damage, this change will 

increase the estimated SCC. This update accounts for a significant portion of the difference in the 

expected SCC estimates reported by the two versions of the model when run probabilistically. 

14-B.3.2.2 Sea Level Rise and Land Loss 

The FUND model explicitly includes damages associated with the inundation of dry land 

due to sea level rise. The amount of land lost within a region is dependent upon the proportion of 

the coastline being protected by adequate sea walls and the amount of sea level rise. In FUND 

3.5 the function defining the potential land lost in a given year due to sea level rise is linear in 

the rate of sea level rise for that year. This assumption implicitly assumes that all regions are 

well represented by a homogeneous coastline in length and a constant uniform slope moving 

inland. In FUND 3.8 the function defining the potential land lost has been changed to be a nonlinear function of sea level rise, thereby assuming that the slope of the shore line is not constant 

moving inland, with a positive first derivative. The effect of this change is to typically reduce the 

vulnerability of some regions to sea level rise based land loss, therefore having an effect of 

lowering the expected SCC estimate. The model has also been updated to assume that the value 

of dry land at risk of inundation is not uniform across a region but will be a decreasing function 

of protection measure, thereby implicitly assuming that the most valuable land will be protected 

first.

 

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.

i

The other damage sectors (water resources, space cooling, land loss, migration, ecosystems, human health, and 

extreme weather) were not the subject of significant updates.
14-B-7

14-B.3.2.3 Agriculture

In FUND, the damages associated with the agricultural sector are measured as 

proportional to the sector’s value. The fraction is made up of three additively separable 

components that represent the effects from carbon fertilization, the rate of temperature change, 

and the level of the temperature anomaly. In both FUND 3.5 and FUND 3.8, the fraction of the 

sector’s value lost due to the level of the temperature anomaly is modeled as a quadratic function 

with an intercept of zero. In FUND 3.5, the linear and quadratic coefficients are modeled as the 

ratio of two normal distributions. Within this specification, as draws from the distribution in the 

denominator approached zero the share of the sector’s value “lost” approaches (+/-) infinity 

independent of the temperature anomaly itself. In FUND 3.8, the linear and quadratic 

coefficients are drawn directly from truncated normal distributions so that they remain in the 

range [0,∞) and (−∞,0], respectively, where the means for the new distributions are set equal to 

the ratio of the means from the normal distributions used in the previous version. In general the 

impact of this change has been to increase the likelihood that increases in the temperature level 

will have either larger positive or negative effects on the agricultural sector relative to the 

previous version (through eliminating simulations in which the “lost” value approached (+/-) 

infinity). The net effect of this change on the SCC estimates is difficult to predict. 

14-B.3.2.4 Temperature Response Model

The temperature response model translates changes in global levels of radiative forcing 

into the current expected temperature anomaly. In FUND, a given year’s increase in the 

cumulative temperature anomaly is based on a mean reverting function where the mean equals 

the equilibrium temperature anomaly that would eventually be reached if that year’s level of 

radiative forcing were sustained. The rate of mean reversion defines the rate at which the 

transient temperature approaches the equilibrium. In FUND 3.5, the rate of temperature response 

is defined as a decreasing linear function of equilibrium climate sensitivity to capture the fact 

that the progressive heat uptake of the deep ocean causes the rate to slow at higher values of the 

equilibrium climate sensitivity. In FUND 3.8, the rate of temperature response has been updated 

to a quadratic function of the equilibrium climate sensitivity. This change reduces the sensitivity 

of the rate of temperature response to the level of the equilibrium climate sensitivity. Therefore 

in FUND 3.8, the temperature response will typically be faster than in the previous version. The 

overall effect of this change is likely to increase estimates of the SCC as higher temperatures are 

reached during the timeframe analyzed and as the same damages experienced in the previous 

version of the model are now experienced earlier and therefore discounted less. 

14-B.3.2.5 Methane

The IPCC notes a series of indirect effects of methane emissions, and has developed 

methods for proxying such effects when computing the global warming potential of methane 

(Forster et al. 2007).8 FUND 3.8 now includes the same methods for incorporating the indirect 

effects of methane emissions. Specifically, the average atmospheric lifetime of methane has been 

set to 12 years to account for the feedback of CH4 emissions on its own lifetime. The radiative 

forcing associated with atmospheric methane has also been increase by 40% to account for its net 
14-B-8

impact on ozone production and increase in stratospheric water vapor. The general effect of this 

increased radiative forcing will be to increase the estimated SCC values, where the degree to 

which this occurs will be dependent upon the relative curvature of the damage functions with 

respect to the temperature anomaly.

14-B.3.3 PAGE

PAGE09 (Hope 2012)9 includes a number of changes from PAGE2002, the version used 

in the 2009 SCC interagency report. The changes that most directly affect the SCC estimates 

include: explicitly modeling the impacts from sea level rise, revisions to the damage function to 

ensure damages are constrained by GDP, a change in the regional scaling of damages, a revised 

treatment for the probability of a discontinuity within the damage function, and revised 

assumptions on adaptation. The model also includes revisions to the carbon cycle feedback and 

the calculation of regional temperatures. More details on PAGE2009 can be found in three 

working papers (Hope 2011a, 2011b, 2011c).10, 11, 12 A description of PAGE2002 can be found 

in Hope (2006).13

14-B.3.3.1 Sea Level Rise

While PAGE2002 aggregates all damages into two categories – economic and noneconomic impacts - PAGE2009 adds a third explicit category: damages from sea level rise. In the 

previous version of the model, damages from sea level rise were subsumed by the other damage 

categories. PAGE09 models damages from sea level rise as increasing less than linearly with sea 

level based on the assumption that low-lying shoreline areas will be associated with higher 

damages than current inland areas. Damages from the economic and non-economic sector were 

adjusted to account for the introduction of this new category. 

14-B.3.3.2 Revised Damage Function to Account for Saturation 

In PAGE09, small initial economic and non-economic benefits (negative damages) are 

modeled for small temperature increases, but all regions eventually experience positive economic 

damages from climate change, where damages are the sum of additively separable polynomial 

functions of temperature and sea level rise. Damages transition from this polynomial function to 

a logistic path once they exceed a certain proportion of remaining Gross Domestic Product 

(GDP) to ensure that damages do not exceed 100 percent of GDP. This differs from PAGE2002, 

which allowed Eastern Europe to potentially experience large benefits from temperature 

increases, and which also did not bound the possible damages that could be experienced. 

14-B.3.3.3 Regional Scaling Factors 

As in the previous version of PAGE, the PAGE09 model calculates the damages for the 

European Union (EU) and then, assumes that damages for other regions are proportional based 

on a given scaling factor. The scaling factor in PAGE09 is based on the length of a region’s 

coastline relative to the EU (Hope 2011b).11 Because of the long coastline in the EU, other 

regions are, on average, less vulnerable than the EU for the same sea level and temperature 
14-B-9

increase, but all regions have a positive scaling factor. PAGE2002 based its scaling factors on 

four studies reported in the IPCC’s third assessment report, and allowed for benefits from 

temperature increase in Eastern Europe, smaller impacts in developing countries, and higher 

damages in developing countries. 

14-B.3.3.4 Probability of a Discontinuity 

In PAGE2002, the damages associated with a “discontinuity” were modeled as an 

expected value. That is, additional damages from an extreme event, such as extreme melting of 

the Greenland ice sheet, were multiplied by the probability of the event occurring and added to 

the damage estimate. In PAGE09, the probability of “discontinuity” is treated as a discrete event 

for each year in the model. The damages for each model run are estimated 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. 

14-B.3.3.5 Adaptation 

As in PAGE2002, adaptation is available to increase the tolerable level of temperature 

change and can help mitigate any climate change impacts that still occur. In PAGE this 

adaptation is the same regardless of the temperature change or sea level rise and is therefore akin 

to what is more commonly considered a reduction in vulnerability. It is modeled by modifying 

the temperature change and sea level rise used in the damage function or by reducing the 

damages by some percentage. PAGE09 assumes a smaller decrease in vulnerability than the 

previous version of the model and assumes that it will take longer for this change in vulnerability 

to be realized. In the aggregated economic sector, at the time of full implementation, this 

adaptation will mitigate all damages up to a temperature increase of 1°C, and for temperature 

anomalies between 1°C and 3°C, it will reduce damages by 15-30 percent (depending on the 

region). However, it takes 20 years to fully implement this adaptation. In PAGE2002, adaptation 

was assumed to reduce economic sector damages up to 3°C by 50-90 percent after 20 years. 

Beyond 3°C, no adaptation is assumed to be available to mitigate the impacts of climate change. 

For the non-economic sector, in PAGE09 adaptation is available to reduce 15 percent of the 

damages due to a temperature increase between 0°C and 2°C and is assumed to take 40 years to 

fully implement, instead of 25 percent of the damages over 20 years assumed in PAGE2002. 

Similarly, adaptation is assumed to alleviate 25-50 percent of the damages from the first 0.20 to 

0.25 meters of sea level rise but is assumed to be ineffective thereafter. Hope (2011c)12 estimates 
14-B-10

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.

14-B.3.3.6 Other Noteworthy Changes

 Two other changes in the model are worth noting. A revised carbon cycle feedback is 

introduced to simulate decreased CO2 absorption by the terrestrial biosphere and ocean as the 

temperature rises. This feedback is linear in the average global and annual temperature anomaly 

but is capped at a maximum value. In the previous version of PAGE, an additional amount was 

added to the CO2 emissions each period to account for a decrease in ocean absorption and a loss 

of soil carbon. Also updated is the method by which the average global and annual temperature 

anomaly is downscaled to determine annual average regional temperature anomalies to be used 

in the regional damage functions. In the previous version of PAGE, the scaling was determined 

solely based on regional difference in emissions of sulfate aerosols. In PAGE09, this regional 

temperature anomaly is further adjusted using an additive factor that is based on the average 

absolute latitude of a region relative to the area weighted average absolute latitude of the Earth’s 

landmass.

14-B.4 REVISED SCC ESTIMATES 

The updated versions of the three integrated assessment models were run using the same 

methodology detailed in the 2010 TSD.1 The approach along with the inputs for the 

socioeconomic emissions scenarios, equilibrium climate sensitivity distribution, and discount 

rate remains the same. This includes the five reference scenarios based on the EMF-22 modeling 

exercise, the Roe and Baker equilibrium climate sensitivity distribution calibrated to the Fourth 

Assessment Report of the IPCC, and three constant discount rates of 2.5, 3, and 5 percent. 

As was previously the case, the use of three models, three discount rates, and five 

scenarios produces 45 separate distributions for the SCC. The approach laid out in the TSD 

applied equal weight to each model and socioeconomic scenario in order to reduce the 

dimensionality down to three separate distributions representative of the three discount rates. The 

interagency group selected four values from these distributions for use in regulatory analysis. 

Three values are based on the average SCC across models and socio-economic-emissions 

scenarios at the 2.5, 3, and 5 percent discount rates, respectively. The fourth value was chosen to 

represent the higher-than-expected economic impacts from climate change further out in the tails 

of the SCC distribution. For this purpose, the 95th percentile of the SCC estimates at a 3 percent 

discount rate was chosen. (A detailed set of percentiles by model and scenario combination is 

available in the Annex.) As noted in the original TSD, “the 3 percent discount rate is the central

value, and so the central value that emerges is the average SCC across models at the 3 percent 

discount rate” (TSD, p. 25). However, for purposes of capturing the uncertainties involved in 

regulatory impact analysis, the interagency group emphasizes the importance and value of 

including all four SCC values. 

Table 14-B.4.1 shows the four selected SCC estimates in five year increments from 2010 

to 2050. Values for 2010, 2020, 2030, 2040, and 2050 are calculated by first combining all 
14-B-11

outputs (10,000 estimates per model run) from all scenarios and models for a given discount rate. 

Values for the years in between are calculated using basic linear interpolation. The full set of 

annual SCC estimates between 2010 and 2050 is reported in the Annex. 
14-B-12

Table 14-B.4.1 Revised Social Cost of CO2, 2010 – 2050 (in 2007 dollars per ton of CO2) 

Discount Rate 5.0% 3.0% 2.5% 3.0%

Year Avg Avg Avg 95th

2010 11 32 51 89

2015 11 37 57 109

2020 12 43 64 128

2025 14 47 69 143

2030 16 52 75 159

2035 19 56 80 175

2040 21 61 86 191

2045 24 66 92 206

2050 26 71 97 220

 The SCC estimates using the updated versions of the models are higher than those 

reported in the TSD due to the changes to the models outlined in the previous section. Figure 14-

B.4.2 illustrates where the four SCC values for 2020 fall within the full distribution for each 

discount rate based on the combined set of runs for each model and scenario (150,000 estimates 

in total for each discount rate). In general, the distributions are skewed to the right and have long 

tails. The Figure also shows that the lower the discount rate, the longer the right tail of the 

distribution. 

Figure 14-B.4.2 Distribution of SCC Estimates for 2020 (in 2007$ per ton CO2) 

As was the case in the original TSD, the SCC increases over time because future 

emissions are expected to produce larger incremental damages as physical and economic systems 

become more stressed in response to greater climatic change. The approach taken by the 

interagency group is to allow the growth rate to be determined endogenously by the models 
14-B-13

through running them for a set of perturbation years out to 2050. Table 14-B.4.2 illustrates how 

the growth rate for these four SCC estimates varies over time.

Table 14-B.4.2 Average Annual Growth Rates of SCC Estimates between 2010 and 2050 

Average Annual 5.0% 3.0% 2.5% 3.0%

Rate (%) Avg Avg Avg 95th

2010-2020 1.2% 3.3% 2.4% 4.4%

2020-2030 3.4% 2.1% 1.7% 2.4%

2030-2040 3.0% 1.9% 1.5% 2.1%

2040-2050 2.6% 1.6% 1.3% 1.5%

 The future monetized value of emission reductions in each year (the SCC in year t 

multiplied by the change in emissions in year t) must be discounted to the present to determine 

its total net present value for use in regulatory analysis. As previously discussed in the original 

TSD, damages from future emissions should be discounted at the same rate as that used to 

calculate the SCC estimates themselves to ensure internal consistency – i.e., future damages from 

climate change, whether they result from emissions today or emissions in a later year, should be 

discounted using the same rate. 

14-B.5 OTHER MODEL LIMITATIONS OR RESEARCH GAPS

 The 2010 interagency SCC technical support report discusses a number of important 

limitations for which additional research is needed. In particular, the document highlights the 

need to improve the quantification of both non-catastrophic and catastrophic damages, the 

treatment of adaptation and technological change, and the way in which inter-regional and intersectoral linkages are modeled. It also discusses the need to more carefully assess the implications 

of risk aversion for SCC estimation as well as the inability to perfectly substitute between 

climate and non-climate goods at higher temperature increases, both of which have implications 

for the discount rate used. EPA, DOE, and other agencies continue to engage in long-term 

research work on modeling and valuation of climate impacts that we expect will inform 

improvements in SCC estimation in the future.
14-B-14

REFERENCES

1. Interagency Working Group on Social Cost of Carbon, Social Cost of Carbon for 

Regulatory Impact Analysis under Executive Order 12866, February, 2010. United States 

Government. <http://www.whitehouse.gov/sites/default/files/omb/inforeg/foragencies/Social-Cost-of-Carbon-for-RIA.pdf> 

2. Nordhaus, W., A Question of Balance. 2008. Yale University Press: New Haver. 

3. Nordhaus, W., Economic aspects of global warming in a post-Copenhagen environment.

Proceedings of the National Academy of Sciences, 2010. 107(26): pp. 11721-11726 

4. Randall, D. A., R.A. Wood, S. Bony, R. Colman, T. Fichefet, J. Fyfe, V. Kattsov, A. 

Pitman, J. Shukla, J. Srinivasan, R.J. Stouffer, A. Sumi and K.E. Taylor, Climate Models 

and Their Evaluation. In Climate Change 2007: The Physical Science Basis. 

Contribution of Working Group I to the Fourth Assessment Report of the 

Intergovernmental Panel on Climate Change, S. Solomon, D. Qin, M. Manning, Z. Chen, 

M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller, Editor. 2007. Cambridge University 

Press: Cambridge, United Kingdom and New York, NY, USA 

5. Nicholls, R. J., N. Marinova, J.A. Lowe, S. Brown, P. Vellinga, D. de Gusmão, J. Hinkel 

and R.S.J. Tol, Sea-level rise and its possible impacts given a ‘beyond 4°C world’ in the 

twenty-first century. Phil. Trans. R. Soc. A 2011. 369(1934): pp. 161-181 

6. National Academy of Sciences, Climate Stabilization Targets: Emissions, 

Concentrations, and Impacts over Decades to Millennia. 2011. National Academies 

Press, Inc: Washington, DC.

7. Anthoff, D. and R. S. J. Tol, The uncertainty about the social cost of carbon: a 

decomposition analysis using FUND. Climatic Change, 2013 (Forthcoming) 

8. Forster, P., V. Ramaswamy, P. Artaxo, T. Berntsen, R. Betts, D.W. Fahey, J. Haywood, 

J. Lean, D.C. Lowe, G. Myhre, J. Nganga, R. Prinn, G. Raga, M. Schulz and R. Van 

Dorland, Changes in Atmospheric Constituents and in Radiative Forcing. In Climate 

Change 2007: The Physical Science Basis. Contribution of Working Group I to the 

Fourth Assessment Report of the Intergovernmental Panel on Climate Change S. 

Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. 

Miller Editor. 2007. Cambridge University Press: Cambridge, United Kingdom and New 

York, NY, USA

9. Hope, C., Critical issues for the calculation of the social cost of CO2: why the estimates 

from PAGE09 are higher than those from PAGE2002. Climatic Change, 

2012(Forthcoming) 
14-B-15

10. Hope, C., The PAGE09 Integrated Assessment Model: A Technical Description. 2011, 

Cambridge Judge Business School 

http://www.jbs.cam.ac.uk/research/working_papers/2011/wp1104.pdf

11. Hope, C., The Social Cost of CO2 from the PAGE09 Model. 2011, Cambridge Judge 

Business School http://www.jbs.cam.ac.uk/research/working_papers/2011/wp1105.pdf

12. Hope, C., New Insights from the PAGE09 Model: The Social Cost of CO2. 2011, 

Cambridge Judge Business School 

http://www.jbs.cam.ac.uk/research/working_papers/2011/wp1108.pdf

13. Hope, C., The Marginal Impact of CO2 from PAGE2002: An Integrated Assessment 

Model Incorporating the IPCC’s Five Reasons for Concern. The Integrated Assessment 

Journal, 2006. 6(1): pp. 19-56 

14. Anthoff D. and Tol, R. S. J., Erratum to: The uncertainty about the social cost of carbon: 

A decomposition analysis using FUND. Climatic Change, 2013. Advance online 

publication. doi: 10.1007/s10584-013-0959-1 
14-B-16

ANNEX A 

Table A1. Annual SCC Values: 2010-2050 (2007$/ton CO2) 

Discount Rate 5.0% 3.0% 2.5% 3.0%

Year Avg Avg Avg 95th

2010 11 32 51 89

2011 11 33 52 93

2012 11 34 54 97

2013 11 35 55 101

2014 11 36 56 105

2015 11 37 57 109

2016 12 38 59 112

2017 12 39 60 116

2018 12 40 61 120

2019 12 42 62 124

2020 12 43 64 128

2021 12 43 65 131

2022 13 44 66 134

2023 13 45 67 137

2024 14 46 68 140

2025 14 47 69 143

2026 15 48 70 146

2027 15 49 71 149

2028 15 50 72 152

2029 16 51 73 155

2030 16 52 75 159

2031 17 52 76 162

2032 17 53 77 165

2033 18 54 78 168

2034 18 55 79 172

2035 19 56 80 175

2036 19 57 81 178

2037 20 58 83 181

2038 20 59 84 185

2039 21 60 85 188

2040 21 61 86 191

2041 22 62 87 194

2042 22 63 88 197

2043 23 64 89 200

2044 23 65 90 203

2045 24 66 92 206

2046 24 67 93 209

2047 25 68 94 211

2048 25 69 95 214

2049 26 70 96 217

2050 26 71 97 220
14-B-17

Table A2. 2020 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton CO2) 

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 6 11 15 27 58 129 139 327 515 991

MERGE 4 6 9 16 34 78 82 196 317 649

MESSAGE 4 8 11 20 42 108 107 278 483 918

MiniCAM Base 5 9 12 22 47 107 113 266 431 872

5th Scenario 2 4 6 11 25 85 68 200 387 955

Scenario DICE

IMAGE 25 31 37 47 64 72 92 123 139 161

MERGE 14 18 20 26 36 40 50 65 74 85

MESSAGE 20 24 28 37 51 58 71 95 109 221

MiniCAM Base 20 25 29 38 53 61 76 102 117 135

5th Scenario 17 22 25 33 45 52 65 91 106 126

Scenario FUND

IMAGE -14 -2 4 15 31 39 55 86 107 157

MERGE -6 1 6 14 27 35 46 70 87 141

MESSAGE -16 -5 1 11 24 31 43 67 83 126

MiniCAM Base -7 2 7 16 32 39 55 83 103 158

5th Scenario -29 -13 -6 4 16 21 32 53 69 103

Table A3. 2020 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 4 7 10 18 38 91 95 238 385 727

MERGE 2 4 6 11 23 56 58 142 232 481

MESSAGE 3 5 7 13 29 75 74 197 330 641

MiniCAM Base 3 5 8 14 30 73 75 184 300 623

5th Scenario 1 3 4 7 17 58 48 136 264 660

Scenario DICE

IMAGE 16 21 24 32 43 48 60 79 90 102

MERGE 10 13 15 19 25 28 35 44 50 58

MESSAGE 14 18 20 26 35 40 49 64 73 83

MiniCAM Base 13 17 20 26 35 39 49 65 73 85

5th Scenario 12 15 17 22 30 34 43 58 67 79

Scenario FUND

IMAGE -13 -4 0 8 18 23 33 51 65 99

MERGE -7 -1 2 8 17 21 29 45 57 95

MESSAGE -14 -6 -2 5 14 18 26 41 52 82

MiniCAM Base -7 -1 3 9 19 23 33 50 63 101

5th Scenario -22 -11 -6 1 8 11 18 31 40 62
14-B-18

Table A4. 2020 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton CO2) 

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 1 2 2 5 10 28 27 71 123 244

MERGE 1 1 2 3 7 17 17 45 75 153

MESSAGE 1 1 2 4 9 24 22 60 106 216

MiniCAM Base 1 1 2 3 8 21 21 54 94 190

5th Scenario 0 1 1 2 5 18 14 41 78 208

Scenario DICE

IMAGE 6 8 9 11 14 15 18 22 25 27

MERGE 4 5 6 7 9 10 12 15 16 18

MESSAGE 6 7 8 10 12 13 16 20 22 25

MiniCAM Base 5 6 7 8 11 12 14 18 20 22

5th Scenario 5 6 6 8 10 11 14 17 19 21

Scenario FUND

IMAGE -9 -5 -4 -1 2 3 6 10 14 24

MERGE -6 -4 -2 0 3 4 6 11 15 26

MESSAGE -10 -6 -4 -1 1 2 5 9 12 21

MiniCAM Base -7 -4 -2 0 3 4 6 11 14 25

5th Scenario -11 -7 -5 -3 0 0 3 5 7 13
14-B-19

ANNEX B 

The November 2013 revision of this technical support document is based on two 

corrections to the runs based on the FUND model. First, the potential dry land loss in the 

algorithm that estimates regional coastal protections was misspecified in the model’s computer 

code. This correction is covered in an erratum to Anthoff and Tol (2013) published in the same 

journal (Climatic Change) in October 2013.

14 Second, the equilibrium climate sensitivity 

distribution was inadvertently specified as a truncated Gamma distribution (the default in FUND) 

as opposed to the truncated Roe and Baker distribution as was intended. The truncated Gamma 

distribution used in the FUND runs had approximately the same mean and upper truncation 

point, but lower variance and faster decay of the upper tail, as compared to the intended 

specification based on the Roe and Baker distribution. The differences between the original 

estimates reported in the May 2013 version of this technical support document and this revision 

are all one dollar or less.
17-A-i

APPENDIX 17-A. REGULATORY IMPACT ANALYSIS: SUPPORTING 

MATERIALS

TABLE OF CONTENTS

17-A.1 INTRODUCTION ............................................................................................... 17-A-2

17-A.2 MARKET SHARE ANNUAL INCREASES BY POLICY ................................ 17-A-3

17-A.3 NIA-RIA INTEGRATED MODEL ..................................................................... 17-A-5

17-A.4 CONSUMER REBATE POLICY MARKET PENETRATION CURVES ........ 17-A-5

17-A.4.1 Introduction .......................................................................................................... 17-A-5

17-A.4.2 Adjustment of XENERGY Penetration Curves ................................................... 17-A-7

17-A.4.3 Interpolation of Penetration Curves ..................................................................... 17-A-8

17-A.5 CONSUMER REBATE PROGRAMS ................................................................ 17-A-9

17-A.6 FEDERAL AND STATE TAX CREDITS........................................................ 17-A-13

17-A.6.1 Federal Tax Credits for Consumers of Residential Appliances ......................... 17-A-13

17-A.6.2 Federal Tax Credits for Manufacturers .............................................................. 17-A-14

17-A.6.3 State Tax Credits ................................................................................................ 17-A-14

LIST OF TABLES

Table 17-A.2.1 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Front-Loading Commercial Clothes Washers ................... 17-A-3

Table 17-A.2.2 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Top-Loading Commercial Clothes Washers ...................... 17-A-4

Table 17-A.5.1 Rebates for Commercial Clothes Washers ............................................ 17-A-10

LIST OF FIGURES 

Figure 17-A.4.1 S-Curves Showing Effects of External and Internal Sources on 

Adoption of New Technologies ............................................................... 17-A-7
17-A-2 

APPENDIX 17-A. REGULATORY IMPACT ANALYSIS: SUPPORTING MATERIALS 

17-A.1 INTRODUCTION

 This appendix contains sections discussing the following topics: 

Projections of annual market share increases for the alternative policies;

NIA-RIA Integrated Model;

XENERGY penetration curves used to analyze consumer rebates, including: 

o Background material,

o DOE’s adjustment of these curves for this analysis, and 

o The method DOE used for interpolating the curves; 

Detailed tables of rebates offered for the considered products; and 

Background material on Federal and state tax credits for appliances. 
17-A-3 

17-A.2 MARKET SHARE ANNUAL INCREASES BY POLICY

Table 17-A.2.1 and Table 17-A.2.2 show the annual increases in market shares for 

commercial clothes washers meeting the target efficiency levels of the proposed TSL (TSL 2). 

DOE used these market share increases as inputs to the NIA-RIA spreadsheet model.

Table 17-A.2.1 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Front-Loading Commercial Clothes Washers

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Voluntary 

Energy 

Efficiency 

Targets

Early 

Replacement

Bulk 

Government 

Purchases

2018 50.0% 5.2% 2.6% 0.5% 2.1% 0.1%

2019 50.0% 8.0% 4.0% 5.6% 2.4% 0.3%

2020 50.0% 10.9% 5.4% 8.9% 2.7% 0.4%

2021 50.0% 12.7% 6.3% 11.2% 3.0% 0.6%

2022 50.0% 13.8% 6.9% 13.0% 3.2% 0.7%

2023 50.0% 14.9% 7.5% 14.5% 3.5% 0.9%

2024 50.0% 15.8% 7.9% 15.8% 3.7% 1.0%

2025 50.0% 14.8% 7.4% 17.0% 3.9% 1.2%

2026 50.0% 13.8% 6.9% 18.0% 3.9% 1.3%

2027 50.0% 12.8% 6.4% 18.8% 4.0% 1.5%

2028 50.0% 11.8% 5.9% 19.7% 4.0% 1.5%

2029 50.0% 10.5% 5.2% 20.4% 3.9% 1.5%

2030 50.0% 9.1% 4.6% 21.1% 3.7% 1.5%

2031 50.0% 7.8% 3.9% 21.7% 3.2% 1.5%

2032 50.0% 6.4% 3.2% 22.3% 2.5% 1.5%

2033 50.0% 5.1% 2.5% 22.9% 1.3% 1.5%

2034 50.0% 3.7% 1.9% 23.4% 0.4% 1.5%

2035 50.0% 2.4% 1.2% 23.9% 0.0% 1.5%

2036 50.0% 1.1% 0.5% 24.3% 0.0% 1.5%

2037 50.0% 0.0% 0.0% 24.8% 0.0% 1.5%

2038 50.0% 0.0% 0.0% 25.2% 0.0% 1.5%

2039 50.0% 0.0% 0.0% 25.6% 0.0% 1.5%

2040 50.0% 0.0% 0.0% 26.0% 0.0% 1.5%

2041 50.0% 0.0% 0.0% 26.3% 0.0% 1.5%

2042 50.0% 0.0% 0.0% 26.7% 0.0% 1.5%

2043 50.0% 0.0% 0.0% 27.0% 0.0% 1.5%

2044 50.0% 0.0% 0.0% 27.3% 0.0% 1.5%

2045 50.0% 0.0% 0.0% 27.7% 0.0% 1.5%

2046 50.0% 0.0% 0.0% 28.0% 0.0% 1.5%

2047 50.0% 0.0% 0.0% 28.2% 0.0% 1.5%
17-A-4 

Table 17-A.2.2 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for Top-Loading Commercial Clothes Washers 

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Voluntary 

Energy 

Efficiency 

Targets

Early 

Replacement

Bulk 

Government 

Purchases

2018 49.8% 5.2% 2.6% 0.5% 15.6% 0.2%

2019 49.8% 7.9% 4.0% 5.6% 14.9% 0.3%

2020 49.8% 10.8% 5.4% 8.9% 14.3% 0.5%

2021 49.8% 12.6% 6.3% 11.2% 13.7% 0.6%

2022 49.8% 13.7% 6.9% 13.0% 13.0% 0.7%

2023 49.8% 14.8% 7.4% 14.5% 12.4% 0.9%

2024 49.8% 15.7% 7.9% 15.8% 11.7% 1.0%

2025 49.8% 14.7% 7.4% 17.0% 11.0% 1.2%

2026 49.8% 13.7% 6.9% 18.0% 10.2% 1.3%

2027 49.8% 12.8% 6.4% 18.8% 9.3% 1.5%

2028 49.8% 11.8% 5.9% 19.7% 8.2% 1.5%

2029 49.8% 10.4% 5.2% 20.4% 6.8% 1.5%

2030 49.8% 9.1% 4.5% 21.1% 5.1% 1.5%

2031 49.8% 7.7% 3.9% 21.7% 2.7% 1.5%

2032 49.8% 6.4% 3.2% 22.3% 0.2% 1.5%

2033 49.8% 5.1% 2.5% 22.9% 0.0% 1.5%

2034 49.8% 3.7% 1.9% 23.4% 0.0% 1.5%

2035 49.8% 2.4% 1.2% 23.9% 0.0% 1.5%

2036 49.8% 1.1% 0.5% 24.3% 0.0% 1.5%

2037 49.8% 0.0% 0.0% 24.8% 0.0% 1.5%

2038 49.8% 0.0% 0.0% 25.2% 0.0% 1.5%

2039 49.8% 0.0% 0.0% 25.6% 0.0% 1.5%

2040 49.8% 0.0% 0.0% 26.0% 0.0% 1.5%

2041 49.8% 0.0% 0.0% 26.3% 0.0% 1.5%

2042 49.8% 0.0% 0.0% 26.7% 0.0% 1.5%

2043 49.8% 0.0% 0.0% 27.0% 0.0% 1.5%

2044 49.8% 0.0% 0.0% 27.3% 0.0% 1.5%

2045 49.8% 0.0% 0.0% 27.7% 0.0% 1.5%

2046 49.8% 0.0% 0.0% 28.0% 0.0% 1.5%

2047 49.8% 0.0% 0.0% 28.2% 0.0% 1.5%
17-A-5 

17-A.3 NIA-RIA INTEGRATED MODEL

 For this analysis, DOE used its integrated NIA-RIAa

 model approach that built on the 

NIA model discussed in Chapter 11 and documented in Appendix 11-A.The resulting integrated 

NIA-RIA model featured both the NIA analysis inputs and results and the RIA inputs and had 

the capability to generate results for each of the RIA policies. A separate module produced 

results summaries for the tables and figures in the RIA document. For the RIA methodology 

documentation in Chapter 17 the module created summaries of parameters calculated by the 

model for the consumer rebates policy, generated the corresponding customized penetration 

curves (discussed in Section17-A.4.3 below), and reported market share impacts for the rebate 

and tax credit policies by product class. For the RIA results reported in Chapter 17 the module 

produced graphs of the market share increases resulting from each of the policies analyzed and 

created summary tables for the national energy savings (NES) and net present value (NPV) 

results. This module also generated tables of market share increases for each policy reported in 

Section 17-A.2 of this Appendix.

17-A.4 CONSUMER REBATE POLICY MARKET PENETRATION CURVES

This section first discusses the theoretical basis for the market penetration curves that 

DOE used to analyze the Consumer Rebates policy. Next it discusses the adjustments it made to 

the maximum penetration rates. It then refers to the method it used to develop interpolated 

penetration curves for each specific equipment class and efficiency level in the analysis. The 

resulting customized curves for front-loading and top-loading commercial clothes washer are in 

chapter 17. 

17-A.4.1 Introduction

XENERGY, Inc.b

, developed a re-parameterized, mixed-source information diffusion 

model to estimate market impacts induced by financial incentives for purchasing energy efficient 

appliances.

1 The basic premise of the mixed-source model is that information diffusion drives 

the adoption of technology. 

Extensive economic literature describes the diffusion of new products as technologies 

evolve. Some research focuses primarily on developing analytical models of diffusion patterns 

applicable to individual consumers or to technologies from competing firms.2, 3, 4

 One study 

records researchers’ attempts to investigate the factors that drive diffusion processes.

5 Because a 

new product generally has its own distinct characteristics, few studies have been able to 

conclusively develop a universally applicable model. Some key findings, however, generally are 

accepted in academia and industry. 

 

a NIA = national impact analysis; RIA = regulatory impact analysis

b XENERGY is now owned by KEMA, Inc. (www.kema.com) 
17-A-6 

 One accepted finding is that, regardless of their economic benefits and technological 

merits, new technologies are unlikely to be adopted by all potential users. For many products, a 

ceiling must be placed on the adoption rate. A second conclusion is that not all adopters purchase 

new products at the same time: some act quickly after a new product is introduced; others wait 

for the product to mature. Third, diffusion processes can be characterized approximately by 

asymmetric S-curves that depict three stages of diffusion: starting, accelerating, and decreasing 

(as the adoption ceiling is approached). 

A so-called epidemic model of diffusion is used widely in marketing and social studies. 

The epidemic model assumes that (1) all consumers place identical value on the benefits of a 

new product, and (2) the cost of a new product is constant or declines monotonically over time. 

What induces a consumer to purchase a new product is information about the availability and 

benefits of the product. In other words, information diffusion drives consumers’ adoption of a 

new product.

3

 The model incorporates information diffusion from both internal sources (spread 

by word of mouth from early adopters to prospective adopters) and external sources (the 

“announcement effect” produced by government agencies, institutions, or commercial 

advertising). The model incorporates both internal and external sources by combining a logistic 

function with an exponential function.4,5

 

The relative degree of influence from the internal and external sources determines the 

general shape of the diffusion curve for a specific product.4,5

 If adoption of a product is 

influenced primarily by external sources of information (the announcement effect), for instance, 

a high rate of diffusion occurs at the beginning of the process. In this scenario, external sources 

provide immediate information exposure to a significant number of prospective adopters. In 

contrast, internal sources (such as a network of prospective adopters) are relatively small in size 

and reach, producing a more gradual exposure to prospective adopters. Graphically speaking, 

information diffusion dominated by external sources is represented by a concave curve (the 

exponential curve in Figure 17-A.4.1). If adoption of a new product is influenced most strongly 

by internal sources of information, the number of adopters increases gradually, forming a convex 

curve (the logistic curve in Figure 17-A.4.1).
17-A-7 

Figure 17-A.4.1 S-Curves Showing Effects of External and Internal 

Sources on Adoption of New Technologies

17-A.4.2 Adjustment of XENERGY Penetration Curves

In consultation with the primary authors of the 2002 XENERGY study who later 

conducted similar California studies, DOE made some adjustments to XENERGY’s original 

implementation (penetration) curves.6 The experiences with utility programs since the 

XENERGY study indicate that incentive programs have difficulty achieving penetration rates as 

high as 80 percent. Consumer response is limited by barriers created by consumer utility issues 

and other non-economic factors. DOE therefore adjusted the maximum penetration parameters 

for some of the curves from 80 percent to the following levels: 

Moderate Barriers: 70% 

High Barriers: 60% 

Extremely High Barriers: 50% 

The low barriers and no barriers curves (the latter used only when a product has a very 

high base-case-market share) remained, respectively, with 80 percent and 100 percent as their 

maximum penetration rates. For the interpolated penetration curves (discussed below), DOE set 

the no barriers and extremely high barriers curves as the upper and lower bounds, respectively, 
17-A-8 

for any benefit/cost ratio points higher or lower than the curves. It set another constraint such 

that the policy case market share cannot be great than 100 percent, as might occur for products 

with high base case market shares of the target-level technology.

17-A.4.3 Interpolation of Penetration Curves 

As discussed above, the XENERGY penetration (implementation) curves followed a 

functional form to estimate the market implementation rate caused by energy efficiency 

measures such as consumer rebates.c The XENERGY report presents five reference market 

implementation curves that vary according to the level of market barriers to technology 

penetration.1

 Such curves have been used by DOE in the Regulatory Impact Analyses for 

rulemakings for appliance energy efficiency standards to estimate market share increases in 

response to rebate programs.d They provide a framework for evaluating technology penetration, 

yet require matching the studied market to the curve that best represents it. This approximate 

matching can introduce some inaccuracy to the analysis. 

Blum et al (2011, Appendix A)7 presents an alternative approach to such evaluation: a 

method to estimate market implementation rates more accurately by performing interpolations of 

the reference curves. The referred report describes the market implementation rate function and 

the reference curves, the method to calibrate the function to a given market, and the limitations 

of the method. 

 

c The RIA chapter refers to these curves as penetration curves. This section, in references to the original source, uses 

the term implementation curve. d DOE has also used this method to estimate market share increases resulting from consumer tax credit and 

manufacturer tax credit programs, since the effects of tax credits on markets can be considered proportional to the 

rebate impacts. 
17-A-9 

17-A.5 CONSUMER REBATE PROGRAMS 

DOE performed a search for rebate programs that offered incentives for commercial 

clothes washers. Some organizations nationwide, comprising electric utilities and regional 

agencies, offer rebate programs for commercial clothes washers. Table 17-A.5.1 provides the 

organizations’ names, states, rebate amounts and program websites. If there is more than one 

entry for an organization, it offers different rebates in different states. When an organization 

offers rebates through several utilities, it is represented only once in each table. 

DOE calculated the rebate amount it used in its analysis of the Rebates policy case from a 

sample of 38 rebates from 26 organizations. The rebate amount DOE calculated for commercial 

clothes washers is $158.16 (in 2012$). This amount refers to the simple average of the individual 

amounts offered by the programs reported in Table 17-A.5.1. 
17-A-10 

 

Table 17-A.5.1 Rebates for Commercial Clothes Washers 

Utility or Agency State Rebate $ Website

City of Palo Alto CA $400 http://www.cityofpaloalto.org/gov/depts/utl/business/rebates/water.asp?Bl

obID=14758

Pacific Power - FinAnswer Express CA $150 http://www.pacificpower.net/content/dam/pacific_power/doc/Business/Sa

ve_Energy_Money/CA_FinAnswer_Express_Appliance_Incentives.pdf

Pacific Power - FinAnswer Express CA $200 http://www.pacificpower.net/content/dam/pacific_power/doc/Business/Sa

ve_Energy_Money/CA_FinAnswer_Express_Appliance_Incentives.pdf

PG&E CA $50 http://www.pge.com/includes/docs/pdfs/mybusiness/energysavingsrebates

/incentivesbyindustry/appliances_generalimprovements_catalog_final.pdf

Riverside Public Utilities CA $75 http://www.greenriverside.com/Green-Rebate-Programs7/Commercial/Energy_Efficiency-112/Rebate325/Energy_Star_Rated_Product_Incentives

Silicon Valley Power CA $400 https://siliconvalleypower.com/index.aspx?page=1932

Questar Gas ID $75 http://www.thermwise.com/business/BusinessRebates.html

Avista Utilities (Electric) ID $200 https://www.avistautilities.com/business/rebates/washington/washer/Page

s/default.aspx

Avista Utilities (Electric) WA $200 https://www.avistautilities.com/business/rebates/washington/washer/Page

s/default.aspx

Cedar Falls Utilities IA $50 http://www.cfu.net/webres/File/2013%20Commerical%20Rebates%20-

%204%20page_web.pdf

Hutchinson Utilities Commission MN $50 http://www.hutchinsonutilities.com/pdf/prescriptiverebate.pdf

New Mexico Gas Company NM $100 http://eeprograms.net/newmexicogas/comsolprescriptive/upgrades.php

NYSERDA: Existing Facilities Program NY $200 http://www.nyserda.ny.gov/BusinessAreas/Energy-Efficiency-andRenewable-Programs/Commercial-and-Industrial/CI-Programs/ExistingFacilities-Program/Pre-Qualified-Incentives.aspx

Dayton Power and Light OH $50 http://www.dpandl.com/save-money/business-government/rapidrebates/motors-drives-and-compressed-air-rebates/

State Rebate Program: Business Energy 

Efficiency Rebate for Existing Buildings

OR $200 http://energytrust.org/library/forms/BE_PI0194F.pdf
17-A-11 

State Rebate Program: Business Energy 

Efficiency Rebate for Existing Buildings

OR $300 http://energytrust.org/library/forms/BE_PI0194F.pdf

Idaho Power OR $200 http://www.idahopower.com/pdfs/EnergyEfficiency/EasyUpgrades/works

heet_Plug2011.pdf

SCE&G (Electric) SC $50 http://www.sceg.com/NR/rdonlyres/A464F2E3-43EF-43B9-A72B93013D6E678D/0/Food_Services_High_Efficiency_v9.pdf

Texas Gas Service TX $100 http://www.texasgasservice.com/en/SaveEnergyAndMoney/Conservation

Programs/AustinConservation/CommercialPrograms/CommAppliancePro

grams/WashWise.aspx

Questar Gas UT $75 http://www.thermwise.com/business/BusinessRebates.html

Cascade Natural Gas WA $180 http://www.cngc.com/docs/conservation-materials/ci-incentives-list-q2-

2012.pdf

NW Natural (Gas) WA $200 http://energytrust.org/library/forms/BE_PI0194F.pdf

NW Natural (Gas) WA $300 http://energytrust.org/library/forms/BE_PI0194F.pdf

NW Natural (Gas) OR $200 http://energytrust.org/library/forms/BE_PI0194F.pdf

NW Natural (Gas) OR $300 http://energytrust.org/library/forms/BE_PI0194F.pdf

Pacific Power - FinAnswer Express WA $150 http://www.pacificpower.net/content/dam/pacific_power/doc/Business/Sa

ve_Energy_Money/WA_FinAnswer_Express_Appliances_Incentives.pdf

Pacific Power - FinAnswer Express WA $200 http://www.pacificpower.net/content/dam/pacific_power/doc/Business/Sa

ve_Energy_Money/WA_FinAnswer_Express_Appliances_Incentives.pdf

Port Angeles Public Works & Utilities WA $180 https://www.cityofpa.us/PDFs/Conservation/Forms/COMMERCIALAPP

LIANCEREBATES.pdf

Port Angeles Public Works & Utilities WA $75 https://www.cityofpa.us/PDFs/Conservation/Forms/COMMERCIALAPP

LIANCEREBATES.pdf

Puget Sound Energy WA $200 http://pse.com/savingsandenergycenter/ForBusinesses/Pages/Highefficiency-Commercial-Clothes-Washers.aspx

Questar Gas WY $150 http://www.thermwise.com/wy/business/BusinessRebateApplicationsWY.

php

Rocky Mountain Power WY $150 http://www.rockymountainpower.net/content/dam/rocky_mountain_powe

r/doc/Business/Save_Energy_Money/WY_FinAnswer_Express_Applianc

es_Incentives.pdf

Rocky Mountain Power WY $200 http://www.rockymountainpower.net/content/dam/rocky_mountain_powe
17-A-12 

r/doc/Business/Save_Energy_Money/WY_FinAnswer_Express_Applianc

es_Incentives.pdf

The United Illuminating Company CT $200 http://www.uinet.com/wps/portal/uinet/business/!ut/p/c5/vY_LkpswEEW

_xR8QSIltGTMY2TzCBJjgTYuwIkHM2BctgHr68fJalKpJJup9F12nzp9gQSP9O

XYHMprcrLN5ADae3ChAbRClOHblMTUoNqacJWCAYYCJBDY8eP94GqVrGjS

iF3X2au6BR57T1-

seaL97qJXU_jmdC5W2hxFkdRO6xhTdDWTz0n2ckjLBYPV_HRxS3j4f

IcnNEYBkj75yfy14vf-R97-IdxICiAxB_4hLgPPktpamMEsQ6yTz6V1cM_6NL1TXGsim6pZT3S3hkiBMLJNYtmlaOtINIGQBsXuKJs_j4-

WVVU_dl31rMDif54bvjANW50Cb4vWtZ2PfFkoeN9kW6SP3GkLUc1

Tm6RsVcJCtX5aoS_3zVRBTsDzqN6LWlWPeyidEw45tb5f1XhTCvLM

h-M7Gs6oDqA1TuCIM1j2u7tVXsbLVdd7XflCHwUka_m3OE7-

ycw8pp0YxDrTDYgHi51P3DQzdGFrMRjjHxbvE29dpA!!/dl3/d3/L2dBISEvZ0FBIS9nQSEh/

Bright Energy Solutions (offered through 5 

utilities in ND)

ND $50 http://www.brightenergysolutions.org/municipalities/?category=business

&state=sd&municipality=37

Bright Energy Solutions (offered through 10 

utilities in SD)

SD $50 http://www.brightenergysolutions.org/municipalities/?category=business

&state=sd&municipality=37

Bright Energy Solutions (offered through 21 

utilities in MN)

MN $50 http://www.brightenergysolutions.org/municipalities/?category=business

&state=mn&municipality=14

Bright Energy Solutions (offered through 16 

utilities in IA)

IA $50 http://www.brightenergysolutions.org/municipalities/?category=business

&state=ia&municipality=67
17-A-13

17-A.6 FEDERAL AND STATE TAX CREDITS

This section summarizes the Federal and State tax credits available to consumers who 

purchase energy efficient appliances. This section also describes tax credits available to 

manufacturers who produce certain energy efficient appliances.

17-A.6.1 Federal Tax Credits for Consumers of Residential Appliances

EPACT 2005 included Federal tax credits for consumers who installed efficient air 

conditioners or heat pumps; gas, oil and propane furnaces and boilers; furnace fans; and/or gas, 

oil, or electric heat pump water heaters in new or existing homes.8, 9 These tax credits were in 

effect in 2006 and 2007, expired in 2008, and were reinstated for 2009–2010 by the American 

Recovery and Reinvestment Act (ARRA).10 There was a $1,500 cap on the credit per home, 

including the amount received for insulation, windows, and air and duct sealing. Congress 

extended this provision for 2011, with some modifications to eligibility requirements, and 

reductions in the cap to $500 per home. The American Taxpayer Relief Act of 2012 extended, 

with some modifications, residential tax credits for air conditioners, heat pumps, furnaces, and 

water heaters placed in service between January 1, 2012 and December 31, 2013.8, 11

The tax credit for furnace fans was $50 in 2011, after which it expired. 

The importance of the Federal tax credits has been emphasized in research in the 

residential heating industry on the impacts of the relatively large credits that were available for 

HVAC (heating, ventilating, and air conditioning) equipment. In a survey of HVAC distributors 

conducted by Vermont Energy Investment Corporation, respondents indicated that the ample 

credit had had a notable impact on sales of higher-efficiency heating and cooling equipment. 

Some distributors combined the Federal tax credits with manufacturer rebates and utility 

program rebates for a greater consumer incentive. However, when the amount of the Federal tax 

credit was reduced, smaller utility rebate incentives had not induced the same levels of 

equipment sales increases. The decrease in incentive size from a $1,500 cap in 2009-2010 to a 

$500 cap in 2011, during a period when the economy continued to be sluggish, resulted in a 

decline in total sales of residential HVAC products. Distributors stated that an incentive needed 

to cover 25 to 75 percent of the incremental cost of the efficient equipment to influence 

consumer choice. The industry publication “2011 HVAC Review and Outlook” noted a decline 

in sales of air conditioning units with >14 SEER in 2011 and a return in sales of units with >16 

SEER to 2009 levels (after an increase in 2010). The large majority of distributor observed no 

impacts from the utility programs with their lower rebate amounts available in 2011. Distributors 

also commented on the advantages of the Federal tax credit being nationwide in contrast to 

utility rebate programs that target regional markets.12, 13

In an effort to evaluate the potential impact of a Federal appliance tax credit program, 

DOE reviewed Internal Revenue Service (IRS) data on the numbers of taxpayers who claimed 

the tax credits during tax years 2006 and 2007. It estimated the percentage of taxpayers who filed 

Form 5695, Residential Energy Credits.14 It also estimated the percentage of taxpayers with 

entries under Form 5695’s section 3, Residential energy property costs, line 3b, qualified natural 

gas, propane, or oil furnace or hot water boiler. DOE reasoned that the percentage of taxpayers 
17-A-14

with an entry on Line 3b could serve as a rough indication of the potential of taxpayer 

participation in a Federal tax credit program for furnaces during the initial program years. DOE 

found that of all residential taxpayers filing tax returns, 0.8 percent in 2006 and 0.6 percent in 

2007, claimed a credit for a furnace or boiler. DOE further found that the percentages of those 

filing Form 5695 for any qualifying energy property expenditure (which also included 

installation of efficient windows, doors and roofs) were 3.1 and 3.2 percent in 2006 and 2007 

respectively. 

 

DOE also reviewed data from an earlier Federal energy conservation tax credit program 

in place in the 1980s. While this tax credit was available from 1979 through 1985, DOE located 

data for only the first three years of the program.

15, 16, 17 For those three years - 1979, 1980, and 

1981 - the percentages of taxpayers filing Form 5695 were 6.4 percent, 5.2 percent, and 4.9 

percent. Given that the data from this earlier tax credit program were not disaggregated by type 

of energy property, this data series served only to indicate a possible trend of greater 

participation in the initial program year, followed by slightly smaller participation in subsequent 

years. However, DOE did not find detailed analysis of this program to indicate the possible 

reasons for such a trend. Also, this trend varies from the more stable trend shown in the EPAct 

2005 energy tax credit program data for its first two program years. 

17-A.6.2 Federal Tax Credits for Manufacturers

EPACT 2005 provided Federal Energy Efficient Appliance Credits to manufacturers that 

produced high-efficiency refrigerators, clothes washers, and dishwashers in 2006 and 2007.18

The Emergency Economic Stabilization Act of 200819 amended the credits and extended them 

through 2010. The credits were extended again to 2011 with modifications in the eligibility 

requirements. Manufacturer tax credits were extended again, by the American Taxpayer Relief 

Act of 2012, for clothes washers, refrigerators, and dishwashers manufactured between January 

1, 2012 and December 31, 2013.20 

Manufacturers who produce these appliances receive the credits for increasing their 

production of qualifying appliances. These credits had several efficiency tiers in 2011. For 2012-

2013, credits for the higher tiers remain but were eliminated for the lowest (least efficient) tiers 

for clothes washers and dishwashers.

11 The credit amounts applied to each unit manufactured. 

The credit to manufacturers of qualifying clothes washers, refrigerators and dishwashers was 

capped at $75 million for the period of 2008-2010. However, the most efficient refrigerator 

(30%) and clothes washer (2.2 MEF/4.5 WCF) models was not subject to the cap. The credit to 

manufacturers was capped at $25 million for 2011, with the most efficient refrigerators (35%) 

and clothes washers (2.8 MEF/3.5 WCF) exempted from this cap.21

17-A.6.3 State Tax Credits

The States of Oregon and Montana have offered consumer tax credits for efficient 

appliances for several years, and the States of Kentucky, Michigan and Indiana began offering 
17-A-15

such credits in 2009. The Oregon Department of Energy (ODOE) has disaggregated data on 

taxpayer participation in credits for eligible products. (See the discussion in chapter 17, section 

17.3.3, on tax credit data for clothes washers.) Montana’s Department of Revenue does not 

disaggregate participation data by appliance, although DOE reviewed Montana's overall 

participation trends and found them congruent with its analysis of Oregon's clothes washer tax 

credits. 

Oregon’s Residential Energy Tax Credit (RETC) was created in 1977. The Oregon 

legislature expanded the RETC program in 1997 to include residential refrigerators, clothes 

washers, and dishwashers, which significantly increased participation in the program. The 

program subsequently added credits for high-efficiency heat pump systems, air conditioners, and 

water heaters (2001); furnaces and boilers (2002); and duct/air sealing, fuel cells, heat recovery, 

and renewable energy equipment. Beginning in 2012, a Tax Credit Extension Bill (HB3672) 

eliminated refrigerators, clothes washers, dishwashers, air conditioners, and boilers from the 

RETC program, leaving credits for water heaters, furnaces, heat pumps, tankless water heaters, 

and heat pump water heaters.22, 23 Those technologies recognized by the Oregon Department of 

Energy as “premium efficiency” are eligible for tax credit of $0.60 per kWh saved in the first 

year (up to $1,500).22, 24 

 Oregon’s business energy tax credit (BETC) includes commercial clothes washers. 

Oregon’s BETC is 35–50 percent of the incremental cost of the efficient equipment.e The

majority of these credits were claimed for laundries and rental dwellings, and included 

commercial clothes washers as well as other appliances. However, ODOE did not track the 

types of appliances involved nor the number of machines per project; the annual counts are the 

total number of projects, with each project involving one or more appliances. DOE was 

therefore unable to use the data to estimate market percentages for specific appliances, i.e. 

commercial clothes washers. However, DOE did note the trend of increasing numbers of credits 

claimed from 1998 through 2006, which contributed to its assumptions regarding the trend line 

for this policy scenario. 

Montana has had an Energy Conservation Tax Credit for residential measures since 

1998.25 The tax credit covers various residential energy and water efficient products, including 

split system central air conditioning; package system central air conditioning; split system air 

source heat pumps; package system heat pumps; natural gas, propane, or oil furnaces; hot water 

boilers; advanced main air circulating fans; heat recovery ventilators; gas, oil, or propane water 

heaters; electric heat pump water heaters; low-flow showerheads and faucets; light fixtures; and 

controls. In 2002 the amount of the credit was increased from 5 percent of product costs (up to 

$150) to 25 percent (up to $500) per taxpayer. The credit can be used for products installed in 

new construction or remodeling projects. The tax credit covers only that part of the cost and 

materials that exceed established standards of construction.

 

e

 The business tax credit is taken over five years: 10 percent in the first and second years and 5 percent each year 

thereafter; the taxpayer may carry the unused credit forward up to eight years. Those with eligible project costs of 

$20,000 or less may take the tax credit in one year.
17-A-16

REFERENCES

1. Rufo, M. and F. Coito, California's Secret Energy Surplus: The Potential for Energy 

Efficiency, 2002. XENERGY Inc. (Last accessed November, 2013.) 

<http://www.issuelab.org/resource/californias_secret_energy_surplus_the_potential_for_

energy_efficiency>

2. Hall, B. H. and B. Khan, Adoption of New Technology. Working Paper No. E03-330, 

2003. Department of Economics, University of California, Berkeley, CA. 

3. Lekvall. P and C. Wahlbin, "A Study of Some Assumptions Underlying Innovation 

Diffusion Functions". Swedish Journal of Economics, 1973 

4. Geroski, P. A., Models of Technology Diffusion. Research Policy, 2000. 29: pp. 603-625 

5. Van den Bulte, C., "Want to Know How Diffusion Speed Varies Across Countries and 

Products? Try a Bass Model". Product Development & Management Association, 2002. 

XXVI(4)

6. Rufo, M., Telephone Conversation with Barbara Atkinson of LBNL, personal 

communication. Itron, Inc. January 2008 and March 2009. 

7. Blum, H., B. Atkinson, and A. Lekov, A Framework for Comparative Assessments of 

Energy Efficiency Policy Measures, 2011. (Last accessed November, 2013.) 

<http://efficiency.lbl.gov/drupal.files/ees/A%20Framework%20for%20Comparative%20

Assessments%20of%20Energy%20Efficiency%20Policy%20Measures_LBNL4749E.pdf>

8. Tax Incentives Assistance Project, Consumer Tax Incentives: Home Heating & Cooling 

Equipment., 2011. (Last accessed November, 2013.) 

<www.energytaxincentives.org/consumers/heating-cooling.php>

9. Energy Policy Act of 2005, in 119 STAT. 594 Public Law 109-58. Section 1333, 26 USC 

25C note,. 2005. http://www.gpo.gov/fdsys/pkg/BILLS-109hr6pcs/pdf/BILLS109hr6pcs.pdf

10. American Recovery and Reinvestment Act of 2009, 2009. Government Printing Office. 

(Last accessed November, 2013.) <http://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=111_cong_bills&docid=f:h1enr.pdf>

11. Tax Incentives Assistance Project, The Tax Incentives Assistance Project (TIAP), 2013.

(Last accessed November, 2013.) <http://energytaxincentives.org/>
17-A-17

12. Foster, R., J. Grevatt, N. Lange, and T. Gee, Efficiency Programs and Distributors: 

Opportunities to Fully Leverage the Supply Chain for Greater Impact, August 12-17, 

2012. American Council for an Energy-Efficient Economy. Asilomar, CA. 

<http://www.aceee.org/files/proceedings/2012/data/papers/0193-000198.pdf> 

13. Tusa, C. S., D. Pierson, P. Mammola, and J. P. Vora, JPM/HARDI 2011 HVAC Survey, 

2011. (Last accessed October 29, 2013.) <http://www.hardinet.org/mid-season-hvacdistributor-survey>

14. Internal Revenue Service, SOI Tax Stats - Individual Income Tax Returns, Estimated 

Data Line Counts, (Last accessed October 29, 2013.) <http://www.irs.gov/uac/SOI-TaxStats-Individual-Income-Tax-Returns,-Estimated-Data-Line-Counts>

15. Internal Revenue Service, 1979 Annual Report, Commissioner of Internal Revenue, (Last 

accessed October 29, 2013.) <http://www.irs.gov/pub/irs-soi/79dbfullar.pdf>

16. Internal Revenue Service, 1980 Annual Report, Commissioner of Internal Revenue., (Last 

accessed October 29, 2013.) <http://www.irs.gov/pub/irs-soi/80dbfullar.pdf>

17. Internal Revenue Service, 1981 Annual Report: Commissioner of Internal Revenue and 

the Chief Counsel for the Internal Revenue Service, (Last accessed October 29, 2013.) 

<http://www.irs.gov/pub/irs-soi/81dbfullar.pdf>

18. U.S. Department of the Treasury–Internal Revenue Service, Form 8909: Energy Efficient 

Appliance Credit, (Last accessed October 29, 2013.) <www.irs.gov/pub/irspdf/f8909.pdf>

19. Emergency Economic Stabilization Act of 2008, (Last accessed October 29, 2013.) 

<http://www.gpo.gov/fdsys/pkg/PLAW-110publ343/pdf/PLAW-110publ343.pdf>

20. Database of State Incentives for Renewables and Efficiency (DSIRE), Energy-Efficient 

Appliance Manufacturing Tax Credit, 2013. (Last accessed October 29, 2013.) 

<http://www.dsireusa.org/incentives/incentive.cfm?Incentive_Code=US42F&re=1&ee=1

&utm_source=FEB2013+DSIRE+NNL&utm_campaign=February2013&utm_medium=e

mail>

21. Tax Incentives Assistance Project, Manufacturers Incentives, (Last accessed November, 

2013.) <http://energytaxincentives.org/builders/appliances.php>

22. Database of State Incentives for Renewables and Efficiency (DSIRE), Oregon 

Incentives/Policies for Renewable Energy, Residential Energy Tax Credit, 2013. (Last 

accessed October 29, 2013.) 

<http://www.dsireusa.org/incentives/incentive.cfm?Incentive_Code=OR17F&re=1&ee=1

>
17-A-18

23. Oregon Department of Energy, HB 3672 (2011)Tax Credit Extension Bill, 2013. (Last 

accessed October 29, 2013.) 

<http://www.oregon.gov/energy/CONS/docs/HB3672summary.pdf>

24. Oregon Department of Energy, ODOE: Energy Conservation, Heat Pumps and Air 

Conditioning Systems, 2013. (Last accessed October 29, 2013.) 

<http://www.oregon.gov/energy/CONS/docs/HB3672summary.pdf>

25. Montana Department of Revenue, Energy Related Tax Relief: Energy Conservation 

Installation Credit, (Last accessed October 29, 2013.) 

<http://revenue.mt.gov/forindividuals/ind_tax_incentives/energy_related_tax_relief.mcpx

#energy>